bluuebunny/crossref_metadata_2025_split huggingface dataset:
Breeding of a Black Soybean Line with Green Cotyledon and Recessive Genotypes for Lectin, KTI, P34, Lipoxygenase, and Stachyose Components
Authors: Ly Sarath , Park Byeong Eon , Shim Sang In , Kim Min Chul , Moon Jin Young , Chung Jong Il 
Published: April 2024

Abstract

Black soybean cultivar with a green cotyledon has long been widely cultivated in Asia due to the high content of health-beneficial anthocyanins and lutein. However, the mature seeds of soybean cultivars with a black seed coat and a green cotyledon contain major anti-nutritional and allergenic components including lectin, Kunitz trypsin inhibitor (KTI), P34, lipoxygenase, and stachyose. Genetically eliminating or reducing the levels of these components might be a method for achieving significantly enhanced quality and nutritional value in the utilization of soybean with a black seed coat and a green cotyledon. The objective of this research is to breed a soybean line with a black seed coat, a green cotyledon, and the penta null genotype (lele-titi-p34p34-lox1lox1lox2lox2 lox3lox3-rs2rs2) for all five components. The ratio for segregation based on the presence or absence of P34 protein was fitted to an expected ratio of 3:1 (χ2 = 2.64, α = 0.05) in 146 F2 seeds. Nine F2 plants with superior agronomic traits from 28 F2 seeds with the p34p34 genotype (lacking the P34 protein) were selected and one F2 plant with the rs2rs2 genotype was finally selected. The penta null genotype for components including lectin, KTI, P34, lipoxygenase, and stachyose was identified in random F3 seeds and 10 F3 plants. The characteristics for the selection line included a black seed coat, a green cotyledon, 31.5g in 100 seed weight, and the penta null genotype for five components. The selection line would be bred into a soybean cultivar with a black seed coat, a green cotyledon, and significantly reduced anti-nutritional and allergenic properties. Worldwide, this paper is the first to demonstrate that accumulation of all five recessive alleles for lectin, KTI, P34, lipoxygenase, and stachyose was achieved for soybean with a black seed coat and a green cotyledon.
Clinical Profile of Cellulitis and Erysipelas in the Tertiary Referral Hospital of West Java, Indonesia
Authors: Gunawan Hendra , Hindritiani Reti , Dharmadji Hartati Purbo , Suwarsa Oki , Widjaja Stephanie 
Published: April 2024

Abstract

Introduction:
Cellulitis and erysipelas belong to a group of diseases that are considered global health burdens. Epidemiological data on these diseases in Indonesia is still limited.
Purposes:
This study aimed to identify the clinical profile of cellulitis and erysipelas in the tertiary referral hospital in West Java, Indonesia.
Method
This was a retrospective, descriptive study using a cross-sectional design. Data were obtained from outpatient and inpatient records of cellulitis and erysipelas patients in the tertiary referral hospital in West Java, Indonesia, in a three-year period during January 2020–December 2022.
Result
The results of the study showed that cellulitis was most common in women within the 45–64-year age group with normoweight nutritional status, while erysipelas mostly occurred in men within the > 65-year age group without a certain nutritional status predominance. In general, the risk factor for cellulitis was skin barrier disruption, while that for erysipelas was metabolic disorders. Fever was present in less than half of cases with cellulitis and erysipelas, and the lower extremities were the most often affected. The most frequent portal of entry in cellulitis was scratched skin, whereas erysipelas had no particular predominance. Additional skin lesions, such as bullae and/or erosions and suppuration, were found in some cases of these diseases and usually showed Gram-positive cocci with leukocytosis. Normal saline dressing was given to all cases, and some of them were treated with additional wound dressings and 2% mupirocin cream. Clindamycin was the most commonly administered oral antibiotic, while ceftriaxone was administered parenterally.
Conclusion
The clinical profile of cellulitis and erysipelas in the tertiary referral hospital in West Java, Indonesia, is consistent with findings from other previous studies. Additionally, clindamycin is the most commonly used oral antibiotic for the treatment of cellulitis and erysipelas.
Exploring biodiversity challenges in Europe: Completeness, geography and environmental representativeness
Authors: García-Roselló Emilio , González-Dacosta Jacinto , Lobo Jorge M. 
Published: April 2024

