Stroke constitutes a significant public health concern due to its impact on mortality and morbidity. This study investigates the utility of machine learning algorithms in predicting stroke and identifying key risk factors using data from the Suita study, comprising 7,389 participants and 53 variables. Initially, unsupervised K-prototype clustering categorized participants into risk clusters, while five supervised models including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosted Machine (Light-GBM) were employed to predict stroke outcomes. Stroke incidence disparities among identified risk clusters using the unsupervised K-prototype clustering method are substantial, according to the findings. Supervised learning, particularly RF was a preferable option because of the higher levels of performance metrics. The Shapley Additive Explanations (SHAP) method identified age, systolic blood pressure, hypertension, estimated glomerular filtration rate, metabolic syndrome, and blood glucose level as key predictors of stroke, aligning with findings from the unsupervised clustering approach in high-risk groups. Additionally, previously unidentified risk factors such as elbow joint thickness, fructosamine, hemoglobin, and calcium level demonstrate potential for stroke prediction. In conclusion, machine learning facilitated accurate stroke risk predictions and highlighted potential biomarkers, offering a data-driven framework for risk assessment and biomarker discovery.

We propose novel spectrometer designs that aim to enhance the measured spectral range 1 of ions on a finite-sized detector. In contrast to the traditional devices that use uniform magnetic 2 field, in which the deflection of particles increases inversely proportional to their momentum, in a 3 gradient magnetic field, the deflection of particles will be decreased due to the reduction of the 4 magnetic field along their propagation. Low energy ions are particularly impacted, as they are 5 deflected less and are more likely to reach the detector compared to being driven out of it while 6 using a uniform magnetic field. By the means of a gradient magnetic field, the nonlinear dispersion 7 of ions in a homogeneous magnetic field can approach a nearly linear dispersion. Nonetheless, 8 the dispersion of low energy ions in a homogeneous magnetic field remains unnecessarily high. 9 In this article, we explore linear, quadratic, and higher-order field profiles in comparison to the 10 homogeneous field case. We discuss the employed methodology and present simulation results of 11 the spectrometer with an extended ion spectral range, focusing on the minimum detectable energy 12 (energy dynamic range) and energy resolution

The movement of liquid droplets on the energy gradient surface has attracted extensive attention. Inspired by biological features in nature, such as the periodic spindle-shaped nodes in spider silks and conical-like barbs of cacti, the structure-property-function relationship of multifunctional gradient surfaces. In this study, a series of specific patterns are fabricated by 3D printing technology, followed by modification via the atmospheric pressure plasma treatment and liquid phase chemical deposition, resulting in enhancing the ability of water droplets of 5μL to travel 18.47mm on a horizontal plane and 22.75mm against gravity up to 20° tilting angle. Additionally, analysis techniques have been employed including contact angle analyzer, ESCA and laser confocal microscope to evaluate the sample performance. This work could further be applied to many applications related to microfluidic devices, drug delivery and water/fog collection.

Chemotherapy treatment against pancreatic ductal adenocarcinoma (PDAC) is thwarted by tumoral activation of multiple therapy-resistance pathways. The growth hormone (GH) – GH receptor (GHR) pair is a covert driver of multimodal therapy resistance in cancer, and is overexpressed in PDAC tumors, yet the therapeutic potential of targeting the same has not been explored. Here, we report that GHR expression is a negative prognostic factor in patients with PDAC. Combinations of gemcitabine with different GHR antagonists (GHRAs) markedly improve therapeutic outcomes in nude mice xenografts. Employing cultured cells, mouse xenografts, and analyses of the human PDAC transcriptome, we identified that attenuation of the multidrug transporter and epithelial-to-mesenchymal transition programs in the tumors underlie the observed augmentation of chemotherapy efficacy by GHRAs. Moreover, in human PDAC patients, GHR expression strongly correlates with a gene signature of tumor-promotion and immune-evasion, which corroborate with that in syngeneic tumors in wild-type vs. GH transgenic mice. Overall, we found that GH action in PDAC promoted a therapy-refractory gene signature in vivo, which can be effectively attenuated by GHR antagonism. Our results collectively present a proof of concept towards considering GHR antagonist to improve chemotherapeutic outcome in the highly chemoresistant PDAC.

Nitrogen is one of the most important macronutrients for crops, and in conjunction with artificial intelligence algorithms, it is possible to estimate it with the aid of vegetation indices through remote sensing. Various indices were calculated and those with a correlation ≥ 0.7 were selected for subsequent use in Random Forest, Gradient Boosting, and Artificial Neural Networks to determine their relationship with nitrogen levels measured in the laboratory. Random Forest showed no relationship, yielding an R2 of zero, whereas Artificial Neural Networks yielded the best results with an R2 of 0.93. Thus, it is reliable to estimate nitrogen levels using this algorithm by feeding it with data from TCARI, MCARI, TCARI/OSAVI, and MCARI/OSAVI, assisted by technological tools.

In the majority of children with growth hormone (GH) deficiency (GHD), normal GH secretion may occur before the attainment of final height. The aim of the study was to assess the incidence of persistent and transient GHD and the effectiveness of recombined human GH (rhGH) therapy in children with isolated idiopathic GHD, with respect to the moment of therapy withdrawal and according to different diagnostic criteria of GHD. Analysis included 260 patients (173 boys, 87 girls) with isolated idiopathic GHD who completed rhGH therapy and have reassessed GH and IGF-1 secretion. The incidence of transient GHD with respect to different pre- and post-treatment criteria was compared together with assessment of GH therapy effectiveness. Incidence of transient GHD, even with respect to pediatric criteria was very high. Normal GH secretion occurred before the attainment of near-final height. Application of more restricted criteria decreased the number of children diagnosed with GHD but not the incidence of transient GHD among them. Poor response to GH therapy was observed mainly in patients with normal IGF-1 before treatment, suggesting that diagnosis of GHD may be false positive. Further efforts should be made to avoid overdiagnosis GHD and overtreatment of the patients.

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

Poikilodermatous plaque-like hemangioma (PPH) is a recently described clinical and pathological entity, with only 18 cases reported in the literature. Although uncommon, this benign condition presents consistent clinical and histological findings. We present a new case of PPH in an 81-year-old male and review the existing literature. The persistence over time and the need to distinguish PPH from more significant lesions underscore the importance of its clinical and pathological recognition.

Dengue, a perilous fever-type illness transmitted by mosquitoes, remains a significant global health concern. The incidence of dengue outbreaks is primarily influenced by climate factors, contributing to fluctuating dengue cases. Therefore, this study aims to develop predictive models for dengue outbreaks employing Machine Learning (ML) techniques. Four distinct ML models, namely K-Nearest Neighbor (KNN), Random Forest (RF), Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR), were employed in this study. Prior to model analysis, thorough data preprocessing was conducted. In some instances, certain attributes exhibiting sparse correlations with dengue occurrence were eliminated based on correlation analysis. Upon finalizing the models, performance evaluation was executed through comparison based on Mean Absolute Error (MAE) metrics. The findings of this comparative analysis revealed that the KNN model exhibited significantly superior performance compared to the other three models. This outcome underscores the potential of KNN in enhancing the accuracy of dengue outbreak predictions.

Earthquake-triggered landslides are one of the most significant hazards worldwide. These landslides, involving small or large volumes, can develop in debris flow or avalanche with high mobility and long-runout distance. The motion processes of this kind of landslide also quite complicated. This study reproduced the motion processes of two landslides using the LS-RAPID program. The Aso Bridge landslide occurred on April 16, 2016, with rapid and long-runout distance characteristics. The Kataragai landslide occurred on April 9, 2018, travelling with flow-like behavior. Through field investigations and laboratory experiments, features and mechanical parameters of these two landslides were obtained. In the LS-RAPID program, the pre-failure models and motion processes of these two landslides were established and reproduced. Results indicated that sliding speed and travelling morphology at different intervals were considered as two indicators to describe their motion process. However, while sliding speed of these two landslides have significant differences with sliding speeds of actual landslides, the final morphologies after failure were consistent with those observed in the field investigation.

Natural killer (NK) cells play a crucial role in innate immunity, particularly in combating infections and tumors. However, in hematological cancers, NK cells often exhibit impaired functions. Therefore, it is very important to activate its endosomal toll-like receptors (TLRs) as a potential strategy to restore its antitumor activity. We stimulate NK cells from the peripheral blood mononuclear cells from children with acute lymphoblastic leukemia and NK cells isolated, the NK cells were stimulated with specific TLR ligands (Poly I:C, Imiquimod, R848, and ODN2006) and evaluated changes in IFN-γ, CD107a, NKG2D, NKp44 expression, Granzyme B secretion, cytokine/chemokine release, and cytotoxic activity. Results revealed that Poly I:C and Imiquimod enhanced activation of both immunoregulatory and cytotoxic NK cells, increasing IFN-γ, CD107a, NKG2D, and NKp44 expression. R848 activated immunoregulatory NK cells, while ODN2006 boosted CD107a, NKp44, NKG2D, and IFN-γ secretion in cytotoxic NK cells. R848 also increased secretion of various cytokines/chemokines. Importantly, R848 and ODN 2006 significantly improved cytotoxicity against leukemic cells. Overall, TLR stimulation enhances NK cell activation, suggesting TLR8 (R848) and TLR9 (ODN 2006) ligands as promising candidates for antitumor immunotherapy.

Introduction: Signet-ring cells are typically associated with mucin-secreting epithelium thus most commonly found in a gastrointestinal tract, however not exclusively. Primary signet ring cell carcinoma of the prostate is a rare and poorly differentiated, aggressive acinar adenocarcinoma variant with a grim prognosis. Clinical case: In June of 2023 a 54-year-old Caucasian male came to the hospital complaining of lower urinary tract obstructive symptoms with occasional macrohematuria, non-specific body aches, and shortness of breath. A prostate specimen obtained in transurethral resection of the prostate was sent for histopathological examination. After a series of extraprostatic diagnostic workups including fibrogastroduodenoscopy, colonoscopy computed tomography imaging, and immunohistochemical studies, the patient was diagnosed with primary prostatic signet-ring cell adenocarcinoma st. IV. Unfortunately, the patient with the disease this advanced, PE, and third-degree thrombocytopenia was not a candidate for chemotherapy and died of cardiopulmonary insufficiency later that week. Discussion: Prostatic signet-ring cell carcinoma accounts for 0.02% of all prostate adenocarcinoma cases. Due to its nature and epidemiology, a diligent extraprostatic investigation has to be carried out. The disease often presents with unremarkable clinical symptoms and variable serum prostate-specific antigen results which may contribute to its late diagnosis which together with inconsistent immunohistochemical findings and unpredictable response to hormonal treatment pose both diagnostic and therapeutic challenges that negatively affect the prognosis. Conclusion: The study highlights the importance of a multidisciplinary approach and the need for diagnostic and therapeutic consensus within the research community in search of the primary site of the disease which may positively influence the prognosis.

Despite the recognized potential of nanoparticles, only a few formulations have progressed to clinical trials, and an even smaller number have been approved by the regulatory authorities and marketed. Virus-like particles (VLPs) have emerged as promising alternatives to most explored nanoparticles because of the absence of viral genetic material, their incapacity to replicate, mimicry of viral structure, and tropism conservation. Furthermore, VLPs can be surface functionalized with small molecules to improve circulation half-life and target specificity. Through the functionalization and coating of VLPs, it is possible to optimize the response properties to a given stimulus, such as heat, pH, an alternating magnetic field, or even enzymes. Surface functionalization can also modulate other properties, such as biocompatibility, stability, and specificity, deeming VLPs as potential vaccine candidates or delivery systems. In this review, we address the different types of functionalization of VLPs, their importance, and their consequent biomedical applications.

Microscopic vision plays an important role in automated micro-assembly. However, some uncertain factors in the assembly process, such as occlusion and stains can lead to the mistakes of feature extraction. Herein, to solve the problem, the deep learning techniques are introduced into the feature recognition tasks, focusing on the attention mechanism and visualizing CNNs for DL-based microscopic vision. The main contributions are summarized as follows: The CBAM attention mechanism is combined with the YOLOv5 algorithm to improve the accuracy and robustness of feature extraction. The micropart feature occlusion experiment results show that at 70% occlusion degree, YOLOV5-CBAM can reach 97.9% mAP@0.5, which is 4.6% higher than the original one. Visualization analysis of DL-based model is conducted using Grad-CAM to make the decision result more transparent and avoid potential visual detection risks during assembly. The heatmap matching degree between GT area and high-light area is increased by 27.81% on average, which further verify the effectiveness of attention mechanism in micropart feature localization. Additionally, micropart surface stain and droplet quality classification models based on ResNet50 are trained to replace the manual sorting. The visual results are consistent with human eye discernment and judgement, confirming the reliability of parts and droplets sorting.

This article presents a description of a new method for solving the factorization problem, based on the gradient descent algorithm. This approach demonstrates significant improvements in efficiency compared to traditional methods proposed in previous research. This article describes a new method for solving the factorization problem, based on the gradient descent algorithm, and demonstrates the transition from algebraic methods to approaches based on functional analysis. The proposed approach not only improves the efficiency of solving the problem but also allows the application of computational algorithms of functional analysis, opening new possibilities for research and optimization.

