Two methods for Pd nanoparticle synthesis were explored with three different carbide-derived carbon (CDC) support materials of which one was nitrogen-doped. These materials were studied for oxygen reduction reaction (ORR) in 0.1 M KOH solution. The prepared CDC/Pd catalysts were characterized using TEM, XRD and XPS. Using these methods, we can observe the influence of support materials for one-pot synthesis methods as we use the standard citrate method and polyol method with polyvinylpyrrolidone (PVP) as a capping agent. Comparing the two methods N-doping of carbon material resulted in smaller nanoparticles only in the case of citrate synthesis, suggesting that the influence of support is weaker using polyol method. Citrate method on CDC1, which was predominantly microporous led to a higher degree of agglomeration and larger particle formation in comparison to CDC2 and CDC3 supports, which possessed higher degree of mesoporosity. Smaller Pd particle sizes were achieved using citrate and NaBH4 in comparison to the ethylene glycol-PVP method. CDC2 and CDC3 did not show significant difference suggesting that the N-doping did not have much of an influence on the ORR. Highest SA value was observed on CDC1/Pd_Cit, which could be attributed to formation of larger particles and agglomerates.

Exosomes are nano-sized extracellular vesicles released by cells that contain nucleic acids, proteins, lipids, and other bioactive compounds that play a role in the body's physiological and pathological processes. Exosomes have significant and distinct benefits over synthetic carriers such as liposomes and nanoparticles in the realm of illness diagnostics and therapy due to their endogeneity and heterogeneity. Exosomes are classified as either natural or designed, depending on whether they have been deliberately altered. Natural exosomes are further classified as either animal-derived or plant-derived exosomes. Plant-derived exosome-like nanoparticles (PDENs) are comprised of numerous kinds of bioactive biomolecules. As an alternative cell-free therapeutic strategy, they have the ability to transfer nano-bioactive chemicals to the human body, resulting in diverse anti-inflammatory, antioxidant, and anti-tumour effects. This review focuses on the beneficial effects of plant-derived exosomes against a range of inflammatory conditions, including as periodontitis, inflammatory bowel disease (IBD), autoimmune skin disorders, and inflammation of the liver, lungs, and intestines. Inflammatory disorders are a major issue because of their widespread influence on both personal health and public healthcare systems. Since these illnesses are persistent, the affected people's quality of life is negatively impacted and long-term morbidity is exacerbated. The complicated interaction of genetic, environmental, and immunological variables in the development of various disorders makes effective prevention and therapy more challenging. Plant exosomes present a promising avenue for developing novel therapeutic strategies against inflammatory diseases, offering unique advantages over other alternatives. In order to exploit this efficient carrier, future research will concentrate on creating a PELNV delivery system with high stability, transdermal volume, and ease of administration. Plant exosomes have the potential to become reliable therapeutic agents for a range of common disorders if they are targeted and generated with interdisciplinary expertise.

Macrophages are well known for the involvement in the biocompatibility, as well as biodistribution, of nano(bio)materials. Although there are a number of rodent cell lines, they may not fully recapitulate primary cell responses; particularly for those of human cells. Isolation of tissue resident macrophages, from humans, is difficult and may result in insufficient cells with which to determine the possible interaction with nano(bio)materials. Isolation of primary human monocytes, and differentiation to monocyte-derived macrophages, may provide a useful tool with which to study, further, these interactions. To that end, we developed a standard operating procedure for this differentiation, as part of the REFINE project, and used it to measure the secretion of bioactive molecules from M1 and M2 differentiated monocytes in response to model nano(bio)materials, following an initial assessment of pyrogenic contamination which may confound, potential, observations. The SOP was deployed in two partner institutions with, broadly, similar results. The work presented here shows the utility of this assay but, highlights the relevance of donor variability in responses to nano(bio)materials. Whilst donor variability can provide some, logistical, challenges to application of such assays, this variability is much closer to the heterogeneous cells that are present in vivo, compared to homogeneous, non-human, cell lines.

We previously reported nano-pulse treatment (NPT), a pulsed power technology, resulted in 4T1-luc mammary tumor elimination and a strong in situ vaccination, thereby completely protecting tumor-free animals against a second live tumor challenge. The mechanism whereby NPT mounts effective antitumor immune responses in the 4T1 breast cancer predominantly immunosuppressive tumor microenvironment (TME) remains unanswered. In this study, orthotopic 4T1 mouse breast tumors were treated with NPT (100 ns, 50 kV/cm, 1000 pulses, 3 Hz). Blood, spleen, draining lymph nodes and tumors were harvested at 4-hour, 8-hour, 1-, 3-, 7-day. Additional studies were conducted over 3-month posttreatment intervals for the analysis of frequencies, death and functional markers of various immune cells in addition to suppressor function of regulatory T cells (Tregs). NPT was verified to elicit strong in situ vaccination (ISV) against breast cancer and promoted both acute and long-term T cell memory. NPT abolished immunosuppressive dominance systemically and in the TME by substantially reducing Tregs, myeloid-derived suppressor cells (MDSCs), and tumor-associated macrophages (TAMs). NPT induced apoptosis in Tregs and TAMs. It also functionally diminished the Treg suppression capacity, likely explained by the downregulation of activation markers, particularly 4-1BB and TGFβ, and a phenotypic shift from predominantly activated (CD44+CD62L-) to naïve (CD44-CD62L+) Tregs. Importantly, NPT selectively induced apoptosis in activated Tregs and spared effector CD4+ and CD8+ T cells. These changes were followed by a concomitant rise in CD8+CD103+ tissue-resident memory T cells and TAM M1 polarization. These findings indicate that NPT effectively switches the TME and secondary lymphatic systems from an immunosuppressive to an immunostimulatory state allowing cytotoxic T-cell function and immune memory formation to eliminate cancer cells and account for the NPT in situ vaccination.

The responses of inflammatory cytokines and brain-derived neurotrophic factor (BDNF) to a high-intensity interval training (HIIT) session were compared between 7 postmenopausal (57±2 y) and 8 premenopausal females (44±3 y) during the luteal phase of their menstrual cycle, when estrogen and progesterone are highest, and. HIIT consisted of 10 x 1 min running intervals (90% maximum heart rate) separated by 1 min recovery intervals. Blood samples were collected at baseline (fasted), pre-exercise (postprandial), and 0, 30 and 90 min post-HIIT and analyzed for interleukin (IL-6) and 10 (IL-10), tumour necrosis factor-alpha (TNF-α) and BDNF. IL-6 significantly increased from pre-exercise to 0 min post-HIIT in postmenopausal (+40%, p=0.01) but 30 min post-HIIT in premenopausal females (+60%, p=0.02). IL-6 remained elevated at 90 min post-HIIT in premenopausal (+104%, p=0.05) and to a higher degree in postmenopausal (+385%, p<0.001). IL-10 showed no response. TNF-α increased from pre- to 0 min post-HIIT (+10%, p=0.05), then decreased to below pre-exercise at 30 min (-10%, p=0.02) and 90 min (-5%, p=0.04) in both groups. BDNF increased immediate post-HIIT in premenopausal (+60%, p<0.001) but not postmenopausal females. The differences in IL-6 and BDNF response to HIIT between pre and postmenopausal females provide evidence of the role of female reproductive hormones in the regulation of these exercise-induced responses.

Our environment has been significantly impacted by man-made pollutants, primarily due to industries make substantial use of synthetic chemicals, resulting in significant environmental consequences. In this research investigation, the co-precipitation approach was employed for the synthesis of cellulose-based ferric oxide (Fe₂O₃/cellulose) and copper oxide nanoparticles (CuOx-NPs). Scanning electron microscopy (SEM) analyses were conducted to determine the properties of the newly synthesised nanoparticles. Furthermore, the synthesized nanoparticles were employed for eliminating chromium from aqueous media under various conditions, including temperature, contact time, adsorbent concentration, adsorbate concentration, and pH. Additionally, the synthesised materials were used to recover Cr(VI) ions from real samples, including tap water, seawater, and industrial water, and the adsorptive capacity of both materials evaluated under optimal conditions. The synthesis of Fe₂O₃/cellulose and CuOx-NPs proved to be effective, as indicated by the outcomes of the study.

This study investigates the effects of subclinical hypothyroidism and BDNF on telomere length in T1DM mothers and their neonates. Methods: In this prospective cohort study, 70 pregnant women with T1DM were enrolled. Subclinical hypothyroidism during the first trimester was characterized by elevated thyroid-stimulating hormone (TSH) levels ranging from 2.5 to 5.0mIU/L alongside normal free thyroxine (FT4) concentrations. Participants were stratified into two groups based on TSH levels: a maternal subclinical hypothyroid group (SHG, n=35) and a maternal euthyroid group (ETG, n=35). Leukocyte telomere length (LTL) measurements were conducted using whole blood samples from the mothers and umbilical veins. Results: The maternal LTL mean was 1.7 (SD=1.1), which was significantly shorter than the neonatal telomeres, with a mean of 2.3 SD (1.2). A significant correlation was observed between maternal and neonatal LTL (r=0.750, p<0.001). A positive association was found between neonatal LTL and BDNF levels in maternal circulation [β=0.421 (95% CI 0.536; 1.873; p=0.001)]. Neonatal LTL was negatively associated with gestational weight gain [β=-0.266 (95% CI-0.138; -0.009)], and with TSH in the first trimester [β=-0.237; (95% CI -0.493; - 0.010)]. Notably, neonates in the SHG exhibited shorter LTL, with a mean of 2.1 (1.0), compared to those in the ETG, who had a mean LTL of 2.7 (1.4). A positive association was found between maternal LTL and BDNF levels in maternal circulation [β=0.523 (95% CI 0.790; 1.950; P<0.001)]. Maternal LTL was negatively associated with gestational weight gain [β=-0.259 (95% CI -0.122; -0.010)], and with TSH in the first trimester [β=-0.231; (95% CI -0.422; - 0.015)]. Conclusions: Genetic factors affect neonatal LTL. Maternal subclinical hypothyroidism reduces neonatal LTL. The link between maternal and neonatal LTL and BDNF reveals the complex endocrine-neurotrophic interaction in T1DM telomere dynamics.

The risk of malignant transformation of induced pluripotent stem cells (iPSC)-derived cells in response to DNA damage is a potential concern as the tumor suppressor genes TP53 and CDKN2A are transiently inactivated during reprogramming. Here, we evaluated the integrity of cellular senescence pathways and DNA double-strand break (DSB) repair in iPSC-derived human fibroblasts (i-HF) compared to their parental skin fibroblasts (HF). Using a variety of functional assays, we show that the capacity of i-HF to enter senescence and repair DSB is not compromised after damage induced by ionizing radiation (IR) or overexpression of H-RASV12. Still, i-HF lines are transcriptionally different from their parental lines, showing enhanced metabolic activity and higher expression of p53-related effector genes. As a result, i-HF lines generally exhibit increased sensitivity to various stresses, have an elevated senescence-associated secretory phenotype (SASP) and cannot be immortalized unless p53 expression is knocked down. In conclusion, while our results suggest that i-HF are not at a greater risk of transformation, their overall hyperactivation of senescence pathways may impede their function as a cell therapy product.

This paper introduces a novel method for accurately calculating the upper biomass of single trees using Light Detection And Ranging (LiDAR) point cloud data. The proposed algorithm involves classifying the tree point cloud into two distinct ones: the trunk point cloud and the crown point cloud. Each part is then processed using specific techniques to create a 3D model and determine its volume. The trunk point cloud is segmented based on individual stems, with each stem further divided into slices that are modeled as cylinders. On the other hand, the crown point cloud is analyzed by calculating its footprint and gravity center. The footprint is further divided into angular sectors, with each being used to create a rotating surface around the vertical line passing through the gravity center. All models are represented in a matrix format, simplifying the process of minimizing and calculating the tree’s upper biomass, consisting of crown biomass and trunk biomass. To validate the proposed approach, both terrestrial and airborne datasets are utilized. A comparison with existing algorithms in the literature confirms the effectiveness of the new method. The study shows that the proposed algorithm achieves an average fit between 0.01 m and 0.49 m for individual trees.

The Simultaneous Localization And Mapping (SLAM) scanner is an easy and portable Light Detection And Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the survey domain, this paper aims to assess the accuracy of two SLAM scanners: the NavVis VLX and the BLK2GO Scanner. This assessment is conducted for both outdoor and indoor environments. In this context, two types of reference data are used: the Total Station (TS) and the static scanner Z+F Imager 5016. To carry out the assessment, four comparisons are tested: cloud to cloud, cloud to mesh, mesh to mesh, and edge detection board assessment. However, the results of the assessments confirm that the accuracy of indoor SLAM scanner measurements (5 mm) is greater than that of outdoor ones (between 10 mm and 60 mm). Moreover, the comparison of cloud to cloud provides the best accuracy regarding direct accuracy measurement without manipulations. Finally, based on the high accuracy, scanning speed, flexibility, and the accuracy differences between tested cases, it is confirmed that SLAM scanners are effective tools for data acquisition.

Digital elevation models (DEMs) have a wide range of applications and play a crucial role in many studies. Numerous public DEMs, frequently acquired using radar and optical satellite imagery, are currently available; however, DEM datasets tend to exhibit elevation values influenced by vegetation height and coverage, which can compromise the accuracy of models in representing terrain elevation. In this study, we developed a digital terrain model for South America using a novel methodology to remove vegetation bias in the Copernicus DEM GLO-30 (COPDEM) model using machine learning, Global Ecosystem Dynamics Investigation (GEDI) elevation data, and multispectral remote sensing products. Our results indicate considerable improvements compared to COPDEM in representing terrain elevation, reducing average errors (BIAS) from 9.6 m to 1.5 m. Furthermore, we evaluated our product (ANADEM) by comparison with other global DEMs, obtaining more accurate results for different conditions of vegetation fraction cover and land use. As a publicly available and open-source dataset, ANADEM will play a crucial role in advancing studies that demand accurate terrain elevation representations at large scales.

Abstract Precision medicine is essentially based on reliable and non-invasive biomarkers. Aim of this review has been to describe the newest biomarkers in the field of kidney transplantation and the kidney rejection, one of the commonest and severe complications. Standard of care tools to identify acute rejection are largely full of errors and of drawbacks. In recent years new and reliable biomarkers have been found. They are avoid of risks, non-invasive and able to detect rejection even in those frequent cases in which acute rejection are clinically asymptomatic and not otherwise identifiable. In recent years several biomarkers have been identified. Very recently have been found new relevant biomarkers able to diagnose rejection with high positive predictive value and low negative predictive value. These are the donor derived cell free DNA found in the recipient, the gene expression profile of the donor found in the recipient and the urinary cytokines that express modification in the graft tissue. The aim of this study has been to find the most recent findings in the literature on this topic and to describe the utility and possible limitation of such new biomarkers on kidney rejection.

