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General General

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia.

In Journal of visualized experiments : JoVE

Segmentation is a complex task, faced by radiologists and researchers as radiomics and machine learning grow in potentiality. The process can either be automatic, semi-automatic, or manual, the first often not being sufficiently precise or easily reproducible, and the last being excessively time consuming when involving large districts with high-resolution acquisitions. A high-resolution CT of the chest is composed of hundreds of images, and this makes the manual approach excessively time consuming. Furthermore, the parenchymal alterations require an expert evaluation to be discerned from the normal appearance; thus, a semi-automatic approach to the segmentation process is, to the best of our knowledge, the most suitable when segmenting pneumonias, especially when their features are still unknown. For the studies conducted in our institute on the imaging of COVID-19, we adopted 3D Slicer, a freeware software produced by the Harvard University, and combined the threshold with the paint brush instruments to achieve fast and precise segmentation of aerated lung, ground glass opacities, and consolidations. When facing complex cases, this method still requires a considerable amount of time for proper manual adjustments, but provides an extremely efficient mean to define segments to use for further analysis, such as the calculation of the percentage of the affected lung parenchyma or texture analysis of the ground glass areas.

Cattabriga Arrigo, Cocozza Maria Adriana, Vara Giulio, Coppola Francesca, Golfieri Rita


General General

Smartphone-based Human Fatigue Level Detection Using Machine Learning Approaches.

In Ergonomics

Human muscle fatigue is the main result of diminishing muscle capability, leading to reduced performance and increased risk of falls and injury. This study provides a classification model to identify the human fatigue level based on the motion signals collected by a smartphone. Twenty-four participants were recruited and performed the fatiguing exercise (i.e., squatting). Upon completing each set of squatting, they walked for a fixed distance while the smartphone attached to their right shank and the gait data were associated to the Borg's Rating of Perceived Exertion (i.e., data label). Our machine-learning model of two (no- vs. strong-fatigue), three (no-, medium-, and strong-fatigue) and four (no-, low-, medium-, and strong-fatigue) levels of fatigue reached to the accuracy of 91%, 78%, and 64%, respectively. The outcomes of this study may facilitate the accessibility of a fatigue-monitoring tool in workplace, which improves the workers' performance and reduce the risk of falls and injury. PRACTITIONAR SUMMARY: This study aimed to develop a machine-learning model to identify human fatigue level using motion data captured by a smartphone attached to the shank. Our results can facilitate the development of an accessible fatigue-monitoring system that may improve the workers' performance and reduce the risk of falls and injury.

Karvekar Swapnali, Abdollahi Masoud, Rashedi Ehsan


Human Muscle fatigue, Machine Learning, Pattern Recognition, Smartphone, Support Vector Machine, Wearable Technology

General General

Exploring and quantifying the relationship between instantaneous wind speed and turbidity in a large shallow lake: case study of Lake Taihu in China.

In Environmental science and pollution research international

Sediment resuspension is critical to the internal nutrient loading in aquatic systems. Turbidity is commonly used as an indicator for sediment resuspension and is proved to be highly correlated to wind speed in large shallow lakes. A field observation of wind speed and turbidity was conducted using a portable weather station and a YSI 6600V2-2, and an observation lasting for 39 days was evaluated in this study (the data points with wind speed > 4 m/s account for 75%). The daily average values (DA dataset) as well as daily maximum (MX dataset) and minimum values (MI dataset) were calculated from the instantaneous observations (IN dataset). Correlations in IN dataset were deduced based on machine learning methods and were compared to those obtained from DA, MI, and MX datasets. Furthermore, the correlation in IN dataset was analyzed by using two statistical methods, and from the view of statistical the turbidity is regarded as a variable. Results indicate that the correlations in IN datasets follow the exponential function or power function pattern with a critical wind speed of 6 m/s, Regression on IN dataset revealed that linear regression model had the best performance on predicting the turbidity in test dataset and no significant differences are observed between exponential function and power function pattern. Correlations in DA and MX datasets exhibit higher maximal information coefficient (MIC) than IN dataset and error of turbidity prediction introduced by using these correlations in IN dataset is within the tolerance level. Statistical analysis on the IN dataset shows that a strong relationship exists among the wind speed and expectation of turbidity with a MIC over 0.99, and follows the exponential function or the power function as well with a different critical wind speed of 4 m/s. Over 95% data points fall in the predicted intervals of turbidity for both methods, suggesting a high predicting accuracy.

Ding Wenhao, Zhao Jinxiao, Qin Boqiang, Wu Tingfeng, Zhu Senlin, Li Yun, Xu Shikai, Ruan Shiping, Wang Yong


Lake Taihu, Machine learning, Maximal information coefficient, Turbidity, Wind disturbance

General General

IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations.

