A single water pollution by dyes is a huge environmental problem; there is a necessity to produce new decolorization methods that are effective, cost-attractive, and suitable in industrial use. Magnetic chitosan polymers offer the advantage of easy separation from the dye solution. In this work, a Chitosan-Fe polymer was synthesized, characterized, and tested for removal of the azo dye Direct Red 83:1 from water. The polymer was characterized in terms of surface morphology (SEM), elemental analysis (EA), differential scanning calorimetry (DSC), thermal gravimetric analysis (TGA), infrared spectrophotometry (IR), X-ray powder diffraction (XRD) and density functional theory (DFT). The reported results hint that 1 g and pH 7.0 were the best conditions to carry out both kinetic and isotherm models. To understand the adsorption behavior of Direct Red 83:1 onto the polymers, adsorption rate and maximum adsorption capacity were calculated using kinetic tests and isotherm curves respectively. The kinetic data and mechanism of the adsorption process were analyzed by three models and the equilibrium data by three adsorption isotherms. The results indicated that the adsorption process follows a pseudo-second-order kinetics (R2= 0.999) and fits the Temkin isotherm (R2= 0.946), indicating that the adsorption occurred on heterogeneous surfaces. The newly synthesized Chitosan-Fe polymer exhibited good adsorption properties and easy separation of cleaned water.

A large part of our understanding of the biological substrates of sex-recognition and mate-recognition is derived by studying animal models. In performing those tasks, rodents rely mostly of pheromones and other olfactory cues, whereas humans rely mostly on visual cues. That may hinder the translation of rodents’ biology to humans’ biology, especially at the neural-networks level, where those cues traverse different networks in humans and rodents brains. That may be called the “pheromonal-visual gap”. A theoretical model presented here addresses those issues. The model merges observations from humans and model-animals, as reported in specific scientific reports, and general biological principles that are accepted by the scientific community. The model suggests that the voices of men and women are the innate cues based on which humans learn to use visual cues in sex-recognition and mate-recognition. Children learn the two tasks in associative learning mechanisms, by being immersed in their community, and observing adult role-models in innocuous, non-sexual scenarios. The model proposes that the human medial-geniculate-nucleus (MGN) is the analog of the rodents’ accessory-olfactory-bulb (AOB) and the main-olfactory-bulb (MOB), and that the human MASH pathway (MGN, amygdala, bnST, hypothalamus) is the analog of the rodents’ VNOP (Vomeronasal-organ-pathway). Considering the differences in the pathways should facilitate the translation from rodents’ brain nuclei and tracks to humans’. Also, the model hypothesizes that innate direct and indirect connections between auditory centers, e.g., MGN, and sex-control centers, e.g., hypothalamus, vary across three groups of children, and those variations determine the individual’s mate-recognition that emerges at puberty.

Migraine is a neurological disorder characterized by severe disabling headaches with a substantial global burden and elevated morbidity rates. Extensive research has established a clear association between migraine and sex/gender, with a notably higher incidence reported in females, reaching up to 18%, which is three times greater compared to males. Additionally, women commonly experience more intense pain and greater frequency of migraine symptoms, both with and without aura, highlighting the differential impact of sex/gender on migraine presentation. This review comprehensively examines the pathophysiology of migraine, with a particular focus on the effects of sex hormones in migraine pathophysiology and the central nervous system. Furthermore, we explore various treatment modalities tailored to gender differences, including pharmacological interventions and emerging therapeutic approaches. Furthermore, we explored alternative treatment strategies such as nutraceuticals and behavioural techniques, including mindfulness and cognitive behavioural therapy. This review aims to provide a comprehensive overview of the multifaceted nature of migraine and the diverse therapeutic approaches available for their management.

The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

Uterine Tumor Resembling Ovarian Sex Cord Stromal Tumor (UTROSCT) is a rare uterine mesenchymal neoplasm, which resembles ovarian sex cord tumors of the ovary. Although, in general, UTROSCT exhibits benign behavior with a favorable prognosis, this neoplasm is nonetheless classified as being of an uncertain ma-lignant potential, given its low rate of recurrence and the fact that it rarely pro-duces metastases (lymph nodes, epiploic appendix, omentum, small bowel, subcutaneous, lungs). Its histogenesis too is uncertain. Typically, UTROSCT oc-curs in peri-menopausal or menopausal women but can sometimes be observed in young women. Usually, this neoplasm can be found in the uterine corpus as a nodular intramural lesion, less frequently submucosal, subserosal or poly-poid/intracavitary. UTROSCT can cause abnormal bleeding, pelvic pain, enlarged uterus, mass sensation, but sometimes it is found purely by chance. This neo-plasm can be considered polyphenotypic on morphological, immunohistochem-ical, and genetic analyses. Generally, on microscopic examination, UTROSCT shows a predominant pattern of the cords, nests, and trabeculae typical of sex cord tumors of the ovary while, immunohistochemically, it is characterized by a co-expression of epithelial, smooth muscle, and sex cord markers. The aim of this review is to report clinical and pathological data and genetic alterations to estab-lish its impact on the prognosis and management of patients affected by this rare entity.

The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.

Lipid-lowering therapy (LLT) is a cornerstone of atherosclerotic cardiovascular disease prevention. Although LLT might lead to different reductions in low-density lipoprotein cholesterol (LDL-C) levels in women and men, LLT diminishes cardiovascular risk equally effectively in both sexes. Despite similar LLT efficacy, the use of high-intensity statin, ezetimibe, and proprotein convertase subtilisin/kexin type 9 inhibitors is lower in women compared to men. Women achieve the guideline-recommended LDL-C levels less often than men. Greater cholesterol burden is particularly prominent in women with familial hypercholesterolemia. In clinical practice, women and men with dyslipidemia present with different cardiovascular risk profiles and disease manifestations. The concentrations of LDL-C, lipoprotein(a) and other blood lipids differ between women and men over a lifetime. Dissimilar levels of LLT’s target molecules partially result from sex-specific hormonal and genetic determinants of lipoprotein metabolism. Hence, to evaluate a potential need for sex-specific LLT, this comprehensive review (i) describes the impact of sex on lipoprotein metabolism and lipid profile, (ii) highlights sex differences in cardiovascular risk among patients with dyslipidemia, (iii) presents recent, up-to-date clinical trial and real-world data on LLT efficacy and safety in women, and (iv) discusses diverse medical needs of women and men with dyslipidemia and increased cardiovascular risk.

Optical coherence tomography (OCT) is a non-invasive imaging technique widely used in ophthalmology for visualizing retinal layers, aiding in early detection and monitoring of retinal diseases. OCT is useful for detecting diseases such as age-related macular degeneration (AMD) and diabetic macular edema (DME), which affect millions of people globally. Over the past decade, the area of application of Artificial Intelligence (AI), particularly Deep Learning (DL), has significantly increased. The number of medical applications is also rising, with solutions from other domains being increasingly applied to OCT. The segmentation of biomarkers is an essential problem that can enhance the quality of retinal disease diagnostics. For 3D OCT scans, AI is beneficial since manual segmentation is very labor-intensive. In this paper, we employ the new SAM 2 and MedSAM 2 for the segmentation of OCT volumes for two open-source datasets, comparing their performance with the traditional U-Net. The model achieved an overall Dice score of 0.913 and 0.902 for macular holes (MH) and intraretinal cysts (IRC) on OIMHS and 0.888 and 0.909 for intraretinal fluid (IRF) and pigment epithelial detachment (PED) on the AROI dataset, respectively.

Motion related human activity recognition using wearable sensors can potentially enable various useful daily applications. So far, most studies view it as a stand-alone mathematical classification problem without considering the physical nature of human motions. Consequently, they suffer from data dependencies and encounter the dimension disaster problem and the over-fitting issue, and their models are never human-readable. In this study, we start from a deep analysis on natural physical properties of human motions, and then propose a useful feature selection method to quantify each feature's classification contribution capability. On one hand, the "dimension disaster" problem can be avoid to some extent, due to the affined dimension of key features; On the other hand, over-fitting issue can be depressed since the knowledge implied in human motions are nearly invariant, which compensates the possible data inadequacy. The experiment results indicate that the proposed method performs superior to those adopted in related works, such as decision tree, k-NN, SVM, neural networks.

Determining the general-name-derived semantic relationships between geographic names often requires consulting many geographic name documents for verification. In geographic name translation and geographic name knowledge graphs, this method cannot meet the needs of large-scale extraction of general-name-derived semantic relationships between geographic entities. To solve this problem, this paper proposes a general-name derivation relation recognition method based on Prompt learning and Semantic Spatiotemporal Distribution Attention Feature Fusion (SSD-AFF). This method adopts a pipe-based relationship recognition strategy. First, the Prompt learning method is adopted to form a general-name derivation pattern with the general-name-derived geographical name category and the original geographical name category as the prompt word, and then the derivation pattern is combined with the geographical name as the input of the model so that the recognition problem of the general-name derivation of the geographical name can be transformed into a classification problem using the bidirectional language model. Then, by analyzing the characteristics of the semantic association and geographical spatio-temporal distribution between the general-name-derived geographical name and the original geographical name, the semantic spatio-temporal distribution characteristics of the general-name-derived geographical name are constructed. Finally, the network model based on the semantic spatio-temporal distribution Attention Feature Fusion (SSD-AFF) is used to distinguish the general-name-derived semantic relationships. The experimental results show that the proposed method has good performance. It can effectively mine the general-name-derived semantic relations between the vector data of geographical entities and has important application value in geographical name translation and geographical name knowledge maps.

In this comprehensive review, we delve into the realm of intention recognition within the context of digital forensics and cybercrime. The rise of cybercrime has become a major concern for individuals, organizations, and governments worldwide. Digital forensics is a field that deals with the investigation and analysis of digital evidence in order to identify, preserve, and analyze information that can be used as evidence in a court of law. Whereas, Intention recognition is a subfield of artificial intelligence that deals with the identification of agents’ intentions based on their actions and change of states. In the context of cybercrime, intention recognition can be used to identify the intentions of cybercriminals and even to predict their future actions. Employing a meticulous six-step systematic review approach, we curated research articles from reputable journals and categorized them into three distinct modeling approaches: logic-based, classical machine learning-based, and deep learning-based. Notably, intention recognition has transcended its historical confinement to network security, now addressing critical challenges across various subdomains, including social engineering attacks, AI black box vulnerabilities, and physical security. While deep learning emerges as the dominant paradigm, its inherent lack of transparency poses unique challenges in the digital forensics landscape. We advocate for hybrid solutions that blend deep learning’s power with interpretability. Furthermore, we propose the creation of a comprehensive taxonomy to precisely define intention recognition, paving the way for future advancements in this pivotal field.

The additional sex combs-like (ASXL) family, a mammalian homolog of the additional sex combs (Asx) of Drosophila, has been implicated in transcriptional regulation via chromatin modifications. Abnormal expression of ASXL family genes leads to myelodysplastic syndromes and various types of leukemia. De novo mutation of these genes also causes developmental disorders. Genes in this family and neighbor genes are evolutionary conserved in humans and mice. This review provides a comprehensive summary of epigenetic regulations associated with ASXL family genes. Their expression is commonly regulated by DNA methylation at CpG islands preceding transcription starting sites. Their proteins primarily engage in histone tail modifications through interactions with chromatin regulators (PRC2, trithorax complex, PR-DUB, SRC1, HP1α, and BET proteins), and with transcription factors, including nuclear hormone receptors (RAR, PPAR, ER, and LXR). Recently, non-coding RNAs have been identified following mutations in the ASXL1 or ASXL3 gene, along with circular ASXLs and microRNAs that regulate ASXL1 expression. The diverse epigenetic regulations linked to ASXL family genes collectively contribute to tumor suppression and developmental processes. Our understanding of ASXL-regulated epigenetics may provide insights into the development of therapeutic epigenetic drugs.

