Browse Articles

Regional disparities in breast cancer mortality in Brazil: a spatial analysis using uncorrected and adjusted data, 2000–2023

Abstract Breast cancer is the leading cause of cancer death among Brazilian women, yet mortality estimates are often underestimated due to ill-defined causes, incomplete diagnoses, underreporting, and data quality limitations. Using national mortality data from 2000 to 2023, we examined the spatial distribution of breast cancer mortality among women aged 20 years and older, comparing uncorrected and adjusted estimates. Adjustments were applied to correct ill-defined causes, incomplete diagnoses, underreporting, and other data quality limitations using methods developed by the World Health Organization and the Brazilian Institute of Geography and Statistics (IBGE). Age-standardized mortality rates were calculated for five time periods using the World Health Organization (WHO) standard population, and spatial patterns were analyzed using choropleth maps, Moran I, and Local Indicators of Spatial Association (LISA). A total of 328,319 breast cancer deaths were reported, increasing to 385,068 (+ 17.3%) after adjustment. Corrections had the greatest effect in the North and Northeast in 2000–2004 (up to + 69.9%), but declined substantially over time. Mortality remained consistently higher in wealthier regions, while adjustments revealed underestimation in historically underserved areas. These findings reveal enduring geographic inequalities in breast cancer mortality and underscore the urgent need for targeted interventions and improved surveillance systems.

DMS-YOLO: Small target detection algorithm based on YOLOv11

To address the challenges in vehicle detection from unmanned aerial vehicle (UAV) overhead images, such as small object size, low resolution, complex background, and scale variation, this paper proposes several targeted improvements to the YOLOv11n model. Firstly, inspired by the Cross Stage Partial Networks (CSPNet), a Dynamic Multi-Scale Edge Enhancement Network (DMS-EdgeNet) is designed to improve robustness to local target features. This module applies multi-scale pooling to extract edge features at various scales and dynamically fuses them through adaptive weighting. Secondly, the DynaScale Aggregation Network (DySAN) module is introduced into the neck network, and a multi-level jump connections structure is adopted to fuse low-level and high-level boundary semantics, thereby improving the detection capability of fuzzy boundary targets and improving target positioning accuracy under complex imaging conditions. Finally, a P2 small target layer is added to further improve the accuracy of small target detection. Based on these innovations, we propose a new architecture named Dynamic Multi-scale and Channel-scaled YOLO (DMS-YOLO), significantly improve the model’s ability to perceive small targets. Experimental results show that DMS-YOLO improves mAP50 and mAP50-95 by 7.0% and 2.9%, respectively, on the Aerial Traffic Images dataset, and by 5.1% and 3.1% on the VisDrone-DET2019 dataset, demonstrating superior performance over the YOLOv11n baseline.

TMEM63 proteins act as mechanically activated cholesterol modulated lipid scramblases contributing to membrane mechano-resilience

Brief mindfulness meditation increases risk-taking behavior

First report of extended-spectrum beta lactamase (ESBL) and carbapenemase-producing MDR Klebsiella pneumoniae from Fuchka

Extended-spectrum beta-lactamase (ESBL) and Carbapenemase-producing Gram-negative bacteria, particularly Klebsiella pneumoniae (ESBL-KP and CP-KP), in the food supply chain, poses a significant public health threat. Ready to eat (RTE) street foods, especially fuchka, a highly popular snack in Bangladesh, India and Southeast Asia, represents a critical breach in food safety. This study investigated the multidrug resistance (MDR) patterns of K. pneumoniae isolated from fuchka with an emphasis on phenotypic and genotypic detection of ESBL-KP and CP-KP. A total of 60 samples were collected from 15 fuchka selling points. K. pneumoniae was isolated and identified via staining, cultural methods, PCR and MALDI-ToF-MS biotyper. Antibiotic susceptibility test (AST) was accomplished using disk diffusion method, phenotypic ESBL producers was detected by combined disk test (CDT) and PCR was used to detect resistance determinant. Thirty K. pneumoniae isolates were confirmed by PCR and MALDI-ToF-MS. All showed resistance to amoxicillin and cefuroxime (100%), while most were sensitive to cefepime (96.7%), norfloxacin (96.7%), imipenem (93.3%) and meropenem (83.3%). All the isolates were MDR, with multiple-antibiotic resistance (MAR) index values ranged from 0.28 to 0.64. CDT confirmed 28 ESBL producers. Among ESBL-producing genes bla TEM, was most prevalent (64%), followed by bla SHV, bla OXA-1, bla CTX-M1, bla CTX-M3. Among carbapenem-resistant genes, bla BIC was most common (46%), while bla VIM was absent. Moreover, other resistance determinants ( aad A1, aac 3IV was most prevalent (40%) but aad A1, qnr A were not detected. The presence of multidrug-resistant, ESBL- and carbapenemase-producing K. pneumoniae in fuchka represents a critical threat to public health.

