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The tradeoff between spatial and spectral resolution in sensor design is inevitable, and spatial–spectral fusion aims to use low spatial resolution hyperspectral image (HSI) and high spatial resolution (HR) multispectral image (MSI) obtained at the same time and in the same area to reconstruct HR HSI. Recently, a large number of deep-learning methods have been applied in this field and achieved success. However, these methods do not fully utilize the characteristics of data for network design, and cannot guarantee effective computational efficiency in extracting local and global features. To solve the above problems, we designed a spatial–spectral interaction super-resolution convolutional neural network (CNN)–Mamba fusion network for satellite HSI and MSI, which uses mutual guidance to improve the spatial and spectral resolution of different data, and obtains the final fused image through feature fusion. In addition, we combined Mamba with CNN to effectively explore global and local features of images. Extensive experiments have proven that our method can reconstruct fused images of high quality and is superior to current state-of-the-art fusion methods.

Introduction: Calcitonin gene-related peptide (CGRP) is involved in trigeminal neuralgia and migraine, and measuring the CGRP concentration in the serum is crucial for the early prediction of these conditions. Current methods for CGRP detection are primarily radioimmunoassay, which needs radioactive substances and enzyme-linked immunosorbent assays (ELISAs) which need long detection time and some have a narrow detection range.Methods: The genes of anti-CGRP antibody variable regions were cloned into pDong1 vector to obtain pDong1/Fab-CGRP, with which phage-Fab was prepared, and the concentration of CGRP was detected by competitive ELISA. The pDong1/Fab-CGRP was modified to obtain pDong1/OS-CGRP, with which the co-expression solution containing phage-displayed heavy chain variable fragments (phage-VH) and light chain was obtained. CGRP was detected by OS-ELISA based on phage-VH, antibody light chain, and anti-light chain antibody. The VL gene was cloned into the pMAL vector to obtain pMAL-VL (CGRP), with which maltose binding protein fused with VL (MBP-VL) was prepared. CGRP was detected by OS-ELISA employing MBP-VL and phage-VH.Results: OS-ELISAs that measure the CGRP concentration by quantifying the interaction between variable regions were investigated. OS-ELISA using phage-VH and secreted light chains in the same culture system exhibited a limit of detection (LOD) of 0.05 nM, offering higher sensitivity than competitive assay with an LOD of 0.75 nM, whereas using phage-VH and separately prepared MBP-VL exhibited an LOD of 0.15 nM and a broader detection range of 0.15–500 nM than competitive ELISA, whose detection range was 0.75–10 nM.Discussion: The combination of the two OS assays achieved high sensitivity and a broad detection range for CGRP, which may have significance in clinical applications.

A wheel flat is a typical wheel defect that significantly impacts the wheel–rail system, posing substantial challenges to vehicle operation safety. In the existing literature, the wheel flat plane model does not account for the contribution of the width direction to the impact response and thus cannot accurately reveal the wheel–rail contact state with a flat. This paper systematically proposes a three-dimensional analytical model that considers multiple worn stages and constructs a spatial complex surface reconstruction model for flats based on NURBS technology. A vehicle–track coupled dynamics model, considering the geometry of the flat, is established to investigate the effects of flat geometry on the wheel–rail impact response and contact relationship in detail. The results show that in the subcritical regime, the wear degree of the flat predominantly affects the impact force, while in the transcritical regime, both the wear degree and velocity together determine the magnitude of the wheel–rail impact force. As the wear degree increases, the moment of wheel lateral jump occurs earlier. The spatial modeling method for flats proposed in this paper offers a novel technical approach for accurately simulating the dynamic behavior of wheel–rail contact when a flat is present.

A group of low-cost Fe40Ni(50-x)Cr10Mox high-entropy alloys (HEAs) without the Co element were proposed. The microstructure and properties were investigated. With the increase of Mo content, the microstructure changes from FCC single-phase to two-phase divorced eutectic, divorced eutectic + hypoeutectic, hypoeutectic, eutectic and hypereutectic, in which the two-phase is composed of FCC phase + σ phase. The yield strength of alloy increases with the increase of Mo content. Its strengthening mechanism transforms from solution strengthening to second phase strengthening and grain boundary strengthening. Mo-20 and Mo-22.5 have comprehensive mechanical properties, and their yield strength, fracture strength and plastic strain are 1103.1 MPa and 1127.9 MPa, 1924.5 MPa and 1881.7 MPa, 30.4% and 28.7% respectively. In addition, the revised equivalent Cr content (ECC) is proposed in the FeNiCrMo HEAs, ECC = at.%[Cr]+8/3 × at.%[Mo]. The eutectic composition can be precisely designed through the revised ECC and atomic content ratio (RI/F) of the intermetallic forming elements to the FCC forming elements in FeNiCrMo HEAs system. When 20 at.%≤Fe and Ni ≤ 40 at.%, and RI/F = ECC/[x × at.%Fe + y × at.%Ni] = 1.05 ± 0.3, FexNiyCrmMon is the eutectic high-entropy alloy.

