bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficiency of genomic selection for developing superior pure lines
Authors: Silva Jean Paulo Aparecido da , Viana José Marcelo Soriano 
Published: September 2024

Abstract

Developing elite pure lines is the most common breeding process for self-pollinating crops. The objective of this study was to assess the efficacy of genomic selection in pure line breeding, using a simulated dataset from F2 plants to F8 selected progeny. The simulated genome included 1000 genes and 49825 SNPs, distributed on 10 chromosomes of 100 cM. We used genomic selection with partial phenotyping over generations and phenotyping only in F2. We assessed the significance of selection intensity, training set size, selecting in F2, dominance, and major genes. The efficacy of the genomic selection was based on genetic gains and probability of selecting elite pure lines. Genomic selection with partial phenotyping in each cycle maximized the probability of selecting superior F8 progeny and provided the higher total genetic gain, comparable to the selection based on the true genotypic value. Training set size and selection intensity are important factors affecting the development of elite pure lines. Dominance does not significantly affect the genomic selection efficiency. The existence of QTLs increases the genomic selection efficiency, proportional to the proportion of the phenotypic variance explained by the QTLs. There is no justification for selecting in F2. Pedigree-based BLUP cannot be disregarded as a genetic assessment method in pure line breeding. All selection methods provided high decrease in the genotypic variance at F8. The prediction accuracies with genomic selection showed almost perfect correlation with the genetic gains. A challenger in pure line breeding is minimizing the random fixation of the unfavorable alleles in heterozygotes.
Sowing Intelligence: Advancements in Crop Yield Prediction Through Machine Learning and Deep Learning Approaches
Authors: Jayanthi S , Priya D Tamil , M Naresh Goud , Rajkumar Arugula , Sriva B 
Published: September 2024

Abstract

Ensuring global food security necessitates precise crop yield prediction for informed agricultural planning and resource allocation. We investigated the impact of temperature, rainfall, and pesticide application on crop yield using a comprehensive, multi-year, multi-region dataset. Our research rigorously compared, for the first time, the effectiveness of fifteen different algorithms encompassing both established machine learning and deep learning architectures, particularly Recurrent Neural Network (RNN), in constructing robust CYP models. Through rigorous experimentation and hyperparameter tuning, we aimed to identify the most optimal model for accurate yield prediction. We leveraged a comprehensive dataset encompassing various agricultural attributes, including geographical coordinates, crop varieties, climatic parameters, and farming practices. To ensure model effectiveness, we preprocessed the data, handling categorical variables, standardizing numerical features, and dividing the data into distinct training and testing sets. The experimental evaluation revealed that Random Forest achieved the highest accuracy, with an impressive (R²=0.99). However, XGBoost offered a compelling trade-off with slightly lower accuracy (R²=0.98) but significantly faster training and inference times (0.36s and 0.02s, respectively), making it suitable for real-world scenarios with limited computational resources. While XGBoost emerged as the most efficient and accurate solution in this investigation, we also explored the potential of deep learning approaches, including RNNs, for crop yield prediction, paving the way for future research into even greater accuracy.
Design and implementation of a natural language processing system at the point of care: MiADE (Medical information AI Data Extractor)
Authors: Jiang-Kells Jennifer , Brandreth James , Zhu Leilei , Ross Jack , Jani Yogini , Costanza Enrico , Amran Maisarah , Kraljevic Zeljko , Bai Xi , Cresta Roberto , Dilan M.M.N.S. , Wijayarathne Jayathri , Wickramaratne Ravi , Asselbergs Folkert W. , Dobson Richard J.B. , Wong Wai Keong , Shah Anoop D. 
Published: September 2024

