68-channel neural signal processing system-on-chip with integrated feature extraction, compression, and hardware accelerators for neuroprosthetics in 22 nm FDSOI.

Introduction: Multi-channel electrophysiology systems for recording of neuronal activity face significant data throughput limitations, hampering real-time, data-informed experiments. These limitations impact both experimental neurobiology research and next-generation neuroprosthetics.

Methods: We present a novel solution that leverages the high integration density of 22nm fully-depleted silicon-on-insulator technology to address these challenges.

From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction.

Accurate in silico prediction of protein-ligand binding affinity is important in the early stages of drug discovery. Deep learning-based methods exist but have yet to overtake more conventional methods such as giga-docking largely due to their lack of generalizability. To improve generalizability, we need to understand what these models learn from input protein and ligand data.

Supramolecular Motifs in the Crystal Structures of Triethylbenzene Derivatives Bearing Pyridinium Subunits in Combination with Pyrimidinyl or Pyridinyl Groups.

A series of mono- and dicationic 1,3,5-trisubstituted 2,4,6-triethylbenzenes containing pyridinium groups in combination with aminopyrimidine-/aminopyridine-based recognition units were synthesized and crystallographically studied. The combination of neutral and ionic building blocks represents a promising strategy for the development of effective and selective artificial receptors for anionic substrates. In the crystalline state, the investigated compounds show a tendency to bind the counterion PF in the cavity formed by the three functionalized side-arms.

Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study.

Background: Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level.