Silicon Photonics – A Review Paper

Silicon Photonics is outlined because the utilization of silicon-based materials for the generation, guide, management and detection of sunshine to speak over distances. Optical technology suffered from a name as an upscale answer, supported high price of hardware parts, as they are usually unreal victimization exotic materials that are dear for producing. These limitations prompted Intel to analysis the development of fiber-optic parts from alternative materials, like chemical element earned abundant attention in recent years attributable to the maturity of chemical element within the industry and its chance of monolithic integration of each photonic and electronic devices on one chip. It develops high-volume low price optical parts victimization standard CMOS method-the IC producing process used nowadays. The varied challenges also because the milestones within the development of chemical element Photonic area unit mentioned. the issue in fabricating optical devices like optical device supply, modulators, detectors etc. on chemical element for prime shift speeds that gives high information rates for communication links also because the solutions hints by the chemical element photonics analysis cluster at Intel area unit projected.

Clustering and Sharing Incentives in BitTorrent Systems

Peer-to-peer protocols play an increasingly instrumental role in Internet content distribution. Consequently, it is important to gain a full understanding of how these protocols behave in practice and how their parameters impact overall performance. We present the first experimental investigation of the peer selection strategy of the popular BitTorrent protocol in an instrumented private torrent. By observing the decisions of more than 40 nodes, we validate three BitTorrent properties that, though widely believed to hold, have not been demonstrated experimentally. These include the clustering of similar-bandwidth peers, the effectiveness of BitTorrent's sharing incentives, and the peers' high average upload utilization. In addition, our results show that BitTorrent's new choking algorithm in seed state provides uniform service to all peers, and that an underprovisioned initial seed leads to the absence of peer clustering and less effective sharing incentives. Based on our observations, we provide guidelines for seed provisioning by content providers, and discuss a tracker protocol extension that addresses an identified limitation of the protocol

NMR identification of a conserved Drp1 cardiolipin-binding motif essential for stress-induced mitochondrial fission

Mitochondria form tubular networks that undergo coordinated cycles of fission and fusion. Emerging evidence suggests that a direct yet unresolved interaction of the mechanoenzymatic GTPase dynamin-related protein 1 (Drp1) with mitochondrial outer membrane-localized cardiolipin (CL), externalized under stress conditions including mitophagy, catalyzes essential mitochondrial hyperfragmentation. Here, using a comprehensive set of structural, biophysical, and cell biological tools, we have uncovered a CL-binding motif (CBM) conserved between the Drp1 variable domain (VD) and the unrelated ADP/ATP carrier (AAC/ANT) that intercalates into the membrane core to effect specific CL interactions. CBM mutations that weaken VD-CL interactions manifestly impair Drp1-dependent fission under stress conditions and induce "donut" mitochondria formation. Importantly, VD membrane insertion and GTP-dependent conformational rearrangements mediate only transient CL nonbilayer topological forays and high local membrane constriction, indicating that Drp1-CL interactions alone are insufficient for fission. Our studies establish the structural and mechanistic bases of Drp1-CL interactions in stress-induced mitochondrial fission

Epidemiological and cohort study finds no association between COVID-19 and Guillain-Barré syndrome

Reports of Guillain-Barré syndrome (GBS) have emerged during the Coronavirus disease 2019 (COVID-19) pandemic. This epidemiological and cohort study sought to investigate any causative association between COVID-19 infection and GBS. The epidemiology of GBS cases reported to the UK National Immunoglobulin Database was studied from 2016 to 2019 and compared to cases reported during the COVID-19 pandemic. Data were stratified by hospital trust and region, with numbers of reported cases per month. UK population data for COVID-19 infection were collated from UK public health bodies. In parallel, but separately, members of the British Peripheral Nerve Society prospectively reported incident cases of GBS during the pandemic at their hospitals to a central register. The clinical features, investigation findings and outcomes of COVID-19 (definite or probable) and non-COVID-19 associated GBS cases in this cohort were compared. The incidence of GBS treated in UK hospitals from 2016 to 2019 was 1.65–1.88 per 100 000 individuals per year. GBS incidence fell between March and May 2020 compared to the same months of 2016–19. GBS and COVID-19 incidences during the pandemic also varied between regions and did not correlate with one another (r = 0.06, 95% confidence interval: −0.56 to 0.63, P = 0.86). In the independent cohort study, 47 GBS cases were reported (COVID-19 status: 13 definite, 12 probable, 22 non-COVID-19). There were no significant differences in the pattern of weakness, time to nadir, neurophysiology, CSF findings or outcome between these groups. Intubation was more frequent in the COVID-19 affected cohort (7/13, 54% versus 5/22, 23% in COVID-19-negative) attributed to COVID-19 pulmonary involvement. Although it is not possible to entirely rule out the possibility of a link, this study finds no epidemiological or phenotypic clues of SARS-CoV-2 being causative of GBS. GBS incidence has fallen during the pandemic, which may be the influence of lockdown measures reducing transmission of GBS inducing pathogens such as Campylobacter jejuni and respiratory viruses

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery