Dynamic GATA4 enhancers shape the chromatin landscape central to heart development and disease.

How stage-specific enhancer dynamics modulate gene expression patterns essential for organ development, homeostasis and disease is not well understood. Here, we addressed this question by mapping chromatin occupancy of GATA4--a master cardiac transcription factor--in heart development and disease. We find that GATA4 binds and participates in establishing active chromatin regions by stimulating H3K27ac deposition, which facilitates GATA4-driven gene expression. GATA4 chromatin occupancy changes markedly between fetal and adult heart, with a limited binding sites overlap. Cardiac stress restored GATA4 occupancy to a subset of fetal sites, but many stress-associated GATA4 binding sites localized to loci not occupied by GATA4 during normal heart development. Collectively, our data show that dynamic, context-specific transcription factors occupancy underlies stage-specific events in development, homeostasis and disease

CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases

CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases

A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals

Diagnostic histopathology is a gold standard for diagnosing hematopoietic malignancies. Pathologic diagnosis requires labor-intensive reading of a large number of tissue slides with high diagnostic accuracy equal or close to 100 percent to guide treatment options, but this requirement is difficult to meet. Although artificial intelligence (AI) helps to reduce the labor of reading pathologic slides, diagnostic accuracy has not reached a clinically usable level. Establishment of an AI model often demands big datasets and an ability to handle large variations in sample preparation and image collection. Here, we establish a highly accurate deep learning platform, consisting of multiple convolutional neural networks, to classify pathologic images by using smaller datasets. We analyze human diffuse large B-cell lymphoma (DLBCL) and non-DLBCL pathologic images from three hospitals separately using AI models, and obtain a diagnostic rate of close to 100 percent (100% for hospital A, 99.71% for hospital B and 100% for hospital C). The technical variability introduced by slide preparation and image collection reduces AI model performance in cross-hospital tests, but the 100% diagnostic accuracy is maintained after its elimination. It is now clinically practical to utilize deep learning models for diagnosis of DLBCL and ultimately other human hematopoietic malignancies

Modeling Rett Syndrome Using TALEN-Edited MECP2 Mutant Cynomolgus Monkeys

Gene-editing technologies have made it feasible to create nonhuman primate models for human genetic disorders. Here, we report detailed genotypes and phenotypes of TALEN-edited MECP2 mutant cynomolgus monkeys serving as a model for a neurodevelopmental disorder, Rett syndrome (RTT), which is caused by loss-of-function mutations in the human MECP2 gene. Male mutant monkeys were embryonic lethal, reiterating that RTT is a disease of females. Through a battery of behavioral analyses, including primate-unique eye-tracking tests, in combination with brain imaging via MRI, we found a series of physiological, behavioral, and structural abnormalities resembling clinical manifestations of RTT. Moreover, blood transcriptome profiling revealed that mutant monkeys resembled RTT patients in immune gene dysregulation. Taken together, the stark similarity in phenotype and/or endophenotype between monkeys and patients suggested that gene-edited RTT founder monkeys would be of value for disease mechanistic studies as well as development of potential therapeutic interventions for RTT

High-efficiency and reliable same-parent thermoelectric modules using Mg3Sb2-based compounds

Thermoelectric modules can convert waste heat directly into useful electricity, providing a clean and sustainable way to use fossil energy more efficiently. Mg3Sb2-based alloys have recently attracted considerable interest from the thermoelectric community due to their nontoxic nature, abundance of constituent elements and excellent mechanical and thermoelectric properties. However, robust modules based on Mg3Sb2 have progressed less rapidly. Here, we develop multiple-pair thermoelectric modules consisting of both n-type and p-type Mg3Sb2-based alloys. Thermoelectric legs based on the same parent fit into each other in terms of thermomechanical properties, facilitating module fabrication and ensuring low thermal stress. By adopting a suitable diffusion barrier layer and developing a new joining technique, an integrated all-Mg3Sb2-based module demonstrates a high efficiency of 7.5% at a temperature difference of 380 K, exceeding the state-of-the-art same-parent thermoelectric modules. Moreover, the efficiency remains stable during 150 thermal cycling shocks (∼225 h), demonstrating excellent module reliability

