bluuebunny/crossref_metadata_2025_split huggingface dataset:
Understanding and overcoming geographical barriers for scaling up dog vaccinations against rabies
Authors: Sambo Maganga , Hampson Katie , Johnson Paul C. D. , Johnson Olatunji O. 
Published: December 2024

AbstractRabies causes 59,000 human deaths annually in over 150 countries. Mass dog vaccination (MDV) is key to controlling dog rabies, requiring 70% coverage in the susceptible dog population to eliminate rabies deaths. MDV campaigns must achieve geographical homogeneity of coverage. Although rabies elimination is feasible, operation challenges exist, especially in hard-to-reach areas. We conducted geospatial modelling to identify geographical factors affecting MDV success in terms of campaign completeness and vaccination coverage across 25 districts in south-eastern Tanzania. From October 2016 to January 2017, about 81,000 dogs were vaccinated in 1,379 (68%) villages within these districts. Multivariable regression analysis revealed that land cover, residence, poverty, and elevation were associated with campaign completeness. The odds of achieving completeness in croplands were 1.75 times higher compared to forests. Vaccination coverage was influenced by residence, area, poverty index, and elevation, with urban areas having lower odds of achieving coverage compared to rural areas. Coverage probabilities exceeding 70% were lower on islands, highlands, urban areas, and areas bordering protected areas. As the 2030 deadline for "zero dog-mediated human rabies deaths" approaches, operational and logistical challenges in MDV campaigns persist. Our data provide insights into MDV success and failure, guiding future control efforts to improve their effectiveness.
Omics-based analysis of mitochondrial dysfunction and BBB integrity in post-COVID-19 sequelae
Authors: Dhariwal Rupal , Dave Kirtan , Jain Mukul 
Published: December 2024

AbstractThe SARS-CoV-2 virus that resulted in the COVID-19 pandemic has been implicated in a range of neurological issues, such as encephalopathy, stroke, and cognitive decline. Although the precise mechanism causing these issues is unknown, mounting evidence shows that blood–brain barrier (BBB) disruption is probable2 a major factor. The integrity of the blood–brain barrier (BBB), a highly selective barrier that divides the brain from the systemic circulation, is crucial for preserving normal brain function. By analysing the multi-transcriptome data, this work explores the neurological impacts of the SARS-CoV-2 virus and provides insight into the molecular mechanisms behind BBB breakdown and neurological symptoms in COVID-19 patients. The endothelial cells of BBB expresses inflammatory genes in response to the systemic inflammation induced due to SARS-CoV-2 remnants in the body. This raises the possibility that systemic inflammation brought on by SARS-CoV-2 and BBB integrity are correlated. Furthermore, the study highlights the pathways involved in oxidative stress and endothelial cell activation, revealing their role in COVID-19 passage through BBB and induction of systemic inflammation and advancement toward neurological disorders. The article showcases the evidence that mitochondrial dysfunction is a major aftermath associated with SARS-CoV-2 infection as the impaired Mitochondria leads to an accumulation of reactive oxygen species (ROS), triggering endothelial dysfunction, and leading to the passage of harmful molecules across the BBB. This study offers insightful information that may open up the possibilities for new treatment plans by targeting biomarkers specifically associated with inflammation and BBB dysfunctioning conditions.
Clustering and classification for dry bean feature imbalanced data
Authors: Lee Chou-Yuan , Wang Wei , Huang Jian-Qiong 
Published: December 2024

AbstractThe traditional machine learning methods such as decision tree (DT), random forest (RF), and support vector machine (SVM) have low classification performance. This paper proposes an algorithm for the dry bean dataset and obesity levels dataset that can balance the minority class and the majority class and has a clustering function to improve the traditional machine learning classification accuracy and various performance indicators such as precision, recall, f1-score, and area under curve (AUC) for imbalanced data. The key idea is to use the advantages of borderline-synthetic minority oversampling technique (BLSMOTE) to generate new samples using samples on the boundary of minority class samples to reduce the impact of noise on model building, and the advantages of K-means clustering to divide data into different groups according to similarities or common features. The results show that the proposed algorithm BLSMOTE + K-means + SVM is superior to other traditional machine learning methods in classification and various performance indicators. The BLSMOTE + K-means + DT generates decision rules for the dry bean dataset and the the obesity levels dataset, and the BLSMOTE + K-means + RF ranks the importance of explanatory variables. These experimental results can provide scientific evidence for decision-makers.
Comparison of large single and small multiple doses of cyclophosphamide exposure in mice during early prepubertal age on fertility outcome
Authors: Salian Sujith Raj , Daddangadi Akshatha , Predheepan Dhakshanya , Bhagat Amonkar Divya Deeleep , Pandya Riddhi Kirit , Laxminarayana Sindhura Lakshmi Koulmane , Uppangala Shubhashree , Kalthur Guruprasad , Anderson Richard A. , Adiga Satish Kumar 
Published: December 2024