The transportation industry is rapidly transitioning from Internal Combustion Engine (ICE) based vehicles to Electric Vehicles (EVs) to promote clean energy. However, large-scale adoption of EVs can compromise the reliability of the power grids by introducing large uncertainty in the demand. Demand response with a controlled charge scheduling strategy for EVs can mitigate such issues. In this paper, a deep reinforcement learning- based charge scheduling strategy is developed for individual EVs by considering user’s dynamic driving behavior and charging preferences. The temporal dynamics of user’s anxiety about charging the EV battery is rigorously addressed. A dynamic weight allocation technique is applied to continuously tune user’s priority for charging and cost-saving with respect to charging duration. The sequential charging control problem is formulated as a Markov decision process, and an episodic approach to the deep deterministic policy gradient (DDPG) algorithm with target policy smoothing and delayed policy update techniques is applied to develop the optimal charge scheduling strategy. A real-world dataset that captures user’s driving behavior, such as arrival time, departure time, and charging duration, is utilized in this study. The extensive simulation results reveal the effectiveness of the proposed algorithm in minimizing energy cost while satisfying user’s charging requirements.

In order to investigate the vertical distribution pattern of soil fauna community in low-altitude mountain areas. On July 8th, 2022, a low hill was selected as the study area to collect soil arthropods were collected through trap. Leaf litter, vegetation type and distribution quantity were investigated at each sampling site while soil fauna were collected. Also, the soil physical and chemical parameters were measured simultaneously. The results of one-way ANOVA showed that there were significant differences (P

Respiratory syncytial virus (RSV) infection is a major cause of severe respiratory disease in infants and young children worldwide. Two vaccines targeting the elderly and pregnant women have been recently approved employing the prefusion form of the RSV-fusion protein (F). However, there are currently no vaccines for infants. Studies have shown a fusion-inactive prefusogenic F form is significantly more immunogenic and produces higher antibody titers to both the prefusion and postfusion structures of the F protein. Here we have developed RSV virus-like particles (RSV-VLPs) consisting of prefusogenic F, RSV glycoprotein and matrix proteins, produced them using the baculovirus expression system, and studied their protective efficacy in Balb/c mice. Morphology and successful assembly of VLPs were confirmed by transmission electron microscopy and western blot. Mice immunized with VLPs developed high levels of serum IgG and neutralizing antibodies as compared with mice immunized with inactivated virus. The VLP vaccine also induced higher levels of IFNγ and IL4, elicited limited proliferation of CD4+ and CD8+ T cells but higher cytotoxic-T-lymphocyte responses. VLP immunization abolished lung pathology in the mice after RSV challenge. Overall, our results indicate that RSV-VLPs consisting of prefusogenic F, glycoprotein and matrix proteins are a potential vaccine candidate against RSV.

The development of reliable predictive algorithm for house price as the housing market is a stand-out among the most involved regarding valuing the price and continues to fluctuate, is constantly a need for socio-economic advancement and welfare of citizen. In this paper, we develop machine learning algorithms for forecasting UK housing Price, and find an optimal algorithm that forecasts housing price accurately on the premises of the presence of many features or covariates. After applying correlation analysis to remove correlated variables in order to avoid multicollinearity, thereby increasing the statistical power, a novel method of using regression analysis to first of all understand and select statistically significant features for the various regions in England based on North South divide is adopted. These features are then used in the machine learning algorithm to further increase the statistical power of the algorithm, increase the level of accuracy for each of them and ultimately increase the predictive values for the algorithms. The model construction involves 3 stages: 1- correlation analysis to identify and remove correlated variables thereby avoiding multicollinearity and increasing the statistical power of the linear regression, 2 - using linear regression to determine variables that are statistically significant and 3 - building the machine learning algorithms based on the variables that are statistically significant from the linear regression. A comprehensive dataset of UK Paid housing Price from 2010 to 2019 was linked to a number of other datasets to generate a total 21 variables or features used for the models. Catboost, Gradient Boosting, Bagging, Random Forest, Extra Tree all achieved the excellent models performance result in all the regions considered. The comparison of the seven models showed that Extra Tree algorithm consistently achieved the best performance in term of level of accuracy in all the regions. K-Nearest Neighbours (KNN) is the only algorithm with less than 50% level of accuracy. Noticeably, the regions considered had varying or differing insignificant variables, implying that although many variables are common (statistically significant) to all the regions, there are regional differences and impact when modelling or predicting housing prices. This study validates the practicability of developing a machine learning methodology for the prediction of housing price. This research offers a reference for future house price prediction based on machine learning.

Hyperplastic nodules (HNs) are benign polypoid lesions of the colorectal mucosa reportedly caused by hyperplasia of the crypt epithelial cells. They differ from hyperplastic polyps because the crypts do not have histologically serrated changes. HNs often occur in the rectal mucosa and tend to be multifocal. We have proposed a new colitis nucleomigrans (CN) concept as the third type of microscopic colitis. Our histopathological criteria of CN include: i) chained nuclear migration to the middle part of the surface-lining columnar epithelium, ii) apoptotic nuclear debris scattered in the eosinophilic cytoplasm underneath the nuclei, and iii) mild to moderate chronic inflammation in the lamina propria. Immunostaining for cleaved-caspase 3 revealed apoptotic bodies. In this study, we report that most HN lesions share the histological features with CN, representing polypoid CN. We evaluated 108 lesions of HN in total, and 94 (87%) were reclassified as polypoid CN. In the polypoid CN, the lamina propria mucosae were uniformly edematous, suggesting that accelerated apoptosis of the mucosal surface epithelium may cause osmotic dysregulation to form localized mucosal elevations.

The attitude dynamics and orbit dynamics of large rigid space structures are coupled with each other under gravity gradient, which may affect the stability of the large rigid space structures. In this paper, the gravity gradient stability of a large rigid space structure is studied under both small and large disturbances. Based on the rigid body dynamics and orbit dynamics, an accurate dynamic model without any linearization of the large rigid space structure is established via the natural coordinate formulation (NCF), which is able to describe the large overall motions of the structures. By using the generalized-α algorithm, the gravity gradient stability of the large rigid space structure is simulated and analyzed via various examples, including the influence of large disturbance angles, the positions at the stabilization and unstabilization regions. Finally, the relationship between spinning stability and gravity gradient stability is also investigated via a large spinning space structure.

This study aimed to introduce a novel machine learning (ML) model designed to predict the need of interventions during endoscopy in patients with upper gastrointestinal bleeding (UGIB). The risk stratification tools in current use, such as the Glasgow Blatchford Score (GBS) and pre-endoscopic Rockall score, have limitations in accurately predicting the need for endoscopic interventions. All patients diagnosed with UGIB from January 2013 to October 2023 who underwent endoscopy were included in the study. Variables extracted included demographics, social history ,clinical history, clinical presentation and symptoms, drug history, management prior to endoscopy, findings during endoscopy, laboratory variables and vitals, and post-endoscopic results. Three machine learning models including Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), and AdaBoost Classifier were used to evaluate the need for intervention during endoscopy in our study. A total of 1372 patients were included from three major hospitals in Jordan, of whom 242 underwent endoscopic intervention. The GBC outperformed all other models and pre-endoscopic scores in predicting the need for endoscopic intervention with an area under the curve of 0.861. Findings during endoscopy, platelet count, pulse rate and systolic blood pressure during admission were the most contributing features in predicting the need for the need of endoscopic intervention. This study highlights the potential of machine learning models in enhancing decision making for UGIB management.

During choriogenesis in insects, chorion (eggshell) is formed by surrounding follicular epithelial cells in ovarioles. However, the regulatory endocrine factor(s) activating choriogenesis and the chemical components on eggshell deserve further exploration. In two representative Coleopterans, a Coccinellid Henosepilachna vigintioctopunctata and a Chrysomelid Leptinotarsa decemlineata, genes encoding the 20-hydroxyecdysone (20E) receptor heterodimer, ecdysone receptor (EcR) and ultraspiracle (USP), and two chitin biosynthesis enzymes UDP-N-acetylglucosamine pyrophosphorylase (UAP) and chitin synthase (ChS1), were highly expressed in ovaries of the young females. RNA interference (RNAi)-aided knockdown of either HvEcR or Hvusp in H. vigintioctopunctata inhibited oviposition, suppressed the expression of HvChS1, and lessened the positive signal of Calcofluor staining on the chorions, which suggests the reduction of a chitin-like substance (CLS) deposited on eggshells. Similarly, RNAi of LdEcR or Ldusp in L. decemlineata constrained oviposition, decreased the expression of LdUAP1 and LdChS1, and declined CLS contents in the resultant ovaries. Knockdown of LdUAP1 or LdChS1 caused similar defective phenotypes, i.e., reduced oviposition and CLS contents in the L. decemlineata ovaries. These results, for the first time, indicate that 20E signaling activates choriogenesis in two Coleopteran species. Moreover, our findings propose the deposition of a CLS on the chorions.

Abstract: Kiwifruit, a perennial horticultural crop known for its nutritional value, faces significant challenges due to biotic and abiotic stresses, affecting its yield and quality. Leucine-rich repeat receptor-like proteins (LRR-RLPs), a major group of receptor-like proteins in plants, have diverse functions in plant physiology, including growth, development, signal transduction, and stress responses. Despite their importance, the specific roles of kiwifruit LRR-RLPs in response to biotic and abiotic stresses remain poorly understood. In this study, we investigated the expression patterns of kiwifruit LRR-RLPs under various stress conditions, including pathogen stress, heat stress, waterlogging stress, and copper stress. Through genome and transcriptome analyses, we identified a total of 101, 164, and 105 LRR-RLPs in Actinidia Chinensis 'Hongyang', Actinidia eriantha 'Huate', and Actinidia chinensis 'Red5' kiwifruit, respectively, classified into 9 subgroups based on phylogenetic relationships. Synteny analysis revealed that the expansion of kiwifruit LRR-RLPs was primarily attributed to segmental duplication events. Using RNA-seq data from pathogen-infected kiwifruits, we identified specific LRR-RLP genes potentially involved in different stages of pathogen infection. Additionally, we observed the potential involvement of kiwifruit LRR-RLPs in abiotic stress responses, with upstream transcription factors possibly regulating their expression. Furthermore, protein interaction network analysis unveiled the participation of kiwifruit LRR-RLP genes in the regulatory network of abiotic stress responses. These findings highlight the crucial roles of LRR-RLPs in mediating both biotic and abiotic stress responses in kiwifruit, offering valuable insights for the breeding of stress-resistant kiwifruit varieties.

The CACCC-box motif emerges as a pivotal cis-regulatory element implicated in diverse developmental processes and diseases, particularly cardiovascular diseases (CVDs). This study centers on the intricate interplay between the CACCC-box and its binding proteins such as: the Krüppel-like family (KLF) of transcription factors as primary effectors in the context of CVDs. Our analysis was through a bioinformatics approach, which reveal significant transcriptional activity among KLF subgroup 2, exhibiting the highest number of interactions focusing on the established roles: pluripotency, cancer, and cardiovascular development and diseases. Our analysis reveals KLF's interactions with GATA4, MEF2C, NKX2.5 and other ~ 90 potential genes that participate in the regulation of the hypertrophic environment (or CVD's Environment). Also, the GO analysis showed genes containing the motif CACCC were enriched for multiple CVD’s, in combination with STRING analysis these results pointed to a link between KLFs and these diseases. The analysis further identifies other potential CACCC-box binding factors, such as SP family members, WT1, VEZF1, and -SALL4, which are implicated in cardiac contraction, remodeling, and inflammation processes.

In the dynamic landscape of the telecommunications industry, understanding and mitigating customer churn is paramount for maintaining competitive advantage and sustaining revenue streams. This comprehensive literature review delves into the intricacies of customer churn prediction, a crucial aspect in fortifying the bond between telecom businesses and their customers. The review methodically analyzes 11 articles published between 2000 and March 2024, spotlighting the strategic role of Customer Relationship Management and the prevalence of high churn rates that necessitate a shift from customer acquisition to retention strategies. Furthermore, the review navigates through the challenges inherent in churn prediction, including the complexities of managing large, imbalanced, and noisy datasets and the intricacies involved in model selection and optimization. Despite these challenges, the paper highlights the superior capability of machine learning techniques in handling complex datasets to provide accurate predictions, albeit acknowledging their practical complexities. The main aim of the study is to provide an overview, serving as a knowledge base for researchers interested in machine learning techniques within the realm of telecommunications.

We delve into nonrelativistic quantum electrodynamics within a background magnetic field, ensuring gauge invariance via a vector potential. Our exploration extends to the Lagrangian, which incorporates electron self-interactions and electromagnetic field interactions. Through the path integral formalism, we elucidate the effective action, with a specific focus on the photon propagator and screening effects. Examining density and current components reveals Laurent expansions in the hydrodynamic limit. The equations of motion in real space are derived, leading to discussions on phenomena such as Poiseuille-like flow and the Navier-Stokes equation. To ground our theoretical framework at practical contexts, we consider applications such as the fluid dynamics at binary star systems. Depending on the velocity of the expelled fluid, our analysis allows for both nonrelativistic and relativistic treatments, providing a versatile tool for understanding the intricacies of fluid behavior at astrophysical scenarios. Additionally, we recognize that at the presence of a magnetic field, magnetohydrodynamics becomes crucial. While our current focus is at nonrelativistic quantum electrodynamics, the insights gained here contribute to a broader understanding, offering a comprehensive foundation for quantum electrodynamics analysis at diverse physical systems.

