Multimodality quantitative ultrasound envelope statistics imaging based support vector machines for characterizing tissue scatterer distribution patterns: Methods and application in detecting microwave-induced thermal lesions.

Ultrasound envelope statistics imaging, including ultrasound Nakagami imaging, homodyned-K imaging, and information entropy imaging, is an important group of quantitative ultrasound techniques for characterizing tissue scatterer distribution patterns, such as scatterer concentrations and arrangements. In this study, we proposed a machine learning approach to integrate the strength of multimodality quantitative ultrasound envelope statistics imaging techniques and applied it to detecting microwave ablation induced thermal lesions in porcine liver ex vivo. The quantitative ultrasound parameters included were homodyned-K α which is a scatterer clustering parameter related to the effective scatterer number per resolution cell, Nakagami m which is a shape parameter of the envelope probability density function, and Shannon entropy which is a measure of signal uncertainty or complexity. Specifically, the homodyned-K log10(α), Nakagami-m, and horizontally normalized Shannon entropy parameters were combined as input features to train a support vector machine (SVM) model to classify thermal lesions with higher scatterer concentrations from normal tissues with lower scatterer concentrations. Through heterogeneous phantom simulations based on Field II, the proposed SVM model showed a classification accuracy above 0.90; the area accuracy and Dice score of higher-scatterer-concentration zone identification exceeded 83% and 0.86, respectively, with the Hausdorff distance <26. Microwave ablation experiments of porcine liver ex vivo at 60-80 W, 1-3 min showed that the SVM model achieved a classification accuracy of 0.85; compared with single log10(α),m, or hNSE parametric imaging, the SVM model achieved the highest area accuracy (89.1%) and Dice score (0.77) as well as the smallest Hausdorff distance (46.38) of coagulation zone identification. We concluded that the proposed multimodality quantitative ultrasound envelope statistics imaging based SVM approach can enhance the capability to characterize tissue scatterer distribution patterns and has the potential to detect the thermal lesions induced by microwave ablation.

Ultrasound k-nearest neighbor entropy imaging: Theory, algorithm, and applications.

Ultrasound information entropy is a flexible approach for analyzing ultrasound backscattering. Shannon entropy imaging based on probability distribution histograms (PDHs) has been implemented as a promising method for tissue characterization and diagnosis. However, the bin number affects the stability of entropy estimation. In this study, we introduced the k-nearest neighbor (KNN) algorithm to estimate entropy values and proposed ultrasound KNN entropy imaging. The proposed KNN estimator leveraged the Euclidean distance between data samples, rather than the histogram bins by conventional PDH estimators. We also proposed cumulative relative entropy (CRE) imaging to analyze time-series radiofrequency signals and applied it to monitor thermal lesions induced by microwave ablation (MWA). Computer simulation phantom experiments were conducted to validate and compare the performance of the proposed KNN entropy imaging, the conventional PDH entropy imaging, and Nakagami-m parametric imaging in detecting the variations of scatterer densities and visualizing inclusions. Clinical data of breast lesions were analyzed, and porcine liver MWA experiments ex vivo were conducted to validate the performance of KNN entropy imaging in classifying benign and malignant breast tumors and monitoring thermal lesions, respectively. Compared with PDH, the entropy estimation based on KNN was less affected by the tuning parameters. KNN entropy imaging was more sensitive to changes in scatterer densities and performed better visualizable capability than typical Shannon entropy (TSE) and Nakagami-m parametric imaging. Among different imaging methods, KNN-based Shannon entropy (KSE) imaging achieved the higher accuracy in classification of benign and malignant breast tumors and KNN-based CRE imaging had larger lesion-to-normal contrast when monitoring the ablated areas during MWA at different powers and treatment durations. Ultrasound KNN entropy imaging is a potential quantitative ultrasound approach for tissue characterization.

Identification of Multiple Genetic Loci Related to Low-Temperature Tolerance during Germination in Maize (Zea maize L.) through a Genome-Wide Association Study.

