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Calculation of standard bodyweights for dogs, cats, rabbits, and guinea pigs
Standard bodyweights are an essential component of calculations that summarise many population-level measures in companion animals, including the defined daily doses for veterinary species (DDDVet) reporting antimicrobial usage. Standard species bodyweights may originate from data derived from clinical records, but current methods to obtain these values risk inaccuracy because they exclude measurements obtained from juvenile animals and consider only individuals that have achieved stable adult bodyweight. This study aimed to improve the accuracy of standard population level species bodyweights through the development of a prediction modelling approach to estimate point mean population bodyweight in dogs, cats, rabbits, and guinea pigs. Data were obtained from the VetCompass database and included bodyweight measurements from approximately three million dogs, two million cats, 220,000 rabbits and 62,000 guinea pigs across 1,800 veterinary practices in the United Kingdom. Initially, Loess models were used to identify the age at which juvenile animals transitioned from growth to stable adult bodyweight. Linear mixed effects models were developed to predict juvenile growth, calibrated such that predicted cessation of growth matched that observed in the Loess models. The prediction models were then used to adjust bodyweight measurements obtained from clinical records of juvenile patients, allowing historical measurements to be included for estimation of a point mean population bodyweight on a subsequent specified target date. Juvenile growth transitioned to stable adult bodyweight at approximately 14 months in dogs, and 13 months in cats, rabbits, and guinea pigs. Point mean whole-population bodyweights estimated on 31st December for each year 2014 – 2023 found that the mean bodyweight of cats, rabbits, and guinea pigs was approximately 4.2 kg, 2.3 kg, and 1.0 kg respectively and changed little over this time period. However, dogs showed a trend to lower mean bodyweight over time, with a mean value of 17.6 kg in 2014, reducing to 16.1 kg by 2023.
Bacteria encode post-mortem protein catabolism that enables altruistic nutrient recycling
Abstract Bacterial death is critical in nutrient recycling. However, the underlying mechanisms that permit macromolecule recycling after bacterial death are largely unknown. We demonstrate that bacteria encode post-mortem protein catabolism via Lon protease released from the dead bacteria. Growth assays reveal that the lysate of Lon protease-null bacteria does not provide a growth benefit to wild type cells. This deficiency is reversed with exogenous recombinant Lon protease, confirming its post-mortem role and is independent of Lon ATPase activity. Biochemistry, growth assays and metabolomics demonstrate that Lon protease facilitates peptide nutrient release, benefitting living cells and acting as a cooperative public good. We also show that the production of Lon protease cannot be explained by a personal benefit to living cells. Although Lon protease can also provide a benefit to living cells under stressful conditions by helping control protein quality, this private benefit does not outweigh the cost under the conditions examined. These results suggest that Lon protease represents a post-mortem adaptation that can potentially be explained by considering the post-mortem indirect benefit to other cells (kin selection). This discovery highlights an unexpected post-mortem biochemistry, reshaping our understanding of nutrient recycling.
Untargeted lipidomics reveals unique lipid signatures of extracellular vesicles from porcine colostrum and milk
Extracellular vesicles (EV) are membranous vesicles considered as significant players in cell-to-cell communication. Milk provides adequate nutrition, transfers immunity, and promotes neonatal development, and milk EV are suggested to play a crucial role in these processes. Milk samples were obtained on days 0, 7, and 14 after parturition from sows receiving either a standard diet (ω-6:ω-3 = 13:1) or a test diet enriched in ω-3 (ω-6:ω-3 = 4:1). EV were isolated using ultracentrifugation coupled with size exclusion chromatography, and characterized by nanoparticle tracking analysis, transmission electron microscopy, and assessment of EV markers via Western blotting. The lipidome was determined following a liquid chromatography–quadrupole time-of-flight mass spectrometry approach. Here, we show that different stages of lactation (colostrum vs mature milk) have a distinct extracellular vesicle lipidomic profile. The distinct lipid content can be further explored to understand and regulate milk EV functionalities and primordial for enabling their diagnostic and therapeutic potential.
