Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks

Automated classification of Schistosoma mansoni granulomatous microscopic images of mice liver using Artificial Intelligence (AI) technologies is a key issue for accurate diagnosis and treatment. In this paper, three grey difference statistics-based features, namely three Gray-Level Co-occurrence Matrix (GLCM) based features and fifteen Gray Gradient Co-occurrence Matrix (GGCM) features were calculated by correlative analysis. Ten features were selected for three-level cellular granuloma classification using a Scaled Conjugate Gradient Back-Propagation Neural Network (SCG-BPNN) in the same performance. A cross-entropy is then calculated to evaluate the proposed Sigmoid input and the ten-hidden layer network. The results depicted that SCG-BPNN with texture features performs high recognition rate compared to using morphological features, such as shape, size, contour, thickness and other geometry-based features for the classification. The proposed method also has a high accuracy rate of 87.2% compared to the Back-Propagation Neural Network (BPNN), Back-Propagation Hopfield Neural Network (BPHNN) and Convolutional Neural Network (CNN)

A distance regularized level-set evolution model based MRI dataset segmentation of brain’s caudate nucleus

The caudate nucleus of the brain is highly correlated to the emotional decision-making of pessimism, which is an important process for improving the understanding and treatment of depression; and the segmentation of the caudate nucleus is the most basic step in the process of analysis and research concerning this region. In this paper, Level Set Method (LSM) is applied for caudate nucleus segmentation. Firstly, Distance Regularized Level Set Evolution (DRLSE), Region-Scalable Fitting (RSF) and Local Image Fitting (LIF) models are proposed for segmentation of the caudate nucleus of Magnetic Resonance Imaging (MRI) images of the brain, and the segmentation results are compared by using selected evaluation indices. The average Dice Similarity Coefficient (DSC) values of the proposed three methods all exceed 85%, and the average Jaccard Similarity (JS) values are over 77%, respectively. The results indicate that all these three models can have good segmentation results for medical images with intensity inhomogeneity and meet the general segmentation requirements, while the proposed DRLSE model performs better in segmentation

Observation of Chern insulator in crystalline ABCA-tetralayer graphene with spin-orbit coupling

Degeneracies in multilayer graphene, including spin, valley, and layer degrees of freedom, are susceptible to Coulomb interactions and can result into rich broken-symmetry states. In this work, we report a ferromagnetic state in charge neutral ABCA-tetralayer graphene driven by proximity-induced spin-orbit coupling from adjacent WSe2. The ferromagnetic state is further identified as a Chern insulator with Chern number of 4, and its Hall resistance reaches 78% and 100% quantization of h/4e2 at zero and 0.4 tesla, respectively. Three broken-symmetry insulating states, layer-antiferromagnet, Chern insulator and layer-polarized insulator and their transitions can be continuously tuned by the vertical displacement field. Remarkably, the magnetic order of the Chern insulator can be switched by three knobs, including magnetic field, electrical doping, and vertical displacement field

SPOC learner's final grade prediction based on a novel sampling batch normalization embedded neural network method

Recent years have witnessed the rapid growth of Small Private Online Courses (SPOC) which is able to highly customized and personalized to adapt variable educational requests, in which machine learning techniques are explored to summarize and predict the learner's performance, mostly focus on the final grade. However, the problem is that the final grade of learners on SPOC is generally seriously imbalance which handicaps the training of prediction model. To solve this problem, a sampling batch normalization embedded deep neural network (SBNEDNN) method is developed in this paper. First, a combined indicator is defined to measure the distribution of the data, then a rule is established to guide the sampling process. Second, the batch normalization (BN) modified layers are embedded into full connected neural network to solve the data imbalanced problem. Experimental results with other three deep learning methods demonstrates the superiority of the proposed method.Comment: 11 pages, 5 figures, ICAIS 202

Low threshold quantum dot lasers directly grown on unpatterned quasi-nominal (001) Si

We report electrically pumped, continuous-wave (cw) InAs/GaAs quantum dot (QD) lasers directly grown on quasi-nominal Si (001) substrates with offcut angle as small as 0.4°. No GaP, Ge buffer layers or substrate patterning is required. An anti-phase boundary free epitaxial GaAs film was grown by metal-organic chemical vapor deposition (MOCVD) with a low threading dislocation density of ${3\times 10^{7} {\rm{cm^{-2}}}}$ . Room-temperature cw lasing at ∼1.3 μm has been achieved, with a minimum threshold current density of 34.6 A/cm2 per layer, a maximum operating temperature of 80 °C, and a maximum single facet output power of 52 mW. A comparison of various monolithic III-V hetero-epitaxy on Si solutions is presented. Direct growth on unpatterned quasi-nominal (001) Si may yield the best material quality at the lowest lifecycle cost

Inhibition of RNF182 mediated by Bap promotes non-small cell lung cancer progression

