bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mathematical Modeling of Groundwater Flow – Foundation Piles in the Vicinity of Danube River Case Study
Authors: Milić Marijana , Jeftenić Goran , Stipić Danilo , Budinski Ljubomir 
Published: April 2023

Abstract

The study investigates an interaction of groundwater flow and foundation piles located in the vicinity of the Danube River. The piles represent an obstacle to the groundwater flow, causing the backwater effect upstream, whilst increasing the local flow velocity. On the other hand, high flow velocity around the piles can cause the suffusion of the surrounding soil in the long term, thus significantly reducing the shaft resistance of the piles. A 3D model of groundwater flow and its impact on the piles was developed in the software package GMS.9.2 based on MODFLOW 2005. It was calibrated by comparing the water level values obtained as a result of the simulations with varying filtration coefficient inputs with the observed values in the monitoring well. After the calibration process, piles were implemented into the model and the underground flow was simulated at the study area location for the calibrated year 2006. The impact analysis was carried out by comparing the groundwater level change over time in the pile zone in three control points, in cases with and without the piles, along with the flow net analysis at the location of the piles themselves. The obtained results indicate an absence of the influence of piles on the groundwater flow at the study area location, both in terms of critical flow velocities and in terms of a possible backwater effect upstream.
CapsANet: Medical image segmentation network based on convolution capsule encoder and attention mechanism
Authors: Qin Chendong , Wang Yongxiong , Zhang Jiapeng 
Published: April 2023

Abstract
Medical images have low contrast and blurred boundaries between different tissues or between tissues and lesions. Because labeling medical images is laborious and requires expert knowledge, the labeled data are expensive or simply unavailable. UNet has achieved great success in the field of medical image segmentation. However, the pooling layer in downsampling tends to discard important information such as location information. It is difficult to learn global and long-range semantic interactive information well due to the locality of convolution operation. The usual solution is increasing the number of datasets or enhancing the training data though augmentation methods. However, to obtain a large number of medical datasets is tough, and the augmentation methods may increase the training burden. In this work, we propose a 2D medical image segmentation network with a convolutional capsule encoder and an attention mechanism block. To extract more local detail and contextual information, the capsule encoder is introduced to learn the information about the target location and the relationship between the part and the whole. Multi-scale features can be fused by a new attention mechanism, which can then selectively emphasize salient features useful for a specific task by capturing global information and suppress background noise. The proposed attention mechanism is used to preserve the information that is discarded by pooling layers of the network. To solve the problem of vanishing/exploding gradients, a novel skip connection structure is added to the last layer of the encoder. Experimental results on the dataset of Liver and ISIC show that our network is superior to the UNet and other previous medical image segmentation networks under the same experimental conditions.
Anaplasma phagocytophilum strain analysis in cattle from Great Britain
Authors: Apaa Ternenge , McFadzean Harriet , Gandy Sara , Hansford Kayleigh , Medlock Jolyon M. , Johnson Nicholas 
Published: April 2023

Abstract
Anaplasma phagocytophilum (A. phagocytophilum) is the aetiological organism of tick-borne fever in cattle and sheep, and granulocytic anaplasmosis in dogs and human. Reports of livestock and wildlife infection and associated zoonotic risk of human infection with A. phagocytophilum have been reported across the world. A previous study has reported two strains, also called ecotypes, in ticks collected from across England and Wales. This study examined A. phagocytophilum strains present in livestock and wildlife in Great Britain (GBR), with a particular focus on cattle. Clinical submissions (EDTA blood) from livestock were received by APHA for tick-borne disease testing. Of these 24 (cattle: n = 21; sheep: n = 3) were confirmed to be infected with A. phagocytophilum using a PCR targeting the msp2 gene. Further submissions from roe deer (n = 2), red deer (n = 2) and Ixodes ricinus ticks (n = 22) were also shown to be infected with A. phagocytophilum. Subsequent analysis using a nested PCR targeting the groEL gene and sequencing, confirmed the presence of ecotype I in cattle, sheep, red deer and Ixodes ricinus, and ecotype II in roe deer and I. ricinus ticks removed from the deer carcass. Despite the presence of two ecotypes, widely distributed in ticks from England and Wales, only ecotype I was detected in cattle in this study.
FT-MIR combined with 3DCOS-ResNet model for rapid identification of wild and cultivated Gastrodia elata
Authors: Shuai Liu , Liu Honggao , Li Jieqing , Wang Yuanzhong 
Published: April 2023

