bluuebunny/crossref_metadata_2025_split huggingface dataset:
U-NeTrans at the Edge: Precision and Adaptability in Medical Image Analysis through Segment-based U-Net and Transformer Integration
Authors: Iqbal Saeed , Qureshi Adnan N. , Alhussein Musaed , Aurangzeb Khursheed , Wang Huihui 
Published: February 2024

Abstract
In the domain of edge computing for medical image analyses, utilizing advanced deep learning algorithms has shown promise in boosting precision and adaptability. Medical image analysis frequently requires striking a compromise between a more comprehensive contextual understanding and specific local information. Even though U-Net with Transformers integration is becoming more and more common, issues like patch flattening and magnification sensitivity still exist. By using a unique approach, U-NeTrans directly addresses these problems by segmenting patches instead of flattening them into tokens. This novel method avoids patch flattening issues while maintaining fine-grained local details. Additionally, U-NeTrans has the benefit of allowing for variable patch sizes in a single architecture, which reduces susceptibility to magnification and supports a range of image resolutions. U-NeTrans demonstrates its effectiveness in improving accuracy in a range of medical image analysis tasks by consistently outperforming its competitors in the thorough examination conducted against publicly available datasets. Notably, using the linear correlation between segmentation accuracy and the divergence in U-NeTrans outputs at various scales, it provides a useful confidence metric for evaluating test image complexity, with values of 98.97\% for Accuracy, 98.81\% for Precision, 99.68\% for Sensitivity, 98.73\% for Specificity, and an AUROC of 99.19\%. In circumstances involving peripheral medical image analysis, when computational performance is crucial, this characteristic is very important.
The K9 lymphoma assay allows a genetic subgrouping of canine lymphomas with improved risk classification
Authors: Aresu Luca , Fanelli Antonella , Licenziato Luca , Mazzone Eugenio , Divari Sara , Rinaldi Andrea , Marino Michele , Maga Ilaria , Bertoni Francesco , Marconato Laura 
Published: February 2024

Abstract
We developed a targeted next-generation sequencing approach designed for the genomic classification of canine lymphoma. Positioned within the evolving landscape of comparative and veterinary oncology, this study underscores the clinical significance of tailored genetic profiling for this tumor in dogs, marking a crucial step in diagnostic high-throughput genetic analysis.
Leveraging a previous dataset, the “K9 lymphoma assay” demonstrated robust comparability and reproducibility in canine diffuse large B-cell lymphoma (DLBCL), when matched with previously tumors examined through whole-exome sequencing.
Expanding our investigation to encompass several B- and T-cell lymphoma histotypes, we unveiled distinct mutational landscapes distinguishing nodal lymphomas derived from immature lymphocytes and nodal lymphomas originating from mature lymphocytes. TRAF3 was the most frequently altered gene in B-cell lymphomas, exhibiting unique mutational patterns.
Key genetic events, such as TP53 and SETD2 mutations, clinically demonstrated universal implications across B-cell lymphomas, positioning them as potential prognostic and therapeutic indicators.
These findings advocate for a tailored therapeutic approach directly impacting treatment decisions and outcomes in managing canine lymphomas. Our study provides the largest and most comprehensive genomic characterization of clinically staged canine lymphomas to date, and it provides pivotal insights for future research and therapeutic strategies in comparative oncology.
Epidemiological and genomic characteristics of global blaNDM-carrying Escherichia coli
Authors: Xia Changyu , Yan Ruyu , Liu Chang , Zhai Junbin , Zeng Jie , Chen Wei , Cao Xiaoli 
Published: February 2024

