bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification of A Novel Hybrid Sterility Locus S67 between temperate japonica subgroup and basmati subgroup in Oryza sativa L
Authors: Lv Yonggang , Li Jing , Yang Ying , Pu Qiuhong , Zhou Jiawu , Deng Xianneng , Zhang Yu , Tao Da-Yun 
Published: June 2024

Abstract

Asian cultivated rice (Oryza sativa) is the most important cultivated species in the AA genome species of the genus Oryza. basmati is a special and famous subgroup in Asian cultivated rice, and temperate japonica is one of the most important cultivated subgroup, too. However, hybrid sterility hinders the introgression of favorable traits and the utilization of hybrid vigour between them. The genetic basis of intraspecific hybrid sterility between temperate japonica and basmati remained elusive. In this study, a novel hybrid sterility locus S67 was identified, which caused hybrid male sterility in hybrids between the temperate japonica rice variety Dianjingyou 1(DJY1) and the basmati rice variety Dom-sufid. Initial mapping with BC1F1, BC4F1, BC4F2 populations and DNA markers located S67 between RM5362(41087022) and K1-40.6(41824986) on the long arm of chromosome 1. Genetic analysis confirmed that S67 caused a transmission advantage for the temperate japonica rice S67-te allele in the hybrid offsprings. This result not only fills the gap in the research on hybrid sterility between basmati and temperate japonica, but also lays a good foundation for the systematic study of the genetic rules of hybrid sterility between basmati and other subgroups, as well as the full exploration and utilization of this subgroup through the creation of wide or specific compatibility lines to overcome hybrid sterility. In addition, this result can also help us broaden our understanding of genetic differentiation within Asian cultivated rice and hybrid sterility between inter-subgroups.
Checkpoint Data-Driven GCN-GRU Vehicle Trajectory and Traffic Flow Prediction
Authors: Guan Deyong , Ren Na , Wang Ke , Wang Qi , Zhang Hualong 
Published: June 2024

Abstract

In order to accurately assess the road traffic flow, improve the efficiency and safety of the transportation system, and provide technical support for vehicle path planning and road congestion early warning, this paper proposes a method for accurately forecast the traffic flow on the urban road network by using trajectory prediction technology. The method uses a combination model of graph convolutional neural network (GCN) and gated recurrent unit (GRU) for vehicle trajectory prediction, and uses the output of trajectory prediction to more accurately forecast the traffic flow. Firstly, transforming the checkpoint data into daily vehicle trajectories with time series characteristics, realizing the division of vehicle trajectory travel chain. Secondly, the adjacency matrix is established by using the spatial relationship of each checkpoint, and the feature matrix of the vehicle's driving trajectory over time is established, which are used as the input of GCN to learn the spatial characteristics of the vehicle during driving on the road network, and then GRU is added to further process the data after GCN training, constructing a GCN-GRU vehicle trajectory prediction model for vehicle trajectory prediction. Finally, the traffic flow of each checkpoint is calculated based on the prediction result of vehicle trajectory, and compared with the real checkpoint flow. In this paper, Qingdao city checkpoint data is used as an example for experiments, and the results show that compared with the traditional GCN model, the GCN-GRU vehicle trajectory prediction model can obtain stronger temporal and spatial correlation characteristics and higher prediction accuracy. The forecasted traffic flow is highly consistent with the real checkpoint traffic flow, which verifies the reliability of the proposed method.
Automated Detection of Potholes in Pavements using Deep Neural Networks with Fine Tuning and Data Augmentation Techniques
Authors: Alencar Juliana P. , Mendes Gabriela M. P. , Mattos Cesar Lincoln C. , Gomes João P. P. , Babadopulos Lucas F. A. L. , Soares Jorge B. , Veras Marcelo B. A. 
Published: June 2024

