bluuebunny/crossref_metadata_2025_split huggingface dataset:
Drug repurposing Based on the DTD-GNN Graph Neural Network: Revealing the Relationships among Drugs, Targets and Diseases
Authors: Li Wenjun , Ma Wanjun , Yang Mengyun , Tang Xiwei 
Published: March 2024

Abstract

Motivation: The rational modelling of the relationship among drugs, targets and diseases is crucial for drug retargeting. While significant progress has been made in studying binary relationships, further research is needed to deepen our understanding of ternary relationships. The application of graph neural networks in drug retargeting is increasing, but further research is needed to determine the appropriate modelling method for ternary relationships and how to capture their complex multi-feature structure.
Results: The aim of this study was to construct relationships among drug, targets and diseases. To represent the complex relationships among these entities, we used a heterogeneous graph structure. Additionally, we propose a DTD-GNN model that combines graph convolutional networks and graph attention networks to learn feature representations and association information, facilitating a more thorough exploration of the relationships. The experimental results demonstrate that the DTD-GNN model outperforms other graph neural network models in terms of AUC, Precision, and F1-score. The study has important implications for gaining a comprehensive understanding of the relationships between drugs and diseases, as well as for further research and application in exploring the mechanisms of drug-disease interactions. The study reveals these relationships, providing possibilities for innovative therapeutic strategies in medicine.
Design and experiment of impeller seed guide device for rice internal suction hole direct seeding device
Authors: Shang Yansong , Zhou Bo , Yang Jitao , Zhang Shun 
Published: March 2024

Abstract

Hole seeding and uniform hole spacing are important agronomic evaluation indexes of rice precision hill-direct-seeding metering device. Based on the principle of internal suction seeding, this paper innovatively designs an impeller seeding device. The main structural parameters of the impeller seed guide device were analyzed by constructing the kinematics model of the rice seed in the impeller seed guide process. The effects of impeller radius, blade offset angle and seeding angle on the performance of the seed guide device were studied by bench seeding test. The seed-guiding experiments of different sizes of seeds show that the impeller has better hole-forming and seed-guiding performance than the traditional seed-guiding tube. The impeller seed guiding device proposed in this paper can improve the performance of fixed-distance multi-grain hill-seeding in the tubular seed guiding device, it provides a reference for the design of seed guide device for multi-grain bunching crop seed metering device.
Shrinkage for Extreme Partial Least-Squares
Authors: Arbel Julyan , Lorenzo Hadrien , Girard Stéphane 
Published: March 2024

Abstract

This work focuses on dimension-reduction techniques for modelling conditional extreme values. Specifically, we investigate the idea that extreme values of a response variable can be explained by nonlinear functions derived from linear projections of an input random vector. In this context, the estimation of projection directions is examined, as approached by the Extreme Partial Least Squares (EPLS) method--an adaptation of the original Partial Least Squares (PLS) method tailored to the extreme-value framework. Further, a novel interpretation of EPLS directions as maximum likelihood estimators is introduced, utilizing the von Mises-Fisher distribution applied to hyperballs. The dimension reduction process is enhanced through the Bayesian paradigm, enabling the incorporation of prior information into the projection direction estimation. The maximum a posteriori estimator is derived in two specific cases, elucidating it as a regularization or shrinkage of the EPLS estimator. We also establish its asymptotic behavior as the sample size approaches infinity. A simulation data study is conducted in order to assess the practical utility of our proposed method. This clearly demonstrates its effectiveness even in moderate data problems within high-dimensional settings. Furthermore, we provide an illustrative example of the method's applicability using French farm income data, highlighting its efficacy in real-world scenarios.
MSC 2020 subject classification: 62G32, 62H25, 62H12, 62E20.
Contracaecum jorgei Sardella, Mancini, Salinas, Simões &amp; Luque, 2020 (Nematoda: Anisakidae) in common snooks Centropomus undecimalis (Bloch, 1792) in southeastern Brazil: new host, locality record and remarks on misidentifications of Contracaecum spp. in Genbank
Authors: Duarte Rayane , Martinatti Bruna Reich , de Lima Viviane Moreira , Tubino Rafael de Almeida , Oliveira Mariana de Souza , Berto Bruno Pereira , Santos-Clapp Michelle Daniele 
Published: March 2024

