Analysis Of Features Research Articles

Influence of gender dimorphism on audience engagement in podcasts: a machine learning analysis of dynamic affective linguistic and paralinguistic features

Influence of gender dimorphism on audience engagement in podcasts: a machine learning analysis of dynamic affective linguistic and paralinguistic features

Mass Spectral Feature Analysis of Ubiquitylated Peptides Provides Insights into Probing the Dark Ubiquitylome.

Ubiquitylation is a structurally and functionally diverse post-translational modification that involves the covalent attachment of the small protein ubiquitin to other protein substrates. Trypsin-based proteomics is the most common approach for globally identifying ubiquitylation sites. However, we estimate that such methods are unable to detect ∼40% of ubiquitylation sites in the human proteome, i.e., "the dark ubiquitylome", including many important for human health and disease. In this meta-analysis of three large ubiquitylomic data sets, we performed a series of bioinformatic analyses to assess experimental features that could aid in uniquely identifying site-specific ubiquitylation events. Spectral predictions from Prosit were compared to experimental spectra of tryptic ubiquitylated peptides, revealing previously uncharacterized fragmentation of the diGly scar. Analysis of the LysC-derived ubiquitylated peptides reveals systematic, multidimensional peptide fragmentation, including diagnostic b-ions from fragmentation of the LysC ubiquitin scar. Comprehensively, these findings provide diagnostic spectral signatures of modification events that could be applied to new analysis methods for nontryptic ubiquitylomics.

Cable Insulation Defect Prediction Based on Harmonic Anomaly Feature Analysis

Cable Insulation Defect Prediction Based on Harmonic Anomaly Feature Analysis

Enhanced prognostic signature for lung adenocarcinoma through integration of adjacent normal and tumor gene expressions

BackgroundCancer prognosis-related signatures have traditionally been constructed based on gene expression profiles derived from tumor or normal tissues. However, the potential benefits of incorporating gene expression profiles from both tumor and normal tissues to improve signature performance have not been explored. MethodsIn this study, we developed three prognostic models for lung adenocarcinoma (LUAD) using gene expression profiles from tumor tissues, normal tissues, and a combination (COM) of both, sourced from The Cancer Genome Atlas (TCGA). To ensure comparability, the same workflow was followed for all three models. ResultsWhen applied to the TCGA LUAD dataset, the tumor-derived model exhibited the best overall performance, except in calibration analysis, where the normal-derived model performed better. The COM-derived model demonstrated intermediate performance. Validation on three independent test datasets revealed that the COM-derived model showed the best performance, while the normal-derived model showed the worst. In overall survival (OS) analysis, the low-risk group defined by the COM-derived model consistently exhibited longer mean survival times. The tumor-derived model did not consistently show this trend, and the normal-derived model produced opposite results. In discrimination analysis, no significant differences were observed. The COM-derived model demonstrated good discrimination ability for short periods, while the tumor-derived model performed better for longer periods. In calibration analysis, both the COM and tumor-derived models had similar absolute prediction errors, which were better than those of the normal-derived model. However, the tumor-derived model tended to underestimate survival rates. The clinical feature analysis and validation in GSE229705 indicate that the risk score (RS) from the COM model is the most clinically significant. These results demonstrate that the COM model's RS aligns more closely with clinical data, maintaining stable performance and the strongest generalizability. ConclusionsOverall, the COM-derived model demonstrated the best generalization ability. The superior performance of the tumor-derived model in the TCGA LUAD dataset might be due to overfitting. Our results suggest that appropriate combinations of gene expression data from tumor and normal tissues can enhance the predictive power of prognostic signatures.