Abstract

Automatic detection of potholes for pavement condition assessment leads to great savings in expenses and allows a better spending of resources destined to road infrastructure management. Out of all the available pothole detection techniques, the use of image-based methods within neural networks frameworks is the one that has offered the best balance between cost and accuracy. Convolutional Neural Networks (CNN) are deep neural network models specially designed for image processing problems. They have achieved remarkable results in many applications. Inspired by the success of such models, in this paper, we have evaluated the performance of 8 CNN methods in the task of pothole detection from pavement images. Along with the evaluation of the CNN architectures, we have also tested the impact of using pre-trained models with fine tuning procedures and data augmentation techniques. Such procedures enable the use of fewer training images. Given the high cost and labor intensiveness of obtaining labeled images, the annotated images were intentionally randomly selected to reduce the dataset from its original size. The dataset of images is from Brazilian roads acquired from a typical setup of a camera attached to a vehicle. The results indicate that pre-trained CNN models with fine tuning constitute a promising technique for pothole detection in pavements, especially when a large amount of labeled data is not available.
Lipidic folding pathway of α-Synuclein via a toxic oligomer
Authors: Griesinger Christian , Sant Vrinda , Matthes Dirk , Mazal Hisham , Antonschmidt Leif , Wieser Franz , Movellan Kumar , Xue Kai , Nimerovsky Evgeny , Stampolaki Marianna , Nathan Magdeline , Riedel Dietmar , Becker Stefan , Sandoghdar Vahid , de Groot Bert , Andreas Loren 
Published: June 2024

Abstract

Aggregation intermediates play a pivotal role in the assembly of amyloid fibrils, which are central to the pathogenesis of neurodegenerative diseases1,2. The structures of filamentous intermediates3 and mature fibrils4 are now efficiently determined by single-particle cryo-electron microscopy. By contrast, smaller pre-fibrillar α-Synuclein (αS) oligomers, crucial for initiating amyloidogenesis, remain largely uncharacterized. We report an atomic-resolution structural characterization of a toxic pre-fibrillar aggregation intermediate (I1) on pathway to the formation of lipidic fibrils. Super-resolution microscopy reveals a tetrameric state, providing insights into the early oligomeric assembly. Time resolved nuclear magnetic resonance (NMR) measurements uncover a structural reorganization essential for the transition of I1 to mature lipidic L2 fibrils. The reorganization involves the transformation of anti-parallel β-strands during the pre-fibrillar I1 state into a β-arc characteristic of amyloid fibrils. This structural reconfiguration occurs in a conserved structural kernel shared by a vast number of αS-fibril polymorphs including extracted fibrils from Parkinson’s and Lewy Body Dementia patients. Consistent with reports of anti-parallel β-strands being a defining feature of toxic αS pre-fibrillar intermediates6, I1 impacts viability of neuroblasts and disrupts cell membranes, resulting in an increased calcium influx. Our results integrate the occurrence of anti-parallel β-strands as unique features of toxic oligomers7-9 with their significant role in the amyloid fibril assembly pathway. These structural insights have implications for the development of therapies and biomarkers.
‘A novel approach to expedite evidence to impact in Pre-eclampsia: co-developed Policy Labs in Zambia and Sierra Leone.’
Authors: Kuhrt Katy , Mabula-Bwalya Chileshe , Boulding Harriet , Beardmore-Gray Alice , Ridout Alexandra , Koroma Osman , Sam Betty , Williams Prince Tommy , Smart Francis , Meleki Isabel , Mwila Meek , Chileshe Mubanga , Mawere Racheal , Hurrell Alice , Mbiiza Christabel , Vwalika Bellington , Fernandez-Turienzo Cristina , Sandall Jane , Shennan Andrew , Bramham Kate 
Published: June 2024

