The strength of certain neural connections can predict how well someone can learn math, and mild electrically stimulating these networks can boost learning, according to a study published on July 1^st in the open-access journal PLOS Biology by Roi Cohen Kadosh from University of Surrey, United Kingdom, and colleagues.

MIT researchers developed a new system that enables a robot to use reflected Wi-Fi signals to identify the shape of a 3D object that is hidden from view, which could be especially useful in warehouse and factory settings. 

The research team proposed a novel discrete-modulated coherent-state quantum key distribution with basis encoding. In this scheme, Alice performs discrete modulation on the coherent state and sends it to Bob. Bob performs coherent detection and encodes the key in the base selection, ultimately publishing the measurement results.

In a paper published in National Science Review, the team of Pro. Liu present an innovative computational framework, the sample-perturbed Gaussian graphical model (sPGGM), designed to analyse disease progression and identify pre-disease stages at the specific sample/cell level based on optimal transport theory and Gaussian graphical models. The proposed sPGGM provides a new single-sample way to identify the pre-disease state and discover signaling molecules leading to potential disease, which showcases exceptional effectiveness and robustness for both bulk and single-cell data analyses, offering a novel perspective for personalized disease prediction.