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.

Viral diseases pose a significant threat to tomato crops (Solanum lycopersicum L.), one of the world's most economically important vegetable crops. The limited genetic diversity of cultivated tomatoes contributes to their high susceptibility to viral infections. To address this challenge, tomato breeding programs must harness the genetic resources found in native populations and wild relatives. Breeding efforts may aim to develop broad-spectrum resistance against the virome. To identify the viruses naturally infecting 19 advanced lines, derived from native tomatoes, high-throughput sequencing (HTS) of small RNAs and validation by PCR and RT-PCR were used. Single and mixed infections with Tomato mosaic virus (ToMV), Tomato golden mosaic virus (ToGMoV), and Pepper huasteco yellow vein virus (PHYVV) were detected. The complete consensus genomes of three variants of Mexican ToMV isolates were reconstructed, potentially forming a new ToMV clade with a distinct 3' UTR. The absence of reported mutations associated with resistance-breaking to ToMV suggests that the Tm-1, Tm-2, and Tm-22 genes could theoretically be used to confer resistance. However, the high mutation rates and a 63 nucleotide insertion in the 3' UTR of Mexican ToMV isolates suggest the need to evaluate known resistance genes against these viral variants. The precise identification and characterization of viruses that naturally infect tomato germplasm could be used as a strategy for selecting the appropriate disease management and guiding breeding efforts.

The addition of 50% vitreous humor to the media bathing lens explants results in the induction of both fiber cell differentiation and a significant immune/inflammatory response. Although Fgfr loss blocks differentiation in lens epithelial explants, this loss is partially rescued by deleting Pten. To investigate the functions of the Fgfrs and Pten during lens fiber cell differentiation, we utilized a lens epithelial explant system and conducted RNA sequencing on vitreous humor-exposed explants lacking Fgfrs, or Pten or both Fgfrs and Pten. We found that Fgfr loss impairs both vitreous-induced differentiation and inflammation while the additional loss of Pten restores these responses. Furthermore, transcriptomic analysis suggested that PDGFR-signaling in FGFR-deficient explants is required to mediate the rescue of vitreous-induced fiber differentiation in explants lacking both Fgfrs and Pten. The blockage of β-crystallin induction in explants lacking both Fgfrs and Pten in the presence of a PDGFR-inhibitor supports this hypothesis. Our findings demonstrate that a wide array of genes associated with fiber cell differentiation are downstream of FGFR-signaling and that the vitreous-induced immune responses also depend on FGFR-signaling. Our data also demonstrates that many of the vitreous-induced gene expression changes Fgfr-deficient explants are rescued in explants lacking both Fgfrs and Pten.

Fraudulent transactions pose a significant threat to financial institutions and e-commerce platforms.Machine learning models, trained on historical labeled data (fraudulent vs. legitimate transactions), are often employed to identify and prevent fraud. However, real-world datasets frequently exhibit class imbalance, where fraudulent transactions (minority class) are significantly outnumbered by legitimate transactions (majority class). Machine learning models may perform poorly as a result of this imbalance, underestimating fraud and favouring the majority class. This paper proposes a novel approach to address class imbalance and improve fraud detection accuracy. We explore the implementation of FROST (Feature space RObust Synthetic saTuration) oversampling, a technique specifically designed to generate synthetic samples for the minority class. The FROST function leverages the k-nearest neighbors (KNN) algorithm and a user-defined amplification factor (m) to create synthetic data points that closely resemble existing minority class instances. We integrate the FROST-enhanced oversampling technique into the machine learning pipeline for fraud detection. The paper evaluates the effectiveness of this approach compared to traditional oversampling methods and analyzes its impact on classification accuracy metrics.

Build a special identical equation, use its calculation characters to prove and search for solution of any odd converging to 1 equation through (*3+1)/2^k operation, change the operation to (*3+2^m-1)/2^k, and get a solution for this equation, give a specific example to verify. Thus prove the Collatz Conjecture is true. Furthermore, analysis the sequences produced by iteration calculation during the procedure of searching for solution, build a weight function model, prove it decrease progressively to 0, build a complement weight function model, prove it increase to its convergence state. Build a (*3+2^m-1)/2^k odd tree, prove if odd in (*3+2^m-1)/2^k long huge odd sequence can not converge, the sequence must outstep the boundary of the tree after infinite steps of (*3+2^m-1)/2^k operation.

The affinity constant, also known as the equilibrium constant, binding constant, equilibrium association constant, or the reciprocal value, the equilibrium dissociation constant (K_d), can be considered as one of the most important characteristics for any antibody-antigen pair. Many methods based on different technologies have been proposed and used to determine this value. However, since a very large number of publications and commercial datasheets do not include this information, significant obstacles in performing such measurements seem to exist. In other cases where such data are reported, the results have often proved to be unreliable. This situation may indicate that most of the technologies available today require a high level of expertise that does not seem to be available in many laboratories. In this paper, we present a simple approach based on standard immunoassay technology that is easy and quick to perform. It relies on the effect that the molar IC₅₀ approaches the K_d value in the case of infinitely small concentrations of the reagent concentrations. A two-dimensional dilution of the reagents leads to an asymptotic convergence to K_d. The approach has some similarity to the well-known checkerboard titration used for the optimization of immunoassays. A well-known antibody against the FLAG peptide, clone M2, was used as a model system and the results were compared with other methods. This approach could be used in any case where a competitive assay is available or can be developed. The determination of an affinity constant should belong to the crucial parameters in any quality control of antibody-related products and assays and should be mandatory in papers using immunochemical protocols.

In Indonesia, types of meat are still identified manually due to increasing prices for beef and buf-falo ahead of Idul Fitri. Traders mix beef with pork to thwart the beef scam. Haar wavelet and GLCM, GLCM with angles of 0°, 45°, 90°, and 135°, as well as matrices using contrast, correlation, energy, homogeneity, and entropy, are used in feature extraction. The following are the results of meat image classification testing using k-NN, Haar wavelet, and GLCM: The k-NN algorithm shows superiority in identifying fresh (99%), frozen (99%), and rotten (96%) meat texture images with the most fantastic accuracy results in every situation. Meanwhile, GLCM routinely provides good results, especially regarding the texture image of fresh meat (183.21) and rotten meat (115.79). However, despite delivering less throughput than k-NN and GLCM, Haar wavelets are still helpful, especially when dealing with fresh meat texture images (89,96).

Fusarium wilt (FW) caused by Fusarium oxysporum f. sp. Cucumerinum (Foc) of cucumber is a destructive soil-borne disease in cucumber (Cucumis sativus.L). There is limited knowledge on molecular mechanisms to FW resistance-mediated defense responses in cucumber. In this study, metabolome and transcriptome profiling were carried out in two FW, resistant (NR) and susceptible (NS), near isogenic lines (NILs) before and after Foc inoculation. The NILs showed consistent and stable FW resistances in multiple greenhouse and laboratory screening tests. Widely targeted metabolomic analysis identified differentially accumulated metabolites (DMAs) with significantly more accumulation in NR in response to Foc infection including many phenolic acid and flavonoid compounds from the flavonoid biosynthesis pathway. Transcriptome analysis identified differentially expressed genes (DEGs) between the NILs upon Foc inoculation including genes for secondary metabolite biosynthesis in or transcription factor genes regulating the flavonoid biosynthesis pathway. Joint analysis of the metabolomic and transcriptomic data identified DAMs and DEGs closely associated with biosynthesis of phenolic acid and flavonoid DAMs. The association of these compounds with NR-conferred FW resistance was exemplified by in vivo assays in which two phenolic acid compounds, bis (2-ethylhexyl) phthalate and diisooctyl phthalate, and the flavonoid compound gallocatechin 3-O-gallate were shown to have significant inhibitory effect on Foc growth. The antifungal effect of three compounds seems to be a novel finding. We conclude that phenolic acids and flavonoids play an important role in NR mediated FW resistance in cucumber.

Understanding the optimal strategy for a real estate investment and how performance changes based on characteristics is crucial for optimising the achievable return. This is prominent in tourist areas such as Paphos where there is no clear distinction as to whether short or long-term approaches are optimal. The study aimed to develop a model for predicting the optimal rental strategy whilst assessing which model performed best and which property attributes impacted its return the greatest. Short-term data was collected from AirDNA and long-term data was manually collected from real estate agents websites. Furthermore, Random Forest, K-Nearest Neighbour and Multiple Linear Regression models were created to predict the highest and best use for each property. Model accuracy varied between data sets with the best performing model for short-term properties being Random Forest (R-Squared: 0.843), and Distance-Based Multiple Linear Regression for long-term approach (R-Squared: 0.843). The study demonstrated that accurate models could be created to predict the optimal rental strategy with the number of bedrooms being the main driver for rental income, followed by luxury finishes and the presence of a pool. It found that locational characteristics didn’t impact the returns significantly assuming that the property was located within a tourist area.

In this project the EEG – electroencephalogram - channel(s) will be characterized to diagnose PTSD – Post-traumatic stress disorder – cases. For this aim, we applied boosting methods including a combination of K-mean and Support Vector Machine (SVM) models to find the feature weights to detect the PTSD cases. We classified 32 channels of 12 subjects including 6 PTSD and 6 healthy controls using a 6-mean classifier. The linear SVM found the weights of distinguished channels within each subject for each cluster. It was found that the significant SVM weights of F4, F8, and Pz are smaller in PTSD than in healthy subjects. This new method can be used as a tool to better understand the interaction of EEG signals and diagnosis.

Several efforts have been made to estimate the prevalence of different types of antibiotic resistance in different regions of Nigeria. The study investigated the prevalence of Klebsiella pneumoniae encoding genes for CTX-M, TEM, and SHV-1 extended-spectrum beta-lactamases (ESBL) enzymes isolated from urine samples of non-hospitalized patients with suspected UTI in Lagos State, Nigeria. A 4-month retrospective study, spanning from May 2023 to August 2023, was conducted. K. pneumoniae was isolated from urine samples using standard microbiological methods. The modified Kirby-Bauer disc diffusion method was used to test for antibiotic susceptibility, and Clinical and Laboratory Standard Institute (CLSI) guidelines were followed for interpretation. The double-disk synergy method was used to confirm the phenotypic identity of ESBL producers, and the results were interpreted in accordance with CLSI guidelines. The phenotypic method was utilized for ESBL screening, employing the standard disk diffusion method. The CTX-M ESBL gene, TEM gene, and SHV gene were found using multiplex PCR. Results revealed that 41 K. pneumoniae isolates were resistant to third-generation Cephalosporins; the isolates showed resistance to ceftazidime in 100% of cases and ceftriaxone in 80.49% of cases respectively. It was determined that 8 (19.51%) of the isolates produced ESBL. Among Klebsiella isolates, the frequencies of the SHV, TEM, and CTX-M genes were 31 (75.61%), 4 (9.76%), and 10 (24.39%), in that order. Four isolates (9.67%) had the SHV, TEM, and CTX-M genes, while eight (19.51%) of the isolates possessed both the SHV and CTX-M genes. This investigation demonstrates high frequency, wide range of patterns and coexistence of ESBL genes in K. pneumoniae isolates from outpatient UTI patients. Although several phenotypic tests have been used for ESBL detection, testing directly for the presence of ESBL genes is always more accurate. The majority of the isolates of Klebsiella pneumoniae ESBL producers had the SHV gene identified.

Abstracts In this article, we describe a simple procedure for evaluating the sterility of nutrient media using a shear-horizontal surface acoustic wave (SH-SAW) biosensor. We used the SH-SAW biosensor platform to monitor the time-dependent phase responses of the material for the media that binds to a monolayer of 3-mercaptopropanyl-N-hydroxy succinimide on the biosensor surface. Increases in the phase shift less than 50% of the starting phase shift recorded 180 minutes after the sterilization indicate a successful sterility test of the media. Our devices were then applied to sterility testing using a viable Escherichia coli (E. coli) K-12 solution to confirm our hypothesis. Filtered LB Broth was the negative control, and a 24-hour culture of E. coli was the positive control. Before testing, the cell solution was pelleted, the media decanted, and then replaced with a 1x phosphate-buffered saline solution (1x PBS). Since we know that the N-hydroxysuccinimide residues will bind to amine groups, only in cases where microbial species are present will the surface binding increase greater than 50%. We confirmed our hypothesis using SH-SAW biosensors and overnight E. coli cultures. The sensitivity for our 250 MHz quartz SH-SAW sensors was calculated based on the response from the E. coli K-12 solution over the linear dynamic range, which appears to be 2x10^4 – 2x10^7 CFU per ml and has a sensitivity of S = 1.655x10^5 o m2 Kg-1. The limit of detection (LOD) for these sensors was calculated to be approximately 2x10^-9 grams (g), which is estimated to be approximately 2,000 Escherichia coli (E. coli) K-12 cells. However, the sensitivity and insertion loss remained approximately unchanged before and after coating the sensor with 3,3'-Dithiobis(succinimidyl) Propionate (DSP).

Gene regulation is vital for cellular function and homeostasis, involving diverse mechanisms controlling specific gene products and contributing to tissue-specific gene expression. Dysregulation leads to diseases, emphasizing the need for understanding these mechanisms. Non-coding regions, notably enhancers and super-enhancers (SEs), act as crucial gene regulators, orchestrating transcriptional activity via transcription factors (TFs). SEs, with high activity, are pivotal in cell identity and disease progression. Current computational approaches focus on individual regulators like TFs and miRNAs, neglecting SE interactions. We emphasize incorporating SEs into models to enhance understanding. Experimental studies have recently linked miRNAs and TFs to SEs. Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) provides valuable SE information. In this review, we categorize computational methods leveraging TF and miRNA data into three areas and explore challenges integrating enhancers/SEs. These include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators, including SEs. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in identifying therapeutic targets and disease biomarkers. We believe that understanding the role of ChIP-Seq data in predicting the effect of miRNA on gene regulation is crucial to construct statistical/computational model that would tackle these challenges.

Predictions of mean values of statistical variables of large scale turbulent flow by the widely used basic k-ϵ model and a new fundamentally based model are verified against published results of Direct Numerical Simulations DNS of the Navier-Stokes equations. The verification concerns turbulent channel flow at shear Reynolds numbers of 950, 2000 and 104. The basic k-ϵ model is largely based on empirical formulations accompanied by calibration constants. This contrasts with the new model where descriptions of leading statistical quantities are based on the general principles of statistical turbulence at large Reynolds number. Predicted values of major output variables such as turbulent viscosity, diffusivity of passive admixture, temperature, and fluid velocities compare well with DNS in case of the new model. Significant differences are seen in case of the basic k-ϵ model.

This paper provides a new discovery that adds to the contemporary theory of modelling of non-stationary queues, by capturing analytically the behavior of the root parameter, for the G1/M/1 PSFFA model of non-stationary Ek/M/1 queue. Additionally, it has been also revealed that the resulting root parameter reduces to the PSFFA model of non-stationary queue. Potential applications of PSFFA to Computer engineering are provided. Some challenging open problems are addressed combined with closing remarks and future research directions.

The interaction between native and invasive plant species plays a crucial role in understanding invasion mechanisms and developing control strategies. This study investigated the differences in photosynthetic and fluorescence parameters, gene expression profiles, and rhizosphere soil microbial composition between the native species Ficus tikoua Bur. and the invasive species Alternanthera philoxeroides. For the first time, it was demonstrated that that F. tikoua Bur. may enhance photoinhibition ability and the expression of genes involved in carbon metabolism, protein processing in the endoplasmic reticulum, and plant-pathogen interactions to maintain growth. Additionally, it recruits beneficial microbes, e.g. Actinomarina (Bacteria), Nitrosotenuis (Archaea) and Laccaria (fungi), to suppress pathogens, thereby preserving soil DMSP and carbon cycling pathways to resist invasion by A. philoxeroides. These findings provide novel insights into the mechanisms underlying plant invasion and suggest potential strategies for invasive plant management. Further research is needed to investigate this relationship and determine the mechanisms underlying the potential resistance of F. tikoua Bur. to plant invasion.

The significance of Kashmir conflict as one of the world’s longest running conflicts has frequently drawn the attention of both communication and international relations’ scholars which makes it an important issue from the perspective of interdisciplinary approach. The researchers have attempted to assemble a brief history of the Indian held Jammu and Kashmir Conflict from the time of partition in 1947 to the developments which took place till 2019 in the light of the available literature. The paper provides substantial historical context of the important events like instrument of accession, UN resolutions on Kashmir conflict, Simla Accord, Gawkadal Massacre , Lahore Declaration, Kargil War, killing of Burhan Wani, Uri attack and Article 370’s abrogation .

Mangrove plant seedling cultivation is crucial for the protection, management and restoration of mangrove ecosystem. In this study, we focused on Kandelia obovata, a typical mangrove, and evaluated nursery cultivation with different combinations of three salinity levels (S1: 0 ppt, S2: 10 ppt, and S3: 20 ppt), three genealogies (EZD, JX, and YZ), and five growth media (M1: 100% loess, M2: 100% sandy, M3: 50% loess + 50% sandy, M4: 40% loess + 40% sandy + 20% peat, and M5: 40% loess + 40% sandy + 20% coir), by measuring the growth parameters such as mortality rate, seedling height, diameter, and biomass partition. These growth indexes were significantly affected by salinity and medium, and genealogies also had significant effects on mortality rate and biomass accumulation. S2 or S3 both had lower mortality and higher growth indexes than S1. M1 was the medium that increased seedlings height, diameter and biomass the most and had the lowest death rate. EZD and JX were also higher levels than YZ in these indicators, but the difference between them was not obvious. S3, M1, and EZD consistently performed well in fuzzy evaluation and quality assessment. Furthermore, combinations involving these treatments also produced highly favorable results. These results furnish both a theoretical and practical foundation for advancing nursery cultivation techniques and germplasm breeding of K. obovata in mangroves.

In a context of increasingly necessary energy transition, precise modelling of profiles for low-voltage (LV) network consumers is crucial to enhance hosting capacity. Typically, load curves for these consumers are estimated through measurement campaigns conducted by Distribution System Operators (DSO) for a representative subset of customers or through the aggregation of load curves from household appliances within a residence. With the instrumentation of smart meters becoming more common, a new approach to modelling profiles for residential customers is proposed to make the most of the measurements from these meters. The disaggregation model estimates the load profile of customers on a low-voltage network by disaggregating the load curve measured at the secondary substation level. By utilizing only the maximum power measured by Linky smart meters, along with the load curve of the secondary substation, this model can estimate the daily profile of customers. For 48 secondary substations in our dataset, the model obtained an average symmetric mean average percentage error (SMAPE) error of 4.91% in reconstructing the load curve of the secondary substation from the curves disaggregated by the model. This methodology can allow the estimation of the daily consumption behaviors of the low-voltage customers. In this way, we can safely envision solutions that enhance the grid hosting capacity.

This study investigates the application of regression neural networks, in particular the fitrnet model, in predicting the hardness of steels. The experiments involve extensive tuning of hyperparameters using Bayesian optimization and employ 5-fold and 10-fold cross-validation schemes. The trained models are rigorously evaluated, and their performances are compared using various metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results provide valuable insights into the effectiveness of the models and their ability to generalize to unseen data. In particular, Model 4208 (8-85-141-1) emerges as the top performer with an impressive RMSE of 1.0790 and an R² of 0.9900. The research paper contains an illustrative example that demonstrates the practical application of the developed model in determining the hardenability band for a specific steel grade and shows the effectiveness of the model in predicting and optimizing heat treatment results.

Rare ginsenoside compound K (CK), is an intestinal microbial metabolite with a low natural abundance that is primarily produced by physicochemical processing, side chain modification, or metabolic transformation in the gut. Moreover, CK exhibits potent biological activity compared to primary ginsenosides, which has raised concerns in the field of ginseng research and development as well as ginsenosides-related dietary supplements and natural products. Ginsenosides Rb1, Rb2, and Rc are generally used as a substrate to generate CK via several bio-conversion processes. Current research shows that CK has a wide range of pharmacological actions including boosting osteogenesis, lipid and glucose metabolism, lipid oxidation, insulin resistance, anti-inflammatory, and anti-apoptosis properties. Further research on the bioavailability and toxicology of CK can advance its medicinal application. The purpose of this review is to lay the groundwork for future clinical studies and the development of CK as a therapy for metabolic disorders. Furthermore, the toxicology and pharmacology of CK are investigated as well in this review. The findings indicate that CK primarily modulates signal-ing pathways associated with AMPK, SIRT1, PPARs, WNTs, and NF-kB. It also demonstrates a positive therapeutic effect of CK on nonalcoholic fatty liver disease, obesity, hyperlipidemia, diabetes, and its complications, as well as osteoporosis. Additionally, the analogues of CK showed more bioavailability, less toxicity, and more efficacy against disease states. Enhancing bioavailability and regulating hazardous variables are crucial for its use in clinical trials.

Background and objective K. pneumoniae appeared to be a significant problem due to its ability to accumulate antibiotic-resistance genes. After 2013, alarming colistin resistance rates among carbapenem-resistant K. pneumoniae have been reported in the Balkans. The study aims to perform an epidemiological, clinical, and genetic analysis of a local outbreak of COLr CR-Kp. Material and methods All carbapenem-resistant and colistin-resistant K. pneumoniae isolates observed among patients in the ICU unit of Military Medical Academy, Sofia from 1 January to 31 October 2023 were included. The results were analyzed according to the EUCAST criteria. All isolates were screened for blaVIM, blaIMP, blaKPC, blaNDM, and blaOXA-48. Genetic similarity was determined using the Dice coefficient as a similarity measure and the unweighted pair group method with arithmetic mean (UPGMA). mgrB genes and plasmid-mediated colistin resistance determinants (mcr-1, mcr-2, mcr-3, mcr-4, mcr-5) were investigated. Results There was a total of 379 MDR-K. pneumoniae isolates, 88% of which were carbapenem-resistant. Of them, there were 9 (2.7%) colistin-resistant isolates in six patients. A time and space cluster for five patients was found. Epidemiology typing showed that two isolates belonged to clone A (pts. 1, 5) and the rest to clone B (pts. 2-4) with 69% similarity. Clone A were coproducers of blaNDM-like and blaOXA-48-like and had mgrB-mediated colistin resistance (40%). Clone B isolates had only blaOXA-48-like and intact mgrB genes. All isolates were negative for mcr-1,-2,-3,-4,-5 genes. Conclusions The study describes a within-hospital spread of two clones of COLr CR-Kp with a 60% mortality rate. Clone A were coproducers of NDM-1 and OXA-48-like enzymes and had mgrB-mediated colistin resistance. Clone B isolates had only OXA-48-like enzymes and intact mgrB genes. No plasmid-mediated was found. The extremely high mortality rate and limited treatment options warrant strict measures to prevent outbreaks.

Flood forecasting helps anticipate floods and evacuate people, but due to the access of a large number of iot data acquisition devices, the explosive growth of multidimensional data and the increasingly demanding prediction accuracy, classical parameter models and traditional machine learning algorithms are unable to meet the high efficiency and high precision requirements of prediction tasks. In recent years, deep learning algorithms represented by convolutional neural networks, recurrent neural networks and Informer models have achieved fruitful results in time series prediction tasks. The Informer model is used to predict the flood flow of the reservoir. At the same time, the prediction results are compared with the prediction results of the traditional method and the LSTM model, and how to apply the Informer model in the field of flood prediction to improve the accuracy of flood prediction is studied. The data of 28 floods in the Wan 'an Reservoir control basin from May 2014 to June 2020 were used, with areal rainfall in five subzones and outflow from two reservoirs as inputs and flood processes with different sequence lengths as outputs. The results show that the Informer model has good accuracy and applicability in flood forecasting. In the flood forecasting with sequence length of 4, 5 and 6, Informer has higher prediction accuracy, and the prediction accuracy is better than other models under the same sequence length, but the prediction accuracy will decline to a certain extent with the increase of sequence length. The Informer model stably predicts the flood peak better, and its average flood peak difference and average maximum flood peak difference are the smallest. As the length of the sequence increases, the number of fields with a maximum flood peak difference less than 15% increases, and the maximum flood peak difference decreases. Therefore, the Informer model can be used as one of the better flood forecasting methods, and it provides a new forecasting method and scientific decision-making basis for reservoir flood control.

The corneal epithelium (CE) is spread between two domains, the outer vascularized limbus and the avascular cornea proper. Epithelial cells undergo constant migration from the limbus to the vision-critical central cornea. Coordinated with this migration the cells undergo differentiation changes where a pool of unique stem/precursor cells at the limbus yields the mature cells that reach the corneal center. Differentiation is heralded by the expression of the corneal-specific Krt12. Processing data acquired by scRNA-Seq we found that the increase of Krt12 expression occurs in four distinct steps within the limbus plus a single continuous increase in the cornea. Differential gene analysis demonstrated that these domains reflect discreet stages of CE differentiation and yielded extensive information of the genes undergoing down or up regulation in the sequential transition from less to more differentiate conditions. The approach allowed the identification of multiple gene cohorts, including a) the genes that have maximal expression in the most primitive, Krt12-negative cell cohort, which is likely to include the stem/precursor cells; b) the sets of genes that undergo continuous increase or decrease along the whole differentiation path, and c) and the genes showing maximal positive or negative correlation with the changes in Krt12.

Platelet-activating factor (PAF) is a phospholipid-derived inflammatory mediator, triggering various inflammatory conditions, including eosinophil activation and recruitment. Despite the unclear mechanism underlying the formation and maintenance of nasal polyp in chronic rhinosinusitis with nasal polyps (CRSwNP), there is strongly indication of its association with type 2 inflammation. This study aimed to quantify PAF-associated gene expression, examining enzymes involved in PAF synthesis (lysophosphatidylcholine acyltransferase 1 (LPCAT1), LPCAT2, LPCAT3, and LPCAT4), and PAF degradation (PAF acetylhydrolases 1 B2 (PAFAH1B2), PAFAH1B3, and PAF acetylhydrolases 2 (PAFAH2)), and PAF receptor (PTAFR). Transcriptomic analysis encompassed CRSwNP, including eosinophilic CRS (ECRS) (n=9), nonECRS (n=8), and a subgroup of ECRS with aspirin exacerbated respiratory disease (Asp) (n=3) based on the Japanese Epidemiological Survey of Refractory Eosinophilic Chronic Rhinosinusitis (JESREC) criteria. Gene expression profiles from nasal polyps in CRSwNP patients and the uncinate process in normal subjects (controls) (n=6) were analyzed using bulk RNA barcoding and sequencing (BRB-seq). Hierarchal analysis, utilizing the average linkage method with iDEP1.1, clarified the relationship between Differentially Expressed Genes (DEG) expressions and cluster formation. Two clusters (cluster 1 (n=11) and cluster 2 (n=9)) of CRSwNP divided by hierarchal analysis showed distinct gene expression profiles in the specific pathway of “cytokine-cytokine receptor interaction” in KEGG pathway and upregulated type 2 inflammatory gene expression in cluster 2. In CRSwNP subclassification of ECRS, nonECRS, and Ctrl, PTAFR was only upregulated in ECRS and nonECRS. In cluster analysis, cluster 1 showed significant downregulation of LPCAT2 and upregulation of PTAFR expression, while cluster 2 showed significant upregulation of LPCAT1, PAFAH1B2, and PTAFR and downregulation of PAFAH2 expression. These findings suggest that cluster 2, characterized by severe type 2 inflammatory gene expression, manifest a prominent PAF-associated pathophysiology. Understanding this high PAF-associated pathophysiology in cluster 2 could provide valuable insights into the treatment and management of CRSwNP.

Apple red flesh coloration is a result of the performance of biochemical pathway involved in the biosynthesis of anthocyanins and anthocyanidins. Based on apple genome analysis, high number of regulatory genes, mainly transcription factors such as MYB, which are components of regulatory complex MYB-bHLH-WD40, and several structural genes (PAL, 4CL, CHS, CHI, F3H, DFR, ANS, UFGT) involved in anthocyanin biosynthesis have been identified so far. In this study, we investigated previously unrecognized genes involved in the constitution of red-flesh apple phenotype. These genes could be concerned as molecular markers for early selection of new apple cultivars. Based on comparative transcriptome analysis of apples with different fruit flesh coloration, we successfully identified and characterized ten genes from plant hormone transduction pathway of: auxin (GH3); cytokinins (B-ARR); gibberellins (DELLA); abscisic acid (SnRK2 and ABF); brassinosteroids (BRI1, BZR1 and TCH4); jasmonic acid (MYC2); and salicylic acid (NPR1). In presented studies an analysis of their expression profiles was performed in immature and ripe fruits of red-fleshed cultivars. Generally, we have uncovered, the set of the novel genes, mediating regulation of the abscisic acid, salicylic acid, cytokinins and jasmonic acid signaling and preliminarily described their role in anthocyanin biosynthesis, accumulation and degradation. Their expression was not characterized in apple fruits so far. Presented study, allowed to underline the relationship of genes from hormone signal transduction pathway with UFGT gene, directly responsible for anthocyanin color transformation as well as confirmed their crucial role of plant hormone regulation and anthocyanin accumulation during apple fruit ripening.

Malaria, leishmaniasis, and African trypanosomiasis are protozoan diseases that constitute a major global health problem, especially in developing countries; however the development of drug resistance coupled with the toxicity of current treatments have hindered their management. The implication of certain enzymes (dihydrofolate reductase, DHFR) or proteins (potassium channels) in the pathogenesis of these protozoan diseases is undeniable. In this study, a series of three DHFR inhibitors (6-5 fused heterocyclic derivatives X, Y and Z) and one K+ channel blocker (E4031) were screened for their inhibitory effect on Leishmania donovani, Plasmodium falciparum, and Trypanosoma brucei. Resazurin assay was used to assess antitrypanosomal and antileishmanial activities of test compounds, whereas the antimalarial activity was appraised through SYBR Green I test. Moreover, cytotoxicity of test compounds was evaluated on Vero, Raw 264.7 and HepG-2 cells using the resazurin-based test, whilst their pharmacokinetic properties were predicted using the online tool pkCSM. As a result, compound Y revealed a selective (selectivity index range: 2.69 - >61.4; Vero, Raw 264.7 and HepG-2 cells) and broad-spectrum antiprotozoal activity against L. donovani promastigotes (IC50: 12.4 µM) and amastigotes (IC50: 4.28 µM), P. falciparum (IC50: 0.028 µM), and T. brucei brucei (IC50: 0.81 µM). In addition, compound X inhibited the growth P. falciparum (IC50: 0.0052 µM) and T. brucei brucei (IC50: 6.49 µM). In silico screening of the active antiprotozoal compounds revealed positive drug-likeness scores as none of Lipinski's rule of five’s criteria was violated by these compounds. However, in-depth pharmacokinetic and mechanistic studies are warranted to support discovery of novel antiprotozoal agents against malaria, leishmaniasis, and African trypanosomiasis by repurposing K+ channel blocker and DHFR inhibitors.

COVID-19 was an inflammatory pneumonia caused by a respiratory infection with a coronavirus named SARS-CoV-2. Severely ill COVID-19 patients could die of acute pulmonary and systemic inflammation. Thus, there was a theory that long COVID-19 shared many similarities with systemic autoimmune diseases. There was also a theory that the SARS-CoV-2 virus could infect many cell- and tissue-types distributed throughout multiple organ systems of the body. I aim to review the above theories using unbiased high-throughput datasets cataloguing gene expression. Studies reviewed used the technology of next generation sequencing, either as bulk tissue RNA sequencing, or as single-cell RNAs sequencing; ingenious methods for signal deconvolution were then used to identify individual cell types. Datasets reviewed suggested that severe COVID-19 induced expression of genes associated with pro-inflammatory signaling, tissue development or remodeling, scarring, or cell death. The datasets also provided evidence for several inflammatory syndromes associated with COVID-19, such as neuronal COVID, acute respiratory disease syndrome, vascular inflammation, or multisystem inflammatory syndrome. Indeed, inflammatory signal during severe COVID-19 was reported in many cell types, for example in structural cells, in infiltrating immune cells, in the vascular system, or in circulating cells in the blood. There was also frequently a change in proportions of immune cell-types in the blood, epithelial cells in the lungs, or among infiltrating immune cells.

Recently, Shehata et al. [37] introduced the r+1Rs(B;C; z) matrix function and established some properties. The aim of this study established to devote and derive certain basic properties including analytic properties, recurrence matrix relations, dierential properties, new integral representations, k- Beta transform, Laplace transform, fractional k-Fourier transform, fractional integral properties, the k-Riemann{Liouville and k-Weyl fractional integral and derivative operators an extended version of r+1Rs;k matrix function. We establish its relationships with other well known special matrix functions which have some particular cases in the context of three parametric Mittag-Leer matrix function, k- Konhauser and k-Laguerre matrix polynomials. Finally, some special cases of the established formulas are also discussed.

Transposable elements (TEs) are repetitive elements which make up around 45% of the human genome. A class of TEs known as SINE-VNTR-Alu (SVA) demonstrate the capacity to mobilise throughout the genome, resulting in SVA polymorphisms for presence or absence within the population. Although studies have previously highlighted the involvement of TEs within neurodegenerative diseases, such as Parkinson’s disease and amyotrophic lateral sclerosis (ALS), however the exact mechanism has yet to be identified. In this study we used whole genome sequencing and RNA sequencing data of ALS patients and healthy controls from the New York Genome Center ALS Consortium, to elucidate the influence of reference SVA elements on gene expression genome-wide within central nervous system (CNS) tissues. To investigate this, we applied matrix expression quantitative trait loci analysis and demonstrated that reference SVA insertion polymorphisms can significantly modulate the expression of numerous genes, preferentially in the trans position, and in a tissue-specific manner. We also highlight that SVAs significantly regulate mitochondrial genes as well as genes within the HLA and MAPT loci, previously associated within neurodegenerative disease. In conclusion, this study continues to bring to light the effects of polymorphic SVAs on gene regulation and further highlights the importance of TEs within disease pathology.

Slave Province in Canada is an Archean granite-supracrustal terrane at the northwestern corner of the Canadian Shield. It is bordered by the Thelon–Taltson orogen (2.0 to 1.9 Ga) to the south-east and the Wopmay orogen (1.9 to 1.8 Ga) to the west. Acasta gneisses exposed in the western-most Slave province and the Wopmay rocks close to the gneisses were collected systematically for K-Ar and laser step-heating 40Ar /39Ar analyses of the biotite and amphibole. The K-Ar biotite ag-es of the four Wopmay samples range from 1816 ± 18 Ma to 1854 ± 26 Ma, which is within the reported U-Pb zircon age range of the Wopmay orogen. The K-Ar biotite ages of the fifteen Acasta gneisses range from 1779 ± 25 Ma to 1877 ± 26 Ma, except for a younger sample (1711 ± 25 Ma). The 40Ar/39Ar biotite analyses of the three samples from the Wopmay orogen give the plateau ages of 1826 ± 21 Ma, 1886 ± 13 Ma, and 1870 ± 18 Ma. On the other hand, the 40Ar/39Ar analyses of the biotite crystals from the Acasta gneiss show a variety of ages. Three samples show the plateau ages of 1877 ± 8 Ma, 1935 ± 14 Ma, and 1951 ± 11 Ma. These plateau ages are a little older than the total gas ages. The 40Ar/39Ar analyses of the amphibole crystals show more varied age rela-tions. The two samples show consistent age relations between the plateau ages (1814 ± 22 Ma and 1964 ± 12 Ma) and the total gas ages (1789 ± 23 Ma and 1967 ± 11 Ma), respectively. Some sam-ples have the apparent old age of 2000 Ma in the middle temperature fractions. These old frac-tions are due to that the originally existed amphibole crystals formed in Archean were affected by the thermal events during the Wopmay orogeny. but did not fully reset. The results suggest that the K-Ar system ages of the biotite and amphibole of the Archean Acasta gneiss that is aged from 4200 to 2600 Ma of zircon U-Pb ages were rejuvenated in the Paleo–Proterozoic ages. The heat source that rejuvenated the K-Ar system ages may have arisen due to the asthenospheric intrusion into the wedge mantle, which took place because of the subduction rollback.

Klebsiella pneumoniae complex is one of the most commonly isolated bacteria within human infections. These microorganisms are opportunistic pathogens that pose a serious threat to public health due to possibility of transmission in the human population. Resistance to carbapenems is one of the most important antimicrobials resistance mechanisms amongst them. In this situation therapeutic options are limited. Therefore, the aim of this study was to evaluate fosfomycin, colistin, ceftazidime-avibactam and meropenem-vaborbactam in vitro activity against multidrug-resistant K. pneumoniae complex strains. The study involved 160 strains of Gram-negative rods: K. pneumoniae – 138 and K. variicola - 22. Minimal inhibitory concentration of fosfomycin was estimated by agar dilution method and for colistin by microdilution method. The susceptibility to ceftazidime-avibactam and meropenem-vaborbactam was determined by gradient strip method. All of the analysed K. pneumoniae complex isolates produced extended-spectrum beta-lactamases and 60.0% carbapenemases. The most of the analysed strains were susceptible to fosfomycin and colistin (62.5%). Among pandrug-resistant K. pneumoniae complex isolates the most were susceptible to colistin (43.9%). Fosfomycin has good activity against ESBL- and VIM-positive isolates from this complex. In turn, colistin has satisfactory in vitro activity against KPC- and VIM-positive isolates from K. pneumoniae complex. Ceftazidime-avibactam has good activity against K. pneumoniae complex strains producing ESBL, KPC and OXA enzymes. In turn, meropenem-vaborbactam has satisfactory in vitro activity against ESBL- and KPC-positive isolates from this complex.

Cardiotonic steroids (CTS) are a group of steroid compounds derived from certain plants and animals. CTS are selective inhibitors of Na,K-ATPase, so for a long time they were widely used for medical purposes to treat heart failure and some other diseases. However, over time, doctors gradually began to refuse this group of drugs due to their narrow therapeutic range and a number of serious side effects. However, in parallel with the rise and fall of interest in CTS as a drug, interest in them as a single class of high-affinity Na,K-ATPase inhibitors has only increased. Numerous data on the effect of CTS on signaling cascades and cell viability made impelling a search for a rationale for the existence of such a subtle biological regulator. A large number of studies devoted to the potential use of CTS in the treatment of neurodegenerative, oncological, immune and other diseases "revitalized" the already "written off" group of the drugs. If initially the role of exogenous (mainly plant origin) CTS was studied, then by the 90s of the XX century the study of the role of endogenous CTS and their regulatory effects became a popular trend in biology and physiology. The role of endogenous CTS was studied in details in the pathogenesis of cardiovascular diseases [1]. A large number of researchers have unanimously stated that endogenous CTS are very complex regulators of the development of pathological processes; however, the exact cause-and-effect relationship that would unambiguously explain the processes of synthesis, utilization, and the role of endogenous CTS has not yet been established. The effects of CTS in the frame of various regulatory schemes have been described, but the predominant number of studies was devoted to arterial hypertension, heart and renal failure, cancer [2–4], aging [5,6], and neuroinflammation. The effects of both endo- and exogenous CTS on blood cells and have been studied to a much lesser extent.

Very disparate types of cell populations exhibit similar transcription specificity for certain genes. We identified 20 genes that were expressed preferentially in myoblasts and cerebellum (Myob/Cbl genes) and examined their cell/tissue-specific epigenetics using a combined approach of comparing DNA methylation, chromatin accessibility, and RNA expression in many different cell populations. Some Myob/Cbl genes shared DNA hypo- or hypermethylated regions in myoblasts and cerebellum. Particularly striking was ZNF556, whose promoter is hypomethylated in expressing cells but highly methylated in the many cell populations that do not express the gene. In reporter gene assays, we demonstrated that its promoter’s activity is methylation-sensitive. The atypical epigenetics of ZNF556 may have originated from its promoter’s hypomethylation and selective activation in sperm progenitors and oocytes. Five of the Myob/Cbl genes (KCNJ12, ST8SIA5, ZIC1, VAX2, and EN2) have much higher RNA levels in cerebellum than in myoblasts and displayed myoblast-specific hypermethylation upstream and/or downstream of their promoters that may downmodulate expression. Differential DNA methylation was associated with alternative promoter usage for Myob/Cbl genes MCF2L, DOK7, CNPY1, and ANK1. Myob/Cbl genes PAX3, LBX1, ZNF556, ZIC1, EN2, and VAX2 encode sequence-specific transcription factors, which likely help drive the myoblast and cerebellum specificity of other Myob/Cbl genes. Our study extends our understanding of epigenetic/transcription associations related to differentiation and may help elucidate relationships between epigenetic signatures and muscular dystrophies or cerebellar-linked neuropathologies.

Trichothecene mycotoxin T-2 toxin is an important environmental pollutant and poses a global threat to human and animal health. T-2 toxin could induce nephrotoxicity, however, the precise molecular mechanism remains unclear. In this study, the mice were intraperitoneally administrated with a single dose of 2 mg/kg T-2 toxin. The kidney function and ultrastructural observation were also assessed and found T-2 toxin exposure could cause kidney damage. Transmission electron microscopy showed a significant swelling and vacuolization of the mitochondria of renal cell. A total of 1122, 58, and 391 differential expression genes (DEGs) that were predominantly expressed in kidney tissues at 1 d, 3 d, and 7 d after T-2 toxin exposure, respectively. Early transcriptomic changes at 1 d after exposure were down-regulation of DEGs involved in the cell cycle, p53 signaling pathway, and cellular senescence, while up-regulation of DEGs referred to the ribosome pathway. The temporal variations in gene expression pattern in T-2 toxin exposure in mice kidney were presented and cellular metabolism was disturbed at the exposure recovery period with 7 d. In conclusion, this study, for the first time, provided a comprehensive comparative transcriptomic analysis of gene regulation in T-2 toxin exposure-induced nephrotoxicity at different temporal periods and explored the nephrotoxicity mechanism of T-2 toxin at the mRNA level.

For years, effort has been devoted to establishing an effective bull breeding soundness evaluation procedure; usual research on the subject is based on BBSE (Bull Breeding Soundness Examination) methodologies, which have significant limitations in terms of their evaluation procedure, such as high cost, time-consuming and administratively difficult. This research focuses on the creation of a prediction model to supplement and/or improve the BBSE approach, through the study of two algorithms, Clustering and Artificial Neural Network (ANN), to find the optimum Machine Learning (ML) approach for our application, with an emphasis on data categorization accuracy. The tool is designed to assist veterinary medicine and farmers in identifying key factors and increasing certainty in their decision-making during the selection of bulls for breeding purposes, providing data from a limited number of factors generated from a deep pairing study of bulls. Zebu Bulls, European Bulls, and Crossbred Bulls were the general groupings. The data utilized in the model's creation (N =359) considers five variables that influence improvement decisions. This approach enhances decision-making by 12%. ANN obtained an accuracy of 90%; Precision was 97% for satisfying, 92% for unsatisfying, and 85% for Bad.

This study explored the effects of pBD114 (porcine β-defensin 114) on inflammatory response. The results in RAW264.7 cells showed that 100 μg/mL of pBD114 significantly increased the concentrations of TNFα and IL-10, and 10 μg/mL of pBD114 significantly decreased the concen-tration of TNFα in LPS-induced RAW264.7 cell cultures at 10 ng/mL. These results suggest that pBD114 can exhibit pro-inflammatory or anti-inflammatory activities under certain conditions. RNA-seq analysis was performed to gain further insight into the effects of pBD114 on the in-flammatory response. Among pBD114 promoting RAW264.7 pro-inflammatory response, pBD114 significantly up-regulated 1170 genes and down-regulated 724 genes. KEGG enrichment found that the DEGs were significantly enriched in immune- and signal-transduction-related signaling pathways. PPI and KDA analyses revealed that Bcl10 and Bcl3 were the key genes. In addition, pBD114 significantly up-regulated 12 genes and down-regulated 38 genes in the an-ti-inflammatory response. KEGG enrichment analysis revealed that the DEGs were mainly en-riched in the "Cytokine-cytokine receptor interaction" signaling pathway, and PPI and KDA analyses showed that Stat1 and Csf2 were the key genes. The results of qRT-PCR verified those of RNA-seq. In vivo mice tests also confirmed the pro- or anti-inflammatory activity of pBD114. Although the inflammatory response is a rapid and complex physiological reaction to noxious stimuli, this study found that pBD114 plays an essential role in the inflammatory response mainly by acting on genes related to immunity, signal transduction, signaling molecules, and interactions.

One of the ways to regulate the sensitivity of human cells to the influenza virus is to knock out genes of the innate immune response. Promising targets for the knockout are genes of the interferon-inducible transmembrane protein (IFITM) family, in particular the IFITM3 gene, whose product limits the entry of a virus into the cell by blocking the fusion of the viral and endosomal membranes. In this study, by means of genome-editing system CRISPR/Cas9, monoclonal cell lines with an IFITM3 knockout were obtained based on WI-38 VA13 cells (human origin). It was found that such cell lines are more sensitive to infection by influenza A viruses of various subtypes. Nonetheless, this feature is not accompanied by an increased titer of newly formed viral particles in a culture medium.

Historically, macrophages have been long implicated in the control of the severity of infectious diseases. This has been based on the observations of a higher risk of severe disease and death upon rechallenge with viral variants due to antibody dependent enhance-ment (ADE) of infection into macrophages. The question remains as to what can account for this potent heterologous protection in macrophages? Here it is argued that the elusive defense mechanism of M1-like pro-inflammatory macrophages may pertain to a novel virus anti-virus response. This system initiates with high replication of human endogenous retrovirus K102 (HERV-K102), a non-pathogenic, protector foamy retrovirus of humans which generates M1-like pro-inflammatory foamy macrophages, glycolysis, and epigenetic changes, all characteristic of trained immunity. This virus-anti-virus system kills virally infected cells by several mechanisms, amplifies the innate interferon response via ‘viral mimicry’, has many unique components that interfere with exoge-nous virus replication, and may be especially adept at neutralizing enveloped exogenous pandemic viruses, such as SARS-CoV-2 and HIV-1. The goal of this treatise is to introduce the multifaceted HERV-K102 protector system, to illustrate how SARS-CoV-2 may target the HERV-K102 protector system by ADE, and to explore how this innate defense system may be exploited for pandemic preparedness.

It is time-consuming for acquiring complete data by fully phase encoding in two orthogonal directions along with one frequency encoding direction. Undersampling in the 3D k-space is promising in accelerating such 3D MRI process. Though 3D undersampling can be conducted according to predefined probability density, the density based method is not optimal. Because of the large amount of 3D data and computational cost, it is challenging to perform data-driven and learning-based 3D undersampling and subsequent 3D reconstruction. To tackle this challenge, this paper proposes a deep neural network called EEUR-Net, realized by optimizing specific undersampling patterns for the fully-sampled 3D k-space data. Innovatively, our undersampling algorithm employs an end-to-end deep learning approach to optimize phase encoding patterns and uses a 3D U-Net for image reconstruction of undersampled data. Through end-to-end training, we obtain an optimized 3D undersampling pattern, which significantly enhances the quality of the reconstructed image under the same acceleration factor. A series of experiments on a knee MRI dataset demonstrated that, in comparison to standard random uniform, radial, Poisson and equispaced Cartesian undersampling schemes, our end-to-end learned undersampling pattern considerably improves the reconstruction quality of undersampled MRI images.

: Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America, and to enhance the utility of drought monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impacts information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and the colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and the warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and indicators are changing over time as the climate warms, and by recommending ways to strengthen the use of indices and indicators in drought decision-making.

High concentrations of sodium (Na+), chloride (Cl-), calcium (Ca2+), and sulphate (SO42-) are fre-quently found in saline soils. Crop plants cannot successfully develop and produce because salt stress impairs the uptake of calcium, potassium, and water into plant cells. Different intracellular and extracellular ionic concentration changes by salinity, including those of calcium, potassium, and protons. These cations serve as stress signaling molecules in addition to being essential for ionic homeostasis and nutrition. Maintaining an appropriate K+: Na+ ratio is one crucial plant mechanism for salt tolerance, which is a complicated trait. Another important mechanism is the ability for fast extrusion of Na+ from the cytosol. Calcium is established as a ubiquitous secondary messenger, which transmits various stress signals into metabolic alterations that cause adaptive responses. When plants are under stress, the cytosolic-free Ca2+ concentration can rise to 10 times or more from its resting level of 50-100 nanomolar. Reactive oxygen species (ROS) are linked to the calcium alterations, and are produced by stress. Depending on the type, frequency and intensity of the stress, the cytosolic calcium signals oscillate, are transient, or persist for a longer period and exhibit specific "signatures". Both the input and efflux of Ca2+ from the cytosol affect the length and amplitude of the signal. According to several reports, under stress calcium alterations can occur not only in the cytoplasm of the cell but also in the cell walls, nucleus and other cell organelles and the calcium waves propagate through the whole plant. Here, we will focus on how wheat and other important crops absorb Na+, K+ and Cl- when plants are under salt stress, as well as how calcium, potassium, and pH cause intracellular signaling and homeostasis. Similar mechanisms in the model plant Arabidopsis will also be considered.

The wind flow patterns and pressure distribution around a plus-shaped building are significantly influenced by the dimensions of its re-entrant corners and the overall building height. The present study aims to find the wind effects and study of the pressure distribution with different flow patterns. Plan area of the building is around 300 square meter while the height of the building is 50 meters by keeping the equal length of the re-entrant corners. The research is performed using ANSYS CFX academic version and the results are depicted in various forms such as pressure coefficient (Cpe), stream lines in both vertical as well as in specific surface and pressure contours. The analysis is carried out by varying the wind incident angles, from 0° to 90° at the interval of 15°. The building dimension are scale down to perform the test on length scale of 1:100. Since the considered building model exhibits symmetry along both the axes, present study mostly emphasis on examining re-entrant corners located in the building. This method yields valuable information about how air circulates and how pressure is distributed across a broad spectrum of wind angles. This study provides valuable insights into flow circulation and pressure distribution for a wide range of wind angles.

As distinct human societies, three unique intentional communities are investigated regarding their preferred consensus decision-making practices. It is Identified that each has adopted a different form of consensus decision-making to solve potential group-wide interpersonal conflict. The individual attributes of these three consensus decision-making practices are considered, both from the perspective of maintaining group stability and in relation to individual member's mental health. The communities are a Canadian self-directed public senior elementary and secondary school, an annual English conference for those self-identifying as on the autistic spectrum, and a self-producing Korean popular music (K-pop) group. It is found that the intentional community and participants’ mental health are sustained regarding each of the three consensus decision-making practices. Nevertheless, the resulting decisions generate various stresses within the communities, both as a whole and concerning the individual members. To retain group cohesion and maintain individual mental health, these stresses must be recognized and understood by participants. The novel finding of this research is that, dependent on the time available for decision-making, and the members’ perspective adopted, intentional communities might practice more than one form of consensus decision-making and still support both group cohesion and individual mental health, maintaining the democracy of these distinct societies.

This article investigates on the radial and non-radial geodesic structures of the generalized K-essence Vaidya spacetime. Within the framework of K-essence geometry, it is important to note that the metric does not possess conformal equivalence to the conventional gravitational metric. This study employs a non-canonical action of the Dirac-Born-Infeld kind. In this work, we categorize the generalized K-essence Vaidya mass function into two distinct forms. Both the forms of the mass functions have been extensively utilized to analyze the radial and non-radial time-like or null geodesics in great details inside the comoving plane. Indications of the existence of wormhole can be noted during the extreme phases of spacetime, particularly in relation to black holes and white holes, which resemble the Einstein-Rosen bridge. In addition, we have also detected the distinctive indication of the quantum tunneling phenomenon around the central singularity.

Historic sites are important components of every city's cultural history because they preserve rich historical knowledge and distinctive values passed down from previous generations to the present. Due to the progress of urbanization and modernization, many historic sites face pressure from damage and transformation. In this paper, a method for assessing cultural heritage damage was developed to measure the extent of spatial damage in historic sites. Using sample data obtained in Xiyang, Qixian, and Xiaoyi, all historic cities in Shanxi Province, Mainland China, and combined weights were estimated using the Delphi technique and the CRITIC weight method. Following this, the Spatial Damage Degree Model (SDDM) based on K-means cluster analysis and K-nearest neighbor (KNN) classification was developed. The findings show that the model efficiently solves the problem of assessing spatial damage levels in historic sites. Through multiple linear regression analysis, it was shown that the damage to historic sites was predominantly caused by three factors: natural erosion, construction damage, and planning and policy. SDDM was used to calculate the spatial damage levels of historic sites, allowing conservators to fully comprehend the features and concerns related to historic sites. As a result, more scientific and rational preservation approaches may be developed, improving the efficiency of historic site restoration and conservation and encouraging the sustainable development of urban and rural heritage.

The Covid-19 crisis has been one of the most relevant economic shocks in the history of the modern economy, mainly due to the restrictions on travel and social interaction taken by the authorities (quarantines, closure of schools, artistic activities, bars, and restaurants, capacity in stores), to reduce the transmission of the virus (Sars-Cov2), having consequences on the national productions of all countries (about 7% for the loss of GDP worldwide). In our study, we analyze, through clustering methods, the effects of the level of contagion, economic projections, and economic mitigation measures taken by governments for managing the pandemic. According to the analyses, it can be concluded that a low infection rate implies a lower loss of product and economic savings when applying economic mitigation measures, taking relevance in the management of sanitary measures to reduce the transmission of the virus (and the temporality of these).

This article is a study on the (k,s)-Riemann-Liouville fractional integral, a generalization of the Riemann-Liouville fractional integral. Firstly, we introduce several properties of the extended integral of continuous functions. Furthermore, we make the estimation of the Box dimension of the graph of continuous functions after the extended integral. It presents that the upper Box dimension of the (k,s)-Riemann-Liouville fractional integral for any continuous functions is no more than the upper Box dimension of the functions on the unit interval I=[0,1], which indicates that the upper Box dimension of the integrand f(x) will not be increased by the σ-order (k,s)-Riemann-Liouville fractional integral ksD−σf(x) where σ>0 on I. Additionally, we prove that the fractal dimension of ksD−σf(x) of one-dimensional continuous functions f(x) is still one.

The Jeryak, a hybrid offspring of Gannan yak and Jersey cattle, exhibits evident hybrid advantages over the Gannan yak in terms of production performance and other various aspects. The small non-coding RNAs known as miRNAs exert a significant regulatory influence on gene expression post-transcriptionally. However, the regulatory mechanism of miRNA associated with muscle development in Jeryak remains elusive. To elucidate the regulatory role of miRNAs in orches-trating the skeletal muscle development in Jeryak, we selected dorsal longissimus muscle tissues from Gannan yak and Jeryak for transcriptome sequencing analysis. A total of 230 (DE)miRNAs and 1819 differentially expressed mRNAs (DEMs) were identified in the longest dorsal muscle of Gannan yak and Jeryak. The functional enrichment analysis revealed a significantly enrichment of target genes from differentially expressed (DE)miRNAs and DEMs in signaling pathways associ-ated with muscle growth, such as the Ras signaling pathway and the MAPK signaling pathway. The network of interactions between miRNA and mRNA suggested that some differentially ex-pressed miRNAs, including miR-2478-z, miR-339-x, novel-m0036-3p and novel-m0037-3p, played a pivotal role in facilitating muscle development. These findings help us to deepen our under-standing of the hybrid dominance of Jeryaks and provide a theoretical basis for further research on the regulatory mechanisms of miRNAs associated with Jeryak muscle growth and development.

Background: We investigated the tumor immune response in gastric cancer patients receiving third line nivolumab monotherapy to identify immune-related biomarkers for better patient se-lection. Methods: Nineteen patients (10 males, median age 67 years) who received nivolumab as a third or later line therapy were enrolled. We analyzed the tumor immune response in durable clinical benefit (DCB) and non-DCB patients. Pre-treatment and early-on-treatment tumor transcriptomes were examined, and gene expression profiles, immunograms, and T cell receptor (TCR) repertoire were analyzed. Results: DCB was observed in 15.8% of patients, with comparable secondary endpoints (ORR, OS, PFS) to previous trials. Individual immunograms showed no significant changes before and ear-ly-on-treatment, nor consistent alterations among DCB cases. The intratumoral immune response was suppressed by previous treatments in most third or later line nivolumab recipients. TCR repertoire analysis revealed newly emerged clonotypes in on-treatment tumors, but clonal re-placement did not impact efficacy. High T cells/Tregs ratios and a low UV radiation response gene signature were linked to DCB and treatment response. Conclusions: This study emphasizes the tumor immune response's importance in nivolumab ef-ficacy for gastric cancer. High T cells/Tregs ratios and specific gene expression signatures show promise as potential biomarkers for treatment response. Larger cohort validation is crucial to optimize immune checkpoint inhibitors in gastric cancer treatment.

The A. japonicus industry has expanded significantly, but no research has focused on how to de-termine the age of A. japonicus during farming. Correctly estimating the age of A. japonicus can provide a decision-making basis for the breeding process, and data for the protection of A. japonicus aquatic germplasm resources. DNA methylation levels in the body wall of Apostichopus japonicus at 4 months, 1 year, 2 years, and 3 years old were determined by MethylRAD-Seq, and differentially methylated genes related to age were screened. The results of the study found that 441 and 966 differentially methylated genes were detected at CCGG and CCWGG sites, respectively. As-partate aminotransferase, succinate semialdehyde dehydrogenase, isocitrate dehydrogenase, the histone H2AX, heat shock protein Hsp90, aminopeptidase N, cell division cycle CDC6, Ras GTPase activating protein (RasGAP), slit guidance ligand slit 1, integrin linked kinase ILK, mechanistic target of rapamycin kinase Mtor, protein kinase A Pka, and autophagy-related 3 atg3 these genes may play key roles in the growth and aging process of A. japonicus. This study provided data for identifying the age of A. japonicus.

The exogenous application of phenolic compounds is increasingly recognized as a valuable strategy for promoting growth and mitigating the adverse effects of abiotic stress. However, the biostimulant effect under optimal conditions have not been thoroughly explored. In this study, we investigated the impact of foliar application of flavonoids, specifically Crop Bio Life (CBL), on tomato plants grown under controlled conditions. Our study focused on determining growth parameters, such as cell size, and assessing the concentration of hormones. Principal component analysis (PCA) from all physiological variables were determined. Additionally, we utilized high-throughput mRNA sequencing technology and bioinformatic methodologies to robustly analyze the transcriptomes of tomato leaves regulated by flavonoids. The findings revealed that CBL primarily influenced cells enlargement, leading to increased growth. Furthermore, CBL-treated plants exhibited higher concentrations of the hormone zeatin but lower concentrations of IAA. Moreover, RNAseq analysis indicated that CBL-treated plants required increased mineral transport and water uptake, as evidenced by gene expression patterns. Genes related to pathways such as fatty acid degradation, phenylpropanoid biosynthesis, and ABC transporters showed regulatory mechanisms governing internal flavonoid biosynthesis, transport, and tissue concentration, ultimately resulting in higher flavonoid concentrations in tomato leaves.

The current study aims to produce a material that has a dual effect of healing and anti-inflammatory. For this target k-carrageenan/polyacrylamide hydrogel film loaded with cetrimide (k-CAR/PAAm/CI) was performed by manual casting technique. Definite concentrations of k-CAR and AAm was heated at 80°C for 2 hours, CI and glycerol were added. The solution was cast without using initiator and cross-linker. The reaction of the sulfonic acid group -SO3H of k-CAR with -CONH2 group of PAAm lead to the formation of a sulfonamide (-SO2NH-) linkage. The characteristics of the produced films were investigated by FTIR, TGA, contact angle, and mechanical properties. Improvement in the thermal stability was performed in k-CAR/PAAm/CI2 film containing 1.5% CI compared to the film has 0.5% CI (k-CAR/PAAm/CI1). The contact angle measurement proved the films are hydrophobic that enhanced by increasing CI content. The tensile strength and elongation percent values are considered adequate for materials used in wound care. k-CAR/PAAm/CI2 (1.5% CI) film showed superior antimicrobial activity against P. aeruginosa, moderate activity against S. aureus, and low activity against E. coli. The film k-CAR/PAAm/CI2 was effectively inhibiting the heat-induced hemolysis and may offer a promising approach for the development of effective wound dressings.

Keywords: Requirements prioritization, Next release plan, Software product planning, Decision support, MoSCoW, AHP, K-Menas, GMM, BIRCH, PAM, Hierarchical, Clustering, Clusters Evaluation

The large quantity of information retrieved from communities, public data repositories, web pages, or data mining can be sparsed and poorly classified. This work shows how to employ unsupervised classification algorithms such as K-means proper to classify user reviews into their closest category, forming a balanced data set. Moreover, we found that the text vectorization technique significantly impacts the clustering formation, comparing TF-IDF and Word2Vec. The value for mapping a cluster with movie genre was 81.34% ± 20.48 of the cases when the TF-IDF was applied, whereas Word2Vec only yielded a 53.51% ± 24.1. In addition, we highlight the impact of the removal of stop-words. Thus, we detected that pre-compiled lists are not the best method to remove stop-words before clustering because there is much ambiguity, centroids are poorly separated, and only 57% of clusters could match a movie genre. Thus, our proposed approach achieved a 94% of accuracy. After analyzing the classifiers’ results, we appreciated a similar effect when divided by the stop-words method removal. Statistically significant changes were observed, especially in precision metric and Jaccard scores in both classifiers, using custom-generated stop lists rather than pre-compiled ones. Reclassifying sparse data is strongly recommended as using custom-generated stop lists.

To verify the concept of the metasurfaced reverberation chamber (MRC) which was proposed. This paper reports on measurements in the reverberation chamber (RC) performance by using a 1-bit random coding metasurfaced stirrer. The measurement results are validated and compared with that from two mechanical stirrers (horizontal stirrer and vertical stirrer). Figures of merit such as quality factor (Q factor), number of samples, standard deviation, angle autocorrelation, average K-factor, total scattering cross section (TSCS) and the enhanced back scattering coefficient (eb) are presented. Results indicate the feasibility of the MRC technique in the operation frequency of the RC. And it is possible to enlarge the test volume of the RC by using the 1-bit random coding metasurface stirrer.

The internet of things and growing demand for smaller and advanced devices has created the problem of high heat production in the electronic equipment which greatly reduces the work performance and life of the electronic instruments. Thermal interface materials (TIMs) are placed in between heat generating micro-chip and the heat dissipater to conduct all the produced heat to the heat sink. Development of suitable TIMs with excellent thermal conductivity (TC) in both in-plane and through-plane directions is a very important need at present. For efficient thermal management, polymer composites are potential candidates. But in general their thermal conductivity is low compared to that of metals. The filler integration into the polymer matrix is one of the two approaches used to increase the thermal conductivity of polymer composites and is also easy to scale up for industrial production. Another way to achieve this is to change the structure of polymer chains, which fall out of the scope of this work. In this review, considering the first approach, the authors have summarized recent development in many types of fillers with different scenarios by providing multiple cases with successful strategies to improve through-plane thermal conductivity (TPTC) (K⊥). For better understanding of TC the comprehensive background is also presented. In the end, it is given a detailed conclusion which provides drawbacks of some filler, multiple significant routes recommended by other researchers to build Thermally Conductive Polymer Composites, future aspects along with direction so that the researchers can get a guideline to design an effective polymer based Thermal interface materials. A number of methods to improve the effective (out-plane) thermal conductivity of polymer composites are also discussed.

Finding the ideal statistical method for grouping phenological data is always an important step for breeders to draw correct conclusions from it possibly. In this paper, K-cluster analysis is presented as a perfect tool for grouping phenological data. The present research was performed based on the phenological data of 61 strawberries (Fragaria × ananassa) cultivars of different geo-graphical origins grown in Estonian conditions. Groups of strawberry cultivars were deter-mined according to flowering and ripening time: early, middle and late, based on the sum of ef-fective temperatures above +5°C. The result of the K-cluster analysis carried out in this way makes it possible to precisely plan the ripening time of berries of different strawberry cultivars. Using such analysis data, it is possible to combine with different early, mid or late strawberry cultivars to extend the picking period. Also, this technique can be used to study the effect of cli-matic changes occurring over the years on the phenology of strawberry cultivars grown in the region.

HIV-1 infection can activate the expression of human endogenous retroviruses (HERVs), particularly HERV-K (HML-2). HIV controllers (HICs) are rare people living with HIV (PLWH) who naturally control HIV-1 replication. The ability of HICs to control the expression of endogenous retroviruses has not been previously addressed. In this study, we measured ERVK-6 RNA expression in PBMCs of HICs (n = 23), antiretroviral (ART)-suppressed subjects (n = 8), and HIV-1-negative (NEG) individuals (n = 10) and correlated the transcript expression of ERVK-6 with multiple HIV-1 restriction factors. Our study reveals that ERVK-6 RNA expression in PBMCs from HICs was significantly downregulated compared with both ART and NEG control groups. Moreover, we detected that ERVK-6 RNA levels in PBMCs across all groups were negatively correlated with the expression levels of p21 and MCPIP1. In our previous study, these two cellular restriction factors were upregulated in HICs compared with control groups. Interestingly, p21 and MCPIP1 limit the activation of macrophages and T cells by downregulating the activity of NF-kB, a transcription factor that stimulates the LTR-driven transcription of HIV-1 and HERV-K proviruses. These findings support that HICs activate innate antiviral mechanisms that may simultaneously downregulate the transcription of both HIV-1 and endogenous retroviruses.

The role of desmoglein-3 (DSG3) in oncogenesis is unclear. This study aimed to uncover molecular mechanisms through comparative transcriptome analysis in oral cancer cells, defining potential key genes and associated biological processes related to DSG3 expression. Four mRNA libraries of oral squamous carcinoma H413 cell lines were sequenced and 599 candidate genes exhibited differential expression between DSG3-overexpressing and matched control lines, with 12 genes highly significantly differentially expressed, including 9 upregulated and 3 downregulated. Genes with known implications in cancer, such as MMP-13, KRT84, OLFM4, GJA1, AMOT, and ADAMTS1, were strongly linked to DSG3 overexpression. Gene ontology analysis indicated that the DSG3-associated candidate gene products participated in crucial cellular processes such as junction assembly, focal adhesion, extracellular matrix formation, intermediate filament organization, and keratinocyte differentiation. Validation of RNA-Seq was performed through qRT-PCR, Western blotting, and immunofluorescence analyses. Furthermore, transmission electron microscopy meticulously examined desmosome morphology, revealing slightly immature desmosome structure in DSG3-overexpressing cells compared to controls. No changes in desmosome frequency and diameter were observed between the two conditions. This study underscores intricate and multifaceted alterations associated with DSG3 in oral squamous carcinoma cells, implying a potential oncogenic role of this gene in biological processes that enable cell communication, motility and survival.

The shoot apical meristem (SAM) gives rise to the aerial structure of plants by producing lateral organs and secondary meristems. The SAM is responsible for plant developmental patterns, thus determining plant morphology and, consequently, many agronomic traits such as the number and size of fruits and flowers and kernel yield. Our current understanding of SAM morphology and regulation is based on studies conducted mainly on some angiosperms, including economically important crops such as maize (Zea mays) and rice (Oryza sativa), and the model species Arabidopsis (Arabidopsis thaliana). However, studies in other plant species from the gymnosperm class are scant, making difficult comparative analyses that help us understand SAM regulation in diverse plant species. This limitation prevents deciphering the mechanisms by which evolution gave rise to the multiple plant structures within the plant kingdom, and determined the conserved mechanisms involved in SAM maintenance and operation. This review aims to integrate and analyze the current knowledge of SAM evolution by combining the morphological and molecular information recently reported from the plant kingdom.

In the particular configuration of the scalar field K-essence in the Wheeler-DeWitt quantum equation, for some age in the Bianchi type I anisotropic cosmological model, a fractional differential equation for the scalar field arises naturally. The order of the fractional differential equation is $\beta=\frac{2\alpha}{2\alpha - 1}$. This fractional equation belongs to different intervals, depending on the value of the barotropic parameter; when $\omega_{X} \in [0,1]$, the order belongs to the interval $1\leq \beta \leq 2$, and when $\omega_{X}\in[-1,0)$, the order belongs to the interval $0<\beta \leq 1$. In the quantum scheme, we introduce the factor ordering problem in the variables $(\Omega,\phi)$ and its corresponding momenta $(\Pi_\Omega, \Pi_\phi)$, obtaining a linear fractional differential equation with variable coefficients in the scalar field equation, then the solution is found using a fractional power series expansion. The corresponding quantum solutions are also given. We found the classical solution in the usual gauge N obtained in the Hamiltonian formalism and without a gauge. In the last case, the general solution is presented in a transformed time $T(\tau)$, however in the dust era we found a closed solution in the gauge time $\tau$.

Nearest-neighbour clustering is a simple yet powerful machine learning algorithm that finds natural application in the decoding of signals in classical optical-fibre communication systems. Quantum k-means clustering promises a speed-up over the classical k-means algorithm; however, it has been shown to currently not provide this speed-up for decoding optical-fibre signals due to the embedding of classical data, which introduces inaccuracies and slowdowns. Although still not achieving an exponential speed-up for NISQ implementations, this work proposes the generalised inverse stereographic projection as an improved embedding into the Bloch sphere for quantum distance estimation in k-nearest-neighbour clustering, which allows us to get closer to the classical performance. We also use the generalised inverse stereographic projection to develop an analogous classical clustering algorithm and benchmark its accuracy, runtime and convergence for decoding real-world experimental optical-fibre communication data. This proposed `quantum-inspired' algorithm provides an improvement in both the accuracy and convergence rate with respect to the k-means algorithm. Hence, this work presents two main contributions. Firstly, we propose the general inverse stereographic projection into the Bloch sphere as a better embedding for quantum machine learning algorithms; here, we use the problem of clustering quadrature amplitude modulated optical-fibre signals as an example. Secondly, as a purely classical contribution inspired by the first contribution, we propose and benchmark the use of the general inverse stereographic projection and spherical centroid for clustering optical-fibre signals, showing that optimizing the radius yields a consistent improvement in accuracy and convergence rate.

Cervical cancer presents a significant global health concern with high-risk human papilloma-viruses (HPVs) being identified as the cause of this cancer. Although current treatment methods for cervical cancer can eliminate lesions, preventing metastatic spread and minimizing tissue damage remains a major challenge. Therefore, the development of a safer and innovative thera-peutic approach is of utmost importance. Natural products like Fig latex, derived from the Ficus Carica tree, have demonstrated promising anti-cancer properties when tested on cervical cancer cell lines. However, the specific mechanisms by which Fig latex exerts its effects are still un-known. In this study, we conducted RNA-seq analysis to explore how Fig latex may counteract carcinogenesis in HPV-positive cervical cancer cell lines, namely CaSki (HPV type 16-positive) and HeLa (HPV type 18-positive). Our data from this investigation indicates that Fig latex influ-ences the expression of genes associated with the development and progression of cervical can-cer, including pathways related to " Nonsense-Mediated Decay (NMD) "cell cycle regulation" and Transcriptional Regulation by TP53. This selective impact of Fig latex on cancer-related pathways suggests a potential novel therapeutic approach for HPV-related cervical cancer.

Keywords: Electrical power systems, Support vector machines, Random Forest, Machine learning, Wavelet transform, Transmission lines fault, Electrical power quality, Short circuit, Classification of faults, Localization of faults, Decision trees, Ensemble learning, K-nearest neighbors.

Spatiotemporal climate variability is a leading environmental constraint to the rain-fed agricultural productivity and food security of communities in the Abbay basin and elsewhere in Ethiopia. The previous one-size-fits-all approach to soil and water management technology targeting did not effectively address climate-induced risks to rain-fed agriculture. This study, therefore, delineates homogenous climatic regions and identifies climate-induced risks to rain-fed agriculture that are important to guide decisions and selection of site-specific technologies for green water management in the Abbay basin. The k-means spatial clustering method was employed to identify homogenous climatic regions in the study area, while the Elbow method was used to determine an optimum number of the climate clusters. The k-mean clustering used the Enhancing National Climate Services (ENACTS) daily rainfall, minimum and maximum temperatures, and other derived climate variables that include daily rainfall amount, length of growing period (LGP), rainfall onset and cessation dates, and rainfall intensity, temperature, potential evapotranspiration (PET), soil moisture and AsterDEM to define climate regions. Accordingly, 12 climate clusters or regions were identified and mapped for the basin. Clustering a given geographic region into homogenous climate classes is useful to accurately identify and target locally relevant green water management technologies to effectively address local-scale climate-induced risks. The study has also provides a methodological framework that can be used in the other river basins of Ethiopia and indeed elsewhere.

Legume-rhizobial symbiosis leads to to the formation of root nodules, where rhizobia reside and develop into bacteroids to reduce nitrogen into ammonium for plant growth, which leaves an opening question as how plant immunity is attenuated in nodules in the presence of large number of foreign bacteria. In Medicago truncatula, mutation in NAD1 (Nodules with Activated Defense 1) only produces necrotic nodules with overactivated immunity, indicating NAD1 is an indispensable component required for suppressing immunity in nodules. In this study, a dual RNA-seq transcriptomic technology was performed to extensively analyze gene expressions in nad1-1 mutant nodules. We indenifited 89 DEGs in symbiotic nitrogen fixation and 89 DEGs in immunity in Medicago truncatula at 10 dpi. Simultaneously, we indenifited 27 DEGs in fix and nif genes in Sinorhizobium meliloti. Then we identified 56 DEGs related to stress, including ROS, NO, and NCR, most of which were upregulated in Sinorhizobium meliloti. Our analyses of nitrogen fixation-defective plant nad1-1 mutants with overactivated defense indicate that host use plant immunity to control massive bacterial colonization during early and late symbiotic stages. Our findings provide insight into the function of NAD1 in improvement or inhibition of plant immune response to maintain symbiosis during rhizobial endosymbiosis.

Intramuscular fat content (IFC) and meat color are vital indicators affecting pork quality. A significant positive correlation between IFC and redness meat color (a* value) indicates that these two traits are likely to be regulated by shared molecular pathways. To identify candidate genes, hub genes, and signaling pathways that regulate these two traits, we measured the IFC and a* value in 147 hybrid pigs and selected individuls with extreme phenotypes for transcriptome analysis. The results revealed 485 and 394 overlapping differentially expressed genes (DEGs) using DESeq2, limma, and edgeR packages affecting IFC and a* value, respectively. Weighted gene co-expression network analysis (WGCNA) identified four modules significantly correlated with IFC and a* value. Moreover, we integrated functional enrichment analysis results based on DEGs, GSEA, and WGCNA conditions to identify candidate genes, and identified 47 and 53 candidate genes affecting IFC and a* value, respectively. The protein-protein interaction (PPI) network analysis of candidate genes showed that 6 and 14 hub genes affecting IFC and a* value, respectively. These genes mainly participated in various pathways related to lipid metabolism and redox reactions. Notably, four crucial hub genes (MYC, SOX9, CEBPB, and PPAGRC1A) were shared for these two traits. After functional annotation of these four hub genes, we hypothesized that the SOX9/CEBPB/PPARGC1A axis could co-regulate lipid metabolism and myoglobin redox response. Further research on these hub genes, especially the SOX9/CEBPB/PPARGC1A axis, will help to understand the molecular mechanism of the co-regulation of IFC and a* value, which will provide the theoretical basis for improving pork quality.

Alternative splicing can produce transcripts that affect cancer development and shows potential for cancer diagnosis and treatment. However, intron retention (IR), a type of alternative splicing, has been less systematically studied in cancer biology research. Here, we generated a pan-cancer IR landscape for more than 10,000 samples across 33 cancer types from The Cancer Genome Atlas (TCGA). We characterized differentially retained introns between tumor and normal samples and identified retained introns associated with survival. We discovered 988 differentially retained introns in 14 cancers, some of which demonstrated diagnostic potential in multiple cancer types. We also inferred a large number of prognosis-related introns in 33 cancer types, and the associated genes included well-known cancer hallmarks such as angiogenesis, metastasis, and DNA mutations. Notably, we discovered a novel intron retention event inside 5′UTR of STN1 that is associated with the survival of lung cancer patients. The retained intron reduces translation efficiency by producing upstream open reading frames (uORFs) and thereby inhibits colony formation and cell migration of lung cancer cells. Besides, the IR-based prognostic model achieved good stratification on certain cancers, as illustrated in acute myeloid leukemia. Taken together, we performed a comprehensive IR survey at a pan-cancer level, and the results implied that IR has the potential to be diagnostic and prognostic cancer biomarkers, as well as new drug targets.

Single-cell RNA sequencing (scRNA-seq) technology has significantly advanced our understanding of the diversity of cells and how this diversity is implicated in diseases. Yet, translating these findings across various scRNA-seq datasets poses challenges due to technical variability and dataset-specific biases. To overcome this, we present a novel approach that employs both an LLM-based framework and explainable machine learning to facilitate generalization across single-cell datasets and identify gene signatures to capture disease-driven transcriptional changes. Our approach uses scBERT, which harnesses shared transcriptomic features among cell types to establish consistent cell-type annotations across multiple scRNA-seq datasets. Additionally, we employ a symbolic regression algorithm to pinpoint highly relevant yet minimally redundant models and features for inferring a cell type’s disease state based on its transcriptomic profile. We ascertain the versatility of these cell-specific gene signatures across datasets, showcasing their resilience as molecular markers to pinpoint and characterize disease-associated cell types. Validation is carried out using four publicly available scRNA-seq datasets from both healthy individuals and those suffering from ulcerative colitis (UC). This demonstrates our approach’s efficacy in bridging disparities specific to different datasets, fostering comparative analyses. Notably, the simplicity and symbolic nature of the retrieved gene signatures facilitate their interpretability, allowing us to elucidate underlying molecular disease mechanisms using these models.

Natural killer cells are associated with the pathogenesis of ulcerative colitis (UC), but their precise contributions remain unclear. The present study sought to investigate the diagnostic value of activated NK-associated genes (ANAGs) in UC. Bulk RNA-seq and scRNA-seq datasets were obtained from the Gene Expression Omnibus (GEO) and Single Cell Portal (SCP) databases. In the bulk RNA-seq, 92 differentially expressed genes (DEGs) were screened out by the “Batch correction” and “Robust rank aggregation” (RRA) methods. The immune infiltration landscape was estimated by single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT, which revealed a higher abundance of activated NK cells in noninflamed UC tissues. 54 DEGs correlated with activated NK cells were identified as ANAGs. Protein-protein interaction (PPI) analysis and least absolute shrinkage and selection operator (LASSO) regression were utilized to screen out 4 key ANAGs (SELP, TIMP1, MMP7, and ABCG2) and establish an activated NK-associated gene score (ANAG score). The ANAG score demonstrated excellent diagnostic value and was validated in three external datasets. The expression of the 4 key ANAGs was validated in UC patients and healthy controls (HC) samples. Through scRNA-seq data analysis, higher expression levels of SELP, TIMP1, MMP7, and ABCG2 were observed in post-capillary venules, inflammatory fibroblasts, enterocytes, and immature enterocytes. The cell scores based on the ANAGs showed enrichment in endothelial cells and fibroblasts. In conclusion, we established and validated an ANAG score with the ability to precisely diagnose UC. The 4 key ANAGs have the potential to serve as therapeutic targets in UC.

(1) Background: Vitamin K deficiency is a common feature of chronic kidney disease (CKD), leading to impaired bone quality and increased risk of vascular calcifications. A method to indirectly measure Vitamin K status is measuring the blood level of Vitamin K dependent proteins (VKDP)- osteocalcin (OC) and matrix GLA protein (MGP). The aim of this study is to correlate the level of OC and MGP with markers of CKD mineral bone disorder (CKD-MBD). (2) Methods: We conducted a single-center cross-sectional study that included 45 CKD G5D patients and measured blood biochemistry, complete blood count and intact osteocalcin and matrix GLA protein. (3) Results: We found a strong statistically significant correlation of OC with the markers of CKD-MBD such as: iPTH , serum calcium and serum phosphorus and a strong indirect statistically significant correlation with abdominal circumference. There was also a statistically significant correlation of MGP with markers of inflammation (CRP). Higher levels of MGP were found in patients treated with vitamin K antagonists, non-calcium based phosphate binders and vitamin D receptor activator- paricalcitol. (4) Conclusions: In our study, we found that vitamin K deficiency, measured indirectly using the level of VKDP is associated to CKD-MBD. The use of medication such as phosphate binders, that reduce vitamin K absorbtion, Vitamin D that increases Vitamin K requirements and also vitamin K antagonists, seem to have an influence on the blood level of VKDPs.

The utilization of tempered blast furnace (BF) slag through the direct fiber forming process to create high-value thermal insulation materials offers a dual benefit: it efficiently harnesses the latent heat within unused slag and substantially enhances the value of blast-furnace slag utilization. However, gauging the melting properties of iron slag under high temperatures is a challenge. In this study, we explore the melting behavior of SiO2 within a high-temperature molten pool. We employ dynamic visual data (video stream) captured via a non-contact charge coupled device (CCD) video recording system to extract SiO2 contours through image processing. The change in image centroid characteristics is used to establish a convolution function relationship, and MATLAB's traversal search algorithm determines SiO2's centroid position. Given that SiO2 is proportionate to crucible pixels, the area of SiO2 is calculated through pixel statistics within these contours. Subsequently, we propose a new indirect method to process image information, yielding SiO2 volume and mass at different time points. An exponential fitting yields the melting rate function of SiO2. Finally, we compare this indirect method with shape from shading (SFS), quantitative characterization, and dimensional analysis techniques. We also discuss the strengths and limitations of each method. Our findings reveal that the indirect solution method presented here boasts straightforward calculation steps and imposes minimal image format requirements. This research provides theoretical and technical support for blast-furnace slag's direct fiber forming process.

In this study, based on frost formation data provided by Harbin Meteorological Bureau, combined with geographical factors, temperature and population density, linear fitting, Mann-Kendall mutation test, pettitt method and sliding T test were used to analyze the spatio-temporal variation characteristics of frost date in Harbin Municipality and the effects of geographical factors, temperature and population density on frost regularity. The results showed that (1) The earliest year of FSD appeared was 1966 and 1967, on the 255th, i.e., August 18th, and the latest was 283 days, i.e., October 10th, 2006. The earliest year in which the FED appeared was April 24th of 2015, the 114th day of that year, and the latest was April 21st of 1974, the 141st day of that year. The year with the most frost was 2012, with 161 days, and the year with the shortest was 1966, with 123 days. (2) Throughout the study period, FSD increased by 7.8 days at a rate of -1.27d/10a, FED increased by 10.9 days at a rate of 1.77d/10a, and FFS increased by 18.9 days at a rate of 3.05d/10a. The tendency rate of FSD and FFS at each site in Harbin showed an increasing trend. For FED, the tendency rate of some sites showed an increasing trend. In general, FSD showed a delayed trend, FED showed an advanced trend, and FFS showed a prolonged trend. Using the method of unary linear regression, FSD of each site showed an increasing trend, FFS showed an increasing trend, and FED showed a decreasing trend in general. (3) Throughout the study period, FSD was mutated in 2000, and the arrival time of it in the study area averaged the 265th, i.e., September 22nd, and after that, the arrival time of it changed to the 272nd, i.e., September 29th of that year. FED was mutated in 2006, and the arrival time of it in the study area averaged 128th, i.e., April 8th. After the mutation, the arrival time of FED changed to 121st, i.e., April 1st of that year. FFS was mutated in 2004. Before the mutation, with an average arrival time of 137days in the study area .But after that, the time changed to 150 days.(4) FSD and FFS in Harbin Municipality were negatively correlated with latitude and positively correlated with temperature, while FED was positively correlated with latitude and negatively correlated with temperature. The FSD, FED and FFS in the central part of Harbin Municipality were the earliest, the latest and the longest, respectively, so the Pearson correlation coefficient method and the influence of longitude could not be reflected in the multiple regression. Several factors were judged by grey correlation analysis method. The longitude is significantly related to FSD, FED and FFS.

Synthetic Aperture Radar (SAR) is an active sensor that uses microwave for sense, it is unrestricted by weather and illumination conditions, and it can observe targets all day and weather. Aircraft targets are important monitoring objects in military and civilian fields, and how to efficiently detect and recognize aircraft targets is an important topic in the field of SAR image interpretation. Based on the features of SAR images, such as complex background, high resolution, and multi-scale, we proposed an improved method based on YOLOv5s. Firstly, this paper proposed the structure of the multi-scale receptive field and channel attention fusion, which is applied to the shallow layer of the backbone of YOLOv5s, it can adjust the weights of the multi-scale receptive field during the training process to enhance the extraction ability of feature information. Secondly, we proposed four de-coupled detection heads to replace the original part in YOLOv5s, which can improve the efficiency and accuracy of SAR image interpretation for small targets. Thirdly, in the case of the limited amount of SAR images, this paper proposed multi methods of data augmentation, which can enhance the diversity and generalization of the network. Fourthly, this paper proposed the K-means++ to re-place the original K-means to improve the network convergence speed and detection accuracy. Finally, Experiments demonstrate that the improved YOLOv5s can enhance the accuracy of SAR image interpretation by 9.3%, and the accuracy of small targets is improved more obviously, reaching 13.1%.

This work introduces a novel numerical method designed to address three-dimensional unsteady free surface flows incorporating integral viscoelastic constitutive equations, specifically, the K-BKZ-PSM (Kaye–Bernstein, Kearsley, Zapas - Papanastasiou, Scriven, Macosko) model. The new proposed methodology employs a second-order finite difference approach along with the deformation fields method to solve the integral constitutive equation, and the marker-particle method (known as \emph{Marker-and-Cell}) to accurately capture the evolution of the fluid's free surface. The newly developed numerical method has proven its effectiveness in handling complex fluid flow scenarios, including confined flows and extrudate swell simulations of Boger fluids. Furthermore, a new semi-analytical solution for velocity and stress fields is derived, considering fully developed flows of a K-BKZ-PSM fluid in a pipe.

Optical “fingerprints” are widely used in chemometrics-assited recognition of samples of different nature. An emerging trend in this area is the transition from obtaining "static" spectral data to reactions occurring over time. The indicator reactions are usually carried out in aqueous solutions; in this study we have developed the reactions that occur in an organic solvent, which makes it possible to recognize fat-soluble samples. In this capacity, we used 5W40, 10W40 and 5W30 motor oils of 4 manufacturers, totally 6 samples. The procedure involved mixing of the dye, sample, and reagents (HNO3, HCl, or t-butyl hydroperoxide) in ethanolic solution in a 96-well plate and measuring absorbance or near-IR fluorescence intensity every several minutes during 20–55 min. The obtained photographic images were processed by linear discriminant analysis (LDA) and k-nearest neighbors algorithm (kNN). The discrimination accuracy was evaluated by using the validation procedure. Reaction of oxidation of a dye with nitric acid allowed to recognize all 6 samples with 100% accuracy by LDA. Merging data of 4 reactions that did not provide complete discrimination ensured an accuracy of 93% by kNN technique. The developed indicator systems have good prospects for the discrimination of other fat-soluble samples. Overall, the results confirm the viability of the kinetic-based discrimination strategy.

Abstract: The surge of diabetes poses a significant global health challenge, particularly in Oman and the Middle East. Early detection of diabetes is crucial for proactive intervention and improved patient outcomes. This research leverages the power of machine learning, specifically, Convolutional Neural Networks (CNNs), to develop an innovative 4D CNN model dedicated to early diabetes prediction. A region-specific dataset from Oman is utilized to enhance health outcomes for individuals at risk of developing diabetes. The proposed model showcases remarkable accuracy, achieving an average accuracy of 98.49% to 99.17% across various epochs. Additionally, it demonstrates excellent F1 score, recall, and sensitivity, highlighting its ability to identify true positive cases. The findings contribute to the ongoing effort to combat diabetes and pave the way for future research in using deep learning for early disease detection and proactive healthcare.

This paper explores the dynamic interplay of K-12 education, the Fourth Industrial Revolution (IR 4.0), and internationalization in the Philippines. Through qualitative content analysis, key themes emerged, shedding light on the impact of K-12 education on global competitiveness, challenges in embracing IR 4.0 technologies, and opportunities arising from internationalization efforts. The findings underscore the significance of curriculum alignment with international standards, comprehensive digital literacy training, and fostering a global mindset to prepare students for the digitally-driven, interconnected world. Understanding the implications of these themes is crucial for the Philippines' education system, as it navigates towards a transformative and globally competitive future. Further, this research offers evidence-based recommendations and addresses research gaps, contributing to a broader understanding of how the convergence of these forces shapes the future of Philippine education. Future research avenues may explore long-term impacts on workforce preparedness, innovative IR 4.0 integration, and the effectiveness of internationalization efforts on educational outcomes. Eventually, this study inspires an education system that nurtures learners equipped to thrive in an ever-evolving global landscape.

Cervical carcinogenesis is the leading cause of cancer related death in women and the role of high-risk human papillomavirus (HR-HPV) as a possible risk factor in the development of this cancer is well recognised. Despite the availability of multi-therapeutic approaches, there is still a major concern regarding the prevention of metastatic dissemination and excessive tissue injuries. Therefore, it is imperative to develop a safer and more efficient treatment modality. Ficus Carica, a natural plant, has shown potential therapeutic properties through its fruit latex when applied to HPV-positive cervical cancer cell lines. However, the mechanisms of action of Ficus Carica (fig) latex are not well understood. This study aims to provide a deeper insight into the biological activities of fig latex on human cervical cancer cell lines expressing high-risk HPV types 16 and 18. The data obtained from this study reveals that fig latex influences the expression of genes in-volved in "Class I MHC-mediated antigen presentation" as well as "Antigen processing: Ubiquitination and Proteasome degradation." These genes play a crucial role in host immune surveillance and the resolution of infection. Findings from this study suggest that fig latex may enhance T cell responses against oncogenic HPV, which could be beneficial for the clearance of early-stage cancer.

Satellite-based data classification performance remains a challenge for research community in the field of land use/land cover mapping. Here we investigated supervised per-pixel classifications performance under different scenarios, based on single and seasonal multispectral data combina-tions of different sensors (Landsat-8 OLI and Sentinel-2 MSI). In case of Landsat, seasonal spectral indices (EVI and NDMI) were included. A typical Mediterranean watershed with a complex landscape comprised of various forest and wetland ecosystems, crops, artificial surfaces, and lake water was selected to test our approach. All available geospatial data from national databases (Forest Map, LPIS, Natura2000 habitats, cadastral parcels, etc.) are used as ancillary data for clas-sification training and validation. We examined and compared the performance of ML, RF, KNN and SVM classifiers under different scenarios for land use/land cover mapping, according to Co-pernicus Land Cover nomenclature. In total, eight land use/land cover classes were identified in Landsat-8 OLI and nine in Sentinel-2 MSI for an acceptable overall accuracy over 85%. A com-parison of the overall classification accuracies shows that Sentinel-2 overall accuracy was slightly higher than Landsat-8 (96.68% vs. 93.02%). Respectively, the best-performed algorithm was ML in Sentinel-2 while in Landsat-8 was KNN. However, machine-learning algorithms have similar results regardless the type of sensor. We concluded that best classification performances achieved using seasonal multispectral data. Future research should be oriented towards integrating time-series multispectral data of different sensors and geospatial ancillary data for land use/land cover mapping.

Aedes aegypti transmits major arboviruses of public health importance, including dengue, chikungunya, Zika, and yellow fever. The use of insecticides represents the cornerstone of vector control, however insecticide resistance in Ae. aegypti has become widespread. Understanding the molecular basis of insecticide resistance in this species is crucial to design effective resistance management strategies. Here, we applied Illumina RNA-Seq to study the gene expression patterns associated with resistance to three widely used insecticides (malathion, alpha-cypermethrin and lambda-cyhalothrin) in Ae. aegypti populations from 2 sites (Manatí and Isabela) in Puerto Rico (PR). Cytochrome P450s were the most over-expressed detoxification genes across all resistant phenotypes. Some detoxification genes (CYP6Z7, CYP28A5, CYP9J2, CYP6Z6, CYP6BB2, CYP6M9, and two CYP9F2 orthologs) were commonly over-expressed in mosquitoes that survived exposure to all three insecticides (independent of geographical origin) while others including CYP6BY1 (malathion), GSTD1 (alpha-cypermethrin), CYP4H29 and GSTE6 (lambda-cyhalothrin) were uniquely overexpressed in mosquitoes that survived exposure to specific insecticides. The gene ontology (GO) terms associated with monooxygenase, iron binding, and passive transmembrane transporter activities were significantly enriched in 4 out of 6 resistant vs susceptible comparisons while serine protease activity was elevated in all insecticide resistant groups relative to the susceptible strain. Interestingly, cuticular-related protein genes (chinase and chitin) were predominantly downregulated, which was also confirmed in the functional enrichment analysis. This RNA-Seq analysis presents a detailed picture of the candidate detoxification genes and other pathways that are potentially associated with pyrethroid and organophosphate resistance in Ae. aegypti populations from PR. These results could inform development of novel molecular tools for detection of resistance-associated gene expression in this important arbovirus vector and guide the design and implementation of resistance management strategies.

Delay in data transmission is one of key performance indicators (KPIs) of a network. The planning and project value of delay in network management is of crucial importance for the optimal allocation of network resources and their performance focuses. To create optimal solutions, predictive models, which are currently most often based on machine learning (ML), are used. This paper aims to investigate the training, testing and selection of the best predictive delay model for a VoIP service in an Long Term Evolution (LTE) network using three ML techniques - Neural Networks (NN), Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). The space of model input variables is optimized by dimensionality reduction techniques: RReliefF algorithm, Backward selection via the recursive feature elimination algorithm and the Pareto 80/20 rule. A three-segment road in the geo-space between the cities of Banja Luka (BL) and Doboj (Db) in the Republic of Srpska (RS), Bosnia and Herzegovina (BiH), covered by the cellular network (LTE) of the M:tel BL operator was chosen for the case study. The results show that, in all three optimization approaches, the k-NN model is selected as the best solution. For the RReliefF optimization algorithm, the best model has 6 inputs and minimum relative error (RE), RE=0.109; for the Backward selection via the recursive feature elimination algorithm, the best model has 4 inputs and RE=0.041; and for the Pareto 80/20 rule, the best model has 11 inputs and RE= 0.049. The comparative analysis of the results concludes that according to observed criteria for the selection of the final model, the best solution is an approach to optimizing the number of predictors based on the Backward selection via the recursive feature elimination algorithm.

Vitamin K is a multifunctional micronutrient essential for human health, and deficiency has been linked to multiple pathological conditions. In this study we aimed to develop and validate a new food frequency questionnaire (FFQ) to estimate total vitamin K intake, over the course of a 30-day interval, in a Portuguese, Mediterranean-based, population. We conducted a prospective study in a non-random sample of 38 healthy adult volunteers. The FFQ was designed based on a validated Portuguese FFQ used in nationally representative studies and on literature reviews, to include foods containing ≥ 5μg of vitamin K/100g and foods with a lower vitamin K content, yet commonly in-cluded in a Mediterranean diet. Vitamin K intake was estimated from 24h recalls and six days of food records. The final FFQ included 54 food items which, according to regression analyses, explains 90% of vitamin K intake. Mean differences in vitamin K intake based on food records (80±47.7 μg/day) and on FFQ (96.5±64.3 μg/day) were statistically non-significant. Further, we found a strong correlation between both methods (r= 0.7; p=0.003). Our results suggest that our new FFQ is a valid instrument to assess the last 30-days of vitamin K intake in the Portuguese Mediterranean population.

Forensic document examiners can determine the authenticity of questioned documents by analyzing the ink used to create them. If an ink mismatch is found, it could be a sign of scam, backdating, or forgery. In this research a Hyperspectral Images of iVision HHID dataset is used to detect number of possible inks used in document. By using Hyperspectral Images, it’s possible to detect ink mismatch in a given document. In this research unsupervised learning method K-means is used to detect number of inks. Approximate number of clusters are determined by Elbow and Silhouette method before implementation of K-means.

Address parsing is a crucial task in natural language processing, particularly for Chinese addresses. The complex structure and semantic features of Chinese addresses present challenges due to their inherent ambiguity. Additionally, different task scenarios require varying levels of granularity in address components, further complicating the parsing process. To address these challenges and adapt to low-resource environments, we propose CapICL, a novel Chinese address parsing model based on the In-Context Learning (ICL) framework. CapICL leverages a sequence generator, regular expression matching, BERT semantic similarity computation, and GPT modeling to enhance parsing accuracy by incorporating contextual information. We construct the sequence generator using a small annotated dataset, capturing distribution patterns and boundary features of address types to model address structure and semantics, mitigating interference from unnecessary variations. We introduce the REB-KNN algorithm, which selects similar samples for ICL-based parsing using regular expression matching and BERT semantic similarity computation. The selected samples, raw text, and explanatory text are combined to form prompts, and inputted into the GPT model for prediction and address parsing. Experimental results demonstrate significant achievements of CapICL in low-resource environments, reducing dependency on annotated data and computational resources. Our model's effectiveness, adaptability, and broad application potential are validated, showcasing its positive impact in natural language processing and geographical information systems.

The transcriptomic analysis of the dehydration rate of mature rice seeds was conducted to explore candidate genes related to the dehydration rate and provide a theoretical basis for breeding and utilization. In this study, we selected Baghlani Nangarhar, an extremely fast dehydration material, and Saturn, a slow dehydration material, based on the results of the previous studies of screening of 165 germplasm materials for dehydration rate phenotypes. Fast dehydration experiment on these two types of seeds was conducted. Four comparative groups were set up under control and dehydration conditions. The differentially expressed genes (DEGs) were quantified by transcriptome sequencing and quantitative real-time PCR (qRT-PCR). GO and KEGG analyses were carried out. The results showed that in Baghlani Nangarhar, 53 DEGs were screened, of which 33 were up-regulated and 20 were down-regulated. Similarly, in Saturn, 25 DEGs were screened, of which 19 were up-regulated and 6 down-regulated. The results of the GO analysis showed that the sites of action of the differentially expressed genes enriched in the fast dehydration modes were concentrated in the cytoplasm, internal components of the membrane and nucleosomes. They play regulatory roles in catalysis, binding, translocation, transcription, protein folding, degradation and replication. They are involved in adaptive responses to adverse external environments such as reactive oxygen species and high temperature. The KEGG analysis showed that the main metabolic pathways enriched are protein processing in the endoplasmic reticulum, amino acid biosynthesis, and oxidative phosphorylation. The key differentially expressed genes and the most important metabolic pathways in the rapidly and slowly dehydrated materials obtained in this study were protein processing in the endoplasmic reticulum and oxidative phosphorylation metabolism, which are presumed to have important regulatory roles in stress/defense, energy metabolism, protein synthesis/folding, and signal transduction during dehydration and drying of mature seeds. The results of this study could potentially provide a valuable reference for further research on the genes and metabolic pathways related to the dehydration rate of mature rice seeds and provide theoretical guidance for the selection and breeding of new rice germplasm that can be rapidly dehydrated at the mature stage.

During the infection by SARS-CoV-2, the virus is changing infected host cell into its own factory producing new viral particles. As infection progresses, infected cell undergoes many changes in various pathways. One of the events caused by changes is cytokine storm, which leads to the severe symptoms. In this study, we investigated transcriptomic changes caused by COVID-19 disease using RNA-seq data obtained from COVID-19-positive patients and COVID-19-negative donors. RNA-seq data were collected for the purpose of identification of potential biomarkers associated with a different course of the disease. Here, the first datasets of 96 samples were analyzed to validate the methods. The aim of this publication is to report pilot results. In search of potential biomarkers associated with different disease severity, we performed differential expression analysis of human transcriptome, focusing on COVID-19 positivity and symptom severity. Since we detected plenty of potential biomarkers, we performed KEGG enrichment analysis to get better view of altered pathways. Results show, that affected were pathways related to immune processes and response to infection, also multiple signaling pathways, while most of them were also reported to be influenced by SARS-CoV-2 infection in previous studies.

Caseous lymphadenitis (CL) is a chronic contagious disease that affects small ruminants and is characterized by the formation of pyogranulomas in lymph nodes and other organs. However, the pathogenesis of this disease and the response of the host genome to infection are not yet fully understood. This study aimed to investigate the whole blood transcriptome and evaluate differential gene expression during the later stages of CL in naturally infected ewes. The study included diseased, serologically positive (EP), exposed, serologically negative (EN) ewes from the same infected flock and healthy ewes (CN) from a different flock. RNA sequencing was performed using the Illumina NextSeq system, and differential gene expression was estimated using DESeq2 and Edge R approaches. The analysis identified 191 annotated differentially expressed genes (DEGs) in the EP group (102 upregulated and 89 downregulated) and 256 DEGs in the EN group (106 upregulated and 150 downregulated). Infection influenced numerous immunoregulatory interactions between lymphoid and nonlymphoid cells in both EP and EN ewes. Immune DEGs were preferentially assigned to antigen presentation through the MHC complex, T-lymphocyte mediated immunity, and extracellular matrix interactions. Furthermore, the EP group showed altered regulation of cytokine and chemokine signalling and activation and recombination of B-cell receptors. Conversely, NF-kappa B signalling, apoptosis, and stress response were the main processes influenced in the EN group. In addition, statistically significant enrichment of the essential immune pathways of Binding and Uptake of Ligands by Scavenger Receptors in EP and p53 signalling in the EN group was found. In conclusion, this study provides new insights into the disease course and host‒pathogen interaction in naturally CL-infected sheep by investigating the blood transcriptome.

The Na+/K+ ATPase is a protein involved in the active transport of ions across the cellular membrane. Ouabain is a cardiotonic glycoside that, by inhibiting the Na+/K+ pump, interferes with cell processes mediated directly by the pump, but also indirectly influences other cellular processes, such as cellular cycle and proliferation, growth, cell differentiation, angiogenesis, migration, adhesion and invasion. We used the SK-BR-3 breast cancer cell line, mesenchymal stem cells (MSCs), and tumor-associated fibroblasts (TAFs) in vitro for determining the changes induced by Ouabain in concentration of 10-5 M, 10-6 M, 10-8 M, 10-9 M on these cellular types, employing specific methods: SEM for morphological evaluation; immunocytochemistry, flowcytometry to assess immunophenotypical markers expression, cell cycle and Annexin V/PI assay for cellular proliferation and viability testing; flowchamber assay to evaluate functional adhesion behavior; RT-PCR for Na+/K+ ATPase subunits molecular expression. Being indicative of the pro-apoptotic and inhibitory effect of Ouabain on tumor invasion and metastasis, the results support the addition of Ouabain to the oncological therapeutic arsenal, trailing the “repurposing drugs” approach.

Finding anomalies in the real-time system is recognized as one of most challenging study in information security. It has so many applications like IoT, and Stock-Market. In any IoT system the data generated are real-time, and temporal in nature. Since due to the extreme exposure to Internet and interconnectivity of devices, the IoT systems often face issues like fraud, anomalies, intrusions etc. Discovering anomaly in such domain can be interesting. Clustering and rough set theory have been tried in many cases. Considering the time-stamp associated with IoT data, time-dependent patterns like periodic clusters can be generated which could be helpful for the efficient detection of anomalies by providing more in-depth analysis of the system. In this paper, a mixed method comprising of nano topology, a modified k-means clustering and an interval superimposition technique is used for finding fuzzy periodic clusters in the subspace generated by the nano topology. For every clusters there will be an associated sequence of time-intervals where it exists. The sequence time-intervals accompanying with each clusters may exhibit some remarkable patterns. For example, there may exist different types of periodicity namely yearly, monthly, daily, and hourly etc. For finding such fuzzy periodicity, an operation called interval-superimposition has been used. The time-intervals associated with each cluster are superimposed if they have reasonable overlapping. Each superimposed time-interval generates a fuzzy time-interval. The data instances are thought to be anomalous if they either belong to sparse clusters or don't belong to any clusters. The efficacy of the method can be assessed by means of both time-complexity analysis and comparative studies with existing clustering-based anomaly detection algorithms with a real-life and a synthetic dataset. It can been found experimentally that our method can extract anomaly with 98% of accuracy and it runs cubic time approximately.

A better understanding of background tropospheric ozone delivers multiple benefits. Robust estimates of regional background ozone assist policy makers to understand the limits to reducing ozone by controlling anthropogenic emissions alone and assist in characterising changes in the atmospheric environment and its composition. Long time series of background ozone measurements can also help quantify the influence of human activity on the global and regional atmosphere. Background tropospheric ozone measurements representative of continental air masses are however scarce in Australia, limiting our knowledge of background atmospheric composition. Here we use k-means clustering to identify a cluster of measurements from the long-term monitoring station at Oakdale, NSW which are likely to be representative of background air. The cluster is associated with NOx-limited air masses of continental origin. From this analysis we estimate background ozone representative of Eastern Australia. We find recent (2017-2022) mean ozone mixing ratios of 28.5 ppb and identify a statistically significant (α=0.05) trend in the mean of +1.8 (1.0 – 2.8) ppb/decade. Our methods demonstrate that some long-term monitoring stations within or near urban areas can provide suitable conditions and datasets for regional Global Atmosphere Watch monitoring.

Falls are extremely damaging to the elderly. The number of elderly who have experienced falls has increased over the years, several of the elderly stay alone or in in badly maintained elderly homes. This makes a low-cost fall detection system a necessity. There has been huge improvements in terms of IoT systems, ML algorithms. Varied data sets have been collected across the world for fall detection. These data sets have a very little in common among them, in terms of user demographics, sensors used, the ADL and Fall activities Hence in this paper we present a data set that has wide user demographics, we used various sensors – such as accelerometer, gyroscope, magnetometer and hear rate. We used wrist worn sensors to collect data. In this paper we present a detailed analysis of the data set we collected using common ML algorithms such as – Naïve Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF) and Support Vector Machines (SVM). We analyzed the performance of these algorithms for variations in accuracy with respect to age, gender, height, weight and health issues and we have identified outliers by analyzing each incorrect prediction. This paper provides the complete details of the data collection methodology, The methods used for analysis and presents the results of analysis in complete detail.

Because rice is native to tropical and subtropical regions, it is generally more sensitive to cold stress compared to other cereals. Low temperature stress has become one of the most important conditions that affects the growth, development, and yield of rice. Plant NAC (NAM, ATAF1/2, and CUC) transcription factors are known to play significant regulatory roles in the stress response. In our research, we found that OsNAC050 (LOC_Os03g60080） is mainly expressed in leaves, and low temperature can further up-regulate its expression. OsNAC050 mutants created using CRISPR-Cas9 gene editing technology showed significantly enhanced tolerance to low temperature treatment. Detection of enzyme activities related to the redox pathway also showed that the mutants had stronger viability under low temperature stress. Comparative transcriptome analysis showed that photosynthesis and soluble sugar metabolism were significantly affected in the osnac050 mutant lines, suggesting that OsNAC050 may participate in the above molecular pathways in the response to low temperature stress. The results will enhance our understanding of molecular mechanisms underlying the responses to cold stress in rice and can provide new strategies for engineering cold-tolerance in high-yielding rice varieties.

The snATAC + snRNA platform allows epigenomic profiling of open chromatin and gene expression with single-cell resolution. The most critical assay step is to isolate high-quality nuclei to proceed with droplet-base single nuclei isolation and barcoding. With the increasing popularity of multiomic profiling in various fields, there is a need for optimized and reliable nuclei isolation methods, mainly for human tissue samples. Herein we compared different nuclei isolation methods for cell suspensions, such as peripheral blood mononuclear cells (PBMC, n=18) and a solid tumor type, ovarian cancer (OC) (n=18), derived from debulking surgery. Nuclei morphology and sequencing output parameters were used to evaluate the quality of preparation. Our results show that NP-40 detergent-based nuclei isolation yields better sequencing results than collagenase tissue dissociation for OC, significantly impacting cell identification and analysis. Given the utility of applying such techniques to frozen samples, we also tested frozen preparation and digestion (n=6). A paired comparison between frozen and fresh samples validated the quality of both specimens. Finally, we demonstrate the reproducibility of scRNA and snATAC + snRNA platform, by comparing the gene expression profiling of PBMC. Our results highlight how the choice of nuclei isolation methods is critical for obtaining quality data in multiomic assays. It also shows that the measurement of expression between scRNA and snRNA is comparable and effective for cell type identification.

Recent data suggest that K-Ras4B (hereafter K-Ras) can drive cancer cell stemness via calmodulin (CaM)-dependent, non-canonical Wnt-signalling. Here we examined whether another Ca2+-binding protein, the CaM-related centrin1, binds to K-Ras and could mediate some K-Ras functions that were previously ascribed to CaM. While CaM and centrin1 appear to distinguish between peptides that were derived from their classical targets, they both bind to K-Ras in cells. Cellular BRET-data suggest that CaM engages more with K-Ras than centrin1 and that the interaction with the C-terminal membrane anchor of K-Ras is sufficient for this. Surprisingly, binding of neither K-Ras nor its membrane anchor alone to CaM or centrin1 is sensitive to inhibition of prenylation. In support of an involvement of the G-domain of K-Ras in cellular complexes with these Ca2+-binding proteins, we find that oncogenic K-RasG12V displays increased engagement with both CaM and centrin1. This is abrogated by addition of the D38A effector-site mutation, suggesting that K-RasG12V is held together with CaM or centrin1 in complexes with effectors. When treated with CaM-inhibitors the BRET-interaction of K-RasG12V with centrin1 was also disrupted in the low micromolar range, comparable to that with CaM. While CaM predominates in regulating functional membrane anchorage of K-Ras, it has a very similar co-distribution with centrin1 on mitotic organelles. Given these results, a significant overlap of the CaM- and centrin1-dependent functions of K-Ras is suggested.

We developed and applied a method for characterizing bacterial promoters genome-wide by in vitro transcription coupled to transcriptome sequencing specific for native 5’-ends of transcripts. This method called ROSE (Run-Off transcription/RNA-SEquencing), only requires chromosomal DNA, ribonucleotides, RNA polymerase (RNAP) core enzyme, and a specific sigma factor, recognizing the corresponding promoters, which have to be analyzed. ROSE was performed on E. coli K-12 MG1655 genomic DNA using E. coli RNAP holoenzyme (including σ70) and yielded 3,226 transcription start sites, 2,167 of which were also identified in in vivo studies, and 598 were new. Many new promoters not yet identified by in vivo experiments might be repressed under the tested conditions. Complementary in vivo experiments with E. coli K-12 strain BW25113 and isogenic transcription factor gene knockout mutants of fis, fur, and hns were used to test this hypothesis. Comparative transcriptome analysis demonstrated that ROSE could identify bona fide promoters that were apparently repressed in vivo. In this sense, ROSE is well-suited as a bottom-up approach for characterizing transcriptional networks in bacteria and ideally complementary to top-down in vivo transcriptome studies.

The achievement of reproductive competence in male mammals is dependent on the testis. Goat testis’ development and spermatogenesis involve physiological events with high complexity. In the current work, 6 testes were respectively collected from immature, sexually mature and physically mature Qianbei Ma goats (1, 6 and 12 months old, respectively). RNA-Seq was carried out to reveal changes in testis mRNA expression levels in Qianbei Ma goats at various developmental stages, and gene expression profiling at different ages was established. Totally 18 libraries were established for screening genes and pathways associated with testis development and spermatogenic processes. Totally 9,724 upregulated and 4,153 downregulated genes were identified between immature (I) and sexually mature (S) testes; 7 upregulated and 3 downregulated genes were detected between sexually mature (S) and physically mature (P) testes, and approximately 4% of genes were alternately spliced between the I and S groups. Selected genes were verified by qRT-PCR, in agreement with sequencing data indicating their reliability. Those genes have critical functions in various developmental stages of goat testicular development and spermatogenesis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were carried out to evaluate differentially expressed genes (DEGs). GO analysis suggested DEGs were involved in “reproduction process”, “channel activity” and “cell periphery part” between I and S, and in “ion transport process”, “channel activity” and “transporter complex part” between S and P. KEGG analysis indicated that pathways such as “glycerolipid metabolism”, “steroid hormone biosynthesis” and “MAPK signaling pathway” may be involved in testis development and spermatogenesis. Genes including IGF1, TGFB1, TGFBR1 and EGFR may regulate the development of the testis from immature to sexually mature, which may be key candidate genes for the development of goat testis. These findings provide novel insights into goat testicular development and spermatogenesis.

A class of nearly Sasakian manifolds is considered. We discussion geometric effects of some symmetries on such manifolds, and show, under a certain condition, that the class of Ricci-symmetric nearly Sasakian manifolds is a subclass of Einstein manifolds. We prove that a nearly Sasakian space form with Ricci tensor satisfying the Codazzi equation is either a Sasakian manifold with a constant $\phi$-holomorphic sectional curvature $\mathcal{H}=1$ or a $5$-dimensional proper nearly Sasakian manifold with a constant $\phi$-holomorphic sectional curvature $\mathcal{H}>1$. We also prove that the spectrum of the operator $H^{2}$ generated by the nearly Sasakian manifold is a set of simple eigenvalue $0$ and an eigenvalue of multiplicity $4$. We show that there exist integrable distributions on the same manifolds with totally geodesic leaves, and prove that there are no proper nearly Sasakian space forms with constant sectional curvature.

In an $m$-person symmetric game, all players are identical and indistinguishable. In this paper, we find that the payoff tensor of the player $k$ in an $m$-person symmetric game is $k$-mode symmetric, and the payoff tensors of two different individuals are the transpose of each other. Furthermore, we reformulate the $m$-person symmetric game as a tensor complementary problem and demonstrate that locating a symmetric Nash equilibrium is equivalent to finding a solution to the resulting tensor complementary problem. Finally, we use the hyperplane projection algorithm to solve the resulting tensor complementary problem, and we present some numerical results to find the symmetric Nash equilibrium.