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Managing free-roaming domestic dog populations using surgical sterilisation: a randomised controlled trial
Abstract Free-roaming domestic dogs (FRDs) are among the most abundant carnivores on earth and have coexisted with humans for over 15,000 years, yet increases in negative interactions and the transmission of zoonotic diseases, precipitates calls for population management. Despite significant investment in FRD sterilisation in India, where rabies is endemic, there is limited evidence of its impact on reducing FRD population sizes. Therefore, robust evaluation of the effectiveness of fertility control programmes is necessary. To address this, we implemented a Before After Control Intervention (BACI) framework in the first multi-site randomised controlled trial for the sterilisation of FRDs. We conducted single intensive sterilisation campaigns in five areas, achieving female sterilisation coverages of 58–66%. We observed a decrease in puppies and lactating females and a reduction in residents’ reports of barking, a common problem associated with FRDs. There were no significant differences in adult FRD counts between intervention and control sites during the 2-year follow-up. However, unmeasured immigration into and emigration out of study areas may have confounded counts. One-off, albeit intense, sterilisation campaigns in open populations require substantial investment and are unlikely to reduce population size in isolation, though there may be some reduction in problematic behaviours and improved animal welfare.
Radical approach to shrink particle colliders gains momentum
Onsite clinic utilization and adherence in semiconductor employees at chronic disease risk
Introduction The objective of this study was to evaluate the utilization and adherence of onsite clinics and identify the factors influencing them in semiconductor employees at risk of chronic diseases, including hypertension, diabetes, and dyslipidemia. Methods A cross-sectional study was conducted through a retrospective review of electronic medical records from onsite clinics at a South Korean semiconductor company. The study focused on employees who visited the onsite clinics between 2013 and 2016 due to the risk of chronic diseases including hypertension, diabetes, and dyslipidemia. Descriptive statistics assessed clinic utilization and adherence, while multivariable logistic regression identified influencing factors, adjusted for age, sex, work type, work shift, workplace, specific onsite clinic, diagnosis, and disease duration. Results Out of the 39,073 employees examined, 8,837 sought care at onsite clinics for managing chronic disease risks. The majority of these participants were male (88.2%) and predominantly aged in their 30s and 40s (84.6%). Among these individuals, 33.0% visited the clinics five or more times, and 28.5% filled prescriptions on two or more occasions. Chronic diseases were the second most common reason for onsite clinic visits. The average adherence to prescription as measured by the Proportion of Days Covered (PDC) was 0.61, with 40% of individuals showing a high adherence. Notably, older age and employment at workplaces located outside metropolitan areas were significant factors positively associated with both the utilization of onsite clinic services and adherence to prescribed treatments. Conclusion This study found that onsite clinics within a semiconductor company were actively utilized for managing chronic diseases, particularly among older employees and those in workplaces located in areas where medical access is limited compared to metropolitan areas. These findings highlight the potential role of onsite clinics in enhancing chronic disease management. Future research across a broader range of workplaces could further support and expand these insights.
Comparative analysis of automated foul detection in football using deep learning architectures
Abstract Automated foul detection in football represents a challenging task due to the dynamic nature of the game, the variability in player movements, and the ambiguity in differentiating fouls from regular physical contact. This study presents a comprehensive comparative evaluation of eight state-of-the-art Deep Learning (DL) architectures — EfficientNetV2, ResNet50, VGG16, Xception, InceptionV3, MobileNetV2, InceptionResNetV2, and DenseNet121 — applied to the task of automated foul detection in football. The models were trained and evaluated using a curated dataset comprising 7000 images, which was split into 70% for training (4,900 images), 20% for validation (1,400 images), and 10% for testing (700 images). To ensure fair evaluation, the test set was balanced to contain 350 images depicting foul events and 350 images representing non-foul scenarios, although perfect balance was subject to class distribution constraints. Performance was assessed across multiple metrics, including test accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC). The results demonstrate that InceptionResNetV2 achieved the highest test accuracy of 87.57% and a strong F1-score of 0.8966, closely followed by DenseNet121, which attained the highest precision of 0.9786 and an AUC of 0.9641, indicating superior discriminatory power. Lightweight models such as MobileNetV2 also performed competitively, highlighting their potential for real-time deployment. The findings highlight the strengths and trade-offs between model complexity, accuracy, and generalizability, underscoring the viability of integrating DL architectures into existing football officiating systems, such as the Video Assistant Referee (VAR). Furthermore, the study emphasizes the importance of model explainability through techniques such as Gradient-weighted Class Activation Mapping++ (GradCAM++), ensuring that automated decisions can be accompanied by interpretable visual evidence. This comparative evaluation serves as a foundation for future research aimed at enhancing real-time foul detection through multimodal data fusion, temporal modeling, and improved domain adaptation techniques.
