Sentinel-3 synergy (SYN) VEGETATION (VGT) products were designed to provide continuity to SPOT/VEGETATION (SPOT VGT). In past years, important changes have been implemented in the SYN VGT processing baseline. Since the PROBA-V mission acted as a gap filler between SPOT VGT and Sentinel-3, and in principle a continuous series of data products from the combined data archives of SPOT VGT (1998 – 2014), PROBA-V (2013-2020) and Sentinel-3 SYN VGT (from 2018 onwards) is available to users, consistency of Sentinel-3 SYN VGT with both the latest SPOT VGT (VGT-C3) and PROBA-V (PV-C2) archives is highly relevant. Spatio-temporal intercomparison of the combined time series of VGT-C3, PV-C2 and Sentinel-3 SYN VGT 10-day NDVI composite products with an external reference from LSA-SAF, and the intercomparison of Sentinel-3 SYN V10 products with a climatology of VGT-C3 resp. PV-C2 for 3 distinct periods with different levels of product quality have shown that the subsequent processing baseline updates have resulted in better quality products. It is therefore essential to reprocess the entire Sentinel-3 SYN VGT archive: a uniform data record of standard SPOT VGT, PROBA-V and Sentinel-3 SYN VGT products, spanning over 25 years, would provide valuable input for a wide range of applications.

Road safety and the effectiveness of the transportation system as a whole are significantly impacted by driver comfort. Road surface quality can play a significant part in the driver’s comfort experienced on roads in any country. This study employs a Random Forest technique to examine the association between road roughness and drivers' comfort during long-distance driving. Using Random Forest, a dependable machine learning technique that can handle big datasets and detect nonlinear correlations, this work aims to shed light on the complex dynamics between road conditions and driver’s comfort. 1,048,576 rows of data from MIRANDA, an application developed at the University of Gustave Eiffel, were used in this study as part of the data collected from a probe vehicle. The data collected includes an International Roughness Index (IRI). The IRI thresholds offer a simple method for assessing driver comfort and road irregularity. While highlighting how uneven and uncomfortable the road is, the research's findings (Road Roughness: SD – 0.73; Driver's Comfort: - Mean, 10.01, SD – 0.64) also contribute to the standardization of road condition evaluation and maintenance communication. This finding is anticipated to aid in the development of strategies for improving the welfare of long-haul drivers and fixing road infrastructure to comply with standard road index, ultimately leading to the creation of more efficient and sustainable transportation systems.

Long noncoding RNAs (lncRNAs) are known to play key roles in breast cancers; however, detailed mechanistic studies of lncRNA function have not been conducted in large cohorts of breast cancer tumors, nor have inter-donor and inter-subtype variabilities been taken into consideration. Here we provide the first identification and annotation of the human BORG lncRNA gene. Using multiple tumor cohorts of human breast cancers, we show that while BORG expression is strongly induced in breast tumors as compared to normal breast tissues, the extent of BORG induction varies widely between breast cancer subtypes and even between different tumors within the same subtype. Elevated levels of BORG in breast tumors are associated with the acquisition of core cancer aggression pathways, including those associated with basal tumor and pluripotency phenotypes, and with epithelial-mesenchymal transition (EMT) programs. While a subset of BORG-associated pathways was present in high BORG-expressing tumors across all breast cancer subtypes, many were specific to triple-negative breast cancer tumors. Finally, we show that genes induced by heterologous expression of BORG in murine models of TNBC both in vitro and in vivo strongly overlap with those associated with high BORG levels in human TNBC tumors, indicating that human BORG is a novel driver of the highly aggressive basal TNBC tumor phenotype.

Cuffless sphygmomanometers may be useful alternative blood pressure monitors for screening and home healthcare. The main requirements for these devices are accuracy in the resting state, accuracy during dynamic blood pressure changes, and long-term stability. Intermittent measurements can ensure that the accuracy of active measurements mirrors that of measurements in the resting and dynamic states. This brief report proposes a new protocol to evaluate long-term stability based on a comparison with a medically approved cuff-based automated sphygmomanometer rather than an auscultatory sphygmomanometer. Dynamic changes in blood pressure are not usually assessed based on intermittent measurement with an over-the-counter (OTC) sphygmomanometer. Thus, validation testing focused on the accuracy of the OTC cuffless sphygmomanometer in the resting state and the long-term stability of the device, and the results were similar to those of the cuff-based sphygmomanometer. Further research and discussions are necessary to develop standards derived from high-quality evidence for manufacturers of OTC sphygmomanometers.

Fibromyalgia (FM) is a chronic central sensitivity syndrome characterized by augmented pain processing at diffuse body sites and presents as a multimorbid clinical condition. Long COVID (LC) is a heterogenous clinical syndrome that affects 10-20% of individuals following COVID-19 infection. FM and LC shares similarities with regards to the pain and other clinical symptoms experienced, thereby posing a challenge for accurate diagnosis. This research explores the feasibility of using surface enhanced Raman spectroscopy (SERS) combined with soft independent modelling of class analogies (SIMCA) to develop classification models differentiating LC and FM. Venous blood samples were collected using two supports, dried bloodspot cards (DBS, n= 48 FM and n= 46 LC) and volumetric absorptive micro sampling tips (VAMS, n= 39 FM and n= 39 LC). A semi-permeable membrane (10 kDa) was used to extract low molecular fraction (LMF) from the blood samples, and Raman spectra was acquired using SERS with gold nanoparticles (AuNPs). Soft Independent Modelling of Class Analogy (SIMCA) models developed with spectral data of blood samples collected in VAMS tips showed superior performance with a validation performance of 100% accuracy, sensitivity, and specificity achieving an excellent classification accuracy of 0.86 area under the curve (AUC). Amide groups, aromatic and acidic amino acids were responsible for the discrimination patterns among FM and LC syndromes, emphasizing the findings from our previous studies. Overall, our results demonstrate the ability of AuNPs SERS to identify unique metabolites that can be potentially used as spectral biomarkers to differentiate FM and LC.

It is challenging to precisely measure the slow interseismic crustal deformation rate from SAR data. The long-wavelength orbital errors, owing to the uncertainties in satellite orbit vectors, commonly exist in SAR interferograms, which degrade the precision of the InSAR products and become the main barrier of extracting interseismic tectonic deformation. In this study, we propose a novel temporal network orbital correction method that is able to isolate the far-fault tectonic deformation from the mixed long-wavelength signals based on its spatio-temporal characteristic. The proposed approach is straightforward in methodology but could effectively separate the subtle tectonic deformation from glaring orbital errors without ancillary data. Both synthetic data and real Sentinel-1 SAR images are used to validate the reliability and effectiveness of this method. The derived InSAR velocity fields clearly present the predominant left-lateral strike-slip motions of the Tuosuo Lake segment of the Kunlun fault in western China. The fault-parallel velocity differences of 5-6 mm/yr across the fault between areas ~50 km away from the fault trace are addressed. The proposed method presents significantly different performance from traditional quadratic approximate method in the far-field. Through the implementation of the proposed method, the RMSE between the LOSGPS and our derived descending InSAR LOS is reduced to less than one-third of the previous study, suggesting its potential to enhance the availability of InSAR technology for interseismic crustal deformation measurement.

Noncoding RNA regions have emerged as pivotal players in the regulation of various cellular processes, and their involvement in HIV/AIDS pathogenesis is an evolving field of research. This review article delves into the intricate interplay between noncoding RNA regions and AIDS, shedding light on the diverse mechanisms by which these RNA molecules influence viral replication, immune responses, and disease progression. The review highlights recent discoveries, potential therapeutic applications, and the challenges ahead, illustrating the promising prospects of noncoding RNA research in the context of HIV/AIDS.

The early detection of small cracks in large structures is a crucial requirement for the implementation of structural health monitoring (SHM) system with a low transducer density. This work presents the results of the analysis of scattering of a submillimeter notch type defect with semielliptical shape with a groove at constant width 100µm in a 4.1mm thick aluminum plate. The defect is investigated with an ultrasonic guided wave (UGW) A0 mode at 550 kHz to cover long range in a thick plate. The mode selection is obtained by interdigital transducers designed to operate with 5mm central wavelength. The novel contribution is the assessment of the scattering amplitudes at different orientations of the defect when the distance is large as 35 cm in pulse-echo mode or 70 cm in pitch-catch mode. The system model is based on Finite Element Model (FEM) simulations and the results are in good agreement with experimental tests. The detection of the defect is obtained by comparing the scattered signals from the defect with base line signals of the pristine laminate. The analysis of scattering features and the efficient excitation with a probing UGW show the potential implementation in real structural health monitoring (SHM) systems.

We propose a new methodology for long-time biopotential recording based on MEMS multisensor integrated platform featuring a commercial electrostatic charge transfer sensor. That family of sensors was originally intended for presence tracking in automotive, so the existing setup has been engineered for the acquisition of electrocardiogram, electroencephalogram, electrooculogram and electromyography, designing a dedicated front-end and writing a proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in domestic environment will be discussed in detail. The excellent results candidate this technology to provide a low power, unexplored solution to biopotentials acquisition. The technology breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, they open to additional possibilities for wearable sensing and strengthen the role of MEMS technology in medical wearable applications for long time synchronous acquisition of a wide range of signals.

The thermal degradation of brakes has a significant impact on the stability of pure electric commercial vehicles during long downhill braking. Based on road slope and using fuzzy control method, design strategies for motor regenerative braking force and friction braking force distribution, with the aim of reducing friction braking force, improving braking stability, and recovering braking energy. By establishing driving conditions on roads with different slopes and using numerical analysis methods to verify the proposed control strategy. the results show that under the 6% constant slope driving condition, the vehicle maintains a constant speed of 30km/h downhill driving, and the braking energy recovery rate reaches 50.93%; Under the condition of 3% -6% gradient driving, the vehicle speed fluctuates slightly, but it can still be maintained at a constant speed of 30km/h for downhill driving through the adjustment of the motor's reverse drag characteristic, with a braking energy recovery rate of 49.97%; During the process of driving downhill at a constant speed, the frictional braking force does not participate in braking; The braking energy recovery rate is determined by the slope and braking deceleration.

The influences of doped hole transporting layer (HTL) on the performances of perovskite solar cells were studied. The influences of electrostatic and surface roughness effect by solvent additive doping were compared. The electrostatic effect of solvent additive doping was compared by addition of 1,8-diiodooctane and 1,8-dichlorooctane to the HTL layer. The surface roughness effect of solvent additive doping was examined using atomic force microscopy. The perovskite crystallinity by doping HTL was studied by X-ray diffraction. The conductivity of HTL was measured using four probe methods and was higher for doped devices. Enhanced conductivity of PEDOT:PSS film was also proved using fluorescence emission quenching. Long-term stability of the device was enhanced by doping HTL. The device with solvent additive exhibited much enhanced stability, retained over 80% of the initial PCE for 400 hours, while PCE of the device without solvent additive decreased to less than 80% after 200 hours. A very weak power microwave treatment was used for post-annealing of the fully fabricated devices. Post-annealing enhanced the lifetime of the device. Comparing different size of cells, the device lifetime was decreased as the cell area increased and as the size of the cell increased, the extent of lifetime enhancement by doping was increased.

Flexible energy storage devices, such as solid-state supercapacitors, are becoming increasingly attractive due to their characteristics of high electrochemical performance, reliability, light weight, flexibility, absence of electrolyte leakage, high power density, and long lifetime. For the optimization of the solid-state symmetrical supercapacitor proposed in this work, it was employed in sodium and lithium form Aquivion electrolyte membrane, which serves as the separator and electrolyte. As electrode materials, carbon xerogels, synthesized by microwave-assisted sol-gel methodology, with designed and controlled properties were obtained. Commercial activated carbon (YP-50F, 'Kuraray Europe' GmbH) was used for comparison. Specifically, the developed solid-state symmetrical supercapacitors deliver sufficient high specific capacitances of 105−110 F g−1 at 0.2 A g−1, along with an energy density of 4.5 Wh kg−1 at 300 W kg−1, and in a voltage window of 0-1.2 V in an aqueous environments, also demonstrating excellent cycling stability up to 10,000 charge/discharge cycles. These results can demonstrate the potential applications of carbon xerogel as an active electrode material and cation exchange membrane as the electrolyte in the development of solid-state supercapacitor devices

With growing regulations on ship emissions, specifically carbon dioxide and nitrogen oxides, this study emphasizes the critical need for improving the performance of existing ship diesel engines. Focusing on easily modifiable injection conditions within the fuel system, the research aims to enhance combustion characteristics for operational ships. The study provides a concise analysis of the combustion performance improvements achievable in the field. The study employed a range of calculation conditions, spanning from fuel injection pressures of 20 MPa to 160 MPa, injection timing from 15 degrees before top dead center (BTDC) to 5 degrees after top dead center (ATDC), and spray angles varying between 10 degrees and 50 degrees. A comprehensive analysis was conducted, focusing on fuel distribution behavior, combustion dynamics, carbon monoxide (CO) production and extinction, as well as nitrogen oxide (NO) production. This thorough examination aimed to provide insights into the complex interplay of these factors in order to optimize combustion performance in ship diesel engines. The analysis indicated that substantial unburned fuel occurred at injection pressures below 40 MPa and spray angles less than 20 degrees. This issue was significantly mitigated when injection pressures exceeded 80 MPa or a spray angle of 40 degrees was employed. High flame propagation speeds were observed at injection pressures over 80 MPa and injection timings before BTDC 10. Carbon monoxide emissions were notably low when injection pressures exceeded 80 MPa and a 40-degree spray angle was used. Nitrogen oxide emissions were minimized under conditions of injection pressure below 40 MPa, injection timing after BTDC 5 degrees, and a spray angle between 30 and 40 degrees. The study concludes that the most effective injection conditions for concurrently reducing fuel consumption, minimizing incomplete combustion products, and lowering nitrogen oxide emissions in the target engine are optimal when the injection pressure is set at 80 MPa, the injection timing is positioned at 5 degrees before top dead center (BTDC), and the spray angle is maintained at 40 degrees.

