Browse Articles

Evaluating the performance of automated detection systems for long-term monitoring of delphinids in diverse marine soundscapes

There is an increasing reliance on passive acoustic monitoring (PAM) as a cost-effective method for monitoring cetaceans, necessitating robust and efficient automated tools for extracting species presence. This work compares two methods, one based on the ‘off-line’ analysis of raw PAM data, using Convolutional Neural Networks (CNNs), and the second based on in-situ detections, implemented within the C-POD. The C-POD is a rapid, low-cost choice for monitoring of odontocetes, while CNNs, requiring large efforts to train, are gaining traction within bioacoustics as they offer performance benefits above standard detection and classification tools. This work represents the first empirical comparison of a C-POD with a system using a CNN on recorded raw acoustic data for monitoring delphinids. The comparison is based on 3000 hours of PAM data, collected off the west coast of Scotland, using a collocated C-POD and SoundTrap acoustic recorder. Results show that the system using a CNN achieves an overall accuracy of 0.82, and an effectiveness (F1-Score) of 0.78 as a click detector, whilst the C-POD achieves scores of 0.71 and 0.62, respectively. The method employing a CNN provides a lower missed detection rate, with the C-POD failing to detect > 90% delphinid positive hours at one focal site. However, the C-POD offered a lower false-positive rate across all analysis sites. This work highlights the importance of incorporating the right automated tools for long-term species monitoring, as the C-POD offers high precision rates for click detections, while the CNN based system provides a robust approach to identifying seasonal and diurnal trends in long-term dolphin occurrence.

Triclinic Liquid Crystal of Counter-Rotating Squashed Double Helices in a Side-Chain Polymer

EBBA-detector: An effective detector for defect detection in solar panel EL images with unbalanced data

Solar panel defect detection, a crucial quality control task in the manufacturing process, often faces challenges such as varying defect sizes, severe image background interference, and imbalanced data sample distribution. To address these issues, this paper proposes the EBBA-Detector. The core of the model lies in an enhanced balanced attention framework, which includes an Enhanced Bidirectional Feature Pyramid Network (EBFPN) and a Balanced-Attention Module (B-A Module). The EBFPN captures defect features of different sizes, significantly improving the recognition ability for small defects, while the B-A Module suppresses background interference, guiding the model to focus more on defect locations. Additionally, this paper designs a Scaled Dynamic Focal Loss (SDFL) function, which enables the model to pay more attention to minority and hard-to-identify defect samples under imbalanced data distribution. Through experimental validation on a large-scale electroluminescence (EL) dataset, the proposed method has achieved significant improvements in detection performance, with a mean Average Precision (mAP) of 89.85%, outperforming other models in multiple defect category detections. Therefore, the EBBA-Detector not only effectively detects small target objects but also demonstrates good handling capabilities for large targets and imbalanced data, providing an efficient and accurate solution for solar panel defect detection.

Open the Pores: Particles with Fully Accessible Hierarchical Pore Networks by Controlling Phase Separation in Confinement

The association between the quality and quantity of carbohydrate intake and the size, depth, and Wagner grade of diabetic foot ulcers in patients with type 2 diabetes

Aims This cross-sectional study aimed to investigate the association between carbohydrate quantity and quality and the size, depth, and Wagner grade of diabetic foot ulcers (DFUs). Methods The study was conducted on 300 participants with DFUs at the Diabetes Clinic of Tehran University of Medical Sciences. Dietary intake was assessed using three 24-hour dietary recalls. Anthropometric measurements, physical activity levels, and socioeconomic factors were evaluated. The location, Wagner grade, length, width, and depth of the diabetic foot ulcer, were assessed by reviewing the patient’s medical records and utilizing the information recorded therein. Results The findings indicated that out of all the carbohydrate indices examined, only the ratio of whole grain to total grain intake had a significant association with the length of the diabetic foot ulcer. Specifically, participants who consumed lower amounts of whole grain had larger diabetic foot ulcers compared to those with higher whole grain intake (0.85 Vs 0.54, Pvalue = 0.04). This relationship remained significant even after adjusting for potential confounding factors such as age, gender, total energy intake, smoking, physical activity, good shoe condition, and BMI (0.71 Vs 0.52, Pvalue = 0.03). Conclusion The findings emphasize the significant role of whole grain intake in the healing process of DFUs. However, further research with larger sample sizes is needed to investigate the impact of other carbohydrate indices.

