Finite-dimensional integrable systems associated with Davey-Stewartson I equation

For the Davey-Stewartson I equation, which is an integrable equation in 1+2 dimensions, we have already found its Lax pair in 1+1 dimensional form by nonlinear constraints. This paper deals with the second nonlinearization of this 1+1 dimensional system to get three 1+0 dimensional Hamiltonian systems with a constraint of Neumann type. The full set of involutive conserved integrals is obtained and their functional independence is proved. Therefore, the Hamiltonian systems are completely integrable in Liouville sense. A periodic solution of the Davey-Stewartson I equation is obtained by solving these classical Hamiltonian systems as an example.Comment: 18 pages, LaTe

A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care

The COVID-19 pandemic highlighted the need for predictive deep-learning models in health care. However, practical prediction task design, fair comparison, and model selection for clinical applications remain a challenge. To address this, we introduce and evaluate two new prediction tasks?outcome-specific length-of-stay and early-mortality prediction for COVID-19 patients in intensive care?which better reflect clinical realities. We developed evaluation metrics, model adaptation designs, and open-source data preprocessing pipelines for these tasks while also evaluating 18 predictive models, including clinical scoring methods and traditional machine-learning, basic deep-learning, and advanced deep-learning models, tailored for electronic health record (EHR) data. Benchmarking results from two real-world COVID-19 EHR datasets are provided, and all results and trained models have been released on an online platform for use by clinicians and researchers. Our efforts contribute to the advancement of deep-learning and machine-learning research in pandemic predictive modeling

SNP-Based Genetic Linkage Map of Soybean Using the SoySNP6K Illumina Infinium BeadChip Genotyping Array

This study reports a high density genetic linkage map based on the ‘Maryland 96-5722’ by ‘Spencer’ recombinant inbred line (RIL) population of soybean [Glycine max (L.) Merr.] and constructed exclusively with single nucleotide polymorphism (SNP) markers. The Illumina Infinium SoySNP6K BeadChip genotyping array produced 5,376 SNPs in the mapping population, with a 96.75% success rate. Significant level of goodness-of-fit for each locus was tested based on the observed vs. expected ratio (1:1). Out of 5,376 markers, 1,465 SNPs fit the 1:1 segregation rate having ≤20% missing data plus heterozygosity among the RILs. Among this 1,456 just 657 were polymorphic between the parents DNAs tested. These 657 SNPs were mapped using the JoinMap 4.0 software and 550 SNPs were distributed on 16 linkage groups (LGs) among the 20 chromosomes of the soybean genome. The total map length was just 201.57 centiMorgans (cM) with an average marker density of 0.37 cM. This is one of the high density SNP-based genetic linkage maps of soybean that will be used by the scientific community to map quantitative trait loci (QTL) and identify candidate genes for important agronomic traits in soybean

Characterization of Soybean STAY-GREEN Genes in Susceptibility to Foliar Chlorosis of Sudden Death Syndrome

Fusarium virguliforme causes sudden death syndrome (SDS) of soybean (Glycine max) in the United States. This fungal pathogen inhabits soil and produces multiple phytotoxins, which are translocated from infected roots to leaves, causing SDS foliar chlorosis and necrosis (Hartman et al., 2015). Because SDS foliar symptoms are solely induced by phytotoxins, it represents a unique pathosystem to study plant-phytotoxin interactions (Chang et al., 2016). SDS foliar symptoms typically appear near flowering through late reproductive growth stages, with chlorotic spots that gradually develop into interveinal chlorosis and necrosis (Fig. 1A). The sudden appearance of SDS foliar symptoms not only explains the origin of the disease name, but also reflects the difficulty of early detection in managing this disease. Yield reductions caused by SDS have been documented at 5% to15%, and the economic loss was estimated up to $669 million U.S. dollars in a single year (Navi and Yang, 2016). Seed treatments have been used to manage SDS, but performance differs by year and location. Alternatively, partially resistant soybean cultivars provide a sustainable option for SDS management, but the genetic architecture of SDS resistance is quantitative and complicated. Among more than 80 quantitative trait loci reported for SDS, only a few quantitative trait loci are reproducible due to the complexity of SDS etiology and environmental interactions (Chang et al., 2018)