This study aimed to investigate the potential of Cord Blood Stem Cell-derived exosome vesicles (CBSC-EVs) in regulating cancer growth and preventing cellular damage. The study aimed to examine the effects of CBSC-EVs on CHL1 cancer cells and healthy fibroblasts. The study results reveal that CBSC-EVs have an anti-cancer/antioxidant effect on CHL1 cancer cells without inducing any toxic effects on healthy lymphocytes. Further analysis of CBSC-EVs revealed the presence of seven miRNAs (noncoding RNA molecules), including three from the Let 7 family, two from the Let 5 family, and two novel miRNAs. The function of the novel miRNAs was the main focus of the study. To assess the efficacy of CBSC-EVs, CHL1 cells (a type of malignant melanoma cell line) and human fibroblasts were grouped into different treatments. One group of those cell lines were treated with CBSC-EVs, and in the second group, the cells were transfected with either miRNA inhibitor Novel 1 (miRNA IN1) plus CBSC-EVs or miRNA inhibitor Novel 2 (miRNA IN2) and CBSC-EVs to knock down those novel miRNAs in the CBSC-EVs. Therefore, by comparing these groups, the function of novel miRNAs is more understandable. In this study, the Comet assay was employed to evaluate cancer cells' DNA damage and CCK8 to measure cell viability. The findings revealed that CBSC-EVs caused an increase in DNA damage in CHL1 cells compared to cells without treatment (p

Tailings generated by mining account for the largest proportion of global waste from industrial activities. Copper is relatively uncommon world-wide, with low concentrations in sediments and waters, yet is very elevated around mining sites. On the Keweenaw Peninsula of Michigan, USA, jutting out into Lake Superior, 140 mines extracted native copper from the Portage Lake Volcanic Series, part of an intercontinental rift system. Between 1901-1932, two mills at Gay (Mohawk, Wolverine) sluiced 22.7 million metric tonnes (MMT) of copper-rich tailings (stamp sands) into Grand (Big) Traverse Bay. Here we examine: 1) 3D coastal elevation and reef bathymetry, 2) particle dispersal, 3) copper concentrations and leaching, plus 4) shoreline toxicity. About 10 MMT of tailings created a migrating coastal beach that stretches 7 km from the Gay pile to the Traverse River Seawall. Another 10 MMT are moving underwater on the coastal shelf, threatening Buffalo Reef, an important lake trout and whitefish breeding ground. Aerial photos, ALS (plane) LiDAR, and relatively inexpensive UAS (drone) surveys characterize shoreline structure and aid initial remediation. Because sands (natural beach quartz, stamp sand basalt) are silicates of similar density, particle dispersal is similar and challenging. However, stamp sand beaches contrast greatly with natural sand beaches in both physical profile and chemistry. Dispersing particles retain copper, and release toxic concentrations. Leaching is elevated by exposure to high DOC and low pH waters. Field experiments, shoreline seining, and benthic sampling confirm serious impacts on aquatic biota, supporting tailings removal.

Metabolic dysfunction-associated steatotic liver disease (MASLD) encompasses metabolic dysfunction-associated fatty liver (MASL) and metabolic dysfunction-associated steatohepatitis (MASH), with MASH posing a risk of progression to cirrhosis and hepatocellular carcinoma (HCC). The global prevalence of MASLD is estimated at approximately a quarter of the population, with significant healthcare costs and implications for liver transplantation. The pathogenesis of MASLD involves intrahepatic liver cells, extrahepatic components, and immunological aspects, particularly the involvement of macrophages. Hepatic macrophages are a crucial cellular component of the liver and play vital roles in liver function, contributing significantly to tissue homeostasis and swift responses during pathophysiological conditions. Recent advancements in technology have revealed the remarkable heterogeneity and plasticity of hepatic macrophage populations and their activation states in MASLD, challenging traditional classification methods like the M1/M2 paradigm and highlighting the coexistence of harmful and beneficial macrophage phenotypes that are dynamically regulated during MASLD progression. This complexity underscores the importance of considering macrophage heterogeneity in therapeutic targeting strategies, including their distinct ontogeny and functional phenotypes. This review provides an overview of macrophage involvement in MASLD progression, combining traditional paradigms with recent insights from single-cell analysis and spatial dynamics. It also addresses unresolved questions and potential therapeutic targets in this area.

The importance of conflict analysis and safety considerations during work zone intervals at signalized intersections cannot be overstated, as these periods pose unique challenges and safety risks for both motorists and pedestrians. Work zones introduce temporary changes to traffic patterns, lane configurations, and signal timings, which can disrupt the flow of traffic and increase the likelihood of conflicts between vehicles and pedestrians. By conducting comprehensive conflict analysis, it is possible to identify potential conflict points, such as merging areas, pedestrian crossings, and lane transitions, and implement targeted safety measures to mitigate risks. Additionally, considering safety considerations during work zone intervals is paramount to safeguarding the well-being of road users and minimizing the occurrence of conflicts or collisions. Measures such as enhanced signage, temporary traffic control devices, and increased enforcement efforts can help promote compliance with traffic regulations and ensure the safe passage of vehicles and pedestrians through work zones. This study investigates the dynamics of traffic behavior and safety implications within work zones at signalized intersections, aiming to address key challenges and enhance intersection management strategies. Through comprehensive analysis, varying frequencies of Vehicle-to-Vehicle (V2V) conflicts were observed across different movement directions, with significant occurrences identified in specific phases of the traffic signal cycle. Specifically, 69 V2V conflicts in phase #1, 181 in phase #2, 31 in phase #3, 207 in phase #4, 1 in phase #5, 274 in phase #6, 93 in phase #7, and 186 in phase #8 were recorded. Furthermore, the examination of Vehicle-to-Pedestrian (V2P) conflicts underscored the importance of prioritizing pedestrian safety, with notable occurrences observed in phases associated with pedestrian crossing movements. Meanwhile, despite the closure of left-turn movements, instances of red light running persisted, with 69 red light running events in phase #1, 181 in phase #2, 31 in phase #3, 207 in phase #4, 1 in phase #5, 274 in phase #6, 93 in phase #7, and 186 in phase #8.To mitigate these safety concerns during work zone intervals, proactive measures are essential. Strategies include improving communication through clear signage and advanced warning systems, enhancing enforcement efforts with red light cameras and increased police presence, and investing in infrastructure improvements such as temporary pedestrian facilities. Integration of real-time data from sensors, particularly LiDAR technology, offers significant advantages in detecting conflicts and red light runners during work zone intervals. LiDAR's precise, real-time data on vehicle and pedestrian movements, combined with its 360-degree coverage and high spatial resolution, facilitates accurate detection and tracking of potential conflict scenarios, contributing to enhanced intersection safety and traffic management efficiency. This study endeavored to conduct a traffic safety analysis during work zone intervals at a signalized intersection in Baltimore City, known for its high rate of traffic crashes, utilizing LiDAR sensor technology.

Background: Since there is no cure for amyotrophic lateral sclerosis (ALS) currently, it is very important for searching diagnostic biomarkers and novel treatments to reduce the severity of this disease; one of these treatment approaches is stem cell transplantation. This study aims to evaluate the safety and efficacy of repeated transplantation of autologous bone marrow-derived mesenchymal stem cells (BM-MSCs) in patients with ALS by analyzing molecular data. Methods: In this single-centre, open-label, phase II clinical trial, retrospective study, fifteen confirmed ALS patients entered the study based on defined inclusion and exclusion criteria and underwent repeated stem cell transplantation (3 times BM-MSCs transplantation (1×10^6) , MSC/Kg BW per injection) concurrently intrathecally (IT) and intravenously (IV), with one-month interval). Clinical assessment of molecular investigation by evaluating specific microRNAs expression (Mir206, 133a-3p, 338-3p) in the patient's serum and Cerebra spinal fluid (CSF) samples were done three times during the 3-month follow-up. Statistical analysis includes paired T-test or Wilcoxon signed ranks. Results: No serious adverse effects were reported following stem cell transplantation. Besides, the mentioned microRNA expression slightly increased but still non-significant (P>0.05). Conclusions: Although Our study demonstrated non-signifying results. However, the still increasing of these MircRNAs in serum and CSF overall, can indicate that repeated transplantation of BM-MSCs was a safe procedure in ALS patients, leading to delay in disease progression and improvement in clinical symptoms. Future studies are needed to confirm these results.

Triple-negative breast cancer (TNBC) is an aggressive and highly metastatic type of tumor. TNBC is often enriched in tumor-infiltrating neutrophils (TINs), which support cancer growth in part by counteracting tumor-infiltrating lymphocytes (TILs). Prior studies identified the enhancer of zeste homolog 2 (EZH2) as a pro-tumor methyltransferase in primary and metastatic TNBC. We hypothesized that EZH2 inhibition in TNBC cells per se would exert anti-tumor activity by altering the tumor immune microenvironment. To test this hypothesis, we used CRISPR to generate EZH2-knockout (KO) and overexpressing lines from parent (wild-type -WT) 4T1 cells, an established murine TNBC model. In vitro, EZH2 KO and OE cells showed little change in replicative capacity and invasiveness, but marked changes in surface marker profile and cytokine/chemokine secretion compared to WT cells. On in vivo injection, EZH2 KO cells showed significantly reduced primary tumor growth and a 10-fold decrease in lung metastasis compared to WT cells, while EZH2 OE cells were unchanged. Compared to WT tumors, TIN:TIL ratios were greatly reduced in EZH2 KO tumors but unchanged in EZH2 OE tumors. Our findings show a tumor-intrinsic role of EZH2 in the 4T1 TNBC model in regulating TIN/TIL poise, primary tumor progression and lung metastasis in vivo.

The therapeutic, attenuative, and morphological effects of proprietary neural stem cell-derived extracellular vesicles (NSC EVs, AB126) produced by Aruna Bio, Inc. were assessed in a range of in vitro and in vivo models of stroke. We emphasize that modulation of neuroinflammation is a key target and suggest further studies to advance our understanding of the mechanisms of action and ultimately how best to dose and monitor the benefits of AB126 in stroke patients.

Bladder cancer (BC) diagnosis is reliant on cystoscopy, an invasive procedure associated with urinary tract infections. This has sparked interest in identifying non-invasive biomarkers in body fluids such as blood and urine. A source of biomarkers in these biofluids are extracellular vesicles (EVs), nanosized vesicles that contain a wide array of molecular cargo, including small non-coding RNA such as microRNA and transfer RNA-derived fragments (tRF). Here we performed small-RNA next-generation sequencing for urine EVs, serum EVs and serum supernatant from 41 patients with non-muscle invasive BC (27 stage Ta, 14 stage T1) and 15 non-cancer patients (NCP) with benign cystoscopy findings. Serum and urine samples were collected before transurethral resection of bladder tumors (TUR-b) and at routine postsurgery check-ups. Expression levels of tRFs in presurgery samples were compared to those at postsurgery check-ups and to NCPs. To strengthen the reliability of our results, samples from 10 patients with stage T1 disease were resequenced. When comparing tRF expression in urine EVs between BC patients with stage T1 disease and NCPs, 14 differentially expressed tRFs (DEtRFs) were identified. In serum supernatant, six DEtRFs were identified among stage T1 patients when comparing presurgery to postsurgery samples, and four DEtRFs were found when comparing presurgery samples to NCPs. By performing a blast search, we found that sequences of DEtRFs aligned with genomic sequences pertaining to processes relevant to cancer development, such as enhancers, regulatory elements and CpG islands. Our findings display a number of tRFs that may hold potential as biomarkers for the diagnosis and recurrence-free survival of BC.

Immune-cell reprogramming driven by mitochondria-derived reactive electrophilic immunometabolites (mt-REMs—e.g., fumarate, itaconate) is an emerging phenomenon of major biomedical importance. Despite their localized production, mt-REMs elicit significantly-large local and global footprints within and across cells, through mechanisms involving electrophile signaling. Burgeoning efforts are being put into profiling mt-REMs’ potential protein-targets and phenotypic mapping of their multifaceted inflammatory behaviors. Yet, precision indexing of mt-REMs’ first-responders with spatiotemporal intelligence and locale-specific function assignments remains elusive. Highlighting the latest advances and overarching challenges, this perspective aims to stimulate thoughts and spur interdisciplinary innovations to address these unmet chemical-biotechnological needs at therapeutic immuno-signaling frontiers.

With the emergence of autonomous functions in road vehicles, there has been an increased use of Advanced Driver Assistance Systems comprising various sensors to perform automated tasks. Light Detection and Ranging (LiDAR) is one of the most important types of optical sensor that detects the positions of obstacles, representing them as clusters of points in 3-dimensional space. LiDAR performance degrades significantly when driving in rain as raindrops adhere to the outer surface of the sensor assembly. The performance degradation behaviors include missing points and reduced reflectivity of the points. It was found that the extent of degradations is highly dependent on the interface material properties, which subsequently affects the shapes of the adherent droplets, causing different perturbations to the optical rays. A fundamental investigation is performed on the protective polycarbonate cover of the LiDAR assembly coated with four classes of materials – hydrophilic, almost-hydrophobic, hydrophobic, and superhydrophobic. Water droplets are controllably dispensed onto the cover to quantify the signal alteration due to each droplet of various sizes and shapes. To further understand the effects of droplet motion on LiDAR signals, sliding droplet conditions are simulated using numerical analysis and validated with physical optical tests using a 905 nm laser source and receiver to mimic the LiDAR detection mechanism. Comprehensive explanations are presented on LiDAR performance degradation in rain from both material and optical perspectives. These can aid component selection and the development of signal enhancing strategies for integrating LiDARs in vehicle designs to minimize the impact of rain.

Polar stratospheric clouds (PSCs) form in polar regions typically between 15 and 25 km, when the local temperature is sufficiently low. PSCs play an important role in the ozone chemistry and the dehydration and denitrification of the stratosphere. Lidars with a depolarization channel may be used to detect and classify different classes of PSCs. The main PSC classes are water ice, nitric acid trihydrate (NAT) and supercooled ternary solutions (STS), the latter being liquid droplets consisting of water, nitric acid and sulphuric acid. PSCs have been observed at the lidar observatory at Concordia Station from 2014 on. The harsh environmental conditions at Concordia during winter render successful lidar operation difficult. In order to facilitate the operation of the observatory several measures have been put in place to achieve an almost complete remote control of the system. PSC occurrence is strongly correlated with local temperatures and is affected by dynamics, as the PSC coverage during the observation season shows.

