bluuebunny/crossref_metadata_2025_split huggingface dataset:
Anthraquinone and its derivatives as a recyclable photocatalyst for Efficient Photocatalytic Degradation of Rhodamine B in Water under visible light
Authors: Jiang Dabo , Zhang Mayin , Meng Wenyi , Zhou Junzuo , Liang Shuting , Yang Tao , Yang Cheng , Su Anqun , Zhang Guanghui , Xu Longjun 
Published: February 2024

Abstract
This study presents findings that suggest it is possible to fine-tune and enhance the optical properties of anthraquinone (AQ) and its photocatalytic performance in the degradation of rhodamine B (RhB) at the molecular level through varying its keleton substituents. Specifically, the electron-withdrawing substituent demonstrates a more pronounced improvement effect compared to the electron-donating substituent. Among them, 2-carboxyanthraquinone (AQ-COOH) demonstrated the most exceptional photocatalytic activity, affording 99.9% degradation rate under visible light irradiation for 30 min. It is noteworthy that a hydrochloric acid solution with a concentration of 0.5 mmol·L− 1 has a considerable promoting impact on the photodegradation of RhB catalyzed by AQ-COOH, and that the total degradation of RhB can be accomplished in 15 min. In addition, AQ-COOH also has good applicability for the degradation of tetracycline, methylene blue, and methyl orange. As a recyclable solid catalyst, the catalytic activity of AQ-COOH remained mostly unchanged after being used five times. Additionally, AQ-COOH did not undergo degradation when exposed to visible light, indicating its excellent stability in the process of catalyzing RhB degradation. According to quenching experiments and EPR spectrum characterizations, the photo-generated active species oxygen vacancies (h+), photogenerated electrons (e−), superoxide free radicals (O2•−), singlet oxygen (1O2) and hydroxyl radical (•OH) are involved in the hypothesized photo-catalytic degradation mechanism.
Sodium butyrate inhibits ferroptosis and ameliorates intestinal ischemia-reperfusion injury by modulating the NRF2/SLC7A11/GPX4 pathway
Authors: Zhao Zicen , Leng Yufang , Chang Liya , Wang Yu , Li Dongbin , Xing Yang , Wu Yuxuan 
Published: February 2024

Abstract
Purpose
Sodium butyrate, a short-chain fatty acid produced by the metabolism of intestinal flora, has been shown to have a protective effect against intestinal ischemia reperfusion injury (IRI), but its effect on intestinal IRI-associated ferroptosis has not yet been demonstrated.
Methods
In this experiment, we used a model of superior mesenteric artery occlusion in mice to examine the effects and principles of sodium butyrate on ferroptosis related to intestinal IRI. We assessed the role of sodium butyrate by constructing mice intestinal IRI models and determining the extent of intestinal tissue damage and changes in ferroptosis-related factors.
Results
Results show that Intestinal IRI mice showed increased tissue damage, massive infiltration of inflammatory cells, severe destruction of villus structure, and elevated MDA. In addition, intestinal IRI led to downregulation of GSH, GPX4, FTH1 and SLC7A11, regulators of ferroptosis, and a rise in Fe2+, as well as downregulation of NRF2 by intestinal IRI. Sodium butyrate had an attenuating effect on intestinal IRI, whereas mice exogenously supplemented with sodium butyrate showed less tissue damage, some restoration of villus structure and decreased MDA, up-regulation of GSH, GSH/GSSG, GPX4, FTH1, SLC7A11, and NRF2, and decreased Fe2+.
Conclusion
This experiment demonstrates that sodium butyrate regulates the SLC7A11/GPX4 pathway by up-regulating NRF2, thereby inhibiting ferroptosis and attenuating intestinal IRI.
Photonic crystal broadband 1×N beam splitter with designable splitting ratio based on genetic algorithm
Authors: Ke Hang , Li Peili , Shi Pengcheng , Shi Weihua 
Published: February 2024