Abstract

Background: Well-organised electronic health records (EHR) are essential for high quality patient care, but EHR user interfaces can be cumbersome for entry of structured information, resulting in the majority of information being in free text rather than a structured form. This makes it difficult to retrieve information for clinical purposes and limits the research potential of the data. Natural language processing (NLP) at the point of care has been suggested as a way of improving data quality and completeness, but there is little evidence as to its effectiveness. We sought to generate such evidence by developing an open source, modular, configurable NLP system called MiADE, which is designed to integrate with an EHR. This paper describes the design of MiADE and the deployment at University College London Hospitals (UCLH), and is intended to benefit those who may wish to develop or implement a similar system elsewhere.
Results: The MiADE system includes components to extract diagnoses, medications and allergies from a clinical note, and communicate with an EHR system in real time using Health Level 7 Clinical Document Architecture (HL7 CDA) messaging. This enables NLP results to be displayed to a clinician for verification before saving them to the patient's record. MiADE utilises the MedCAT library (part of the Cogstack family of NLP tools) for named entity recognition (NER) and linking to SNOMED CT, as well as context detection. MedCAT models underwent unsupervised and supervised training on patient notes from UCLH, achieving precision of 83.2% (95% CI 77.0, 88.1), and recall of 85.2% (95% CI 79.1, 89.8) for detection of diagnosis concepts. In simulation testing we found that MiADE reduced the time taken for clinicians to enter structured problem lists by 89%. We have commenced a trial implementation of MiADE at UCLH in live clinical use, integrated with the Epic EHR at UCLH.
Conclusions: We have developed an open source point of care NLP system called MiADE and successfully integrated it with a live EHR at a major hospital for an evaluation study.
Multimodal Machine Learning for Language and Speech Markers Identification in Mental Health
Authors: Drougkas Georgios , Bakker Erwin , Spruit Marco 
Published: September 2024

Abstract

Background There are numerous related papers focusing on diagnosing mental health disorders using unimodal and multimodal approaches. However, our literature review shows that the majority of these studies either use unimodal approaches to diagnose a variety of mental disorders or employ multimodal approaches to diagnose a single mental disorder instead. In this research we combine these approaches by identifying mental health disorder markers for a wide range of mental illnesses using both unimodal and multimodal methods, and consequently determining whether the multimodal approach can outperform the unimodal ones.
Methods For this study we used a well known and robust dataset derived from clinical interviews: DAIC-WOZ. First, we constructed two unimodal models to analyze text and audio data independently using feature extraction, based on the mental disorder markers that had been identified earlier through related studies. Through the unimodal text model, we also propose an initial pragmatic binary label creation process. Then, we employed an early fusion strategy to combine our text and audio features before model processing. Our fused feature set was then given as input to various machine and deep learning algorithms, including Support Vector Machines, Logistic Regression, Random Forests, and a fully connected neural network classifier (Dense Layers). Ultimately, the performance of our models was evaluated using accuracy, AUC-ROC score, and two F1 metrics: one for the prediction of positive cases and one for the prediction of negative cases.
Results Overall, the unimodal text models achieved an accuracy ranging from 78% to 87% and an AUC-ROC score between 85% and 93%, while the unimodal audio models attained an accuracy of 64% to 72% and AUC-ROC scores of 53% to 75%. The experimental results indicated that our multimodal models achieved comparable accuracy (ranging from 80% to 87%) and AUC-ROC scores (between 84% and 93%) to those of the unimodal text models. However, the majority of the multimodal models managed to outperform the unimodal models in F1 scores, particularly in the F1 score of the positive class (F1 of 1s), which reflects how well the models perform in identifying the presence of a marker.
Conclusions In conclusion, by refining the binary label creation process and by improving the feature engineering process of the unimodal acoustic model, we argue that the multimodal model can outperform both unimodal approaches. This study underscores the importance of multimodal integration in the field of mental health diagnostics and sets the stage for future research to explore more sophisticated fusion techniques and deeper learning models.
Taurine enhances antioxidant enzyme activity and immune response in Seriola rivoliana juveniles after lipopolysaccharide injection
Authors: Teles Andressa , Guzmán-Villanueva Laura , Dios Marco A. Hernández-de , Maldonado-García Minerva , Tovar-Ramírez Dariel 
Published: September 2024

