bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Influence of Multi-generational High-Fiber Diet on the Gut Microbiota of Root Voles (Microtus oeconomus)
Authors: zhang yan , Wang Yihong , Wanyan Ruijun , Yao Baohui , Tan Zhaoxian , Wang Rong , Li Huan , Qu Jiapeng 
Published: October 2024

Abstract

Fiber influences animal metabolism by affecting the gut microbiota, and high-fiber diets are often considered beneficial. However, existing research primarily focuses on the short-term effects of high-fiber diets, with limited studies on their long-term and trans-generational impacts on gut microbiota. This study investigated the long-term high-fiber diets and trans-generational effects on root voles (Microtus oeconomus)’ gut microbiota over five generations (F0 to F4) using 16S rRNA gene sequencing. Results showed that high-fiber diet significantly increased the diversity and complexity of gut microbiota and upregulated genes related to metabolism and immunity. The proportion of non-cellulose-degrading bacteria such as Proteobacteria and Spirochaetes decreased, while cellulose-degrading Firmicutes increased, raising the Firmicutes/Bacteroidetes ratio. Generational factors significantly influenced microbial community structure, reducing similarity. Over generations, both diets led to a reduction in beneficial bacteria such as Lactobacillus, Sporanaerobacter, and Clostridium, impairing the breakdown of proteins and starches. Meanwhile, potentially harmful bacteria like Desulfovibrio and Oscillospira increased, and the Firmicutes/Bacteroidetes ratio decreased, suggesting that a long-term, trans-generational uniform high-fiber diet may cause unfavorable shifts in gut microbiota. In summary, a high-fiber diet can increase gut microbiota abundance and diversity, promote cellulose-degrading bacteria, and upregulate certain metabolic genes, but long-term, uniform diets may cause gut microbiota imbalance, reducing beneficial bacteria and increasing potentially harmful ones.
Service function chain mapping method based on delay guarantee
Authors: Cai Xiaodan , Zhuge Bin , Zhang Zitian , Ren Qianye , Dong Ligang , Jiang Xian 
Published: October 2024

Abstract

Network function virtualization achieves the softwareization of network functions through virtualization technology, reducing operational costs but introducing challenges in network orchestration complexity. To deploy service function chains efficiently and cost-effectively in a network function virtualization environment, a latency-guaranteed, optimized mapping method is proposed. Firstly, a model that minimizes the total network deployment cost for service function chains is established, comprehensively considering user latency requirements and network resource constraints. Secondly, an improved Sparrow Search Algorithm-based mapping method for service function chains is introduced. Simulation results demonstrate that this algorithm exhibits higher service function chain request acceptance rates and lower average network costs compared to the control algorithm, showcasing significant advantages in enhancing network performance and reducing costs.
A Multi-Dilated Convolution Network for Speech Emotion Recognition
Authors: Madanian Samaneh , Adeleye Olayinka , Templeton John Michael , Chen Talen , Poellabauer Christian , Zhang Enshi , Schneider Sandra 
Published: October 2024

Abstract

Speech emotion recognition (SER) is an important application in the field of Affective Computing and Artificial Intelligence. Recently, there has been a significant interest in Deep Neural Networks using speech spectrograms. As the two-dimensional representation of the spectrogram includes more speech characteristics, research interest in convolution neural networks (CNNs) or advanced image recognition models is leveraged to learn deep patterns in a spectrogram to effectively perform SER. Accordingly, in this study, we propose a novel SER model based on the learning of the utterance-level spectrogram. First, we use the Spatial Pyramid Pooling (SPP) strategy to remove the size constraint associated with CNN-based image recognition task. Then, the SPP layer is deployed to extract both the global-level prominent feature vector and multi-local-level feature vector, followed by an attention model to weigh the feature vectors. Finally, we apply the ArcFace layer, typically used for face recognition, to the SER task, thereby obtaining improved SER performance. Our model achieved an unweighted accuracy of 67.9 % on IEMOCAP and 77.6 % on EMODB datasets.
Integrative analysis of the metabolome and transcriptome revealed the regulatory mechanisms of lignan synthesis in Polygonatum
Authors: Shi Guangdi , Wan Xiaolin , Xiao Qiang 
Published: October 2024