Abstract
A novel broadband Y-shaped 1×N beam splitter based on two-dimensional photonic crystal is proposed in this paper. Firstly, a broadband 1×2 beam splitting structure with designable splitting ratio is proposed. The influence of the offset along the z-axis of the dielectric rods adjacent to the input and output waveguides, and the offset of the dielectric rods at the junctions on the broadband performance of the 1×2 splitting structure is studied. Then, based on the structure, the broadband 1×4 and 1×8 beam splitters with designable splitting ratio are proposed. According to the target of the specific performance, using genetic algorithm, the broadband 1×4 and 1×8 beam splitters with different splitting ratios can be inversely designed, which not only improves the optimization efficiency, but also achieves excellent broadband splitting performance. The results show that the 1×4 beam splitters proposed have the additional loss less than 0.352dB and the response time less than 0.2ps within 1515~1590nm. The 1×8 beam splitters proposed have the additional loss less than 0.382dB and the response time less than 0.3ps within 1519~1587nm. Due to the advantages of broad operating bandwidth, flexible design of splitting ratio, high transmittance and fast response speed, the proposed 1×N beam splitters have wide application prospects in the fields of photonic integrated circuits, passive optical net-work, etc.
PACS: 42.79.Fm, 42.70.Qs, 42.82.Et
Classification of cyberbullying in social media using Natural Language Programming method
Authors: Vivekananth P , Sharma Navneet 
Published: February 2024

Abstract
The evolution of internet describes the usage of Social Media (SM) which expands drastically. This involvement continues to increase in context with the current global epidemic since people frequently use SM platforms to vent their feelings. Similarly, the widespread adoption of SM sites like Facebook and Twitter by many organizations has raised the volume of essential individual input regarding the state of affairs, events, goods, and services. One of the major drawbacks that widespread SM usage is cyberbullying. The frequency of cyberbullying on SM platforms has raised serious concerns for people, organizations, and society at large. To minimize negative impacts due to cyberbullying on SM, early detection is essential. As a result, Sentiment Analysis (SA) utilizing Twitter data has gained prominence. Text analysis-related Natural Language Processing (NLP) and in Artificial Intelligence (AI) technologies have gained increased attention because of increasing demand for SM analysis. Moreover, the important fields utilized to proactively extract and train high-quality characteristics from low-level text involved is Machine Learning (ML). The Convolution Neural Network (CNN) models are Deep Learning (DL) methods to train data like text, picture, and video data. Improved text classification is achieved by preparing these data types using CNN as a persuasive method. To categorize the bullying text, this study discusses the Ensemble model that integrates Modified Term Frequency and Inverse Document Frequency (MTF-IDF), and Deep Neural Network (DNN) with sophisticated feature extraction approaches. From the text data, the cyberbullying feature patterns are extracted using the techniques of feature extraction.
Food Segmentation and Calorie Estimation of Coastal Food Delicacies
Authors: Khaleel Ibrahim Khaleel , Shetty Navya S , Poojary Manish , K Dhanush , Chaithra  , Mallya Rakesh , K Soorya Krishna , Kannadaguli Prashanth 
Published: February 2024

Abstract
With the growing interest in using artificial intelligence (AI) for nutritional analysis, complex models have been developed to calculate the calorie content of various foods. In this study, we employ Machine Learning (ML) techniques for estimating calories in seafood dishes. To enhance accuracy and resilience, the model combines two popular algorithms: linear regression and k-nearest neighbors (KNN). Seaside food, renowned for its rich and varied flavors, presents a unique challenge in calorie estimation due to the variety of ingredients and cooking techniques used. The proposed machine learning model addresses these challenges by identifying complex patterns in the dataset while considering the unique qualities of coastal cuisine. The KNN algorithm, by finding local patterns in the dataset, enhances the model's efficacy, making it adept at capturing the subtleties of regional variations in coastal food. Additionally, the linear regression model complements the KNN approach by highlighting more general patterns and connections among different components, cooking methods, and caloric content. The training and assessment dataset comprise an extensive compilation of seaside cuisine dishes, each labeled with precise calorie counts. The model is trained to generalize from this data, enabling it to predict the calorie content of previously unseen dishes accurately. Performance evaluation indicates that the combined KNN and linear regression model outperforms individual algorithms in terms of accuracy and generalization across a variety of coastal cuisines.
Is Skin Thinning a Natural Consequence of Aging or a Result of Sarcopenia? A Case-Control Study
Authors: Tuna Serpil , Eren Sevil 
Published: February 2024