Abstract

Background
Escherichia. coli is the most frequent host for New Delhi metallo-β-lactamase (NDM) which hydrolyzes almost all β-lactams except aztreonam. The worldwide spread of blaNDM-carrying E. coli heavily threatens public health.
Objective
This study aimed to explore the global genomic epidemiology of blaNDM- carrying E. coli isolates, providing information for preventing the dissemination of such strains.
Methods
Global E. coli genomes were downloaded from NCBI database and blaNDM was detected using BLASTP. Per software was used to extract meta information on hosts, resources, collection data, and countries of origin from GenBank. The sequence types (STs) and distribution of antimicrobial resistance gene (ARG) were analyzed by CLC Workbench; Plasmid replicons, serotypes and virulence genes (VFs) were analyzed by submitting the genomes to the websites. Statistical analyses were performed to access the relationships among ARGs and plasmid replicons.
Results
Until March 2023, 1,774 out of 33,055 isolates collected during 2003–2022 were found to contain blaNDM in total. Among them, 15 blaNDM variants were found with blaNDM-5 (74.1%) being most frequent, followed by blaNDM-1 (16.6%) and blaNDM-9 (4.6%). Among the 213 ARGs identified, 27 blaCTX-M and 39 blaTEM variants were found with blaCTX-M-15 (n = 438, 24.7%) and blaTEM-1B (n = 1092, 61.6%) being the most frequent ones, respectively. In addition, 546 (30.8%) plasmids mediated ampC genes, 508 (28.6%) exogenously acquired 16S rRNA methyltransferase encoding genes and 262 (14.8%) mcr were also detected. Among the 232 distinct STs, ST167 (17.2%) were the most prevalent. As for plasmids, more than half of isolates contained IncFII, IncFIB and IncX3. The VF terC, gad, traT and iss as well as the serotypes O101:H9 (n = 231, 13.0%), O8:H9 (n = 115, 6.5%) and O9:H30 (n = 99, 5.6%) were frequently observed.
Conclusions
The study delves into the intricate relationship between plasmid types, virulence factors, and ARGs, which provides valuable insights for clinical treatment and public health interventions, and serves as a critical resource for guiding future research, surveillance, and implementation of effective strategies to address the challenges posed by blaNDM-carrying E. coli. The findings underscore the urgent need for sustained global collaboration, surveillance efforts, and antimicrobial stewardship to mitigate the impact of these highly resistant strains on public health.
The effect of inter-connection properties on the lateral behaviour of moment-frame modular steel structures
Authors: Farajian Mostafa , Khodakarami Mohammad Iman , Kontoni Denise-Penelope N. , Pishgahi Farnoush 
Published: February 2024

Abstract

Recently, there has been a push for the adoption of pre-fabricated modular units in constructing low to high-rise buildings mainly due to the reduction of material waste and construction time. These units are manufactured in a factory and then transported to the construction site for assembly through inter-connections to create a building block. The unique nature of these inter-connections is crucial for the structural performance of modular buildings as they provide modular structure integrity. Despite the growing popularity of modular construction, there is a limited number of published studies examining how inter-connection mechanical characteristics influence the lateral performance of moment-frame modular structures. The stiffness of inter-connections plays a crucial role in determining the overall rigidity of these structures, indicating the importance of studying their impact on the seismic performance of moment-frame modular buildings. The objective of this study is to investigate the impact of inter-connection mechanical characteristics on the lateral performance of moment-frame modular buildings using nonlinear static analysis. For this purpose, three frames with four, eight, and twelve storeys are assumed for analysis. Various inter-connections with different stiffness properties are considered in the nonlinear static analysis. Three performance levels are taken into account, and the responses of the structures at these levels are assessed and compared based on different inter-connection mechanical properties. The findings suggest that a decrease in inter-connections' stiffness results in a reduction of lateral capacity up to around 32.98% for the structural base shear.
Investigating the Mechanical and Physical Enhancements of Silk-sisal Hybrid Composites Through Granite-nano-filler Integration: Synergizing Strength, Damping, and Form
Authors: K Sripriyan 
Published: February 2024

Abstract
This paper focuses on the effect of granite-nano-filler on static mechanical properties, damping behavior, and physical characteristics of silk-sisal hybrid composite. The composites structures are fabricated by using three layers of plain weaves mat with varying weight percentages (2, 4, and 6 wt. %) of granite-nano-filler by hand layer process. The effect of granite-nano-filler on silk-sisal is analyzed and results are compared with pure silk-sisal (without granite-nano-filler) hybrid composite. The results infer that; the tensile strength and flexural strength value of the developed silk-sisal hybrid composites are increased with the increase in wt.% of granite-nano-filler content than pure silk-sisal, approximately 5% and 9% improved value in order by adding 6 wt. % granite-nano-filler on silk-sisal. The tensile and flexural fracture morphology analysis indicated that composed plain weave of silk-sisal fiber mat with two (longitudinal and transverse) directions possesses higher mechanical properties and also observed that, granite-nano-filler dispersed on silk-sisal fiber mat and resin with closely packed. The improved damping factor is obtained by adding 6 wt. % of granite-nano-filler content, which is approximately 75.8% higher than pure silk-sisal. The physical properties of silk-sisal hybrid composite have also been analyzed and well discussed in this paper.
The Impact of Artificial Intelligence (A.I) on Recruitment and Selection of Human Resources Management (HRM)
Authors: Abdelraouf Mohamed , Emad Aya , Kadry Mona 
Published: February 2024

