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Development of a spatial risk indicator for monitoring residential pesticide exposure in agricultural areas

Eligibility and GDMT up-titration success in heart failure: A real-world assessment

Background Implementation of GDMT in HFrEF remains incomplete. Prescription manners may vary based on the development of adverse effects. An HFA position paper proposed patient profiling and individualized prescription manners. This study aims to assess the eligibility for GDMT up-titration and its success in the context of clinical profiles in chronic severe HFrEF outpatients. Methods Clinical characteristics of 900 HFrEF patients at first presentation were assessed, and GDMT up-titration limiting factors were identified by applying thresholds of mutual consent. GDMT prescription was analyzed at 6 months and 1 year. Results 75% of patients had no GDMT up-titration limiting factor at baseline. Significant up-titration could be achieved in all four HF drug classes, especially within the first 6 months, irrespective of GDMT up-titration limiting factors (p ≤ 0.035 for administration and dosage, all drug classes). During up-titration, there was a balanced transition between up-titration limiting factors. 35% of patients received triple therapy on target dosages with a 2.6% one-year mortality rate. Regarding the HFA profiles, 62% of patients could not be classified into a specific HFA phenotype, including most severe patients. 98% of classifiable patients belonged to only four phenotypes, while GDMT up-titration could be achieved in all of these HFA profiles (p ≤ 0.007). Conclusion In this real-world study, 75% of patients with chronic severe HFrEF are eligible for GDMT up-titration towards target dosages. The clinical profile concept of the HFA might be adapted, as most classifiable patients can be up-titrated, and most severe patients are missed by the classification.

Microchemical system for simultaneous measurement of surface-enhanced Raman scattering and electrochemical reactions

Abstract Electrochemical reactions have been extensively studied and used in various fundamental research and engineering applications such as electroplating, surface treatment, secondary batteries, and fuel cells because they are scalable, economically viable, and practical processes. Research on electrochemical reactions is ongoing because they still contain unknown phenomena. In particular, the ability to conduct reactions at interfaces while precisely controlling non-equilibrium chemical reaction states electrically contributes to the analysis of reaction mechanisms and industrial applications. In this study, we fabricated a gold nanofève structure as a working electrode and traced the chemical reaction processes during electrochemical reactions by simultaneous measurement of surface-enhanced Raman scattering (SERS) spectra. In addition to confirming the reattachment of 4-mercaptobenzoic acid by electrochemical manipulation, the redox reactions and formation of copper nanoparticles in aqueous copper acetate solutions were tracked by in situ SERS measurements while controlling electrochemical reactions. This enabled in situ real-time SERS measurements on the surface of the working electrode in the microsystem during electrochemical reactions. The developed system can be used to measure non-equilibrium chemical reaction dynamics at solid–liquid interfaces to explore related phenomena.

Swim-Rep fusion net: A new backbone with Faster Recurrent Criss Cross Polarized Attention

Deep learning techniques are widely used in the field of medicine and image classification. In past studies, SwimTransformer and RepVGG are very efficient and classical deep learning models. Multi-scale feature fusion and attention mechanisms are effective means to enhance the performance of deep learning models. In this paper, we introduce a novel Swim-Rep fusion network, along with a new multi-scale feature fusion module called multi-scale strip pooling fusion module(MPF) and a new attention module called Faster Recurrent Criss Cross Polarized Attention (FRCPA), both of which excel at extracting multi-dimensional cross-attention and fine-grained features. Our fully supervised model achieved an impressive accuracy of 99.82% on the MIT-BIH database, outperforming the ViT model classifier by 0.12%. Additionally, our semi-supervised model demonstrated strong performance, achieving 98.4% accuracy on the validation set. Experimental results on the remote sensing image classification dataset RSSCN7 demonstrate that our new base model achieves a classification accuracy of 92.5%, which is 8.57% better than the classification performance of swim-transformer-base and 12.9% better than that of RepVGG-base, and increasing the depth of the module yields superior performance.

