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Surgery Surgery

Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning.

In Biophysical reviews

Cardiovascular diseases are the leading cause of mortality, morbidity, and hospitalization around the world. Recent technological advances have facilitated analyzing, visualizing, and monitoring cardiovascular diseases using emerging computational fluid dynamics, blood flow imaging, and wearable sensing technologies. Yet, computational cost, limited spatiotemporal resolution, and obstacles for thorough data analysis have hindered the utility of such techniques to curb cardiovascular diseases. We herein discuss how leveraging machine learning techniques, and in particular deep learning methods, could overcome these limitations and offer promise for translation. We discuss the remarkable capacity of recently developed machine learning techniques to accelerate flow modeling, enhance the resolution while reduce the noise and scanning time of current blood flow imaging techniques, and accurate detection of cardiovascular diseases using a plethora of data collected by wearable sensors.

Moradi Hamed, Al-Hourani Akram, Concilia Gianmarco, Khoshmanesh Farnaz, Nezami Farhad R, Needham Scott, Baratchi Sara, Khoshmanesh Khashayar

2023-Feb

Cardiovascular diseases, Computational fluid dynamics, Flow imaging, Machine learning, Wearable sensors

General General

Deep learning-based urban morphology for city-scale environmental modeling.

In PNAS nexus

Herein, we introduce a novel methodology to generate urban morphometric parameters that takes advantage of deep neural networks and inverse modeling. We take the example of Chicago, USA, where the Urban Canopy Parameters (UCPs) available from the National Urban Database and Access Portal Tool (NUDAPT) are used as input to the Weather Research and Forecasting (WRF) model. Next, the WRF simulations are carried out with Local Climate Zones (LCZs) as part of the World Urban Data Analysis and Portal Tools (WUDAPT) approach. Lastly, a third novel simulation, Digital Synthetic City (DSC), was undertaken where urban morphometry was generated using deep neural networks and inverse modeling, following which UCPs are re-calculated for the LCZs. The three experiments (NUDAPT, WUDAPT, and DSC) were compared against Mesowest observation stations. The results suggest that the introduction of LCZs improves the overall model simulation of urban air temperature. The DSC simulations yielded equal to or better results than the WUDAPT simulation. Furthermore, the change in the UCPs led to a notable difference in the simulated temperature gradients and wind speed within the urban region and the local convergence/divergence zones. These results provide the first successful implementation of the digital urban visualization dataset within an NWP system. This development now can lead the way for a more scalable and widespread ability to perform more accurate urban meteorological modeling and forecasting, especially in developing cities. Additionally, city planners will be able to generate synthetic cities and study their actual impact on the environment.

Patel Pratiman, Kalyanam Rajesh, He Liu, Aliaga Daniel, Niyogi Dev

2023-Mar

WUDAPT, deep neural network, urban boundary layer, urban canopy parameters, urban climate, weather research and forecasting model

Public Health Public Health

An anti-infodemic virtual center for the Americas.

In Revista panamericana de salud publica = Pan American journal of public health

The Pan American Health Organization/World Health Organization (PAHO/WHO) Anti-Infodemic Virtual Center for the Americas (AIVCA) is a project led by the Department of Evidence and Intelligence for Action in Health, PAHO and the Center for Health Informatics, PAHO/WHO Collaborating Center on Information Systems for Health, at the University of Illinois, with the participation of PAHO staff and consultants across the region. Its goal is to develop a set of tools-pairing AI with human judgment-to help ministries of health and related health institutions respond to infodemics. Public health officials will learn about emerging threats detected by the center and get recommendations on how to respond. The virtual center is structured with three parallel teams: detection, evidence, and response. The detection team will employ a mixture of advanced search queries, machine learning, and other AI techniques to sift through more than 800 million new public social media posts per day to identify emerging infodemic threats in both English and Spanish. The evidence team will use the EasySearch federated search engine backed by AI, PAHO's knowledge management team, and the Librarian Reserve Corps to identify the most relevant authoritative sources. The response team will use a design approach to communicate recommended response strategies based on behavioural science, storytelling, and information design approaches.

Brooks Ian, D’Agostino Marcelo, Marti Myrna, McDowell Kate, Mejia Felipe, Betancourt-Cravioto Miguel, Gatzke Lisa, Hicks Elaine, Kyser Rebecca, Leicht Kevin, Pereira Dos Santos Eliane, Saw Jessica Jia-Wen, Tomio Ailin, Garcia Saiso Sebastian

2023

Americas, COVID-19, Public Health Informatics, artificial intelligence, communication, social media

General General

DeepDetect: Deep Learning of Peptide Detectability Enhanced by Peptide Digestibility and Its Application to DIA Library Reduction.

