Multi-strategy learning-based particle swarm optimization algorithm for COVID-19 threshold segmentation.

With advancements in science and technology, the depth of human research on COVID-19 is increasing, making the investigation of medical images a focal point. Image segmentation, a crucial step preceding image processing, holds significance in the realm of medical image analysis. Traditional threshold image segmentation proves to be less efficient, posing challenges in selecting an appropriate threshold value. In response to these issues, this paper introduces Inner-based multi-strategy particle swarm optimization (IPSOsono) for conducting numerical experiments and enhancing threshold image segmentation in COVID-19 medical images. A novel dynamic oscillatory weight, derived from the PSO variant for single-objective numerical optimization (PSOsono) is incorporated. Simultaneously, the historical optimal positions of individuals in the particle swarm undergo random updates, diminishing the likelihood of algorithm stagnation and local optima. Moreover, an inner selection learning mechanism is proposed in the update of optimal positions, dynamically refining the global optimal solution. In the CEC 2013 benchmark test, PSOsono demonstrates a certain advantage in optimization capability compared to algorithms proposed in recent years, proving the effectiveness and feasibility of PSOsono. In the Minimum Cross Entropy threshold segmentation experiments for COVID-19, PSOsono exhibits a more prominent segmentation capability compared to other algorithms, showing good generalization across 6 CT images and further validating the practicality of the algorithm.

H-ACO with Consecutive Bases Pairing Constraint for Designing DNA Sequences.

DNA computing is a novel computing method that does not rely on traditional computers. The design of DNA sequences is a crucial step in DNA computing, and the quality of the sequence design directly affects the results of DNA computing. In this paper, a new constraint called the consecutive base pairing constraint is proposed to limit specific base pairings in DNA sequence design. Additionally, to improve the efficiency and capability of DNA sequence design, the Hierarchy-ant colony (H-ACO) algorithm is introduced, which combines the features of multiple algorithms and optimizes discrete numerical calculations. Experimental results show that the H-ACO algorithm performs well in DNA sequence design. Finally, this paper compares a series of constraint values and NUPACK simulation data with previous design results, and the DNA sequence set designed in this paper has more advantages.

Integrating Genome-Wide Association Study, Transcriptome and Metabolome Reveal Novel QTL and Candidate Genes That Control Protein Content in Soybean.

Protein content (PC) is crucial to the nutritional quality of soybean [Glycine max (L.) Merrill]. In this study, a total of 266 accessions were used to perform a genome-wide association study (GWAS) in three tested environments. A total of 23,131 high-quality SNP markers (MAF ≥ 0.02, missing data ≤ 10%) were identified. A total of 40 association signals were significantly associated with PC. Among them, five novel quantitative trait nucleotides (QTNs) were discovered, and another 32 QTNs were found to be overlapping with the genomic regions of known quantitative trait loci (QTL) related to soybean PC. Combined with GWAS, metabolome and transcriptome sequencing, 59 differentially expressed genes (DEGs) that might control the change in protein content were identified. Meantime, four commonly upregulated differentially abundant metabolites (DAMs) and 29 commonly downregulated DAMs were found. Remarkably, the soybean gene Glyma.08G136900, which is homologous with Arabidopsis hydroxyproline-rich glycoproteins (HRGPs), may play an important role in improving the PC. Additionally, Glyma.08G136900 was divided into two main haplotype in the tested accessions. The PC of haplotype 1 was significantly lower than that of haplotype 2. The results of this study provided insights into the genetic mechanisms regulating protein content in soybean.

Integrated improved Harris hawks optimization for global and engineering optimization.

