This study presents the first genome assembly of the freshwater saprobe fungus Neonectria lugdunensis and a comprehensive phylogenomics analysis of the Nectriaceae family, examining genomic traits according to fungal lifestyles. The Nectriaceae family, one of the largest in Hypocreales, includes fungi with significant ecological roles and economic importance as plant pathogens, endophytes, and saprobes. The phylogenomics analysis identified 2684 single-copy orthologs, providing a robust evolutionary framework for the Nectriaceae family. We analyzed the genomic characteristics of 17 Nectriaceae genomes, focusing on their carbohydrate-active enzymes (CAZymes), biosynthetic gene clusters (BGCs), and adaptations to environmental temperatures. Our results highlight the adaptation mechanisms of N. lugdunensis, emphasizing its capabilities for plant-litter degradation and enzyme activity in varying temperatures. Comparative genomics of different Nectriaceae lifestyles revealed significant differences in genome size, gene content, repetitive elements, and secondary metabolite production. Endophytes exhibited larger genomes, more effector proteins, and BGCs, while plant pathogens had higher thermo-adapted protein counts, suggesting greater resilience to global warming. In contrast, the freshwater saprobe shows less adaptation to warmer temperatures and is important for conservation goals. This study underscores the importance of understanding fungal genomic adaptations to predict ecosystem impacts and conservation targets in the face of climate change.

Recognizing the need to enhance immune cells' ability to target and eliminate tumor cells by focusing on tumor-associated antigens is crucial for the success of immunotherapy. Numerous cell-based therapies, such as CAR-T cell therapy, tumor-infiltrating lymphocyte (TIL) therapy, CAR-NK cell therapy, and T-cell receptor (TCR)-based therapies, are currently undergoing clinical and pre-clinical evaluation across various cancer types. However, a significant challenge lies in managing the inflammatory responses that are harmful to cancer patients. To overcome this, developing advanced strategies to target tumor cells precisely while preserving immune balance in cancer patients is imperative. One promising approach involves using modified tumor-associated macrophages (TAMs), which constitute approximately 50% of the tumor microenvironment (TME) and play a pivotal role in tumor progression. CAR-Macrophage (CAR-M) therapy is an emerging potential asset of immunotherapy for a wide range of tumors.

The world is facing a major health crisis, the global pandemic of COVID-19 caused by the SARS-CoV-2 coronavirus, for which no approved antiviral agents or vaccines are currently available. Here we describe a collection of codon-optimized coding sequences for SARS-CoV-2 cloned into Gateway-compatible entry vectors, which enable rapid transfer into a variety of expression and tagging vectors. The collection is freely available via Addgene. We hope that widespread availability of this SARS-CoV-2 resource will enable many subsequent molecular studies to better understand the viral life cycle and how to block it.

In the coded pulse scanning light detection and ranging (LIDAR) system, the number of laser pulses used at a given measurement point changes depending on the modulation and the method of spreading used in optical code-division multiple access (OCDMA). The number of laser pulses determines the pulse width, output power, and duration of the pulse transmission of a measurement point. These parameters determine the maximum measurement distance of the laser and the number of measurement points that can be employed per second. In this paper, we evaluate the performance and characteristics of combinations of modulation and spreading technology that can be used for OCDMA, and study optimal combinations according to varying operating environments.

A small phylogenetically conserved sequence of 11,231 bp termed FAM247 is repeated in human chromosome 22 by segmental duplications. This sequence forms part of diverse genes that span evolutionary time, the protein genes being the earliest as they are present in zebrafish and/or mice genomes, the long non-coding RNA genes and pseudogenes the most recent as they appear to be present only in the human genome. We propose that the conserved sequence provides a nucleation site for new gene development at evolutionary conserved chromosomal loci where the FAM247 sequences reside. The FAM247 sequence also carries information in its open reading frames that provides protein exon amino acid sequences; one exon plays an integral role in immune system regulation, specifically, the function of ubiquitin specific protease (USP18) in the regulation of interferon. An analysis of this multifaceted sequence and the genesis of genes that contain it are presented.

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. In contemporary times, the transition from conventional electricity grids to Smart Grids constitutes an area where extensive research is conducted on a global scale. Among the research challenges, the investigation of Deep Transfer Learning (DTL) in the field of electricity load forecasting represents a fundamental effort that imparts generality to Artificial Intelligence applications, due to new capabilities, such as knowledge transfer and computational power reduction. In this paper a comprehensive study is conducted for day-ahead electricity load forecasting. For this purpose, three Sequence-to-Sequence (Seq2seq) Deep Learning (DL) models are used, namely the Multilayer Perceptron (MLP), the Convolutional Neural Network (CNN) and the Ensemble Learning Model (ELM), which is consisted of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL and emphasizing the way the datasets are trained and fine-tuning for higher forecasting accuracy. In order to implement the forecasting strategies using Deep Learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1-24 hours) for each month of the year for Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. After several trials, four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) contains the training of the DL models using Lesvos dataset, and the testing period performed directly on the Chios dataset without fine-tuning. The fourth strategy is a Multi-task Deep Learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. In this paper, we explore the performance of DTL and compare the results with those produced with MTDL. The results demonstrated that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, the two cases with fine-tuning (DTL Case 1 and 2) outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it has been concluded that the MLP model presents best monthly forecasts with a MAPE of 6.24% and 6.01% for the first two cases, the CNN model presents best monthly forecasts with a MAPE of 5.57% and 5.60% respectively and the ELM model achieves best monthly forecasts with a MAPE of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

The comorbidity of osteoarthritis and type 2 diabetes mellitus represents a complex clinical challenge and complicates patient management due to the interplay of overlapping pathophysiological mechanisms. The research aimed to analyze the exacerbation of clinical symptoms and biochemical markers in patients with osteoarthritis and type 2 diabetes mellitus compared to those with osteoarthritis. We utilized various assessment methods to measure the patient cohorts' inflammation, oxidative stress, and glycemic control. The study also involved the administration of alpha-lipoic acid to patients with comorbid osteoarthritis and type 2 diabetes mellitus and monitored its effects on joint function, inflammatory markers, oxidative stress levels, and glycemic control. Our findings revealed that the presence of type 2 diabetes mellitus significantly intensified osteoarthritis symptoms and altered biochemical markers, including increased inflammation, elevated oxidative stress, and disrupted immunological balance. The treatment with alpha-lipoic acid resulted in improved joint function, reduced inflammation and oxidative stress levels, and better glycemic control. Additionally, it was identified several potential predictors for developing osteoarthritis and type 2 diabetes mellitus comorbidity. The results underscore the detrimental impact of type 2 diabetes mellitus on osteoarthritis and highlight the beneficial effects of alpha-lipoic acid in alleviating some of the adverse consequences associated with this comorbidity. Nonetheless, further research is necessary to comprehensively evaluate the therapeutic efficacy of alpha-lipoic acid and its long-term effects on managing comorbidity of osteoarthritis and type 2 diabetes mellitus.

Using numerical simulation method, combined with the pre-branch material characteristics of the measurement results, based on the LS-DYNA software to simulate the branch crushing process, the analysis found that not only the existence of the cutter cutting in the branch destruction process, there is also the cutting of the opposing surface of the bending fracture, with the increase in the speed of the knife rollers, the cutter cutting resistance increases, the cutting resistance reaches 2690 N when the cutter at 2500 r/min, the cutter cutting resistance is the smallest in the cutting simulation at the blade angle of 55°, the blade angle is more suitable for the cutting of the branch. In the cutting simulation under different cutting edge angles, the cutting resistance of the tool is the smallest when the edge angle is 55°, which is 1860 N. This edge angle is more suitable for branch crushing and cutting. Using Fluent software to analyse the airflow characteristics of the crushing device, it was found that with the increase of the knife roller speed, the inlet flow and negative pressure of the crushing chamber are increasing, the speed of 2500 r/min, the inlet flow and negative pressure were 1.92 kg/s and 37.16 Pa, which will be conducive to the feeding of branches, but the speed is too high and it will also lead to the enhancement of the vortex of part of the area in the crushing device, which will affect the feeding of branches and the throwing of the crushed material. affect the feeding of branches and the throwing out of crushed materials, and finally determined the speed range of the crushing knife roller is 1800 ~ 2220 r/min. Based on ANSYS/Model module modal analysis of the crushing knife roller, the knife roller of the first six orders of the intrinsic frequency and vibration pattern, crushing knife roller of the lowest order of the modal intrinsic frequency of 137.42 Hz, much larger than the crushing knife roller operating frequency of 37 Hz, the machine will not resonate during operation.

Against the background of the sky, imaging and perception of aircraft are cru- cial. Among these, the location of aircrafts is not a very difficult problem in general, because that deep learning algorithms and object detection models based on convolutional neural network(CNN) are ever-evolving. In order to have a better identification of various aircrafts, or to accurately locate different types of flying targets in the sky, it becomes significant to distinguish and recognize different types of aircrafts. One question is that, distinguishing and recognizing between sub-categories of aircrafts pose great challenges. Although fine-grained recognition focuses on exploring and studying such problems, aircrafts under different sub-categories and granularities lead us to rethink the application of features. We noticed that features in swin-transformer demonstrates the under- standing and processing of images, fully showcasing the encoding and indexing of information. Through this research and proposed approach, we discovered a better understanding of features encoding and use, which are inspired by the feature encoding and computation in swin-transformer. In our paper, our ap- proach has achieved effects on features encoded graphically, manifested from the architecture design and convolutional neural network computation, and outper- forms other famous fine-graiend classification models on this issue. Not only approach we proposed has demonstrated superior performance in fine-grained aicraft classification, but also the mechanisms of the feature encoding under different sample space partitions is revealed for this issue, which provides a re- search for the representation of aircraft fineness characteristics. The relationship between representation orientation of aircraft feature information under various grained divisions, shows that the recognition of different categories according to man-made, such as aircraft, can be achieved by means of the feature represen- tations studied in this paper, for more specific defined classification tasks and various man-made targets partition criteria, which may influences the princi- ple of design for calculation and feature extraction in fine-grained classification models.

The rising challenge of polyethylene terephthalate (PET) waste necessitates efficient collection methods to mitigate environmental impacts. The Analytic Hierarchy Process (AHP) is one of the Multi-Criteria Decision Analysis (MCDA) approaches utilized in this study. The Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) was used to rank each alternative according to the objective weight that AHP had produced. Also, sensitivity analysis was performed to determine how robust the findings were when considering equal weights and entropy weights to maximize PET waste collection techniques. The alternative achieved the objective of obtaining the best collection method, Threshold Plastic Bottle Waste Collection (Tpbw), out of all the three alternatives considered. Another MCDA approach, VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), was used to compare the results and validate the ranking result achieved by the TOPSIS method. The VIKOR technique's validation of the TOPSIS approach showed that the outcomes were highly consistent. Data for the study were gathered from the archives of waste management companies on possible practices of plastic waste collection addressing costs, environmental impacts, social acceptance, feasibility, and efficiency. The findings provide a prioritized framework for enhancing PET waste collection strategies, contributing to sustainable waste management. Many criteria are considered when deciding the best collecting method for PET waste recycling, making it challenging. By using criteria importance, multi-criteria decision analysis (MCDA) was applied in this study, and the objective weight of the criteria was determined using the analytic hierarchy process (AHP). The five criteria considered in this study are Initial investment cost, Operational cost, Transportation cost, Environmental risk, Employment potential, and the objective weights allocated to them. AHP results 0.4952, 0.1997, 0.1565, 0.0870, and 0.0616 are respectively determined.

Previous studies demonstrate the critical importance of non-coding RNAs interfacing with chromatin-modifying machinery resulting in promoter-enhancer-based gene regulation and raise the possibility that many other enhancer-like RNAs may operate via similar mechanisms. Critically, more than 80% of the disease-linked variations identified in genome-wide studies are located in the non-coding regions of genomes, especially non-coding RNA, suggesting non-coding RNAs are relevant to disease. Thus, a critical path forward for understanding non-coding RNAs' role, especially long non-coding RNAs, is to understand the genomic regions' transcriptional regulation, especially non-coding regions. Here, we developed a user-friendly R package called SomaGene for studying and identifying enhancer-like non-coding RNAs with enriched somatic mutations in the cancer genome. SomaGene accepts different genomic variants (whole genome/exome somatic point mutations, structural variations, copy number variations) to identify those RNAs that significantly mutated in diseases (e.g., cancer). It then uses multiple publicly available genomics and epigenetics datasets including ENCODE epigenomics annotations, FANTOM5 tissue-specific expression profiles, disease-associated genome-wide association SNPs, and tissue-specific eQTL pairs to identify those RNAs with potentially enhancer function. SomaGene, as a powerful R package, can provide the opportunity to cancer scientists to study the roles of non-coding RNAs in different cancer genomes.