Abstract

In the current study, Contracaecum jorgei Sardella, Mancini, Salinas, Simões & Luque, 2020 is reported from the common snook Centropomus undecimalis (Bloch, 1792) in the Rodrigo de Freitas lagoon in the State of Rio de Janeiro, southeastern Brazil. This anisakid species was recently described in wolffish in Argentina. Through comparative analyzes of genetic sequences deposited in GenBank with the sequencing of a locus of the cytochrome oxidase subunit 2 (cox2) gene provided in the present study, the identification of C. jorgei was confirmed, at the same time that misidentified sequences of Contracaecum spp. were observed. These misidentified sequences were then discussed and reevaluated, expanding the reports of C. jorgei and, consequently, its geographic distribution in the Neotropical region. Finally, the current study highlights the importance of the findings and study of anisakid nematodes in commercial fish for the monitoring and prevention of human anisakiasis, which is openly neglected or confused with other gastrointestinal diseases worldwide.
Efficient Composite Model to Detect Facial Paralysis using Generative Adversarial Network and Facial Key Points Analysis
Authors: Naganjaneyulu Satuluri , Srinivasa Narisetty , Sesha Srinivas. , Mahmood Ali Mirza 
Published: March 2024

Abstract
Facial paralysis is the inability of one or both sides of the face's muscles to move, and it can impair a person's ability to talk, blink, swallow saliva, eat, or express themselves naturally with their faces. To detect this facial paralysis, the best technique is facial key point analysis. Even though it is the best technique, there are several limitations and drawbacks of facial key point analysis, including limited diversity, sensitivity to lighting, occlusion, pose variability, real-time performance, privacy concerns, etc. This paper proposes a method to overcome the limitation of light sensitivity, making it possible for facial key point analysis to detect key points on the face. In this paper, a composite model is implemented using a "generative adversarial network" (GAN) and "facial key point analysis". GAN is implemented to make the facial picture into a high-resolution picture. This GAN output is given for the input of the facial key point analysis. Facial key point analysis is the process of identifying and tracking specific points on a person's face, such as the corners of the mouth, the tip of the nose, and the eyebrows, to understand the movements and expressions of the face. The results of this composite model help detect facial paralysis more efficiently and accurately than before.
EGFR-TKIs with or without Stereotactic Body Radiotherapy to Primary Lesion in the Advanced EGFR-Mutated Non- Small Cell Lung Cancer
Authors: Yuan Ze , Tao Dan , Yang Dingyi , Jiang Yong , Munai Erha , Zeng Siwei , Wang Dehuan , Wang Qiang , Zhou Wei , Wu Yongzhong 
Published: March 2024

Abstract

Background: Advanced non-small cell lung cancer (NSCLC) harboring epidermal growth factor receptor (EGFR) mutations presents a clinical challenge due to eventual resistance to EGFR tyrosine kinase inhibitors (TKIs). This study aimed to evaluate the efficacy and safety of combining EGFR-TKI therapy with stereotactic body radiotherapy (SBRT) to primary lesions in treating EGFR-mutant NSCLC patients.
Methods: A retrospective analysis was conducted on 58 EGFR-mutant NSCLC patients treated between 2018 and 2023. Patients were divided into two groups: EGFR-TKI alone (n=43) and EGFR-TKI combined with SBRT (n=15). Clinical outcomes were assessed, including progression-free survival (PFS), overall survival (OS), and treatment-related toxicities. Statistical analyses were performed to compare the effectiveness of the treatment modalities.
Results: A total of 485 patients with advanced NSCLC were screened, and 58 patients were eligible for enrollment in this study. Of the 58 eligible patients, 15 (25.9%) received EGFR-TKI treatment alone and 43(74.1%) received EGFR-TKI treatment plus with SBRT to lung primary tumor. The TKIs plus SBRT group exhibited a significant extension in PFS compared to the TKIs alone group (not reached vs 10.2 months, p < 0.001). Multivariate analysis identified SBRT as the sole significant positive predictor for PFS. Adverse event profiles were comparable between groups, with manageable toxicities and no Grade 4-5 events reported. Meta-analysis revealed that combined TKI and SBRT treatment significantly improved PFS (HR 0.39, 95% CI 0.24-0.64, P < 0.001) but showed no significant OS benefit. After excluding retrospective studies, a significant OS benefit was observed. (HR 0.47, 95% CI 0.33-0.69, P < 0.001). No increased incidence of grade ≥3 AEs with SBRT was observed, but the TKIs plus SBRT group had higher probabilities of pneumonitis and radiation dermatitis than the TKIs alone group.
Conclusions: Our study suggests that the addition of SBRT to EGFR-TKI therapy significantly extends PFS and OS in EGFR-mutant NSCLC patients. The results emphasize the potential of combining local radiotherapy with targeted therapy, offering a promising approach to improve outcomes in this patient population.
Investigating the Impact of Institutional Quality Under the Petroleum Price Deregulation Policy Regime on the Economic Growth of an Emerging Economy
Authors: Sackey Frank Gyimah , Orkoh Emmanuel , Musah Mohammed 
Published: March 2024

