bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mechanism of Qingwei San in Treatment of Chronic Gastritis Disease Based on Network Pharmacology and experimental validation
Authors: Yongguang Han , Yuexin Chen , Yalan Tan , Xing Lv , Ma Qiong 
Published: June 2024

Abstract

Chronic gastritis (CG) is a type of chronic inflammatory response of the gastric mucosa. Qingwei San is mainly used to treat accumulation of heat in the stomach and the upward attack of fire, which has the effect of clearing the stomach and cooling the blood. This prescription is clinically effective in the treatment of chronic gastritis. However, its mechanism of action is not fully understood and needs further study. To explore effective compounds and the potential targets and molecular mechanism of Qingwei San (QWS) on chronic gastritis, the main active components of QWS were obtained through the TCMSP, ETCM and HERB databases. The target prediction of QWS was carried out through Swiss Target Prediction database. The related genes of Chronic gastritis were obtained through Genecards, OMIM and DisGeNet databases. The Cytoscape 3.8.1 software was used to draw drug-component-target network. Protein-protein interaction (PPI) network was constructed by String database. Based on Metascape database, GO and KEGG Pathway were enriched and analyzed to obtain the action pathway of QWS. The prediction results were verified by Western blotting. The 43 main active components and 175 targets were obtained from QWS. It can regulate epithelial cell infected Helicobacter pylori signal transduction, JAK-STAT signal pathway, inflammatory bowel disease (IBD), TRP pathway and other inflammatory mediators through SRC, STAT3, PIK3CA, MAPK1 to treat chronic gastritis. QWS significantly reduced the TNF-α, IL-6, and IL-1β and increased the level of IL-10 in rats with CG. QWS could clearly reduce the mRNA level of JAK1, AKT1, VEGFA and EGFR. The result of Western blotting confirmed that QWS plays a role in the treatment of chronic gastritis by down regulating the expression levels of SRC and STAT3.The treatment of QWS for chronic gastritis has the characteristics of multi-component, multi-target and multi-channel effects, which provides a basis for further exploring the mechanism of QWS in the treatment of chronic gastritis.
Sequential Semi-Supervised Classification Considering Core Clustering: A Hyper-Heuristic Based Approach
Authors: Mojarad Musa 
Published: June 2024

Abstract

The current models for learning from streaming data with limited labeling have several drawbacks. One major issue is that the algorithms struggle to adapt to changes in the data, known as concept drift. Therefore, it becomes crucial for the algorithm to tackle the hurdle of dynamically updating internal parameters or managing concept drift. Nonetheless, relying solely on neural network-based semi-supervised learning proves inadequate in promptly adapting to changes in data distribution and characteristics, while also avoiding the influence of outdated knowledge retained in neural networks (NN). This paper presents a comprehensive framework that amalgamates neural networks, a genetic algorithm-based meta-heuristic, and an online-offline clustering approach. The framework trains the neural network on labeled data and leverages its knowledge to compute the error of the clustering block. The genetic optimization process is tasked with selecting the optimal model parameters to minimize this error, with the aim of effectively addressing concept drift. This model is termed the hyper-heuristic framework for semi-supervised classification (HH-F). Experimental findings underscore the superiority of this framework over existing methods when applied to sequential classification data characterized by an evolving nature.
Characterization of quartz sand deposits from Al Qaryatyien area in Syria
Authors: Dayoub Rami , Al-Masri Mohammad Said , Ghafar Muhammad , Abazli Muhammad Hicham 
Published: June 2024