Abstract

The genus Polygonatum is an essential resource for traditional Chinese medicine (TCM). To investigate the molecular mechanism of lignan biosynthesis in Polygonatum sibiritumRed and Polygonatum kingianum var. grandifolium, we performed transcriptomic and metabolomic analyses of these two plants. Seventeen lignans were detected a total of 17 lignans. Among them,Cycloolivil-6-O-glucoside,Matairesinol-4,4'-di-O-glucoside,5'-Methoxyisolariciresitnol-9'-O-glucoside,Syringaresinol,Syringaresinol-4'-O-glucoside,Isolariciresinol-9'-O-glucoside, dihydrodehydrodiconiferyl alcohol-4-O-glucoside, Pinoresinol and Eleutheroside E were reported in the genus Polygonatum for the first time. Transcriptome sequencing identified 75 genes related to the lignin biosynthesis pathway belonging to 12 classes of transcription factor families, among which genes from the SET, C2H2, and GARP-G2-like families were dominant in lignin biosynthesis. In addition, a family of transcription factors such as SET, GARP-G2-like, C2H2, Trihelix, SWI/SNF-BAF60b, PHD, FAR1, C3H, AP2/ERF-ERF, and BES1 were found to have essential roles in lignin biosynthesis by combined metabolome and transcriptome analyses. This study lays the foundation for an in-depth understanding of Polygonatum lignan biosynthesis and metabolic mechanisms. This has significant reference value for the future cloning of critical genes for lignans and the selection and breeding of Polygonatumgermplasm resources with high lignan content.
Potential distribution modeling and future perspectives for neotropical snakes of the genus Oxyrhopus
Authors: Quintana Paola De la , Solé Mirco 
Published: October 2024

Abstract

Recent increase in research on the genus Oxyrhopus, which comprises 15 species distributed throughout the entire Neotropical region. Although presence data is available, the potential distribution and temporal stability of these species remain underexplored areas. Understanding the impacts of climate change and the need to identify effective conservation measures is crucial. In this study, the future scenarios of the IPCC are examined, projecting changes up to 2061-2080, using optimistic (SSP2), intermediate (SSP3), and pessimistic (SSP5) scenarios. The use of maximum entropy models to predict the distribution of these snakes both in the present and in the future is emphasized. Available data records from the internet, scientific articles, and museum data were employed, filtered by species, resulting in 6,413 records for model creation. The potential distribution of 14 out of the 15 species of the genus Oxyrhopus was successfully calculated, using specific bioclimatic parameters for each one. Our findings represent the first potential distribution models for Oxyrhopus species. Most species exhibit wide distributions that could be maintained over time according to future scenarios. However, some populations are not represented in the models, suggesting a risk due to suboptimal conditions in the future.
M1 macrophage membrane-functionalized nanovesicles for free radical-medicated chemotherapy against liver cancer
Authors: Wang Feiran , Xue Qiang , Xu Xiaodong , Liu Xiancheng , Liu Tianqing 
Published: October 2024

Abstract

Free radical chemistry including free radical reactive oxygen species (ROS) and reactive nitrogen species (RNS) has received a great deal of attention for cancer treatment due to their ability to produce excessive free radicals, leading to cancer cell death. In this study, we developed M1 macrophage membrane-coated MnO2-silica loaded with doxorubicin (DOX) and a nitric oxide (NO) donor (M1@MS/MnO2/DOX/NO) to enhance therapeutic efficacy for hepatocellular carcinoma therapy. Specifically, superoxide anion O2•− produced from DOX reacted with NO to form ONOO − for RNS-mediated treatment. Meanwhile, Mn2+ produced from the MnO2 layer under a reductive microenvironment functioned as a catalyst to transform endogenous H2O2 into the highly toxic ·OH to achieve ROS-mediated treatment. Our results demonstrated that this novel combination of reactive species therapy synergistically improved the therapeutic efficiency against liver cancer, leading to a significant reduction of cancer cell proliferation, as well as in vivo tumor growth.
Circumvention of Hepato-toxicity of Paracetamol by Co-administering with Flavonoids in the treatment of Osteoarthritis
Authors: kukkala sai priyanka , Gran G Srikar 
Published: October 2024

