bluuebunny/crossref_metadata_2025_split huggingface dataset:
Parental Perspectives Regarding the Return of Genomic Research Results in Neurodevelopmental Disorders in South Africa: Anticipated Impact and Preferences
Authors: Diedericks Angelique , Bruwer Zandré , Laing Nakita , Eastman Emma , De Vries Jantina De Vries , Donald Kirsten A , Robinson Elise B , Newton Charles R , Abubakar Amina 
Published: June 2024

Abstract

Few policies and little research exist regarding the disclosure of genomic results to research participants in Africa. As understanding participant preferences would be pivotal to the success of the feedback process, this study set out to address this issue by engaging with enrolled participants from an ongoing genomics research project on neurodevelopmental disorders with the aim to assess the anticipated impact of receiving pertinent results and explore the preferences for feedback in a South-African context. Twelve semi-structured interviews were conducted with 17 parents of children participating in the research study. Transcribed interview data and observational notes were analysed using thematic analysis and framework matrices. Participants linked their own meaning to the impact of receiving a pertinent result and perceived the information as useful for reasons other than only clinical utility. These included closure, improved management of their child’s condition and information regarding recurrence risks. In terms of preferences for feedback, an in-person result delivery session, conducted by a member of the study team or medical professional familiar with their child was preferred. In addition, participants felt a sense of ownership over their blood or their contribution to the research study, finding meaning even in non-pertinent (secondary findings) or negative results. These findings provide insight into the type of discussions that may be valuable in enabling the development of best practices and guidelines for the return of individual genetic research results, in a culturally appropriate manner, within South-African communities.
The Neural Basis of Self-ambivalence: An ERP Study
Authors: Chen Dian , Liu Ying , Zhang Yulin , Guo Yiting 
Published: June 2024

Abstract

The primary objective of this study was to investigate the neural basis of self-ambivalence, a phenomenon firmly established by behavioral research but whose underlying brain mechanisms have been less explored. Employing EEG methods and a modified self-reference paradigm, we analyzed event-related potentials using a linear mixed model to determine whether self-ambivalence processing exhibits a distinct neural representation. The results indicated that self-ambivalence processing primarily affected the late components (N2, N450, and P3), with N450 activation in the midline brain regions showing a significant positive correlation with scores on the Dialectical Self Scale. This finding suggests that individuals with higher levels of dialectical self-tendencies may engage in more extensive processing of self-ambivalent information. The current study confirms the importance of the cortical midline in self-ambivalence and provides the first evidence of a distinct EEG representation of self-ambivalence processing. These findings contribute to our understanding of the neural mechanisms underlying self-ambivalence and highlight the potential role of individual differences in shaping the neural processing of self-ambivalent information.
Design, synthesis, and mechanism study of novel 1-arylisoquinoline derivatives as antifungal agents
Authors: Chen Yang , Jin Yanxi , Wang Luyao , Wang Wanxiang , Zhou Haiping , Chen Wei 
Published: June 2024

Abstract

In screening for natural-based fungicides, a series of 32 novel 1-arylisoquinoline derivatives were designed, synthesized, and evaluated for their antifungal activities. Their structures were verified by 1H NMR, 13C NMR, HRMS and single X-ray crystal diffraction analysis. Most of the target products exhibited medium to excellent antifungal activity against 6 phytopathogenic fungi in vitro at a concentration of 50 mg/L. Interestingly, compounds A13 and A25 with EC50 values of 2.375, 2.251 mg/L s against A. alternate, that were similar to boscalid (EC50 = 1.195 mg/L). The in vivo experiments revealed that A13 presented 51.61% and 70.97% protection activities against A. alternate at the dosage of 50 and 100 mg/L, respectively, which were equal to that of boscalid (64.52% and 77.42%). The SEM analysis indicated that compound A13 could strongly damage the mycelium morphology. Molecular electrostatic potential and molecular docking analysis revealed that A13 was covered by negative potential contour, and strongly interacts with the residues of SDH. These results revealed that compounds A13 and A25 could be as promising antifungal candidates for the development of natural-based fungicides.
Metabolic syndrome decouples insulin resistance from liver fibrosis in patients with Metabolic Associated Steatohepatitis
Authors: Zeng Cheng , Yang Yang , Jin ZhiQiang , Xu Hua , Zhong Shan , Xie Yurui , Zhang Chaohua , Zhou Zhi 
Published: June 2024

