In-Datacenter Performance Analysis of a Tensor Processing Unit

Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs (caches, out-of-order execution, multithreading, multiprocessing, prefetching, ...) that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X - 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X - 80X higher. Moreover, using the GPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.Comment: 17 pages, 11 figures, 8 tables. To appear at the 44th International Symposium on Computer Architecture (ISCA), Toronto, Canada, June 24-28, 201

BIOINFORMATICS APPLICATIONS NOTE Databases and ontologies

MtbRegList, a database dedicated to the analysis o

Comparison of crystal structures of human type 3 3α-hydroxysteroid dehydrogenase reveals an “induced-fit” mechanism and a conserved basic motif involved in the binding of androgen

The aldo-keto reductase (AKR) human type 3 3α-hydroxysteroid dehydrogenase (h3α–HSD3, AKR1C2) plays a crucial role in the regulation of the intracellular concentrations of testosterone and 5α-dihydrotestosterone (5α-DHT), two steroids directly linked to the etiology and the progression of many prostate diseases and cancer. This enzyme also binds many structurally different molecules such as 4-hydroxynonenal, polycyclic aromatic hydrocarbons, and indanone. To understand the mechanism underlying the plasticity of its substrate-binding site, we solved the binary complex structure of h3α–HSD3-NADP(H) at 1.9 Å resolution. During the refinement process, we found acetate and citrate molecules deeply engulfed in the steroid-binding cavity. Superimposition of this structure with the h3α–HSD3-NADP(H)-testosterone/acetate ternary complex structure reveals that one of themobile loops forming the binding cavity operates a slight contraction movement against the citrate molecule while the side chains of many residues undergo numerous conformational changes, probably to create an optimal binding site for the citrate. These structural changes, which altogether cause a reduction of the substrate-binding cavity volume (from 776 Å3 in the presence of testosterone/acetate to 704 Å3 in the acetate/citratecomplex), are reminiscent of the “induced-fit” mechanism previously proposed for the aldose reductase, another member of the AKR superfamily. We also found that the replacement of residues Arg301 and Arg304, localized near the steroid-binding cavity, significantly affects the 3α–HSD activity of this enzyme toward 5α-DHT and completely abolishes its 17β–HSD activity on 4-dione. All these results have thus been used to reevaluate the binding mode of this enzyme for androgens

Elevated Calprotectin and Abnormal Myeloid Cell Subsets Discriminate Severe from Mild COVID-19

International audienc

Comparison of crystal structures of human androgen receptor ligand-binding domain complexed with various agonists reveals molecular determinants responsible for binding affinity

Androgens exert their effects by binding to the highly specific androgen receptor (AR). In addition to natural potent androgens, AR binds a variety of synthetic agonist or antagonist molecules with different affinities. To identify molecular determinants responsible for this selectivity, we have determined the crystal structure of the human androgen receptor ligand-binding domain (hARLBD) in complex with two natural androgens, testosterone (Testo) and dihydrotestosterone (DHT), and with an androgenic steroid used in sport doping, tetrahydrogestrinone (THG), at 1.64, 1.90, and 1.75 Å resolution, respectively. Comparison of these structures first highlights the flexibility of several residues buried in the ligand-binding pocket that can accommodate a variety of ligand structures. As expected, the ligand structure itself (dimension, presence, and position of unsaturated bonds that influence the geometry of the steroidal nucleus or the electronic properties of the neighboring atoms, etc.) determines the number of interactions it can make with the hARLBD. Indeed, THG—which possesses the highest affinity—establishes more van der Waals contacts with the receptor than the other steroids, whereas the geometry of the atoms forming electrostatic interactions at both extremities of the steroid nucleus seems mainly responsible for the higher affinity measured experimentally for DHT over Testo. Moreover, estimation of the ligand–receptor interaction energy through modeling confirms that even minor modifications in ligand structure have a great impact on the strength of these interactions. Our crystallographic data combined with those obtained by modeling will be helpful in the design of novel molecules with stronger affinity for the AR

Low incidence of SARS-CoV-2, risk factors of mortality and the course of illness in the French national cohort of dialysis patients

International audienceThe aim of this study was to estimate the incidence of COVID-19 disease in the French national population of dialysis patients, their course of illness and to identify the risk factors associated with mortality. Our study included all patients on dialysis recorded in the French REIN Registry in April 2020. Clinical characteristics at last follow-up and the evolution of COVID-19 illness severity over time were recorded for diagnosed cases (either suspicious clinical symptoms, characteristic signs on the chest scan or a positive reverse transcription polymerase chain reaction) for SARS-CoV-2. A total of 1,621 infected patients were reported on the REIN registry from March 16th, 2020 to May 4th, 2020. Of these, 344 died. The prevalence of COVID-19 patients varied from less than 1% to 10% between regions. The probability of being a case was higher in males, patients with diabetes, those in need of assistance for transfer or treated at a self-care unit. Dialysis at home was associated with a lower probability of being infected as was being a smoker, a former smoker, having an active malignancy, or peripheral vascular disease. Mortality in diagnosed cases (21%) was associated with the same causes as in the general population. Higher age, hypoalbuminemia and the presence of an ischemic heart disease were statistically independently associated with a higher risk of death. Being treated at a selfcare unit was associated with a lower risk. Thus, our study showed a relatively low frequency of COVID-19 among dialysis patients contrary to what might have been assumed