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Estimation of free-roaming dog populations using Google Street View: A methodological study

Controlling and eliminating zoonotic pathogens such as rabies virus, Echinococcus granulosus, and Leishmania spp. require quantitative knowledge of dog populations. Dog population estimates are fundamental for planning, implementing, and evaluating public health programs. However, dog population estimation is time-consuming, requires many field personnel, may be inaccurate and unreliable, and is not without danger. Our objective was to evaluate a remote method for estimating the population of free-roaming dogs using Google Street View (GSV). Adopting a citizen science approach, participants from Arequipa and other regions in Peru were recruited using social media and trained to use GSV to identify and count free-roaming dogs in 20 urban and 6 periurban communities. We used correlation metrics and negative binomial models to compare the counts of dogs identified in the GSV imagery with accurate counts of free-roaming owned dogs estimated via door-to-door (D2D) survey conducted in 2016. Citizen scientists detected 862 dogs using GSV. After adjusting by the proportion of streets that were scanned with GSV we estimated 1,022 free-roaming dogs, while the 2016 D2D survey estimated 1,536 owned free-roaming dogs across those 26 communities. We detected a strong positive correlation between the number of dogs detected by the two methods in the urban communities (r = 0.85; p < 0.001) and a weak correlation in periurban areas (r = 0.36; p = 0.478). Our multivariable model indicated that for each additional free-roaming dog estimated using GSV, the expected number of owned free-roaming dogs decreased by 2% in urban areas (p < 0.001) and increased by 2% in peri-urban areas (p = 0.004). The type of community (urban vs periurban) had an effect on the predictions, and fitting the models in periurban communities was difficult because of the sparsity of high-resolution GSV images. Using GSV imagery for estimating dog populations is a promising tool, especially in urban areas. Citizen scientists can help to generate information for disease control programs in places with insufficient resources.

Care mobilities and associated contexts of hospital-based informal caregiving in Nigeria: Towards an explanatory framework

Hospital-based informal caregiving in Nigeria is shaped by care mobilities and contextual factors such as policy contradictions and normative care philosophies. This study explores how these factors influence caregiving practices in a Nigerian tertiary health facility. Using a qualitative approach, data were gathered through interviews and observations, involving 75 participants, including 36 in-depth interviews with caregivers and inpatients, and 39 key informant interviews with staff like nurses, doctors, security guards, and ad-hoc caregivers. Findings showed that many informal caregivers traveled long distances to assist hospitalized relatives, often “hanging around” the hospital and engaging in micro-mobilities, such as running errands. Geographical distance, policy contradictions, and the financial costs of hospitalization significantly affected caregiving dynamics. Care mobilities caregivers moving within the hospital environment emerged as a critical aspect of the caregiving process. Understanding these mobilities and how they intersect with contextual factors is essential to improving caregiving experiences. The study highlights the need for policies that support informal caregivers and enhance patient outcomes, especially in terms of reducing the burdens caregivers face due to long travel distances, hospital policies, and financial challenges.

Spinal needles versus conventional needles for fine-needle aspiration biopsy of thyroid nodules—A multicenter randomized controlled trial

Objective Ultrasound-guided fine-needle aspiration biopsy (FNAB) is essential for evaluating thyroid nodules but often yields inadequate samples, leading to repeated procedures, increased discomfort, and higher costs. Previous non-randomized studies found promising results of spinal needles to improve diagnostic adequacy. Therefore, we conducted a multicenter randomized controlled trial to validate these findings. Methods Between July 1st, 2021, and April 13th, 2023, patients with suspicious thyroid nodules were randomized to receive FNAB with either a 25G spinal needle or conventional needle. The primary outcome was the rate of adequate diagnostic cytology. Secondary outcomes included procedure-related pain, sensitivity and specificity of FNAB, and adverse events. Results A total of 359 patients (75.6% female), with a mean age of 59.7 years (range 23−94) were randomized. The rate of adequate diagnostic FNAB was 86.2% (156/181) for the spinal group compared to 84.8% (151/178) for the control group (OR 1.01; 95% CI: 0.95–1.08). The mean pain scale score was 4.0 (SD = 1.8) in the spinal group and 3.9 (SD = 2.0) in the control group (p = 0.40). No complications were observed in either group. We found a significantly better cytological adequacy rate of FNABs performed by physicians with more than four years of experience in the procedure (OR=1.07; 95% CI, 1.01–1.14). Conclusions No significant improvement was found using spinal needles with a stylet compared to conventional needles. Given the significantly higher cost of spinal needles and comparable diagnostic outcomes, their routine use for thyroid FNAB is not recommended.