Abstract

Background
The current study suggests that insulin resistance plays a significant role in liver fibrosis. However, animal studies imply a potential dissociation between liver fibrosis and insulin resistance. We aim to determine whether this dissociation exists in the human population and validate whether metabolic syndrome (MS) serves as an intermediary factor leading to this dissociation.
Methods
Data from NHANES 2017-March 2020 Pre-pandemic was employed in this study. The assessment of hepatic steatosis and fibrosis in participants was conducted using liver elastography, while the degree of insulin resistance was evaluated through the utilization of HOMA-IR from laboratory tests.
Results
The study revealed that the median age of participants was 55 years, with 1168 individuals diagnosed with Metabolic Associated Steatohepatitis (MASLD). Among these patients, 69% were identified as having Metabolic Syndrome (MS), while the remaining 31% did not. In the subset of MASLD patients without MS, logistic regression analysis showed no significant association between insulin resistance and either liver fibrosis or significant fibrosis. However, in MASLD patients with MS, individuals in the highest quartile of HOMA-IR had a respective 11.5(5.20, 25.6) and 13.0(4.96, 34.2) times higher risk of liver fibrosis or significant fibrosis compared to those in the lowest quartile of HOMA-IR.
Conclusion
In patients with MASLD, there is a dissociation between insulin resistance and liver fibrosis, and this dissociation is mediated by MS.
Secure and Efficient General Matrix Multiplication On Cloud Using Homomorphic Encryption
Authors: Gao Yang , Gang Quan , Homsi Soamar , Wen Wujie , Wang Liqiang 
Published: June 2024

Abstract

Despite the enormous technical and financial advantages of cloud computing, security and privacy have always been the primary concerns for adopting cloud computing facilities, especially for government agencies and commercial sectors with high-security requirements. Homomorphic Encryption (HE) has recently emerged as an effective tool in ensuring privacy and security for sensitive applications by allowing computing on encrypted data. One major obstacle to employing HE-based computation, however, is its excessive computational cost, which can be orders of magnitude higher than its counterpart based on the plaintext. In this paper, we study the problem of how to reduce the HE-based computational cost for general Matrix Multiplication (MM), i.e., a fundamental building block for numerous practical applications, by taking advantage of the Single Instruction Multiple Data (SIMD) operations supported by HE schemes. Specifically, we develop a novel element-wise algorithm for general matrix multiplication, based on which we propose two HE-based General Matrix Multiplication (HEGMM) Approved for Public Release on 06 Mar 2024. Distribution is Unlimited. Case Number: 2024-0184 (original case number(s): AFRL-2024-0944) algorithms to reduce the HE computation cost. Our experimental results show that our algorithms can significantly outperform the state-of-the-art approaches of HE-based matrix multiplication.
A Scale-Up of Fluidized Bed Granulation Process for Calcium-Tryptophan
Authors: Han Hyeong-seok , Sung Woo Chang , Kim Jun-Woo , Kim Kyochan , Lee Donghyun 
Published: June 2024

Abstract

The importance of tryptophan in both human and animal nutrition continues to drive its demand and market growth. The fluidized bed granulation is one of the crucial processes for industrial production of tryptophan from microbial fermentation, particularly as the simplified process of evaporation and granulation to efficiently generate commercial granular products. In this study, solid flow analysis for scaling up fluidized bed granulators in the manufacturing of calcium-tryptophan granules was conducted in terms of solid flow characteristics of the granules, flow regime within the granulator, and design considerations of the granulator. Based on the established scale-up case of calcium-tryptophan granules, it was founded that the flow regime of bubbling fluidization occurred similarly in both pilot and plant scale fluidized bed granulator. Then, the desired height (transport disengaging height, TDH) and entrainment rate (to secure the capacity of entrained particle recovery equipment such as cyclones or bag filters) were determined to obtain basic design data for scale-up of the fluidized bed granulation.
Antibiotic treatment induces microbiome dysbiosis and reduction of neuroinflammation following traumatic brain injury in mice
Authors: Flinn Hannah , Marshall Austin , Holcomb Morgan , Cruz Leonardo , Soriano Sirena , Treangen Todd J. , Villapol Sonia 
Published: June 2024