Urethral tissue characterization using multiparametric ultrasound imaging

A decrease in urethral closure pressure is one of the primary causes of stress urinary incontinence in women. Atrophy of the urethral muscles is a primary factor in the 15 % age-related decline in urethral closure pressure per decade. Incontinence not only affects the well-being of women but is also a leading cause of nursing home admission. The objective of this research was to develop a noninvasive test to assess urethral tissue microenvironmental changes using multiparametric ultrasound (mpUS) imaging technique. Transperineal B-scan ultrasound (US) data were captured using clinical scanners equipped with curvilinear or linear transducers. Imaging was performed on volunteers from our institution medical center (n = 15, 22 to 76 y.o.) during Valsalva maneuvers. After expert delineation of the region of interest in each frame, the central axis of the urethra was automatically defined to determine the angle between the urethra and the US beam for further analysis. By integrating angle-dependent backscatter with radiomic texture feature analysis, a mpUS technique was developed to identify biomarkers that reflect subtle microstructural changes expected within the urethral tissue. The process was repeated when the urethra and US beam were at a fixed angle. Texture selection was conducted for both angle-dependent and angle-independent results to remove redundancies. Ultimately, a distinct biomarker was derived using a random forest regression model to compute the urethra score based on features selected from both processes. Angle-dependent backscatter analysis shows that the calculated slope of US mean image intensity decreased by 0.89 (±0.31) % annually, consistent with the expected atrophic disorganization of urethral tissue structure and the associated reduction in urethral closure pressure with age. Additionally, textural analysis performed at a specific angle (i.e., 40 degrees) revealed changes in gray level nonuniformity, skewness, and correlation by 0.08 (±0.04) %, −2.16 (±1.14) %, and −0.32 (±0.35) % per year, respectively. The urethra score was ultimately determined by combining data selected from both angle-dependent and angle-independent analysis strategies using a random forest regression model with age, yielding an R2 value of 0.96 and a p-value less than 0.001. The proposed mpUS tissue characterization technique not only holds promise for guiding future urethral tissue characterization studies without the need for tissue biopsies or invasive functional testing but also aims to minimize observer-induced variability. By leveraging mpUS imaging strategies that account for angle dependence, it provides more accurate assessments. Notably, the urethra score, calculated from US images that reflect tissue microstructural changes, serves as a potential biomarker providing clinicians with deeper insight into urethral tissue function and may aid in diagnosing and managing related conditions while helping to determine the causes of incontinence.

Impact of extracorporeal blood pump gap sizes on the performance and hemocompatibility under off-design operation.

Hemocompatibility remains the dominant challenge in rotary blood pumps, and more information on the relationship between individual pump design features, hemodynamics, and blood trauma in various operation conditions is necessary. The study evaluated the variation of gap sizes in extracorporeal blood pumps concerning their influence on blood compatibility, particularly during off-design conditions. We developed a parametric generic blood pump framework for in-silico and in-vitro design feature analysis. Thirty-six designs with varying axial and radial gap sizes between 0.5 mm and 3 mm were generated. CFD was applied to calculate and compare device hemodynamics and evaluate the performance and hemocompatibility during off-design and target operation conditions. The following quantities were analyzed: pressure difference, hemolysis potential, residence times, hydraulic efficiency, and recirculation ratio. The in-vitro prototype showed excellent agreement with in-silico predictions regarding hydraulic performance (R2 = 0.996 with a RMSE = 2.07). Our results show a modest impact of gap size variations ±10% on key metrics. Domain-resolved analyses revealed a significant contribution of the gap regions to the device's overall hemolytic performance, with an increasing contribution for off-design flow rates. Overall elevated hemolysis levels were identified if at least one gap size was held minimal. We introduced and showed the feasibility of a parametric rotary blood pump framework to systematically investigate design feature impact. Results suggest, larger and uniformly sized gaps being overall beneficial regarding hemocompatibility.

Multimodal, microspectroscopic speciation of legacy phosphorus in two US mid‐Atlantic agricultural soils

AbstractTo understand phosphorus (P) mobility in agricultural soils and its potential environmental risk, it is essential to directly measure solid phase P speciation. Often, bulk P K‐edge X‐ray absorption near edge structure (XANES) spectroscopy followed by linear combination fitting (LCF) is utilized to determine the solid P phases in soil. However, this method may limit results to only a few major phases. Additionally, XANES spectra for different P species may have very similar features, leading to an over‐ or underestimate of their contribution to LCF. Here, an improved P speciation by pairing multimodal microbeam‐X‐ray fluorescence (µ‐XRF) mapping coupled with µ‐XANES (microbeam‐X‐ray absorption near edge structure) analysis to directly speciate major and minor P phases on the micron scale is provided. We combined maps of both tender (P, sulfur, aluminum, and silicon) and hard energy (calcium, iron [Fe], and manganese) elements to evaluate the elemental co‐locations with P. To better account for uncertainty assigning XANES peaks to individual compounds, a more quantitative fingerprinting by “spectral feature analysis” was completed. With this analysis, an R‐factor is reported for the fit. These results were compared to traditional LCF. Pre‐edge fitting results revealed the presence of a two‐component pre‐edge feature for phosphate adsorbed to ferrihydrite. Additionally, phytate co‐precipitated with ferrihydrite (Phytate‐Fe‐Cop) had a pre‐edge feature, indicating direct association with Fe. Lastly, a unique P species associated with manganese oxide was identified in the soil via multimodal mapping and µ‐XANES. These results allow for better prediction of P dissolution and mobility.