A heterogeneous population code at the first synapse of vision

Abstract Vision begins when photoreceptors convert fluctuations in light intensity into temporal patterns of glutamate release that drive the retinal network. The input-output relation at this first stage has not been studied in vivo so it is not known how it operates across a photoreceptor population. Using glutamate imaging in zebrafish, we find that individual type 1 cones (PR1; ancestral red cones), which dominate daylight vision in non-avian vertebrates, encode visual stimuli with high reliability and time-precision but routinely vary in sensitivity to luminance, contrast and frequency across the population. Variations in input-output relations are generated by feedback from the horizontal cell network that effectively decorrelate feature representation. A model capturing how zebrafish sample their visual environment indicates that heterogenous cone outputs expand the dynamic range of the retina to improve the coding of natural scenes. Moreover, we find that different kinetic release components are used to encode distinct stimulus features in parallel: sustained release linearly encodes low amplitude light and dark contrasts, but transient release encodes large amplitude dark contrasts. This study reveals an unexpected degree of functional heterogeneity within a population of cones and illustrates how separation of different visual features begins in the first synapse in vision.

Preparation and characterization of holmium doped ZIF-8 nanocrystals for white light emitting phosphors

Abstract We report on the generation of white light from Ho-doped ZIF-8 nanocrystals synthesized by a simple wet chemical technique at room temperature. The influence of Holmium (Ho) concentration and heat treatment on both structural and optical responses has been investigated. The crystal structure and morphology of as-synthesized (As) and post-thermal annealed (TA) nanocrystals have been examined using X-ray diffraction (XRD) and Transmission Electron Microscope (TEM).  In addition, the thermal stability and bond structure have been studied via Thermogravimetric analysis (TGA) and Fourier Transform Infrared Spectroscopy (FTIR), respectively. Moreover, the photoluminescence (PL) measurements were performed using two excitation wavelengths: 445 nm and 355 nm. The XRD and TEM evidenced the sodalite nanocrystalline nature of the samples. The TGA and FTIR analyses demonstrated the degradation behavior of ZIF-8 due to the incorporation of Ho ions and thermal calcination. The PL results showed intense luminescence response, in particular, from the heat-treated samples. Furthermore, the PL exhibited a naked-eye observed white light emission. This was attributed to the structural modifications induced by the dopant and thermal treatment. Our findings could be used as a guideline towards the potential application of lanthanide-doped MOFs (ZIF-8) in light-emitting purposes.

Intergenerational transmission of violence in Bangladesh: Mediated through maternal attitudes towards intimate partner violence, disciplinary beliefs, and life satisfaction