The original Harris hawks optimization (HHO) algorithm has the problems of unstable optimization effect and easy to fall into stagnation. However, most of the improved HHO algorithms can not effectively improve the ability of the algorithm to jump out of the local optimum. In this regard, an integrated improved HHO (IIHHO) algorithm is proposed. Firstly, the linear transformation escape energy used by the original HHO algorithm is relatively simple and lacks the escape law of the prey in the actual nature. Therefore, intermittent energy regulator is introduced to adjust the energy of Harris hawks, which is conducive to improving the local search ability of the algorithm while restoring the prey's rest mechanism; Secondly, to adjust the uncertainty of random vector, a more regular vector change mechanism is used instead, and the attenuation vector is obtained by modifying the composite function. Finally, the search scope of Levy flight is further clarified, which is conducive to the algorithm jumping out of the local optimum. Finally, in order to modify the calculation limitations caused by the fixed step size, Cardano formula function is introduced to adjust the step size setting and improve the accuracy of the algorithm. First, the performance of IIHHO algorithm is analyzed on the Computational Experimental Competition 2013 (CEC 2013) function test set and compared with seven improved evolutionary algorithms, and the convergence value of the iterative curve obtained is better than most of the improved algorithms, verifying the effectiveness of the proposed IIHHO algorithm. Second, the IIHHO is compared with another three state of the art (SOTA) algorithms with the Computational Experimental Competition 2022 (CEC 2022) function test set, the experiments show that the proposed IIHHO algorithm still has a strong ability to search for the optimal value. Third, IIHHO algorithm is applied in two different engineering experiments. The calculation results of minimum cost prove that IIHHO algorithm has certain advantages in dealing with the problem of search space. All these demonstrate that the proposed IIHHO is promising for numeric optimization and engineering applications.

Multi-omics analysis reveals novel loci and a candidate regulatory gene of unsaturated fatty acids in soybean (Glycine max (L.) Merr).

BACKGROUND: Soybean is a major oil crop; the nutritional components of soybean oil are mainly controlled by unsaturated fatty acids (FA). Unsaturated FAs mainly include oleic acid (OA, 18:1), linoleic acid (LLA, 18:2), and linolenic acid (LNA, 18:3). The genetic architecture of unsaturated FAs in soybean seeds has not been fully elucidated, although many independent studies have been conducted. A 3 V multi-locus random single nucleotide polymorphism (SNP)-effect mixed linear model (3VmrMLM) was established to identify quantitative trait loci (QTLs) and QTL-by-environment interactions (QEIs) for complex traits. RESULTS: In this study, 194 soybean accessions with 36,981 SNPs were calculated using the 3VmrMLM model. As a result, 94 quantitative trait nucleotides (QTNs) and 19 QEIs were detected using single-environment (QTN) and multi-environment (QEI) methods. Three significant QEIs, namely rs4633292, rs39216169, and rs14264702, overlapped with a significant single-environment QTN. CONCLUSIONS: For QTNs and QEIs, further haplotype analysis of candidate genes revealed that the Glyma.03G040400 and Glyma.17G236700 genes were beneficial haplotypes that may be associated with unsaturated FAs. This result provides ideas for the identification of soybean lipid-related genes and provides insights for breeding high oil soybean varieties in the future.

DNA Sequences Under Multiple Guanine-Cytosine (GC) Base Pairs Constraint.

DNA computing is a new computing method that has high efficiency in solving large-scale nonlinear and Non-deterministic Polynomial complete problems. The design of DNA sequences is an important step in DNA computing, and the quality of the DNA sequences directly affects the accuracy of DNA computing results. Efficiently designing high-quality DNA sequences is currently a significant challenge. In order to improve the efficiency of DNA sequence design, a sparrow evolutionary search algorithm (SESA) is proposed by us. It inherits the fast convergence of the sparrow search algorithm and avoids the situation that the sparrow search algorithm is prone to fall into a local optimum, which greatly improves the search performance of the algorithm on discrete numerical problems. In order to improve the quality of DNA sequence, a new constraint, multiple GC constraint, has been proposed in this paper. Simulated experiments in NUPACK show that this constraint can greatly improve the quality of the DNA sequences designed by us. Compared with previous results, our DNA sequences have better stability.

Experimental and Simulation Study of the Fracture Instability Behavior in Polypropylene Fiber-Reinforced Concrete.