A Study of the effect of eigenvalues on the Moore Gibson Thompson model under photoacoustic excitation of semiconductors

Abstract Regarding this investigation, the Moore-Gibson-Thompson (MGT) model was developed with the impact of acoustic pressure. This research’s light is spotted on semiconductor material undergoing thermo-acoustic and optical deformation in the context of a theory of photo-thermoelasticity (PTE). The governing equations are formulated using a modified photo-excitation model, where (MGT) equation represents the heat conduction during processes of optical transport. This model represents the coupling between plasma, thermal, mechanical-elastic, and acoustic wave propagation. Analytical solutions for the main physical quantities are obtained utilizing the Laplace transform method combined with the vector–matrix differential equation method. Boundary conditions for the acoustic, plasma, and thermo-mechanical effects are applied at the outer surface of the medium. Numerical inversion of Laplace transforms is performed to obtain complete space–time solutions for primary fields. Silicon is utilized as a representative semiconductor material for numerical computations, with the results presented graphically and discussed with various influencing parameters. This study is significant because it provides a novel way to analyze the behavior of semiconducting materials under photo-acoustic excitation, applying the eigenvalue approach to a system previously modeled using simple methods. It fills existing gaps in the literature related to the application of the MGT model in semiconducting photo-acoustics and provides more detailed and reliable predictions for real-world applications.

Determinants of adverse childhood experiences and substance use among emerging adults in the context of sustainable development goals

Introduction Adverse childhood experiences (ACEs) are linked to an increased risk of developing substance use among emerging adults, which adversely affects achieving the Sustainable Development Goals (SDGs). This study explored various sociocultural and socio-economic factors associated with emerging adults as predictors of ACEs and substance use. Methods Data was collected from 957 emerging adults studying bachelor’s degrees in 12 colleges at Mangalore University in the Udupi district, Karnataka, India. The tools for data collection included sociodemographic pro forma, Alcohol Smoking and Substance Involvement Screening tool developed by the World Health Organization (WHO ASSIST version 3.0) to assess substance use, and the Adverse Childhood Experiences Scale to determine the adverse experiences in childhood. Results The study found that the prevalence of substance use was 17.97%, while the prevalence of Adverse Childhood Experiences (ACEs) was 18.91% in the sample examined (172 and 181 participants among the total sample of 957, respectively). The mean age for substance use initiation was 17.26 years. ACEs operated as a risk element for emerging adults’ substance use, whereas living with parents protected against it. In addition, the parents who lived together and served as primary caregivers could protect against ACEs. Family and peer group substance use was found to be associated with an increased likelihood of ACEs. Conclusion Various sociocultural and socioeconomic factors predicted substance use and ACEs. The significance of family was revealed in the study since families without substance use and parental supervision served as a defense against both substance use and ACEs.

Weight loss dietary knowledge, attitudes, and practices among different body weight groups in Northeast China

Research on emotion recognition using sparse EEG channels and cross-subject modeling based on CNN-KAN-F2CA model

Emotion recognition plays a significant role in artificial intelligence and human-computer interaction. Electroencephalography (EEG) signals, due to their ability to directly reflect brain activity, have become an essential tool in emotion recognition research. However, the low dimensionality of sparse EEG channel data presents a key challenge in extracting effective features. This paper proposes a sparse channel EEG-based emotion recognition method using the CNN-KAN-F2CA network to address the challenges of limited feature extraction and cross-subject variability in emotion recognition. Through a feature mapping strategy, this method maps features such as Differential Entropy (DE), Power Spectral Density (PSD), and Emotion Valence Index (EVI) - Asymmetry Index (ASI) to pseudo-RGB images, effectively integrating both frequency-domain and spatial information from sparse channels, providing multi-dimensional input for CNN feature extraction. By combining the KAN module with a fast Fourier transform-based F2CA attention mechanism, the model can effectively fuse frequency-domain and spatial features for accurate classification of complex emotional signals. Experimental results show that the CNN-KAN-F2CA model performs comparably to multi-channel models while only using four EEG channels. Through training based on short-time segments, the model effectively reduces the impact of individual differences, significantly improving generalization ability in cross-subject emotion recognition tasks. Extensive experiments on the SEED and DEAP datasets demonstrate the proposed method’s superior performance in emotion classification tasks. In the merged dataset experiments, the accuracy of the SEED three-class task reached 97.985%, while the accuracy for the DEAP four-class task was 91.718%. In the subject-dependent experiment, the average accuracy for the SEED three-class task was 97.45%, and for the DEAP four-class task, it was 89.16%.