Abstract
Background: It is widely accepted that skin thickness decreases and fragility increases with aging. However, the relationship between skin thickness and sarcopenia, a common condition in the elderly associated with an increased risk of fragility, remains unknown.
Objective: We aimed to investigate the presence of skin thinning in sarcopenia patients and explore the relationship between age, sarcopenia, and skin thickness.
Methods: This case-control study included a total of 66 individuals (33 individuals with sarcopenia, 33 healthy controls) aged 40 to 80 years. In all patients, total skin (epidermal + dermal) thickness was measured using ultrasound (US) at the leg, hand, forearm, and upper arm.
Results: Skin thickness in all regions was significantly lower in patients with sarcopenia compared to controls (all p < 0.05). Controlling for potential confounders, including age, sex, and body mass index, revealed a significant relationship between sarcopenia and skin thickness.
Conclusion: Sarcopenia was found to be associated with skin thinning in all regions and BMI is positively correlated only with leg skin thickness. However, age and gender were not found to be associated with skin thickness.
Depression and Long-term Mortality among 5-year Breast Cancer Survivors in Korea: A Population-based Cohort Study
Authors: Lee Su Kyoung , Park Sangwoo , Park Sang Min 
Published: February 2024

Abstract

Background
Depression is common and harmful among breast cancer survivors, but it may be neglected and untreated in non-Western settings. We examined the association of depression after breast cancer diagnosis with long-term mortality risk in a Korean cohort.
Methods
We used data from the National Health Insurance Service cancer patients cohort database of South Korea. We included women aged 40 or older who were diagnosed with breast cancer between 2007 and 2013, survived at least 5 years, and had no depression history. Depression was defined as hospitalization for more than 2 days due to depression (ICD-10 code F22 or F23) after diagnosis. We assessed all-cause, cancer-specific, and non-cancer-specific mortality by Cox proportional hazards model, adjusting for covariates.
Results
Among 30,371 women (mean [SD] age, 56.5 [11.4] years), 30,371 had depression. During the 5-year period, 1,904 deaths occurred (33.6 per 1,000 person-years). Depression patients had higher risks of all-cause mortality (adjusted hazard ratio [aHR], 1.38; 95% CI, 1.03–1.86) and non-cancer-specific mortality (aHR, 1.81; 95% CI, 1.14–2.86) compared to non-depression patients with Charlson comorbidity index of 2 or more. Cancer mortality was not significantly different. All-cause mortality was also higher for depression patients aged 65 or older (aHR, 1.96; 95% CI, 1.27–3.03).
Conclusions
Depression was linked to increased mortality among 5-year breast cancer survivors, especially for non-cancer causes. This study implies the need for depression screening and treatment among breast cancer patients, especially in non-Western settings where depression may be underdiagnosed and undertreated.
VGLL4, a promising biomarker, promotes epithelial-mesenchymal transition and cancer stemness via the Wnt pathway in hepatocellular carcinoma
Authors: Pan Juan-yong , Wang Gao-Xiong , Chen Shi 
Published: February 2024

Abstract
Vestigial-like family member 4 (VGLL4) is involved in cell proliferation, migration and invasion. Pancancer analysis revealed that VGLL4 is differentially expressed in multiple tumors and may be associated with poor survival in patients. There are still few reports about the role of VGLL4 in hepatocellular carcinoma (HCC), and these findings are controversial. Herein, we present our findings on VGLL4 in HCC and suggest strategies for further research. Through bioinformatics analyses, we found that VGLL4 was highly expressed in HCC and that this high expression was associated with poor prognosis. In addition we found that VGLL4 had greater diagnostic efficacy for HCC than AFP. To verify the results of the bioinformatics analyses, we conducted a series of experiments. First, we constructed HepG2 cell lines with high VGLL4 expression. Cell Counting Kit-8 (CCK-8), wound healing and Transwell assays revealed that HepG2 cells overexpressing VGLL4 had advantages in terms of cell proliferation, migration and invasion. Further bioinformatics analysis revealed that the expression level of VGLL4 in HCC cells was positively correlated with the number of stromal cells. In view of the relationship between stromal cells and epithelial-mesenchymal transition (EMT) and cancer stemness, we found by further western blot that β-catenin, transcription factor 4 (TCF4), c-Myc, vimentin, N-cadherin, CD44 and CD133 were significantly elevated in HepG2 cells overexpressing VGLL4. We conclude that VGLL4 is a promising novel marker of HCC that promotes EMT and cancer stemness via the Wnt pathway in HCC.
From past habitats to present threats: Tracing North American weasel distributions through a century of climate and land use change
Authors: Cheeseman Amanda E. , Jachowski David S. , Kays Roland 
Published: February 2024

Abstract

Context: Shifts in climate and land use have dramatically reshaped ecosystems, impacting the distribution and status of wildlife populations. For many species, data gaps limit inference regarding population trends and links to environmental change. This deficiency hinders our ability to enact meaningful conservation measures to protect at risk species.
Objectives: We investigated historical drivers of environmental niche change for three North American weasel species (American ermine, least weasel, and long-tailed weasel) to understand their response to environmental change.
Methods: Using species occurrence records and corresponding environmental data, we developed species-specific environmental niche models for the contiguous United States (1938-2021). We generated annual hindcasted predictions of the species’ environmental niche, assessing changes in distribution, area, and fragmentation in response to environmental change.
Results: We identified a 53.96% decline in suitable habitat alongside high levels of fragmentation for least weasels and region-specific trends for American ermine and long-tailed weasels; declines in the West and increased suitability in the East. Climate and land use were important predictors of the environmental niche for all species. Changes in habitat amount and distribution reflected widespread land use changes over the past century while declines in southern and low-elevation areas are consistent with impacts from climatic change.
Conclusions: Our models uncovered land use and climatic change as potential historic drivers of population change for North American weasels and provide a basis for management recommendations and targeted survey efforts. We identified potentially at-risk populations and a need for landscape-level planning to support weasel populations amid ongoing environmental changes.
Endoscopic Submucosal Dissection Versus Surgery for T1b Esophageal Carcinoma: A Single-Center Retrospective Study
Authors: Zhang Ting , Wang Hanying , Jin Tian , Wu Zeyu , Li Xiuqing , Zhang Qide 
Published: February 2024

Abstract

INTRODUCTION: Endoscopic submucosal dissection (ESD) is a preferred treatment option for superficial esophageal squamous cell carcinoma (SESCC). However, only few studies compared long-term survival outcomes of ESD with surgery, especially for T1b SESCC. This study compared the overall survival (OS), disearse free survival (DSS), recurrence-free survival(RFS), and complication rates of both, To evaluate the value of ESD in patients with T1b SESCC.
METHODS: We reviewed patients who underwent ESD (n = 47) or surgery (n =73) for T1b SESCC at Affiliated Hospital of Nanjing University of Chinese Medicine from 2009 to 2021. To increase the precision of our results interpretation, subgroups were analyzed according to the depth of tumor invasion and elderly people.
RESULTS: In the ESD and surgery groups,the overall mortality rates were 0/100 and 12.3/100 person years , incidence rates of recurrence were 2.13/100 and 11/100 person years,respectively. Kaplan-Meier survival analysis revealed no significant different in OS,DSS and RFS. Charlson comorbidity index(CCI) and depth of submucosal invasion were identified as risk factors for cancer recurrence in multivariate analysis.For elderly people, no significant differences in OS, DSS and RFS between different treatments.
CONCLUSION: ESD are related to lower complication rates and shorter hospital stay than surgery in long-term outcomes for patients with pT1b SESCC. But in pT1b-SM2 patients, we still need long-term follow-up.
Factors affecting the consideration of Public-Private Partnerships (PPPs) for smart city projects in Indore city
Authors: Lende Pranav K. , Ambadkar S. D. 
Published: February 2024

