Verifying the inclusivity of molecular detection methods gives indications about the reliability of viral infection diagnosis because of the tendency of viral pathogens to undergo sequence variation. This study was aimed at selecting inclusive probes based on reverse transcription–quantitative PCR (RT-qPCR) assays for the diagnosis of the most widespread and detrimental viruses infecting honeybees, namely the acute bee paralysis virus (ABPV), the black queen cell virus (BQCV), the chronic paralysis bee virus (CBPV), the deformed wing virus variants A (DWVA) and B (DWVB), and the sacbrood virus (SBV). Therefore, previously described detection methods were re-evaluated in silico for their specificity and inclusivity. Based on this evaluation, selected methods were modified, or new ones were designed and tested in duplex RT-qPCR reactions. The limits of detection (LODs), effect of multiplexing on sensitivity and the viral RNA quantification potential in bees and hive debris were assessed. This study made available diagnostic assays able to detect an increased number of virus variants compared with previously described tests and two viral pathogens in a single PCR reaction. Full article

During the fermentation process of Oolong tea, significant changes occur in both its external characteristics and its internal components. This study aims to determine the fermentation degree of Oolong tea using visible–near–infrared spectroscopy (vis-VIS-NIR) and image processing. The preprocessed vis-VIS-NIR spectral data are fused with image features after sequential projection algorithm (SPA) feature selection. Subsequently, traditional machine learning and deep learning classification models are compared, with the support vector machine (SVM) and convolutional neural network (CNN) models yielding the highest prediction rates among traditional machine learning models and deep learning models with 97.14% and 95.15% in the prediction set, respectively. The results indicate that VIS-NIR combined with image processing possesses the capability for rapid non-destructive online determination of the fermentation degree of Oolong tea. Additionally, the predictive rate of traditional machine learning models exceeds that of deep learning models in this study. This study provides a theoretical basis for the fermentation of Oolong tea. Full article

Dangerous human behavior in the driving sense may cause traffic accidents and even cause economic losses and casualties. Accurate identification of dangerous human behavior can prevent potential risks. To solve the problem of difficulty retaining the temporal characteristics of the existing data, this paper proposes a human behavior recognition model based on utilized optical flow and hybrid deep learning model-based 3D CNN-LSTM in stacked autoencoder and uses the abnormal behavior of humans in real traffic scenes to verify the proposed model. This model was tested using HMDB51 datasets and JAAD dataset and compared with the recent related works. For a quantitative test, the HMDB51 dataset was used to train and test models for human behavior. Experimental results show that the proposed model achieved good accuracy of about 86.86%, which outperforms recent works. For qualitative analysis, we depend on the initial annotations of walking movements in the JAAD dataset to streamline the annotating process to identify transitions, where we take into consideration flow direction, if it is cross-vehicle motion (to be dangerous) or if it is parallel to vehicle motion (to be of no danger). The results show that the model can effectively identify dangerous behaviors of humans and then test on the moving vehicle scene. Full article

It is expected that the number of elected female mayors in local government will increase globally, yet no major progress has been registered lately despite the increased focus on the topic. At the European level, no country exceeds 40% female mayors or other leaders of the municipal council (or equivalent), with the highest descriptive representation of 39.1% in Iceland. Following the 2020 elections in Romania, only around 5% of mayors were female with a strong over-representation of male mayors. The current study aims to analyze the male–female distribution of mayors, the degree of re-election, the relationship between the number of candidates and re-election of incumbents, and how these factors impact female political representation at the local level in Romania. Thus, we argue that a high degree of re-election of incumbents may be a barrier to women’s access to the position of mayor. In addition, it is important to determine whether female incumbents are as successful as their male counterparts in being re-elected. While there is an extensive body of literature on incumbency that covers a range of topics, there is a gap in the literature regarding the proposed subject. The present research aims to fill the gap and contribute to a better understanding of the political representation of women in Eastern Europe. We utilized a dataset of Romanian elections from 2008 to 2020 to test our hypotheses. Our findings indicate that during the studied period, more than 95% of mayors were male, the re-election was a frequent occurrence in Romania with a percentage ranging from 70.82% (2008–2012) to 72% (2012–2016 and 2016–2020), and female incumbents were just as likely to be re-elected as their male counterparts. Full article