This study investigated the fracture characteristics of plain concrete and polypropylene fiber-reinforced concrete (PFRC) using pre-notched three-point bending beam tests with the digital speckle correlation method (DSCM). Then, the fracture instability behavior of the two types of beams was simulated in finite elements based on the plastic damage model and the cohesion model, for which the applicability was assessed. Furthermore, the stability of the Big Gang Mountain Dam made from plain concrete or PFRC subjected to the earth-quake loading was simulated with the plastic damage model. The results show that the limiting length of the non-local deformation zone can be used as an indicator of instability damage in a concrete structure. The simulation results of the plastic damage model agreed well with the local deformation in the pre-notched three-point bending beam test obtained from the DSCM. The plastic damage model was found to be capable of describing the residual strength phenomenon, which the cohesive model was not capable of. The damage evolution regions of the PFRC dam are strictly constrained in some regions without the occurrence of the local deformation band across the dam, and PFRC can dramatically reduce the failure risk under earthquake loading. The numerical solution proves that PFRC is an advisable material for avoiding failure in concrete dams.

Identification of QTL, QTL-by-environment interactions, and their candidate genes for resistance HG Type 0 and HG Type 1.2.3.5.7 in soybean using 3VmrMLM.

Introduction: Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) is an important disease affecting soybean yield in the world. Potential SCN-related QTLs and QTL-by-environment interactions (QEIs) have been used in SCN-resistant breeding. Methods: In this study, a compressed variance component mixed model, 3VmrMLM, in genome-wide association studies was used to detect QTLs and QEIs for resistance to SCN HG Type 0 and HG Type 1.2.3.5.7 in 156 different soybean cultivars materials. Results and discussion: The results showed that 53 QTLs were detected in single environment analysis; 36 QTLs and 9 QEIs were detected in multi-environment analysis. Based on the statistical screening of the obtained QTLs, we obtained 10 novel QTLs and one QEI which were different from the previous studies. Based on previous studies, we identified 101 known genes around the significant/suggested QTLs and QEIs. Furthermore, used the transcriptome data of SCN-resistant (Dongnong L-10) and SCN-susceptible (Suinong 14) cultivars, 10 candidate genes related to SCN resistance were identified and verified by Quantitative real time polymerase chain reaction (qRT-PCR) analysis. Haplotype difference analysis showed that Glyma.03G005600 was associated with SCN HG Type 0 and HG Type 1.2.3.5.7 resistance and had a haplotype beneficial to multi-SCN-race resistance. These results provide a new idea for accelerating SCN disease resistance breeding.

Combined Detection of IFN-γ and Lymphocyte Subsets with Activation Indicators in the Clinical Application of Mycobacterium Tuberculosis Infection at Different Times.

The immune status of mycobacterium tuberculosis (MTB) infection is essential for the diagnosis and treatment of this disease. In this work, we aim to evaluate the clinical significance of the combination of serum IFN-γ, IGRAs (Interferon-Gamma Release Assay), lymphocyte subset with activation indicators detection in active and latent tuberculosis infection patients. For this study, anticoagulant whole blood were collected from 45 active tuberculosis (AT group), 44 latent tuberculosis (LT group) and 32 healthy controls (HCs group). The serum IFN-γ and IGRAs detected by chemiluminescence, and the percentage of lymphocyte subsets and activated lymphocytes detected by flow cytometry. The results showed combined IGRAs, serum IFN-γ and NKT cells not only has good diagnostic efficiency for the AT, but also provides a laboratory diagnostic method to distinguish AT from LT. Activation indicator of CD3+HLA-DR+T and CD4+HLA-DR+T can effectively distinguish LT from HCs. While combined CD3+T, CD4+T, CD8+CD28+T, Treg and CD16+CD56+CD69+ cells can distinguish AT from HCs. This study showed combined direct detection of serum IFN-γ and IGRAs as well as lymphocyte subsets with activation indicators which may provide laboratory basis for the diagnosis and differential diagnosis of active and latent MTB infection.

DNA Sequence Optimization Design of Arithmetic Optimization Algorithm Based on Billiard Hitting Strategy.