Abstract
Wild Gastrodia elata resources are in short supply and the market is dominated by cultivated Gastrodia elata resources and the medicinal value of both is very different. It is significant to find a highly accurate and stable technique to identify wild and cultivated Gastrodia elata. It could prevent market fraud and protect the rights of consumers. In this context, this study is the first to combine three-dimensional correlated spectral (3DCOS) images with deep learning to identify wild and cultivated Gastrodia elata. Also, partial least squares discrimination analysis (PLS-DA) and support vector machine (SVM) models are compared with this model. The PLS-DA and SVM models are built based on Fourier transform mid-infrared (FT-MIR) spectral data after nine different preprocessing. The PLS-DA model with second-order derivatives (2D) gives the best results when comparing the effects of the models with different preprocessing. the SVM model with parameters c, g in a reasonable range also gives satisfactory model results. The advantage of the deep learning model over them is that no processing of the original spectral data is required. With only 46 iterations, the accuracy of the model is stable at 100% for the training set, test set and external validation set. The excellent performance of the model allows it to be used as a technical reference to solve studies on the qualitative aspects of Gastrodia elata.
Identification of a novel Human Milk Oligosaccharides utilization cluster in the infant gut commensal Bacteroides dorei
Authors: Yassour Moran , Kijner Sivan , Ennis Dena , Shmorak Shimrit , Florentin Anat 
Published: April 2023

Abstract
The infant gut microbiome is impacted by early-life feeding, as human milk oligosaccharides (HMOs) found in breastmilk, cannot be digested by infants, and serve as nutrients for their gut bacteria. While the vast majority of HMO-utilization research has focused on Bifidobacterium species, recent studies have suggested additional HMO-utilizers, mostly Bacteroides, yet their utilization mechanism is completely unknown.
Here, we investigated Bacteroides dorei isolates from breastfed-infants and identified that polysaccharide utilization locus (PUL) 32 enables B. dorei to utilize sialylated HMOs. We performed transcriptional profiling and identified upregulated genes when growing on sialylated HMOs. Using CRISPR-Cas12 to knock-out four PUL32 genes, with combined complementation assays, we identified GH33 as the critical gene in PUL32 for sialylated HMO-utilization.
This is the first demonstration of an HMO-utilization system by a Bacteroides species isolated from infants, opening the way to further characterization of additional such systems, to better understand HMO-utilization in the infant gut.
Analysis of Hemodynamics During Blood Transfusion Utilizing High-Fidelity Realtime Telemetry After the Arterial Switch Operation
Authors: Mikulski Matthew F. , Linero Antonio , Stromberg Daniel , Affolter Jeremy T. , Fraser Charles D. , Mery Carlos M. , Lion Richard P. 
Published: April 2023

Abstract
Background Red blood cell transfusions (pRBCTx) are given to many patients after congenital heart surgery to augment hemodynamics, but our current understanding is limited by hourly vital sign recordings. The goal of this study was to analyze hemodynamic parameters surrounding pRBCTx with high-fidelity, real-time telemetry monitoring.Methods This is a retrospective review of patients after the arterial switch operation receiving post-operative pRBCTx from 07/15/2020-07/15/2021. Continuous, 1-Hz vital sign data were analyzed in five-minute intervals up to six hours before, four hours during, and six hours after pRBCTx—up to 57,600 data points per pRBCTx. Oxygen delivery was assessed using pre- and post-pRBCTx laboratory data, hourly renal and cerebral near-infrared spectroscopy, sedation medication doses, and vasoactive-inotropic scores.Results Six patients, median age 8.5[IQR:5–22] days and weight 3.1[IQR:2.8–3.2]kg, underwent the arterial switch operation. There were 10 pRBCTx administered with a median dose of 10[IQR:10–15]mL/kg over 169[IQR:110–190]min; at median post-operative hour 36[IQR:10–40]. There was an increase in systolic and mean arterial blood pressures by 5-12.5% at three hours after pRBCTx, but returned to baseline at six hours. There were no changes in heart rate or oxygen saturations. Renal spectroscopy increased by 6.2% after pRBCTx. There were no changes in ventilation, sedation, vasoactive support, or laboratory variables related to oxygen delivery.Conclusions pRBCTx given to arterial switch operation patients increases arterial blood pressures three hours post-pRBCTx without evidence of sustained effects at six hours. High-fidelity real-time data can be used to better assess hemodynamic parameters after congenital heart surgery and provide nuanced, patient-specific care.
Design Devanagari script online digital Handwritten Dataset of 44,000 images with its Recognition using Deep Learning
Authors: Pate Sanjay E.Pate S.E. , Ramteke Prof .Rakesh J. Ramteke Dr. Rakesh 
Published: April 2023

