bluuebunny/crossref_metadata_2025 huggingface dataset:
Morphological and fruit variability in grape (Vitis vinifera L.) germplasm under Indian condition
Authors: Somkuwar R G , Nale Rutuja D , Karande Pooja , Sharma Ajay Kumar , Nikumbhe P H , Naik Sharmistha , Bhagat Anju
Published: March 2024

Grapevine (Vitis vinifera L.) is one of the oldest and most important cultivated fruit crops. The aim of present investigation was to evaluate grape accessions that have been under cultivation in India for a long time. In the current study, ampelographic and fruit character of forty-nine accessions were investigated. A high variability was found in the evaluated grape accessions, and significant differences were found among the studied accessions for the measured parameters. Mature leaf, shoot tip, bunch parameter, berry parameter, total soluble solids (TSS), and total acidity (TA) showed a wide variation. Significant positive correlation found between bunch and berry traits and negative correlation between TSS and TA. Cluster analysis and principal component analyses showed a considerable fruit diversity in the studied grape germplasm. The obtained data revealed phenotypic and genotypic variation within studied grape accessions might be considered as characterizing gene pool and that contributed to the future breeding process of grape.
Identification of Critical Signature in Posttraumatic Stress Disorder Using Bioinformatics Analysis and Vitro Analyses
Authors: Liu Lifen , Liu Yang , Li Rui , Teng Yue , Zhao Shuyang , Chen Jinhong , Li Changjiang , Hu Xinyu , Sun Lin
Published: March 2024

Background: Post-traumatic stress disorder (PTSD) is a complicated psychiatric disorder after experiencing trauma that affects people’s natural lives. As such, the objective of the study was mining related pathways and key genes of PTSD validated through vitro experiments.
Method: Retrieving RNA sequencing (RNA-seq) datasets from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened using GEO2R. KEGG pathway assessment was carried out for enrichment analysis. The plugin MCODE from Cytoscape software was utilized to analyze the protein-protein interaction (PPI) network. Assessment of anxiety and depression in a mice model of stress was carried out by the open-field test (OFT) and elevated plus maze test (EPMT). Subsequently, Real-Time quantitative PCR (qRT-PCR) was used to verify these key genes in stress-exposed models.
Results: A total of 157 common upregulated DEGs and 53 common downregulated DEGs were identified between the amygdala (AMY) and the hippocampus (HIP). Notably enriched pathways (Neuroactive ligand-receptor interaction, mTOR signaling pathway, Nicotine addiction and dopaminergic synapse, etc.) were screened by Gene functional annotations and enrichment analysis. The PPI network identified the 4 hub genes , finding that the closely related pathways were Nicotine addiction and dopaminergic synapse. qRT-PCR was used to confirm these four genes and the expressing trends were consistent with the microarray analysis. The results of the OFT and the EMPT were remarkable changes.
Conclusion: Using bioinformatic analysis and in vitro experiments, we explored a variety of genes and pathways that may represent key mechanisms in the development of PTSD.
Fleeting Extinction? Unraveling the persistence of Noise Traders in Financial Markets with Learning and Replacement
Authors: Gerotto Luca , Pellizzari Paolo , Tolotti Marco
Published: March 2024

We describe an agent-based model of a financial market where agents can learn whether to buy costly information on returns, to use noise as if it were information, or to disregard any signals. We show that while learning alone drives all noise traders to extinction in stationary populations, allowing for small rates of replacement of existing agents with new ones suffices to generate substantial levels of persistent noise trading, with the equilibrium share of agents using irrelevant news reaching double digits. Remarkably, the presence of noise traders, when replacement is realistically considered, inflates the share of agents who use costly information relative to the benchmark scenario without replacement.
JEL Classification: C63 , D83 , G11
Going paperless: the strengths and limitations of electronic Research Ethics Information Management System in a health training institution in Tanzania
Authors: Mkumbwa Rebecca D. , Pancras Godwin , Sirili Nathanael S.
Published: March 2024

Over the years, the processing of research proposals for ethical approvals has been done manually through a review of printed proposals. The system faced many challenges which led to inefficiency of the review process, and thus longer turn-around time. To address these challenges, an electronic or web-based application for research proposal submission and processing was introduced. However, the advantages of going paperless and inherent drawbacks remain unsubstantiated. This study aimed to explore the strength and limitations of electronic Research Ethics Information Management Systems (REIMS) in a health training institution in Tanzania. An exploratory qualitative study was adopted where 16 faculty members of the Muhimbili University of Health and Allied Sciences were interviewed. The faculty were purposefully recruited to include only those who had been exposed both to the paper-based research ethics review system and electronic Research Ethics Information Management System. The faculty were stratified into two groups of researchers and members of the research ethics committee. Thematic analysis using inductive reasoning was used to analyze the gathered information. The strengths and limitations of REIMS were unveiled in this study as two important themes. The strengths included convenience in use of the system and improved records management. The imitations included demand for reliable ICT resources and limited reviewer-researcher interaction. The findings of this study underscore the strengths and limitations of transformation in handling research ethics review following an introduction of an electronic system and abandoning the paper-based system. Institutions aspiring to adapt the system can learn from this experience and devise ways to overcome the limitations. We recommend system automation, strengthening the institutional capacity and further studies to be conducted on the use of the REIMS.
Re-Resection and Deltopectoral Flap Reconstruction For Recurrent Anastomotic Esophageal Cancer
Authors: Yang Tzu-Yi , Li Chih-Ying , Tsao Chung-Kan , Chou Pin-Li , Chao Yin-Kai , Chiu Chien-Hung
Published: March 2024