Clinical features, hemorrhage risk and epilepsy outcomes of familial cerebral cavernous malformation: A 20-year observational pragmatic single-center study

IntroductionFamilial Cerebral Cavernous Malformations (fCCMs) are rare, hereditary conditions characterized by multiple central nervous system lesions. Despite their rarity, CCMs can cause significant clinical challenges when symptomatic, manifesting as seizure and symptomatic hemorrhage (CASH). Guidelines suggest neurosurgical intervention for symptomatic or previously symptomatic lesions, while conservative management is recommended for new-onset epilepsy. However, the natural history and optimal management remain unclear, necessitating further research. ObjectiveThis study aims to provide a comprehensive analysis of the clinical features, hemorrhage risk, and epilepsy outcomes in fCCM patients over an extended follow-up period, offering a more precise estimate of CASH and epilepsy rates in this population. MethodsThis retrospective longitudinal cohort study included fCCM patients enrolled from 2001 to May 2024. Data collected included demographic information, new neurological symptoms, symptomatic hemorrhages, seizures, and modified Rankin Scale (mRS) scores. Incidence rates of first symptomatic events and Kaplan-Meier survival curves were calculated, with logistic and Cox-proportional hazard regression models used to evaluate outcomes. ResultsA total of 47 patients were included in this study, with a mean age at diagnosis of 37.51 years. At diagnosis, 68 % were symptomatic, with 30 % having CASH and 36 % experiencing seizures without CASH. During a median follow-up of 126.0 months (interquartile range, 110.5 months), 17 % had a new CASH event, 20 % had seizures without CASH, and 60 % remained asymptomatic. The bleeding rate was 1.02 % per patient-year, with new focal neurological symptoms at 2.045 per 1000 patient-years and new CASH at 10.225 per 1000 patient-years. Most patients maintained minimal or no disability (mRS 0 or 1). Presenting with epilepsy at baseline significantly increased the odds of future seizures (OR 18.13, p = 0.001). ConclusionThis study highlights the complex presentation and progression of fCCMs, emphasizing the necessity for long-term monitoring. Baseline epilepsy is a significant predictor of future seizures, underscoring the need for individualized management strategies. Future research with larger cohorts and standardized criteria is essential to refine the understanding and management of fCCMs.

Exploring the Uncommon: A Series on Dermatofibrosarcoma Protuberans with Rare Locations, Unusual Sites of Metastasis, and Their Close Mimics

Abstract Dermatofibrosarcoma protuberans (DFSP) is an uncommon, slowly growing soft-tissue tumor that involves the dermis, subcutaneous fat and very rarely involves, muscle and deep fascia. It is locally aggressive and an intermediate-grade fibroblastic neoplasm that rarely metastasizes. It is important to know about this entity as it is locally aggressive and has a higher rate of transformation into fibrosarcoma which has an aggressive growth pattern and frequently metastasizes. Apart from this, there are diagnostic challenges, especially in small biopsies, where they resemble other spindle cell neoplasms. This article reviews the clinical and histological features of DFSP as well as possible pitfalls in their diagnosis and how to resolve them. Diagnosed cases of DFSP were retrieved from pathology archives. Their locations and histopathological features along with Immunohistochemistry (IHC) features were reviewed. Our study identified 26 cases of DFSP for 5 years from January 2019 to January 2024 in a tertiary care hospital. The age of study subjects was in the range of 11–70 years. Male:female ratio was 1.30:1. The most common site was shoulder (n = 5). Immunohistochemistry (HC) was performed in cases with diagnostic dilemmas and cases of high-grade transformation. The aim of this study was to perform a retrospective analysis of histopathological features of DFSP to distinguish it from other mimics and emphasize its local aggressiveness, reoccurrence, and rare site of metastasis, especially in cases with fibrosarcomatous transformation. The careful histomorphological examination particularly in smaller biopsies can be challenging, especially in peripheral laboratories and in resource-limited settings.