A long-term localization and mapping system for autonomous inspection robots in large-scale environments using 3D LiDAR sensors

Inspection mobile robots equipped with 3D LiDAR sensors are now widely used in substations and other critical circumstances. However, the application of traditional LiDAR sensors is restricted in large-scale environments. Prolonged operation poses the risk of sensor degradation, while the presence of dynamic objects disrupts the stability of the constructed map, consequently impacting the accuracy of robot localization. To address these challenges, we propose a 3D LiDAR-based long-term localization and map maintenance system, enabling autonomous deployment and operation of inspection robots. The whole system is composed of three key subsystems: a hierarchical SLAM system, a global localization system, and a map maintenance system. The SLAM subsystem includes Local Map Representation, LiDAR Odometry, Global Map Formulation and Optimization, and Dense Map Generation. Specifically, we construct an efficient map representation that voxelizes only the occupied space and computes local geometry within each voxel. The design of LiDAR Odometry ensures high consistency with this map representation mechanism. Then, to address drift errors, we formulate the global map as a graph of local submaps that undergo global optimization. Furthermore, we utilize marching cubes to generate a mesh model of the map. Our system outperforms the state-of-the-art LiDAR odometry method, LOAM, reducing average absolute position error by 30 % and 38 % on two public datasets. The comparative evaluation highlights the system’s superior accuracy and robustness, and demonstrates its high SLAM ranking in real-world scenarios. For global localization, we propose a novel ScanContext-ICP method, which integrates our improved ScanContext method, termed ScanContext++, for place recognition and global pose initialization. The Iterative Closest Point (ICP) algorithm is then employed for precise point cloud alignment and pose refinement, enabling the recovery of the robot’s position on the offline map when localization is lost. Finally, the map maintenance system tracks environmental changes, distinguishing stable features from dynamic ones. The system assigns higher weight to stable voxels, thereby improving localization accuracy. Furthermore, our time distribution mechanism refines map updates by filtering unstable points through temporal and segment-level analysis, which further enhances map maintenance. We conduct extensive experiments on public datasets to validate our system. The experimental results demonstrate that our system is effective and can be deployed on inspection mobile robots.

Optimal Anisotropic Guided Filtering in retinal fundus imaging: A dual approach to enhancement and segmentation

Retinal vascular tree segmentation and enhancement has significant medical imaging benefits because, unlike any other human organ, the retina allows non-invasive observation of blood microcirculation, making it ideal for the detection of systemic diseases. Many traditional methods of segmentation and enhancement encounter issues with visual distortion, ghost artifacts, spatially inconsistent structures, and edge information preservation as a result of the diffusion of spatial intensities at the edges. This article introduces an Optimal Anisotropic Guided Filtering (OAGF) framework tailored for retinal fundus imaging, addressing both enhancement and segmentation needs in a unified approach. The proposed methodology consists of three stages, in the first stage, we perform the illumination correction and then convert the source RGB image to YCbCr format. The luminance (Y) component is further processed through OAGF. In the second stage, optimized top-hat transform and homomorphic filtering has been performed to get segmented image. In the third stage, the enhanced image is produced by converting YCbCr to RGB format. To validate the effectiveness of the suggested approach, extensive experiments with the open-source DRIVE and STARE datasets were performed. Quantitative and qualitative assessments prove that the OAGF-enhancement and segmentation methodology surpasses current algorithms with better values in Dice Coefficient (0.860, 0.854), Precision (0.845, 0.834), and F1 Score (0.827, 0.817) on both databases.