Background. Malignant pleural mesothelioma (MPM) is still a rather incurable disease. Many classes of compounds with various mechanisms of action have been tested in vivo and ex-vivo, with rather disappointing results. This is partly due to preclinical studies mostly performed in models that fail to recapitulate MPM complexity and do not retain the intra-tumor heterogeneity, a propelling force for tumor progression. Additionally, we and others have shown that, under stress-induced chemotherapy, profound rearrangements take place in MPM cultures to fuel the emergence of chemoresistant cell subpopulations. Thus, finding clinically actionable modulators of MPM resistance is still a very urgent need. Methods. Setup and propagation of patient-derived organoid cultures from MPM pleural effusions; flow cytometry-based validation of organoid cultures, isola-tion and propagation of mesothelioma-associated fibroblasts; co-cultures, Lu-minex-based Cytokine array, ELISA assay to quantitate IL-6 in the conditioned media, qRT-PCR to assess mRNA expression of cancer stem cell markers and fibroblast markers. Results. Organoid cultures were obtained from eight MPM patients by using specific growth media and conditions to propagate pleural effusion-derived cells. We proved that primary MPM specimens and serially passaged (mesothelio-ma-Patient-Derived-Organoids) mPDOs were cytologically similar, with the obtained mPDOs expressing mesothelial markers similarly to the originating specimen. As expected from a clinically relevant model, the size and number of mPDOs were altered, in a patient-specific way, when pharmacologically relevant doses of chemotherapy drugs were added. We derived Mesothelio-ma-Associated-Fibroblasts (MAFs) and the addition of MAFs, to various ratios, altered the response of the mPDOs to pemetrexed + cisplatin, toward a reduced sensitivity to the latter anticancer agents. Additionally, organoid formation and size were altered by MAF-conditioned media, and this correlated with increased expression of cancer stem cell markers. We showed increased levels of IL-6 in the media of the treated cocultures and showed that this latter was derived from the MAFs. Conclusions. Increased secretion of IL-6 by mesothelioma-associated fibroblasts (MAFs) contributes to the resistance of the MPM to therapy.

Biochar (BC) is a carbonaceous material obtained by pyrolysis at 200−1000 °C in the limited presence of O2 of different vegetable and animal biomass feedstocks. BC has demonstrated great potential, mainly in environmental applications, due to its high sorption ability and persistent free radicals (PFRs) content. These characteristics enable BCs to carry out the direct and PFRs-mediated removal/degradation of environmental organic and inorganic contaminants. The types of PFRs possibly present in BC depend mainly on the pyrolysis temperature and the kind of pristine biomass. Since they also can cause ecological and human damage, a systematic evaluation of the environmental behavior, risks, or management techniques of BC-derived PFRs is urgent. PFRs generally could consist of a mixture of carbon- and oxygen-centered radicals, and of oxygenated carbon-centered radicals, depending on the pyrolytic conditions. Here, to promote a more productive and beneficial use of BC and related PFRs and stimulate further studies to make them environmentally safer and less hazardous to humans, we have first reviewed the most common methods used to produce BC, its main environmental applications, the primary mechanisms by which BC remove xenobiotics, as well as the reported mechanisms for PFRs formation in BCs. Secondly, we have discussed the PFRs environmental migration and transformation; we have reported the main PFRs-mediated applications of BCs to degrade inorganic and organic pollutants, the correlated environmental potential risks, and the possible strategies to limit them.

Light detection and ranging (LIDAR) sensors using a polarization-diverse receiver are able to capture polarimetric information about the target under measurement. This information can be used in a classifier to make an instantaneous assessment of the type of material present, enabling a shot-by-shot prediction of the material type and distance to the target.

Background: Extracellular Vesicles are pivotal mediators in intercellular communication, facilitating the exchange of biological information among healthy, pathological and tumor cells. Between the diverse subtypes of extracellular vesicles, exosomes have unique properties, clinical and therapeutical applications, even towards cancer. Breast cancer ranks as one of the most prevalent malignancies across the globe. Both the tumor core and its surrounding microenvironment engage in a complex, orchestrated interaction that facilitates the cancer’s growth and spread. Methods: The most valuable and significant PubMed Literature about extracellular vesicles and adipose–derived stem cells exosomes was selected and analyzed in order to report their biological properties and potential applications, in particular on the Triple–Negative Breast Cancer. Results: Adipose–derived stem cells exosomes represent a potential tool to target the tumor core of the Triple–Negative Breast Cancer, its microenvironment and surrounding tissues including metastasis. The authors are developing a supportive Triple–Adipose–derived stem cells Exosomes [T.A.E.] Technology against breast cancer in order to perform a selective preconditioning of the cancer before or while the traditional oncological treatments are running. Conclusion: The possibility to exploit the impact of Adipose–derived stem cells exosomes within the T.A.E. Technology against the Triple–Negative breast cancer is real, highlighting a new era for supportive measures against breast cancer.Keywords: Extracellular vesicles; Exosomes; Adipose–derived stem cells, Tumor microenvironment, Triple–Negative Breast Cancer, T.A.E. Technology.

Depression is the commonest mental disorder worldwide. Both antidepressants and psychotherapy are effective in treating depression, but response to these treatments is often incomplete. Yoga-based interventions (YBIs) have emerged as a promising adjunctive treatment for depression. Recent research has attempted to identify the biological mechanisms associated with the antidepressant actions of YBIs. In this scoping review, conducted according to the PRISMA-ScR guidelines, the PubMed and Scopus databases were searched to retrieve research on biomarkers of response to YBIs in patients with depression. Eleven studies were included in the review. Based on these studies, YBIs may improve depression through beneficial effects on systemic inflammation, stress axis regulation, cardiac autonomic functioning, and possibly reductions in oxidative stress-related cellular damage. Increases in brain-derived neurotrophic factor (BDNF) may represent a final common pathway upon which these processes converge. Though these results are theoretically plausible, they should be interpreted with caution due to certain methodological limitations in the existing research. Replication of these results and improvements in study methodology, and the evaluation of other biomarkers relevant to the pathophysiology of depression, could further elucidate the mechanism of action of YBIs in this disorder, and identify which patients would benefit most from this treatment approach.

The result of the multidisciplinary collaboration of researchers from different areas of knowledge to validate a solar radiation model is presented. The MAPsol is a 3D local-scale adaptive solar radiation model that allows to estimate direct, diffuse and reflected irradiance for clear sky conditions. The model includes the adaptation of the mesh to complex orography and albedo, and considers the shadows cast by the terrain and buildings. The surface mesh generation is based on surface refinement, smoothing and parameterisation techniques and allows the generation of high quality adapted meshes with a reasonable number of elements. Another key aspect of the paper is the generation of a high-resolution digital elevation model (DEM). This high-resolution DEM is constructed from LiDAR data and its resolution if two times more accurate than the publicly available DEMs. The validation process uses direct and global solar irradiance data obtained from pyranometers at the University of Salamanca located in an urban area affected by systematic shading from nearby buildings. This work provides an efficient protocol for studying solar resources, with particular emphasis on areas of complex orography and dense buildings where shadows can potentially make solar energy production facilities less efficient.

Background: Monocyte-derived dendritic cells (mo-DCs) play a vital role in the innate immune response by activating Th17 cells, which in turn release the pro-inflammatory cytokine IL-17. We hypothesised that following repetitive mechanical strain, tenocytes activate monocyte-derived dendritic cells, which in turn, drive the pathogenesis of tendinopathy. Aim: The aim of the study was to determine if monocyte-derived dendritic cells are upregulated in a human tendinopathy model. Method: Torn supraspinatus tendon and matched intact subscapularis tendon samples were collected from 7 patients undergoing arthroscopic shoulder surgery. Healthy control samples of subscapularis tendon were collected from 5 patients undergoing arthroscopic stabilization surgery. Tendon biopsy samples were evaluated immunohistochemically by quantifying the presence of monocyte-derived dendritic cells (CD11C and CD206). Results: Tendinopathic tendon samples exhibited significantly greater (2-3-fold) monocyte-derived dendritic cell expression when compared with healthy control tissue (P < .05). Conclusion: This study provides evidence for a monocyte-derived dendritic cell upregulation in early-stage human tendinopathy.

Precise individual tree or sample-based inventories derived from 3D point cloud data of mobile laser scanning can improve our comprehensive understanding of the structure, function, resilience, biodiversity, and ecosystem services of urban forests. This study assessed the performance of a hand-held laser scanning system for the extraction of tree position, diameter at breast height (DBH) and tree height (H) in an urban area. A total of 2083 trees of 13 species from 34 plots were analyzed. The results showed that the registration of tree positions using ground control points (GCPs) demonstrated high accuracy, with errors consistently below 0.4 m, except for a few instances. The extraction accuracy of DBH for all trees and individual species remained consistently high, with a total root mean square error (RMSE) of 2.06 cm (6.89%) and a bias of 0.62 cm (2.07%). Notably, broad-leaved trees outperformed coniferous trees, with RMSE and bias values of 1.86 cm (6%) and 0.76 cm (2.46%) compared to 2.54 cm (9.46%) and 0.23 cm (0.84%). Tree height extraction accuracy varied significantly among different species, with R2 values ranging from 0.65 to 0.92. Generally, both DBH and height were underestimated. Linear mixed effects models (LME) were applied to evaluate factors affecting the performance of HLS with the plot as a random factor. LME analysis revealed that plant type and terrain significantly influenced the accuracy of DBH and H derived from HLS data. Other fixed factors of plot area, tree density and trajectory length didn’t have significance. With a large sample size, we concluded that the HLS demonstrated sufficient accuracy in extracting individual tree parameters in urban forests.

This paper shows the firsts results of a methodological approach in which Remote Sensing technologies (Satellite imagery and LiDAR) combined with Least Cost Path algorithm on GIS are being tested like tools to overcome locally-based interpretations about populational patterns and communication routes during Flavian municipality in the Comarca de El Condado region. The identification of fourteen hypothetical communication routes between already known archaeological sites thanks to a recent archaeological prospection, and the confirmation of three sections of them and a new archaeological site demonstrate that the combined use of the aforementioned technics has a very useful to delve more deeply, more widely and at a variety of different scales into the relation between the twelve known/selected Early Roman Empire sites and the surrounding landscape in the right bank of Guadalén River. It is expected that, in next phases, this methodological approach will allow us to go deeper in analysing and understanding spatial distribution, exploitation, and interrelation between different Early Roman Empire sites, helping us to fill the aforementioned gaps in our understanding of the archaeological continuum in the region reducing time and costs.

The homeostasis of cellular calcium is fundamental for many physiological activities, while calcium levels maintain inhomogeneous within cells. During the onset of asthma, epithelial and inflammatory cells secrete platelet derived growth factor (PDGF), inducing proliferation and migration of airway smooth muscle (ASM) to the epidermal layer, narrowing the airway. The regulation of ASM cells by PDGF is closely related to the conduction of calcium signals. In this work, we generated subcellular-targeted FRET biosensors to investigate calcium regulations at different compartments of ASM cells. PDGF-induced cytoplasmic calcium [Ca2+]C increase was attributed from both extracellular calcium influx and endoplasmic reticulum (ER) calcium [Ca2+]ER release, which was partially regulated by PLC-IP3R pathway. Interestingly, removal of extracellular calcium influx led to inhibited ER calcium release, likely through inhibitory effect on calcium-dependent activation of ER ryanodine receptor. Inhibition of L-type calcium channel on plasma membrane, or SERCA pump on ER resulted in both reduced [Ca2+]C and [Ca2+]ER from PDGF stimulation, while IP3R channel inhibition led to reduced [Ca2+]C only. Inhibited SERCA pump caused immediate [Ca2+]C increase and [Ca2+]ER decrease, indicating an active calcium exchange between cytosol and ER storage in resting cells. PDGF-induced calcium at outer mitochondrial membrane sub-region showed similar regulatory response to cytosolic calcium, not influenced by inhibition of mitochondrial calcium uniporter channel. Therefore, our work identified calcium flow pathways among extracellular medium, cell cytosol, and ER by regulatory calcium channels. Particularly, extracellular calcium flow has an essential function to fully activate ER calcium release.

An innovative mobile Lidar device, developed to monitor volcanic plumes during explosive eruptions at Mt. Etna (Italy) and analyze the optical properties of volcanic particles was upgraded in October 2023 with the aim to improve volcanic plume retrievals. The new configuration of the lidar allows to obtain new data of both the optical and the microphysical properties of the atmospheric aerosol. In fact, after its upgrade, the lidar has the possibility to measure 3β, 2α and 2δ in a configuration defined as “state of the art lidar” where properties such as the particle size distribution and refractive index can be derived. During the lidar implementation we were able to to test the system performance through specific calibration measurements. In this work, the first measurement results are shown and compared with results obtained by other instruments aimed at proving the ability of the upgraded system to more precisely characterize the aerosol optical and microphysical properties.

The pathophysiology of Nonketotic Hyperglycinemia (NKH), a rare neuro-metabolic disorder associated with severe brain malformations and life-threatening neurological manifestations, remains incompletely understood. Therefore, a valid human neuronal model is essential. We aimed to investigate the impact of GLDC gene variants, which cause NKH, on cellular fitness during the differentiation process of human induced pluripotent stem cells (iPSCs) into iPSC-derived astrocytes and to identify sustainable mechanisms capable of overcoming GLDC deficiency. We developed the GLDC27-FiPS4F-1 line and performed metabolomic, mRNA abundance and protein analyses. The study showed that although GLDC27-FiPS4F-1 maintained the parental genetic profile, it underwent a metabolic switch to an altered serine-glycine-one-carbon metabolism with a coordinated cell growth and cell cycle proliferation response. We then differentiated the iPSCs into neural progenitor cells (NPCs) and astrocyte-lineage cells. Analysis showed that GLDC-deficient NPCs had shifted towards a more heterogeneous astrocyte lineage with increased expression of the radial glial markers GFAP and GLAST and the neuronal markers MAP2 and NeuN. In addition, we detected changes in other genes related to serine and glycine metabolism and transport, all consistent with the need to maintain glycine at physiological levels. These findings improve our understanding of the pathology and offer new perspectives for therapeutic options.

Diamond-like carbon (DLC) layers are known for their high corrosion and wear resistance, low friction, and high biocompatibility. However, it is often necessary to dope DLC layers with additional chemical elements to strengthen their adhesion to the substrate. Ti-DLC layers (doped with 0.4, 2.1, 3.7, 6.6, 12.8 at.% of Ti) were prepared by dual pulsed laser deposition, and pure DLC, glass, and polystyrene (PS) were used as controls. In vitro cell-material interactions were investigated with an emphasis on cell adhesion, proliferation, and osteogenic differentiation. We observed slightly increasing roughness and contact angle and decreasing surface free energy on Ti-DLC layers with increasing Ti content. Three-week biological experiments were performed using adipose tissue-derived stem cells (ADSCs) and bone marrow mesenchymal stem cells (bmM-SCs) in vitro. The cell proliferation activity was similar or slightly higher on the Ti-doped materials than on glass and PS. Osteogenic cell differentiation on all materials was proved by collagen and osteocalcin production, ALP activity, and Ca deposition. The bmMSCs exhibited greater initial proliferation potential and an earlier onset of osteogenic differentiation than the ADSCs. The ADSCs showed a slightly higher formation of focal adhesions, higher metabolic activity, and Ca deposition with increasing Ti content.

Lathyrane-type diterpenes have a wide range of biological activities. Among them, euphoboetirane A (1) exerts neurogenesis-promoting activity. In order to increase the structural diversity of this type of lathyranes to explore their potential use in neurodegenerative disorders, the biotransformation of 1 by Streptomyces puniceus BC-5GB.11 has been investigated. The strain BC-5GB.11, isolated from surface sediments collected from the intertidal zone of the inner Bay of Cadiz, was identified as Streptomyces puniceus, as determined by phylogenetic analysis using 16S rRNA gene sequence. Biotransformation of 1 by BC-5GB.11 afforded five products (3-7), all of them reported here for the first time. The main biotransformation pathways involved regioselective oxidation at non-activated carbons (3-5) and isomerization of the D^12,13 double bond (6). In addition, a cyclopropane-rearranged compound was found (7). The structures of all compounds were elucidated on the basis of extensive NMR and HRESIMS spectroscopic studies.

