bluuebunny/crossref_metadata_2025_split huggingface dataset:
Spatial Analysis of Racial Capitalism and COVID-19 in Louisiana
Authors: Oh Hyunsu , Thakkar Shriya 
Published: May 2024

Abstract

Background
. Louisiana has experienced one of the highest COVID-19 burdens in the United States. This article seeks to investigate the geospatial pattern of COVID-19 in Louisiana using the perspective of racial capitalism.
Methods
. Using data from the Louisiana Department of Health and American Community Survey, we employed spatial autoregressive models to assess how racial income disparity between White and Black residents connected to COVID-19 cases in Louisiana parishes, controlling for other parish-level characteristics.
Results
. Greater racial income disparity between White and Black residents yielded more COVID-19 cases in Louisiana parishes. A rise in income had a buffering effect on the role of racial income disparity aggravating COVID-19 severity.
Conclusions
. African Americans from lower socioeconomic backgrounds were likely at a higher risk of COVID-19 in the state of Louisiana. Based on Louisiana's unique historical and sociocultural contexts, implications are further discussed.
The Novel-B-Cell-Related Gene Signature Predicts the Prognosis and Immune Status of Patients with Esophageal Carcinoma
Authors: Li xinhong , Liu surong , Sun Tongyu , Li Hongyan , Liu Juan , Huang nan 
Published: May 2024

Abstract

The current understanding of the prognostic significance of B cells and their role in the tumor microenvironment (TME) in esophageal carcinoma (ESCA) is limited. Therefore, we conducted a screening for B-cell-related genes through the analysis of single-cell transcriptome data. Subsequently, we developed a B-cell-related gene signature (BRGrisk) using LASSO-regression analysis. We compared the immune infiltration profiles between the risk groups. The BRGrisk prognostic model indicated significantly worse outcomes for patients with high BRGrisk scores (p < 0.001). The BRGrisk-based nomogram exhibited good prognostic performance. Patients in the high-BRGrisk group had notably higher levels of immune cell infiltration and were more likely to be in an immunoresponsive state. Furthermore, enrichment analysis showed a strong correlation between the prognostic gene signature and cancer-related pathways. IC50 results indicated that patients in the low-BRGrisk group were more responsive to common drugs compared to those in the high-BRGrisk group. In summary, this study presents a novel BRGrisk that can be used to stratify the prognosis of ESCA patients and may offer guidance for personalized treatment strategies aimed at improving prognosis.
TP53 deletion as an MRD-dependent risk factor in childhood B-ALL: a post hoc analysis from a prospective cohort
Authors: ZHU XIAOFAN , Gao Yangyang , Li Jun , Wang Ning , An Wenbin , Yin Zixi , Wang Junxia , chen xia , Chen Yumei , Guo Ye , Yang Wenyu , Zhang Li , Zou Yao , Chen Xiaojuan 
Published: May 2024

Abstract

The effect of TP53 alterations on childhood B-cell acute lymphoblastic leukemia (B-ALL) remains unclear. To investigate the impact of TP53 deletion (TP53del) and TP53 mutation (TP53mut) on prognosis, this post-hoc study used fluorescence in situ hybridization test to detect TP53del in 914 newly diagnosed B-ALL children from a prospective Chinese Children’s Cancer Group ALL-2015 cohort. Targeted gene sequencing was used to identify TP53mut in 345 out of the 914 patients. TP53del was detected in 4.4% of cases. The frequency of hypodiploidy was higher in TP53del subgroup (7.5% vs. 0.5%, P = 0.002), but patients with TP53del were less likely to have other recurrent genetic abnormalities, including BCR::ABL1, ETV6::RUNX1, TCF3::PBX1 and MLL rearrangement. Univariable and multivariable analyses indicated that TP53del was an independent risk factor for overall and disease-free survival. Furthermore, stratification analysis revealed that TP53del was associated with adverse outcomes in patients with positive MRD after induction (0.0% vs. 58.2%, P < 0.001), suggesting an MRD-dependent pattern. But TP53mut was not associated with poor survival (79.2% vs. 85.3%, P = 0.317). In summary, TP53del may serve as a predictor for poor prognosis in pediatric B-ALL. Especially children in intermediate-risk group with positive MRD and TP53del may deserve more aggressive treatment.
Effect of Gas-Grafting Pretreatment of Cellulose on Properties of Cellulose-Polypropylene Composites
Authors: Noh Kyu Hwan , Lee Cheol Woo , Choi Kyoung-Hwa , Lee Kwang-Seob , Martinez Philippe , Ryu Jeong-Yong 
Published: May 2024

