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General General

Mitigating climate and health impact of small-scale kiln industry using multi-spectral classifier and deep learning

Tackling Climate Change with Machine Learning workshop at ICLR 2023

Industrial air pollution has a direct health impact and is a major contributor to climate change. Small scale industries particularly bull-trench brick kilns are one of the major causes of air pollution in South Asia often creating hazardous levels of smog that is injurious to human health. To mitigate the climate and health impact of the kiln industry, fine-grained kiln localization at different geographic locations is needed. Kiln localization using multi-spectral remote sensing data such as vegetation index results in a noisy estimates whereas use of high-resolution imagery is infeasible due to cost and compute complexities. This paper proposes a fusion of spatio-temporal multi-spectral data with high-resolution imagery for detection of brick kilns within the "Brick-Kiln-Belt" of South Asia. We first perform classification using low-resolution spatio-temporal multi-spectral data from Sentinel-2 imagery by combining vegetation, burn, build up and moisture indices. Then orientation aware object detector: YOLOv3 (with theta value) is implemented for removal of false detections and fine-grained localization. Our proposed technique, when compared with other benchmarks, results in a 21x improvement in speed with comparable or higher accuracy when tested over multiple countries.

Usman Nazir, Murtaza Taj, Momin Uppal, Sara Khalid

2023-03-21

General General

ALDH2 as a potential stem cell-related biomarker in lung adenocarcinoma: Comprehensive multi-omics analysis.

In Computational and structural biotechnology journal

Lung adenocarcinoma (LUAD) is the most prevalent lung cancer and one of the leading causes of death. Previous research found a link between LUAD and Aldehyde Dehydrogenase 2 (ALDH2), a member of aldehyde dehydrogenase gene (ALDH) superfamily. In this study, we identified additional useful prognostic markers for early LUAD identification and targeting LUAD therapy by analyzing the expression level, epigenetic mechanism, and signaling activities of ALDH2 in LUAD patients. The obtained results demonstrated that ALDH2 gene and protein expression significantly downregulated in LUAD patient samples. Furthermore, The American Joint Committee on Cancer (AJCC) reported that diminished ALDH2 expression was closely linked to worse overall survival (OS) in different stages of LUAD. Considerably, ALDH2 showed aberrant DNA methylation status in LUAD cancer. ALDH2 was found to be downregulated in the proteomic expression profile of several cell biology signaling pathways, particularly stem cell-related pathways. Finally, the relationship of ALDH2 activity with stem cell-related factors and immune system were reported. In conclusion, the downregulation of ALDH2, abnormal DNA methylation, and the consequent deficit of stemness signaling pathways are relevant prognostic and therapeutic markers in LUAD.

Tran Thi-Oanh, Vo Thanh Hoa, Lam Luu Ho Thanh, Le Nguyen Quoc Khanh

2023

4-HNE, 4-Hydroxynonenal, AJCC, American Joint Committee On Cancer, ALDH, Aldehyde Dehydrogenase, Aldehyde Dehydrogenase 2, CGI, Cpg Island, CPTAC, Clinical Proteomic Tumor Analysis Consortium, CSCs, Cancer Stem Cells, Cancer stem cells, DNA methylation, Gene expression, IHC, Immunohistochemical, LCSCs, Liver Cancer Stem Cells, LUAD, Lung Adenocarcinoma, Lung adenocarcinoma, MAPK, Mitogen-Activated Protein Kinase, MDA, Malondialdehyde, NSCLC, Non-Small Cell Lung Cancer, OS, Overall Survival, Protein expression, ROS, Reactive Oxygen Species, SCLC, Small Cell Lung Cancer, Survival analysis, TCGA, The Cancer Genome Atlas, TMT, Tandem Mass Tags, TNM, Tumor-Node-Metastasis, UICC, International Union For Cancer Control, XRCC1, X-Ray Repair Cross-Complementing Protein 1

Radiology Radiology

Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling.