Low-temperature stress during the germination stage is an important abiotic stress that affects the growth and development of northern spring maize and seriously restricts maize yield and quality. Although some quantitative trait locis (QTLs) related to low-temperature tolerance in maize have been detected, only a few can be commonly detected, and the QTL intervals are large, indicating that low-temperature tolerance is a complex trait that requires more in-depth research. In this study, 296 excellent inbred lines from domestic and foreign origins (America and Europe) were used as the study materials, and a low-coverage resequencing method was employed for genome sequencing. Five phenotypic traits related to low-temperature tolerance were used to assess the genetic diversity of maize through a genome-wide association study (GWAS). A total of 14 SNPs significantly associated with low-temperature tolerance were detected (-log10(P) > 4), and an SNP consistently linked to low-temperature tolerance in the field and indoors during germination was utilized as a marker. This SNP, 14,070, was located on chromosome 5 at position 2,205,723, which explained 4.84-9.68% of the phenotypic variation. The aim of this study was to enrich the genetic theory of low-temperature tolerance in maize and provide support for the innovation of low-temperature tolerance resources and the breeding of new varieties.

Novel variants in the CLCN4 gene associated with syndromic X-linked intellectual disability.

Objective: The dysfunction of the CLCN4 gene can lead to X-linked intellectual disability and Raynaud-Claes syndrome (MRXSRC), characterized by severe cognitive impairment and mental disorders. This study aimed to investigate the genetic defects and clinical features of Chinese children with CLCN4 variants and explore the effect of mutant ClC-4 on the protein expression level and subcellular localization through in vitro experiments. Methods: A total of 401 children with intellectual disabilities were screened for genetic variability using whole-exome sequencing (WES). Clinical data, including age, sex, perinatal conditions, and environmental exposure, were collected. Cognitive, verbal, motor, and social behavioral abilities were evaluated. Candidate variants were verified using Sanger sequencing, and their pathogenicity and conservation were analyzed using in silico prediction tools. Protein expression and localization of mutant ClC-4 were measured using Western blotting (WB) and immunofluorescence microscopy. The impact of a splice site variant was assessed with a minigene assay. Results: Exome analysis identified five rare CLCN4 variants in six unrelated patients with intellectual disabilities, including two recurrent heterozygous de novo missense variants (p.D89N and p.A555V) in three female patients, and two hemizygous missense variants (p.N141S and p.R694Q) and a splicing variant (c.1390-12T > G) that are maternally inherited in three male patients. The p.N141S variant and the splicing variant c.1390-12(T > G were novel, while p.R694Q was identified in two asymptomatic heterozygous female patients. The six children with CLCN4 variants exhibited a neurodevelopmental spectrum disease characterized by intellectual disability (ID), delayed speech, autism spectrum disorders (ASD), microcephaly, hypertonia, and abnormal imaging findings. The minigene splicing result indicated that the c.1390-12T > G did not affect the splicing of CLCN4 mRNA. In vitro experiments showed that the mutant protein level and localization of mutant protein are similar to the wild type. Conclusion: The study identified six probands with CLCN4 gene variants associated with X-linked ID. It expanded the gene and phenotype spectrum of CLCN4 variants. The bioinformatic analysis supported the pathogenicity of CLCN4 variants. However, these CLCN4 gene variants did not affect the ClC-4 expression levels and protein location, consistent with previous studies. Further investigations are necessary to investigate the pathogenetic mechanism.

WPConvNet: An Interpretable Wavelet Packet Kernel-Constrained Convolutional Network for Noise-Robust Fault Diagnosis.

Deep learning (DL) has present great diagnostic results in fault diagnosis field. However, the poor interpretability and noise robustness of DL-based methods are still the main factors limiting their wide application in industry. To address these issues, an interpretable wavelet packet kernel-constrained convolutional network (WPConvNet) is proposed for noise-robust fault diagnosis, which combines the feature extraction ability of wavelet bases and the learning ability of convolutional kernels together. First, the wavelet packet convolutional (WPConv) layer is proposed, and constraints are imposed to convolutional kernels, so that each convolution layer is a learnable discrete wavelet transform. Second, a soft threshold activation is proposed to reduce the noise component in feature maps, whose threshold is adaptively learned by estimating the standard deviation of noise. Third, we link the cascaded convolutional structure of convolutional neutral network (CNN) with wavelet packet decomposition and reconstruction using Mallat algorithm, which is interpretable in model architecture. Extensive experiments are carried out on two bearing fault datasets, and the results show that the proposed architecture outperforms other diagnosis models in terms of interpretability and noise robustness.

Transcranial Alternating Current Stimulation Improves Memory Function in Alzheimer's Mice by Ameliorating Abnormal Gamma Oscillation.

