Escherichia coli DsbD transports electrons across the plasma membrane, a pathway that leads to the reduction of protein disulfide bonds. Three secreted thioredoxin-like factors, DsbC, DsbE, and DsbG, reduce protein disulfide bonds whereby an active site C-X-X-C motif is oxidized to generate a disulfide bond. DsbD catalyzes the reduction of the disulfide of DsbC, DsbE, and DsbG but not of the thioredoxin-like oxidant DsbA. The reduction of DsbC, DsbE, and DsbG occurs by transport of electrons from cytoplasmic thioredoxin to the C-terminal thioredoxin-like domain of DsbD (DsbD(C)). The N-terminal domain of DsbD, DsbD(N), acts as a versatile adaptor in electron transport and is capable of forming disulfides with oxidized DsbC, DsbE, or DsbG as well as with reduced DsbD(C). Isolated DsbD(N) is functional in electron transport in vitro. Crystallized DsbD(N) assumes an immunoglobulin-like fold that encompasses two active site cysteines, C103 and C109, forming a disulfide bond between beta-strands. The disulfide of DsbD(N) is shielded from the environment and capped by a phenylalanine (F70). A model is discussed whereby the immunoglobulin fold of DsbD(N) may provide for the discriminating interaction with thioredoxin-like factors, thereby triggering movement of the phenylalanine cap followed by disulfide rearrangement.

Power ISA™ Version 3.1 has introduced a new family of matrix math instructions, collectively known as the Matrix-Multiply Assist (MMA) facility. The instructions in this facility implement numerical linear algebra operations on small matrices and are meant to accelerate computation-intensive kernels, such as matrix multiplication, convolution and discrete Fourier transform. These instructions have led to a power-and area-efficient implementation of a high throughput math engine in the future POWER10 processor. Performance per core is 4 times better, at constant frequency, than the previous generation POWER9 processor. We also advocate the use of compiler builtins as the preferred way of leveraging these instructions, which we illustrate through case studies covering matrix multiplication and convolution.

For applications in healthcare, physics, energy, robotics, and many other fields, designing maximally informative experiments is valuable, particularly when experiments are expensive, timeconsuming, or pose safety hazards. While existing approaches can sequentially design experiments based on prior observation history, many of these methods do not extend to implicit models, where simulation is possible but computing the likelihood is intractable. Furthermore, they often require either significant online computation during deployment or a differentiable simulation system. We introduce Reinforcement Learning for Deep Adaptive Design (RL-DAD), a method for simulation-based optimal experimental design for non-differentiable implicit models. RL-DAD extends prior work in policy-based Bayesian Optimal Experimental Design (BOED) by reformulating it as a Markov Decision Process with a reward function based on likelihood-free information lower bounds, which is used to learn a policy via deep reinforcement learning. The learned design policy maps prior histories to experiment designs offline and can be quickly deployed during online execution. We evaluate RL-DAD and find that it performs competitively with baselines on three benchmarks.

Propagation of ultrasonic wave in Carbon Fiber Reinforced Polymer (CFRP) is greatly influenced by the material’s matrix, resins and fiber volume ratio. Laser ultrasonic broadband spectral technique has been demonstrated for porosity and fiber volume ratio extraction on unidirection aligned CFRP laminates. Porosity in the matrix materials can be calculated by longitudinal wave attenuation and accurate fiber volume ratio can be derived by combined velocity through the high strength carbon fiber and the matrix material with further consideration of porosity effects. The results have been benchmarked by pulse-echo ultrasonic tests, gas pycnometer and thermal gravimetric analysis (TGA). The potentials and advantages of the laser ultrasonic technique as a non-destructive evaluation method for CFRP carbon fiber volume fraction evaluation were demonstrated.