bluuebunny/crossref_metadata_2025_split huggingface dataset:
Eco-friendly extraction carrageenan from seaweed using a ball mill: celite effect during filtration and ion addition on carrageenan physicochemical properties
Authors: - Amalia , Hidayat Chusnul , Yanti Rini 
Published: June 2024

Abstract

The aim of this study was to evaluate the extraction method of carrageenan using a new eco-friendly process, namely a ball mill, to produce good carrageenan characteristics and to assess the physicochemical properties. The variables evaluated included the seaweed : water ratio and celite concentration. Results showed that seaweed : water ratio and celite concentration affected yield, gel strength, sulfate content, and viscosity. Optimum conditions occurred at 14:1 w/v seaweed : water ratio and 4% celite concentration, which resulted in 68.89% carrageenan yield, 161.34 g/cm2 gel strength, 15.53% sulfate content, 5.4 cP viscosity, 45.32°C melting point, 30.50°C gel point, 5.56% water content, 25.10% ash content, pH 8.75, and 57% purity. Fourier Transform Infrared (FTIR) proves that the type of carrageenan is kappa-carrageenan. The addition of celite has been shown to simplify the filtration process, and the ball mill method effectively achieves high results quickly. However, the physicochemical properties obtained were inferior to those of the alkali method, especially in gel strength. The gel strength, viscosity, melting point, and gel point of carrageenan are significantly affected by ion variations (K+ and Ca2+).
Single-ended protection scheme for three-terminal hybrid DC transmission system based on refractive coefficients
Authors: Wu Chuanjian , Li Kuan , Li Chenhao , Zhang Yan , Zhang Xiaodong 
Published: June 2024

Abstract

Three-terminal hybrid DC transmission technology has the advantages of long-distance and large capacity, and there is no phase change failure and other problems, which is an effective means of sending out new energy bases. Correspondingly, the reliability of the existing single-ended traveling wave protection is further degraded by the unknown fault characteristics introduced by the greater variety and combination of converters and add-ons in the three-terminal hybrid DC transmission system. Therefore, a single-ended protection scheme based on the refractive coefficient is proposed. First, an expression for the refraction coefficient of a three-terminal hybrid DC system is derived. Subsequently, the variability of the refraction coefficients for different faults is analyzed. Finally, a single-ended protection scheme using the fault characteristics of the refraction coefficients is proposed and validated by simulation. The program has the advantages of theoretical clarity, reliability, and the ability to recognize near-end faults.
Development and psychometric evaluation of nutrigenomics and personalized nutrition-related knowledge, attitude, and behavior questionnaire in dietetic students and professionals
Authors: Moitra Panchali , Nemani Janvi , Madre Saba , Madan Jagmeet 
Published: June 2024

Abstract

This study aimed to develop and test the psychometric properties of a questionnaire assessing knowledge, attitudes, and behaviors (KAB) related to nutrigenomics and personalized nutrition (PN) in dietetic students and professionals in India. A literature review, six focus group discussions (n = 37), and nine in-depth interviews guided initial item generation. The face, item, and scale content validity indices were calculated and a cross-sectional sample (n = 297) examined the item difficulty (IDI), item discrimination (DI), and internal consistency. Test-retest reliability was estimated in a sub-sample (n = 64) and confirmatory factor analysis method (CFA) using model fit indices - factor loadings, Tucker Lewis Fit Index, and Root Mean Square Error of Approximation established construct validity. The IDI ranged from 0.60 to 0.72 and the mean Cronbach α was 0.82. The CFA results presented four constructs − 1) knowledge and understanding of nutrigenomics, 2) attitude, and willingness to learn nutrigenomics, 3) behaviors toward gene-based dietary advice, 4) training and confidence to practice PN. The intraclass correlation coefficients for knowledge, attitude, behavior, and training domains were 0.74, 0.83, 0.72, and 0.81, respectively. The psychometric evaluation indicated that the 45-item KAB_PN is a valid and reliable instrument to assess dietitians’ knowledge and perspectives toward nutrigenomics and PN.
Mechanistic insights into how the single point mutation change the autoantibody repertoire
Authors: Ni Zhong , Xu Ying , Zhou Huimin , Song Fangyuan , Wang Zhiguo , Chen Dongfeng 
Published: June 2024