AbstractCyclophosphamide (CY) exposure is known to affect the ovary and impair fertility. Clinically, treatment is generally given over multiple doses, but research models have generally used single doses. The relative effects of administering multiple small doses of CY in the prepubertal period are not elucidated. Two-week-old early-prepubertal Swiss albino female mice were administered with either large single (200 mg/Kg x 1; CY200X1) or small multiple (75 mg/Kg x 4; CY75X4) CY doses, thus a 50% higher total dose. Surviving females were assessed for estrous cyclicity, ovarian follicle reserve, oocyte functional competence, and postnatal assessment of first-generation (F1) pups. Exposure to CY75X4 reduced the loss of ovarian follicles (p < 0.05), and body weight (p < 0.001), and resulted in a larger population of cycling females (p < 0.01) with higher oocyte yield (p < 0.05) compared to CY200X1. Although CY200X1 exposed cycling females had comparable oocyte quality, and fertility index, the postnatal mortality was higher in F1 pups (p < 0.05) in comparison to the CY75X4 group. Although both strategies affect oocyte quality and functional competence similarly, CY75X4, despite the higher overall dose, results in reduced follicle loss, produces higher oocyte/blastocyst yield, and exhibits lower postnatal mortality rates, suggesting a potential advantage over CY200X1 for later fertility and offspring health. The differences in effects of the two treatment models show the need for designing animal model studies that more closely mimic the clinical administration of gonadotoxic therapies such as cyclophosphamide.
Potato late blight leaf detection in complex environments
Authors: Li Jingtao , Wu Jiawei , Liu Rui , Shu Guofeng , Liu Xia , Zhu Kun , Wang Changyi , Zhu Tong 
Published: December 2024

AbstractPotato late blight is a common disease affecting crops worldwide. To help detect this disease in complex environments, an improved YOLOv5 algorithm is proposed. First, ShuffleNetV2 is used as the backbone network to reduce the number of parameters and computational load, making the model more lightweight. Second, the coordinate attention mechanism is added to reduce missed detection for leaves that are overlapping, damaged, or hidden, thereby increasing detection accuracy under challenging conditions. Lastly, a bidirectional feature pyramid network is employed to fuse feature information of different scales. The study results show a significant improvement in the model’s performance. The number of parameters was reduced from 7.02 to 3.87 M, and the floating point operations dropped from 15.94 to 8.4 G. These reductions make the model lighter and more efficient. The detection speed increased by 16 %, enabling faster detection of potato late blight leaves. Additionally, the average precision improved by 3.22 %, indicating better detection accuracy. Overall, the improved model provides a robust solution for detecting potato late blight in complex environments. The study’s findings can be useful for applications and further research in controlling potato late blight in similar environments.
Type 1 diabetes genetic risk score variation across ancestries using whole genome sequencing and array-based approaches
Authors: Arni Ankit M. , Fraser Diane P. , Sharp Seth A. , Oram Richard A. , Johnson Matthew B. , Weedon Michael N. , Patel Kashyap A. 
Published: December 2024