Abstract

The use of additives in fish feeds is a practice used worldwide to provide better productive results and improve the fish's health and immune capacity to face disease outbreaks. This study aimed to analyze the effects of the different doses of taurine in fish feed and its impact on the immune-related parameters and antioxidant enzyme activity after LPS (lipopolysaccharide) injection. For this, immune-related gene expression was evaluated as well as lysozyme activity and antioxidant enzyme activity. The results showed that using taurine at 2% (TAU 2%) improved the expression of il1-β, tnf-α, and tlr-3 compared to the other treatments at 24 hours post LPS injection. Lysozyme activity and antioxidant activity such as superoxide dismutase (SOD) and catalase were higher in the treatment with both taurine 1% (TAU 1%) and TAU 2% when compared with the negative control (C-) and the positive control (C+) treatments after 72 hours post LPS injection. These results suggest that using 2% of exogenous taurine added to a commercial fish feed for juveniles of Seriola rivoliana can improve their immunocompetency and counteract the oxidative stress caused by exposure to LPS.
Assessment of Serum Melatonin Levels, Sleep Patterns, and Clinical Symptoms in Children with Autism Spectrum Disorder: A Case-Control Study
Authors: Jaicks Çağlar Charles Daniel , Ray Perihan Çam , Öztürk Özlem Görüroğlu , Çelik Gonca Gül , Tahiroğlu Ayşegül Yolga , Haytoğlu Zeliha , Döğüş Yusuf 
Published: September 2024

Abstract

Objective: This study aims to evaluate the relationship between serum melatonin levels, sleep habits, and clinical features in children with autism spectrum disorder (ASD) compared to healthy controls.
Methods: In accordance with DSM (The Diagnostic and Statistical Manual of Mental Disorders)-V, children aged 2-8 years diagnosed with autism spectrum disorder (n:38) and healthy children (n:33) of similar age and gender in the control group were evaluated. Vitamin D levels, ferritin, serum iron, and iron binding capacity of all participants were examined, as well as endogenous melatonin levels, and a pediatric sleep habits questionnaire was filled out. Autism Behavior Checklist (ABC-Autism Behavior Checklist) and The Modified Checklist for Autism in Toddlers (M-CHAT) were applied to the autism spectrum disorder group. The relationship between the blood values obtained and the completed questionnaire results was evaluated.
Results: Melatonin levels were compared between the patients with autism spectrum disorder and the control group. In total, 71 participants were included in it. The gender of 81.7% (n=58) of the participants was male.The mean age of the autism spectrum disorder group was 44.4±20.4 months, and that of the control group was 51.2±20 months (p=0.104). When children’s sleep habits questionnaire data were compared between the two groups, the "daytime sleepiness" subscale score was higher in the control group, while the "bed-wetting" subscale score was higher in the autism spectrum disorder group (p=0.036 and p=0.008, respectively). No significant correlation was found between the questionnaire scores of the patients and their melatonin levels. Melatonin levels were on average 823.2±237.9 U/L in the autism spectrum disorder group and 677.4±254.7 U/L in the control group. This difference between the two groups was found to be statistically significant (p=0.027). When the participants were compared by dividing according to their gender, it was found that there was a difference between the two groups only in male patients (p=0.020). It was observed that there was no significant difference between girls in terms of melatonin levels (p=0.608).
Conlucion: In the light of the data we obtained in our study, it was observed that there are sleep problems in autism spectrum disorder and therefore, melatonin levels measured during the daytime are higher than in healthy children. This makes us think that there may be desensitization to melatonin receptors in the ASD group, and the fact that melatonin levels differ according to gender, suggesting that melatonin treatment modalities applied in ASD patients may be regulated according to gender.
Modeling a Novel Circular Diaphragm Microelectromechanical System for Intraocular Pressure Evaluation
Authors: Taherian Sina , Taherian Nima 
Published: September 2024