Abstract

A recent study showed that just one point mutation F33 to Y in the H-CDR1 could lead to the autoantibody losing its DNA binding ability. However, the potential molecular mechanisms have not been well elucidated. In this study, we investigated how the antibody lost the DNA binding ability caused by mutation F33 to Y in the H-CDR1. We found that the electrostatic force was not the primary driving force for the interaction between anti-DNA antibodies and the antigen ssDNA, and that the H-CDR2 largely contributed to the binding of antigen ssDNA, even larger than H-CDR1. The H-F33Y mutation could increase the hydrogen-bond interaction but impair the pi-pi stacking interaction between the antibody and ssDNA. We further found that F33H, W98H and Y95L in the wiletype antibody could form the stable pi-pi stacking interaction with the nucleotide bases of ssDNA. However, the Y33 in mutant could not form the parallel sandwich pi-pi stacking interaction with the ssDNA, which could be verified by the result that any functional mutation in three key residues (F33H, W98H, and Y95L) could lead to the loss of ssDNA binding ability of mutant antibody. Our findings may not only deepen the understanding of the underlying interaction mechanism between autoantibody and antigen, but also broad implications in the field of antibody engineer.
Solar poloidal magnetic field generation rate from observations and mean-field dynamos
Authors: Pipin Valery 
Published: June 2024

Abstract

To estimate the hemispheric flux generation rate of the large-scaleradial magnetic field in the solar cycles 23 and 24 we use the photosphericobservations of the solar magnetic fields and results of the mean-fielddynamo models. Our analysis suggests that the hemispheric flux generationrate can be determined by the turbulent diffusion of the dynamo generatedmagnetic field at the solar equator. Observational data suggest thatspeed of magnetic field reconnection across equator, which representsthe effect of the latitudinal turbulent diffusion, can be insufficientto explain the magnitude of the hemispheric poloidal flux generationrate in the solar cycles 23 and 24. In addition, the results of dynamomodel show the powerful impact of the radial turbulent diffusion ofthe large-scale poloidal magnetic field on the hemispheric magneticflux generation rate and the global magnetic field distribution insidethe convection zone.
Effect of beta-glucan on oxidative stress, inflammation, hormonal and histopathological changes in dehydroepiandrosterone-induced polycystic ovary syndrome
Authors: Yüce Hande , Türkmen Neşe Başak , Aydın Muhterem , Taşlıdere Aslı , Özek Dilan Aşkın , Şenkal Selinay , Aslan Sümeyye , Doğan Ayşegül , Çiftçi Osman , Ünüvar Songül 
Published: June 2024

Abstract

A class of dietary fibers and biologically active polysaccharides from natural sources, beta-glucans (βTGs) have bioactive capabilities. The anti-tumor, anti-inflammatory, prebiotic, anti-obesity, anti-allergic, anti-microbial, antiviral, anti-osteoporotic, and immunomodulating effects of βTGs are well documented. Although many biological activities of βTG have been proven, its mechanism in DHEA-induced PCOS has not been investigated. We aimed to investigate the protective effects of βTG treatment on PCOS and its capacity to reverse PCOS-induced changes. Female Sprague-Dawley (SD) rats were divided into four groups at random (n = 8): control, PCOS, PCOS + βTG, and βTG groups. Biochemical markers linked to oxidative stress, antioxidant state, inflammation, cytokines, and hormone levels were assessed. Analyses using immunohistochemistry and histopathology were also carried out. Membrane array analysis detected growth factors, cytokine, and chemokine protein profiles. βTG did not cause any change in body, uterus, and ovarian weights in rats. βTG normalized the deviations in the oestrus cycle caused by PCOS. It was observed that βTG had a positive effect on the reproductive system. βTG can reduce the inflammatory response in PCOS rats by decreasing inflammatory cytokines. Oxidative stress was significantly reduced, whereas antioxidant enzyme activities were significantly elevated in the βTG group. βTG also prevented histopathological alterations. βTG induced the expression of some essential proteins, including bNGF, TIMP-1, Agrin, CINC-1, BDNF, and FGF-2 (bFGF). The results of this study showed that treatment with βTG protects against oxidative stress, inflammation, hormone imbalance, and histopathological damage in ovarian tissue caused by PCOS.
Prediction algorithms using genetic and non genetic factors inducing vitamin D deficiency among healthy adults
Authors: AMMAR Mariem , ABDERRAHMANE Amani , HENI Syrine , TIRA Mohamed Sahbi , Moussa Amira , KHALIJ Yassine , Ksibi Sonia , Bouslama Ali , Omezzine Asma 
Published: June 2024