AbstractA Type 1 Diabetes Genetic Risk Score (T1DGRS) aids diagnosis and prediction of Type 1 Diabetes (T1D). While traditionally derived from imputed array genotypes, Whole Genome Sequencing (WGS) provides a more direct approach and is now increasingly used in clinical and research studies. We investigated the concordance between WGS-based and array-based T1DGRS across genetic ancestries in 149,265 UK Biobank participants using WGS, TOPMed-imputed, and 1000 Genomes-imputed array genotypes. In the overall cohort, WGS-based T1DGRS demonstrated strong correlation with TOPMed-imputed array-based score (r = 0.996, average WGS-based score 0.0028 standard deviations (SD) lower, p < 10− 31), while showing lower correlation with 1000 Genomes-imputed array-based scores (r = 0.981, 0.043 SD lower in WGS, p < 10− 300). Ancestry-stratified analyses between WGS-based and TOPMed-imputed array-based score showed the highest correlation with European ancestry (r = 0.996, 0.044 SD lower in WGS, p < 10− 300) followed by African ancestry (r = 0.989, 0.0193 SD lower in WGS, p < 10− 14) and South Asian ancestry (r = 0.986, 0.0129 SD lower in WGS, p < 10 − 6). These differences were more pronounced when comparing WGS based score with 1000 Genomes-imputed array-based scores (r = 0.982, 0.975, 0.957 for European, South Asian, African respectively). Population-level analysis using WGS-based T1DGRS revealed significant ancestry-based stratification, with European ancestry individuals showing the highest scores, followed by South Asian (average 0.28 SD lower than Europeans, p < 10− 58) and African ancestry individuals (average 0.89 SD lower than Europeans, p < 10− 300). Notably, when applying the European ancestry-derived 90th centile risk threshold, only 0.71% (95% CI 0.41–1.13) of African ancestry individuals and 6.4% (95% CI 5.6–7.2) of South Asian individuals were identified as high-risk, substantially below the expected 10%. In conclusion, while WGS is viable for generating T1DGRS, with TOPMed-imputed genotypes offering a cost-effective alternative, the persistence of ancestry-based variations in T1DGRS distribution even using whole genome sequencing emphasises the need for ancestry-specific or pan-ancestry standards in clinical practice.
Personality traits and their correlation with behavior in the bonnet macaque (Macaca radiata) in southern India
Authors: Arlet Małgorzata E , Sakson-Obada Olga , Zakrzewska Marzena , Singh Mewa 
Published: December 2024

AbstractThe purpose of our research was to identify the primary personality traits of bonnet macaques using an observer-based questionnaire and to assess the accuracy of this method in predicting behavior. Additionally, we examined the relationship between personality traits and variables such as sex, age, and rank. We rated 25 males and 38 females on 50 personality questionnaire items using a 7-point scale, with reliable ratings and good agreement among raters (the lowest of the calculated ICC (3:k) index ≥ 0.62). Behavioral observations of 38 females over two-years were analyzed. Exploratory factor analysis revealed five traits: Friendliness, Aggression, Playfulness, Fearfulness, and Opportunism. Every trait correlated with a certain number of observed behaviors. Our results suggest that the questionnaire-based method is sufficiently valid as it confirmed high behavioral convergence with Fearfulness, Playfulness and Aggressiveness. Further studies are needed to understand the functional value of behaviors associated with Friendliness and Opportunism. Our study confirmed a tolerant social style of bonnet macaques, characterized by similar Aggression levels in both males and females, and lack of difference in Aggression level between subadult and adult males. Moreover, we identified a species-characteristic trait Opportunism which, unlike in other studied macaques, did not involve aggression-related characteristics.
In Vitro conservation and genetic diversity analysis of rare species Ribes janczewskii
Authors: Nurtaza Aidana , Dyussembekova Damira , Islamova Symbat , Samatova Indira , Zhanybekova Zhanargul , Umirzakova Alima , Magzumova Gulmira , Muranets Anna , Kakimzhanova Almagul 
Published: December 2024

AbstractRibes janczewskii is a rare and valuable plant known for its resistance to spring frosts, pests, and diseases. It is used in hybridization to develop resistant currant varieties but is on the verge of extinction, listed in Kazakhstan Red Book. This study developed a micropropagation and slow-growth storage protocol for conservation. Genotypes were identified through DNA barcode analysis (rbcL, ITS, and matK) and sequences uploaded to the National Center for Biotechnology Information database. Genetic diversity was assessed using iPBS primers, generating 98 fragments with 88–94% polymorphic bands. Biochemical analysis of fruits showed vitamin C content from 4.64 to 5.61 mg/100 g, vitamin E from 2.26 to 3.16 mg/100 g, vitamin B5 from 3.18 to 4.93 mg/100 g, and quercetin up to 12.5 mg/100 g. Micropropagation stages were optimized with 12% hydrogen peroxide for surface sterilization, achieving up to 73.3% explant viability. Effective hormonal combinations for in vitro culture included WPM with BAP 0.2 mg L−1 and GA 0.5 mg L−1, and for propagation, BAP 0.25 mg L−1, GA 0.5 mg L−1, and IBA 0.5 mg L−1. Mannitol (20 g L−1) was used for slow-growth storage, keeping explants viable for 4 months without re-cultivation.
The impact of transient air pollution exposure on worker performance in Chinese soccer players
Authors: Zhang Ning , Wei Xiahai , Huang Xuhui , He Pan , Liu Yunxiao , Liu Hongyou , Lin Tao , Shi Xin , Liu Zhu , Freeman Richard B. 
Published: December 2024

