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.

Informing antimicrobial management in the context of COVID-19: understanding the longitudinal dynamics of C-reactive protein and procalcitonin.

Background: To characterise the longitudinal dynamics of C-reactive protein (CRP) and Procalcitonin (PCT) in a cohort of hospitalised patients with COVID-19 and support antimicrobial decision-making.

Methods: Longitudinal CRP and PCT concentrations and trajectories of 237 hospitalised patients with COVID-19 were modelled. The dataset comprised of 2,021 data points for CRP and 284 points for PCT.

Prediction of Cellular Burden with Host-Circuit Models.

Heterologous gene expression draws resources from host cells. These resources include vital components to sustain growth and replication, and the resulting cellular burden is a widely recognized bottleneck in the design of robust circuits. In this tutorial we discuss the use of computational models that integrate gene circuits and the physiology of host cells.

Development and Delivery of a Real-time Hospital-onset COVID-19 Surveillance System Using Network Analysis.

Background: Understanding nosocomial acquisition, outbreaks, and transmission chains in real time will be fundamental to ensuring infection-prevention measures are effective in controlling coronavirus disease 2019 (COVID-19) in healthcare. We report the design and implementation of a hospital-onset COVID-19 infection (HOCI) surveillance system for an acute healthcare setting to target prevention interventions.

Methods: The study took place in a large teaching hospital group in London, United Kingdom.

Detecting carbapenemase-producing Enterobacterales (CPE): an evaluation of an enhanced CPE infection control and screening programme in acute care.

Objectives: The transmission of carbapenemase-producing Enterobacterales (CPE) poses an increasing healthcare challenge. A range of infection prevention activities, including screening and contact precautions, are recommended by international and national guidelines. We evaluated the introduction of an enhanced screening programme in a multisite London hospital group.

Growth Defects and Loss-of-Function in Synthetic Gene Circuits.

Synthetic gene circuits perturb the physiology of their cellular host. The extra load on endogenous processes shifts the equilibrium of resource allocation in the host, leading to slow growth and reduced biosynthesis. Here we built integrated host-circuit models to quantify growth defects caused by synthetic gene circuits.

Sources, propagation and consequences of stochasticity in cellular growth.

Growth impacts a range of phenotypic responses. Identifying the sources of growth variation and their propagation across the cellular machinery can thus unravel mechanisms that underpin cell decisions. We present a stochastic cell model linking gene expression, metabolism and replication to predict growth dynamics in single bacterial cells.

Signaling Tug-of-War Delivers the Whole Message.

How do cells transmit biochemical signals accurately? It turns out, pushing and pulling can go a long way.

Mechanistic links between cellular trade-offs, gene expression, and growth.

Intracellular processes rarely work in isolation but continually interact with the rest of the cell. In microbes, for example, we now know that gene expression across the whole genome typically changes with growth rate. The mechanisms driving such global regulation, however, are not well understood.

Saving lives, not sacrificing them: the inevitable clash between medical research and the protection of medical subjects.

Throughout history, medical practitioners have been admonished to do nothing in treating their patients that might result in harming them. It was not until the 20th century that such teaching was codified in specific legislation. Spurred on by the perversity of Nazi doctors during the Holocaust, world leaders produced the Nuremberg Code in 1947 and the Declaration of Helsinki in 1964.

Divide and conquer the Hilbert space of translation-symmetric spin systems.

Iterative methods that operate with the full Hamiltonian matrix in the untrimmed Hilbert space of a finite system continue to be important tools for the study of one- and two-dimensional quantum spin models, in particular in the presence of frustration. To reach sensible system sizes such numerical calculations heavily depend on the use of symmetries. We describe a divide-and-conquer strategy for implementing translation symmetries of finite spin clusters, which efficiently uses and extends the "sublattice coding" of H.

Cumulative signal transmission in nonlinear reaction-diffusion networks.

Quantifying signal transmission in biochemical systems is key to uncover the mechanisms that cells use to control their responses to environmental stimuli. In this work we use the time-integral of chemical species as a measure of a network's ability to cumulatively transmit signals encoded in spatiotemporal concentrations. We identify a class of nonlinear reaction-diffusion networks in which the time-integrals of some species can be computed analytically.

Reduced Oct4 expression directs a robust pluripotent state with distinct signaling activity and increased enhancer occupancy by Oct4 and Nanog.

Embryonic stem cell (ESC) pluripotency is governed by a gene regulatory network centered on the transcription factors Oct4 and Nanog. To date, robust self-renewing ESC states have only been obtained through the chemical inhibition of signaling pathways or enforced transgene expression. Here, we show that ESCs with reduced Oct4 expression resulting from heterozygosity also exhibit a stabilized pluripotent state.