Reagent-Regulated Organocatalytic Enantiodivergent Synthesis of Chiral Sulfinimidate Esters

DeepSeek-AI-enhanced virtual reality training for mass casualty management: Leveraging machine learning for personalized instructional optimization

Objective This study aimed to evaluate the effectiveness of a virtual reality (VR) training system for mass casualty management, integrating artificial intelligence (AI) and machine learning (ML) to analyze trainee performance and error patterns. The goal was to identify key predictors of performance, generate personalized feedback, and provide actionable recommendations for optimizing VR-based medical training. Materials and methods A total of 196 medical professionals participated in a 1-hour VR training session, followed by a 20-question assessment and a post-training evaluation survey. The DeepSeek AI framework was employed to analyze the data, utilizing clustering analysis, principal component analysis (PCA), and random forest models. Descriptive statistics, error rates, and correlation analyses were performed using R software (version 4.1.2). Machine learning models were trained to predict performance outcomes, and feature importance was assessed using the Gini index. Personalized feedback reports were generated based on clustering and error analysis results. Results The study identified three distinct trainee clusters, with the highest-performing group excelling in Trauma Assessment and Clinical Case Analysis. However, high error rates were observed in Clinical Case Analysis (69.4%) and Trauma Assessment (67.3%), indicating areas for targeted improvement. Machine learning models highlighted replacing traditional teaching methods (IncNodePurity = 25.76) and stimulating learning interest (IncNodePurity = 13.08) as the most critical factors influencing learning outcomes. AI-driven feedback provided actionable recommendations, such as redesigning complex scenarios and enhancing system usability. Conclusions This study demonstrates the potential of integrating AI with VR training to create a more personalized and effective learning experience for medical professionals. The findings underscore the importance of adaptive, data-driven approaches in medical education, particularly in high-stakes environments such as emergency medicine. Future research should explore hybrid training models and incorporate physiological data to further enhance the efficacy of VR-based training systems.

Bioinspired Amine-Guided Polyphenol Coatings for Selective Bacterial Disruption and Osseointegration on Orthopedic Implants

Atherosclerosis is associated with amyloid and tau pathology via blood–brain barrier dysfunction in the hippocampus of aged human brains

Atherosclerosis, a chronic vascular condition characterized by lipid accumulation and arterial plaque formation, has emerged as a significant contributor to neurodegenerative diseases, including Alzheimer’s disease (AD). This study investigated the association between severe atherosclerosis and hippocampal changes in aged human brains, focusing on blood–brain barrier (BBB) dysfunction and its potential role in amyloid-beta (Aβ) and phosphorylated tau (pTau) pathology. Using multiplex immunohistochemical staining of postmortem brain tissue, we demonstrated that atherosclerosis-associated vascular damage leads to endothelial and smooth muscle cell apoptosis, exacerbates cerebral amyloid angiopathy (CAA), and promotes perivascular tau accumulation. These findings highlight a potential association between vascular health and neurodegeneration, offering insights into potential therapeutic targets for mitigating AD progression.

Formation Mechanism of Polycatenane by Direct Catenation

Spatiotemporal impact of urban development on nighttime light intensity and its hotspot distribution

Nighttime light (NTL) data serve as a valuable proxy for accessing urbanization and socio-economic activities at various scales. This study investigated the spatiotemporal evolution of NTL intensity in Taipei City from January 2018 to June 2023 using data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) via the Google Earth Engine (GEE) platform. A grid system comprising 1,211 cells (500-m resolution) was established to integrate land use, road networks, population, electricity consumption, and business prosperity into temporal, spatial, and spatiotemporal models using Integrated Nested Laplace Approximations (INLA). Additionally, spatiotemporal patterns were analyzed through the space–time cube in ArcGIS Pro. This finding highlights the strong influence of commercial activities and electricity consumption on NTL intensity, with persistent hotspots in commercial and industrial areas and cold spots in forested and agricultural zones. This study underscores the potential of NTL data to capture the interplay between urbanization, land use, and socioeconomic factors. Emphasizing land use as a central analytical focus provides a scalable framework for future urban studies and policy development that can be applied to diverse urban contexts.