Abstract

Objective
An alarming increase in vitamin D (vitD) deficiency even in sunny regions highlights the need for a better understanding of the mechanisms controlling vitD variability. We aimed to study potential variables involved in vitD deficiency among healthy Tunisian adults in order to establish two prediction algorithms: a composite algorithm (CA) that included genetic and non genetic factors and a simple one (SA) including only environmental non genetic factors. These algorithms could be used to predict vitD status and help identify individuals at high risk of vitD deficiency.
Methods
We screened six key genes (DBP, CYP2R1, CYP27B14, CYP24A1 and VDR) within the vitD metabolic pathway using 15 single nucleotide polymorphism (SNP) markers in across a cohort of 394 unrelated healthy individuals. After giving an informed consent, all participants were asked to complete a generalized questionnaire. Significant confounding factors that may influence the variability in serum 25(OH)D levels were used as covariates for association analyses. Statistical study was carried out with SPSS26.0.
Results
VitD deficiency correlated positively with albumin (r = 0.135, p = 0.007) and negatively with serum PTH (r = − 0.303, p < 0.001), age (r = − 0.198, P < 0.001), and BMI (r = − 0.143, p = 0.04). Multivariate logistic regression revealed that season, sun screen use, phototype, age, VDR- rs2228570 and CYP24A1- rs6013897 were significant predictors of hypovitaminosis D. Non genetic factors explained 15.6% of the variance in 25(OH)D concentrations while genetic polymorphisms (VDR- rs2228570 and CYP24A1- rs6013897) explained a lower variance of 12%. When combined together, genetic and non genetic factors contributed up to 27.6% in 25(OH)D concentrations variability.
Conclusion
25(OH)D deficiency is highly prevalent among healthy adults in Tunisia. It is related to seasonal fluctuations, increasing age, darker skin tones, excessive sunscreen usage, and genetic polymorphisms in the VDR and CYP24A1 genes. The genetic markers could be used as tools in Mendelian randomization analyses of vitD, and they should well be considered when establishing a supplementation protocol in order to prevent 25(OH)D deficiency in the Tunisian population.
Erianin inhibits cell migration and induces apoptosis by in-hibiting VEGF-α/PI3K/AKT signaling pathway in melanoma A375 cells
Authors: Sun Hao , Wang Gaolei , Ren Cong , Zhang Xi , Zhao Ping , Guo Bin 
Published: June 2024

Abstract

Erianin, extracted from Dendrobium chrysotoxum Lindl, has shown significant antitumour activity in various cancers, including bladder, cervical, and nasopharyngeal carcinoma. This study explores erianin's impact on melanoma, a prevalent intraocular malignancy in adults, emphasizing cell growth inhibition and its mechanism. Vascular endothelial growth factor-α (VEGF-α), a validated therapeutic target in melanoma angiogenesis, was considered. Despite its origin from Dendrobium plants in the Orchidaceae family, erianin's anti-melanoma effects and its potential to inhibit the PI3K/AKT/mTOR signalling pathway to decrease VEGF-α secretion in melanoma cells remain unexplored. Thus, erianin's effects on A375 cells and its underlying molecular mechanisms were investigated. Erianin induced apoptosis in A375 cells, as demonstrated by flow cytometry and western blot assays. Additionally, erianin inhibited A375 cell migration, proliferation, and invasion in vitro, evidenced by cell scratch, EdU stain, and transwell cell invasion assays. Mechanistically, erianin downregulated VEGF-α and VEGF Receptor 2 expression while inhibiting PI3K/AKT pathway activation, indicated by western blot and immunofluorescence analyses. Furthermore, both western blot and immunofluorescence analyses confirmed erianin's regulation of VEGF-α and VEGF-R2 expression. This study suggests that erianin is a novel compound capable of inducing cell apoptosis while suppressing the activation of the VEGF/PI3K/AKT signalling pathway in A375 cells, thereby inhibiting cell proliferation.
An Open Dataset for Individual Tree Detection in UAV LiDAR Point Clouds and RGB Orthophotos in Dense Mixed Forests
Authors: Dubrovin Ivan , Fortin Clement , Kedrov Alexander 
Published: June 2024

