bluuebunny/crossref_metadata_2025_split huggingface dataset:
OpenMP offload at the Exascale using Intel® GPU Max 1550: evaluation of STREAmS compressible solver
Authors: Salvadore Francesco , Rossi Giacomo , Sathyanarayana Srikanth , Bernardini Matteo 
Published: April 2024

Abstract
Nearly 20 years after the birth of general purpose GPU computing, the HPC landscape is now dominated by GPUs. After years of undisputed dominance by NVIDIA, new players have entered the arena in a convincing manner, namely AMD and more recently Intel, whose devices currently power the first two clusters in the Top500 ranking. Unfortunately, code porting is still a major problem, even more so with the presence of different vendors, but at the same time the emergence of simplified standard paradigms suggests an encouraging prospect for developers.
In this work, we analyze the porting and performance of STREAmS, a community code for compressible fluid dynamics, on Intel® Data Center GPU Max 1550 (formerly called Ponte Vecchio or PVC) based architectures. First, we discuss the porting, based on the offload functionality of the OpenMP 5.x paradigm, and in particular using a hybrid directives/APIs approach that fits smoothly into the multi-backend software ecosystem of STREAmS-2. Second, we analyze the performance of the code on two benchmark clusters powered by PVC, including the exascale Aurora cluster. The performance is evaluated at the different levels 1 of parallelism involved, i.e., the intrinsic parallelism of the PVC tile, the inter-tile parallelism within the GPU configuration, between the GPUs within the node, and between the nodes within the cluster. The analysis shows that although the implementation complexity of the OpenMP porting is limited, it is necessary to follow some important guidelines to achieve satisfactory performance. The PVC GPU shows about 40% higher performance than the NVIDIA A100 or AMD MI250X GPUs, which however were released about 3 years earlier. Both intra-node and inter-node scalability show good results. Overall, the introduction of PVC into the GPU computing HPC landscape represents a positive step forward for diversification and competitiveness in the sector.
Enhanced Diagnostic Efficiency of a Novel Fecal Methylated Gene Model for Early Colorectal Cancer Detection
Authors: Yun Peng , Kulaixijiang Kamila , Pan Jiang , Yang Luping , Wang Nengzhuang , Xu Zheng , Zhang Yaodong , Cai Haifang , Zhao Zi-Ye , Zhu Min , Yan Hongli 
Published: April 2024

Abstract
Background and Aims
Methylation of stool DNA (sDNA) is a reliable noninvasive early diagnostic marker for colorectal cancer (CRC). Our study aimed to identify a new gene panel for the early diagnosis of CRC.
Methods
We conducted methyl-CpG binding domain isolated genome sequencing (MiGS) on 3 CpG island methylation phenotype (CIMP)-positive and 3 CIMP-negative CRC tissues and their corresponding normal adjacent tissues. Subsequently, by utilizing both the aforementioned data and public datasets, we identified a set of promising methylated sDNA markers for CRC. Finally, we developed a combined diagnostic model (CDM) for CRC based on the methylation status of PRDM12, FOXE1, and SDC2 and evaluated its performance in an independent multicenter validation cohort.
Results
A total of 1,062 participants were included in this study. The area under the curve (AUC) of the CDM was 0.979 (95% CI: 0.960–0.997), and the optimal sensitivity and specificity were 97.35% and 99.05%, respectively, in the training cohort (n = 231). In the independent validation cohort (n = 800), the AUC was 0.950 (95% CI: 0.927–0.973), along with the optimal sensitivity of 92.75% and specificity of 97.21%. When CRC and advanced adenoma (AAD) were used as diagnostic targets, the model AUC was 0.945 (95% CI: 0.922–0.969), with an optimal sensitivity of 91.89% and a specificity of 95.21%. The model sensitivity for nonadvanced adenoma patients was 68.66%.
Conclusion
The sDNA diagnostic model CDM, developed from both CIMP-P and CIMP-N, exhibited exceptional performance in CRC and could serve as a potential alternative strategy for CRC screening.
Prediction of PD-L1 tumor positive score in lung squamous cell carcinoma with H&amp;E staining whole slide images and deep learning
Authors: Wang Qiushi , Deng Xixiang , Ma Qiang , Zhao Lianhua , Feng Yangyang , Wang Yiying , Zhao Yuan , Chen Yan , Zhong Peng , He Peng , Ma Mingrui , Feng Peng , Xiao Hualiang 
Published: April 2024