Depression among older adults in Norway 1995–2019: Time trends, correlates, and future projections in a population study: The HUNT study

Objectives To investigate patterns and correlates of depression among Norwegian older adults (age 70+), 1995–2019, and estimate the number of older adults with depression by 2050. Design Population-based cross-sectional study Setting and participants Three surveys of the Trøndelag Health Study (Norway): HUNT2 (1995−96), HUNT3 (2007−08), and HUNT4 (2017−19). 22,822 home dwellers aged 70 + who participated in at least one of the three surveys. Methods Depression was defined as scores ≥8 on the depression subscale of the Hospital Anxiety and Depression Scale. Covariates included sex, age, education, marital status, and reported loneliness. Depression prevalence (%) was standardized to the Norwegian population by age, sex, and education for years close to the initial HUNT survey year (1995, 2006, and 2016). Projection of the total number of individuals with depression in the coming decades were estimated. Predictors of depression were analyzed with logistic regression and the potential reduction in depression prevalence by reducing the prevalence of loneliness was estimated. Results Standardized depression prevalence decreased from 16.7% (HUNT2) to 14.9% (HUNT3), and 11.5% (HUNT4), and was highest among men, the oldest (85+), the lower-educated, and in earlier surveys (all p < 0.001). Living alone was also associated with higher depression prevalence, but only if loneliness was present. While depression rates are falling, we expect the number of depressed individuals to double by 2050 as the population ages. Conclusion and implications Depression rates among adults aged 70 + decreased by 50% from 1995 to 2019, but less so among the oldest old. The rates were highest among single older men. Despite decreasing prevalence, the number of depressed older adults will increase significantly in the future. Given the major individual and societal costs of depression, this trend is alarming for societies preparing for the challenges posed by population aging. This can, however, be addressed by addressing predictors of depression.

Ageing stem cells in the knees drive arthritis damage

Predicting pulmonary function using thoracic deformity parameters in early onset scoliosis patients

Introduction Thoracospinal deformities in early onset scoliosis (EOS) patients often lead to thoracic insufficiency syndrome, in which respiration or lung growth is impaired. Pulmonary function tests (PFTs) are used to assess pulmonary deficits but are challenging to comply with for EOS patients, who typically are between 5 and 10 years old. Thus, the objective was to predict PFT values in EOS patients directly from deformity parameters measured on routine radiographs. Methods Corresponding preoperative radiographs and PFT values were retrospectively obtained from 47 EOS patients (13M/34F; mean age: 9.8 ± 3.0 years), and 19 literature-based deformity parameters were measured. Multiple linear regression (MLR) analyses using an exhaustive search feature selection method were used to estimate percent predicted forced vital capacity (%FVC) and forced expiratory volume in one second (%FEV1). Ten percent of the dataset was set aside to validate the predictive accuracy of the MLR models. Results The additive contributions of multiple thoracospinal deformity parameters successfully yielded significant (p < 0.001) MLR models that predicted %FVC (R2 = 0.54) and %FEV1 (R2 = 0.59) in EOS patients. For the validation test, no significant differences (p > 0.05) in prediction error magnitudes were found. Conclusion The developed MLR models provide the highest reported precision for predicting PFT values in EOS patients from radiographic deformity parameters. Additionally, a key subset of deformity parameters was identified, and their relative contributions to predicting PFT values provide quantitative metrics to guide surgical treatment.

“The future depends on what we do in the present” - Development positions of EU countries by levels of sustainable development and living standards

Sustainable development and standard of living of households have recently become central topics in economic analysis, scientific research, and public debate. This study aims to evaluate the positions of European Union (EU) countries and the changes occurring between 2016 and 2023 in terms of sustainable development and living standard. A hybrid multi-criteria decision-making process based on the modified positional technique for order of preference by similarity to the ideal solution (MP-TOPSIS) was employed to assess these dimensions. The research utilized data from the Eurostat database for the specified period. Both the standard of living of EU inhabitants and their countries’ levels of sustainable development were assessed. The findings revealed a clear relationship between the households’ living standards and the degree of sustainable development in EU countries. Furthermore, the analysis highlighted significant differentiation in these levels across countries, alongside a general upward trend throughout the period analyzed.