In our previous studies, the marine-derived fungus Emericellopsis maritima BC17 was found to produce new eremophilane-type sesquiterpenoids on solid media. In order to explore its potential to produce more metabolites, E. maritima BC17 was subjected to a one strain-many compounds (OSMAC) analysis leading to the discovery of three new eremophilanes (1-3) and fourteen known derivatives (4-17) in the liquid media Czapek Dox and PDB. Their structures were established by extensive analyses of the 1D and 2D NMR, and HRESIMS data, as well as ECD data for the assignment of their absolute configurations. Antitumoral and antimicrobial activities of the isolated metabolites 1, 3, 11, and 15 were investigated. PR toxin 3-deacetyl (15) exhibited cytotoxic activity against HepG2, MCF-7, A549, A2058 and Mia PaCa-2 human cancer cell lines with IC₅₀ values ranging from 2.5 to 14.7 µM. In addition, 15 exhibited selective activity against methicillin-sensitive S. aureus ATCC29213 at the highest concentration tested of 128 µg/mL.

In mobile robotics, laser scanners have a wide spectrum of indoor and outdoor applications, both in structured and unstructured environments, due its accuracy and precision. Most works that use this sensor have their own data representation and their own case-specific modeling strategies, and no common formalism is adopted. To address this issue, this manuscript presents an analytical approach for identification and localization of objects using a 2D LiDARs Our main contribution lies in formally defining laser sensor measurements and their representation, the identification of objects, their main properties and their location in a scene. We validate our proposal with experiments in generic semi-structured environments common in autonomous navigation, and we demonstrate its feasibility on multiple object detection and identification, following strictly its analytical representation. Finally, our proposal further encourages and facilitates the design, modeling and implementation of other applications that use laser scanners as a distance sensor.

Leishmaniases are zoonotic vector-borne diseases caused by a wide variety of Leishmania species with complex transmission cycles involving different reservoirs, potential new hosts and vectors. Similarly, to other eukaryotes, Leishmania release extracellular vesicles (LEVs) to play important initial interactions that are crucial to modulate the subsequent systemic immune response on the establishment of infection in humans and others hosts like dogs. Recent studies in endemic areas of Brazil concluded that canine infections were predominantly due to L. amazonensis and not restricted to L. infantum (syn. Leishmania chagasi). Under these premises, the diagnosis of leishmaniasis needs to be improved with the identification of current etiological agent and the histopathologic features, highlighting the differential diagnosis and the molecular components of LEVs. In this way, the dual aim of that study is to register collected observations of natural canine infections, enhancing the relevance of differential diagnosis in companion animals and inserting in vitro results in the field of LEVs that still research gaps to be filled to understand the mechanisms and biological aspects involving the parasite-host interactions. Therefore, improve these studies of Parasitology research is important for diagnostic, prognostic, treatment advances and continuing need for global prevention, control, elimination/eradication of these parasitic infections.

Chagas disease, caused by the protozoan parasite Trypanosoma cruzi, stands as a significant public health challenge in endemic regions of the Americas. Current treatment options, limited and associated with adverse effects, underscore the need for novel therapeutic approaches. This systematic review comprehensively examines existing research on natural products as potential agents against Chagas disease. Employing a systematic search strategy across diverse databases, studies meeting predefined inclusion criteria were assessed for methodological quality and relevance. The synthesis of findings reveals a spectrum of natural products showcasing promising anti-Trypanosoma cruzi properties. The review discusses the limitations of current evidence, highlights gaps in knowledge, and proposes avenues for future research. By elucidating the landscape of natural products in Chagas disease treatment, this review contributes to the ongoing pursuit of innovative and accessible therapeutic strategies.

Through long-term Rayleigh lidar observations during 2010-2022, atmospheric gravity wave (AGW) activities in the middle atmosphere (30–65 km) are statistically investigated at Haikou (110.3°E, 19.9°N) in China. In total, 827 quasi-monochromatic AGW events are identified from the lidar dataset, and the vertical wavelength, observed wave period, and vertical propagation direction of dominant waves are extracted for each AGW event by analyzing the power spectra of wave-induced temperature perturbations. It is revealed that 81.7% of all AGW events were associated with upward-propagating waves, and the proportion of upward-propagating AGWs reached a maximum in autumn and a minimum in winter. The most common vertical wavelengths and wave periods were 6–11 km and 4–10 hours, respectively. In particular, 68 AGW events associated with simultaneous upward and downward propagating waves were observed by lidar, corresponding to 8.2% of the events. The occurrence of such special AGW phenomena is qualitatively discussed, and multiple formation mechanisms may exist. Comparisons are made with previously reported lidar observation results, and the statistical features of AGW activity in the low-latitude middle atmosphere may vary over a relatively wide range depending on the location and altitude. However, a majority of AGWs propagate upward in the middle atmosphere.

Our previous study demonstrated that mesenchymal stem/stromal cells (MSCs) induced the differentiation of myeloid-derived suppressor cells (MDSCs) in the bone marrow (BM) under inflammatory conditions. In the present study, we aimed to explore the signaling pathway in BM cells responsible for MDSC generation. RNA-seq revealed that the mitogen-activated protein kinase (MAPK) pathways, among biochemical pathways, exhibited the highest number of differentially expressed genes in BM cells with MSC coculture relative to those without. Western blot confirmed strong phosphorylation of c-Jun N-terminal kinase (JNK) in BM cells cocultured with MSCs under GM-CSF stimulation, whereas p38 kinase activation remained unaltered by MSCs. Inhibition of JNK by SP600125 significantly abolished the expression of Arg1 and Nos2, the hallmark genes of MDSCs, as well as Hif1α, a molecule mediating monocyte functional re-programming into suppressive phenotype, in MSC-cocultured BM cells, while increasing TNF-α in response to lipopolysaccharides stimulation. Furthermore, JNK inhibition markedly abrogated the effect of MSCs on the production of TGF-β1, TGF-β2 and IL-10 in BM cells. These results suggest that MSCs induce MDSC differentiation in BM during inflammation, at least in part, through the activation of the JNK MAPK signaling pathway.

Background: Brain-derived neurotrophic factor (BDNF) has attracted increasing attention as a therapeutic agent because of its potentially biological activities, including osteogenesis. However, the molecular mechanisms involved in the osteogenic activity of BDNF have not been fully investigated. This study aimed to investigate the action of BDNF on the osteoblast differentiation in bone marrow stromal cells, and its influence on signaling pathways. Methods: Preosteoblast cells (MC3T3-E1), bone marrow-derived stromal cells (ST2), and a direct 2D co-culture system were treated with BDNF. The effect of BDNF on cell proliferation was determined using the CCK-8 assay. Osteoblast differentiation was assessed based on alkaline phosphatase (ALP) activity and staining and the protein expression of multiple osteoblast markers. Calcium accumulation was examined by Alizarin red S staining. Results: BDNF significantly increased ALP activity, calcium deposition, and the expression of osteoblast differentiation-related proteins, such as ALP, osteopontin, etc. in both ST-2 and the MC3T3-E1 and ST-2 co-culture system. Moreover, the effect of BDNF on osteogenic differentiation was diminished by blocking tropomyosin receptor kinase B as well as inhibiting c-Jun N-terminal kinase and p38 MAPK signals. Conclusion: Our study results showed positive effect of BDNF on osteogenesis. It provides essential evidence for clinical applications of BDNF in treating osteoporosis.

In this article, we combine datasets from multiple research projects and remote sensing tech-nologies to evaluate the conservation conditions of La Fortaleza de Kuelap, a pre-Hispanic monument in Peru’s northeastern Andes that suffered significant damage during historically high seasonal rains in April 2022. Our analyses seek to identify the main causes of the collapse and locate places where the monument is likely at risk of further deterioration. To do so, we model surface hydrology using a digital elevation model (DEM) derived from drone LiDAR data, re-construct a history of previous collapses, and calculate the volume of the most recent by fusing terrestrial LiDAR and photogrammetric datasets. In addition, we examine subsurface water ac-cumulation through electrical resistivity data, reconstruct the stratification of the monument from seismic refraction data, and analyze vegetation loss and ground moisture accumulation using high resolution satellite imagery. Our results point to water accumulation as the most significant source of risk for La Fortaleza’s perimeter walls. Combined with adverse contemporary conser-vation interventions and natural conditions, this led to the collapse of April 2022. This integration of analytical results demonstrates how multi-scalar and multi-instrumental approaches provide comprehensive and timely assessments of conservation needs.

The 21st century has seen the launch of new space-borne sensors based on LiDAR (light detection and ranging) technology developed in the second half of the 20th century. LiDAR was initially developed to integrate laser-focused imaging with the capability to determine distances through the measurement of signal return times, utilizing suitable sensors and data acquisition electronics. Nowadays, these sensors have transformed into robust instruments, offering novel opportunities for mapping terrain, canopy heights, and estimating above-ground biomass (AGB) across local to regional scales. This work aims to analyze the scientific impact of these sensors on large-scale for-est mapping to retrieve 3D canopy information, monitor forest degradation, estimate AGB, and model key ecosystem variables such as primary productivity and biodiversity. In this way, a worldwide bibliometric analysis of this topic was carried out based on up to 412 publications in-dexed in the Scopus database during the period 2004-2022. The results showed that the number of published documents increased exponentially in the last five years, coinciding with the commis-sioning of two new LiDAR space missions: Ice, Cloud and Land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI). These missions are providing data since 2018 and 2019, respectively. The journal that demonstrated the highest productivity in this field was "Remote Sensing," and among the leading contributors, the top five countries in terms of publica-tions were the USA, China, the UK, France, and Germany. In the realm of prominent research in-stitutions, France boasted six, the USA had four, China had three, while the UK and Canada each had one. The upward trajectory in the number of publications recorded from 2004 to 2022 catego-rizes the subject under investigation as a highly trending research topic, particularly within the context of enhancing the administration of forest resources and engaging in global climate treaty frameworks mandating the surveillance and reporting of carbon stocks in forests. The recent launch in August 2022 of the Terrestrial Ecosystem Carbon Monitoring Satellite (TECMS; China State Administration of Forestry and Grassland), along with the planned launch in the coming years of up to three new space sensors, such as the Multi-footprint Observation LiDAR and Im-ager (Japan Aerospace Exploration Agency), the BIOMASS P-band Synthetic Aperture Radar (SAR) (European Space Agency), and the LiDAR Surface Topography (LIST; NASA), will greatly contribute to expanding the ability to map and monitor forest systems at very large scales. In this context, the integration of space-borne data, including imagery, SAR, and LiDAR, is anticipated to steer the trajectory of this research in the upcoming years.

Cell junction that is usually associated with dynamic cytoskeletons is essential for a wide range of cellular activities such as cell migration, cell communication, barrier function and signal transduction. Real-time observation of cell junction facilitates understanding of the mechanisms by which cell junction regulate relevant cellular activities. In this study, we examined the binding capacity of a modified tridecapeptide from Cx43 to cell junction protein ZO-1 and sought to create a fluorescent peptide that could be used to label cell junction. To introduce the fluorescent peptide into the cells, a cell-penetrating peptide was linked to the modified tridecapeptide and synthesized. The binding of the modified tridecapeptide was tested using pull down and immunoprecipitation assay. The ability of the peptide to label cell junction was assessed by adding it to fixed or cultured Caco-2 cells. Testing assay revealed that the Cx43-derived peptide can bind to ZO-1. Besides, the peptide was able to label cell junction of fixed cell, although no obvious cell junction labeling was observed clearly in living cells, probably due to the inadequate affinity. These results indicate that a peptide-based strategy for labeling cell junction may be feasible, and efforts to improve its affinity are warranted in the future.

Segmental bone defects caused by trauma, infection, tumor resection or osteoporotic fractures present significant surgical treatment challenges. Host bone autograft is considered the gold standard to restore function but comes with the cost of harvest site comorbidity. Allograft bone is a secondary option but has its own limitations in incorporation with host bone as well as cost. Therefore, developing new bone tissue engineering strategies to treat bone defects is critically needed. In the past three decades, the use of stem cells delivered with different scaffolds or growth factors for bone tissue engineering has made tremendous progress. Many varieties of stem cells have been isolated from different tissues for use in bone tissue engineering. This review summarizes the progress in using different postnatal stem cells including bone marrow mesenchymal stem cells, muscle-derived stem cells, adipose-derived stem cells, dental pulp stem cells/periodontal ligament stem cells, periosteum stem cells, umbilical cord-derived stem cells, peripheral blood stem cells and urine-derived stem cells for bone tissue engineering and repair. This review also summarizes the progress using exosomes or extracellular vesicles delivered with various scaffolds for bone repair. The advantages and disadvantages of each type of stem cell are also discussed and explained in detail. It is hopeful in the future that these preclinical results will translate into new therapies for bone defect repair.

Lipofilling is an option for breast reconstruction after tumor resection to avoid the complications of an implant-based reconstruction. Although some concerns exist regarding the oncological safety of a tissue rich in mesenchymal stem cells with their proangiogenic and proliferation-supportive properties, there are also reports that adipose tissue-derived stem cells can exhibit antitumoral properties. We isolated primary adipose tissue-derived stem cells. Both conditioned medium and exosomes were harvested from the cell culture and used to treat the breast cancer cell line MCF-7. Cell viability, cytotoxicity, and gene expression of MCF-7 cells in response to the indirect co-culture were evaluated. MCF-7 cells incubated with exosomes from adipose tissue-derived stem cells show reduced cell viability in comparison to MCF-7 cells incubated with adipose-tissue derived stem cell-conditioned medium. Expression of proapoptotic genes was upregulated, expression of antiapoptotic genes was downregulated. The debate about the oncological safety of autologous fat grafting after tumor resection continues. Here, we show that exosomes from adipose tissue-derived stem cells exhibit some antitumoral properties on breast cancer cell line MCF-7.