Abstract

We present an open access dataset for development, evaluation, and comparison of algorithms for individual tree detection in dense mixed forests. The dataset consists of a detailed field inventory and overlapping UAV LiDAR and RGB orthophoto, which make it possible to develop algorithms that fuse multimodal data to improve detection results. Along with the dataset, we describe and implement a basic local maxima filtering baseline and an algorithm for automatically matching detection results to the ground truth trees for detection algorithm evaluation.
Study on the Impact of Dietary Patterns on Cardiovascular Metabolic Comorbidities among Adults
Authors: Mao Danhui , Li Gongkui , Li Yajing , Wang Shixun , Zhang Mohan , Ma Mingyan , Ren Xiaojun 
Published: June 2024

Abstract

Background
The prevalence of cardiovascular metabolic comorbidities (CMM) among adults is relatively high, imposing a heavy burden on individuals, families, and society. Dietary patterns play a significant role in the occurrence and development of CMM. This study aimed to identify the combined types of CMM in adult populations and investigate the impact of dietary patterns on CMM.
Methods
Participants in this study were from the sixth wave of the China Health and Nutrition Survey (CHNS). Dietary intake was assessed using a three-day 24-hour dietary recall method among 4,963 participants. Latent profile analysis was used to determine dietary pattern types. Two-step cluster analysis was performed to identify the combined types of CMM based on the participants' conditions of hyperuricemia, dyslipidemia, diabetes, renal dysfunction, hypertension, and stroke. Logistic regression analysis with robust standard errors was used to determine the impact of dietary patterns on CMM.
Results
Participants were clustered into three dietary patterns (Pattern 1 to 3) and five CMM types (Class I to V). Class I combined six diseases, with a low proportion of diabetes. Class II also combined six diseases but with a high proportion of diabetes. Class III combined four diseases, with a high proportion of hypertension. Class IV combined three diseases, with the highest proportions of hyperuricemia, diabetes, and renal dysfunction. Class V combined two diseases, with high proportions of dyslipidemia and renal dysfunction. Patients with Class III CMM had a significantly higher average age than the other four classes (P ≤ 0.05). Compared to those with isolated dyslipidemia, individuals with a low-grain, high-fruit, milk, and egg (LCHFM) dietary pattern had a higher risk of developing dyslipidemia combined with renal dysfunction (Class V CMM) with an odds ratio of 2.001 (95% CI: 1.011–3.960, P ≤ 0.05).
Conclusion
For individuals with isolated dyslipidemia, avoiding a low-grain, high-fruit, milk, and egg (LCHFM) dietary pattern may help reduce the risk of developing dyslipidemia combined with renal dysfunction (Class V CMM).
GMS-YOLO：Improved water meter reading recognition algorithm based on YOLOv8
Authors: Wang Yu , Xiang Xiaodong 
Published: June 2024

Abstract

Owing to the disorderly arrangement of water meter pipelines and the random rotation angles of their mechanical numeral wheels, the captured images of water meters reveal issues such as tilt, blurriness, and missing characters. It is evident that traditional optical character recognition fails to meet the detection requirements, and the two-stage detection method, positioning first and then recognizing, needs to be revised. In this study, water meter reading recognition is treated as an object detection task, whereby extracting detection box information output by the object detection algorithm facilitates the acquisition of water meter readings.YOLOv8n is chosen as the baseline model, and a target detection algorithm, GMS-YOLO, based on group multi-scale convolution, is put forward. Firstly, by replacing convolution in the Bottleneck module with group multi-scale convolution, the model achieves different scales of receptive fields, thereby enhancing its feature extraction capability. Secondly, the large-kernel separable attention (LSKA) is incorporated into the SPPF module to augment the perceptual ability of fine features. Lastly, the ShapeIoU bounding box loss function is opted to replace CIoU, enhancing the model's positioning ability and expediting its convergence speed. Evaluated on a self-compiled water meter image dataset, GMS-YOLO attained a mAP@0.5 of 92.4% and a precision rate of 93.2%, representing increments of 2.0% and 2.1% over YOLOv8n and demonstrating significant superiority over the baseline model. The average detection time of GMS-YOLO per image is ten milliseconds, a capability well-suited to practical detection tasks.
Forecasting of G7 Countries' Total Energy Production: A Rigorous Exploration with Artificial Neural Networks and Multiple Linear Regression
Authors: BAYIR Gökhan , KILIÇ Faruk , DİRİ Faik Ümit , ERKAYMAZ Hande 
Published: June 2024