Abstract
The importance of institutional quality as a means of influencing economic growth is being given much attention. This paper examines the extent to which institutional quality under the price deregulation policy impacts GDP growth. The study employs the Bayesian linear regression and the auto regressive distributed lag (ARDL) models in estimating a quarterly data that spans from the first quarter of 2005 to the fourth quarter of 2020. We observe that petroleum prices impact positively on GDP growth but only in the long run while inflation impacts negatively on GDP growth in the short run. Again, we observe a short run positive relationship between foreign direct investment and GDP growth. While institutional quality impacts positively on GDP growth in the short run but negatively in the long run, the interactions of petroleum prices and institutional quality on the other had reduces GDP growth in the short run but tend to increase GDP growth comparatively more than the decrease in the short run suggesting that institutional quality under the price deregulation policy impacts on GDP growth in the long run.
Design and Development Writing Robot Using Speech Processing for Medical Prescription
Authors: Boddu IndraSena Reddy , Akkala Subba Reddy , V Vijayakumar 
Published: March 2024

Abstract
The paper's main goal is to outline the direction and the process for creating swift, fluid movements for a universal robot that can write like a doctor signs a prescription. The majority of consumers and certain pharmacists find it difficult to understand a handwritten prescription from a doctor; this issue can have unfavorable consequences if the prescription is misread Prescriptions can be confusing since doctors often employ Latin and medical phrases that are abbreviated and difficult for the general public to understand. To complete the work, online human signature criteria must first be devised. Robot signatures are then produced as a result of writing robots in compliance with these recommendations. Finally, suggestions for improving robot movement are provided.
Short-Term Forecasting in Smart Grid Environment using N-BEATS
Authors: Singh Neelesh Pratap , Alam Mahamad Nabab 
Published: March 2024

Abstract

Forecasting is increasingly becoming necessary in the modern Smart Grid to properly operate and control renewable integrated power systems. Forecasting accuracy plays a vital role in the adequate scheduling of conventional resources to allow the maximum utilization of renewable sources and provide proper operation of the system. Mainly, accurate short-term forecasting of load, electricity price, solar photovoltaic (PV), and wind turbine (WT) based renewable power generations are of central importance to manage remaining sources optimally to supply cost-effective power to the consumers and reduce carbon emissions. This paper proposes a Neural Basis Expansion Analysis Time Series Forecasting (N-BEATS) algorithm for highly accurate short-term forecasting of SG load, electricity price, PV and WT-based power generations. Data preprocessing, such as normalisation, outlier management, and new cyclic feature building, is employed in the proposed algorithm to enhance its performance. The developed N-BEATS algorithm performs excellently on dynamically fluctuating datasets and peak load prediction. The suitability and effectiveness of the N-BEATS algorithm are validated on four different datasets. Further, the superiority of the algorithm is demonstrated by comparing the performance of the algorithm with several well-explored state-of-the-art forecasting algorithms.
Thermopower probing emergent local moments in magic-angle twisted bilayer graphene
Authors: Das Anindya , Ghosh Ayan , Chakraborty Souvik , Dutta Ranit , Agarwala Adhip , Watanabe Kenji , Taniguchi Takashi , Banerjee Sumilan , Trivedi Nandini , Mukerjee Subroto 
Published: March 2024