DNA computing is a very efficient way to calculate, but it relies on high-quality DNA sequences, but it is difficult to design high-quality DNA sequences. The sequence it is looking for must meet multiple conflicting constraints at the same time to meet the requirements of DNA calculation. Therefore, we propose an improved arithmetic optimization algorithm of billiard algorithm to optimize the DNA sequence. This paper contributes as follows. The introduction to the good point set initialization to obtain high-quality solutions improves the optimization efficiency. The billiard hitting strategy was used to change the position of the population to enhance the global search scope. The use of a stochastic lens opposites learning mechanism can increase the capacity of the algorithm to get rid of locally optimal. The harmonic search algorithm is introduced to clarify some unqualified secondary structures and improve the quality of the solution. 12 benchmark functions and six other algorithms are used for comparison and ablation experiments to ensure the effectiveness of the algorithms. Finally, the DNA sequences we designed are of higher quality compared to other advanced algorithms.

Evaluation Soybean Cultivars for Reaction to Heterodera glycines Populations HG Types 7 and 1.3.4.7 in Northeast China.

Soybean cyst nematode Heterodera glycines (SCN) is a major threat to global soybean production. Effective management of this disease is dependent on the development of resistant cultivars. Two SCN HG Types, 7 and 1.3.4.7. were previously identified as prevalent H. glycines populations in Northeast China. In order to evaluate soybean cultivars resistant to local SCN populations, 110 domestic commercial soybeans from different regions of Northeast China were assessed in the greenhouse to determine their potential as novel sources of resistance. The results suggested that cultivars responded differently to the two HG types. Of the 110 soybean cultivars evaluated, 24 accessions were classified as resistant or moderately resistant to HG Type 7, and five cultivars were classified as resistant or moderately resistant to HG Type 1.3.4.7. Among the tested cultivars, Kangxian 12 and Qingdou 13 had resistance response to both HG types 7 and 1.3.4.7. Thus, these broad-based SCN cultivars will be the valuable materials in the SCN resistance breeding program.

Research Progress in Construction and Application of Enzyme-Based DNA Logic Gates.

As a research hotspot in the field of information processing, DNA computing exhibits several important underlying characteristics-from parallel computing and low energy consumption to high-performance storage capabilities-thereby enabling its wide application in nanomachines, molecular encryption, biological detection, medical diagnosis, etc. Based on DNA computing, the most rapidly developed field focuses on DNA molecular logic-gates computing. In particular, the recent advances in enzyme-based DNA logic gates has emerged as ideal materials for constructing DNA logic gates. In this review, we explore protein enzymes that can manipulate DNA, especially, nicking enzymes and polymerases with high efficiency and specificity, which are widely used in constructing DNA logic gates, as well as ribozyme that can construct DNA logic gates following various mechanism with distinct biomaterials. Accordingly, the review highlights the characteristics and applications of various types of DNAzyme-based logic gates models, considering their future developments in information, biomedicine, chemistry, and computers.

Changes in the m6A RNA methylome accompany the promotion of soybean root growth by rhizobia under cadmium stress.

Cadmium (Cd) is the most widely distributed heavy metal pollutant in soil and has significant negative effects on crop yields and human health. Rhizobia can enhance soybean growth in the presence of heavy metals, and the legume-rhizobia symbiosis has been used to promote heavy-metal phytoremediation, but much remains to be learned about the molecular networks that underlie these effects. Here, we demonstrated that soybean root growth was strongly suppressed after seven days of Cd exposure but that the presence of rhizobia largely eliminated this effect, even prior to nodule development. Moreover, rhizobia did not appear to promote root growth by limiting plant Cd uptake: seedlings with and without rhizobia had similar root Cd concentrations. Previous studies have demonstrated a role for m6A RNA methylation in the response of rice and barley to Cd stress. We therefore performed transcriptome-wide m6A methylation profiling to investigate changes in the soybean RNA methylome in response to Cd with and without rhizobia. Here, we provide some of the first data on transcriptome-wide m6a RNA methylation patterns in soybean; m6A modifications were concentrated at the 3' UTR of transcripts and showed a positive relationship with transcript abundance. Transcriptome-wide m6A RNA methylation peaks increased in the presence of Cd, and the integration of m6A methylome and transcriptome results enabled us to identify 154 genes whose transcripts were both differentially methylated and differentially expressed in response to Cd stress. Annotation results suggested that these genes were associated with Ca2+ homeostasis, ROS pathways, polyamine metabolism, MAPK signaling, hormones, and biotic stress responses. There were 176 differentially methylated and expressed transcripts under Cd stress in the presence of rhizobia. In contrast to the Cd-only gene set, they were also enriched in genes related to auxin, jasmonic acid, and brassinosteroids, as well as abiotic stress tolerance. They contained fewer genes related to Ca2+ homeostasis and also included candidates with known functions in the legume-rhizobia symbiosis. These findings offer new insights into how rhizobia promote soybean root growth under Cd stress; they provide candidate genes for research on plant heavy metal responses and for the use of legumes in phytoremediation.

