bluuebunny/crossref_metadata_2025_split huggingface dataset:
An investigation into the correlation between visual performance in simulated complex environments and academic attainment among primary school students
Authors: Tian Yu-Jing , Chen Chen , Zhang Xiao-Han , Cao Yu-Juan , Yu Ying-Qing 
Published: March 2024

AbstractTraditional vision screenings in schools are limited to simple visual tasks, yet students in their daily learning face more complex visual environments. Binocular rivalry tasks can partially simulate the visual challenges of real visual environments and activate advanced visual processing mechanisms that simple visual tasks cannot. Therefore, by superimposing binocular rivalry-state tasks onto simple visual tasks, we have developed an innovative vision screening program to rapidly and extensively assess students’ visual performance in complex environments. This is a cross-sectional study in which we investigated the performance of 1126 grade 1–6 students from a primary school in Wuxi, China, in rivalry-state stereoscopic vision tasks. The correlation between the screening results of 1044 students and their academic achievements was also statistically analyzed. The study results revealed pass rates of 53.5–60.5% across various visual tests. Specifically, for first-grade students, there was a statistically significant difference in standardized Chinese scores between the group that failed and the group that passed the rivalry-state stereoscopic vision test (− 0.49 ± 3.42 vs. 0.22 ± 0.58, t =  − 2.081, P = 0.04). This result underscores the importance of focusing on the visual adaptability of first graders in complex environments.Trail registration: Ethics Committee of Affiliated Children’s Hospital of Jiangnan University-Certificate number: WXCH2022-04-027
The transcriptomic insight into the differential susceptibility of African Swine Fever in inbred pigs
Authors: Banabazi Mohammad Hossein , Freimanis Graham , Goatley Lynnette C. , Netherton Christopher L. , de Koning Dirk-Jan 
Published: March 2024

AbstractAfrican swine fever (ASF) is a global threat to animal health and food security. ASF is typically controlled by strict biosecurity, rapid diagnosis, and culling of affected herds. Much progress has been made in developing modified live virus vaccines against ASF. There is host variation in response to ASF infection in the field and under controlled conditions. To better understand the dynamics underlying this host differential morbidity, whole transcriptome profiling was carried out in twelve immunized and five sham immunized pigs. Seventeen MHC homozygous inbred Large white Babraham pigs were sampled at three time points before and after the challenge. The changes in the transcriptome profiles of infected animals were surveyed over time. In addition, the immunization effect on the host response was studied as well among the contrasts of all protection subgroups. The results showed two promising candidate genes to distinguish between recovered and non-recovered pigs after infection with a virulent African swine fever virus (ASFV) pre-infection: HTRA3 and GFPT2 (padj < 0.05). Variant calling on the transcriptome assemblies showed a two-base pair insertion into the ACOX3 gene closely located to HTRA3 that may regulate its expression as a putative genomic variant for ASF. Several significant DGEs, enriched gene ontology (GO) terms, and KEGG pathways at 1 day and 7 days post-infection, compared to the pre-infection, indicate a significant inflammation response immediately after ASF infection. The presence of the virus was confirmed by the mapping of RNA-Seq reads on two whole viral genome sequences. This was concordant with a higher virus load in the non-recovered animals 7 days post-infection. There was no transcriptome signature on the immunization at pre-infection and 1 day post-infection. More samples and data from additional clinical trials may support these findings.
Binarized neural network of diode array with high concordance to vector–matrix multiplication
Authors: Shin Yunwoo , Cho Kyoungah , Kim Sangsig 
Published: March 2024