Abstract

This paper presents a new Microelectromechanical capacitive pressure device designed for continuous intraocular pressure (IOP) measurement in glaucoma patients. The device is capable of accurately measuring IOP within the range of 0-8000 Pascal (Pa). A key focus of this research is on reducing the device’s size compared to previous designs, which results in benefits like better energy efficiency, lighter weight, and lower costs. These improvements contribute to more effective glaucoma treatment and enhanced patient care. The device’s design features a circular diaphragm combined with circular spiral meanders, increasing flexibility and reducing stiffness, which improves overall performance. The diaphragm is made of 3.5 μm thick polysilicon, with a radius of 0.1 mm and a 3 μm air gap separating it from the fixed plate. Under pressures up to 8000 Pa, the resonant frequency decreases to 88.243 kHz. The capacitive sensitivity is calculated at 2.6175×10-4 (1/Pa), while the mechanical sensitivity is 0.265 nm/Pa. The performance of the device is thoroughly evaluated through simulations using IntelliSuite software. A comparative analysis shows that this device is smaller and more efficient than previous designs, with dimensions of 200 μm and 100 μm in diameter. This development indicates notable progress in glaucoma treatment and utilizing biological sensing in many applications.
Development of a Disease Modeling Framework for Glutamatergic Neurons Derived from Neuroblastoma Cells in 3D Microarrays
Authors: Nguyen Duc Long , Le My Phuong Thi , Lee Kyung Won , Kim Jae-Ho , Yoon Hyun C. , Pham Huyen T. M. 
Published: September 2024

Abstract

Neurodegenerative diseases (NDDs) present significant challenges due to limited treatment options and the ethical concerns of traditional animal models and iPSC-derived neurons. We addressed these issues by developing a 3D culture protocol for differentiating SH-SY5Y cells into glutamatergic neurons, enhancing physiological relevance with a 3D microarray culture plate. Our protocol optimized serum concentration and incorporated retinoic acid (RA) to improve differentiation. We analyzed the proportions of N-type and S-type cells, observing that RA in the maturation stage not only reduced cell proliferation but also enhanced the expression of MAP2 and VGLUT1, indicating effective neuronal differentiation. Our approach demonstrates the strong expression of glutamatergic neuron phenotypes in 3D SH-SY5Y neural spheroids, offering a promising tool for high-throughput NDD modeling and advancing drug discovery and therapeutic development. This method overcomes limitations associated with conventional 2D cultures and animal models, providing a more effective platform for NDD research.
An Analysis of Vertical Infiltration Responses in Unsaturated Soil Columns
Authors: Li Lincui , Li Cheng , Li Xi-An , Li Yonghong , Li Yong , He Yiping , Yao Chaowei , Wang Li 
Published: September 2024

Abstract

Rainfall infiltration affects slope stability by changing the pore water pressure in soil. In this study, indoor vertical infiltration tests were performed on silt columns. The instantaneous profile method and filter paper method were used to obtain the soil water characteristic curve (SWCC) and hydraulic conductivity function(HCF). During the rainfall infiltration test, the vertical patters of volumetric moisture contents, total hydraulic head or suction and the wetting front were recorded. Advancing displacement and rate of the wetting front, the cumulative infiltration, the instantaneous infiltration rate, the average infiltration rate were determined to assess rainfall infiltration process comprehensively, combining with SWCC and HCF. Additionally, the effects of dry density and runoff on the one-dimensional (1D) vertical infiltration process of soil columns were evaluated. The results showed that the variation curve of the wetting front displacement versus time obeys a power function relationship, the infiltration rate–time relationship curve and the unsaturated permeability curve could be roughly divided into three stages, and the SWCC and HCF calculated by volumetric moisture content are more sensitive to changes in dry density than to changes in runoff or hydraulic head height.
Dependence of peak force during granular impact on projectile geometry
Authors: Williams Hollis 
Published: September 2024

Abstract

Force measurements were performed for conical projectiles impacting a granular bed. The diameter and density of the grains was varied, along with the mass, density, and tip shape of the projectiles. At medium speeds, the data can be described by a scaling law which varies as the square root of the densities of the projectile and the granular material and linearly with respect to impact speed and cone angle.
ORCID ID: 0000 − 0003 − 3292 − 602X
Nitrogen and sulfur co-doped carbon dots for ratiometric fluorometric determination of mercury ions
Authors: Li Fenglan , Lu Liqin , Wu Yutong , Meng Liang , Zhu Binling , Xu Quanming , Zhuang Guoxin , zhuang Junyang 
Published: September 2024