Abstract
Recent experiments on magic-angle twisted bilayer graphene (MATBLG) indicate an unusual coexistence of heavy 1-6 and light 7-10 fermions. Yet, these experiments lack direct evidence of the existence of local moments arising from the flatband, a key component of heavy-fermion physics 11-16. Utilizing thermopower as a sensitive probe for measuring entropy 17, we unveil the presence of local moments through their impact on entropy. Remarkably, in addition to sign changes at the Dirac point (ν = 0) and full band filling (ν = ±4), the thermopower demonstrates additional sign changes at the location, νcross ∼ ±1, which does not vary with temperature from 5K to ∼ 60K. This observation cannot be explained solely from the contributions of band fermions; instead, our data is consistent with the dominant contribution arising from the entropy of the localized moments. To validate our claim, we have investigated the effect of magnetic field (B) on the thermopower, both B∥ and B⊥. Our results show a 30% and 50% reduction, respectively, and can be attributed to the partial polarization of the local moments (spin/valley), resulting in decreased entropy. Our thermopower results highlight the contribution from local moments arising from flatbands, distinct from the band contributions to the resistivity in MATBLG.
Urinary polycyclic aromatic hydrocarbon metabolites and their association with oxidative stress among pregnant women in Los Angeles
Authors: Meng Qi , Mitra Sanjali , Rosario Irish Del , Jerrett Michael , Janzen Carla , Devaskar Sherin U. , Ritz Beate 
Published: March 2024

Abstract

Background
Polycyclic aromatic hydrocarbons (PAHs) have been linked to adverse birth outcomes, but few epidemiological studies to date have evaluated associations between urinary PAH metabolites and oxidative stress biomarkers in pregnancy.
Methods
We measured a total of 7 PAH metabolites and 2 oxidative stress biomarkers (malondialdehyde (MDA), 8-hydroxy-2’-deoxyguanosine (8-OHdG)) in urine samples collected up to three times during pregnancy in 159 women enrolled at antenatal clinics at the University of California Los Angeles during 2016–2019. Using multiple linear regression models, we estimated the percentage change (%) and 95% confidence interval (CI) in 8-OHdG and MDA measured at each sample collection time per doubling of PAH metabolite concentrations.
Results
Most PAH metabolites were positively associated with both urinary oxidative stress biomarkers, MDA and 8-OHdG, with stronger associations in early and late pregnancy. Women pregnant with male fetuses exhibited slightly larger increases in both MDA and 8-OHdG in association with PAH exposures in early and late pregnancy.
Conclusion
Urinary OH-PAH biomarkers are associated with increases in oxidative stress during pregnancy, especially in early and late pregnancy. Sex differences in associations between PAH exposures and oxidative stress need to be further explored in the future.
A method for assessing and localizing protection measurement loop errors based on an improved similarity algorithm
Authors: Mu ZongJun , Liu Deng Xin , Hu Bin , Li Zhen 
Published: March 2024

Abstract

This paper proposes the EWM-EDTS algorithm for localizing errors in measurement loops of power system relay protection devices. The algorithm integrates Euclidean distance and Tanimoto similarity using the entropy weight method. Two sets of measurement data with correlated physical information are selected based on the power station's topology. The Euclidean distance and Tanimoto similarity algorithms are used to calculate a sequence of distance values that characterize the dispersion of the two sets of data. then the entropy power method is introduced to calculate the dynamic entropy value of the two sets of sequences, which serves as a distance value weighting and fuses to generate the distance value of the EWM-EDTS, and compares it with the distance threshold value constituted by the measurement data under the normal working condition to realize the error localization of the real-time measurement data. Real-time measurement data error localization.
Following a route at a safe distance of the leader in 2D\3D environments with dynamic obstacles
Authors: Rybka Roman , Skorokhodov Maksim , Gryaznov Artem , Sboev Alexander , Moloshnikov Ivan , Kudryashov Nikolay , Liu Yuanchao 
Published: March 2024