Lasso-shaped molecule is a new type of broad-spectrum antibiotic
Mitochondria-targeted ROS scavenger JP4-039 improves cardiac function in a post-myocardial infarction animal model and induces angiogenesis in vitro
Background This study aimed at evaluating the effects of JP4-039, a mitochondria-specific reactive oxygen species (mito-ROS) scavenger, on coronary angiogenesis and cardiac function in a post-myocardial infarction (MI) animal model. Methods Mice underwent ligation of the left anterior descending (LAD) artery to induce MI and received intraperitoneal (i.p.) injections of JP4-039 or vehicle (n=8 animals/group) three times/week for four weeks. Echocardiography for cardiac function and immunohistochemistry for Infarction area and capillary density were carried out. Angiogenic potential of endothelial cells (EC) was assessed by ex vivo tube formation using mouse heart EC (MHEC) and by aortic and atrial sprouting. Western blots were conducted using mouse cardiac tissue and lysates from HCAECs that were treated with or without JP4-039. Results Cardiac function including ejection fraction, fractional shortening, and fractional area change were improved significantly in JP4-039-treated animals compared to the vehicle group. JP4-039-treated hearts demonstrated significant reduction in infarction size and increased capillary density in the ischemic area. These findings were consistent with increased ex vivo endothelial sprouting of the aortae and atrial tissue from the mice treated with JP4-039. Western blots using cardiac tissue lysates from JP4-039-treated animals showed decrease in phosphorylation of AMPKα at the Threonine 172, suggesting a plausible increase in the ATP:AMP ratio. Interestingly, JP4-039 increased expression of mitochondrial complexes I and IV and increased ATP synthesis in EC. Conclusions JP4-039-mediated reduction in mito-ROS results in significantly increased coronary vascular density in ischemic myocardium, improved ATP synthesis, and recovery of post-MI cardiac function. Together, these results suggest that nitroxide nanodrug-mediated reduction in mito-ROS may help recover post-MI cardiac function.
A new multi objective crested porcupines optimization algorithm for solving optimization problems
Proteomic analysis of hippocampus reveals metabolic reprogramming in a piglet model of mild hypoxic ischemic encephalopathy
Neonatal hypoxic-ischemic encephalopathy (HIE) remains a leading cause of long-term neurologic morbidity. Fifty percent of HIE cases are mild and do not have clearly defined therapeutic interventions. Emergent evidence now demonstrates that up to 25% of children with mild HIE suffer motor and developmental delay by 18 months and 35% have cognitive impairments by age 5 years. Interestingly, the hippocampus, which is responsible for learning and memory, does not show overt injury but does demonstrate volume changes on imaging that correlate with cognitive and behavioral outcomes. Although there is extensive data regarding pathophysiological changes following moderate and severe HIE, there is a paucity of understanding regarding the extent, duration, and compensatory adaptations in the mild neonatal HIE brain. We performed hippocampal proteomic analysis using a swine model of mild neonatal hypoxia-asphyxia. Hippocampi were collected at 24 or 72 hours after injury, and proteomics was performed by liquid chromatography tandem mass spectrometry (LC-MS/MS). Pathway analysis demonstrated that several metabolic pathways are temporally regulated after mild HIE. Specifically, amino acid, carbohydrate, and one-carbon metabolism increased at 24 hours while fat metabolism and oxidative phosphorylation decreased at 24 hours. Downregulation of oxidative phosphorylation was more pronounced at 72 hours. Our data demonstrate that metabolic reprogramming occurs after mild HIE, and these changes persist up to 72 hours after injury. These results provide new evidence that mild HIE disrupts brain metabolism, emphasizing the need for a better understanding of the underlying pathophysiology of mild HIE and development of targeted therapeutic interventions for this population.
The shear mechanical properties of rocks with variable angle joints
Genome-wide association study uncovers key genomic regions governing agro-morphological and quality traits in Indian mustard [Brassica juncea (L.) Czern. and Coss.]