Regeneration is usually regarded as a unique plant or some animal species process. In reality, regeneration is a ubiquitous process in all multicellular organisms. It ranges from response to wounding by healing the wounded tissue to whole body neoforming (remaking of the new body). In a larger context, regeneration is one facet of two reproduction schemes that dominate the evolution of life. Multicellular organisms can propagate their genes asexually or sexually. Here I present the view that the ability to regenerate tissue or whole-body regeneration is also determined by the sexual state of the multicellular organisms (from simple animals like hydra and planaria to plants and complex animals). The above idea is manifested here by showing evidence that many organisms, organs, or tissues show inhibited or diminished regeneration capacity when in reproductive status compared to the same organism organs or tissues in nonreproductive conditions or by exposure to sex hormones.

In pattern recognition domain, deep architectures are widely used nowadays and they have achieved fine grades. However, these deep architectures need special demands, especially big datasets and GPU. Aiming to gain better grades without deep networks, we propose a simplified algorithm framework using fusion features extracted from the salient areas of faces. Furthermore, the proposed algorithm has achieved a better result than some deep architectures. For extracting more effective features, this paper firstly defines the salient areas on the faces. This paper normalizes the salient areas of the same location in the faces to the same size, therefore it can gain more similar features from different subjects. LBP and HOG features are extracted from the salient areas, fusion features’ dimensions are reduced by Principal Component Analysis( PCA) and we apply softmax to classify the six basic expressions at once. This paper proposes a salient areas definitude method which uses peak expressions frames to compare with their neutral faces. This paper also proposes and applies the idea of normalizing the salient areas to align the specific areas which express the different expressions. This makes the salient areas found from different subjects have the same size. Besides, gamma correction method is firstly applied on LBP features in our algorithm framework which improves our recognition rates significantly. By applying this algorithm framework, our research has gained state-of-the-art performances on CK+ database and JAFFE database.

Astrocytes, the most abundant glial cells in the brain, are an integral part of the synaptic compartment and contribute to synaptic pruning, a key process for refining neural circuits during early postnatal development (PND). Dysregulations in this process are implicated in various neuropsychiatric disorders, including major depressive disorder (MDD). To investigate astrocyte functions in a physiologically relevant context, organotypic brain slice cultures (OBSCs) offer a powerful model, reproducing more closely in vivo conditions than traditional cell cultures and preserving complex brain architecture and interactions. Here, we present OBSCs as an ex vivo culturing method to provide a platform to explore astrocyte-mediated synaptic pruning dynamics in the rat prefrontal cortex (PFC) during PND. Our approach is based on assessing the role of MEGF10, a key protein involved in synaptic pruning, alongside the synaptic markers synaptophysin and PSD95, using Western blotting to analyse the expression levels of these markers in the cortex of developing rat pups. Additionally, we combine immunofluorescence staining with confocal imaging and IMARIS 9.8 software-assisted analysis to investigate the co-localization of the lysosomal marker LAMP1 with synaptic and astrocytic markers to evaluate the precise rate of synaptic engulfment. The methods presented here allow a deeper examination of an astrocyte-mediated synaptic remodelling in healthy and pathophysiological conditions.

Prompt removal of misfolded membrane proteins and misassembled membrane protein complexes is essential for membrane homeostasis. However, the elimination of these toxic proteins from the hydrophobic membrane environment has high energetic barriers. Transmembrane FtsH is the only known ATP-dependent protease responsible for this task, unlike other well-studied soluble ATP-dependent proteases. The mechanisms by which FtsH recognizes, unfolds, translocates, and proteolyzes its substrates remain unclear. Here, we report the crystal structures of the Thermotoga maritima FtsH periplasmic domain (PD) in an associative trimeric state at a 1.5-1.95 Å resolution. We also describe the pH-dependent oligomerization states of the isolated PD using dynamic light scattering. These observations help us understand how FtsH recognizes membrane-anchored misfolded proteins.

In this paper, we proposed the fusion of two projection based face recognition algorithms: local binary Patterns in DCT domain and singular value decomposition (SVD) characterized by its simplicity and efficiently. Standard databases ORL are used to test the experimental results which prove that proposed system achieves more accurate face recognition as compared to individual method.

This paper describes the first results of an Image Recognition Based Location (IRBL) for mobile application focusing on the procedure to generate a Database of range images (RGB-D). In an indoor environment, to estimate the camera position and orientation, a prior spatial knowledge of the surrounding is needed. In order to achieve this objective a complete 3D survey of two different environment (Bangbae metro station of Seoul and E.T.R.I. building in Daejeon – Republic of Korea) was performed using LiDAR (Light Detection And Ranging) instrument and the obtained scans were processed in order to obtain a spatial model of the environments. From this, two databases of reference images were generated using a specific software realized by the Geomatics group of Politecnico di Torino (ScanToRGBDImage). This tool allow to generate synthetically different RGB-D images) centered in the each scan position in the environment. Later, the external parameters (X, Y, Z, ω, φ, κ) and the range information extracted from the DB images retrieved, are used as reference information for pose estimation of a set of acquired mobile pictures in the IRBL procedure. In this paper the survey operations, the approach for generating the RGB-D images and the IRB strategy are reported. Finally the analysis of the results and the validation test are described.

This research introduces a hybrid feature extraction approach that combines Linear Discriminant Analysis (LDA) and Multilayer Perceptron (MLP) methods to address the challenges of reducing feature vector dimensionality and accurately classifying smartphone-based human activities. Moreover, to refine activity classification accuracy, Support Vector Machine (SVM) optimization with Stochastic Gradient Descent (SGD) is employed. LDA, a statistical tool, is leveraged to derive a new feature space for data projection, enhancing class separation and test feature label prediction. The proposed approach, named LMSS, was evaluated using the UCI-HAR dataset and compared with state-of-the-art models. The results demonstrate that the proposed approach outperformed the best-performing method over this dataset. It achieved an accuracy rate of 99.52%, precision of 99.55%, recall of 99.53%, and an F1-score of 99.54%, highlighting the effectiveness of the proposed method in accurately classifying the data.

Musical instrument recognition is a relatively unexplored area of machine learning due to the need to analyze complex spatial-temporal audio features. Traditional methods using individual spectrograms, like STFT, Log-Mel, and MFCC, often miss the full range of features. We propose a hierarchical residual attention network using a scaled combination of multiple spectrograms, including STFT, Log-Mel, MFCC, and CST features (chroma, spectral contrast, and Tonnetz), to create a comprehensive sound representation. This model enhances focus on relevant spectrogram parts through attention mechanisms. Experimental results with the OpenMIC-2018 dataset show significant improvement in classification accuracy, especially with the "Magnified 1/4 Size" configuration. Future work will optimize CST feature scaling, explore advanced attention mechanisms, and apply the model to other audio tasks to assess its generalizability.

The purpose of this study was to establish the association between total and segmental body composition (BC) variables and anaerobic performance and to create optimal models that best predict such performance in CrossFit® (CF) athletes. Fifty athletes, 25 males and 25 females (age: 33.26 ± 6.81 years; body mass: 72.57 ± 12.17 kg; height: 169.55 ± 8.71 cm; BMI: 25.06 ± 2.31 kg·m−2) were recruited to participate and underwent BC analysis using dual-energy X-ray absorptiometry (DXA) and an all-out laboratory test on a cycle ergometer (Wingate) to determine their anaerobic performance. The results show a significant correlation between BC values and performance, ranging from moderate (r = -0.34, p = 0.015) to near-perfect (r = 0.96, p < 0.01). Furthermore, the created performance prediction models exhibited predictive capacities ranging from 19% (p = 0.017) to 93% (p < 0.001). All prediction models were created using total or segmental lean mass variables, excluding others. BC and performance variables found significant differences between males and females. The findings demonstrate that BC variables are crucial indicators of anaerobic performance in CF athletes. Professionals responsible for athlete performance should use this information to monitor athletes and design training programs.

Geckos demonstrate a remarkable variability in sex determination systems, but our limited knowledge prohibits accurate conclusions on the evolution of sex determination in this group. Eyelid geckos (Eublepharidae) are of particular interest, as they encompass species with both environmental and genotypic sex determination. We identified for the first time the X-specific gene content in the Yucatán banded gecko, Coleonyx elegans, possessing X1X1X2X2/X1X2Y multiple sex chromosomes by comparative genome coverage analysis between sexes. The X-specific gene content of Coleonyx elegans was revealed to be partially homologous to genomic regions linked to the chicken autosomes 1, 6 and 11. A qPCR-based test was applied to validate a subset of X-specific genes by comparing the difference in gene copy numbers between sexes, and to explore the homology of sex chromosomes across 11 eublepharid, two phyllodactylid and one sphaerodactylid species. Homologous sex chromosomes are shared between Coleonyx elegans and Coleonyx mitratus, two species diverged approximately 34 million years ago, but not with other tested species. As far as we know, the X-specific gene content of Coleonyx elegans / Coleonyx mitratus was never involved in the sex chromosomes of other gecko lineages, indicating that the sex chromosomes in this clade of eublepharid geckos evolved independently.

In recent years, electroencephalogram (EEG) emotion recognition has emerged as a research hotspot in the field of artificial intelligence (AI). Despite advancements in EEG-based emotion recognition, room for enhancing performance persists due to EEG signal redundancy and limitations in feature extraction, leading to inefficient models and emotional information loss. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification, and 97.69% and 98.05% in ternary classification, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

With the development of educational technology, machine learning and deep learning provide technical support for traditional classroom observation assessment. However, in real classroom scenarios, the technique faces challenges such as lack of clarity of raw images, complexity of datasets, multi-target detection errors and complexity of character interactions.Based on the above problems, a student classroom behavior recognition network incorporating super-resolution and target detection is proposed. To cope with the problem of unclear original images in the classroom scenario, SRGAN (Super Resolution Generative Adversarial Network for Images) is used to improve the image resolution and thus the recognition accuracy.To address the dataset complexity and multi-targeting problems, feature extraction is optimized and multi-scale feature recognition is enhanced by introducing AKConv and LASK attention mechanisms into the Backbone module of YOLOv8s algorithm. To improve the character interaction complexity problem, the CBAM attention mechanism is integrated to enhance the recognition of important feature channels and spatial regions. Experiments show that it can detect six behaviors of students raising their hands, reading, writing, playing cell phones, looking down, and lying on the table in high-definition images. And the accuracy and robustness of this network are verified.Compared with small target detection algorithms such as Faster R-CNN, YOLOv5, and YOLOv8s, this network has better detection performance and can efficiently deal with the behavior recognition of multiple students.

Train fault detection primarily relies on the comparative analysis of collected images with standard reference images, making the accurate identification of the collected image types crucial for effective fault detection. Most current systems achieve indirect classification of carriage images by using Automatic Equipment Identification (AEI) devices to recognize carriage identification number information while capturing the carriage images. However, damaged RF tags or blurred characters can hinder these devices, obstructing further train fault analyses. The unique features of carriage linear array images, such as high resolution, extreme aspect ratios, and local non-linear distortions, pose significant challenges to recognition algorithms. This paper proposes a method specifically designed to recognize these types of images. First, we implement an object detection algorithm to locate the positions of key components in carriage linear array images. By leveraging the spatial layout mapping between these key components and the carriage type classification, we simplify the complex image recognition problem into a sparse point set alignment task. To ensure effective registration despite local distortions in the linear array images, we propose a weighted radial basis function. To address the issues of unknown matching relationships and quantity mismatches during point set alignment, the proposed objective function is designed to maximize the similarity between Gaussian mixtures of point sets, thereby determining the RBF weights. Extensive experiments have demonstrated that our algorithm achieves 100% recognition accuracy under local nonlinear scale distortions of less than 15%. The algorithm remains effective even in the presence of some false positives or missed detections in key component object detection results. It can accurately identify the target category from 79 image categories within 24 milliseconds on an i7 CPU without GPU support. It is of great significance to reduce system cost and promote automatic exterior fault detection for train rolling stock.