Pure silver as well as silver compounds have been used as antiseptics for centuries. Nowadays, the most frequently applied form of silver is a nanoparticle form. Several studies demonstrate the ability of silver nanoparticles and silver salts to accumulate in the brain of laboratory ani-mals as well as the ability of silver nanoparticles to influence behavioral and cognitive functions of laboratory mammals. For the first time, the influence of a silver salt, in particular, silver citrate on the change dynamics of behavioral and cognitive functions of laboratory mice at the daily oral exposure during 30, 60, 120 and 180 days is investigated in the present research. We observed the anxiety increase after 30 days of the exposure as well as the ability of mice to adapt to the prolonged oral exposure of silver citrate manifested in the anxiety decrease, increase of locomotor activity and the tendency of long-term contextual memory improvement. Increase of locomotor activity and memory improvement might be associated with the sensitivity increase and regarded as compensatory mechanism to anxiety increase. The observed phenomenon is classified as a hormetic effect. A comparison with the analogous study implemented polyvi-nylpyrrolidone coated silver nanoparticles is carried out. It was concluded that the nanoform is more neurotoxic.

Background and Objective: There is a paucity of literature comparing unilateral instrumented transforaminal lumbar interbody fusion (UITLIF) with bilateral instrumented TLIF (BITLIF) regarding radiological alignment, including the coronal balance, even though UITLIF might have asymmetric characteristics in the coronal plane. This retrospective study aimed to compare the clinical and long-term radiological outcomes of 1-level UITLIF and BITLIF in lumbar degenerative diseases (LDD). Materials and Methods: Patients who underwent 1-level UITLIF with two rectangular polyetheretherketone (PEEK) cages or BITLIF with ≥ 5 years of follow-up at a single hospital were included. We compared the clinical and radiological outcomes between the UITLIF and BITLIF. Results: In total, 63 and 111 patients who underwent UITLIF and BITLIF, respectively, were enrolled. The median follow-up was 85.55 months (range: 60–130). The UITLIF group had a significantly shorter operation time (185.0 [170.0–210.0] vs. 225.0 [200.0–265.0], p < 0.001) and lower estimated blood loss (300.0 [250.0–500.0] vs. 550.0 [400.0–800.0], p < 0.001) than BITLIF group. Regarding the clinical outcomes, there were no significant differences in the intermittent claudication score (p = 0.495) and Kirkaldy–Willis criteria (p = 0.707) at 1 year postoperatively. The interval changes of the local coronal Cobb angle at the index level, L1-S1 lordotic angle, and coronal off-balance from the immediate postoperative radiograph to the last follow-up were not significantly different (p = 0.687, p = 0.701, and p = 0.367, respectively). Conclusion: UITLIF with two rectangular PEEK cages is a viable option for providing comparable clinical outcomes and radiological longevity to BITLIF in 1-level LDD. In addition, UITLIF has advantages over BITLIF in terms of operative time and blood loss.

Background: In March 2020, a COVID-19 outbreak associated with the Falles Festival in Borriana, Spain, led to significant infections. Methods: In June 2022, we conducted a cross-sectional follow-up study of 473 socially engaged adults aged 18 to 64, examining the cumulative experience after SARS-CoV-2 infection and vaccination responses. Data included demographic details, lifestyle habits, medical history, infection records, and vaccinations from a population-based vaccine registry. Blood samples were analyzed for SARS-CoV-2 antibodies and cellular immunity. We employed a doubly robust inverse weighted probability analysis to estimate the booster vaccine dose's impact on long COVID prevalence and symptom count. Results: 28.1% of participants met the WHO criteria for long COVID, with older individuals showing higher rates. Not complying with long COVID criteria associated with factors including O blood group, higher occupational status, physical activity, three vaccine doses, strong SARS-CoV-2-S-reactive IFNγ‐producing‐CD8+ response, and infection during the Omicron period. Conversely, increased age, high or low social activity, underlying health conditions, a severe initial COVID episode, and reinfection associated with higher long COVID likelihood. A booster dose, compared to one or two doses, reduced long COVID risk by 74% (95% CI: 56.4% to 91.7%) and symptom count by 55.2% (95% CI: 31.6% to 78.7%). Conclusion: Long COVID was prevalent in a significant portion of those who contracted COVID-19, underscoring the need for sustained follow-up and therapeutic strategies. Vaccinations, notably the booster dose, had a substantial beneficial effect on long-term infection outcomes, affirming the vaccination’s role in mitigating SARS-CoV-2 infection consequences.

Hypertension is one of the most common and significant cardiovascular risk factor. With the emergence of COVID-19, concerns have arisen about its potential to exacerbate cardiovascular morbidity and mortality. Several theories have emerged linking hypertension with COVID-19. However, the relationship between SARS-CoV-2 infection and long-term risk of hypertension remains insufficiently explored. Therefore, our study aimed to investigate the impact of COVID-19 infection on blood pressure and long-term risk of hypertension developing. A comprehensive search was conducted in the PubMed and SCOPUS databases up to 10th August 2023 for relevant papers. The analysis included 35 eligible articles. The majority of the latest studies pointed to increase BP after COVID-19 infection. However, several observations contradicted the influence of SARS-CoV-2 on the hypertension development or suggesting a transient effect. The possible role of the loss of angiotensin-converting enzyme 2, cytokine storm, increased sympathetic activity, and endothelial dysfunction were considered. On the other hand, BP changes following COVID-19 could be a consequence of lifestyle changes, which often accompanied COVID-19 infection. Our findings highlight the urgent need for comprehensive research on the COVID-19 and hypertension link. These insights are crucial for implementing effective prevention and management strategies for individuals with the history of COVID-19 infection.

Some recent studies show that filters in convolutional neural networks (CNNs) have low color selectivity in datasets of natural scenes such as Imagenet. CNNs, bio-inspired in the visual cortex, are characterized by their hierarchical learning structure, which appears to gradually transform the representation space. Inspired by the direct connection between the LGN and V4, which allows V4 to handle low-level information closer to the trichromatic input in addition to processed information that comes from V2/V3, we propose the addition of a long skip connection (LSC) between the first and last blocks of the feature extraction stage to allow deeper parts of the network to receive information from shallower layers. This type of connection improves classification accuracy by combining simple-visual and complex-abstract features to create more color-selective ones. We have applied this strategy to classic CNN architectures and quantitatively and qualitatively analyzed the improvement in accuracy while focusing on color selectivity. The results show that, in general, skip connections improve accuracy, but LSC improves it even more and enhances the color selectivity of the original CNN architectures. As a side result, we propose a new color representation procedure for organizing and filtering feature maps, making their visualization more manageable.

Allergies are a common and increasing health problem affecting millions of people worldwide. In addition to genetic predisposition, this increase in allergic cases is attributed to air pollution, climate change, more time spent indoors, lack of physical activity, and alterations in eating habits. The Mediterranean diet (MD), which includes a lot of fruits and vegetables, whole grains, legumes, nuts, olive oil and fish has been linked to a variety of health benefits, including a lower risk of chronic and allergic diseases. The purpose of this paper is to explore the effects of the dietary components of the MD on food allergies. Electronic databases PubMed, Scopus, Science Direct, and EBSCO were used to conduct this systematic review up to March 2023. 696 studies were initially identified and nine were included (five human and four animal studies). The findings of this review showed an overall beneficial effect between the food components of the MD and food allergies. Although the results are promising, the limited number of studies highlights the need for more research.

Improper lifting measures of long-span spatial steel structures during construction process may delay the construction period and even cause safety accidents. Few studies on long-span spatial steel structures considered time-varying mechanical characteristic during construction process. In order to achieve safe and efficient installation in a long-span spatial steel structure, this research presents a time-varying mechanical analysis of the synchronous and asynchronous integral lifting and a single and interlaced point’s asynchronous integral lifting analysis method of a long-span spatial steel structure. The results showed that in the case of asynchronous lifting of single point, the displacement influence on other members is related to the distance. The closer the distance from the lifting point, the greater the influence. In asynchronous integral lifting, the lifting point with large lifting force is first installed to the specified position, and the lifting point with small lifting force is installed to the specified position.

In this paper, we propose, implement, and test two novel downlink LTE scheduling algorithms. The implementation and testing of these algorithms were in Matlab, and they are based on the use of Reinforcement Learning, more specifically, the Qlearning technique for scheduling two types of users. The first algorithm is called a Collaborative scheduling algorithm, and the second algorithm is called a Competitive scheduling algorithm. The first type of the scheduled users is the Primary Users, and they are the licensed subscribers that pay for their service. The second type of the scheduled users is the Secondary Users, and they could be unlicensed subscribers that dont pay for their service, device to device communications, or sensors. Each user whether it is a primary or secondary is considered as an agent. In the Collaborative scheduling algorithm, the primary user agents will collaborate in order to make a joint scheduling decision about allocating the resource blocks to each one of them, then the secondary user agents will compete among themselves to use the remaining resource blocks. In the Competitive scheduling algorithm, the primary user agents will compete among themselves over the available resources, then the secondary user agents will compete among themselves over the remaining resources. Experimental results show that both scheduling algorithms converged to almost ninety percent utilization of the spectrum, and provided fair shares of the spectrum among users.

Energy harvesting from wind turbines has been explored by researchers for more than a century from conventional turbines up to the latest bladeless turbines. Amongst these bladeless turbines, vortex bladeless wind turbine (VBT) harvests energy from oscillation of a turbine body. Due to the novelty of this science and the widespread researches around the world, one of the most important issues is to optimize and predict produced power. To enhance the produced output electrical power of VBT, the fluid-solid interactions (FSI) were analyzed to collect a dataset for predicting procedure. Long short-term memory (LSTM) method has been used to predict the produced power of VBT from the collected data. The reason of choosing LSTM from various artificial neural network methods is that the parameters of VBT study are all time- dependent and the LSTM is one of the most accruable algorithms for predicting time series data. In order to find the relationship between the parameter and the variables used in this research, a correlation matrix was presented. According to the value of 0.3 for the root mean square error (RMSE), a comparative analysis between the simulation results and its prediction shows that the LSTM method is very accurate for these types of research. Furthermore, the LSTM method has significantly reduced the computation time so that the prediction time of desired values has been reduced from an average of 2 and a half hours to two minutes. Also, one of the most important achievements of this study is to suggest a mathematical relation of VBT output power which helps to extend it in a different size of VBT with a high range of parameter variations.

Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the Internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link have fractal behavior when the Hurst exponent is in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between –0.5, 0. Based on these results, it is ideal to characterize both the singularities of the traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyses, the fact that the traffic flows of current computer networks exhibit fractal behavior with a long-range dependency is reaffirmed.

The clinical significance of carbon-ion radiotherapy (CIRT) for adenocarcinoma (AC) of the uterine cervix has been assessed in several single-institutional studies. To validate the significance, we conducted a multi-institutional survey of CIRT for locally advanced AC (LAAC) of the uterine cervix. We retrospectively analyzed the clinical outcomes of patients with stage IIB–IVA LAAC of the uterine cervix who underwent chemo-CIRT or CIRT alone between April 2010 and April 2016. Patients received 74.4 Gy (relative biological effectiveness [RBE]) in 20 fractions of CIRT or 55.2 Gy (RBE) in 16 fractions of CIRT plus 3 sessions of brachytherapy. Patients aged ≤70 years with adequate bone marrow and organ function were administered cisplatin weekly (40 mg/m2 per week for up to 5 weeks). Fifty-five patients were enrolled in this study. The median follow-up period was 67.5 months. The 5-year overall survival (OS) and local control (LC) rates were 68.6% and 65.2%, respectively. Multivariate analysis showed that the initial tumor response within 6 months was significantly associated with LC and OS. The present study represents promising outcomes of CIRT or chemo-CIRT for LAAC of the uterine cervix, especially in the cases showing initial rapid regression of the tumor.

PM2.5 and PM>2.5 were separately collected in Kanazawa, Japan in every season from the spring of 2017 to the winter of 2018, and nine polycyclic aromatic hydrocarbons (PAHs) and six nitropolycyclic aromatic hydrocarbons (NPAHs) were determined by HPLC with fluorescence and chemiluminescence detections, respectively. Atmospheric concentrations of both PAHs and NPAHs showed seasonal changes (highest in the winter and lowest in the summer), which were different from the variations of TSP and PM2.5 (highest in the spring). Contributions of major sources to combustion-derived particulate (Pc) in PM2.5 were calculated by the NP-method using pyrene and 1-nitropyrene as representative markers of PAHs and NPAHs, respectively. The annual average concentration of Pc accounted for only 2.1% of PM2.5, but showed the same seasonal variation as PAHs. The sources of Pc were automobiles (31%) and coal heating facilities/industries (69%). The source of Pyr was almost entirely coal heating facilities/industries (98%). A backward trajectory analysis showed that automobile-derived Pc was mainly from Kanazawa and its surroundings and that coal heating facilities-derived Pc was transported from city areas in central and northern China in the winter and during the Asian dust event in the spring. These results show that large amounts of PAHs were long-range transported from China in the winter. Even in spring when the coal heating season was over in China, PAHs came over to Japan after Asian dust storms passed through Chinese city areas. The main contributor of NPAHs was automobiles in Kanazawa and its surroundings. The recent Pc concentrations were much lower than those in 1999. This decrease was mostly attributed to the decrease in the contribution of automobiles. Thus, changes of atmospheric concentrations of Pc, PAHs and NPAHs in Kanazawa were strongly affected not only by the local emissions but also long-range transport from China.