Abstract

Biases and gaps in biodiversity data lead to significant disparities in knowledge among species descriptions and distributions of different taxonomic groups. These gaps could be addressed by utilizing predictive models, but this requires ensuring that available information is environmentally representative. In this study we utilize data from GBIF to investigate geographical biases, gaps and spatial completeness patterns concerning species distribution for the main classes of terrestrial organism in Europe. By identifying the spatial units with comprehensive inventories for each class, we offer insights into their quantity, distribution, and ability to capture the environmental variability of the European subcontinent. The results clearly demonstrate a high spatial heterogeneity and variability between taxa in the number of well-surveyed spatial units, showing that the units with high completeness for vertebrates and vascular plants are several times more numerous than those available for invertebrates and mosses. Regarding the environmental variability represented by the available data, results demonstrate the uncoordinated and contingent character of the accumulation process of biodiversity information and the need of an extra effort, which should be more intense in those taxa with a lower geographical coverage of their data. These challenges raise doubts about the reliability of these data in providing a comprehensive understanding of biodiversity distribution, as well as hindering model estimations. Extra compilation efforts should be mainly directed towards those spatial units capable of improving the current environmental representation of the spatial units considered well-surveyed, to reach a representative sample capable of producing effective interpolations and reliable predictions of species distributions.
Networks and Yardstick Competition in the Digital Age: Evidence from Italy
Authors: Lockwood Ben , Porcelli Francesco , Redoano Michela , Schiavone Antonio 
Published: April 2024

Abstract

This paper examines the impact of OpenCivitas, a data disclosure program introduced by the Italian government that allows mayors to access detailed expenditure data of other municipalities through a dedicated website. Through analysing website views, we construct a directed network and investigate the characteristics and strategic behaviors of mayors in this network. Mayors in the network are on average younger, more educated, often from larger cities and are more likely to be affiliated to traditional parties, although populist parties are the ones usually relying more on the Web for communication and political activities. Mayors in this network do not engage in yardstick competition with neighboring municipalities as the other mayors do; rather, they interact with each other despite the physical distance. We find that the network existed prior to the website opening, but after data disclosure, yardstick competition within the network becomes strongly driven by mayors who are up for re-election, which was not the case before. For the other municipalities, yardstick competition between neighbours remains uncorrelated with mayors’ term limits.
JEL codes: H11, H71, H77
Integration of ex-situ multiplication approaches with in-situ conservation of Gentiana kurroo through reintroduction and recovery programs: a critically endangered and endemic species of the North-West Himalaya
Authors: Panwar Giriraj Singh , Kumar Puneet , Bhandari Aakriti , Srivast Amber , Mao A. A. 
Published: April 2024

Abstract

Gentiana kurroo Royle (Gentianaceae), a highly medicinal, critically endangered and endemic species of the Western Himalayas with restricted distribution in India and Pakistan. The perspective focuses on the ex-situ mass multiplication and in-situ conservation of the species through reintroduction under the species recovery initiative. The integration of effective conservation strategies viz macro and micro and intervention of modern technological tools for in-situ restoration of species were invoked. The macro propagation was carried out through seed germination and vertically segregating the profusely branched tuft of plants into small tufts along with the rhizome. The species was micro propagated through axillary bud explants in MS medium fortified with 4.4 µM BAP and 0.53 µM NAA was found to be the optimum for shoot induction having 98.23% of shoot formation with 11.32 mean shoot number per culture and shoot length of 4.6 cm. Shoots rooted in half strength MS medium supplemented with IBA (4.9 µM) and 0.3% activated charcoal. Profuse rooting with average 24.58 roots per shoot was reported after 3-weeks of incubation. The plants were acclimatized in the glass house and were finally transferred to the open environment with 83% survival percentage. Under the species reintroduction and recovery programs, the propagated saplings were introduced into three different wild habitats located in Chakrata and Mussoorriee forest divisions of Uttarakhand and 44% survival was reported after six months of field transfer. The research demonstrates the successful coherence of ex-situ propagation methods with in-situ conservation through reintroduction initiatives. The efforts might be helpful in in-situ preservation of the germplasm and simultaneously to cater the escalating medicinal requirements of Gentiana kurroo.
Selection of Superior sugar beet (Beta vulgaris L.) genotypes using Selection Indexes of multi-trai under rhizomania-contaminated areas
Authors: Hamze Hamze , Mohammadian Rahim , Darabi Saeed , Idiliki Jamshid Soltani , khodadadi Shahram , KHURSHID Abdul Majid 
Published: April 2024