AbstractIn this study, a binarized neural network (BNN) of silicon diode arrays achieved vector–matrix multiplication (VMM) between the binarized weights and inputs in these arrays. The diodes that operate in a positive-feedback loop in their p+-n-p-n+device structure possess steep switching and bistable characteristics with an extremely low subthreshold swing (below 1 mV) and a high current ratio (approximately 108). Moreover, the arrays show a self-rectifying functionality and an outstanding linearity by an R-squared value of 0.99986, which allows to compose a synaptic cell with a single diode. A 2 × 2 diode array can perform matrix multiply-accumulate operations for various binarized weight matrix cases with some input vectors, which is in high concordance with the VMM, owing to the high reliability and uniformity of the diodes. Moreover, the disturbance-free, nondestructive readout, and semi-permanent holding characteristics of the diode arrays support the feasibility of implementing the BNN.
Towards a universal mechanism for successful deep learning
Authors: Meir Yuval , Tzach Yarden , Hodassman Shiri , Tevet Ofek , Kanter Ido 
Published: March 2024

AbstractRecently, the underlying mechanism for successful deep learning (DL) was presented based on a quantitative method that measures the quality of a single filter in each layer of a DL model, particularly VGG-16 trained on CIFAR-10. This method exemplifies that each filter identifies small clusters of possible output labels, with additional noise selected as labels outside the clusters. This feature is progressively sharpened with each layer, resulting in an enhanced signal-to-noise ratio (SNR), which leads to an increase in the accuracy of the DL network. In this study, this mechanism is verified for VGG-16 and EfficientNet-B0 trained on the CIFAR-100 and ImageNet datasets, and the main results are as follows. First, the accuracy and SNR progressively increase with the layers. Second, for a given deep architecture, the maximal error rate increases approximately linearly with the number of output labels. Third, similar trends were obtained for dataset labels in the range [3, 1000], thus supporting the universality of this mechanism. Understanding the performance of a single filter and its dominating features paves the way to highly dilute the deep architecture without affecting its overall accuracy, and this can be achieved by applying the filter’s cluster connections (AFCC).
Impact of ecological presence in virtual reality tourism on enhancing tourists’ environmentally responsible behavior
Authors: Su Zhen , Lei Biman , Lu Dandan , Lai Shuchen , Zhang Xijing 
Published: March 2024

AbstractIn an era where environmental conservation is increasingly critical, identifying pathways through which technological innovations like virtual reality tourism (VRT) can promote sustainable behaviors is vital. This study investigates the impact of ‘ecological presence’, a newly proposed sub-dimension of presence in VRT, on tourists’ environmentally responsible behavior (TERB). Through structural equation modeling and fuzzy set qualitative comparative analysis of data from 290 participants, we unveil that ecological presence—defined as the authenticity and immersion of tourists in virtual ecological environments—significantly bolsters biospheric values, environmental self-identity, and personal norms. Additionally, our findings indicate that ecological presence in VRT indirectly promotes TERB, predominantly through the mediation of enhanced biospheric values and environmental self-identity. Notably, ecological presence, biospheric values, and environmental self-identity constitutes a sufficient condition for achieving a high level of TERB. This research highlights the potential of VRT as an innovative tool for tourism administrators to foster environmental stewardship, offering a novel approach to leveraging technology for conservation efforts.
Impact of intrapartum oxytocin administration on neonatal sucking behavior and breastfeeding
Authors: Omaru Machiko , Kajiwara Setsu , Wakamatsu Eri , Kuroishi Sumiko , Ochiai Yukifumi , Oniki Kentaro , Kato Kiyoko , Morokuma Seiichi 
Published: March 2024