Climate change has become the greatest threat to the survival of world and its ecosystem. With the irreversible impact on the ecosystem, problems like rise in sea level, food-insecurity, natural resources scarcity, seasonal disorders have increased over the past few years. Among these problems, the issue of water scarcity due to the lack of water resources and global warming has plagued several nations. Owing to the rising concerns over water scarcity United Nations (UN) has acknowledged water as a primary resource to the development of societies under the ‘Water Goal’ of the sustainable development goals. As the changing climate and intermittent availability of water resources pose major challenges to forecast demand, especially in countries like the United Arab Emirates (UAE) which has one of the highest per capita residential water consumption rates in the world. Therefore, the aim of this study is to propose an accurate water demand forecasting technique that incorporates all significant factors to predict the future water demands of the UAE. The forecasting model used is the Long Short Term Memory (LSTM), with the factors considered are mean temperature, mean rainfall, relative humidity, Gross Domestic Product (GDP), Consumer Price Index (CPI) and population growth. The LSTM model predicts the water demand forecasting in the UAE showing that the future demand will decrease from 1821 million m3 in 2018 to 1809.9 million m3 in 2027.

Organic carbon (OC) accumulation in soil mitigates greenhouse gases emission and improves soil health. We aimed to quantify the dynamics of OC stock in soils and to justify technologies that allow annual increasing OC stock in the arable soil layer by 4‰. We based the study on a field experiment established in 1936 in the 9-field crop rotation with a fallow on Chernozem in European Russia. The RothC version 26.3 was used for the reproducing and forecasting OC dynamics. In all fertilizer applications at FYM background, there was a decrease in the OC stock with preferable loss of active OC, except the period 1964-71 with 2-5‰ annual OC increase. The model estimated the annual C input in the arable soil layer as 1,900 kg·ha-1. For increasing OC stocks by 4‰ per year, one should raise input to 2400 kg·ha-1. Simulation was made for 2016-2090 using climate scenarios RCP4.5 and RCP8.5. Crop rotation without fallowing provided an initial increase of 3‰ and 6‰ of stocks in the RCP8.5 and RCP4.5 scenarios accordingly, followed by a loss in accumulated OC. Simulation demonstrates difficulties to increase OC concentration in Chernozems under intensive farming and potential capacity to rise OC stock through yield management.

DNA double-strand breaks (DSBs) are deleterious lesions that are generated in response to ionizing radiation or replication fork collapse that can lead to genomic instability and cancer. Eukaryotes have evolved two major pathways, namely homologous recombination (HR) and non-homologous end joining (NHEJ) to repair DSBs. Whereas the roles of protein-DNA interactions in HR and NHEJ have been fairly well defined, the functions of small and long non-coding RNAs and RNA-DNA hybrids in the DNA damage response is just beginning to be elucidated. This review summarizes recent discoveries on the identification of non-coding RNAs and RNA-mediated regulation of DSB repair

Congenital Pouch Colon (CPC) is a rare anorectal anomaly common to North Western India specifically Rajasthan. Despite efforts to understand the clinical genetic makeup of CPC, no attempt on identifying non-coding RNAs was done. We have earlier reported CPC's rare variants from whole exome sequencing across 18 affected samples in a total of 64 subjects. A Smith-Waterman algorithm was used to infer a couple of lncRNAs from WES samples of CPC with predictions from the Noncode database. Further screening and quantification using PCR, we ascertained interactions using Micro Scale Thermophoresis (MST). We report the role of lnc-EPB41-1-1 shown to be promiscuously interacting with KIF13A substantiating their role in regulation.

A comprehensive model for designing robust all-in-fiber microresonator-based optical sensing setups is illustrated. The investigated all-in-fiber setups allow light to selectively excite high-Q whispering gallery modes (WGMs) into optical microresonators, thanks to a pair of identical long period gratings (LPGs) written in the same optical fiber. Microspheres and microbubbles are used as microresonators and evanescently side-coupled to a thick fiber taper, with a waist diameter of about 18 µm, in-between the two LPGs. The model is validated by comparing the simulated results with the experimental data. A good agreement between the simulated and experimental results is obtained. As an application example, the sensing of the concentration of an aqueous glycerol solution is demonstrated. The model is general and by exploiting the refractive index and/or absorption characteristics at suitable wavelengths, the sensing of other substances or pollutants can be also predicted.

Formalin-Fixed Paraffin Embedded (FFPE) tissues are a valuable resource in studying different markers and mechanistic molecules (protein, DNA and RNA) in order to understand the etiology of different cancers as well as many other diseases. Degradation and modification of RNA is the major challenge in utilizing FFPE tissue samples in medical research. Recently, non-protein coding transcripts long non-coding RNAs (lncRNAs), have gained significant attention due to their important biological actions and potential involvement in cancer. There is no validated method except qRTPCR or RNAseq to evaluate and study lncRNA expression. We have standardized and are reporting a sensitive Z probe based in situ hybridization method to identify, localize and quantitate lncRNA in FFPE tissues. This assay is sensitive to single transcript and localizes lncRNA in individual cells within tumor. We have characterized a tumor suppressor lncRNA-NRON (non coding repressor of NFAT), which is scarcely expressed, a moderately expressed oncogeneic lncRNA UCA1 (urothelial cancer associated 1), and a highly studied and expressed lncRNA MALAT1 (metastasis associated lung adenocarcinoma transcript1) in different cancers. High MALAT1 staining was found in colorectal, breast and pancreatic cancer. MALAT1 expression increased with the progression of the stage in colorectal cancer and invasiveness in breast cancer.

Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition, which remains challenging for traditional methods due to the complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include: 1) The convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; 2) A large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and 3) Experimental results demonstrate that HDMF is super capable of copping with the AMC problem, and achieves much better performance when compared with the independent network. The source code and the database will be publically available.

We present an innovative method for dual band mid-wave infrared (MWIR) and long-wave infrared (LWIR) reflectors. By using double-sided metasurfaces, two high reflection bands can be generated with a single device. As individual guided-mode resonance (GMR) reflectors are combined with interlayer (or substrate) on the top and bottom sides, we achieve high reflection in the MWIR and LWIR bands simultaneously. Each GMR reflector is optimized as a germanium (Ge) grating structure on a potassium bromide (KBr) substrate. In our analysis, it is found that the transparency of the interlayer is critical to produce the dual-band reflection. The simulation results on the Ge/KBr/Ge double-sided metasurfaces demonstrate wideband reflection from ~3.3 to 4.8 μm and ~8.8 to 11 μm. Additionally, the device exhibits favorable angular tolerance. The work contributes to developing capability of metasurface technologies in various application fields.

Background: The term “Long COVID” is commonly used to describe persisting symptoms after acute COVID‑19. Until now, proposed mechanisms for the explanation of Long COVID have not related quantitative measurements to basic laws. In this work, a common framework for the Long COVID pathophysiological mechanism is presented, based on the blood supply deprivation and the flow diffusion equation. Methods: Case-control studies with statistically significant differences between cases (post-COVID patients) and controls, from multiple tissues and geographical areas, were gathered and tabulated. Microvascular loss (ML) was quantified by vessel density reduction (VDR), foveal avascular zone enlargement (FAZE), capillary density reduction (CDR), and percentage of perfused vessels reduction (PPVR). Both ML and hemodynamic decrease (HD), were incorporated in the tissue blood supply reduction (SR) estimation. Results: ML data were found from 763 post-COVID patients with an average VDR, FAZE, CDR, and PPVR of 16%, 31%, 14%, and 21%, respectively. The average HD from 72 post-COVID patients was 37%. The estimated SR for multiple tissues with data from 634 post-COVID patients reached a sizeable 47%. This large SR creates conditions of lower mass diffusion rates, hypoxia, and undernutrition, which at a multi-tissue level, for a long time, can explain the wide variety of the Long COVID symptoms. Conclusions: Disruption of peripheral tissue blood supply by the contribution of both ML and HD is proposed here to be the principal cause of the mechanism leading to Long COVID symptoms.

(1) Background: Sudden Infant Death Syndrome (SIDS) represents unexplained and sudden deaths during the sleep of infants under one year of age, despite thorough investigation. Screening for prolonged QTc or Long QT Syndrome (LQTS) should be conducted on all newborns to reduce the incidence of SIDS. Neonatal electrocardiogram (ECG) could identify congenital heart defects (CHD) early, especially those not detected at birth. Infants with prolonged QT intervals typically undergo genetic analysis for Long QT Syndrome; (2) Methods: The study involved infants aged 20-40 days, born clinically healthy, with initial ECG screening. Infants with prenatal diagnoses or signs/symptoms of CHD identified immediately after birth, as well as those who underwent ECG or echocardiogram for other clinical or historical indications, were excluded from the study. The data were analyzed using SPSS version 22.0; (3) Results: Of the 42,200 infants involved, 2,245 were enrolled, with 39.9% being males. Subsequently, 164 children (37.8% males) with prolonged QT intervals underwent further diagnostic investigations. Genetic mutations were identified in 11 out of 18 tested children. The most common mutations were LQT1 (54.5%), LQT2 (36.4%), and LQT3 (1 patient). Medications used included propranolol (39.8%), nadolol (22.2%), inderal (11.1%), metoprolol (11.1%), and no treatment (16.7%). The most common abnormalities were focal right bundle branch block (54.5%), left axis deviation (9.2%), and nonspecific ventricular repolarization abnormalities (7.1%). Multiple anomalies were found in 0.47% of children with focal right bundle branch block. Structural abnormalities were associated with specific features in 267 patients (11.9%), primarily isolated patent foramen ovale (PFO) at 61.4%; (4) Conclusions: The screening has proven effective in early diagnosing Long QT Syndrome and other cardiac rhythm anomalies, with additional benefits in detecting mutations and/or QTc prolongations in family members. Identifying other ECG abnormalities and congenital heart malformations further enhances the benefits of the screening.

Non-pharmaceutical interventions (NPIs), including social distancing, wearing personal protective equipment, and lockdown measures, have been at the forefront of outbreak control in nursing homes. We used a mixed methodology to assess which NPIs nursing homes in the canton of Geneva, Switzerland, followed for their staff and residents during the first wave of the pandemic, between March 1, 2020 and June 1, 2020. For the qualitative component, we interviewed the attending physicians and/or director of each nursing home. Based on in-vivo codes, NPIs for nursing home workers and residents in each nursing home were thematically classified as: maximally restrictive, moderately restrictive, and minimally restrictive. In the quantitative component, we calculated incident rate ratios (IRR) for infection between the three levels of COVID-19-related measures taken in these nursing homes. We found an equal distribution of maximally (n=4), moderately (n=4), and minimally (n=4) restrictive NPIs. The extent of restriction did not show to be significantly associated with the cumulative incidence of COVID-19 cases among residents (maximally restrictive IRR = 3.90, 95%CI 0.82-45.54, p = 0.184; moderately restrictive IRR = 3.55, 95%CI 0.75-41.42, p = 0.212 ; minimally restrictive IRR = reference). Variabilities in NPIs adopted by nursing homes, and the number of COVID-19 cases appear to be randomly affected.

Hundreds of thousands of people living along the Yatsushiro Sea coast have been exposed to methylmercury from the contaminated water of the Chisso factory in Minamata. Methylmercury causes neurological deficits in the cerebrum and cerebellum, with symptoms of ataxia, dysarthria, and visual field constriction in severe cases, but the most common symptom of a milder disorder is somatosensory disturbance due to parietal lobe damage. We investigated the characteristics of the somatosensory disturbances in detail by studying subjective complaints, neurological findings, and quantitative sensory tests in 197 residents (63.2 ± 10.7 years old) from contaminated areas exposed to methylmercury from seafood and 130 residents (63.7 ± 9.3 years old) from control areas. The most commonly affected sensory modalities were superficial peripheral touch and pain in the extremities, followed by two-point discrimination and deep senses, and in the most severe cases, full-body sensory dysfunction and impairment of all sensory submodalities. Although the severity of the sensory submodalities correlated with each other, there was almost no correlation between the severity of these submodalities and indices of peripheral nerve conduction studies, suggesting that the primary cause of the sensory deficits was damage to the central nervous system. The health effects of chronic methylmercury toxicosis can be elucidated by a detailed examination of sensory deficits.

With the development of sensor and communication technologies, Internet of Things (IoT) subsystem is gradually becoming a crucial part in vehicles. It can effectively enhance functionalities of vehicles. However, new attack types are also emerged. For example, a driver with the smart key in the pocket can push the start button to start a car when. At the same time, security issues in the push-to-start scenario are alerted, such as smart key forgery. In this paper, we propose a vehicle Passive Entry Passive Start (PEPS) system that uses deep learning algorithms to recognize the driver by the Electrocardiogram (ECG) signals measured by his or her smart watch. ECG signals are used for verification. Smart watches, as a new smart key of PEPS system, can replace traditional smart keys to improve security. Experiment results show that Long Short-Term Memory (LSTM) models have achieved the best accuracy score for identity recognition (91%) when single ECG cycle is used. However, it takes at least 30 minutes for training. The training time of Auto Encoder successfully reduces to 5 minutes. When 15 continuous ECG cycles are used, it can achieve 100% identity accuracy.