Commercially cultivated Prunus species are commonly grown in adjacent or mixed orchards, and can be infected with unique or commonly shared viruses. Apple (Malus domestica), another member of the Rosacea and distantly related to Prunus, can share the same growing regions and common pathogens. Pollen can be a major route for virus transmission, and analysis of the pollen virome in tree fruit orchards can provide insights into these virus pathogen complexes from mixed production sites. Commercial honey bee (Apis mellifera) pollination is essential for improved fruit set and yield in tree fruit production systems. Here we describe the metagenomics-based detection of plant viruses through bee and pollen samples collected during the spring bloom period from a mixed Prunus and Malus orchard. Twenty-one unique viruses were detected in samples collected during apricot (Prunus armeniaca), sweet cherry (Prunus avium), peach (Prunus persica), and apple blooms. Ilarviruses prune dwarf virus (PDV) and prunus necrotic ringspot virus (PNRSV), Secoviridae family members tomato necrotic ringspot virus, tobacco necrotic ringspot virus, and prunus virus F, and Capillovirus cherry virus A (CVA) were detected in all time points, while other viruses were detected with restricted ranges. These results demonstrate the benefits of area-wide monitoring through bee pollination activities, and provide new insights into the diversity of viruses in tree fruit pollination ecosystems.

Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder characterized by cognitive decline and neuronal loss, representing a most challenging health issue. We present a computational analysis of transcriptomic data of AD tissues vs. healthy controls, focused on elucidation of functional roles played by long non-coding RNAs (lncRNAs) throughout the AD progression. We first assembled our own lncRNA transcripts from the raw RNA-Seq data generated from 527 samples of dorsolateral prefrontal cortex, resulting in the identification of 31,574 novel lncRNA genes. Based on co-expression analyses between mRNAs and lncRNAs, a co-expression network is constructed. Maximal subnetworks with dense connections are identified as functional clusters. Pathway enrichment analyses are conducted over mRNAs and lncRNAs in each cluster, which serve as the basis for the inference of functional roles played by lncRNAs involved in each of the key steps in an AD development model that we have previously build based on transcriptomic data of protein-encoding genes. Detailed information is presented about the functional roles by lncRNAs in activities related to stress response, reprogrammed metabolism, cell-polarity, and development. Our analyses have also revealed that lncRNAs have discerning power in distinguishing between AD samples of each stage and healthy controls. This study represents the first of its kind.

A great deal of studies have assessed the expression of noncoding RNAs (ncRNAs) as well as their relevant molecular functions and biological mechanisms in the individuals of alcohol dependence. Alcohol dependence is one of the most prevalent neuropsychological disorders worldwide, and its pathogenesis is intricate and inadequately understood. There is considerable evidence demonstrating substantial links between multiple genetic factors and the development of alcohol dependence. Critical roles of ncRNAs have been emphasized in the pathophysiology of mental diseases including alcohol dependence. In the comprehension of ncRNAs action and their mechanisms of modification, they have emerged as therapeutic targets for a variety of psychiatric illness including alcohol dependence. Worth mentioning, dysregulated expression of ncRNAs has been regularly detected in individuals of alcohol dependence. An in-depth knowledge of roles of ncRNAs and m6A modification may be valuable for the development of a novel treatment against alcohol dependence. In general, a deeper comprehension of functional roles ncRNAs may make significant contributions to the precise diagnosis and/or actual treatment of alcohol dependence. Here in this review, we mostly focused on the up-to-date knowledge regarding alterations and/or modifications in the expression of ncRNAs in alcohol dependence, and then, presented their prospects for future research and therapeutic application with a novel concept of engram-system.

We assessed depression, anxiety, and stress in healthcare workers (HCWs) in 2023 and the evo-lution of depression in 2023 compared with 2022. In September-November 2023, 181 HCWs from the Infectious Diseases Hospital, Cluj-Napoca, Romania, completed the Depression, Anxiety, and Stress 21 Scale (DASS-21 R), the Patient Health Questionnaire-9 (PHQ-9), and the Hamilton Anxiety Rating Scale (HAM-A). The prevalence of moderate-to-severe levels was 13.81% for DASS-Depression, 18.79% for DASS-Anxiety, 16.02% for DASS-Stress, 19.89% for PHQ-9, and 12.16% for HAM-A. Moderate-to-severe PHQ-9 clinical depression was found in 19.89% of respondents, and moderate-to-severe HAM-A clinical anxiety in 12.16%. Moderate-to-severe PHQ-9 depres-sion was significantly lower in the 2023 study group (19.89%, N=181) compared with 2022 (30.60%, N=114) (p=0.036), also within the 2022-2023 follow-up study group (N=88) (p=0.026). We did not find significant statistical differences between those infected vs. non-infected, vaccinated vs. non-vaccinated, working with vs. non-working with SARS-CoV-2 infected patients. Males were significantly more stressed (p=0.018) and anxious (p=0.034), and physicians in training had the highest prevalence of moderate-to-severe depression (31.60%), followed by physicians (25.64%). In 2023, depression, anxiety, and stress symptoms decreased but remained within a concerning range. By addressing these psychological issues, we can prevent professional crises in the healthcare system.

Non-coding RNAs (ncRNAs) comprise a diverse class of non-protein coding transcripts that regulate critical cellular processes associated with cancer. Advances in RNA-sequencing (RNA-Seq) have led to the characterization of non-coding RNA expression across different types of human cancers. Through comprehensive RNA-Seq profiling, a growing number of studies demonstrate that ncRNAs, including long non-coding RNA (lncRNAs) and microRNAs (miRNA), play central roles in progenitor B-cell Acute Lymphoblastic Leukemia (B-ALL) pathogenesis. Furthermore, due to their central roles in cellular homeostasis and their potential as biomarkers, the study of ncRNAs continues to provide new insight into the molecular mechanisms of B-ALL. This article reviews the ncRNA signatures reported for all B-ALL subtypes, focusing on technological developments in transcriptome profiling and recently discovered examples of ncRNAs with biologic and therapeutic relevance in B-ALL.

This study investigates the possibility of using machine learning models created in DeepLabCut and Create ML to automate aspects of behavioral coding and aid in behavioral analysis. Two models with different capabilities and complexities were constructed and compared to a manually observed control period. The accuracy of the models was assessed before being applied to 7 nights of footage of the nocturnal behavior of two African elephants (Loxodonta africana). The resulting data was used to draw conclusions regarding behavioral differences between the two elephants and between individually observed nights, thus proving that such models can aid researchers in be-havioral analysis. The models were capable of tracking simple behaviors with high accuracy, but had certain limitations regarding detection of complex behaviors, such as the stereotyped behavior sway, and displayed confusion when deciding between visually similar behaviors. Further expansion of such models may be desired to create a more capable aid with the possibility of automating behavioral coding.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

The investigation of membrane proteins, key constituents of cells, is hampered by the difficulty and complexity of their in vitro synthesis, of unpredictable yield. Cell-free synthesis is herein employed to unravel the impact of the expression construct on gene transcription and translation, without the complex regulatory mechanisms of cellular systems. Through the systematic design of plasmids in the immediacy of the start of the target gene, it was possible to identify translation initiation and the conformation of mRNA as the main factors governing the cell-free expression efficiency of the human voltage dependent anion channel (VDAC), a relevant membrane protein in drug-based therapy. A simple translation initiation model was developed to quantitatively assess the expression potential for the designed constructs. A scoring function is proposed that quantifies the feasibility of formation of the translation initiation complex through the ribosome-mRNA hybridization energy and the accessibility of the mRNA segment binding to the ribosome. The scoring function enables to optimize plasmid sequences and semi-quantitatively predict protein expression efficiencies. This scoring function is publicly available as webservice XenoExpressO at rna.tbi.univie.ac.at.

The aim of this study was to identify differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) in the endometrium of individuals with and without EMS during the proliferative (P) and secretory (S) phases of the menstrual cycle. Tissues were obtained from 18 control (CT; P-phase [pCT], n=8; S-phase [sCT], n=13) and 23 EMS patients (P-phase [pEMS], n=13; S-phase [sEMS], n=12). DElncRNAs and DEmRNAs were analyzed using total RNA-sequencing. In P-phase, expression of NONHSAG019742.2 and NONHSAT120701.2 was significantly higher in EMS than control patients, that of while NONHSAG048398.2 and NONHSAG016560.2 was lower in EMS patients. In S-phase, expression of NONHSAT000959.2, NONHSAT203423.1, and NONHSAG053769.2 was significantly increased in EMS patients, while that of NONHSAG012105.2 and NONHSAG020839.2 was lower. In addition, the expression of HSD11B2, THBS1, GPX3, and SHISA6 was similar to that of neighboring lncRNAs in both P- and S-phases. In contrast, ELP3 and NR4A1, respectively, were up- or downregulated in pEMS tissues. In sEMS, expression of LAMB3 and HIF1A was increased, while expression of PAM was reduced. Our findings on lncRNAs and mRNAs encourage not only to explore the potential clinical applications of lncRNAs and mRNAs as prognostic or diagnostic biomarkers for EMS but also to gain valuable insights into its pathogenesis.

This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Sixteen features are extracted and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and that collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values being greater than 95%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups.

The present grievous situation of the tuberculosis disease can be improved by efficient case management and timely follow-up evaluations. With the advent of digital technology this can be achieved by quick summarization of the patient-centric data. The aim of our study was to assess the effectiveness of the ODK Scan paper-to-digital system during testing period of three months. A sequential, explanatory mixed-method research approach was employed to elucidate technology use. Training, smartphones, application and 3G enabled SIMs were provided to the four field workers. At the beginning, baseline measures of the data management aspects were recorded and compared with endline measures to see the impact of ODK Scan. Additionally, at the end, users’ feedback was collected regarding app usability, user interface design and workflow changes. 122 patients’ records were retrieved from the server and analysed for quality. It was found that ODK Scan recognized 99.2% of multiple choice bubble responses and 79.4% of numerical digit responses correctly. However, the overall quality of the digital data was decreased in comparison to manually entered data. Using ODK Scan, a significant time reduction is observed in data aggregation and data transfer activities, however, data verification and form filling activities took more time. Interviews revealed that field workers saw value in using ODK Scan, however, they were more concerned about the time consuming aspects of the use of ODK Scan. Therefore, it is concluded that minimal disturbance in the existing workflow, continuous feedback and value additions are the important considerations for the implementing organization to ensure technology adoption and workflow improvements.

End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational complexity, learned image compression methods inherently align with learning-based data processing and analytic pipelines due to their well-suited internal representations. The concept of Video Coding for Machines has garnered significant attention from both academic researchers and industry practitioners. This concept reflects the growing need to integrate data compression with computer vision applications. In light of these developments, we present a comprehensive survey and review of lossy image compression methods. Additionally, we provide a concise overview of two prominent international standards, MPEG Video Coding for Machines and JPEG AI. These standards are designed to bridge the gap between data compression and computer vision, catering to practical industry use cases.

The paper presents attribute based characterization of nanomaterials method for computer storage and retrieval as knowledgebase. The knowledgebase permits indepth understanding and comparison between nanomaterials available with the scientists and product developers to satisfy their research and development (R & D) needs. Techniques for order preference by similarity to ideal solution (TOPSIS) is proposed to evaluate nanomaterials in the presence of multiple attributes. The method normalizes attributes to nullify the effect of different units and their values in the range of 0 to 1. The relative importance of different attributes for different applications is considered. The weight vector is derived using Eigen value formulation. The positive and negative benchmark solutions are derived. Euclidean distance of alternatives from these best and worst solution leads to the development of proximity /goodness/suitability index for ranking. Final decision is taken by decision makers by SWOT analysis and short and long term strategies of the organisation. The methodology is illustrated with the help of an example and step-by-step procedure. Results, discussion and conclusion highlights the importance and practical application.

Information molecules of DNA and RNA should obey principles of quantum mechanics where unitary operators in form of unitary matrices have key meanings. Unitary matrices are the basis of calculations in quantum computers. This article presents some author's results, which show that matrix forms of the representation of structured systems of molecular-genetic alphabets can be considered as sets of sparse unitary matrices related with phenomenologic features of the degeneracy of the genetic code. These sparse unitary matrices have orthogonal systems of functions in their rows and columns. A complementarity exists among some unitary genetic matrices in relation each other. Decompositions of numeric genetic matrices into sets of sparse unitary matrices are connected with the logical operation of modulo-2 addition used in quantum computers as well. Tensor (or Kronecker) families of unitary genetic matrices with their fractal-like properties are also considered. The described results are discussed in the frame of development of quantum-information approaches for modeling genetic systems.

This paper investigates the improvement in solar energy collection and conversion using Al₂O₃-Water nanofluids in a solar flat plate collector (SFPC). The efficiency of a solar flat plate collector using water as the fluid medium is analyzed experimentally and theoretically. For theoretical analysis, a mathematical model in MATLAB is used to simulate and is validated by the experimental results. To enhance the solar energy collection and conversion efficiency of the SFPC, Al₂O₃-Water nanofluid was selected as the fluid medium. The nanofluid properties like density, specific heat capacity, thermal conductivity and viscosity are analyzed and compared for several models of Al₂O₃-Water nanofluids and the best model was selected to modify the simulation. Effect of particle diameter in the nanofluid was found to be marginal on the nanofluid properties. The optimum volume concentration of the nanofluid was found to be 4% giving an efficiency increase of 7.78% in the SFPC over the use of water. This reduces the area of the SFPC by 10.5%.

Genes encoding regulatory RNAs known as short RNAs (sRNAs) or non-coding sRNAs (ncRNAs), modulate physiological responses through different mechanisms, through RNA-RNA interaction or RNA-protein interaction. These molecules transcribed in trans and in cis relative to the target RNA. They are located between the coding regions of proteins, i.e., in the intergenic regions of the genome and show signals of promoters and termini sequences generally Rho-independent. The size of the ncRNAs genes ranges from ~ 50 to ~ 500 nucleotides and several transcripts are processed by RNase with smaller end products, which modulate physiological responses through different mechanisms, by RNA-RNA interaction or RNA-protein interactions and some interactions may be stabilized by the Hfq chaperone. The Riboswitches constitute another class of ncRNAs, located in the 5'UTR region of an mRNA that promote transcriptional regulation through their interaction with a linker molecule. Recently, in prokaryotes, CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) regions have described, which repeats of sequences of palindromic bases are. Each replicate consists of short segments of "spacer DNA" from exposures prior to a bacteriophage virus or exogenous plasmid. The CRISPR system consists of an immune system of resistance to exogenous molecules.

Recycling waste products is an environmental-friendly activity that can bring benefits to accompany, saving manufacturing costs and improving economic efficiency. For the beer industry, recycling bottles can reduce manufacturing costs and reduce the industry's carbon footprint. This paper presents a model for a multi-objective collection-distribution center location and allocation problem in a closed loop supply chain for the beer industry, in which the objective is to minimize total costs and transportation pollution. Uncertainties in the form of randomness and fuzziness are jointly handled in this paper to ensure a more practical problem solution, for which returned bottle sand unusable bottles are considered fuzzy random variables. A heuristic algorithm based on priority-based global-local-neighbor particle swarm optimization (pb-glnPSO) is applied to ensure reliable solutions for this NP-hard problem. A case study on a beer operation company is conducted to illustrate the application of the proposed model and demonstrate the priority-based global-local-neighbor particle swarm optimization.

This paper focuses on the role of the most common mineralogical techniques applied to the identification of the different statuary qualities in white, grey and black Göktepe marble. For this purpose, a Roman case of sculpture marble collection from the rural villa of Valdetorres de Jarama (central Iberia), of the 4th century AD, is presented. The mythological statuary, combining white, grey and black marbles, are one of the most outstanding marble collections in Aphrodisian style found in Hispania. The analytical results (Petrography, Cathodoluminescence, C and O isotopes, and Sr and Mn concentration) support the identification of two varieties of “black” Göktepe, traditionally named as bigio morato and bigo antico, as well as the best statuary quality on white Göktepe. In addition, the analytical identification of other Asiatic marbles in Valdetorres collection, a white coarse-grained originally from the quarries of Aphrodisias city and one small piece identified as Carian red from Iasos, corroborates the already suggested strong connection existing between artists and the stone material they chose for their works. Finally, it is noteworthy the identification carried out on the marble of the bases that served as seats for the sculptures, as it is a white marble of lower quality whose analytical characteristics are consistent with the Microasiatic marble of Denizli. The use of these exotic and exceptional raw materials confirms the taste for luxury and decorative richness in Late Antique Hispanic rural villae and contributes to a better understanding of the distribution of Aphrodisian productions in the Western Roman provinces.

This paper analyzes the collection of George Viau (1855-1939) from a computational perspective. Indeed, this dental surgeon, close to the Impressionists, collected several hundred works of art in the first half of the 20th century: the auctions of his collection, in 1907, 1930 and after his death, in 1942, 1943 and 1948, accounted for 642 artworks and, during the Occupation, the 1942 sale produced a total of more than 46 million francs. Thanks to statistics, econometrics, network analysis and cartography, it is possible to understand the salient features of the collection and its place in the world of Parisian auctions.

Today massive amounts of sequenced metagenomic and -transcriptomic data from different ecological niches and environmental locations are available. Scientific progress depends critically on methods that allow extracting useful information from the various types of sequence data. Here, we will first discuss types of information contained in the various flavours of biological sequence data, and how this information can be interpreted to increase our scientific knowledge and understanding. We argue that a mechanistic understanding is required to consistently interpret experimental observations, and that this understanding is greatly facilitated by the generation and analysis of dynamic mathematical models. We conclude that, in order to construct mathematical models and to test mechanistic hypotheses, time-series data is of critical importance. We review diverse techniques to analyse time-series data and discuss various approaches by which time-series of biological sequence data was successfully used to derive and test mechanistic hypotheses. Analysing the bottlenecks of current strategies in the extraction of knowledge and understanding from data, we conclude that combined experimental and theoretical efforts should be implemented as early as possible during the planning phase of individual experiments and scientific research projects.

This study aims at highlighting and illustrating innovative actions for achieving and/or improving accurate medical coding within a DRG system. Already grouped DRG cases recorded in the first DRG implementation year in the region of Crete were examined. A sample of 130,000, from a total of 200,000, cases were analyzed and audited, through a process consisting of three stages: i) Digitalization, ii) Auditor training, and iii) Control & consultation. The results indicated that a significant proportion of DRG coding, with a length of stay exceeding one day, were reclassified into different DRG categories. This was not primarily due to errors in the selection of the principal diagnosis, but resulted from coding errors, including omission of secondary diagnoses coding, medical procedures that should have been included and utilization of less specific codes. The findings indicate that medical coding control, consulting services and the adoption of targeted actions can be instrumental for proper implementation of the DRG coding system, and for ensuring transparent allocation of resources within hospitals. Ultimately, this shortens the coding learning curve, from four years typically reported in the international literature, to a more efficient duration of two years, leading to rational and documented clinical and financial management of hospitals.

Cardiovascular diseases, which are the leading cause of death in Croatia, are linked to the high prevalence of hypertension. Both are associated with high salt intake which was determined almost two decades ago when Croatian Action on Salt and Health (CRASH) was launched. The main objective of the present study was to evaluate salt, potassium, and iodine intake using 24-hour urine samples in a random sample of adult Croatian population, and to analyse trends in salt consumption after the CRASH has intensively started. Methods: In this national population-based EH-UH 2 study performed in 2018-2021 (participation rate 72 %) in which we analysed a single 24-hour urine sample, 1067 adult participants (mean age 57.12 (SD 13.9), men 35%) were included. Results: Mean salt and potassium intakes were 8.6 g/day (IQR 6.2-11.2) and 2.8 g/day (IQR 2.1-3.5), respectively, with sodium-to-potassium ratio of 2.6 (IQR 1.8-3.3). We detected a decrease of 17.6% (2 g/day less) in salt consumption compared to our previous salt-mapping study. However, only 13.7%, and 8.9 % met the WHO salt and potassium recommended target of 5 g/day, and 3.5 g/day, respectively. Salt intake was higher, and potassium ingestion was lower in rural vs. urban regions, and in continental vs. Mediterranean parts of Croatia. Moderate to severe iodine insufficiency was determined in only 3% adult participants. Conclusion: In the last fifteen years, salt consumption has been significantly reduced in Croatian adult population because of intensive and broad CRASH program led by the Croatian Society of Hypertension and the Croatian Hypertension League. However, salt intake is still too high, and potassium ingestion is too low. With expected more active engagement and help of the government, obtained results could be significantly improved. Salt reduction programs are most cost-effective methods of cardiovascular disease prevention and should be considered more seriously by the government and health policy makers.

Background: Intrauterine transfusion (IUT) of the donor and partial exchange (pET) of the recipient is a temporizing treatment for pregnancies with Twin Anemia Polycythemia Sequence (TAPS). We sought to give a detailed description of the procedural approach and outcomes for sequential donor IUT and recipient pET in TAPS. Methods: Retrospective study of spontaneous TAPS referred to the Johns Hopkins Center for Fetal Therapy treated with donor IUT followed by recipient pET utilizing a double-syringe set up. Procedure characteristics and outcomes as well as accuracy of existing transfusion formulas were analyzed and compared with the literature. Results: 5 of 78 patients with spontaneous TAPS underwent a total of 19 combined IUT/pET procedures (median first procedure to delivery interval 5.6 weeks [interquartile range IQR 1.9-6.0]). One pET was stopped for fetal decelerations. Patients delivered at 33.0 weeks [IQR 31.9-33.3] with two survivors and no neonatal transfusion requirements. IUT volumes were 48 ml [IQR 39-63 ml] and pET volumes 32 ml [IQR 20-50] utilizing aliquots of 5 – 20 mls for the latter (p=0.021). For IUTs, assumption of a fetal blood volume below 150 ml/kg underestimated required transfusion volume. For pETs, all formulas required adjustment of dilution volume based on bedside testing (p<0.05 for all). Conclusions: Donor transfusion followed by partial exchange in the recipient can prolong pregnancy in spontaneous TAPS and obviate the need for neonatal transfusion. A double-syringe setup facilitates efficient saline exchange. Because the accuracy of volume formulas is limited, bedside testing is recommended to achieve the target hemoglobin.

Debt collection companies buy overdue debts on the market in order to collect them and recover the highest possible amounts of a debt. The pursuit of debt recovery by employees of collection agencies is a very demanding task. The aim of the article is to propose a rule-based model for managing the process of mass debt collection in a debt collection company, which will make the debt collection process more efficient. To achieve this, we have chosen a decision tree as a machine learning technique best suited for creating rules based on extensive data from the debt collection company. The classification accuracy of the decision tree, regardless of the possibility of acquiring rule-based knowledge, proved to be the highest among the tested machine learning methods, with an accuracy rate of 85.5%. Through experiments, we generated 16 stable rules to assist in the debt collection process. The proposed approach allows for the elimination of debts that are difficult to recovery at the initial stage of recovery process and to decide whether to pursue amicable debt collection or to escalate the debt recovery process to legal action. Our approach also enables the determination of specific actions during each stage of the proceedings. Abandoning certain actions or reducing their frequency will alleviate the burden on collection agency employees and help avoid the typical burnout associated with this line of work. This is the path to making the organizational culture of a collection agency more sustainable. Our model confirms also the possibility of using data from debt collection companies to automatically generate procedural rules and automate the process of purchasing and collecting debts. However, this would require a larger set of attributes than what we currently possess.

Automatic and blind parameter estimation based on the inherent features of wireless signals is a major research area due to the fact that these techniques lead to the simplification of receivers, especially in terms of coarse synchronization, more importantly reduce down the signaling load at the control channels. Thus, in the literature many techniques are proposed to estimate a vast set of parameters including modulation types and orders, data and chip rates, phase and frequency offsets and so on. In this paper, a cyclostationary feature detection based method is proposed to estimate the carrier numbers and carrier spacing of carrier-aggregated direct sequence spread spectrum cellular signals blindly. The particular chip rate of the signal is also estimated through the process jointly. In the paper, after the proposed method is formulated, the measurement setup which is developed to collect the data for the validation of the proposed method is introduced. The measurement results are post-processed for performance analysis purposes. To that end, the method is investigated in terms of SNR values, different channel conditions, and measurement durations. Furthermore, the performance of the proposed method is compared with that of energy detection. Measurement results indicate superior performance of the proposed method under significant wireless channel impairments and in low-SNR regions.

Long non-coding RNAs (lncRNAs) are involved in numerous biological processes and serve crucial regulatory functions in both animals and plants. Nevertheless, there is limited understanding of lncRNAs and their patterns of expression and roles in sharks. In the current study, we systematically identified and characterized lncRNAs in the blue shark Prionace glauca from four tissues (liver, spleen, muscle, and kidney) using high-throughput sequencing and bioinformatics tools. A total of 21,932 high confidence lncRNAs were identified with 8,984 and 3,067 stably and tissue-specific expressed lncRNAs, respectively. In addition, a total of 45,007 differentially expressed (DE) lncRNAs were obtained among tissues, with kidney versus muscle having the largest numbers across tissues. DE lncRNAs trans target protein-coding genes were predicted, and functional gene ontology enrichment of these genes showed GO terms such as muscle system processes, cellular/metabolic processes, and stress and immune responses, all of which correspond with the specific biological functions of each tissue analyzed. These results advance our knowledge of lncRNAs in sharks and present novel data on tissue-specific lncRNAs, providing key information to support future functional shark investigations.

Genetic enhancement of grain production and quality is a priority in wheat breeding projects. In this study, we assessed two key agronomic traits - grain protein content (GPC) and thousand kernel weight (TKW) - across 179 Bulgarian contemporary and historic varieties, and landraces in three growing seasons. Significant phenotypic variation existed for both traits among genotypes and seasons, and no discernible difference was evident between the old and modern accessions. To understand the genetic basis of the traits, we conducted a genome-wide association study with MLM using phenotypic data from the crop seasons, best linear unbiased estimators, genotypic data from the 25K Infinium iSelect array, and a Bonferroni-adjusted p-value. As a result, 776 significant quantitative trait nucleotides (QTNs) organized into quantitative trait loci were discovered, including 25 loci associated with GPC, and 35 loci linked to TKW. Additionally, 14 environmentally stable QTNs were identified for TKW. Certain loci on chromosomes 2A, 3A, and 6B favorably in-fluenced GPC, while the locus on chromosome 6D impacted TKW positively. These loci may reflect novel associations to the two traits. Annotations for proteins involved in the remobilization of carbon and nitrogen from vegetative tissues to developing grain during senescence, as well as in the following buildup of resources required for seed germination, have been found for selected pu-tative candidate genes. These include genes coding for 11S globulin seed storage protein, glutamine synthetase, metalloendopeptidase, components of the ubiquitin/proteasome system, sucrose transporter, alpha-amylase, NAC and bZIP transcription factors. Our findings highlight promising genomic regions for targeted breeding programs aimed at improving grain yield and protein content.

In the global fight against malaria, standard vector control methods such as indoor residual spraying (IRS) and insecticide-treated nets (ITNs) are intended to protect inside residential structures and sleeping spaces. However, these methods can still leave individuals vulnerable to residual transmission from vectors that they may be exposed to outdoors. Nchelenge District in northern Zambia experiences high persistent malaria transmission even with ITNs and IRS in place. However, very few studies have examined outdoor vector activity. To assess diversity and abundance of outdoor foraging female anopheline mosquitoes, CDC light traps were used as proxy measures for mosquito host-seeking, set in three outdoor trapping schemes randomly assigned on different nights: 1) locations where people congregate at night outside of the house within the peri-domestic space, 2) animal pens or shelters, and 3) high human-trafficked areas, such as paths to latrines, where traps were baited with BG-Lure®. 1,087 total female anophelines were collected over a total of 74 trap nights. Anopheles funestus s.s. comprised the majority of the collection (86%), with An. gambiae s.s. (2%) and a highly diverse sampling of other anophelines (12%) making up the remainder. Plasmodium falciparum parasites were only detected in An. funestus (1%). No significant difference in species diversity or female anopheline abundance was detected between trapping schemes. Outdoor foraging anopheline mosquitoes, including a number of infectious An. funestus, may partially explain the difficulty of controlling malaria transmission in Nchelenge District where vector control is only targeted indoors. BG-Lure® shows some promise as an alternative to human-baited landing catch collections in this resource-poor setting.

A herbarium is a library of preserved dried samples of vascular plants, bryophytes, fungi, algae and/or lichens and is maintained for educational and scientific purposes, and provides detailed information on taxonomy, systematic, biodiversity, ecological and evolutionary research studies. Globally, there are more than 3,000 active public herbaria that preserve around 400 million exsiccates. In Brazil there are 216 active herbaria, of which 162 are indexed in the Index Herbariorum and in the Central region there are 21 herbaria. In this context, the Herbarium of the University of Brasília (international code UB) was founded in 1963 together with the Department of Botany of UnB and is located at the Institute of Biological Sciences of the Campus Darcy Ribeiro, Brasília and it is fifth largest herbarium in Brazil and presents approximately 277 thousand samples. In this study, we present here the bryophytes collection at the UB Herbarium. The collection of bryophytes of UB herbarium has 30,902 samples of all continents and 79 countries and most come from Brazil, Papua New Guinea, Malaysia, United States of America and Argentina.

Background: Acute ischemic stroke treatment has been thoroughly studied to identify strategies to reduce treatment times. However, many centers still do not meet the benchmark time metrics. Additionally, smaller centers often face longer treatment times, yet studies focus primarily on larger, more advanced centers. Objectives: The aim of this study is to analyze existing literature to understand the strategies implemented in primary and comprehensive stroke centers to reduce their treatment times, and categorize the studies based on methods used to improve their processes Results: Three main categories of improvements were identified in the literature: 1) standardization of processes, 2) resource management, and 3) data collection. Both primary and comprehensive stroke centers were able to reduce treatment times through standardization of processes. However, challenges such as variations in resources between hospitals and difficulties in integrating data collection software into workflow were highlighted. Additionally, many strategies to optimize resource management and data collection were conducted in comprehensive stroke centers, which can benefit primary stroke centers. Conclusions: Many existing strategies to improve treatment times are viable for both primary and comprehensive stroke centers. However, while data collection is recognized as crucial for process improvement, challenges persist in integrating data collection methods into clinical workflow. Proposed solutions include the development of easy-to-use software tailored to clinician needs.

Open source software such as OpenDSS has given a lot of help to distribution network researchers and educators. With high penetration of distributed renewable energy resources into distribution network, tradition distribution steady state analysis software such as OpenDSS is faced with difficulty in handling distributed generators. Three-phase distributed generators are often modeled in sequence frame while unbalanced distribution network are usually modeled in phase frame. So a load flow in sequence-phase coupled frame is proposed to handle models described in both frames. Voltage controlled DGs which are difficult to cope with in OpenDSS are handled in proposed program. The steady state analysis platform is programmed with open source Modelica language and the main aim of this paper is to introduce an open source platform for active distribution network steady analysis include load flow and short circuit analysis which can be easily adopted and improved by other educators and researchers.

Butirosins are naturally occurring aminoglycoside (AG) antibiotics featuring a 4,5-disubstituted 2-deoxystreptamine (2-DOS) with a (2S)-4-amino-2-hydroxybutyrate (AHBA) side chain. This side chain has been shown to confer resistance against AG modifying enzymes, leading to ongoing studies on the butirosin biosynthetic pathway and the corresponding enzymes. Butirosin is produced by Niallia (formerly Bacillus) circulans and Bacillus vitellinus, with most research focused on the former strain. To date, no whole genome analysis has been performed on B. vitellinus. In this study, we sequenced the complete genome of B. vitellinus NBRC 13296 and performed a comparative analysis of different butirosin biosyntheric gene clusters (BGCs), including those from N. circulans. The complete genome of B. vitellinus NBRC 13296 comprises a 6,331,192 base circlular chromosome with a GC content of 52.68%. The annotation revealed the presence of 5,605 CDSs, 70 tRNA genes, 30 rRNA genes, and 3 ncRNA genes in NBRC 13296. The highest dDDH and ANI values between NBRC 13296 and the most closely related type strain, Paenibacillus chitinolyticus KCCM 41400, were 97.8% and 98.66%, respectively. Based on these genome-based comparative analyses, we propose reclassifying B. vitellinus NBRC 13296 as P. chitinolyticus. Genome mining revealed 18 gene clusters encoding the biosynthesis of diverse secondary metabolites in the genome of B. vitellinus NBRC 13296, indicating the enormous biosynthetic potential of this strain. The predicted structural diversity of secondary metabolites includes aminoglycoside, PKS, NRPS, PKS-NRPS hybrids, metallophores, phosphonates, terpenes, β-lactones, and RiPP peptides. We then comparatively characterized butorosin BGCs previously studied in several N. circulans strains. Additionally, comparative genome analysis revealed complete butirosin BGCs identified from P. chitinolyticus KCCM 41400, P. chitinolyticus NRRL B-23119, P. chitinolyticus NRRL B-23120, P. chitinolyticus B-14908, P. chitinolyticus YSY-3.1, P. chitinolyticus JMW06, Paenibacillus sp. GbtcB18, Paenibacillus sp. HGH0039, Paenibacillus sp. MZ04-78.2. Finally, we identified the core region consisting of BtrS, BtrN, BtrM, BtrL, BtrA, BtrB, BtrC, BtrD, BtrD, BtrE, BtrF, BtrG, BtrH, BtrI, BtrI, BtrJ, BtrK, BtrO, BtrP, and BtrV, followed by an upstream region organizing BtrQ, BtrW, BtrX, BtrY, and BtrZ in the same transcriptional direction and sequential genetic arrangement, and a downstream region organizing various proteins based on BtrT, BtrR2, BtrU, and BtrR1. Our study provided insights into the reclassification of B. vitellinus NBRC 13296 to P. chitinolyticus and suggested the need for continued studies on butirosin biosynthesis from an enzymatic perspective.

The aim of this paper is to use the elementary method and the properties of the second kind Stirling numbers to study the congruence properties of a new sequence, which is closely related to Fubini polynomials and Euler numbers, and give some interesting congruences for it. This solves a problem proposed by the first author at an unpublished paper.

Most non-infectious diseases are associated with dysfunction of proteins or protein complexes. Аssociation between sequence and structure is analyzed since a long time, and analysis of sequence organization in domains and motifs is actual research area. A mathematical method is proposed here to identify the hierarchical organization of protein sequences. The method is based on pentapeptide as a unit of protein sequences. This method was applied on a non-homologous dataset of protein sequences. The analysis revealed 11 hierarchical levels of protein sequence organization, showing the relationship of these multiple fragments of sequences. Using different examples, we illustrated how the fragments of the spatial structure of protein correspond to the elements of the hierarchical structure of the protein sequence. A hierarchical structure is observed in the protein sequence. This methodology is an interesting basis for mathematically based classification of elements of spatial organization of proteins. Elements of the hierarchical structure of different levels of the hierarchy can be used for biotechnological and medical problems.

Magnetic resonance (MR) image acquisitions are usually time-consuming, limiting utilization in neuroimaging. For multiple sclerosis (MS) patients, MR imaging plays a major role in drug therapy decision-making. The purpose of this study was to evaluate whether an ultrafast, T2-weighted (T2w), deep-learning-enhanced (DL), echo-planar-imaging-based (EPI) fluid-attenuated inversion recovery (FLAIR) sequence (FLAIRUF) that has been targeting neurological emergencies so far might even be an option to detect MS lesions of the brain compared to conventional FLAIR sequences. Therefore, 17 MS patients were enrolled prospectively in this exploratory study. Standard MR protocols and ultrafast acquisitions were conducted at 3 tesla (T), including three-dimensional (3D)-FLAIR, turbo/fast spin echo (TSE)-FLAIR, and FLAIRUF. Inflammatory lesions were grouped by size and location. Lesion conspicuity and image quality were rated on an ordinal five-point Likert scale, and lesion detection rates were calculated. Statistical analyses were performed to compare results. Altogether, 568 different lesions were found. Data indicated no significant differences in lesion detection (sensitivity and positive predictive value [PPV]) between FLAIRUF and axially reconstructed 3D-FLAIR (lesion size ≥ 3 mm × ≥ 2 mm) and no differences in sensitivity between FLAIRUF and TSE-FLAIR (lesion size ≥ 3 mm total). Lesion conspicuity in FLAIRUF was similar in all brain regions except for superior conspicuity in the occipital lobe and inferior conspicuity in central brain regions. Further findings include location-dependent limitations of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) as well as artifacts such as spatial distortions in FLAIRUF. In conclusion, FLAIRUF could potentially be an expedient alternative to conventional methods for brain imaging in MS patients since the acquisition can be performed in a fraction of time while maintaining good image quality.

Hypoxia contributes to tumor progression and metastasis, and hypoxically dysregulated RNA molecules may thus be implicated in poor outcomes. Canine oral melanoma (COM) has a par-ticularly poor prognosis, and some hypoxia-mediated miRNAs are known to exist in this cancer; however, equivalent data on hypoxically dysregulated other non-coding RNAs (ncRNAs) are lacking. Accordingly, we aimed to elucidate non-miRNA ncRNAs that may be mediated by hy-poxia, targeting primary-site and metastatic COM cell lines and clinical COM tissue samples in next generation sequencing (NGS), with subsequent qPCR validation and quantification in COM primary and metastatic cells, plasma and extracellular vesicles (EVs) for any identified ncRNA of interest. Findings suggested that a number of non-miRNA ncRNA species are hypoxically up- or downregulated in COM. We identified one ncRNA, the long ncRNA fragment EN-SCAFT00000084705.1, as a molecule of interest due to its consistent downregulation in COM tissues, hypoxically and normoxically cultured primary and metastatic cell lines, when compared to the oral tissues from healthy dogs. However, this molecule was undetectable in plasma and plasma EVs, suggesting that its expression may be tumor tissue-specific and it has little potential as a biomarker. Here, we provide evidence of hypoxic transcriptional dysregulation for ncRNAs other than miRNA in COM for the first time, and suggest that ncRNA ENSCAFT00000084705.1 is a molecule of interest for future research on the role of the transcriptome in hypoxia-mediated progression of this aggressive cancer.