Traumatic Brain Injury (TBI) is one of the leading causes of death and disability worldwide. Patient treatment and management pose a challenge for healthcare systems. The prognosis of these patients may be influenced by factors such as sex. This study aims to describe the clinical parameters of patients hospitalized in the Intensive Care Unit (ICU) of a Spanish hospital due to Trauma Brain Injury and to establish differences based on sex. Methodology: An observational, cross-sectional, and analytical study was conducted from March 2015 to December 2019. All patients admitted to the ICU during this period who presented isolated significant Traumatic Brain Injury, defined as an abbreviated injury scale (AIS) ≥ 3, were included. Data on baseline characteristics, mechanism of injury, substance use, and acute management in the prehospital and hospital stages (including the need for hospitalization) were recorded. A descriptive and in-ferential analysis was conducted using the chi-square test. Results: A total of 950 patients were ultimately included. The majority of the patients were men (723) with a mean age of 45.16 (± 17.55). There were statistically significant differences in terms of the mechanism of injury (p

Human Activity Recognition (HAR) technology enables continuous behavior monitoring, particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR, were employed for classification. Results demonstrate the ear sensor's efficacy, with deep learning models achieving 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear's efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital signs monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes.

Over the past decades, overweight and obesity has become a global epidemic and the leading threat for death. To prevent the serious risk, an overweight or obese individual must apply a long-term weight-management strategy to control food intake and physical activities, which is however, not easy. Recently, with the advances of information technology, more and more people can use wearable devices and smartphones to obtain physical activity information, while they can also access various health-related information from Internet online social networks (OSNs). Nevertheless, there is a lack of an integrated approach that can combine these two methods in an efficient way. In this paper, we address this issue and propose a novel mobile-social framework for health recognition and recommendation, namely, H-Rec². The main ideas of H-Rec² include (1) to recognize the individual's health status using smartphone as a general platform, and (2) to recommend physical activity and food intake based on personal health information, life science principles, and health-related information obtained from OSNs. To demonstrate the potentials of the H-Rec² framework, we develop a prototype that consists of four important components: (1) an activity recognition module that senses physical activity using accelerometer, (2) a health status modeling module that applies a novel algorithm to generate personalized health status index, (3) a restaurant information collection module that collects relevant information from OSN, and (4) a restaurant recommendation module that provides personalized and context-aware recommendation. To evaluate the prototype, we conduct both objective and subjective experiments, which confirm the performance and effectiveness of the proposed system.

Background: Microscopic examination of acid-fast stained sputum smears for detecting mycobacterial acid-fast bacilli (AFB) remains the most economical and readily available method for laboratory diagnosis of tuberculosis (TB). However, this conventional approach has limitations, including low sensitivity and labor-intensive procedures. Methods: An automated microscopy system incorporating artificial intelligence (AI) and machine learning for AFB identification was evaluated. The study was conducted at an Infectious Disease Hospital in Jiangsu Province, China, utilizing an intelligent microscope system (TB-Scan, Wellgen Medical, Kaohsiung). A total of 1,000 sputum smears were included in the analysis, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFB. Referee technicians provided the gold standard for discrepant results. Results: The automated system demonstrated an overall accuracy of 95.00% (950/1,000), sensitivity of 91.24% (177/194), and specificity of 95.91% (773/806). Notably, the system identified 21 smears as positive that were previously reported as negative, with referee technicians confirming 17 of these as true positive and retracting the test results accordingly. Recalculating the performance, the accuracy increased to 96.70% (967/1,000), sensitivity to 91.94% (194/211), and specificity to 97.97% (773/789), with a false negative rate of 8.06% (17/211) and a false positive rate of 2.03% (16/789). Conclusions: The incorporation of AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

Modification of electrode surface with a selective layer gives the possibility to detect target ana-lytes of forensic interest. 3-(4-trifluoromethyl)-phenyl)-thiophene (ThPhCF3) was deposited on a graphite (G) electrode by electrochemical oxidation and characterized as a receptor for the recognition of synthetic stimulants (2-aminoindane, buphedrone, naphyrone). First, the structural characterization of the polymeric layer were obtained from Raman spectroscopy. Second, the electrochemical profiles of selected synthetic stimulants and their affinity to ThPhCF3/G-modified electrodes were studied using square wave voltammetry and electrochemical impedance spec-troscopy. The values of the adsorption constant show the importance of – PhCF3 groups in the interaction with synthetic stimulants, namely recognition by the formation of hydrogen bonds (Kads (buphedrone) < Kads (2-AI)) and interaction with aromatic H atoms (Kads (buphedrone) < Kads (naphyrone)). It was demonstrated that the polymeric layer derived from thiophene with – PhCF3 group is capable to increase the intensity of the electrochemical signal.

Based on non-invasive neuroimaging techniques and graph theory-based network analyses, many researchers have tried to use small-world network model to elucidate sex differences in the brain. This manuscript aims to compile the related research findings from the past few years and summarize the sex differences in human brain networks from the perspective of small-world properties. We reviewed published reports examining altered small-world properties in both the functional and structural brain networks between males and females. Based on four patterns of altered small-world properties proposed--randomization, regularization, stronger small-worldization and weaker small-worldization, we found that current results point to a significant trend toward more regularization in females and more randomization in males in the functional brain networks. As for particular topological properties, the most consistent findings include a significant shift toward higher global efficiency in males and higher local efficiency in females in the functional network studies. On the other hand, there seems to be no consensus to date on the sex differences in small-world properties of the structural brain networks. Nevertheless, we noticed that the sample sizes in many published studies are limited, and future studies with larger samples are warranted to obtain more reliable results. We anticipate that the conclusions in this manuscript will contribute to a deeper understanding of neurobiological mechanisms underlying the sex differences in the brain.

This paper proposes a novel facial expression recognition network called the Lightweight Facial Network with Spatial Bias (LFNSB). The LFNSB model balances model complexity and recognition accuracy. It has two key components: a lightweight feature extraction network (LFN) and a Spatial Bias (SB) module for aggregating global information. The LFN introduces combined channel operations and depthwise convolution techniques, effectively reducing the number of parameters while enhancing feature representation capability. The Spatial Bias module enables the model to focus on local facial features while also capturing the dependencies between different facial regions. Additionally, a novel loss function called Cosine-Harmony Loss is designed. This function optimizes the relative positions of feature vectors in high-dimensional space, resulting in better feature separation and clustering. Experimental results on the AffectNet and RAF-DB datasets show that the proposed LFNSB model performs excellently in facial expression recognition tasks. It achieves high recognition accuracy while significantly reducing the number of parameters, thus substantially lowering model complexity.

Digital photogrammetry has attracted widespread attention in the field of geotechnical and geological survey due to its low-cost, ease of use and contactless mode. In this work, with the purpose of studying the progressive block surficial detachments of a landslide we developed a monitoring system based on fixed multi-view time-lapse cameras. Thanks to a new-developed photogrammetric algorithm based on the comparison of photo sequences through a structural similarity metric and the computation of the disparity map of two convergent views we can quickly detect the occurrence of collapse events, determine their location and calculate the collapse volume. With the field data obtained at the Perarolo landslide site (Belluno Province, Italy), we have preliminary tested the effectiveness of the algorithm and its accuracy in the volume calculation. The method of quickly and automatically obtaining collapse information proposed in this paper can extend the potentials of landslide monitoring system based on videos or photo sequence and it will be of great significance for further research on the link between the frequency of collapse events and the driving factors.

Sick leave is influenced by various modifiable lifestyle factors and sex differences. This study examines the impact of body mass index, fruit and vegetable consumption, physical activity, smoking, and alcohol consumption on sick leave among Serbia's working population, focusing on sex differences. Data from the 2019 National Health Survey of Serbia were analyzed, focusing on a representative sample of 4,652 individuals aged 18-65. Chi-square tests and logistic regression models assessed the relationships between lifestyle factors and sick leave, adjusting for sociodemographic, work-related, and health-related variables. Among respondents, 15.8% reported sick leave in the past 12 months, with higher rates among women in both, short-term (13.9% vs. 10.6%) and long-term sick leave (3.4% vs. 2.2%). For men, obesity (OR = 2.6), poor dietary habits (fruit OR = 2.1; vegetables OR = 2.8), smoking (OR = 1.9), and risky alcohol consumption (OR = 4.1) significantly increase the likelihood of sick leave. In women, smoking (OR = 1.8) and risky alcohol consumption (OR = 3.1) are major predictors, while BMI and physical activity show less impact. Fruit and vegetable consumption and physical activity have inconsistent effects on sick leave, indicating the need for further research. This study identifies obesity, poor dietary habits, smoking, and alcohol consumption as key predictors of sick leave in men. In women, smoking and risky alcohol consumption are major predictors. Smoking is a risk factor for both sexes, while risky alcohol consumption emerges as a prominent predictor and is a significant predictor overall. These findings highlight the need for targeted public health interventions to address these lifestyle factors and reduce sick leave rates. Further research is needed to clarify the roles of dietary habits and physical activity in influencing sick leave.

Recent advancements in brain stimulation and nanomedicine have ushered in a new era of therapeutic interventions for psychiatric and neurodegenerative disorders. This review explores the cutting-edge innovations in brain stimulation techniques, including their applications in alleviating symptoms of main neurodegenerative disorders and addiction. Emphasis is placed on the underlying main neurotransmitter modifications and their specific brain area location, particularly focusing on the dopaminergic system, which plays a critical role in these conditions. Furthermore, the review delves into the groundbreaking developments in nanomedicine, highlighting how nanotechnology can be utilized to target aberrant signaling in neurodegenerative diseases, with a specific focus on the dopaminergic system. The discussion extends to emerging technologies such as magnetoelectric nanoparticles (MENPs), which represent a novel intersection between nanoformulation and brain stimulation approaches. These innovative technologies offer promising avenues for enhancing the precision and effectiveness of treatments by enabling non-invasive, targeted delivery of therapeutic agents as well as on site, on-demand stimulation. By integrating insights from recent research and technological advances, this review aims to provide a comprehensive understanding of how brain stimulation and nanomedicine can be synergistically applied to address complex neuropsychiatric and neurodegenerative disorders, paving the way for future therapeutic strategies.

Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration (1). The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health’s (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER)) policies to motivate researchers to consider sex differences (2). However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses (1,3–5). Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information (3,6,7). They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex (8). Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods (3). However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods (9,10). Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.

The current plant two-sex model makes the assumption that there are only two sexual reproductive states: male and female. However, the application of this model to the plant alternation of generations requires the subtle redefinition of several common terms related to sexual reproduction, which also seems to obscure aspects of one or the other plant generation: For instance, the homosporous sporophytic plant is treated as being “asexual,” and the gametophytes of angiosperms treated like mere gametes. In contrast, the proposal is made that the sporophytes of homosporous plants are indeed sexual reproductive organisms, as are the gametophytes of heterosporous plants. This view requires the expansion of the number of sexual reproductive states we accept for plants, therefore a three-sex model for homosporous plants and a four-sex model for heterosporous plants are described and then contrasted with the current two-sex model. These new models allow the use of sexual reproductive terms in a manner largely similar to that seen in animals, and may better accommodate the plant alternation of generations life cycle than does the current plant two-sex model. These new three-sex and four-sex models may also help stimulate new lines of research, and examples of how they might alter our view of the flower, and may lead to new perspectives in terms of sexual determination, are presented. Thus it is suggested that plants have more than merely two sexual reproductive states, and that recognition of this may promote our study and understanding of plants.

In this review, we provide an overview of the current knowledge on the different sexual systems and sex determining mechanisms in bivalves, with a focus on the various epigenetic and genetic factors that may be involved. The final section of the review provides recent discoveries on sex-specific mitochondrial genes in bivalves possessing the unconventional system of doubly uniparental inheritance of mitochondria (which is found in several members of the orders Mytiloida, Unionoida, Veneroida and Nuculanoida). The genes involved in this developmental pathway could represent the first sex determination system in animals in which mitochondrially-encoded genes are directly involved.

