bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characterization of a Native Microbial Consortium from Mining Tailings of the Arequipa Region and its potential application in Biolixiviation
Authors: Arequipa-Loayza Daniela V. , Valdes Jorge H. , Lazarte-Rivera Antonio M. 
Published: June 2024

Abstract

This study addresses the issue of mining environmental liabilities in the southeastern region of Arequipa, Peru, specifically in Yarabamba, pristine areas for molecular characterization studies through metagenomics, where the microbial consortia present in mining tailings were determined, exploring their diversity and biolyxative capacity. In the studied region, treatments with native and adapted microbial consortia were implemented, using seawater in the latter. Over 14 days, analyses of cell count, pH, and oxidation-reduction potential (ORP) were performed, showing an increase in microbial biomass and changes in the medium conditions, indicating a positive impact of the microorganisms on the sustainable recovery of copper. Atomic absorption analysis of the copper content revealed a significant increase at the end of the treatments, reaching 4.42% for the native microbial consortium (NMC) and 4.61% for the adapted microbial consortium (AMC), compared to the initial sample's 2.19%. The final results revealed significant species diversity, with Cutibacterium namnetense, Staphylococcus caprae, and Pelomonas aquatica predominating. This would indicate a positive impact of microbial consortia on sustainable copper recovery, and the microbial diversity suggests that the presence of the identified species could correspond to a signal of natural recovery of the environment, through a possible ecological succession over time. This would be an adaptation to address associated environmental challenges, and it would also highlight the importance of long-term strategies in the bioremediation of affected mining areas.
MRD-Net: Multi-scale Refinement Dehazing Network for Autonomous Driving Perception Images
Authors: Wang Juan , Wang Sheng , Wu Minghu , Yang Hao , Cao Ye , Hu Shuyao , Shao Jixiang , Zeng Chunyan 
Published: June 2024

Abstract

In hazy weather conditions, a significant accumulation of haze poses a severe challenge to the quality of image capture for autonomous driving systems, thereby heightening safety risks for autonomous vehicles. To tackle this issue, we propose the Multi-scale Refinement Dehazing Network (MRD-Net), an innovative architecture comprising a front-end module, a backbone module, and a tail-end module, specifically designed to eradicate haze with precision. To enhance the extraction of multi-scale features, the backbone module employs the Squeeze-Excitation Residual Dense Block (SRD). It not only learns the intricate multi-scale features of the image but also adaptively recalibrates the feature response of each feature map, ultimately bolstering the network's performance and resilience. The tail-end module, crafted with the Dilation Refinement Block (DRB), serves as a compensatory measure for any detail loss or pseudo-artifacts that might arise from the backbone module's operations. By incorporating this refinement block, the overall dehazing effect is further optimized. Empirical evaluations reveal that the proposed MRD-Net achieves impressive results, with a PSNR value of 28.12, an SSIM value of 0.964, and an LPIPS value of 0.032. These figures indicate that the network is adept at removing haze from images while preserving intricate details, ensuring the efficacy and reliability of autonomous driving systems in hazy environments.
Object/Scene Recognition based on Directional PixelVoting Descriptor from Edge-based Segmentation
Authors: Aguilar-González Abiel , Santiago Alejandro Medina , Osuna-Coutiño J. A. de Jesús 
Published: June 2024

Abstract

Detecting objects in images is crucial across a wide range of applications, including surveillance, autonomous navigation, augmented reality, and more. While AI-based approaches such as Convolutional Neural Networks (CNNs) have proven highly effective in object detection, in some industrial applications where the objects being recognized are confidential, it is difficult to train an AI for such tasks. On the other hand, feature-based approaches like SIFT, SURF, and ORB offer the capability to search any template but may struggle with complex visual variations. In this work, we introduce a novel edge-based object/scene recognition method. We propose that utilizing feature edges, instead of feature points, offers high performance under complex visual variations. Our primary contribution is a directional pixel voting descriptor based on image segments. Experimental results are promising; compared to previous approaches, ours demonstrates superior performance under complex visual variations, enabling real-time processing with embedded capabilities.
Time-series transcriptome analysis reveals the cascade mechanism of biological processes after perturbation of the MVA pathway in Salvia miltiorrhiza
Authors: Liu Fang , Li Nan , Yan Zhu-Yun , Chen Xin 
Published: June 2024