Abstract

This study investigated a method to modify cellulose for enhanced hydrophobicity through gas-phase grafting with palmitoyl chloride, facilitating easier blending of fibers and polypropylene. In addition, cellulose sheets were produced by substituting water in the cellulose fiber stock with ethanol during sheet molding to prevent the matting of macrofibrils on the cellulose surface, thereby improving the hydrophobization efficiency achieved by gas grafting. The results revealed that alcohol-molded sheets, which expanded in volume, exhibited more than twice the amount of reacted fatty acids compared to conventional water-based cellulose sheets after gas grafting. Composite films composed of the pretreated fibers exhibited superior tensile strength relative to those made from non-grafted fibers. In particular, the addition of maleic-anhydride-grafted polypropylene (MAPP) improved the dispersibility of the cellulose fibers within the composite film, indicating the positive contribution of gas grafting and MAPP treatment.
High-throughput plant phenotyping analysis of Melon (Cucumis melo L.) germplasm resources using deep learning methods and computer vision
Authors: Xu Shan , Shen Jia , Wei Yuzhen , Li Yu , He Yong , Hu Hui , Feng Xuping 
Published: May 2024

Abstract

Cucumis melo L., commonly known as melon, is a crucial horticultural crop. The selection and breeding of superior melon germplasm resources play a pivotal role in enhancing its marketability. However, current methods for melon appearance phenotypic analysis rely primarily on expert judgment and intricate manual measurements, which are not only inefficient but also costly. Therefore, to expedite the breeding process of melon, we analyzed the images of 117 melon varieties from two annual years utilizing artificial intelligence (AI) technology. By integrating the semantic segmentation model Dual Attention Network (DANet), the object detection model RTMDet, the keypoint detection model RTMPose, and the Mobile-Friendly Segment Anything Model (MobileSAM), a deep learning algorithm framework was constructed, capable of efficiently and accurately segmenting melon fruit and pedicel. On this basis, a series of feature extraction algorithms were designed, successfully obtaining 11 phenotypic traits of melon. Linear fitting verification results of selected traits demonstrated a high correlation between the algorithm-predicted values and manually measured true values, thereby validating the feasibility and accuracy of the algorithm. Moreover, cluster analysis using all traits revealed a high consistency between the classification results and genotypes. Finally, a user-friendly software was developed to achieve rapid and automatic acquisition of melon phenotypes, providing an efficient and robust tool for melon breeding, as well as facilitating in-depth research into the correlation between melon genotypes and phenotypes.
Study of an MoS2 Phototransistor Using a Compact Numerical Method Enabling Detailed Analysis of 2D Material Phototransistors
Authors: Islam Raonaqul , An Ishraq Md , Menyuk Curtis R. , Simsek Ergun 
Published: May 2024

Abstract

We present a detailed numerical framework for solving the drift-diffusion equations to characterize two-dimensional material-based phototransistors and we apply this framework to a phototransistor made from monolayer molybdenum disulfide (MoS2) that is placed on top of a back-gated silicon-oxide-coated silicon substrate. Numerical results, which show good agreement with the experimental results found in the literature, emphasize the necessity of including the inhomogeneous background for the accurate calculation of device metrics such as quantum efficiency and bandwidth. We also calculate the phase noise of these phototransistors, which is a crucial performance metric for many applications where precise timing and synchronization are critical.
Exploring the Relationship Between Gastric Cancer and Nutritional Risk Factors: Insights from the Korea National Health Insurance Database
Authors: Kim You Na , Kim Chi Young 
Published: May 2024