In Belgium, sex work was recently decriminalized to enhance street-based sex workers’ (SSWs) health. This qualitative study explores stakeholders' perceptions of the impact of the working environment on street-based sex workers’ occupational safety, health and well-being in Brussels. In-depth interviews with seven stakeholders directly involved with SSWs were conducted with data analyzed using qualitative content analysis. The analysis of study data resulted in one theme: “A power imbalance and a lack of concerted efforts among stakeholders are detrimental to the occupational safety, health, and well-being of street-based sex workers”. In Brussels, the majority of SSWs face various challenges due to structural vulnerabilities, thus limiting their choices regarding their work environment, and further compromising their health. Limited access to resources and severe constraints within their working environment contribute to a lack of safety and adverse health outcomes. A stigma mechanism fueled by ideology and personal interests prevent the enforcement of their rights regarding their working environment, safety and health promotion. The lack of involvement of key stakeholders, especially from the public sector, prevents the majority of SSWs in Brussels from benefiting from a sound working environment and the promotion of their health, safety and well-being.

The sleeping and grazing behaviors of horses play a crucial role in their health and welfare. In the horse industry, with the prevalent trend of individual single-stall farming in stable, accurately identifying various behaviors of horses within stables is imperative. This study has established a spatio-temporal dataset for horse postures and behaviors, proposed an algorithm utilizing a SE-SlowFast network with an enhanced loss function. This algorithm achieves automatic recognition of multiple postures and behaviors of horse simultaneously. The model developed in this study demonstrates high accuracy in identifying three postures within the stable: Standing, Crouching, and Lying, achieving respective accuracies of 92.73%, 91.87%, and 92.58%. Additionally, it accurately recognizes two behaviors: Sleeping and Grazing, achieving best accuracies of 93.56% and 98.77%. The overall best accuracy of the model is 93.9%. Experiments on video recognition confirm that the suggested model recognizes horse video data accurately. This study marks the first time computer vision technology has been used in the horse farming industry, achieving non-intrusive recognition of multiple horse postures and behaviors. This study provides valuable data support for livestock managers to evaluate the health conditions of horses, enhances the welfare of horses in stables, and contributes to the advancement of modern horse husbandry practices.

Identifying punch types as part of punch kinematic analysis is a key component for both coaches and players, providing critical insights into the variety and effectiveness of punches, which are essential for refining overall performance. Existing research indicates that punch type classification typically relies on data from either wearables or video data. Both approaches have their merits and demerits but require a significant amount of labeled data for modeling the classification algorithm. In this work, we propose a novel approach that significantly reduces the labeling effort by one-sixth by leveraging video data to enhance the training information of sensor data. This enriched training phase enables more accurate punch classification with reduced labeled data. Subsequently, the real-time punch identification and classification use only the wearable data. On a real dataset, with 15% of the labeling efforts, our approach achieved an accuracy rate of 91.41% for rear hand punch recognition and 91.91% for lead hand punch recognition, along with 92.33% and 94.56% for punch classification, respectively. Through the development of our Smart Boxer system, we aim to revolutionize punch analytics in boxing, providing comprehensive insights while enhancing the overall training and competitive experience for athletes and enriching fan engagement with the sport.

Pipelines extend thousands of kilometers to transport and distribute oil and gas. Given the challenges often faced with corrosion, fatigue, and other issues on steel pipes, the demand for glass fiber reinforced plastics (GFRP) pipes is increasing in the oil and gas gathering and transmission systems. However, the medium transported through these pipelines contains multiple acid gases such as CO2 and H2S, as well as ions including Cl-、Ca2+、Mg2+、SO42-、CO32- and HCO3-. These substances can cause a series of problems, such as aging, debonding, delamination, and fracture. In this study, a series of aging damage experiments were conducted on V-shaped GFRP pipes with depths of 2mm and 5mm. The aging and failure of GFRP were studied under the combined effects of external force and acidic solution using acoustic emission (AE) techniques. It was found that the acidic aging solution promoted matrix damage, fiber/matrix desorption, and delamination damage in GFRP pipes over a short period. However, the overall aging effect was relatively weak. Based on the experimental data, the SSA-LSSVM algorithm was proposed and applied to the damage pattern recognition of GFRP. An average recognition rate of up to 90% was achieved, indicating that this method is highly suitable for analyzing AE signals related to GFRP damage.

Rett syndrome (RTT) is an X-linked neurodevelopment disorder associated with the single monogenic mutation in methyl-CpG binding protein 2 (MeCP2) in up to 95% of cases. The growing number of genome-wide association studies and incomplete concordance for autoimmune diseases in monozygotic twins concur to support the involvement of environmental factors, like infectious agents or chemicals, in the breakdown of tolerance leading to autoimmunity. In fact, the coexistence of a perturbation of the immune system in RTT patients has been previously hypothesized. We herein explored the hypothesis that an autoimmune component derived from environmental bacterial infection of non typeable Haemophilus influenzae may coexist in RTT. At this purpose we screened sera from RTT syndrome patients, non-RTT pervasive developmental disorders patients and healthy controls with the hyperglucosylated adhesin protein HMW1ct(Glc) used as a precise antigen in ELISA in order to identify specific antibodies to N-glucosylation sites. Results showed that HMW1ct(Glc) is able to significantly discriminate antibodies among RTT sera and controls. Competitive ELISA confirmed the specific interaction between antibodies characteristic of RTT syndrome and the N-glucosylation motifs of the bacterial adhesin protein HMW1ct(Glc).

Background/Objectives: An age-associated increase in serum levels of sex hormone binding globulin (SHBG) is considered to be one of the factors leading to the development of male hy-pogonadism. Aim of the study: to study the relationship between the concentration of sex hor-mone-binding globulin in the blood serum and the age of the man, the level of total and free testosterone. A single-center retrospective cross-sectional study was conducted. The results of laboratory tests of 162 male patients aged from 18 to 56 years (median - 34 years) were analyzed. Methods: The level of total testosterone and sex hormone binding globulin (SHBG) was deter-mined by chemiluminescent immunoassay. The level of free testosterone was determined by the calculation method according to Vermeullen. Statistical analysis was performed using SPSS Sta-tistics v23. Results: Correlation analysis did not reveal a significant relationship between age and SHBG levels, as well as between age and total testosterone levels. Serum SHBG concentration showed a significant (p

Understanding the distribution of rock glaciers provides key information for investigating and recognizing the status and changes of the periglacial environment. Deep learning algorithms and red-green-blue (RGB) bands from high-resolution satellite images have been extensively employed to map rock glaciers. However, the near-infrared (NIR) band offers rich spectral information and sharp edge features that could significantly contribute to semantic segmentation tasks, but it is rarely utilized in constructing rock glacier identification models due to the limitation of three input bands for classical semantic segmentation networks, like DeeplabV3+. In this study, a dual-encoder DeeplabV3+ network (DEDNet) was designed to overcome the flaws of classical DeeplabV3+ network (CDNet) when identifying rock glaciers using multispectral remote sensing images by extracting spatial and spectral features from RGB and NIR bands, respectively. This network, trained with manually labeled rock glacier samples from QiLian Mountains, established a model with accuracy, precision, recall, specificity, and mIoU (Mean Intersection over Union) of 0.9131, 0.9130, 0.9270, 0.9195, and 0.8601, respectively. The well-trained model was applied to identify new rock glaciers in a test region, achieving a producer's accuracy of 93.68% and a user's accuracy of 94.18%. Furthermore, the model was employed to two study areas in northern Tien Shan (Kazakhstan) and Daxue Shan (Hengduan Shan, China) with high accuracy, which proved that the DEDNet offers an innovative solution to more accurately map rock glaciers on a larger scale due to its robustness across diverse geographic regions.

Anxiety disorders manifest in women more than in men by almost twofold. This narrative review aims to summarize the sex-related biological factors, which underpin anxiety, focusing on the interactions of sex and tryptophan/serotonin with anxiety.A literature search was conducted using Google Scholar, PubMed/MEDLINE, Scopus, and EMBASE databases from inception until December 31, 2017. This review shows that sex may interact with many serotonin functions thereby modulating anxiety, including 5-HT1A and 5-HT2C receptors, 5-HT transporter and central 5-HT concentrations and metabolism. Sex-steroids modulate the expression of serotonin transporter genes, creating a difference in serotonin availability. Sex and estrous cycle phases lead to varying anxiety responses to tryptophan depletion. Testosterone, progesterone and estrogen are important factors in mediating sex differences in serotonin responses to anxiety-generating behavioral tests. At prenatal levels, there are sex-related differences in the reciprocal relationships between serotonin and the HPA-axis, which modulate anxiety-like behaviors. Activated immune-inflammatory pathways induce indoleamine-2,3-dioxynease (IDO) and the tryptophan catabolite (TRYCAT) pathway thereby increasing tryptophan degradation and increasing the production of TRYCATs including kynurenine and quinolinic acid, which may create an overall anxiogenic effect. The effects of immune activation on IDO are significantly more pronounced in women than men and therefore females may show increased levels of anxiogenic TRYCAT following immune challenge. Aberrations in the IDO-activated TRYCAT pathway are found in pregnant females and parturients and are associated with increased anxiety levels in the postnatal period. The results of this review underscore the necessity of studying the associations between serotonin and anxiety in both sexes taking into account the effects of immune activation on IDO and production of anxiogenic TRYCATs. Future anxiety research should focus on the interactions between serotonin/tryptophan and sex, sex hormones, the menstrual cycle, pregnancy, the HPA axis and the immune system through production of anxiogenic TRYCATs.

Crosstalk between the BMP and TGF-β signaling pathways regulates many complex developmental processes from the earliest stages of embryogenesis throughout adult life. In many situations, the two signaling pathways act reciprocally. For example, TGF-β signaling is generally pro-fibrotic whereas BMP signaling is anti-fibrotic and pro-calcific. Sex-specific differences occur in many diseases including cardiovascular pathologies. Differing ratios of fibrosis and calcification in stenotic valves suggests that BMP/TGF-β signaling may vary in men and women. In this review, we focus on the current understanding of the interplay between sex and BMP/TGF-β signaling and pose several unanswered questions.

Parkinson’s Disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assist in recognizing the PD with good accuracy. The decorrelation function reduces the non-linear correlation between features and bias in order to learn bias-invariant features. The Parkinson’s Progression Markers Initiative (PPMI) dataset, referred to as a single scanner imbalanced dataset in this study used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new Decorrelated Convolutional Neural Networks (DcCNN) framework by applying decorrelation-based optimization to Convolutional Neural Networks(CNN). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN model performs significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner invariant features without significantly impacting the performance of a model. The obtained dataset is a multi-scanner dataset which leads to scanner bias due to the differences in acquisition protocols and scanners. The multi-scanner dataset is a combination of two datasets, namely PPMI and FTLDNI - frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed Feature Extraction-DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multi-scanner dataset for differentiating PD from healthy control, which is superior to the DcCNN model trained on a single scanner imbalanced dataset.

Cross-lingual sentiment analysis is crucial for understanding and interpreting emotions expressed in text across diverse linguistic contexts. However, cross-lingual sentiment analysis faces challenges such as differences in emotional lexicons, data imbalance, and the need for multi-language sentiment normalization. In this study, we propose a novel approach to address these challenges by leveraging low-resource language training techniques to enhance cross-lingual sentiment analysis. Our method aims to bridge the gap in emotional lexicons by adapting sentiment analysis models to diverse linguistic contexts. Additionally, we tackle issues of data imbalance through innovative data augmentation strategies tailored to each language, and we introduce a multi-language sentiment normalization technique to ensure consistent sentiment interpretation across different languages. Our method achieved state-of-the-art results in twelve languages across four domains, demonstrating superior performance in both text-level and sentence-level sentiment analysis tasks. By conducting comprehensive evaluations across diverse linguistic contexts and domains, we showcase the versatility and effectiveness of our approach in achieving top-tier results in cross-lingual sentiment analysis.