Transcranial alternating current stimulation (tACS) is considered to have a positive effect on the rehabilitation of Alzheimer's disease (AD) as an intervention method that matches stimulation frequency to neurogenesis frequency. However, when tACS intervention is delivered to a single target, the current received by brain regions outside the target may be insufficient to trigger neural activity, compromising the effectiveness of stimulation. Therefore, it is worth studying how single-target tACS restores gamma-band activity in the whole hippocampal-prefrontal circuit during rehabilitation. We used Sim4Life software to conduct finite element methods (FEM) on the stimulation parameters to ensure that tACS intervened only in the right hippocampus (rHPC) and did not activate the left hippocampus (lHPC) or prefrontal cortex (PFC). We stimulated the rHPC by tACS for 21 days to improve the memory function of AD mice. We simultaneously recorded local field potentials (LFPs) in the rHP, lHPC and PFC and evaluated the neural rehabilitative effect of tACS stimulation with power spectral density (PSD), cross-frequency coupling (CFC) and Granger causality. Compared to the untreated group, the tACS group exhibited an increase in the Granger causality connection and CFC between the rHPC and PFC, a decrease in those between the lHPC and PFC, and enhanced performance on the Y-maze test. These results suggest that tACS may serve as a noninvasive method for Alzheimer's disease rehabilitation by ameliorating abnormal gamma oscillation in the hippocampal-prefrontal circuit.

Ultrasound Homodyned-K Contrast-Weighted Summation Parametric Imaging Based on H-scan for Detecting Microwave Ablation Zones.

The homodyned-K (HK) distribution is a generalized model of envelope statistics whose parameters α (the clustering parameter) and k (the coherent-to-diffuse signal ratio) can be used to monitor the thermal lesions. In this study, we proposed an ultrasound HK contrast-weighted summation (CWS) parametric imaging algorithm based on the H-scan technique and investigated the optimal window side length (WSL) of the HK parameters estimated by the XU estimator (an estimation method based on the first moment of the intensity and two log-moments, which was used in the proposed algorithm) through phantom simulations. H-scan diversified ultrasonic backscattered signals into low- and high-frequency passbands. After envelope detection and HK parameter estimation for each frequency band, the α and k parametric maps were obtained, respectively. According to the contrast between the target region and background, the (α or k) parametric maps of the dual-frequency band were weighted and summed, and then the CWS images were yielded by pseudo-color imaging. The proposed HK CWS parametric imaging algorithm was used to detect the microwave ablation coagulation zones of porcine liver ex vivo under different powers and treatment durations. The performance of the proposed algorithm was compared with that of the conventional HK parametric imaging and frequency diversity and compounding Nakagami imaging algorithms. For two-dimensional HK parametric imaging, it was found that a WSL equal to 4 pulse lengths of the transducer was sufficient for estimating the α and k parameters in terms of both parameter estimation stability and parametric imaging resolution. The HK CWS parametric imaging provided an improved contrast-to-noise ratio over conventional HK parametric imaging, and the HK αcws parametric imaging achieved the best accuracy and Dice score of coagulation zone detection.

ZmG6PDH1 in glucose-6-phosphate dehydrogenase family enhances cold stress tolerance in maize.

Glucose-6-phosphate dehydrogenase (G6PDH) is a key enzyme in the pentose phosphate pathway responsible for the generation of nicotinamide adenine dinucleotide phosphate (NADPH), thereby playing a central role in facilitating cellular responses to stress and maintaining redox homeostasis. This study aimed to characterize five G6PDH gene family members in maize. The classification of these ZmG6PDHs into plastidic and cytosolic isoforms was enabled by phylogenetic and transit peptide predictive analyses and confirmed by subcellular localization imaging analyses using maize mesophyll protoplasts. These ZmG6PDH genes exhibited distinctive expression patterns across tissues and developmental stages. Exposure to stressors, including cold, osmotic stress, salinity, and alkaline conditions, also significantly affected the expression and activity of the ZmG6PDHs, with particularly high expression of a cytosolic isoform (ZmG6PDH1) in response to cold stress and closely correlated with G6PDH enzymatic activity, suggesting that it may play a central role in shaping responses to cold conditions. CRISPR/Cas9-mediated knockout of ZmG6PDH1 on the B73 background led to enhanced cold stress sensitivity. Significant changes in the redox status of the NADPH, ascorbic acid (ASA), and glutathione (GSH) pools were observed after exposure of the zmg6pdh1 mutants to cold stress, with this disrupted redox balance contributing to increased production of reactive oxygen species and resultant cellular damage and death. Overall, these results highlight the importance of cytosolic ZmG6PDH1 in supporting maize resistance to cold stress, at least in part by producing NADPH that can be used by the ASA-GSH cycle to mitigate cold-induced oxidative damage.