Abstract

Nitrogen and sulfur co-doped carbon dots (N,S-CDs) were prepared for dual-channel ratiometric fluorescence determination of mercury ions (Hg2+). The dual-emission N,S-CDs were synthesized using a simple one-pot hydrothermal treatment. When excited with visible light, N,S-CDs exhibited two emission peaks at 390 and 500 nm. Notably, the presence of Hg2+ caused a considerable decrease in the fluorescence of N,S-CDs at 500 nm, mainly due to the static quenching effect. In comparison, the fluorescence at 390 nm was almost unchanged. With a limit of detection (LOD) of 0.21 µM for Hg2+, the N,S-CDs were successfully applied to the unlabeled ratiometric fluorescence determination of Hg2+ in actual water samples with good recoveries (94.5–107.8%). In conclusion, this developed ratiometric fluorescent sensor provides a reliable, environmentally friendly, rapid, and efficient platform for detecting Hg2+ in environmental applications.
Molecular Identification of Fasciola and Dicrocoelium species isolates in ruminants live stock from Kashan and Arak in center of Iran
Authors: Arbabi Mohsen , Hooshyar Hossein , Delavari Mahdi 
Published: September 2024

Abstract

Fascioliasis and Dicrocoeliasis are important trematode infections that affect humans and ruminants worldwide. Molecular techniques have a conclusive role in detection liver flukes. The purpose of the current study was to find out the genotypic diversity of Fasciola and Dicrocoelium spp. isolated from different hosts in Iran. Totally, 160 and 200 adult Fasciola and Dicrocoelium spp. isolates collected from infected cattle, sheep, and goats from two abattoirs in the center of Iran. PCR-RFLP, and DNA sequences nuclear markers (18S, 28S, ITS) and the mitochondrial marker (ND1, CO1) were applied. PCR products of Dicrocoelium and Fasciola samples, were subjected to digestion by Bfa1, TruiI, BsrB1, ECO881, and Hind III enzymes. DNA from 60 isolates of Fasciola and Dicrocoelium of different hosts were sequenced and evaluated. The PCR reaction showed the length of 18S, 28S, ND1, CO1 of Fasciola at 260bp, 618bp, 700bp, and 500bp, and the length of the ITs2 and 28S of Dicrocoelium was 236bp and 963bp respectively. D. dendriticum has an RFLP pattern of 110, and 126bp (ITS2), and 116, 293, 409bp (28s) using, Bfa1 and Tru1I restriction enzymes. F. gigantica has a profile of 333, and 285bp (28s) using Bsrb1 enzyme. The RFLP pattern of genotype F. hepatica was 73, 120, and 507bp (ND1) and 119 and 381bp (CO1) in size using Hind III and ECO881 enzymes. Using the PCR-RFLP, three species of F. hepatica, F. gigantica, and D.dendriticum were identified. To uncover the genetic population structure of liver flukes across the country, future studies are still required.
Nighttime Lights Data as an Indicator of Electricity Outage Vulnerability: Case Study of Winter Storm Uri
Authors: Kahl Alexandra Claire , Tran Liem , Bhaduri Budhendra 
Published: September 2024

Abstract

Power outage data aggregated at a specific administrative level, such as census tract or census block, is not publicly available. This creates barriers to understanding spatial distribution of outage vulnerability, resulting in challenges in outage preparedness and disaster response. We work to better understand spatial power outage vulnerability by deriving simulated values through implementation of maximum entropy models, which represent likelihood of outage occurrence at the census tract level in Houston, TX. We develop a model utilizing nighttime light data to produce power outage likelihood values at the census block level. Nighttime lights outages during Winter Storm Uri (February 11–18, 2021) are used as inputs in the MaxEnt machine learning model. Energy transmission, generation, hospitals, emergency services and tree coverage data are used as predictors within MaxEnt. Results show that the model performs relatively well with a mean area under the curve of 0.758 (a common threshold for model evaluation is 0.70). Power line density, tree coverage and proximity to schools are the most influential variables in power outage vulnerability (contributing percentages are 73.0%, 7.1% and 5.2%, respectively). Utilizing MaxEnt prediction, we generate likelihood of outage occurrence values between 0 and 1 for each census block. Our work provides a novel methodology for nighttime lights processing and new applications for MaxEnt. These results provide insight into which census tracts are the most vulnerable to power outage during extreme weather events.