Abstract Point cloud matching is an important procedure in a variety of computer vision tasks. Traditional point cloud matching methods have made great progress, while neural network‐based approaches are becoming a trend, powered by their strong capabilities of feature extraction. Existing point matching neural networks, however, mainly focus on the rigid transformation. More complex transformations should also be considered in many scenarios. In this regard, the authors extend the rigid registration to non‐rigid cases and propose a network called the Scale Robust Point Matching (SRPM)‐Net for scale point matching. This robust structure‐preserving network is implemented by incorporating Lie group parametrisation. It is conducted by Lie group linearisation representation with the constraints of parameters under the corresponding basis of Lie algebra. SRPM‐Net preserves the structure of the solution and avoids degeneration. The contributions of this paper lie in two aspects: Most importantly, SRPM‐Net provides an extendable framework for handling complicated transformations. Secondly, it introduces a new feature learning module, which better preserves the shape structure by aggregating the high‐dimensional feature and calculating the normal vector of point cloud surface automatically. Experimental results show that SRPM‐Net is more robust and accurate than existing traditional and recent deep learning methods under various situations.

IntroductionThis study aims to explore the risk factors associated with suicidal behavior and establish predictive models in female patients with mood disorders, specifically using a nomogram of the least absolute shrinkage and selection operator (LASSO) regression.MethodsA cross-sectional survey was conducted among 396 female individuals diagnosed with mood disorders (F30-F39) according to the International Classification of Diseases and Related Health Problems 10th Revision (ICD-10). The study utilized the Chi-Squared Test, t-test, and the Wilcoxon Rank-Sum Test to assess differences in demographic information and clinical characteristics between the two groups. Logistic LASSO Regression Analyses were utilized to identify the risk factors associated with suicidal behavior. A nomogram was constructed to develop a prediction model. The accuracy of the prediction model was evaluated using a Receiver Operating Characteristic (ROC) curve.ResultThe LASSO regression analysis showed that psychotic symptoms at first-episode (β = 0.27), social dysfunction (β = 1.82), and somatic disease (β = 1.03) increased the risk of suicidal behavior. Conversely, BMI (β = −0.03), age of onset (β = −0.02), polarity at onset (β = −1.21), and number of hospitalizations (β = −0.18) decreased the risk of suicidal behavior. The area under ROC curve (AUC) of the nomogram predicting SB was 0.778 (95%CI: 0.730–0.827, p

Abstract Background Melon is a very important horticultural crop produced worldwide with high phenotypic diversity. Fruit size is among the most important domestication and differentiation traits in melon. The molecular mechanisms of fruit size in melon are largely unknown. Results Two high-density genetic maps were constructed by whole-genome resequencing with two F2 segregating populations (WAP and MAP) derived from two crosses (cultivated agrestis × wild agrestis and cultivated melo × cultivated agrestis). We obtained 1,871,671 and 1,976,589 high quality SNPs that show differences between parents in WAP and MAP. A total of 5138 and 5839 recombination events generated 954 bins in WAP and 1027 bins in MAP with the average size of 321.3 Kb and 301.4 Kb respectively. All bins were mapped onto 12 linkage groups in WAP and MAP. The total lengths of two linkage maps were 904.4 cM (WAP) and 874.5 cM (MAP), covering 86.6% and 87.4% of the melon genome. Two loci for fruit size were identified on chromosome 11 in WAP and chromosome 5 in MAP, respectively. An auxin response factor and a YABBY transcription factor were inferred to be the candidate genes for both loci. Conclusion The high-resolution genetic maps and QTLs analyses for fruit size described here will provide a better understanding the genetic basis of domestication and differentiation, and provide a valuable tool for map-based cloning and molecular marker assisted breeding.