Abstract

Objectives
The objective of present study is to develop novel pharmaceutical dosage form to reduce the hepatotoxicity of Paracetamol when induced in higher doses in treatment of Osteoarthritis. The effect of Quercetin in controlling paracetamol toxicity was planned to be studied by conducting in vivo studies on rats for 21days1. The outcome of the present study is to show that Quercetin shows high impact in reducing the hepatotoxicity caused by paracetamol due to release of toxic metabolite NAPQI by cytochrome P-450 during metabolism process.
Materials and methods
Rats were treated with Paracetamol in combination with different dose strengths of quercetin, and with silymarin as standard.
Results
Quercetin is given in doses of 5, 10, and 15 mg/kg body weight. with paracetamol lowered the blood levels of SGOT, SGPT, ALP, DB, and TB while restoring albumin & total protein levels to normal in a dose-varying way in comparison to the toxic control. Histopathology of liver and kidney were done for cell necrosis2. Chrsyin therapy at low doses resulted in a modest recovery from necrosis, vacuolization, inflammation, and fatty alterations. Moderate recovery from necrosis, vacuolization, lipid alterations, and inflammation was observed with medium dose therapy3. Treatment at a high dose maintained the liver's natural architecture.
Conclusion
The current study found that Quercetin inhibits the CYP2E1 mediated metabolism, as a result reduces the formation of toxic metabolite NAPQI by suppressing the liver and kidney biomarkers4. Hence reducing the liver toxicity
Unsteady Cavity Pressure Distribution Recovery for Underwater Axisymmetric Body via Deep Learning
Authors: Liu Yu-bo , He Zhen-min , Gao Qi , Chu Xue-sen , Deng Jian , Shao Xue-ming 
Published: October 2024

Abstract

The underwater launch of an axisymmetric body involves complex cavity-structure interactions. Studying the evolution of cavity pressure around an axisymmetric body is crucial for researching its motion stability. In this work, we propose a deep neural network model for cavity pressure distribution recovery, called CPDR-net. This model can reconstruct the full-domain distribution of surface pressure based solely on the local pressure distribution. The CPDR-net model was trained using numerical simulation data with different launch depths and initial velocities, and subsequently tested on two simulation datasets under new conditions. Results demonstrated that CPDR-net can accurately predict the pressure distribution along each longitudinal line of the axisymmetric body and provide the pressure evolution over time for each point on the surface. Thus, we can obtain the evolution of surface pressure distribution throughout the entire voyage process based on the CPDR-net model. The findings from this study may provide a valuable reference for subsequent research on underwater launches.
Dynamics of Southern Hemisphere Super Gyre Response to External Forcings
Authors: Liu Jianyu , Luo Yiyong , Liu Fukai , Yang Jason 
Published: October 2024

Abstract

The subtropical gyres in the Southern Hemisphere are interlinked through the Tasman and Agulhas Leakages and extend throughout the mid-latitude ocean basins of all major oceans. This vast ocean circulation system is called the Southern Hemisphere Super Gyre (SHSG). Previous studies have found a significant strengthening and poleward shift of the SHSG in recent decades. By analyzing multi-member ensembles from the Canadian Earth System Model and the Community Earth System Model, as well as a multi-model ensemble from the Detection and Attribution Model Intercomparison Project, this study investigates the relative contributions of greenhouse gas, aerosol, and ozone depletion forcings to changes in the SHSG since the 1950s. Results show that the strengthening and poleward shift of the SHSG have been dominated by the greenhouse gas forcing, which induces an intensification and poleward shift of the westerlies in the Southern Hemisphere, leading to a southward migration of the zero wind stress curl line and an increase in the positive wind stress curl over the southern SHSG. In contrast, the ozone depletion forcing plays a secondary role in changing the SHSG because its effect on the westerlies is further south than the greenhouse gas forcing. The aerosol forcing has little influence on the SHSG due to its weak effect on the winds in the Southern Hemisphere. The dominance of changes in wind stress curl is further validated through a set of partially coupled experiments in which the contribution of buoyancy and wind stress forcings are separated.
Predicting the Optimal Insulin Dose and Controlling the Blood Glucose Levels with Deep Reinforcement Learning
Authors: Symeonidis Panagiotis , Rizos Evangelos C. , Sotiriou Dimosthenis , Zanker Markus 
Published: October 2024