Abstract

In this study, 13 modern genotypes were examined based on the list provided by the Iraniai Seed Registration and Certification Institute. Genotypes grown in a randomized complete block design, with four replications in Iran's Karaj, Mashhad, Miandoab, Zarghan, and Hamedan stations in the 2022 and 2023 crop seasons. The mean genotype comparison results revealed that the F-21375 hybrid had the highest root yield (RY), while the F-21376 and F-21374 genotypes had the highest sugar content (SC), white sugar content (WSC), and white sugar yield (WSY). The results of AMMI analysis revealed that RY, SC, WSC, and WSY were significantly influenced by genotype (G), environment (E), and the multiplicative effect of G×E. Based on the WAASB and WAASBY indexes, F-21376, F-21410, F-21411, and F-21092 in terms of RY, F-21375, F-21370, and F-20940 in terms of SC and WSC, and F-21375, F-21376, and F-21092 in terms of WSY were stable with favorable quantitative and qualitative characteristics. The predicted mean for RY, SC, WSC, and WSY in F -21375 and F-21092 was higher than the total mean. The biplot analysis showed that the first two components contributed 65.1%, 80.80%, 88.07%, and 69.7% of GEI variance of RY, SC, WSC, and WSY, respectively. In this experiment, F-21092 and F-21376 were selected as stable genotypes for all quantitative and qualitative characteristics; in addition, F-21375, F-21092, and F-21411 were recognized as the most ideal among the studied genotypes. Finally, the F-21092 genotype can be introduced as a variety with high performance and stability to areas with rhizomania contamination.
SNP-based analysis reveals high genetic structure and diversity in umbu tree (Spondias tuberosa Arruda), a native and endemic species of the Caatinga biome
Authors: Nascimento Wellington Ferreira do , Costa Flaviane Malaquias Malaquias , Alves-Pereira Alessandro , Batista Carlos Eduardo de Araújo , de Carvalho Igor Araújo Santos , Garcia Caroline Bertocco , Silva Allison Vieira da , Silva Edson Ferreira da , Dias Márcia Maria de Souza Gondim , Pereira Fábio Rodrigo Araújo , Zucchi Maria Imaculada , Veasey Elizabeth Ann 
Published: April 2024

Abstract

Umbu (Spondias tuberosa Arruda) is an endemic fruit tree restricted to the Brazilian seasonally dry tropical forest called Caatinga. This study aimed to evaluate the structure and genomic diversity of umbu trees from seven locations in the Caatinga biome, distributed among four Brazilian states. Using genotyping-by-sequencing (GBS), a total of 5,336 SNPs were obtained, of which 250 showed outlier behavior. Therefore, 5,086 neutral SNPs were used for population structure and genetic diversity analyses. Both discriminant analysis of principal components (DAPC) and neighbor-joining cluster analyses classified the accessions into four groups, with a genetic structure observed among groups, disagreeing with our initial hypothesis of low genetic structure between locations. Isolation by distance (r2 = 0.974; p = 0.0015) was detected. Moderate to high levels of genetic diversity were found, with the average observed heterozygosity (HO = 0.221) higher than the expected heterozygosity (HE = 0.199) and with negative inbreeding coefficient (FIS) values. Most genetic variation was found within locations, although high diversity between locations (22.1%) was observed. The results obtained are important for understanding the levels and distribution of genetic variation, suggesting that most locations are priorities for conservation actions, contributing with different alleles to the species' gene pool in Brazil.
Hand gestures classification of sEMG signals based on BiLSTM- Metaheuristic Optimization and Hybrid U-Net-MobileNetV2 Encoder Architecture
Authors: Khavari Safoura Farsi , Rezaee Khosro , Ansari Mojtaba , Zare Fatemeh , Roknabadi Mohammad Hossein Alizadeh 
Published: April 2024