Chlamydia trachomatis (Ct) infections are the most common sexually-transmitted infection (STI). Despite effective antibiotics for Ct, undetected infections or delayed treatment can lead to infertility, ectopic pregnancies, and chronic pelvic pain. Besides humans, chlamydia poses similar health challenges in animals such as C. suis (Cs) in pigs. Based on the similarities between humans and pigs as well as their chlamydia species, we use pigs as a large biomedical animal model for chlamydia research. In this study, we use the pig model to develop a vaccine candidate against Ct. The vaccine candidate consists of TriAdj-adjuvanted chlamydial protease-like activity factor (CPAF) protein. We tested two weekly administration options – twice intranasal (IN) followed by twice intramuscular (IM), or twice IM followed by twice IN. We assessed the humoral immune response not only in serum by CPAF-specific IgG including antibody avidity determination but also in cervical and rectal swabs by CPAF-specific IgG and IgA ELISAs. The systemic T-cell response was analyzed upon in vitro CPAF-restimulation via IFN-γ and IL-17 ELISpots as well as intracellular cytokine staining flow cytometry. Our data demonstrate that while the IN/IM vaccination mainly led to non-significant systemic immune responses, the vaccine candidate is highly immunogenic if administered IM/IN. This vaccination strategy induced high serum anti-CPAF IgG levels with strong avidity as well as high IgA and IgG levels in vaginal and rectal swabs and in uterine horn flushes. In addition, this vaccination strategy prompted a pronounced cellular immune response: Besides inducing IL-17 production, the vaccine candidate induced a strong IFN-γ response by CD4 T cells. In the IM/IN vaccinated pigs, these cells also significantly downregulated their CCR7 expression – a sign for differentiation into peripheral tissue homing effector/ memory cells. Conclusively, this study demonstrates strong immunogenicity of the IM/IN administered TriAdj-adjuvanted Ct CPAF vaccine candidate. Future studies will test vaccine efficacy of this promising Ct vaccine candidate. In addition, this project demonstrates suitability of the Cs pre-exposed outbred pig model for Ct vaccine development. Thereby, we aim to open the bottleneck of large animal models to facilitate the progression of Ct vaccine candidates into clinical trials.

Parkinson’s disease (PD) is a progressive neurodegenerative disease characterized by resting tremor, bradykinesia, rigidity, postural instability, that also includes non-motor symptoms such as mood dysregulation. Dopamine (DA) is the primary neurotransmitter involved in this disease, but cholinergic imbalance has also been implicated. Current intervention in PD is focused on replenishing central DA, which provides remarkable temporary symptomatic relief but does not address the neuronal loss and the progression of the disease. It has been well established that neuronal nicotinic cholinergic receptors (nAChRs) can regulate DA release and that nicotine itself may have neuroprotective effects. Recent studies have identified nAChRs in nonneuronal cell types including glial cells, where they may regulate inflammatory responses. Given the crucial role of neuroinflammation in dopaminergic degeneration, and the involvement of microglia and astrocytes in this response, glial nAChRs may provide a novel therapeutic target in prevention and/or treatment of PD. In this review, following a brief discussion of PD, we focus on the role of glial cells and specifically their nAChRs in PD pathology and/or treatment.

Abstract: The venom-containing barb attached to their ‘whip-like’ tail provides stingrays a defensive mechanism for evading predators such as sharks. From human encounters, dermal stingray envenomation is characterized by intense pain often followed by tissue necrosis occurring over several days to several weeks. The bioactive components in stingray venoms (SRV’s) and their molecular targets and mechanisms that mediate these complex responses are not well understood. Given the utility of venom-derived proteins from other venomous species for biomedical and pharmaceutical applications, we set out to characterize the bioactivity of SRV from three local species that belong to the Dasyatoidea ‘whiptail’ superfamily. Multiple cell-based assays were used to quantify and compare the in vitro effects of these SRV’s on different cell lines. All three SRV’s demonstrated concentration-dependent growth inhibitory effects on three different human cell lines tested. In contrast, a mouse fibrosarcoma cell line was markedly resistant to all three SRV’s, indicating the molecular target(s) for mediating the SRV effects are not expressed on these cells. The multifunctional SRV responses were characterized by an acute disruption of cell adhesion leading to apoptosis. These findings aim to guide future investigations of identified SRV proteins and their molecular targets for potential use in biomedical applications.

Chronic diseases, such as type 2 diabetes (T2D), are difficult to manage because they demand continuous therapeutic review and monitoring. Beyond achieving the target HbA1c, new guidelines for the therapy of T2D have been introduced with the new groups of antidiabetics (GLP-1ra and SGLT2-in). Despite new guidelines, clinical inertia, that can be caused by physicians, patients or healthcare system, results in T2D not being effectively managed. This opinion paper explores the shift in T2D treatment, challenging assumptions and evidence-based recommendations, particularly for family physicians, considering the patient's overall situation in decision-making. We looked for the possible reasons of clinical inertia and poor application of guidelines in the management of T2D. Guidelines for antidiabetic drugs should be more precise, providing case studies and clinical examples to define clinical contexts and contraindications. Knowledge communication can improve confidence and should include clear statements on areas of decision-making not supported by evidence. Precision medicine initiative in diabetes aims to identify subcategories of T2D patients (including frail patients) using clustering techniques from data science applications, focusing on CV and poor treatment outcomes. Clear, unconditional recommendations for personalized T2D management may encourage drug prescription, especially for family physicians dealing with diverse patient contexts and clinical settings.

Endoscopy has made a significant and noteworthy contribution to the field of medical science and technology. Nevertheless, its potential remains constrained due to the limited availability of rigid or flexible endoscopes. This paper introduces a novel hydraulic actuator based on electrorheological fluid (ERF) as a pivotal advancement in bridging the existing gap within the realm of endoscopy. Following a comprehensive introduction that briefly outlines the electrorheological effect, the subsequent section is dedicated to the elucidation of the actuator’s development process. Challenges arise, particularly in terms of miniaturization and the realization of a hydraulically sealed system with integrated valve electrodes. An internal electrorheological valve system consisting of four valves that are controlled using a pulse-width modulated high voltage was suitable for position control of the antagonistic hydraulic actuators. High-precision stereolithography (SLA) printing has proven practical for manufacturing the actuator components. For functional testing, a test bench was set up in which the actuator follows a setpoint through a PI control loop. The control deviation ranged from 0.6 to 1 degree, with a response time between 6 and 8 seconds. The experiments have demonstrated that, through the use of ERF and integrated valve electrodes, a miniaturized functional actuator can be constructed.

Background and Objectives: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent world-wide. It progresses from simple steatosis to non-alcoholic steatohepatitis (NASH). Fibrosis is often present during NAFLD progression; however, factors determining which subjects develop NASH or fibrosis are unclear. Insulin-like growth factor binding proteins (IGFBP) is a family of secreted proteins involved in senescence and scarring mainly synthetized in the liver. Here we aimed to study the association of IGFBP and its induced senescence with the progression of NAFLD and liver fibrosis. Materials and Methods: 16 weeks old male C57BL/6 mice weighing 23 ± 3 g were fed either Methionine-Choline Deficient or control diet for 2, 8 or 12 weeks. Blood and liver samples were collected; histological assessment of NAFLD and fibrosis was performed. Fat contents were measured. Cellular senescence was evaluated in the liver. IGFBP levels were assessed in liver and serum. Data was expressed as Mean ± SD and analyzed by one-way ANOVA followed Tukey’s test. Lineal regression models were applied for NAFLD and fibrosis progression. p

We prove an analog of the removable singularity theorem for bounded harmonic functions on stratified sets. The harmonic functions are understood in the sense of the soft Laplacian. The result can become one of the main technical components for extending the well-known Poincaré–Perron’s method of proving the solvability of the Dirichlet problem for the soft Laplacian.

We delve into the axion-dilaton model within the supergravity framework, with a specific focus on the intricacies of domain wall construction and stability. Employing holographic vitrification, we unravel the dynamics of domain wall formation in gauge theories featuring periodic vacuum structures. Our model, incorporating a QCD-like axion term and a stabilizing dilaton, undergoes scrutiny for conductivity variations under weak disorder. The investigation reveals the model's resilience, manifesting near-perfect conductivity under mild disorder conditions. However, the rigorous mathematical motivation for the holographic setup demands further elucidation. The scattered nature of our results prompts the necessity for a more systematic interpretation of QCD phenomena and conductivity transitions. This study contributes to the mathematical understanding of the axion-dilaton model's behavior, highlighting the imperative for a refined holographic framework and a more coherent interpretation of observed phenomena.

During public health events, Active Mobility (walking and cycling) plays an important role in securing residents' travel. To enhance the resilience of urban transportation, this article integrates the travel environment elements for walking and cycling and constructs a travel environment for Active Mobility evaluation index system. This article uses survey data from the main urban area of Harbin in the context of the COVID-19 pandemic in May 2022, and combines the Gradient Boosting Decision Tree (GBDT) and Impact-Asymmetry Analysis (IAA) methods to analyze the satisfaction of the travel environment for Active Mobility. It proposes strategies for optimizing the travel environment for Active Mobility, ensuring residents' travel during public health events, guiding them to adopt Active Mobility, and improving urban transportation resilience. This research shows that Active Mobility can enhance urban transportation resilience. In addition, there are differences in resident demand for walking and cycling environments in the travel environment for Active Mobility. Residents are more concerned about the comfort of the walking environment, while they prioritize the convenience of the cycling environment.

The origin of neurodegenerative diseases is linked to the abnormal folding of disease-associated proteins. The traditional amyloid hypothesis has been a focal point of research, but recent findings suggest a more complex picture. This review explores the role of physiological conformation of aggregation-prone domain (also known as prion-like low complexity domain) and multiple co-misfolded proteins in neurodegenerative diseases. We highlight the importance of targeting multi-heterotypic misfolded proteins for effective treatments and emphasize the irreversible neurodegeneration stemming from the loss of physiological folding in prion-like disease-causing proteins underscores the criticality of refolding. Additionally, we introduce the concept of ReproFold Therapeutics, a novel approach aimed at restoring the cellular prion-like folding of disease-causing proteins to rebuild normal cross-β interactomes and mitigate neurodegeneration. Baicalein, a potential drug candidate for this approach, shows promise in vitro and in animal models. Moreover, prion-like low complexity domains implicated in neurodegeneration are also associated with other disorders such as diabetes, cancers, and mental illnesses. Understanding the core pathology of neurodegenerative diseases and the broader implications of prion-like LC domains could revolutionize healthcare approaches and policies worldwide.

Many protein expression systems are primary utilised to produce a single, specific recombinant protein. In contrast, most biological processes such as virus assembly rely upon a complex of several interacting proteins rather than the activity of a sole protein. The high complexity of the baculovirus genome, coupled with a multiphase replication cycle incorporating distinct transcriptional steps, made it the ideal system to manipulate for high-level expression of a single or co-expression of multiple foreign proteins within a single cell. We have developed and utilised a series of recombinant baculovirus systems to unravel the sequential assembly process of a complex non-enveloped model virus, Bluetongue virus (BTV). The high protein yields expressed by the baculovirus system not only facilitated structure-function analysis of each viral proteins but were also advantageous to crystallography studies and supported the first atomic level resolution of a recombinant viral protein, the major BTV capsid protein. Further, the formation of recombinant double-shelled virus-like particles (VLPs) provided insights into the structure-function relationships among the four major structural proteins of the BTV whilst also representing a potential candidate for a viral vaccine. The baculovirus multi-gene expression system facilitated the study of structurally complex viruses (both non-enveloped and enveloped viruses) and heralded in a new generation of viral vaccines.

We delve into the intricate dynamics governing anisotropic fluids within the cosmological paradigm, elucidating the nuanced influence of conformal anomalies upon the stress-energy tensor. Through the presentation of a sophisticated theoretical framework, we seamlessly intertwine unconventional fluidic properties with profound cosmological considerations, thereby augmenting and refining our comprehension of the primordial universe.

Recent studies have elucidated the diversity of genes encoding venom in sea anemones. However, most of those genes are yet to be explored in an evolutionary context. Insulin is a common peptide across metazoans and has been coopted into a predatory venom in many venomous lineages. In this study, we focus on the diversity of insulin-derived venoms in sea anemones and on elucidating their evolutionary history. We sourced data for 34 species of sea anemones and found sequences belonging to two venom families that have Insulin PFAM annotations (Cono-Insulin, VP302). Our findings show that both families have undergone duplication events. Members of each of the independently evolving clades have consistent predicted protein structures and distinct dN/dS values. Our work also shows that VP302 is part of a multidomain venom contig and has experienced a secondary gain into the venom system of cuticulate sea anemones.

