bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep learning-based 3D cerebrovascular segmentation workflow on bright and black blood sequences magnetic resonance angiography
Authors: Zhou Langtao , Wu Huiting , Luo Guanghua , Zhou Hong 
Published: March 2024

Abstract
Background
Cerebrovascular diseases have emerged as significant threats to human life and health. Effectively segmenting brain blood vessels has become a crucial scientific challenge. We aimed to develop a fully automated deep learning workflow that achieves accurate 3D segmentation of cerebral blood vessels by incorporating classic convolutional neural networks (CNNs) and transformer models.

Methods
We used a public cerebrovascular segmentation dataset (CSD) containing 45 volumes of 1.5 T time-of-flight magnetic resonance angiography images. We collected data from another private middle cerebral artery (MCA) with lenticulostriate artery (LSA) segmentation dataset (MLD), which encompassed 3.0 T three-dimensional T1-weighted sequences of volumetric isotropic turbo spin echo acquisition MRI images of 107 patients aged 62 ± 11 years (42 females). The workflow includes data analysis, preprocessing, augmentation, model training with validation, and postprocessing techniques. Brain vessels were segmented using the U-Net, V-Net, UNETR, and SwinUNETR models. The model performances were evaluated using the dice similarity coefficient (DSC), average surface distance (ASD), precision (PRE), sensitivity (SEN), and specificity (SPE).

Results
During 4-fold cross-validation, SwinUNETR obtained the highest DSC in each fold. On the CSD test set, SwinUNETR achieved the best DSC (0.853), PRE (0.848), SEN (0.860), and SPE (0.9996), while V-Net achieved the best ASD (0.99). On the MLD test set, SwinUNETR demonstrated good MCA segmentation performance and had the best DSC, ASD, PRE, and SPE for segmenting the LSA.

Conclusions
The workflow demonstrated excellent performance on different sequences of MRI images for vessels of varying sizes. This method allows doctors to visualize cerebrovascular structures.

Critical relevance statement
A deep learning-based 3D cerebrovascular segmentation workflow is feasible and promising for visualizing cerebrovascular structures and monitoring cerebral small vessels, such as lenticulostriate arteries.

Key points
• The proposed deep learning-based workflow performs well in cerebrovascular segmentation tasks.
• Among comparison models, SwinUNETR achieved the best DSC, ASD, PRE, and SPE values in lenticulostriate artery segmentation.
• The proposed workflow can be used for different MR sequences, such as bright and black blood imaging.

Graphical Abstract
Genomic characterization of equine influenza A subtype H3N8 viruses by long read sequencing and functional analyses of the PB1-F2 virulence factor of A/equine/Paris/1/2018
Authors: Kleij Lena , Bruder Elise , Raoux-Barbot Dorothée , Lejal Nathalie , Nevers Quentin , Deloizy Charlotte , Da Costa Bruno , Legrand Loïc , Barrey Eric , Chenal Alexandre , Pronost Stéphane , Delmas Bernard , Dhorne-Pollet Sophie 
Published: March 2024

AbstractEquine influenza virus (EIV) remains a threat to horses, despite the availability of vaccines. Strategies to monitor the virus and prevent potential vaccine failure revolve around serological assays, RT-qPCR amplification, and sequencing the viral hemagglutinin (HA) and neuraminidase (NA) genes. These approaches overlook the contribution of other viral proteins in driving virulence. This study assesses the potential of long-read nanopore sequencing for fast and precise sequencing of circulating equine influenza viruses. Therefore, two French Florida Clade 1 strains, including the one circulating in winter 2018–2019 exhibiting more pronounced pathogenicity than usual, as well as the two currently OIE-recommended vaccine strains, were sequenced. Our results demonstrated the reliability of this sequencing method in generating accurate sequences. Sequence analysis of HA revealed a subtle antigenic drift in the French EIV strains, with specific substitutions, such as T163I in A/equine/Paris/1/2018 and the N188T mutation in post-2015 strains; both substitutions were in antigenic site B. Antigenic site E exhibited modifications in post-2018 strains, with the N63D substitution. Segment 2 sequencing also revealed that the A/equine/Paris/1/2018 strain encodes a longer variant of the PB1-F2 protein when compared to other Florida clade 1 strains (90 amino acids long versus 81 amino acids long). Further biological and biochemistry assays demonstrated that this PB1-F2 variant has enhanced abilities to abolish the mitochondrial membrane potential ΔΨm and permeabilize synthetic membranes. Altogether, our results highlight the interest in rapidly characterizing the complete genome of circulating strains with next-generation sequencing technologies to adapt vaccines and identify specific virulence markers of EIV.
Energetic particle flux measurements from the Korean space weather monitor particle detector: a comparative study with the MPS-HI onboard GOES-16
Authors: Oh Daehyeon , Kim Jiyoung , Loto’aniu Paul T. M. , Lim Han-Cheol , Lee Dae-Young , Kim Dohyeong 
Published: March 2024