This paper introduces and explores the dynamics of a novel three-dimensional (3D) fractional map with hidden dynamics. The map is constructed through the integration of a discrete sinusoidal memristive into a discrete Duffing map. Moreover, a mathematical operator, namely, a fractional variable-order Caputo-like difference operator, is employed to establish the fractional form of the map with short memory. The numerical simulation results highlight its excellent dynamical behavior, revealing that the addition of the piecewise fractional order makes the memristive-based Duffing map even more chaotic. It is characterized by distinct features, including the absence of an equilibrium point and the presence of multiple hidden chaotic attractors. Full article

This study focuses on understanding the transcriptional heterogeneity of activated platelets and its impact on diseases such as sepsis, COVID-19, and systemic lupus erythematosus (SLE). Recognizing the limited knowledge in this area, our research aims to dissect the complex transcriptional profiles of activated platelets to aid in developing targeted therapies for abnormal and pathogenic platelet subtypes. We analyzed single-cell transcriptional profiles from 47,977 platelets derived from 413 samples of patients with these diseases, utilizing Deep Neural Network (DNN) and eXtreme Gradient Boosting (XGB) to distinguish transcriptomic signatures predictive of fatal or survival outcomes. Our approach included source data annotations and platelet markers, along with SingleR and Seurat for comprehensive profiling. Additionally, we employed Uniform Manifold Approximation and Projection (UMAP) for effective dimensionality reduction and visualization, aiding in the identification of various platelet subtypes and their relation to disease severity and patient outcomes. Our results highlighted distinct platelet subpopulations that correlate with disease severity, revealing that changes in platelet transcription patterns can intensify endotheliopathy, increasing the risk of coagulation in fatal cases. Moreover, these changes may impact lymphocyte function, indicating a more extensive role for platelets in inflammatory and immune responses. This study identifies crucial biomarkers of platelet heterogeneity in serious health conditions, paving the way for innovative therapeutic approaches targeting platelet activation, which could improve patient outcomes in diseases characterized by altered platelet function. Full article

Sesamum indicum L. (Pedaliaceae) is one of the most economically important oil crops in the world, thanks to the high oil content of its seeds and its nutritional value. It is cultivated all over the world, mainly in Asia and Africa. Well adapted to arid environments, sesame offers a good opportunity as an alternative subsistence crop for farmers in Africa, particularly Niger, to cope with climate change. For the first time, the variation in genome size among 75 accessions of the Nigerien germplasm was studied. The sample was collected throughout Niger, revealing various morphological, biochemical and phenological traits. For comparison, an additional accession from Thailand was evaluated as an available Asian representative. In the Niger sample, the 2C DNA value ranged from 0.77 to 1 pg (753 to 978 Mbp), with an average of 0.85 ± 0.037 pg (831 Mbp). Statistical analysis showed a significant difference in 2C DNA values among 58 pairs of Niger accessions (p-value < 0.05). This significant variation indicates the likely genetic diversity of sesame germplasm, offering valuable insights into its possible potential for climate-resilient agriculture. Our results therefore raise a fundamental question: is intraspecific variability in the genome size of Nigerien sesame correlated with specific morphological and physiological traits? Full article

Background: The clinical impact of the withdrawal of sodium–glucose cotransporter 2 inhibitors (SGLT2i) on all-cause readmission in patients with heart failure remains unknown. Methods: We enrolled a total of 212 consecutive patients who were hospitalized for heart failure and received SGLT2i during their index hospitalization between February 2016 and July 2022. Of these patients, 51 terminated SGLT2i during or after their index hospitalization. We evaluated the prognostic impact of the withdrawal of SGLT2i on the primary outcome, which was defined as the all-cause readmission rate/times. Results: Over a median of 23.2 months, all-cause readmission occurred in 38 out of 51 patients (74.5%) withdrawn from SGLT2i and 93 out of 161 patients (57.8%) with continuation of SGLT2i (p = 0.099). The incidence of all-cause readmissions per year was 0.97 [0–1.50] in patients withdrawn from SGLT2i and 0.50 [0–1.03] in patients with continuation of SGLT2i (p = 0.030). There was no significant difference in total medical costs (62,906 [502–187,246] versus 29,236 [7920–180,305] JPY per month, p = 0.866) between both patient groups. Conclusions: Termination of SGLT2i may be associated with incremental all-cause readmission and no benefit in reducing total medical costs. Full article