Abstract

Surface electromyography (sEMG) data has been extensively utilized in deep learning algorithms for hand movement classification. This paper aims to introduce a novel method for hand gesture classification using sEMG data, addressing accuracy challenges seen in previous studies. We propose a U-Net architecture incorporating a MobileNetV2 encoder, enhanced by a novel Bidirectional Long Short-Term Memory (BiLSTM) and metaheuristic optimization for spatial feature extraction in hand gesture and motion recognition. Bayesian optimization is employed as the metaheuristic approach to optimize the BiLSTM model's architecture. To tackle the non-stationarity of sEMG signals, we utilize a windowing and overlapping method, augmented with additional signals in deep learning architectures. The MobileNetV2 encoder and U-Net architecture extract relevant features from sEMG spectrogram images. Edge computing integration is leveraged to further enhance innovation by enabling real-time processing and decision-making closer to the data source. Six standard databases were utilized, achieving an average accuracy of 90.23% with our proposed model, showcasing a 3-4% average accuracy improvement and a 10% variance reduction. Notably, Mendeley Data, BioPatRec DB3, and BioPatRec DB1 surpassed advanced models in their respective domains with classification accuracies of 88.71%, 90.2%, and 88.6%, respectively. Experimental results underscore the significant enhancement in generalizability and gesture recognition robustness. This approach offers a fresh perspective on prosthetic management and human-machine interaction, emphasizing its efficacy in improving accuracy and reducing variance for enhanced prosthetic control and interaction with machines through edge computing integration.
Long-Term Risk of Autoimmune Diseases After mRNA-based COVID-19 Vaccination: A Korean Population-based Nationwide Cohort Study
Authors: Lee Solam , Jung Seung-Won , Jeon Jae Joon , Choe Sung Jay 
Published: April 2024

Abstract

The long-term association between mRNA-based Coronavirus Disease 2019 (COVID-19) vaccination and the development of autoimmune connective tissue diseases (AI-CTDs) remains unclear. In this nationwide population-based cohort study involving 9,258,803 individuals, we investigated the incidence of AI-CTDs associated with mRNA vaccines for ≥1 year and further analysed the risk by stratifying by sex, age, mRNA vaccine type, COVID-19 diagnosis, cross-vaccination with a non-mRNA vaccine, and booster vaccination. The risk of developing most AI-CTDs did not increase following mRNA vaccination. However, systemic lupus erythematosus and bullous pemphigoid were significantly more prevalent in vaccinated individuals than in controls. Comparable results were reported in the subgroup analyses, especially in the subgroups comprising women or patients aged ≥40 years who showed a higher risk of developing bullous pemphigoid following mRNA vaccination. Our results suggest that mRNA-based COVID-19 vaccinations are not associated with a high incidence of most AI-CTDs. However, further research is needed regarding its potential association with certain conditions including systemic lupus erythematosus and bullous pemphigoid.
Neural Adaptive Video Streaming with OfflineReinforcement Learning
Authors: Qin Yongbin , Huang Ruizhang 
Published: April 2024

Abstract

Learning adaptive bitrate (ABR) algorithms arecurrently an effective means for video players to optimize userquality of experience (QoE) under diverse network conditions.Nonetheless, reinforcement learning (RL) approaches demandextensive trial-and-error learning with Internet adaptive videostreaming, and the dynamic and heavy-tailed nature of networkcharacteristics poses a challenge. As a result, off-the-shelf RLtechniques face difficulties in efficient learning and fast adaptation to diverse network conditions.
In this work, we propose Offline Meta-RL ABR (OMA) algorithm, which utilizes offline datasets to automatically generatehighly-efficient meta-ABR policies based on specific networkconditions. First, traditional learned ABR algorithm techniquesrequire lengthy online meta-training from video streaming sessions, which we replace with demonstration and and offline data,eliminating the need for expensive online learning and enablingsafer exploration. Second, meta-ABR inevitably fail to generalizeto unseen network conditions that differ significantly duringmeta-training. We address this issue by incorporating contextualmeta-learning for online fine-tuning. If the new network conditions are similar to the prior data, then the contextual meta-ABRlearner adapts immediately, and if it’s significantly different, itgradually adapts through fine-tuning.
Comparing OMA under different network conditions, the experimental results demonstrate that it outperforms existing stateof-the-art ABR algorithms. OMA achieves up to 8× improvementduring training and effectively generalizes to unseen networkconditions and video streams.
Experimental and Numerical Investigations on Amalgamation of Mixed Frequency Carrier Based Asymmetrical 15 Level Multilevel Inverter
Authors: Paramasivan Muthukumar , Prasad Narasimha , K Vanchinathan 
Published: April 2024