Abstract

This paper addresses the critical challenge of predicting future insulin dosages for diabetic patientsin Intensive Care Units (ICUs), a setting where precise glycemic control is crucial. The work proposes aunique methodology that sidesteps the complications associated with traditional glucose-insulin interactionmodels, due to data sparsity. We introduce a simplified version of Bergman’s Insulin-Glucose Interaction Modeland construct an extended dataset based on the MIMIC III database. This dataset includes 870 predictivefeatures encompassing demographic data, prior insulin administrations, and average glucose levels. The paperalso introduces a Reinforcement Learning approach, utilizing Deep Q-Learning, to optimize both the thetraining population and the feature selection for individualized predictions. We found that our CompositeMultilinear Regression Model algorithm outperforms the single-patient regression model in terms of MeanAbsolute Error (MAE). Specifically, the MAE values for the Type I and Type II Diabetic groups were 2.33 and3.68, respectively, significantly better than the single-patient regression model. The work contributes a novelapproach to insulin dose prediction, offering a promising pathway for more effective glucose management inICU settings.
Charging ahead: Firm-level optimisation strategies for sustainable and cost-effective electric vehicle workplace charging
Authors: Seger Marcel , Brand Christian , Clement Christoph , Dixon James , Wilson Charlie 
Published: October 2024

Abstract

Expanding electric vehicle (EV) charging infrastructure is essential for transitioning to an electrified mobility system. With rising EV adoption rates, firms face increasing regulatory pressure to build up workplace charging facilities for their employees. However, the impact of EV charging loads on businesses’ specific electricity consumption profiles remains largely unknown. Our study addresses this challenge by presenting a mathematical optimisation model, available via an open-source web application, that empowers business executives to manage energy consumption effectively, enabling them to assess peak loads, charging costs, and carbon emissions specific to their power profiles and employee needs. Using real-world data from a global car manufacturer in South East England, UK, we demonstrate that smart charging strategies can reduce peak loads by 28% and decrease charging costs and emissions by 9% compared to convenience charging. Our methodology is widely applicable across industries and geographies, offering data-driven insights for planning EV workplace charging infrastructure.
A Single Amino Acid Mutation Alters the Neutralization Epitopes in the Respiratory Syncytial Virus Fusion Glycoprotein
Authors: Oraby Ahmed K. , Stojic Aleksandra , Elawar Farah , Bilawchuk Leanne M. , McClelland Ryley D. , Erwin Kaci , Granoski Madison , Griffiths Cameron D. , Frederick Justin D. , Arutyunova Elena , Lemieux M. Joanne , West Frederick G. , Ramilo Octavio , Mejias Asuncion , McLellan Jason S. , Marchant David J. 
Published: October 2024

Abstract

Respiratory syncytial virus (RSV) is the leading cause of infant hospitalization. All current available RSV therapeutics, including antibody prophylaxis and adult vaccination, target the RSV fusion glycoprotein (RSV-F). The seven neutralization sites on RSV-F are highly conserved and infrequently mutated. Here, we show that a single amino acid mutation at position 305 in RSV-F significantly alters antigenic recognition of RSV-F binding sites and reduces the susceptibility of RSV to neutralizing antibodies. In an in vitro evolution assay, we show that RSV-F L305I occurs in the majority of RSV quasi-species. Computational modeling predicted that the L305I mutation alters the epitope landscape of RSV-F, resulting in changes to neutralizing antibody sensitivity and affinity towards the RSV-F glycoprotein. Screening of published RSV-F sequences revealed that position 305 in RSV-F is a conserved leucine and isoleucine in RSV-A and RSV-B subtypes respectively. Our study suggests that select amino acids in RSV-F may act as conformational switches for RSV to evade host serum antibodies. This work has important implications in understanding RSV evolution and resistance as it suggests that mutational resistance to neutralizing antibodies can occur at sites distal to antigenic epitopes, significantly altering antibody sensitivity to viral infection. These unique antigenic landscape changes should be considered in the context of vaccine and therapeutic development in order to better understand viral mechanisms of evasion and resistance.
Adaptation of thermal reaction norms in constant and fluctuating environments
Authors: Suleiman Abdulrahaman Lawal , Landi P. , Hui C. 
Published: October 2024

