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fMRI insights into differential brain activation, executive function, and physical activity in older adults

Background Executive function is vital for cognitive health, particularly in older adults, where declines can lead to an increased risk of cognitive impairment. Physical activity (PA) has been linked to improvements in executive function, yet the underlying mechanisms remain poorly understood. Methods This cross-sectional study involved 41 Chinese adults (21 young: 23.0 ± 2.12 years; 20 older: 63.30 ± 2.36 years) who were categorized as physically active (≥3000 metabolic equivalent (MET)-min/week) or inactive (<3000 MET-min/week). Participants performed fMRI while completing executive function tasks (Flanker, N-back, Switching). Brain activation patterns were analyzed using Statistical Parametric Mapping (SPM), with significance thresholds set at p < 0.01 (voxel-level) and p < 0.05 (whole-brain corrected). Results Physically active older adults showed significantly better accuracy and faster reaction times on the Flanker task than inactive peers. In young adults, those who were inactive exhibited greater activation in prefrontal regions during executive tasks. No significant differences in brain activation were found in older adults for these tasks. Additionally, activation in the right medial/paracentral cingulate gyrus (BA 6) negatively correlated with working memory reaction times in active young adults (r= −0.804, p < 0.05), whereas cognitive flexibility in active older adults positively correlated with activation in the right dorsolateral frontal gyrus (BA 32; r = 0.589, p < 0.05). Conclusion Active older adults require less brain activation to perform executive function tasks, suggesting enhanced cognitive efficiency. In contrast, young adults showed different patterns of brain activation, indicating potential compensatory mechanisms. These results underscore PA’s role in optimizing age-specific cognitive strategies and underscore the need for longitudinal research to clarify causality.

Is AI watching you? The hidden links between research and surveillance

Design and comparative analysis of laser-based array systems for UAV detection in surveillance zones

Identifying unmanned aerial vehicles (UAVs) is critical to protecting vital locations and infrastructures from potential attacks. The literature suggests a variety of detection methods, including conventional radar systems, acoustic detection, radio frequency signal detection, LiDAR, and camera-based techniques. LiDAR systems, in particular, offer high-resolution 3D mapping and precise distance measurements, which prove to be highly effective for detecting and tracking UAVs under various environmental conditions. This study presents two innovative LiDAR systems for UAV detection: a multi-array static LiDAR system and a one-array rotating LiDAR system. The multi-array static LiDAR employs arrays of laser light sources and concentrators arranged along a spherical shape. A central photodiode receives the transmittance of the reflected optical energy captured by the concentrators, enabling the precise identification of UAVs. The system’s design focuses on achieving continuous, high-resolution coverage with minimal delay, making it suitable for monitoring wide areas. In contrast, the one-array rotating LiDAR utilizes a single array with rotational motion to scan the surveillance area. This approach prioritizes compactness and energy efficiency, which makes it advantageous for cost-sensitive applications. However, the rotational mechanism introduces trade-offs, such as increased mechanical wear and scanning latency. By conducting a comprehensive analysis of the design characteristics, this study evaluates the practicability and efficiency of these LiDAR solutions. Parameters such as the dimensions of the monitored region, sensor characteristics, component arrangement, and interspacing are considered to assess the effectiveness of both systems in identifying potential UAV threats.

Correction: The effects of tryptophan loading on Attention Deficit Hyperactivity Disorder in adults: A remote double blind randomised controlled trial

Screening of a lignin decomposing bacterium and its application in bamboo biomechanical pulping

Bamboo is an excellent raw material for papermaking, offering advantages such as a simple papermaking process, abundant availability, short growth cycle, and significant ecological effect. However, the lignin content in bamboo greatly restricts its effective utilization of bamboo pulp. In this study, a strain of SF-6, an efficient ligninolytic bacterium, was screened from bamboo rat feces under restrictive culture conditions and identified as Enterobacter sichuanensis by 16S rDNA. The main factors affecting the fermentation of SF-6 was determined by a one-way test, and the optimal culture temperature of the strain was 36.8 °C. The inoculum amount and pH value were determined by response surface analysis. The optimum culture temperature was 36.8 °C, the inoculum amount was 7.5%, and the initial pH was 5.2. Under these conditions, the decomposition rate of lignin was 38.59%, which was 55.42% higher than that before optimization, the paper tear resistance was improved by 73.2%, the breakage strength was improved by 62.41%, and the amount of alkali used was only 1.5 times that of the traditional chemical method. In conclusion, bamboo pulping with SF-6 resulted in a good pulping performance, less energy consumption, and no harm to the environment. Therefore, this is a feasible preparation method. This study provides a new microbial source for lignin degradation.