Multispectral and 3D LiDAR remote sensing data sources are valuable tools for characterizing the 3D vegetation structure and thus understanding the relationship between forest structure, biodiversity and microclimate. This study focuses on mapping riparian forest species in the canopy strata using a fusion of Airborne LiDAR data and multispectral multi-sources and multi-resolution satellite imagery; Sentinel-2 and Pleiades at tree level. The idea is to assess the contribution of each data source in the tree species classification at the considered level. The data fusion was processed at feature-level and decision level. At feature level, LiDAR 2D attributes were derived and combined with vegetation indices. At decision level, LiDAR data was used for 3D tree crown delimitation providing unique trees or groups of trees that are used as a support for the species classification. Data augmentation techniques were used to improve the training process. Best results were obtained by the fusion of Sentinel-2 time series and LiDAR data with a Kappa of 0.66 thanks to red-edge based indices that better discriminate vegetation species and the temporal resolution of Sentinel-2 images that allows monitoring the phenological stages helping to discriminate the species.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent form of renal cancer and its treatment is hindered by resistance to targeted and immunotherapies and combinations ob both. We have reported that knockdown of the antisense noncoding mitochondrial RNAs (ASncmtRNAs) with chemically-modified antisense oligonucleotides induces proliferative arrest and apoptotic death in tumor cells from many human and mouse cancer types. These studies have been mostly performed in vitro and in vivo on commercially-available cancer cell lines and have shown that, in mouse models, tumor growth is stunted by the treatment. The present work was performed on cells derived from primary and metastatic ccRCC tumors. We established primary cultures fom primary and metastatic ccRCC tumors, which were subjected to knockdown of ASncmtRNAs in vitro and in vivo in an orthotopic xenograft model in NOD/SCID mice. We found that these primary ccRCC cells are affected in the same way as tumor cell lines and, in the orthotopic model, tumor growth was significantly reduced by the treatment. This study on patient-derived ccRCC tumor cells represents a model closer to actual patient ccRCC tumors and shows that knockdown of ASncmtRNAs poses a potential treatment option for these patients.

Viruses are silent enemies that intrude and take control of the plant cell's machinery for their own multiplication. Infection by viruses and the resulting damage is still a major challenge in the agriculture sector. Plants have the ability to fight back, but the ability of viruses to counteract and mutate at a fast rate helps them to hide from the host response. Therefore, classical approaches for introgressing resistance genes by breeding have obtained limited success in counteracting the virus menace. Genetic modification (GM) based strategies have been successful in engineering artificial resistance in plants. Several different approaches based on pathogen derived resistance, antisense constructs, hairpin RNA, double stranded RNA etc. have been used to enhance plant resistance to the virus. Recently, genome editing (GE) strategies mainly involving the CRISPR/Cas mediated modifications are being used for virus control. In this review, we discuss the developments and advancements in the GM and GE based methods in tackling virus infection in plants.

Both brain-derived neurotrophic factor (BDNF) and glucocorticoids (GCs) have multiple roles in the various aspects of neurons, including cell survival and synaptic function. BDNF and its receptor TrkB express in the central nervous system (CNS) neurons extensively, and contribution of BDNF/TrkB system in neuronal function is evident, thus, its downregulation has been considered to be involved in the pathogenesis of Alzheimer’s disease (AD). GCs, a stress-related molecule, and glucocorticoid receptor (GR) are also considered to be associated with AD, in addition to mental disorders such as depression. Importantly, a growing body of evidence suggest a close relationship between BDNF/TrkB-mediated signaling and GCs/GR system in the CNS. Here, we introduce current studies on the interaction among neurotrophic system and stress in CNS neurons, and discuss their involvement in the pathophysiology of AD.

Adult mesenchymal stem cells (MSCs) are a promising cell source for tissue regeneration. However, ex vivo expansion results in cell senescence; cells lose their proliferation and differentiation capacity. Fetal MSCs can be an alternative due to their robust proliferation and differentiation capacities as well as immune privilege properties. Given the rejuvenation effect of decellularized extracellular matrix (dECM) on adult MSCs, it remains unknown whether dECM influences the regenerative potential of fetal stem cells. In this study, passage five fetal nucleus pulposus cells (fNPCs) and fetal synovium-derived stem cells (fSDSCs) were expanded on dECMs deposited by fNPCs (NECM) and fSDSCs (SECM) for one passage with expansion on tissue culture plastic (Plastic) as a control. We found that dECM expanded fNPCs and fSDSCs exhibited both similarities and differences in expression of stemness genes and surface markers. Expanded fNPCs yielded more differentiated pellets after chondrogenic induction but no adipogenic differentiation following adipogenic induction in both Plastic and dECM groups than the corresponding fSDSC group. Despite a significant increase in fNPCs, dECM expanded fSDSCs exhibited no increase in chondrogenic potential; however, compared to the Plastic group, dECM expanded fSDSCs exhibited a small increase in osteogenic potential and a great increase in adipogenic potential. These results suggest that fNPCs are more sensitive to NECM rejuvenation for cartilage tissue engineering and regeneration; in contrast, dECMs exhibited limited effects on fSDSC rejuvenation in chondrogenic capacity except for enhanced adipogenic capacity following expansion on SECM.

This paper is a follow-up to a previous study that was conducted in a research forest and identified the optimal method for Diameter at Breast Height (DBH) estimation as a circular scanning and fitting ellipses to 4cm stem cross-sections at breast height. The aim of this study was to determine whether the identified method is applicable to natural boreal forests as well. The iPad Pro LiDAR scanner was used to acquire point clouds for 15 sites representing a range of natural boreal forest conditions in Ontario, Canada, and estimate DBH. The secondary objective was to determine if tested stand (Species composition, age, density, understory) or tree (Species, DBH) factors affected the accuracy of estimated DBH. Overall, estimated DBH values were within 1cm of actual DBH values for 78 of 133 measured trees (59%). An RMSE of 1.5cm (8.6%) was achieved. Stand age had a large effect (>0.15) on the accuracy of estimated DBH values, while density, understory, and DBH had moderate effects (0.05-0.14). No trend was identified between accuracy and stand age. Accuracy improved as understory density decreased and as tree DBH increased. Inertial Measurement Unit (IMU) and positional accuracy errors with the iPad Pro scanner limit the feasibility of using this device for forest inventories.

Effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level Of Detail or LOD 2. Similarly, automated solutions for 3D modeling of individual trees at different levels of details are required for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT). The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our work in this paper is based on the assumption that, similar to the canopy shape, the structure of tree trunk and branches is also an important consideration. An algorithm for automated modeling of tree trunks is proposed. Aiming to generate 3D models of tree trunks, the suggested approach starts by extracting the trunk point cloud, which is then segmented into single stems. Finally, the individual stems are modeled by dividing them into slices. The geometry of the developed tree models is represented in a matrix format. This solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands and promoting sustainable forest management. Despite the huge variation of trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models.

Coastal resources in Lian, Batangas, have undergone significant degradation due to a combination of human activities and natural factors, including sea-level rise, changes in sea-surface temperature, fluctuations in rainfall, increased storm frequency, and sedimentation. This has led to adverse effects on key ecosystems such as corals, mangroves, and seagrasses. To assess the extent of climate change hazards on these coastal resources, a Vulnerability Assessment was conducted in the municipality of Lian, Batangas. The assessment employed a combination of the Coastal Integrity Vulnerability Assessment Toolkit (CIVAT) and Integrated Coastal Sensitivity, Exposure, and Adaptive Capacity to Climate Change (ICSEA-C) guidelines. Additionally, the analysis incorporated detailed mapping using Light Detection and Ranging (LiDAR) data for mangroves and LandSat 8 imagery for corals and seagrasses. The findings at the municipal scale revealed that a staggering 90% of mangroves in Lian, Batangas, were highly vulnerable. This vulnerability was attributed to a high score in intrinsic characteristics and a low adaptive capacity. Similarly, a majority of seagrasses in the area were classified as medium and highly vulnerable at the municipal level. Only 10–20% of seagrasses received low to medium vulnerability scores. Shockingly, almost 100% of the coral reefs in Lian, Batangas, were deemed highly vulnerable, indicating a critical threat to this crucial marine ecosystem.

The retrieval of tree height percentiles with satellite lidar waveforms over the mountainous ar-eas is greatly challenged due to slanted and rugged topographic relief of underlying ground. A novel algorithm is proposed to extract the height percentiles based on the target response waveform (TRW) that is resolved by using a Richardson–Lucy deconvolution algorithm with an adaptive iterative time. The height metrics of the energy percentiles of 25%, 50%, 75% and 95% for the cumulative TRW distribution are determined by their vertical distances relative to the ground elevation in this study. To validate the proposed algorithm, we select the received waveforms of the Global Ecosystem Dynamics Investigation (GEDI) lidar over the Pahvant Mountains of central Utah, USA. The results reveal that the resolved TRWs resemble closely to the actual target response waveforms from the coincident airborne lidar data, with the mean values of the coefficient of correlation, total bias and root mean square error (RMSE) taking val-ue of 0.92, 0.0813 and 0.0016. In addition, the accuracies of the derived height percentiles from the proposed algorithm have been greatly improved compared with the conventional Gaussian de-composition method and the slope-adaptive waveform metrics method. The RMSE values de-crease by the mean values of 1.96 m and 2.72 m, respectively. It demonstrates that the proposed algorithm has good potential in the extraction of forest structure parameter over rugged moun-tainous areas.

In this paper, an intelligent blind guide system based on 2D LiDAR and RGB-D camera sensing is proposed, and the system is mounted on the smart cane. The intelligent guide system relies on 2D LiDAR, RGB-D camera, IMU, GPS, Jetson nano B01, STM32 and other hardware. The main advantage of the intelligent guide system proposed by us is that the distance between the smart cane and obstacles can be measured by 2D LiDAR based on Cartographer algorithm, thus achieving Simultaneous localization and mapping (SLAM). At the same time, through the improved yolov5 algorithm, pedestrians, vehicles, pedestrian crosswalks, traffic lights, warning posts, stone piers, tactile paving and other objects in front of the visually impaired can be quickly and effectively identified. Laser SLAM and improved yolov5 obstacle identification tests were carried out inside a teaching building on the campus of Hainan Normal University and on a pedestrian crossing on Longkun South Road in Haikou City, Hainan Province. The results show that the intelligent guide system developed by us can drive the wheels and omnidirectional wheels at the bottom of the smart cane, and give the smart cane a self-leading blind guide function like a "guide dog", which can effectively guide the visually impaired to avoid obstacles and reach the predetermined destination, and can quickly and effectively identify the obstacles on the way out. The laser SLAM speed of this system is 25~31FPS, which can realize the short-distance obstacle avoidance and navigation function both in indoor and outdoor envionments. The improved yolov5 helps to identify 86 types of objects, the recognition rate for pedestrian crosswalks and for vehicles are 84.6% and 71.8%, respectively; the overall recognition rate for 86 types of objects is 61.2%, and the obstacle recognition rate of the intelligent guide system is 25-26FPS.

Background: Anthropometric measurements play a crucial role in medico-legal practices. Actually, several scanning technologies are employed in post-mortem investigations for forensic anthropological measurements. This study aims to evaluate the precision, in-ter-rater reliability, and accuracy of a hand-held scanner in measuring various body parts. Methods: Three independent raters measured seven longitudinal distances using an iPad Pro equipped with a LiDAR sensor and specific software. These measurements were sta-tistically compared to manual measurements conducted by an operator using a laser level and a meterstick (considered the gold standard). Results: The Friedman’s test revealed minimal intra-rater variability in digital measurements. Inter-rater variability analysis yielded an ICC=1, signifying high agreement among the three independent raters. Addi-tionally, the accuracy of digital measurements displayed errors below 2%. Conclusions: Preliminary findings demonstrate that the pairing of LiDAR technology with the Polycam app showcases high precision, inter-rater agreement, and accuracy. Hand-held scanners show potential in forensic anthropology due to their simplicity, affordability, and porta-bility. However, further validation studies under real-world conditions are essential to es-tablish the reliability and effectiveness of handheld scanners in medico-legal settings.

Blood-derived products (BP) like citrate-platelet-rich plasma (CPRP) or hyperacute serum (HAS) are known to contain many growth factors. Combined mitogenic and DNA damaging stimuli might lead to increased senescent cell burden and altered senescence-associated secretory pheno-type (SASP). Therefore, the senescent state was extensively tested by γH2AX staining, p21 Q-PCR and western blot, growth curves and senescence associated ß-galactosidase staining. Two main treatments with BP were performed early (immediately after etoposide) and late (after addition-al 11days). Effects of the BP treatment was evaluated by IL-6 and IL-8 measurement as well as collagen (COL1) and p21 mRNA expression. Additionally, XTT assays, cell size measurements, viability assays, and cell number calculations were performed. HAS treated cells in early treat-ment had the lowest observed IL-6 and IL-8. In contrast, there was a clear inflammatory response for IL-8 on HAS and CPRP treated cells in late treatment. For COL1 expression an upregulation in early treatment could be shown, meanwhile cells in the late treatment group remained unaf-fected. In CPRP treated cells, the COL1 expression decreased. All in all, BP treatment seems to have slightly positive effects regarding skin rejuvenation.

Exosomes, a subset of extracellular vesicles, have emerged as pivotal mediators of intercellular communication, revolutionizing our comprehension of cellular interactions. Their diverse cargo, comprising proteins, nucleic acids, and bioactive molecules, plays a crucial role in various physiological and pathological processes. In the intricate landscape of adult neurogenesis, exosomes wield significant influence, actively participating in the generation of new neurons within the adult mammalian brain. Originating predominantly from non-niche cells, these vesicles contribute substantially to the regulation of neuroinflammation, thereby impacting neurogenesis and offering therapeutic promise for neurological disorders. In the specific context of Parkinson's disease (PD), exosomes assume a multifaceted role, prominently implicated in the propagation of α-synuclein oligomers and the orchestration of neuroinflammation. Stem cell-derived exosomes present themselves as promising therapeutic agents, capable of reshaping the microenvironment, repairing neuronal damage, and mitigating apoptosis. The ongoing exploration of exosomes continues to unravel intricate biological processes, paving the way for innovative medical interventions and treatments, particularly within the challenging landscape of neurodegenerative diseases such as PD. The comprehensive understanding of exosomal functions holds the potential to advance our therapeutic approaches, offering new avenues for intervention and treatment strategies in the realm of neurodegenerative disorders.

Hepatocellular carcinoma (HCC) results in high mortality due to ineffective systemic therapy. Human immortalized cell lines are commonly used to study anti-tumour effects in the context of new anti-tumour therapies and tumour biology. As immortalized cell lines have limited biological relevance and heterogeneity compared to primary cells, patient-derived tumour tissues and corresponding immune cells are the gold standards for studying the complexity of individual tumour entities. However, culturing primary HCC cells has a low success rate. Here, we aimed to establish a reproducible approach to preserve the patient-derived liver cancer cells for in vitro and in vivo studies. The underlying study aimed to establish an in vitro pre-screening platform to test treatment options' effectivity and dosage, e.g., for new substances, autologous modified immune cells, or combined therapies in HCC. We initially employed 15 surgical resection specimens from patients with different HCC entities for isolation and preservation. The isolated liver cancer cells from four HCC-diagnosed patients were used for orthotopic transplantation into the healthy liver of immunodeficient mice, allowing them to grow for six months before human liver cancer cells were isolated and cultured. As a result, we generated and characterized four new primary-like liver cancer cell lines. Compared to immortalized HCC cell lines, freshly generated liver cancer cells displayed individual morphologies and heterogenous protein-level characteristics. We assessed their ability to proliferate, migrate, form spheroids, and react to common medications compared to immortalized HCC cell lines. All four liver cancer cell lines exhibit strong migration and colony-forming characteristics in vitro, comparable to extensively investigated immortalized HCC cell lines. Moreover, the four etiological different liver cancer cell lines displayed differences in the response to 5-FU, Sorafenib, Axitinib and Interferon-alpha treatment, ranking from non-responders to responders depending on the applicated medication. In sum, we generated individual patient-derived liver cancer cell lines suitable for predictive in vitro drug screenings and moreover for xenograft transplantations to realize the in vivo investigation of drug candidates. We overcame the low cultivation success rate of liver cancer cells derived from patients and analyzed their potential to serve a pre-clinical model.