Analysis of the morpho-genetic features of the phylogenesis of the сlass Reptilia

Aim. The aim of the study was to establish the species composition of the extinct herpetofauna of reptiles in the European continent, identify plesiomorphic, apomorphic characteristics, autapomorphies, and idioadaptations, as well as to analyze the patterns of morphogenesis of the class Reptilia. Methods. Used methods of literature analysis, mapping of places of discovery of dinosaurian paleontological remains in Europe, comparative, mathematical-statistical, and correlation analysis methods of metric measurements of the detected species. Results. It was established that in the extinct fauna of the class Reptilia of the European continent, with taxonomic richness (equal to 27); the species composition consisted of seven species. A correlation dependence between ontogenetic growth parameters of animals was revealed. In particular, in the pair of absolute (length - mass) and relative (growth per year) growth parameters, a strong functional relationship is traced, while the correlation between growth (L) and age parameters is medium, and between body mass (M) and animal age is weak. Conclusions. According to the hypothesis of additive typogenesis, it has been confirmed that new morphological structures (apomorphies) do not arise as a result of simultaneous changes in all organ systems but are the result of individual gradual mutations that arise within narrow limits, possibly in separate populations of reptiles.

OCTA-ReVA: an open-source toolbox for comprehensive retinal vessel feature analysis in optical coherence tomography angiography.

Optical coherence tomography angiography (OCTA) has significantly advanced the study and diagnosis of eye diseases. However, current clinical OCTA systems and software tools lack comprehensive quantitative analysis capabilities, limiting their full clinical utility. This paper introduces the OCTA Retinal Vessel Analyzer (OCTA-ReVA), a versatile open-source platform featuring a user-friendly graphical interface designed for the automated extraction and quantitative analysis of OCTA features. OCTA-ReVA includes traditional established OCTA features based on binary vascular image processing, such as blood vessel density (BVD), foveal avascular zone area (FAZ-A), blood vessel tortuosity (BVT), and blood vessel caliber (BVC). Additionally, it introduces new features based on blood perfusion intensity processing, such as perfusion intensity density (PID), vessel area flux (VAF), and normalized blood flow index (NBFI), which provide deeper insights into retinal perfusion conditions. These additional capabilities are crucial for the early detection and monitoring of retinal diseases. OCTA-ReVA demystifies the intricate task of retinal vasculature quantification, offering a robust tool for researchers and clinicians to objectively evaluate eye diseases and enhance the precision of retinal health assessments.

Unveiling the therapeutic significance of shikimate pathway-derived phenolic acids against penicillin-binding protein 3 of Pseudomonas aeruginosa

ABSTRACT This study investigated the modulatory potential of Shikimate pathway-derived phenolic acids against penicillin-binding protein 3 (PBP3) of Pseudomonas aeruginosa using computational and in vitro methods. The binding and inhibitory capabilities of 22 phenolic acids against the catalytic site of PBP3 were evaluated and revealed ellagic and chlorogenic acids as the top inhibitors, showing superior docking scores of −8.4 kcal/mol each compared to the standard, cefotaxime (−7.5 kcal/mol). Dynamics simulation of their PBP3 complexes indicated chlorogenic acid’s efficacy in inhibiting PBP3 (−33.65 kcal/mol) relative to cefotaxime (−30.10 kcal/mol), while interacting with key catalytically relevant residues. The quantum features analysis using DFT/B3LYP suggested chlorogenic acid’s superior reactivity and compatibility with the PBP3 active site. In vitro evaluation revealed an MIC of 200 mg/ml for chlorogenic acid and 0.8 mg/ml for cefotaxime against Pseudomonas aeruginosa ATCC 27,853, while their combination showed a synergistic action mechanism (FIC index of 0.5). These observations were validated in time-kill kinetics (gradual decrease in viable cells over 24 h). The study concludes chlorogenic acid as promising PBP3 inhibitor. Nonetheless, further studies could focus on the structural modification of chlorogenic acid to improve its pharmacokinetic properties and potential as an antibacterial drug candidate in vivo.