Improved Bare Bones Particle Swarm Optimization for DNA Sequence Design.

DNA computing has efficient computational power, but requires high requirements on the DNA sequences used for coding, and reliable DNA sequences can effectively improve the quality of DNA encoding. And designing reliable DNA sequences is an NP problem, because it requires finding DNA sequences that satisfy multiple sets of conflicting constraints from a large solution space. To better solve the DNA sequence design problem, we propose an improved bare bones particle swarm optimization algorithm (IBPSO). The algorithm uses dynamic lensing opposition-based learning to initialize the population to improve population diversity and enhance the ability of the algorithm to jump out of local optima; An evolutionary strategy based on signal-to-noise ratio(SNR) distance is designed to balance the exploration and exploitation of the algorithm; Then an invasive weed optimization algorithm with niche crowding(NCIWO) is used to eliminate low-quality solutions and improve the search efficiency of the algorithm. In addition, we introduce the triplet-bases unpaired constraint to further improve the quality of DNA sequences. Finally, the effectiveness of the improved strategy is demonstrated by ablation experiments; and the DNA sequences designed by our algorithm are of higher quality compared with those generated by the six advanced algorithms.

Chemical Reaction Networks' Programming for Solving Equations.

The computational ability of the chemical reaction networks (CRNs) using DNA as the substrate has been verified previously. To solve more complex computational problems and perform the computational steps as expected, the practical design of the basic modules of calculation and the steps in the reactions have become the basic requirements for biomolecular computing. This paper presents a method for solving nonlinear equations in the CRNs with DNA as the substrate. We used the basic calculation module of the CRNs with a gateless structure to design discrete and analog algorithms and realized the nonlinear equations that could not be solved in the previous work, such as exponential, logarithmic, and simple triangle equations. The solution of the equation uses the transformation method, Taylor expansion, and Newton iteration method, and the simulation verified this through examples. We used and improved the basic calculation module of the CRN++ programming language, optimized the error in the basic module, and analyzed the error's variation over time.

A nonlinear neural network based on an analog DNA toehold mediated strand displacement reaction circuit.

The DNA toehold mediated strand displacement reaction is one of the semi-synthetic biology technologies for next-generation computers. In this article, we present a framework for a novel nonlinear neural network based on an engineered biochemical circuit, which is constructed by several reaction modules including catalysis, degradation and adjustment reaction modules. The proposed neural network possesses an architecture that is similar to that of an error back propagation neural network, and is built of an input layer, hidden layer and output layer. As a proof of concept, we utilize this nonlinear neural network based on an analog DNA toehold mediated strand displacement reaction circuit to learn the standard quadratic form function and analyze the robustness of the nonlinear neural network toward DNA strand concentration detection, DNA strand displacement reaction rate and noise. Unlike in error back propagation neural networks, the adaptive behavior of this DNA molecular neural network system endows it with supervised learning capability. This investigation will highlight the potential of analog DNA displacement reaction circuits for implementing artificial intelligence.

K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm.