In Analytical chemistry

In tandem mass spectrometry-based proteomics, proteins are digested into peptides by specific protease(s), but generally only a fraction of peptides can be detected. To characterize detectable proteotypic peptides, we have developed a series of methods to predict peptide digestibility and detectability. Here, we propose a bidirectional long short-term memory (BiLSTM)-based algorithm, named DeepDetect, for the prediction of peptide detectability enhanced by peptide digestibility. Compared with existing algorithms, DeepDetect is featured by its improved prediction accuracy for a wide range of commonly used proteases, covering trypsin, ArgC, chymotrypsin, GluC, LysC, AspN, LysN, and LysargiNase. On 11 test data sets from E. coli, yeast, mouse, and human samples, DeepDetect achieved higher prediction accuracies than PepFormer, a state-of-the-art deep-learning-based peptide detectability prediction algorithm. The results further demonstrated that peptide digestibility can substantially enhance the performance of peptide detectability predictors. As an application, DeepDetect was used to reduce the in silico predicted spectral libraries in data-independent acquisition mass spectrometry data analysis. Experiments using DIA-NN software showed that DeepDetect can significantly accelerate the library search without loss of peptide and protein identification sensitivity.

Yang Jinghan, Cheng Zhiyuan, Gong Fuzhou, Fu Yan

2023-Mar-12

Pathology Pathology

Artificial intelligence-based HDX (AI-HDX) prediction reveals fundamental characteristics to protein dynamics: Mechanisms on SARS-CoV-2 immune escape.

In iScience

Three-dimensional structure and dynamics are essential for protein function. Advancements in hydrogen-deuterium exchange (HDX) techniques enable probing protein dynamic information in physiologically relevant conditions. HDX-coupled mass spectrometry (HDX-MS) has been broadly applied in pharmaceutical industries. However, it is challenging to obtain dynamics information at the single amino acid resolution and time consuming to perform the experiments and process the data. Here, we demonstrate the first deep learning model, artificial intelligence-based HDX (AI-HDX), that predicts intrinsic protein dynamics based on the protein sequence. It uncovers the protein structural dynamics by combining deep learning, experimental HDX, sequence alignment, and protein structure prediction. AI-HDX can be broadly applied to drug discovery, protein engineering, and biomedical studies. As a demonstration, we elucidated receptor-binding domain structural dynamics as a potential mechanism of anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody efficacy and immune escape. AI-HDX fundamentally differs from the current AI tools for protein analysis and may transform protein design for various applications.

Yu Jiali, Uzuner Ugur, Long Bin, Wang Zachary, Yuan Joshua S, Dai Susie Y

2023-Apr-21

Immunology, Virology

General General

Object Detection During Newborn Resuscitation Activities

IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 3, pp. 796-803, March 2020

Birth asphyxia is a major newborn mortality problem in low-resource countries. International guideline provides treatment recommendations; however, the importance and effect of the different treatments are not fully explored. The available data is collected in Tanzania, during newborn resuscitation, for analysis of the resuscitation activities and the response of the newborn. An important step in the analysis is to create activity timelines of the episodes, where activities include ventilation, suction, stimulation etc. Methods: The available recordings are noisy real-world videos with large variations. We propose a two-step process in order to detect activities possibly overlapping in time. The first step is to detect and track the relevant objects, like bag-mask resuscitator, heart rate sensors etc., and the second step is to use this information to recognize the resuscitation activities. The topic of this paper is the first step, and the object detection and tracking are based on convolutional neural networks followed by post processing. Results: The performance of the object detection during activities were 96.97 % (ventilations), 100 % (attaching/removing heart rate sensor) and 75 % (suction) on a test set of 20 videos. The system also estimate the number of health care providers present with a performance of 71.16 %. Conclusion: The proposed object detection and tracking system provides promising results in noisy newborn resuscitation videos. Significance: This is the first step in a thorough analysis of newborn resuscitation episodes, which could provide important insight about the importance and effect of different newborn resuscitation activities

Øyvind Meinich-Bache, Kjersti Engan, Ivar Austvoll, Trygve Eftestøl, Helge Myklebust, Ladislaus Blacy Yarrot, Hussein Kidanto, Hege Ersdal

2023-03-14