Abstract

A spectroscopic, physiochemical, and radioactivity characterization of quartz sand samples from Al Qaryatyien area in Syria has been carried out. XRF analysis showed the dominance of SiO2 in the chemical composition of the sand (95.10% on average) with a tiny portion of other minerals. The index of XRD spectrum revealed the quartz structural for SiO2 in the sand where the calculated unit constants (acal=4.9070, ccal=5.3539) were so close to those found in the references (aref=4.91120, cref=5.4040), which refer to a high degree of purity. Moreover, the good crystallinity of quartz was confirmed by FTIR analysis where the calculated crystal Index was (CIcal=1.415). Thermal analysis manifested the stability of the sample almost, the TGA curve indicated slight changes in mass (1.45%), and most of those changes were due to losing both moisture and crystalized water. The DTA curve showed stability in slope with no changes except the characteristic endothermic peak of the phase transition of quartz from α to β. On the other hand, the natural radioactivity of the decay chains of 238U, 232Th, and 235U was measured using HPGe spectrometry and the mean values of radionuclide activity concentrations were below the limit of detection.
A Simulated Two-Stream Network via Multi-levelDistillation of Reviewed Features and DecoupledLogits for Video Action Recognition
Authors: Gao Zitao , Liu Xiangjian , Lin Liyu , Wang Anna K 
Published: June 2024

Abstract

Video-based action recognition, which models human activities and classifiesactions from video frame sequences, offers significant utility in computer visionand computer graphics applications. The methods based on two-stream architectureare commonly used, which consist of an appearance stream for RGBvideo frames and a motion stream for optical flow frames. Due to the highcomputational complexity of optical flow, recent studies have attempted to useoptical flow only in the training phase and avoid computing optical flow in thetesting phase. However, these approaches fail to fully leverage the benefits ofoptical flow during training, leading to limited improvements in accuracy. Toaddress this issue, we introduce a Simulated Two-Stream Network (STS-Net),utilizing a multi-level knowledge distillation approach to capture motion representations.First, we try to distill motion feature of optical flow across variouslevels through a review mechanism, thereby capturing both low-level feature andhigh-level semantic information. Second, we apply a decoupled logit distillationloss to obtain a more comprehensive knowledge transfer. Additionally, we analyzethe role of the activation function in fusing the two streams, and propose aneffective fusion strategy named “ActivNo”. The experimental results on benchmarkdatasets (i.e., HMDB51, UCF101, and Kinetics400) demonstrated that theexploited model STS-Net achieves superior performance, surpassing comparable methods in terms of both efficiency and accuracy. The code is openly availableat https://github.com/BiBiKo219/STS-Net.
Cross-polarized stimulated Brillouin scattering empowered photonics
Authors: Huang Shu-Wei , Nie Mingming , Musgrave Jonathan 
Published: June 2024

Abstract

This paper explores the integration of cross-polarized stimulated Brillouin scattering (XP-SBS) with Kerr and quadratic nonlinearities in lithium niobate (LN) to enhance photonic device performance. Three novel applications are demonstrated: (i) a reconfigurable stimulated Brillouin laser (SBL) with 0.7-Hz narrow linewidth and 40-nm tunability, enabled by XP-SBS’s thermo-optic phase matching; (ii) an efficient coherent mode converter achieving 55% conversion efficiency via intracavity Brillouin-enhanced four-wave mixing; (iii) a Brillouin-quadratic laser and frequency comb operational in near-infrared and visible bands, benefiting from the interaction between XP-SBS and quadratic nonlinearity. These advancements promise significant improvements in photonic technologies, including narrow-linewidth laser, microcomb generation, precision metrology, and optical signal processing, paving the way for more robust and versatile applications.
Crystalline Cyclic (Alkyl)(Amino)Stannylene via Transient Stannyne
Authors: Wang Xin-Feng , Hu Chaopeng , Lu Zhihao , Liu Liu Leo 
Published: June 2024

Abstract

We demonstrate that a bulky cyclic amino ligand, specifically 1,1,3,3-tetraphenylisoindolinyl, can facilitate the formation of a transient stannyne, endowed with adjacent ambiphilic carbon and tin centers. This stannyne, manifesting carbene-like behavior, engages in an unprecedented sequence of reactions, including Buchner ring expansion, [1,3]-sigmatropic, and [1,5]-sigmatropic shifts, culminating in the synthesis of a stable cyclic (alkyl)(amino)stannylene. This compound represents the first tin analogue of the well-known cyclic (alkyl)(amino)carbenes. Both the bonding configuration and the formation process of this stannylene are thoroughly analyzed through quantum chemical computations. Our findings pave the way for further synthesis of unusual main group species using bulky isoindolinyl substituents, emphasizing their potential as a versatile tool in synthetic chemistry.
Determining the Elemental Compositions in the trichome of five sampled herbarium species in the genus Solanum (Solanaceae) using SEM-EDS
Authors: Ojo Funmilola Mabel , Benedito Vagner A. 
Published: June 2024