Abstract

Background
Various biological processes are interconnected in plants. Transcription factors (TFs) often act as regulatory hubs to regulate plant growth and responses to stress by integrating various biological pathways. Despite extensive studies on TFs functions in various plant species, our understanding of the details of TFs regulation remains limited. In this study, clonal seedlings of Salvia miltiorrhiza were exposed to specific inhibitors for 12 hours. Time-series transcriptome data, sampled hourly, were used to construct co-expression networks and gene regulatory networks (GRNs). Transcriptome dynamic analysis was utilized to capture the gene expression dynamics of various biological processes and decipher the potential molecular mechanisms that regulate these processes.
Results
The perturbation results showed the growth and development processes of S.miltiorrhiza were primarily affected at the early stage, whereas stress response-related biological processes were mainly influenced at the later stage. And there was a correlation between the series of key differentially expressed genes in terpenoid biosynthesis pathways and the topological distribution of these pathways. Furthermore, the GRNs based on TFs indicate that TFs play a crucial role in connecting various biological processes. In the cytoplasmic lysate gene regulatory module, SmWRKY48-SmTCP4-SmWRKY28 constituted a regulation hub regulating S.miltiorrhiza responses to perturbation of the MVA pathway. The regulation hub mediated various pathways, including pyruvate metabolism, glycolysis/gluconeogenesis, amino acid metabolism, and ubiquinone and other terpenoid-quinone biosynthesis.
Conclusion
Our findings suggest that perturbation of a key biological pathway in S.miltiorrhiza has time-dependent effects on other biological processes. And SmWRKY48-SmTCP4-SmWRKY28 constitutes the regulatory hub in S.miltiorrhiza responses to perturbation of MVA pathway.
Comparative Analysis of Outlier Detection Approaches in MetOcean Domain: Extreme Value Analysis, Functional Data Analysis, and Unsupervised Learning
Authors: Sazali Faris Haziq , Jawaddi Siti Nuraishah Agos , Ismail Azlan , Zain Jasni Mohamad , Jaafar Jafreezal , Hasan Mohd Hilmi , Aziz Izzatdin Abdul 
Published: June 2024

Abstract

Detecting outliers within MetOcean datasets is crucial for identifying extreme weather conditions to facilitate informed decision-making whilst various outlier detection approaches and methods are available to be selected. However, choosing the most appropriate one requires an in-depth understanding of their capabilities and this poses a great challenge to the novice within the domain. Therefore, this study is proposed to empirically compare these approaches and provide insights on their performance when dealing with large MetOcean data. The comparison is driven by three outlier detection approaches using the SEAFINE dataset: Extreme Value Analysis (EVA), Functional Data Analysis (FDA), and Unsupervised Learning (UL). A framework is designed to systematically conduct the comparative analysis, which involves five phases: data preprocessing, model training, outlier detection, parameter estimation, and evaluation. This analysis provides insights into the performance of each outlier detection method for each approach and the comparison between them. Overall, the results indicate that the EVA approach exhibits the fastest average execution time compared to the FDA and UL. Within the EVA approach, Peak-Over-Threshold is the most effective, closely aligning the distribution of extreme values with the estimated distribution. Functional Boxplot emerges as the optimal method for FDA, capable of detecting outliers far from the median. Cluster-Based Local Outlier Factor (CBLOF) is the most effective UL method, predicting outliers close to the expected total, with high Excess-Mass values and fast execution.
Improving Citrus Fruit Classification Accuracy in Xray Images Using Features Enhanced Vision Transformer Architecture
Authors: Raza Syed Mudassir , Raza Awais , Babeker Mohamed Ibrahim Abdullah , Haq Zia-Ul , Islam Muhammad Adnan , Li Shanjun 
Published: June 2024