Background
Anastomotic recurrence following esophagectomy is a rare occurrence. While complete resection yields better long-term outcomes, surgical management in this scenario poses challenges due to the impact of prior surgery and the fragile condition of patients. Here, we present an alternative surgical approach and reconstruction method to address this challenging scenario.
Case presentation:
Our patient, a 60-year-old man, developed anastomotic recurrence after neoadjuvant chemoradiotherapy and esophagectomy with gastric tube reconstruction via the retrosternal route. To tackle this issue, we employed an innovative surgical technique involving esophagogastrectomy via hemisternotomy, reconstruction using a deltopectoral fasciocutaneous local flap, and the establishment of a feeding jejunostomy. There were no major complications post-procedure, and the patient achieved good cancer control.
Conclusions
This approach not only offers significant technical advantages but also provides nutritional benefits. By adopting this novel approach, there is potential to enhance the current management of recurrent anastomotic esophageal cancer post-esophagectomy.
Fourier Feature Network for 3D Vessel Reconstruction from Biplane Angiograms
Authors: Wu Sean , Kaneko Naoki , Liebeskind David , Scalzo Fabien
Published: March 2024

3D reconstruction of biplane cerebral angiograms remains a challenging, unsolved research problem due to the loss of depth information and the unknown pixelwise correlation between input images. The occlusions arising from only two views complicate the reconstruction of fine vessel details and the simultaneous addressing of inherent missing information. In this paper, we take an incremental step toward solving this problem by reconstructing the corresponding 2D slice of the cerebral angiogram using biplane 1D image data. We developed a coordinate-based neural network that encodes the 1D image data along with a deterministic Fourier feature mapping from a given input point, resulting in a slice reconstruction that is more spatially accurate. Using only one 1D row of biplane image data, our Fourier feature network reconstructed the corresponding volume slices with a peak signal-to-noise ratio (PSNR) of 26.32±0.36, a structural similarity index measure (SSIM) of 61.38±1.79, a mean squared error (MSE) of 0.0023±0.0002, and a mean absolute error (MAE) of 0.0364±0.0029. Our research has implications for future work aimed at improving backprojection-based reconstruction by first examining individual slices from 1D information as a prerequisite.
No-Reference Point Cloud Quality Assessment Based on Multi-Projection and Hierarchical Pyramid Network
Authors: Miao Yizhuang , Xiao Shuyan , Pan Lingjiao , Zhang Lin , Yang Zhengkai
Published: March 2024

Within the realms of 3D vision and computer graphics, point clouds play a pivotal role as a dominant technology in various applications. However, practical processes such as acquisition, storage, and transmission introduce diverse quality defects in point clouds, making point cloud quality assessment a pressing issue. Presently, research on no-reference methods for evaluating point cloud quality often overlooks multi-scale feature analysis, resulting in suboptimal performance. This study presents a novel approach for evaluating the quality of point clouds based on multi-projection and a hierarchical pyramid network without using any reference data. Specifically, the point cloud is initially projected onto multiple 2D images. Subsequently, a pyramid network is employed to decompose the point cloud into multiple layers, extracting features at various scales. These features are subsequently inputted into a quality regression module for obtaining predicted scores at diverse levels. Finally, a weighted average of predicted scores from different levels is calculated to derive a point cloud quality score that encompasses information from multiple scales. The proposed method is applied on two databases, namely SJTU-PCQA and WPC, to assess the quality of distorted point clouds. Our experiments demonstrate that the proposed method surpasses traditional NR-PCQA methods regarding accuracy and generalization.
Occurrence of neonicotinoid pesticides in outdoor air particulate matters from 2019 to 2021 in China
Authors: Du hairong , Yao Yunying , Yao Wei , Zhu Xiaoling
Published: March 2024

Neonicotinoid insecticides (NNIs), as a new type of insecticide, are now widely used in agriculture and daily life. Because of the problematic volatility of NNIs, few studies have evaluated them in atmospheric particulate matter. In this study, a total of 101 outdoor PM2.5 samples were collected from the Wuhan urban area from 2019 to 2021, and seven NNIs and three metabolites of NNIs were detected. The frequency of detection for all 10 substances was more than 60%, with DIN and IMI reaching 100%. DIN (53.3 pg/m3) and IMI (42.8 pg/m3) had higher median concentrations than did the other substances. The distributions of four neonicotinoids and one metabolite were significantly different (p<0.05) across the four seasons: spring, summer, fall and winter. The median concentration of imidacloprid-equivalent total neonicotinoids (IMIeq: generated by the relative potency factor method) was 256.1 pg/m3. Finally, the estimated daily intake (EDI) of NNIs via respiration was greater in infants and young children than in the rest of the population, suggesting that infants and young children are more likely to be exposed to the health effects of airborne residual PM2.5.
Aseptic Technique Training Course Teaching Practices Based on Mastery Learning
Authors: Luo Binlin , Ding Qiang , Chen Liling , Du Junjie , Wang Xiaozhi , Chen Huanhuan , Jiang Surong
Published: March 2024

