bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring the Antioxidant and Antiproliferative Properties of Flacourtia indica Extracts on Lung Cancer Cells: A Comprehensive Analysis Utilizing GC-MS, Molecular Docking, and PASS Analysis.
Authors: Bashera Machi Al , Parvin Mst. Shahnaj , Islam Md. Badrul , Rana G.M. Masud , Rony Satyajit Roy , Islam Md. Ekramul 
Published: April 2024

Abstract
Background
The study aimed to explore the anti-proliferative potential against a lung cancer cell line for the first time, alongside investigating phytochemical composition, antioxidant activity, and conducting GCMS analysis of Flacourtia indica extracts. This was supplemented by in-silico molecular docking and PASS prediction study for selected compounds. Traditionally decoctions of Flacourtia indica are utilized as anti-helminthic and antibacterial in Bangladesh. Multiple studies have shown its antioxidant, antibacterial, and anti-colorectal cancer effects.
Methods
The aerial plant was extracted with n-hexane, chloroform, and methanol in order of polarity and the phytoconstituents were determined by GC-MS spectroscopy techniques. MTT assay was used to measure lung cancer cell line viability to determine extract's anti-proliferative effect. The phenolic, flavonoid, antioxidant content analysis was carried out by spectroscopic method. The antineoplastic (breast cancer) potential of selected phytoconstituents were unveiled using the PASS program. Molecular docking analysis further assessed the binding affinity of these bioactive compounds with proteins including topoisomerase II, calvasculin, porin, and TP53.
Results
GCMS analysis of crude chloroform extract revealed Cyclopropanepentanoic acid, 2-undecyl-, methyl ester (13.072%), phytol (12.667%), benzene,1,2,4-trimethyl, decane, pentadecanoic acid,14-methyl, and 9,12-Octadecanoic acid, methyl ester, and 20 other components. Total phenolics and flavonoids in crude chloroform extract are 2.3882 ± 1.47 mg/gm Gallic acid equivalent and 99.167 ± 32.842 mg/gm quercetin equivalent. The crude chloroform extract (CCE) has antioxidant properties, with IC50 values of 38.691 ± 0.582 µg/mL for DPPH scavenging and 59.81 ± 0.272 µg/mL for iron reducing methods, and 148.75 ± 1.25 mg/gm for catechine antioxidants. The in-silico PASS prediction analysis of the prominent compounds were also carried out against different cell line of lung cancer. The Pa value ranges from 0.502 to 0.639 revealed considerable anti-proliferative activity of the constituents. Compared to tamoxifen as positive control, crude chloroform extract (CCE) inhibited lung-cancer cell proliferation by 22.87%. Molecular docking of discovered chemicals to four proteins supported the anti-carcinogenic theory.
Conclusion
This comprehensive study provides valuable insights into the anti-carcinogenic potential of Flacourtia indica extracts, particularly against lung cancer cell lines. Further research is warranted to explore its therapeutic potential and pave the way for the development of novel anti-cancer agents derived from natural sources
Texture Recognition Using a Biologically Plausible Spiking Phase-Locked Loop Model for Spike Train Frequency Decomposition
Authors: Mastella Michele , Tiemens Tesse , Chicca Elisabetta 
Published: April 2024

Abstract
Neural spikes can encode a rich set of information, ranging from the perceived intensity of light sources to the likelihood associated with decisions made in the cortex. Among these capabilities, previous studies demonstrated that spikes can also encode in their activity multiple frequencies at the same time, such as those generated by skin vibrations during textures scanning. However, the mechanism responsible for decoding spikes containing multiple frequencies is yet to be uncovered. In this paper, we introduce a novel spiking neural network model tailored for frequency decomposition of spike trains. Our model mimics neural microcircuits hypothesized in the somatosensory cortex, making it a biologically plausible candidate for decoding spike trains observed in tactile peripheral nerves. We showcase the ability of simple neurons and synapses to replicate the functionality of a phase-locked loop (PLL) and delve into the emergent properties when multiple spiking phase-locked loops (sPLLs) interact with diverse inputs. Furthermore, we demonstrate how these sPLLs can decode textures by leveraging the spectral features of spike trains generated in peripheral nerves. By harnessing our model's frequency decomposition capabilities, we achieve significant performance enhancements over state-of-the-art approaches on a Multifrequency Spike Train (MST) dataset. Our findings underscore the potential of sPLLs in elucidating the mechanisms behind texture decoding in the brain, while also showcasing their potential to outperform conventional SNNs in handling spike trains with multiple frequencies. We believe this study sheds light into the neuronal mechanisms behind texture decoding, while presenting a practical framework for augmenting the capabilities of artificial neural networks in intricate pattern recognition tasks.
The Teaching Experience of Parents of Children with Special Needs During the COVID-19 Pandemic
Authors: ghiasi shiva , kakabraee keyvan 
Published: April 2024