Introduction Child discipline, while intended to instill appropriate behavior, often manifests as violent practices in low- and middle-income countries, including Bangladesh. Maternal exposure to violence, attitude towards intimate partner violence (IPV), and disciplinary beliefs serve as key determinants of physical disciplinary practices. These dynamics illustrate how exposure to violence in adulthood can shape parenting behaviors, highlighting the urgency of addressing cultural attitudes that sustain harsh physical discipline. Materials and methods This study analyzed nationally representative cross-sectional data from the 2019 Bangladesh Multiple Indicator Cluster Survey (MICS) which included 30044 mother-child (children aged between 2 and 14 years) pairs. Physical disciplinary practice is analyzed as an ordered outcome, considering maternal experience of physical violence as the primary exposure along with their attitudes toward IPV and disciplinary beliefs as mediators. This study used ordinal logistic regression within a structural equation modeling framework and bootstrapping technique to analyze indirect associations, providing robust inference that accounts for sampling variability and accommodates binary mediators. Results Mothers exposed to violence had significantly higher odds of physically disciplining their children (odds ratio, OR=1.77 and 95% confidence interval, CI=[1.60, 1.95]). Three mediators significantly increased the odds of adopting harsh physical disciplinary practice by 2% through maternal positive attitudes toward IPV, by 51% through their disciplinary beliefs, and by 6% through their overall life satisfaction. The total association indicated that maternal exposure to violence nearly tripled the odds (OR = 2.89 and 95% CI= [2.52, 3.31]) of physical disciplinary practices. Conclusion This study suggested that supportive environment for children can be fostered by reducing violence against women, promoting mothers’ life satisfaction, and reshaping women’s perceptions of spousal abuse and disciplinary beliefs.

The biological role of local and global fMRI BOLD signal variability in multiscale human brain organization

Abstract Variability drives the organization and behavior of complex systems, including the human brain. Understanding the variability of brain signals is thus necessary to broaden our window into brain function and behavior. Few empirical investigations of macroscale brain signal variability have been undertaken, given the difficulty in separating biological sources of variance from artefactual noise. Here, we characterize the temporal variability of the most predominant macroscale brain signal, the fMRI BOLD signal, and systematically investigate its statistical, topographical, and neurobiological properties. We contrast fMRI acquisition protocols, and integrate across histology, microstructure, transcriptomics, neurotransmitter receptor and metabolic data, fMRI static connectivity, and empirical and simulated magnetoencephalography data. We show that BOLD signal variability represents a spatially heterogeneous, central property of multi-scale multi-modal brain organization, distinct from noise. Our work establishes the biological relevance of BOLD signal variability and provides a lens on brain stochasticity across spatial and temporal scales.

Serial femtosecond crystallography data processing at the global science data hub center at KISTI

Rare event detection by progressive clustering undersampling

Capturing rare events in severely imbalanced datasets is challenging, as the learning and optimization processes are often biased toward the majority class. To address this issue, this study explores various resampling techniques and introduces a novel method called Progressive Clustering Undersampling (PCU). This technique removes negative instances that are distant from positive ones. PCU was compared with eight common undersampling and two oversampling techniques, consistently outperforming them on highly imbalanced and noisy datasets. The workflow demonstrates that rare anomalies can be effectively predicted using unsupervised methods based on frequency-driven decision boundaries. Progressive clustering ultimately identifies clusters with the highest concentration of positive instances. These delineated clusters are then saved by supervised models and used in the preparatory phase before prediction. The proposed method produces two outputs: one optimized for a high F1-score and the other for high precision. Overall, this approach presents a promising solution for identifying rare anomalies in complex, imbalanced data environments.

Phased-assembly-driven pangenome graphs for structural variant genotyping and complex trait mapping in dairy cattle

Abstract Structural variants are an underexplored source of genetic diversity. As part of the FarmGTEx Project, here we report a Holstein breed-specific pangenome graph (H20D) using Minigraph-Cactus and 40 phased haploid assemblies from 20 cows. H20D outperforms both assembly- and read-based long-read callers, and far exceeds short-read approaches, identifying over 10,000 additional structural variants per sample. It also significantly improves structural variant detection and genotyping relative to graphs built across breeds or from fewer/unphased assemblies, with particular advantages in complex regions. Using H20D, we genotype variants in 173 cattle and performed a GWAS, where a larger fraction of structural variants than SNPs reach genome-wide significance, implicating them as potential causal variants. Together, these results demonstrate the power of phased, within-breed pangenome graphs for accurate SV genotyping and trait mapping in dairy cattle.