Abstract

Background
Gastric cancer is a global public health concern, ranking as the third most common cause of cancer-related deaths despite its decreasing incidence over the last five decades. Gastric cancer rates vary across regions, which may be attributable to factors such as Helicobacter pylori infection, environmental factors, and genetic predispositions.
Objective
We aimed to examine the association between gastric cancer, nutrient intake, and lifestyle parameters in Korean adults.
Methods
The study was conducted in Korea and utilised the KNHANES dataset (2012–2016) to explore nutritional risk factors associated with gastric cancer. Multivariable analysis was conducted to confirm the association of micronutrients and specific food items using questionnaires designed to collect data on individuals' consumption frequency and nutrient intake.
Results
This study enrolled 18,894 participants, including individuals diagnosed with gastric cancer. Factors associated with gastric cancer included male sex, advanced age, low body mass index, and regular consumption of meals prepared outside the home. Multivariate analysis indicated that lower intake of protein (OR 0.98, 95% CI 0.97–0.99, p < 0.001), fat (OR 0.99, 95% CI 0.98–0.99, p < 0.004), and thiamine (OR 0.59, 95% CI 0.45–0.76, p < 0.001) and higher intake of niacin (OR 1.05, 95% CI 1.02–1.08, p < 0.001) correlated with an increased risk of gastric cancer. Additionally, specific dietary items such as tteok (rice cake) and soju contributed to an elevated risk of gastric cancer (OR 1.21, 95% CI, 1.01–1.40; OR 1.14, 95% CI, 1.03–1.25; p < 0.001).
Conclusions
We found an association between gastric cancer and various nutrient and lifestyle parameters in a nationally representative sample of Korean adults. Nutrient intake and lifestyle-related factors play a pivotal role in influencing the prevalence of gastric cancer, suggesting that tailored interventions could mitigate gastric cancer risk in specific populations.
Stakeholder Analysis for One-Health Preparedness and Operationalization in Kenya: The One-Health Scorecard Approach
Authors: Estambale Benson , Mutua Edna , Ochieng Alfred , Osok Damar , Ong’amo George , Oludhe Christopher , Oyugi Julius , Olago Daniel , Ongore Dismas , Nyamongo Isaac K. 
Published: May 2024

Abstract

Background: Rift Valley Fever (RVF) is a climate-sensitive mosquito-borne zoonotic disease with the potential to cause major economic losses due to its high mortality rate in domestic animals including cattle, sheep, goats, and camels. Disease outbreaks are closely associated with climate-driven ecosystem changes that modify interfaces between animals, humans, and the ecosystem. This study evaluated Baringo County, an administrative county in the Rift Valley of Kenya, to determine its preparedness to implement the One-Health Approach (OHA) to combat Rift Valley Fever.
Methods: A cross-sectional study design was used to interview 31 officers from One Health-related government departments in Baringo County. A structured questionnaire based on a One-Health (OH) scorecard system was used to interrogate four components of One-Health. These included capacity, intervention science, risk management, and impact. A binary characterization of the responses was used to assign affirmative responses corresponding to good intervention practices a value of 1 and negative responses a value of zero. Chi-square tests of association were used to determine the associations between existing intervention practices and OHA preparedness.
Results: The study established that Baringo County lacked a structured governance system for operationalizing OHA activities. This was characterized by poor organizational culture and insufficient resource allocation. There was no coordinated implementation of interventions between the different sectors, no implementation research to assess the efficiency of interventions, and no systems analysis within OHA interfaces. The County was, therefore, not resilient to environmental drivers of RVF and had weak risk mitigation strategies in the three One-Health interfaces that include animal, human, and ecosystems. RVF risk potential was determined to be highest in the previous RVF hotspots where its impact was severe.
Conclusion: Baringo County lacks the preparedness for operationalizing the OHA in controlling RVF outbreaks. Based on these findings, the paper recommends: 1) The establishment of a County One-Health Unit comprising interdisciplinary experts from relevant sectors, 2) Capacity building to enhance OHA preparedness, 3) A continuous systems analysis to identify vulnerabilities and changes in the three One Health interfaces and 4) Integrated multisectoral planning and intervention against RVF threat.
Combined ameliorative effect of N-Acetylcysteine and Vitamin C on Cyclophosphamide-Induced Immunosuppression in Balb/C mice via Immunomodulatory, anti-oxidant and downregulation of pro-inflammatory markers
Authors: Bethala Krupavaram , Lakshmi BVS 
Published: May 2024