This research aims to explore aseptic technique training course teaching practices based on mastery learning in the field of medical education. Aseptic techniques are indispensable in health care services, which makes it crucial to provide training for medical students to master these skills proficiently. Traditional teaching methods have focused primarily on theoretical knowledge, whereas mastery learning emphasizes student engagement, autonomous study, and practical application. In designing the course, we first establish clear learning objectives and plans, breaking the content into a series of graduated tasks and skills. Students progressively grasp the essentials of aseptic techniques through video demonstrations, lecture explanations, and targeted deliberate practice until they meet the minimum standards for passing the course. Timely feedback and peer assessment are highlighted in the course, with students' progress being evaluated through on-site practical examinations to ensure that the learning objectives of each stage are achieved. This comprehensive assessment method helps promptly identify and correct issues in the learning process, thereby enhancing teaching outcomes. In summary, an aseptic technique training course based on mastery learning provides medical students with an efficient and systematic learning approach to enhancing their ability to apply aseptic techniques in real clinical settings, thus improving the quality and safety of clinical operations.
Evaluation Of Segment Anything Model (SAM) For Automated Labelling in Machine Learning Classification of UAV Geospatial Data
Authors: Parulekar Bhargav , Singh Nischal , Ramiya Anandakumar M
Published: March 2024

With the present trend toward digitization in many areas of urban planning and development, accurate object classification is becoming increasingly vital. To develop machine learning models that can effectively classify the broader region, it is crucial to have accurately labeled datasets for object extraction. However, the process of generating sufficient labeled data for machine learning models remains challenging. A recently developed AI-assisted segmentation approach called Segment Anything Model (SAM) offers a solution to enhance the semi-automated labeling of complex and intricate image structures. By utilizing SAM, the accuracy and consistency of annotation results can be improved, while also significantly reducing the time required for annotation. This paper aims to assess the efficiency of SAM annotated labels for training machine learning models using high-resolution remote sensing data captured by UAVs (Unmanned Aerial Vehicles) in the peri-urban region of Anad, Kerala, India. A comparative analysis was conducted to evaluate the performance of training datasets generated using SAM and semi-automated labeling with existing tools. Multiple machine learning models, including Random Forest, Support Vector Machine, and XGBoost, were employed for this analysis. The results, as indicated by the classification accuracy of the machine learning models, demonstrate that the SAM-based segmentation approach is more efficient in generating semantic labels for accurately training the models.
Polydiacetylene Based Bio Sensing Strategy for the Detection of Ndv and Csf Antibodies for Diva Strategy
Authors: Nachinarkiniyan Deepa. , Mahalingam Vidhya. , Das Priyadip , Pabisetti Vayunandana Kishore , Kirubaharan John , Juliet Asokan Vimala
Published: March 2024

Influenza viruses have become a major threat to humans, animals and agriculture posing a huge serious economic loss. In this connection, a rapid, high sensitive and cost effective detection method is the need of the day to keep a regular surveillance and to control the spreading of the viruses. Newcastle disease virus (NDV) is most prevalent virus in avian species causing disastrous losses in worldwide poultry industries. Despite of the availability of several vaccines, eradication of the disease still remains a challenge due to the inability of the conventional vaccines to differentiate the infected from vaccinated animals (DIVA).This led to the need for the development of marker vaccines which in conjunction with diagnostic tools that are able to differentiate the infected animals from the vaccinated animals. The limitations possessed by conventional diagnostic tools has paved the way to the implementation of biosensors for its rapid, cost effective, on site detecting features. This research work advocates, the development of a bio sensing strategy for the detection of anti-bodies developed when the chickens exposed to Newcastle disease virus (NDV) and Classical Swine Fever (CSF) using peptide-functionalized polydiacetylene (PEP-PDA) based on the unique color variation (bio-chromism) of the PDA. The PDA vesicle is formed from the ratio of 10, 12-pentacosadionic acid (PCDA), 1, 2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) using a peptide of NDV and CSF attached to the surface of the vesicle. The specificity, sensitivity and proficiency of the method is evaluated. The developed method is very cost effective, advantageous than existing methods.
Retention strategies in a dominant market
Authors: Amorim Andreia
Published: March 2024

With the developments in technology, firms can gather information about consumers' purchase history which can be use to price discrimination accordingly - Behaviour-Based Price Discrimination (BBPD). Introducing the capacity of firms to implement retention strategies, the aim of this paper is to analyze the effects of BBPD where firms apply retention strategies under a switching costs approach. Assuming that there is a dominant position in the market with one firm with a higher inherit market share, retention strategies are good for consumers and bad for profits.
Keywords: retention strategies, price discrimination, dominant firm.
An Intrinsic Evaluator for Embedding Methods in Malicious URL Detection
Authors: Chen Qisheng , Omote Kazumasa
Published: March 2024

Nowadays, machine learning is used in many fields. Not only in fields such as image recognition, machine learning is also used for malicious detection. Especially in recent years, there have been many studies using machine learning for malicious URL detection to replace traditional blacklists. In order to compare the performance of the malicious URLs detection method, researches used the F-score or other detection accuracy to evaluate, but there are some difficulties in evaluating the URL embedding method used in malicious URLs detection because the detection accuracy is also effect by machine learning or deep learning models and data sets. An evaluation method of URL embedding method that is not affected by other factors is particularly important. In this paper, we proposed an intrinsic evaluation method for URL embedding method that is not affected by machine learning models or deep learning models and data sets. Besides, We analyse some URL embedding methods according to intrinsic and extrinsic methods and offer a guidance in selecting suitable embedding methods in URL by analysing the results.
ITPNet: Initial Trajectory Prediction Network - A deep learning framework for predicting initial trajectories to warm start optimization-based motion planners
Authors: Natarajan Sankaranarayanan , Kirchner Frank
Published: March 2024

