Volume 1 Issue 9, September 2021

A new study uses longitudinal mobility data to identify how individuals behave at different stages of the COVID pandemic, elucidating benefits and challenges of using this type of data for decision-making by epidemiologists and policy-makers.

A framework called Detect is proposed to detect subtle effects of brain disorders, making it possible to delineate anomalous brain connections within specific individuals.

Recent work introduces a powerful new web tool that enables a faster and statistically more reliable data mining of transcriptomics and metatranscriptomics for inflammatory bowel disease (IBD) research.

The authors show that accurate bootstrap confidence limits on inferred evolutionary relationships of species can be estimated by bootstrapping a collection of little samples of very long sequence alignments. Little bootstraps take a fraction of computer time and memory compared to the standard bootstrap, enabling big data analytics on personal computers.

An efficient method for parallelizing the contraction of tensor networks pushes the boundaries for the classical simulation of quantum computation, and aids the development of quantum algorithms and hardware.

Combining human mobility data and nonlinear mathematical analysis techniques, this study offers insights into the interplay between human behavior, disease transmission and non-pharmaceutical interventions during the COVID-19 pandemic.

The authors propose Detect, a browser-based anomaly detection framework for diffusion magnetic resonance imaging tractometry data. The tool leverages normative microstructural brain features derived from healthy participants using deep autoencoders to detect anomalies at the individual level.

The authors demonstrate how neural systems can encode cognitive functions, and use the proposed model to train robust, scalable deep neural networks that are explainable and capable of symbolic reasoning and domain generalization.

The authors propose EPICS, a method to predict microbial community structures by estimating effective pairwise interactions that subsume high-order interactions between species. EPICS is more efficient and applicable to larger communities than current approaches.