Abstract

The problem of controlling an agent, following by the leader, which moves by some complicated route, unknown for the follower is relevant both from a methodical point of view, and the practical one. In the first case, it is a rich polygon for investigation and prospective development of reinforcement learning (RL) methods. In the second case, a solution of the issue gives a means to control the route for an agent-follower which does not possess a detailed map of the area and the ability to navigate in space using external systems such as ”Global Positioning System” (GPS). The agent is obliged to remain on the route at a given distance from the leader. We consider the problem in the next statement: the agent follows by the leader, taking into account the avoidance of dynamical obstacles in a defined narrow area relative to a given or dynamically generated route. To train a reinforcement learning-based model we developed a two-dimensional (2D) environment simulator, where the "Light Detection and Ranging" (LIDAR) data from the last few steps are used as features, and the linear and angular velocities of the agent are used as the model's output. The paper presents the results of a study of various experimental configurations: the number and types of obstacles, variability of movement routes, and signs of the current state of the environment. The result shows the applicability of the developed model to the problem of following the leader along previously unknown routes in a three-dimensional (3D) environment built using the Gazebo simulator. Code is available at: https://github.com/sag111/continuous-grid-arctic.
Orally administered Thymoquinone mitigates cypermethrin-induced dentate gyrus oxidative stress, preventing GABAergic interneuron degeneration and memory impairment in rats via the Nrf2/ARE pathway.
Authors: Imam Abubakar Lekan , Okesina Akeem Ayodeji , Sulamon Fatimo Ajoke , Imam Aminu , Ibiyeye Ruqayyah Yetunde , Oyewole Lukuman Aboyeji , Biliaminu Sikiru Abayomi , Shehu Monsur , Abdulhameed Alli Oluwatomi , Omoola Oluwatosin Olasheu , Ajao Salihu Moyosore 
Published: March 2024

Abstract

Background
Exposure to chemical toxins, including insecticides, has harmful effects on body organs such as the brain. This study examined the neuroprotective of thymoquinone on the cypermethrin's harmful effects on the histoarchitecture of the dentate gyrus as well as motor deficit.
Methods
Forty adult male rats (180-200g) were randomly divided into 5 groups (n = 8 per group). Groups I, II, III, and IV received oral administration of 0.5 ml of phosphate buffered saline, 20 mg/kg of cypermethrin, 10 mg/kg of thymoquinone, 20 mg/kg cypermethrin plus 5 mg/kg of thymoquinone, and 20 mg/kg of cypermethrin plus 10 mg/kg of thymoquinone for 14 days respectively. The novel Object recognition test assesses intermediate-term memory at days 14 and 21 of the experiment. At the end of these treatments, the animals were euthanized and taken for cytoarchitectural analysis and immunohistochemical studies.
Result
The study shows that thymoquinone at 5 and 10 mg/kg improved Novelty preference and discrimination index. Thymoquinone enhanced Nissl body integrity, increased GABBAergic interneuron expression, nuclear factor erythroid 2-derived factor 2, and enhanced Bcl-2 expression in the dentate gyrus. It also improved the concentration of nuclear factor erythroid 2-derived factor 2, increased the activities of superoxide dismutase and glutathione, and decreased the concentration of malondialdehyde level against cypermethrin-induced neurotoxicity.
Conclusion
thymoquinone could be a therapeutic agent against cypermethrin poisoning.
Prediction of microbiological non-compliances using a Boosted Regression Trees model: application on the drinking water distribution system of a whole country
Authors: Barcia Mariana , Sixto Alexandra , Pia Cerdeiras Maria 
Published: March 2024

ABSTRACT
Universal access to safe drinking water is a fundamental human right and a requirement for a healthy life. Therefore, monitoring the quality of the supplied water is of utmost importance. To achieve this goal, there is a need to develop tools that support monitoring activities and improve efficiency. Forecasting models enable the prediction of pollution levels and facilitate the implementation of action plans. In this study, the Boosted Regression Trees method was employed to investigate the variables influencing water quality failures (WQFs) due to microbial contamination at the delivery point. The dataset used was obtained from localities across the country's distribution systems. The variables under consideration included physicochemical parameters such as pH, turbidity (NTU), and free chlorine (mg L−1), along with contextual parameters like the year, season, geographic location, and locality population. Indicators of microbial contamination assessed were the presence of total coliforms, Escherichia coli, and Pseudomonas aeruginosa. The most significant variables were geographic location, free chlorine content, and the population of the locality. The model achieved an AUC value of 0.77 and provided adequate predictions in the conducted tests. It enables the exploration of key factors affecting microbiological water quality, allowing for informed action to reduce associated risks.