Motion planning for robot manipulators in a cluttered environment is one of the most researched areas in the field of robotics.For high degrees of freedom (DOF) robotic systems, optimization-based motion planners are often preferred for trajectory planning, as they are computationally efficient and provide a smooth, locally optimal solution.To converge to an optimal solution, an optimization-based motion planner needs a good initial trajectory. Finding an initial trajectory, which is not far away from the basin of attraction of the optimum is not a trivial task. In this work, we propose an Initial Trajectory Prediction Network (ITPNet), a deep neural network framework for predicting an initial trajectory to warm start optimization-based motion planners.Given a planning task in the form of task and environment features, the ITPNet predicts the best initial trajectory for warm starting an optimization-based motion planner. Two different task features: joint and Cartesian features, and three types of environment features extracted using: Principal Component Analysis (PCA), Variational Autoencoder (VAE), and Signed Spatial Distance (SSD) techniques are compared.The learned models are evaluated on an upper-torso humanoid system in two different scenarios. The results show that the model, using the Cartesian task and the SSD-based environment features, efficiently learns the mapping between the planning tasks and the optimal trajectories. Warm-starting the planner with the predicted initial trajectory, even for an unseen environment results in a higher success rate and requires fewer iterations.
Deep Learning-Based Approaches for Early Diagnosis and Tracking of COVID-19
Authors: LHIADI REDOUANE , Amaanaoui Taoufiq , Kaaouachi Abdelali , Jaddar Abdessamad
Published: March 2024

The COVID-19 pandemic has prompted governments and specialists worldwide to collaborate in search of effective solutions and strategies for containment and eventual societal recovery. With advancements in equipment capabilities, remote communications, and on-board/distributed computing, information-based technologies are playing an increasingly critical role in identifying, detecting, and diagnosing potential COVID-19 cases. This study aims to explore factors for early diagnosis, tracking, and identification of COVID-19 spread, focusing on data collection and discussing opportunities for improvement. The study recognizes that deep learning models are well-suited for mitigating the impact of COVID-19, given the availability of a large volume of pandemic data through various technologies and collaborative efforts. While deep learning and big data approaches may not have been extensively implemented or clinically tested, they offer quick responses and valuable insights to medical staff and decision-makers. However, designing deep learning algorithms for COVID-19 presents numerous challenges. The quality and quantity of COVID-19 datasets need further improvement, demanding ongoing efforts from the research community to enhance data quality and reliability.
Addressing treatment switching bias in the ALTA-1L trial with g-methods: exploring the impact of model specification
Authors: Tawil Amani Al , McGrath Sean , Ristl Robin , Mansmann Ulrich
Published: March 2024

Background: Treatment switching in randomized clinical trials introduces challenges in performing causal inference. Intention To Treat (ITT) analyses often fail to fully capture the causal effect of treatment in the presence of treatment switching. Consequently, decision makers may instead be interested in causal effects of hypothetical treatment strategies that do not allow for treatment switching. For example, the phase 3ALTA-1L trial showed that brigatinib may have improved Overall Survival (OS) compared to crizotinib if treatment switching had not occurred. Their sensitivity analysis using Inverse Probability of Censoring Weights(IPCW), reported a Hazard Ratio (HR) of 0.50 (95% CI, 0.28–0.87), while their initial ITT analysis estimated an HR of 0.81 (0.53–1.22).
Methods: We used a directed acyclic graph to depict the clinical setting of the ALTA-1L trial in the presence of treatment switching, illustrating the concept of treatment-confounder feedback and highlighting the need for g-methods. In a re-analysis of the ALTA-1L trial data, we used IPCW and the parametric g-formula to adjust for baseline and time-varying covariates to estimate the effect of two hypothetical treatment strategies on OS: “always treat with brigatinib” versus “always treat with crizotinib”. We conducted various sensitivity analyses using different model specifications and weight truncation approaches.
Results: Applying the IPCW approach in a series of sensitivity analyses yielded Cumulative HRs (cHRs)ranging between 0.38 (0.12, 0.98) and 0.73 (0.45,1.22) and Risk Ratios (RRs) ranging between 0.52 (0.32,0.98) and 0.79 (0.54,1.17). Applying the parametric g-formula resulted in cHRs ranging between 0.61(0.38,0.91) and 0.72 (0.43,1.07) and RRs ranging between 0.71 (0.48,0.94) and 0.79 (0.54,1.05).
Conclusion: Our results consistently indicated that our estimated ITT effect estimate (cHR: 0.82 (0.51,1.22)may have underestimated brigatinib’s benefit by around 10-45 percentage points (using IPCW) and 10-20 percentage points (using the parametric g-formula) across a wide range of model choices. Our analyses underscore the importance of performing sensitivity analyses, as the result from a single analysis could potentially stand as an outlier in a whole range of sensitivity analyses.
Trial registration: Clinicaltrials.gov Identifier: NCT02737501 on April 14, 2016
A sport, mint az életmentés és a testi- és lelki egészségmegőrzés eszköze
Authors: Mayer Krisztina
Published: March 2024