Abstract

Background:
The present study investigated immunomodulatory effects of N -Acetylcysteine and Vitamin C on Cyclophosphamide induced Immunosuppression in Balb/C mouse models.
Methods:
Thirty two Balb/c mice randomly divided into 4 groups. One group of healthy mice acted as normal control. 80 mg/kg/day cyclophosphamide (i.p.) was administered on day 1-3 and 9 for all other groups. From day 4 to 21, the cyclophosphamide treated mice were administered as: saline in positive control, N- Acetylcysteine (200mg/kg), N-Acetylcysteine (200mg/kg) + Vitamin C (200mg/kg). On 22nd day, thymus and spleen tissues isolated, processed for histopathological examination. Blood samples were analyzed for hematological, biochemical, immunological and parameters.
Results: NAC and Vit C prevented the Cyclophosphamide induced spleen and thymus injury compared with normal control, prevented the decrease in numbers of peripheral RBC, WBC, Hb and platelets, providing supportive evidence for their anti-leukopenia activities; reversed decreased activities of catalase (CAT), glutathione (GSH), GR and rise in MDA. NAC and Vit C also enhanced humoral immune function through increasing level of IgG; significantly decreasing the proinflammatory cytokines-TNF-α and IL-2 levels in serum; produced an increase in circulating serum immunoglobulins in haemagglutinating antibody titre test. Decrease in foot pad edema in delayed-type hypersensitivity reaction test; showed an increase in the neutrophil adhesion to the nylon fibres.
Conclusion: Taken together, our results provide scientific evidence for synergic effect between NAC and Vit C as a co-treatment for enhancing humoral and cellular immune functions, augmenting antioxidant, anti-inflammatory and immunomodulatory effects.
Individual differences in the acquisition of shared syntactic representations: A re-analysis of studies using an artificial language learning paradigm
Authors: Muylle Merel , Bernolet Sarah , Hartsuiker Robert J. 
Published: May 2024

Abstract

A series of artificial language (AL) learning studies investigated the development of shared syntactic representations during early stages of second language (L2) acquisition. In this paradigm, the sharing of syntax is measured by means of structural priming: if structures are shared between two languages, a sentence in one language (e.g., the AL) should prime the structure of a sentence in another language (e.g., Dutch) and vice versa. According to Hartsuiker and Bernolet’s (2017) developmental theory, syntactic representations evolve gradually from item-specific to more abstract with increasing L2 proficiency. The current study tested this hypothesis by focusing on individual differences in AL proficiency during the first day of acquisition in a re-analysis of three AL studies. We predicted that individuals with higher AL proficiency levels show more cross-linguistic priming than those with lower levels. AL proficiency was indeed related to the magnitude of structural priming, although the strongest evidence for modulation of priming by proficiency was obtained for item-specific priming. Additionally, we observed that working memory (WM) capacity and L1 proficiency predicted AL proficiency and priming in general. Finally, WM capacity predicted the magnitude of priming in ditransitive sentences, but not in transitive sentences, suggesting a larger role for WM in ditransitive vs. transitive priming.
Multi-task and multi-stage commodity sorting algorithm for distributed e-commerce logistics system
Authors: Gao Li , Yang Heyu , Gu Kai 
Published: May 2024

Abstract

With the increasing scale of e-commerce and logistics, factors such as the types, quantities, and volumes of goods sorted in logistics have made the sorting process more difficult. A single serial logistics workshop sorting method cannot meet the growing business needs, with low sorting speed and inability to make reasonable use of sorting machine resources. This article proposes a distributed logistics system commodity sorting algorithm, which sorts multiple types of goods through multiple machines and divides the sorting task into multiple stages for processing. We present the Distributed Dynamic Programming Memetic System (DDPMS): (1) Parallel sorting process architecture realizes multi-stage parallel acceleration, and dynamic programming algorithm plans the execution order of commodity sorting tasks to reduce total sorting time. (2) The meme algorithm first initializes the sorting machine scheduling scheme through an improved dragonfly algorithm, and the mutation operator prevents the solution from falling into local optima. (3) And a local reinforcement search optimization solution based on fuzzy Q-Learning was designed, using the Softmax Green strategy to cope with more complex machine resource sizes and quantities. Improve resource utilization, reduce sorting time, and ensure system load balancing. Through experimental verification, the execution efficiency of distributed sorting technology has been improved by 2 and 4 times compared to traditional algorithms; By comparing the improved Dragonfly algorithm with different numbers of sorting machines and single machine instances, the algorithm converges to a more optimal solution compared to the baseline algorithm. Fuzzy Q-Learning also has advantages in terms of search ability and efficiency.
Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things
Authors: Mahabadi Aminollah , Abdolazimi Faezeh 
Published: May 2024