Abstract
The offline technique was used to build the datasets of Devanagari alphabets and numerals. In the offline character dataset, the writer writes on the pages, which are then optically or digitally scanned, requiring an image processing technique to remove noise. In this paper, we designed, I. an online digital handwritten Devanagari dataset, II. To recognize the dataset by using the deep learning technique CNN. Dataset design-i. Devanagari alphabets, 34 (4 vowels and 30 consonants), and 10 numerals total of 44 characters are selected. ii. Designed a Canvas using Python Jupiter to draw an image, the writer uses an input tool, like a mouse, and the data is recorded in digital form.iii. One hundred individuals of varied ages are chosen, and the total number of characters produced by each person is 440. A dataset of 44,000-character images is produced.Each image has a grayscale data type and is in the (dot).JPEG format. The image requires 2 kb of storage and had a resolution of 65 by 65 pixels. Converted images dataset into Comma Separated Values (CSV) files/ dataset, the dataset is divided into 70 % for training and 30 % for testing. Published the same on the IEEE data port and Mendeley repository Now the objective is to recognize our dataset using the deep learning concept Convolution Neural Network is applied to extract features and classify the input images, recognizing digital Devanagari characters. There are 44 classes, image size is (65,65,1). In this model, two Conv2D, a MaxPooling2D, a Flatten, a Dropout, and a Dense (fully connected) layers are used. The CNN-trained model achieved recognition accuracy of 95.00 %,95.45 %, and 96.00 % using 10,20, and 30 epochs. The model with 30 epochs has higher recognition accuracy, tested 100 images on trained 1000 images, of each class and designed a confusion matrix resulting in the accuracy rating ranging from 0.994 to 1.0. However, a multi-class accuracy score of above 0.99 is regarded as outstanding, and a high accuracy score is generally desired in any classification task.
Energy and Security Aware Clustering in Internet of Things with Improved SandPiper Optimization Model
Authors: Gorikapudi Sateesh , Kondaveeti Hari Kishan 
Published: April 2023

Abstract
Internet of Things (IoT) is a networking paradigm shift that aims to link almost everything on the world. Due to the limited form of smart devices, energy-efficient routing would be critical to their effective implementation. Clustering techniques divide a network's nodes into groups or clusters, each of which is led by a specified node known as the cluster head. Clustering methods have been presented in the area of Wireless Sensor Networks (WSN), while their application in IoT might solve comparable problems. By transferring a major portion of communication overhead to the cluster head, clustering would promote topology maintenance and energy efficient routing. Therefore, this article aims to establish a novel clustering model in IoT, in which the cluster head selection is performed via a proposed Sandpiper Optimization with Cycle Crossover Process (SOCCP) model. Moreover, this cluster head selection includes certain constraints including (i) Distance (ii) Energy (iii) security (based on the risk levels), and (iv) Cluster Radius, respectively. At first, the clustering is done by the Optimized Fuzzy C-Means (FCM) algorithm. Here, the determination of the cluster head and cluster radius is tuned optimally via an adopted SOCCP model. At the end, the results of the adopted technique are computed to extant based on various metrics including alive node analysis, risk analysis, distance analysis, cluster radius analysis, as well as normalized energy analysis, respectively.
Chronic viral infection compromises the quality of circulating mucosal-invariant T cells and follicular T helper cells via expression of both activating and inhibitory receptors
Authors: Vimali Jaisheela , Yong Yean Kong , Murugesan Amudhan , Tan Hong Yien , Zhang Ying , Ashwin Rajeev , Raju Sivadoss , Balakrishnan Pachamuthu , Larsson Marie , Velu Vijayakumar , Shankar Esaki M 
Published: April 2023