Ez a tanulmány az idősebb, szakértő tűzoltók rekreációs sporttevékenységébe enged betekintést, különös tekintettel a motivációra. Mennyit sportolnak hetente átlagosan a vonulós tűzoltók? Mióta végeznek rendszeres sporttevékenységet? Mi a motivációjuk? Jelen kutatás ezekre a kérdésekre keresi a választ kvantitatív módszert alkalmazva. A vizsgálatban vonulós tűzoltók vettek részt (46 fő, átlagos életkor 47,87 ±3,35). A vizsgálatba kerülés kritériuma volt a legalább 45 éves életkor és a legalább 15 éves tűzoltó szakmai tapasztalat.  Az eredmények arra engednek következtetni, hogy a vizsgált vonulós állományt elsősorban a kikapcsolódás és a mindennapi stresszel való megküzdés motiválja a sporttevékenységekben való részvételre. E mellett megjelent a munka iránti elkötelezettség is, mely a rendszeres spottevékenységben való részvételre sarkallja a tűzoltókat.
Szembenézés önmagunkkal egy okos tükör segítségével
Authors: Juhász Annamária
Published: March 2024

Jelen tanulmány bemutat egy olyan egészségmegőrző eszközt, amelyet én csak okos tükörnek neveztem el annak az okán, hogy 30 másodperc alatt képes bárki mobil telefonján egy applikáción keresztül arcdiagnosztikai elemzést készíteni, valamint egy pillanatképet mutatni arról, mi zajlik a szervezünkben testi-lelki és mentális szinten. Sőt, még ajánlást is megfogalmaz arra vonatkozóan, hogy milyen balanszereket lehet célszerű aznap használni az optimális és kiegyensúlyozott működéshez.
Determinants of UK companies’ dividend policy
Authors: Momany Munther , Bataineh Khaled , Al-Bataineh Omar
Published: March 2024

This study examines the major factors influencing UK companies listed on the Financial Times Stock Exchange (FTSE) 100 stock market's dividend policy (as determined by the dividend payout ratio) over 32 years, from 1990 to 2022. The dividend premium and free cash flow components make up the catering dividend. The outcomes of a wide range of panel data analysis regressions, such as Generalized Method of Moments (GMM) and Two-Stage Least Squares (2SLS) regressions, clearly show that the catering dividend significantly impacts UK firms' dividend policy. On the other hand, the dividend policy benefits from the dividend premium, which increases it by 12% to 17% on average. Free cash flow, on the other hand, has a negligible negative impact on the dividend policy by just 5%. It is crucial to mention that this outcome varies depending on the models and regression techniques used. Furthermore, this study emphasizes how important it is for a firm's size and profitability to play a key role in determining how it will implement its dividend policy. Financial leverage also becomes important since a company's dividend payment ratio decreases when it relies more heavily on debt in its capital structure. By using GMM and 2SLS regressions, this study carefully tackles the endogeneity issue, and the results hold up even when the endogeneity effect is reduced. Ultimately, this study emphasizes how important dividend catering components are in guiding UK companies' dividend policies, arguing that CEOs and legislators should pay more attention to this.
Ionic Liquid-promoted the Synthesis of Structurally Diverse Pyrans,
Pyran-annulated Heterocycles, and Spiropyrans
Authors: Banerjee Bubun , Kaur Manmeet , Priya Anu , Sharma Aditi , Singh Arvind
Published: March 2024

During the last two decades, non-conventional solvents, especially various ionic
liquids, have been utilized as efficient reaction media as they can play a dual role as solvents
and promoters. The use of ionic liquids as a medium increases the efficiency of the
reactions due to their inherent features like high thermal stability, ability to act as a catalyst,
non-volatility, high polarity, reusability, ability to dissolve a large number of organic and
inorganic compounds, etc. Under this direction, various structurally diverse ionic liquids
have been employed as efficient reaction media for various organic transformations. On the
other hand, among many other important synthetic scaffolds, during the last two decades,
the synthesis of pyrans, pyran-annulated heterocyclic scaffolds, and spiropyrans have
gained huge attention as they possess a wide range of significant biological efficacies,
which include antibacterial, anticancer, antimycobacterial, antioxidant, xanthine oxidase inhibitory, etc. activities.
Almost every day, many new methods are being added to the literature related to synthesizing pyrans, pyran-
annulated heterocyclic scaffolds, and spiropyrans. Among many other alternatives, various ionic liquids
have also played an efficient role as promoters for synthesizing structurally diverse pyrans, pyran-annulated
heterocyclic scaffolds, and spiropyrans. In this review, we have summarized a large number of literature reported
during the last two decades related to the ionic liquid-promoted synthesis of pyrans, pyran-annulated heterocyclic
scaffolds, and spiropyran derivatives.
CXCL13-neutralizing Antibody Alleviate Chronic Skeletal Muscle Degeneration in a Mouse Model
Authors: Xie Zhongcheng , Yang Jimin , Jiao Chunmeng , Chen Hui , Ouyang Siyu , Liu Zhiyang , Hou Qin , Liu Jifeng
Published: March 2024

Introduction:

Skeletal muscle degeneration is a common effect of chronic
muscle injuries, including fibrosis and fatty infiltration, which is the replacement of preexisting
parenchymal tissue by extracellular matrix proteins and abnormal invasive
growth of fibroblasts and adipocytes.