Abstract Phytophthora blight is one of the most serious diseases affecting melon (Cucumis melo) production. Due to the lack of highly resistant germplasms, the progress on disease-resistant research is slow. To understand the genetics of melon resistance to Phytophthora capsici, an F2 population containing 498 individuals was developed by crossing susceptible line E31 to highly resistant line ZQK9. Genetic analysis indicated that the resistance in ZQK9 was controlled by a dominant gene, tentatively named MePhyto. Through bulked-segregant analysis (BSA-Seq) and chromosome walking techniques, the MePhyto gene was mapped to a 52.44 kb interval on chromosome 12. In this region, there were eight genes and their expression patterns were validated by qRT-PCR. Among them, one wall-associated receptor kinase (WAK) gene MELO3C002430 was significantly induced in ZQK9 after P. capsici inoculation, but not in E31. Based on the non-synonymous mutation site in MELO3C002430, a cleaved amplified polymorphic sequence (CAPS) marker, CAPS2430, was developed and this maker was co-segregated with MePhyto in both F2 population and a collection of 36 melon accessions. Thus MELO3C002430 was considered as the candidate gene and CAPS2430 was a promising marker for marker-assisted selection (MAS) in breeding. These results lay a foundation for revealing the resistance mechanism of melon to P. capsici.

Objectives: A multidrug-resistant Morganella morganii strain (CQ-M7), isolated from the kidney of a diseased Chinese giant salamander in China, was examined with whole genome sequencing to better understand drug tolerance and its pathogenicity. Methods: The draft genome of the investigated strain was assembled using HGA assembler and annotated using Rapid Annotations Subsystems Technology (RAST) server. The contigs were annotated by the appropriate bioinformatics tools available on the National Center for Biotechnology Information (NCBI) website. Antibiotic resistance genes were detected by PCR. Pathogenicity of the isolate was performed on 30 healthy Chinese giant salamanders with different infection dosages. Results: The CQ-M7 strain showed resistance to multiple antimicrobials, especially to aminoglycoside and β-lactam antibiotics. Seventeen drug-resistance genes were detected, which were related to β-lactams, aminoglycosides, fluoroquinolones, tetracyclines, peptide antibiotic, and fosfomycin resistance. Sequence analysis showed the assembled genome size to be 4 966 326 bp with 51.16% of GC content, containing 4587 protein-coding genes, 71 pseudogenes, five rRNAs, 80 tRNAs, and five noncoding RNAs. The genome sequence was deposited in GenBank under accession number RQIJ00000000. Artificial infection results indicated that the CQ-M7 strain was a low-virulence strain for the Chinese giant salamander. Conclusion: It is believed that this is the first draft genome of Chinese giant salamander original Morganella morganii strain harbouring multiple antibiotic resistance genes in China. The reported genome sequence could provide insights into antibiotic resistance mechanisms and control strategies of Morganella morganii.

Logic locking is a technique that can help hinder reverse-engineering-based attacks in the IC supply chain from untrusted foundries or end-users. In 2015, the Boolean Satisfiability (SAT) attack was introduced. Although the SAT attack is effective in deobfuscating a wide range of logic locking schemes, its execution time varies widely from a few seconds to months. Previous research has shown that Graph Convolutional Networks (GCN) may be used to estimate this deobfuscation time for locked circuits with varied key sizes. In this paper, we explore whether GCN models truly understand/capture the structural/functional sources of deobfuscation hardness. In order to tackle this, we generate different curated training datasets: traditional ISCAS benchmark circuits locked with varying key sizes, as well as an important novel class of synthetic benchmarks: Substitution-Permutation Networks (SPN), which are circuit structures used to produce the most secure and efficient keyed-functions used today: block-ciphers. We then test whether a GCN trained on a traditional benchmark can predict the simple fact that a deeper SPN is superior to a wide SPN of the same size. We find that surprisingly the GCN model fails at this. We propose to overcome this limitation by proposing a set of circuit features motivated by block-cipher design principles. These features can be used as stand-alone or combined with GCN models to provide deeper topological cues than what GCNs can access.

Melon is an important horticultural crop worldwide. The high diversity of melon makes it a model plant for various different properties. Some quantitative trait loci or candidates have been discovered, but few were verified as limiting genetic transformation and genome editing systems. Identifying new genetic resources with resistance and special fruit quality traits is imperative to develop effective and useful breeding technologies in melon. This review describes the advances in genetics, genomics, and the breeding of melon and puts forward some recommendations in these areas.