Polarization-Induced Breaching of the Liquid/Liquid Interface Formed with Water-in-Salt Electrolytes

Extraction, purification, in vitro antioxidant and cytoprotective ability of oligostilbenes from paeonia seeds threshing residues

Objective Oligostilbenes, which have been associated with multiple biological activities, are a kind of oligomeric resveratrol compound and widely exist in Paeonia seeds threshing residues. The re-use of the Paeonia seeds threshing residues as value-added materials is, not only cost-effective, but also environmentally beneficial. It is therefore important to develop a high-efficiency method for extraction of oligostilbenes. Methods In this investigation, different extraction methods (soxhlet extraction, high temperature and pressure extraction, cold soaking extraction, heat reflux extraction, and ultrasonic extraction) were used to extract oligostilbenes from Paeonia seeds threshing residues. By comparing the extraction yield and in vitro antioxidant ability of oligostilbenes obtained from different extraction ways, the optimal extraction technology of Paeonia seeds threshing residues oligostilbenes was selected. The macroporous resin was used to purify oligostilbenes crude extract samples, and the purification conditions were determined. The protective effect of purified oligostilbenes on oxidative damage of MODE-K cells was evaluated. Results Ultrasonic extraction with ethanol (UA-E) possessed the highest extraction yield of oligostilbenes, and the extraction yield was (3.45 ± 0.07)%. The oligostilbenes extracts obtained by different extraction methods had scavenging ability on DPPH· and ABTS+·, and UA-E showed relatively stronger scavenging ability at different concentration levels. The best resin for purifying oligostilbenes was X-5, and the adsorption and desorption rates were (93.12 ± 0.16)% and (91.33 ± 0.40)%, respectively. The optimal adsorption/desorption conditions were sample loading rate of 2 BV/h, ethanol concentration of 70%, and elution flow rate of 1.0 BV/h. There was a dose-response relationship between the scavenging ability of purified oligostilbenes on DPPH· and ABTS+· and the concentration of the samples. The oligostilbenes could relieve the oxidation effect of hydrogen peroxide (H2O2) on MODE-K cells, and enhance the protection of MODE-K cells by regulating the relative SOD activity, MDA, and ROS production. Conclusion This research lays a theoretical foundation and scientific reference for the extraction, purification and application of Paeonia seed threshing residues in food and medicine.

Developing Pharmaceutically Relevant Pd-Catalyzed C–N Coupling Reactivity Models Leveraging High-Throughput Experimentation

Approaches to predict future type 2 diabetes mellitus and chronic kidney disease: A scoping review

Background Demographic change and changing lifestyles are leading to a steady increase in so-called population diseases such as type 2 diabetes mellitus and chronic kidney disease. Both conditions are often preceded by a latency period during which lifestyle changes and/or medications have the potential to delay or even prevent disease onset. Thus, detection of those at an increased risk of these diseases is of great importance. A scoping review was conducted to collate different prediction approaches for type 2 diabetes mellitus and chronic kidney disease. Methods Literature searches were performed in PubMed, Embase, Web of Science, and Google Scholar. A stepwise approach was used, consisting of searches for systematic reviews and primary literature, and additional Google searches for novel approaches. Included was literature that (1) presented an approach for risk prediction of incident type 2 diabetes mellitus or chronic kidney disease, (2) contained information on the risk factors considered and application, (3) targeted the general population, (4) was written in English or German language, and (5) for which an abstract and full-text was available. Literature screening was carried out by two persons independently. Results Studies extracted literature from 1940 to 2023. Prediction approaches were included from 25 literature reviews, eight primary studies and nine studies found in additional searches. Several different approaches were identified, including methods based on clinical parameters, biological parameters (blood, urine, microbiome, genetics), the combinations of those, sequential approaches, and exposure and lifestyle factors. Most of the identified approaches were risk surveys that usually ask for simple and readily available parameters. Novel approaches cover transdermal optical imaging, prediction based on facial blood flow and using deoxyribonucleic acid methylation data. Conclusion This scoping review provides an overview of different tools for the risk prediction of type 2 diabetes mellitus and chronic kidney disease. In addition to established tools, which are primarily risk surveys, innovative approaches have been developed and evaluated in recent years in which the potential of machine learning is utilized. As cardio-renal-metabolic diseases share predicting factors and given the social and economic importance of these diseases, approaches that address multiple relevant diseases such as type 2 diabetes mellitus, chronic kidney disease and cardiovascular disease can be of great interest, especially in time- and resource-constrained healthcare settings.