Named Entity Recognition (NER) serves as a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries.To address these challenges, a corpus, labeled datasets and a lexicon specific to pig diseases for Chinese named entity recognition were constructed. Subsequently, a Pig Disease Chinese Named Entity Recognition (PDCNER) model was proposed. The model integrates external lexicon knowledge of pig disease by employing Lexicon-enhanced BERT and enhance feature representation by incorporating contrastive learning. Experimental results show that the model achieved the best recognition results, with a precision of 86.92%, a recall of 85.08%, and an F1-score of 85.99% respectively. Furthermore, the model exhibits robustness and generalizability across few-shot and publicly available datasets. Experimental results illustrate the proposed model could effectively identify Chinese named entities of pig diseases, outperforming several existing baseline methods. Moreover, the proposed model can be extended to other animal disease domains, such as chicken and cattle, thereby facilitating seamless adaptation for named entity identification across diverse contexts.

The predatory mirids Macrolophus pygmaeus Ramber and Nesidiocoris tenuis Reuter (Heteroptera: Miridae) are used for the biological control of Tuta absoluta Meyrick (Lep.: Gelechiidae) and other pests in tomato greenhouses. The functional response of 1 (young) and 10 (old) days old adult females and males of M. pygmaeus and N. tenuis on eggs of T. absoluta, was determined on tomato at two temperatures (20° and 25°C) and LD 16:8 and found to be Type II. M. pygmaeus females exhibited higher predation efficiency, than males at both tested temperatures. Young M. pygmaeus females had a higher efficiency than old ones, whereas males had a low efficiency irrespective of age. The predation efficiency of N. tenuis was high (but lower than M. pygmaeus) in both young females and males, although old females had a higher efficiency than the respective males. Our results show that the two predatory species have different functional response characteristics to their prey depending mainly on sex and age which may affect their role as biological control agents.

Cannabidiol (CBD) is a non-intoxicating compound extracted from Cannabis sativa, showing antidepressant-like effects in different rodent models. However, inconsistent results have been described depending on the species and the strain used to assess depressive-like behaviour. Moreover, only a few studies have investigated the effect of CBD in female rodents. Therefore, we aimed to i) investigate the effects of CBD in two different strains of mice (Swiss and C57BL/6) and in a rat model of depression based on selective breeding (Flinders Sensitive and Resistant Lines, FSL and FRL) subjected to tests predictive of antidepressant-like effects; and ii) investigate the influence of sex in the effects of CBD in both mice and rats. CBD induced an antidepressant-like effect in male Swiss but not in female Swiss or C57BL/6 mice in the tail suspension test (TST). In male FSL rats, CBD produced an antidepressant-like effect one-hour post-injection. However, in female FSL, CBD induced a bimodal effect, increasing the immobility time at one hour and decreasing it at two hours. Ketamine produced an antidepressant-like effect in male and female FSL rats at different doses. In conclusion, strain, sex, and administration time affect CBD's behavioural response to rodents exposed to tests predictive of antidepressant effects.

The prevalence and incidence of cardiac pro-arrhythmic disorders are often influenced by sex due to specific effects on the QT interval. Androgens shorten QT, which may be protective against acquired long QT syndromes and their related arrhythmias in men such as torsade de pointes (TdP). On the other hand, androgens can potentiate Brugada and early repolarization syndromes, which are most prevalent in men. In this case series, we highlight four male patients with aborted SCD in the setting of abnormal testosterone status; two patients with TdP in a setting of testosterone deprivation (of which one drug-induced) and 2 patients with ventricular fibrillation associated with exogenous androgenic booster (Tribulus terrestris) intake. From this case series, we review the current available literature of the effects of androgen as a double-edged sword on the QTc interval and emphasize the importance of QTc monitoring in this subset of patients.

Many dioecious plants have sex chromosomes that are cytologically heteromorphic, but about half of species lack cytological differences between males and females and are thus homomorphic. Very little is known about the size and content of the non-recombining sex-determining region (SDR) in these species. Here, we assess the size and content of the SDR of the diploid dioecious herb Mercurialis annua, which has homomorphic sex chromosomes and shows signatures of mild Y-chromosome degeneration. We used RNAseq to identify new Y-linked markers for M. annua. Twelve of 24 transcripts with male-specific and male-biased expression could only be PCR-amplified from males and are thus Y-linked. We found a further six Y-linked sequences that were present in males but not females using genome capture data from multiple populations. We used the Y-linked sequences to identify and sequence 17 sex-linked bacterial artificial chromosomes (BACs), which form 11 groups of non-overlapping sequence, covering a total sequence length of about 1.5 Mb. Content analysis of this region suggests it is enriched for repeats, has a low gene density and contains few candidate sex-determining genes. The BACs map to a subset of the sex-linked region of the genetic map, which is estimated to be at least 14.5 Mb. This is substantially larger than estimates for other dioecious plants with homomorphic sex chromosomes, especially given the small genome size of M. annua. Our data provide a rare, high-resolution view of the homomorphic Y chromosome of a dioecious plant.

Background. The possible interactions between anger and fear have not been widely explored in the psychological literature. Fear and anger are currently beginning to be studied by looking at their interrelationships, rather than seeing them as simply opposing emotions. Furthermore, there is a tendency to think that anger is more typical of men and fear of women. Our contribution proposes a particular perspective of affective neuroscience. The objectives of the study are: 1) To assess possible differences in affective systems, states, and traits of anger in relation to biological sex; 2) To assess correlations between ANGER, FEAR, and PANIC, state and trait anger in both a female and male sample; 3) To assess possible differences in basic affective systems in relation to different levels of ANGER, FEAR, and PANIC, state and trait anger, in sample of female and in sample of male. Methods. A non-clinical sample of 339 females and 99 males completed the ANPS 3.1 to assess basic affective states and the STAXI 2 to assess anger states and traits. Results. No significant differences were found for ANGER and FEAR scores and for state and trait scores between the male and female samples. Clear correlations emerged (p < .01) between FEAR. PANIC and FEAR in both the female and male samples. Among the differences that emerged in the affective systems, we emphasise that in the female group as FEAR increases, there are significant variations in systems of SEEKING, PLAY, and CARE. Furthermore, only in the female sample as PANIC increases, PLAY progressively decreases, and CARE shows an irregular trend. Discussion. Given the importance of the SEEKING and PLAY systems for well-being and gender equality the hypoactivation of these systems to increases in FEAR and PANIC highlights the need to monitor conditions that may trigger these affective systems.

With the advancement of technology, signal modulation types are becoming increasingly diverse and complex. The phenomenon of signal time-frequency overlap during transmission poses significant challenges for the classification and recognition of mixed signals, including poor recognition capabilities and low generality. This paper presents a recognition model for fine-grained analysis of mixed signal characteristics, proposing a Geometry Coordinate attention mechanism and introducing a low-rank bilinear pooling module to more effectively extract signal features for classification. The model employs a residual neural network as its backbone architecture and utilizes the Geometry Coordinate attention mechanism for time-frequency weighted analysis based on information geometry theory. This analysis targets multiple-scale features within the architecture, producing time-frequency weighted features of the signal. These weighted features are further analyzed through a low-rank bilinear pooling module, combined with the backbone features, to achieve fine-grained feature fusion. This results in a fused feature vector for mixed signal classification. Experiments were conducted on a simulated dataset comprising 39,600 mixed signal time-frequency plots. The model was benchmarked against a baseline using a residual neural network. The experimental outcomes demonstrated an improvement of 9% in the exact match ratio and 5% in the Hamming score. These results indicate that the proposed model significantly enhances the recognition capability and generalizability of mixed signal classification.

Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, Named Entity Recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named entities, coupled with a scarcity of labeled data. To address these challenges, we compiled a corpus from 50 books on chicken diseases, covering 28 different disease types. Utilizing this corpus, we developed a nested NER model, MFGFF-BiLSTM-EGP. This model integrates the Multiple Fine-Grained Feature Fusion (MFGFF) module with a BiLSTM neural network and employs an Efficient Global Pointer (EGP) for predicts the entity location encoding. In MFGFF module we designed three encoders, character encoder, word encoder and sentence encoder, this design effectively captures fine-grained features and improves the recognition accuracy of nested entities. Experimental results show that the model performs robustly, with F1 scores of 91.98%, 73.32%, and 82.54% on the CDNER, CMeEE V2, and CLUENER datasets, respectively, outperforming other commonly used NER models. Specifically, on the CDNER dataset, the model achieved an F1 score of 79.68% for nested entity recognition. This research not only advances the development of knowledge graph and intelligent question-answering system for chicken diseases but also provides a viable solution for extracting disease information that can be applied to other livestock species.

As climate changes, our daily life has been much influenced by abnormal meteorological phenomena such as heavy rainfall, heat wave, heavy snowfall, and fine dust. Atmospheric air quality is worsening day by day and indoor air quality is also affected by interconnected daily activities throughout the inside and outside of buildings and houses. Nowadays, pollutants from various sources are emitted, transformed by sunlight, vapor, and ozone and transported into the city from country to country. Due to these reasons, there have been high demands to monitor the transportation of particulate matters and improve air quality. Monitoring of pollutants and identification of type and its concentration enables us to track and control its generation and consequently find out the solution. However, monitoring of pollutants, especially, particulate matter generation and its transportation is still not fully operated in atmospheric air due to its open nature and meteorological factors. Even though indoor air is relatively easy to monitor and control than outdoor in the aspect of specific volume and contaminant source, but it still needs to consider the meteorological parameters because indoor air is not fully separated from the outdoor air flow and contaminants transportation. In this study, optical approach using spectral sensor was attempted to reveal the feasibility of wavelength and chromaticity values of reflected light from specific particles. From the analysis of reflected light of various particulate matters according to different liquid additives, parameter studies were performed to investigate which experimental conditions can contribute to the enhanced selective sensing of particulate matters. Five different particulate matters such as household dust, soil, talc powder, gypsum powder and yellow pine tree pollen were utilized and observed to elucidate the relationship between property of particulate matter and detected light spectrum. Applicable approaches to assist current particle matter sensors and improve the selective sensing were suggested.

Superantigens are unconventional antigens which recognise immune receptors outside the usual binding sites e.g. complementary determining regions (CDRs), to elicit a response within the target cell. T-cell superantigens crosslink T-cell receptors and MHC Class II molecules on antigen-presenting cells, leading to lymphocyte recruitment, induction of cytokine storms and T-cell anergy or apoptosis among many other effects. B-cell superantigens, on the other hand, bind immunoglobulin receptors on B-cells affecting opsonisation, IgG-mediated phagocytosis, and drive B-cells into apoptosis. Here, through a review of the structural basis for recognition of immune receptors by superantigens, we show that their binding interfaces share specific physicochemical characteristics when compared with other protein-protein interaction complexes. Given that antibody-binding superantigens have been exploited extensively in industrial antibody purification, these observations could facilitate further protein engineering to optimize the use of superantigens in this and other areas of biotechnology.

It is a challenging problem that air-written Arabic letters has received a lot of attention in the past decades when compared to commonly spoken languages like English languages. To fill this gap, we propose a strong model that brings together machine learning (ML) and optical character recognition (OCR) methods. The model applied several ML methods, (i.e., Neural Networks (NN), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), with deep convolutional neural networks (CNNs) such as VGG16, VGG19, and SqueezeNet for effective feature extraction. Our study utilizes the AHAWP dataset, which consists of varied writing styles and variations in hand signs, to train and evaluate the model. Preprocessing systems are applied to improve data quality by reduction bias. Besides, OCR methods are combined into our model to sequestrate individual letters from continuous air-written gestures and refine recognition results. Results of this study show that the proposed model has achieved the extreme accuracy of 88.8% using NN with VGG16. This study presents an inclusive approach that combines ML, deep CNNs, and OCR methods to address the issue of Arabic in air writing recognition research.