AbstractThe Korean space weather monitor (KSEM) aboard the GEO-KOMPSAT-2A (GK2A) has been continuously measuring energetic particle flux in geostationary orbit at 128.2°E longitude since July 2019. The particle detector (PD) component of KSEM comprises six particle telescopes viewing different directions that provide near-real-time electron flux observations within the energy range of 100 keV to 3.8 MeV. The KSEM PD on the GK2A satellite, together with the MPS-HI on the GOES-16 satellite 156.6° away, can provide valuable simultaneous observations of the asymmetric space environment and contributes to our understanding of this dynamic region. In this study, we present recent energetic electron flux measurements obtained from the KSEM PD and conduct preliminary cross-comparisons with data from the Magnetosphere Particle Sensor–High (MPS-HI) PD, which is part of the SEISS instrument suite onboard GOES-16. The inter-comparisons show that the electron fluxes of the two detectors are in reasonable similarity except for some energy bands under quiet conditions. Additionally, we provide a brief overview of the electron flux responses of both KSEM PD and MPS-HI under enhanced space environment conditions, which shows differences based on sensor characteristics and satellite location.
Graphical Abstract
Development of Epigallocatechin 3-gallate-Loaded Hydrogel Nanocomposites for Oral Submucous Fibrosis
Authors: Mehta Chetan Hasmukh , Velagacherla Varalakshmi , Manandhar Suman , Nayak Yogendra , Pai Sreedhara Ranganath K , Acharya Shruthi , Nayak Usha Yogendra 
Published: March 2024

AbstractOral submucous fibrosis (OSF) is a chronic progressive disease associated with increased collagen deposition and TGF-β1 release. The current therapy and management have been a limited success due to low efficacy and adverse drug reactions. This study aimed to evaluate epigallocatechin 3-gallate (EGCG) encapsulated nanoparticles loaded mucoadhesive hydrogel nanocomposite (HNC) for OSF. Developed HNC formulations were evaluated for their permeation behaviour using in vitro as well as ex vivo studies, followed by evaluation of efficacy and safety by in vivo studies using areca nut extract-induced OSF in rats. The disease condition in OSF-induced rats was assessed by mouth-opening and biochemical markers. The optimized polymeric nanoparticles exhibited the required particle size (162.93 ± 13.81 nm), positive zeta potential (22.50 ± 2.94 mV) with better mucoadhesive strength (0.40 ± 0.002 N), and faster permeation due to interactions of the positively charged surface with the negatively charged buccal mucosal membrane. HNC significantly improved disease conditions by reducing TGF-β1 and collagen concentration without showing toxicity and reverting the fibroid buccal mucosa to normal. Hence, the optimized formulation can be further tested to develop a clinically alternate therapeutic strategy for OSF.
Graphical Abstract
Effects of microRNA-305 knockdown on brain gene expression associated with division of labor in honey bee colonies (Apis mellifera)
Authors: Stuart Sarai H. , Ahmed Amy C. Cash , Kilikevicius Laura , Robinson Gene E. 
Published: March 2024