Abstract

The classification of large image data using traditional methods has been based on changes in image gray features, extraction of edge and contour feature information, or conversion between image coordinate sets. However, due to the growth of new image size and big image data (BID) in real-time multimedia communication and future online big data (BD) applications, these methods have become increasingly complex and resulting in poor real-time performance. These complex methods suffer from complex algorithms, massive data communication, slow processing speed, unintelligent predictive modeling, weak data classification, limited accuracy, uncleaned usage data, combined destructive artificial and natural noise, exchanging data values over time, and exhausting updating data for accurate operation of media storage. In the era of the Internet of Multimedia Things (IoMT), most modern devices can accurately capture vast amounts of observational data in the form of valuable images, text, and acoustic recordings. This reliable data must be kept valid for local data processing at different times and for globally extracting knowledge from information at various data levels to address forthcoming challenges related to big image data. The challenges of secure communication require accurate, reliable, fast, and effective information gathering for local decision-making and global knowledge mining of BID to support cyber-physical systems for speedy data virtualization of smart city devices. Complex and uncontrollable problems persist on the outskirts of grids, fogs, and cloud networks regarding data cleansing and privacy protection for intelligent data ocean management. This study developed a new 15Vs model empirically to examine distributed big image data processing based on a modern BID connectivity approach. The model accurately extracts textured features from visible data images and overcomes a unique set of key challenges. The model introduces a strategic analyzer, uses intelligent local agents, and recruits clever global bots to provide a suitable platform for generous support of bot-oriented BD processing. This instantly forms a hierarchical data level for better data acquisition and cleansing, safe privacy protection, suitable information diffusion, and speedy knowledge extraction. Our study presents a novel definition of BID to address the ultimate challenges of data management in a high-risk environment for further big image data modeling research needs.
Robust Multi-Target Tracking with Integrated Knowledge Model, Graph Structures, and Statistical Analysis in Unobservable Environments
Authors: Mehrizi Ali , Yazdi Hadi Sadoghi 
Published: May 2024

Abstract

This paper presents an innovative approach to vehicle tracking in video streams, utilizing graph structures, integrated prior knowledge, and forecasting techniques. The method encompasses statistical analysis and prediction of non-identical periodic events, specifically tailored to the characteristics of vehicles in motion. Through the generation of pseudo-observations, auxiliary information is provided to the tracker, effectively capturing the underlying dynamics of vehicle movements. The approach is evaluated on tracking datasets, demonstrating substantial improvements in tracking performance, especially in scenarios with camera sensor occlusion caused by physical obstructions, which is a common challenge encountered in camera-based tracking systems.
Machine Learning Based Photovoltaic Power and Energy Prediction in time-frequency domain
Authors: aouni Abdelaziz El , Naimi Salah Eddine , Ayat Yassine 
Published: May 2024

Abstract

Efficient energy management in smart-grid systems relies heavily on accurate photovoltaic (PV) power production forecasting. In this study, we explore the benefits of employing frequency domain methods for PV panel power production forecasting. moreover two methods are proposed in this article, Discrete Wavelet Transform with Long Short-Term Memory (DWT-LSTM) for short-term forecasting and LSTM combined with Short-Time Fourier Transform and an Artificial Neural Network (LSTM-STFT-ANN) for long-term forecasting. to assess the robustness of the proposed methods, extensive testing and comparison was conducted with the state of the art methods LSTM combined with a conventional neural network (CNN-LSTM) on an unknown dataset spanning two years, enabling prediction performance evaluation under varying weather conditions, including foggy and cloudy scenarios. Additionally, we evaluated the total energy produced across different time horizons. The performance assessment results for 1-hour horizon power forecasting revealed that the DWT-LSTM method achieved an average normalized root mean squared error (HH) of approximately 0.16, outperforming the classical CNN-LSTM method, which yielded an HH of more than 0.22. For longer-horizon forecasts, the LSTM-STFT-ANN method exhibited higher efficiency. For instance, for 2-day horizon, the HH indicator was recorded as 0.35 for LSTM-STFT-ANN, compared to around 0.54 for the CNN-LSTM method.