A clinically applicable and generalizable deep learning model for anterior mediastinal tumors in CT images across multiple institutions

Abstract Rare diseases are often difficult to diagnose, and their scarcity also makes it challenging to develop deep learning models for them due to limited large-scale datasets. Anterior mediastinal tumors—including thymoma and thymic carcinoma—represent such rare entities. A few diagnostic support systems for these tumors have been proposed; however, no prior studies have tested them across multiple institutions, and clinically applicable and generalizable models remain lacking. A total of 711 computed tomography (CT) images were collected from 136 hospitals, each from a different patient with pathologically proven anterior mediastinal tumors (339 males, 372 females). Of these, 485 images were used for training, 62 for tuning, and 164 for external testing. The external testing dataset comprised CT images from 121 unique institutions not involved in the other datasets. A 3D U-Net-based model was trained on the training dataset, and the model with the best performance on the tuning dataset was selected. This model was then evaluated on the external testing dataset for its segmentation and detection performance across different institutions. Based on the reference standards provided by board-certified diagnostic radiologists, the trained model achieved average Dice scores of 0.82, Intersection over Union (IoU) of 0.72, Precision of 0.85, and Recall of 0.82 for tumor segmentation at the CT-image level. The free-response receiver operating characteristic curve—derived from lesion-wise IoU thresholds—demonstrated high sensitivity and a low false-positive rate for tumor detection. Even under a stricter IoU threshold of 0.50, the model maintained a sensitivity of 0.87 with only 0.61 false positives per scan. Our model achieved clinically applicable segmentation and detection performance for anterior mediastinal tumors, demonstrating broad generalizability across 121 institutions and overcoming the data-scarcity challenges inherent to such rare diseases.

Optimization of decompression angles in facial nerve decompression surgery: A decompression model

Facial nerve decompression is a surgical procedure performed for severe facial nerve paralysis associated with conditions such as Bell’s palsy and Ramsay Hunt syndrome. Classical Western studies by Fisch first established the surgical principles of facial nerve decompression, providing the foundation for subsequent work on decompression extent and outcomes. However, the optimal extent of bony decompression of the facial nerve canal remains unclear, and a 180° removal of the surrounding bone has traditionally been performed based on empirical judgment. Nevertheless, more extensive bone removal may increase the risk of surgical complications. This study aimed to evaluate the relationship between the angle of bony decompression around the facial nerve canal and pressure reduction, in order to determine the optimal decompression angle. To achieve this, a simplified experimental model was employed to quantitatively evaluate the relationship between decompression angle and internal pressure reduction, providing mechanical insight into facial nerve decompression rather than clinical data. To evaluate this relationship, a decompression model was created, and pressure changes were measured at opening angles ranging from 30° to 180°. The results revealed that a 150° decompression provided a comparable reduction in pressure to that of a 180° decompression. These findings suggest that the extent of bone removal can be minimized while still achieving sufficient pressure reduction, potentially lowering the risk of nerve injury. We also observed significant pressure reduction at 30°, suggesting utility in regions where extensive bone removal is difficult. The finding that a 150° decompression produced an effect comparable to that of 180° is an important contribution toward improving surgical safety. Moving forward, we aim to refine the decompression model and conduct further investigations using more detailed angle settings, with the goal of establishing a practical surgical technique.

Trustworthy prediction of enzyme commission numbers using a hierarchical interpretable transformer

Dissociable age-dependent effects of emotion on scene and location memory

Morphological traits and microbiome diversity in the free-living nematodes Acrobeles complexus and Zeldia punctata