In Indian mustard, improving agro-morphological and quality traits through conventional methods are both cumbersome and resource-intensive. Marker-aided breeding presents a promising solution to these challenges. Hence, the present research aimed to identify genomic regions governing agro-morphological and quality traits using genome-wide association studies (GWAS). The GWAS panel comprised 142 diverse genotypes of Indian mustard were evaluated for 20 different agro-morphological and quality traits, revealing significant difference among genotypes. Subsequently, the GWAS panel genotyped using the Brassica 90K SNP array (Illumina). Structure and diversity analysis grouped the GWAS panel into 3 sub-populations or groups, and LD decay of 1.05 Mb was confirmed through genotypic analysis. GWAS using the BLINK model revealed a total of 49 marker-trait associations (MTAs), in which 28 and 21 MTAs were observed during rabi 2020–21 and rabi 2021–22 for various agro-morphological and quality traits, respectively. Amongst them, twelve MTAs demonstrated stable associations with the studied traits, including days to 50% flowering (DF), days to 100% flower termination (DFT), days to maturity (DM), plant height (PH), main shoot length (MSL), siliqua length (SL), seeds per siliqua (SPS), oil content (OC), and glucosinolates content (Glu) in both years. Moreover, in silico analysis of nearby regions of these stable SNPs revealed their association with 31 candidate genes known to be involved in various molecular, physiological, and biochemical pathways relevant to the studied traits. These genes can be further characterized and deciphered for more precise utilization in breeding programs in the future.
Multi-scene image fusion via memory aware synapses
Characterization of single neurons reprogrammed by pancreatic cancer
Abstract The peripheral nervous system (PNS) orchestrates organ function in health and disease. Most cancers, including pancreatic ductal adenocarcinoma (PDAC), are infiltrated by PNS neurons, and this contributes to the complex tumour microenvironment (TME)1,2. However, neuronal cell bodies reside in various PNS ganglia, far from the tumour mass. Thus, cancer-innervating or healthy-organ-innervating neurons are lacking in current tissue-sequencing datasets. To molecularly characterize pancreas- and PDAC-innervating neurons at single-cell resolution, we developed Trace-n-Seq. This method uses retrograde tracing of axons from tissues to their respective ganglia, followed by single-cell isolation and transcriptomic analysis. By characterizing more than 5,000 individual sympathetic and sensory neurons, with about 4,000 innervating PDAC or healthy pancreas, we reveal novel neuronal cell types and molecular networks that are distinct to the pancreas, pancreatitis, PDAC or melanoma metastasis. We integrate single-cell datasets of innervating neurons and the TME to establish a neuron–cancer–microenvironment interactome, delineate cancer-driven neuronal reprogramming and generate a pancreatic-cancer nerve signature. Pharmacological denervation induces a pro-inflammatory TME and increases the effectiveness of immune-checkpoint inhibitors. The taxane nab-paclitaxel causes intratumoral neuropathy, which attenuates PDAC growth and, in combination with sympathetic denervation, results in synergistic tumour regression. Our multi-dimensional data provide insights into the networks and functions of PDAC-innervating neurons, and support the inclusion of denervation in future therapies.
Molecular activity of bioactive phytocompounds for inhibiting host cell attachment and membrane fusion interacting with West Nile Virus envelope glycoprotein
West Nile virus is an arbovirus primarily spread by mosquitoes, which are the principal carriers and belong to the Flaviviridae category. This widespread disease lacks specific treatments despite its potential lethality, urgently demanding novel pharmaceutical research and development aims to prevent severe or long-term complications and improve overall outcomes. Pandemic awareness, increasing global incidence, fatal illness effects, expenses associated with outbreaks, reducing suffering, and other broader implications highlight the study’s wider significance. Drug design as a novel treatment approach to reduce the risk of resistance to the virus resulting from overuse of broad-spectrum antiviral therapies for unrelated viral diseases has been evaluated using computational techniques. Initially, molecular docking targeted the envelope glycoprotein of the WNV, utilizing a set of 5375 phytochemicals found in the IMPPAT database. Their binding affinities were −7.464, −5.802, −5.617, and −4.92, kcal/mol for CID: 359 (Phloroglucinol), 9064 (Cianidanol), 25310 (L-Rhamnose), and 492405 (Favipiravir), respectively. The lead compounds and the control ligand both bind at the common active site of the macro-molecule, as evidenced by their interactions with the same amino acid residues at LEU281, ASN47, THR282, SER29, MET48, MET46, and MET45, correspondingly. In post-docking MM-GBSA the negative binding energy of the P-L complex for the compounds CIDs: 359, 9064, 25310, and 492405 (control) were −29.16, −33.45, −32.02, and −3.16 kcal/mol, correspondingly. The selected compounds are secure and efficient since they demonstrate excellent toxicological and Pk characteristics. The compounds were further evaluated to confirm their stability and binding affinity to the target protein by molecular dynamics simulation (RMSD, RMSF, Rg, SASA, H-bond, P-L, and L-P contact). Following this, principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM) studies were conducted using the MD trajectory data. The ligands evaluated in this study demonstrated considerable stability of the proteins’ binding site when complexed with CID: 9064 and CID: 25310, respectively, in the MD simulation, which also revealed a high negative binding free energy value, indicating a robust interaction between the target and lead compounds. The three principal components (PC1, PC2, PC3) for the lead compounds corresponding to CID: 9064 (40.37%, 23.02%, and 8.82%) and CID: 25310 (73.04%, 10.06%, and 3.77%), respectively, indicate that their complexes are more stable than the other L-P complexes. Consequently, both the compounds derived from the plants Tamarindus indica and Plantago ovate, respectively, may potentially impede the viral activity of the WNV envelope glycoprotein, indicating the possibility of these compounds as prospective phytochemical therapeutic candidates. This preclinical study can be used in further drug development processes, including in vivo studies and animal trials.