Long-term gamma transcranial alternating current stimulation improves the memory function of mice with Alzheimer's disease.

Background: The main manifestation of Alzheimer's disease (AD) in patients and animal models is impaired memory function, characterized by amyloid-beta (Aß) deposition and impairment of gamma oscillations that play an important role in perception and cognitive function. The therapeutic effect of gamma band stimulation in AD mouse models has been reported recently. Transcranial alternating current stimulation (tACS) is an emerging non-invasive intervention method, but at present, researchers have not completely understood the intervention effect of tACS. Thus, the intervention mechanism of tACS has not been fully elucidated, and the course of treatment in clinical selection also lacks theoretical support. Based on this issue, we investigated the effect of gamma frequency (40 Hz) tACS at different durations in a mouse model of AD. Materials and methods: We placed stimulating electrodes on the skull surface of APP/PS1 and wild-type control mice (n = 30 and n = 5, respectively). Among them, 20 APP/PS1 mice were divided into 4 groups to receive 20 min 40 Hz tACS every day for 1-4 weeks. The other 10 APP/PS1 mice were equally divided into two groups to receive sham treatment and no treatment. No intervention was performed in the wild-type control mice. The short-term memory function of the mice was examined by the Y maze. Aß levels and microglia in the hippocampus were measured by immunofluorescence. Spontaneous electroencephalogram gamma power was calculated by the average period method, and brain connectivity was examined by cross-frequency coupling. Results: We found that the long-term treatment groups (21 and 28 days) had decreased hippocampal Aß levels, increased electroencephalogram spontaneous gamma power, and ultimately improved short-term memory function. The treatment effect of the short-term treatment group (7 days) was not significant. Moreover, the treatment effect of the 14-day treatment group was weaker than that of the 21-day treatment group. Conclusion: These results suggest that long-term gamma-frequency tACS is more effective in treating AD by reducing Aß load and improving gamma oscillation than short-term gamma-frequency tACS.

A Review of Quantitative Ultrasound-Based Approaches to Thermometry and Ablation Zone Identification Over the Past Decade.

Percutaneous thermal therapy is an important clinical treatment method for some solid tumors. It is critical to use effective image visualization techniques to monitor the therapy process in real time because precise control of the therapeutic zone directly affects the prognosis of tumor treatment. Ultrasound is used in thermal therapy monitoring because of its real-time, non-invasive, non-ionizing radiation, and low-cost characteristics. This paper presents a review of nine quantitative ultrasound-based methods for thermal therapy monitoring and their advances over the last decade since 2011. These methods were analyzed and compared with respect to two applications: ultrasonic thermometry and ablation zone identification. The advantages and limitations of these methods were compared and discussed, and future developments were suggested.

Inheritance and Linkage of Virulence Genes of Puccinia striiformis f. sp. hordei.

Puccinia striiformis f. sp. hordei (Psh) causing barley stripe rust has only recently been known to be heteroecious, for which reason the inheritance of its virulence has not been analyzed. Herein, we selfed a Psh isolate, XZ-19-972, on Berberis aggregata and obtained 53 progenies. The virulence phenotypes (VPs) for these progenies were identified on 11 barley differentials, and their genotypes were assessed with 22 Kompetitive allele specific PCR-single nucleotide polymorphism (KASP-SNP) markers. In total, 18 VPs were detected among progenies, 17 (VP2-VP18) of which, corresponding to 43 isolates, were different from the parental isolate showing VP1. Of the 53 progenies, 8 exhibited increased virulence and 34 decreased virulence. One progeny, belonging to VP18, showed a different virulence formula but without a virulence increase or decrease. The parental isolate and all progenies were avirulent to yrc6 but virulent to yrc7. The parental isolate was heterozygous in terms of avirulence/virulence to nine barley resistance gene loci. KASP-SNP marker analysis identified 36 multilocus genotypes, based on which a linkage map was constructed, with total genetic distance intervals of 516.07 cM, spanning 16 avirulence or virulence loci. Taken together, our results provide important insights into the inheritance and virulence diversity of Psh.