Abstract

This study assessed the teaching experience of parents of children with special educational needs during the COVID-19 pandemic. The online questionnaire was completed by 57 parents and the data was analyzed using SPSS software. Descriptive and inferential statistical measures were used to assess the relationship between variables. The results revealed a significant relationship between the quality of virtual teaching and the availability of supports and resources for online learning. However, available resources did not contribute to parental satisfaction. Parental satisfaction was not related to the quality of education or the age of the child. Additionally, there was no significant difference in educational performance between children who received virtual training and those who attended school in person. Overall, the virtual training was found to be satisfactory, but improvements to online educational resources could enhance the quality of education.
Testing the efficacy of minocycline treatment in an awake, female rat model of repetitive mild head injury
Authors: Hightower Rosemarie , Brengel Eric , Prom Sophia , Kulkarni Praveen , Ferris Craig 
Published: April 2024

Abstract
Minocycline is being tested in clinical trials for the treatment of stroke. As an antibiotic it reduces microglia activation. Can minocycline be used to treat mild head injury? To that end, minocycline was tested in a novel, closed-head, momentum exchange model of repetitive mild head injury in female rats impacted while fully awake. MRI revealed there was no brain damage or contusion attesting to the mild nature of the head impacts in this model. It was hypothesized that drug treatment would reduce edema and brain neuroinflammation. Female rats maintained on a reverse light-dark cycle were head impacted three times while fully awake with and without drug treatment. The impacts, separated by 24 hrs each, were delivered under red light illumination. Within 1-2 hrs of the last impact, rats were assessed for changes in water diffusion using diffusion weighted imaging. The data were registered to a 3D MRI rat atlas with 173 segmented brain areas providing site specific information on altered brain gray matter microarchitecture. Postmortem histology was performed 18 days post head injury. Head injury without minocycline treatment was characterized by multiple areas of increased fractional anisotropy, evidence of cytotoxic edema. Treatment with minocycline reversed these measures in many of the same areas and several others (e.g., hippocampus, basal ganglia, prefrontal cortex, sensory and motor cortices and thalamus). Histology for gliosis showed no evidence of neuroinflammation in the thalamus, hippocampus and cerebellum for control or experimental groups in this female model of mild head injury. These studies provide clear evidence that treatment with minocycline within hours after mild repetitive head injury significantly reduce measures of cytotoxic edema in a female rat model of mild repetitive head injury.
Evaluation and comparison of infiltration models for estimating infiltration capacity of different textures of irrigated soils: Case of Northern Ethiopia.
Authors: Mesele Halefom , Grum Berhane , Aregay Gebremeskel , Teklay Gebremeskel 
Published: April 2024