Abstract

Trichomes, specialized epidermal structures on plant surfaces, are integral to plant defense and adaptation. However, their elemental composition in Solanum species remains understudied. This research investigates the trichomes' elemental compositions from five Solanum species—Solanum berthaultii, Solanum galapagense, Solanum lycopersicum, Solanum pennellii, and Solanum tuberosum—using herbarium samples from the Royal Botanic Gardens, Kew. Samples were prepared using standard coating techniques with a Quorum-Q150T ES series and imaged under a Hitachi Regulus 8230 SEM with a 5 kV electron beam. Elemental compositions were analyzed using SEM-EDS with an Oxford EDS detector and BSE detector. EDS spectra were acquired from the base, midsection, and tip of the trichomes and processed using AZtech software. Results revealed significant variations in elemental content among the five Solanum species. S. berthaultii trichomes showed high potassium levels, especially at the tips, indicating a role in osmoregulation and defense. S. galapagense exhibited notable chlorine and calcium presence, suggesting ionic balance and pest deterrence. S. lycopersicum and S. pennellii had higher silicon concentrations in midsections and tips, enhancing structural integrity and herbivory resistance. S. tuberosum displayed significant variability in calcium, particularly at the base, highlighting its role in cellular structure and rapid damage response. This study provides detailed insights into the elemental strategies of Solanum species trichomes, contributing to our understanding of their ecological and physiological roles. The findings underscore the importance of elemental composition in plant adaptation and evolution, offering a foundation for future research in plant defense mechanisms and environmental interactions.
Improving Learning Efficiency in Large Language Models through Shortcut Learning
Authors: Meibuki Amane , Nanao Renshu , Outa Mugen 
Published: June 2024

Abstract

Large-scale neural networks have demonstrated remarkable capabilities in natural language processing tasks, yet they often face challenges related to computational efficiency and scalability. The introduction of shortcut learning mechanisms offers a novel and significant advancement by enhancing information flow and reducing computational overhead, thereby improving model performance and training speed. This research explores the integration of shortcut learning into the GPT-Neo architecture, resulting in a model that exhibits faster convergence, higher accuracy, and improved resource management. Through meticulous architectural modifications, such as residual connections, skip layers, and gating mechanisms, the modified model achieved superior performance across various benchmarks, including GLUE, SQuAD, and WMT, demonstrating its proficiency in complex linguistic tasks. The experimental results underscored the model's robustness and generalization capabilities, making it a competitive alternative to existing state-of-the-art models. Comprehensive evaluation metrics, including accuracy, F1 score, and BLEU score, were used to validate the effectiveness of the proposed modifications, highlighting substantial improvements in training efficiency and model accuracy. This study contributes significantly to the field of artificial intelligence by providing a scalable and efficient framework for the design and training of advanced LLMs, ultimately paving the way for more effective and accessible AI technologies.
Computational Discovery of AngiomiRs and Evaluating the Synergistic Effects of Target miRNA with Tranexamic Acid in Colorectal Cancer
Authors: Ahmadizad Firouzjaei Ali , Sharifee Kazem , Khazaei Majid , Mohammadi-Yeganeh Samira , Aghaee-Bakhtiari Seyed Hamid 
Published: June 2024

Introduction:
microRNA (miRNA) levels are dysregulated in many cancers,
suggesting that miRNA-based therapy may be effective. The molecular pathways of colorectal
cancer (CRC) development are unknown.