Named Entity Recognition (NER) is a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question-answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries. To address these challenges, we propose PDCNER, a Pig Disease Chinese Named Entity Recognition method leveraging lexicon-enhanced BERT and contrastive learning. Firstly, we construct a domain-specific lexicon and pre-train word embeddings in the pig disease domain. Secondly, we integrate lexicon information of pig diseases into the lower layers of BERT using a Lexicon Adapter layer, which employs char-word pair sequences. Thirdly, to enhance feature representation, we propose a lexicon-enhanced contrastive loss layer on top of BERT. Finally, a Conditional Random Field (CRF) layer is employed as the model's decoder. Experimental results show that our proposed model demonstrates superior performance over several mainstream models, achieving a precision of 87.76%, a recall of 86.97%, and an F1-score of 87.36%. The proposed model outperforms BERT-BiLSTM-CRF and LEBERT by 14.05% and 6.8%, respectively, with only 10% of the samples available, showcasing its robustness in data scarcity scenarios. Furthermore, the model exhibits generalizability across publicly available datasets. Our work provides reliable technical support for the information extraction of pig diseases in Chinese and can be easily extended to other domains, thereby facilitating seamless adaptation for named entity identification across diverse contexts. The codes have been open-sourced at https://github.com/tufeifei923/pdcner.

Relatively little research attention has been paid to sex issues in late life suicidal behaviour. The aim was to compare clinical characteristics of women and men aged 70+ who were hospitalized after a suicide attempt. We hypothesized higher depression and anxiety scores in women, and we expected to find that men would more often attribute the attempt to health problems and compromised autonomy. Participants (56 women and 47 men, mean age 80) were interviewed by a psychologist. In addition to psychiatric and somatic health assessments, participants responded to an open-ended question concerning attributions of the attempt. There were no sex differences in depression and anxiety, but women were more likely to report that they found their situation hopeless (67.9% vs. 43.8%, p = 0.02). At least one serious physical disability was noted in 60.7% of the women and 53.2% of the men (p = 0.55). Proportions attributing their attempt to somatic illness did not differ (women, 14.5% vs. men 17.4%, p = 0.79), and similar proportions attributed the attempt to reduced autonomy (women, 21.8% vs. men, 26.1 %, p = 0.64). The unexpected lack of sex differences might be influenced by cultural context in which sex norms play a part.

Abstract: Hand gestures are an essential kind of nonverbal communication that let people engage and be expressive with one another. As technology has progressed, hand gesture recognition systems have become an important field of study, providing a link between human activities and artificial intelligence. An overview of the field of hand gesture recognition systems is given in this abstract, along with an explanation of its importance, difficulties, and possible uses. This abstract begins by examining the role that hand gestures have in communication. It then goes on to show the cultural significance and wide range of variations in gestural expressions that occur in many contexts and societies. Artificial intelligence systems face a unique problem in comprehending and interpreting these motions, which calls for the combination of computer vision, machine learning, and pattern recognition approaches. The abstract goes on to address why people develop hand motions. The purpose behind creating systems to recognize hand gestures is also covered in the abstract, with a focus on how these systems could improve human-computer interaction in several fields, including robotics, virtual reality, healthcare, and assistive technologies. These systems have the potential to enhance accessibility, efficiency, and user experience by allowing people to engage with devices and interfaces naturally and intuitively. The abstract also discusses environmental elements including backdrop clutter and lighting, as well as the technological difficulties in identifying and understanding hand gestures, which include variations in hand forms, orientations, and movements. To tackle these obstacles, strong algorithms that can record and interpret complex hand movements in real-time must be developed.

To date, the development of unmanned technologies using autonomous underwater vehicles (AUV) has become an urgent demand for solving the problem of inspection of industrial sub-sea structures. A key issue here is precise localization of AUVs relative to underwater objects. However, the well-known challenges of recognizing and coordinating underwater objects (impossibility to use GPS, undercurrents, etc.) do not allow high-precision navigation if based solely on a standard suite of AUV navigation tools (sonars, etc.). An alternative is a technology of processing of optical images that, at short distances, can provide higher accuracy of AUV navigation compared to a technology of acoustic measurement processing. In this study, in the context of the problem of subsea production system inspection, we propose a method to recognize underwater objects and provide coordinate reference of AUV to them, based on stereo-image processing. Its distinctive features are the use of a non-standard technique for generating a geometric model of an object from its views (foreshortening) taken from positions of a pre-made overview trajectory, the use of various characteristic geometric elements when recognizing objects, and the original algorithms for comparing visual data of the inspection trajectory with an a priori model of the object.

Myasthenia gravis (MG) is a disease characterized by typical clinical and immunopathogenic pe-culiarities, which correlate with age and sex/gender. Accordingly, MG patients show thymic al-terations and auto-antibodies productions, that can be the expression of a different pathogenetic mechanism related to age and gender. This determines, in relation to age at and sex/gender, a dif-ferent MG onset, prognosis, and outcome. Therefore, diverse MG forms can be identified, classi-fied, and characterized in relation to their different features, and precisely there are early onset and late-onset forms of MG. It has been hypothesized that the cause of appearance the two diverse forms of MG can be ascribed mainly to the diverse genetic and epigenetic factors. In this narrative review, we will analyze and discuss about the interplay of age- and sex-related factors (i.e. genet-ic and epigenetic factors) related to two different MG forms and how they contribute to their di-verse pathogenesis.

Suboptimal nutrition is a leading cause of cardiometabolic disease and mortality. Biological sex is a variable that can play a role in individual response to nutritional factors and may modulate the impact of nutrition on disease. This narrative review explores the intricate interplay between sex, nutrition, and health outcomes, and the consequent importance of considering sex in nutrition research, dietary recommendations, and disease management. We examine the impact of sex on how various dietary patterns, including the Mediterranean diet, macronutrient content and quality, Western diet, and energy restriction associate with or affect metabolic health and disease risk. Understanding how sex influences the response to the diet can inform precision nutrition interventions and approaches for disease prevention and management.

Background: We aimed to 1) Understand the level of knowledge about sex trafficking of minors among school personnel and the determinants of such knowledge; 2) Test the effectiveness of short educational videos in increasing awareness about sex trafficking of minors among school personnel. Methods: We employed an online survey to gather responses from 741 teachers and school counselors living in the US. The McNemar test was used to test for differences in awareness before and after exposure to the videos. Logistic regression was used to identify predictors of awareness based on the respondents’ characteristics. Results: Predictors of knowledge about sex trafficking were years of experience, level of education and being a female. Exposure to the educational videos improved the respondents’ level of awareness about this crime and prompted respondents to seek additional educational material. There is a need to develop training initiatives for school personnel on sex trafficking and response protocols to support victims of this crime. Conclusion: School personnel have a high level of awareness of risk factors for sexual abuse but less awareness of what constitutes sex trafficking in children. Exposure to short educational videos can increase such awareness.

Radiosensitivity, the susceptibility of cells to ionizing radiation, plays a critical role in understanding the effects of radiation therapy and exposure on tissue health and regeneration. Identifying characteristics that predict how a patient may respond to radiotherapy enables clinicians to maximize the therapeutic window. Limited clinical data suggested a difference in male and female radiotherapy outcomes. Radiotherapy for gastrointestinal malignancy is still a challenge due to intestinal sensitivity to radiation toxicity. In this manuscript we demonstrated sex specific differences in intestinal epithelial radiosensitivity. In mice model of abdominal irradiation, we observed significant increase in oxidative stress and injury in male compared to female. Lgr5+ve intestinal stem cells from male mice showed higher sensitivity to radiation induced toxicity. However, sex specific differences in intestinal radiosensitivity are not dependent on sex hormone as we demonstrated similar sex specific radiosensitivity differences in pediatric mice. In ex-vivo study we found that human patient derived intestinal organoid (PID) derived from male showed higher sensitivity to irradiation compared to female as evident from loss of budding crypt, organoid size, and membrane integrity. Transcriptomic analysis of human Lgr5+ intestinal stem cells suggested radiation induced upregulation of mitochondrial oxidative metabolism in male compared to female possible mechanism for radiosensitivity differences.

Saturated lights appear brighter than white lights of the same luminance. This phenomenon is known as the Helmholtz-Kohlrausch (H-K) effect and can be estimated by modeling achromatic luminance and saturation to total brightness. These models have been shown to differ between women and men and are more variable in women. The variation in brightness models among women may be due to hormonal changes across the menstrual cycle. To test this, total brightness and achromatic luminance were measured across blue, green, yellow-green, yellow, and red hues. These data were measured along with salivary hormone levels for nine cycling women at points representing the menstrual, peri-ovulation, and luteal phases. These data were also collected for seven oral contraceptive (OC) users. There were no main effects of OC use nor menstrual cycle phase on B/L ratios, but ratios were higher for the red stimulus in cycling women than OC users. Red B/L ratios were also higher for cycling women than OC users during the luteal phase. Estrogen, progesterone, and their interaction predicted 18% of the variation in brightness for cycling women. These models could not be fit for OC users, and estrogen only accounted for 5% of brightness variance with progesterone terms omitted. These findings and potential mechanisms are discussed in the context of previous results.

Individual response to drugs is highly variable and largely influenced by genetic variants and gene-expression profiles. Also, it has been shown that response to drugs is strongly sex-dependent, both in terms of efficacy and toxicity. To expand current knowledge on sex differences in the expression of genes relevant for drug response, we generated a catalogue of differentially expressed human transcripts encoded by 289 genes in 41 human tissues from 838 adult individuals of the Genotype-Tissue Expression project (GTEx, v8 release) and focused our analysis on relevant transcripts implicated in drug response. We have detected significant sex-differentiated expression of 99 transcripts encoded by 59 genes in the tissues most relevant for human pharmacology (Liver, Lung, Kidney, Small intestine terminal ileum, Skin not sun-exposed, and Whole Blood). Among them, as expected, we observed significant differences in the expression of transcripts encoded by the cytochromes in the liver, CYP2B6, CYP3A7, CYP3A5, and CYP1A1. Our systematic investigation on differences between male and female in the expression of drug response related genes, reinforce the need to overcome the sex bias of clinical trials.

The article proposes to use a radio holography for the detection and identification of unmanned aerial vehicles (UAVs).

Abstract: The aim of the present study was to identify the age and sex-related associations between marital status with PA and TV time. We used data from Vigitel, an annual telephone survey started in 2006 in Brazil. We applied a complex sample logistic regression model to estimate the odds for PA and TV time comparing marital statuses according to age and sex subgroups, independent of obesity, hypertension, diabetes, self-assessed poor health, and smoking. Our sample included 561,837 individuals with a TV time &gt; 3 h/day (prevalence = 25.2%) and PA &gt; 150 min/week (prevalence = 35%). Compared to single individuals, married men and women were less likely to watch TV more than 3 h/day in participants &gt; 30 years old. When compared to single, married participants were less likely to do more than 150 min of PA/week at younger age groups. Married women older than 40 years were more likely to do more than 150 min of PA/week than the single ones, while there were no differences among married men by age group. In conclusion, married individuals are less likely to spend more than 3 hours a day watching TV than single individuals. Single men and women were more likely to do more than 150 min of PA/week at younger age groups and married women older than 40 years were more likely to do 150 min of PA/week than single women.

Egyptian mongoose (Herpestes ichneumon) is a medium-size carnivore that in Europe is restricted to Iberia. The bio-ecology of this species remains to be elucidated in several dimensions, including gut microbiota that is nowadays recognized as a fundamental component of mammals. In this work, we investigated the gut microbiota of this herpestid by single-molecule real-time sequencing of twenty paired male (n=10) and female (n=10) intestinal samples. This culture-independent approach enabled microbial profiling based on 16S rDNA and investigation of taxonomical and functional features. The core gut microbiome of the adult subpopulation was dominated by Firmicutes, Fusobacteria, Actinobacteria, and Proteobacteria. Eight genera were uniquely found in adults and five in non-adults. When comparing gut bacterial communities across sex, four genera were exclusive of females and six uniquely found in males. Despite these compositional distinctions, alpha- and beta-diversity analyses showed no statistically significant differences across sex or between adult and non-adult specimens. However, males presented a significantly higher abundance of amino acid and citrate cycle metabolic pathways, compared to the significant overrepresentation in females of galactose’ metabolic pathways. Adults showed a significantly higher abundance of cationic antimicrobial peptide resistance pathways, while non-adults bared a significant overrepresentation of two-component systems associated with antibiotic synthesis, flagellin production and chemotaxis control. This study adds new insights into mongoose bio-ecology palette, highlighting taxonomical and functional microbiome dissimilarities across sex and age classes, possibly related to primary production resources and life-history traits that impact on behavior, diet and gut ecosystem.