Image segmentation plays an important role in daily life. The traditional K-means image segmentation has the shortcomings of randomness and is easy to fall into local optimum, which greatly reduces the quality of segmentation. To improve these phenomena, a K-means image segmentation method based on improved manta ray foraging optimization (IMRFO) is proposed. IMRFO uses Lévy flight to improve the flexibility of individual manta rays and then puts forward a random walk learning that prevents the algorithm from falling into the local optimal state. Finally, the learning idea of particle swarm optimization is introduced to enhance the convergence accuracy of the algorithm, which effectively improves the global and local optimization ability of the algorithm simultaneously. With the probability that K-means will fall into local optimum reducing, the optimized K-means hold stronger stability. In the 12 standard test functions, 7 basic algorithms and 4 variant algorithms are compared with IMRFO. The results of the optimization index and statistical test show that IMRFO has better optimization ability. Eight underwater images were selected for the experiment and compared with 11 algorithms. The results show that PSNR, SSIM, and FSIM of IMRFO in each image are better. Meanwhile, the optimized K-means image segmentation performance is better.

Geometric features of middle cerebral artery are associated with spontaneous basal ganglia intracerebral haemorrhage.

BACKGROUND AND PURPOSE: Haemodynamics around the middle cerebral artery (MCA) and lenticulostriate arteries is believed to play important roles in the vascular rupture and local haemodynamics is subject to vascular geometry. Nonetheless, the relationship between the geometric features of MCA and spontaneous basal ganglia intracerebral haemorrhage (ICH) has not been investigated. To examine the relationship between the MCA geometric features and spontaneous basal ganglia ICH. METHODS: This study was of retrospective and observational nature. The study recruited 158 consecutive hospitalised patients with consecutive CT-confirmed unilateral spontaneous basal ganglia ICH. Clinical data were extracted from electronic medical records, and imaging data were evaluated by two trained radiologists. The MCA-related geometric features were examined and their relationship with spontaneous basal ganglia ICH was analysed. Haemodynamic analyses under different MCA structural features were conducted. RESULTS: Compared with the contralateral MCA, the ipsilateral MCA had greater M1 diameter ratio (proximal/distal) and a smaller M1/M2 angle and MCA bifurcation angle (p＜0.01). Imaging study showed differences in the MCA shape in both sides on coronal plane (p＜0.05). These MCA features were significantly correlated with the spontaneous ICH in basal ganglia. The greater M1 diameter ratio (proximal/distal), the inferior-oriented M1, the smaller M1/M2 angle and the superior-oriented M1 conditions increased the pressure, from high to low. The greater M1 diameter ratio (proximal/distal) and the inferior-oriented M1 increased the shear stress at the distal end of M1 segment. CONCLUSIONS: The geometric features of MCA were significantly related to the spontaneous ICH in basal ganglia. The risk of haemorrhage, from high to low, included the greater M1 diameter ratio (proximal/distal), the inferior-oriented M1 (distal end), the smaller M1/M2 angle and the superior-oriented M1. Mechanistically, these vascular structural features contribute to increased vascular wall pressure and shear stress, which eventually lead to haemorrhage.

Stock prediction based on bidirectional gated recurrent unit with convolutional neural network and feature selection.

With the development of recent years, the field of deep learning has made great progress. Compared with the traditional machine learning algorithm, deep learning can better find the rules in the data and achieve better fitting effect. In this paper, we propose a hybrid stock forecasting model based on Feature Selection, Convolutional Neural Network and Bidirectional Gated Recurrent Unit (FS-CNN-BGRU). Feature Selection (FS) can select the data with better performance for the results as the input data after data normalization. Convolutional Neural Network (CNN) is responsible for feature extraction. It can extract the local features of the data, pay attention to more local information, and reduce the amount of calculation. The Bidirectional Gated Recurrent Unit (BGRU) can process the data with time series, so that it can have better performance for the data with time series attributes. In the experiment, we used single CNN, LSTM and GRU models and mixed models CNN-LSTM, CNN-GRU and FS-CNN-BGRU (the model used in this manuscript). The results show that the performance of the hybrid model (FS-CNN-BGRU) is better than other single models, which has a certain reference value.