In Frontiers in cellular and infection microbiology ; h5-index 53.0

BACKGROUND : There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVID-19 triage based on chest computed tomography (CT) images, including normal, pneumonia, and COVID-19 cases.

METHODS : A total of 2,809 chest CT scans (1,105 COVID-19, 854 normal, and 850 non-3COVID-19 pneumonia cases) were acquired for this study and classified into the training set (n = 2,329) and test set (n = 480). A U-net-based convolutional neural network was used for lung segmentation, and a mask-weighted global average pooling (GAP) method was proposed for the deep neural network to improve the performance of COVID-19 classification between COVID-19 and normal or common pneumonia cases.

RESULTS : The results for lung segmentation reached a dice value of 96.5% on 30 independent CT scans. The performance of the mask-weighted GAP method achieved the COVID-19 triage with a sensitivity of 96.5% and specificity of 87.8% using the testing dataset. The mask-weighted GAP method demonstrated 0.9% and 2% improvements in sensitivity and specificity, respectively, compared with the normal GAP. In addition, fusion images between the CT images and the highlighted area from the deep learning model using the Grad-CAM method, indicating the lesion region detected using the deep learning method, were drawn and could also be confirmed by radiologists.

CONCLUSIONS : This study proposed a mask-weighted GAP-based deep learning method and obtained promising results for COVID-19 triage based on chest CT images. Furthermore, it can be considered a convenient tool to assist doctors in diagnosing COVID-19.

Zhang Hong-Tao, Sun Ze-Yu, Zhou Juan, Gao Shen, Dong Jing-Hui, Liu Yuan, Bai Xu, Ma Jin-Lin, Li Ming, Li Guang, Cai Jian-Ming, Sheng Fu-Geng

2023

artificial intelligence, computed tomography (CT), coronavirus disease 2019 (COVID-19), deep learning, global average pooling (GAP)

General General

Applying Blockchain Technology in Network Public Opinion Risk Management System in Big Data Environment.

In Computational intelligence and neuroscience

Network public opinion represents public social opinion to a certain extent and has an important impact on formulating national policies and judgment. Therefore, China and other countries attach great importance to the study of online public opinion. However, the current researches lack the combination of theory and practical cases and lack the intersection of social and natural sciences. This work aims to overcome the technical defects of traditional management systems, break through the difficulties and pain points of existing network public opinion risk management, and improve the efficiency of network public opinion risk management. Firstly, a network public opinion isolation strategy based on the infectious disease propagation model is proposed, and the optimal control theory is used to realize a functional control model to maximize social utility. Secondly, blockchain technology is used to build a network public opinion risk management system. The system is used to conduct a detailed study on identifying and perceiving online public opinion risk. Finally, a Chinese word segmentation scheme based on Long Short-Term Memory (LSTM) network model and a text emotion recognition scheme based on a convolutional neural network are proposed. Both schemes are validated on a typical corpus. The results show that when the system has a control strategy, the number of susceptible drops significantly. Two days after the public opinion is generated, the number of susceptible people decreased from 1,000 to 250; 3 days after the public opinion is generated, the number of susceptible people stabilized. 2 days after the public opinion is generated, the number of lurkers increased from 100 to 620; 3 days after the public opinion is generated, the number of lurkers stabilized. The data demonstrate that the designed isolation control strategy is effective. Changes in public opinion among infected people show that quarantine control strategies played a significant role in the early days of Corona Virus Disease 2019. The rate of change in the number of infections is more affected when quarantine controls are increased, especially in the days leading up to the outbreak. When the system adopts the optimal control strategy, the influence scope of public opinion becomes smaller, and the control becomes easier. When the dimension of the word vector of emergent events is 200, its accuracy may be higher. This method provides certain ideas for blockchain and deep learning technology in network public opinion control.

Luo Zhenqing, Zhang Cheng

2023

Public Health Public Health

Prediction of red blood cell transfusion after orthopedic surgery using an interpretable machine learning framework.