Key-based circuit obfuscation or logic-locking is a technique that can be used to hide the full design of an integrated circuit from an untrusted foundry or end-user. The technique is based on creating ambiguity in the original circuit by inserting “key” input bits into the circuit such that the circuit is unintelligible absent a correct secret key. Clock signals have traditionally been avoided in locking in order to not corrupt the timing behavior of the locked circuit. In this paper, we explore the case where the clock signal itself may be obfuscated by ambiguating its frequency or pattern. Along with discussing formal notions of security in this context, we present practical ways to deobfuscate such designs using techniques from multi-rate model-checking. We present experimental data on deobfuscation runtime on a set of sequential benchmark circuits. Our results show that naive random clock obfuscation may not provide more security per overhead than traditional random keyed-gate insertion. We discuss how clock obfuscation may be a more attractive choice for special circuit designs that are inherently multi-clock/asynchronous.

Phytophthora blight caused by Phytophthora capsici is a devastating disease for melon plant. However, the underlying resistance mechanisms are still poorly understood. In this study, the transcriptome differences between the resistant ZQK9 and susceptible E31 at 0, 3, and 5 days post-inoculation (dpi) were identified by RNA-seq. A total of 1,195 and 6,595 differentially expressed genes (DEGs) were identified in ZQK9 and E31, respectively. P. capsici infection triggered massive transcript changes in the inoculated tissues. Genes related to plant defense responses were activated, which was reflected by a lot of up-regulated DEGs involved in pathogenesis-related (PR) genes, hormones biosynthesis and signal transduction, secondary metabolites biosynthesis and cell wall modification in resistant ZQK9. The dataset generated in this study may provide a basis for identifying candidate resistant genes in melon against P. capsici and lay a foundation for further research on the molecular mechanisms.

The numerical simulation of ship flows has evolved into a highly practical approach in naval architecture. In typical virtual towing tanks, the principle of Galilean relativity is invoked to maintain the ship as fixed, while the surrounding water is prescribed to flow past it. This assumption may be identified, at least partly, as being responsible for the wide-scale adoption of computational solutions within practitioners’ toolkits. However, it carries several assumptions, such as the levels of inlet turbulence and their effect on flow properties. This study presents an alternative virtual towing tank, where the ship is simulated to advance over a stationary fluid. To supplement the present work, the free surface disturbance is processed into Fourier space to determine the Kelvin half-angle for an example case. The results suggest that it is possible to construct a fully unsteady virtual towing tank using the overset method, without relying on Galilean relativity. Differences between theoretical and numerical predictions for the Kelvin half-angle are predominantly attributed to the assumptions used by the theoretical method. The methods presented in this work can potentially be used to validate free-surface flows, even when one does not have access to experimental wave elevation data.

Shewanella xiamenensis, a newly virulent zoonotic pathogen belonging to the genus Shewanella is the causative organism of emerging intra-abdominal infection, acute skin ulceration, rotten limbs and ascites in humans and animals. The global spread of S. xiamenensis entails severe economic impact. However, it was rarely reported as a cause of infection and no reports were found that S. xiamenensis isolated from clinical samples. The isolate was identified as a S. xiamenensis strain by 16S rDNA amplification and DNA sequencing identification method. Even if co-infection by other bacteria could not be ruled out, this is the first report of acute disease caused by S. xiamenensis in the Chinese giant salamander in China. By using the Kirby-Bauer disc diffusion method, the sensitivity of the isolate to clinical antibiotics was evaluated. Antibiotic susceptibility test indicated that the isolate was resistant to 32 antibacterial drugs such as kanamycin, florfenicol and ceftriaxone suggesting that the isolate was a multi-drug resistant strain. [ABSTRACT FROM AUTHOR]

The gosling gout, a newly emerged disease, has widely broken out in China since 2017. Typical signs for the disease include diarrhea, anorexia, depression, dehydration, emaciation and paralysis. At autopsy, uratosis was the main pathological change which could be found at kidney, pericardium, air sac, muscle and leg joint. In this study, gosling gout was firstly diagnosed by metagenomic analysis. Samples of kidney, Fabricius bursa, spleen and jejunum were collected and submitted to next-generation DNA sequencing. Our results demonstrated that goose avastrovirus was highly related with this disease. We confirmed the sequencing results by reverse transcription polymerase chain reaction method and artificial infection experiment and got consistent results. In summary, metagenomic sequencing method combined with traditional molecular identification was applied toward diagnosis of a novel gosling gout disease in China and revealed that goose avastrovirus was highly related with this disease. It has been proved to be a powerful tool for rapid and sensitive diagnosis of animal diseases, especially for some exceptional pathogens. In addition, host range, variation, molecular pathogenesis and potential zoonotic infection of this novel goose astrovirus need to be further studied. [ABSTRACT FROM AUTHOR]