Predicting pedestrian-vehicle interaction severity at unsignalized intersections

Objectives This study aims to develop and validate a novel deep-learning model that predicts the severity of pedestrian-vehicle interactions at unsignalized intersections, distinctively integrating Transformer-based models with Multilayer Perceptrons (MLP). This approach leverages advanced feature analysis capabilities, offering a more direct and interpretable method than traditional models. Methods High-resolution optical cameras recorded detailed pedestrian and vehicle movements at study sites, with data processed to extract trajectories and convert them into real-world coordinates via precise georeferencing. Trained observers categorized interactions into safe passage, critical event, and conflict based on movement patterns, speeds, and accelerations. Fleiss Kappa statistic measured inter-rater agreement to ensure evaluator consistency. This study introduces a novel deep-learning model combining Transformer-based time series data capabilities with the classification strengths of a Multilayer Perceptron (MLP). Unlike traditional models, this approach focuses on feature analysis for greater interpretability. The model, trained on dynamic input variables from trajectory data, employs attention mechanisms to evaluate the significance of each input variable, offering deeper insights into factors influencing interaction severity. Results The model demonstrated high performance across different severity categories: safe interactions achieved a precision of 0.78, recall of 0.91, and F1-score of 0.84. In more severe categories like critical events and conflicts, precision and recall were even higher. Overall accuracy stood at 0.87, with both macro and weighted averages for precision, recall, and F1-score also at 0.87. The variable importance analysis, using attention scores from the proposed transformer model, identified ‘Vehicle Speed’ as the most significant input variable positively influencing severity. Conversely, ‘Approaching Angle’ and ‘Vehicle Distance from Conflict Point’ negatively impacted severity. Other significant factors included ‘Type of Vehicle’, ‘Pedestrian Speed’, and ‘Pedestrian Yaw Rate’, highlighting the complex interplay of behavioral and environmental factors in pedestrian-vehicle interactions. Conclusions This study introduces a deep-learning model that effectively predicts the severity of pedestrian-vehicle interactions at crosswalks, utilizing a Transformer-MLP hybrid architecture with high precision and recall across severity categories. Key factors influencing severity were identified, paving the way for further enhancements in real-time analysis and broader safety assessments in urban settings.

Multivariate analysis of morphological variability of the initial stock intrabreed type of Sarboyan carp (Cyprinus Carpio L.)

The analysis of morphological variability of the initial stock within the breed type Sarboyan carp (Cyprinus carpio L.) by the principal component method is presented. The research was conducted in 2022, 2023 in the Novosibirsk region. The Sarboyan carp stock (males and females) from the fish breeding farm was studied according to eight valuation measurements: body weight, absolute body length, body length without tail fin, maximum height, width and girth. A comparative analysis of the body features of the Sarboyan carp belonging to four haplotypes (H1, H15, H16, H17) was carried out. The morphometric features of the Sarboyan carp were processed using principal component method. It was found that sexual dimorphism was noted on the basis of total size: females have larger sizes than males. In the fish stock under study, a trend towards the difference between H1 and H16 haplotypes was established. The individuals in the H1 haplotype are relatively thicker and shorter compared to H16. There is a group of fish with unknown haplotypes in the data structure, all of which show a shift towards the third component relative to H1, i.e. they have a higher back and smaller body thickness. No haplotypic differences were found in the component showing an apparent inverse relationship between head length trait, body length and absolute fish length. Multivariate analysis of morphological variability also showed that distribution of the individuals in the three-dimensional space PC2-PC3-PC4, free from size-age variability, allows pre-selection of fish with the required proportions of morphological features for further reproduction.

Construction of a novel model based on PVT1-MYC duet-related genes for predicting survival and characterization of the tumor microenvironment in pancreatic cancer.