Abstract

This paper provides an in-depth analysis of Public-Private Partnerships (PPPs) in the context of urban development in Indore city, with a focus on initiatives undertaken under the Smart City scheme. It explores the conceptual framework of PPPs, elucidating governance structures, risk allocation mechanisms, and contractual arrangements crucial for effective collaboration between the public and private sectors. The rationale for PPPs in Indore's smart city initiatives, encompassing access to resources, risk mitigation, innovation, efficiency, and public accountability, underscores their significance in urban development. The paper examines the existing PPP projects implemented under the Smart City scheme in Indore, highlighting key achievements, challenges encountered, and lessons learned. It also discusses proposed PPP projects and future recommendations for enhancing urban infrastructure, service delivery, and quality of life for residents. The analysis encompasses diverse PPP models and structures, including Build-Operate-Transfer (BOT), Design-Build-Operate (DBO), Concession Agreements, and Service Contracts, tailored to the specific needs and characteristics of Indore's smart city projects. Furthermore, the paper addresses indexing, providing a comprehensive overview of the literature on PPPs in Indore city and related urban development initiatives. By synthesizing existing knowledge and offering insights into future research directions, this paper contributes to the understanding of PPPs in urban development and provides practical recommendations for policymakers, urban planners, and stakeholders involved in Indore's smart city transformation.
Research on Blood Cell Detection and Counting Based on YOLO-BC Algorithm
Authors: Liu Zeyu , Yuan Dan , Zhu Guohun 
Published: February 2024

Abstract
Blood cell detection and counting have always been of great medical importance because healthcare professionals can identify possible abnormalities in patients based on the numbers of different types of blood cells. However, traditional counting methods have certain limitations, such as smaller blood cells being ignored or misclassified, and the accuracy of identifying scenes with occlusion and overlap between blood cells is low. To solve the problem of blood cells being missed or misidentified in existing models, this study introduces the improved YOLO-BC algorithm to solve the pixel-level differences of different categories of blood cells by combining efficient multi-scale attention and full-dimensional dynamic convolution models, thereby achieving fast and accurate identification and counting of blood cells. The BCCD (Blood Cell Count and Detection) dataset was used for related experiments and performed data augmentation. The mAP@50 score based on YOLO-BC is 3.1% higher than that of YOLOv8, the value of mAP@50:95 increases by 3.7%, and F1-score increases by 2% on the same dataset and iou parameters, where small objects such as platelets can also be clearly detected. YOLO-BC shows a certain degree of applicability for automated testing of blood cells by experimental results.
1-(4-aminophenyl)-3-morpholinopiperidin-2-one: A new process related impurity of Apixaban- An anticoagulant drug
Authors: Kumar Vipin , Sharma Sachin Kumar , Singh Govind , Dhuriya Shyambaran , Balthu Lingaiah , Kumar Sushil , Devi Rashmi , Yadav Krishna Kumar , Chourasiya Sumit S. 
Published: February 2024

Abstract
In this report, we describe the identification of new process related impurity observed during the preparation of Apixaban-An anticoagulant drug. As a part of the investigation, we isolated the impurity by column chromatography with > 96% purity and analyzed the sample of impurity by LC-MS, IR and NMR spectroscopy. The LC-MS analysis coupled with IR and NMR analysis suggested that the impurity is 1-(4-aminophenyl)-3-morpholinopiperidin-2-one, generated during preparation of Apixaban intermediate (APN-05). The impurity is a reduced form of APN-05 possibly formed due to reduction of ring double bond of dihydropyridin-2(1H)-one moiety of APN-05.
Privacy-Preserving Vital node Identification in Complex Networks using Machine Learning
Authors: Diallo Diaoulé , Hecking Tobias 
Published: February 2024

Abstract

Identifying vital nodes in complex networks is critical in various research areas, including social network analysis, epidemiology, and physics. Centrality measures are commonly used and combined for this purpose. However, the impact of growing privacy concerns on the identification of vital nodes, particularly in networks containing sensitive data like Bluetooth-based contact networks, is not well understood. Our study evaluates the effectiveness of vital node identification algorithms under privacy-preserving network settings. Through simulations, we identify algorithms that are most effective for estimating node vitality when only limited network information is available. Furthermore, we demonstrate that machine learning models provided with aggregated output of the most promising approaches and trained with only 20 percent of the data can significantly outperform state-of-the-art approaches, especially when network information is strongly limited. This research advances the understanding of privacy-centric approaches in complex network analysis and shows how machine learning-based methods can empower advanced network analysis under privacy-preserving conditions.