Quantitative Evaluation of Changes in Three-Dimensional CT Density Distributions in Pulmonary Alveolar Proteinosis after GM-CSF Inhalation.

BACKGROUND: A previous clinical trial for autoimmune pulmonary alveolar proteinosis (APAP) demonstrated that granulocyte-macrophage colony-stimulating factor (GM-CSF) inhalation reduced the mean density of the lung field on computed tomography (CT) across 18 axial slice planes at a two-dimensional level. In contrast, in this study, we challenged three-dimensional analysis for changes in CT density distribution using the same datasets. METHODS: As a sub-study of the trial, CT data of 31 and 27 patients who received GM-CSF and placebo, respectively, were analyzed. To overcome the difference between various shooting conditions, a newly developed automatic lung field segmentation algorithm was applied to CT data to extract the whole lung volume, and the accuracy of the segmentation was evaluated by five pulmonary physicians independently. For normalization, the percent pixel (PP) in a certain density range was calculated as a percentage of the total number of pixels from -1,000 to 0 HU. RESULTS: The automatically segmented images revealed that the lung field was accurately extracted except for 7 patients with minor deletion or addition. Using the change in PP from baseline to week 25 (ΔPP) as the vertical axis, we created a histogram with 143 HU bins set for each patient. The most significant difference in ΔPP between GM-CSF and placebo groups was observed in two ranges: from -1,000 to -857 and -143 to 0 HU. CONCLUSION: Whole lung extraction followed by density histogram analysis of ΔPP may be an appropriate evaluation method for assessing CT improvement in APAP.

Magneto-optical diffractive deep neural network.

We propose a magneto-optical diffractive deep neural network (MO-D2NN). We simulated several MO-D2NNs, each of which consists of five hidden layers made of a magnetic material that contains 100 × 100 magnetic domains with a domain width of 1 µm and an interlayer distance of 0.7 mm. The networks demonstrate a classification accuracy of > 90% for the MNIST dataset when light intensity is used as the classification measure. Moreover, an accuracy of > 80% is obtained even for a small Faraday rotation angle of π/100 rad when the angle of polarization is used as the classification measure. The MO-D2NN allows the hidden layers to be rewritten, which is not possible with previous implementations of D2NNs.

Prediction of Microcystis Occurrences and Analysis Using Machine Learning in High-Dimension, Low-Sample-Size and Imbalanced Water Quality Data.

Machine learning, Deep learning, and water quality data have been used in recent years to predict the outbreak of harmful algae, especially Microcystis, and analyze outbreak causes. However, for various reasons, water quality data are often High-Dimension, Low-Sample- Size (HDLSS), meaning the sample size is lower than the number of dimensions. Moreover, imbalance problems may arise due to bias in the occurrence frequency of Microcystis. These problems make predicting the occurrence of Microcystis and analyzing its causes with machine learning difficult. In this study, a machine learning model that applies Feature Engineering (FE) and Feature Selection (FS) algorithms are used to predict outbreaks of Microcystis and analyze the outbreak factors from imbalanced HDLSS water quality data. The prediction performance was verified with binary classification to determine whether Microcystis would occur in the future by applying three machine learning models to four data patterns. The cause analysis of Microcystis occurrence was performed by visualizing the results of applying FE and FS. For the test data, the predictive performance of FE and FS methods was significantly better than that of the conventional method, with an accuracy of .108 points and an F-value of .691 points higher than the conventional method. A prediction performance increase was observed with a smaller model capacity. Data-driven analysis suggested that total nitrogen, chemical oxygen demand, chlorophyll-a, dissolved oxygen saturation, and water temperature are associated with Microcystis occurrences. The results also indicated that basic statistics of the water quality distribution (especially mean, standard deviation, and skewness) over a year, not the concentrations of water components, are related to the occurrence of Microcystis. These are new findings not found in previous studies and are expected to contribute significantly to future studies of algae. This study provides a method for analyzing water quality data with high-dimensionality and small samples, imbalance problems, or both.

Improving work detection by segmentation heuristics pre-training on factory operations video.

The measurement of work time for individual tasks by using video has made a significant contribution to a framework for productivity improvement such as value stream mapping (VSM). In the past, the work time has been often measured manually, but this process is quite costly and labor-intensive. For these reasons, automation of work analysis at the worksite is needed. There are two main methods for computing spatio-temporal information: by 3D-CNN, and by temporal computation using LSTM after feature extraction in the spatial domain by 2D-CNN. These methods has high computational cost but high model representational power, and the latter has low computational cost but relatively low model representational power. In the manufacturing industry, the use of local computers to make inferences is often required for practicality and confidentiality reasons, necessitating a low computational cost, and so the latter, a lightweight model, needs to have improved performance. Therefore, in this paper, we propose a method that pre-trains the image encoder module of a work detection model using an image segmentation model. This is based on the CNN-LSTM structure, which separates spatial and temporal computation and enables us to include heuristics such as workers' body parts and work tools in the CNN module. Experimental results demonstrate that our pre-training method reduces over-fitting and provides a greater improvement in detection performance than pre-training on ImageNet.

Exploration of an Inflection Point of Ventilation Parameters with Anaerobic Threshold Using Strucchange.

(1) Background: When measuring anaerobic work threshold (AT), the conventional V-slope method includes the subjectivity of the examiner, which cannot be eliminated completely. Therefore, we implemented an engineering method using strucchange to objectively search for the inflection point of AT. (2) Methods: Seventeen subjects (15 men and 2 women) were included in the study. The subjects rode an ergometer and performed a ramp load test for 18 min and 30 s. (3) Results: In VE (Ventilation), 11 out of 12 subjects had the same results with 95% confidence intervals for the AT by the strucchange and respiratory metabolic apparatus. In VCO2 (Carbon dioxide emissions), 9 out of 12 subjects had the same results with 95% confidence intervals for the AT with the strucchange and respiratory metabolic apparatus. In VE, 3 out of 12 subjects showed the same results for respiratory metabolic analysis and the AT by the V-slope method. In VCO2, 3 out of 12 subjects showed the same results for the respiratory metabolic analysis and AT by the V-slope method in VCO2. (4) Conclusions: Strucchange was more objective and significant in identifying the AT than the V-slope method.

Use of a Deep Learning Approach for the Sensitive Prediction of Hepatitis B Surface Antigen Levels in Inactive Carrier Patients.

Deep learning is a subset of machine learning that can be employed to accurately predict biological transitions. Eliminating hepatitis B surface antigens (HBsAgs) is the final therapeutic endpoint for chronic hepatitis B. Reliable predictors of the disappearance or reduction in HBsAg levels have not been established. Accurate predictions are vital to successful treatment, and corresponding efforts are ongoing worldwide. Therefore, this study aimed to identify an optimal deep learning model to predict the changes in HBsAg levels in daily clinical practice for inactive carrier patients. We identified patients whose HBsAg levels were evaluated over 10 years. The results of routine liver biochemical function tests, including serum HBsAg levels for 1, 2, 5, and 10 years, and biometric information were obtained. Data of 90 patients were included for adaptive training. The predictive models were built based on algorithms set up by SONY Neural Network Console, and their accuracy was compared using statistical analysis. Multiple regression analysis revealed a mean absolute percentage error of 58%, and deep learning revealed a mean absolute percentage error of 15%; thus, deep learning is an accurate predictive discriminant tool. This study demonstrated the potential of deep learning algorithms to predict clinical outcomes.

Relationship between detection of hepatitis B virus in saliva and periodontal disease in hepatitis B virus carriers in Japan.

INTRODUCTION: Although hepatitis B virus infection is well-described, the additional risk posed by oral bleeding in individuals with chronic hepatitis B virus infection has not been determined. This study aimed to determine the quantity of hepatitis B virus in the saliva of carriers in Japan, as a means of understanding the potential risk for horizontal transmission. METHODS: Saliva samples from 48 confirmed hepatitis B virus carriers were included in the analysis. Hepatitis B virus concentrations and the presence of occult blood as periodontal disease were evaluated in each sample. RESULTS: Hepatitis B surface antigen was identified in 46 of the 48 samples (98%), with hepatitis B virus DNA identified in 19 of the 48 saliva samples (40%). Occult blood was detected in 32 (67%) samples with the prevalence increasing as a function of age (r = 0.413; P = 0.003). There was a significantly positive correlation between hepatitis B virus DNA levels in the serum and saliva specimens (r = 0.895; P < 0.001). CONCLUSIONS: Occult blood in saliva was detected in most participants. The detection of hepatitis B virus DNA correlated positively with hepatitis B virus in the serum and occult blood in the saliva. Therefore, improved care of periodontal disease among older people is important for preventing horizontal transmission of hepatitis B virus.