For many engineering applications it is sufficient to use the concept of simple materials. However, higher gradients of the kinematic variables are taken into account to model materials with internal length scales as well as to describe localization effects using gradient theories in finite plasticity or fluid mechanics. In many approaches length scale parameter have been introduced that are related to a specific micro structure. An alternative approach is possible, if a thermodynamically consistent framework is used for material modelling as shown in the present contribution. However, even if sophisticated and thermodynamically consistent material models can be established there are still not yet standard experiments to determine higher order material constants. In order to contribute to this ongoing discussion system identification based on the method of self-adaptive filtering is proposed in this paper. To evaluate the effectiveness of this approach it has been applied to second-order gradient materials considering longitudinal vibrations. Based on thermodynamically consistent models that have been solved numerically it has been possible to prove that system identification based on self-adaptive filtering can be used effectively for both narrow-band and broadband signals in the field of second-order gradient materials. It has also been found that the differences identified for simple materials and gradient materials allow for condition monitoring and detection of gradient effects in the material behavior.

Leukemia, contributing to 3% of all cancer cases in the United States, presents a significant challenge in treatment due to its diverse subtypes, each dis-playing unique genetic and molecular abnormalities. The aggressive growth of ab-normal blood cells compromises immune function and poses challenges in developing effective therapeutic approaches. Unlike solid tumors, surgical resection is not an option, limiting treatment strategies to chemotherapy, immunotherapy, and radiation therapy, which may be nonspecific and lead to organ damage and toxicity. Anthracyclines, widely used in hematological tumors, face limitations due to severe cardiotoxicity, causing progressive cardiomyopathy and, in severe cases, congestive heart failure. Heart failure incidence is significantly higher in cancer patients treated with anthracyclines over 5, 10, and 15 years. Early detection of left ventricular dysfunction and cardiotoxicity is crucial to mitigate progression to congestive heart failure (CHF) in this population. Recent research by Bayrón-Marrero et al. reveals that lower levels of plasma soluble TREM-Like Transcript-1 (s-TLT-1), a platelet-specific receptor, correlate with left ven-tricular dysfunction in cardiovascular disease patients. Those with s-TLT-1 levels ex-ceeding 544 pg/mL exhibit a significant association with congestive heart failure. These findings suggest the potential use of s-TLT-1 as a diagnostic screening tool for early detection and treatment of anthracycline-induced CHF in leukemia patients.

In addition to their importance in statistical thermodynamics, probabilistic entropy measurements are crucial for understanding and analyzing complex systems, with diverse applications in time series and one-dimensional profiles. However, extending these methods to two- and three-dimensional data still requires further development. In this study, we present a new method to classify spatiotemporal processes based on entropy measurements. To test and validate the method, we selected four classes of similar processes related to the evolution of random patterns: dynamic colored noises ((i)~white and (ii)~red); (iii)~weak turbulence from reaction-diffusion; (iv)~hydrodynamic fully developed turbulence, and (v)~plasma turbulence from MHD. Considering seven possible ways to measure entropy from a matrix, we present the method as a parameter space composed of the two best separating measures of the five selected classes. The results highlight better combined performance of Shannon Permutation Entropy ($S^p_H$) and a new approach based on Tsallis Spectral Permutation Entropy ($S^s_q$). Notably, our observations reveal the segregation of reaction terms in this $S^p_H \times S^s_q$ space, a result that identifies specific sectors for each class of dynamic process, and can be used to train machine learning models for automatic classification of complex spatiotemporal patterns.

Intestinal epithelial cell activities during homeostasis and regeneration are well described, but their potential interactions with stromal cells remain unresolved. Exploring the functions of these heterogeneous intestinal mesenchymal stromal cells (iMSCs) remains challenging due to the lack of specific markers for most functionally homogenous subpopulations. In recent years, however, novel clustering techniques such as single-cell RNA sequencing (scRNA-seq), fluorescence-activated cell sorting (FACS), confocal microscope, and computational remodeling of intestinal anatomy have helped identify and characterize some specific iMSC subsets. These methods help researchers learn more about the localization and functions of iMSC populations during intestinal morphogenic and homeostatic conditions. Furthermore, it is imperative to understand their cellular pathways for activation and how they interact with surrounding cellular components, particularly during intestinal epithelial regeneration that follows mucosal injury. This review provides insights into the spatial distribution and functions of some identified iMSC subtypes in intestinal morphogenesis, homeostasis, and regeneration. We reviewed related signaling mechanisms implicated during epithelial and subepithelial stromal cell crosstalk. Future research should focus on elucidating the molecular intermediates of these regulatory pathways to open a new frontier for potential therapeutic targets that can alleviate intestinal mucosa-related injuries.

Glypicans are a family of heparan sulfate proteoglycans that are attached to the outer plasma membrane leaflet of the producing cell by a glycosylphosphatidylinositol anchor. Glypicans are involved in the regulation of many signalling pathways, including those that regulate the activities of Wnts, Hedgehog (Hh), Fibroblast Growth Factors (FGFs) and Bone Morphogenetic Proteins (BMPs), among others. In the Hh signalling pathway, glypicans have been shown to be essential for ligand transport and the formation of Hh gradients over long distances, for the maintenance of Hh levels in the extracellular matrix and for its unimpaired reception in distant recipient cells. Recently, two mechanistic models have been proposed to explain how Hh can form the signalling gradient and how glypicans may contribute to it. In this review, we describe the structure, biochemistry and metabolism of glypicans and their interactions with different components of the Hh signalling pathway that are important for the release, transport and reception of Hh.

Glucagon-like peptide-1 (GLP-1) is an incretin hormone that acts on the pancreas to increase insulin and decrease glucagon secretion. Albuminuria is the pivotal marker for diabetic nephropathy, which is the leading microvascular complication of diabetes mellitus. Most of the previous studies mainly focused on the clinical trials of the therapeutic renal protective effect after administration of GLP-1 receptor agonists (GLP-1 RAs), not prior to administration. Therefore, this study aimed to investigate the association between fasting plasma GLP-1 levels and albuminuria before GLP-1 RA administration. A cross-sectional study was designed to evaluate the association between fasting plasma GLP-1 levels and albuminuria in patients with type 2 diabetes mellitus (T2DM). A total of 68 T2DM participants were analyzed from the data at Wonkwang University Hospital in Iksan, Korea. Logistic regression analysis was performed to evaluate the odds ratio (OR) and 95% confidence interval (CI) of the incidence of albuminuria between the GLP-1 low group (Group L) and the high group (Group H). The OR (95% CI) for the incidence of albuminuria comparing the Group L with the Group H of fasting plasma GLP-1 levels was 3.410 (1.160-10.023), p = 0.026 after adjustment for age, gender, fasting plasma glucose, HbA1c, C-peptide, the use of ACE inhibitors or ARBs, SGLT-2 inhibitors, and DPP-4 inhibitors. As a continuous variable, the OR (95% CI) for the incidence of albuminuria associated with the reduction of each 1 pmol/L in fasting plasma GLP-1 levels was 1.673 (1.173-1.870), p = 0.018, after full adjustment. This study showed the negative association between fasting plasma GLP-1 levels and the incidence of albuminuria in Korean patients with T2DM, before GLP-1 RA administration. This result suggests that endogenous GLP-1 may play a role in attenuating albuminuria.

Background Few data exist on comparative long-term outcomes of severe aortic stenosis (AS) patients with different flow-gradient patterns undergoing transcatheter aortic valve implantation (TAVI). This study sought to evaluate the impact of pre-TAVI flow-gradient pattern on long-term clinical outcomes after TAVI and assess changes in left ventricular ejection fraction (LVEF) of different subtypes of AS patients following TAVI. Methods Consecutive patients with severe AS undergoing TAVI in our institution were screened and prospectively enrolled. Patients were divided in four subgroups according to pre-TAVI flow/gradient pattern: (i) low flow - low gradient (LF-LG): stroke volume indexed (SVi) ≤ 35ml/m2 and mean gradient (MG) < 40mmHg), (ii) normal flow - low gradient (NF-LG): SVi > 35ml/m2 and MG < 40mmHg, (iii) low flow - high gradient (LF-HG) : SVi ≤ 35ml/m2 and MG ≥ 40mmHg and (iv) normal flow - high gradient (NF-HG): SVi > 35 ml/m2 and MG ≥ 40mmHg. Transthoracic echocardiography was repeated at 1-year follow-up. Clinical follow-up was obtained at 12 months, and yearly thereafter until 5-year follow up was complete for all patients. Results A total of 272 patients with complete echocardiographic and clinical follow-up were included in our analysis. Their mean age was 80±7 years and the majority of patients (N=138, 50.2%) were women. 62 patients (22.8% of the study population) were distributed in LF-LG group, 98 patients (36%) were LF-HG patients, 95 patients (34.9%) were NF-HG, while 17 patients (6.3%) were NF-LG. There was a greater prevalence of comorbidities among LF-LG AS patients. One-year all-cause mortality differed significantly between the four subgroups of AS patients (log rank p: 0.022) and was more prevalent among LF-LG patients (25.8%) compared to LF-HG (11.3%), NF-HG (6.3%) and NF-LG patients (18.8%). At 5-year follow-up, global mortality remained persistently higher among LF-LG patients (64.5%) compared to LF-HG (47.9%), NF-HG (42.9%) and NF-LG patients (58.8%) (log rank p: 0.029). At multivariable Cox hazard regression analysis, baseline SVi (HR: 0.951, 95% C.I.; 0.918 – 0.984), the presence of at least moderate tricuspid regurgitation at baseline (HR: 3.091, 95% C.I: 1.645 - 5.809) and at least moderate paravalvular leak (PVL) post-TAVI (HR: 1.456 , 95% C.I.: 1.106 - 1.792 ) were significant independent predictors of late global mortality. LF-LG patients and LF-HG patients exhibited a significant increase in LVEF at 1-year follow-up. A lower LVEF (p<0.001) and a lower Svi (p<0.001) at baseline were associated with LVEF improvement at 1 year. Conclusions Patients with LF-LG AS have acceptable 1-year outcomes with significant improvement in LVEF at 1-year follow-up, but exhibit an exceedingly high 5-year mortality following TAVI. The presence of low transvalvular flow and at least moderate tricuspid regurgitation at baseline and significant paravalvular leak post-TAVI were associated with poorer long-term outcomes in the entire cohort of AS patients. The presence of a low LVEF or a low SVi predicts LVEF improvement at 1-year.

Biofilm-associated bacterial infections are the major reason for treatment failure in many diseases including burn trauma infections. Uncontrolled inflammation induced by bacteria leads to materiality, tissue damage and chronic diseases. Specialized proresolving mediators (SPMs), including maresin-like lipid mediators (MarLs), are enzymatically biosynthesized from omega-3 essential long-chain polyunsaturated fatty acids, especially docosahexaenoic acid (DHA) by macrophages and other leukocytes. SPMs exhibit strong inflammation-resolving activities, especially inflammation provoked by bacterial infection. In this study, we explored the potential direct inhibitory activities of three MarLs on gram-positive (Staphylococcus aureus) and gram-negative (Pseudomonas aeruginosa and Escherichia coli) bacteria in their biofilms that are leading bacteria in burn trauma related infections. We also examined the effects of MarLs on the bactericidal activities of a typical broad-spectrum antibiotics carbenicillin on these bacteria in their preformed biofilms. The results revealed that MarLs combined with carbenicillin can inhibit the survival of gram-positive and gram-negative bacteria in their biofilms although MarLs alone did not exhibit bactericidal activity. Thus, our findings suggest that the combination of MarLs and carbenicillin can lower the antibiotic requirements to kill the bacteria in preformed biofilms.

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

The classification of tumors in subtypes, characterized by phenotypes determined by specific differentiation pathways, aids diagnosis and directs therapy towards targeted approaches. However, with the advent and explosion of next-generation sequencing, cancer phenotypes are turning out to be far more heterogenous than initially thought, and the classification is continually being updated to include more subtypes. Tumors are indeed highly dynamic and they can evolve and undergo various changes in their characteristics during disease progression. The picture becomes even more complex when tumor responds to a therapy. In all these cases, cancer cells acquire the ability to trans-differentiate, changing subtype, and adapt to changing microenvironments. These modifications affect the tumor's growth rate, invasiveness, response to treatment, and overall clinical behavior. Studying tumor subtype transitions is crucial for understanding tumor evolution, predicting disease outcomes, and developing personalized treatment strategies. We discuss this emerging hallmark of cancer and the molecular mechanisms involved at the crossroads between tumor cells and their microenvironment, focusing on four different human cancers in which tissue plasticity causes a subtype switch: breast cancer, prostate cancer, glioblastoma and pancreatic adenocarcinoma.