The novel coronavirus disease (COVID-19) pandemic has resulted in over 720 million confirmed cases and 7 million deaths worldwide, with insufficient treatment options. Innumerable efforts are being made around the world for faster identification of therapeutic agents to treat the deadly disease. Postacute sequelae of SARS-CoV-2 infection or COVID-19 (PASC), also called Long COVID, is still being understood and lacks treatment options as well. A growing list of drugs are being suggested by various in silico, in vitro and ex vivo models, however currently only two treatment options are widely used: the RNA-dependent RNA polymerase (RdRp) inhibitor remdesivir, and the main protease inhibitor nirmatrelvir in combination with ritonavir. Computational drug development tools and in silico studies involving molecular docking, molecular dynamics, entropy calculations and pharmacokinetics can be useful to identify new targets to treat COVID-19 and PASC, as shown in this paper. We have investigated bisphosphonates which can bind competitively to nidovirus RdRp-associated nucleotidyl (NiRAN) transferase domain, and systematically down selected seven candidates for further evaluation (CHEMBL608526, CHEMBL196676, CHEMBL164344, CHEMBL4291724, CHEMBL4569308, CHEMBL387132, CHEMBL98211). Interestingly, one of these (CHEMBL608526) very closely resembles the approved drug minodronate, and another (CHEMBL98211) resembles the approved drug zoledronate.

The escalation of climate change and the increasing frequency of extreme weather events have amplified the importance of precise and timely lightning prediction. This predictive capability is pivotal for the preservation of life, protection of property, and maintenance of crucial infrastructure safety. Recently, the rapid advancement and successful application of data-driven deep learning across diverse sectors, particularly in computer vision and spatio-temporal data analysis, have opened up innovative avenues for enhancing both the accuracy and efficiency of lightning prediction. This article presents a comprehensive review of the broad spectrum of existing lightning prediction methodologies. Starting from traditional numerical forecasting techniques, we traverse the path to the most recent breakthroughs in deep learning research. We encapsulate these diverse methods, shedding light on their progression and summarizing their capabilities, while also predicting their future development trajectories. This exploration is designed to enhance our understanding of these methodologies, allowing us to better utilize their strengths, navigate their limitations, and potentially integrate these techniques to create novel and powerful lightning prediction tools. Through such endeavors, our aim is to bolster our preparedness against the growing unpredictability of our climate and ensure a proactive stance towards lightning prediction.

Data-driven models (DDMs) are extensively used in environmental modeling but face challenges due to limited training data and potential results not adhering to physical laws. To address this challenge, this study developed a process-guided deep learning (PGDL) model, integrating a long short-term memory (LSTM) neural network and a process-based model (PBM), CE-QUAL-W2 (W2), to predict water temperature in a stratified reservoir. The PGDL included an energy constraint term from W2's thermal energy equilibrium into the cost function of the LSTM, besides the mean square error term. In PGDL, parameters were optimized by penalizing deviations from the energy law, ensuring adherence to physical constraints. Compared to LSTM, PGDL demonstrated enhanced satisfaction with the energy balance and superior performance in water temperature prediction. Even with less field data for training, PGDL outperformed both LSTM and calibrated W2 after pre-training with data generated using the uncalibrated W2. Therefore, integration of DDM with a PBM ensured physical consistency in water temperature prediction for complex stratified reservoirs with limited data. Moreover, pre-training the PGDL with PBM proved highly effective in mitigating bias and variance due to insufficient field measurement data.

Deformation monitoring is beneficial to ensure the service safety of long tunnel. An three-direction (vertical, transverse and axial) deformation estimation method using long-gauge fiber Bragg grating (FBG) sensing technology is proposed and applied to a long tunnel by integrating mechanical analysis and field monitoring during the service state. The key issues of the proposed method for identifying the three-direction deformation are discussed, such as establishing the separation model of coupling strain on cross section, deriving the theory of deformation identification in three directions, and determining the sensor layout of the long tunnel. A major advantage of the proposed method is that the three-direction deformation of the tunnel can be monitored in real time using distributed long-gauge strain sensors. The numerical analysis and field test results verify the feasibility of the proposed method.

Cross-species investigations on cancer invasiveness are a new approach that identified new biomarkers potentially useful for improving tumor diagnosis and prognosis in clinical medicine and veterinary science. In this study, we combined proteomic analysis of four experimental rat malignant mesothelioma (MM) tumors with analysis of ten patient-derived cell lines to identify common features associated to mitochondrial proteome rewiring. The comparison of significant abundance changes between invasive and non-invasive rat tumors rat tumors gave a list of 433 proteins, including twenty-six proteins reported to be exclusively located in mitochondria. Next, we analyzed the differential expression of genes encoding the mitochondrial proteins of interest in five primary epithelioid and five primary sarcomatoid human MM cell lines, and the most impressive increase was observed in the expression of the long-chain acyl coenzyme A dehydrogenase (ACADL). To evaluate the role of this enzyme in the migration/invasiveness, two epithelioid and two sarcomatoid human MM cell lines derived from patients with the highest and lowest overall survival were studied. Interestingly, sarcomatoid vs epithelioid cell lines were characterized by higher migration and fatty oxidation rates, in agreement with ACADL findings. These results suggest that evaluating mitochondrial proteins in MM specimens might identify tumors with higher invasiveness.

Based on the example of wintering waterbirds in the Baltic Sea, we show a method that is useful in setting priorities for species management. The Value Factor (VF) shows which species in the region are the most valuable and on which ones conservation measures need to focus. Every year 4,400,000 waterbirds winter in the Baltic. Among which the highest priority species are velvet scoter Melanitta fusca (VS, VF=153) and long-tailed duck Clangula hyemalis (LTD, VF=204): 74% and 40%, respectively, of the world populations and over 90% of the EU populations of both species spend the winter there. Management plans (MP) regulating the protection of marine Natura 2000 sites (MPA) and dedicated to the protection of VS and LTD have been implemented in 65% and 51%, respectively, of MPAs in the Baltic. Poland, a key country for the survival of these species, has not implemented a single MP despite the existence of documentation confirming their crucial importance for seaducks, and on the pressures occurring there. We suggest using the VF concept to define priority species. On this basis, it will be possible to identify gaps in the protection of the most seriously threatened species and implement conservation measures in the most appropriate sites

Every month teachers face the dilemma of what exercises should their students practice, and what their consequences are on long-term learning. Since teachers prefer to pose their own exercises, this generates a large number of questions, each one attempted by a small number of students. Thus, we couldn’t use models based on big data such as deep learning. Instead, we developed a simple to understand state-space model that predicts end-of-year national test scores. We used 2,386 online fourth-grade mathematic questions designed by teachers and each attempted by some of the 500 students in 24 low socioeconomic schools. We found that the state-space model predictions improved month-by-month and that in most months it outperformed linear regression models. Moreover, the state-space estimator provides for each month a direct mechanism to simulate different practice strategies and compute their impact on the end-of-year standardized national test. We built iso-impact curves based on two critical variables: the number of questions solved correctly in the first attempt and the total number of exercises attempted. This allows the teacher to visualize the trade-off between asking students to do exercises more carefully or doing more exercises. To the best of our knowledge, this model is the first of its kind in education. It is a novel tool that supports teachers drive whole classes to achieve long-term learning targets.

Software reliability is an important characteristic for ensuring the qualities of software products. Predicting the potential number of bugs from the beginning of a development project allows practitioners to make the appropriate decisions regarding testing activities. In the initial development phases, applying traditional software reliability growth models (SRGMs) with limited past data does not always provide reliable prediction result for decision making. To overcome this, herein we propose a new software reliability modeling method called deep cross-project software reliability growth model (DC-SRGM). DC-SRGM is a cross-project prediction method that uses features of previous projects’ data through project similarity. Specifically, the proposed method applies cluster-based project selection for training data source and modeling by a deep learning method. Experiments involving 15 real datasets from a company and 11 open source software datasets show that DC-SRGM can more precisely describe the reliability of ongoing development projects than existing traditional SRGMs and the LSTM model.

Alzheimer’s disease and related dementias (ADRD) remain a public health challenge in developing counties. We developed a needs-based essential care service package (ECSP) for care planning of persons living with dementia (PLWD) using a cross-sectional survey among PLWD in institutions in six cities in China (n= 1,299). Face-to-face interviews were conducted with caregivers of PLWD by trained staff between 2018 and 2019. Care service needs and utilization by the level of cognitive impairment were summarized. The average age of PLWD was 80.7 years. 76% of participants had severe cognitive impairment. The needs-based ECSP with 30 service items would be sufficient in supporting care services of PLWD in China, of which seven items are core care. The selection plan for ECSP at different levels is designed as “General Care Services + Selective Care Services”, in which service items for low-, mid-and high-level care for PLWD are 7+3, 7+6, and 7+10, respectively. The findings provide the first large-scale data on service needs and utilization of PLWD in mainland China. The ECSP for PLWD advanced in the paper was a practicable and effective quantitative management means. It is deserved to application in a large scale.

The rise of Asian and the stagnation of Western middle classes over the last thirty years have resulted in gradual convergence of income of large parts of the world’s population. Recent global crises ‐ the Great Recession and the COVID-19 pandemic ‐ have led to a decline in income and increase in income uncertainty. Rise in consumption of lower quality goods of shorter durability and an overall decline in demand and economic activity resulted as challenges to the global economy. In this paper, we argue that generational responsibility in consumption can be an environmentally sustainable response to crises which enables the economies to overcome the crisis of confidence and reaffirms community ties. As an element of long‐term orientation in consumption, generational responsibility is a cultural phenomenon dependent on solidarity within family and the wider community. It is characterized by consideration of consequences of consumption choices on the environment, and the abundance of savings and the usability of goods to be inherited by future generations. For companies, willing to revisit their traditional business models and incorporate principles of sustainability in their competitive strategies, promotion of generational responsibility can become a new source of competitive advantage and a driver of economic recovery.

Wearable technology will become available and allow prolonged electroencephalography (EEG) monitoring in the home environment of patients with epilepsy. Neurologists analyse the EEG visually and annotate all seizures, which patients often under report. Visual analysis of a 24 hour EEG recording typically takes one to two hours. Reliable automated seizure detection algorithms will be crucial to reduce this analysis. We study a dataset of behind-the-ear EEG measurements. Our first aim was to develop a methodology to reduce the EEG dataset by classifying part of the data automatically, while retaining 100% detection sensitivity (DS). Prediction confidences are determined by temperature scaling of the classification model outputs and trust scores. A DS of approximately 90% (99%) can be achieved when automatically classifying around 90% (60%) of the data. Perfect DS can be achieved when automatically classifying 50% of the data. Our second contribution demonstrates that a common modelling strategy, where predictions from several short EEG segments are used to obtain a final prediction, can be improved by filtering out untrustworthy segments with low trust scores. The false detection rate shows a relative decrease between 21% and 43%, and the DS shows a small increase or decrease.

Soil organic carbon (SOC) sequestration in arable soils is a challenging goal for soil management. Multiple factors should be considered for the prediction of the soil capacity to fix atmospheric carbon. In this study, we focused on the effect of crop rotation and previous land use for future carbon sequestration on two experimental fields with identical soils (Retisols) and input of organic fertilizers. We analyzed the SOC dynamics and used the Roth C model to forecast SOC changes under RCP4.5 and RCP8.5 scenarios. Our experimental and modelling results indicated a consistent increase in SOC stocks and the stable fractions of soil organic matter (SOM). The increase in SOC was higher in the experiment with the crop-grassland rotation that in the experiment with a rotation of row crops and barley. With similar total SOC stocks, the efficiency of soil management differed as reflected by the contrasting composition of SOM, as fields with a long cultivation history showed higher SOM stability. The goal of 4‰ annual increase of SOC stocks may be reached under crop- grassland rotation in 2020-40 and 2080-90 when applying mineral or organic fertilizer system for scenario RCP4.5, and mineral fertilizer system in 2080-2090 for scenario RCP8.5.

Despite advances in algorithms and computational platforms, de-novo genome assembly remains a challenging process. Due to the constant innovation in sequencing technologies (Sanger, SOLiD, Illumina, 454, PacBio and Oxford Nanopore), genome assembly has evolved to respond to the changes in input data type. This paper includes a broad and comparative review of the most recent short-read, long-read and hybrid assembly techniques. In this review, we provide (1) an algorithmic description of the important processes in the workflow that introduces fundamental concepts and improvements; (2) a review of existing software that explains possible options for genome assembly; and (3) a comparison of the accuracy and the performance of existing methods executed on the same computer using the same processing capabilities and using the same set of real and synthetic datasets. Such evaluation allows a fair and precise comparison of accuracy in all aspects. As a result, this paper identifies both the strengths and weaknesses of each method. This comparative review is unique in providing a detailed comparison of a broad spectrum of cutting-edge algorithms and methods.

In the case of vascular plants the process of water loss by leafs and water absorption by the root is well known. There is agreement on the passive nature of long-distance moisture movement in the dead cells of the xylem; however, controversy exists focusing on the long-distance water transport principle. Hales (1726) founded a view of bulk flow based on water suction after experiments with cut twigs. The previous doctrine of long-distance water transport within vessel elements and tracheid of the xylem of intact plants – the relevant cohesion theory in text books – was developed mainly by Boehm (1893), Renner (1911) and Dixon (1914) with plant artefacts. Water movement according to this theory is based on an assumed hydrodynamic bulk fluid flow in xylem in continuous water columns (free of water vapour space), under tension, according to the law of Poiseuille (see e.g. Dixon 1914). Physically hydrodynamics is part of fluid mechanics, as a result Poiseuille’s law is usually valid only for hydrodynamic bulk flow in ideal capillaries (Sutera & Skalak, 1993). Besides the basic requirement for transport, according to cohesion theory, the existence of ideal capillaries is not compatible with either: “Because vessel elements and tracheid do not stand as ideal capillaries. …” (Bresinsky et al. 2008, translated from German). Unlike ideal capillaries, the walls of vessel elements and tracheid interact with the transported water. These walls are able to function as a source or as a sink for the transported water because of interaction with the cell walls. With the interaction, vessel elements and tracheid, part of the xylem, can shrink and swell, unlike ideal capillaries. Because the xylem (in woody plants part of the wood) is inconsistent with the basic law of fluid flow, the equation of mass balance (Zimmermann et al. 2004) and cohesion theory are not strictly followed.Many plant physiologists view the cohesion theory as appropriate, however, this theory remains controver­sial, i.e. by Eisenhut (1988), Laschimke (1990) and Hahn (1993). Nultsch (1996) gives doubts referring to the present doctrine of plant water transport. Zimmermann et al. (2004) reject the cohesion theory and conclude: “... that the arguments of the proponents of the Cohesion Theory are completely misleading” (Zimmermann et al. 2004). Hence cohesion theory can be treated as inapplicable and the question arises: how does water transport in fact function? In the following, it is gone into in more detail. A sorption hypothesis of actual water transport, based on empirical fact, shall be addressed in this paper.

