bluuebunny/crossref_metadata_2025_split huggingface dataset:
AI-enabled Legacy Data Integration with Privacy Protection: a Case Study on Regional Cloud Arbitration Court
Authors: Song Jie , Fu Haifei , Jiao Tianzhe , Wang Dongqi 
Published: June 2023

Abstract
This paper reports an interesting case study on the Legacy Data Integration (LDI for short) for a Regional Cloud Arbitration Court. Due to the inconsistent structure and presentation, legacy arbitration cases can hardly integrate into the Cloud Court unless processed manually. In the case study, we aim to build an AI-enabled LDI method to replace the high-cost manual one and protect privacy during the process. Our method employs Optical Character Recognition (OCR), text classification, Named Entity Recognition (NER), and entity relation extraction to transform legacy data into system format. We train AI models to replace the tasks of the Court staff, such as reading and understanding legacy cases, removing privacy information, composing new records of cases to fit the Cloud Court, and inputting them through the system interfaces. With the applications of a Cloud Arbitration Court in Liaoning Provence, China, our intelligent LDI has similar effectiveness but greater efficiency than the manual LDI. Our method saves 90% of the workforce and achieves a 60%-70% information extraction rate of manual work. Our method achieves a comparable filtering effect for privacy while retaining the maximum amount of information. With the continuous development of informationalization and intelligentization in judgment and arbitration, many courts are building the court system using ABC technologies, namely Artificial intelligence, Big data, and Cloud computing. Our method could provide a practical reference when integrating legal data into the system.
A metric and its derived protein network for evaluation of ortholog database inconsistency
Authors: Yang Weijie , Ji Jingsi , Ling Shuyang , Fang Gang 
Published: June 2023

Abstract

Ortholog prediction, essential for various genomic research areas, faces growing inconsistencies amidst the expanding array of ortholog databases. The common strategy of computing consensus orthologs introduces additional arbitrariness, underscoring the need to identify proteins prone to ortholog prediction inconsistency. To address this, we introduce the Signal Jaccard Index (SJI), a novel metric based on unsupervised genome context clustering, to assess protein similarity. Utilizing SJI, we construct a protein network, revealing that proteins at the network peripheries primarily contribute to prediction inconsistency. Importantly, we show that a protein's degree centrality can gauge its assignment reliability to a consensus set, facilitating the refinement of ortholog predictions.
Genetic Polymorphisms of N-acetyl Transferase 2 and the Risk of Hepatotoxicity in Patients on Isoniazid Preventive Therapy at Kenyatta National Hospital
Authors: Akumu Reveszt M. , Guantai Eric M. , Oluka Margaret Ngwono , Kimotho James , Zablon Jeremiah O. 
Published: June 2023

Abstract
Background
N-acetyl transferase (NAT2) affects metabolism and disposition of isoniazid. Polymorphisms of NAT2 may affect pharmacokinetics of isoniazid (INH) by decreasing concentration of active drug in fast acetylators. High concentrations of drug in slow acetylators may lead to hepatotoxicity; this affects clinical outcomes of patients on therapy.
Objectives
The main objective was to characterize the distribution of selected single nucleotide polymorphisms (SNPs) of NAT2 in patients on IPT at the Comprehensive Care Centre (CCC) of Kenyatta National Hospital (KNH) and to investigate for a relationship between acetylator status and isoniazid induced hepatotoxicity (as indicated by elevated ALT levels).
Methods
The study design was a cross sectional study and entailed collection of patient data. The QIAamp ® DNA Mini kit was used for extraction of DNA and purification of Genomic DNA, followed by DNA sequencing. Data analysis was conducted using SPSS version 25(IBM USA).
Results
The prevalence of the homozygous NAT2 genotype was 19% and that of the heterozygous genotype was 50%. The proportion of the population with slow acetylator alleles was 56% and the proportion of fast acetylator alleles was 44%. Fisher's exact test showed no significant association between ALT levels and NAT2 genotype (P = 0.330).
Conclusion
The study found no significant association between NAT2 genotypes and ALT levels (P = 0.33)
Meta-learning-based Inductive Logistic Matrix Completion for Prediction of Kinase Inhibitors
Authors: Du Ming , Xie XingRan , Luo Jing , Li Jin 
Published: June 2023