Statistical and machine learning models for predicting university dropout and scholarship impact

Although student dropout is an inevitable aspect of university enrollment, when analyzed, universities can gather information which enables them to take preventative actions that mitigate dropout risk. We study a data set consisting of 4,424 records from a Portuguese higher institution. In this study, dropout is defined from a micro-perspective, where field and institution changes are considered as dropouts independently of the timing these occur. The purpose of this analysis is twofold. First, we aim to build predictive models to learn of the significant socioeconomic and academic features associated with students’ dropout risk. Another goal is to understand the relationship between financial status and dropout, especially the causal effect of being a scholarship holder. Propensity score matching is conducted first with the training set to better estimate the causal effect of being a scholarship holder on dropout status while controlling confounding variables. The predictive classifiers evaluated are Lasso regression, generalized additive model, random forest, XGBoost and single-layer neural network. The XGBoost model has the highest F1-score 0.904. According to this model, the most important features predicting dropout status are the student’s second semester grades and the number of units they are credited. Whether a student’s tuition fee is up to date, whether they owe money to a debtor, whether they are scholarship holders, and students’ age at enrollment are also found to be important features. The Generalized Additive Model (GAM) performs competitively and offers clear interpretability, revealing how changes in actionable variables influence dropout risk. It shows that receiving a scholarship leads to the reduction in the odds of dropping out by nearly 40%, or by 22.2% in terms of probability when holding other factors fixed. As the study is based on data from a single institution and time period, and unobserved confounders cannot be fully ruled out, results should be interpreted with caution.

Association of long working hours with psychological distress in men with pregnant partners: An observational study from the Japan Environment and Children’s Study

Background It has been suggested that working long hours affects workers’ mental health, although findings have been inconsistent. In this study, we investigated the association of working hours with psychological distress in a population of Japanese men with pregnant partners, using data from the Japan Environment and Children’s Study. Methods Data from 44,996 men were analyzed and weekly working hours were classified into six groups. The Kessler Psychological Distress Scale (K6) was used to assess mental health. Each of the six items were assessed on a 5-point scale (0–4), with a total score of 0–24 and higher scores indicating greater psychological distress. A total score of 5–12 was considered to indicate moderate psychological distress and a score of ≥13 to indicate severe psychological distress. To investigate the association of working hours with psychological distress, multinomial logistic regression analysis was performed to calculate odds ratios (ORs) and 95% confidence intervals (CIs). Results The results showed that after adjusting for covariates, weekly working hours was positively associated with moderate and severe psychological distress. Compared with men who worked ≤40 h per week, those who worked >55 to ≤65 h or >65 h per week had significantly higher ORs (95% CIs) for moderate psychological distress, 1.12 (1.03–1.21) and 1.34 (1.24–1.45), respectively, and those working >65 h per week had significantly higher OR, 1.84 (1.47–2.32) for severe psychological distress. For these two outcomes, a significant p for trend (<.0001) was observed in both the crude and adjusted models. Conclusion The results of this study suggest that the greater time constraints resulting from working long hours are associated with psychological distress in Japanese men with pregnant partners.