The Ca Mau Peninsula situated in the Mekong Delta of Vietnam, features a low-lying terrain. Over recent years, the region has encountered the adverse impacts of climate change, leading to both land subsidence and rising sea levels. In this study, we assessed the land subsidence susceptibility in the Ca Mau peninsula utilizing three Boosting machine learning models: AdaBoost, Gradient Boosting, and Extremely Gradient Boosting (XGB). Eight key factors were identified as the most influential in land subsidence within Ca Mau: land cover (LULC), Groundwater levels, distance to roads, Digital Terrain Model (DTM), normalized vegetation index (NDVI), geology, soil composition, and proximity to rivers and streams. A dataset comprising 1950 subsidence sample points was employed for the model, with 1910 points obtained from the PSInSAR Radar method, and the remaining points derived from leveling measurements. The sample points were split, with 70% allocated to the training set and 30% to the testing set. Following computation and execution, the three models underwent evaluation for accuracy using statistical metrics such as the ROC curve, Area under the curve (AUC), Specificity, Sensitivity, and Overall Accuracy. The research findings revealed that the XGB model exhibited the highest accuracy, achieving an AUC above 0.9 for both the training and test sets. Consequently, XGB was chosen to construct a land subsidence susceptibility map for the Ca Mau peninsula.

Modern power systems are confronted with a spread concern on the frequency stability issue due to the widespread integration of randomly fluctuating renewable resources. To address the above concern, this work introduces a load frequency control (LFC) scheme based on a hydrogen based energy-storage unit and a parameter tuning strategy for fractional-order proportional-integral-derivative (FOPID) controller. Firstly, a two-area interconnected power system (IPS) model, including thermal, hydro, solar, wind, gas power generator, and hydrogen based energy-storage unit, is established. Then, a FOPID controller is designed for this IPS model, and an improved gradient-based optimizer (IGBO) is developed to adaptively regulate the parameters of the FOPID controllers. Finally, the effectiveness of the offered LFC scheme are tested through load disturbance and renewable energy fluctuations test scenarios, and provides comparison and robustness analysis among different schemes. The test results validated that the offered LFC scheme can effectively suppress the frequency fluctuations of IPS and has excellent robustness.

A novel reinforcement learning deep deterministic policy gradient agent-based sliding mode control (DDPG-SMC) approach is proposed to suppress the chattering phenomenon in the attitude control for quadrotors, in the presence of external disturbances. First, the attitude dynamics model of the studied quadrotor is derived and the attitude control problem is described by formulas. Second, a sliding mode controller including its sliding mode surface and reaching law is selected for the nonlinear dynamic system, and the stability of the designed SMC system is supported by Lyapunov stability theorem. Third, a reinforcement learning (RL) agent based on deep deterministic policy gradient (DDPG) is trained to adjust the switching control gain adaptively. During the training process, the input signals of agent are the actual and desired attitude angles, and the output action is the time-varying control gain. Finally, the above trained agent is applied to the SMC as a parameter regulator, to implement the adaptive adjustment of the switching control gain related to reaching law, and the simulation results verify the robustness and effectiveness of the proposed DDPG-SMC method.

Technical components should be produced sustainably and are subject to growing demands in terms of durability and reliability. To meet these expectations, the development of thermochemical processes is auspicious. Titanium alloys, which have a comparatively high gas solubility, allow temporary hydrogen (H) loading, often called thermo hydrogen treatment (THT). THT causes lattice deformation and reduces the β transformation temperature. These effects enable an adjustment of the microstructure, which improves the mechanical properties as compared to those produced conventionally. Furthermore, the process is applicable to complex geometries that cannot be surface hardened by mechanical surface treatment techniques. The investigation presented intends to realize a local microstructure gradient in additively via laser-powder bed fusion (L-PBF) manufactured cylindrical Ti–6Al–4V specimens by changing the distribution and morphology of strengthening precipitates as well as the β grain size depending on the distance to the surface, which should improve the material fatigue properties. To evaluate the mechanical properties of such THT-induced microstructure gradients, the resulting change in fatigue life in the LCF and HCF range was determined by stress-controlled cyclic deformation tests. Besides, the resulting microstructure was characterized by means of XRD and TEM. In addition to the additively manufactured and thermo hydrogen treated cylindrical specimens, conventionally produced tube-like specimens were thermo hydrogen treated and examined. For the THT-induced microstructure gradients, numerically simulated H concentration profiles as well as experimentally determined hardness profiles were used for the evaluation of the THT process. The study shows that H-loading at 500°C and H-degassing at 750°C, followed by aging at 550°C, allows the establishment of a microstructure gradient that shows an 8% improvement in fatigue properties compared to a reference condition without THT. The reason for this improvement is an increased volume fraction secondary alpha phase in the near-surface area of the specimens. Moreover, the results show that the process is applicable a tube-like specimen with varying wall thicknesses.

In this paper, a novel nonlocal strain gradient anisotropic elastic shell model is developed to analyse the vibrations of simply supported single‒walled carbon nanotubes (SWCNTs). Sanders‒Koiter shell theory is used to obtain the strain‒displacement relationships. Eringen nonlocal elasticity theory and Mindlin strain gradient theory are adopted to derive the constitutive equations, where the anisotropic elastic constants are expressed via Chang molecular mechanics model. The complex variable method is used to analytically solve the equations of motion and to obtain the natural frequencies of SWCNTs. First, the anisotropic elastic shell model is validated via comparisons with the results of molecular dynamics simulations reported in the literature. Then, the effect of nonlocal and material parameters on the natural frequencies of SWCNTs with different geometries and wavenumbers is analysed. From the numerical simulations it is obtained that the natural frequencies decrease with increasing nonlocal parameter, while they increase with increasing material parameter. Moreover, the decrease of natural frequencies with increasing SWCNT radius is exponential as the material parameter increases, while it is linear as the nonlocal parameter increases. Finally, as the number of waves increases, the natural frequencies linearly vary with increasing nonlocal parameter, while they exponentially increase with increasing material parameter.

In this study, we once again use a set of Fibonacci-like sequences to examine the symmetries within the genetic code. This time, our focus is on the physiological state of the amino acids, considering them as charged, in contrast to our previous work where they were seen as neutral. In a pH environment around 7.4, there are four charged amino acids. We utilize the properties of our sequences to accurately describe the symmetries in the genetic code table. These include Rumer’s symmetry, the third-base symmetry and the "ideal" symmetry, along with the "supersymmetry" classification schemes. We also explore the special chemical structure of the amino acid proline, presenting two perspectives—shCherbak’s view and the Downes-Richardson view, which perspectives are included in the description of the above-mentioned symmetries. Our investigation employs also elementary modular arithmetic to precisely describe the chemical structure of proline, connecting the two views seamlessly. Finally, our Fibonacci-like sequences prove instrumental in quickly establishing the multiplet structure of non-standard versions of the genetic code. We illustrate this with an example, showcasing the efficiency of our method in unraveling the complex relationships within the genetic code.

This theoretical study presents a novel perspective on the potential anti-inflammatory properties of Bromelain, a natural agent extracted from pineapple stems. The investigation explores the binding capabilities of Bromelain to Apoptosis-associated speck-like containing CARD (ASC), a crucial adapter molecule known for its involvement in inflammatory processes and inflammasome formation. For the first time, these findings suggest that Bromelain exhibits a notable affinity for ASC, indicating its promising role as a natural anti-inflammatory agent. This study sheds light on the molecular interactions that may contribute to Bromelain's therapeutic potential in modulating inflammatory responses.

Novel segmented tick-borne RNA viruses belonging to the group of Jingmenviruses (JMVs) are widespread across Africa, Asia, Europe, and America. In this work, we obtained whole-genome sequences of two Kindia tick virus (KITV) isolates and performed modeling and functional annotation of the secondary structure of 5’ and 3’ UTRs from JMV and KITV viruses. UTRs of various KITV segments are characterized by: (1) polyadenylated 3’ UTRs; (2) 5’ DAR and 3’ DAR motifs; (3) a highly conserved 5’-CACAG-3’ pentanucleotide; (4) a La protein binding site; (5) multiple UAG sites providing interactions with the MSI1 protein; (6) three homologous sequences in the 5’ UTR and 3’ UTR of segment 2; (7) the segment 2 3’ UTR of a KITV/2017/1 isolate comprises two consecutive 40 nucleotide repeats forming a Y-3 structure; (8) a 35-nucleotide deletion in the second repeat of the segment 2 3’ UTR of KITV/2018/1 and KITV/2018/2 isolates, leading to a modification of the Y-3 structure; (9) two pseudoknots in the segment 2 3’ UTR; (10) the 5’ UTR and 3’ UTR are represented by patterns of conserved motifs; (11) the 5’-CAAGUG-3’ sequence occurs in early UTR hairpins. Thus, we identified regulatory elements in the UTRs of KITV, which are characteristic of orthoflaviviruses. This suggests their functional significance for the replication of JMVs and the evolutionary similarity between orthoflaviviruses and segmented flavi-like viruses.

The transition region is a very thin but most peculiar layer in the solar atmosphere, located between the solar chromosphere and the corona. It is a key region for understanding the coronal heating, the solar eruptions triggering, and the origin of solar winds. Here, the gradients of all physical parameters are very high, including plasma density, ionization degree, temperature, and magnetic fields, etc. Therefore, the transition region should be highly dynamic, such as fast energy releasing and transporting, plasma heating, and even particle accelerating. They include two physical aspects: thermal and non-thermal processes. The thermal processes can be observed from the ultraviolet (UV) and extreme ultraviolet (EUV) emission by multi-wavelength images. The non-thermal processes, however, should be related to radio emissions especially at millimeter wavelengths, and the frequency range should span from several GHz to beyond 300 GHz, and the emission mechanism should be highly related to the magnetic fields and variations. The ultra-wide broadband spectral observations at millimeter wavelengths with high spectral resolutions may provide rich dynamic information about the transition region, such as non-thermal energy release and transmission, the flows of plasma and energetic particles, the magnetic fields and their variations, the generation and transportation of various waves, and the formation and evolution of the source regions of solar eruptions. Here, we discussed the spectral characteristics of millimeter-wave emission from the transition region and proposed a new conception of ultra-wide broadband millimeter-wave spectrometers to observe them.

Human sepsis is one of the leading cause of death worldwide and is known to be a harmful damaging host inflammatory condition, primarily caused by endotoxin released by gram-negative bacteria. Despite antibiotics administration being widely used to treat disease, effective targeted therapeutic strategies for the sepsis are still lacking. Here, we demonstrate that CM1, a derivative of natural polyphenol chrysin, exerts anti-inflammatory activity by inducing the ubiquitin-editing protein TNFAIP3 and NAD-dependent deacetylase sirtuin 1 (SIRT1). We found that CM1 attenuated Toll-like receptor 4 (TLR4)-induced the generation of inflammatory cytokines by inhibiting the extracellular-signal-regulated kinase (ERK)/MAPK and nuclear factor kappa B (NF-κB) signaling pathway. In addition, the treatment of macrophages with CM1 induced the expression of TNFAIP3 and SIRT1 in TLR4-stimulated primary macrophages, however the anti-inflammatory properties of CM1 was disappeared by siRNA silencing of TNFAPI3 or by the genetic and pharmacologic inhibition of SIRT1. Importantly, intravenously administration of CM1 resulted in the decreased susceptibility to endotoxin-induced sepsis, leading to inhibition of the generation of proinflammatory cytokines and neutrophils infiltration into lung than control mice. Collectively, these findings suggest that CM1 has the potential to be a treatment candidate for diverse inflammatory diseases including sepsis.

Qinghai spruce (Picea crassifolia) is a ecologically important species in the forest ecosystem of the Qilian Mountains region in China. Natural regeneration of this species is critical to maintaining forest ecosystem function. We here analyzed several biological indicators among naturally re-generating Qinghai spruce across several elevations in the Pailugou watershed. Specifically, seedling density, basal diameter (BD), and plant height were measured, as were soil physico-chemical parameters, at 2700 m, 3000 m, and 3300 m above sea level. Differences in the biological indicators and correlations between the indicators and soil parameters were then assessed at each elevation. The results showed that soil stoichiometry was more sensitive to changes in el-evation than seedling indicators were. Furthermore, seedling density was positively correlated with soil pH, whereas BD was positively correlated with the carbon to nitrogen ratio (C/N), the carbon to phosphorus ratio, and soil organic carbon (SOC) contents. None of the analyzed soil parameters had a significant impact on elevation-specific differences in seedling density. However, soil pH, SOC, and C/N significantly affected variations in seedling diameter at each elevation. Finally, soil pH, SOC, C/N, and the carbon to phosphorus ratio significantly affected variations in seed-ling height at each elevation. This study provides a strong theoretical basis for further under-standing of the mechanisms associated with Qinghai spruce regeneration, ulti-mately contrib-uting to rational protection and management strategies for this important natural resource.

The red fox, found on all continents except Antarctica, occupies diverse habitats. In Croatia it is an indigenous wild species with a population density of 0.7 animals per square kilometer. While tumors in wild animals from the Canidae family are scarce, the true prevalence and diversity of tumors are likely underestimated due to limited research. So far, a limited number of tumors were observed among the red fox population, either in their natural habitat or in captivity. As part of the National rabies control program, we examined 1,890 red fox carcasses over a four-year period. Our focus was on identifying abnormalities on the skin and internal organs that suggest potential neoplastic proliferation. Five red foxes, three males and two females, were found to have growths resembling potential tumors. Their age distribution spanned from 2 to 7 years. Microscopic investigation revealed two collagenous hamartomas, two Meibomian gland adenomas and one intra-abdominal teratoma within a cryptorchid testis. This retrospective study aims to provide a comprehensive description of tumor and tumor-like lesions observed in free-range red foxes from Croatia, marking the first research of its kind in Croatia.