Abstract

Background
Detecting programmed death ligand 1 (PD-L1) expression based on immunohistochemical (IHC) staining is an important guide for the treatment of lung cancer with immune checkpoint inhibitors. However, this method has problems such as high staining costs, tumor heterogeneity, and subjective differences among pathologists. Therefore, the application of deep learning models to segment and quantitatively predict PD-L1 expression in digital sections of Hematoxylin and eosin (H&E) stained lung squamous cell carcinoma is of great significance.
Methods
We constructed a dataset comprising H&E-stained digital sections of lung squamous cell carcinoma and used a Transformer Unet (TransUnet) deep learning network with an encoder-decoder design to segment PD-L1 negative and positive regions and quantitatively predict the tumor cell positive score (TPS).
Results
The results showed that the dice similarity coefficient (DSC) and intersection overunion(IoU) of deep learning for PD-L1 expression segmentation of H&E-stained digital slides of lung squamous cell carcinoma were 80% and 72%, respectively, which were better than the other seven cutting-edge segmentation models. The root mean square error (RMSE) of quantitative prediction TPS was 26.8, and the intra-group correlation coefficients with the gold standard was 0.92 (95% CI: 0.90–0.93), which was better than the consistency between the results of five pathologists and the gold standard.
Conclusion
The deep learning model is capable of segmenting and quantitatively predicting PD-L1 expression in H&E-stained digital sections of lung squamous cell carcinoma, which has significant implications for the application and guidance of immune checkpoint inhibitor treatments.
Trial registration:
ChiCTR1900022640(2019-04-19).
A Systematic Literature Review on Mammography: Deep Learning in Redefining Breast Cancer Diagnosis for the Asian Perspective
Authors: Amin Ashwini , Acharya Dinesh , C Siddalingaswamy P , K Prakashini , Mathew Stanley 
Published: April 2024

Abstract

Breast cancer (BC) is a leading cause of mortality in women worldwide, and its incidence and prognosis vary greatly, especially in low-income areas such as Asia and Africa, compared to America and Europe. This review examines the critical role of computer-aided diagnostic systems utilizing Deep Learning (DL) techniques in improving the precision of BC detection, which can help researchers and practitioners better understand the obstacles and emerging trends in the field. The comprehensive analysis and synthesis of the published articles is provided by the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA). Following several searches, 287 articles were found eligible for the assessment followed by data extraction and synthesis. This work provides insight into the pathology of BC and its corresponding mammographic appearance, a comprehensive analysis of publication trends, important contributors, and emerging themes using bibliometric techniques in addition to a thorough review of research articles. Focus maps were also created to identify and shed light on the body of knowledge. In contrast to other research, this review sheds light on the subtle identification of breast density, mass, and calcification from mammography images by emphasizing the critical evaluation of image pre- processing, augmentation, detection, segmentation, and classification techniques used from 2018 to 2023, further highlighting articles related to the Asian dataset. Furthermore, the review examines contributions made by authors, countries, and experimental datasets, providing insights into various approaches.
Prospero registration: CRD42022478896
Optimizing Photocatalytic Efficiency and Durability of Zeolite-Based Catalysts via Cation Doping for Effective Dye Contaminant Removal
Authors: Moosavifar Maryam , Akbari Sara , Nejat-Dehkordi Maryam 
Published: April 2024

Abstract

Decoration of Ce and Fe in the dealuminated Y zeolite (DAZY) framework occurred using the isomorphous substitution method in two steps post-synthesis. At first, Y zeolite was dealuminated by EDTA treatment, creating vacant T sites and silanol groups followed by an exchange of Ce and Fe impregnation of the resultant DAZY zeolite with cerium (IV) sulfate and ferric nitrate in one step due to their solubility of related salts to the formation of Si-O-Ce and Si-O-Fe bonds. For this purpose, we have selected three distinct ratios of metals/zeolite, which are 1:3, 1:10, and 1:20. An important outcome was achieved with a 1:20 ratio of metals to zeolite, providing strong evidence for the superiority of this configuration. The catalyst was characterized using spectroscopic techniques. The photocatalytic activity was investigated in the degradation of para-nitrophenol. To optimize the process, we conducted the effect of various parameters, including reaction time, catalyst amount, temperature, pH, isoelectric point, and 4-NP concentration on the photodegradation process. The optimal conditions for the removal of 4-nitrophenol were obtained in the presence of DAZY decorated with Fe and Ce under ultraviolet radiation, 40 µL of hydrogen peroxide, 0.03 g of photocatalyst, and a concentration of 5×10-4 M 4-nitrophenol during 90 minutes. Studying Hinshelwood's equations showed that the reaction rate was pseudo-first order. Meanwhile, the reaction rate increases with the increase of the initial concentration. The catalyst can be reused up to five times while maintaining its catalytic activity. COD experiments proved the mineralization of dye contamination.
Nutritional profile and infection-related hospital admissions in children with chronic kidney disease
Authors: Madhileti Sravani , Selvam Sumithra , Iyengar Arpana 
Published: April 2024

