Bioterrorism

Acoustic Denoising Using Artificial Intelligence for Wood-Boring Pests Larvae Early Monitoring.

Acoustic detection technology is a new method for early monitoring of wood-boring pests, and the effective denoising methods are the premise of acoustic detection in forests. This paper used sensors to record larval feeding sounds and various environmental noises, and two kinds of sounds were mixed to obtain the noisy feeding sounds with controllable noise intensity. Then, the time domain denoisi...

Defining the Morbidity of Robot-Assisted Radical Cystectomy with Intracorporeal Urinary Diversion: Adoption of the Comprehensive Complication Index.

The Clavien-Dindo Classification (CDC) only reports the postoperative complication of highest grade. It is thus of limited value for radical cystectomy, after which patients usually experience multiple complications. The Comprehensive Complication Index (CCI) is a novel scoring system, which incorporates all postoperative events in one single value. The study aimed to adopt the CCI for the evalua...

Probabilistic Seismic Response Prediction of Three-Dimensional Structures Based on Bayesian Convolutional Neural Network.

Seismic response prediction is a challenging problem and is significant in every stage during a structure's life cycle. Deep neural network has proven...

Multi-End Physics-Informed Deep Learning for Seismic Response Estimation.

As a structural health monitoring (SHM) system can hardly measure all the needed responses, estimating the target response from the measured responses...

Automatic microscopic diagnosis of diseases using an improved UNet++ architecture.

Anthrax is a severe infectious disease caused by the Bacillus anthracis bacterium. This paper aims to design and implement a fast and reliable system ...

Combining mathematical modeling and deep learning to make rapid and explainable predictions of the patient-specific response to anticoagulant therapy under venous flow.

Anticoagulant drugs are commonly prescribed to prevent hypercoagulable states in patients with venous thromboembolism. The choice of the most efficien...

A multimodal deep learning model for cardiac resynchronisation therapy response prediction.

We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and car...

Defining Normal Ranges of Skeletal Muscle Area and Skeletal Muscle Index in Children on CT Using an Automated Deep Learning Pipeline: Implications for Sarcopenia Diagnosis.

Skeletal muscle area (SMA), representing skeletal muscle cross-sectional area at the L3 vertebral level, and skeletal muscle index (SMI), representin...

Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease.

Inflammatory bowel diseases (IBDs), including ulcerative colitis and Crohn's disease, affect several million individuals worldwide. These diseases are...

Digital Twins-Based Impact Response Prediction of Prestressed Steel Structure.

Civil infrastructure O&M requires intelligent monitoring techniques and control methods to ensure safety. Unfortunately, tedious modeling efforts and ...

Mental speed is high until age 60 as revealed by analysis of over a million participants.

Response speeds in simple decision-making tasks begin to decline from early and middle adulthood. However, response times are not pure measures of men...

Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy.

In this study, a novel deep learning-based methodology was investigated to predict breast cancer response to neo-adjuvant chemotherapy (NAC) using the...

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition.

The industrial manufacturing sector is undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Unde...

COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization.

The coronavirus outbreak 2019, called COVID-19, which originated in Wuhan, negatively affected the lives of millions of people and many people died fr...

PRCTC: a machine learning model for prediction of response to corticosteroid therapy in COVID-19 patients.

Corticosteroid has been proved to be one of the few effective treatments for COVID-19 patients. However, not all the patients were suitable for cortic...

Comparison of antibody response to SARS-CoV-2 after two doses of inactivated virus and BNT162b2 mRNA vaccines in kidney transplant.

BACKGROUND: Antibody response against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) after mRNA or adenoviral vector-based vaccines is w...

Artificial Neural Network Analysis-Based Immune-Related Signatures of Primary Non-Response to Infliximab in Patients With Ulcerative Colitis.

Infliximab (IFX) is an effective medication for ulcerative colitis (UC) patients. However, one-third of UC patients show primary non-response (PNR) to...

Mitigating Cold Start Problem in Serverless Computing with Function Fusion.

As Artificial Intelligence (AI) is becoming ubiquitous in many applications, serverless computing is also emerging as a building block for developing ...

Phase response approaches to neural activity models with distributed delay.

In weakly coupled neural oscillator networks describing brain dynamics, the coupling delay is often distributed. We present a theoretical framework to...

Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays.

SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficien...