Mass Effect is some of the best science fiction ever made. That may sound like a grandiose comment, but it's true. As a trilogy, the original games from 2007-2013 effortlessly plucked the most cerebral ideas from the sci-fi genre and slotted them into a memorable military role-playing game that had players invested from beginning to controversial end. Whether you prefer the hopeful, optimistic outlook of Asimov, the dark and reflective commentary of Shelley, the accessible thought experiments of Star Trek, or the arch melodrama of Battlestar Galactica, Mass Effect has it all. The trilogy is as happy grazing on the western-inspired tropes of Star Wars as the "hard" sci-fi of Iain M Banks, blending all its moods and micro-stories into a compelling, believable galaxy that somehow walks a line between breathless optimism and suffocating bleakness.

It appears that Microsoft is planning to expand voice control of Xbox One consoles beyond Cortana. Windows Central reports that Xbox One support for Amazon's Alexa and the Google Assistant is on the way. While Cortana lets you control an Xbox One through either a Kinect or a headset, voice control will be much more convenient through a Home, Echo or smartphone. If and when the new feature rolls out, a "Digital Assistants" section will appear in the Kinect & Devices menu. Enable your preferred assistant on the Xbox One, install an Xbox app on your phone or voice-activated speaker and you should be good to go.

Cluster analysis methods are used to identify homogeneous subgroups in a data set. In biomedical applications, one frequently applies cluster analysis in order to identify biologically interesting subgroups. In particular, one may wish to identify subgroups that are associated with a particular outcome of interest. Conventional clustering methods generally do not identify such subgroups, particularly when there are a large number of high-variance features in the data set. Conventional methods may identify clusters associated with these high-variance features when one wishes to obtain secondary clusters that are more interesting biologically or more strongly associated with a particular outcome of interest. A modification of sparse clustering can be used to identify such secondary clusters or clusters associated with an outcome of interest. This method correctly identifies such clusters of interest in several simulation scenarios. The method is also applied to a large prospective cohort study of temporomandibular disorders and a leukemia microarray data set.