Abstract

Chronic viral infection results in impaired immune responses rendering viral persistence. Here, we investigated the role of immune activation and compared the quality of T-cell responses in chronic HBV, HCV, and HIV infections. Cytokines were measured using a commercial Bio-plex Pro Human Cytokine Grp I Panel 17-plex kit (BioRad, Hercules, CA, USA). Inflammation was assessed by measuring an array of plasma cytokines, and peripheral CD4+ T cells including circulating Tfh cells, CD8+ T cells, and TCR iVα7.2+ MAIT cells in chronic HBV, HCV, and HIV-infected patients and healthy controls. The cells were characterized based markers pertaining to immune activation (CD69, ICOS, and CD27) proliferation (Ki67), cytokine production (TNF-α, IFN-γ) and exhaustion (PD-1). The cytokine levels and T cell phenotypes together with cell markers were correlated with surrogate markers of disease progression. The activation marker CD69 was significantly increased in CD4+ hi T cells, while CD8+ MAIT cells expressing IFN-γ were significantly increased in chronic HBV, HCV and HIV infections. Six cell phenotypes, viz., TNF-α+CD4+ lo T cells, CD69+CD8+ T cells, CD69+CD4+ MAIT cells, PD-1+CD4+ hi T cells, PD-1+CD8+ T cells, Ki67+CD4+ MAIT cells were independently associated with decelerating the plasma viral load (PVL). TNF-α levels showed a positive correlation with increase in cytokine levels and decrease in PVL. Chronic viral infection negatively impacts the quality of peripheral MAIT cells and TFH cells via expression of both activating and inhibitory receptors.
Prevalence, effectiveness, and usefulness of using YouTube among medical students in clinical years /physical examination
Authors: Sharayah Mohammad , Hadidi Fadi Al , AlSamhori Jehad Feras , Ebdah Abdulsalam , Diwan Ola Abbas , Alna'san Ali Wa'el , Sharayah Abdallah , Quba Qutaiba Abu , alsabi’ Abdel rahman ahmad 
Published: April 2023

Abstract
Introduction:
A recent study aimed to investigate the usefulness of YouTube as an educational resource for medical students studying physical examination in Jordanian universities. The questionnaire aimed to determine the prevalence, effectiveness, and usefulness of YouTube in learning physical examination and to identify which medical topics were studied more through YouTube.
Methods
The study used a Google Forms questionnaire to survey 413 medical students in their 4th, 5th, and 6th clinical years.
Results
The study found that the majority (91%) of the students used YouTube as a learning tool, and most (96.4%) found it useful in their studies. Nearly all students (93.7%) reported learning additional information they did not previously have access to. Most students (88.4%) used YouTube to learn physical examination, and a vast majority (90.1%) found it useful for practicing before OSCE exams. Almost 80% of the students who used YouTube received higher marks in OSCE exams. Furthermore, the study also found that YouTube usage varied among students, with the majority using it in their 4th year. The two medical topics that students found most useful in YouTube videos were internal medicine and general surgery, with 82.8% and 69.5% of students finding them useful, respectively.
Conclusion
the study suggests that YouTube is a valuable educational tool for medical students studying physical examination and medical topics in Jordanian universities. The study recommends that educators encourage students to use YouTube and create updated physical examination videos and general clinical themes to supplement medical education.
Methods of improving rural water ecological environment and promoting the development of ecological economy from the perspective of the digital economy
Authors: Wang Jingyi , Yin Hong , Jin Jiaming , Cui Luoyuan 
Published: April 2023

Abstract

With the liberation of productive forces and the rapid development of cities, the development of rural areas is very fast, and the pace of rural industrialization and urbanization is also accelerating. However, with the rapid development of the economy, the rural ecological environment has also been greatly affected. Based on the literature and field investigation, the paper made a thorough analysis of the water environment and pollution in rural areas and used the water environment-bearing capacity pressure model to analyze the current problems in rural areas. Economic development is at the cost of the ecological environment. The higher the level of economic development, the greater its impact on environmental carrying capacity. Based on the above analysis, this paper proposed rural water resources carrying capacity system based on ecological carrying capacity and organically combined it with the theory of system and ecological carrying capacity to evaluate it. According to the current situation of water pollution in rural areas, this article preliminarily discussed and put forward corresponding countermeasures.
Optimal operating method of gate and pump switching in water transfer project
Authors: Meng Yujie , Wang Weiping , Xu Donglai , Qu Shisong , Zhao Weidong 
Published: April 2023