Epigenome and three-dimensional genome architecture remodeling during NDM29-mediated retro-transformation of neuroblastoma cells

Neoplastic transformation of mammalian cells involves intricate interactions between genetic, epigenetic and architecture modifications of the nucleus. Neuroblastoma is a malignant pediatric tumor with high biological and clinical heterogeneity representing a challenging model of study. We aimed to explore the changes in genome architecture and epigenetics being associated with neuroblastoma malignancy. We employed the neuroblastoma cell line SKNBE2 overexpressing the ncRNA NDM29 to differentiate from highly malignant into neuron-like cells. By 3D confocal microscopy, we explored the nuclear architecture (volume, elongation, compactness, and chromatin density). Using super-resolution microscopy (STED) and histone H3 immunolabelling we assessed the epigenetic rearrangement, and by enzyme-linked immunoassay the global DNA methylation. Then we assessed the mRNA expression of the main epigenetic modifying enzymes by quantitative PCR, and the expression of NF-κB-regulated genes by cDNA microarray. Compared to malignant NB cells, the NDM29-overexpressing cells, assuming a neuron-like phenotype, exhibited smaller and more elongated nuclei, redistribution of H3K9-acetylated and -methylated chromatin domains and DNA hypermethylation. In line with these results, in neuron-like cells the acetyltransferase KAT2A and the DNA methyltransferase DNMT1 were up-regulated, while most of NF-κB-regulated genes were down-regulated. Our findings reveal modifications of the nuclear structure and epigenome during neuroblastoma retro-transformation induced by NDM29 overexpression, with impacts on gene expression. These results offer potential insights into better understanding the mechanism of neuroblastoma malignancy in terms of chromatin rearrangements, opening exciting prospects for prognostic and therapeutic approaches with a focus on the nuclear level.

Safety of median nerve electrical stimulation in disorders of consciousness: A systematic review and meta-analysis of randomized controlled trials

Background Median nerve electrical stimulation (MNS) is a noninvasive treatment technique that can improve the brain functional activity of patients with disorders of consciousness (DoC) and promote their awakening. However, there is no research on whether MNS increases the incidence and severity of DoC complications. In this study, we evaluated the safety of MNS for the treatment of DoC. Methods We searched relevant studies in PubMed, Cochrane, Web of Science (Medline), and Chinese databases, including CNKI, VIP, and WanFang Databases. For dichotomous outcomes, the pooled risk ratio (RR) with its 95% confidence interval (CI) was used as the effect estimate. We applied a fixed-effects or random-effects model to test the robustness of the forecast. A funnel plot was used to test for publication bias. Heterogeneity was evaluated using I². Subgroup analysis, sensitivity analysis were also performed. The quality of evidence was assessed using the GRADE approach. This study protocol has been registered in PROSPERO. Results The results of the meta-analysis showed that MNS may not increase the incidence of complications and adverse events such as seizures, increased sympathetic activity, arrhythmia, nausea and vomiting, lethargy, pulmonary infection, intracranial hemorrhage or hematoma, and gastrointestinal bleeding in DoC patients. However, the methodological quality of most studies was poor, and accurate conclusions could not be drawn. Conclusion MNS does not increase the incidence of DoC complications and adverse events, however, the quality of evidence for it’s safety is low and high-quality randomized controlled trials are needed to further confirm this conclusion.

Unusual fossil skin appendage is not a feather

AI-Driven fetal distress monitoring SDN-IoMT networks

The healthcare industry is transforming with the integration of the Internet of Medical Things (IoMT) with AI-powered networks for improved clinical connectivity and advanced monitoring capabilities. However, IoMT devices struggle with traditional network infrastructure due to complexity and eterogeneous. Software-defined networking (SDN) is a powerful solution for efficiently managing and controlling IoMT. Additionally, the integration of artificial intelligence such as Deep Learning (DL) algorithms brings intelligence and decision-making capabilities to SDN-IoMT systems. This study focuses on solving the serious problem of information imbalance in cardiotocography (CTG) characteristics with clinical data of pregnant women, especially fetal heart rate (FHR) and deceleration. To improve the performance of prenatal monitoring, this study proposes a framework using Generative Adversarial Networks (GAN), an advanced DL technique, with an auto-encoder model. FHR and deceleration are important markers in CTG monitoring, which are important for assessing fetal health and preventing complications or death. The proposed framework solves the data imbalance problem using reconstruction error and Wasserstein distance-based GANs. The performance of the model is assessed through simulations performed using Mininet, according to criteria such as accuracy, recall, precision and F1 score. The proposed framework outperforms both the basic and advanced DL models and achieves an effective accuracy of 94.2% and an F1 score of 21.1% in very small classes. Validation using the CTU-UHB dataset confirms the significance compared to state-of-the-art solutions for handling unbalanced CTG data. These findings highlight the potential of AI and SDN-based IoMT to improve prenatal outcomes.