Impact of monitor unit optimization in volumetric modulated arc therapy planning for nasopharyngeal carcinoma

Purpose To evaluate the impact of monitor units (MUs) optimization on volumetric modulated arc therapy (VMAT) plan for nasopharyngeal carcinoma (NPC). Methods Twenty-one NPC patients were retrospectively analyzed. Dual-arc VMAT plan were designed using photon optimization algorithms without the monitor unit objective (MUO) tool, denoted as the base plan. Each base plan was re-optimized with the MUO tool with the Maximum MU parameter set to 30% of the base plans’ total MUs and Strength parameters set to 50, 80, and 100, generating plans S50, S80, and S100. Target and organ-at-risk (OAR) dose distributions, MUs, beam delivery time, and gamma passing rates were compared between re-optimized and base plans. Statistical analysis was performed using SPSS 17.0 (paired t-tests; significance: P < 0.05). Results Plan S100 reduced target PCTV2 D98% by >4% (relative to the base plan) in four patients. Plan S80 reduced target PGTV and PGTVnd Dmax and target PCTV2 D98% for >3% but <4% in two patients, while other target dose parameters changed by <2%. Compared to the base plan, all re-optimized plans increased the brainstem Dmax (P < 0.05), though the maximum increase was < 1.5%. Plan S50 reduced both parotid glands D50% and Dmean (P < 0.001), while plan S80 reduced both parotids Dmean and the left parotid D50% (P < 0.001). Conversely, S100 increased both parotids D50% and Dmean and the spinal cord Dmax (P < 0.05). Plan S80 and S100 increased the thyroid V40 (P < 0.05). MU reductions averaged 5.1% (S50), 21.4% (S80), and 30.9% (S100), with consistent beam delivery times (~2.5 minutes). Gamma passing rates improved sequentially from the base plan to S50, S80, and S100. Conclusion MU optimization in NPC VMAT planning effectively reduces MUs and enhances delivery accuracy (improved gamma passing rates). While target coverage and OAR sparing were generally maintained, higher MUO strengths (e.g., S100) may necessitate careful consideration of dosimetric trade-offs. Moderate MUO settings (e.g., S80) offer a favorable balance between MU reduction and plan fidelity.

Improved swin transformer-based thorax disease classification with optimal feature selection using chest X-ray

Thoracic diseases, including pneumonia, tuberculosis, lung cancer, and others, pose significant health risks and require timely and accurate diagnosis to ensure proper treatment. Thus, in this research, a model for thorax disease classification using Chest X-rays is proposed by considering deep learning model. The input is pre-processed by resizing, normalizing pixel values, and applying data augmentation to address the issue of imbalanced datasets and improve model generalization. Significant features are extracted from the images using an Enhanced Auto-Encoder (EnAE) model, which combines a stacked auto-encoder architecture with an attention module to enhance feature representation and classification accuracy. To further improve feature selection, we utilize the Chaotic Whale Optimization (ChWO) Algorithm, which optimally selects the most relevant attributes from the extracted features. Finally, the disease classification is performed using the novel Improved Swin Transformer (IMSTrans) model, which is designed to efficiently process high-dimensional medical image data and achieve superior classification performance. The proposed EnAE + ChWO+IMSTrans model for thorax disease classification was evaluated using extensive Chest X-ray datasets and the Lung Disease Dataset. The proposed method demonstrates enhanced Accuracy, Precision, Recall, F-Score, MCC and MAE of 0.964, 0.977, 0.9845, 0.964, 0.9647, and 0.184 respectively indicating the reliable and efficient solution for thorax disease classification.

Daily briefing: The neuroscience behind eureka moments

CATransU-Net: Cross-attention TransU-Net for field rice pest detection

Accurate detection of rice pests in field is a key problem in field pest control. U-Net can effectively extract local image features, and Transformer is good at dealing with long-distance dependencies. A Cross-Attention TransU-Net (CATransU-Net) model is constructed for paddy pest detection by combining U-Net and Transformer. It consists of encoder, decoder, dual Transformer-attention module (DTA) and cross-attention skip-connection (CASC), where dilated residual Inception (DRI) in encoder is adopted to extract the multiscale features, DTA is added into the bottleneck of the model to efficiently learn nonlocal interactions between encoder features, and CASC instead of skip-connection between encoder/decoder is designed to model the multi-resolution feature representation. Compared with U-Net and Transformer, CATransU-Net can extract multiscale features through DRI and DTA, and enhance feature representation to generate high-resolution insect images through CASC and decoder. The experimental results on the large-scale multiclass IP102 and AgriPest benchmark datasets verify that CATransU-Net is effective for rice pest extraction with precision of 93.51%, about 2% more than other methods, especially 9.36% more than U-Net. The proposed method can be applied to the field rice pest detection system. Code is available at https://github.com/chenchenchen23123121da/CATransU-Net.