Planarity Is Not Plain: Closed- vs Open-Shell Reactivity of a Structurally Constrained, Doubly Reduced Arylborane toward Fluorobenzenes

Detectable SARS-CoV-2 specific immune responses in recovered unvaccinated individuals 250 days post wild type infection

Memory T cells play an important role in mediating long-lasting adaptive immune responses to viral infections, such as SARS-CoV-2. In the context of the latter, much of our current knowledge stems from studies in vaccinated individuals or repeatedly infected individuals. However, limited knowledge is available on these responses in fully naive individuals in German communities. We performed immunophenotyping of a previously naive SARS-CoV-2 cohort in convalescent individuals after asymptomatic to moderate COVID-19. The samples were collected median 250 days post infection during the first wave of the COVID pandemic in Germany (March – May 2020). In this cohort of 174 individuals, we phenotyped different leukocyte cell populations in peripheral blood (B, T and Natural Killer cells). We then assessed the serostatus against the SARS-CoV-2 antigens Nucleocapsid (N) and Spike subunit (S1) with its receptor binding domain (RBD), as these are important correlates of protection, by testing for presence of immunoglobulin G (IgG) antibodies. We also measured IgG antibody responses against the N antigen of the common cold coronaviruses HCoV-OC43, HCoV-HKU1, HCoV-NL63 and HCoV-229E, to determine possible cross-reactivity. In a subset of the cohort (n = 76), we performed intracellular staining assays (ICS) after stimulation with SARS-CoV-2 and HCoV antigens. Key findings are significant differences in frequency of CD4+ memory T cell populations, notably CD4+ TEM and CD4+ TEMRA cells, between the group of SARS-CoV-2 positive individuals and the control group. These differences correlated with cytokine production (TNFα, IFNγ) after stimulation with SARS-CoV-2 peptides, indicating a specific T cell immune response. In conclusion, a clear memory T cell and humoral response can be detected up to 250 days post mild to moderate COVID-19 disease. Our results underline findings reported by others indicating a lasting cellular immune response even in a population which previously had not been exposed to SARS-CoV-2.

Unveiling Ultra-High Ionic Conductivity in W-Doped Na₃SbS₄: Grain Boundary Effects and Pure Bulk Transport

Identifying determinants of under-5 mortality in Bangladesh: A machine learning approach with BDHS 2022 data

Background Under-5 mortality in Bangladesh remains a critical indicator of public health and socio-economic development. Traditional methods often struggle to capture the complex, non-linear relationships influencing under-5 mortality. This study leverages advanced machine learning models to more accurately predict under-5 mortality and its key determinants. By enhancing prediction accuracy, the study aims to provide actionable insights for improving child survival outcomes in Bangladesh. Methods Multiple machine learning (ML) algorithms were applied to data from the 2022 Bangladesh Demographic Health Survey, including Random Forest, Decision Tree, K-Nearest Neighbors, Logistic Regression, Support Vector Machine, XGBoost, LightGBM and Neural Networks. Feature selection was performed using the Boruta algorithm and model performance was evaluated by comparing accuracy, precision, recall, F1 score, MCC, Cohen’s Kappa and AUROC. Results The Random Forest (RF) model emerged as the most effective predictive model for under-5 mortality in Bangladesh, surpassing other models in various performance metrics. The RF model delivered impressive results, achieving 98.75% Accuracy, 98.61% Recall, 98.88% Precision, 98.74% F1 Score, 97.5% MCC, 97.5% Cohen’s Kappa and an AUROC of 99.79%. These metrics highlight its exceptional predictive accuracy and robustness. Key factors influencing under-5 mortality identified by the model included the number of household members, wealth index, parents’ education (both father’s and mother’s), the number of antenatal care (ANC) visits, birth order and the father’s occupation. Conclusions The Random Forest model excelled in predicting under-5 mortality in Bangladesh identifying key predictors such as household size, wealth, parental education, ANC visits, birth order and father’s occupation. These findings underscore the efficacy of machine learning in predicting under-5 mortality and identifying critical determinants these also provide a data-driven foundation for policymakers to design targeted interventions, such as improving access to maternal healthcare, promoting parental education and addressing socio-economic inequalities, ultimately contributing to enhanced child survival outcomes in Bangladesh.

Iridium-Catalyzed Asymmetric Hydrogenation of Aryl(4-hydroxyphenyl)ketones