Abstract

The G7 countries, consisting of Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States, have important collaborations in energy production to achieve energy security. One of the main systems of Artificial intelligence's, artificial neural networks (ANN), is crucial to this area of study, comparatively using Multiple Linear Regression (MLR) comparatively. ANN and MLR are feasible to use across the G7 countries' total energy production numbers, and these numbers were determined using ANN and MLR forecasting techniques. The data included the years 1990–2020, focusing on GDP intensity, refined oil product production, electricity production, and renewable energy proportion. In ANN modeling, the best regression results at 10*10 have been obtained with two hidden layers. All regression values were 0.99947, with the training regression value being 0.99912, the validation regression value being 0.99997, and the test regression value being 0.99997. The results showed high accuracy, with regression scores exceeding 99% and smaller prediction error values. A paired sample t test has been applied to see whether the distinction between the average values is significant or not. The results of the test between the actual and predicted values (p = 0.7462 > 0.05) revealed that the forecasted values have been quite close to the actual values. Total energy production Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) parameters have been calculated as 4.364, 34.072, 5.837, and 0.162, respectively. The research proved that ANNs are effective in forecasting total energy output. And, with MLR, error values for MAD, MSE, RMSE, and MAPE were also found to be 5.364, 34.352, 5.861, and 1.609, respectively, using MLR modeling. By 2035, the G7 nations are expected to produce 50,652.746 Mtoe of energy collectively. The research proved that ANNs are effective in forecasting total energy output.
Drought Characteristics Prediction Using a Hybrid Machine Learning Model with Correction
Authors: Xue Ruihua , Luo Jungang , Li Shaoxuan , Zuo Ganggang , Yang Xue 
Published: June 2024

Abstract

Drought prediction is a crucial aspect of drought risk assessment, playing a significant role in effectively minimizing losses associated with droughts. While many previous studies have focused on the variations of drought indices, understanding the impact of drought events requires a transformation into drought characteristics such as severity and duration. These drought features provide insights into the processes and hazards associated with drought events. The main objectives of this study are twofold: improving prediction accuracy and evaluating model performance based on drought characteristics.By decomposing the data into high and low-frequency sequences and effectively addressing these with optimally chosen models, a hybrid model (LSL) model is constructed.This model incorporates an error correction sequence to reduce errors during the prediction process and improve accuracy. Simultaneously, various aspects of drought characteristics, such as duration, start and end times, intensity, and peak value, were analyzed to enhance drought event prediction and impact assessment. The results show that, the LSL model is effective in predicting the drought class and all aspects of drought characteristics. Overall, the LSL model can be well applied to the prediction of drought events.
Neural Correlates of Perceiving Animacy in Robotic Objects
Authors: Yizhar Or , Maimon Amber , Tal Zohar , Wald Iddo Yehoshua , Erel Hadas , Friedman Doron , Zuckerman Oren , Amedi Amir 
Published: June 2024

Abstract

The prevalence of robots in modern society calls for gaining a fundamental understanding of the underlying principles of human-robot interaction. In particular, we need to understand how people perceive robots and how different physical and social characteristics affect this perception. Here, we examined the neural correlates of observing simple gestures performed by a non-humanoid social robot, focusing on the role of the Ventral Occipitotemporal (VOT) cortex in processing perceived animacy. Our results reveal that a broad bilateral cortical network is activated, comprising areas related to action observation, the social brain, and the visual cortex, including the VOT. We also found a correlation between the level of neural responses in the VOT, especially in the left hemisphere, and individual animacy ratings of the robotic object. This correlation provides a physical basis for such subjective ratings. These results suggest a complex representation of perceived animacy across the lateral-medial axis of the VOT, explained by spatial cortical location and the amplitude of neural responses. Furthermore, we discuss the implications of these findings for understanding how people perceive robots, their behaviors and intentions, which are crucial for developing social robots that can interact with humans more naturally.