Abstract

Background
Pre-eclampsia (PE) is a leading causes of maternal and neonatal mortality; 30,000 PE-related maternal deaths occur annually, with 70% in Sub-Saharan Africa (SSA) and 16% in South Asia. We have shown that early, accurate detection of hypertension combined with planned early delivery in women with suspected pre-eclampsia between 34 and 37 weeks of gestation significantly reduces stillbirth and severe maternal hypertension. There is an urgent need for this novel evidence to be included clinical management of women and babies suffering the worst outcomes. This article describes co-development and delivery of Policy Labs in Sierra Leone and Zambia as part of a strategy to expedite integration of new knowledge into Pre-eclampsia care pathways.
Methods
Working with King’s Policy Institute, we co-developed and delivered two Policy Labs based on their 8-step process and the ‘trust-translation-timing’ model. Policy Labs were undertaken in Freetown, Sierra Leone and Lusaka, Zambia. Participants were purposively selected in each setting to represent a diverse range of relevant stakeholders. Main outcome measures included Policy Lab attendance; Policy Lab delivery; Policy Lab report and agreed outputs.
Results
Two Policy Labs were held: in Zambia (14th February 2023) and Sierra Leone (7th March 2023), attended by 35 and 39 diverse stakeholders respectively, included women via pre-recorded video clips. Both labs were successfully facilitated by local, well-respected female team members, and addressed co-developed questions (‘What are enablers and barriers to offering planned early delivery between 34 and 37 weeks?’ – Zambia; ‘How can we improve timely detection and appropriate action in women with Pre-eclampsia?’ – Sierra Leone) in answer to context-specific emerging evidence. Participants at both labs identified lack of Pre-eclampsia awareness as a key barrier, and recommended development of locally designed, community based strategies to increase access to timely, evidence-based Pre-eclampsia management.
Conclusions
Policy Labs are a novel approach to facilitate transfer of new knowledge into policy and action. Future Policy Labs should feature the women, participating in real time, at the forefront of the discussion.
Exploring Novel Molecular Mechanisms Underlying Recurrent Pregnancy Loss in Decidual Tissues
Authors: Ding Hui , Gao Yajie , Gao Yuan , Chen Yulu , Liu Ruimin , Wang Caili , Gao Yuqing 
Published: June 2024

Abstract

Background
Recurrent pregnancy loss (RPL) is a common reproductive complication, and the specific pathogenesis is still unclear. This study aimed to investigate RPL-related biomarkers and molecular mechanisms from the transcriptome of RPL decidua tissue using modern bioinformatics techniques, providing new perspectives for the etiology and clinical diagnosis and treatment of RPL.
Methods
Three gene expression profiles of RPL decidua tissue were retrieved and downloaded from the GEO database. Differential analysis, WGCNA analysis, and functional enrichment analysis were performed on the merged data. Subsequently, three machine learning methods (LASSO, SVM-RFE, and RF) were used to select the optimal feature genes for RPL, which were experimentally validated by RT-qPCR. The immune cell infiltration in RPL was evaluated using the ssGSEA algorithm, and the biological functions of the optimal feature genes were explored. Lastly, a heatmap was constructed to assist clinical physicians.
Results
10 key differentially expressed genes were identified: CFHR1, GPR155, TIMP4, WAKMAR2, COL15A1, LNCOG, C1QL1, KLK3, XG, and XGY2. Enrichment analysis showed associations with complement and coagulation cascade pathways. The three machine learning algorithms identified CFHR1 as the optimal feature gene for RPL, and RT-qPCR confirmed its high expression in RPL. ROC curve and nomogram demonstrated its diagnostic efficacy for RPL. Immune infiltration analysis revealed increased macrophages and γδT cells in RPL decidua tissue, with a significant positive correlation between CFHR1 and macrophages.
Conclusion
Transcriptomic abnormalities exist in RPL decidua tissue, with key genes closely related to complement and coagulation cascade pathways; CFHR1 is identified as the optimal feature gene for RPL. Abnormal immune infiltration and correlation with CFHR1 are observed in RPL decidua tissue.
Exploring Temperature-Dependent Photoluminescence Dynamics in Colloidal CdSe Nanoplatelets using Machine Learning Approach
Authors: Malashin Ivan P. , Daibagya Daniil , Tynchenko Vadim , Nelyub Vladimir , Borodulin Aleksei , Gantimurov Andrei , Selyukov Alexandr , Ambrozevich Sergey , Vasiliev Roman 
Published: June 2024

Abstract

The study explore machine learning techniques to predict temperature-dependent photoluminescence spectra in colloidal CdSe nanoplatelets, leveraging polynomial regression models trained on experimental data from 85 to 270 K spanning temperatures to forecast photoluminescence spectra backward to 0 K and forward to 300 K. 6th-degree polynomial models with Tweedie regression were optimal for band energy B1 predictions up to 300 K, while 9th-degree models with LassoLars and Linear Regression regressors were suitable for backward predictions to 0 K. For exciton energy B2, the Lasso model of degree 5 and the Ridge model of degree 4 performed well up to 300 K, while the Tweedie model of degree 2 and Theil-Sen model of degree 2 showed promise for predictions to 0 K. Furthermore, we utilize a GA-based approach to fit experimental data to theoretical model of Fan and Varshni equations, facilitating a comparative analysis with the ML-predicted curves.
Vehicle-to-grid response to a frequency contingency in a national grid – successes and shortcomings
Authors: Sturmberg Bjorn C. P. , Lucas-Healey Kathryn , Jones Laura , Hapuarachchi Lahiru , Biljon Justus van 
Published: June 2024