Abstract

Quality assessment is a cornerstone of fruit production and distribution, particularly regarding storage conditions and duration. Citrus fruits, a staple in global consumption patterns, serve as aultimate example. This study employs a non-destructive analytical technique, X-ray Computed Tomography (CT) scanning to meticulously analyze a substantial sample of 300 citrus fruits, specifically satsuma, subjected to both ambient (20-22°C, 50-60% humidity) and refrigeration conditions (6-8°C, 65-75% humidity). Through a methodologically rigorous approach, we conduct Stratified Dataset Splitting, allocating 60% of the X-ray datasets for training, with 20% dedicated to validation and testing, respectively. Our research introduces a pioneering methodology termed Feature Enhancement Vision Transformers (FEViT), meticulously designed to augment precision in 1 citrus fruit classification via X-ray image analysis. By leveraging the transforma-tive potential of Vision Transformer architecture, FEViT integrates convolutional layers and expanded input dimensions to enhance classification performance substantially. Our empirical findings unequivocally demonstrate the superior efficacy of FEViT models vis-à-vis conventional ViT counterparts across diverse datasets. Particularly noteworthy are the marked performance gains exhibited by FEViT-Large variants, evidenced by notable increases in precision (5.08%), accuracy (5.47%), recall (4.55%), and F1 scores (5.28%). This underscores the distinguish-able enhanced discriminatory prowess of FEViT models. Extensive validation through rigorous experimentation ratifies FEViT’s supremacy over traditional deep learning architectures, affirming heightened accuracy, precision, recall, and F1 scores. Our study heralds the advent of FEViT architecture as a milepost in citrus fruit image classification, promising augmented accuracy and robustness vis-à-vis extant methodologies. This research holds profound implications for the agricultural sector, especially in domains such as citrus and broader fruit classification , where nuanced image analysis is indispensable for quality assessment and informed decision-making.
An efficient method for the isolation and transfection of Pea (Pisum sativum) mesophyll protoplasts
Authors: Jo Leonardo , Xu Qiuyu , Kajala Kaisa 
Published: June 2024

Abstract

Background
Pea (Pisum sativum L.) is an important crop with a wide range of uses and benefits, including food source, forage, and potential use as a biofuel crop. Developing methods for the transformation in this species can provide opportunities to improve this important crop. The transient transformation of protoplasts serves as a method for plant engineering, providing a quick and cheap way to study gene and protein function in plants. Developing a protocol for protoplast transfection is especially important for species like pea, where stable transformation methods are laborious and inefficient.
Results
In this report, we established and optimized an efficient protoplast isolation method for pea mesophyll cells. Using the tape-sandwich method we were able to isolate an average of 6.7x105 protoplasts per pea leaflet. Additionally, we determined the optimal conditions for the PEG4000 mediated transfection to achieve about 40% to 60% transfection efficiency in pea mesophyll protoplasts. Finally, we showed that with the protoplast transfection system, we were able to quickly assess protein subcellular localization in pea.
Conclusion
With the inclusion of this efficient and fast protoplast isolation and transfection system to the pea transformation toolbox, we hope to further aid many molecular and biochemical studies in this important legume crop.
Organic Nutrient Management Affecting Growth and Yield in Strawberry (Fragaria× ananasa Duch.) cv. Flavia under Punjab Conditions
Authors: Sharma Jyoti Bharti , Wani Ab. Waheed , Chauhan Nidhi , Jabroot Khan , Chaudhuri Madhurima 
Published: June 2024