Abstract
The aim of this research is to examine the potential effects of incorporating artificial intelligence (AI) on the processes of recruitment and selection within the human resources management (HRM) industry in Egypt. By employing a quantitative research methodology, this study employs an empirical and descriptive focus. The data was collected from 109 participants via an internet-based survey utilising cluster random sampling. The collected data underwent analysis utilising the Structural Equation Model (SEM) of SmartPLS. The study identifies the significant impact of the attribute on human resource management for selection and recruitment. The results of this research emphasise the effects of artificial intelligence (AI) on selection and recruitment processes. These findings provide practical and implementable suggestions for human resources professionals in Egyptian organisation. Nevertheless, the research also emphasises the significance of conscientiously implementing AI, while remaining cognizant of possible biases. Practical suggestions comprise the implementation of specialised training programmes for human resources professionals, the development of strategies to mitigate biases, and a thorough assessment of AI tools.
Challenges in assessing the immunization status of adults in Germany – Lessons from a population-based VACCELERATE survey on polio vaccination
Authors: Nacov Julia , Stemler Jannik , Salmanton-García Jon , Cremer Louise M. , Zeitlinger Markus , Mallon Patrick , Pana Zoi Dorothea , Schmitt Heinz-Josef , Cornely Oliver A. 
Published: February 2024

Abstract

Purpose
Considering the re-emergence of poliomyelitis (PM) in non-endemic regions, it becomes apparent that vaccine preventable diseases can rapidly develop epi- or even pandemic potential. Evaluation of the current vaccination status is required to inform patients, health care providers and policy makers about vaccination gaps.
Methods
Between October 28 2022 and November 23 2022, 5,989 adults from the VACCELEREATE Volunteer Registry completed an electronic case report form on their previous PM vaccine doses including number, types/-valencies and the time of administration based on their vaccination records. A uni-/multivariable regression analysis was performed to assess associations in participant characteristics and immunization status.
Results
Among German volunteers (n = 5,449), complete PM immunization schedule was found in 1,981 (36%) participants. Uncertain immunization, due to unknown previous PM vaccination (n = 313, 6%), number of doses (n = 497, 9%), types/-valencies (n = 1,233, 23%) or incoherent immunization schedule (n = 149, 3%) was found in 40% (n = 2,192). Out of 1,276 (23%) participants who reported an incomplete immunization schedule, 62 (1%) never received any PM vaccine. A total of 5,074 (93%) volunteers reported having been vaccinated at least once and 2,087 (38%) indicated that they received vaccination within the last ten years. Female sex, younger age, as well as availability of first vaccination record were characteristics significantly associated with complete immunization (p < 0.001).
Conclusion
Full PM immunization schedule was low and status frequently classified as uncertain due to lack of details on administered doses. There is an obviousneed for improved recording to enable long-term access to detailed vaccination history in the absence of a centralized immunization register.
Synthesis of Porous Molecularly Imprinted Polymerized High Internal Phase Emulsion (MIP-PolyHIPE) Adsorbents for removal of water pollutants: Experimental and Molecular Dynamics Studies
Authors: Seyedkhademi Mehrnaz Sadat , Karimian Hossein , Bahlakeh Ghasem , Mokhtari Ali 
Published: February 2024