AbstractThis study aimed to examine the effect of intrapartum oxytocin administration on neonatal sucking behavior and breastfeeding. A total of 64 pairs (29 in the group treated with intrapartum oxytocin and 35 in the control group) of normal infants within 24–48 h of birth and their mothers were recruited. Sucking ability was evaluated by measuring Non-Nutritive Sucking (NNS) for 5 min. Data on the rate of exclusive breastfeeding at 1 month postpartum were collected. In the adjusted multiple regression models, intrapartum oxytocin exposure was significantly associated with fewer total NNS bursts (95% confidence interval (CI), −7.02 to −0.22), longer pause times (95% CI, 1.33 to 10.21), and greater pause-time variability (95% CI, 3.63 to 63.92). Effects estimated using structural equation modeling revealed that intrapartum oxytocin exposure had a significant negative and direct effect on the practice of exclusive breastfeeding 1 month postpartum (β = −0.238, p = 0.047). However, no NNS-mediated indirect effects were observed. This report demonstrates that infants born to mothers who receive intrapartum oxytocin may have impaired sucking ability for at least the first 48 h after birth, and breastfeeding support should be provided.
Development and validation of prognostic machine learning models for short- and long-term mortality among acutely admitted patients based on blood tests
Authors: Jawad Baker Nawfal , Shaker Shakir Maytham , Altintas Izzet , Eugen-Olsen Jesper , Nehlin Jan O. , Andersen Ove , Kallemose Thomas 
Published: March 2024

AbstractSeveral scores predicting mortality at the emergency department have been developed. However, all with shortcomings either simple and applicable in a clinical setting, with poor performance, or advanced, with high performance, but clinically difficult to implement. This study aimed to explore if machine learning algorithms could predict all-cause short- and long-term mortality based on the routine blood test collected at admission. Methods: We analyzed data from a retrospective cohort study, including patients > 18 years admitted to the Emergency Department (ED) of Copenhagen University Hospital Hvidovre, Denmark between November 2013 and March 2017. The primary outcomes were 3-, 10-, 30-, and 365-day mortality after admission. PyCaret, an automated machine learning library, was used to evaluate the predictive performance of fifteen machine learning algorithms using the area under the receiver operating characteristic curve (AUC). Results: Data from 48,841 admissions were analyzed, of these 34,190 (70%) were randomly divided into training data, and 14,651 (30%) were in test data. Eight machine learning algorithms achieved very good to excellent results of AUC on test data in a of range 0.85–0.93. In prediction of short-term mortality, lactate dehydrogenase (LDH), leukocyte counts and differentials, Blood urea nitrogen (BUN) and mean corpuscular hemoglobin concentration (MCHC) were the best predictors, whereas prediction of long-term mortality was favored by age, LDH, soluble urokinase plasminogen activator receptor (suPAR), albumin, and blood urea nitrogen (BUN). Conclusion: The findings suggest that measures of biomarkers taken from one blood sample during admission to the ED can identify patients at high risk of short-and long-term mortality following emergency admissions.
An integrated approach for the benthic habitat mapping based on innovative surveying technologies and ecosystem functioning measurements
Authors: Piazzolla Daniele , Scanu Sergio , Mancuso Francesco Paolo , Bosch-Belmar Mar , Bonamano Simone , Madonia Alice , Scagnoli Elena , Tantillo Mario Francesco , Russi Martina , Savini Alessandra , Fersini Giorgio , Sarà Gianluca , Coppini Giovanni , Marcelli Marco , Piermattei Viviana 
Published: March 2024

AbstractAmong marine ecosystems globally, those in the Mediterranean Sea, are facing many threats. New technologies are crucial for enhancing our understanding of marine habitats and ecosystems, which can be complex and resource-intensive to analyse using traditional techniques. We tested, for the first time, an integrated multi-platform approach for mapping the coastal benthic habitat in the Civitavecchia (northern Latium, Italy) coastal area. This approach includes the use of an Unmanned Surface Vehicle (USV), a Remote Operated Vehicle (ROV), and in situ measurements of ecosystem functionality. The echosounder data allowed us to reconstruct the distribution of bottom types, as well as the canopy height and coverage of the seagrass Posidonia oceanica. Our study further involved assessing the respiration (Rd) and net primary production (NCP) rates of P. oceanica and its associated community through in situ benthic chamber incubation. By combining these findings with the results of USV surveys, we were able to develop a preliminary spatial distribution model for P. oceanica primary production (PP-SDM). The P. oceanica PP-SDM was applied between the depths of 8 and 10 m in the studied area and the obtained results showed similarities with other sites in the Mediterranean Sea. Though in the early stages, our results highlight the significance of multi-platform observation data for a thorough exploration of marine ecosystems, emphasizing their utility in forecasting biogeochemical processes in the marine environment.
Cascade and pre-slip models oversimplify the complexity of earthquake preparation in nature
Authors: Martínez-Garzón Patricia , Poli Piero 
Published: March 2024