Method:

This remodeling limits muscle function and strength, eventually leading to
reduced quality of life for those affected. Chemokines play a major role in the
regulation of immunocyte migration, inflammation, and tissue remodeling and are
implicated in various fibrotic and degenerative diseases. In this study, we aimed to
investigate the role of the B-cell chemokine CXCL13 in the gastrocnemius muscle of
the Achilles tendon rupture model mouse. We hypothesize that CXCL13 may promote
fibrosis and aggravate skeletal muscle degeneration. We performed RNA sequencing
and bioinformatics analysis of gastrocnemius muscle from normal and model mice to
identify differentially expressed genes and signal pathways related to skeletal muscle
degeneration and fibrosis.

Results:

Our results show that CXCL13 is highly expressed in chronically
degenerating skeletal muscle. Furthermore, CXCL13-neutralising antibodies with
therapeutic potential were observed to inhibit fibrosis and adipogenesis in vivo and in
vitro.

Conclusion:

Our study reveals the underlying therapeutic implications of CXCL13
inhibition for clinical intervention in skeletal muscle degeneration, thereby improving
patient prognosis.
Another CCTA Incidental Finding: Case Report of an Idiopathic Pulmonary
Vein Pseudo-thrombosis
Authors: Bordon Abraham , Weg Noah , Miller Raphael , Leb Jay , Sokol Seth I , Guelfguat Mark
Published: March 2024

Introduction::

While pulmonary vein filling defects on CT are typically considered diagnostic for thrombus, under certain circumstances, they can be artifactual
as a result of flow phenomena.

Case Presentation::

We report a case of a 53-year-old female with chest pain who was found to have filling defects in pulmonary vein branches on CCTA that were
initially treated as thromboses. However, follow-up cardiac MRI was negative for thrombi, and pseudo-thrombosis was therefore diagnosed.

Conclusion::

Pulmonary vein pseudo-thrombosis should be considered in the differential diagnosis of pulmonary vein filling defects.
Choc-Tadalafil Fusion: Unlocking Solubility and Taste Harmony with
β-CD-Infused Medicated Chocolate
Authors: Modi Chetna , Sinha Manobika , Thakkar Vaishali , Rana Hardik , Chavda Dipika
Published: March 2024

Objective:

The primary limitations of tadalafil in treating erectile dysfunction are its low
solubility and unpleasant bitter taste, which ultimately result in inadequate patient adherence. The
present study aimed to develop and characterize a medicated chocolate formulation containing
Tadalafil and ß-CD (solubility enhancer) employing the concept of Design of Experiment (DoE) using
chocolate as a user-friendly excipient.

Methods:

An inclusion complex was formulated by incorporating the drug into ß-CD using the
kneading method for solubility improvement and also as a taste masker for Tadalafil. The ratio of
drug: ß-CD inclusion complex was selected based on a phase solubility study. The inclusion complex
was molded into a chocolate base and optimized using the DoE approach. Further, drug excipient
interaction was evaluated by DSC and FTIR study.

Results:

Phase solubility study suggested a 1:1 ratio of Tadalafil: β-CD for better solubility. DSC
spectra suggested the conversion of crystalline structure into an amorphous state which indicates
improvement of the drug solubility. DSC and FTIR studies revealed that there was no significant
interaction between drug and excipients. Next, %CDR (cumulative drug release) at 30 min revealed
the immediate effect of Tadalafil from chocolate formulation and free drug analysis (an unbound
drug with ß-CD) proved reduced bitterness of the drug in the complex. Additionally, the medicated
chocolate was found to be stable at room temperature as per stability study.

Conclusion:

β-CD was found to be a promising multifunctional excipient as a solubility enhancement
carrier and taste masker for bitter-tasting drugs.
Detection and risk assessment of COVID-19 through machine learning
Authors:   Escuela Superior de Cómputo, Instituto Politécnico Nacional, Mexico City, Mexico, Luna-Benoso B. , Martínez-Perales J. C. ,   Escuela Superior de Cómputo, Instituto Politécnico Nacional, Mexico City, Mexico, Cortés-Galicia J. ,   Escuela Superior de Cómputo, Instituto Politécnico Nacional, Mexico City, Mexico, Morales-Rodríguez U. S. ,   Escuela Superior de Cómputo, Instituto Politécnico Nacional, Mexico City, Mexico
Published: March 2024

COVID-19, also known as coronavirus disease, is caused by the SARS-CoV-2 virus. People infected with COVID-19 may show a range of symptoms from mild to severe, including fever, cough, difficulty breathing, tiredness, and nasal congestion, among others. The goal of this study is to use machine learning to identify if a person has COVID-19 based on their symptoms and to predict how severe their illness might become. This could lead to outcomes like needing a ventilator or being admitted to an Intensive Care Unit. The methods used in this research include Artificial Neural Networks (specifically, Multi-Layer Perceptrons), Classification and Regression Trees, and Random Forests. Data from the National Epidemiological Surveillance System of Mexico City was analyzed. The findings indicate that the Multi-Layer Perceptron model was the most accurate, with an 87.68% success rate. It was best at correctly identifying COVID-19 cases. Random Forests were more effective at predicting severe cases and those requiring Intensive Care Unit admission, while Classification and Regression Trees were more accurate in identifying patients who needed to be put on a ventilator.
Carbon Conscious Consumption: Impact of expenditure and renewable energy in Malaysia
Authors: Othman Norashida , Ismail Farah Raihana , Wahab Siti Norida , Kamal Safwan
Published: March 2024