The rapid growth of equipment tasks in 5G large-scale machine-type communication scenarios presents challenges due to user equipment’s (UE) limited computing power and power constraints. To address these limitations, task offloading to unmanned aerial vehicles (UAVs) has gained attention. However, traditional cloud computing centres far from the UE fail to meet latency requirements. Mobile Edge Computing (MEC) emerges as a solution by deploying computing servers at the edge of the cellular network to enhance service responsiveness. However, traditional MEC solutions need more flexibility. This paper explores the advantages of UAVs, including flexibility and rapid deployment, and considers UAV power constraints. It proposes a joint optimization approach for user offloading strategies and UAV trajectories to minimize the overall energy consumption of UE. By leveraging the benefits of UAVs and MEC, the proposed method aims to improve task execution efficiency in energy-constrained 5G machine-type communication scenarios.

The pandemic SARS-CoV-2 continues to evolve to give rise to variants of concern that can escape vaccine-induced immunity. As such, more effective vaccines are urgently needed. In this study, we evaluated virus-like particle (VLP) as a vaccine platform for SAS-CoV-2. The spike, envelope, and membrane proteins of the SARS-CoV-2 Wuhan strain were expressed by a single recombinant baculovirus BacMam and assembled into VLPs in cell culture. The morphology and size of the SARS-CoV-2 VLP as shown by transmission electron microscopy was found to be similar to the authentic SARS-CoV-2 virus particle. In a mouse trial, two intramuscular immunizations of the VLP BacMam with no adjuvant elicited spike-specific binding antibodies in both sera and bronchoalveolar lavage fluids. Importantly, BacMam VLP -vaccinated mouse sera showed neutralization activity against SARS-CoV-2 spike pseudotyped lentivirus. Our results indicated that the SARS-CoV-2 VLP BacMam stimulated spike-specific immune responses with neutralization activity.

Neurodegeneration is becoming one of the leading causes of death worldwide as the population expands and grows older. There is a growing desire to understand the mechanisms behind prion proteins as well as the prion-like proteins that make up neurodegenerative diseases (NDs) including Alzheimer’s Disease (AD). Both amyloid β (Aβ) and hyperphosphorylated tau (p-tau) proteins behave in ways similar to that of the infectious form of the prion protein, PrPSc, such as aggregating, seeding, and replicating under not yet fully understood mechanisms, thus the designation of prion-like. This review aims to highlight the shared mechanisms between prion-like proteins and prion proteins in the structural variations associated with aggregation and disease development. These mechanisms are largely focusing on the dysregulation of protein homeostasis, self-replication, and protein aggregation, and how this knowledge could contribute to diagnoses and treatments for the given NDs.

Utilizing unmanned aerial vehicles (UAVs) to facilitate wireless communication has emerged as a viable and promising strategy aimed at enhancing both current and prospective wireless systems. This approach offers many advantages by establishing line-of-sight connections, optimizing operational efficiency, and enabling flexible deployment capabilities in various terrains. Thus, in this paper, we investigate UAV communication in a relaying network in which a UAV helps communication between a source and two destination users while flying to a location. To have a complete view of our proposed system, we consider both orthogonal multiple access as OFDM and non-orthogonal multiple access (NOMA) scenarios. Moreover, we apply successive convex optimization (SCO) and the Block-coordinate gradient descent (BCGD) for the sum-rate optimization problems to improve the system performance under constraints of total bandwidth and total power at the ground base station and UAV. The experimental results validate that the achievable secrecy rates are notably enhanced by using our proposed algorithms and show optimal trends for critical parameters, such as transmit powers, the flight trajectory and speed of UAV, and resource allocation of OFDM and NOMA.

The Thailand automotive industry grapples with complex challenges in service parts management, demanding a strategic approach for post-sales operations. This research unveils a tailored methodology, leveraging historical sales data, refined part classifications, and strategic decision rules. A high-level overview of the automotive supply chain emphasizes interconnected stakeholders. The systematic methodology delves into part categorization, decision rules, and data analysis. Decision rule outcomes, exceptions, and contingency solutions showcase efficacy. A cost impact assessment highlights substantial savings. Visualization tools offer nuanced perspectives. Optimized service parts classification, robust stocking decision rules, and cost-effective strategies emerge. Support Vector Regression excels in forecasting, with recommendations for dynamic stocking. Implications extend to the broader industry, offering efficiency and service quality blueprints. Acknowledging limitations and suggesting future research, this study contributes a valuable framework for Thai automotive service parts management.

The problem of intensification of melt crystal growth process has been analyzed using CdTe as an actual material. Numerical simulation of 100 mm diameter CdTe crystal growth by VGF technique has been carried out. The heat-mass transfer was controlled by introducing of low-frequency oscillating baffle into the melt, so called axial vibrational control (AVC) technique. The baffle configuration has been optimized to destroy solid «tails», which were formed near the crucible walls at high cooling rates due to the low thermoconductivity and the corresponding latent heat. Analysis of CdTe homogeneity range showed that during fast crystal cooling Te micro precipitations were formed resulting from decay of oversaturated Cd-riched nonstoichiometric solid solution at Bridgman crystal growth technique. A VGF grown CdTe crystal after full crystallization stays inside the phase field of the high temperature wurtzite polymorph. This makes it possible to go through the polymorph transition without Te micro-precipitating using the advantages of VGF specific feature of very slow cooling.

Co-delivery of antigens and adjuvants to the same antigen presenting cells (APCs) can significantly improve the efficacy and safety profiles of vaccines. Here, we report amine-grafted silica nanoparticles (A-SNPs) as a tunable vaccine co-delivery platform for TLR7/8 agonists along with the recombinant influenza antigen hemagglutinin H7 (H7) to APCs. A-SNPs of two different sizes (50 and 200 nm) were prepared and coated with INI-4001 at different coating densities, followed by co-adsorption of H7. Both INI-4001 and H7 showed &gt; 90% adsorption to the tested A-SNP formulations. TNF-α and IFN-α cytokine release by human peripheral blood mononuclear cells as well as TNF-α, IL-6, and IL-12 release by mouse bone marrow-derived dendritic cells revealed that the potency of the INI-4001-adsorbed A-SNPs (INI-4001/A-SNP) formulations can be improved relative to aqueous formulation control. This improved potency was dependent on particle size and ligand coating density. In addition, slow-release profiles of INI-4001 were measured from INI-4001/A-SNP formulations in plasma with 30-50% INI-4001 released after 7 days. In vivo murine immunization studies demonstrated significantly improved H7-specific humoral and Th1/Th17-polarized T cell immune responses with no observed adverse reactions. Low-density 50 nm INI-4001/A-SNP elicited significantly higher IFN-γ and IL-17 induction over that of the H7 antigen only group and INI-4001 aqueous formulation controls. In summary, this work introduces an effective and biocompatible SNP-based co-delivery platform that enhances the immunogenicity of TLR7/8 agonist-adjuvanted subunit influenza vaccines.

СrAss-like phages play an important role in maintaining ecological balance in the human intestine microbiome. However, their lifestyle and genetic diversity are still insufficiently studied. In this study, a novel CrAssE-Sib phage genome belonging to the epsilon сrAss-like phage genomes was found. Detailed analysis indicated that CrAssE-Sib encodes unusual proteins – DNA primase, virion RNA polymerase, prophage antirepressor, and transcriptional repressor, as well as reverse transcriptase (RT) and receptor binding protein (RBP) that are part of diversity-generating retroelement (DGR). Comparative analysis showed that all epsilon сrAss-like phages are divided into two putative genera, which were proposed to name Epsilonunovirus and Epsilonduovirus; CrAssE-Sib belongs to the first of them. RT from CrAssE-Sib and several Epsilonunoviruses contains a specific motif in its fourth domain that was not previously found in RTs from phage and bacterial DGRs. In addition, RBP from CrAssE-Sib and all Epsilonunoviruses has a unique structure. The presence of the conservative prophage repressor and anti–repressors that belong to the lysogenic-to-lytic cycle switches indicate that CrAssE-Sib and other Epsilonunoviruses could be temperate. Epsilon сrAss-like phage substantially differ from other crAss-like phages, including zeta сrAss-like phages. This fact indicates the need to isolate these phages into a separate family.

Nonlinear compression of narrow-band (Δ ≈ 0.2 cm-1) 20 ns KrF laser pulses in SF6 at 10 atm and CH4 at 50 atm pressure was studied. Both SBS and SRS optically phase conjugated backward reflected radiation was registered with energy reflectivity 10−14% in SF6 and CH4. In SF6 SBS pulses gradually shortened from 10 ns to 2−3 ns with pumping decrease to the SBS threshold ~ 10 mJ, while the SRS pulse had the shortest length of 30−60 ps for the maximal pumping of 120 mJ and broadened near the SRS threshold of ~30 mJ. For the SRS pulse energy ~ 2 mJ peak power 5×107 W was tenfold higher than the pump power. The theoretical model predicted a soliton-like SRS pulse compression to a temporal length of the order of vibrational relaxation time. There was no pulse compression of backward SBS and SRS radiation in CH4, while in the forward direction SRS pulses shortened to 3−4 ns at reduced pumping.

The paper covers the development of formulations and technology for low energy building materials based on the alkaline alumino-silicate binder and appropriate for special purposes. The microstructure of the binder was investigated using scanning electron microscopy and atomic force microscopy. The identification of the phase compositions was done by means of X-ray diffraction. By leach rate measurement and chemical analysis of pore liquid, the linkage of the alkali metal ions in water-insoluble composites was researched. The dependence of structure formation on mix design and curing conditions was investigated. Specific examples of manufacture and application of binder-based glues and adhesives have been considered, incl. ones for low-temperature insulating composite materials, heat insulation perlite-based materials, heat insulation of industrial boilers, and the fire resistant heat insulating system. The advantages of the binder-based temperature insulating composites compared to traditionally used ones are shown.

The 2-Acetyl-1-pyrroline (2-AP) is the most important volatile in determining the aroma of fragrant rice, however, the transcriptional regulation mechanism of 2-AP biosynthesis in fragrant rice is still unclear. In this study, the Osp5cs1 knockout mutant lines and OsP5CS1 over-expression lines were constructed by genetic transformation of indica rice cultivar i.e., ‘Zhonghua11’, which was knocked out OsBADH2 to produce fragrance in aromatic rice. The OsP5CS1 gene was also identified as a key gene in the 2-AP biosynthesis pathway of aromatic rice. The OsP5CS1 promoter was used as bait, and the transcription factor OsbZIP60-like was screened by yeast one-hybrid assays. The transcription factor OsbZIP60-like specifically bound to the OsP5CS1 gene. The dual luciferase reporting system found that OsbZIP60-like transcription factor promoted the transcriptional activation of OsP5CS1. Compared with the wild type, the OsP5CS1 gene expression was significantly down-regulated in Osbzip60-like mutant, and resulted in substantial reduction in 2-AP biosynthesis. Moreover, the OsP5CS1 gene expression was significantly up-regulated in OsbZIP60-like over-expressed plants, and the 2-AP concentrations were also increased whereas the Osbzip60-like mutants were found to be sensitive to Zn deficiency. Overall, the OsbZIP60-like transcription factor promoted the 2-AP accumulation. This study provides a theoretical basis for the transcriptional regulation mechanism of 2-AP biosynthesis and explores the function of OsbZIP transcription factor in fragrant rice.

Despite the availability of effective vaccines against COVID-19, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to spread worldwide, pressing the need for new vaccines with improved breadth and durability. We developed an adjuvanted subunit vaccine against SARS-CoV-2 using recombinant receptor-binding domain (RBD) of spike with synthetic adjuvants targeting TLR7/8 (INI-4001) and TLR4 (INI-2002), co-delivered with aluminum hydroxide (AH) or aluminum phosphate (AP). The formulations were characterized for the quantities of RBD, INI-4001, and INI-2002 adsorbed onto the respective aluminum salts. Results indicated that at pH 6, the uncharged RBD (5.73 ± 4.2 mV) did not efficiently adsorb to the positively charged AH (22.68 ± 7.01 mV), whereas it adsorbed efficiently to the negatively-charged AP (-31.87 ± 0.33 mV). Alternatively, pre-adsorption of the TLR ligands to AH converted it to a negatively-charged particle, allowing for the efficient adsorption of RBD. RBD could also be directly adsorbed to AH at a pH of 8.1, which changed the charge of the RBD to negative. INI-4001 and INI-2002 efficiently to AH. Following vaccination in C57BL/6 mice, both aluminum salts promoted Th2-mediated immunity when used as the sole adjuvant. Co-delivery with TLR4 and/or TLR7/8 ligands efficiently promoted a switch to Th1-mediated immunity instead. Measurements of viral neutralization by serum antibodies demonstrated that the addition of TLR ligands to alum also greatly improved the neutralizing antibody response. These results indicate that the addition of a TLR7/8 and/or TLR4 agonist to a subunit vaccine containing RBD antigen and alum is a promising strategy for driving a Th1 response and neutralizing antibody titers targeting SARS-CoV-2.