The gut dysbiosis of mothers with gestational diabetes and its correlation with diet

Lightweight wavelet-CNN tea leaf disease detection

Tea diseases can significantly impact crop yield and quality, necessitating accurate and efficient recognition methods. This study presents WaveLiteNet, a lightweight model designed for tea disease recognition, addressing the challenge of inadequate disease feature extraction in existing approaches. By integrating 2D discrete wavelet transform (DWT) with MobileNetV3, the model enhances noise suppression and feature extraction through an adaptive thresholding strategy in the 2D DWT. The extracted frequency-domain features are fused with depth features from the Bneck structure, enabling a more comprehensive representation of disease characteristics. To further optimize feature extraction, a convolutional block attention module (CBAM) is incorporated within the Bneck structure, refining the network’s ability to assign optimal weights to feature channels. A focal loss function also replaces traditional cross-entropy loss to mitigate sample category imbalance, improving recognition accuracy across varying distributions. Experimental results show that WaveLiteNet achieves a 98.70% recognition accuracy on five types of tea leaf diseases, with a parameter count of 3.16 × 10⁶. Compared to MobileNetV3, this represents a 2.15 percentage point improvement in accuracy while reducing the parameter count by 25.12%. These findings underscore WaveLiteNet’s potential as a highly efficient and lightweight real-time crop disease recognition solution, particularly in resource-constrained agricultural environments.

Scene consistency enhances state representations of real-world objects

Abstract Previous research has shown that the context in which objects are located significantly influences how efficiently they are categorized. However, less is known about whether scene consistency can also affect the processing of finer object features, such as the state of an object (e.g., the angle of a Swiss army knife or the fill level of a bottle). Therefore, across three experiments, we presented a subset of the JURICS stimulus set, in which each object exists in 20 continuously varying states (e.g., from fully closed to fully open) in scenes that were either contextually consistent or inconsistent. Participants were asked to report the specific state of the object using a continuous report task. Our results showed that scene consistency enhanced the precision of state judgments; that is, participants made significantly larger errors in reporting object states when objects were presented in inconsistent compared to consistent scenes. These findings suggest that scene context exhibits its effect already at the level of fine-grained perceptual processing of objects, affecting not only object categorization but also the accuracy of its perceived features.

The social representations of Covid-19 among primary health care’ users in the Federal district, Brazil: A psychosocial approach

Brazil was one of the countries most affected by the COVID-19 pandemic, including severe psycho-social effects. This study aimed to analyze the Federal District Primary Health Care users’ Social Representations (SR) regarding Covid-19 through exploration of the elements that comprise their field, identification of the variations of their prominence, as well as analyze the relations and differences among them according to their socio-demographic characteristics and the participants’ exposure to the disease. It was based on the Moscovici theory of social representations. 1,714 users from the Federal District Primary Health Units regions participated in the study: Central-South, North, West, Southwest, East and South. For the data collection, the free evocation technique was used. The data were analyzed by the Iramuteq software in the prototypical modality, analyzing similitude and analyzing x2 test of the most prominent words, considering the socio-demographic and Covid-19 exposure variables, establishing p < 0.05. It has been found that the social representations of the pandemic among the Federal District PHC’ users formed a dyad: doctor and death. It was observed, however, that the doctor-death relationship was organized according to the vulnerabilities present in the face of the pandemic context and the affective-emotional aspects, the most excluded social segments experiencing the terrifying feelings caused by the virus, particularly the confrontation with death and its consequences, along with the absence of hope. In particular, it was evident that the representation of an absent or omissive State, due to the lack of protective measures for people already socially excluded, who demanded agile, more equitable policies, configured from the perspective of bioethics a moral debt on the part of the State to this population.