Abstract

This research encompasses the investigations of a multicarrier based 15 level multilevel inverter in an asymmetrical mode. The series connection of three voltage sources (12V, 24V and48 V) with three metal oxide semiconductor field effect transistor (MOSFET) switches and a parallel H Bridge are used to construct the multilevel inverter. The Phase Disposition configurations of triangle carrier and inverted sine carrier are selected for the investigations. The indigenous thorough Pulse-width modulation (PWM) has been generated for three series switches from the comparison of seven carriers with a reference. The two frequencies (1 kHz and 4 kHz) are chosen for carriers. The 4 kHz frequency carrier will be active only at the peak of the sine reference i.e between 5π/12 ≥ θ ≤ 7π/12 in positive half cycle and between 17π/12 ≥ θ ≤ 19π/12 in negative half cycle, remaining period 1 kHz frequency carrier has been active. These schemes have been verified with single phase MLI with RL load. The MATLAB based simulation results of fours schemes are presented. Field programmable gate array based experimental result of best scheme among four is reported and corresponding hardware results are evidenced. Xilinx Vivado design suite has been used for design synthesis and bit file generation for the FPGA.
Decision-making in suicidal acute psychiatric patients
Authors: Reiråskag Marie Aaslie , Brokke Silje Støle , Rohde Gudrun , Bertelsen Thomas Bjerregaard , Landrø Nils Inge , Haaland Vegard Øksendal 
Published: April 2024

Abstract

Background
Suicidality is a major health problem. Decision-making deficits, including a lack of cognitive control (e.g., impulsivity and risk-taking behavior), have been associated with an increased risk of suicide.
Methods
This study examined decision-making in a clinical group of 23 adult, suicidal acute psychiatric patients and compared their data to that of a control group of 17 healthy adults using the Cambridge Gambling Task (CGT) from the Cambridge Neuropsychological Test Automated Battery (CANTAB). Group differences in outcomes on the six CGT subtests were compared using chi-square tests, t tests, and Mann‒Whitney U tests where appropriate. Multiple regression analysis was used to explore whether background variables were associated with CGT outcomes.
Results
The main findings were significantly lower scores for risk-taking, quality of decision-making, and risk adjustment in the clinical group than in the control group. Within the clinical group, differences were observed in which suicide ideators scored worse in some measures than did suicide attempters. These findings suggest that suicidal acute psychiatric patients may struggle with making low-risk decisions that are considered reasonable.
Conclusion
These results support the potential for cognitive control training, specifically aimed at enhancing decision-making abilities, in suicide prevention efforts. The observed decision-making deficits in suicidal patients underscore the importance of further investigating these findings in a larger population to solidify the foundation for targeted interventions.
Towards Resource-Efficient DDoS Detection in IoT: Leveraging Feature Engineering of System and Network Usage Metrics
Authors: Gavric Nikola , Bhandari Guru Prasad , Shalaginov Andrii 
Published: April 2024

Abstract

The Internet of Things (IoT) is omnipresent, exposing a large number of devices that often lack security controls to the public Internet. In the modern world, many everyday processes depend on these devices, and their service outage could lead to catastrophic consequences. There are many Deep Packet Inspection (DPI) based intrusion detection systems (IDS). However, their linear computational complexity induced by the event-driven nature poses a power-demanding obstacle in resource-constrained IoT environments. In this paper, we shift away from the traditional IDS as we introduce a novel and lightweight framework, relying on a time-driven algorithm to detect Distributed Denial of Service (DDoS) attacks by employing Machine Learning (ML) algorithms leveraging the newly engineered features containing system and network utilization information. These features are periodically generated, and there are only ten of them, resulting in a low and constant algorithmic complexity. Moreover, we leverage IoT-specific patterns to detect malicious traffic as we argue that each Denial of Service (DoS) attack leaves a unique fingerprint in the proposed set of features. We construct a dataset by launching some of the most prevalent DoS attacks against an IoT device, and we demonstrate the effectiveness of our approach with high accuracy. The results show that standalone IoT devices can detect and classify DoS and, therefore, arguably, DDoS attacks against them at a low computational cost with a deterministic delay.