ABSTRACT
Division of labor in honey bee colonies is based on the behavioral maturation of adult workers that involves a transition from working in the hive to foraging. This behavioral maturation is associated with distinct task-related transcriptomic profiles in the brain and abdominal fat body that are related to multiple regulatory factors including juvenile hormone (JH) and queen mandibular pheromone (QMP). A prominent physiological feature associated with behavioral maturation is a loss of abdominal lipid mass as bees transition to foraging. We used transcriptomic and physiological analyses to study whether microRNAs (miRNAs) are involved in the regulation of division of labor. We first identified two miRNAs that showed patterns of expression associated with behavioral maturation, ame-miR-305-5p and ame-miR-375-3p. We then downregulated the expression of these two miRNAs with sequence-specific antagomirs. Neither ame-miR-305-5p nor ame-miR-375-3p knockdown in the abdomen affected abdominal lipid mass on their own. Similarly, knockdown of ame-miR-305-5p in combination with JH or QMP also did not affect lipid mass. By contrast, ame-miR-305-5p knockdown in the abdomen caused substantial changes in gene expression in the brain. Brain gene expression changes included genes encoding transcription factors previously implicated in behavioral maturation. The results of these functional genomic experiments extend previous correlative associations of microRNAs with honey bee division of labor and point to specific roles for ame-miR-305-5p.
Population, distribution, biomass, and economic value of Equids in Ethiopia
Authors: Asteraye Girma Birhan , Pinchbeck Gina , Knight-Jones Theodore , Saville Klara , Temesgen Wudu , Hailemariam Alemayehu , Rushton Jonathan 
Published: March 2024

Background
Equids play a crucial role in the Ethiopian economy, transporting agricultural inputs and outputs in the dominant subsistence agricultural systems and the critical link for value chains throughout the country. However, these species are often neglected in policies and interventions, which reflects the data and information gaps, particularly the contribution of working equids to Ethiopia.

Objective
To assess population dynamics, distribution, biomass, and economic value of equids in Ethiopia.

Materials and methods
Equine population data were obtained from the Ethiopian Central Statistics Agency (CSA) annual national agriculture surveys published yearbooks from 2004 to 2020. Parameters such as the number of effective service days and daily rental value were obtained from interviews and literature to estimate the stock monetary and service value of equids. Descriptive statistics were used to assess population dynamics and the geographical distribution was mapped.

Results
The estimated total Ethiopian equid population increased by more than doubled (by 131%) between 2004 and 2020 from 5.7 (4.9–6.6) million to 13.3 (11.6–15) million with 2.1 million horses, 10.7 million donkeys, and 380 thousand mules. Similarly, the number of households owning a working equid has increased. Equine populations are unevenly distributed across Ethiopia, although data were lacking in some districts of the country. The per human-capita equine population ranged from 0–0.52, 0–0.13, and 0–0.02 for donkeys, horses, and mules, respectively. The equid biomass was 7.4 (6.3–8.4) million Tropical livestock unit (TLU) (250 kg liveweight), 10% of total livestock biomass of the country. The stock monetary value of equids was USD 1,229 (651–1,908) million, accounting for 3.1% of total livestock monetary value and the services value of equids was USD 1,198 (825–1,516) million, which is 1.2% of Ethiopian 2021 expected GDP.

Conclusion
The Ethiopian equine population has grown steadily over the last two decades. Equids play a central role in transportation and subsistence agriculture in Ethiopia and contribute significantly to the national economy. This pivotal role is insufficiently recognized in national livestock investments.
Metabolic impairments associated with type 2 diabetes mellitus and the potential effects of exercise therapy: An exploratory randomized trial based on untargeted metabolomics
Authors: Zhang Furong , Chen Xixi , Yang Mingxiao , Shen Xiaoyu , Wang Yiliang , Zhong Dongling , Zeng Fang , Jin Rongjiang 
Published: March 2024

Background
Type 2 diabetes mellitus (T2DM) is a common condition that is characterized by metabolic impairments. Exercise therapy has proven effective in improving the physiological and psychological states of patients with T2DM; however, the influence of different exercise modalities on metabolic profiles is not fully understood. This study first aimed to investigate the metabolic changes associated with T2DM among patients and then to evaluate the potential physiological effects of different exercise modalities (Tai Chi and brisk walking) on their metabolic profiles.