Method:
Understanding miRNAs implicated in CRC formation may reveal new diagnostic
and therapeutic targets. Angiogenesis is a key mechanism in tumor growth. CRC
treatment may involve inhibiting angiogenesis, but existing drugs can cause negative effects.
Tranexamic acid, an FDA-approved medication, may reduce the adverse effects of
angiogenesis inhibitors. This work examined miRNAs implicated in CRC angiogenesis
and how miR-16 and tranexamic acid may synergistically decrease CRC cell migration
and angiogenesis. We identified miRNAs targeting CRC angiogenesis genes using bioinformatic
databases. Proteins were docked with tranexamic acid utilizing the PyRx software.
Quantitative Real-time PCR was used to analyze the effects of overexpressed miRNA
and tranexamic acid on the expression of target genes. Scratch, transwell migration,
and Chicken Chorioallantoic Membrane (CAM) assays were used to evaluate the effect
of selected miRNA and tranexamic acid on the invasion and angiogenesis of CRC cells.
in silico studies identified hsa-miR-16-5p, -101-3p, and 34a-5p as possible CRC angiogenesis
modulators.

Results:
The study found that miR-16 and tranexamic acid influence the expression of
VEGFA, ANGPT2, MMP9, and HIF1A. miR-16 and tranexamic acid influenced CRC
cell movement in scratch tests and transwell migration assays. Furthermore, the CAM assay
results demonstrated that miR-16 and tranexamic acid can alter angiogenesis in
CRC.

Conclusion:
These findings highlight the potential of miR-16 and tranexamic acid as
combination therapeutic agents for CRC, with the ability to simultaneously target tumorigenesis
and angiogenesis.
In vitro Effect of Cannabidiol on Red Blood Cells: Implication in Long-Lasting Pathology Treatment
Authors: Gómez Claudia Taborda , Borda Natalia , Moscovicz Franco , Fernandez Florencia , Lazarowski Alberto , Auzmendi Jerónimo 
Published: June 2024

Background:
Cannabidiol (CBD) is the principal non-hallucinogenic compound of Cannabis plants
with high clinical interest because CBD has been described as having anti-inflammatory, analgesic and anticonvulsant
properties. CBD is considered a multitarget compound as it can interact with a wide range of targets,
explaining their multiplicity of effects. Some clinical studies have indicated certain side effects of CBD, including
somnolence, anemia and diarrhea, while the elevation of transaminases is considered as an exclusion
criterion from the trial. Since the red blood cells (RBCs) are a source of transaminase, we assayed in vitro effect
on RBCs stability.

Methods:
We performed in vitro experiments with RBCs obtained from human peripheral blood with normal
hematological parameters exposed to CBD in the range of therapeutic uses. We evaluated RBCs morphological
changes, membrane fragility and hemoglobin release as a reflection of hemolysis.

Results:
CBD induced an increase in the hemoglobin release (3.27 μg/106 RBC), without altered RBC osmotic
fragility. When RBCs suspensions were incubated with CBD the initial number of elements (RBCs + vesicles)
was increased up to 65% after 20 min and returned to basal level after 40 min of incubation. In the first 20
min, the accounts of elements were enriched in the smaller vesicles that disappeared after the remaining 20
minutes.

Conclusion:
These results suggest that CBD affects the indemnity of erythrocytes in vitro, inducing the formation
of hemolytic vesicles that can provide the basis for the development of anemia, transaminase elevation
and underlying tissular iron overload in patients chronically treated with CBD.
The Potential Radioprotective Effect of Piperine against Radiation-induced Lung Injury in Mice: Histopathological and Biochemical Evaluations
Authors: Safarbalou Asal , Ebrahimi Fatemeh , Talebpour Amiri Fereshteh , Hosseinimehr Seyed Jalal 
Published: June 2024

Introduction:
It has been hypothesized that piperine, the main alkaloid component of black pepper,
possesses a unique radioprotective effect. This study aimed to investigate the protective effect of piperine
against Radiation-Induced Lung Injury (RILI) in mice.

Methods:
Firstly, eighty male mice were divided into eight groups; the control group did not receive any
dosage of piperine and radiation (6 Gy), and the other groups received piperine alone at doses 10, 25, and 50
mg/kg, radiation, and radiation-piperine combination (6 Gy + 10, 25, and 50 mg/kg). Animals received
piperine by gavage for 7 consecutive days. To investigate the effect of piperine pretreatment in mice that were
exposed to radiation, histopathological and biochemical evaluations (markers of oxidative stress) were performed.
Irradiation led to an increase in oxidative stress (increase in MDA and PC). Pretreatment of piperine
in all three doses in irradiated mice was able to reduce oxidative stress compared to mice that were only exposed
to radiation.

Results:
Piperine at a dose of 25 mg/kg exhibited the highest protective effect as compared to other doses. Also,
in the histopathological examination, it was seen that pretreatment with piperine was able to improve the infiltration
of inflammatory cells and reduce the thickness of the alveolar septum and air sac damage.

Conclusion:
The outcomes completely proved significant lung protection by piperine in mice through reducing
oxidative stress. This natural compound could be considered a protective agent against lung injury induced
by ionizing radiation.
Prostate Segmentation in MRI Images using Transfer Learning based Mask R-CNN
Authors: Shabbir Maryam , Suhail Zobia , Hafeez Nida , Saqib Najmus , Farooq Muhammad , Guizani Sghaier , Ur Rehman Ateeq , Hamam Habib 
Published: June 2024

Introduction:
The second highest cause of death among males is Prostate Cancer (PCa) in America. Over the globe, it’s the usual case in men, and the annual
PCa ratio is very surprising. Identical to other prognosis and diagnostic medical systems, deep learning-based automated recognition and detection
systems (i.e., Computer Aided Detection (CAD) systems) have gained enormous attention in PCA

Methods:
These paradigms have attained promising results with a high segmentation, detection, and classification accuracy ratio. Numerous researchers
claimed efficient results from deep learning-based approaches compared to other ordinary systems that utilized pathological samples.

Results:
This research is intended to perform prostate segmentation using transfer learning-based Mask R-CNN, which is consequently helpful in prostate
cancer detection

Conclusion:
Lastly, limitations in current work, research findings, and prospects have been discussed.
Enhancing Alzheimer's Disease Classification with Transfer Learning: Finetuninga Pre-trained Algorithm
Authors: Boudi Abdelmounim , He Jingfei , El Kader Isselmou Abd 
Published: June 2024

Objective:
The increasing longevity of the population has made Alzheimer's disease (AD) a significant public health concern. However, the challenge of
accurately distinguishing different disease stages due to limited variability within the same stage and the potential for errors in manual
classification highlights the need for more precise approaches to classifying AD stages. In the field of deep learning, the ResNet50V2 model stands
as a testament to its exceptional capabilities in image classification tasks.

Materials:
The dataset employed in this study was sourced from Kaggle and consisted of 6400 MRI images that were meticulously collected and rigorously
verified to assure their precision. The selection of images was conducted with great attention to detail, drawing from a diverse array of sources.

Methods:
This study focuses on harnessing the potential of this model for AD classification, a task that relies on extracting disease-specific features.
Furthermore, to achieve this, a multi-class classification methodology is employed, using transfer learning and fine-tuning of layers to adapt the
pre-trained ResNet50V2 model for AD classification. Notably, the impact of various input layer sizes on model performance is investigated,
meticulously striking a balance between capacity and computational efficiency. The optimal fine-tuning strategy is determined by counting layers
within convolution blocks and selectively unfreezing and training individual layers after a designated layer index, ensuring consistency and
reproducibility. Custom classification layers, dynamic learning rate reduction, and extensive visualization techniques are incorporated.

Results:
The model's performance is evaluated using accuracy, AUC, precision, recall, F1-score, and ROC curves. The comprehensive analysis reveals the
model's ability to discriminate between AD stages. Visualization through confusion matrices aided in understanding model behavior. The rounded
predicted labels enhanced practical utility

Conclusion:
This approach combined empirical research and iterative refinement, resulting in enhanced accuracy and reliability in AD classification. Our model
holds promise for real-world applications, achieving an accuracy of 96.18%, showcasing the potential of deep learning in addressing complex
medical challenges.