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General General

Kinetic Analysis of Label-Free Microscale Collagen Gel Contraction Using Machine Learning-Aided Image Analysis.

In Frontiers in bioengineering and biotechnology

Pulmonary fibrosis is a deadly lung disease, wherein normal lung tissue is progressively replaced with fibrotic scar tissue. An aspect of this process can be recreated in vitro by embedding fibroblasts into a collagen matrix and providing a fibrotic stimulus. This work expands upon a previously described method to print microscale cell-laden collagen gels and combines it with live cell imaging and automated image analysis to enable high-throughput analysis of the kinetics of cell-mediated contraction of this collagen matrix. The image analysis method utilizes a plugin for FIJI, built around Waikato Environment for Knowledge Analysis (WEKA) Segmentation. After cross-validation of this automated image analysis with manual shape tracing, the assay was applied to primary human lung fibroblasts including cells isolated from idiopathic pulmonary fibrosis patients. In the absence of any exogenous stimuli, the analysis showed significantly faster and more extensive contraction of the diseased cells compared to the healthy ones. Upon stimulation with transforming growth factor beta 1 (TGF-β1), fibroblasts from the healthy donor showed significantly more contraction throughout the observation period while differences in the response of diseased cells was subtle and could only be detected during a smaller window of time. Finally, dose-response curves for the inhibition of collagen gel contraction were determined for 3 small molecules including the only 2 FDA-approved drugs for idiopathic pulmonary fibrosis.

Yamanishi Cameron, Parigoris Eric, Takayama Shuichi


aqueous two-phase systems, collagen contraction, fibroblasts, machine learning, phenotypic assay, pulmonary fibrosis

General General

Satellite-Based Estimates of Wet Ammonium (NH4-N) Deposition Fluxes Across China during 2011-2016 Using a Space-Time Ensemble Model.

In Environmental science & technology ; h5-index 132.0

Wet NH4-N deposition plays a significant role in the ecosystem safety in China, and thus it is highly imperative to estimate the national wet NH4-N deposition flux accurately. In this study, a new methodology named space-time ensemble machine-learning model was first applied to constrain the high-resolution NH4-N deposition fluxes over China based on the satellite data, assimilated meteorology, and various geographical covariates. A small gap between site-based cross-validation (CV) R2 value (0. 73) and 10-fold CV R2 value (0.76), along with remarkable improvement in predictive accuracy (0.76) compared with previous studies (0.61), demonstrated the strong prediction capability of the space-time ensemble model in data mining. The higher wet NH4-N deposition fluxes mainly occurred in North China Plain (NCP), Sichuan Basin, Hunan, Jiangxi, and Guangdong provinces, whereas other regions retained the lower values. In addition, the wet NH4-N deposition fluxes, removing the precipitation effect in some major developed regions (e.g., Beijing and Shanghai) of China, displayed gradual increases from 2011 to 2014, while they suffered from dramatic decreases during 2014-2016, which was due to the strict implementation of the Action Plan for Air Pollution Prevention and Control (APPC-AP). The high-quality NH4-N deposition data sets are greatly useful to assess the potential ecological risks.

Li Rui, Cui Lulu, Fu Hongbo, Zhao Yilong, Zhou Wenhui, Chen Jianmin


General General

Comparing the accuracy of several network-based COVID-19 prediction algorithms.

In International journal of forecasting

Researchers from various scientific disciplines have attempted to forecast the spread of the Coronavirus Disease 2019 (COVID-19). The proposed epidemic prediction methods range from basic curve fitting methods and traffic interaction models to machine-learning approaches. If we combine all these approaches, we obtain the Network Inference-based Prediction Algorithm (NIPA). In this paper, we analyse a diverse set of COVID-19 forecast algorithms, including several modifications of NIPA. Among the diverse set of algorithms that we evaluated, original NIPA performs best on forecasting the spread of COVID-19 in Hubei, China and in the Netherlands. In particular, we show that network-based forecasting is superior to any other forecasting algorithm.