Exploring the induction and measurement of positive affective state in equines through a personality-centred lens

Abstract There is increasing focus on how to induce and measure positive affective states in animals and the development of social license to operate has brought this to the forefront within equestrianism. This study aimed to utilise a range of methods to induce and measure positive affect in horses in real-world settings. Twenty healthy horses were scored for personality, exposed to four induction methods (wither scratching, high value food provision, positive reinforcement training and the addition of an affiliative conspecific), and data collected on their behaviour (QBA and ethograms) and physiology (heart and respiratory rate, heart rate variability, eye and ear thermography and salivary cortisol). Analyses identified potentially sensitive and specific behavioural (ear and eye position, QBA items, frustration items) and physiological (RR mean, HF power, LF power, LF/HF ratio, mean HR, RMSSD and pNN50) measures of affective state across the four quadrants of core affect. Individual difference effects were found, and personality traits such as unfriendly, nervous and unresponsive were associated with differing responses to induction stimuli indicating that all four induction stimuli are potentially useful for inducing positive affect depending on their salience to the individual. Research measuring and inducing positive affect in animals rarely considers personality, but this study underscores its importance. The dimensional approach taken allowed for assessment of the broad arousal and valence components of affect without ascribing measures to discrete emotions. Accurate, real-world measures of affect could benefit 116 million equines globally, and exploring ways to promote positive affect in horses can significantly enhance their welfare.

Numerical study on performance of a vertical axis wind rotor with S-shaped blades

Traditional Savonius wind rotor has simple structure and can operate in any wind direction. However, its aerodynamic efficiency is lower than other types. A novel S-shaped wind rotor with three blades is proposed in order to improve the aerodynamic performance. The blade of the rotor is composed of two opposing convex circular arcs and its shape likes an ‘S’. The flow characteristics of the rotor are studied and analyzed by computational fluid dynamics (CFD) numerical simulation method. The steady and transient performances are studied using SST k-ω model and sliding mesh method, and are compared with that of traditional Savonius rotors. The results show that the average static torque coefficient of the rotor is 0.291, which is higher than the 0.222 of the Savonius rotor. The static vibration amplitude of the rotor is 0.375, which is lower than 0.709 of the Savonius rotor. The maximum power coefficient of the rotor is 0.228, which is also higher than the 0.226 of the Savonius rotor. The dynamic vibration amplitude of the novel rotor is 0.183, which is lower than the 0.492 of the Savonius rotor. The flow field analyses show that structure of the S-shaped blades can smooth the flow field and reduce the blocking effect in the overlap area. The study indicates that the proposed navel rotor can not only overcome the problems of sharp change in the internal flow field of traditional Savonius rotors, but also provide better operating stability and higher wind energy utilization.

Laminar fluid ejection device enables high yield and preservation of mRNA and SaRNA LNP formulations

Identifying potential biomarkers for early evaluating mechanical compression injuries to skeletal muscle through proteomic analysis: A rat model

The skeletal muscle is highly susceptible to injury in daily life. Severe skeletal muscle injuries often result in incomplete regeneration, leading to functional impairment. In clinical practice, understanding the extent of skeletal muscle injury in limb trauma patients is crucial for selecting treatment modalities and assessing prognosis. Currently, there is a lack of specific indicators for evaluating the severity of mechanical skeletal muscle injury. Therefore, the aim of this study is to develop biomarkers for the early evaluation of different degrees of skeletal muscle injury. A rat model of skeletal muscle mechanical compression injury was established with varying degrees of injury severity, one control group, and two compression groups (Mild Injury and Severe Injury Group). LC-MS/MS-4D-DIA quantitative proteomics technology was used to detect the plasma proteome profile of rats in different injury groups at 3 hours post-injury, followed by bioinformatics analysis for data decoding. Rats in the mild and severe injury groups exhibited completely different degrees of injury and prognosis. The proteomic results of the plasma revealed that the relative quantification of 37 proteins increased along with the increase in injury, while 2 proteins decreased. These differentially expressed proteins (DEPs) included not only muscle-specific structural proteins but also metabolic-related proteins that might play crucial roles in tissue injury control, repair, and regeneration. Overall, the study has identified several potential protein biomarkers that can distinguish different degrees of skeletal muscle injury at an early stage. These protein biomarkers may be further developed to help clinicians identify patients with varying degrees of skeletal muscle injury, paving the way for personalized treatments.

