Integrated Proteomics Analysis of Baseline Protein Expression in Pig Tissues.

The availability of an increasingly large amount of public proteomics data sets presents an opportunity for performing combined analyses to generate comprehensive organism-wide protein expression maps across different organisms and biological conditions. Sus scrofa, a domestic pig, is a model organism relevant for food production and for human biomedical research. Here, we reanalyzed 14 public proteomics data sets from the PRIDE database coming from pig tissues to assess baseline (without any biological perturbation) protein abundance in 14 organs, encompassing a total of 20 healthy tissues from 128 samples. The analysis involved the quantification of protein abundance in 599 mass spectrometry runs. We compared protein expression patterns among different pig organs and examined the distribution of proteins across these organs. Then, we studied how protein abundances were compared across different data sets and studied the tissue specificity of the detected proteins. Of particular interest, we conducted a comparative analysis of protein expression between pig and human tissues, revealing a high degree of correlation in protein expression among orthologs, particularly in brain, kidney, heart, and liver samples. We have integrated the protein expression results into the Expression Atlas resource for easy access and visualization of the protein expression data individually or alongside gene expression data.

CSANet: a lightweight channel and spatial attention neural network for grading diabetic retinopathy with optical coherence tomography angiography.

Background: Diabetic retinopathy (DR) is one of the most common eye diseases. Convolutional neural networks (CNNs) have proven to be a powerful tool for learning DR features; however, accurate DR grading remains challenging due to the small lesions in optical coherence tomography angiography (OCTA) images and the small number of samples. Methods: In this article, we developed a novel deep-learning framework to achieve the fine-grained classification of DR; that is, the lightweight channel and spatial attention network (CSANet). Our CSANet comprises two modules: the baseline model, and the hybrid attention module (HAM) based on spatial attention and channel attention. The spatial attention module is used to mine small lesions and obtain a set of spatial position weights to address the problem of small lesions being ignored during the convolution process. The channel attention module uses a set of channel weights to focus on useful features and suppress irrelevant features. Results: The extensive experimental results for the OCTA-DR and diabetic retinopathy analysis challenge (DRAC) 2022 data sets showed that the CSANet achieved state-of-the-art DR grading results, showing the effectiveness of the proposed model. The CSANet had an accuracy rate of 97.41% for the OCTA-DR data set and 85.71% for the DRAC 2022 data set. Conclusions: Extensive experiments using the OCTA-DR and DRAC 2022 data sets showed that the proposed model effectively mitigated the problems of mutual confusion between DRs of different severity and small lesions being neglected in the convolution process, and thus improved the accuracy of DR classification.

Image segmentation of mouse eye in vivo with optical coherence tomography based on Bayesian classification.

OBJECTIVES: Optical coherence tomography (OCT) is a new imaging technology that uses an optical analog of ultrasound imaging for biological tissues. Image segmentation plays an important role in dealing with quantitative analysis of medical images. METHODS: We have proposed a novel framework to deal with the low intensity problem, based on the labeled patches and Bayesian classification (LPBC) model. The proposed method includes training and testing phases. During the training phase, firstly, we manually select the sub-images of background and Region of Interest (ROI) from the training image, and then extract features by patches. Finally, we train the Bayesian model with the features. The segmentation threshold of each patch is computed by the learned Bayesian model. RESULTS: In addition, we have collected a new dataset of mouse eyes in vivo with OCT, named MEVOCT, which can be found at URL https://17861318579.github.io/LPBC. MEVOCT consists of 20 high-resolution images. The resolution of every image is 2048 × 2048 pixels. CONCLUSIONS: The experimental results demonstrate the effectiveness of the LPBC method on the new MEVOCT dataset. The ROI segmentation is of great importance for the distortion correction.

Memristor-based adaptive neuromorphic perception in unstructured environments.

Efficient operation of control systems in robotics or autonomous driving targeting real-world navigation scenarios requires perception methods that allow them to understand and adapt to unstructured environments with good accuracy, adaptation, and generality, similar to humans. To address this need, we present a memristor-based differential neuromorphic computing, perceptual signal processing, and online adaptation method providing neuromorphic style adaptation to external sensory stimuli. The adaptation ability and generality of this method are confirmed in two application scenarios: object grasping and autonomous driving. In the former, a robot hand realizes safe and stable grasping through fast ( ~ 1 ms) adaptation based on the tactile object features with a single memristor. In the latter, decision-making information of 10 unstructured environments in autonomous driving is extracted with an accuracy of 94% with a 40×25 memristor array. By mimicking human low-level perception mechanisms, the electronic neuromorphic circuit-based method achieves real-time adaptation and high-level reactions to unstructured environments.

Effectiveness of fecal DNA syndecan-2 methylation testing for detection of colorectal cancer in a high-risk Chinese population.

BACKGROUND: Colorectal cancer (CRC) is among the most prevalent and life-threatening malignancies worldwide. Syndecan-2 methylation (mSDC2) testing has emerged as a widely used biomarker for early detection of CRC in stool and serum samples. Cancer (CRC) is among the most prevalent and life-threatening malignancies worldwide. mSDC2 testing has emerged as a widely used biomarker for early detection of CRC in stool and serum samples. AIM: To validate the effectiveness of fecal DNA mSDC2 testing in the detection of CRC among a high-risk Chinese population to provide evidence-based data for the development of diagnostic and/or screening guidelines for CRC in China. METHODS: A high-risk Chinese cohort consisting of 1130 individuals aged 40-79 years was selected for evaluation via fecal mSDC2 testing. Sensitivity and specificity for CRC, advanced adenoma (AA) and advanced colorectal neoplasia (ACN) were determined. High-risk factors for the incidence of colorectal lesions were determined and a logistic regression model was constructed to reflect the efficacy of the test. RESULTS: A total of 1035 high-risk individuals were included in this study according to established criteria. Among them, 16 suffered from CRC (1.55%), 65 from AA (6.28%) and 189 from non-AAs (18.26%); 150 patients were diagnosed with polyps (14.49%). Diagnoses were established based upon colonoscopic and pathological examinations. Sensitivities of the mSDC2 test for CRC and AA were 87.50% and 40.00%, respectively; specificities were 95.61% for other groups. Positive predictive values of the mSDC2 test for CRC, AA and ACN were 16.09%, 29.89% and 45.98%, respectively; the negative predictive value for CRC was 99.79%. After adjusting for other high-risk covariates, mSDC2 test positivity was found to be a significant risk factor for the occurrence of ACN (P < 0.001). CONCLUSION: Our findings confirmed that offering fecal mSDC2 testing and colonoscopy in combination for CRC screening is effective for earlier detection of malignant colorectal lesions in a high-risk Chinese population.

De novo transcriptome assembly database for 100 tissues from each of seven species of domestic herbivore.

Domesticated herbivores are an important agricultural resource that play a critical role in global food security, particularly as they can adapt to varied environments, including marginal lands. An understanding of the molecular basis of their biology would contribute to better management and sustainable production. Thus, we conducted transcriptome sequencing of 100 to 105 tissues from two females of each of seven species of herbivore (cattle, sheep, goats, sika deer, horses, donkeys, and rabbits) including two breeds of sheep. The quality of raw and trimmed reads was assessed in terms of base quality, GC content, duplication sequence rate, overrepresented k-mers, and quality score distribution with FastQC. The high-quality filtered RNA-seq raw reads were deposited in a public database which provides approximately 54 billion high-quality paired-end sequencing reads in total, with an average mapping rate of ~93.92%. Transcriptome databases represent valuable resources that can be used to study patterns of gene expression, and pathways that are related to key biological processes, including important economic traits in herbivores.