Correction: The miR-183/96/182 cluster regulates sensory innervation, resident myeloid cells and functions of the cornea through cell type-specific target genes

Correction: Correlation between novel inflammatory markers and carotid atherosclerosis: A retrospective case-control study

Solvent free spray coated n type PEDOT PSS thin film for high performance homojunction diode

Abstract Pristine PEDOT: PSS is a p-type material with salient features. However, studies have revealed that the electrical property of pristine PEDOT: PSS can be switched to n-type by solvent treatment. Notwithstanding that this method is simple, it performs inconsistently and primarily affects the samples’ surfaces due to the migration of the solvent molecules into the polymer network. These solvents are also hazardous and unfavourable to the environment. In this work, we report solvent-free switching electrical characteristics of pristine PEDOT: PSS to n-type using a spray coating technique. The obtained n-PEDOT: PSS exhibits excellent optical and electrical characteristics. In addition, high Seebeck coefficient of – 1320.06 ± 38.42 µVK−1 and power factor of 4.32 ± 0.25 µWm−1K−2 were found. The obtained n-PEDOT: PSS was used in the fabrication of a homojunction diode. The I-V curve showed rectification characteristics with rectification ratio and barrier height of 17.2 and 0.047 eV, respectively, which is one of the best reported values in the literature for all PEDOT: PSS-based diode.

Is AI powering Big Brother? Surveillance research is on the rise

Psychometric properties and factorial structure of the Spanish version of the HLS-EU-Q16 questionary in Venezuelan adults

Health literacy is a variable determined by personal skills and resources for information management and health-related decision making. Studies have shown that it positively influences health maintenance and disease prevention. To contribute to the lack of validated and adapted instruments in Latin America, this study aimed to analyze the psychometric properties of the HLS-EU-Q16 questionnaire short version in Spanish, in Venezuelan adult population. A non-probabilistic intentional sample of 972 (mean age 41.55 years, SD = 15.64; 67.5% female) was surveyed using a cross-sectional design. Data analysis included exploratory (EFA) and confirmatory factor analysis (CFA), factorial invariance, assessment of internal consistency and group contrasting method according to educational level and economic capacity. The three-factor model showed the best fit to the data (X2/df = 1.52; CFI = .990; TLI = .988; SRMR = .056; RMSEA = .033) and such model remained invariant across sex. The internal consistency was adequate, with Alpha and Omega coefficients for the total scale (α = .88, ω = .88) and three factors: health care (α = .83, ω = .81), disease prevention (α = .73, ω = .73) and health promotion (α = .88, ω = .88). The results indicate that people with postgraduate studies report higher levels of health literacy and people who have borrowed money to buy food or medicines, who have stopped seeing their doctor or taking medicines due to lack of money have low levels of health literacy. The HLS-EU-Q16 questionnaire adapted to Venezuela is reliable, valid and easy to apply. Hence, it will be useful for measuring health literacy and generating preventive programs.

Elevated long-to-very-long-chain ceramide ratio correlates with disease severity in inflammatory bowel disease and primary sclerosing cholangitis

Abstract There is strong evidence that ceramides play a significant role in the pathology of inflammatory bowel disease (IBD) and chronic liver injury. Long-chain (LC) and very long-chain (VLC) ceramides have opposing functions, yet the associations of circulating levels of ceramide species in patients with IBD and primary sclerosing cholangitis (PSC)—as inflammatory biliary-hepatic disease closely linked to IBD— with disease severity remain poorly studied. This study investigates whether serum levels of ceramide (Cer) and hexosylceramide, a glycated ceramide derivative, are associated with disease severity in these conditions. Serum levels of eight ceramide and five hexosylceramide species were measured in 16 healthy controls, 57 patients with IBD, 7 patients with PSC, and 13 patients with PSC-IBD. Lipid levels were determined using direct flow injection analysis with a triple quadrupole mass spectrometer. Patients with IBD exhibited higher levels of Cer 18:1;O2/16:0 and Cer 18:1;O2/18:0 compared to controls. Their LC/VLC ceramide ratio was elevated and positively correlated with C-reactive protein and fecal calprotectin. However, ceramide and hexosylceramide levels were not associated with stool consistency, disease localization, or extra-intestinal manifestations. Patients with PSC and PSC-IBD also had increased LC/VLC ceramide ratios, primarily due to a decline in VLC ceramide species. In PSC-IBD, this ratio correlated positively with cholestasis markers. Additionally, serum hexosylceramide 18:1;O2/16:0 and 24:1 levels were specifically elevated in PSC. This study demonstrates that an altered LC/VLC ceramide balance is associated with disease severity in IBD, PSC-IBD, and PSC, highlighting its potential as a biomarker for IBD, PSC-IBD, and PSC. As our PSC cohorts were small, a confirmatory study is required.