Methods
This study included 20 T2DM patients and 11 healthy subjects. Patients were randomly allocated to either the Tai Chi or walking group to perform Dijia simplified 24-form Tai Chi or brisk walking (80–100 m/min), with 90 minutes each time, three times per week for 12 weeks, for a total of 36 sessions. The healthy group maintained daily living habits without intervention. Glycemic tests were conducted at the baseline and after 12 weeks. Serum and urine samples were collected for untargeted metabolomic analyses at baseline and 12 weeks to examine the differential metabolic profiles between T2DM and healthy subjects, and the metabolic alterations of T2DM patients before and after exercise therapy.

Results
Compared to the healthy group, T2DM patients exhibited metabolic disturbances in carbohydrates (fructose, mannose, galactose, glycolysis/gluconeogenesis), lipids (inositol phosphate), and amino acids (arginine, proline, cysteine, methionine, valine, leucine, and isoleucine) metabolism, including 20 differential metabolites in the serum and six in the urine. After exercise, the glycemic results showed insignificant changes. However, patients who practiced Tai Chi showed significant improvements in their post-treatment metabolic profiles compared to baseline, with nine serum and six urine metabolites, including branch-chained amino acids (BCAAs); while those in the walking group had significantly altered nine serum and four urine metabolites concerning steroid hormone biosynthesis and arachidonic acid metabolism compared to baseline.

Conclusion
T2DM patients displayed impaired carbohydrate, lipid, and amino acid metabolism, and exercise therapy improved their metabolic health. Different modalities may act through different pathways. Tai Chi may improve disrupted BCAAs metabolism, whereas brisk walking mainly regulates steroid hormone biosynthesis and arachidonic acid metabolism.
Paradoxical Reaction and Parastomal Pyoderma Gangrenosum Emergence With Secukinumab Therapy
Authors: Zaher Anas , Castillo Michael 
Published: March 2024

ABSTRACTParadoxical reactions are characterized by the emergence or worsening of a pathological condition that typically responds to the administered drug while treating the patient for another condition. Certain drugs, such as tumor necrosis factor inhibitors, can induce pyoderma gangrenosum. Recently, the drug secukinumab has been implicated in a few case reports as a potential cause of pyoderma gangrenosum after initiation. We report the first case of biologic-refractory ulcerative colitis presenting with parastomal pyoderma gangrenosum after the initiation of secukinumab. While these associations are still being investigated, they highlight the importance of monitoring patients for possible adverse skin reactions when starting new medications.
Corpus linguistics meets historical linguistics and construction grammar: how far have we come, and where do we go from here?
Authors: Hilpert Martin 
Published: March 2024

Abstract
This paper aims to give an overview of corpus-based research that investigates processes of language change from the theoretical perspective of Construction Grammar. Starting in the early 2000s, a dynamic community of researchers has come together in order to contribute to this effort. Among the different lines of work that have characterized this enterprise, this paper discusses the respective roles of qualitative approaches, diachronic collostructional analysis, multivariate techniques, distributional semantic models, and analyses of network structure. The paper tries to contextualize these approaches and to offer pointers for future research.
Effect of cooling rate and Nb synergistic strengthening on microstructure and mechanical properties of high-strength rebar
Authors: Jiang Yafei , Gu Shangjun , Wang Jie , Wei Fulong , Li Zhiying , Li Changrong , Wang Tianyou , Zeng Zeyun 
Published: March 2024

AbstractRebar is an extremely important building material. The cooling rate and the presence of niobium (Nb) element are key factors influencing the overall performance of rebars. In this work, the high-strength rebar’s microstructure, precipitated phase, and mechanical properties were characterized using scanning electron microscopy, transmission electron microscopy, HVS-1000 hardness tester, and MTS810 universal tensile testing machine. The results showed that a shift in cooling rate from 0.3 to 3°C·s−1resulted in noticeable changes in the microstructures of rebars, particularly between Nb-free and Nb-containing variants. In the case of Nb-containing rebars, there was an increase of 8.26% in the proportion of pearlite, along with a decrease of 10.63 μm in the average grain size of ferrite. Furthermore, the lamellar spacing of pearlite experienced a decrease of 0.0495 μm, the proportion of low-angle grain boundaries saw an increment of 4.13%, and the size of the precipitated phase (Nb, Ti, V) C reduced by 18.9 nm. These changes collectively led to a significant increase in hardness (98.56 HV), yield strength (179.02 MPa), and ultimate strength (199.43 MPa). The resultant fracture morphology manifested as a dimple pattern.
Commentary: exploring “the pinch” of emotion labor in language teacher research
Authors: Miller Elizabeth R. 
Published: March 2024