In Journal of computer-aided molecular design

Redox-sensitive cysteine (RSC) thiol contributes to many biological processes. The identification of RSC plays an important role in clarifying some mechanisms of redox-sensitive factors; nonetheless, experimental investigation of RSCs is expensive and time-consuming. The computational approaches that quickly and accurately identify candidate RSCs using the sequence information are urgently needed. Herein, an improved and robust computational predictor named IRC-Fuse was developed to identify the RSC by fusing of multiple feature representations. To enhance the performance of our model, we integrated the probability scores evaluated by the random forest models implementing different encoding schemes. Cross-validation results exhibited that the IRC-Fuse achieved accuracy and AUC of 0.741 and 0.807, respectively. The IRC-Fuse outperformed exiting methods with improvement of 10% and 13% on accuracy and MCC, respectively, over independent test data. Comparative analysis suggested that the IRC-Fuse was more effective and promising than the existing predictors. For the convenience of experimental scientists, the IRC-Fuse online web server was implemented and publicly accessible at .

Hasan Md Mehedi, Alam Md Ashad, Shoombuatong Watshara, Kurata Hiroyuki


Feature selection, Machine learning, PseAAC, Redox-sensitive cysteine, Sequence profile information

General General

Predicting PAMPA permeability using the 3D-RISM-KH theory: are we there yet?

In Journal of computer-aided molecular design

The parallel artificial membrane permeability assay (PAMPA), a non-cellular lab-based assay, is extensively used to measure the permeability of pharmaceutical compounds. PAMPA experiments provide a working mimic of a molecule passing through cells and PAMPA values are widely used to estimate drug absorption parameters. There is an increased interest in developing computational methods to predict PAMPA permeability values. We developed an in silico model to predict the permeability of compounds based on the PAMPA assay. We used the three-dimensional reference interaction site model (3D-RISM) theory with the Kovalenko-Hirata (KH) closure to calculate the excess chemical potentials of a large set of compounds and predicted their apparent permeability with good accuracy (mean absolute error or MAE = 0.69 units) when compared to a published experimental data set. Furthermore, our in silico PAMPA protocol performed very well in the binary prediction of 288 compounds as being permeable or impermeable (precision = 94%, accuracy = 93%). This suggests that our in silico protocol can mimic the PAMPA assay and could aid in the rapid discovery or screening of potentially therapeutic drug leads that can be delivered to a desired tissue.

Roy Dipankar, Dutta Devjyoti, Wishart David S, Kovalenko Andriy


3D-RISM-KH, Classification, Machine learning, Molecular solvation theory, PAMPA

Radiology Radiology

A diagnostic strategy for Parkinsonian syndromes using quantitative indices of DAT SPECT and MIBG scintigraphy: an investigation using the classification and regression tree analysis.

In European journal of nuclear medicine and molecular imaging ; h5-index 66.0

PURPOSE : We aimed to evaluate the diagnostic performances of quantitative indices obtained from dopamine transporter (DAT) single-photon emission computed tomography (SPECT) and 123I-metaiodobenzylguanidine (MIBG) scintigraphy for Parkinsonian syndromes (PS) using the classification and regression tree (CART) analysis.

METHODS : We retrospectively enrolled 216 patients with or without PS, including 80 without PS (NPS) and 136 with PS [90 Parkinson's disease (PD), 21 dementia with Lewy bodies (DLB), 16 progressive supranuclear palsy (PSP), and 9 multiple system atrophy (MSA). The striatal binding ratio (SBR), putamen-to-caudate ratio (PCR), and asymmetry index (AI) were calculated using DAT SPECT. The heart-to-mediastinum uptake ratio (H/M) based on the early (H/M [Early]) and delayed (H/M [Delay]) images and cardiac washout rate (WR) were calculated from MIBG scintigraphy. The CART analysis was used to establish a diagnostic decision tree model for differentiating PS based on these quantitative indices.

RESULTS : The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 87.5, 96.3, 93.3, 92.9, and 93.1 for NPS; 91.1, 78.6, 75.2, 92.5, and 83.8 for PD; 57.1, 95.9, 60.0, 95.4, and 92.1 for DLB; and 50.0, 98.0, 66.7, 96.1, and 94.4 for PSP, respectively. The PCR, WR, H/M (Delay), and SBR indices played important roles in the optimal decision tree model, and their feature importance was 0.61, 0.22, 0.11, and 0.05, respectively.

CONCLUSION : The quantitative indices showed high diagnostic performances in differentiating NPS, PD, DLB, and PSP, but not MSA. Our findings provide useful guidance on how to apply these quantitative indices in clinical practice.

Iwabuchi Yu, Kameyama Masashi, Matsusaka Yohji, Narimatsu Hidetoshi, Hashimoto Masahiro, Seki Morinobu, Ito Daisuke, Tabuchi Hajime, Yamada Yoshitake, Jinzaki Masahiro


123I-FP-CIT, 123I-Ioflupane, Artificial intelligence, CART, Data mining