Keywords: Wall-like RC columns; Strengthening; FRP; NSM rebars; Steel plates; Testing; FE modeling; Preloading.

Radiation therapy (RT) is one of the main approaches of cancer therapy, including non-small cell lung cancer (NSCLC). Radiation-induced DNA double-stranded breaks (DSBs), single-stranded DNA breaks (SSBs) and oxidized nucleobases cause replication fork stalling, chromosomal instability or point mutagenesis and can lead to cell death. Translesion DNA synthesis (TLS) is one of the DNA damage tolerance (DDT) pathways promoting replication of damaged DNA. Here, we explored the effects of the knockout of primase-polymerase PRIMPOL (PRIMPOL −/−) and TLS polymerase POLI (POLI −/−) genes in two different monoclonal lines derived from human NSCLC A549 cells. Our study aimed to investigate the impact of their absence on the susceptibility of these cells to ionizing radiation (IR)-induced stress. Without IR, cells that do not have PRIMPOL or POLI experience a considerable rise in apoptosis by day 7 of continuous cultivation and a noteworthy decline in cell migration under spatial confinement when compared to the parental wild-type cells. Despite this, we have found that PRIMPOL −/− exhibited higher clonogenic survival compared to wild-type cell lines in response to IR stress. Both of these cell lines demonstrated remarkable resilience to radiation stress, surpassing the survival rate of the original A549 cells. POLI deficiency reduces cellular γH2AX foci accumulation, while the presence of highest number of γH2AX foci in PRIMPOL−/− cells was ATM-independent. We report for the first time that increased post-irradiation clonogenic survival of PRIMPOL −/− A549 cells might be associated with the increase of CD133+ but not CD44+ populations of cancer stem-like cells.

Respiratory viral infections (RVI) are common reasons for healthcare consultations. The inpatient management of RVI consumes significant resources. From 2009-2014, we assessed the costs of RVI management in 4776 hospitalized chil-dren aged 0-18 years participating in a quality improvement program, where all ILI patients underwent virologic testing at the National Reference Centre followed by de-tailed recording of their clinical course. The direct (medical or non-medical) and indirect costs of inpatient management outside the ICU (“non-ICU”) versus management requiring ICU-care (“ICU”) added up to 2,767.14€ (non-ICU) vs. 29,941.71€ (ICU) for Influenza, 2,713.14€ (non-ICU) vs. 16,951.06€ (ICU) for RSV infections, and 2,767.33€ (non-ICU) vs. 14,394.02€ (ICU) for human rhinovirus (hRV) infections, respectively. Non-ICU inpatient costs were similar for all 8 RVI studied: Influenza, RSV, hRV, ade-novirus (hAdV), metapneumovirus (hMPV), parainfluenzavirus (hPIV), bocavirus (hBoV) and seasonal coronavirus (hCoV) infections. ICU-costs for Influenza however, ex-ceeded all other RVI. At the time of the study, Influenza was the only RVI with antivi-ral treatment options available for children, but only 9.8% of Influenza patients (non-ICU) and 1.5% of ICU-patients with Influenza received antivirals; only 2.9% were vac-cinated. Future studies should investigate the economic impact of treatment and pre-vention of Influenza-, COVID-19-, and RSV post vaccine introduction.

Diamond-like carbon (DLC) films have a promising tribological application in green precision manufacturing, but their toughness represents a serious issue in the way of their application perspective. The film thickness is on the micrometer scale and makes conventional methods unsuitable for its toughness characterization. The absence of an accepted method for characterizing the DLC toughness has hindered the related research on the development of toughening methods and understanding the mechanism. This work intends to explore a relatively simple and acceptable characterization method to measure the film toughness. Moreover, toughening mechanisms of DLC films are proposed during the characterization to reveal the toughening mechanism. The toughness can be fully characterized through combining impact toughness and scratch toughness. The impact toughness can be obtained by observing the size of the indentation and the crack state around the indentation in the impact experiment. The scratch toughness could be assessed by combining the scratch morphology and Crack Propagation Resistance (CPRS). The improvement of film toughness should be carried out from two aspects: preventing microcrack initiation and suppressing crack propagation. Appropriate doping of non-carbide metal particles can effectively improve the toughness of the film, and there are seven main factors affecting the toughening of the film. Five toughening mechanisms of non-carbide metals from film preparation to test stage are revealed during the investigation of the change patterns.

Diversity-generating retroelements (DGRs) are prokaryotic systems providing rapid modification and adaptation of target proteins. In phages, the main targets of DGRs are receptor-binding proteins that are usually parts of tail structures and the variability of such host-recognizing structures enables phage adaptation to changes on the bacterial host surface. Sometimes, more than one target gene containing a hypermutated variable repeat (VR) can be found in phage DGRs. The role of mutagenesis of two functionally different genes is unclear. In this study, several phage genomes that contain DGRs with two target genes were found in the gut virome of healthy volunteer. Bioinformatics analysis of these genes indicated that they encode proteins with different topology; however, both proteins contain the C-type lectin (C-lec) domain with a hypermutated beta-hairpin on its surface. One of the target proteins belongs to a new family of proteins with a specific topology: N-terminal C-lec domain followed by one or more immunoglobulin domains. Proteins from the new family were named tentaclins after TENTACLe+proteIN. The genes encoding such proteins were found in the genomes of prophages and phages from the gut metagenomes. We hypothesized that tentaclins are involved in binding either to bacterial receptors or intestinal/immune cells.

Anastomosing rivers are wetland systems with multiple channels, and there are many constraints limiting in-situ observations of their hydrodynamics. Therefore, there are few reports on the hydrodynamic data of such rivers. In order to obtain the hydrodynamic parameters of the river channels without hydrological observations in the anastomosing rivers, this study proposes an expression method namely expression of channel morphological parameters (ECMP) for hydrodynamic calculations. The calculation formula of the ECMP method based on the shape factor, scale factor, and gradient factor of the flow cross-section as independent variables. This method can be used to calculate the average velocity, discharge, specific stream power, gross stream power and other hydrodynamic parameters of the flow cross-section at different water levels, only requiring the measurement of morphological parameters such as the average depth, width-depth ratio, and gradient of the flow (channel) cross-section. The applicability of the ECMP method was verified using measured hydrological data. The results show that the ECMP method is a practical calculation method with high computational accuracy and convenient application for calculating river hydrodynamic parameters. It has great application value in study on fluvial geomorphology and hydrodynamics, as well as in the reconstruction of ancient river hydrology and hydrodynamics.

Cardiac hypertrophy typically follows myocardial damage, a process known to involve members of the Krüppel-Like Factors (KLFs) which have pro-hypertrophic and anti-hypertrophic roles. Our study delved into the molecular mechanisms underlying KLFs-cardiac hypertrophy interplay post myocardial infraction. We induced myocardial dam-age in rats using isoproterenol. Total RNA was extracted from the left ventricle and Quantitative Real Time RT-PCR was conducted to assess the expression of KLFs, cardiac commitment genes, inflammatory markers, and certain conduction-related genes. We devel-oped a computational approach to construct a proteomic network centered on KLFs. Initial results revealed early expression (2-3 days post-induction) of KLF3, KLF4 and KLF6, followed by the subsequent expression of KLF11 and KLF15 (5-8 days after induction). In the maturation stage, KLF12 and KLF13 regulators were found upregulated. IL-6 was gradually found to be upregulated through. Interaction analysis revealed KLF3, KLF8 and KLF12 interacted with cardiac electrical functions. RT-PCR confirmed up-regulation in cardiac genes linked to electrical function and scar maturation. Our findings underscore the central role of KLFs during the modulation of the cardiac hypertrophic response. Dysregulation of KLF expression resulted in damaged myocardium, participating in the progression of abnormal hypertrophy, highlighting their potential as therapeutic targets for heart diseases.

Objective: To analyze the relation between alterations in the growth hormone (GH)/insulin-like growth factor 1 (IGF-1) axis during the first 6 months of life and weight in children born in the Lower Middle São Francisco region. Methods: This is an analytical cohort, with a quantitative approach and a translational perspective. Thirty children with low and normal birth weight were initially identified in a hospital and reapproached at 3 and 6 months of age. Birth weight and alterations in GH/IGF-1 curves at birth, third month, and sixth month of life. Results: Weight gain during the 6 months of follow-up in newborns with low birth weight was greater compared to newborns with normal birth weight. All children who were born with low birth weight had an altered GH/IGF-1 curve at birth (p = 0.002). Most newborns with low birth weight maintained the alteration in the GH/IGF-1 curve at the third month of life (p = 0.027). Regarding the GH/IGF-1 curve at the sixth month, alteration persisted in greater proportion among children with low birth weight. Conclusion: Alterations in insulin resistance markers, demonstrated by increased GH without a proportional increase in IGF-1, were observed to be significant in children with low birth weight, with greater adiposity in this group, which may increase the risk of metabolic diseases in later life.

The new coronavirus SARS-CoV-2 is the cause of the COVID-19 pandemic, which has created an incomparable global health problem. PLpro is a key protein involved in viral polyprotein processing and immune system evasion, making it a prime target for the development of antiviral drugs to combat COVID-19. The review begins by elucidating the functional and structural properties of SARS-CoV-2 PLpro, underscoring its significance in viral pathogenicity and replication. Employing homology modeling, a tertiary structure model of PLpro is constructed, laying the groundwork for subsequent investigations. To expedite the search for potential therapeutic candidates, the study harnesses computational methodologies. Molecular docking techniques are employed to explore binding sites for antiviral drugs within the catalytic region of PLpro. This computational approach not only aids in drug development but also provides crucial preliminary data before experimental research commences. The stability of drug-PLpro complexes is assessed through comprehensive all-atom molecular dynamics (MD) simulations, affording dynamic insights into drug-PLpro interactions. By evaluating binding energy estimates from MD simulations, stable drug-PLpro complexes with potential antiviral properties are identified. However, it is emphasized that experimental validation is imperative to confirm these computational findings, necessitating in vitro and in vivo tests to assess safety and efficacy. This pivotal stage bridges the computational realm with real-world clinical applications. The review emphasizes the imperative need for enhancements in the existing PLpro-targeting drugs currently on the market. Their actions are insufficient for clinical use and necessitate thorough characterization and improvement. The Supervised Molecular Dynamics (SuMD) simulation technique reveals critical information about drug unbinding pathways, shedding light on opportunities for refinement. In addition to optimizing existing drugs, the investigation of sub-structurally similar molecules emerges as a promising avenue for drug development. In conclusion, the review underscores the pivotal role of targeting SARS-CoV-2 PLpro in the ongoing battle against COVID-19. By integrating molecular dynamics simulations, structural modeling, and computational insights, a robust foundation is established for identifying promising antiviral drug candidates. The continual need for improvement in existing drugs and the exploration of novel compounds remain paramount in the global effort to combat COVID-19. The evolution and management of COVID-19 hinge on the symbiotic relationship between computational insights and experimental validation, illustrating the interdisciplinary synergy crucial to this endeavor.

In this paper, it is shown that the bivariate beta statistical manifold of the first kind with three parameters has a gradient system Hamiltonian and is completely integrable. It is shown that this system admits a Lax pair representation. The question here is how to construct a gradient system and show that it is completely integrable while determining the integrable curves on the three-parameter bivariate beta manifold of the first kind admitting a potential function?To do this, we prove the existence of the potential on the manifold using ovidiu in Ovidiu, then using Amari's theorems in Amari, we show that this coordinate system admits a dual coordinate system. This will allow us to determine the Riemannian metric on the variety and to construct the gradient system. We linearise this system using Nakamura's method. We prove that it is Hamiltonian and completely integrable using the theorem in \cite{mama-proceeding}. It is shown that the Bivariate Beta of the first kind with three parameters, is an exponential function. The gradient system is linearizable. It is proven that the associate gradient system is Hamiltonian with $ \mathcal{H} $ which is in involution and satisfying $d\mathcal{H}=0.$ Therefore, the gradient system obtained is a sub-dynamical system of a $4$-dimensional system and is a completely integrable system. We show that the gradient system on the statistical model has the following Lax pair representation: $ \dot{L}= \left[L, N\right] $ where $L$ is a symmetric matrix and $N$ is the diagonal matrix. The gradient system defined by the Bivariate Beta family of the first kind with three parameters odd-dimensional manifold is a completely integrable Hamiltonian system.

As renewable energy generation prediction systems have been introduced into the energy trading market, accurate prediction of solar photovoltaic (PV) energy generation has become a crucial challenge for ensuring stable trade of variable energy. Therefore, it is essential to quantitatively investigate and analyze current prediction technology and develop more advanced prediction systems. In this study, three models for predicting PV energy generation were investigated. Multiple regression, random forest, and gradient boosting machine (GBM) models were con-structed for 15 utility-scale power plants throughout South Korea. Model performance was evaluated in terms of root mean square error (RMSE) and mean absolute error (MAE). The multiple regression-based model had an RMSE of 12.00% and MAE of 9.06%. The random forest model had an RMSE of 11.69% and MAE of 8.61%. The GBM model had an RMSE of 11.34% and MAE of 8.18%, indicating its superior accuracy among the three models in predicting PV energy generation. These findings provide valuable insights that may contribute to the development of advanced PV energy generation models and their application in grid operations.