Abstract

Background
The gut microbiome is linked to brain pathology in cases of traumatic brain injury (TBI), yet the specific bacteria that are implicated are not well characterized. To address this gap, in this study, we induced traumatic brain injury (TBI) in male C57BL/6J mice using the controlled cortical impact (CCI) injury model. After 35 days, we administered a broad-spectrum antibiotics (ABX) cocktail (ampicillin, gentamicin, metronidazole, vancomycin) through oral gavage for 2 days to diminish existing microbiota. Subsequently, we inflicted a second TBI on the mice and analyzed the neuropathological outcomes five days later.
Results
Longitudinal analysis of the microbiome showed significant shifts in the diversity and abundance of bacterial genera during both acute and chronic inflammation. These changes were particularly dramatic following treatment with ABX and after the second TBI. ABX treatment did not affect the production of short-chain fatty acids (SCFA) but did alter intestinal morphology, characterized by reduced villus width and a lower count of goblet cells, suggesting potential negative impacts on intestinal integrity. Nevertheless, diminishing the intestinal microbiome reduced cortical damage, apoptotic cell density, and microglial/macrophage activation in the cortical and thalamic regions of the brain.
Conclusions
Our findings suggest that eliminating colonized gut bacteria via broad-spectrum ABX reduces neuroinflammation and enhances neurological outcomes in TBI despite implications to gut health.
Enhancing Patient Engagement with Machine Learning at a Novel Care Transition Clinic
Authors: Lee Seung-Yup , Eagleson Reid , Hearld Larry , Gibson Madeline , Hearld Kristine , Hall Allyson , Burkholder Greer , McMahon Jacob , Mahmood Shoaib , Spraberry Corey , Baker Thalia , Garretson Alison , Bradley Heather , Mugavero Michael 
Published: June 2024

Abstract

Objective
This study applies predictive analytics to identify patients at risk of missing appointments at a novel post-discharge clinic (PDC) in a large academic health system. Recognizing the critical role of appointment adherence in the success of new clinical ventures, this research aims to inform future targeted interventions to increase appointment adherence.
Materials and Methods
We analyzed electronic health records (EHR) capturing a wide array of demographic, socio-economic, and clinical variables from 2,168 patients with scheduled appointments at the PDC from September 2022 to August 2023. Logistic regression, decision trees, and XGBoost algorithms were employed to construct predictive models for appointment adherence.
Results
The XGBoost machine learning model outperformed logistic regression and decision trees with an area under the curve of 72% vs. 65% and 67%, respectively, in predicting missed appointments, despite limited availability of historical data. Key predictors included patient age, number of days between appointment scheduling and occurrence, insurance status, marital status, and mental health and cardiac disease conditions.
Discussion
Findings underscore the potential of machine learning predictive analytics to significantly enhance patient engagement and operational efficiency in emerging healthcare settings. Optimizing predictive models can help balance the early identification of patients at risk of non-adherence with the efficient allocation of resources.
Conclusion
The study highlights the potential value of employing machine learning techniques to inform interventions aimed at improving appointment adherence in a post-discharge transition clinic environment.
Assessment of Lipid peroxidation and total antioxidant capacity in patients with breast carcinoma
Authors: Babakr Abdullatif Taha , Eldein Mohamed Mahmoud Nour 
Published: June 2024

Abstract

Background: Breast cancer is a prevalent life-threatening disease worldwide. Oxidative stress has been implicated in the development and progression of various cancers, including BC. Assessing lipid peroxidation and overall antioxidant status in breast cancer offers valuable information regarding the advancement, prognosis, and effectiveness of treatment options.
Methods: A total of one hundred and fifty women, categorized into three groups Normal, Benign BC, and Malignant BC cases. Patients were selected and examined in the oncology clinic, fasting blood samples were collected and measurements of Total Antioxidant Capacity (TAC), Ox-LDL, CA 15.3, and CEA were performed. Then statistical analysis was done to compare the levels of these parameters in different groups and measure the analytical performance of TAC and Ox-LDL in BC.
Results:
The serum level of TAC in malignant cases was significantly decreased compared to benign group, 8.3 U/ml and 16.04 U/ml (P<0.001) respectively. Healthy controls show higher levels of TAC (43.4 U/ml). The levels of Ox-LDL in BC was significantly increased in malignant cases and benign group, 3831, and 1234 pg/ml, respectively compared to normal controls (682 pg/ml) (P<0.001). CEA and CA15-3 sharply increased in BC groups compared to control group. A significant area under the curve (AUC) for TAC (0.975, P<0.001), and Ox-LDL (0.986, P<0.001) was observed in ROC curve analysis.
Conclusion:
The present study revealed that breast cancer patients had lower TAC and higher Ox-LDL serum levels, indicating elevated oxidative stress. Additionally, TAC and Ox-LDL levels may serve as promising monitoring parameters in BC.
GaitHF: Height Fused Gait Recognition
Authors: Zhu Jinchang , Sun Dayang , Cheng Yu , Wang Hailong , Chen Yujing , Chen Yaowei 
Published: June 2024