Abstract

Vehicle-to-grid technology enables electric vehicles to contribute their large batteries and high (dis)charging powers to power systems reserves. Here we report the first demonstration of a fleet of vehicles discharging to support system security after a frequency contingency in a national grid. Our results highlight the potential of vehicle-to-grid, with vehicles discharging within 6 seconds of the contingency event, and shortcomings, with vehicles recommencing charging before the power system had fully recovered.
Porphyromonas gingivalis protease Rgp induces M1-type polarization and pyroptosis in BV-2 cells by inhibiting SIRT1 expression
Authors: CAI Hongxuan , Yaguang TIAN , Weixing SI , Zan ZHANG , Jingyi DAI , Zhurui WANG , LI Mengsen 
Published: June 2024

Abstract

Background
Periodontitis and Alzheimer's disease (AD) are age-related diseases that reciprocally act as risk factors. It has been reported that periodontal pathogen Porphyromonas gingivalis and its gingipains contribute to neuroinflammation mediated by microglial cells, playing a crucial role in the onset of AD. However, it remains unclear whether gingipains play a pro-inflammatory role by inducing senescent phenotypic changes in microglial cells.
Methods
BV-2 cells were cultured and stimulated with gingival protease (Rgp), in combination with or not SRT1720, an inhibitor of SIRT1. SA-βgal staining was used to observe the altered cellular senescent phenotype. Immunoprotein blotting and real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) were used to confirm the results of SIRT1, NLRP3, Caspase-1 and GSDMD expression. ELISA and flow cytometry were used to detect IL-1β and IL-18 levels in supernatants and altered M1 polarization in BV-2 cells.
Results
Rgp induced BV-2 cells to present a senescent phenotype and downregulated the expression of senescence-related protein SIRT1. BV-2 cells with the senescent phenotype showed a concentration-dependent upregulation of NLRP3 upon Rgp stimulation, accompanied by a significant increase in the M1-type polarization phenotype. Simultaneously, the expression of pyroptosis-related proteins Caspase-1 and GSDMD increased, and flow cytometry analysis indicated an increase in pyroptosis in BV-2 cells. Further restoration verification using the SIRT1 activator SRT1720 showed that, compared to the Rgp stimulation group, the SRT1720 intervention group exhibited increased SIRT1 protein expression in BV-2 cells, decreased NLRP3 expression, and a significant reduction in M1-type polarization. Additionally, the expression of Caspase-1 and GSDMD proteins decreased, the levels of IL-1β and IL-18 in the supernatants decreased, and cell pyroptosis was significantly reduced.
Conclusions
Porphyromonas gingivalis protease Rgp induced a senescent phenotype in BV-2 cells and promoted M1-type polarization and pyroptosis of cells by inhibiting SIRT1 expression, thereby exacerbating the inflammatory response.
The use of a methylene blue and glyceryl trinitrate- based cream for the treatment of chronic anal fissures: a phase II randomized pilot trial from a referral coloproctological unit
Authors: Lobascio Pierluigi , Tomasicchio Giovanni , Cassetta Noadia , Altomare Donato Francesco , Gallo Gaetano , Pezzolla Angela , Laforgia Rita 
Published: June 2024