An in situ grown amorphous ZrO2 layer on zeolite for enhanced phosphate adsorption.

Zeolite supported amorphous metal oxide nanolayers with high specific surface area, abundant adsorption sites, and excellent reusability hold a bright prospect in the efficient removal of contaminants, yet it is proven to be still challenging to precisely regulate and control their synthesis. Herein, we reported a facile synthetic strategy for rational design and achieving the uniform and firm in situ growth of an amorphous ZrO2 layer decorated on the surface of zeolite (ZEO@AZ) for enhanced phosphate adsorption. The Langmuir isotherm model and pseudo-second order kinetic equation well described the adsorption process towards phosphate solution, and the synthetized ZEO@AZ exhibited an excellent maximum adsorption amount of 24.98 mgP g-1. Furthermore, the adsorption of phosphates on ZEO@AZ was confirmed to be chemisorption, endothermic and spontaneous. This approach for fabricating amorphous metal oxide nanolayers on a robust matrix may provide a new route for constructing composites with superb phosphate adsorption performance.

Virtual Reality-Based Sensory Stimulation for Pediatric Disorders of Consciousness: A Pilot Study.

Objective: The purpose of this study was to determine whether virtual reality-based sensory stimulation has the ability to improve the level of consciousness in pediatric disorders of consciousness compared with general rehabilitation. Methods: Thirty subjects were divided into a virtual reality (VR) group (n = 15) and a control group (n = 15). Subjects in the VR group received both general rehabilitation and exposure to VR videos; the control group received only general rehabilitation. The Glasgow Coma Scale (GCS), Coma Recovery Scale-Revised (CRS-R), and amplitude-integrated electroencephalogram (EEG) (aEEG) were used to measure the clinical behavioral response and neuroelectrophysiology before and after the treatment. The Glasgow Outcome Scale Extended Pediatric Revised (GOS-E Peds) was used to measure the social and personal functional ability after 3 months. Results: After 2 weeks of treatment, the CRS-R and GCS improved in both groups. However, the VR group had better results than the control group in the CRS-R (p = 0.003) and GCS (p = 0.045). There were no significant differences on aEEG in the two groups after treatment. According to the GOS-E Peds, the improvement of social and personal functional ability had no significant differences in the two groups. Additionally, there were no obvious adverse reactions in the two group during the treatment. Conclusions: This pilot study indicates potential benefit from the addition of VR to standard rehabilitation in pediatric disorders of consciousness. To further explore the efficacy of VR, a large-sample randomized controlled trial is warranted.

Understanding different cultural ecosystem services: An exploration of rural landscape preferences based on geographic and social media data.

Rural landscapes offer a variety of cultural ecosystem services (CESs). However, the relationship between rural landscape characteristics and different CESs is still poorly understood. Therefore, this study explored the rural areas of Huzhou city, China, as a case study to assess the main rural landscape characteristics of different CESs based on public preferences. First, 148 scenic spots were classified into four CESs (physical, experiential, intellectual and inspirational), and the public preferences for each scenic spot were determined by combining tourists' scores obtained from social media and government assessment scores. Then, the landscape characteristic indicators were constructed from the natural, infrastructural and sensory perspectives by combining geographic and social media data. Finally, the random forest model was used to evaluate the public preferences for rural landscape characteristics overall and for different CESs. The word frequency analysis showed that, in addition to the nature landscape, infrastructure and service had a strong influence on public preferences. The relationship with rural landscape characteristics varied across different CESs. For physical CESs, the convenience of infrastructure played a greater role than natural landscape characteristics. Experiential CESs, on the other hand, were affected by natural landscape characteristics themselves. Intellectual CESs had higher requirements for both infrastructure and nature. Inspirational CESs included sensory evaluation indicators, in addition to their focus on natural landscape characteristics and infrastructure, indicating that this category of CESs was more concerned with inner experience. The use of social media data has enriched the dimensions of sensory elements and provided new ideas and information supplements for comprehensively understanding different CESs, thus better supporting the management, planning and protection of rural landscapes.

Detection of microwave ablation coagulation areas using ultrasound Nakagami imaging based on Gaussian pyramid decomposition: A feasibility study.