Disseminated leishmaniasis (DL) caused by L. braziliensis is characterized by the presence of 10 to more than 1000 lesions spread on the body. While protection against Leishmania is mediated by macrophages upon activation by IFN-γ produced by CD4⁺T cells, the pathology of disseminated leishmaniasis (DL) could be mediated by macrophages, NK, and CD8⁺T cells. Herein, we evaluate the participation of senescent CD8⁺T cells in the pathogenesis of DL. Methods: Peripheral blood mononuclear cells (PBMCs), biopsies, co-cultures of CD8⁺T cells with uninfected and infected macrophages (MØ), and PBMC cultures stimulated with soluble L. braziliensis antigen (SLA) for 72 h from patients with cutaneous leishmaniasis (CL) and DL were used to characterize senescent CD8⁺T cells. Statistical analysis was performed using the Mann–Whitney and Kruskal–Wallis tests, followed by Dunn’s. Results: Patients with DL have an increase in the frequency of circulating CD8⁺T cells that present a memory/senescent phenotype, while lesions from DL patients have an increase in the frequency of infiltrating CD8⁺T cells with a senescent/degranulation phenotype. In addition, after specific stimuli, DL patients’ circulating CD8⁺T with memory/senescent profile, showing degranulation characteristics, increased upon SLA stimuli, and those specific CD8⁺T cells from DL patients had an increased degranulation phenotype, causing more apoptosis of infected target cells. Conclusions: DL patients show a higher frequency of cytotoxic senescent CD8⁺T cells compared to CL patients, and that could promote the lysis of infected cells, although without parasite killing, releasing Leishmania to the extracellular compartment, contributing to the spread of parasites. Full article

In this article, we consider the statistical analysis of the parameter estimation of the Marshall–Olkin extended generalized extreme value under liner normalization distribution (MO-GEVL) within the context of progressively type-II censored data. The progressively type-II censored data are considered for three specific distribution patterns: fixed, discrete uniform, and binomial random removal. The challenge lies in the computation of maximum likelihood estimations (MLEs), as there is no straightforward analytical solution. The classical numerical methods are considered inadequate for solving the complex MLE equation system, leading to the necessity of employing artificial intelligence algorithms. This article utilizes the genetic algorithm (GA) to overcome this difficulty. This article considers parameter estimation through both maximum likelihood and Bayesian methods. For the MLE, the confidence intervals of the parameters are calculated using the Fisher information matrix. In the Bayesian estimation, the Lindley approximation is applied, considering LINEX loss functions and square error loss, suitable for both non-informative and informative contexts. The effectiveness and applicability of these proposed methods are demonstrated through numerical simulations and practical real-data examples. Full article

Finding passages related to a sentence over a large collection of text documents is a fundamental task for claim verification and open-domain question answering. For instance, a common approach for verifying a claim is to extract short snippets of relevant text from a collection of reference documents and provide them as input to a natural language inference machine that determines whether the claim can be deduced or refuted. Available approaches struggle when several pieces of evidence from different documents need to be combined to make an inference, as individual documents often have a low relevance with the input and are therefore excluded. We propose GRAAL (GRAph-based retrievAL), a novel graph-based approach that outlines the relevant evidence as a subgraph of a large graph that summarizes the whole corpus. We assess the validity of this approach by building a large graph that represents co-occurring entity mentions on a corpus of Wikipedia pages and using this graph to identify candidate text relevant to a claim across multiple pages. Our experiments on a subset of FEVER, a popular benchmark, show that the proposed approach is effective in identifying short passages related to a claim from multiple documents. Full article