Abstract

Protein energy wasting (PEW) and undernutrition are highly prevalent in children with chronic kidney disease (CKD), but their impact on clinical outcomes is not well-described. This prospective longitudinal study in children with CKD assessed the association of nutritional parameters with infection-related hospital admissions (IRHA).
Methods: Children with CKD2-5D aged 2-18years, infection-free for 1 month were recruited over 5 years. Evaluation for undernutrition by subjective global nutritional assessment and for PEW using pediatric criteria were undertaken and categorised as mild (>2criteria), standard (>3criteria) and modified PEW (>3criteria with short stature). The IRHA (severe viral, bacterial or fungal infections) were recorded.
Results: Among 137 children (45 on dialysis; age 123±46 months; 70% males) undernutrition was seen in 60% and PEW in 52%. Over 38±21 months follow-up, 107(78%) required hospital admissions (67% IRHA). The incidence rate of IRHA in days per patient-year was higher in those with undernutrition compared to well-nourished children [1.74(1.27,2.31) vs 0.65(0.44,0.92) p<0.0001] and higher in those with PEW compared to no PEW [1.74(1.30,2.28) vs 0.56(0.36,0.82) p<0.0001] respectively. On adjusted analysis, independent risk factors for IRHA were undernutrition, low BMI, hypoalbuminemia and dialysis status with modified PEW [OR5.34(2.16,13.1) p<0.001] and raised CRP [OR4.66(1.56,13.9) p=0.006] having the highest risk. Additionally, modified PEW and BMI were noted to have a 2-fold risk for recurrent infections.
Conclusion: In children with CKD2-5D, incidence rate of IRHA was significantly high in those with undernutrition and PEW. While dialysis, poor nutritional status, inflammation were risk factors for IRHA, modified PEW and BMI were associated with recurrent infections.
Exploring Potential Drug Targets for Pancreatic Cancer Based on Mendelian Randomization
Authors: Yao Peishan , huang junming , dang yi , Wang Ya 
Published: April 2024

Abstract
Objective
Genome-wide association studies (GWAS) provide a rich resource for identifying risk factors and biomarkers associated with cancer susceptibility. This study aims to use Mendelian randomization (MR) analyses within the proteome and transcriptome to explore potential protein markers and therapeutic targets for pancreatic cancer.
Methods
Exposure data were derived from expression quantitative trait loci (eQTL) data from GTEx V8 and the eQTLGen Consortium, covering 838 and 31,684 participants, as well as protein quantitative trait loci (pQTL) data for 3,703 proteins with a sample size of 27,698 participants. The pancreatic cancer GWAS dataset was obtained from the FinnGen Consortium, including 1,626 pancreatic cancer patients and 314,193 controls. The inverse variance weighted(IVW) and Wald ratio were the main analytical methods to assess the causal relationship between the proteome/transcriptome and pancreatic cancer. Cochran's Q test and MR-Egger intercept were used to evaluate heterogeneity and pleiotropy. Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction networks revealed functional characteristics and biological relevance.
Results
A total of 16,059 mRNAs and 1,608 proteins were included in the study. MR analysis using pQTL and eQTL data showed that 88 proteins and 811 mRNAs were causally related to pancreatic cancer based on the IVW and Wald ratio methods. Among these 88 proteins and 811 mRNAs, eight genes overlapped, including HAGH, FGF2, DTD2, IDUA, and CD248, demonstrating consistent causal effects with pancreatic cancer at both the protein and mRNA levels. However, IRF3, PILRA, and AMY2B showed inconsistent effects on pancreatic cancer at the protein and mRNA levels. GO analysis highlighted processes related to cellular transport, and KEGG pathway analysis suggested involvement in metabolic pathways and signaling pathways.
Conclusion
This study identified key proteins and mRNAs associated with pancreatic cancer, enhancing our understanding of the disease's molecular mechanisms and providing insights for future research and therapeutic development.
Structure-based chemical ontology improves chemometric prediction of antibacterial essential oils
Authors: Yabuuchi Hiroaki , Fujiwara Makiko , Shigemoto Akihiko , Hayashi Kazuhito , Nomura Yuhei , Nakashima Mayumi , Ogusu Takeshi , Mori Megumi , Tokumoto Shin-ichi , Miyai Kazuyuki 
Published: April 2024

Abstract
Plants are valuable resources for drug discovery as they produce diverse bioactive compounds. However, the chemical diversity makes it difficult to predict the biological activity of plant extracts via conventional chemometric methods. In this research, we propose a new computational model that integrates chemical composition data with structure-based chemical ontology. For a model validation, a training dataset was prepared from literature on antibacterial essential oils to classify active/inactive oils. A random forest classifier constructed from the data showed improved prediction performance in a test dataset. Prior feature selection using hierarchical information criterion further improved the performance. Furthermore, an antibacterial assay using a standard strain of Staphylococcus aureus revealed that the classifier correctly predicted the activity of commercially available oils with an accuracy of 83% (= 10/12). The results of this study indicate that machine learning of chemical composition data integrated with chemical ontology can be a highly efficient approach for exploring bioactive plant extracts.
A qualitative exploration of student cognition when answering text-only or image-based histology multiple-choice questions.
Authors: Holland Jane , McGarvey Alice , Flood Michelle , Joyce Pauline , Pawlikowska Teresa 
Published: April 2024

Abstract
Interpretation of images and spatial relationships is essential in medicine, but the evidence-base on how to assess these skills is sparse. Thirty medical students were randomized into two groups (A and B), and invited to “think aloud” while completing 14 histology MCQs. All students answered six identical MCQs, three with only text and three requiring image interpretation. Students then answered eight “matched” questions, where a text-only MCQ on Version A was “matched” with an image-based MCQ on Paper B, or vice versa. Students’ verbalizations were coded with a realist, inductive approach and emerging codes were identified and integrated within overarching themes. High-performing students were more likely to self-generate an answer as compared to middle and lower-performing students, who verbalized more option elimination. Images had no consistent influence on item statistics, and students’ self-identified visual-verbal preference (“learning style”) had no consistent influence on their results for text or image-based questions. Students’ verbalizations regarding images depended on whether interpretation of the adjacent image was necessary to answer the question or not. Specific comments about the image were present in 95% of student-item verbalizations (142 of 150) if interpreting the image was essential to answering the question, whereas few students referred to images if they were an unnecessary addition to the vignette. In conclusion, while assessing image interpretation is necessary for authenticity and constructive alignment, MCQs should be constructed to only include information and images relevant to answering the question, and avoid adding unnecessary information or images that may increase extraneous cognitive load.
Unveiling the DHX15 - G-patch Interplay in Retroviral RNA Packaging
Authors: Rumlová Michaela , Dostálková Alžběta , Krizova Ivana , Junková Petra , Racková Jana , Kapisheva Marina , Novotný Radim , Danda Matěj , Zvonařová Karolína , Šinkovec Larisa , Večerková Kateřina , Bednarova Lucie , Ruml Tomas 
Published: April 2024

Abstract
We explored how a simple retrovirus, Mason-Pfizer monkey virus (M-PMV) to facilitate its replication process, utilizes DHX15, a cellular RNA helicase, typically engaged in RNA processing. Through advanced genetic engineering techniques, we showed that M-PMV recruits DHX15 by mimicking cellular mechanisms, relocating it from the nucleus to the cytoplasm to aid in viral assembly. This interaction is essential for the correct packaging of the virus's genetic material and critical for its infectivity. Our findings offer new insights into the mechanisms of viral manipulation of host cellular processes, highlighting a sophisticated strategy that viruses employ to leverage cellular machinery for their replication. This study adds valuable knowledge to the understanding of viral-host interactions but also suggests a common evolutionary history between cellular processes and viral mechanisms. This discovery opens a new perspective on the export mechanism of cellular intron-retaining mRNAs on packaging of viral genetic information and potentially develop ways to stop them.
Multi-objective optimization for balancing surface roughness and material removal rate in milling hardened SKD11 alloy steel with SIO2 nanofluid MQL
Authors: Bui Gia-Thinh , Do The-Vinh , Nguyen Quoc Manh , Thi Minh Hue Pham , Vu Minh Hung 
Published: April 2024

In manufacturing practice, manufacturers always strive to achieve both quality and productivity targets simultaneously. In the first part, this study examines the relationship between input factors, including cutting speed, depth of cut, and feed rate, and the output response, which is surface roughness, when milling hardened SKD11 alloy steel under minimum coolant lubrication conditions using SiO2 nanofluid. The input parameters are divided into four levels to determine their influence on surface roughness and to find the optimal conditions for achieving the minimum surface roughness. The experimental design was conducted using an L16 array. A second-order regression model was developed to describe the relationship between the input variables and the output response. In the second part, multi-objective optimization was performed to simultaneously achieve the minimum surface roughness and the maximum material removal rate (MRR). The Response Surface Methodology (RSM) was employed in this study. The results indicated that to achieve the minimum surface roughness, machining should be performed at a cutting speed of 100 m/min, a cutting depth of 0.2 mm, and a feed rate of 0.01 mm/tooth. With these settings, the predicted surface roughness could reach 0.0451 µm. On the other hand, for the multi-objective optimization, to achieve the minimum surface roughness and the maximum MRR simultaneously, machining should be carried out at a cutting speed of 100 m/min, a cutting depth of 0.36 mm, and a feed rate of 0.0168 mm/tooth. With this cutting condition, the predicted surface roughness could reach 0.1069 µm, and the predicted MRR could reach 775.06 mm3/min
Case study on water quality index for wastewater treatment in Mumbai
Authors: Shrivastava Preeti , Mategaonkar Meenal 
Published: April 2024

Abstract

The traditional wastewater treatment process is laborious and demanding in terms of time, energy, and space. With rapid advancements, there's a quest for more efficient techniques that can achieve comparable treatment outcomes while minimizing these demands. This study proposes a solution by employing ozone as an Advanced Oxidation Process (AOP) to expedite the treatment while enhancing the water quality. The Water Quality Index (WQI) offers a consolidated representation of overall water quality by considering multiple quality parameters. Although typically used for assessing surface water quality, this study utilizes WQI to gauge the enhancement in parameters post-treatment. The research investigates the impact of varied ozone doses on raw sewage samples collected from four different sites in Mumbai, focusing on parameters such as dissolved oxygen, turbidity, hardness, chemical oxygen demand (COD), and WQI. The results demonstrate a notable percentage enhancement in WQI, ranging from 30 to 60% across various sites within a short 25-minute timeframe, attributed to the application of ozone.
Identification, degradation characteristics, and application of a newly isolated pyridine-degrading Paracidovorax sp. BN6-4
Authors: Liang Die , Xie Yifei , Jiang Yi , Xu Wenlai , Wang Zicheng , Zhang Dan 
Published: April 2024

ABSTRACT

The Paracidovorax sp. BN6-4 capable of degrading high concentrations of pyridine was isolated from the coking sludge. The removal rate of BN6-4 to 1,000 mg/L pyridine during 48 h was 97.49 ±1.59%. The primary intermediate metabolites of pyridine degradation by strain BN6-4 were identified by gas chromatography-mass spectrometry (GC-MS), including N-Ethylurea, acetamidoacetaldehyde, and N-Hydroxymethylacetamide, etc. Subsequently, two different biodegradation pathways of pyridine were proposed. First, the hydroxylation of pyridine to form the intermediates pyridin-2(1H)-one and 5,6-dihydropyridine-2,5-diol, the former undergoing oxidative ring opening and the latter oxidative ring opening via N-C2 and C2–C3 ring opening to ammonia and carbon dioxide. Furthermore, the organic matter was greatly degraded by the bioremediation of real coking wastewater using BN6-4. This study enriched the microbial resource for pyridine degradation and provided new insights about the biodegradation pathway of pyridine, which is of great significance for the pyridine pollution control and coking wastewater treatment.