AbstractEarthquake precursory processes have been central to scientific inquiry for nearly a century. Recent advancements in earthquake monitoring, geodesy, and data analysis including artificial intelligence, have substantially improved our understanding of how earthquake sequences unfold leading to the mainshock. We examine the available seismological and geodetic evidence describing preparatory processes in 33 earthquake sequences with MW [3.2–9.0] across different tectonic and stress conditions. Our analysis reveals common patterns, and sheds light on the interplay of structural, tectonic and other boundary conditions that influence the dynamics of earthquake sequences, and hence, in the seismo-geodetic observables prior to the mainshock. We place particular emphasis on connecting observed phenomena to the underlying physical processes driving the sequences. From our findings, we propose a conceptual framework viewing earthquake preparation as a process involving several juxtaposed driving physical mechanisms on different temporal and spatial scales, jointly leading to the stress increase in the future epicenter.
Kupffer cells dictate hepatic responses to the atherogenic dyslipidemic insult
Authors: Di Nunzio Giada , Hellberg Sanna , Zhang Yuyang , Ahmed Osman , Wang Jiawen , Zhang Xueming , Björck Hanna M. , Chizh Veronika , Schipper Ruby , Aulin Hanna , Francis Roy , Fagerberg Linn , Gisterå Anton , Metso Jari , Manfé Valentina , Franco-Cereceda Anders , Eriksson Per , Jauhiainen Matti , Hagberg Carolina E. , Olofsson Peder S. , Malin Stephen G. 
Published: March 2024

AbstractApolipoprotein-B (APOB)-containing lipoproteins cause atherosclerosis. Whether the vasculature is the initially responding site or if atherogenic dyslipidemia affects other organs simultaneously is unknown. Here we show that the liver responds to a dyslipidemic insult based on inducible models of familial hypercholesterolemia and APOB tracing. An acute transition to atherogenic APOB lipoprotein levels resulted in uptake by Kupffer cells and rapid accumulation of triglycerides and cholesterol in the liver. Bulk and single-cell RNA sequencing revealed a Kupffer-cell-specific transcriptional program that was not activated by a high-fat diet alone or detected in standard liver function or pathological assays, even in the presence of fulminant atherosclerosis. Depletion of Kupffer cells altered the dynamic of plasma and liver lipid concentrations, indicating that these liver macrophages help restrain and buffer atherogenic lipoproteins while simultaneously secreting atherosclerosis-modulating factors into plasma. Our results place Kupffer cells as key sentinels in organizing systemic responses to lipoproteins at the initiation of atherosclerosis.
AI-guided pipeline for protein–protein interaction drug discovery identifies a SARS-CoV-2 inhibitor
Authors: Trepte Philipp , Secker Christopher , Olivet Julien , Blavier Jeremy , Kostova Simona , Maseko Sibusiso B , Minia Igor , Silva Ramos Eduardo , Cassonnet Patricia , Golusik Sabrina , Zenkner Martina , Beetz Stephanie , Liebich Mara J , Scharek Nadine , Schütz Anja , Sperling Marcel , Lisurek Michael , Wang Yang , Spirohn Kerstin , Hao Tong , Calderwood Michael A , Hill David E , Landthaler Markus , Choi Soon Gang , Twizere Jean-Claude , Vidal Marc , Wanker Erich E 
Published: March 2024