Abstract

BACKGROUND (MAIN AIM): Chronic anal fissures (CAFs) are the second most common anorectal disease. Non-surgical treatment includes several options with controversial efficacy. The aim of this study was to evaluate the efficacy and safety of a new ointment based on methylene blue in addition to glyceryl trinitrate. METHODS: A phase II randomized single-centre triple-blinded study was carried out in a tertiary proctology unit on patients with CAF. The enrollment started after local ethical approval (Study n.6461, Protocol approval n. 0045085). Eligible consecutive patients were randomized to create three different groups, each receiving a different ointment. The efficacy of the treatment was evaluated with the REALISE score. RESULTS: Nine patients with Cream A (median age 47 years, IQR 40–56, 22% female), nine with B (median age 52 years, IQR 49–57, 33% female), and nine with C (median age 58 years, IQR 46–62, 55% female). In group A, REALISE scores decreased significantly from a median of 22 (IQR 12–25) to 6 (IQR 4–8) (p < 0.05) after 40 days. In group B, REALISE scores improved significantly from a median of 20 (IQR 17–22) to 5 (IQR 4–9) (p < 0.05). In group C, REALISE scores decreased significantly from a median of 19 (IQR 19–20) to 4 (4–5) (p < 0.05). No statistically differences were recorded. The healing rate was 77% with Creams A and C, while it was 44% with Cream B. CONCLUSION: Methylene blue-based ointments could be a new and innovative treatment for the non-operative management and healing of CAFs.
A Two-Phase Approach for Solving the Multi-Skilled and Irregular Personnel scheduling Problem
Authors: Xue Ning , Bai Ruibin , Landa-Silva Dario , Cui Tianxiang , Jin Huan 
Published: June 2024

Abstract

In the realm of personnel scheduling, where demand is subject to variability, the creation of a work schedule for irregular worker presents a significant challenge. This difficulty is amplified when the specific shift types are not predefined and when workers possess varying levels of availability and distinct skill sets. Additionally, due to legal and cost constraints, personal preferences, and fairness considerations, the scheduling problem becomes even more complex. The typical personnel scheduling process generally consists of two main stages: shift design and shift assignment. During the shift design stage, a scheduler designs a set of shifts and calculates the required number of workers to meet the demand. The shift assignment stage involves allocating workers to each individual shift, while satisfying various constraints to achieve certain objectives.The shift design and assignment stages can be approached as a single problem or solved as two separate problems. In this paper, a two-phase method is investigated, where the first phase is addressed by an integer programming approach, and the second phase is solved using a memetic approach. Real-world retail store scheduling instances are used to test and compare these methods. The solution time and quality suggest that the proposed methods can be effectively applied to solve the irregular scheduling problem commonly encountered in retail stores.
Two-Stage Genetic Algorhitm for Optimization Logistics Network for Groupage Delivery
Authors: Malashin Ivan P. , Tynchenko Vadim S. , Masich Igor S. , Sukhanov Denis A. , Kudryavtsev Alexey A. , Ageev Daniel A. , Neluyb Vladimir A. , Gantimurov Andrei P. , Borodulin Alexey S. 
Published: June 2024

Abstract

This study explores groupage delivery optimization through a two-stage GA framework. The goal of optimization is to define locations of regional branch warehouses of Logistics Network to reduce transportation costs. The first stage of the GA focuses on identifying branch warehouses from a list of cities and uses the standard binary string approach, while the second stage solves Vehicle Routing Problem (VRP) using combinatorial GA. This research contributes to advancing logistics optimization methodologies and provides valuable insights for solving Location Routing Problem (LRP) which entails two problems: the Facility Location Problem (FLP) and the Multiple Vehicle Routing Problem (MVRP).
First record of Culicoides deltus as a putative vector of Onchocerca flexuosa in Slovak red deer (Cervus elaphus)
Authors: Kočišová Alica , Schreiberová Andrea , Kasičová Zuzana , Janošková Nikola 
Published: June 2024

Abstract

In the research conducted in years 2016–2022, 63,950 biting midges were trapped during 74 collections conducted in the area of Volovsky Mountains in Slovakia for the purpose of identifying the species composition, the host preference and the transmission or potential parasites in wild nature. The collected biting midges were of 29 species, while the most frequently detected were the Culicoides biting midges (C. obsoletus/C. scoticus/C. montanus), representing 47.9% of all biting midges. The species identification of biting midges was carried out based on their morphology and by applying molecular methods. The host preference of biting midges was molecularly identified by sequencing a 350-bp sequence of the mitochondrial cytochrome b gene (cyt b). The presence of Onchocerca flexuosa DNA in Culicoides deltus was confirmed by the sequencing of fragments of mitochondrial genes cox1. The sequences matched the previously published sequences for O. flexuosa. Data on high prevalence of infections caused by Onchocerca worms in red deer in Slovakia has already been published and indicates that there are favourable conditions for vectors and a suitable environment for parasite circulation in Slovakia. According to the authors’ knowledge, this was the first-ever detection of O. flexuosa in C. deltus in wild nature.