Establishing intervention effectiveness is an important component of a broader knowledge translation (KT) process. However, mobilizing the implementation of these interventions into practice is perhaps the most important aspect of the KT cycle. The purpose of the current study was to conduct an umbrella review to (a) identify promising interventions for SI&L in older adults, (b) interpret (translate) the findings to inform clinical knowledge and practice interventions in different settings and contexts, and (c) highlight research gaps that may hinder the uptake of these interventions in practice. The broader purpose of this study was to inform evidence-based clinical practice guidelines on SI&L for HCSSPs. In line with other reviews, our study noted variations in methods and intervention designs that prohibit definitive statements about intervention effectiveness. Perhaps, the most significant contribution of the current review was in identifying knowledge-to-practice gaps that inhibit the implementation of interventions into practice-based realities. Full article

This study aimed to characterize and quantify reasons for the removal of nurse sows and identify the removal associated with their extended lactation length (ELL). A total of 100,756 removed nurse sows within a period of 2016–2022 from 53 sow herds in the Midwest USA were analyzed. Reproductive failure was the most common removal reason (χ² = 8748.421, p < 0.001) affecting P1, P2, and P3 nurse sows. Failure to conceive and absence of estrus were the main causes of reproductive failure (χ² = 352.480, p < 0.001) affecting P1 and P2 nurse sows and P1 and P5 nurse sows, respectively. When P2 and P6 nurse sows had an ELL of 0–7 d, they faced a high chance (χ² = 13.312, p = 0.021) of removal due to conception failure and failure to return to heat, respectively. When P2 and P5 nurse sows had an ELL of 8–14 d, they were highly vulnerable (χ² = 59.847, p < 0.001) to removal due to failure to conceive and showing heat, respectively. Finally, when ELL was at 15–21 days, P4 and P5 nurse sows were more likely (χ² = 41.751, p < 0.001) to be removed due to failure to express heat, whereas at the same time, P2 and P3 nurse sows experienced the same removal threat due to failing to conceive. These results could help producers manage nurse sow systems. Full article

This study investigates the empowering potential of digital technologies for women entrepreneurs, a transformative force that transcends all fields of knowledge. It specifically examines how technology can equip women to overcome socio-cultural and economic barriers, focusing on the case of Iran. The research employs a mixed-methods approach, utilizing a literature review within the qualitative framework to identify key empowerment drivers. Subsequently, a quantitative approach leverages DEMATEL to pinpoint the most impactful drivers. This investigation aims to provide stakeholders with actionable insights, highlighting the critical role of technology in fostering equitable and sustainable economic advancement for women entrepreneurs. Furthermore, the study emphasizes the importance of gathering information from a developing nation like Iran, as its findings can hold significant implications for other countries experiencing similar developmental stages. Ultimately, the research seeks to inform the creation of effective policies, support initiatives, and educational programs. These interventions aim to empower women entrepreneurs to leverage digital tools for sustainable business growth, ultimately contributing to a more equitable and environmentally conscious future. Full article

This study investigates the effect of extrusion screw speed and carbon nanotube (CNT) concentration on the thermal, mechanical, and electromagnetic interference shielding effectiveness (EMI SE) properties of Polycarbonate (PC)/acrylonitrile-butadiene-styrene (ABS) and its polymer nanocomposites (PNCs) by means of design of experiments (DoE) approach. A masterbatch method was employed to obtain the best dispersion of the CNTs throughout the polymer matrix. This study evaluates the thermo-mechanical characterisation of the polymers and PNCs at varying screw speeds to assess filler matrix bonding. The results highlight that CNT concentration has a significant effect on all mechanical properties, while screw speed only affects the Charpy impact strength and flexural properties of the samples. Compounding at 200 rpm has the best flexural and tensile strength, which is attributed to the best filler matrix bonding (highest storage modulus) of the PNCs. The best EMI SE results were obtained at 10 wt.% CNTs. This research contributes valuable insights into the effect of CNT concentration and extrusion screw speed on the mechanical, thermal and EMI SE properties of PC/ABS and its PNCs. Full article