Abstract
Protein kinases become an important source of potential drug targets. Developing new, efficient, and safe small-molecule kinase inhibitors has become an important topic in the field of drug research and development. In contrast with traditional wet experiments which are time-consuming and expensive, machine learning-based approaches for predicting small molecule inhibitors for protein kinases are time-saving and cost-effective that are highly desired for us. However, the issue of sample scarcity (known active and inactive compounds are usually limited for most kinases) poses a challenge for the research and development of machine learning-based kinase inhibitors active prediction methods. To alleviate the data scarcity problem in the prediction of kinase inhibitors, in this study, we present a novel Meta-learning based inductive logistic matrix completion method for the Prediction of Kinase Inhibitors (MetaILMC). MetaILMC adopts a meta-learning framework to learn a well-generalized model from tasks with sufficient samples, which can fast adapt to new tasks with limited samples. As MetaILMC allows the effective transfer of the prior knowledge learned from kinases with sufficient samples to kinases with a small number of samples, the proposed model can produce accurate predictions for kinases with limited data. Experimental results show that MetaILMC has excellent performance for prediction tasks of kinases with few-shot samples and is significantly superior to the state-of-the-art multi-task learning in terms of AUC, AUPR, etc., various performance metrics. Case studies also provided for two drugs to predict Kinase Inhibitory scores, further validating the proposed method's effectiveness and feasibility.
Bovine Mastitis: Prevalence and Antimicrobial Patterns of Staphylococcus aureus and Escherichia coli in Smallholder Dairy Farms of Adawa and Enticho Towns, Tigray, Ethiopia
Authors: Hailay Tekledimanos , Gugsa Getachew , Awol Nesibu , Tsegaye Yisehak , Ahmed Meselu 
Published: June 2023

Abstract
Mastitis is a costly and one of the most economically important production diseases affecting the dairy industry. A cross-sectional study was conducted from December 2015 to May 2016 in Adwa and Enticho Towns, Tigray, Ethiopia with the objectives of estimating the prevalence of bovine mastitis, isolation, characterization, and determination of antimicrobial profiles of E. coli and S. aureus from milk samples of dairy cows. A total of 156 lactating dairy cows from 23 smallholder dairy farms were sampled. Data related to herd size, breed, body condition, milking practice, farm hygiene, udder, and milk abnormalities were collected. California Mastitis Test (CMT) was performed. CMT-positive samples were collected. Bacteriological isolation, identification, and antibiogram testing were conducted. Data were recorded and analyzed for descriptive statistics. The overall prevalence rates of bovine mastitis, clinical mastitis, and subclinical mastitis in the study areas were found to be 35.9%, 3.85%, and 32%, respectively. Breed-based prevalence of mastitis was 21.74% and 41.8% for local and crossbreeds, respectively, (χ²=5.683; p ≤ 0.05). The overall prevalence of E. coli and S. aureus were 14.1% and 8.3%, respectively. The antimicrobial susceptibility test revealed that S. aureus was 100% susceptible to amoxicillin, kanamycin, and penicillin but 95.5% of E. coli isolates were susceptible to kanamycin, oxytetracycline, and streptomycin. Hence, regular CMT testing should be undertaken on every farm to screen subclinical mastitis-positive cows and all dairy farms should follow a good hygienic milking practice.
A NEW CLIMATOLOGICAL PERCEPTION OF HURRICANES IN THE NORTH ATLANTIC.
Authors: Neto José Augusto Ferreira , Mendes David , Gonçalve Weber Andrade , Cintra Marcio Machado , Júnior José Francisco de Oliveira 
Published: June 2023

Abstract
This study conducts an in-depth analysis of hurricane trajectories and their variabilities across categories 1 to 5 over several decades. Utilizing HURDAT2 data from 1961 to 2021, the analysis categorizes hurricanes based on the rate of pressure drop within a six-hour interval, revealing distinct patterns in intensification and weakening among different categories. The K-means clustering method synthesized hurricane trajectories into representative paths, illustrating significant variations among decades. The research indicates that Category 1 and 2 hurricanes predominantly originate from tropical depressions, with this trend slightly intensifying in Categories 3 and 4. In contrast, Category 5 displayed variation, revealing an increased frequency in subsequent decades. Moreover, the study analyzes the monthly distribution of hurricanes, finding September as the peak month across categories. The analysis further detects a significant interannual variability with a noticeable intensification in hurricane activity since the 1990s, albeit with some reductions in the early 2010s. The Accumulated Cyclone Energy (ACE) is used to summarize cyclonic activities, with results indicating a decrease from 1970–1995, followed by a consistent surge over the last 15 years. This is aligned with previous research suggesting an approximately 60% increase in ACE since the 1980s. Additionally, an analysis of North Atlantic basin data reflects a progressive increase in the frequency of named storms and hurricanes, particularly from 1991 onwards. In conclusion, the study highlights not only an escalating frequency of hurricanes but also an enhanced variability and unpredictability, which necessitates further research to comprehend the underlying causes and evaluate the potential socio-economic and environmental consequences.
Nuclei-Net: A multi-stage fusion model for nuclei segmentation in microscopy images
Authors: Roy Kaushiki , Saha Subhadeep , Banik Debapriya , Bhattacharjee Debotosh 
Published: June 2023

Abstract
In this study, we proposed Nuclei-Net: A multi-stage fusion model for segmenting nuclei in microscopy images. Our proposed model works in two stages. In the first stage, we incorporated a deep learning (DL) based model namely Mask Region-Based-CNN (Mask-RCNN) for the coarse segmentation of the nuclei. However, it can be observed that the predicted mask of Mask-RCNN is unable to segment some complex overlapping nuclei boundaries. So, in the second stage of Nuclei-Net, we detect the complex overlapping nuclei and pass only those overlapping nuclei for fine segmentation. We proposed a marker generation method for each nucleus in a clump. These markers serve as seed points for the marker-controlled watershed algorithm. This algorithm fine segments those overlapping nuclei which were failed by Mask R-CNN. Finally, we fuse the results from the two stages to develop the final segmentation map. Our proposed Nuclei-Net is trained and tested on Kaggle 2018 Data Science Bowl dataset and compared with some standard DL models. Our proposed model achieved a DSC of 83.3%, AJI of 70.03%, Pre of 89.80%, and Rec of 87.20% respectively
An Efficient Framework for Sensor Data Collection by UAV Based on Clustering, Two-Fold Ant Colony Optimization and Node Grouping
Authors: Shayboub Magdy , Reda Eman , Al-Mahdi Hassan , Nassar Hamed 
Published: June 2023

Abstract
Unmanned Aerial Vehicles (UAVs) are a promising solution for sensor data collection (DC) in large-scale area. The challenge is to minimize the DC route, which will reduce UAV energy consumption and data latency. We propose a three-phase efficient solution with innovative algorithms to address this challenge. First, an initial shortest route that traverses all SNs is established based on the ant colony optimization (ACO) and the $K$-means algorithms. Second, we group the sensor nodes (SNs) along the initial route using the footprint of the UAV, so that the latter can collect the data of the group in one stop, instead of stopping at each SN. By sequencing the hovering locations, we obtain a (shorter) intermediate route. Third, we shorten this route even further, by applying ACO to the set of hovering locations of the intermediate route. The solution has been implemented fully in Python. The results show that the route length gets shorter progressively with each phase. To evaluate the performance of the solution objectively, we have compared it with four states of the art solutions. The results show vividly that the proposed solution produces a DC route 19.28\% shorter than the shortest route produced by the four competitive solutions. Moreover, it demonstrates a remarkable improvement by retaining 44\% of energy in most SNs while over 99\% energy depletion observed in the five state-of-the-art competitive solutions.
The inhibitory effect of common food spices bioactive compounds against KRAS G12C and KRAS G12D: an in-silico approach
Authors: Agosile Oluwafemi Ojo , Njoku Prisca Chinonso , Oduwe Ugochukwu , Aremu Ayokanmi Joseph , Fajobi Sunday Joseph , Akachukwu Sunday Onyedikachukwu , Adebisi Aderinsola Racheal , Abolaji Amos Olalekan , Omirin Emmanuel Sunday 
Published: June 2023

Abstract
More than 19 percent of all human cancer cases globally express mutant RAS proteins, which are the most frequently altered oncogenic proteins. Targeting cancer cells becomes difficult because these oncogenic proteins and cancer cells that are driven by Ras mutation show very aggressive traits. KRAS is the most expressed among the three isoforms of Ras mutation, which usually occur at position 12. KRAS G12C and KRAS G12D are common types of KRAS that have been proven difficult to target for many years. In recent times, many studies started to show that a lot of food spices possess strong anticancer properties that can target many cancer cells. Therefore, computational methods to access the anticancer properties of some selected compounds that are present in common food spices against KRAS G12C and KRAS G12D in their inactive GDP-bound state. These compounds were molecularly docked with standard drugs after their ADMET screening revealed their druglikeness. Chlorogenic acid and Rosmarinic acid displayed better binding affinity to the targets than all other drugs studied in this research. Molecular dynamics simulation was used to verify this compound's stability. Therefore, after extensive experimental effort, it is most likely that Rosmarinic acid would be effective as a KRAS G12C medicine.
Flow Separation Control of a Vertical Stabiliser using a Rudder-mounted Slat
Authors: Muhammad Mahmud Jamil , Wang Yaxing , Mao Xuerui , Choi Kwing-So 
Published: June 2023

Abstract
A joint study utilising experimental and numerical methods was carried out to investigate the aerodynamic effect of a rudder-mounted slat on a vertical stabiliser. The wind tunnel test results showed that the side force coefficient was increased by up to 4% while the drag coefficient was unchanged when the rudder deflection angle was set to δ = 30°. Large eddy simulation (LES) results suggested that the rudder-mounted slap can increase the circulation around the vertical stabiliser, showing that the flow from the upstream recirculating regions was drawn towards the rudder surface. Associated changes in the turbulent flow field, including the mean and turbulent flow field and the vortical structure are also presented to understand the flow control mechanism by the rudder-mounted slat.
Microglia activation induced by a rat model of mild acute pancreatitis
Authors: Cabral-França Tamires , Cruz Fernanda F. , Silva Paulo C. , Pannain Vera L. N. , Fernandes Arlete , Eulálio José M. R. , Paiva Maurício M. , Macedo-Ramos Hugo , Manso Jose E. F. , Baetas-da-Cruz Wagner 
Published: June 2023

Abstract
Background Acute pancreatitis is an inflammation of the pancreatic glandular parenchyma that causes injury with or without the destruction of pancreatic acini. Clinical and experimental evidence point to some systemic pro-inflammatory mediators as responsible for triggering the basic mechanisms involved in microglial reactivity. Here, we investigated the possible repercussions of mild acute pancreatitis (AP) on the production of inflammatory mediators in the brain parenchyma focusing on microglial activation in the hippocampus.
Methods The acute pancreatic injury in rats was induced by a pancreas ligation surgical procedure (PLSP) on the splenic lobe, which corresponds to approximately 10% of total mass of the pancreas. Blood samples were collected via intracardiac puncture for the measurement of serum amylase. After euthanasia, frozen or paraffin-embedded brains and pancreas were analyzed using qRT-PCR or immunohistochemistry, respectively.
Results Immunohistochemistry assays showed a large number of Iba1 and PU.1 positive-cells in the CA1, CA3, and dentate gyrus (DG) regions of the hippocampus of the PLSP group. TNF-α mRNA expression was significantly higher in the brain from PLSP-group. NLRP3 inflammasome expression was found to be significantly increased in the pancreas and brain of rats of the PLSP-group. High levels of BNDF mRNA were found in the rat brain of PLSP-group. In contrast, NGF mRNA levels were significantly higher in the control group versus PLSP-group.
Conclusion Our results suggest that AP has the potential to induce morphological changes in the microglia compatible with the activated phenotype.
Mass spectrometry-based proteomics data from thousands of HeLa control samples
Authors: Webel Henry , Perez-Riverol Yasset , Nielson Annelaura Bach , Rasmussen Simon 
Published: June 2023

Abstract
Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable amounts of time for researchers. Therefore we provide machine based metadata for easy selection and overview along the 7,444 raw files and MaxQuant search output. For convenience, we provide three filtered and assembled development datasets for three data levels ready for use: on protein groups, peptides and precursors level. Next to providing easy to access training data, we provide a SDRF file annotating each raw file with instrument settings allowing automated reprocessing. We encourage others to enlarge this data set by instrument runs of further HeLa samples from different machine types by providing our workflows and analysis scripts.
Revisiting the Direct Fourier Filtering Technique for the Maximal Decay Rate of Boundary-damped Wave Equation by Finite Differences and Finite Elements
Authors: Ozer Ahmet Ozkan , Emran Rafi 
Published: June 2023

Abstract
The one-dimensional PDE model of the wave equation with a state feedback controller at its boundary, which describes wave dynamics of a wide-range of controlled mechanical systems, has exponentially stable solutions. However, it is known that the reduced models of the wave equation by the standard Finite Differences and Finite Elements suffer from the lack of exponential stability (and exact observability without a state feedback controller) uniformly as the discretization parameter tends to zero. This is due to the loss of uniform gap among the high-frequency eigenvalues as the discretization parameter tends to zero. One common remedy to overcome this discrepancy is the direct Fourier filtering of the reduced models, where the high-frequency spurious eigenvalues are filtered out. After filtering, besides from the strong convergency, the exponential decay rate, mimicking the one for the partial differential equation counterpart, can be retained uniformly. However, the existing results in the literature are solely based on an observability inequality of the control-free model, to which the filtering is implemented. Moreover, the decay rate as a function of the filtering parameter is implicit. In this paper, exponential stability results for both filtered Finite Difference and Finite Element reduced models are established directly by a Lyapunov-based approach and a thorough eigenvalue estimation.The maximal decay rate is explicitly provided as a function of the feedback gain and filtering parameter. Our results, expectedly, mimic the ones of the PDE counterpart uniformly as the discretization parameter tends to zero. Several numerical tests are provided to support our results.
Mathematics Subject Classification (2010). 35Q60, 35Q93; 74F15; 35Q74, 93B52.
An image patch selection algorithm for the detection of Oral Squamous Cell Carcinoma using textural and morphological features
Authors: Subhija Eranjoli Nalupurakkal , Reju Vaninirappuputhenpurayil Gopalan 
Published: June 2023

Abstract
A significant proportion of cancer-related deaths are caused by oral cancer, and oral squamous cell carcinoma is prevalent. Systems for computer-aided diagnosis can lower subjective errors and assist the pathologist in making a more accurate diagnosis. Feature extraction from the whole histopathology images is difficult due to the structural variation of the oral tissue images. A new patch selection algorithm can be used to create and select image patches containing nuclei-specific information and extract their textural and morphological characteristics, improving cancer diagnosis. We extract the morphological characteristics of the nucleus from the selected patches using five pretrained networks. The texture from the regions of the selected patches is also extracted from the Haar wavelet decomposed components using the gray-level co-occurrence matrix. Then, we combine the textural and morphological features to create the final feature vector, followed by feature selection using an extra trees classifier. In order to detect oral squamous cell carcinoma, we examined the feasibility of using six classifiers, including voting classifier, logistic regression, random forest, Naive Bayes, K nearest neighbor, and support vector machine. The performance of the algorithm is evaluated using accuracy, precision, sensitivity, confusion matrix, ROC curves, and AUC values of characteristic curves. Together, ResNet 50 and the voting classifier produce results with a high accuracy of 97.66 % and a precision of 98.00 %. The suggested patch-based method outperforms the image based method and is accurate and efficient for identifying oral squamous cell carcinoma and will be a reliable and precise support tool for oral pathologists.
Improving Detection Capabilities of YOLOv8-n for Small Objects in Remote Sensing Imagery: Towards Better Precision with Simplified Model Complexity
Authors: Bai Ruihan , Shen Feng , Wang Mingkang , Lu Jiahui , Zhang Zhiping 
Published: June 2023

Abstract
This study presents a comprehensive analysis and improvement of the YOLOv8-n algorithm for object detection, focusing on the integration of Wasserstein Distance Loss, FasterNext, and Context Aggravation strategies. Through a detailed ablation study, each strategy was systematically evaluated individually and collectively to assess its contribution to the model's performance. The results indicate that each strategy uniquely enhances the model's performance, significantly increasing mAP and reducing model complexity when all three are integrated. Visualizations through Grad-CAM further substantiate the improved model's capacity to extract and focus on key object features. Comparisons with existing models, such as YOLOv5-n, YOLOv5-s, YOLOX-n, YOLOX-s, and YOLOv7-tiny, the improved YOLOv8-n model achieves an optimal balance between accuracy and model complexity, outperforming other models in terms of model accuracy, model complexity, and model inference speed. Further image inference tests validate the model's performance, showcasing its superior detection capabilities.