AbstractUsing a unique dataset on the performance of soccer players in China (retrieved from 632 matches involving 24 teams during the 2014 to 2016 seasons), we investigate the effect of air pollution on different performance indicators that rely on different mixtures of the physical and cognitive inputs of players. To ensure a causal interpretation, we implement an instrumental variable (IV) approach using thermal inversion as the instrument for air pollution. We found that players’ performance indicators, especially those more related to cognitive factors, are more strongly influenced by air pollution. One standard deviation (SD) increase in the Air Quality Index (AQI) leads to 2.5% decrease in the number of players’ passes and 5.1% increase in the number of fouls. However, for performance indicators that are more related to players’ physical condition, e.g., running distances, no such significant impact is identified. Overall, these findings suggest that the negative impact of short-term air pollution exposure on outdoor worker performance is mainly cognitive, which we believe could lead to important policy implications, not only for competitive sports but also across a much broader spectrum of business sectors.
Whole genome sequencing reveals candidate causal genetic variants for spastic syndrome in Holstein cattle
Authors: Jacinto Joana G.P. , Letko Anna , Häfliger Irene M. , Akyürek Eylem Emek , Sacchetto Roberta , Gentile Arcangelo , Drögemüller Cord 
Published: December 2024

AbstractBovine spastic syndrome (SS) is a progressive, adult-onset neuromuscular disorder (NMD). SS is inherited but the mode of inheritance is unclear. The aim of this study was to characterize the phenotype and to identify a possible genetic cause of SS by whole-genome sequencing (WGS) and focusing on protein-changing variants. Seven affected unrelated Holstein cattle of both sexes were referred for SS at a mean age of 5.3 years (S.D.±1.1) showing intermittent spasm of the skeletal muscles of the pelvic girdle. Assuming monogenic recessive inheritance, analysis of the WGS data did not reveal any private variants common to all cases. Searching for homozygous rare variants considering each case individually, allowed the identification of a rare recessive likely pathogenic missense variant in TOR3A for one case with an allele frequency of 1.69% in a global Holstein population. In the remaining six SS cases, we identified seven potentially dominant de novo mutations or inherited alleles as private heterozygous, mostly missense, variants of uncertain significance involving seven different NMD candidate genes: MPEG1, LHX8, WHAMM, NGRN, TTN, ATP1A1, PCDH1. All eight candidate causal variants identified were predicted to be deleterious. This study describes for the first time WGS findings in confirmed cases of bovine SS and provides evidence for a heterogeneous genetic cause of SS in cattle.
Data-driven assessment of well stimulation in unconventional gas reservoirs
Authors: Yang Jian , Li Song , Zeng Ji , Yang Zhaozhong , Li Xiaogang , He Tingting , Yi Liangping , Kong Bing 
Published: December 2024

AbstractUnconventional gas reservoirs, characterized by their complex geologies and challenging extraction conditions, demand innovative approaches to enhance gas production and ensure economic viability. Well stimulation techniques, such as hydraulic fracturing and acidizing, have become indispensable tools in unlocking the potential of these tight formations. However, the effectiveness of these techniques can vary widely depending on the specific characteristics of the reservoir. In this context, a data-driven approach to assess well stimulation practices offers a promising avenue to optimize recovery processes and reduce uncertainties. This paper presents a comprehensive study that leverages the power of big data analytics and machine learning to analyze and improve well stimulation strategies in unconventional gas reservoirs. By systematically gathering and processing vast arrays of geological, operational, and production data, this study aims to identify patterns and correlations that can predict stimulation outcomes more accurately. The ultimate goal is to develop a robust framework that allows for tailored stimulation designs based on the unique properties of each reservoir, thereby maximizing efficiency and minimizing environmental impacts. This study introduces a new procedure for assessing well stimulation performance, which involves analyzing the EUR through Duong’s model, calculating the key performance indicator of the treatment, and establishing a data-driven model to predict the treatment KPI.
Explainable AI improves task performance in human–AI collaboration
Authors: Senoner Julian , Schallmoser Simon , Kratzwald Bernhard , Feuerriegel Stefan , Netland Torbjørn 
Published: December 2024

AbstractArtificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human–AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains opaque. This makes it difficult for humans to validate a prediction made by AI against their own domain knowledge. For this reason, we hypothesize that augmenting humans with explainable AI improves task performance in human–AI collaboration. To test this hypothesis, we implement explainable AI in the form of visual heatmaps in inspection tasks conducted by domain experts. Visual heatmaps have the advantage that they are easy to understand and help to localize relevant parts of an image. We then compare participants that were either supported by (a) black-box AI or (b) explainable AI, where the latter supports them to follow AI predictions when the AI is accurate or overrule the AI when the AI predictions are wrong. We conducted two preregistered experiments with representative, real-world visual inspection tasks from manufacturing and medicine. The first experiment was conducted with factory workers from an electronics factory, who performed $$N=9,600$$ assessments of whether electronic products have defects. The second experiment was conducted with radiologists, who performed $$N=5,650$$ assessments of chest X-ray images to identify lung lesions. The results of our experiments with domain experts performing real-world tasks show that task performance improves when participants are supported by explainable AI with heatmaps instead of black-box AI. We find that explainable AI as a decision aid improved the task performance by 7.7 percentage points (95% confidence interval [CI]: 3.3% to 12.0%, $$P=0.001$$) in the manufacturing experiment and by 4.7 percentage points (95% CI: 1.1% to 8.3%, $$P=0.010$$) in the medical experiment compared to black-box AI. These gains represent a significant improvement in task performance.
Experimental and computational analysis of SnSx encapsulated into carbonized chitosan as electrode material for potassium ion batteries
Authors: Nowak Andrzej P. , Rokicińska Anna , Wang Zhilong , Prześniak-Welenc Marta , Zarach Zuzanna , Tao Kehao , Roda Daria , Szkoda Mariusz , Trzciński Konrad , Li Jinjin , Kuśtrowski Piotr 
Published: December 2024

AbstractTin sulphide compounds (SnSx, x = 1, 2) are potential anode materials for potassium-ion batteries (PIBs) due to their characteristic layered structure, high theoretical capacity, non-toxicity and low production cost. However, they suffer from significant volume changes resulting in poor performance of such anodes. In this work incorporation of SnSx into the carbon structure was expected to overcome these disadvantages. Two SnS-based electrode materials encapsulated into chitosan, as a natural carbon source, are fabricated by two different synthesis routes: (a) solvothermal, and (b) solvothermal followed by pyrolysis. The results indicate that the synthesis route is a crucial factor affecting the composition and electrochemical performance of the negative electrode. The electrode material, exhibiting a high reversible capacity (304 mAh/g at 50 mA/g), and good rate capability (128 mAh/g at 1000 mA/g for 500 cycles) is produced by the solvothermal method. The relationship between specific capacity and synthesis procedure is analyzed using the results obtained from XRD, XPS. Additionally, density functional theory is employed to provide deeper insights into the underlying mechanisms governing the electrochemical performance of the SnSx@C electrode materials.
Effects of minipuberty disruption on the expression of sexual behavior in female mice
Authors: Torres Thomas , Parmentier Caroline , Guigon Céline J. , Mhaouty-Kodja Sakina , Naulé Lydie 
Published: December 2024

AbstractSex steroids influence early organization of neural structures involved in expression of sexual behavior. A critical perinatal period during which testosterone surges occur has been identified in male rodents. Data are lacking for females, whose ovarian activity starts later in the postnatal period. Minipuberty has been described in female as a first postnatal period of gonadotropic axis activation. We transiently disrupted the minipubertal period, using daily injections of GnRH antagonist or aromatase inhibitor. The results showed unchanged puberty initiation and mild disturbance of estrus cyclicity. Minor modifications of some components of sexual behavior (olfactory preference, ability to attract male) were observed, but had no impact on lordosis behavior. Interestingly, letrozole-treated female mice showed increased anxiety state level at adulthood. This period therefore does not seem to be a key organizational window for female sexual behavior but appears to participate in the organization of neural structures involved in anxiety-related behavior.