Abstract

Thermal reaction norms depict how temperature influences biological performances, thus also known as thermal performance curves (TPCs). Arguably, the interplay of the thermal environmental conditions and the TPC can shape the strength of intraspecific competition. Such competition can then drive the long-term evolution of the TPC. We develop a Lotka-Volterra model, using adaptive dynamics (AD), to investigate how intraspecific competition among individuals of ectotherms drives the TPC adaptation under constant versus periodically fluctuating environmental temperatures. The competition coefficient from one individual to another is assumed proportional to the ratio of their thermal performances at the current environmental temperature, with the individuals’ thermal performance being adaptive and captured by a beta distribution. Results show that, under a constant temperature, the optimal temperature of the TPC evolves to align perfectly with the environmental temperature, with the TPC breadth shrinking to zero, reflecting local adaptation to complete thermal specialisation. In fluctuating thermal environments, adaptation produces broader TPCs, with their optimal temperature potentially mismatching the average environmental temperature. When the TPC’s optimal temperature matches the average temperature, large temperature fluctuations lead to broad TPCs (thermal generalisation). Our model also shows the emergence of bimodal TPCs under rapid and large temperature fluctuations, indicating adaptation to extreme temperatures and potentially a divergence of thermal strategies within the population. Our theoretical model has demonstrated that adaptation of TPCs in periodic thermal regimes promotes the evolution of thermal generalists and possible character divergence, compared to complete thermal specialisation in constant environments.
Spatial and temporal scales of patterning in open-air surface scatters along the Doring River, South Africa
Authors: Shaw Matthew , Phillips Natasha , Ames Chris H.J. , Mackay Alex 
Published: October 2024

Abstract

Individuals are considered the primary agents responsible for introducing objects into the archaeological record through discard. However, the intricate spatial distribution of artefacts within archaeological contexts is complex, being influenced not only by individual actions, but also by the aggregation of individual behaviours, temporal dynamics, and post-depositional processes. This study employs spatial data on stone tools from accumulating open-air localities in the Doring River catchment, using R and Geographic Information Systems, to explore the scales at which meaningful information can be extracted from these open-air sediment bodies. Although statistical analyses of the spatial data reveal a correlation between the distribution of artefacts and the geomorphology of the locality, it is evident that the patterning cannot solely be attributed to geomorphological factors. The findings from the study suggest the representation of various discrete temporal and spatial scales of behaviour at these sites, ranging from the individual to the broader ‘collective of systems’ scale. This diversity persists even in scenarios where temporally constrained artefacts are being lagged onto a single ancient surface. The outcomes highlight the potential of open-air sites in providing insights into the complexities of human-environment interactions.
Robust 3D Localization for UAV Navigation Using Event Cameras and IMU for Indoor Environments
Authors: Tejero-Ruiz David , Solís-Martín David , Pérez-Grau Francisco J. , Galán-Páez Juan 
Published: October 2024

Abstract

For proper navigation of Unmanned Aerial Vehicles (UAVs), it is necessary to know their position in real-time to ensure safe navigation. Determining position in outdoor spaces is quite well solved. On the other hand, in indoor spaces, existing solutions are either imprecise or excessively costly. In this paper, the 3D localization problem is addressed in the context of UAV navigation. The main purpose of this work is to develop and evaluate a robust real-time localization scheme using exclusively the information from an embedded Event Camera and an IMU (Inertial Measurement Unit). Deep learning techniques and robust computer vision algorithms are implemented together to accurately compute the UAV pose, leveraging the strengths of well-established visual-inertial odometry algorithms and the intrinsic advantages of Event Cameras, such as high dynamic range and absence of motion blur. Throughout this study, state-of-the-art techniques are selected, refined, implemented, and evaluated. The proposed system demonstrated good performance and acceptable precision specially in situation with abrupt lighting changes.