Abstract

Gait recognition, an emerging field at the intersection of computer vision and biometrics, has garnered significant attention for its potential applications in surveillance, security, and healthcare. In this paper, we present a novel method that combines appearance-based gait recognition with human height data. The proposed approach aims to enhance the accuracy and robustness of gait recognition systems by incorporating complementary features derived from both gait patterns and human height. We believe that incorporating height information can offer additional discriminative power to gait recognition models, enabling them to better distinguish individuals in various scenarios. Many gait recognition convolutional neural networks using deep learning methods have made good data progress in recent years, so we also adopt this approach, e.g., deep learning methods such as (CNN) and Recurrent Neural Networks (RNN), which can automatically learn hierarchical representations of gait and height features to capture intricate patterns and relationships. Our experiments involve a comprehensive analysis using benchmark gait datasets, demonstrating the effectiveness of the proposed approach in comparison to traditional gait recognition methods. The results highlight the potential of leveraging human height information to enhance the overall performance of gait recognition systems. Our experimental data show that the results achieved by many appearance-based gait recognition models on the CASIA-B and OU-MVLP datasets progress in most conditions after using our proposed new approach, which are eye-catching in that the average accuracy improves by 1.875% and 6% on BG and CL of CASIA-B, respectively, and the average accuracy improves on the large dataset OU-MVLP is also improved by 1.35%.
Design of Flexible and Green Chemistry Synthesis Method for Highly Crystalline COFs for Supercapacitor Applications
Authors: Xiong Shanxin , Fang Ke , Zhang Kerui , Guo Jingru , Chen Min , Wu Juan , Zhang Yukun , Wang Xiaoqin , Hua Chunxia , Chu Jia , Zhang Runlan , Wang Chenxu , Gong Ming , Wu Bohua , Zhang Juan 
Published: June 2024

Abstract

Covalent organic frameworks (COFs) have attracted much attention in energy storage due to their porous network structure, large specific surface area, high crystallinity, and pseudocapacitive ability brought by redox reactions. However, the traditional synthesis method of COFs involves toxic solvents and requires high temperatures and pressure. Therefore, it is necessary to develop simple synthesis methods for large-scale practical application of COFs. This study investigated the synthesis and electrochemical properties of two kinds of COFs, which were synthesized through the reflux heating method and solvothermal method using Tri(4-aminophenyl)amine (TAPA) and tris(benzaldehyde) (TFB) as monomers. The results show that COFs synthesized by reflux heating (Re-COF-TAFB) outperforms COFs Synthesized by solvothermal method (So-COF-TAFB) in specific surface area, thermal stability, and electrochemical properties. Re-COF-TAFB has a specific capacitance of 248 F·g− 1 at 0.1 A·g− 1 and a capacitance retention rate of 104.13% after 10,000 charge and discharge cycles. This paper contributes to understanding COFs' synthesis methods and their impact on material properties. Reflux heating is highlighted as an efficient technique for developing high-performance COF-based supercapacitors.
Bioactive compounds from Sargassum horneri attenuates inflammation and obesity regulating by Nrf2/HO-1 and AMPK signaling pathways
Authors: Chilakala Ramakrishna , Moon Hyeon Jeong , Kim Min Ju , Ko Kang Ho , Han Jong Won , Jung Min Seouk , Cheong Sun Hee 
Published: June 2024