Prestress loss evaluation in prestressed strand is essential for prestressed structures. However, the sensors installed outside the duct can only measure the total prestress loss. The sensors attached on strand inside the duct also have several problems, such as inadequate durability in an aggressive environment, vulnerable damage at tensioning and so on. This paper proposes a new installation method for long-gauge fiber Bragg grating (LFBG) sensor to prevent accidental damage. Then the itemized prestress losses were determined in each stage of the pre-tensioning and post-tensioning according to the LFBG measurements. We verified the applicability of the LFBG sensors for prestress monitoring and the accuracy of the proposed prestress loss calculation method during pre-tensioning and post-tensioning. In the pre-tensioning case, the calculated prestress losses had less deviation from the true losses than those obtained from foil-strain gauges, and the durability of the LFBG sensors was better than foil-strain gauges, whereas in post-tensioning case, the calculated prestress losses were close to those derived from theoretical predictions. Finally, we monitored prestress variation in the strand for 90 days. The itemized prestress losses at each stages of post-tensioning were obtained by the proposed calculation method to show the prospect of the LFBG sensors in practical evaluation.

The main features of the instrumental global mean surface temperature (GMST) are reasonably well described by a simple linear response model driven by anthropogenic, volcanic and solar forcing. This model acts as a linear long-memory filter of the forcing signal. The physical interpretation of this filtering is the delayed response due to the thermal inertia of the ocean. This description is considerably more accurate if El Ni\~{n}o Southern Oscillation (ENSO) and the Atlantic Multi-decadal Oscillation (AMO) are regarded as additional forcing of the global temperature and hence subject to the same filtering as the other forcing components. By considering these as predictors in a linear regression scheme, more than 92\% of the variance in the instrumental GMST over the period 1870-2017 is explained by this model, and in particular all features of the 1998 -- 2015 hiatus, including its death. While the more prominent pauses during 1870 -- 1915 and 1940 -- 1970 can be attributed to clustering in time of strong volcanic eruptions, the recent hiatus is an unremarkable phenomenon that is attributed to ENSO and a small contribution from solar activity.

Hereditary angioedema (HAE) is a rare genetic disorder clinically characterized by recurrent episodes of severe swelling, affecting the skin, gastrointestinal tract, and airways. Lanadelumab, a monoclonal antibody that inhibits plasma kallikrein, is approved for long-term prophylaxis (LTP) in HAE patients, demonstrating effectiveness in reducing disease burden. This study evaluated the real-world impact of lanadelumab on healthcare resource utilization, angioedema episode frequency, and quality of life (QoL) in adult HAE type 1 patients treated at the Allergy Department of Hospital Universitario de Canarias, Tenerife Spain. A retrospective review of electronic medical records was conducted for patients who iniciated LTP with lanadelumab between March 2021 and June 2024. Results confirmed a 75.27% reduction in hospital visits, and a 94.1% decrease in the average number of angioedema episodes during the study period. Additionally, the use of on-demand rescue medication (icatibant) was significantly reduced by 61% (p<0.05), with HAEQoL scores improving from 62.2 to 99.5 points, and no significant adverse effects were reported. These findings indicate that lanadelumab effectively reduces healthcare resource utilization and angioedema episodes, while notably enhancing the QoL in selected HAE type 1 patients.

Balancing the levels of ammonia, in poultry facilities plays a role in enhancing the welfare and productivity of animals. Various environmental factors such as temperature, humidity, ammonia concentrations, carbon dioxide concentration and exposure to light have an impact on the wellbeing and efficiency of poultry. It is essential to monitor these elements to achieve outcomes necessitating practical, reliable and cost-effective solutions. Leveraging the Internet of Things (IoT) in conjunction with LoRa-WAN (Long Range Wide Area Network) technology presents an opportunity in the industry by enhancing connectivity among devices and facilitating real time data transmission. This research introduces a hardware and software setup for monitoring conditions at poultry farms through LoRa WAN and IoT technologies. More-over researchers employed Random Forest Machine Learning prediction models to tune the concentration of Ammonia (NH3) in poultry facilities across a wide area or multiple Poultry houses. Comparative assessments were conducted to evaluate the proposed system against devices revealing a correlation exceeding 0.9830 between the sensors and standard equipment while costing 28% less than traditional tools. The prototype displayed outcomes underscoring its potential, for managing poultry facilities across multiple farms with centralized oversight.

It is meaningful to monitor and identify the concrete cracks as they will seriously reduce the fatigue life of pavement. In this paper a new method based on distributed long-gauge optic fiber sensing is proposed to implement the crack identification. Firstly, the damage indicator, namely strain curve envelope area ratio (SCEAR), is proposed and calculated with the measured strain in time series when the plane goes across the pavement where the sensors are installed. Then the indicators with different sensors are normalization by the value of the indicator from one referenced sensor where damage normally will not happen. If the crack happens within the sensing gauge of the sensor, the value of damage indicator SCEAR from the sensor will increase. Therefore, the crack will be identified with the change of the damage indicator. The effect of the proposed method is first verified by some simulations of prefabricated pavement as the cracks can be accurately identified by the proposed method. Moreover, the influence from the aircraft, such as taxiing position, aircraft load and aircraft type, is not obvious enough to mislead the identification results. Lastly the static loading tests were implemented with one small-scale model of prefabricated concrete pavement in the lab. From the experimental results the prefabricated cracks inside the pavement slab can be identified accurately with the proposes method as the parameter SCEAR is increased by about 100% at the crack position, while it is only changed by about 3% at the undamaged position.

Coronaviruses constitute a global threat to human and animal health. It is essential to investigate the long-distance RNA-RNA interactions that approximate remote regulatory elements in strategies, including genome circularization, discontinuous transcription, and transcriptional enhancers, aimed at rapid replication of their large genomes, pathogenicity, and immune evasion. Based on the primary sequences and modeled RNA-RNA interactions of two experimentally de-fined coronaviral enhancers, we detected via in silico primary and secondary structural analysis potential enhancers in various coronaviruses, from the phylogenetically ancient avian IBV to the recently emerged SARS-CoV-2. These potential enhancers possess a core duplex-forming region that could transition between closed and open states, as molecular switches directed by viral or host factors. The open state would permit the enhancer to pair with remote sequences in the viral genome and modulate the expression of crucial genes involved in viral replication and host immune evasion. Consistently, variations in the predicted IBV enhancer region or its distant targets coincide with cases of viral attenuation possibly driven by decreased expression of the ORF3a immune evasion protein. The annotated enhancer sequences could inform structural prediction tools and antiviral interventions if validated experimentally.

Exosomal microRNAs (miRNAs) from cancer cells play a key role in mediating the oral squamous cell carcinoma (OSCC) microenvironment. The objective aimed to investigate how long non-coding RNA (lncRNA) MEG3 affects OSCC angiogenesis through exosomal miR-421. Global miRNA microarray analysis and quantitative real-time PCR (qRT-PCR) were performed to determine the level of miRNAs in OSCC cells-derived exosomes. Cell migration, invasion, tube formation, immunohistochemistry and hemoglobin concentration were used to study the effects of exosomal miR-421 in angiogenesis. Western blotting was used to determine the expression level of HS2ST1 and VEGFR2-related downstream proteins. MiRNA array and qRT-PCR identified upregulation of miR-421 in OSCC cell-derived exosomes. Furthermore, exosome-miR-421 can be taken up by human umbilical vein endothelial cells (HUVEC) and then targets HS2ST1 through VEGF-mediated ERK and AKT phosphorylation, thereby promoting HUVEC migration, invasion, and tube formation. Additionally, enforced expression of lncRNA MEG3 in OSCC cells reduced exosomal miR-421 levels and then increased HS2ST1 expression, thereby reducing the VEGF/VEGFR2 pathway in HUVEC. Our results demonstrate a novel mechanism by which lncRNA MEG3 can act as a tumor suppressor and regulate endothelial angiogenesis through the exosomal miR-421/HS2ST1 axis, which provides a potential therapeutic strategy for OSCC angiogenesis.

Accurate streamflow forecasting is crucial for effectively managing water resources, particularly in countries like Colombia, where hydroelectric power generation significantly contributes to the national energy grid. Although highly interpretable, traditional deterministic, physically-driven models often suffer from complexity and require extensive parameterization. Data-driven models like Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) networks offer simplicity and performance but cannot quantify uncertainty. This work introduces Sparse Variational Gaussian Processes (SVGPs) for forecasting streamflow contributions. The proposed SVGP model reduces computational complexity compared to traditional Gaussian Processes, making it highly scalable for large datasets. The methodology employs optimal hyperparameters and shared inducing points to capture short-term and long-term relationships among reservoirs. Training, validation, and analysis of the proposed approach consider the streamflow dataset from 23 geographically dispersed reservoirs recorded during twelve years in Colombia. Performance assessment reveals that the proposal outperforms baseline Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) models in three key aspects: adaptability to changing dynamics, provision of informative confidence intervals through Bayesian inference, and enhanced forecasting accuracy. Therefore, the SVGP-based forecasting methodology offers a scalable and interpretable solution for multi-output streamflow forecasting, thereby contributing to more effective water resource management and hydroelectric planning.

The knowledge of COVID-19 impact on the human body has increased rapidly. Although many people recover from COVID-19, some continue to experience persistent symptoms that have been identified as Long COVID. This condition can have a severe impact on quality of life, and it remains a significant concern for medical professionals and researchers. One of the key components of the SARS-CoV-2 virus that enables it to enter human cells is the spike (S) protein. Recent studies have revealed a complex network of interactions between G proteins, spike (S) protein, and the Renin-Angiotensin System (RAS) may be responsible, at least in part, for long COVID. SARS-CoV-2 can also affect the brain, leading to neurological symptoms such as confusion, memory loss, and fatigue. Increasing evidence suggests that COVID-19 is not just a respiratory illness since it is likely that the virus could influence signal transduction pathways such as G-protein-coupled receptor (GPCR), among others, in the brain, either directly or indirectly, affecting neural functions. These interactions with the spike (S) protein and RAS, alongside the brain, are complex and require deep research to understand their implications for Long COVID-19 manifestation fully. While recent research has shed light on the complex interactions between G proteins, spike (S) protein, the brain, and the angiotensin system, this article explores these interconnected pathways and their implications for long COVID-19 manifestations. The present review summarises current research on different molecular mechanisms in Long COVID pathophysiology and may help identify possible targets or new strategies for the diagnosis and treatment.

Docosahexaenoic acid (DHA) is an essential fatty acid (FA) with proven health effects, whom bioavailability improvement is becoming a public health issue. Unlike fish oils, the bioavailability of DHA from microalgal (A) has not been fully assessed, particularly with regard to the molecular structuring capabilities offered by A-oil. We explored the impact of 5 formulas rich in DHA, different by i) the molecular structure: ethyl ester (EE) or monoglyceride (MG) or Triglyceride (TG) and ii) the supramolecular form: emulsified TG or TG+phospholipids (blend PL), on the lymphatic kinetics of DHA absorption and the lipid characteristics of resulting lipoproteins. We demonstrated that in rat, the DHA absorption of the conventional A-DHA TG structure was more effective than the EE structure (+ 23%). More, the A-DHA MG and A-DHA Emulsion were the most favourable DHA-vectors (AUC: 89% and +42%, respectively), thanks to an improved lipolysis. The A-DHA MG and -Emulsion presented the richest DHA content in TG (+40%) and PL (+50%) of lymphatic chylomicrons, which could impact the metabolic fate of DHA. We concluded that structuring A-DHA in TG or EE would be more prone for tissue and hepatic metabolism whereas in A-DHA MG and A-DHA Emulsion could target nerve tissues.

Post-viral fatigue syndrome (PVFS) encompasses a wide range of complex neuroimmune disorders of unknown cause characterized by disabling post-exertional fatigue, myalgia and joint pain, cognitive impairments, unrefreshing sleep, autonomic dysfunction, and neuropsychiatric symptoms. It includes myalgic encephalomyelitis, also known as chronic fatigue syndrome (ME/CFS), fibromyalgia (FM), and more recently post-COVID-19 condition (Long COVID). To date, there are no definitive clinical case criteria and no FDA-approved pharmacological therapies for PVFS. Given the current lack of effective treatments, there is a need to develop novel therapeutic strategies for these disorders. Mitochondria, the cellular organelles responsible for tissue energy production, have recently garnered attention in research into PVFS due to their crucial role in cellular bioenergetic metabolism in these conditions. Accumulating literature has identified a link between mitochondrial dysfunction and low-grade systemic inflammation in ME/CFS, FM, and Long COVID. To address this issue, this article aimed to critically review the evidence relating to mitochondrial dysfunction in the pathogenesis of these disorders; in particular, to evaluate the effectiveness of coenzyme Q10 supplementation on chronic fatigue and pain symptoms as a novel therapeutic strategy for the treatment of PVFS.

