Fast and robust small infrared target detection using absolute directional mean difference algorithm

Small infrared target detection using absolute average difference weighted by cumulative directional derivatives

https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(20)30085-9/pdf

Machine learning in the clinical microbiology laboratory: has the time come for routine practice?

Antimicrobial resistance (AMR) remains one of the most challenging phenomena of modern medicine. Machine learning (ML) is a subfield of artificial intelligence that focuses on the development of algorithms that learn how to accurately predict outcome variables using large sets of predictor variables that are typically not hand selected and are minimally curated. Models are parameterized using a training dataset and then applied to a test dataset on which predictive performance is evaluated. The application of ML algorithms to the problem of AMR has garnered increasing interest in the past 5 years due to the exponential growth of experimental and clinical data, heavy investment in computational capacity, improvements in algorithm performance and increasing urgency for innovative approaches to reducing the burden of disease. Here, we review the current state of research at the intersection of ML and AMR with an emphasis on three domains of work. The first is the prediction of AMR using genomic data. The second is the use of ML to gain insight into the cellular functions disrupted by antibiotics, which forms the basis for understanding mechanisms of action and developing novel anti-infectives. The third focuses on the application of ML for antimicrobial stewardship using data extracted from the electronic health record. Though the use of ML for understanding, diagnosing, treating and preventing AMR is still in its infancy, the continued growth of data and interest ensures it will become an important tool for future translational research programs.

Applications of Machine Learning to the Problem of Antimicrobial Resistance: an Emerging Model for Translational Research

By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.

/pdf/accelerating-antibiotic-discovery-through-artificial-4c5o9yarw3.pdf

Accelerating antibiotic discovery through artificial intelligence.

Power grid operators assess situational awareness using time-tagged measurements from phasor measurement units (PMUs) placed at multiple locations in a network. However, synchrophasor measurements are prone to anomalies which may impact the performance of phasor based applications. Anomalies include any deviation from expected measurements resulting from power system events or bad data. Bad data include data errors or loss of information due to failures in supporting synchrophasor cyber infrastructure. It is necessary to flag bad data before utilizing for an application. This work proposes a tool for the detection and classification of anomalous data using an unsupervised stacked ensemble learning algorithm. The proposed synchrophasor anomaly detection and classification (SyADC) tool analyzes a selected window of data points using a combination of three unsupervised methods, namely: isolation forest, KMeans and LoOP. The method classifies the data as anomalies or normal data with more than 99% recall. The method also provides a probability of the data to be an event or bad data with more than 99% recall. Results for the IEEE 14 and 68 bus systems with synchrophasor data obtained using Real-Time Digital Simulator and data of industrial PMUs highlight the superiority of the algorithm to detect and classify anomalies.

Real-Time Synchrophasor Data Anomaly Detection and Classification Using Isolation Forest, KMeans, and LoOP

Aims Predicting bacterial resistance provides valuable information that can assist in clinical decisions. With recent advances in whole genome sequencing technology, the detection of antibiotic resistance (AR) proteins directly from genomic data is becoming feasible. AR genes/proteins can be identified using best-hit methods that work by comparing candidate sequences with known AR genes in public databases. However, these approaches may fail to detect resistance genes with sequences that differ significantly from known sequences. Our goal is to develop a machine learning technique to accurately predict capreomycin resistance in Mycobacteria with low false discovery rates. Methods and results We present a stacked ensemble learning model as an alternative to traditional DNA sequence alignment-based methods using optimal features generated from the physicochemical, evolutionary and secondary structure properties of protein sequences. We train logistic regression, C5.0 and support vector machine (SVM) algorithms as our base classifiers, and our stacked ensemble predictors combine the results from the base classifiers to achieve higher accuracy. Compared with our most accurate base classifier (SVM), our most accurate stacked ensemble predictor increases training accuracy by 2·43%. Our stacked ensemble predictors achieve test accuracy up to 81·25%. Conclusions We developed a stacked ensemble model to predict capreomycin resistance for Mycobacteria with an accuracy >80% using protein sequences with sequence similarity ranging between 10% and 70%. This performance cannot be achieved with best-hit methods due to differences in sequence similarity. Significance and impact of the study Today an estimated one-half million cases of multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis (TB) occur annually worldwide at a great cost. Because capreomycin is a second-line drug used to treat drug-resistant TB, the ability to use a machine learning approach to classify capreomycin-resistant TB in a timely manner is crucial for the successful treatment of MDR or XDR TB.

Capreomycin resistance prediction in two species of Mycobacterium using a stacked ensemble method.

In this paper, a new method to detect small targets in infrared images is presented. The proposed method is based on the optical responses of infrared photodetectors, mainly point spread function (PSF). The effects of PSF on gray level and shape of the targets is used to distinguish the targets from background clutter. Also, a novel operator called Lapalacian of Point Spread Function (LoPSF) is proposed to identify location and size of the infrared targets. Since detecting the variable-size targets in infrared search and track systems (IRST) is of critical importance, unlike previous methods of target detection, the proposed method can robustly detect variable-size targets. The proposed algorithm is applied on a set of real infrared images which are captured under different conditions such as target size, target distance, background clutter etc.. Simulation results demonstrate the effectiveness and validity of the proposed method in comparison with the other conventional methods.

A new method for detecting variable-size infrared targets

http://www.thelancet.com/article/S2352396422001748/pdf

Sequence determinants of human-cell entry identified in ACE2-independent bat sarbecoviruses: A combined laboratory and computational network science approach

Reconstructing and visualizing phylogenetic relationships among living organisms is a fundamental challenge because not all organisms share the same genes. As a result, the first phylogenetic visualizations employed a single gene, e.g., rRNA genes, sufficiently conserved to be present in all organisms but divergent enough to provide discrimination between groups. As more genome data became available, researchers began concatenating different combinations of genes or proteins to construct phylogenetic trees believed to be more robust because they incorporated more information. However, the genes or proteins chosen were based on ad hoc approaches. The large number of complete genome sequences available today allows the use of whole genomes to analyze relationships among organisms rather than using an ad hoc set of genes. We present a systematic approach for constructing a phylogenetic tree based on simultaneously clustering the complete proteomes of 360 bacterial species. From the homologous clusters, we identify 49 protein sequences shared by 99% of the organisms to build a tree. Of the 49 sequences, 47 have homologous sequences in both archaea and eukarya. The clusters are also used to create a network from which bacterial species with horizontally-transferred genes from other phyla are identified.

Ehdieh Khaledian

Papers

Real-Time Synchrophasor Data Anomaly Detection and Classification Using Isolation Forest, KMeans, and LoOP

Capreomycin resistance prediction in two species of Mycobacterium using a stacked ensemble method.

A new method for detecting variable-size infrared targets

Sequence determinants of human-cell entry identified in ACE2-independent bat sarbecoviruses: A combined laboratory and computational network science approach

A Systematic Approach to Bacterial Phylogeny Using Order Level Sampling and Identification of HGT Using Network Science.