Northern Nigeria has had historically low antenatal care (ANC) utilization rates with poor health outcomes. Group antenatal care (gANC) improves ANC behavior and pregnancy outcomes. The gANC has been adopted in Kaduna and Kano states, Nigeria. This paper presents baseline findings from the evaluation of the implementation of the gANC program to support scaling in real-world setting. Baseline data were collected from a cohort of 1,269 and 1,200 pregnant women enrolled in gANC at health facilities in Kaduna and Kano states respectively from March to April 2024. Analyses of socio-demographic and pregnancy behavior data were done. Participants were mostly between ages 19-31 years, married or living with a partner, with over 50% having their own businesses. Over 62% and 34% had completed secondary or higher-level education with 60% and 80% living in urban areas in Kaduna and Kano States, respectively. In Kano State, >60% of the women had their last delivery at home with 41.6% not assisted by a skilled birth attendant. In Kaduna, >63% delivered in the hospital and >50% had skilled attendance during labor. Almost half had not used contraceptives previously. Subsequent longitudinal data will examine the impact of gANC utilization on perinatal outcomes.

Transportation systems through the ages have seen drastic evolutions in terms of transportation methods, speed of transport, infrastructure, technology, connectivity, influence on the environment, and accessibility. The massive transformation seen in the transportation sector has been fueled by the industrial revolutions which have continued expansion and progress into the fourth industrial revolution. However, the methodologies of data collection and processing used by the many drivers of this progress differ. The significance of standardized data and its mode of collection cannot be overstated as it has a major impact on the process outcomes. In order to achieve a better understanding of the impact of this, this study methodically reviewed the literature on the subject of the data collection and processing mechanism of 4IR technologies in the context of the transport industry with an emphasis on the areas of accomplishment of several studies and drawing attention to their limitations. Although the industry has advanced significantly as a result of the adoption and application of various research studies in this field, considerable focus needs to be placed in the areas of standardized data collection and computational approaches, which will ultimately assist in achieving various benefits in the transportation industry.

Seed longevity is a crucial trait for the seed industry and genetic resource preservation. To develop excellent cultivars with extended seed lifespans, it is important to understand the mechanism of keeping seed germinability long-term and to find useful genetic resources as prospective breeding materials. This study was conducted to identify the best cultivars with a high and stable seed longevity trait in the germplasm of rice (Oryza sativa L.) and to analyse the correlation between seed longevity and embryonic RNA integrity. Seeds from 69 cultivars of the world rice core collection selected by the NIAS in Japan were harvested in different years and subjected to long-term storage or controlled deterioration treatment (CDT). The long-term storage (4 °C, RH under 35%, 10 years) was performed on seeds harvested in 2010 and 2013. The seeds harvested in 2016 and 2019 were used for CDT (36 °C, RH of 80%, 40 days). Seed longevity and embryonic RNA integrity were estimated by a decrease in germination rate and RNA integrity number (RIN) after long-term storage, or CDT. The RIN value was obtained by electrophoresis of total RNA extracted from seed embryos. Seeds of ‘Vandaran (Indica)’, ‘Tupa 729 (Japonica)’, and ‘Badari Dhan (Indica)’ consistently showed higher seed longevity and embryonic RNA integrity both under long-term storage and CDT conditions, regardless of the harvest year. A strong correlation (R2 = 0.93) was observed between germination rates and RIN values of the seeds after long-term storage or CDT among nine cultivars selected based on differences in their seed longevity. The study findings revealed the relationship between rice seed longevity and embryo RNA stability and suggested potential breeding materials, including both Japonica and Indica cultivars, for improving rice seed longevity.

A solid structure, such as a road, building wall or envelop, used as a solar collector is considered an effective and new way to use renewable energy. This paper focused on the temperature characteristics of four structures exposed to sunshine: asphalt, red brick, composite cement and concrete road slab. Furthermore, the collected heat based on a hydraulic system was investigated experimentally. For the four structure slabs, their temperature differences are due to solar radiation absorption varied greatly by the material’s heat absorptance and color. Through the test, asphalt slab attained the highest temperature and had the weakest reflection among the structures. Compared with the others, the temperature of the asphalt slab was greater by 8.1%, 14.9% and 16.4% than the brick, composite cement and concrete, respectively. The reflection intensity growth ratio was defined and indicates the growth potential for absorbing radiation in the solid slab surface. From the experiments, it was concluded that a suitable selection of road materials can greatly improve the thermal absorption, conduction and penetration into the solid slab. The collected heat capability was approximately 250 W/m2 to 350 W/m2 in the natural summer condition. A black coating or a surface modification can collect more heat, reaching greater than 250 W/m2. The solar collecting heat efficiency with a surface configuration of the road slab can reach above 30% in the summer time.

Seventy-two clavarioid specimens from Johannes Rick's collection at PACA and BPI were revised. Eleven taxa are presented. Illustrations of the microstructures are provided for nine taxa. The description of Scytinopogon robustus includes information from recently collected specimens and SEM photographs of the basidiospores. A new combination, Clavulina ridleyi, is proposed.

Background. Inferior quality of biological material compromises data, slows discovery, and wastes research funds. The gut microbiome plays a critical role in human health and disease, yet little attention has been given to optimizing collection and processing methods of human stool. Methods. We collected the entire bowel movement from 2 healthy volunteers: one to examine stool sample heterogeneity and one to test stool sample handling parameters. Sequencing and bi-oinformatic analyses were used to examine the microbiome composition. Results. The microbiome profile varied depending on where the subsample was obtained from the stool. The exterior cortex of the stool was rich in specific phyla and deficient in others while the interior core of the stool revealed opposite microbiome profiles. Sample processing also re-sulted in varying microbiome profiles. Homogenization and stabilization at 4°C gave superior microbial diversity profiles compared to the fresh or frozen subsamples of the same stool sample. Bacterial proliferation continued in the fresh subsample when processed at ambient temperature. Bacteroidetes proliferated and Firmicutes diminished during the 30-minute processing of fresh sample. The frozen sample had good overall diversity but Proteobacteria diminished likely be-cause of the freeze/thaw. Conclusions. The microbiome profile is specific to the section of the stool being sampled. Stool sample collection, homogenization, and stabilization at 4°C for 24 hours provides a “neat”, high-quality sample of sufficient quantity that can be banked into aliquots with nearly identical microbial diversity profiles. This collection pipeline is essential to accelerate our understanding of the gut microbiome in health and disease.

The nucleosome is a major modulator of DNA accessibility to other cellular factors. Nucleosome positioning has a critical importance in regulating cell processes such as transcription, replication, recombination or DNA repair. The DNA sequence is a major factor influencing the position of nucleosomes on genomes. Different sequence motifs can promote or inhibit the nucleosome formation, thus influencing the accessibility to the DNA. Sequence-encoded nucleosome positioning having functional consequences on cell processes can then be selected or counter-selected during evolution. We review the interplay between sequence evolution and nucleosome positioning evolution. We first focus on the different ways to encode nucleosome positions in the DNA sequence, and to which extent these mechanisms are responsible of genome-wide nucleosome positioning in vivo. Then, we discuss the findings about selection of sequences for their nucleosomal properties. Finally, we illustrate how the nucleosome can directly influence sequence evolution through its interactions with DNA damage and repair mechanisms. This review aims to provide an overview of the mutual influence of sequence evolution and nucleosome positioning evolution, possibly leading to complex evolutionary dynamics.

Personalized recommendation is an important part of ecommerce platform. In the recommendation system, the neural network is used to enhance collaborative filtering to accurately capture user preferences, so as to obtain better recommendation performance. Traditional recommendation methods focused on the results of a single user behavior, ignoring the modeling of multiple interactive behaviors of users, such as click, add to cart and purchase. Although many studies had also focused on Multi-Behavior modeling, two important challenges remained. 1)Since multiple behavioral context information was ignored, it was still a challenge to identify the multimodal relationships of behaviors. 2) Surveillance signals were still sparse. In order to solve this problem, this paper proposes Two-path Multi-behavior Sequence Modeling(TP_MB). First, a two-path learning strategy is introduced to maximize the multiple interaction information of user items learned by the two paths, which effectively enhances the robustness of the model. Second, a multi-behavior dependent encoder is designed. Contextual information is obtained through behavior dependencies in the interaction of different user. In addition, three contrastive learning methods are designed, which not only obtain additional auxiliary supervision signals, but also alleviate the problem of sparse supervision signals. Extensive experiments on two real datasets demonstrate that our method outperforms state-of-the-art multi-behavior recommendation methods.

We study various properties of the polygonal numbers; such as their recurrence relations; fundamental identities; weighted binomial and ordinary sums; partial sums and generating functions of their powers; and a continued fraction representation for them. A feature of our results is that they are presented naturally in terms of the polygonal numbers themselves and not in terms of arbitrary integers; unlike what obtains in most literature.

Alzheimer´s Disease (AD) has currently no effective treatment; however, preventive measures can significantly delay the progress/onset of the disease. Thus, accurate and early prediction of risk is an important strategy to alleviate the AD burden. Neuroinflammation is a major factor prompting the onset of the disease. Inflammation exerts its toxic effect via multiple mechanisms. Amongst others, it is affecting gene expression via modulation of non-coding RNAs (ncRNAs), such as miRNAs. Recent evidence supports that inflammation can also affect long non-coding RNAs (lncRNAs) expression. While the association between miRNAs and inflammation in AD has been extensively studied, the role of lncRNAs in neurodegenerative diseases has been less explored. In this review, we focus on lncRNAs and inflammation in the context of AD. Furthermore, since plasma-isolated extracellular vesicles (EVs) are increasingly recognized as an effective monitoring strategy of brain pathologies, we have focused on the studies reporting dysregulated lncRNAs in EVs isolated from AD patients and controls. The revised literature shows a positive association between pro-inflammatory lncRNAs and AD. However, the reports evaluating lncRNAs alterations in EVs isolated from plasma of patients and controls, although still limited confirm the value of specific lncRNAs associated with AD as reliable biomarkers. This is an emerging field that will open new avenues to improve risk prediction, patients’ stratification and may lead to the discovery of potential novel therapeutic targets for AD

For decades since the central dogma, cancer biology research has been focusing on the involvement of genes encoding proteins. It has been not until more recent times that a new molecular class has been discovered, named non-coding RNA (ncRNA), which has been shown to play crucial roles in shaping the activity of cells. An extraordinary number of studies into shown that ncRNAs represent an extensive and prevalent group of RNAs, including both oncogenic or tumor suppressive molecules. Henceforth, various clinical trials involving ncRNAs as extra ordinary biomarkers or therapies have started to emerge. In this review, we will focus on the prognostic and diagnostic role of ncRNAs for breast cancer.

Although Shannon mutual information has been widely used, its effective calculation is often difficult for many practical problems, including those in neural population coding. Asymptotic formulas based on Fisher information sometimes provide accurate approximations to the mutual information but this approach is restricted to continuous variables because the calculation of Fisher information requires derivatives with respect to the encoded variables. In this paper, we consider information-theoretic bounds and approximations of the mutual information based on Kullback--Leibler divergence and Rényi divergence. We propose several information metrics to approximate Shannon mutual information in the context of neural population coding. While our asymptotic formulas all work for discrete variables, one of them has consistent performance and high accuracy regardless of whether the encoded variables are discrete or continuous. We performed numerical simulations and confirmed that our approximation formulas were highly accurate for approximating the mutual information between the stimuli and the responses of a large neural population. These approximation formulas may potentially bring convenience to the applications of information theory to many practical and theoretical problems.

As an important part of coastal wetlands, tidal flats provide unique ecosystem services and functions. China's tidal flats are significantly threatened by industrialization, urbanization, aquaculture expansion and coastline reconstruction, and there is an urgent need for sustainable strategies to balance the protection and utilization of tidal flats. Remote sensing technology can realize large-scale spatiotemporal research of tidal flats, comprehensively improve the ecological environment of coastal zones, and help achieve the overall planning goals of major projects for the protection and restoration of important ecosystems in China. In this study, based on GEE (Google Earth Engine) platform, Sentinel-2 (MSI), Landsat 8 (OLI) and Landsat 9 (OLI-2) remote sensing images were used to construct multi-source intensive time series remote sensing image collection, combined with MSIC-OA algorithm. The tidal flat data of China in 2018 and 2023 are extracted and analyzed. The results show as follows: 1) The overall classification accuracy of the tidal flat in 2023 is 95.19%, and the Kappa coefficient is 0.89; In 2018, they were 92.77% and 0.83 respectively. 2) The total tidal flat area in 2018 and 2023 is 8300.34km2 and 8151.54km2, respectively, a decrease of 148.80km2; 3) In 2023, estuarine and bay tidal flats will account for 54.88% of the total area, and most of the tidal flats will be distributed near river inlets and bays; 4) In 2023, the total length of the coastline adjacent to the tidal flat is 10196.17km, of which the artificial shoreline accounts for 67.06%, and the development degree of the tidal flat is 2.04, indicating that most of the tidal flat has been developed and utilized. The research results provide valuable reference for the scientific planning and rational utilization of tidal flat resources in China.