Aging induces numerous physiological alterations, with immunosenescence emerging as a pivotal factor. This phenomenon has attracted both researchers and clinicians, prompting pro-found questions about its implications for health and disease. Among the contributing factors, one intriguing actor in this complex interplay is human cytomegalovirus (CMV), a member of the herpesvirus family. Latent CMV infection exerts a profound influence on the aging immune system, potentially contributing to age-related diseases. This review delves into the intricate relationship between immunosenescence and CMV, revealing how chronic viral infection im-pacts the aging immune landscape. We explore the mechanisms through which CMV can im-pact both the composition and functionality of immune cell populations and induce shifts in in-flammatory profiles with aging. Moreover, we examine potential role of CMV in such pathol-ogies as cardiovascular diseases, cancer, neurodegenerative disorders, COVID-19, and Long COVID. This review underlines the importance of understanding the complex interplay be-tween immunosenescence and CMV. It offers insights into the pathophysiology of aging and age-associated diseases, as well as COVID-19 outcomes among the elderly. By unraveling the connections between immunosenescence and CMV, we gain a deeper understanding of aging's remarkable journey and the profound role that viral infections play in transforming the human immune system.

Air pollution includes particle-bound Polycyclic Aromatic hydrocarbons (PAHs) which eventually reach the placenta triggering adverse perinatal outcomes by long-term exposure. Late-ly, air pollution has increased over the Metropolitan Area of Medellin-Colombia (MAMC) but its effects on pregnancy are still unknown. In this research, we made a real-time analysis of airborne total PAHs using a photoelectric sensor for residential places influenced by industrial and traffic sources contrasting southern and northern MAMC during the second peak of the bimodal ten-dency for PM2.5 emissions in this region. Additionally, we analyzed individual PAHs by GC/MS coupled to pressurized hot water extraction methodology. Data was applied in an Inhalation In-take Model to assess pregnancy exposure. The average concentration of PAHs over southern MAMC was three times higher than over northern MAMC where the abortion rate has been 1.4 times higher presented in database. Previous research found that PAHs act as an Endo-crine-Disrupting Chemical (EDC) during pregnancy and even heavy congeners could reside in umbilical cord blood. Finally, the annual series of abortion rates in the MAMC showed a signifi-cant correlation with the annual average levels of PM2.5 which are associate to PAHs. Although this significant correlation does not imply causality, our results suggest an important connection between both variables. This latter finding opens a gap in deeply understanding how regions with high PAHs-convergence influences abortion rates in MAMC.

As sessile organisms, plants face a wide range of abiotic stresses, with salinity being a prominent constraint affecting their growth, development, and productivity, particularly in arid and semi-arid regions. This study focused on understanding how salinity impacts Jatropha curcas, an important oilseed plant for biodiesel production. By examining the anatomy and ultrastructure of stomata and chloroplasts, we investigated the effects of prolonged salinity stress on J. curcas. This stress led to changes in stomatal density, stomatal index, and ostiole aperture, which case an imbalance of water conductivity in the xylem. Through transmission electron microscopy, we explored the subcellular organization of J. curcas chloroplasts and their contribution to plant photosynthetic efficiency, providing insights into their role in this process. Notably, salinity treatment resulted in a significant increase in starch granules accumulation, leading to impaired the granal and stromal grana lamellae, destroying this ultrastructure. Our findings indicate that the anatomy and ultrastructure of chloroplasts play a crucial role in influencing photosynthetic efficiency and hydraulic conductivity. This study offers new perspectives on the structure and function of chloroplasts in J. curcas, presenting innovative opportunities to develop strategies that enhance biofuel production in areas with high soil salinity.

This paper presents a methodology for indirectly estimating the scale of seabird bycatch using existing data, eliminating the need for regular monitoring. The study focuses on five species of ducks that winter in the Polish waters of the Baltic Sea and are highly susceptible to bycatch: Long-tailed Duck, Velvet Scoter, Common Scoter, Greater Scaup, and Tufted Duck. The evaluation units used are divided into the Bornholm and Gotland Groups within the Polish Exclusive Economic Zone (PEEZ). The analysis acknowledges the lack of data for certain areas known to have high concentrations of diving birds and fishing activity. The population sizes of waterbirds were assessed using ship-based surveys and a distance sampling approach. Fishing effort data from the winter seasons between 2015/2016 and 2019/2020 were analyzed. Bycatch rates were estimated based on surveys conducted in previous seasons, and threshold values for bycatch were established using the concept of "small numbers" defined in EU directives. The results show that within the Gotland subdivision of the PEEZ, the estimated average abundance of all diving waterbirds was 207,114 individuals, with an average bycatch of 7,921 birds (3.8% of the total). The Velvet Scoter was the most abundant species, followed by the Long-tailed Duck. Benthivorous ducks constituted the majority of the bird population in the area. In the Bornholm subdivision, the estimated average abundance of diving waterbirds was 600,845 individuals, with an average bycatch of 5,056 birds (0.8% of the total). The Long-tailed Duck was the most numerous species, followed by the Velvet Scoter. Benthivorous ducks also dominated in this area. The findings highlight the need for effective management and conservation measures to address the high mortality of seabirds due to bycatch. The methodology presented in this study offers a valuable approach for estimating bycatch scale and can support efforts to minimize the impact on seabird populations in the Baltic Sea.

In the present work, we investigate the possibility that long-range airborne transport of infectious aerosols could initiate an epidemic outbreak at distances downwind beyond one hundred kilometers. For this, we have developed a simple atmospheric transport box-model which, for a hypothetical case of a COVID-19 outbreak, was compared to a more sophisticated 3-dimensional transport-dispersion model (HYSPLIT) calculation. Coupled with an extended Wells-Riley description of infection airborne spread, it shows that, the very low probability of outdoor transmission can be compensated by high numbers and densities, such as occurs in large cities, of infected and susceptible people in the source upwind and in the target downwind respectively. This may result in the creation of a few primary cases. It is worth pointing out that the probability of being infected remains very small at the individual level. Therefore, this process alone, which depends on population sizes, geography, seasonality and meteorology, can only “trigger” an epidemic which could then spread by the standard infection routes

Ad- and desorption forces move water in living xylem/wood from the root to the leaf thermodynamically. The doctrine of plant water transport, the so-called cohesion- or cohesion-tension theory, postulates however that the process is physically based on a hydraulic fluid flow with negative pressure in water conducting tubes originating from the leaves. Lower pressure (suction) driven volume flow is physically a branch of mechanics. Moisture absorbed from the soil via the root is thought to be pulled up the stem by the leaves in continuous and tensioned threads of water. It is assumed that the hydraulic Hagen/Poiseuille flow law, derived for tubes, applies in the xylem. In a textbook of botany you can find the opinion: "Just as the pipes of a water pipe supply necessary water to each household, leaf nerves supply water and nutrient salts to each individual cell.” (Translated from German). Many plant physiologists consider this hydraulic principle to be correct, but it does not remain unchallenged. Doubts are repeatedly expressed. The question arises: How does water transport actually take place? It is shown how the diffusive/adsorption transport principle works. The partial dehydration (desorption) of the plant, driven by the diffusive process of transpiration, forms a combined concentration and adsorption-site gradient for water in the xylem matrix. Especially with open stomata the lowest moisture concentration and the highest number of adsorption-sites for water (sites with free van der Waals forces), can be found in the mesophyll cell walls at the liquid/vapor boundary in the leaf. The water taken up by the root moves spontaneously in the direction of this boundary and can thus partially or completely compensate for the existing concentration- and adsorption-site- differences for water. Thus, a thermodynamic overlapping diffusive/adsorptive movement of moisture along the stationary xylem/wood takes place. After the introduction and a review of some controversies with cohesion theory, the physiology of the processes associated with long-distance water displacement is mentioned below. A thermodynamic adsorption hypothesis of the natural water transport in plants, based on known facts, is presented.

The genus Pseudomonas hosts an extensive genetic diversity and is one of the largest genera among Gram-negative bacteria. Type strains of Pseudomonas are well-known to represent only a small fraction of this diversity and the number of available Pseudomonas genome sequences is increasing rapidly. Consequently, new Pseudomonas species are regularly reported and the number of species within the genus is in constant evolution. In this study, whole genome se-quencing enabled us to define 43 new Pseudomonas species and to provide an update of the Pseu-domonas evolutionary and taxonomic relationships. Phylogenies based on the rpoD gene and whole genome sequences, including 316 and 313 type strains of Pseudomonas, respectively, re-vealed sixteen groups of Pseudomonas and justified the partitioning of the P. putida group into fifteen subgroups. Pairwise average nucleotide identities were calculated between type strains and a selection of 60 genomes of non-type strains of Pseudomonas. Forty-one strains were incor-rectly assigned at the species level and among those, 19 strains were shown to represent an addi-tional 13 new Pseudomonas species that remain to be formally classified. This work pinpoints the importance of correct taxonomic assignment and phylogenetic classification in order to perform integrative studies linking genetic diversity, lifestyle and metabolic potential of Pseudomonas spp.

Athletes living with and beyond cancer can continue to train and, in some cases, compete during treatment. Following cancer treatment, athletes can return to competitive sport but need to learn to adapt their physical strength and training to lingering effects of cancer. It is critical for oncology healthcare providers to use the principles of assess, refer and advise to exercise oncology programs that are appropriate for the individual. Managing side effects of treatment is key to being able to train during and immediately following cancer treatment. Keen attention to fatigue is important at any point in the cancer spectrum to avoid overtraining and optimize the effects of training.

With the latest advances in information and communication technologies, greater amounts of sensitive user and corporate information are constantly shared across the network making it susceptible to an attack that can compromise data confidentiality, integrity and availability. Intrusion Detection Systems (IDS) are important security mechanisms that can perform a timely detection of malicious events through the inspection of network traffic or host-based logs. Throughout the years, many machine learning techniques have proven to be successful at conducting anomaly detection but only a few considered the sequential nature of data. This work proposes a sequential approach and evaluates the performance of a Random Forest (RF), a Multi-Layer Perceptron (MLP) and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset. The resulting performance measures of this particular approach are compared with the ones obtained from a more traditional one, that only considers individual flow information, in order to determine which methodology best suits the concerned scenario. The experimental outcomes lead to believe that anomaly detection can be better addressed from a sequential perspective and that the LSTM is a very reliable model for acquiring sequential patterns in network traffic data, achieving an accuracy of 99.94% and a f1-score of 91.66%.

Hand and arm gesture recognition using radio frequency (RF) sensing modality proves valuable in man-machine interface and smart environment. In this paper, we use time series analysis method for accurately measuring the similarity of the micro-Doppler (MD) signatures between the training and test data, thus providing improved gesture classification. We characterize the MD signatures by the maximum instantaneous Doppler frequencies depicted in the spectrograms. In particular, we apply the dynamic time warping (DTW) method and compare its performance with that of the long short-term memory (LSTM) network. Both methods take into account the values as well as the temporal evolution and trends of time series data. It is shown that the DTW method achieves high gesture classification rates and is robust to time misalignment.

Course recommendation is an important service of Intelligent tutoring systems (ITS), the rapid development of large-scale online courses has generated a substantial amount of learning process data for online learning platforms, providing valuable support for understanding learners' interests and preferences. However, despite made considerable achievements, there are still unresolved challenges:(1) Current research neglects the sequential relationship between the course learners learn. (2) It does not take advantage of the differences in learners' interest in different courses during the knowledge dissemination process. To overcome these challenges, we propose an innovative solution called PGDB (Preference-aware graph diffusion network and BiLSTM) for course recommendation. Specifically, we analyze learners' preferences for courses and relational paths using a relation-aware multi-head attention network, learn the semantic diversity of different contexts, effectively distinguish the difference of learners' interest in different courses in the process of knowledge transmission. In addition, the temporal preference modeling module employs a bi-directional long short-term memory network to mine learners' interest evolution patterns, generating learner-dependent representations, efficient use of the sequential relationship between the courses taken by learners. Experimental results on real datasets demonstrate that the proposed model greatly exceeds the performance of the most recent baseline models, thereby validating the model's effectiveness.

This research presents a comparative analysis of various deep learning models—including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Attention LSTM—in predicting stock prices of major companies in the Indian stock market, specifically HDFC, TCS, ICICI, Reliance, and Nifty. The study evaluates model performance using key regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-Squared (R²). The results indicate that CNN and GRU models generally outperform the others, depending on the specific stock, and demonstrate superior capabilities in forecasting stock price movements. This investigation provides insights into the strengths and limitations of each model while highlighting potential avenues for improvement through feature engineering and hyperparameter optimization.

Hepatocellular carcinoma (HCC) is a liver cancer that originates from hepatocytes. It is a primary health issue and one of the most common malignant tumors. The US FDA approved Sorafenib as the first-line anti-cancer drug for advanced HCC patients. A few patients with advanced HCC may benefit from Sorafenib treatment, but most of the patients eventually develop resistance to Sorafenib, which leads to its poor prognosis. Long-noncoding RNAs (lncRNA) have been found to play a critical role in tumorigenesis and the development of HCC, as well as various cancers. It also plays a vital role in tumor drug resistance, while the mechanisms of LncRNAs in HCC sorafenib resistance have not been characterized yet. In this review article, we summarized the molecular mechanisms, such as the involvement of transporters, receptors, cell death regulation, and other factors impacting sorafenib resistance in HCC.