The coal and gas outburst and rockburst coupling disaster is becoming increasingly serious due to deep mining. To clarify the mechanism inducing the outburst–rockburst coupling disaster, a true triaxial single-sided unloading mechanical test was conducted with the aid of a true triaxial solid–thermal–gas coupling test device, an industrial computed tomography (CT) system, and an acoustic emission system. Through this test, the mechanical characteristics, meso crushing characteristics, and acoustic characteristics in the disaster formation process were obtained. Additionally, the outburst–rockburst coupling instability disaster law was verified by numerical simulation. The results demonstrated that the stress unloading degree of the coal body was negatively correlated with the initial gas pressure in the outburst–rockburst coupling disaster. The time domain parameter count and energy of acoustic emission exhibited a “bimodal” distribution pattern in the instability stage. The rockburst would occur when the peak value was in a “low-count and high-energy” state, while coal and gas outburst would occur when the peak value was in a “high-count and low-energy” state. The meso slice revealed that gas degradation promoted the development of microcracks in the coal body, and the penetration of cracks resulted in the main cracks of structural instability during rockburst. The coal and gas outburst was mainly attributed to the “cross” shear failure pattern of the coal body. These research findings may lay a foundation for the effective prevention and control of outburst–rockburst coupling disasters. Full article

Moment-resisting frames (MRFs) and eccentrically braced frames (EBFs) can be effectively combined in multi-story steel buildings. In fact, MRFs provide redundancy and ductility, while EBFs provide initial and post-yield stiffness with high energy dissipation capacity. In addition, if detachable links are designed as dissipative fuses, it is possible to activate their almost contemporary yielding and prevent the collapse of structures during severe earthquakes, ensuring easy repair and rehabilitation measures. The seismic responses of dual frames with MRFs and EBFs are investigated by means of pushover analyses. Different buildings were designed, varying the number of floors (two, four, and eight stories), the number of MRF spans (zero, one, and two), and the length of the short links (0.8 M_p/V_p and 1.6 M_p/V_p). The obtained results confirmed the superior behavior of dual frames with respect to simple EBFs. In addition, it was observed that the frames with the shorter links exhibited higher resistance and rigidity but smaller overall displacement capacity because of the anticipated failure of the links. Full article

Psoriasis is a chronic inflammatory disease with specific cutaneous and nail lesions. Recent data has emphasized its systemic nature, highlighting metabolic conditions found in patients. Insulin resistance was identified in adult psoriasis, sometimes related to psoriasis severity. Data regarding this relationship in children are limited. Consequently, we tested the association between the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) and Psoriasis Area and Severity Index (PASI) using a retrospective dataset of 43 children with various types of psoriasis. First, we attempted to replicate the relationship between the HOMA-IR and PASI. Second, we explored potential associations between these variables and others in the dataset. The results illustrated no association between HOMA-IR and PASI (p-value = 0.512). The exploratory findings hinted at a connection between nail pitting and insulin resistance (p-value = 0.038), yet Bonferroni adjustments suggested the risk of a false-positive finding. Noteworthy associations were found between the HOMA-IR and body mass index (BMI) (p-value = 0.001), the PASI and quality of life impairment (p-value = 0.005), and psoriasis severity and type (p-value = 0.001). The null hypothesis that insulin resistance in children is not positively associated with psoriasis severity cannot be rejected. Pilot estimates of variables and covariates of interest are provided for further confirmatory studies assessing this hypothesis. Full article

Congenital heart defects (CHDs) are common human birth defects. Genetic mutations potentially cause the exhibition of various pathological phenotypes associated with CHDs, occurring alone or as part of certain syndromes. Zebrafish, a model organism with a strong molecular conservation similar to humans, is commonly used in studies on cardiovascular diseases owing to its advantageous features, such as a similarity to human electrophysiology, transparent embryos and larvae for observation, and suitability for forward and reverse genetics technology, to create various economical and easily controlled zebrafish CHD models. In this review, we outline the pros and cons of zebrafish CHD models created by genetic mutations associated with single defects and syndromes and the underlying pathogenic mechanism of CHDs discovered in these models. The challenges of zebrafish CHD models generated through gene editing are also discussed, since the cardiac phenotypes resulting from a single-candidate pathological gene mutation in zebrafish might not mirror the corresponding human phenotypes. The comprehensive review of these zebrafish CHD models will facilitate the understanding of the pathogenic mechanisms of CHDs and offer new opportunities for their treatments and intervention strategies. Full article

