• A Nature paper demonstrates that unsupervised word-embedding models trained on the materials-science literature can automatically rediscover latent knowledge and predict previously unknown thermoelectric compounds, illustrating how NLP can accelerate scientific discovery.  
• A biorxiv preprint describes a DNA-based “ticker-tape” recorder that encodes temporal ion-concentration data into a growing DNA strand with minute-scale resolution, offering a potential route to logging neural activity directly into genomic memory.  
• A Science article reports that precise spike-train patterns in developing neurons act as a temporal code to drive differential gene expression required for olfactory-circuit wiring, highlighting activity-dependent developmental programming.  
• A biorxiv study presents complete 3-D reconstructions of single molecularly defined claustral and cortical neurons, revealing unexpected morphological diversity within genetically identical cell classes.  
• A Reason magazine essay argues for the permissibility and desirability of human germline editing and “designer babies,” framing the debate around parental rights and technological inevitability.  
• Bloomberg reports on the U.S. government’s widening investigations that have led to the dismissal or resignation of numerous Chinese-American cancer researchers, raising concerns about racial profiling and its impact on open scientific collaboration.  
• Facebook AI released a ResNeXt-101-32×8d model pre-trained on 3.5 billion Instagram images and fine-tuned on ImageNet, achieving a then-record 85.4 % top-1 accuracy and demonstrating the power of weakly supervised learning at web scale.  
• An Ars Technica op-ed proposes building a global, real-time DNA sequencing network to detect emerging pathogens early, treating genomic surveillance as critical infrastructure for pandemic prevention.