2D (NH4)BiI3 enables non-volatile optoelectronic memories for machine learning
Correction: Correlation between cellular uptake and cytotoxicity of polystyrene micro/nanoplastics in HeLa cells: A size-dependent matter
Highly porous metal-organic framework glass design and application for gas separation membranes
Mixed fleet-based two-echelon vehicle routing optimization for cold chain logistics with diverse recharging strategies
The expansion of cold chain logistics necessitates a substantial fleet of fuel-refrigerated trucks, which presents environmental challenges. Electric vehicles (EVs) offer an environmentally friendly solution for low-carbon development despite the issue of range anxiety. Diverging from the conventional two-echelon distribution structure, this paper explores alternative recharging strategies and introduces an innovative scheme: employing fuel vehicles in suburban areas and EVs in urban central zones. The presented model optimizes economic and environmental considerations to mitigate air pollution and reduce dependence on non-renewable energy sources while providing feasible routes. This study proposes an allocation algorithm and an enhanced ant colony algorithm to address a single-objective two-echelon vehicle routing problem with the mixed fleet (2EVRPMF). The mixed fleet outperforms in terms of both cost and carbon emissions based on numerical experiments. Additionally, the study investigates the influence of battery capacity and recharging rate under various recharge strategies, including their correlation with costs. The findings can provide valuable insights for decision-making in implementing environmentally-friendly logistics within the cold chain industry.
Characterising the asynchronous resurgence of common respiratory viruses following the COVID-19 pandemic
Abstract The COVID-19 pandemic and relevant non-pharmaceutical interventions (NPIs) interrupted the circulation of common respiratory viruses. These viruses demonstrated an unprecedented asynchronous resurgence as NPIs were relaxed. We compiled a global dataset from a systematic review, online surveillance reports and unpublished data from Respiratory Virus Global Epidemiology Network, encompassing 92 sites. We compared the resurgence timings of respiratory viruses within each site and synthesised differences in timings across sites, using a generalised linear mixed-effects model. We revealed a distinct sequential timing in the first post-pandemic resurgence: rhinovirus resurged the earliest, followed by seasonal coronavirus, parainfluenza virus, respiratory syncytial virus, adenovirus, metapneumovirus and influenza A virus, with influenza B virus exhibiting the latest resurgence. Similar sequential timing was observed in the second resurgence except influenza A virus caught up with metapneumovirus. The consistent asynchrony across geographical regions suggests that virus-specific characteristics, rather than location-specific factors, determining the relative timing of resurgence.
Experimental study on the shear creep behavior of residual soil with varying rock content
Using residual soil from the Shanghecun landslide in the western Henan Province, shear creep tests of residual soil samples with different rock contents (RCs) were performed to explore the creep characteristics, creep rate, and long-term strength of the residual soil. The test results indicate that the residual soil samples with different RCs display typical creep characteristics. With increasing RC, both the instantaneous deformation and the total creep deformation of the residual soil gradually decrease. The shear strain of the residual soil increases gradually with increasing shear stress for the different RCs. With increasing time, the slope of the isochronous stress–strain curves of the residual soil samples with different RCs increases gradually. The Burgers model can simulate the rheological process of the residual soil samples with different RCs. The RC has a significant effect on the shear strength and the long-term strength of the residual soil. With increasing RC, the shear strength and the long-term strength of the residual soil gradually increase, with the long-term strength being approximately 39%–63% of the shear strength.
Comprehensive dissection of cis-regulatory elements in a 2.8 Mb topologically associated domain in six human cancers
Research on quantitative assessment of translation quality from the perspective of phraseology
Supported by the relevant theories of phraseology, this study examined the translation quality of three genres of explanatory, argumentative, and narrative essays at the phrase level and aimed to construct a translation quality assessment model. In this study, a total of six variables were extracted from both linguistic form and linguistic meaning in strict accordance with the phrase screening criteria, among which the linguistic form features contained a 2-4 gram match degree and the linguistic meaning features contained a part-of-speech tagged 2-4 gram match degree. The results showed that, first, bigram-related variables were the strongest predictors of translation scores for the three genres. The trigram-related variables were slightly weaker, and the fourgram-related variables were the lowest. The bigram match degree had the highest correlation coefficient of .752** with explanatory text translation scores. Second, all translation quality assessment models of the three genres fit well, with the highest correlation coefficient for the explanatory text model, R = 0.820, R2 = 0.673, followed by argumentative text, and the lowest for narrative text. This study realized the automatic assessment of the meaning and form of translations of different genres, which had certain theoretical and practical significance.