Many studies have addressed sex differences in patients with psoriatic arthritis, although these are aimed more at describing the phenotype than at investigating the causes underlying the differences. Cross-sectional study of 203 patients. The clinical and demographic variables included age, years of education, time since onset, smoking, type of treatment, clinical presentation, number of entheses affected, dactylitis, fatigue, and cutaneous involvement. Disease activity,functioning and disease was measured. The comorbid conditions evaluated included obesity (and leptin as an associated parameter), anxiety and depression, and sleep quality. Mean age was 54.6, and 46.8% of patients were women. Women less frequently presented axial involvement and more frequently presented enthesitis. They also presented greater disease activity, greater disease impact, and poorer functioning, as well as greater fatigue. As for comorbid conditions, women presented a higher leptin/BMI ratio, higher levels of anxiety and depression, and poorer sleep quality. By sex, pain affecting women was associated with the leptin/BMI ratio and sleep quality. The leptin/BMI ratio was not associated with any component of disease activity in men. Sex was associated with several clinical manifestations, disease activity, functioning, and disease impact. Leptin was associated with intensity of pain in women.

Poplar (Populus) is a genus of woody plants of value for the pulp industry, green energy, urban landscaping, soil phytoremediation and as an ornamental plant. Due to the increasing economic importance of poplar, there is a need to ensure its stable growth by increasing its resistance to pathogens, as one of the main problems in poplar cultivation is its susceptibility to numerous diseases. Currently, breeding poplars with reduced susceptibility to infections is receiving increasing attention. Genetic engineering can produce organisms with improved traits faster than traditional methods, and with the development of CRISPR/Cas-based genome editing systems, scientists have a new highly efficient tool at their disposal to create valuable genotypes. In this review, we summarize recent research data on poplar diseases, the biology of their pathogens, and how these plants resist pathogens. In the final section, we discuss genetic engineering, genome editing, and microbiome strategies that we believe are suitable for obtaining new improved poplar lines.

Colon cancer (CRC) is a prevalent malignancy that exhibits distinct differences in incidence, prognosis, and treatment responses between males and females. These disparities have long been attributed to hormonal differences, particularly the influence of estrogen signaling. This review aims to provide a comprehensive analysis of recent advances in our understanding of the molecular mechanisms underlying sex differences in colon cancer and the protective role of membrane and nuclear estrogen signaling in CRC development, progression, and therapeutic interventions. We discuss the epidemiological and molecular evidence supporting sex differences in colon cancer, followed by an exploration of the impact of estrogen in CRC through various genomic and non-genomic signaling pathways involving membrane and nuclear estrogen receptors. Furthermore, we examine the interplay between estrogen receptors and other signaling pathways, in particular the Wnt/-catenin proliferative pathway and hypoxia in shaping biological sex differences and estrogen protective actions in colon cancer. Lastly, we highlight the potential therapeutic implications of targeting estrogen signaling in the management of colon cancer and propose future research directions to address the current gaps in our understanding of this complex phenomenon.

In the present era, there is no longer a necessity to rely on outdated and laborious methods for attendance management in the field of education. Managing attendance for large groups of students in a classroom can be a challenging and time-consuming task when it comes to recording data. Fortunately, modern technologies offer us the opportunity to streamline attendance management and make it more efficient. One effective approach is Real-Time Face Recognition, which simplifies the process of tracking attendance for numerous students daily. We employ the Haar cascade classifier to identify positive and negative facial attributes, along with the LBPH algorithm for accurate face recognition. These techniques are implemented using Python programming language and the OpenCV library while incorporating a user-friendly Tkinter GUI (Graphical User Interface) for seamless usability. By embracing these advancements, we can revolutionize how attendance is managed in educational environments.

Transgender (TG) describes individuals whose gender identity differs from the social norms. Some TG people undergo gender-affirming hormone therapy (HT) and may be considered as a sub-group of population susceptible to environmental contaminants for their targets and modes of action. Aim of the work is to set appropriate HT doses and identify specific biomarkers to implement TG animal models. Four adult rats/group/sex are subcutaneously exposed to 3 doses of HT (plus control) selected starting from available data. Demasculinizing-feminizing model (dMF): β-estradiol plus cyproterone acetate: 0.09+0.33, 0.09+0.93 and 0.18+0.33 mg, 5 times/week. Defeminizing-masculinizing model (dFM): testosterone 0.45, 0.95 and 2.05 mg, 2 times/week. Clitoral gain and sperm count, histophatological analysis of reproductive organs and liver, hormone serum levels and gene expression of sex-dimorphic CYP450 are evaluated. In dMF model, the selected doses, leading to T serum levels at the range of the corresponding cisgender, induced strong general toxicity and cannot be used in long-term studies. In dFM model, 0.45 mg of testosterone represents the correct dose. In addition, the endpoints selected are considered suitable and reliable to implement the animal model. The sex-specific CYP expression is a suita-ble biomarker to set proper (de)masculinizing/(de)feminizing HT and to implement TG animal models.

There is increasing evidence that many molecular processes exhibit differences with age 1 and sex. Such differences produces also differences in the insurgence and progression of many 2 complex diseases. For instances, demographic data on insurgence of comorbidities of mellitus 3 diabetes, on lethality of COVID-19, and on some cancers, shows differences between sex and age 4 groups. Therefore, the growing interest on such area requires the management of related data as 5 well as the development of algorithms and tool for the analysis. The availability of omics data 6 annotated with metadata related to age and sex is mandatory for building pipeline of the analysis. 7 The number of databases containing data related to age and sex is hencefort growing. We here show 8 some databases and tools storing such data. Finally, future research direction are highlighte

The practice of physical activity is an effective non-pharmacological strategy for preventing and treating chronic diseases. A large body of evidence has contributed to establishing a positive correlation between a physically active lifestyle and health benefits, including enhanced vaccination responses, lower numbers of senescent T-cells, increased T-cell proliferative capacity, lower levels of inflammatory cytokines, and improved neutrophil and macrophage function. While females are generally thought to exert more robust immune responses than males in response to a variety of challenges, and both male and female sex hormones have been suggested as mediators of immune function, research on this topic has not always been designed with a sex-specific lens. The goal of this review is to summarize the available experimental and clinical evidence linking exercise and immune function in male and female subjects, with an emphasis on sex differences and sex-specific mechanisms. Overall, the available evidence indicates that moderate exercise and physical activity improves immune function in both sexes, whereas prolonged and high-intensity exercise temporarily impairs immune responses at a higher degree in females. A role of male and female sex hormones in these sex-specific effects is also discussed.

This brief essay explores both the social and biological dimensions of human sexuality in light of the possibility of a substantial commerce in sexually-enabled robots, and concludes with some potentially strategic considerations for those who find themselves involved with their design, production, and/or marketing.

Ferroptosis is implicated in viral neuropathogenesis and may underlie HIV-associated neurocognitive impairment (NCI). Emerging data also suggest differences in brain iron transport by sex. We hypothesized that circulating ferritins that inhibit ferroptosis associate with neurocognitive function and NCI in people with HIV (PWH) in a sex-biased manner. Serum ferritin heavy-chain-1 (Fth-1), ferritin light-chain (Ftl), and urinary F2-isoprostanes (uF2-isoPs, specific lipid peroxidation marker) were quantified in 324 PWH (including 61 women) with serial global (NPZ4) and domain-specific neurocognitive testing. Biomarker associations with neurocognitive test scores and NCI were evaluated by multivariable regression; correlations with uF2-isoPs were also assessed. Higher Ftl and Fth-1 associated with less NCI in all PWH (adjusted odds ratios 0.53, 95% Confidence Interval (95% CI) 0.36-0.79 and 0.66, 95% CI 0.45-0.97, respectively). In women, higher Ftl and Fth-1 associated with better NPZ-4 (Ftl: adjusted beta (β)=0.15, 95% CI 0.02-0.29; Ftl-by-sex βinteraction=0.32, p=0.047) and domain-specific neurocognitive test scores. Effects on neurocognitive performance persisted for up to 5 years. Levels of both ferritins correlated inversely with uF2-isoPs in women (Ftl: rho=-0.47, p<0.001). Circulating Ftl and Fth-1 exert sustained, sex-biased neuroprotective effects in PWH, possibly by protecting against iron-mediated lipid peroxidation (ferroptosis). Larger studies are needed to confirm the observed sex differences and further delineate underlying mechanisms.

Accurate and robust deep learning models for facial expression recognition are challenging to achieve, given the diversity of human faces and variations in images, including different facial poses and lighting conditions. In this work, we proposed a clock-driven convolutional Spiking Neural Network (SNN) and STDP fine-tune architecture, meticulously calibrated its hyperparameters, and experimented with various optimization methods. The best model resulted was trained and evaluated on the Fer2013 and FER+ database, obtaining an accuracy of 61.87% and 79.97% without requiring auxiliary training data or face registration. To our best knowledge, the proposed SNN achieved comparable accuracy to CNNs of similar depth and possessed the advantages of low energy consumption and high computational efficiency. The computational efficiency of the proposed SNN is approximately three times that of CNNs. Along with this, we introduced the very recent cumulative spike guided encoder visualization technique and revealed the strong encoding capability of the proposed SNN.

The canonical decomposition of a bipartite graph is a new decompositionmethod that involves three operators, parallel, series and K+S. The class of weak bisblit graphs is totally decomposable with respect of these operators, and the class of bicographs is totally decomposable with respect of parallel and series operators. We prove in this paper that the class of bipartite (P_6,C_6)-free graphs is exactly the class of bipartite graphs that are totally decomposable with respect of parallel and K+S operators. We simplify the recognition algorithm of weak-bisplit graphs to adepte with bipartite (P_6,C_6)-free graphs. As a result of this adapted algorithm, we prsent efficient solutions in this class of graphs for two optimization graph problems, the first is the maximm balanced bi-clique problem and the the second is the maximum independent set problem.

The research on the distribution of rural buildings is one of the fundamental works of urban-rural development in Vietnam. Adopting Mask R-CNN deep learning framework and collecting sub-meter remote sensing images, this research used a remote sensing interpretation model of rural buildings trained based on East Asian characteristics of rural buildings and successfully recognized about 2.87 million rural buildings in 34 Vietnamese provincial administrative districts with a total area of rural buildings of 2,492 million square meters. The reliability of the identification results was verified by manual detection and quantitative statistics, and a multi-scale database of rural buildings in Vietnam based on individual rural buildings was created. Based on the database, this paper analyzes the distribution characteristics of rural buildings and summarizes characteristics of rural buildings distribution at the country, regional, and provincial scales. The identification results lay the foundation for the next study of urban-rural relations in Southeast Asia and the construction of a basic database on villages.

Indoor scene recognition and semantic information can be helpful for social robots. Recently, in the field of indoor scene recognition, researchers have incorporated object-level information and shown improved performances. This paper demonstrates that scene recognition can be performed solely using object-level information in line with these advances. A state-of-the-art object detection model was trained to detect objects typically found in indoor environments and then used to detect objects in scene data. These predicted objects were then used as features to predict room categories. This paper successfully combines approaches conventionally used in computer vision (YOLO) and Term Frequency-Inverse Document Frequency (TF-IDF). These approaches could be further helpful in the field of embodied research and dynamic scene classification, which we elaborate on.

The ability to express and recognize emotion via facial expressions is well known to change with age. The present study investigated the differences in the facial recognition and facial expression of the elderly (n = 57) and the young (n = 115) and measure how each group uses different facial muscles for each emotion with Facial Action Coding System (FACS). In facial recognition task, the elderly did not recognize facial expressions better than young people and reported stronger feelings of fear and sad from photographs. In making facial expression task, the elderly rated all their facial expressions as stronger than the younger, but in fact, they expressed strong expressions in fear and anger. Furthermore, the elderly used more muscles in the lower face when making facial expressions than younger people. These results help to understand better how the facial recognition and expression of the elderly change, and show that the elderly do not effectively execute the top-down processing concerning facial expression.