Pancreatic cancer is an extremely malignant tumor. PVT1 and MYC signaling has been considered as a therapeutic target recently. Nonetheless, the prognostic values and critical regulatory networks of PVT1-MYC duet in pancreatic cancer remain unclear. Firstly, we identified PVT1-MYC duet-related genes using public databases. Then we analyzed our Hi-C and ChIP-seq data to confirm PVT1-MYC duet. We performed LASSO regression and multivariate Cox regression analysis to build a prognostic model whose effectiveness and robustness were validated by Cox regression, ROC analysis, calibration curve, and nomogram. Besides, we conducted functional enrichment analyses, mutation profiles analyses and the immune features analyses to compare low- and high-risk group. Functional enrichment analyses revealed that several terms associated with cancer progression were enriched in the high-risk group. Mutation profile analysis showed that high-risk group had higher tumor mutation burden, and immune analysis demonstrated high-risk group had more immunosuppressive tumor microenvironment. Finally, we detected PVT1 expression in pancreatic cancer and paracancer tissues from the PUMCH cohort, which showed that PVT1 was significantly upregulated in pancreatic cancer and associated with invasion, metastasis, and poor prognosis. We further performed transwell and proliferation assays and found that PVT1, CDC6, and COL17A1 could promote migration or proliferation of PDAC cells. This study constructed a prognostic model based on three PVT1-MYC duet-related genes, which had a significant potential in predicting the prognosis and tumor microenvironment of pancreatic cancer. These results suggested that targeting PVT1-MYC duet or its regulatory processes could be a therapeutic option with great interests.

INAP 2.0: Harnessing metabolic complementarity in microbial network analysis.

With the widespread adoption of metagenomic sequencing, new perspectives have emerged for studying microbial ecological networks, yielding metabolic evidence of interspecies interactions that traditional co-occurrence networks cannot infer. This protocol introduces the integrated Network Analysis Pipeline 2.0 (iNAP 2.0), which features an innovative metabolic complementarity network for microbial studies from metagenomics sequencing data. iNAP 2.0 sets up a four-module process for metabolic interaction analysis, namely: (I) Prepare genome-scale metabolic models; (II) Infer pairwise interactions of genome-scale metabolic models; (III) Construct metabolic interaction networks; and (IV) Analyze metabolic interaction networks. Starting from metagenome-assembled or complete genomes, iNAP 2.0 offers a variety of methods to quantify the potential and trends of metabolic complementarity between models, including the PhyloMint pipeline based on phylogenetic distance-adjusted metabolic complementarity, the SMETANA (species metabolic interaction analysis) approach based on cross-feeding substrate exchange prediction, and metabolic distance calculation based on parsimonious flux balance analysis (pFBA). Notably, iNAP 2.0 integrates the random matrix theory (RMT) approach to find the suitable threshold for metabolic interaction network construction. Finally, the metabolic interaction networks can proceed to analysis using topological feature analysis such as hub node determination. In addition, a key feature of iNAP 2.0 is the identification of potentially transferable metabolites between species, presented as intermediate nodes that connect microbial nodes in the metabolic complementarity network. To illustrate these new features, we use a set of metagenome-assembled genomes as an example to comprehensively document the usage of the tools. iNAP 2.0 is available at https://inap.denglab.org.cn for all users to register and use for free.

Unlocking the Potential of Food Waste: A Review of Multifunctional Pectins.

This review comprehensively explores the multifunctional applications of pectins derived from food waste and by-products, emphasizing their role as versatile biomaterials in the medical-related sectors. Pectins, known for their polyelectrolytic nature and ability to form hydrogels, influence the chemical composition, sensory properties, and overall acceptability of food and pharmaceutical products. The study presents an in-depth analysis of molecular parameters and structural features of pectins, such as the degree of esterification (DE), monosaccharide composition, galacturonic acid (GalA) content, and relative amounts of homogalacturonan (HG) and rhamnogalacturonan I (RG-I), which are critical for their technofunctional properties and biological activity. Emphasis is placed on pectins obtained from various waste sources, including fruits, vegetables, herbs, and nuts. The review also highlights the importance of structure-function relationships, especially with respect to the interfacial properties and rheological behavior of pectin solutions and gels. Biological applications, including antioxidant, immunomodulatory, anticancer, and antimicrobial activities, are also discussed, positioning pectins as promising biomaterials for various functional and therapeutic applications. Recalled pectins can also support the growth of probiotic bacteria, thus increasing the health benefits of the final product. This detailed review highlights the potential of using pectins from food waste to develop advanced and sustainable biopolymer-based products.