AbstractProtein–protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.
Integrated annotation prioritizes metabolites with bioactivity in inflammatory bowel disease
Authors: Bhosle Amrisha , Bae Sena , Zhang Yancong , Chun Eunyoung , Avila-Pacheco Julian , Geistlinger Ludwig , Pishchany Gleb , Glickman Jonathan N , Michaud Monia , Waldron Levi , Clish Clary B , Xavier Ramnik J , Vlamakis Hera , Franzosa Eric A , Garrett Wendy S , Huttenhower Curtis 
Published: March 2024

AbstractMicrobial biochemistry is central to the pathophysiology of inflammatory bowel diseases (IBD). Improved knowledge of microbial metabolites and their immunomodulatory roles is thus necessary for diagnosis and management. Here, we systematically analyzed the chemical, ecological, and epidemiological properties of ~82k metabolic features in 546 Integrative Human Microbiome Project (iHMP/HMP2) metabolomes, using a newly developed methodology for bioactive compound prioritization from microbial communities. This suggested >1000 metabolic features as potentially bioactive in IBD and associated ~43% of prevalent, unannotated features with at least one well-characterized metabolite, thereby providing initial information for further characterization of a significant portion of the fecal metabolome. Prioritized features included known IBD-linked chemical families such as bile acids and short-chain fatty acids, and less-explored bilirubin, polyamine, and vitamin derivatives, and other microbial products. One of these, nicotinamide riboside, reduced colitis scores in DSS-treated mice. The method, MACARRoN, is generalizable with the potential to improve microbial community characterization and provide therapeutic candidates.
A trusted and regulated data trading scheme based on blockchain and zero‐knowledge proof
Authors: Li Dongmei , Ke Xiaohui , Zhang Xiaomei , Zhang Yujin 
Published: March 2024

AbstractTo meet the demand for high‐quality healthcare services, data trading can effectively promote the circulation of medical data and improve the level of healthcare services. To address the existing problems of data regulation difficulties and data privacy leakage in medical data trading, a trusted and regulated data trading scheme based on blockchain and zero‐knowledge proof is proposed. In this scheme, a regulatory institution is introduced to control the issuance of authorized tokens and ensure the controllability of data sharing activities. The blockchain takes over the task of generating public parameters to reduce the computational overhead of the system. Based on homomorphic proxy re‐encryption technology, users can perform data analysis in the cloud to ensure data security. Smart contracts and zero‐knowledge proof technology can automatically verify the validity of data to protect the rights and interests of data users; at the same time, efficient consensus algorithms can also increase the rate of transactions processed by the blockchain system. Finally, as the security and performance analysis shows, the scheme in this paper has better security, higher efficiency and more comprehensive functions.
Probabilistic 3D motion model for object tracking in aerial applications
Authors: Mirtajadini Seyed Hojat , Amiri Atashgah MohammadAli , Shahbazi Mohammad 
Published: March 2024

AbstractVisual object tracking, crucial in aerial applications such as surveillance, cinematography, and chasing, faces challenges despite AI advancements. Current solutions lack full reliability, leading to common tracking failures in the presence of fast motions or long‐term occlusions of the subject. To tackle this issue, a 3D motion model is proposed that employs camera/vehicle states to locate a subject in the inertial coordinates. Next, a probability distribution is generated over future trajectories and they are sampled using a Monte Carlo technique to provide search regions that are fed into an online appearance learning process. This 3D motion model incorporates machine‐learning approaches for direct range estimation from monocular images. The model adapts computationally by adjusting search areas based on tracking confidence. It is integrated into DiMP, an online and deep learning‐based appearance model. The resulting tracker is evaluated on the VIOT dataset with sequences of both images and camera states, achieving a 68.9% tracking precision compared to DiMP's 49.7%. This approach demonstrates increased tracking duration, improved recovery after occlusions, and faster motions. Additionally, this strategy outperforms random searches by about 3.0%.