In Frontiers in surgery

OBJECTIVE : Postoperative red blood cell (RBC) transfusion is widely used during the perioperative period but is often associated with a high risk of infection and complications. However, prediction models for RBC transfusion in patients with orthopedic surgery have not yet been developed. We aimed to identify predictors and constructed prediction models for RBC transfusion after orthopedic surgery using interpretable machine learning algorithms.

METHODS : This retrospective cohort study reviewed a total of 59,605 patients undergoing orthopedic surgery from June 2013 to January 2019 across 7 tertiary hospitals in China. Patients were randomly split into training (80%) and test subsets (20%). The feature selection method of recursive feature elimination (RFE) was used to identify an optimal feature subset from thirty preoperative variables, and six machine learning algorithms were applied to develop prediction models. The Shapley Additive exPlanations (SHAP) value was employed to evaluate the contribution of each predictor towards the prediction of postoperative RBC transfusion. For simplicity of the clinical utility, a risk score system was further established using the top risk factors identified by machine learning models.

RESULTS : Of the 59,605 patients with orthopedic surgery, 19,921 (33.40%) underwent postoperative RBC transfusion. The CatBoost model exhibited an AUC of 0.831 (95% CI: 0.824-0.836) on the test subset, which significantly outperformed five other prediction models. The risk of RBC transfusion was associated with old age (>60 years) and low RBC count (<4.0 × 1012/L) with clear threshold effects. Extremes of BMI, low albumin, prolonged activated partial thromboplastin time, repair and plastic operations on joint structures were additional top predictors for RBC transfusion. The risk score system derived from six risk factors performed well with an AUC of 0.801 (95% CI: 0.794-0.807) on the test subset.

CONCLUSION : By applying an interpretable machine learning framework in a large-scale multicenter retrospective cohort, we identified novel modifiable risk factors and developed prediction models with good performance for postoperative RBC transfusion in patients undergoing orthopedic surgery. Our findings may allow more precise identification of high-risk patients for optimal control of risk factors and achieve personalized RBC transfusion for orthopedic patients.

Chen Yifeng, Cai Xiaoyu, Cao Zicheng, Lin Jie, Huang Wenyu, Zhuang Yuan, Xiao Lehan, Guan Xiaozhen, Wang Ying, Xia Xingqiu, Jiao Feng, Du Xiangjun, Jiang Guozhi, Wang Deqing

2023

RBC transfusion, interpretability, machine learning, orthopedic surgery, prediction model

General General

Field oriented control dataset of a 3-phase permanent magnet synchronous motor.

In Data in brief

This paper presents a dataset of a 3-phase Permanent Magnet Synchronous Motor (PMSM) controlled by a Field Oriented Control (FOC) scheme. The data set was generated from a simulated FOC motor control environment developed in Simulink; the model is available in the public GitHub repository. The dataset includes the motor response to various input signal shapes that are fed to the control scheme to verify the control capabilities when the motor is subjected to real life scenarios and corner conditions. Motor control is one of the most widespread fields in control engineering as it is widely used in machine tools and robots, the FOC scheme is one of the most used control approaches thanks to its performance in speed and torque control, with the drawback of having to handcraft the Proportional-Integrative-Derivative (PID) regulators using Look Up Tables (LUT). The test conditions are designed by setting a motor desired speed. Different input speed variations shapes are proposed as well as extreme scenarios where the linear behaviour of the PID regulator is challenged by applying fast and high magnitude speed variations so that the PID controller is not able to correctly follow the reference. The measured data includes both the outer and inner-loop signals of the FOC, which opens the possibility to develop non-linear control approaches such as Machine Learning (ML) and Neural Networks (NN) with different topologies to replace the linear controllers in the FOC scheme.

Nustes Juan Camilo, Pau Danilo Pietro, Gruosso Giambattista

2023-Apr

ID control, Motor control, Neural networks, Simulink