Abstract

The demand for increased food production while minimizing environmental impacts poses a significant challenge for sustainable agricultural intensification. Organic farming is considered eco-friendly but less productive compared to conventional farming. To boost yields, a balanced application of organic manures, bio-fertilizers, and inorganic fertilizers is recommended. However, appropriate combinations of organic sources alone can also meet crop requirements. Therefore, an experiment was conducted at Lovely Professional University, Punjab, to investigate the effect of different organic sources of nutrients, alone or in combinations, on the growth and yield attributes of the strawberry cultivar Flavia. The study comprised ten treatments structured using a randomized block design (RBD). The results indicated that all treatments contributed to increased growth and yield of strawberry compared to the control, but treatment combination including 60% RDN – 30% through Vermicompost (90 g/m2) + 30% through neem cake (45 g/m2) and biochar (800 g/m2) + Panchgavya + VAM each @ 50 mL/plant (T9) resulted in significant increase in vegetative growth parameters viz. plant height (6.7 and 13.33 cm), plant spread EW (15.87 and 24.63 cm), plant spread NS (14.00 and 25.59 cm), and number of trifoliate leaves (5.97 and 17.40) at 45 and 90 days after planting (DAP) respectively. Similarly, this treatment combination also contributed to maximum chlorophyll index (57.90 SPAD), leaf area (48.76 cm2) and stem girth (3.41 cm) along with greater yield and yield attributing parameters such as number of fruits per plant (28.14), average berry weight (16.53 g), yield per plant (447.88 g) and yield efficiency (0.47 kg/cm2) compared to other treatment combinations and control.
Antiviral Activity of Probiotics in the Prophylaxis and Therapy of Respiratory Infections Associated with Coronavirus (COVID-19): Meta-analysis of Randomized Controlled Trials
Authors: Mokadem Slimane Chawki , Naimi Mostefa , Alami Omar 
Published: June 2024

Abstract

Introduction:
Probiotics, living microorganisms administered in sufficient quantities, exert beneficial effects on host health. Given the high prevalence of SARS-CoV-2, recent studies suggest potential positive impacts of probiotics on COVID-19 patients.
Methods
A predetermined search strategy encompassing seven databases: NCBI, PubMed, Science Direct, Springer Link, Embase, CNKI, and Cochrane Library Databases, was implemented. Human RCTs studies were scrutinized independently, involving data extraction, quality and risk of bias assessment, and statistical analysis. Pooled data, employing the random-effects model, were expressed as Standardized Mean Differences (SMD) with a 95% Confidence Interval (CI). Assessments of (p) value and heterogeneity (I²) were conducted and quantified.
Results
Five studies, comprising 282 out of 375 participants, were included. Meta-analysis revealed effects on various parameters: CRP (SMD = 0.26 MG/L, 95% CI [0.10, 0.43], p = 0.002, (I²=67%, p = 0.03)), BMI (SMD = 0.28 KG/m², 95% CI [0.07, 0.50], p = 0.01, (I²=67%, p = 0.40)), T-cells (SMD = 0.09 G/L, 95% CI [-0.07, 0.26], p = 0.26, (I²=0%, p = 0.73)), Albumin (SMD = 0.28 G/DL, 95% CI [0.04, 0.52], p = 0.02, (I²=7%, p = 0.34)), IL-6 (SMD = 0.67, 95% CI [0.45, 0.90], p = 0.00001, (I²=94%, p = 0.0001)), LDH (SMD = 0.12mmol/L, 95% CI [-0.05, 0.30], p = 0.17, (I²=55%, p = 0.13)), and Ferritin (SMD = 0.19mmol/L, 95% CI [-0.27, 0.66], p = 0.41, (I²=77%, p = 0.04)).
Conclusion
This meta-analysis suggests significant positive effects of probiotics on various measures for COVID-19 treatment.
Hardware Implementation of Multi-LFSR Pseudo Random Number Generator
Authors: Teo Tee Hui , Xiang Maoyang , Xie Jiacheng , Gerald Ek Hong Sheng , Augustine Lee Kang Yi , Yi Isabel Judith Koh Wee 
Published: June 2024

Abstract

This is an implementation of a hardware pseudo-random number generator (pRNG) using multiple LFSR (Multi-LFSR) for generation, and ambient electromagnetic noise for seed generation. The pRNG algorithm is evaluated in field-programmable-gate-array (FPGA) board, which is CMOS A7. 16-bit random numbers are generated at a frequency of 50 kHz. In the FPGA setup, a new seed is set by clicking the reset button. A dedicated high speed UART is designed with a baud rate of 1,152,000 to transfer the generated numbers from the FPGA to PC.
Cannabinoid Ethers: Identification, Binding interactions, and their ADME properties
Authors: Docampo-Palacios  , Ramirez Giovanni. A. , Tesfatsion Tesfay. T. , Cruces Westley 
Published: June 2024