Association of elevated Delta-like canonical Notch ligand 1 levels with clinical outcomes in patients hospitalized for SARS-CoV2 infection

Abstract Soluble Delta-like ligand 1 (sDLL1) has demonstrated promising results as an early biomarker of bacterial sepsis, but its role in viral infections remains unclear. This study investigated the association between sDLL1 levels and clinical outcomes in patients hospitalized for COVID-19. In this secondary analysis of a single-center prospective observational trial, we measured plasma sDLL1 levels in 46 patients admitted with PCR-confirmed SARS-CoV2 infection between December 2020 and April 2021. Patients were divided into a high-sDLL1 group, upper quartile of patients with the highest measured sDLL1 levels, and low-sDLL1 group, lower three quartiles of patients. Clinical outcomes, including secondary infections, organ dysfunction, and mortality, were compared between groups. Patients in the high-sDLL1 group (n = 11, 24%) showed higher rates of secondary infections (63% vs. 20%, OR 7, CI 1.6 to 31, p = 0.01) with higher odds of pulmonary secondary infections (46% vs. 11%, OR 6.5, CI 1.3 to 31, p = 0.03). Organ dysfunction was more prevalent in the high-sDLL1-group, indicated by a higher maximal Sequential Organ-Failure Assessment (SOFA) score (median (IQR) 11 (8.5–14) vs. 3 (0.5-8), p < 0.01) as well as higher rates of vasopressor support (64% vs. 26%, OR 5.1, CI 1.2 to 21, p = 0.03) and renal replacement therapy (36% vs. 9%, OR 6.1, CI 1.1 to 3.6, p < 0.05). The high-sDLL1 group also showed increased 90-day mortality (45% vs. 11%, OR 6.5, CI 1.3 to 31, p = 0.03). These findings suggest that high levels of sDLL1 are associated with adverse outcomes in viral sepsis, warranting further investigation in larger, prospective studies.

The citizen’s perception of a shared responsibility during the COVID-19 management: Insights from a focus group study across four European countries

Background The World Health Organisation emphasises the importance of a whole-of-society approach to the management of health emergencies, which includes a need to involve citizens. Yet, little guidance is found on how this should occur and, more importantly, the citizen’s current perspective of their role. Understanding citizens’ perceptions is the first step in preparing for citizen engagement during a future pandemic. Methods A qualitative research study of 16 focus groups comprising 89 participants was conducted in Finland, Slovenia, Spain, and the Netherlands. Four age-stratified focus groups in each country were organised online in the country’s primary language. Inductive analysis of each focus group transcript was used to identify important themes that captured the participants’ perceptions of their role in COVID-19 preparedness, response, and recovery. Results Three common themes were found in the cross-country analysis. The first theme was the citizen’s personal involvement, with specific references to the citizen’s responsibilities and their concrete actions during COVID-19 preparedness, response, and recovery. The second theme was that of the citizen as an information receiver, with specific references to the quantity and quality of the information received. The final theme was the relationship between the citizen and decision-makers, with specific references to the citizen’s level of trust in the decision-maker, the decision-maker’s communication style as perceived by the citizens, as well as the level of interaction between the citizens and decision-makers as perceived by the citizens. Conclusions Generally, citizens included in this study from Finland, the Netherlands, Slovenia, and Spain felt a shared responsibility in curbing the spread of the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) during the pandemic. However, they emphasised the imperative need to be better informed about the likelihood of, and the developments during a pandemic in their country. Furthermore, the quality of the information provision emerged as an important theme.

Inhibition of CDK5 signaling mediated inflammation in macrophages promotes cutaneous wound healing

What kinds of government trust structures affect political participation? Evidence from Chinese Youth Netizens

Based on 2018 research data on Chinese netizens’ social awareness, this paper examines how different government trust structures influence youth netizens’ political participation. The findings indicate that the reinforcing effect of government trust on political participation follows a declining order: paradoxical government trust, equal trust, hierarchical government trust, and equal distrust. In this context, subjective well-being is introduced as a mediating variable. The results show that the positive impact of subjective well-being on political participation varies across different trust structures, decreasing in the following order: equal trust holders, paradoxical government trust holders, hierarchical government trust holders, and equal distrust holders. Furthermore, heterogeneity analysis reveals that the negative impact of equal distrust and hierarchical government trust is weaker on online political participation than on offline political participation, while paradoxical government trust has a stronger positive effect online than offline. Overall, the influence of government trust structure is weaker for online political participation than for offline political participation.