Achterberg Massimo A, Prasse Bastian, Ma Long, Trajanovski Stojan, Kitsak Maksim, Van Mieghem Piet


Bayesian methods, Epidemiology, Forecast accuracy, Machine learning methods, Network inference, SIR model, Time series methods

Cardiology Cardiology

Early Detection of Sepsis using Ensemblers

2019 Computing in Cardiology

This paper describes a methodology to detect sepsis ahead of time by analyzing hourly patient records. The Physionet 2019 challenge consists of medical records of over 40,000 patients. Using imputation and weak ensembler technique to analyze these medical records and 3-fold validation, a model is created and validated internally. The model achieved an accuracy of 93.45% and a utility score of 0.271. The utility score as defined by the organizers takes into account true positives, negatives and false alarms.

Shailesh Nirgudkar, Tianyu Ding


Surgery Surgery

Ultrasound-Based Radiomics Analysis for Preoperatively Predicting Different Histopathological Subtypes of Primary Liver Cancer.

In Frontiers in oncology

Background : Preoperative identification of hepatocellular carcinoma (HCC), combined hepatocellular-cholangiocarcinoma (cHCC-ICC), and intrahepatic cholangiocarcinoma (ICC) is essential for treatment decision making. We aimed to use ultrasound-based radiomics analysis to non-invasively distinguish histopathological subtypes of primary liver cancer (PLC) before surgery.

Methods : We retrospectively analyzed ultrasound images of 668 PLC patients, comprising 531 HCC patients, 48 cHCC-ICC patients, and 89 ICC patients. The boundary of a tumor was manually determined on the largest imaging slice of the ultrasound medicine image by ITK-SNAP software (version 3.8.0), and then, the high-throughput radiomics features were extracted from the obtained region of interest (ROI) of the tumor. The combination of different dimension-reduction technologies and machine learning approaches was used to identify important features and develop the moderate radiomics model. The comprehensive ability of the radiomics model can be evaluated by the area under the receiver operating characteristic curve (AUC).

Results : After digitally processing tumor ultrasound images, 5,234 high-throughput radiomics features were obtained. We used the Spearman + least absolute shrinkage and selection operator (LASSO) regression method for feature selection and logistics regression for modeling to develop the HCC-vs-non-HCC radiomics model (composed of 16 features). The Spearman + statistical test + random forest methods were used for feature selection, and logistics regression was applied for modeling to develop the ICC-vs-cHCC-ICC radiomics model (composed of 19 features). The overall performance of the radiomics model in identifying different histopathological types of PLC was moderate, with AUC values of 0.854 (training cohort) and 0.775 (test cohort) in the HCC-vs-non-HCC radiomics model and 0.920 (training cohort) and 0.728 (test cohort) in the ICC-vs-cHCC-ICC radiomics model.

Conclusion : Ultrasound-based radiomics models can help distinguish histopathological subtypes of PLC and provide effective clinical decision making for the accurate diagnosis and treatment of PLC.

Peng Yuting, Lin Peng, Wu Linyong, Wan Da, Zhao Yujia, Liang Li, Ma Xiaoyu, Qin Hui, Liu Yichen, Li Xin, Wang Xinrong, He Yun, Yang Hong


histopathological subtype, identification, primary liver cancer, radiomics, ultrasound

General General

Systems biology approaches integrated with artificial intelligence for optimized food-focused metabolic engineering.

In Metabolic engineering communications

Metabolic engineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. This requires detailed understanding of underlying metabolic pathways involved in the production of the targeted substances, and how the cellular processes or growth conditions are regulated by the engineering. To achieve this goal, a large system of experimental techniques, compound libraries, computational methods and data resources, including the multi-omics data, are used. The recent advent of multi-omics systems biology approaches significantly impacted the field by opening new avenues to perform dynamic and large-scale analyses that deepen our knowledge on the manipulations. However, with the enormous transcriptomics, proteomics and metabolomics available, it is a daunting task to integrate the data for a more holistic understanding. Novel data mining and analytics approaches, including Artificial Intelligence (AI), can provide breakthroughs where traditional low-throughput experiment-alone methods cannot easily achieve. Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms.

Helmy Mohamed, Smith Derek, Selvarajoo Kumar