Abstract

Determination of infiltration capacity is a very important parameter during the design and evaluation of irrigation systems. Accurate estimation of infiltration rates helps in optimizing irrigation scheduling, preventing soil erosion, and improving water use efficiency. This study was conducted to evaluate and compare selected infiltration models for estimating water infiltration rates of five soil textures in the Shillanat-iv- irrigation scheme in northern Ethiopia. Soil samples were taken from selected sites in the irrigation scheme to determine soil textural classes using the hydrometer texture laboratory analysis and the USDA textural triangle. A soil textural map of the irrigation area was prepared using the inverse distance weight interpolation technique in ArcGIS version 10.4. The double-ring infiltrometer was used to measure the infiltration rates of different soil textures in the irrigation scheme. Six selected infiltration models namely Kostiakov, Modified Kostiakov, Revised Modified Kostiakov, Philip, Horton, and Novel models were used to estimate infiltration rates for five soil textural classes namely, clay loam, loam, sandy clay loam, clay and sandy loam soils. To evaluate the performance of the models, infiltration rate was measured in 38 points of the study area, out of which 70% of the data was calibrating model parameters and 30% of the data was used for model validation. Parameter values of the infiltration models were optimized using the least-squares errors in SPSS statistical software. Five statistical parameters including the Coefficient of determination (R2), Maximum absolute error (MAE), Bias, Root mean square error (RMSE) and Percentage average error (PAE) were used to evaluate the performance of the infiltration models. Results indicated that the Revised Modified Kostiakov’s, Modified Kostiakov’s, and Novel’s infiltration models had better capability in estimating infiltration rates for clay loam, loam and sandy loam soil textures respectively. Similarly, the Hortons’s model had better performances in estimating infiltration rates of both sandy clay loam and clay soil textures compared to other models. In the design of the irrigation system, the best-fitted infiltration models can be used for estimating the infiltration rate of soil textures in the selected irrigation scheme and other sites with similar soil textures.
Remote Sensing and Aeromagnetic Mapping for Iron Ore Mineralization in Parts of Kakanda and Its Environs, Northcentral Nigeria
Authors: Akinlalu Ayokunle Adewale 
Published: April 2024

Abstract

Kogi State, Northcentral Nigeria has been known for its iron ore deposits and Kakanda village is one of those places that possess iron ore deposits. However, little information about the extent and locations of possible iron ore mineralized zone is available due to limited researches in that area. Hence, this study utilized aeromagnetic and Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) datasets for the delineation of Iron ore deposits in Kakanda Village and its environs. Enhancement techniques such as residual magnetic amplitude and analytic signal amplitude carried out on the aeromagnetic data revealed the concentration of iron ore deposits especially in the southern, eastern and western parts of the study area. This is consistent with signatures derived from other data enhancement techniques involving the total horizontal derivative, tilt derivative and 3D Euler deconvolution technique which were principally used to map structures guiding mineralization in the study area. Furthermore, analyses of the ASTER dataset using the true and false color composite and combination of band ratios indicate the occurrence of iron oxide and clay alterations relating to iron ore mineralization in the study area. It was observed that the signatures relating to iron ore mineralization of the aeromagnetic data and ASTER dataset are consistent with each other. The overlap of these signatures was used to produce the iron ore prospectivity map of the study area. The study showed that areas of delineated lineaments coincide with areas of iron ore mineralization. In the same vein, areas of dense lineaments coincide with areas of iron ore mineralization especially in the southern and eastern parts of the study area. The study area therefore concluded that mineralization in the study area is structurally controlled. Hence, further exploration activities should be focused on areas where there is an evident overlap of lineament and iron ore mineralization in the study area.
A new efficient medoid based divisive hierarchical clustering method for large-scale data
Authors: Wang Xiaochun 
Published: April 2024

Abstract

Clustering in data mining groups a set of data objects into a collection of different classes which are called clusters. Being one category of the most popular clustering methods, hierarchical clustering has found applications in various disciplines as an effective tool for data analysis. However, a run of hierarchical clustering requires multiple iterations to compute and update the pairwise distances between all intermediate clusters, making the conventional linkage methods of hierarchical clustering algorithms inevitably suffer from the quadratic time and space complexity. Given the fact that the choice of linkage profoundly affects not only the quality but also the efficiency of hierarchical clustering, we propose in this paper a new linkage method, named the medoid linkage, for hierarchical clustering and design an efficient divisive algorithm, referred to as MEDIA, for it to address the scalability issue of hierarchical clustering large-scale data. Experiments performed on both synthetic and real datasets demonstrate that our scheme can not only provide better clustering results but also scale well for large-sized high-dimensional data.
The fate of an antibody labelled with zirconium-89 in cynomolgus macaques
Authors: Sasaki Takanori , Kimura Sadaaki , Noda Akihiro , Murakami Yoshihiro , Miyoshi Sosuke , Akehi Masaru , Ochiai Kazuhiko , Watanabe Masami , Higuchi Takahiro , Matsuura Eiji 
Published: April 2024

Abstract

Background
Preclinical pharmacokinetic studies of therapeutic antibodies in non-human primates are desired because of the difficulty in extrapolating ADME data from animal models to humans. We evaluated the pharmacokinetics of 89Zr-labelled anti-KLH human IgG and its metabolites to confirm their non-specific/physiological accumulation in healthy cynomolgus macaques.
Methods
Selected IgG was conjugated to desferrioxamine (DFO), labelled with 89Zr, and injected into healthy cynomolgus macaques (9–16 MBq of 89Zr-IgG/head). PET/CT images at the whole-body level were acquired at different time points, and standard uptake values (SUV) in regions of interest, such as the heart, liver, spleen, kidneys, bone, and muscles, were calculated. The distribution of a shortened antibody variant, 89Zr-Fab, as well as that of [89Zr]Zr-DFO and [89Zr]Zr-oxalate, the expected metabolites of 89Zr-IgG, was also assessed.
Results
After 89Zr-IgG injection, the SUV in the heart, vertebral body, and muscle decreased, in line with the 89Zr concentration decrease in the circulation, whereas radioactivity increased over time in the kidneys and liver. Autoradiography of the renal sections indicated that most of the 89Zr-IgG radioactivity accumulated in the renal cortex. Relatively high accumulation in the kidneys was also observed in 89Zr-Fab-injected macaques, and renal autoradiographs of these animals showed that the renal cortex was the preferred accumulation site. However, [89Zr]Zr-DFO was rapidly excreted into the urine, whereas [89Zr]Zr-oxalate was highly accumulated in the epiphysis of the long bones and vertebral body.
Conclusion
In the non-human primate cynomolgus macaque, 89Zr-IgG accumulated in the kidneys and the liver. However, [89Zr]Zr-DFO and 89Zr did not accumulate in these organs. This preclinical pharmacokinetic study performed with human IgG in a non-human primate model using PET is of great significance as it sheds light on the basic fate and distribution of 89Zr-IgG.
Enhancing Software Reliability through Naive Bayes-based Defect Prediction
Authors: P Dhavakumar , Lakshmikant Kumar 
Published: April 2024

Abstract

Software defects can be costly to fix and can lead to reduced system reliability, decreased user satisfaction, and increased development time. To mitigate these risks, software defect prediction techniques have been proposed to identify potentially problematic areas of code before defects occur. In this paper, we propose an effective method to detect software flaws using the Naive Bayes classifier. We used a publicly available dataset for our study and performed preprocessing steps such as removing duplicate records and missing values. We splitted the data into training and testing and trained a Naive Bayes classifier on training. We evaluated the performance of our approach using precision, recall, and F1 score metrics. Our results demonstrate that the Naive Bayes classifier was effective in detecting software defects, achieving an accuracy of 98.16% on the testing set and area under ROC curve of 0.965. These findings suggest that the Naive Bayes classifier could be a valuable tool for software defect prediction and could help practitioners and researchers improve the quality of software systems.
Vision Transformer for Skin Cancer Identification Based on Contrastive Learning and Adaptive-Scale Fragmentation
Authors: Naeem Muhammad Ahtsam , Yang Shangming , Sharif Abida , Saleem Muhammad Asim , Sharif Muhammad Imran 
Published: April 2024

Abstract

The approach of image processing and deep learning has shown to be a breakthrough in the field of medical image diagnosis such as dermoscopic image analysis for skin cancer recognition and their classification. Skin cancer cases are increasing every year and pose a significant threat for health. In recent studies, convolutional neural network (CNN) has accomplished remarkable success in classifying skin cancer images. CNN is limited to extracting features from minor objects from input dermoscopic image and fails to pinpoint significant regions. Consequently, the researchers of this study have utilized vision transformers (VIT), known for their robust performance in conventional classification assignments. The self-attention mechanism (SAM) aims to enhance the significance of pivotal characteristics while modifying the influence of noise-inducing features. Specifically, an enhanced transformer network architecture has been introduced in this context. To assess its effectiveness, several enhancements have been applied to the model. Initially, a ViT network is implemented to evaluate its efficacy in identifying skin cancer. Subsequently, Adaptive-scale image fragmentation is utilized to sequentially process the image, emphasizing adaptive-scale features through patch embedding. Furthermore, contrastive learning is employed to ensure that similar skin cancer data is encoded differently, aiming for distinct encoding outcomes for different data. Skin cancer dataset namely ISIC 2019 is retrieved in this study, locally accessible at Kaggle’s official website. This dataset consists of dermoscopic images of skin cancer having several types: dermatofibroma, melanoma, actinic keratosis, basal cell carcinoma, nevus, vascular lesion, and pigmented benign keratosis. The ViT model has achieved 99.66% accuracy, 94.85% precision, 93.74% recall, and 94.52% f1-score. Three deep learning models Inception V3, MobileNet, and ResNet-50 were also applied with transfer learning approach as comparison to proposed ViT model for performance evaluation that resulted in 72%, 94.3, and 89% accuracies, respectively. The transformer network has shown remarkable success in natural language processing and in the domain of image analysis. These achievements establish a solid groundwork to classify skin cancer using multimodal data. This paper is confident to captivate the attention of medical researchers, computer engineers, dermatologists, and scholars across various related disciplines. Its insights promise to offer enhanced convenience for patients in their respective fields.
Interaction between Acute Pancreatitis and Gastrointestinal Microbiome: A Bidirectional Two-sample Mendelian Randomization Study
Authors: Wang Xuefei , Wang Ao , Zhang Yu , An Yu , Tu Zhengwei , Cui Yunfeng 
Published: April 2024

Abstract

Background: Previous studies have shown that the severity of acute pancreatitis correlates with the gastrointestinal microbiome and that the development of acute pancreatitis causes changes in the gastrointestinal microbiome. However, there is no known genetic mechanism of action to explain the relationship between acute pancreatitis and the gastrointestinal microbiome.
Methods: We performed a two-sample MR analysis of publicly available GWAS data on the gastrointestinal microbiome and acute pancreatitis. We applied several MR methods to explore causal associations, including inverse variance weights, weighted medians, MR‒Egger, Wald ratios, and MR-PRESSO. Sensitivity analyses and Cochrane's Q tests were also performed to exclude the effects of pleiotropy and heterogeneity.
Results: IVW showed that enrichment of the Eubacterium rectale group, Holdemania and Terrisporobacter was correlated with the severity of acute pancreatitis. Inverse MR analyses revealed that the acute pancreatitis affected the abundance of Allisonella, Anaerotruncus, Paraprevotella, Erysipelotrichaceae UCG003, Family XIII AD3011 group, Haemophilus, Intestinibacter, Turicibacter and Tyzzerella 3.
Conclusions: This study elucidates the association between acute pancreatitis and gastrointestinal microbiome at the level of genetic variation, but the mechanism of action needs to be further explored.
A novel in situ AuNPs synthesis by electrospinning technique
Authors: Castro Cynthia , Arizmendi-Morquecho Ana , García-Gutiérrez Domingo , ZúñigaMartínez Jorge Alexis , Lucio-Porto Raúl , Gonzalez-Perez Giovanni , Estrada-Villegas Gethzemani Mayeli 
Published: April 2024

Abstract

This study presents a new route for in situ synthesis of gold nanoparticles (AuNPs) and nanofibers using PEO-Gelatin as the supporting polymer via the electrospinning technique. Variation in the size and shape of the AuNPs significantly impacts their properties and applications, emphasizing the importance of precise methods for their synthesis. The proposed approach in this study aims to synthesize AuNPs and nanofibers simultaneously during the electrospinning process without the need for external reducing agents. This methodology offers significant advantages, such as conducting the reaction at room temperature while simultaneously synthesizing nanoparticles and fibers without requiring additional purification processes. The current work describes the synthesis conditions by adding chloroauric acid precursor (HAuCl4▪3H20) to the polymer solution for electrospinning and in situ reduction at a voltage of 14 kV. Additionally, the morphology of the fibers and particles was explored using Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM), revealing hexagonal particles with an average size of 12 nm. The presence of AuNPs was confirmed via UV-Vis spectroscopy through Surface Plasmon Resonance (SPR) at λmax 532 nm. Finally, it was found that the precursor reduction is dependent on pH, polymer functional groups, and applied voltage.