Identification of Tomato Leaf Diseases based on LMBRNet

Tomato Disease Image Identification Plays a Very Important Role in the Field of Agricultural Production. Aiming at the Problems of Large Intraclass Differences, Small Inter-Class Differences and Difficult Feature Extraction of Some Tomato Leaf Diseases, This Paper Proposes an Identification of Tomato Leaf Diseases based on LMBRNet. Firstly, a Comprehensive Grouped Differentiated Residual (CGDR) is Built,The Multi-Branch Structure of CGDR is Used to Capture the Diversified Feature Information of Tomato Leaf Diseases in Different Dimensions and Receptive Fields. Then, a Multiple Residual Connection Scheme is Adopted,using Residuals to Connect All Layers, to Ensure the Maximum Information Transmission between Layers in the Network and to Solve the Problems of Network Degradation and Gradient Disappearance in the Network Training Process. Secondly,the Visual Enhancement Effectively Fuses the Results Obtained by Three Different Downsampling Strategies using Average Pooling, Max Pooling, and 1*1 Convolution. Avoid the Loss of Information Caused by Downsampling and Improve the Accuracy of the Network. Moreover, Deep Separable Convolution is Used to Optimize the Network Structure, Reduce the Amount of Model Parameters and Reduce the Computational Resources Occupied by Training and Deploying the Model.we Found that the Depthwise Separable Convolution with a Kernel Size of 1*1 Can Slightly Improve the Efficiency of the Model under the Premise that It Has Little Effect on the Number of Model Parameters. the Application Results of More Than 8000 Images Show that the overall Identification Accuracy is About 99.7%,higher Than ResNet50(97.48%),GoogleNet(98.96%) Etc. Conventional Models. the Parameter Amount of LMBRNet is 4.1M. Less Than ResNet50(23M),GoogleNet(5.7M) Etc. Conventional Models. It is Worth Noting that the Accuracy of LMBRNet(99.7%) is Similar to that of InceptionResNetV2(99.68%), But the Amount of Parameters of LMBRNet(4.1M) is Much Lower Than that of InceptionResNetV2(54M). Moreover, the Parameter Amount of LMBRNet (4.1M) is Slightly Lower Than that of MobileNetV2(2.2M), But the Accuracy Rate of LMBRNet(99.7%) is Higher Than that of MobileNetV2(97.87%). LMBRNet Was Tested on RS, SIW, Plantvillage-Corn Public Datasets, All Obtained High Recognition Accuracy, 82.32% on RS, 88.37% on SIW and 97.25% on Plantvillage-Corn, Indicating that LMBRNet Has Good Generalization. Compare LMBRNet with Advanced Methods. in Four Different Classification Tasks, the Performance of LMBRNet is Similar to ResMLP12 and DCCAM-MRNet, and the Difference of Recognition Accuracy between LMBRNet and ResMLP12 and DCCAM-MRNet is Not More Than 1%. However, the Parameters of LMBRNet (4.1M) Are Lower Than ResMLP12 (14.94M) and DCCAM MRNet (22.8M). LMBRNet is Compared with MobileNetV3, an Advanced Lightweight Classification Model. LMBRNet(88.37% on SIW,82.32% on RS) is Used on Certain Datasets and Performs Better Than MobileNetV3S(83.76% on SIW,75 on RS) and MobileNetV3L(84.34 on SIW,73.39 on RS). the Parameters of LMBRNet(4.1M) Are Lower Than MobileNetV3L(5.4M) and Slightly Higher Than MobileNetV3S(2.9M). This Indicates that LMBRNet Has Good Generality Even Though It Has a Small Number of Parameters

Information Assurance through Redundant Design: A Novel TNU Error Resilient Latch for Harsh Radiation Environment

International audienc

Deep learning bird song recognition based on MFF-ScSEnet

Bird diversity plays an important role in ecological balance, and bird song identification is of great practical significance. The spectrum generated by feature extraction shows good performance on classification. However, the information extracted by the filter in the process of spectrogram generation can cause information loss, which limits the learning ability of birdsong recognition. This study proposes a feature fusion network (MFF-ScSEnet) to solve this problem. The audios of the birdsong extracted the Mel-spectrogram with low-frequency feature advantage by the Mel-filter, and the Sinc-spectrogram with timbral feature advantage by the Sincnet-filter, respectively, and perform the early fusion strategy. The ScSEnet attention module is introduced into the backbone network ResNet18 to enhance the sound ripple information of the spectrogram, reduce the influence of spectrogram noise information on the recognition and improve the recognition performance of the network. Based on the feature fusion network MFF-ScSEnet in this paper, the accuracy of the experimental results on the self-built birdsong dataset (Huabei_dataset), the public datasets of Urbansound8K and Birdsdata reached 96.28%, 98.34%, and 96.66%, respectively. The results indicated that the method proposed in this paper is superior to the recent and latest birdsong recognition method