Abstract
Highly porous molecularly imprinted polymers (MIPs) with molecular memory have the potential to be used for identifying various compounds. This study investigated the removal of widely used fungicides in agriculture, namely penconazole and carboxin, from aqueous solutions using molecularly imprinted polymerized high internal phase emulsions (MIP-polyHIPEs). The synthesized MIPs were characterized by SEM, TEM, FTIR, BET, and contact angle analyses. The adsorption capacity of MIPs was determined by the UV-Vis analysis. Preliminary studies showed that the MIP-polyHIPEs respectively adsorbed 85% and 57% of penconazole and carboxin. The successful desorption of fungicides from the MIPs indicated its reusability in the adsorption process. The surface hydrophilicity increased by functionalizing the MIP surface with sulfuric acid, leading to a higher adsorption percentage for penconazole. Molecular dynamics (MD) calculations also approved the capability of MIP-PolyHIPEs in adsorbing penconazole and carboxin.
Effectiveness of Neurofeedback in Reducing Rumination and Cognitive Avoidance in Patients with Obsessive Compulsive Disorder: A Clinical Trial
Authors: Azizi Elahe , Kalan Mehdi Emami , Dolatabad Mohanna Mohammadi , Kalhor Masoomeh , Alavikolaei Sedigheh Ebrahimi , Jahromizadeh Amir Mohammad 
Published: February 2024

Abstract
Introduction: Obsessive-compulsive disorder (OCD) is a psychiatric condition characterised by persistent, intrusive thoughts and ritualistic behaviours. This study assesses the impact of qEEG-assisted neurofeedback on two critical components of OCD: rumination, a maladaptive focus on problem causes and consequences, and cognitive avoidance (CA), the tendency to evade distressing thoughts aiming to evaluate neurofeedback’s effectiveness in reducing rumination and CA severity in patients with OCD. Methods: This controlled prospective clinical trial with parallel design included patients diagnosed with OCD, with Yale-Brown Obsessive Compulsive Scale (YB-OCS) scores ≥ 16. Subjects were alternately assigned to either the neurofeedback or control groups maintaining a 1:1 ratio. The neurofeedback group underwent 25 sessions over six weeks, with outcomes measured through the Rumination Response Scale (RRS) and the Cognitive Avoidance Questionnaire (CAQ) pre- and post-intervention. Results: Of the initial cohort, 30 participants finished the study. Significant reductions in Rumination and CA were observed in the neurofeedback group with multivariate ANCOVA showing a significant impact on CAQ and RRS scores (Lambda Wilks p = 0.001) and univariate ANCOVA indicating marked decreases in CA (p = 0.001, Eta2 = 0.687) and Rumination (p = 0.001, Eta Squared = 0.636) compared to controls. Discussion: The findings substantiate qEEG-assisted neurofeedback’s role in significantly reducing rumination and cognitive avoidance in OCD, indicating neurofeedback’s potential to modulate brain regions implicated in OCD pathology, such as orbitofrontal cortex and anterior cingulate, thus enhancing self-regulation and reducing symptoms. Limitations: Limitations include no long-term follow-up, reliance on self-report measures, a small, single-centred sample, and convenience sampling, all of which affect the generalizability of the results. INTRODUCTION
Liver cancer detection using Artificial Intelligence
Authors: Badrawy Noha , .T.Khalil Apeer , .M.Amer Hanan 
Published: February 2024

Abstract
A noticeable increase in statistics of liver cancer in Egypt and all over the world. Therefore, using Artificial Intelligence (AI) to increase the detection accuracy and minimize human errors during manual classification of liver images. Where the manual classification of liver Computed Tomography (CT) scan images require a very great effort and time-consuming tasks. This study aims to improve a high-performance computer detection system. The proposed model used to detect liver tumor is based on Convolutional Neural Network (CNN) techniques and the machine learning techniques, which are of the most application of AI that used in biomedical image classification and recognition. The dataset used in this study is composed of 9255 CT scan images. The proposed model consists of three main steps. The first step aims to compare between three deep learning model which that Liver Tuned High-Resolution Network (LTHR-Net), Deep Residual Network (ResNet50) and Visual Geometry Group Network (VGG19-Net) to get the most suitable deep learning model that improve the system detection accuracy. The next step aims to apply three machine learning classifiers and compare their performance to increase the system detection accuracy. These classifiers are Logistic Regression (LR), Random Forest (RF) and Support Vector Machine (SVM). The final step improves the system detection accuracy by applying decision fusion techniques at the classifiers classification result using majority voting algorithm. The accuracy of the proposed model achieved 99.9% by using LTHR-Net as based model and applied majority voting algorithm on the classification output of the three machine learning classifiers.
Geographical, seasonal, and growth-related dynamics of gut microbiota in a grapevine pest, Apolygus spinolae (Heteroptera: Miridae)
Authors: MORIMURA Hiroyuki , ISHIGAMI Kota , SATO Tomoyuki , SONE Teruo , KIKUCHI Yoshitomo 
Published: February 2024