Abstract
This brief Commentary examines how the nine articles in the Special Issue on Second Language Teacher Emotion Labor build on sociologist Arlie Hochschild's conceptualization of emotional labor as well as more recent poststructuralist orientations to language teacher emotion labor by scholars in applied linguistics. To that end, it focuses on two common themes that run throughout these articles: the effects of feeling rules on language teachers and the role of power and its beneficiaries. It discusses how the articles in this Special Issue expand and develop our understanding of language teacher emotion labor through applying it to new contexts by drawing on additional, complementary theoretical perspectives, and through using a broader range of research methods. Recommendations for future research are offered in the conclusion.
Dietary supplementation with seeds of Sesamum indicum L. (pedaliaceae) mitigates the 7,12-dimethylbenz(a)anthracene-induced breast tumor burden in rats
Authors: Beyegue Eric , Afna Florence , Walantini Jonas , Tata Charlotte Mungho , Abdoulaye Malla Gambo , Njamen Dieudonné , Zingue Stéphane , Ndinteh Derek Tantoh 
Published: March 2024

Abstract

Objectives

Sesamum indicum L. seeds; rich in zinc and lignans are endowed with antioxidant and immunomodulatory properties which attract research on their anticancer potential. Although many studies have reported the in vitro antitumor potential of S. indicum and its phytoconstituents, much is yet to be known about its in vivo effects. To fill this gap, the effects of dietary supplementation with seeds of S. indicum in 7,12-dimethylbenz(a)anthracene-exposed rats was assessed.

Methods
42 rats aged 30–35 days were randomized into six groups (n=6) as follows: the normal (NOR) and negative (DMBA) control groups were fed with standard diet; the positive control group (DMBA + Zinc) was fed with standard diet supplemented with commercial zinc (0.01 %); the test groups were fed with standard diet supplemented with S. indicum seeds in different proportions (6.25 , 12.5 and 25 %). Breast cancer was induced by a single administration of DMBA (50 mg/kg BW, s.c.) diluted in corn oil. The experiment lasted 20 weeks and afterward, tumor incidence; tumor burden, tumor volume, tumor micro-architecture and some biochemical parameters were evaluated.

Results
As salient result, 100 % of rats in the DMBA group developed tumors, while rats feed with rat chow supplemented with S. indicum seeds (25 %) had a reduced incidence of tumors (33.3 %) and tumor volume (2.71 cm3 in sesame 25 % vs. 4.69 cm3 in the DMBA group, p˂0.01). The seeds (25 %) also slowed DMBA-induced neoplasm expansion in mammary ducts as compared to rats of DMBA group.

Conclusions
In summary, supplementation with S. indicum seeds slowed breast tumorigenesis via its antioxidant capacity.
Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis
Authors: Bahiuddin Irfan , Mazlan Saiful Amri , Imaduddin Fitrian , Shapiai Mohd. Ibrahim , Ubaidillah  , Sugeng Dhani Avianto 
Published: March 2024

Abstract
Machine learning’s prowess in extracting insights from data has significantly advanced fluid rheological behavior prediction. This machine-learning-based approach, adaptable and precise, is effective when the strategy is appropriately selected. However, a comprehensive review of machine learning applications for predicting fluid rheology across various fields is rare. This article aims to identify and overview effective machine learning strategies for analyzing and predicting fluid rheology. Covering flow curve identification, yield stress characterization, and viscosity prediction, it compares machine learning techniques in these areas. The study finds common objectives across fluid models: flow curve correlation, rheological behavior dependency on variables, soft sensor applications, and spatial–temporal analysis. It is noted that models for one type can often adapt to similar behaviors in other fluids, especially in the first two categories. Simpler algorithms, such as feedforward neural networks and support vector regression, are usually sufficient for cases with narrow range variability and small datasets. Advanced methods, like hybrid approaches combining metaheuristic optimization with machine learning, are suitable for complex scenarios with multiple variables and large datasets. The article also proposes a reproducibility checklist, ensuring consistent research outcomes. This review serves as a guide for future exploration in machine learning for fluid rheology prediction.