In this paper, we explored the feasibility of using ultrasound Nakagami-m parametric imaging based on Gaussian pyramid decomposition (GPD) to detect microwave ablation coagulation areas. Monte Carlo simulation and phantom simulation results demonstrated that a 2-layer GPD model was sufficient to achieve the same m parameter estimation accuracy, smoothness and resolution as 3-layer and 4-layer. The performances of GPD, moment-based estimator (MBE) and window-modulated compounding (WMC) algorithms were compared in terms of parameter estimation, smoothness, resolution and contrast-to-noise (CNR). Results showed that the m parameter estimation obtained by GPD algorithm was better than that of MBE and WMC algorithms except the small window size (27 × 5). When using a window size of >3 pulse lengths, GPD algorithm could achieve better smoothness and CNR than MBE and WMC algorithms, but there was a certain loss of axial resolution. The computation time of GPD algorithm was less than that of WMC algorithm, while about 2.24 times that of MBE algorithm. Experimental results of porcine liver microwave ablation ex vivo (n = 20) illustrated that the average areas under the operating characteristic curve (AUCs) of Nakagami mGPD, mMBE and mWMC parametric imaging and homodyned-K (HK) α and k parametric imaging to detect coagulation areas were significantly improved by polynomial approximation (PAX). Kruskal-Wallis test showed that the accuracy of coagulation area detection obtained by PAX imaging of mGPD parameter had no significant difference with that of mMBE, mWMC, HK_α and HK_k parameters. This preliminary study suggested that Nakagami imaging based on GPD algorithm may have the potential to detect microwave ablation coagulation areas.

Understanding Consumers' Food Waste Reduction Behavior-A Study Based on Extended Norm Activation Theory.

Based on norm activation theory, a research framework was built to explore the food waste reduction behavior when consumers eat out. The framework included behavior intentions and four psychological factors: awareness of consequence (persons understanding that actions have consequences), ascription of responsibility (duty to respond), self-efficacy (belief in own skills and capacity), personal norm (individuals' values to act by socially accepted rules and reduce food waste as a code of conduct and moral obligation). A total of 514 samples from different regions of China were collected through an online survey platform, and the research framework was tested by applying structural equation modeling (SEM). This study found that ascription of responsibility and self-efficacy can effectively activate personal norm to reduce food waste. Personal norm and self-efficacy had a significant positive effect on behavior intentions to reduce food waste. Specifically, self-efficacy had the greatest effect on personal norm, followed by ascription of responsibility, and on behavior intentions to reduce food waste, followed by personal norm. Interestingly, while ascription of responsibility and self-efficacy had an impact on personal norm, awareness of consequence did not significantly influence personal norm to reduce food waste, suggesting that emotional factors are more likely to trigger personal norms that motivate consumers to take action to reduce food waste than cognitive factors. Based on the findings, several suggestions are provided for more effective interventions by restaurants to promote food waste reduction behavior, such as information intervention strategies, displaying information related to food consumption, and reducing the size of plates for some meals.

Metabolic Insight into Cold Stress Response in Two Contrasting Maize Lines.

Maize (Zea mays L.) is sensitive to a minor decrease in temperature at early growth stages, resulting in deteriorated growth at later stages. Although there are significant variations in maize germplasm in response to cold stress, the metabolic responses as stress tolerance mechanisms are largely unknown. Therefore, this study aimed at providing insight into the metabolic responses under cold stress at the early growth stages of maize. Two inbred lines, tolerant (B144) and susceptible (Q319), were subjected to cold stress at the seedling stage, and their corresponding metabolic profiles were explored. The study identified differentially accumulated metabolites in both cultivars in response to induced cold stress with nine core conserved cold-responsive metabolites. Guanosine 3',5'-cyclic monophosphate was detected as a potential biomarker metabolite to differentiate cold tolerant and sensitive maize genotypes. Furthermore, Quercetin-3-O-(2″'-p-coumaroyl)sophoroside-7-O-glucoside, Phloretin, Phloretin-2'-O-glucoside, Naringenin-7-O-Rutinoside, L-Lysine, L-phenylalanine, L-Glutamine, Sinapyl alcohol, and Feruloyltartaric acid were regulated explicitly in B144 and could be important cold-tolerance metabolites. These results increase our understanding of cold-mediated metabolic responses in maize that can be further utilized to enhance cold tolerance in this significant crop.

Explainable Graph Wavelet Denoising Network for Intelligent Fault Diagnosis.