Earliest short-tailed bird from the Late Jurassic of China
Author Correction: Benchmarking machine learning methods for synthetic lethality prediction in cancer
Relationship between chromatin configuration and maturation ability of rat oocytes in vitro and in vivo
Purpose Embryo engineering requires a large number of oocytes, which undergo in vitro maturation (IVM). Understanding how to select the best quality oocytes is key to improving IVM efficiency. Oocytes have different germinal vesicle (GV) chromatin configurations, which may explain the heterogeneity in oocyte quality during IVM. However, no reports have categorized, the chromatin configuration of rat GVs or evaluated, the association between the chromatin configuration and oocytes development. Methods The GV chromatin configuration of rat oocytes was divided into seven types according to the degree of chromatin compaction: non-surrounded nucleolus (NSN), prematurely condensed NSN, partly NSN, partly surrounded nucleolus (SN-1), SN-1, condensed SN-1, and aggregated (SN-2). The chromatin configuration distribution was compared during the different stages of oocyte growth and maturation. We also analyzed the changes in the chromatin configuration at different GV stages during IVM. Moreover, the factors affecting the chromatin configuration were analyzed. Results The SN-2 configuration increased with rat oocyte growth and maturation, suggesting that SN-2 facilitates oocyte development. RNA transcription activity in rat oocyte GVs was inversely correlated with oocyte IVM. Conclusions The SN-2 chromatin configuration was related to rat oocyte growth and maturation. RNA transcription activity in rat oocytes in the GV stage was inversely correlated with oocyte maturation.
Unveiling the role of South Tropical Atlantic in winter Atlantic Niño inducing La Niña
Knowledge of behavioral risk factors for type 2 diabetes mellitus and its associated factors among women of reproductive age
Background Type 2 diabetes accounts for over 90% of all diabetes cases and is caused by a combination of behavioral risk factors. It is currently a serious health issue, particularly among women of reproductive age, as it is associated with reproductive disorders. Preventing it requires knowledge, but there is limited data on behavioral risk factors in Ethiopia. Objective To assess knowledge of the behavioral risks of type 2 diabetes mellitus and its associated factors among women of reproductive age. Methods A community-based cross-sectional study was conducted, with all women in the town serving as the source population. A multistage sampling method was utilized to recruit kebeles, and a systematic random technique was employed to select households at every 13th interval. We completed interview questionnaires for 623 samples. The crude odds ratio was calculated using a bivariate logistic model, and multivariate analysis was performed to control for confounding and identify associated factors among model-fitting variables using an adjusted odds ratio (AOR). Result The knowledge of behavioral risk factors (BRF) among women of reproductive age (WRA) is 47.0% [95% CI, 43.5–50.9], and significant associations were found with the following factors: average family income of between 3000 and 5000 Ethiopian Birr(ETH) 1.81 [95% CI, 1.03–3.18], > = 5001 ETH 1.93 [95% CI, 1.02–3.68], diabetes mellitus (DM) in the friend or relatives 4.03 [95% CI, 1.56–10.46], family history of DM 9.47 [95% CI, 4.74–18.90], source of information: health workers 1.87 [95% CI, 1.04–3.34] and friend or relatives 1.65 [95% CI, 1.04–2.62]. Conclusion Knowledge of behavioral risk factors for type 2 diabetes was poor among study participants. Factors such as family income, diabetes mellitus (DM) in friends or relatives, family history of DM, and sources of information were strongly associated with good knowledge. It is essential to emphasize health education about behavioral risk factors for women.
Observation of an ultra-high-energy cosmic neutrino with KM3NeT
Proactive and reactive construction of memory-based preferences
The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports
This study aims to solve the problems of insufficient accuracy and low efficiency of the existing methods in sprint pattern recognition to optimize the training and competition strategies of athletes. Firstly, the data collected in this study come from high-precision sensors and computer simulation, involving key biomechanical parameters in sprint, such as step frequency, stride length and acceleration. The dataset covers multiple tests of multiple athletes, ensuring the diversity of samples. Secondly, an optimized machine learning algorithm based on decision tree is adopted. It combines the advantages of Random Forest (RF) and Gradient Boosting Tree (GBT), and improves the accuracy and efficiency of the model in sprint pattern recognition by adaptively adjusting the hyperparameter and tree structure. Specifically, by introducing adaptive feature selection and ensemble learning methods, the decision tree algorithm effectively improves the recognition ability of the model for different athletes and sports states, thus reducing the over-fitting phenomenon and improving the generalization ability. In the process of model training, cross-validation and grid search optimization methods are adopted to ensure the reasonable selection of super parameters. Moreover, the superiority of the model is verified by comparing with the commonly used algorithms such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The accuracy rate on the test set is 94.9%, which is higher than that of SVM (87.0%) and CNN (92.0%). In addition, the optimized decision tree algorithm performs well in computational efficiency. However, the training data of this model comes from the simulation environment, which may deviate from the real game data. Future research can verify the generalization ability of the model through more actual data.