Nowadays, speed up development and use of digital devices such as smartphones have put people at risk of internet crimes. The evidence of present crimes in a computer file can be easily unreachable by changing the prefix of a file or other algorithms. In more complex cases, either file divided into different parts or the parts of a file that has information about the file type are deleted, where the file fragment recognition issue is discussed. The known files are divided into different fragments, and different classification algorithms to solve the problems of file fragment recognition. A confusion matrix measures the accuracy of type recognition. In the present study, first, the file is divided into different fragments. Then, the file fragment features, which are obtained from Binary Frequency Distribution (BFD), are reduced by 2 feature reduction algorithms; Sequential Forward Selection algorithm (SFS) as well as Sequential Floating Forward Selection algorithm (SFFS) to delete sparse features that result in increased accuracy and speed. Finally, the reduced features are given to 3 classifier algorithms, Multilayer Perceptron (MLP), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN) for classification and comparison of the results. In this paper, we proposed the algorithm of file type recognition that can recognize 6 types of useful files ( pdf, txt, jpg, doc, html, exe), which may distinguish a type of file fragments with higher accuracy than the similar works done.

The dominant paradigm holds that spontaneous and therapeutically induced anti-tumor responses are mediated mainly by CD8 T cells and directed against tumor-specific antigens (TSAs). The presence of specific TSAs on cancer cells can only be proven by mass spectrometry analyses. Bioinformatic predictions and reverse immunology studies cannot provide this type of conclusive evidence. Most TSAs are coded by unmutated non-canonical transcripts that arise from cancer-specific epigenetic and splicing aberrations. When searching for TSAs, mass spectrometry analyses must therefore interrogate not only the canonical reading frame of annotated exome but all reading frames of the entire translatome. The majority of aberrantly expressed TSAs (aeTSAs) derive from unstable short-lived proteins that are good substrates for direct MHC I presentation but poor substrates for cross-presentation. This is an important caveat because cancer cells are poor antigen-presenting cells and the immune system therefore depends on cross-presentation by dendritic cells (DCs) to detect the presence of TSAs. We therefore postulate that, in the untreated host, most aeTSAs are undetected by the immune system. We present evidence suggesting that vaccines inducing direct aeTSA presentation by DCs represent an attractive strategy for cancer treatment.

Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera-subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration.

This paper propose an original approach of achieving a Cymatics based visual perception of isolated speech commands. The idea is to smartly combine the effective speech processing and analysis methods with the phenomena of Cymatics. In this context, an effective approach for automatic isolated speech based message recognition is proposed. The incoming speech segment is enhanced by applying the appropriate pre-emphasis filtering, noise thresholding and zero alignment operations. The Mel-Frequency Cepstral coefficients (MFCCs), Delta coefficients and Delta-Delta coefficients are extracted from the enhanced speech segment. Later on, the Dynamic Time Warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The classification decision is transformed into a methodical excitation. Finally, this excitation is converted into the systematic visual perceptions via the phenomenon of Cymatics. The system functionality is tested with an experimental setup and results are presented. The approach is novel and can be employed in various applications like visual art, encryption, education, archeology, architecture, integration of impaired people, etc.

Studies report variable effects of sex hormones on serious diseases. Severe disease and mortality rates in COVID-19 show marked gender differences that may be related to sex hormones. Sex hormones regulate the expression of the viral receptors ACE2 and TMPRSS2, which affect the extent of viral infection and consequently cause variable outcomes. In addition, sex hormones have complex regulatory mechanisms that affect the immune response to viruses. These hormones also affect metabolism through obesity, and severe disease can result from complications such as thrombosis. This review presents the latest research on the regulatory functions of hormones in viral receptors, immune responses, complications as well as their role in COVID-19 progression. It also discusses the therapeutical possibilities of these hormones by reviewing the recent findings of clinical and assay studies.

Background: Limited research has explored sex-specific differences in death predictors of HF pa-tients with ischemic (iCMP) and nonischemic (niCMP) cardiomyopathy. This study assessed sex differences in niCMP and iCMP prognosis. Methods: We studied 7,487 patients with HF between February 2017 and September 2020. Clini-cal features and echocardiographic findings were collected. We used Kaplan-Meier, Cox propor-tional hazards models, and score chi-square of Cox regression to determine death predictors in women and men. Results: mean age was 64.3±14.2 years, with 4,417 (59%) males. Women with iCMP and niCMP exhibited significantly higher mean age, higher mean left ventricular ejection fraction, and smaller left ventricular diastolic diameter than men. Over 2.26 years of follow-up, 325 (14.7%) women and 420 (15.7%) men, and 211 women (24.5%) and 519 men (29.8%) died in niCMP (p=NS) and iCMP (p=0.004), respectively. Cumulative incidence of death was higher in men with iCMP (log-rank p<0.0001) but similar in niCMP. Cox regression showed chronic kidney disease, dia-betes, stroke, atrial fibrillation, age, and myocardial infarction, as main predictors of death for iCMP in women and men. Conclusion: Women exhibited a better prognosis than men in iCMP, but similar for niCMP. Nevertheless, sex was not an independent predictor of death for both CMP.

Plant reproduction is a very important process on Earth from the perspective of biodiversity, biomass gain and crop productivity. It is therefore important to understand sex determination process and many researchers are investigating the molecular basis of this phenomenon. However, information on the influence of transcription factors (TFs) on this process is limited, although cucumber is a model plant in this regard. In the present study, based on RNA-seq data for differential gene expression (DEG) analyses, we aimed to investigate the regulatory TFs that may influence the metabolomic processes in the shoot apex containing the forming flower buds. Therefore, a robust TF database was established for the B10v3 cucumber genome. Sex-specific interactome network maps were generated, indicating the regulatory TFs by their effects on DEGs and further on processes leading to the formation of different sex flowers. The network analysis identified major families of regulatory TFs. The most abundant families were: MYB, AP2/ERF, NAC and bZIP, and those with the greatest impact on developmental processes were identified, namely the AP/ERF family, followed by DOF, MYB, MADS and others. Thus, the central nodes and key regulators in the networks were identified with respect to male, female and hermaphrodite. Here, we proposed the first model of the regulatory network of TFs that influences the metabolism of sex development in cucumber. These findings may help to understand the molecular genetics and functional mechanisms underlying sex determination processes.

The impact of sex in the development of long-term toxicities affecting the quality of life of cancer survivors has not been investigated experimentally. To address this issue, a series of neurologic and cardiologic endpoints were used to investigate sex-based differences triggered by paclitaxel treatment and radiotherapy exposure. Male and female WT mice were treated with Paclitaxel (150 and 300 mg/kg) administered weekly over 6 weeks or exposed to 19 Gy cardiac irradiation. Cohorts were analyzed for behavioral and neurobiologic endpoints to assess systemic toxicity of paclitaxel or cardiovascular endpoints to assess radiotherapy toxicity. Interestingly, female WT mice exhibited enhanced tolerance compared to male WT mice regardless of the treatment regimen. To provide insight into the possible sex-specific protective mechanisms, rhoB deficient animals and elderly mice (22 months) were used with a focus on the possible contribution of sex hormones including estrogen. RhoB deficiency and advanced age had no impact on neurocognitive impairment induced by Paclitaxel but reversed the cardioprotection was observed in females after radiotherapy. Conversely, rhoB deficiency protected males from radiation toxicity. In sum, rhoB was identified as a molecular determinant driving estrogen dependent cardioprotection in female mice, whereas neuroprotection was not sex-hormone dependent. To our knowledge, this study revealed for the first time sex- and organ-specific responses to paclitaxel and radiotherapy.

The unusual occurrence and developmental diversity of asexual eukaryotes still remain a puzzle. Despite asexual organisms have a theorized two-fold reproductive advantage over sexuals, asexual lineages are rare among multicellular eukaryotes. Justification of such disparity relies on the consequences of a lack of meiosis, which restricts genotype diversity and adaptation to novel conditions while accelerating the genetic degeneration that drives asexual lineages to an early demise. However, evidence indicates asexuals use different strategies to limit negative consequences of ameiosis, and age estimates show some asexual vertebrates and plants are much older than expected. If rapid extinction is not a factor influencing asexuals lifespan, then why asexuals are not more frequent? Here we review traditional ideas and new data and provide a novel unified evolutionary frame to understand the intriguing nature, developmental diversity and maintenance of asexual lineages. As a rule, de novo formation of a functioning asexual genome requires a unique assemblage combining particular sets of genes or gene states to disrupt cellular mechanisms of meiosis and gametogenesis, and affect discrete components of sexuality to produce clonal or hemiclonal offspring. We highlight two usually overlooked but essential conditions to understand the molecular nature of clonal organisms, i.e. a non-recombinant genomic assemblage retaining modifiers of the sexual program, and a complementation between altered reproductive components. These subtle conditions are the basis for physiologically viable and genetically balanced transitions between generations. Genomic and developmental evidence from asexual animals and plants indicates the lack of complementation of molecular changes in the sexual reproductive program is likely the main cause of asexuals' rarity, and can provide an explanatory frame for the developmental diversity and lability of developmental patterns in some asexuals as well as for the discordant time to extinction estimations.

The dioecious property of sea buckthorn (Hippophae rhamnoides L.) prevents sex recognition via traditional observation at the juvenile stage, thus impeding breeding and economic cropping; RAPD and SCAR markers were used to identify the sexes. A total of 45 random decamer primers were used to screen genomic DNA pools of staminate and pistillate genotypes for genetic polymorphisms. One female sex-linked marker was identified. D15 (5′-CATCCGTGCT-3′) amplified a particular band of 885 bp, which showed polymorphism among staminate and pistillate genotype plants. The SCAR marker Hrcx-15 was obtained by sequencing the fragment. The alleles of 140 pistillate genotypes were examined but not of the 140 staminate genotypes discerned via taxonomy. Staminate and pistillate genotypes of sea buckthorn plants can be distinguished, using Hrcx-15 as a genetic marker for sex identification and for expediting cultivation for commercial applications.

Machine learning is a fundamental aspect of artificial intelligence that involves the development of algorithms and models that allow computers to learn and make predictions or decisions without explicit programming. With the development of neural networks, back-propagation algorithms and deep learning, machine learning has made breakthroughs in the fields of image recognition, natural language processing and handwriting recognition using machine learning techniques. The advent of deep learning has revolutionized the field of handwriting recognition, using convolutional neural networks, recurrent neural networks, and sequence-to-sequence models to provide solutions that go beyond machine learning methods and significantly improve the accuracy and robustness of handwriting recognition systems. But challenges remain, including the need for large labelled datasets, computational resources and addressing potential biases. As research in deep learning techniques continues to drive handwriting recognition closer towards realisability, machine learning approaches remain at the forefront.

Pneumonia is a leading cause of death worldwide, and one of the most significant approaches to diagnose pneumonia is Chest X-ray (CXR) since it was used in clinical scenes. Convolutional neural networks (CNNs) have been widely used in computer vision community. Along with the development of CNNs, we want to make use of CNNs to recognize CXR of people who get pneumonia and make classification. It is important, especially during epidemic period. In this paper, we present a new type of residual learning framework, PEPX-Resnet, which makes use of a type of lightweight residual, and apply this network to CXR dataset. The result shows that PEPX-Resnet is easier to optimize and can have better results, especially for COVID-19 cases. PEPX-Resnet could reach higher accuracy, f1 score and some other evaluations for CXR dataset.

Over the last decade, the field of Human Attribute Recognition (HAR) has dramatically changed, mainly due to the improvements brought by deep learning solutions. This survey reviews the progress obtained in HAR, considering the transition from the traditional hand-crafted to deep-learning approaches. The most relevant works on the field are analyzed concerning the advances proposed to address the HAR's typical challenges. Furthermore, we outline the applications and typical evaluation metrics used in the HAR context. Finally, we provide a comprehensive review of the publicly available datasets for the development and evaluation of novel HAR approaches.