The IPIN 2019 Indoor Localisation Competition—Description and Results

IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks

Research on Torsional Characteristic and Stiffness Reinforcement of Main Girder of Half-Through Truss Bridge

The stronger stability of a half-through truss bridge can improve the bridge performance for resisting extreme loads, such as earthquakes and shock. To improve the bridge stability, it is necessary to improve the torsional stiffness of the half-through truss bridge. To study the torsional characteristics of the main girder of the half-through truss bridge, the half-through truss is equivalent to an open slot thin-walled member, and the calculation formula of the free torsional moment of inertia of the main girder is deduced. Because the main truss can resist warping deformation through bending, it has a great contribution to the torsional stiffness. Based on the vertical bending action of the main truss, the calculation formula of the correction of the torsional moment of inertia of the main girder is deduced. Taking a half-through truss pedestrian bridge as an example, the torsional moment of inertia of the bridge under different width-span ratios is calculated by theoretical and finite element analysis. The results show that when calculating the torsional moment of inertia of the main girder of the half-through truss bridge, the free torsional moment of inertia calculated by the equivalent open slot section is very different from the actual torsional stiffness, and the bending correction value must be considered. The theoretical solution after taking into account the corrected value is well-fitted with the finite element results. The theoretical formula can be used to explain the torsional mechanism of this kind of bridge. According to the mechanism research, the method of installing X-shaped longitudinal supports between the upper transverse girders to improve the torsional stiffness is finally formulated. Installing the X-shaped longitudinal supports not only can keep the size of the half-through truss bridge unchanged but can also have a considerable enhancement effect, which will significantly improve the torsional stiffness and stability of existing bridges

A boosting approach for prediction of protein-RNA binding residues

Abstract Background RNA binding proteins play important roles in post-transcriptional RNA processing and transcriptional regulation. Distinguishing the RNA-binding residues in proteins is crucial for understanding how protein and RNA recognize each other and function together as a complex. Results We propose PredRBR, an effectively computational approach to predict RNA-binding residues. PredRBR is built with gradient tree boosting and an optimal feature set selected from a large number of sequence and structure characteristics and two categories of structural neighborhood properties. In cross-validation experiments on the RBP170 data set show that PredRBR achieves an overall accuracy of 0.84, a sensitivity of 0.85, MCC of 0.55 and AUC of 0.92, which are significantly better than that of other widely used machine learning algorithms such as Support Vector Machine, Random Forest, and Adaboost. We further calculate the feature importance of different feature categories and find that structural neighborhood characteristics are critical in the recognization of RNA binding residues. Also, PredRBR yields significantly better prediction accuracy on an independent test set (RBP101) in comparison with other state-of-the-art methods. Conclusions The superior performance over existing RNA-binding residue prediction methods indicates the importance of the gradient tree boosting algorithm combined with the optimal selected features

Robust Vehicle Speed Measurement Based on Feature Information Fusion for Vehicle Multi-Characteristic Detection

A robust vehicle speed measurement system based on feature information fusion for vehicle multi-characteristic detection is proposed in this paper. A vehicle multi-characteristic dataset is constructed. With this dataset, seven CNN-based modern object detection algorithms are trained for vehicle multi-characteristic detection. The FPN-based YOLOv4 is selected as the best vehicle multi-characteristic detection algorithm, which applies feature information fusion of different scales with both rich high-level semantic information and detailed low-level location information. The YOLOv4 algorithm is improved by combing with the attention mechanism, in which the residual module in YOLOv4 is replaced by the ECA channel attention module with cross channel interaction. An improved ECA-YOLOv4 object detection algorithm based on both feature information fusion and cross channel interaction is proposed, which improves the performance of YOLOv4 for vehicle multi-characteristic detection and reduces the model parameter size and FLOPs as well. A multi-characteristic fused speed measurement system based on license plate, logo, and light is designed accordingly. The system performance is verified by experiments. The experimental results show that the speed measurement error rate of the proposed system meets the requirement of the China national standard GB/T 21555-2007 in which the speed measurement error rate should be less than 6%. The proposed system can efficiently enhance the vehicle speed measurement accuracy and effectively improve the vehicle speed measurement robustness