Abstract

The Yellow River diversion project consists of three gates, two pumping stations, and one plain reservoir, with a total river length of 17.46 km in the Panzhuang Yellow River irrigation area, which provides the primary water source for the Dezhou central city, Shandong Province, China. The project is subject to the constraints of the Yellow River diversion sluice diverting water time, reservoir diverting water time, and combinations of pumping stations with self-flowing gates. The opening and closing forms of the gates and pumps are complicated and irregular, and the switching of the gates and pumps mainly depends on the experience of the staff, which makes the water transfer cost high and the water transfer time long. Based on engineering compositions in the study area, an optimal mathematical model is constructed by the dynamic programming method. By combining the flow and water level, the objectives of the minimum operation cost, the shortest water transfer time and the combination of the two are established. A new optimization scheme of the switch between gates and pumps combined with water level and flow is proposed, which reduces the operation cost by 20% and the water transfer time by 8%, providing an efficient operation mode.
Study on the Composition and Mechanism of Santiao Decoction in Treating Insomnia Based on UPLC and Network Pharmacology and Molecular Docking Technology
Authors: Wang Lixiang , Liu Feixiang , Li Weixia , Zhang Hui , Wang Weitao , Liu Menglin , Zhang Daopei , Zhang Huailiang 
Published: April 2023

Objective:
To identify the chemical components of Santiao Decoction (STD) using Ultra
Performance Liquid Chromatography (UPLC) and to conduct a network pharmacological study of
STD for the treatment of insomnia based on this technique.

Methods:
An ACQUITY UPLC BEH C18 column (2.1 mm×100 mm, 1.7 µm) was used to identify
the chemical components of STD by relative molecular weight, mass spectrometry information,
and comparison with the control. The active ingredients of the formula and their corresponding
gene targets and targets for insomnia were retrieved from several databases, and a visual network
diagram of "drug-active ingredient-target-disease" was constructed using Cytoscape 3.8.2 software,
and GO functional annotation and KEGG pathway enrichment analysis were performed using various databases such as DAVID.

Result:
Five active ingredients were identified from STD by UPLC technique, 268 active ingredients of STD were screened from the TCMSP database, and 109 genes related to STD for insomnia
were screened by network pharmacology, among which IL6, MMP9, VEGFA, IL10, CCL2 may be
the key targets of STD for insomnia. KEGG pathway analysis showed that STD acts on membrane
rafts, plasma membrane micro-regions, and other related pathways, such as Toll-like receptor signaling pathway, prolactin signaling pathway, dopaminergic synapse, relaxin signaling pathway,
ErbB signaling pathway, steroid hormone biosynthesis and NF-kappa B signaling pathway for regulation.

Conclusion:
The active ingredients in STD, such as (+)-catechin, Swertisin, quercetin, baicalein,
and wogonin, may act on IL6, CCL2, VEGFA, MMP9, and other targets to regulate Toll-like receptor signaling pathway, ErbB signaling pathway, NF-kappa B and other signaling pathways, and
exert certain therapeutic effects on insomnia, which provide a reference and basis for further research on the mechanism of action of STD.
Application of Biomimetic Nanoparticles based on the Cell Membrane in
Tumor Therapy
Authors: Gong Ya , Huang Jingbin 
Published: April 2023

Abstract:
Due to their unique biological functionality, nanocarriers can be designed to deliver va-rious anti-tumor drugs in vivo, which has a wide and important application prospect in the field of tumor therapy. However, poor biosafety, short blood circulation time, and weak targeting ability still limit the application of nanoparticles in tumor therapy. In recent years, with the development of biomedicine, the biomimetic technology-based biomembrane-mediated drug delivery system is ex-pected to achieve a breakthrough in tumor-targeted therapy due to low immunogenicity, tumor tar-geting, the adjustability and versatility of intelligent nanocarrier design. This paper mainly reviews the research process of different types of the cell membrane (erythrocyte membrane, cancer cell membrane, bacterial membrane, stem cell membrane, and hybrid membrane)-camouflaged nanopar-ticles in tumor therapy, as well as the challenges and development prospects in clinical application.