Abstract

New cannabinoid analogs are constantly being synthesized and marketed to the public as ways to increase psychotropic activity or to remain competitive within the market. The issue with new analogs is the lack of proper testing and characterization—companies are misrepresenting what they sell, and customers are consuming unknown research compounds. Our group performed in-silico docking in CB1 and CB2 receptors, drug metabolism, and pharmacokinetics (DMPK) studies of methyl-, ethyl-, and pivaloy- ether cannabinoids analogs. A dataset of eight cannabinoid ethers was docked to the model to further analyze key interacting residues on the CB1 and CB2 receptors. In addition, the methyl ether derivatives of D8 tetrahydrocannabinol and hexahydrocannabinol were synthesized and the full NMR characterization of these compounds was achieved.
A Longchain Approach to Reduce Hallucinations in Large Language Models
Authors: Li Jinchao , Hong Quan 
Published: June 2024

Abstract

The increasing deployment of natural language processing models in critical domains necessitates addressing the issue of hallucinations, where generated outputs may be factually incorrect or nonsensical. The longchain approach, which involves an iterative refinement process, offers a novel and significant method to mitigate hallucinations by enhancing both the accuracy and coherence of model outputs. The methodology involved modifying the GPT-3 architecture to incorporate additional layers for intermediate evaluations and corrections, followed by rigorous training and evaluation using the MMLU dataset. Quantitative results demonstrated that the modified model significantly outperformed baseline models across various performance metrics, including precision, recall, F1-score, logical coherence, and hallucination rate. Qualitative analysis further supported these findings, showcasing the practical benefits of the longchain approach in producing accurate and contextually relevant outputs. The study emphasizes the theoretical foundations of iterative learning and continuous improvement, providing a robust framework for enhancing the reliability of natural language processing models. The implications of the findings are substantial for applications in healthcare, legal advice, and education, where the generation of accurate and reliable text is paramount. By reducing hallucinations and improving coherence, the longchain approach contributes to the development of more trustworthy and effective language models.
Utilization and predictors of postpartum modern family planning methods in Felegehiwot referral hospital, Northwest Ethiopia
Authors: Moges Fisseha 
Published: June 2024

Abstract

Achieving adequate spacing between pregnancies is a major pathway through which contraceptive practice can render benefits. Directing efforts to increase modern contraception among postpartum women could have a significant impact on increasing contraceptive prevalence rate and other health benefits. However, contraceptive use in the postpartum period is less emphasized by policymakers and program designers in Ethiopia. Therefore, information about the current utilization of modern contraceptives and its associated factors among postpartum women is needed to design strategies and improve service delivery practices in health institutions. The objective of the study is to assess the utilization and factors associated with modern contraceptives use among postpartum women in Felegehiwot referral hospital, Bahirdar city, Northwest Ethiopia. A facility-based cross-sectional study was employed from January 1-31, 2019 among postpartum women in Felegehiwot referral hospital. The data were collected through a face to face interview for 425 women who were attending basic maternal and child health services. Bivariable and multivariable logistic regression was employed to identify the association between the dependent variable, utilization of postpartum family planning, and the explanatory variables. The association was considered statistically significant when P-value was < 0.05. A total of 425 postpartum women participated in the study with a 100% response rate. Over half (53%) of women interviewed used modern contraceptives during the postpartum period. Women who are aware of PPFP during ANC and PNC visits (AOR, 16.99; 95% CI, 2.85 to 101.16); married (AOR 3.052; 95% CI, 1.644 to 5.667); and higher age (AOR, 5.825; 95%CI, 2.519 to 13.467) were more likely to use modern contraceptives in the postpartum period. However, educational status, parity, the spousal reaction for FP related discussions, and contraceptive education failed short of any statistically significant association with modern postpartum contraceptive use. The utilization of modern contraceptives in the postpartum period was found to be relatively lower. Proper counseling on available methods for postpartum women at different instances coupled with increasing contraceptive method mix, enhancing male engagement in family planning programs, and expanding contraceptive information during facility visits for antenatal and postnatal care using different media outlets are important measures to boost contraceptive use.