A contemporary technique for calculating seismic risk is earthquake nowcasting (EN), which analyzes the progression of the earthquake (EQ) cycle in fault systems. 'Natural time' is a novel idea of time that serves as the foundation for EN assessment. The earthquake potential score (EPS), which has been discovered to have useful uses regionally and worldwide, is a unique tool EN uses to predict seismic risk. For the estimation of the EPS for the occurrences with the highest magnitude among these applications, we have since 2021 concentrated on Greece territory, applying sophisticated Artificial Intelligence (AI) algorithms, both wise (semi)supervised and unsupervised models, along with a customized dynamic sliding window technique that performs as a stochastic filter able to fine-tune the geo seismic occurrences. Long short-term memory (LSTM) neural networks, random forests, and clustering (geospatial) models are three machine learning techniques that are particularly good at finding patterns in vast databases and may be used to enhance earthquake prediction performance. In this study, we attempt to forecast whether practical Machine-learning and AI/Game-theoretic-based approaches can help predict big earthquakes and the normal future seismic cycle for 6-12 months. Specifically, we focus on answering two questions for a given region: (1) Is there a chance that a significant earthquake—say, one with a magnitude of M ≥ 6.0 will happen in the upcoming year? (2) What is the largest earthquake magnitude predicted in the upcoming year, and with which exact geographic coordinates (GCS)? Our results are quite promising and project a high precision accuracy score (≥ 98%) for seismic nowcasting in terms of four predictive parameters: the approximate (a) latitude, (b) longitude, (c) focal depth, and (d) magnitude of the phenomena.

Predicting heart disease is crucial for early diagnosis and intervention, significantly improving patient outcomes and reducing mortality rates. This study compares various machine learning models, including Logistic Regression, Random Forest, Gradient Boosting, XGBoost, and Long Short-Term Memory (LSTM) networks, using the Cleveland Heart Disease dataset. Comprehensive preprocessing steps were undertaken, such as handling missing values, converting categorical variables to numeric forms, and binarizing the target variable for binary classification. Each model was rigorously evaluated using performance metrics, including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). SHapley Additive exPlanations (SHAP) values were employed to provide insights into feature importance, ensuring model transparency and interpretability. The results indicate that XGBoost outperformed all other models, achieving an accuracy of 90% and an AUC-ROC of 0.94, demonstrating its superior ability to capture complex patterns in the data through advanced optimization techniques and regularization. This study highlights the significant potential of advanced machine learning techniques, particularly ensemble methods like Gradient Boosting and XGBoost, in enhancing heart disease prediction. These models offer higher accuracy and valuable interpretability, making them practical tools for early diagnosis in clinical settings. Future research should focus on integrating these models into healthcare systems and exploring hybrid approaches to further improve predictive performance and clinical applicability.

Keywords: Chronic Obstructive Pulmonary Disease (COPD), Long Non-Coding RNAs (lncRNAs), Molecular Pathogenesis, Inflammation, Lung Cancer, Diagnostic Biomarkers

This review investigates links between post-acute sequelae of SARS-CoV-2 infection (PASC), post-infection viral persistence, mitochondrial involvement and aberrant innate immune response and cellular metabolism during SARS-CoV-2 infection. Advancement of proteomic and metabolomic studies now allows deeper investigation of alterations to cellular metabolism, autophagic processes and mitochondrial dysfunction caused by SARS-CoV-2 infection while computational biology and machine learning have advanced methodologies of predicting virus-host gene and protein interactions. Particular focus is given to interaction between viral genes and proteins with mitochondrial function and that of the innate immune system. Finally, the authors hypothesise that viral persistence may be a function of mitochondrial involvement in sequestration of viral genetic material. While further work is necessary to understand the mechanisms definitively, a number of studies now point to resolution of questions regarding the pathogenesis of PASC.

Long-acting (LA) drug-delivery system (DDS) successes are linked to their abilities to harness biocompatible polymers and materials for sustained and predictable release of therapeutic agents over an extended period of time. For human immune deficiency virus type one (HIV-1) infections, LA DDSs hold great promise in closing the gaps of daily oral therapy for treatment and prevention. Examples are Cabenuva, Apretude and Sunlenca, which have been shown to be safe and effective. Alternative promising drug delivery technologies such as implants, prodrugs, vaginal rings, and microarray patches are being explored to further meet patients’ needs. We posit that improved physicochemical properties of the formulation payloads and material chemistry design with optimal drug release kinetics holds the potential to optimize release profiles. Furthermore, for the treatment and prevention of HIV-1 infection, the landscape of LA medicines now in clinical or preclinical development offers hope that the strategic design of polymers will further improve upon controlled delivery of drugs to simplify dosing schedules.

This study introduces session-aware recommendation models, leveraging GRU (Gated Recurrent Unit) and attention mechanisms for advanced latent interaction data integration. A primary ad-vancement is enhancing latent context, a critical factor for boosting recommendation accuracy. We address existing models' rigidity by dynamically blending short-term (most recent) and long-term (historical) preferences, moving beyond static period definitions. Our approaches, pre-combination (LCII-Pre) and post-combination (LCII-Post) with fixed (Fix) and flexible learning (LP) weight configurations, are thoroughly evaluated. We conducted extensive experiments to as-sess our models' performance on public datasets such as Amazon and MovieLens 1M. Notably, on the MovieLens 1M dataset, LCII-PreFix achieved a 1.85% and 2.54% higher Recall@20 than II-RNN and BERT4Rec+st+TSA, respectively. On the Steam dataset, LCII-PostLP outperformed these models by 18.66% and 5.5%. Furthermore, on the Amazon dataset, LCII showed a 2.59% and 1.89% improvement in Recall@20 over II-RNN and CAII. These results affirm the significant enhancement our models bring to session-aware recommendation systems, showcasing their po-tential for both academic and practical applications in the field.

Coronavirus disease 2019 (COVID-19), an infectious disease pandemic, affected millions of people globally, resulting in high morbidity and mortality. Causing further concern, significant proportions of COVID-19 survivors suffer from the lingering health effects of severe acute respiratory syndrome virus 2 (SARS-CoV-2), the pathogen that causes COVID-19. One of the diseases manifesting as a post-acute sequela of COVID-19 is new-onset diabetes. This systematic review and meta-analysis will perform a comprehensive and systematic literature search to estimate the burden of new-onset diabetes after COVID-19. Specifically, this study will estimate the magnitude of the incidence, risk, and population-attributable fraction of new-onset diabetes. The study will also explore and summarize the data on the natural history or clinical course of the new-onset diabetes cases. Five bibliographic databases, including PubMed, MEDLINE, Embase, Scopus, and Web of Science, will be searched for eligible studies. The World Health Organization COVID-19 Research Database, preprint servers, and conference abstracts will also be searched. Cohort studies of COVID-19 patients of all ages providing data on new cases of diabetes in the post-acute phase of the illness will be included. The comparators to estimate the pooled risk ratio will be those with no diagnosis of COVID-19 or those infected with other respiratory tract infections. The findings of this study will likely inform clinical practice, public health guidelines, and policies for early detection and treatment of new-onset diabetes cases in the long-COVID phase. This protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO: No.CRD42020200432).

Recently, various applications of indoor location-based services using Wi-Fi fingerprint data have been studied. However, since fingerprint data collected at a specific location does not have continuous information between signals and correlation information between signals, path data cannot exist in principle. Therefore, most indoor positioning technologies are based on predicting the position of a stationary positioner at a specific location based on signal data collected at a specific location in the indoor space. Due to the discontinuous nature of these signals, they are unable to account for user movement. Therefore, there is a need for techniques to improve the accuracy of indoor positioning in moving situations. This paper proposes a method for enriching all data points with relevant information to obtain improved indoor positioning results and an improved technique for generating movement path data. The proposed technique generates relevant information with continuity by expressing data points as Bounding Boxes (BB) and subsequently clustering them using grid cells. Through the proposed method, we represented and expanded the indoor location data, which consists of data points, as BB. However, data points represented by BB do not have any adjacency information with each other, and as a result, it is not possible to create a movement path between neighboring BB. To solve the problem, we used clustering, a machine learning technique that categorizes data points into groups, to group BBs. This was done by constructing a YOLO (You Only Look Once) grid cell that included all BBs and clustered them into grid cells that were divided into specific regions through the suggestion phase. The clustering results align with the indoor structure of the building, indicating that the data points have been appropriately clustered. BBs inside a grid cell are contained within a moveable area and have related information. We also generated association information between neighboring grid cells through the proposal technique, and then generated path information that can be traveled between grid cells. In this paper, we use the movement path information generated from a dataset as training data for machine learning, and through this, we propose an enhanced indoor positioning technology.

Long Covid is a heterogeneous clinical condition in which Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and brain fog stand out among the different clinical symptoms and syndromes. The cerebral metabolic alterations and neuroendocrine disorders seem to constitute an important part of Long Covid. Given the substantial lack of drugs and effective therapeutic strategies, hypothalamic phospholipid liposomes which have been on the market for several years as adjuvant therapy of cerebral metabolic alterations resulting from neuroendocrine disorders, can be taken into consideration in an overall therapeutic strategy that aims to control the Long Covid associated symptoms and syndromes. Their pharmacological mechanisms and clinical effects strongly support their usefulness in Long Covid. Our initial clinical experience corroborates this rationale. Further research is imperative in order to obtain robust clinical evidence.

Advancements in technology have revolutionized communication on social media platforms, where reviews and comments greatly influence product selection. Current opinion mining methods predominantly focus on textual content, overlooking the rich information within customer-posted images, termed here as Multus-Medium. This research introduces an innovative deep learning approach, Multus Medium Opinion Mining (MMOM), capitalizing on both text and image data for comprehensive product review analysis. MMOM employs an integrated model of Bidirectional Long Short-Term Memory (BiLSTM) and embedded Convolutional Neural Network (CNN), incorporating architectures of GoogleNet and VGGNet, thus enabling efficient extraction and fusion of textual and visual features. This synergistic approach enables data collection, preprocessing, feature extraction, fusion strategy-based feature vector generation, and subsequent product recommendation. Performance evaluation on two diverse real-world datasets name as “flicker8k” and “t4sa”. These datasets show substantial improvement over existing methods. MMOM outperforms standard benchmark models, achieving an accuracy, F1 score and ROC over fliker8k are 90.38%, 88.75% and 93.08%, whereas for twitter dataset are 88.54%, 86.34%, and 92.26% respectively, the accuracy of purposed model is 7.34% and 9.54% higher than the other two mentioned techniques. These statistics highlight the robustness and applicability of MMOM across various domains. The compelling results underscore the potential of MMOM, providing a more holistic and precise approach to opinion mining in the era of social media product reviews. This research, product recommendation helps the customer to make purchasing decision. Last but not the least, the purposed scheme can further be expanded in any other sentiment task like hospital recommendation system, crop farming recommendation and medical diagnostic system etc.

Hepatocellular carcinoma (HCC) is one of the most common cancers and the main cause of cancer-related death globally. Immune dysregulation of CD4+ T cells has been identified as a role in the development of HCC. Nevertheless, the underlying molecular pathways of CD4+ T cells in HCC are not completely known. Thus, a better understanding of the dysregulation of lncRNA-miRNA/mRNA network might yield novel insights into the etiology or progression of HCC. In this study, circulating CD4+ T cells were isolated from the whole blood of 10 healthy controls and 10 HCC patients for next-generation sequencing of the expression of lncRNAs, miRNAs, and mRNAs. Our data showed that there was different expression of 34 transcripts (two lncRNAs, XIST and MIR222HG, 29 mRNA, and 3 other types of RNA) and 13 miRNAs in the circulating CD4+ T cells of HCC patients. The expression of the lncRNA XIST-related miRNAs and their target mRNAs was confirmed using real-time quantitative polymerase chain reaction (qPCR) on samples from 100 healthy controls and 60 HCC patients. The lncRNA–miRNA/mRNA regulation network was created using interaction data generated from ENCORI and revealed there are positive correlations in the infiltration of total CD4+ T cells and negative correlations in the infiltration of Th1 CD4+ T cells.

ME/CFS (Myalgic Encephalomyelitis / Chronic Fatigue Syndrome) is a chronic, complex, heterogeneous disease that affects millions and lacks both diagnostics and treatments. Big data, or the collection of vast quantities of data that can be mined for information, has transformed the understanding of many complex illnesses like cancer (1,2) and multiple sclerosis (3,4), by dissecting heterogeneity, identifying subtypes, and enabling the development of personalized treatments. It is possible that big data can reveal the same for ME/CFS. Solve M.E. developed and launched the You + ME Registry to collect longitudinal health data from people with ME/CFS, people with Long COVID (LC) and control volunteers using rigorous protocols designed to harmonize with other groups collecting data from similar groups of people. The Registry is an invaluable resource because it integrates with a symptom tracking app, as well as a biorepository, to provide a robust and rich dataset that is available to qualified researchers. Accordingly, it facilitates collaboration that may ultimately uncover causes and help accelerate the development of therapies.

(1) Background: Nursing homes’ preparedness in managing a public health crisis has been fragile, with effects on safety culture. The objective of this study was to assess nursing homes’ COVID-19 preparedness in Southern Portugal, including personnel’s work experiences during the pandemic. (2) Methods: We used a COVID-19 preparedness checklist to be completed by management teams, followed by follow-up calls to nursing homes. Thereafter, a survey to personnel was applied. Data analysis included descriptive statistics, exploratory factor analysis, and thematic analysis of open-end questions. (3) Results: In total, 71% (138/195) of eligible nursing homes returned the preparedness checklist. We conducted 83 follow-up calls and received 720 replies to the personnel survey. On average, 25% of nursing homes did not have an adequate decision-making structure to respond to the pandemic. Outbreak capacity and training were fragile areas among nursing homes’ contingency plans. We identified compliance with procedures and nonpunitive response to mistakes as fragile areas of safety culture, and teamwork as a strong safety area. (4) Conclusions: To strengthen how nursing homes cope with upcoming phases of the COVID-19 pandemic or future public health emergencies, nursing homes’ preparedness and safety culture should be fostered and closely monitored.