The methodology of teaching psychological and pedagogical disciplines in agricultural higher education institutions

Introduction: The introduction of psychological and pedagogical disciplines contributes to integrating higher education into European standards, leading to the development of a competitive specialist in changing employer requirements. Agrarian education slowly introduced psychological and pedagogical methods, which was the reason for the slow reformation of educational approaches in the field and did not contribute to the growth of the profession's prestige.Objective: The study aimed to determine the methodology of psychological and pedagogical disciplines based on the analysis of structural subdivisions, teaching staff and features of the pedagogical and psychological profile disciplines in agrarian universities.Methods: The method of synthesis, analysis, survey, grouping, comparison, visualisation and generalisation of results is applied.Results: The study results established that there are departments of psychology and pedagogy and professional teaching staff in the studied universities. The presence of compulsory and optional subjects, which differ in different institutions, was determined, which is explained by the autonomy of institutions in determining the form of teaching subjects by individual requests.Conclusions: At the same time, autonomy does not involve monitoring the effectiveness of educational activities, which was confirmed by the results of a survey of students and teachers, which revealed the popularity of outdated educational methods among teachers and low motivation among students in methods that develop creativity and the practical application of acquired knowledge. Instead, some pedagogical practices have found wide application and have shown effectiveness in developing communication skills, teamwork, and organisation

Cancer detection with various classification models: A comprehensive feature analysis using HMM to extract a nucleotide pattern

This work presents a novel feature extraction method for identifying complex patterns in genomic sequences by employing the Hidden Markov Model (HMM). In this study, we use HMM to identify gene nucleotide patterns that are specific to malignant and non-malignant cells. Crucial genetic components DNA and RNA are involved in many biological processes that impact both healthy and malignant cells. Early patient identification is essential to successful cancer diagnosis and therapy. Varying nucleotide patterns indicate different cellular responses, which are important to understanding the molecular causes of cancer and associated disorders. We present a detailed study of nucleotide patterns in whole raw nucleotide sequences with variations in both protein sequence (CDS) and non-protein sequence (NCDS) in both malignant and non-malignant cells. Nucleotide prediction has been achieved while computational expenses are reduced by using the proposed HMM for feature extraction and selection. The classification models implemented in this work for cancer detection are Gradient-Boosted Decision Trees (GBDT), Random Forests (RF), Decision Trees (DT), and Support Vector Machines (SVM) with kernels. The suggested classification model's accuracy and 10-fold cross-validation have been validated via comprehensive case studies. The results reveal that DT and ensemble learning techniques significantly differentiate between malignant and non-malignant DNA sequences. SVM with suitable kernels improves cancer detection accuracy significantly. Combining feature reduction approaches with nucleotide pattern classifiers based on Hidden Markov models improves performance and ensures reliable cancer detection.

Analysis of HER2-Low Breast Cancer in Aotearoa New Zealand: A Nationwide Retrospective Cohort Study.

To perform the first national analysis of demographic and clinicopathological features associated with the HER2 positive, HER2-low, and HER2-zero invasive breast cancers in New Zealand. The study will reveal the proportion of women who may benefit from new HER2-targeted antibody drug conjugate (ADC) therapies. Utilising data from Te Rēhita Mate Ūtaetae (Breast Cancer Foundation NZ National Register), the study analysed data from women diagnosed with invasive breast cancer over a 21-year period. The HER2 status of tumours was classified into three categories-HER2-zero, HER2-low, HER2-positive. From 2009-2021, 94% of women underwent HER2 testing, with 14% diagnosed with HER2-positive breast cancer. For advanced-stage disease, 38% of those formerly classified as HER2-negative were reclassified as HER2-low. Including HER2-positive breast cancers, this indicates that 60% of women with advanced breast cancer may potentially benefit from the new HER2-directed ADCs (approximately 120 women per year). The findings suggest a significant proportion of women with invasive breast cancer in New Zealand could benefit from new HER2-targeted treatments. There is a need to standardise HER2 testing to enhance personalised treatment and improve outcomes.