Exploring the chemical composition and processes of submicron aerosols in Delhi using aerosol chemical speciation monitor driven factor analysis
Abstract Wintertime non-refractory submicron particulate matter (NR-PM 1 ) species were measured in Delhi with an Aerodyne Aerosol Chemical Speciation Monitor (ACSM) during February–March 2018. The average NR-PM 1 mass concentration throughout the study was 58.0 ± 42.6 µg m −3 , where the contribution of organic aerosol (OA) was 69% of the total NR-PM 1 . In Delhi, chloride (10%) was the main inorganic contributor, followed by ammonium (8%), sulfate (7%), and nitrate (6%), contrasting with the prevalence of sulfate in most urban environments. Source apportionment analysis of the OA identified five major factors, including three primary contributors: hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-related OA (COA) and two secondary contributors: oxygenated primary OA (OPOA), and more-oxidized oxygenated OA (MO-OOA). A 19% rise in OPOA concentration was observed during high chloride episodes, suggesting the potential role of chloride in the atmospheric chemical transformation of OA. Traffic emissions significantly contribute to ambient OA, accounting for at least 41% of the total OA mass. Furthermore, the OA exhibited low oxidation levels regardless of its source. The f 44 : f 43 analysis revealed slower atmospheric oxidization of OA compared to other urban locations worldwide. Further investigations, including chamber experiments tailored to the Delhi atmosphere, are necessary to elucidate the atmospheric oxidants and the genesis of secondary OA alongside primary emissions.
Multisensory gamma stimulation enhances adult neurogenesis and improves cognitive function in male mice with Down Syndrome
Generalized spatial modulation for underwater backscatter communication using acoustic metasurfaces
Examining wage inequality among women in India: A multidimensional analysis of socio-economic disparities
Using the nationally representative Indian Human Development Surveys 2004–05 and 2011–12 and multiple inequality measures/frameworks, we investigate both vertical (within-group/interpersonal) and horizontal (between-group/inter group) socioeconomic (based on caste, religion, location and region) inequalities in wages among women in India. We find that the wage inequality (WI) is extremely high (around 60%) and has increased during 2004–12 driven by within-group inequalities which are very high and have increased, whereas between-group inequalities have reduced. There are stark rural-urban divides be it wage labour participation or mean wages; at the same time the WI itself is substantially higher in urban areas. Caste-based WIs are enormous with women belonging to scheduled groups and other backward castes earning substantially lower than their “upper” caste counterparts. The wages of Muslim women are consistently lower than women from other religions. There are vast inter-regional WIs, with the regions of Central and East having lower wages but higher inequalities.
Development of COPMAN-Air method for high-sensitivity detection of SARS-CoV-2 in air
Abstract Several studies have successfully detected SARS-CoV-2 in air samples. However, most of these studies focused on validating the air collection method, and there was no report on the development of a virus detection method. In this study, to detect viruses in air samples with greater sensitively than conventional detection methods, we utilized COPMAN, a highly sensitive virus detection method originally used for wastewater samples. We applied COPMAN to air samples, thereby developing COPMAN-Air. Briefly, this method efficiently detects the extremely low levels of viral RNA in air samples via three reaction steps: RT, preamplification, and qPCR, as it is performed with COPMAN. We evaluated COPMAN-Air using samples from a fever clinic for COVID-19 patients. COPMAN-Air demonstrated a higher detection rate of viral RNA compared with conventional methods, detecting the virus in 22 out of 23 samples (95.7%) vs. 14 out of 23 samples (60.9%). Additionally, a positive correlation ( r = 0.70) was detected between the amount of viral RNA detected by COPMAN-Air and the number of confirmed COVID-19 cases, suggesting that COPMAN-Air could estimate the number of SARS-CoV-2-positive individuals in a given space based on the quantitative values of SARS-CoV-2 RNA in air samples. Surveillance systems for airborne pathogens using COPMAN-Air are expected to be valuable for estimating the number of infected individuals and for guiding the implementation of public health measures.