Applications of gradient method for nonlinear optimization in development of Gradient Neural Network (GNN) and Zhang Neural Network (ZNN) are investigated. Particularly, the solution of the matrix equation AXB=D which changes over time is studied using the novel GNN model, termed as GGNN(A,B,D). The GGNN model is developed applying GNN dynamics on the gradient of the error matrix used in the development of the GNN model. The convergence analysis shows that the neural state matrix of the GGNN(A,B,D) design converges asymptotically to the solution of the matrix equation AXB=D, for any initial state matrix. It is also shown that the convergence result is the least square solution which is defined depending on the selected initial matrix. A hybridization of GGNN with analogous modification GZNN of the ZNN dynamics is considered. The Simulink implementation of presented GGNN models is carried out on the set of real matrices.

Keywords: Artificial Neural Network (ANN), Support Vector Machine (SVM), Support Vector Regression (SVR), Lightweight Gradient Boosting Machines (Light GBM), Machine Learning, Solar Irradiance (SI), Solar forecasting

Due to the complexity of the microstructure of porous media, it is of great significance to explore the heat transport mechanism in porous media in many engineering applications. In this study, an expression for effective thermal conductivity（ETC） of porous media with randomly distributed damaged tree-like bifurcation networks is derived based on the theory of thermodynamics and fractal features of tree-like bifurcation networks. We investigate the effect of heat conduction and heat convection in porous media with randomly distributed damaged tree-like bifurcation networks on the ETC of the porous media. It is found that our fractal model is in good consistency with the existing available experimental data. In addition, the influence of the micro-structural parameters of the model on heat transfer in the porous media have been analyzed in detail. The research results can provide significant theoretical guidance for the development and design of heat transfer systems.

Globally, we are undergoing compelling changes in virtually every branch of the society and every aspect of our daily lives over the last couple of years due to the COVID-19 pandemic. In the words of Melinda Gates, the famous American Philanthropist, the pandemic has literally “magnified every existing inequality”. There has been much talk and an ongoing debate on the origin of SARS-CoV-2 (or, the novel coronavirus) as to whether have evolved purely naturally or has the contaminating element of human intervention somewhere in its trajectory. Proponents of the pure ‘natural evolution theory’, some of whom are bigshots in the field of virology have seem to take up great effort to stamp their authority right from the very early days of the pandemic on the slow natural evolution of the virus from its ancestrors by several publications in high-profile journals. These well-cited papers however provide rationals that are somewhat contradictory and are backed up with data which maybe argued as far from being complete and/or all-inclusive. As time progressed, alternative theories to that of the pure natural evolution of the virus emerged with strong counter-logics and gradually compiling genomic and epidemiological data along with detailed structural studies of relevant molecular interactions. All said and done, till date, compelling evidences lack on either end of the spectrum making the COVID origin debate still very mucg wide open. Regardless of whether purely natural or human-intervented the damage caused by the virus is unmistakable and has been an alarming concern to medical science lately. To that end, a section of the scientific community is devoting committed efforts using immuno-informatics to design and develop possible multi-epitope combination vaccines to cover a plethora of viral antigens and their plausible future mutations, alongside with the vaccines that are currently available.

This paper presents three types of tensor Conjugate-Gradient methods for solving large-scale linear discrete ill-posed problems based on the t-product between third-order tensors. An automatical determination strategy of a suitable regularization parameter is proposed for the tensor conjugate gradient (tCG) method. A truncated version and a preprocessed verion of the tCG method are further presented. The discrepancy principle is employed to determine a suitable regularization parameter. Several numerical examples are given to show the effectiveness of the proposed tCG methods in image and video restoration.

Quantum computing is an emerging field that have given a significant impact on optimization. Among the diverse quantum algorithm, the quantum gradient descent has become a prominent technique for solving unconstrained optimization problems. In this paper, we propose a quantum-spectral Polak-Ribi{\'e}re-Polyak (PRP) conjugate gradient approach. The technique is considered as a generalization of the spectral PRP method which employs a $q$-gradient that approximate the classical gradient with quadratically better dependence on the quantum variable $q$. Additionally, the proposed method reduces to the classical variant as quantum variable $q$ gets closer to $1$. The quantum search direction always satisfies the sufficient descent condition and does not depend on any line search. This approach is globally convergent with the standard Wolfe line search without any convexity assumption. Numerical experiments are conducted and compared with the existing approach to demonstrate the improvement of the proposed strategy.

Abstract—Online learning platforms provide diverse course resources, but this often result in the issue of information overload. Learners always want to learn courses that are appropriate for their knowledge level and preferences quickly and accurately. Effective course recommendation plays a key role in helping learners select appropriate courses and improving the efficiency of online learning. However, when a user is enrolled in multiple courses, Existing course recommendation methods face the challenge in accurately recommending the target course that is most relevant to the user, because of the noise courses. In this paper, we propose a novel reinforcement learning model named Actor-Critic Hierarchical Reinforcement Learning (ACHRL). The model incorporates the Actor-Critic method to construct the profile reviser. This can remove noise courses and make personalized course recommendation effectively. Furthermore, we propose a policy gradient based on temporal difference error to reduce the variance in the training process, to speed up the convergence of the model, and improves the accuracy of the recommendation. We evaluate the proposed model on two real datasets, and the experimental results show that the proposed model is significantly outperforms the existing recommendation models (improving 3.77% to 13.66% in terms of HR@5).

In this study, we investigate the behavior of randomly moving particles with a defined average energy, focusing on their biased motion around a specific point. These particles' velocity magnitudes follow a Maxwell distribution with a specific scale parameter. Our analysis derives the stochastic rotation rules for these particles and introduces the Ito equation conditions for biased stochastic rotation at a given curl value. We also consider the special relativistic-like effects in this context. The findings are validated through a representative example.

The effect of ferromagnetic CaMnO3 (CMO) addition on structural, magnetic, dielectric, and ferroelec-tric properties of BiFeO3 are presented. X ray diffraction and Raman investigation allowed the identifi-cation of single pseudo cubic perovskite structure. The magnetic measurement showed that the pre-pared films exhibit a ferromagnetic behavior at low temperature with both coercive field and remnant magnetization increased with increasing CMO content. However, a deterioration of magnetization was observed at room temperature. Ferroelectric study revealed an antiferroelectric like behavior with a pinched P-E hysteresis loop for 5%CMO doping BFO resulting in low remnant polarization and double hysteresis loops. Whereas, high remnant polarization and coercive field with a likely square hysteresis loop are obtained for 10 % CMO addition. Furthermore, a bipolar resistive switching behavior with a threshold voltage of about 1.8V is observed for high doped film that can be linked to the ferroelectric polarization switching.

Intelligent transportation systems (ITS) have recently evolved rapidly, which requires development of highly trustworthy and effective communication technologies for uses, including vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communication; where Orthogonal Frequency Division Multiplexing (OFDM) is regarded as a strong candidate and highly popular option technique among these methods. However, the movement of vehicles introduces Doppler frequencies which produce inter-carrier interference (ICI), that is frequently occurs in V2X channels. This interference has the potential to compromise the integrity of subcarrier orthogonality within OFDM, leading to lower-quality communication and an increased likelihood of data transmission errors. When employing channels with doubly dispersive fading, OFDM necessitates the usage of a complex equalization based on the minimum mean-square error (MMSE) equalizer, which requires channel matrix inversion. Several low-complexity equalizers for OFDM have been developed and are based on band factorization, time domain LSQR (Least-Square QR) iterative computing, and banded minimum mean squared error (BMMSE). This paper proposes Conjugate Gradient Least Squares (CGLS), which is a novel iterative computation algorithm integrated with nonlinear equalizers. The suggested nonlinear equalization technique determines the trade-off between computations and performance. According to simulation data, the suggested nonlinear equalizer performs better than the current BMMSE and linear CGLS algorithms across doubly dispersive fading channels.

In a particle swarm with a fixed kinetic energy, the constituent particles move randomly and their velocity magnitudes follow a Maxwell distribution characterized by specific parameters. Within a certain time frame, particles in a sub-swarm may exhibit a bias in their movement direction. Thus, the particles may exhibit a special characteristic (biased random motion), where the group velocity in a particular direction remains constant u. For this biased particle swarm, composed of randomly moving particles with a fixed average speed c, particles have a higher probability of moving in a specific direction. Conversely, their likelihood of moving in all other directions is equally reduced. This study starts from the perspective of biased random walks and using Mathematica software proves that the diffusion rate of particles (in the reference frame R_u observed from R₀) in all directions is slower than that of an unbiased particle swarm with the same average speed c. Specifically, the degree of reduction is determined by the Lorentz-like factor. Lastly, we present the Ito equation for this biased random motion and provide associated verification examples. This study aims to serve as a reference for comprehending the underlying principles of the special relativity effect.

For massive multiple-input multiple-output (MIMO) communications, the minimum mean square error (MMSE) scheme provides close to optimal recognition but involves inverting the high-dimensional matrix. A Gauss-Seidel (GS) detector based on conjugate gradient and Jacobi iteration (CJ) joint processing is introduced to address this problem. Firstly, the signal is initialized with the best of the three initialization regimes for faster algorithm convergence. Secondly, the signal is processed together with CJ. Finally, the pre-processed result is transferred to the GS detector. The simulation results indicate that the proposed iterative algorithm has a lower BER performance than the GS and improved iterative scheme based on GS in channels with different correlation levels.

Magnetorheological (MR) fluids have been known to react to magnetic fields of sufficient magnitudes. While in the presence of the field the material develops a yield stress. The tunable property has made it attractive in e.g. semi-active damper applications in vibration control domain in particular. Within the context of a given application the fluids can be exploited in at least one of the fundamental operating modes of which the so-called gradient pinch mode has been least explored. Contrary to the other operating modes, the MR fluid volume in the flow channel is exposed to a non-uniform magnetic field in such a way that the Venturi-like contraction is developed in a flow channel solely by means of a solidified material in the regions near the walls rather than mechanically driven changes of the channel’s geometry. The pinch mode rheology of the material has made it a potential candidate in developing on a new category of MR valves. By convention, a pinch mode valve features a single flow channel with poles over which non-uniform magnetic field is induced. In this study the authors examine ways of extending the dynamic range of pinch mode valves by employing a number of such arrangements (stages) in series. To accomplish this, the authors developed a prototype of a multi-stage (three-stage) valve, and then compared its performance against that of a single-stage valve across a wide range of hydraulic and magnetic stimuli. To summarize, improvements in the pinch mode valve dynamic range are evident, however, at the same time it is hampered by the presence of serial air gaps in the flow channel.

This research undertakes a quantitative exploration of the impact of procurement mo-dalities on economic efficiency and inclusive procurement within the Dominican Repub-lic's public sector. Applying data science methodologies and machine learning techniques, this study analyzes a rich dataset of public procurement contracts spanning from 2018 to 2023. A focus is placed on understanding how different procurement modalities affect contract amounts and processing times—two key indicators of economic efficiency. Machine learning models, including Random Forest and Gradient Boosting, are em-ployed to predict these essential metrics across different procurement modalities. The findings indicate a significant positive correlation between certain procurement modali-ties and economic efficiency. The machine learning models reveal that procurement strategies focused on local sourcing and transparent bidding processes tend to yield high-er economic returns and more equitable outcomes. This research underscores the poten-tial of quantitative analysis to inform procurement policies, fostering a more economi-cally efficient and inclusive procurement ecosystem in the Dominican Republic's public sector. The insights garnered from this study could have significant implications for the design of more inclusive procurement strategies, contributing to socio-economic growth and corruption reduction in the Dominican Republic.

This comprehensive analysis thoroughly examines the topic of adversarial attacks in artificial intelligence (AI), providing a detailed overview of the various methods used to compromise machine learning models. It explores different attack techniques, ranging from the simple Fast Gradient Sign Method (FGSM) to the intricate Carlini and Wagner Attack (C&W), emphasising the wide range of adversarial approaches and their intended goals. The discussion also distinguishes between targeted and non-targeted attacks, highlighting the adaptability and versatility of these malicious efforts. Additionally, the study delves into the realm of black-box attacks, revealing the capability of adversarial strategies to compromise models even with limited knowledge. Real-life examples illustrate the tangible consequences and potential dangers of adversarial attacks in various fields such as self-driving cars, multimedia, and voice assistants. These cases highlight the difficulties in ensuring the legitimacy and dependability of AI-powered technologies and programs. The article stresses the importance of ongoing research and innovation to address the growing difficulties posed by advanced methods like deepfakes and disguised voice commands in preserving the security of AI systems. This study provides valuable insights on how different adversarial strategies and defence mechanisms interact within AI. The results emphasise the urgent need for stronger and more secure AI models to combat the increasing number of adversarial threats in today's AI landscape. These findings can guide future research and innovations in developing more resilient AI technologies that can better withstand various adversarial vulnerabilities and challenges.