The switching game Lights Out and its variants were studied extensively as recreational mathematics problems. The game board of the ordinary Lights Out is a rectangular grid of lights, where each light is either on or off. By clicking a light, the clicked light and its adjacent rectilinear neighbors are toggled. Given an arbitrary initial configuration of lights, the final mission is to “solve” this game by switching off all the lights. Most studies on Lights Out and its variants focused on the solvability of given games or the number of solvable games, but when the game is viewed in a coding-theoretical perspective, more interesting questions with special meanings in coding theory will naturally pop up, such as finding the minimal number of lit lights among all solvable games except the solved game, or finding the minimal number of lit lights that the player can achieve from a given unsolvable game, etc. However, these problems are usually hard to be solved in general in terms of algorithmic complexity. This study considers a natural extension of the Lights Out game, which enlarges the toggle pattern in a way that all the lights in the same row and those in the same column of the clicked light are toggled. This variant of Lights Out is a two-state version of a switching game called Alien Tiles. In this paper, we investigate the properties of the two-state Alien Tiles, and discuss several coding-theoretical problems about this game. Then, we apply this game as an error correction code and investigate its optimality. We also give a brief overview on algorithmic complexity and coding theory for readers who are not familiar with these topics. The purpose of this paper is to propose ways of playing switching games in a think-outside-the-box manner, which benefit the recreational mathematics community.

Network coding approaches typically consider an unrestricted recoding of coded packets in the relay nodes for increased performance. However, this can expose the system to pollution attacks that cannot be detected during transmission, until the receivers attempt to recover the data. To prevent these attacks while allowing for the benefits of coding in mesh networks, the Cache Coding was proposed. This protocol only allows recoding at the relays when the relay has received enough packets to decode an entire generation of packets. At that point, the relay node recodes and signs the recoded packets with its own private key allowing for the system to detect and minimize the effect of pollution attacks and make relays accountable for changes on the data. This paper analyzes the delay performance of Cache Coding to understand the security-performance trade-off of this scheme. We introduce an analytical model for the case of two relays in an erasure channel relying on an Absorbing Markov Chain and a approximate model to estimate the performance in terms of the number of transmissions before successfully decoding at the receiver. We confirm our analysis using simulation results. We show that Cache Coding can overcome security issues of unrestricted recoding with only a moderate decrease in system performance.

Small non-coding RNAs (sRNAs) are ubiquitously found in the three domains of life playing large-scale roles in gene regulation, transposable element silencing, and defense against foreign elements. While a substantial body of experimental work has been done to uncover function of sRNAs in Bacteria and Eukarya, the functional roles of sRNAs in Archaea are still poorly understood. Recently, high throughput studies using RNA-sequencing revealed that sRNAs are broadly expressed in the Archaea, comprising thousands of transcripts within the transcriptome during non-challenged and stressed conditions. Antisense sRNAs, which overlap a portion of a gene on the opposite strand (cis-acting), are the most abundantly expressed non-coding RNAs and they can be classified based on their binding patterns to mRNAs (3’ UTR, 5’ UTR, CDS-binding). These antisense sRNAs target many genes and pathways, suggesting extensive roles in gene regulation. Intergenic sRNAs are less abundantly expressed and their targets are difficult to find because of a lack of complete overlap between sRNAs and target mRNAs (trans-acting). While many sRNAs have been validated experimentally, a regulatory role has only been reported for very few of them. Further work is needed to elucidate sRNA-RNA binding mechanisms, the molecular determinants of sRNA-mediated regulation, whether protein components are involved, and how sRNAs integrate with complex regulatory networks.

Rhodes grass (Chloris gayana Kunth) is one of the most important forage grasses used throughout the tropical and subtropical regions of the world. Enhancing the conservation and use of genetic resources requires the development of knowledge and understanding about the existing global diversity of the species. In this study, 104 Rhodes grass accessions, held in trust in the ILRI forage genebank, were characterized using DArTSeq markers to evaluate the genetic diversity and population structure, and to develop representative subsets, of the collection. The genotyping produced 193,988 SNP and 142,522 SilicoDArT markers with an average polymorphic information content of 0.18 and 0.26, respectively. Hierarchical clustering using selected informative markers showed the presence of two and three main clusters using SNP and SilicoDArT markers, respectively, with a cophenetic correction coefficient of 82 %. Bayesian population structure analysis also showed the presence of two main subpopulations using both marker types indicating the existence of significant genetic variation in the collection. A representative subset, containing 21 accessions from diverse origins, was developed using the SNP markers. In general, the results revealed substantial genetic diversity in the Rhodes grass collection and the generated molecular information, together with the developed subset, should help enhance the management, use and improvement of Rhodes grass germplasm in the future.

Faba bean (Vicia faba L.) is one of the popular pulse crops grown for traditional consumption, not only in Oman but also in Arabian Peninsula and other countries. In Oman, the famers living in and around high altitude mountain areas mostly grow it. This paper highlights features of variation in seed color and diversity in seed traits found in collected accessions from the joint collecting missions of 2008-2010 between the staff of College of Agriculture, Sultan Qaboos University and the Ministry of Agriculture & Fisheries throughout all the governorates of the Sultanate of Oman. 41 seed samples/accessions were collected from 38 sites. Of these, the highest number of accessions was collected from Batinah South governorate (38) represented mostly by the mountains of Rustaq, followed by the mountains of Interior/Al-Dakhliyah (8), and Dhahira & Buraimi (4). Only one accession was collected from mountains of the Eastern/ Al-Sharqiyah governorates. The seed accessions were found diverse with respect to all seed characters studied, i.e. seed length (cm) and width (cm) and 1000-seed weight (g) besides seed color. The faba bean accessions were classified into 9 genetically diverse clusters based on the Principal Component Analysis, which indicated that the contribution of seed width and 100-seed weight to the total variation existing in indigenous germplasm collected from all the governorates of Oman. It was found from the critical analysis of seed colors that 38 accessions were homogenous (pure) with one color and three were heterogeneous with combinations of colors.

The adoption of recycling and proper disposal techniques for electronic waste is crucial in advancing sustainable development. The traditional approaches of motivating people for collection of electronic waste are often insufficient due to inadequate methods reaching out, given incentives, calculating appropriate incentives associate to the task of gathering the electronic waste and a lack of transparency in the entire process. Insufficient collection of electronic waste could result in the release of numerous harmful substances into the environment. Prior research studies have been carried out to tackle the issue of electronic waste management in a broad sense. The findings of those studies indicated diverse strategies, each of which is relevant solely to a restricted range of electronic waste reprocessing circumstances. The current study has presented a proposed technique for incentivizing tasks and activities associated to collection of electronic waste through the adoption of vector space technique. Blockchain smart contact technology, has been used to implemented the strategy. The method used involves various well defined mapping of tasks, nature of activities, and their magnitude in order to derived an incentive. Some scenarios for the calculations of incentives are presented, and the findings reveals the based case is by utilizing weighting scale scheme where each tasks and activities are mapped to its associated inventive rather than providing fixed incentive values. Ethereum was used as digital token for each unit of incentive. This concept has contributed in encouraging personal accountability in the management of e-waste collection in order to cultivate sustainable behaviors for a long term solution.

The newly discovered coronavirus (COVID-19) has disrupted traditional methods of conducting research, particularly qualitative research. However, there remains a number of methods by which qualitative data can still be collected. These include the use of digital voice, video, and text-based tools, online surveys, and content analysis. Text-based sources can help to overcome the limitations of time and space, and also can be cost-effective. This chapter draws from data collected from 12 participants across Zimbabwe and demonstrates how these tools can be used to generate data or to sample data that is already available to satisfy research questions and meet research objectives. It recommends researchers to experiment with new ways of collecting qualitative data while also observing safety protocols and ethical considerations.

Reptilians or reptoids are staple characters of Hollywood cinema, science fiction literature, ufology and conspiracy theories. Despite its popularity, the reptoid has been dismissed by the scientific community as a figment of the imagination; born out of a speculation, paranoia and wanton anthropocentricism. In this paper, however, the Life Sequence Conjecture is advanced as a supportive framework for the notion of reptilian extraterrestrials. This paper is not an argument in support of the existence of extraterrestrials. Rather, it proposes that if extraterrestrials do exist, then a reptilian morphology is not untenable.

This article presents description of social structures such as, for example, states, in the context of their consideration as systems. Moreover, it seeks to develop K. Marx’s model in terms of systematic approach, as well as to justify the application of the Fibonacci sequence to systems in general and, in particular, to social systems. The description covers a wide range of issues, ranging from the assertion of the basic principle of living systems as patterns in resources flows to description of functions of various aspects of states and their economics in terms of objective needs of such structures. In addition, this article justifies synergetic effect based on proposed model, and the way this effect is applied to state systems. Moreover, it includes the description of main changes of socio-economic formations throughout the history as a natural development of social systems, including possible modern stages. The fundamental tool of this description is description of essence of the mechanism of property rights and its formation options. The differences of two extreme directions of such development are also described on the basis of the objective factor of resource provision. Special attention is given to the concept of surplus value as a feature of social production systems.

Mitochondrial carriers facilitate the transfer of small molecules across the inner mitochondrial membrane (IMM) to support mitochondrial function and core cellular processes. In addition to the classical mitochondrial carrier family SLC25, the past decade led to the discovery of additional protein families that exhibit IMM localization and transporter-like properties. These include mitochondrial pyruvate carriers, sideroflexins and mitochondrial cation/H+ exchangers that have been linked to vital physiological functions and disease. Their structures and transport mechanisms are still largely unknown and understudied. Protein sequence analysis per se can often pinpoint hotspots that are of functional or structural importance. In this review, we summarize current knowledge about sequence features of mitochondrial transporters with a special focus on the newly included SLC54, SLC55 and SLC56 families of the SLC solute carrier superfamily. Taking a step further, we combined sequence conservation analysis with transmembrane segment and secondary structure prediction methods to extract residue positions and sequence motifs that likely play a role in substrate binding, binding site gating or structural stability. We hope that our review will help guide future experimental efforts by the scientific community to unravel the transport mechanism and structure of these novel mitochondrial carriers.

Myelodysplastic syndromes or neoplasms (MDS) are a heterogeneous group of myeloid clonal disorders characterized by peripheral blood cytopenias, blood and marrow cell dysplasia and an increased risk for evolution to acute myeloid leukemia (AML). Non-coding RNAs, especially microRNAs and long non-coding RNAs serve as regulators of normal and malignant hematopoiesis and have been implicated in carcinogenesis. This review will present a comprehensive summary of the biology and role of non-coding RNAs, including the less studied circRNA, siRNA, piRNA, and snoRNA as potential prognostic and/or predictive biomarkers or therapeutic targets in MDS.

Thyroid cancer is the most prevalent malignancy of the endocrine system and the ninth most common cancer globally. Despite the advances in the management of thyroid cancer, there are critical issues with the diagnosis and treatment of thyroid cancer that result in the poor overall survival of undifferentiated and metastatic thyroid cancer patients. Recent studies have revealed the role of different non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) that are dysregulated during thyroid cancer development or the acquisition of resistance to therapeutics, and may play key roles in treatment failure and poor prognosis of the thyroid cancer patients. Here, we systematically review the emerging roles and molecular mechanisms of ncRNAs that regulate thyroid tumorigenesis and drug response. We then propose the potential clinical implications of ncRNAs as novel diagnostic and prognostic biomarkers for thyroid cancer.

RNA polymerase II (RNAPII) frequently transcribes non-protein coding DNA sequences in eukaryotic genomes into long non-coding RNA (lncRNA). Here, we focus on the impact of the act of lncRNA transcription on nearby functional DNA units. Distinct molecular mechanisms linked to the position of lncRNA relative to the coding gene illustrate how non-coding transcription controls gene expression. We review the biological significance of the act of lncRNA transcription on DNA processing, highlighting common themes, such as mediating cellular responses to environmental changes. This review presents the background in chromatin signaling to appreciate examples in different organisms where we can interpret functions of non-coding DNA through the act of RNAPII transcription.

Exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access network. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this work, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.

With the advancement of next generation sequencing in past several years, a rising number of non-coding RNAs have been found as new actors to regulate gene expression. Non-coding RNAs not only play important roles in carcinogenesis, but also affect the clinical treatment strategies. They have been proved to be deeply correlated with chemoresistance in several cancers. Ovarian cancer is the leading cause of death in gynecological malignancy, with low 5-year survival rate. Most patients are identified when they have late-stage disease. This review mainly makes a compilation of the most relevant research literature in this field with the purpose of shedding light on the relation between ncRNAs and chemoresistance in ovarian cancer.