Decoding corporate communication strategies: Analysing mandatory published information under Pillar 3 across turbulent periods with unsupervised machine learning

This study explores the communication patterns of Slovak banks with stakeholders through mandatory disclosures mandated by Basel III’s Pillar 3 framework and annual reports in 2007−2022. Our primary objective is to identify key topics communicated by banks and analysing the sentiment of this communication during turbulent periods (i.e., alternating periods of stability and crisis) in 2007−2022. Textual data was collected from Pillar 3 disclosures, annual reports, and additional regulatory reports. A hybrid model was developed to extract the most important keywords from each collected document chapter. This hybrid model (model combining multiple approaches) combines elements of statistical approaches to keyword extraction, (keyword frequency dictionary), linguistic approaches (pair-of-speech tagging in order to select noun-phrases), and machine-learning based approaches (BERT) to extract meaningful keywords. Subsequently, a sentiment analysis was performed on the extracted keywords using a Loughran-McDonald lexicon (list of words labelled with sentiment) specially designed for financial texts. Based on the adjusted univariate results, we can reject the global null hypothesis of independence of the sentiment category of keywords from time for negative sentiment at p = 0.0000 for positive sentiment at p = 0.0005, and for neutral sentiment at p = 0.0000 significant level. The multilevel comparison revealed that negative sentiment was most frequent during the global financial crisis and the COVID-19 pandemic, likely impacting stakeholder confidence and trust. Conversely, positive sentiment dominated during periods of financial stability, potentially enhancing stakeholder satisfaction and investment decisions. This research points out that the sentiment of the selected commercial bank documents changes depending on the years. A commercial bank can use this knowledge and include sentiment information as predictors when modelling financial distress. For bank management of selected commercial bank the examined documents are an important communication tool, the wording of which can have a significant impact on stakeholder behaviour towards the bank, their styling is very important.

Protect the integrity of the US National Institutes of Health

CSCA-YOLOv8: A lightweight network model for evaluating drought resistance in mung bean

Drought is one of the main factors affecting mung bean production in China. Screening drought-resistant germplasm resources and cultivating drought-resistant varieties are of great significance to the development of the mung bean industry in China. Combined with chlorophyll fluorescence imaging technology, this paper proposes a lightweight mung bean drought resistance identification network model based on YOLOv8, referred to as CSCA-YOLOv8. The model uses StarNet to replace the backbone network of YOLOv8 to reduce the size of the model. The C2f_Star module is introduced in the neck structure instead of the original C2f module. Then, in order to enhance the network’s attention to the key regions in the feature map, the Context Anchor Attention Mechanism (CAA) module is also introduced into the fourth C2f_Star module. Then, a CGBD module is proposed in the neck structure to reconstruct the ordinary convolution to improve the feature extraction ability of the model for small targets. Finally, the SIoU loss function is used to replace CIoU to accelerate the convergence of the model. In the actual data analysis, we used the collected 4808 chlorophyll fluorescence images of the natural mung bean population under drought stress to make the Mungbean Drought Datatset(MDD) and made classification labels for each image according to different drought resistance levels, which were 0, 1, 2, 3, 4 and 5. We also verified the excellent performance and generalization performance of the model using the collected MDD dataset. The final experimental results show that compared with the YOLOv8s baseline model, the number of parameters of our proposed algorithm is reduced by 24%, the floating point number is reduced by 35%, and the accuracy is improved by 2.52%, which supports the deployment on embedded edge devices with limited computing power. Therefore, our proposed algorithm has great potential in the field of drought resistance identification and germplasm selection of mung bean.