Celiac disease is an autoimmune disease triggered by oral ingestion of gluten, with certain gluten residues resistant to digestive tract enzymes. Within the duodenum, the remaining peptides incite immunogenic responses, including the generation of autoantibodies and inflammation, leading to irreversible damage. Our previous exploration unveiled a glutenase called Bga1903 derived from the Gram-negative bacterium Burkholderia gladioli. The cleavage pattern of Bga1903 indicated its moderate ability to mitigate the toxicity of pro-immunogenic peptides. The crystal structure of Bga1903, along with the identification of subsites within its active site, was determined. To improve its substrate specificity toward prevalent motifs like QPQ within gluten peptides, the active site of Bga1903 underwent site-directed mutagenesis according to structural insights and enzymatic kinetics. Among the double-site mutants, E380Q/S387L exhibits an approximately 38-fold increase in its specificity constant toward the QPQ sequence, favoring glutamines at the P1 and P3 positions compared to the wild type. Not only does the heightened specificity of E380Q/S387L improve the hydrolytic activity against pro-immunogenic peptides, but also holds this version of the enzyme as a promising candidate as an oral therapy enzyme for celiac disease.

Severe COVID-19 is a major cause of morbidity and mortality worldwide, especially to those with co-morbidities, the elderly and the immunocompromised. However, the molecular determinants critical for severe COVID-19 progression remain to be fully elucidated. Meta-analysis of transcriptomic RNAseq and single-cell sequencing datasets comparing between severe and mild COVID-19 patients have demonstrated that the early expansion of myeloid-derived suppressor cells (MDSCs) could a key feature of severe COVID-19 progression. Besides serving as potential early prognostic biomarkers for severe COVID-19 progression, several studies have also indicated the functional roles of MDSCs in severe COVID-19 pathogenesis and possibly even long COVID. Given the potential links between MDSCs and severe COVID-19, we examined the existing literature summarizing the characteristics of MDSCs, the evidence of MDSCs in facilitating severe COVID-19 pathogenesis and discuss the potential therapeutic avenues that can be explored to reduce the risk and burden of severe COVID-19. We also provide a web app where users can visualise the temporal changes of specific genes or MDSC-related gene sets during severe COVID-19 progression and disease resolution at https://temporal-severe-covid.streamlit.app/, based on our previous study described by Ong et al., 2021.

Global agriculture has been forced to increase food production to feed the growing human population, while confronting various environmental obstacles such as global warming, resistance to pathogens, and constraints on arable land caused by soil salinity, drought, rising sea level, saltwater intrusion, and urbanization. Regarding abiotic stresses, salinity is a worldwide problem for agricultural production. Many efforts, therefore, have been made to cope with the environmental challenges, however, the progress of salinization, which is mainly caused and accelerated by anthropogenic activities, is likely faster than our progress in finding ways to deal with this problem. In addition, drought represents a global threat to the production of major crops. In addition, pests and pathogens cause significant crop losses and diminish global food security. Among the various strategies that have been investigated and applied in plant science, antimicrobial peptides derived from plants have caught widespread attention from scientists since these peptides exhibit beneficial biological activities. In agricultural science, there have been reports on the roles of antimicrobial peptides with active properties against biotic and abiotic stresses. Non-specific plant lipid transfer proteins, thionins, systemins, defensins, cyclotides, and heveins-like antimicrobial peptides are common antimicrobial peptides that have been found to be involved in the defense system against fungi and insect pests. Based on their potential ability to protect crops from pests, bacteria, and pathogenic fungi, the use of antimicrobial peptide genes in creating transgenic plants has been largely conducted during the last decades, and these studies have obtained positive results against the growth of fungi and bacteria. This review will focus on the latest progress in studies of antimicrobial peptides related to biotic and abiotic stress tolerance in plants. We will also update the current progress in the development of antimicrobial peptide-based transgenic crops.

A recent study published in Nature Medicine (Mautner et al., Nat Med 2023 Nov 2. doi: 10.1038/s41591-023-02632-w. Epub ahead of print) reported findings regarding a four-arm parallel, multicenter, single-blind, randomized, controlled clinical trial with 480 patients, aimed at comparing the therapeutic effectiveness of orthobiologic interventions against corticosteroid injections for the treatment of knee osteoarthritis (OA). The study concluded that, one-year post-treatment, all orthobiologic therapies examined were equivalent in effectiveness to each other and to corticosteroid injections. While we commend the scope and methodological rigor of the trial, the publication could be enhanced by a more comprehensive presentation of data to support the conclusions drawn. Particularly, the omission of baseline data for primary outcomes, the missing context to evaluate absolute values, the lack of discussion regarding interindividual variability, and the unexpectedly favorable results for corticosteroids that contrast the established literature, are areas that merit additional examination and insight. Here we aim to highlight these points for consideration, and advocate for the release of supplementary data to facilitate a comprehensive understanding and utilization of this rich dataset.

Traditional underground pipeline inspection primarily relies on closed-circuit television (CCTV) systems, capturing visual data of deformations within sewer systems for manual assessment of their types and severity. However, these methods heavily rely on human expertise, which leads to subjective detection with limited accuracy. Moreover, they lack the capability for quantitative analysis of deformation extent, hindering accurate assessments and limiting overall inspection effectiveness. To address these challenges, this paper proposes a method for quantitatively detecting geometric deformations in underground pipe corridors using laser point cloud data. The approach, employing laser scanning with a 3D scanner, enables objective detection of internal pipeline deformations and quantitative assessment of blockage levels. In comparison to traditional CCTV-based methods, this approach offers advantages in objectivity and quantification, thereby improving detection reliability, accuracy, and overall efficiency.

This study explores the intricate relationship between the angle of vehicle-to-vehicle (V2V) conflicts and the severity of conflicts at signalized intersections during traffic signal failures, leveraging the advanced capabilities of LiDAR sensor technology. The research delves into the impact of conflict angles on collision outcomes and introduces LiDAR as a novel and precise tool for capturing spatial dynamics during unregulated traffic movements. Findings reveal that as the angle of V2V conflicts increases, indicating a wider approach between vehicles, the severity of conflicts tends to decrease. This reduction in severity is attributed to the detailed spatial data provided by LiDAR, enabling the identification of glancing impacts rather than direct head-on collisions. The mitigation of forces involved in collisions at increased angles contributes to a decreased likelihood of severe consequences, such as extensive vehicle damage and serious injuries to occupants.Conversely, as the angle of V2V conflicts decreases, signifying a narrower approach between vehicles, the potential for severe collisions tends to increase. LiDAR technology facilitates precise measurements of spatial coordinates and temporal dynamics, revealing that collisions occurring at smaller angles involve more direct and concentrated impact forces. This heightened risk of extensive damage to vehicles and increased injury severity is crucially captured by LiDAR, providing valuable insights into the relationship between conflict angles and collision outcomes during traffic signal failures. This study underscores the efficacy of LiDAR sensor technology in understanding the nuanced dynamics of V2V conflicts and highlights its role in developing targeted safety interventions. The integration of LiDAR data into traffic safety analyses offers a sophisticated approach to mitigating the impact of conflicts and enhancing overall safety at signalized intersections during signal failures. The research also highlighted that as the angle of V2P conflicts widens, PET experiences a proportional increase. A larger conflict angle signifies a more complex interaction between vehicles and pedestrians, leading to prolonged PET values. Conversely, a decrease in the angle of V2P conflicts is associated with a reduction in PET during traffic signal failures.The study commences with an examination of traffic characteristics at the E Cold Spring Ln – Hillen Rd intersection in Baltimore City, MD, utilizing a deployed LiDAR sensor. When the signal at this intersection experienced a temporary malfunction on a regular working day in September 2023, the LiDAR captured data related to vehicle and pedestrian volumes, as well as V2V and V2P conflicts. The primary objective of the research is to evaluate the repercussions of traffic signal failures on both traffic flow and safety, specifically V2V and V2P conflicts, occurring during periods of improper signal functionality. Additionally, this investigation delves into various factors that impact traffic signal performance, encompassing traffic demand, geometric layout, pedestrian interactions, and the incorporation of emerging technologies.

Non-suicidal self-injurious behavior (NSSI), prevalent in patients with non-psychotic mental disorders (NPMD), is associated with numerous adverse outcomes. Despite active research into the clinical and psychological aspects of NSSI, the underlying biological mechanisms remain obscure. Early adverse experiences are believed to induce long-lasting changes in neuroendocrine mechanisms of stress control playing a key role in NSSI development. The aim of the study was to evaluate parameters potentially predicting development of NSSI in female patients with NPMD. Eighty female patients over 18 years with NPMD and suicidal ideation (40 with and 40 without NSSI) and 48 age matching women without evidence of mental illness (healthy controls) were enrolled. Diagnostic interviews and self-report measures were used to assess childhood maltreatment, presence, frequency, and characteristics of suicidal and self-injurious thoughts and behaviors, the Beck Depression Inventory scale to assess severity of depression. Hypothalamic-pituitary-adrenal axis markers, hormones, and neurotrophic factors were measured in blood serum. The likelihood of developing NSSI in patients with NPMD was associated with early adverse family history and elevated adrenocorticotropic hormone levels. Dysregulation of hypothalamic-pituitary-adrenal axis as a result of early chronic stress experiences may represent critical biological mechanism promoting the development of NSSI behaviors in patients with NPMD.

Healthcare faces a major problem with the increased emergence of antimicrobial resistance due to the over-prescription of antibiotics. This causes dysbiosis and major disruption of the gut microbiome, leading to the development of intestinal diseases, abnormalities in the regulation of immune responses, malfunctioning of entero-endocrine signaling, and an imbalance in communication with the central nervous system (CNS). Bacteriophages may provide a solution to the treatment of bacterial infections given their specificity. This approach increased rapidly over the last ten years, because of the rapid rise of multi-drug-resistant bacteria worldwide coupled with a decline in the development and production of novel antibacterial agents. Eradication of multidrug-resistant bacteria is often only possible with bacteriophage treatment. Phage proteins are used to stimulate immune responses against specific pathogens, improve antibiotic susceptibility, prevent biofilm formation, and characterize bacterial pathogens. This review discusses the therapeutic potential of bacteriophage-derived proteins.

Efficient traffic management at signalized intersections is integral to urban infrastructure development, requiring accurate estimation of delay times to mitigate congestion and enhance overall transportation systems. Traditional methodologies, including empirical observations and microsimulation software, have been prevalent in assessing delay times; however, their limitations have prompted the exploration of novel technologies like LiDAR (Light Detection and Ranging) sensors. This research study investigates and compares the accuracy of delay time estimations obtained from LiDAR sensor technology with those derived from microsimulation in AIMSUN. LiDAR sensors, known for their high-resolution, real-time data collection capabilities, offer a promising avenue for precise measurement of delay times at signalized intersections. Nonetheless, challenges in sensor placement, environmental influences, and data processing complexities suggest the need for further development and validation. In parallel, microsimulation software, exemplified by AIMSUN, provides a virtual platform for scenario testing but relies on assumptions that may not always mirror real-world traffic dynamics accurately. The comparative analysis conducted in this study aims to critically examine the discrepancies and potential complementarity between delay times obtained from LiDAR sensor technology and those derived from microsimulation in AIMSUN. The research involves an in-depth evaluation of real-time, high-resolution data collected by LiDAR sensors, assessing their accuracy in capturing the intricate movements and behavior of vehicles at signalized intersections. Simultaneously, AIMSUN microsimulation delay time models are scrutinized for their ability to accurately replicate these observed delay times. The disparities identified serve as critical insights into the challenges of both methodologies, prompting the discussion on the prospects of integrating LiDAR-derived data and microsimulation calibration processes to enhance the precision and reliability of delay time estimations. Future traffic management strategies can significantly benefit from a more accurate understanding of delay times, and this study endeavors to contribute to the advancement of methodologies in traffic engineering for more effective urban transportation systems.The paper's findings illuminate the potential and limitations of both LiDAR sensor technology and microsimulation in estimating delay times at signalized intersections. The results highlighted that the LiDAR sensors could accurately calculate delay times at a signalized intersection. Furthermore, the calculated delay time differences by LiDAR and AIMSUN at three days with the highest vehicle volumes (counts) are always less than 6.5%.

As urban populations continue to grow, efficient traffic management becomes paramount in reducing congestion, enhancing air quality, and improving overall quality of life. This study addresses the critical issue of intersection efficiency through the implementation of smart green time allocation strategies at a signalized intersection equipped with two LiDAR sensors. This research aims to investigate optimal green time allocations provided by two LiDAR sensors and analyze the LiDAR results by microsimulation in AIMSUN. The research first introduces the concept of LiDAR-equipped signalized intersections and their potential to enhance traffic control precision. Two LiDAR sensors are strategically placed at the intersection of Marlboro Pike and Brooks Dr. in Coral Hills, MD, USA to capture real-time data on vehicle and pedestrian movements. The data are then processed to generate accurate and dynamic traffic profiles, ensuring the responsiveness of the green time allocation system to varying traffic conditions.The heart of this study lies in the integration of AIMSUN microsimulation with LiDAR data. Through meticulous modeling and simulation, the research explores the optimal green allocation at morning, mid-day, and afternoon peak hours’ scenarios to comprehensively assess the impact of LiDAR-enabled dynamic signal control. The findings demonstrate that smart green time allocation, informed by real-time LiDAR data and implemented through AIMSUN microsimulation, significantly enhances intersection efficiency. By adapting signal timings to real-time traffic demands, congestion, travel times, and emissions are reduced. Furthermore, this research highlighted that the optimal green time allocation in morning, mid-day, and afternoon peak hour intervals can improve the delay time by 55.3%, 59.7%, and 55.6%, respectively.In conclusion, this paper sheds light on the potential of LiDAR technology to transform intersection management. Through a case study involving two LiDAR sensors and AIMSUN microsimulation, it reveals the tangible benefits of dynamic signal control in enhancing intersection efficiency and creating more sustainable urban environments. These findings are pivotal in advancing the discourse on modern urban traffic management and promoting data-driven solutions for the challenges of today's cities.

Our previous studies showed that in patients with brain diseases neurotrophic factors in lacrimal fluid (LF) may change more prominently than in blood serum (BS). Since glial cell line-derived neurotrophic factor (GDNF) is involved in control of neuronal networks in epileptic brain, we aimed to assess GDNF levels in LF and BS as well as BDNF, hypothalamic-pituitary-adrenocortical and inflammation indices in BS of patients with focal epilepsy (PWFE), epilepsy and comorbid depression (PWFE+MDD) compared with major depressive disorder (PWMDD) and healthy controls (HC). GDNF levels in BS were similar in patients and HC and higher in PWFE taking valproates. GDNF levels in LF were significantly lower in all patient groups compared to controls, and independent of drugs used. LF and BS GDNF concentrations positively correlated in HC, but not patient groups. BDNF level was lower in BS of patients compared with HC and higher in PWFE+MDD taking valproates. Logistic regression models demonstrated that GDNF decrease in LF may be an important predictor of focal epilepsy. Probability of focal epilepsy can be evaluated using GDNF in LF and BDNF in BS; MDD - GDNF in LF, cortisol and TNF-α in BS; epilepsy with MDD - GDNF in LF, TNF-α and BDNF in BS.