The goal of newborn screening is to improve health outcomes by identifying and treating affected newborns. This manuscript provides an overview of a data tool to facilitate the longitudinal collection of health information on newborns diagnosed with a condition through NBS. The Newborn Screening Translational Research Network (NBSTRN) developed the Longitudinal Pediatric Data Resource (LPDR) to capture, store, analyze, visualize, and share genomic and phenotypic data over the lifespan of NBS identified newborns to facilitate understanding of genetic disease, and to assess the impact of early identification and treatment. NBSTRN developed a consensus-based process using clinical care experts to create, maintain, and evolve question and answer sets organized into common data elements (CDEs). The LPDR contains 24,172 core and disease-specific CDEs for 118 rare genetic diseases, and the CDEs are being made available through the NIH CDE Repository. The number of CDEs for each condition average of 2,200 with a range from 69 to 7,944. The LPDR is used by state NBS programs, clinical researchers, and community-based organizations. Case level, de-identified data sets are available for secondary research and data mining. The development of the LPDR for longitudinal data gathering, sharing, and analysis supports research and facilitates the translation of new discoveries into clinical practice.

Immunotherapy has gained great momentum with chimeric antigen receptor T cell (CAR-T) therapy, in which patient’s T lymphocytes are genetically manipulated to recognize tumor-specific antigens increasing tumor elimination efficiency. In the last years, CAR-T cell immunotherapy for hematological malignancies achieved a great response rate on patients and is a very promising therapy for several other malignancies. Each new CAR design requires a preclinical proof-of-concept experiment using immunodeficient mouse models. The absence of a functional immune system in these mice makes them simple and suitable to be mathematically modeled. In this work, we developed a three population mathematical model to describe tumor response to CAR-T cell immunotherapy in immunodeficient mouse models, encompassing interactions between a non-solid tumor and CAR-T cells (effector and long-term memory). We account for several phenomena, such as tumor-induced immunosuppression, memory pool formation, and conversion of memory into effector CAR-T cells in the presence of new tumor cells. Individual donor and tumor specificities were considered as uncertainties in the model parameters. Our model is able to reproduce several CAR-T cell immunotherapy scenarios, with different CAR receptors and tumor targets reported in the literature. We found that therapy effectiveness mostly depends on some specific parameters such as the differentiation of effector to memory CAR-T cells, CAR-T cytotoxic capacity, tumor growth rate, and tumor-induced immunosuppression. In summary, our model can contribute to reduce and optimize the number of in vivo experiments with in silico tests to select specific scenarios that could be tested in experimental research. Such in silico laboratory was made available in a Shiny R-based platform called CARTmath. It is an open-source, easy to run simulator, available at github.com/tmglncc/CARTmath or directly on the webpage cartmath.lncc.br, containing this manuscript results as examples and documentation. The developed model, together with the CARTmath platform, provides potential use for assessing different CAR-T cell immunotherapy protocols and associated efficacy, becoming an accessory towards in silico trials.

This study aimed to characterize the long non-coding RNA (lncRNA) expression in the bovine mammary gland and to infer their functions in dietary response to 5% linseed oil (LSO) or 5% safflower oil (SFO). Twelve cows (six per treatment) in mid lactation were fed a control diet for 28 days followed by a treatment period (control diet supplemented with 5% LSO or 5% SFO) of 28 days. Mammary gland biopsies were collected from each animal on day-14 (D-14, control period), D+7 (early treatment period) and D+28 (late treatment period) and were subjected to RNA-Sequencing and subsequent bioinformatics analyses. Functional enrichment of lncRNA was performed via potential cis regulated target genes located within 50 Kb flanking regions of lncRNAs and having expression correlation of >0.7 with mRNAs. A total of 4955 lncRNAs (325 known and 4630 novel) were identified which potentially cis targeted 59 and 494 genes in LSO and SFO treatments, respectively. Enrichments of cis target genes of lncRNAs indicated potential roles of lncRNAs in immune function, nucleic acid metabolism and cell membrane organization processes as well as involvement in Notch, cAMP and TGF-β signaling pathways. Thirty-two and 21 lncRNAs were differentially expressed (DE) in LSO and SFO treatments, respectively. Six genes (KCNF1, STARD13, BCL6, NXPE2, HHIPL2 and MMD) were identified as potential cis target genes of six DE lncRNAs. In conclusion, this study indicated potential roles for lncRNAs in mammary gland immune functions and development and provided potential candidate genes and pathways via which lncRNAs can function in diet responses.

With the gradual development and integration of artificial intelligence into various industries, there is also a great range of integration in the financial industry. Therefore, this article focuses on the trend prediction model and financial risk management problems of deep reinforcement learning (DRL), one of the largest branches of artificial intelligence, in the cryptocurrency market. In addition, in the experimental part of this paper, the artificial intelligence machine learning Long short-term memory network (LSTM) model is used to make effective time series prediction and analysis on the relevant data of the cryptocurrency market, so as to make a large-scale analysis to improve the accuracy of market trend prediction and the effectiveness of risk management. In addition, in this experiment, technology-related indicators, emotional states of financial market customers and other content related to large language models are combined. While optimizing investment strategy by using deep reinforcement learning algorithm, machine learning prediction model is also used to capture the time dependence of financial market. The experimental results also show that the predicted results are consistent with the actual value. Therefore, the model has high practical application value in predicting the time series price trend of cryptocurrency in the financial market and indicates that the integrated DRL model framework can further optimize and manage the price and trading strategy of the financial market. Future research should focus on improving the LSTM model and incorporating more features to improve prediction accuracy and adapt to market changes.

Increases in water temperature caused by climate change will challenge water and species management in the San Francisco Estuary. Our goals were to describe spatial and seasonal patterns in water temperature across the upper Estuary, and evaluate how temperature stress and suitability vary across the aquatic ecosystem. We synthesized 10 years of continuous water temperature data at 75 stations across six regions of the Estuary between 2010 and 2019. We identified stressful temperature thresholds for species of interest using published physiological limits and observed distribution, including ESA-listed native (e.g., osmerids, salmonids), non-ESA-listed native (e.g., cyprinids), non-native (e.g., centrarchids, bivalves), and nuisance species such as invasive aquatic vegetation, and harmful cyanobacteria. We then quantified thermal stress across varying spatial and temporal scales and metrics. Analyses indicated there were detectable regional temperature differences, and that Suisun Bay was the only region to provide regular thermal refuge during the warmer parts of the year, though portions of the Confluence and Suisun Marsh also provided potential thermal refuge during summer/fall and during cooler parts of the day. Meanwhile, the Central and South Delta experienced the warmest temperatures, exceeding thresholds for listed species throughout summer and early fall. We found that listed species such as juvenile salmonids and osmerids experience more thermal stress across a number of metrics. Fishes with higher heat tolerance (including non-natives and select natives), aquatic vegetation, and Microcystis (a harmful cyanobacteria) had the lowest average number of thermal stress days. This study demonstrates that the Estuary is already exhibiting stressful conditions for species of concern, and thermal stress will only increase with climate change. We identify at what times of year and where the Estuary may provide refuge from thermal stress conditions which has important implications for restoration prioritization and design and species management.

Children account for a significant proportion of the world's population and are a particularly vulnerable subpopulation regarding environmental factors. In a scoping review of a total of 3130 articles compiled from relevant medical literature, including 107 publications, we provide an empirical overview of the literature on masks in connection with children, particularly with regard to its' expected effects on viruses during the pandemic 2020-2023 versus scientific reality and evidence, including the side effects. While masks lack ecological validity from a strictly empirical point of view (transferability of experimental results from laboratories and modelling to the real world, e.g. to situations or environments typical of everyday life) and high-quality studies are showing little evidence of their effectiveness in real-world scenarios, whereas the side effects appear to be clear. An individual risk-benefit analysis is necessary and also a consideration of the situations, in which the use of masks in children could be justified. This review including the literature found and analysed will be able to help when making decisions in this regard.

With the advent of Web 3.0, the swift advancement of technology confronts an imminent threat from quantum computing. Security protocols safeguarding the integrity of Web 2.0 and Web 3.0 are growing more susceptible to both quantum attacks and sophisticated classical threats. The article introduces our novel long-distance free-space quantum secure direct communication (LF QSDC) as a method to safeguard against security breaches in both quantum and classical contexts. Differing from techniques like quantum key distribution (QKD), LF QSDC surpasses constraints by facilitating encrypted data transmission sans key exchanges, thus diminishing the inherent weaknesses of key-based systems. The distinctiveness of this attribute, coupled with its quantum mechanics base, protects against quantum computer assaults and advanced non-quantum dangers, harmonizing seamlessly with the untrustworthy tenets of the Web 3.0 age. The focus of our study is the technical design and incorporation of LF QSDC into web 3.0 network infrastructures, highlighting its efficacy for extended-range communication. LF QSDC is based on the memory DL04 protocol and enhanced with our novel Quantum-Aware Low-Density Parity Check (LDPC), Pointing, Acquisition, and Tracking (PAT) technologies, and Atmospheric Quantum Correction Algorithm (AQCA). Utilizing this method not only bolsters the security of worldwide Web 3.0 networks but also guarantees their endurance in a time when quantum and sophisticated classical threats exist simultaneously. Consequently, LF QSDC stands out as a robust security solution, well-suited for Web 3.0 systems amidst the constantly evolving digital environment.

Ballastless track has high smoothness, but the laying requirements are strict. Therefore, the maximum span of cable-stayed bridges laying ballastless tracks is 392m. For laying ballastless track structures over larger spans, the deformation characteristics of long-span cable-stayed Bridges under complex loads are incomplete, and the interaction between long-span bridge-track structures is unclear. The influence of wavelength of cosine wave on the track- bridge mapping of different orbital structures was explored. The wavelength characteristics of vertical deformation under complex loads were investigated. The track-bridge integrated model for the cable-stayed bridge was establish to analyze the mapping relationship between rail and bridge and wavelength characteristics of deformation. Based on mapping relationships and wavelength characteristics of deformation, the Train-Track-Bridge dynamic simulation model was simplified. The results show that when the minimum wavelength of bridge deformation surpasses 6 m, 10 m, and 16 m, the rail deformation in the ballasted track, the longitudinal-connected track, and the unit slab-type ballastless track accurately mirrors the deformation of the bridge. For the span of bridge ranging from 200 m to 600 m, the wavelength of vertical deformation ranges from 21 to 1270 m under complex loads. During local loads, the vertical deformation below the 200 m wavelength constitutes a significant proportion near the pie. Considering the influence of the deformation on train vibration response, the Train-Bridge dynamic coupling model can be employed to treat the track structure as a load to reduce complexity of model and enhance the calculation efficiency.

Recent studies have shown the potential of Transformer-based neural networks in increasing prediction capacity. However, classical transformers present several problems such as computational time complexity and high memory requirements, which make Long Sequence Time-Series Forecasting (LSTF) challenging. The contribution to the prediction of time series of flood events using deep learning techniques is examined, with a particular focus on evaluating the performance of the Informer model (a particular implementation of transformer architecture), which attempts to address the previous issues. This study explores the predictive capabilities of the Informer model compared to statistical methods, stochastic models and traditional deep neural networks. The accuracy, efficiency as well as the limits of the approaches are demonstrated via numerical benchmarks relating to real river streamflow applications. Using daily flow data from the River Test in England as the main case study, we conduct a rigorous evaluation of the Informer efficacy in capturing the complex temporal dependencies inherent in flood time series. Among other things, the present work extends its analysis to encompass diverse time series datasets from various locations (>100) in the United Kingdom, providing insights into the generalizability of the Informer. The results highlight the significant superiority of the Informer model over established forecasting methods, especially regarding the LSTF problem. Furthermore, it is observed that the structure of time series, as expressed by climacogram, affects the performance of the Informer network.

Long COVID is the collective term used to describe the persistence of symptoms in individuals who have recovered from SARS-CoV-2 infection. Some people who have healed from COVID-19 continue to experience new or persistent symptoms that last for weeks or months. While Long COVID is becoming more widely recognized, its fundamental causes and effective therapies remain largely unclear. This study aims to explore the potential of phytochemical medications as a cutting-edge therapeutic strategy for treating Long COVID, also known as post-acute sequelae of SARS-CoV-2 infection (PASC).G protein-coupled receptors (GPCRs), which play a critical role in cell signaling, were the focus of this study. GPCRs are known to be involved in immunological and inflammatory responses and are considered potential targets for Long COVID therapy. The GPCRs examined included chemokine receptors, serotonin receptors, adrenergic receptors, and histamine receptors. The docking results demonstrated high binding affinity between phytochemical drug structures and these receptors for most drug-receptor combinations.The study consists of three main parts. First, data on receptors and drugs were retrieved from the Protein Data Bank (PDB) and PubChem, respectively. The receptors selected are those predicted to be linked to Long COVID. The structure of the drugs was converted from SDF to PDB format using OpenBabel from ChemInfo. Docking studies were performed to assess the binding affinity between the drugs (ligands) and their respective receptors (binding sites) using iGemDock v2.1.In conclusion, the findings of this study could have significant implications for the development of alternative therapies for Long COVID.

The neurologic effects of long-COVID are a therapeutic challenge. This article discusses the immunologic manifestations of long-COVID and proposes a non-androgenic, synthetic analog of dehydroepiandrosterone (DHEA), bromo-epi-androsterone (BEA), as a candidate for clinical trials for this exigent collection of neurologic conditions. Moreover, because there is an association between long-COVID and Alzheimer’s disease, the evaluation of BEA in long-COVID may serve as a proxy for its effect on neuroinflammatory biomarkers of Alzheimer’s disease that are shared with long-COVID.