Academic language has become a proxy for European culture wars

Increasing proportion of mildly aged population in rural mitigates farmland abandonment in the farming-pastoral ecotone of northern China

Rural population aging has emerged as a widespread phenomenon, which can lead to farmland abandonment and pose unprecedented challenges to agricultural production and ecological sustainability in the farming-pastoral ecotone of northern China (FPENC). The dynamic changes in farmland abandonment from 2000 to 2020 were systematically explored using a trajectory-based land use change detection approach. Binary logit regression models were employed to analyze the driving mechanism of the current farmland abandonment based on 1,195 questionnaires, and then random forest models were used to predict future farmland abandonment trends under various scenarios. The results showed that (1) rural population aging had emerged as a significant challenge for Ulanqab, with the proportion of people aged 60 and above increasing from 12.3% in 2000 to 44.9% in 2020. Members of mildly aged households (60–69) were identified as the main agricultural labor force, accounting for 46.6%; (2) an overall downward trend of farmland abandonment was observed, decreasing from 295 km2 in 2000 to 273 km2 in 2020. However, the abandonment rate increased slightly from 3.03% to 3.66%, with higher abandonment rates concentrated in northern Ulanqab; (3) an increase in the proportion of the mildly aged population mitigated farmland abandonment, while an increase in the proportion of the severely aged population (≥70) exacerbated it. When other conditions remained unchanged, a 5% and 10% increase in the proportion of the mildly aged population corresponded to a decrease in farmland abandonment rates to 11.4% and 10.5%, respectively; (4) the mechanisms underlying abandonment behavior differed between young and elderly households. For each additional year of age for elderly households, the probability of farmland abandonment increased by 4.1%. Elderly households with higher levels of education, fewer farm laborers, smaller per capita farmland area, a higher proportion of dryland, larger quantities of farmland, and non-contracted farmland were more likely to abandon farmland; (5) under three Shared Socioeconomic Pathway scenarios (SSP1, SSP2, and SSP5), the proportion of abandoned farmland by farmers was projected to rise to 58.8%, 20.1%, and 58.8%, respectively. These findings provided new insights into the issue of abandoned farmland, particularly from a demographic perspective.

Mechanical underwater adhesive devices for soft substrates

Abstract Achieving long-term underwater adhesion to dynamic, regenerating soft substrates that undergo extreme fluctuations in pH and moisture remains a major unresolved challenge, with far-reaching implications for healthcare, manufacturing, robotics and marine applications1–16. Here, inspired by remoras—fish equipped with specialized adhesive discs—we developed the Mechanical Underwater Soft Adhesion System (MUSAS). Through detailed anatomical, behavioural, physical and biomimetic investigations of remora adhesion on soft substrates, we uncovered the key physical principles and evolutionary adaptations underlying their robust attachment. These insights guided the design of MUSAS, which shows extraordinary versatility, adhering securely to a wide range of soft substrates with varying roughness, stiffness and structural integrity. MUSAS achieves an adhesion-force-to-weight ratio of up to 1,391-fold and maintains performance under extreme pH and moisture conditions. We demonstrate its utility across highly translational models, including in vitro, ex vivo and in vivo settings, enabling applications such as ultraminiaturized aquatic kinetic temperature sensors, non-invasive gastroesophageal reflux monitoring, long-acting antiretroviral drug delivery and messenger RNA administration via the gastrointestinal tract.

Psychological distress and compliance with sanitary measures during the Covid-19 pandemic

Background This study aims to understand the relationship between the experience of psychological distress and compliance with COVID-19 sanitary measures. We testeed whether this relationship was modified by individuals’ gender and socioeconomic status (i.e., educational level and employment). Methods Data from four European cohort studies (n = 13,635), were analysed using an Individual Participant Data (IPD) meta-analytic approach. Mixed effect models were employed to examine associations between mental health difficulties and compliance with sanitary measures, as well as effect modification by socioeconomic status. Statistical models were stratified by gender. Results We found a statistically significant association between mental health difficulties and increased compliance with sanitary measures in women, while amongst men the statistically significant association observed was opposite. Moreover, there was a statistically significant interaction between participants’ educational level and mental health difficulties amongst men only, indicating especially low compliance levels with COVID-19 sanitary measures amongst individuals with only primary schooling and who reported psychological distress. Conclusion The association between psychological distress and compliance with sanitary measures is complex–positive in women, negative in men. Men experiencing mental health difficulties, especially those with lower educational attainment, exhibit low levels of compliance with sanitary measures. These results suggest that psychological distress and its possible consequences should be considered when designing measures addressing infectious disease spread.