IntroductionUbiquitylation that mediated by ubiquitin ligases plays multiple roles not only in proteasome-mediated protein degradation but also in various cellular process including DNA repair, signal transduction and endocytosis. RING finger (RNF) proteins form the majority of these ubiquitin ligases. Recent studies have demonstrated the important roles of RNF finger proteins in tumorigenesis and tumor progression. Benzo[a]pyrene (BaP) is one of the most common environmental carcinogens causing lung cancer. The molecular mechanism of Bap carcinogenesis remains elusive. Considering the critical roles of RNF proteins in tumorigenesis and tumor progression, we speculate on whether Bap regulates RNF proteins resulting in carcinogenesis.MethodsWe used GEO analysis to identify the potential RING finger protein family member that contributes to Bap-induced NSCLC. We next used RT-qPCR, Western blot and ChIP assay to investigate the potential mechanism of Bap inhibits RNF182. BGS analyses were used to analyze the methylation level of RNF182.ResultsHere we reported that the carcinogen Bap suppresses the expression of ring finger protein 182 (RNF182) in non-small cell lung cancer (NSCLC) cells, which is mediated by abnormal hypermethylation in an AhR independent way and transcriptional regulation in an AhR dependent way. Furthermore, RNF182 exhibits low expression and hypermethylation in tumor tissues. RNF182 also significantly suppresses cell proliferation and induces cell cycle arrest in NSCLC cell lines.ConclusionThese results demonstrated that Bap inhibits RNF182 expression to promote lung cancer tumorigenesis through activating AhR and promoting abnormal methylation

Protective Effect of Akkermansia muciniphila against Immune-Mediated Liver Injury in a Mouse Model

Accumulating evidence indicates that gut microbiota participates in the pathogenesis and progression of liver diseases. The severity of immune-mediated liver injury is associated with different microbial communities. Akkermansia muciniphila can regulate immunologic and metabolic functions. However, little is known about its effects on gut microbiota structure and function. This study investigated the effect of A. muciniphila on immune-mediated liver injury and potential underlying mechanisms. Twenty-two C57BL/6 mice were assigned to three groups (N = 7–8 per group) and continuously administrated A. muciniphila MucT or PBS by oral gavage for 14 days. Mouse feces were collected for gut microbiota analysis on the 15th day, and acute liver injury was induced by Concanavalin A (Con A, 15 mg/kg) injection through the tail vein. Samples (blood, liver, ileum, colon) were assessed for liver injury, systemic inflammation, and intestinal barrier function. We found that oral administration of A. muciniphila decreased serum ALT and AST and alleviated liver histopathological damage induced by Con A. Serum levels of pro-inflammatory cytokines and chemokines (IL-2, IFN-γ, IL-12p40, MCP-1, MIP-1a, MIP-1b) were substantially attenuated. A. muciniphila significantly decreased hepatocellular apoptosis; Bcl-2 expression increased, but Fas and DR5 decreased. Further investigation showed that A. muciniphila enhanced expression of Occludin and Tjp-1 and inhibited CB1 receptor, which strengthened intestinal barriers and reduced systemic LPS level. Fecal 16S rRNA sequence analysis indicated that A. muciniphila increased microbial richness and diversity. The community structure of the Akk group clustered distinctly from that of mice pretreated with PBS. Relative abundance of Firmicutes increased, and Bacteroidetes abundance decreased. Correlation analysis showed that injury-related factors (IL-12p40, IFN-γ, DR5) were negatively associated with specific genera (Ruminococcaceae_UCG_009, Lachnospiraceae_UCG_001, Akkermansia), which were enriched in mice pretreated with A. muciniphila. Our results suggested that A. muciniphila MucT had beneficial effects on immune-mediated liver injury by alleviating inflammation and hepatocellular death. These effects may be driven by the protective profile of the intestinal community induced by the bacteria. The results provide a new perspective on the immune function of gut microbiota in host diseases

Robust elbow angle prediction with aging soft sensors via output-level domain adaptation

Wearable devices equipped with soft sensors provide a promising solution for body movement monitoring. Specifically, body movements like elbow flexion can be captured by monitoring the stretched soft sensors’ resistance changes. However, in addition to stretching, the resistance of a soft sensor is also influenced by its aging, which makes the resistance a less stable indicator of the elbow angle. In this paper, we leverage the recent progress in Deep Learning and address the aforementioned issue by formulating the aging-invariant prediction of elbow angles as a domain adaption problem. Specifically, we define the soft sensor data (i.e., resistance values) collected at different aging levels as different domains and adapt a regression neural network among them to learn domain-invariant features. However, unlike the popular pairwise domain adaptation problem that only involves one source and one target domain, ours is more challenging as it has “infinite” target domains due to the non-stop aging. To address this challenge, we novelly propose an output-level domain adaptation approach which builds on the fact that the elbow angles are in a fixed range regardless of aging. Experimental results show that our method enables robust and accurate prediction of elbow angles with aging soft sensors, which significantly outperforms supervised learning ones that fail to generalize to aged sensor data