This study aims to assess the impact of domestic consumption and renewable energy on environmental pollutants, with national income (GDP) included as a control variable. Employing the auto-regressive distributed lag (ARDL) econometric technique, the analysis spans time series data from 1990 to 2020. The bound F-test and Johansen cointegration tests validate that domestic consumption substantially contributes to environmental pollution. By addressing existing gaps in knowledge, this research provides valuable insights for shaping sustainable energy policies tailored to the unique circumstances of Malaysia.
Igal Ritual Theatre: A Cultural Sustainable Development Goals (SDGs) Perspective
Authors: Abdul Rahman Mohd Kipli , Ahmad Umi Kalsom , Musa Syahrul Fithri , Hayimasae Numan
Published: March 2024

This study explains the spiritual aspect of igal ritual theatre. It justifies the relationship between the concept of Cultural Sustainable Development Goals (SDGs) and the role of the igal ritual theatre in the well-being of society and the preservation of heritage. This research applies the ethnographic method through an 'inductive phenomenological character' approach. The result explains how the igal ritual theatre relates to the concept of Cultural SDGs presented by the International Council on Monuments and Sites (ICOMOS). This study affirms that igal ritual theatre aligns with the agenda of the Cultural SDGs to improve society and preserve heritage.
Border Area Development Miangas Island as a Tourism Attraction in the District of Talaud Islands North Sulawesi
Authors: Laluas Efraim , Lanya Indayati , Paturusi Syamsul Alam , Putra I Nyoman Darma
Published: March 2024

This research explores the potential of Miangas Island as a tourist destination using a mixed methods approach. The findings highlight the importance of involving stakeholders and propose the MULTIPOL approach for enhancing the island's attractiveness. Recommendations are provided for policymakers and community leaders in developing border areas to maximize tourism potential.
Suppression of Coffee-ring Effect (CRE) in the Development of Low-cost Diagnostic Kit
Authors: Ideris Norhidayah , Abu Bakar Noor Fitrah , Ahmad Abdul Latif , Kusumastuti Adhi
Published: March 2024

One of the applications of membrane technology is using the polymeric membrane as an adsorber or assay-capturing matrix in the diagnostic kits' assembly. This study explores the addition of NaCl into a protein solution to suppress the coffee-ring effect (CRE) in developing a low-cost diagnostic kit. The highest concentration of NaCl addition shows the optimum results with no formation of CRE and high color intensity (low grey scale value). Adding NaCl into the protein solution is a safe and cheap alternative for lowering the cost of assembly, benefiting people in low-resource places.
Development of a Safe Driver’s Attitude Intervention Module Based on the SaringSikap Screening Method
Authors: Masuri Mohamad Ghazali , Md Isa Khairil Anuar , Ishak Siti Zaharah , Wiyono Adrianto Sugiarto
Published: March 2024

The human factors are consistently the leading cause of road crashes in Malaysia and worldwide. This study would like to support the government's effort to reduce road crashes by proposing a comprehensive driver's intervention module based on the SaringSikap/ASDS46 screening method. A total of 240 volunteers had undergone the screening process. Domain 1 (D1) scored the highest high-risk attitude score, and Domain 2 (D2) was the lowest. This intervention will benefit all groups of road users in Malaysia. It also will assist the government in reducing road crashes in the future through education and empowerment programs.
Islamic Values, Donation Behaviour and Muslim Financial Behaviour among Malaysians and Indonesians
Authors: Johan Zaimy Johana , Canggih Clarashinta
Published: March 2024

This paper explores the impact of Islamic values on Muslim donation and financial behaviours in Indonesia and Malaysia. This associative quantitative research applies structural equation modeling (SEM), the SmartPLS approach. 427 data were collected using a closed questionnaire distributed to respondents who met the research criteria from Malaysia and Indonesia. The result postulates slight differences in the donation and financial behaviours between the two countries. Since the research is purely quantitative and only focused on individual donation and financial behaviours, researchers propose a qualitative study to further explore other determinants of Muslim donation and financial behaviours.
Competency of Autism Spectrum Disorder Parents in Home-Based Occupational Therapy Intervention
Authors: Azri Muhammad Azwan , Che Daud Ahmad Zamir , Berahim Juliana , Paramasivam Revathi
Published: March 2024

During COVID-19, parents only relied on online materials to provide occupational therapy intervention at home. However, their competency is less studied. Thus, the objectives of the study were to examine parents’ sense of competence and the level of stress in providing home-based therapy. Data were collected via an online survey using the Parenting Sense of Competence Scale and Parental Stress Scale. 125 parents were recruited using a purposive sampling technique. The result shows that parents have a high level of sense of competence and a low level of stress in handling children with ASD during the pandemic.
Grammar and Technology: A Bibliometric Exploration of Research Trends and Influential Works
Authors: Paris Nazatul , Samsuri Maimanah , Abas Hafiza
Published: March 2024