Ruderal plants designate native or exotic plant species that grow spontaneously in anthropized environments, being important from the ecological succession perspective. The present study aimed to identify ruderal plants that occur in an anthropized area of the Campus Floresta of the Federal University of Acre (UFAC), in the municipality of Cruzeiro do Sul, in the Southwest Amazon. Toward this end, botanical material was collected and herborized, and identified based on consultation of the specialized literature, virtual herbariums, and by comparison with the Campus Floresta Herbarium (CFCZS) collection. Finally, these data were incorporated to the referred collection, according to the usual plant systematics procedures. The results revealed 28 species and 28 genera, distributed in 14 families. The most representative families were Fabaceae, Malvaceae and Poaceae, with four species each; followed by Asteraceae with three; Cyperaceae, Rubiaceae and Amaranthaceae with two each; and the others, Plantaginaceae, Ochnaceae, Urticaceae, Verbenaceae, Iridaceae, Lamiaceae and Dennstaedtiaceae with one species each. The occurrence of predominant species in planted pastures, agricultural use, and mining area ecosystems is highlighted, such as Pteridium aquilinum (L.) Kuhn., Andropogon bicornis L., and Crotalaria micans Link.

This study analyzes the relevance of governance structure for efficiency in revenue collection by Brazilian Water Utilities (WUs). Data was collected from the National Information System on Sanitation (SNIS) for 127 Brazilian WUs, covering a balanced longitudinal panel from 2018 to 2022. Governance structures evaluated included Ownership (public or private) and Corporatization (publicly traded or not). In scientific databases, we did not find studies on the efficiency of specific WUs in collecting customer bills for services rendered and its relationships with this type of governance; thus, this would be the main innovative contribution of the study to the previous literature. In the first stage, the study utilized the Dynamic Slack Based Model (DSBM) to assess revenue collection efficiency. In the second stage, an econometric model with Generalized Estimating Equations (GEE) to explain the efficiency. The findings revealed a global average inefficiency of 50.47% in revenue collection. Corporatization was linked to higher collection efficiencies, while ownership type was significant, but with lower collection efficiency. Factors such as tariff accessibility, urbanization, and the COVID-19 pandemic also influenced efficiency. The study suggests regulatory bodies should consider these insights to implement policies that prevent inefficiencies from affecting the tariff system for services.

Drinking water, in addition to the best-known chemical and biological agents, contains radionuclides of both natural and artificial origin, which can contribute significantly to the overall effective dose received by the population. The Italian Decree Law 28/2016, implementing 2013/51/EURATOM Directive, establishes the activities for risk management and the parameter values for different radionuclides activity concentrations. In addition to the institutions involved, annually the National Inspectorate for Nuclear Safety and Radiation Protection (ISIN), publishes the monitoring reports of environmental radioactivity in Italy, including radioactivity in drinking water. The purpose of the study was to integrate ISIN reports with 2018 to 2020 data by collecting measurements performed by institutional laboratories to obtain a more complete information and adding, for the Campania region, some data not yet published. This new updated report was not significantly different from ISIN’s one, meaning that those publications are nevertheless extremely representative of the radioactivity in Italian drinking water. However, the study allowed to obtain a more detailed data also including measurements not considered in ISIN reports, for instance, radon-222 activity concentrations. This may be of great usefulness for all radiation protection stakeholders in order to ensure environmental protection, pollution prevention and the population safety.

A macro network loading Model for multi-flow lines, time-varying and pedestrian congestion is proposed. The station hub is abstracted as a network of different types of nodes, and the flow of passengers at each node is calculated in real time for the purpose of simulating the hub's collection and distribution process. For correct transmission of passenger flow on heterogeneous networks, three types of indexes are proposed to distinguish the nodes, and then match the corresponding fundamental diagrams. This paper divides the update process of the dynamic network loading model into multiple processes by flow lines, and improves computational speed of DNL model. The proposed model is applied to the simulation of passenger flow collection and distribution in an actual hub station with multi-flow lines. The analysis results illustrate that the model can accurately reflect the realistic congestion facilities and explain the formation process of high-density areas. A rolling passenger flow control model based on optimal control theory is proposed. The effectiveness of the control model is verified based on simulation data.

Recently, inter-vehicle communication, which helps to avoid collision accidents (by driving safety support system) and facilitate self-driving (by dissemination of road and traffic information), has attracted much attention. In this paper, in order to efficiently collect road / traffic information in the request / response manner, first a basic method, Content-centric network (CCN) for Vehicular network (CV), is proposed, which applies CCN cache function to inter-vehicle communication. Content naming and routing, which take vehicle mobility into account, are investigated. On this basis, the CV method is extended (named as ECV) to avoid the cache miss problem caused by vehicle movement, and is further enhanced (named as ECV+) to more efficiently exploit cache buffer in vehicles, caching content according to a probability decided by channel usage rate. Extensive evaluations on network simulator Scenargie, with realistic open street map, confirm that the CV method and its extensions (ECV, ECV+) effectively reduce the average number of hops of data packets (by up to 47%, 63%, 83% respectively) and greatly improve the content acquisition success rate (by up to 356%, 444%, 689%, respectively), compared to the method without cache mechanism.

Aging kills 100,000 people a day - more than any other cause of death combined. The exact causes of aging have been much discussed, but the most pressing issue with regard to aging appears to me to be lipofuscin accumulation. That is, the accumulation of indigestible cellular garbage that needs to be removed from our cells, then the body. In this piece, I will explain why I think “getting rid of the garbage” should be at least one of our main goals with regard to longevity research for now.

Histone deacetylases inhibitors (HDACi) have shown promising therapeutic outcomes in haematological malignancies such as leu-kaemia, multiple myeloma and lymphoma, with dis-appointing results in solid tumours when used as monotherapy. As a result, combination therapies either with radiation or other deoxyribonucleic acid (DNA) damaging agents have been suggested as ideal strategy to improve their efficacy in solid tumours. Numerous in vitro and in vivo studies have demonstrated that HDACi can sensitise malignant cells to both electromagnetic and particle types of radiation by inhibiting DNA damage repair. Although the radiosensitising ability of HDACi has been reported as early as 1990s, the mechanisms of radiosensitisation are yet to be fully understood. This review brings forth the various protocols used to sequence the administration of radiation and HDACi treatments in the different studies. The possible contribution of these various protocols on the ambiguity that surrounds the mechanisms of radiosensitisation is also highlighted.

In archaea, the interaction of HerA helicase and NurA nuclease is required for 3' end resection during DNA double-stranded break repair, and its disruption results in unfaithful DNA repair. Interactions transpire by the four conserved domains, including the N-terminal HAS domain (HerA-ATP synthase), which serves as a binding site for the exonuclease NurA. As a HerA hexamer between its two subunits, the conserved C-terminal brace extension functions as an adjacent brace. DBD serves as a location for DNA binding, which causes the DNA strands to be propelled into the HerA pocket to begin unwinding. The putative pore entrance is made up of four helix coils, which provide a suitable charged surface for guiding DNA into the pore. The crystal structure of the complex HerA-NurA allows for the identification of key residues for protein-protein interaction. This allows us to predict that most archaeal HerA orthologs will be able to interact with one another. Archaeal HerA is a homo-hexameric assembly featuring a DNA-binding domain that is conserved. It also features distinctive ATP and NurA binding domains. Following the structure and sequence conservation criteria, we find one HerA ortholog in Pyrobaculum calidifontis and multiple HerA paralogs, but the role of none is established yet.Interestingly, orthologous HerA is part of an Mre11-Rad50 gene neighborhood while paralogous HerA is not, which could be misleading. As a result, it sought to describe the criteria that may be used as a possible fingerprint for identifying orthologous genes, as well as the cloning procedures and tactics that would aid in the confirmation of the HerA helicase in P. calidifontis. Their confirmation is based on PCR cloning and characterization following HerA construct creation.

The advent of RNA-sequencing (RNA-Seq) technologies has markedly improved our knowledge and expanded the compendium of small non-coding RNAs, most of which derive from the processing of longer RNA precursors. In this review article, we will discuss about the biogenesis and function of small non-coding RNAs derived from eukaryotic ribosomal RNA (rRNA), called rRNA fragments (rRFs), and their potential role(s) as regulator of gene expression. This relatively new class of ncRNAs remained poorly investigated and underappreciated until recently, due mainly to the a priori exclusion of rRNA sequences—because of their overabundance—from RNA-Seq datasets. The situation surrounding rRFs resembles that of microRNAs (miRNAs), which used to be readily discarded from further analyses, for more than five decades, because we could not believe that RNA of such a short length could bear biological significance. As if we had not yet learned our lesson not to restrain our investigative, scientific mind from challenging widely accepted beliefs or dogmas, and from looking for the hidden treasures in the most unexpected places.

Standardized protocols were specifically designed and developed for clinical information collection and obtaining trio genomic information from infants affected with congenital anomalies (CA) and their parents, as well as securing human biological resources. Two large areas of these protocols include clinical and genomic information collection on CA which were difficult to diagnose using pre-existing screening methods. The following are three essential items for the clinical information collection protocol: establishment of a consent system for collecting and utilizing research resources, establishment of criteria for target patient selection, and collection of clinical and epidemiological information. A total of 138 cases consisting of 45 families of infants with CA and their parent trios have provided human-derived resources and genomic information. Whole genome sequencing data have been generated for all participants, and standardized protocols for resource collection and manufacturing have been developed. Phenotype information according to the Human Phenotype Ontology term and major test findings were recorded. It is important to keep updating and recording clinical symptoms and genetic diagnosis that are newly added or changed over time. The unique significance of this study is to develop a protocol that enables long-term tracking by adding growth and development tests that reflect important characteristics of newborns. By using these clinical and genetic information collection protocols for CA, it will be possible to establish an essential platform for early genetic diagnosis and diagnostic research, and further present new genetic diagnostic guidelines in the near future.

Let an odd integer be represented as $\sum \limits_{n > m} 2^n + 2^m - 1$ for $m \geq 1$. To transform $2^m - 1$ according to the Collatz rules, the $3x + 1$ step is applied, resulting in the even integer $2^{m + 1} + 2^m - 2$. This even integer, being exactly once divisible by 2, becomes the next odd integer $2^m + 2^{m - 1} - 1$. This represents the growth phase, as exactly one even step follows an odd step. However, while the overall value of the integer increases, terms with decreasing indices are generated after each odd-even cycle. After $m$ such cycles, a term $2^{m - m}$ (which equals 1) is generated, canceling the negative 1. Additional even steps are then performed until the integer becomes odd again, which occurs when the next smallest index becomes zero. The positive 1 thus obtained is written as $2^1 - 1$. If uninterrupted, the term $2^1 - 1$ has the trivial cycle $2^1 - 1 \rightarrow 2^2 \rightarrow 2^1 \rightarrow 2^1 - 1$. This represents the shrinkage phase, as two even steps follow an odd step. To interrupt the trivial cycle, a sequence such as $2^1 + 2^2 + \cdots$ must be available to combine with $2^1 - 1$ and produce $2^{\mathcal{M}} - 1$. This starts another growth phase that lasts for $\mathcal{M}$ odd-even cycles. Finally, there is an attempt to craft an integer that can escape the shrinkage phase in the Collatz dynamics.

In this paper, we investigate the recurrence properties of the generalized Tribonacci sequence and present how the generalized Tribonacci sequence at negative indices can be expressed by the sequence itself at positive indices.

Sequence alignment and comparison through pairwise, multiple, global and local techniques are the main principles that underpin comparative genomics. However, most of the algorithms used are alignment-based which imposed some limitations on their use and application. In an attempt to provide an alignment-free alternative approaches, a methodology of comparative optinalysis and statistical mirroring was used and adopted to provide a suitable alternative for multiple genomic sequence comparison. In this article, methods comparison with MUSCLE, MUFFT, Clustal Omega, and T-Coffee was designed to assess the suitability and statistical power of statistical mirroring as an alternative method for multiple genomic sequences comparison using different sets of logically generated biological sequence datasets with different problems and computational complications. The results of the comparisons validate that statistical mirroring is a suitable alignment-free alternative approach for multiple genomic sequence comparison. The applied method (statistical mirroring) distinguishes itself over MUSCLE, MUFFT, Clustal Omega, and T-Coffee in specificity to a position-specific changes, specificity to a base-specific changes, cladogram and phylogenetic linearity, alignment independency, computational simplicity, and limit of input capacity.