Navigating pathways to develop self-compassion in sport, dance, and music: Qualitative insights from volunteer participants on balancing criticism and compassion, the pivotal influence of the social environment and strategies to overcome setbacks

Self-compassion has emerged as a valuable skill for performers, contributing to both their well-being and performance enhancement. However, the question remains: how can it be effectively integrated and cultivated within performers’ daily practice? This study examines the extent to which self-compassion is integrated by performers, its practical application, and the role of the social environment in shaping these skills. A total of 27 performers (athletes, dancers, and musicians), aged 14–27, practicing at a pre-professional level, participated in semi-structured interviews. These interviews explored performers’ responses to challenging situations during practice, the coping strategies employed, the use or non-use of self-compassion, and the influence of their social environment. Through a thematic analysis, six key themes emerged, each comprising subthemes: Explore your inner critic; Balance over-identification; Use strategies to overcome setbacks; Create a virtuous circle; Acknowledge your social environment; and, Learn about yourself. The findings revealed that performers continue to place significant emphasis on self-criticism, often neglecting self-kindness when most needed. Despite this, they recognize the benefits of self-compassion and report various strategies to manage difficulties. Additionally, the importance of the coach’s communication style and the performers’ desire to be better understood by their coach were emphasized. This qualitative study provides deeper insights into the experiences of performers and offers practical guidance on how self-compassion can be more effectively integrated into their daily practice.

Immunomodulation by extracorporeal ozone-based bactericide system in porcine Pseudomonas aeruginosa septic shock

Abstract Sepsis is associated with substantial mortality rates. Traditional treatment strategies often fail to address the underlying dysregulation in immune response, necessitating novel therapeutic approaches. Ozone (O3) is an inorganic molecule with no evident function in the body. We investigated the properties of ozone, using a system of extracorporeal ozone blood treatment in Pseudomonas aeruginosa septic shock. We hypothesized that extracorporeal ozonation would decrease bacteria in blood, have immunomodulating properties, and improve organ function. In this 4-h sepsis model swine were allocated to P. aeruginosa (PA-103, ATCC 29260, CCUG31589) infusion and ozone treatment (n = 7) or P. aeruginosa infusion and no ozone treatment (n = 6). Bacteria were infused in a peripheral vein. Mean (SD) duration of ozone treatment was 134 (67) min. A single pass through the system decreased viable P. aeruginosa by 53%, mean 2193 to 1023 colony forming units/mL, mean of differences -1170 (95% CI − 1689 to − 651, P < 0.0001). No difference in viable bacterial concentration was detected in peripheral venous blood between groups (P = 0.68). IL-1β, IL-4, IL-6, IL-8 and IFN-γ decreased by ozonation. Classical and alternative complement pathways were not affected. Blood hemoglobin, hematocrit and noradrenaline doses decreased in the treatment group. Breathing frequency and pulmonary peak airway pressure decreased in the ozone treatment group. Median survival in ozone treatment was 134 min and no treatment 159 min, with no statistical difference. Extracorporeal ozone blood treatment modulated the immune response in P. aeruginosa septic shock, which decreased mostly proinflammatory cytokines and was associated with indications of decreased vascular permeability and improved lung function and warrants further investigation for potential use in clinical settings.

Cancer cells get power boost by stealing mitochondria from nerves