Deep learning (DL)-based intelligent fault diagnosis methods have greatly promoted the development of the field of fault diagnosis due to their powerful feature extraction ability for handling massive monitoring data. However, most of them still suffer from the following three limitations. First, many existing DL-based intelligent diagnosis methods cannot extract proper discriminative features from signals with strong noise. Second, the interactions or relationships between signals are ignored, while they mainly focus on extracting temporal features from the signal. Third, owing to their black-box nature, the learned features lack interpretability, which hinders their application in the industry. To tackle these issues, an explainable graph wavelet denoising network (GWDN) is proposed to achieve intelligent fault diagnosis under noisy working conditions in this article. In GWDN, the collected signals are first transformed into graph-structured data to consider the interactions among signals. Then, the graph wavelet denoising convolution (GWDConv) is proposed based on the discrete graph wavelet frame, which allows GWDN to achieve multiscale feature extraction for graph-structured data and realize signal denoising. Extensive experiments are implemented to verify the efficacy of the proposed GWDN, and the experimental results show that GWDN can achieve state-of-the-art performance among the comparison methods. Besides, by using the square envelope spectrum to analyze the extracted features of GWDConv, we find that it can well retain the fault-related components of the signal and realize signal denoising, which further proves that GWDN is explainable.

Analysis of the C2H2 Gene Family in Maize (Zea mays L.) under Cold Stress: Identification and Expression.

The C2H2 zinc finger protein is one of the most common zinc finger proteins, widely exists in eukaryotes, and plays an important role in plant growth and development, as well as in salt, low-temperature, and drought stress and other abiotic stress responses. In this study, C2H2 members were identified and analyzed from the low-temperature tolerant transcriptome sequencing data of maize seedlings. The chromosome position, physical and chemical properties, evolution analysis, gene structure, conservative motifs, promoter cis elements and collinearity relationships of gene the family members were analyzed using bioinformatics, and the expression of the ZmC2H2 gene family under cold stress was analyzed by fluorescent quantitative PCR. The results showed that 150 members of the C2H2 zinc finger protein family were identified, and their protein lengths ranged from 102 to 1223 bp. The maximum molecular weight of the ZmC2H2s was 135,196.34, and the minimum was 10,823.86. The isoelectric point of the ZmC2H2s was between 33.21 and 94.1, and the aliphatic index was 42.07-87.62. The promoter cis element analysis showed that the ZmC2H2 family contains many light-response elements, plant hormone-response elements, and stress-response elements. The analysis of the transcriptome data showed that most of the ZmC2H2 genes responded to cold stress, and most of the ZmC2H2 genes were highly expressed in cold-tolerant materials and lowly expressed in cold-sensitive materials. The real-time quantitative PCR (qRT-PCR) analysis showed that ZmC2H2-69, ZmC2H2-130, and ZmC2H2-76 were significantly upregulated, and that ZmC2H2-149, ZmC2H2-33, and ZmC2H2-38 were significantly downregulated. It is hypothesized that these genes, which function in different metabolic pathways, may play a key role in the maize cold response. These genes could be further studied as candidate genes. This study provides a theoretical reference for further study on the function analysis of the maize C2H2 gene family.

An etiological study of intellectually disabled children under 14 years old in Anhui Province, China.

OBJECTIVE: To explore the etiological factors of intellectually disabled children in Anhui Province using a multicenter etiological study. METHODS: A total of 200 children aged 0 to 14 years in Anhui Province who were diagnosed with intellectual disabilities were recruited as the study cohort. Their general information (perinatal information, parental educational levels, family environments, etc.) was collected through questionnaires, and the Gesell Developmental Scale and the Wechsler Intelligence Scale were used to assess the intelligence development of the enrolled children. RESULTS: Among the 528 children, 270 (51.14%) had severe intellectual disabilities and 258 (48.86%) had mild intellectual disabilities. It was found that various perinatal factors (premature birth, asphyxia, ischemic hypoxic encephalopathy, etc.), severe cerebral palsy, and psychosocial factors were the main etiological factors, accounting for 27.42%, 22.29%, and 17.16% respectively. There was a significant difference in the distribution of the etiologies between the rural and urban areas (P<0.01). The educational levels of most of the parents in the rural areas were lower than the parents' educational levels in the cities. CONCLUSION: Correlation analyses are helpful for the early diagnosis of children suspected of having intellectual disabilities and they provide a scientific basis for improving the children's quality of life and their early rehabilitation treatment.