In industry 4.0 era, video applications such as surveillance visual systems, video conferencing, or video broadcasting have been playing a vital role. In these applications, for manipulating and tracking objects in decoded video, the quality of decoded video should be consistent because it largely affects to the performance of the machine analysis. To cope with this problem, we propose a novel perceptual video coding (PVC) solution in which a full reference quality metric named Video Multimethod Assessment Fusion (VMAF) is employed together with a deep convolutional neural network (CNN) to obtain the consistent quality while still achieving the high compression performance. First of all, to achieve the consistent quality requirement, we propose a CNN model with an expected VMAF as input to adaptively adjust the quantization parameters (QP) for each coding block. Afterwards, to increase the compression performance, a Lagrange coefficient of Rate-Distortion optimization (RDO) mechanism is adaptively computed under Rate-QP and Quality-QP models. Experimental results show that the proposed PVC has achieved two targets simultaneously: the quality of video sequence is kept consistently with an expected quality level and the bit rate saving of the proposed method is higher than traditional video coding standards and relevant benchmark, notably with around 10% bitrate saving in average.

Long non-coding RNAs (LncRNAs) are mRNA-like molecules that do not encode for proteins and that are longer than 200 nucleotides. LncRNAs play important biological roles in normal cell physiology and organism development. Therefore, deregulation of their activities is involved in disease processes such as cancer. Lung cancer is the leading cause of cancer-related deaths due to late stage at diagnosis, distant metastasis, and high rates of therapeutic failure. LncRNAs are emerging as important molecules in lung cancer for their oncogenic or tumor suppressive functions. LncRNAs are highly stable in circulation, presenting an opportunity for use as non-invasive and early-stage cancer diagnostic tools. Here, we summarize latest works providing in vivo evidence available for LncRNAs role in cancer development, therapy-induced resistance, and their potential as biomarkers for diagnosis and prognosis, with a focus on lung cancer. Additionally, we discuss current therapeutic approaches to target LncRNAs. The evidence discussed here strongly suggests that investigation of LncRNAs in lung cancer in addition to protein-coding genes will provide a holistic view of molecular mechanisms of cancer initiation, development, and progression, and could open a new avenue for cancer treatment.

An adaptor signature can be viewed as a signature concealed with a secret value and, by design, any two of the trio yield the other. In a multiparty setting, an initial adaptor signature allows each party create additional adaptor signatures without the original secret. Adaptor signatures help address scalability and interoperabity issues in blockchain. They can also bring some important advantages to cryptocurrencies, such as low on-chain cost, improved transaction fungibility, and less limitations of a blockchain’s scripting language. In this paper, we propose a new two-party adaptor signature scheme that relies on quantum-safe hard problems in coding theory. The proposed scheme uses a hash-and-sign code-based signature scheme introduced by Debris-Alazard et al. and a code-based hard relation defined from the well-known syndrome decoding problem. To achieve all the basic properties of adaptor signatures formalized by Aumayr et al., we introduce further modifications to the aforementioned signature scheme. We also give a security analysis of our scheme and its application to the atomic swap. After providing a set of parameters for our scheme, we show that it has the smallest pre-signature size compared to existing post-quantum adaptor signatures.

The operations of digitization, transmission and storage of medical data, particularly images require increasingly effective encoding methods not only in terms of compression ratio and flow of information but also in terms of visual quality. At first, there was DCT (discrete cosine transform) then DWT (discrete wavelet transform) and their associated standards in terms of coding and image compression. After that, the 2nd generation wavelets seeks to be positioned and confronted to the image and video coding methods currently used. It is in this context that we suggested a method combining bandelets and SPIHT (set partitioning in hierarchical trees) algorithm. There are two main reasons for our approach: the first lies in the nature of the bandelet transform to take advantage by capturing the geometrical complexity of the image structure. The second reason stems in the suitability of encoding the bandelet coefficients by the SPIHT encoder. Quality measurements shows that in some cases (for low bit rates) the performances of the proposed coding compete with the well-established ones and opens up new application prospects in the field of medical imaging.

Information Technology (IT) has become an essential part of our lives and due to the emergence of Internet-of-Things (IoT), technology has encompassed a majority of things that humans rely on in their daily lives. Further, as IT becomes more relevant in daily lives, the need for IT to serve public emergency services has become more important. However, due to the infancy status of IoT, there is a need for a data consortium that would prove to be best used in servicing policing in a technological driven society. This paper will discuss the plausibility of creating a universal format for use in carrying out public services, such as emergency response by the police and regular law maintenance. In this research we will discuss what the police requires in their line-of-duty and how smart devices can be used to satisfy those needs. A data formatting framework is developed and demonstrated, with the goal of showing what can be done to unifying data from smart city sensors.

The Combined Elevation Test (CET) is a musculoskeletal screening technique (MST) replicates the streamline position in swimming and is commonly used in various sports. Although the CET is widely used, no normative data exist within an adolescent population. Therefore, the purpose of this study was to develop a normative data set for the CET within an adolescent population and to evaluate the influence various demographic and anthropometric variables. Data was collected for 416 participants aged between 8 and 18 years old. Age and arm span showed a significant correlation with CET scores (arm span r_s (105) = .478, p = .000, age r_s (416) = .238 p = .000). Regression analysis further quantified the influence of arm span and age on CET scores accounting for 23.1% and 5.3% of variability respectively. These results can be used as a reference point for clinicians and coaches who are using the CET within their assessment.

Abstract: The study used whole-genome sequencing (WGS) and bioinformatics analysis for the genomic characterization of 60 isolates of Listeria monocytogenes isolated from cattle farms, cattle abattoirs, and retail outlets in Gauteng province, South Africa. The isolates' sequence types (STs), clonal complexes (CCs), and lineages were determined using in silico multilocus sequence typing (MLST). We used BLAST-based analyses to identify virulence and antimicrobial genes, plasmids, proviruses, and the CRISPR-Cas system. The study investigated any association of the detected genes to the origin in the beef production chain of the L. monocytogenes isolates. Overall, in 60 isolates of Listeria monocytogenes, there were 7 STs, 6 CCs, 44 putative virulence factors, 2 resistance genes, 1 plasmid with AMR genes and 3 with conjugative genes, 1 CRISPR gene, and all 60 isolates were positive for proviruses. Among the 7 STs detected, ST204 (46.7%) and ST2 (21.7%) were the most prominent, with ST frequency varying significantly (p<0.001). The predominant CC detected were CC2 (21.7%) and CC204 (46.7%) in lineages I and II, respectively. Of the 44 virulence factors detected, 26 (across Listeria Pathogenicity Islands, LIPIs) were present in all the isolates. The difference in the detection frequency varied significantly (P<0.001). The two AMR genes (fosX and vga(G)) detected were present in all 60 (100%) isolates of L. monocytogenes. The only plasmid, NF033156, was present in 3 (5%) isolates. A CRISPR-Cas system was detected in 6 (10%), and all the isolates carried proviruses. Significant differences were detected in the frequencies of STs and virulence factors regarding the source and sample type of the L. monocytogenes isolates. The presence of both fosX and vga(G) genes in all the isolates from the three industries (cattle farms, abattoirs, and retail outlets) can potentially cause therapeutic implications. Our study, which characterized L. monocytogenes recovered from the three levels in the beef production chain in the country, provides the first evidence of the distribution of the pathogen with potential food safety and therapeutic implications.

Taking a DNA-sequence, a word with letters/bases A, T, G and C, as the relation between the generators of an infinite group $\pi$, one can discriminate two important families: (i) the cardinality structure for conjugacy classes of subgroups of $\pi$ is that of a free group on $1$ to $4$ bases and the DNA word, viewed as a substitution sequence, is aperiodic. (ii) The cardinality structure for conjugacy classes of subgroups of $\pi$ is not that of a free group, the sequence is generally not aperiodic and topological properties of $\pi$ have to be determined differently. The two cases rely on DNA conformations such as A-DNA, B-DNA, Z-DNA, G-quadruplexes, etc. We found a few salient results: Z-DNA, when involved in transcription, replication and regulation in a healthy situation implies (i). The sequence of telomeric repeats comprising $3$ distinct bases, most of the time, satisfies (i). For $2$-base sequences in the free case (i) or non free case (ii), the topology of $\pi$ may be found in terms of the $SL(2,\mathbb{C})$ character variety of $\pi$ and the attached algebraic surfaces. The linking of two unknotted curves --the Hopf link-- may occur in the topology of $\pi$ in cases of biological importance, in telomeres, G-quadruplexes, hairpins and junctions, a feature we already found in the context of models of topological quantum computing. For $3$- and $4$ base sequences, other knotting configurations are noticed and a building block of the topology is the $4$-punctured sphere. Our methods have the potential to discriminate potential diseases associated to the sequences.

Autonomous driving vehicles rely on sensors for the robust perception of surroundings. Such vehicles are equipped with multiple perceptive sensors with a high level of redundancy to ensure safety and reliability in any driving condition. However, multi-sensor, such as camera, LiDAR and radar, systems bring up the requirements related to sensor calibration and synchronization, which are the fundamental blocks of any autonomous system. On the other hand, sensor fusion and integration have become important aspects of autonomous driving research and directly determine the efficiency and accuracy of advanced functions such as object detection and path planning. Classical model-based estimation and data-driven models are two mainstream approaches to achieving such integration. Most recent research is shifting to the latter, showing high robustness in real-world applications but requiring large quantities of data to be collected, synchronized, and properly categorized. To generalize the implementation of the multi-sensor perceptive system, we introduce an end-to-end generic sensor dataset collection framework that includes both hardware deploying solutions and sensor fusion algorithms. The framework prototype integrates a diverse set of sensors, such as cameras, LiDAR, and radar. Furthermore, we present a universal toolbox to calibrate and synchronize three types of sensors based on their characteristics. The framework also includes the fusion algorithms, which utilize the merits of three sensors , namely, camera, LiDAR and radar, and fuse their sensory information in a manner that is helpful for object detection and tracking research. The generality of this framework makes it applicable in any robotic or autonomous applications, also suitable for quick and large-scale practical deployment.

Fusarium Head Blight (FHB) is a destructive disease affecting the grain yield and quality of wheat, barley, rye and triticale. Developing varieties with genetic resistance is integral to successfully managing FHB. However, significant knowledge gap exists in the genetic diversity present in triticale for FHB resistance. This information is critical for breeding new varieties of triticale as its production continues to increase. In the present study, a set of 298 winter triticale accessions from a worldwide collection were screened for their type-2 FHB resistance in an artificially inoculated misted nursery with high levels of inoculum density. Most of the triticale accessions were susceptible to FHB, and only 8% of accessions showed resistance in the field nursery screening. The resistant accessions identified in the nursery screening were selected and further screened for three years in greenhouse conditions. Seven accessions were found to show robust FHB resistance over the three years of greenhouse testing. Thirteen accessions showed significantly lower levels of Deoxynivalenol accumulation when compared to the susceptible triticale control. The accessions identified in the study will be useful in triticale and wheat breeding programs for enhancing FHB resistance and reducing DON accumulation.

General Variable Neighborhood Search (GVNS) is a well known and widely used metaheuristic for efficiently solving many NP-hard combinatorial optimization problems. Quantum General Variable Neighborhood Search (qGVNS) is a novel, quantum inspired extension of the conventional GVNS. Its quantum nature derives from the fact that it takes advantage and incorporates tools and techniques from the field of quantum computation. Travelling Salesman Problem (TSP) is a well known NP-Hard problem which has broadly been used for modelling many real life routing cases. As a consequence, TSP can be used as a basis for modelling and finding routes for Geographical Systems (GPS). In this paper, we examine the potential use of this method for the GPS system of garbage trucks. Specifically, we provide a thorough presentation of our method accompanied with extensive computational results. The experimental data accumulated on a plethora of symmetric TSP instances (symmetric in order to faithfully simulate GPS problems), which are shown in a series of figures and tables, allow us to conclude that the novel qGVNS algorithm can provide an efficient solution for this type of geographical problems.

Attention-Deficit/Hyperactivity Disorder (ADHD) is traditionally conceptualized within a framework emphasizing deficits in attention regulation and executive function. However, emerging evidence from the fields of predictive coding and active inference offers a novel lens through which to understand ADHD not merely as a disorder of attention but as a distinctive mode of neurodevelopmental variation. This article synthesizes current research and theoretical models to propose a redefined understanding of ADHD. By integrating insights from Ryan Smith, Paul B. Badcock, and Karl J. Friston (2020), we explore the premise that individuals with ADHD exhibit unique differences in how their brains generate and update predictions about the environment, leading to the characteristic behavioral patterns observed in ADHD. We argue that ADHD involves a divergence in the brain's predictive models and its ability to minimize prediction error, impacting sensory processing, attention allocation, and motor control. This perspective illuminates the adaptive aspects of ADHD, suggesting that what are often labeled as deficits could also reflect an adaptive fit to certain environmental contexts, underscoring the role of neurodiversity in human evolution. Furthermore, we discuss the implications of this reconceptualization for clinical practice, including diagnosis, treatment, and support for individuals with ADHD, emphasizing approaches that align with their unique neurocognitive profiles. This article calls for a shift in the narrative surrounding ADHD, advocating for a model that appreciates the complexity and adaptiveness of neurodevelopmental diversity.