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)

Predictive short/long-term efficacy biomarkers and resistance mechanisms of CD19-directed CAR-T immunotherapy in relapsed/refractory B-cell lymphomas

Genetically modified T-cell immunotherapies are revolutionizing the therapeutic options for hematological malignancies, especially those of B-cell origin. Impressive efficacies of CD19-directed chimeric antigen receptor (CAR)-T therapy have been reported in refractory/relapsed (R/R) B-cell non-Hodgkin lymphoma (NHL) patients who were resistant to current standard therapies, with a complete remission (CR) rate of approximately 50%. At the same time, problems of resistance and relapse following CAR-T therapy have drawn growing attention. Recently, great efforts have been made to determine various factors that are connected to the responses and outcomes following CAR-T therapy, which may not only allow us to recognize those with a higher likelihood of responding and who could benefit most from the therapy but also identify those with a high risk of resistance and relapse and to whom further appropriate treatment should be administered following CAR-T therapy. Thus, we concentrate on the biomarkers that can predict responses and outcomes after CD19-directed CAR-T immunotherapy. Furthermore, the mechanisms that may lead to treatment failure are also discussed in this review

Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved

Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved

Eau

Modeling aluminum (Al) particle-air detonation is extremely difficult because the combustion is shock-induced, and there are multi-phase heat release and transfer in supersonic flows. Existing models typically use simplified combustion to reproduce the detonation velocity, which introduces many unresolved problems. The hybrid combustion model, coupling both the diffused- and kinetics-controlled combustion, is proposed recently, and then improved to include the effects of realistic heat capacities dependent on the particle temperature. In the present study, 2D cellular Al particle-air detonations are simulated with the realistic heat capacity model and its effects on the detonation featured parameters, such as the detonation velocity and cell width, are analyzed. Numerical results show that cell width increases as particle diameter increases, similarly to the trend observed with the original model, but the cell width is underestimated without using the realistic heat capacities. Further analysis is performed by averaging the 2D cellular detonations to quasi-1D, demonstrating that the length scale of quasi-1D detonation is larger than that of truly 1D model, similar to gaseous detonations