This study investigates the application of Light Detection and Ranging (LiDAR) sensor technology for data collection at signalized intersections characterized by a high rate of traffic crashes. The research aims to provide valuable insights into the potential of LiDAR-based data analysis to enhance road safety and traffic management at signalized intersections. The research methodology involved the deployment of LiDAR sensors at Marlboro Pike & Brooks Dr. signalized intersection in Coral Hills, Maryland. Two LiDAR sensors installed in this intersection to collect high-resolution, three-dimensional data of the intersection area from June, 1^st to July, 7^th 2023. The data included information on vehicle trajectories, speeds, and behaviors, as well as pedestrian and cyclist movement patterns. Concurrently, historical traffic crashes recorded and traffic flow data were obtained for the same intersection.The analysis of LiDAR data involved several key aspects including LiDAR data allowed for a precise evaluation of traffic flow patterns, including congestion points, traffic volume fluctuations, and peak-hour behavior. This information provided insights into potential factors contributing to crashes. By analyzing LiDAR data, the study identified near-miss incidents, which are often precursors to actual crashes. This proactive approach could assist in identifying crash-prone areas within the intersection. The LiDAR data analysis also focused on pedestrian and cyclist movements, including jaywalking and bike lane usage. The aim was to identify areas where infrastructure improvements could enhance safety for vulnerable road users. LiDAR data was compared with historical crash data to identify specific locations within the intersection that exhibited a high frequency of crashes. This information can guide targeted safety interventions. Last but not least, the study explored opportunities to optimize traffic signal timings based on real-time traffic data from LiDAR. Adaptive signal control could help mitigate congestion and reduce the risk of crashes.The results of this study demonstrated the potential of LiDAR sensor technology in collecting detailed data for traffic analysis in signalized intersections. By combining LiDAR data with historical crash records and traffic flow data, traffic engineers and urban planners can develop evidence-based strategies to reduce the frequency and severity of crashes in high-risk areas. Ultimately, this research contributes to a comprehensive understanding of how LiDAR technology can be employed to enhance road safety and traffic management, providing valuable insights for traffic engineers, urban planners, and policymakers seeking to improve the safety and efficiency of signalized intersections with a history of high traffic crashes.

Traffic signal control plays a key role in managing urban traffic flow, enhancing safety, and minimizing congestion and conflicts. Effective green time allocation is a critical element of this control process. This research explores the utilization of LiDAR sensor technology in the optimization of green time allocation for one phase of a traffic signal at a signalized intersection. LiDAR sensors provide precise and real-time data on vehicle presence and traffic patterns, enabling a data-driven approach to traffic signal control. The study begins with an analysis of the limitations of traditional traffic signal control strategies, which often rely on fixed-time plans or rudimentary vehicle detection systems. These approaches can lead to suboptimal green time allocation, resulting in inefficient traffic management and increased vehicle delays.The integration of LiDAR sensors provides detailed information on vehicle queues, arrival rates, and vehicle types. The research presents a practical framework for green time allocation optimization, considering factors such as intersection geometry, traffic volumes, and signal coordination. An intelligent control algorithm was developed that uses LiDAR data to determine the optimal green time for a specific phase, thereby reducing unnecessary waiting times and enhancing intersection efficiency. The effectiveness of the proposed LiDAR-based green time allocation strategy is demonstrated through extensive field tests. The results indicate significant improvements in intersection throughput, reduced delays, and enhanced traffic safety. In conclusion, this research highlights the transformative potential of LiDAR sensor technology in traffic signal control, specifically in the context of optimizing green time allocation. The findings support the notion that adaptive and data-driven strategies, when integrated with LiDAR sensors, can contribute to more efficient, sustainable, and safe urban traffic management. This research aims to provide valuable insights for transportation engineers, policymakers, and researchers seeking innovative solutions for modern urban traffic control.

Traffic congestion is a persistent and challenging problem in urban areas, leading to increased travel times, fuel consumption, and environmental pollution. Signalized intersections play a pivotal role in regulating traffic flow, and their efficiency has a direct impact on the overall traffic performance of a city. This study investigates the effect of traffic signal in managing traffic volume and reducing congestion and delays at signalized intersections through a comprehensive analysis of existing research, data collection, and simulations.The research begins by analyzing the traffic characteristics by an installed LiDAR sensor at E Cold Spring Ln – Hillen Rd intersection in Baltimore City, MD. When the signal at this intersection stopped working for some hours during a working day in September 2023, the LiDAR recorded vehicle and pedestrian counts, vehicle-vehicle and vehicle-pedestrian conflicts, and jaywalking events conflicts. The research aims to assess the impact of traffic signal failures on traffic flow, congestion, safety (V2V and V2P conflicts), and the frequency of jaywalking events before, during, and after improper performance of the traffic signal. Furthermore, this study explores the factors influencing traffic signal performance, including traffic demand, geometric layout, pedestrian interactions, and the integration of emerging technologies. The analysis results highlighted the importance of signal control systems existence at this intersection that can adjust signal timing in response to changing the real-time traffic conditions.Reduced congestion, minimized delays, and enhanced traffic flow are observed outcomes, contributing to a more sustainable and efficient urban transportation system. However, it is crucial to consider the trade-offs and challenges associated with traffic signal optimization, such as the potential for increased travel times for certain modes of transportation and the need for ongoing maintenance and updates. In conclusion, this study underscores the pivotal role of traffic signals in managing traffic volume and reducing congestion and delays at signalized intersections. Through evidence-based analysis and innovative signal control strategies, urban planners and transportation authorities can work towards creating more efficient, sustainable, and less congested transportation networks. The insights derived from this research can inform policy decisions and guide the development of future traffic management solutions, ultimately leading to improved quality of life in urban areas.

This research paper investigates ultra-wideband (UWB) localization systems by focusing on the use of average filter (AVG), Kalman filter (KF), and extended Kalman filter (EKF) algorithms, as well as a novel integrated filtering method that incorporates low-pass filter (LPF) into AVG, KF, and EKF. The study aims to improve localization loss in indoor environments using a TurtleBot robot equipped with a camera to observe ground truth positions. To evaluate the effectiveness of the proposed algorithms, a comprehensive comparison of the raw and filtered data with the camera-based ground truth observations is performed. Quantitative analyses of the results, including max, min, max-min, and mean error, are performed to evaluate the localization performance of the algorithms and the integrated filtering method. The results reveal that the integrated filtering method has performed better accuracy in comparison with existing methods.

This paper provides a data set collected with an underground shaft mobile mapping system equipped with rotated Velodyne VLP-16, Velodyne Ultra Puck VLP-32c, Livox Tele-15, and IMU Xsens MTi-30. The ground truth data was acquired with a geodetic survey including 15 ground control points and 6 Faro Focus 3D Terrestrial Laser Scanner stations of a total 273,784,932 of 3D measurement points. The aim of the paper is to describe the data set of a mining shaft. Obtained mobile mapping data can be the basis for detailed analyzes of the technical condition of the shaft. This data set provides an end-user case study of realistic applications in mobile mapping technology. It provides ground truth data, mobile mapping data and software tools for manipulation and visualization. The project is released and maintained at https://michalpelka.github.io/mine-mapping-dataset/.

The rhizome of Drynaria fortunei (Kunze ex Mett.) J. Sm., which is known as “Golsebo” in Korea, has been traditionally used to treat various inflammatory conditions, including bone metabolism disorders. It relieves blood extravasation, stops bleeding, repairs broken bone tissue, treats bone fractures, and kills bacteria. Recently, D. fortunei-derived polysaccharides (DFP) have been identified as bioactive compounds that act against numerous human diseases. However, the molecular mechanism underlying the bone metabolism-improving effect of DFP has not been elucidated. In this study, we evaluated the modulatory effects of DFP on the differentiation of bone marrow-derived macrophages into osteoclasts. We performed tartrate-resistant acid phosphatase assays using DFP (different concentrations and molecular weights) to evaluate the degree of bone resorption in the receptor activator of nuclear factor-κB ligand (RANKL)-induced osteoclastogenesis of bone marrow-derived macrophages. DFP significantly inhibited RANKL-induced osteoclastogenesis and suppressed RANKL-mediated overexpression of c-Fos and nuclear factor of activated T cells 1, thereby downregulating osteoclast-specific gene (Atp6v0d2, DC-STAMP, and cathepsin K) expression. DFP has potential as a nutraceutical candidate for treating bone loss diseases, including osteoporosis in postmenopausal women.

Although the COVID-19 pandemic appears to be diminishing, the emergence of SARS-CoV-2 variants represents a threat to humans due to its inherent transmissibility, immunological evasion, virulence, and invulnerability to existing therapies. The previous COVID-19 pandemic affected more than 500 million people and caused over 6 million deaths. Vaccines are essential, but in circumstances when vaccination is not accessible or in individuals with compromised immune systems, drugs can give additional protection. Targeting host signaling pathways is recommended due to their genomic stability and resistance barriers. Plant-derived products, which have been used in traditional medicine for treating pathological conditions, offer structurally novel therapeutic compounds including anti-viral. In addition, plant-derived bioactive substances might serve as the ideal basis for developing sustainable/efficient/cost-effective anti-viral alternatives. Epigenetic modifications in the genome can affect gene expression without altering DNA sequence. Host cells can use epigenetic gene regulation as a mechanism to silence incoming viral DNA molecules, while viruses have recruited cellular epi transcriptomic (covalent modifications of RNAs) modifiers to increase the translational efficiency and transcript stability of viral transcripts to enhance viral gene expression and replication. Moreover, viruses manipulate host cells' epigenetic machinery to ensure productive viral infections. Environmental factors, such as natural products, may influence epigenetic modifications. In this review, we are exploring the potential of plant-derived substances as epigenetic modifiers for broad-spectrum anti-viral activity, reviewing modulation processes and anti-viral effects on DNA and RNA viruses, and addressing future research objectives in this rapidly emerging field.

Non-rigid registration presents a significant challenge in the domain of point cloud processing. The general objective is to model complex non-rigid deformations between two or more overlapping point clouds. Applications are diverse and span multiple research fields, including registration of topographic data, scene flow estimation, and dynamic shape reconstruction. To provide context, we begin with a general introduction to the topic of point cloud registration, including a categorization of methods. Next, we introduce a general mathematical formulation for point cloud registration and extend it to address non-rigid registration. A detailed discussion and categorization of existing approaches to non-rigid registration follows. We then introduce our own method where the usage of piece-wise tricubic polynomials for modeling non-rigid deformations is proposed. Our method offers several advantages over existing methods. These advantages include easy control of flexibility through a small number of intuitive tuning parameters, a closed-form optimization solution, and an efficient transformation of huge point clouds. We demonstrate our method through multiple examples that cover a broad range of applications, with a focus on remote sensing applications - namely, the registration of Airborne Laser Scanning (ALS), Mobile Laser Scanning (MLS), and Terrestrial Laser Scanning (TLS) point clouds. The implementation of our algorithms is open source and can be found on GitHub.

Early spaceborne laser altimetry mission development starts in pre-phase A design, where diverse ideas are evaluated against mission science requirements. A key challenge is predicting realistic instrument performance through forward modeling at arbitrary spatial scale. Analytical evaluations compromise accuracy for speed, while radiative transfer modeling is not applicable at global scale due to computational expense. Instead of predicting arbitrary properties of a lidar measurement, we develop a baseline theory to predict only the distribution of uncertainty specifically for the terrain elevation retrieval based on terrain slope and fractional canopy cover features through a deep neural network gaussian mixture model, also known as a mixture density network (MDN). Training data was created from differencing geocorrected GEDI L2B elevation measurements with 32 independent reference lidar datasets in the contiguous U.S. from the National Ecological Observatory Network. We trained the MDN and selected hyperparameters based on regional distribution predictive capability. On average, the relative error of equivalent standard deviation of predicted regional distributions was 15.9%, with some anomalies in accuracy due to generalization and insufficient feature diversity and correlation. As an application, we predict the percent of elevation residuals of a GEDI-like lidar within a given mission threshold from 60°S to 78.25°N, which correlate to qualitative understanding of prediction accuracy and instrument performance.

Mathematical models of non-small cell lung cancer are powerful tools that use clinical and experimental data to describe various aspects of tumorigenesis. The developed algorithms capture phenotypic changes in the tumor and predict changes in tumor behavior, drug resistance, and clinical outcomes of anti-cancer therapy. The aim of this study was to propose a mathematical model that predicts the changes in the cellular composition of patient-derived tumor organoids over time with a perspective of translation of these results to the parental tumor, and therefore to possible clinical course and outcomes for the patient. Using the data on specific biomarkers of cancer cells (PD-L1), tumor-associated macrophages (CD206), natural killer cells (CD8), and fibroblasts (αSMA) as input, we proposed a model that accurately predicts the cellular composition of patient-derived tumor organoids at a desired time point. Combining the obtained results with “omics” approaches will improve our understanding of the nature of NSCLC. Moreover, their implementation into clinical practice will facilitate a decision-making process on treatment strategy and develop a new personalized approach in anti-cancer therapy.

Self-driving vehicles demand efficient and reliable depth sensing technologies. Lidar, with its capacity for long-distance, high-precision measurement, is a crucial component in this pursuit. However, conventional mechanical scanning implementations suffer from reliability, cost, and frame rate limitations. Solid-state lidar solutions have emerged as a promising alternative, but the vast amount of photon data processed and stored using conventional direct time-of-flight (dToF) prevents long-distance sensing unless power-intensive partial histogram approaches are used. In this paper, we introduce a groundbreaking ‘guided’ dToF approach, harnessing external guidance from other onboard sensors to narrow down the depth search space for a power and data-efficient solution. This approach centers around a dToF sensor in-which the exposed time widow of independent pixels can be dynamically adjusted. We utilize a 64-by-32 macropixel dToF sensor and a pair of vision cameras to provide the guiding depth estimates. Our demonstrator captures a dynamic outdoor scene at 3 fps with distances up 75 m. Compared to a conventional full histogram approach, on-chip data is reduced by over 25 times, while the total laser cycles in each frame are reduced by at least 6 times compared to any partial histogram approach. The capability of guided dToF to mitigate multipath reflections is also demonstrated. For self-driving vehicles where a wealth of sensor data is already available, guided dToF opens new possibilities for efficient solid-state lidar.