Abstract

Sargassum horneri is rich in bioactive compounds, including phytosterols, exhibits antioxidant, anti-obesity and anti-inflammation properties; however, its underlying mechanisms remain unclear. In this study, we evaluated that the antioxidant and anti-inflammatory effect of S. horneri ethanolic extract and its subfractions using lipopolysaccharide (LPS)-treated RAW264.7 cells. Furthermore, we examined the in vivo anti-obesity efficacy of S. horneri using obese mice fed a high-fat diet. Results revealed that the in vitro treatment increases the inflammatory cytokines such as iNOS, NO, COX-2, PGE2, TNF-α, and IL-6 including NF-κB subunit p65 protein expressions in LPS treated RAW264.7 cells. However, these pro-inflammatory cytokines were significantly reduced by inhibiting the NF-κB- p65 translocation pathway in S. horneri treatment. In addition, the S. horneri extracts increased the Nrf2 translocation into the nucleus as well as their heme oxygenase (HO-1) target gene expression. Whereas, in vivo treatment with S. horneri reduces body and organ weight, including pathological damage in liver and adipose tissue. Moreover, S. horneri decreases serum triglycerides (TG), LDL-cholesterol, total cholesterol (TC), arteriosclerosis index (AI), and cardiovascular risk index (CRF), but increases HDL-cholesterol concentration-dependently. The liver antioxidant enzyme activities and AMPK protein expressions were raised in S. horneri treated groups; while SREBP-1, FAS, and ACC expressions was reduced with dose-dependent manner. These findings provide an innovative pharmacological basis for the antioxidant, anti-inflammatory and anti-obesity effect of S. horneri. It specifies the potential of S. horneri as a candidate for preventing inflammation, obesity and other related disorders.
Research on multimodal link prediction method based on Vision Transformer and convolutional neural network
Authors: Liu Yang , Ren Zehong , Liu Xuemei , Wang Xingzhi , Wang Yize , Wang HuiYu 
Published: June 2024

Abstract

To address the problems of inadequate feature representation and low generalisation ability of existing link prediction methods. A multimodal link prediction method based on Vision Transformer and convolutional neural network is proposed. Firstly, PHash is employed at the filter gate to filter out irrelevant images. Secondly, picture features are extracted using Vision Transformer model and computed using MRP through forgetting gate. Multi-layer Convolutional Neural Networks are used to fuse spatial location feature information during entity relationship embedding to effectively obtain richer semantic information. Meanwhile, a multi-scale null convolution kernel is used to capture rich explicit interaction features in different scale spaces. Finally, the picture features are fused with entity-relationship features in the fusion gate. The experimental results show that the MRR metrics are improved by 17.3% compared with the DistMult model on the public dataset and 4.1% compared with the TuckER model on the e-commerce dataset.
WITHDRAWN: Frequency Constrained Optimal Power Flow Incorporating UPFC Controller Using Driving Training-Based Optimization
Authors: Roy Adhit , Dutta Susanta , Bhattacharya Anagha , Biswas Soumen , chatterjee Rajesh kumar , Akhtar Md Amir Khusru , Kumar Mohit , Verma Sahil , Khurma Ruba Abu , Mallik Saurav , Shah Mohd Asif 
Published: June 2024

Abstract

Optimal power flow (OPF) solution is being attained for IEEE 30-bus system considering thermal power (TP) sources. This paper presents a methodology to incorporate UPFC with frequency-based security constraints in the OPF problem framework. Objectives include reducing generation costs, emissions, and frequency deviation. This paper has looked at three different scenarios: the best power flow when there is no frequency-based security limitation, the best power flow when there is a frequency-based security constraint, and the combination of UPFC and frequency-based security constraint. It is necessary to maintain the system frequency within a safe range in order to display the balance between generation and consumption. Therefore, the power flow optimization solution should provide frequency stability in addition to provide the lowest possible generation cost under operational conditions. To achieve this solution, a new constraint is implemented on the power dispatch problem to achieve frequency stability. The inclusion of this limitation is demonstrated to induce the OPF to choose greater inertia generators more favorably when it comes to meet the frequency based security criterion. Outcomes of the test reveal a better result in resolving the OPF problem, by incorporating a unified power flow controller (UPFC) with frequency-based security constraint OPF problem. A driving training-based optimization (DTBO) approach has been employed to obtain optimal solutions without violating any equality and inequality network constraints.