Abstract
A number of insects are associated with gut microorganisms, wherein symbionts play pivotal metabolic roles such as nutrient supplementation, diet degradation, and pesticide detoxification. Despite the ecological and evolutionary importance of gut microbial communities in insects, their diversity and dynamics need to be better investigated in many insect species. The green plant bug Apolygus spinolae, a notorious grapevine pest in Japan, damages grape shoots and severely reduces grape berry yield and quality. The plant bug possesses a simple tubular gut housing ~ 104 bacteria. Here we investigated geographic, seasonal, and growth-related dynamics of gut microbiota by high-throughput sequencing in the grapevine pest. In Japanese populations of the plant bug, gut bacterial community was dynamically altered depending on geographic region, season, and developmental stage, in which particularly Serratia, Lactococcus and Caballeronia were abundantly detected. In one vineyard population, Spiroplasma was prevalent. Among the gut bacterial microorganisms, Serratia was consistently and abundantly detected, but significantly affected by seasonal change. In addition, Caballeronia, known as a specific symbiont of stinkbugs, was abundantly detected, especially in insects collected in late summer. The increase of Caballeronia was negatively correlated with Serratia, strongly suggesting an antagonistic interaction between gut bacterial members in A. spinolae.
Increased Global Tensions Jeopardize Climate Targets
Authors: Dong Wenjie , Yang Jie , Ran Qi , Liu Fei , Deng Rong , Wang Kaixi , Wang Xinyue , Zheng Duofan , Li Chenhao , Liang Wenjun , Chou Jieming , Yuan Wenping 
Published: February 2024

Abstract
The Sixth Assessment Report of the Intergovernmental Panel on Climate Change highlights the reliance on optimistic scenarios such as shared socioeconomic pathways SSP1-1.9 and SSP1-2.6 to achieve the 1.5°C or 2°C climate targets by the end of this century[1]. However, these scenarios have not quantitatively assessed the impact of global tensions (measured as global military expenditure as a percentage of GDP) on CO2 emissions. Our research reveals that events such as the 2001–2011 War on Terror and the 2021 prelude to the Russian–Ukrainian war have led to a 0.041% (95% CI: 0.030–0.052) kg/USD increase in CO2 emission intensity (CO2 emissions per unit of GDP) for each 1% rise in global tensions. This increment accounts for 30% of the total change in CO2 emission intensity from 1995 to 2021. In the case of escalating global tensions, with the global military expenditure ratio exceeding thresholds of 11% (for SSP1-1.9) or 23% (for SSP1-2.6), the global surface temperature increase would fail to return below 1.5°C after the initial overshoot in SSP1-1.9, and the 2°C climate target would become unattainable in SSP1-2.6 by the end of this century. These findings underscore the potential of escalating global tensions to undermine climate targets, emphasizing the critical need for a more peaceful international environment to effectively limit global warming to 1.5°C or 2°C.
A review of hybrid solar desalination systems: structure and performance
Authors: Alghassab Mohammed A. 
Published: February 2024

Abstract
The purpose of this study is to explore the architecture and functioning of hybrid solar desalination systems and investigate their potential as a sustainable solution for water purification. The study reveals that solar-powered desalination systems offer a remarkable alternative to traditional methods, as they rely on clean solar energy and produce no noise or sound pollution. In addition, they have demonstrated cost-effectiveness in generating drinking water, especially in desert regions and inaccessible areas. Furthermore, the research highlights the significance of incorporating waste heat energy into the desalination process. Also shows that utilizing waste heat energy can significantly reduce expenses and enhance the overall effectiveness of water desalination. Through an in-depth analysis of the fundamental principles and real-world applications, this study underscores the importance and rationale for implementing hybrid solar desalination systems. By effectively utilizing solar energy, these systems provide a sustainable approach to address water scarcity and ensure the efficient management of water and energy resources. This study emphasizes the fundamental importance of the structure of hybrid solar desalination systems fueled by solar energy in the efficient management of water resources. By combining technological innovations with renewable energy sources, these systems pave the way for a sustainable future.