We discuss a remote sensing system that is used to simultaneously detect range-resolved differential absorption LIDAR (light detection and ranging; DIAL) signals and integrated path DIAL signals (IP-DIAL) from aerosol targets for ranges up to 22 km. The DIAL/IP-DIAL frequency converter consists of an OPO pumped at 1064 nm to produce light at 1.6 μm and operates at 100 Hz pulse repetition frequency. The probe light is free space coupled to a movable platform that contains one transmitter and two receiver telescopes. Hybrid photon counting/current systems increase the dynamic range for detection by two orders of magnitude. Range resolved and column integrated dry-air CO2 and CH4 mixing ratios are obtained from line shape fits of CO2 and CH4 centered at 1602.2 nm and 1645.5 nm, respectively and measured at 10 different frequencies over ≈ 1.5 cm-1 bandwidth. The signal-to-noise ratios (SNR) of the IP-DIAL returns from cloud aerosols approach 1000:1 and the uncertainties in the mixing ratios weighted according to the integrated counts over the cloud segments range from 0.1 % to 1 %. The range averaged DIAL mixing ratios are in good agreement with the IP-DIAL mixing ratios at the 1 % to 2 % level for both CO2 and CH4. These results can serve as a validation method for future active and passive satellite observational systems.

Super Typhoon Saola came very close to Hong Kong on 1 and 2 September 2023, necessitating the issuance of No. 10 hurricane signal, the highest tropical cyclone warning signal, in Hong Kong. While there were widespread damages in Hong Kong, no people were killed in the event with effective early warning. It is rare that a super typhoon came very close to Hong Kong and this paper is the first part in the series of the documentation of Saola to summarize the interesting observations of Saola near Hong Kong for future reference by weather forecasters, including sur-face observations, upper air observations, microburst alert from weather radars, and turbulence intensity based on spectral width measurement of radars.

The current deformation and stable state of slopes with historical shatter signs is a concern for engineering construction. Suspected landslide scarps were discovered at the rear edge of the slope of the Genie in the Sichuan-Tibet transportation corridor during a field investigation. In order to qualitatively determine the current status of the surface deformation of this slope, this paper uses high-resolution optical remote sensing, airborne LiDAR and InSAR technologies for comprehensive analysis. The interpretation of high-resolution optical and airborne LiDAR data revealed that the rear edge of the slope exhibits three levels of scarps. However, no deformation was detected with the D-InSAR analysis of ALOS-1 radar images from 2007 to 2008 or with the Stacking-InSAR and SBAS-InSAR processing of Sentinel-1A radar images from 2017 to 2020. A geological model of the slope was established in combination with field investigation stipulating that the slope is composed of steep anti-dip layered dolomite limestone and that the scarps at the rear edge of the slope were caused by historical shallow toppling. Further research is recommended to determine the extent of toppling deformation and evaluate the slope stability under the disturbance of tunnel excavation.

Actinobacteria are known for their production of bioactive specialized metabolites, but still under-exploited due to silent biosynthetic gene clusters (BGCs) not expressed in conventional laboratory cultures conditions. In this study, three marine-derived actinobacteria were cultured using the one strain many compounds method (OSMAC) to explore the real potential of these microorganisms. Salinispora arenicola (SH-78) and two Micromonospora sp. (SH-82 and SH-57) were grown under varying parameters such as time, medium composition, and culture support. The microbial extracts produced were chemically screened by high-resolution analysis (HRMS/MS). The data were used to build ion identity molecular networks (IIMN) and annotate the specialized metabolites detected. A selection of extracts was biologically screened for their cytotoxic and antiplasmodial activities. The IIMNs and the annotations produced were used to assess the impact of culture parameters on the metabolomic diversity of the selected actinobacteria and highlight the presence of unique molecules to specific culture conditions. Micromonospora sp. SH-57 produced a greater quantity of metabolites in liquid culture, with higher associated biological activity. The method employed allowed to explore deeper the specialized metabolites production and to identify the best culture conditions to produce bioactive natural products.

Brain-derived neurotrophic factor (BDNF) is a key molecule in promoting neurogenesis, dendritic and synaptic health, and neuronal survival, plasticity, and excitability, all of which are disrupted in neurological and cognitive disorders such as Alzheimer’s disease (AD). Extracellular aggregates of amyloid-β (Aβ) in the form of plaques and intracellular aggregates of hyperphosphorylated tau protein have been identified as major pathological insults in AD brain, along with immune dysfunction, oxidative stress, and other toxic stressors. Although aggregated Aβ and tau lead to decreased brain BDNF expression, early losses in BDNF prior to plaque and tangle formation may be due to other insults such as oxidative stress and contribute to early synaptic dysfunction. Physical exercise, on the other hand, protects synaptic and neuronal structure and function, with increased BDNF as a major mediator of exercise-induced enhancements in cognitive function. Here, we review recent literature on mechanisms behind exercise induced BDNF upregulation and its effects on improving learning and memory and on Alzheimer’s disease pathology. Mechanisms include elevations in peripheral BDNF-inducing hormones such as osteocalcin, FNDC5/irisin, and lactate. The fundamental mechanisms of how exercise impacts BDNF and cognition are unclear but are a prerequisite to developing new biomarkers and therapies to delay or prevent cognitive decline.

It was for long time believed that lidar systems based on the use of high-repetition micro-pulse lasers could be effectively used to only stimulate atmospheric elastic backscatter echoes, and thus only exploited in elastic backscatter lidar systems. Their application to stimulate rotational and roto-vibrational Raman echoes, and consequently their exploitation in atmospheric thermodynamic profiling, was considered not feasible based on the technical specifications possessed by these laser sources until a few years ago. However, recent technological advances in the design and development of micro-pulse lasers, presently achieving high UV average powers (1-5 W) and small divergences (0.3-0.5 mrad), in combination with the use of large aperture telescopes (0.3-0.4 m diameter primary mirrors), allow to presently develop micro-pulse laser-based Raman lidars capable to measure the vertical profiles of atmospheric thermodynamic parameters, namely water vapour and temperature, both in daytime and nighttime. This paper is aimed at demonstrating the feasibility of these measurements and at illustrating and discussing the high achievable performance level, with a specific focus on water vapour profile measurements. The technical solutions identified in the design of the lidar system and their technological implementation within the experimental setup of the lidar prototype are also carefully illustrated and discussed.

Rice canopy height and density are directly usable crop phenotypic traits for the direct estimation of crop biomass. Therefore, it is crucial to rapidly and accurately estimate rice canopy phenotypic parameters. To achieve non-destructive detection and estimation of essential phenotypic parameters in rice, a platform based on LiDAR point cloud data for rice phenotypic parameter detection was established. Data collection of rice canopy layers was performed across multiple plots. The LiDAR-detected canopy top point clouds were selected using a method based on the highest percentile, and the rice canopy surface model was calculated. Canopy height estimation was the difference between ground elevation and percentile value. To determine the optimal percentile defining the rice canopy top, testing was conducted incrementally from 0.8 to 1 with an increment of 0.005. The optimal percentile value was found to be 0.975. The root mean square error (RMSE) between LiDAR-detected canopy height and manually measured canopy height was calculated. The prediction model based on canopy height (R2=0.941, RMSE=0.019) exhibited a strong correlation with actual canopy height. Linear regression analysis was conducted between gap fraction of different plots and manually detected average Leaf Area Index (LAI) of rice canopy. Prediction models for canopy LAI based on ground return counts (R2=0.24, RMSE=0.1) and ground return intensity (R2=0.28, RMSE=0.09) showed strong correlations but had lower correlation with rice canopy LAI. Regression analysis was performed between LiDAR-detected canopy height and manually measured rice canopy LAI. The results indicated that the prediction model based on canopy height (R2=0.77, RMSE=0.03) was more accurate.

The concept of targeted drug delivery can be described in terms of the drug’s ability to mimic the other biological objects’ property to localize to target cells or tissues. For example, drug delivery systems based on red blood cells or mimicking some of their useful features, such as long circulation in stealth mode, have been known for decades. On the contrary, therapeutic strategies based on macrophages have gained very limited attention until recently. Here we review two biomimetic strategies associated with macrophages, that can be used to develop new therapeutic modalities: First, the mimicry of certain types of macrophages (i.e., the use of macrophages, including tumor-associated, or macrophage-like particles as a carrier for targeted delivery of therapeutic agents); and Second, the mimicry of ligands, naturally absorbed by macrophages (i.e., the use of therapeutic agents specifically targeted at macrophages). We discuss potential applications of biomimetic systems involving macrophages for the new advancements in treatment of infections, inflammatory diseases, and cancer.

Background: Obtaining adipose-derived stem cells is limited by its capacity to differentiate. Stimulation by platelet-rich fibrin appears to have the beneficial effects to accelerate differentiation. Objective: To obtain immunohistochemistry evidence of platelet-rich fibrin to accelerate differentiation of adipose-derived stem cells. Methods: We used post-test only controlled-group design. Adipose-derived stem cells were isolated and cultured until 4 passages in α-MEM culture medium. Results: GATA-4 expression increased in platelet-rich fibrin group compared with control group (68.20±6.82 vs 58.15±1.23 counts; p<0.05; 68.20±6.82 vs 52.96±2.02 counts; p<0.05). Troponin expression also increased in platelet-rich fibrin group (50.66±7.2 vs 10.73±2.39 counts; p<0.05; 50.66±7.2 vs 26.00±0.4 counts; p<0.05). Conclusion: There is an immunohistochemistry benefit of platelet-rich fibrin to accelerate differentiation of adipose-derived stem cells into cardiomyocyte-like cells.

Extensive surface and upper air measurements of a typhoon over the northern part of the South China Sea, namely, Typhoon Talim in July 2023, are documented and analyzed in this paper. A number of features have been observed from the upper air measurements. First, the log law and the power law are found to be appropriate in fitting the wind profiles of the typhoon in the first 1000 m or so above the sea surface. A low level jet is observed in the lower troposphere from the observations of the radar wind profilers. The paper is also novel from the perspectives that the vertical wind profile from a Doppler LIDAR on an offshore platform over the northern part of the South China sea, and that ocean radar data are used to analyze the surface wind observations of a typhoon in the region. The results of this paper would be useful in understanding the structure of tropical cyclones, e.g. in wind engineering applications.

Krill oil (KO) shows promise as a natural marine-derived ingredient for improving skin health. This study investigated its antioxidant, anti-inflammatory, anti-wrinkle, and moisturizing effects on skin cells and UVB-induced skin photoaging in hairless mice. In vitro assays on HDF, HaCaT, and B16/F10 cells, and in vivo experiments on 66 hairless mice, were conducted. Mice received oral KO administration (100, 200, or 400 mg/kg) once a day for 15 weeks and UVB radiation three times a week. In vitro, KO significantly countered UVB-induced oxidative stress, reduced wrinkles, and prevented skin water loss by enhancing collagen and hyaluronic synthesis. In vivo, all KO dosages showed dose-dependent inhibition of oxidative stress-induced inflammatory photoaging-related skin changes. Skin mRNA expressions for hyaluronan synthesis and collagen synthesis genes also increased dose-dependently after KO treatment. Histopathological analysis confirmed that Krill Oil (KO) ameliorated the damage caused by UVB-irradiated skin tissues. These findings suggest that KO may be a valuable intervention to mitigate UVB-induced skin photoaging and address various skin concerns.

Background: This review aims to explore the regulatory mechanism of microglial exosomes, particularly focusing on the role of miR-124-3p, in modulating neuroinflammation and promoting neuronal repair following traumatic brain injury (TBI). Methods: Multiple studies investigating the impact of microglial exosomal miRNAs, specifically miR-124-3p, on injured neurons and brain microvascular endothelial cells (BMVECs) in the context of TBI were reviewed. Results: Animal models of TBI, in vitro cell culture experiments, RNA sequencing analysis, and functional assays were employed to elucidate the mechanisms underlying the effects of miR-124-3p-loaded exosomes on neuroinflammation and neuronal repair. The increased expression of miR-124-3p in microglial exosomes after TBI inhibited neuronal inflammation and promoted neurite outgrowth. This effect was achieved by promoting the anti-inflammatory M2 polarization of microglia, suppressing mTOR signaling through downregulation of PDE4B, and inducing autophagy in BMVECs. These actions collectively contributed to neuroprotection, reduced blood-brain barrier leakage, and improved neurologic outcomes in animal models of TBI. Conclusion: Microglial exosomes, particularly those carrying miR-124-3p, have emerged as promising candidates for therapeutic interventions in TBI. These exosomes exhibit neuroprotective effects, attenuate neuroinflammation, and promote neuronal repair and plasticity. Targeting miR-124-3p and utilizing microglial exosomes as therapeutic agents hold great potential for future clinical translation in TBI management. However, further research is required to fully elucidate the underlying mechanisms and optimize their delivery strategies for effective treatment in human TBI cases.

Solid-state LiDAR offers multiple advantages over mechanism mechanical LiDAR, including higher durability, improved coverage ratio, and lower prices. However, solid-state LiDARs typically possess a narrow field of view, making them less suitable for odometry and mapping systems, especially for mobile autonomous systems. To address this issue, we propose a novel rotating solid-state LiDAR system that incorporates a servo motor to continuously rotate the solid-state LiDAR, expanding the horizontal field of view to 360∘. Additionally, we propose a multi-sensor fusion odometry and mapping algorithm for our developed sensory system that integrates an IMU, wheel encoder, motor encoder and the LiDAR into an iterated Kalman filter to obtain a robust odometry estimation. Through comprehensive experiments, we demonstrate the effectiveness of our proposed approach in both outdoor open environments and narrow indoor environments.

The wind observations for multiple levels (40–200 m) have been conducted for a long time (2016–2020) on Jeju Island of South Korea. This study aims at understanding the vertical and temporal characteristics of lower atmosphere. Jeju Island is a region located at mid-latitude and is affected by seasonal monsoon wind. The maximum wind speed appears in the lower layer during day time and is delayed in the upper layer during latter time in diurnal cycle. In summer season, the surface layer increases up to 160 m during day time via dominant solar radiation effect, which is higher than those for other seasons. However, the maximum wind speed in winter season appears irregularly among altitudes, and the surface layer is ~100 m, which is lower than that in summer season. It can be attributed to the increase in the mean wind speed in diurnal cycle caused by the strong northwestern wind for winter season. These results imply that the relationship between near-surface and higher altitudes is primarily affected by solar radiation and seasonal monsoon winds. These results are expected to contribute to site selection criteria for wind farms and to the assessment concerning planetary boundary layer modeling.

Pesticides are chemical products are used to protect crops to prevent or eliminate undesirable organisms. Pesticides can contaminate continental waters, air, soil and food products. The presence of pesticide residues must therefore be monitored in water and food using sensitive, reliable and broad-spectrum analytical methods, as there is a huge variety of pesticides. Biosensors are one of the screening methods with great potential for detecting residues below the maximum residue limits. The different sensing principles of enzymatic biosensors for pesticide analysis will be described. This review highlights the great potential of colorimetric, enzymatic biosensors for detecting pesticide residues in water and food. Moreover the development of dual colorimetric and fluorimetric sensors during the past years is presented. These methods are most often based on the enzyme acetylcholinesterase, whose activity is inhibited by pesticides. The different approaches used in the development of these biosensors will be detailed, based on different chemical substrates, fluorophores and nanomaterials. The advantages and disadvantages of each approach will be discussed. Finally, the development prospects for improving these biosensors will be presented.