Phytoplankton are the foundation of marine ecosystems and play a crucial role in determining the optical properties of seawater, which are critical for remote sensing applications. However, passive remote sensing techniques are limited to obtaining data from the near surface, and cannot provide information on the vertical distribution of the subsurface phytoplankton. In contrast, active LiDAR technology can provide detailed profiles of the subsurface phytoplankton layer (SPL). Nevertheless, the large amount of data generated by LiDAR brought a challenge, as traditional methods for SPL detection often require manual inspection. In this study, we investigated the application of supervised machine learning algorithms for the automatic recognition of SPL, with the aim of reducing the workload of manual detection. We evaluated five machine learning models—support vector machine (SVM), linear discriminant analysis (LDA), a neural network, decision trees, and RUSBoost—and measured their performance using metrics such as precision, recall, and F₃ score. The study results suggest that RUSBoost outperforms the other algorithms, consistently achieving the highest F₃ score in most of the test cases, with the neural network coming in second. To improve accuracy, RUSBoost is preferred, while the neural network is more advantageous due to its faster processing time. Additionally, we explored the spatial patterns and diurnal fluctuations of SPL captured by LiDAR. This study revealed a more pronounced presence of SPL at night during this experiment, thereby demonstrating the efficacy of LiDAR technology in the monitoring of the daily dynamics of subsurface phytoplankton layers. Full article

Cell mechanics is gaining attraction in drug screening, but the applicable methods have not yet become part of the standardized norm. This review presents the current state of the art for atomic force microscopy, which is the most widely available method. The field is first motivated as a new way of tracking pharmaceutical effects, followed by a basic introduction targeted at pharmacists on how to measure cellular stiffness. The review then moves on to the current state of the knowledge in terms of experimental results and supplementary methods such as fluorescence microscopy that can give relevant additional information. Finally, rheological approaches as well as the theoretical interpretations are presented before ending on additional methods and outlooks. Full article

The early detection of esophageal cancer presents a substantial difficulty, which contributes to its status as a primary cause of cancer-related fatalities. This study used You Only Look Once (YOLO) frameworks, specifically YOLOv5 and YOLOv8, to predict and detect early-stage EC by using a dataset sourced from the Division of Gastroenterology and Hepatology, Ditmanson Medical Foundation, Chia-Yi Christian Hospital. The dataset comprised 2741 white-light images (WLI) and 2741 hyperspectral narrowband images (HSI-NBI). They were divided into 60% training, 20% validation, and 20% test sets to facilitate robust detection. The images were produced using a conversion method called the spectrum-aided vision enhancer (SAVE). This algorithm can transform a WLI into an NBI without requiring a spectrometer or spectral head. The main goal was to identify dysplasia and squamous cell carcinoma (SCC). The model’s performance was evaluated using five essential metrics: precision, recall, F1-score, mAP, and the confusion matrix. The experimental results demonstrated that the HSI model exhibited improved learning capabilities for SCC characteristics compared with the original RGB images. Within the YOLO framework, YOLOv5 outperformed YOLOv8, indicating that YOLOv5’s design possessed superior feature-learning skills. The YOLOv5 model, when used in conjunction with HSI-NBI, demonstrated the best performance. It achieved a precision rate of 85.1% (CI95: 83.2–87.0%, p < 0.01) in diagnosing SCC and an F1-score of 52.5% (CI95: 50.1–54.9%, p < 0.01) in detecting dysplasia. The results of these figures were much better than those of YOLOv8. YOLOv8 achieved a precision rate of 81.7% (CI95: 79.6–83.8%, p < 0.01) and an F1-score of 49.4% (CI95: 47.0–51.8%, p < 0.05). The YOLOv5 model with HSI demonstrated greater performance than other models in multiple scenarios. This difference was statistically significant, suggesting that the YOLOv5 model with HSI significantly improved detection capabilities. Full article