Morphological adaptations play a key role in shaping the feeding behavior and microbiome associations of Cephalobidae nematodes. To investigate how morphology influences nematode-associated microbiomes, we selected two widely distributed species: Acrobeles complexus , exhibiting elaborated oral structures, and Zeldia punctata , with simpler oral morphology. Unlike earlier studies that reported the microbiomes of A. complexus and Z. punctata independently, this study is the first to directly compare the two species. By integrating in silico re-analysis of our previously published microbiome datasets with new light microscopy and scanning electron microscopy (SEM) observations, we demonstrate how morphological adaptations, such as labial probolae and cuticle structures, shape associated bacterial communities. Our results revealed that A. complexus harbored a more diverse bacterial community than Z. punctata . Morphology showed that the complex oral structures of A. complexus facilitated selective bacterial capture, supporting greater microbial diversity compared to the simpler morphology of Z. punctata . Although statistical significance was not observed, the two species showed distinct patterns of microbial richness and diversity. Principal Coordinate Analysis (PCoA) revealed clearly separated bacterial community structures between the species. Linear discriminant analysis effect size identified potential microbial biomarkers at the genus level, including Firmicutes and Clostridium in A. complexus and Actinobacteria and Pseudomonas in Z. punctata . Predicted functional pathway analysis revealed notable differences in microbial metabolism, such as enrichment of bacterial secretion systems in A. complexus and amoebiasis and lipid metabolism pathways in Z. punctata . This study highlights the role of morphological adaptations in shaping microbiome composition in Cephalobidae nematodes and provides insights into the contribution of free-living bacterivorous nematodes to soil microbial balance. These findings lay the groundwork for further studies on nematode-mediated microbial interactions in soil ecosystems.

Understanding alkali metal promotion in hydrogenation catalysis through Strong Metal–Base Interaction

Synergistic protective and regenerative effects of hyaluronic acid and polynucleotides against UVA-induced oxidative stress in dermal fibroblasts

Abstract Ultraviolet A (UVA) radiation, a principal driver of skin photoaging, generates excessive reactive oxygen species (ROS) in dermal fibroblasts, causing oxidative stress, loss of viability, inflammatory signaling, and extracellular matrix (ECM) degradation. Hyaluronic acid (HA) and polynucleotides (PN) are clinically used dermal biomaterials; however, their protection against UVA injury remains insufficiently defined. We evaluated HA, PN, and their combination in human dermal fibroblasts (HDFs) subjected to photodamage. HDFs were pretreated with HA, PN, or both, irradiated with 20 J/cm 2 UVA, and then maintained in treated media to mimic therapeutic recovery. UVA reduced viability and proliferation, downregulated ECM genes ( COL1A1 , FN1 ), and increased intracellular and mitochondrial ROS and proinflammatory cytokine gene ( TNF-α ). Monotherapy partially alleviated these changes. In contrast, combined HA + PN synergistically improved survival and proliferation, lowered ROS to near baseline, restored ECM transcription, and upregulated antioxidant enzymes ( GPX1 , S OD2 ). HA + PN also increased fibroblast invasion, indicating regenerative activity beyond cytoprotective effects. Under basal conditions, neither HA nor PN showed cytotoxicity or prooxidant effects, while modestly enhancing ECM transcription. These findings demonstrate that HA and PN act synergistically to counter UVA-induced oxidative stress and support dermal regeneration, highlighting a combinatorial bioactive strategy for photoaged skin.

Use of Kaplan-Meier and Cox regressions in the distribution of length of stay in animal shelters for pre-specified calendar periods: Definition, computation, and examples of dog length of stay in orange county California

Computations of length of stay in animal shelters rely on fixed animal cohorts. This is appropriate for research studies that pre-select cohorts, but it is problematic for operational assessments of animal shelters in fixed calendar periods or for comparisons among periods or shelters. Considering only the length of stay of animals whose stay ended within the study period leads to misinterpretation. The use of the Kaplan-Meier and Cox proportional hazards methods with left-truncation and right-censoring is proposed to correctly account for all animals present in the shelter for any fraction of a study period, including those that were present at the beginning and those that remain in care at the end of the period. Examples of dog length of stay in Orange County Animal Care in California show that this computation method corrects the misleading view of historically used calculations of length of stay. Statistically significant changes in length of stay are observed in 8 out of 23 quarterly periods. In a comparison of length of stay before and after the COVID-19 pandemic, the observed significant change in length of stay cannot be explained by variations in sizes and ages of incoming dogs and may be connected to operational policies that restricted visitor access. The proposed approach enables timely tracking of length of stay, accurate comparisons, and assessment of shelter practices and resource needs.