The study delves into the pivotal role of Grammar in scholarly communication, emphasising the need for a nuanced understanding of evolving grammatical research. Conducting a bibliometric analysis of 381 papers from the Scopus database (2015-2023), it explores grammar teaching and learning via technology. Employing advanced methods, the research identifies influential authors, institutions, and countries, tracks evolving themes, and assesses funding sources. The study utilises tools like Microsoft Excel, VOS Viewer, and Harzing to unveil partnerships, current research areas, and impact assessment. By categorising five clusters, it sheds light on underexplored research areas, offering insights for future investigations into grammar research.
Effect of Environmental Factors on Performance in Martial Arts Competitions
Authors: Zhang Tingxiu , Boon Hooi Lim , Lu Lu , Tie Yuhe
Published: March 2024

This study investigates the nexus between environmental conditions, precisely temperature and humidity, and Martial arts performance scores in Shandong, China. Analyzing data from 200 participants reveals significant impacts on athletes' Martial arts performance scores. The insights garnered empower athletes, trainers, and organizers to make informed decisions on training approaches, venue selection, and preparation strategies. Recognising the diverse effects of environmental factors, subsequent research may offer tailored recommendations for distinct martial arts disciplines. These findings contribute to developing targeted training strategies and preparation techniques, offering a roadmap to optimise martial arts performance scores in competitive settings
Shanxi Local Opera and Malaysian Chinese Opera Clothing Fabrics
Authors: Xing Furong , Azian Tahir  , Syed Alwi Syed Abu Bakar  , Ge Shuqian
Published: March 2024

This paper, literature research, and comparative research were used to systematically study the costumes and fabrics. The paper finds that the clothing fabrics of Shanxi local opera have the magnificence of ancient Chinese costumes, and the process technology is mainly color embroidery, gold embroidery, and mixed embroidery. The fabrics of Malaysian Cantonese opera costumes are relatively more luxurious, with a very obvious Lingnan style. In the end, the article argues that historical and cultural changes have dominated the differentiation of fabrics. The research can further promote the theoretical and practical exploration of the intrinsic value of theatrical costumes.
Effect of Arbuscular Mycorrhizal Fungi on vascular wilt control caused by Fusarium oxysporum in cape gooseberry plantlets
Authors: Ramírez Gómez María Margarita , Pérez Moncada Urley Adrián , Serralde Ordóñez Diana Paola , Peñaranda Rolón Andrea María
Published: March 2024

Colombia es uno de los productores de uchuva más grandes del mundo. Este estudio tuvo como objetivo evaluar el efecto de un inóculo de mezcla de HMA (Rhizoglomus sp y Acaulospora mellea), en condiciones de invernadero en plántulas de grosella, con el fin de reducir el impacto de Fusarium spp y la mejora del desarrollo de la planta.
Métodos Geostadísticos aplicados en el control de poblaciones del picudo Conotrachelus psidii Marshall (Coleoptera: Curculionidae)
Authors: Montes Prado Millerlandy , Carabali Muñoz Arturo
Published: March 2024

La geoestadística es una herramienta útil para la caracterización y modelización de los patrones de distribución espacial (Moral 2004). El picudo Conotrachelus psidii es un insecto plaga de importancia económica en el cultivo de la guayaba en Colombia, el conocimiento de los patrones de distribución espacial del picudo a partir de métodos geoestadísticos permitirán optimizar un programa de manejo y reducir la aplicación de insecticidas, generando un menor impacto ambiental. Cultivos tecnificados realizan aplicaciones tipo calendario para su control. Desde el reporte de ingreso de C. psidii al país Corpoica ha adelantado trabajos de investigación que conlleven a la disminución de poblaciones del insecto, durante los últimos estudios y con la aplicación de herramientas geoestadísticas, muestran que C. psidii presenta un patrón de distribución agregado. En el presente estudio se aplican métodos geoestadísticos para el análisis de los datos obtenidos durante la validación de estrategias de manejo de C. psidii en dos ciclos fenológicos de un cultivo comercial de guayaba de la variedad Pera Ica 1
Cadena del fique y su agroindustria : anexo 5
Authors: Ramírez Durán Julio , Castillo Jennifer Andrea , Contreras Pedraza Carlos Alberto
Published: March 2024

El presente documento se preparó con el fin de proveer información básica sobre el desempeño de la cadena productiva, sus vínculos con la orientación de la ciencia, la tecnología y la innovación y las demandas del sector sobre conocimiento técnico, para superar problemas productivos, mejorar la competitividad, la sostenibilidad, aprovechar oportunidades de mercado, potenciar capacidades y focalizar la inversión pública. Esta información se consolidó a través del proceso de revisión y ajuste de la Agenda Dinámica Nacional de Investigación, Desarrollo Tecnológico e Innovación (Agenda l+O+i), como insumo para orientar las estrategias del PECTIA
Contexto general del PECTIA Mega tendencias vinculadas a algunos aspectos del desarrollo agrícola: anexo 4
Authors:   Corporación colombiana de investigación agropecuaria - AGROSAVIA,   Departamento Administrativo de Ciencia, Tecnología e Innovación,   Ministerio de Agricultura y Desarrollo Rural
Published: March 2024

Dada la creciente vinculación del país a la economía global y el creciente impacto del cambio climático mundial en la base ambiental de nuestra agricultura, la ejecución de un Plan Estratégico para la Ciencia, la Tecnología y la Innovación (PECTIA) en el sector agropecuario colombiano en los próximos años, debe tener en cuenta como aspectos orientadores, las tendencias en el mundo relacionadas con algunos temas que en este documento se tratan como mega-tendencias vinculadas al desarrollo agrícola