The effect of symmetrically tilt grain boundary of aluminum on hydrogen diffusion

High-strength aluminum alloys are widely used in industry. Hydrogen embrittlement greatly reduces the performance and service safety of aluminum alloys. The hydrogen traps in alumi-num profoundly affect the hydrogen embrittlement of aluminum. Here, we took a coinci-dence-site lattice (CSL) symmetrically tilted grain boundary (STGB) Σ5(120)[001] as an example to carry out molecular dynamics (MD) simulations of hydrogen diffusion in aluminum at dif-ferent temperatures, and to obtain results and rules consistent with the experiment. At 700 K, three groups of MD simulations with concentrations of 0.5, 2.5 and 5 atomic % hydrogen (at. % H) were carried out for STGB models at different angles. By analyzing the simulation results and the MSD curves of hydrogen atoms, we found that, in the low hydrogen concentration of STGB models, the grain boundaries captured hydrogen atoms and hindered their movement. In high-hydrogen-concentration models, the diffusion rate of hydrogen atoms was not affected by the grain boundaries. The analysis of the simulation results showed that the diffusion of hydro-gen atoms at the grain boundary is anisotropic

The in Vitro Estrogenic Activities of Polyfluorinated Iodine Alkanes

Background: Polyfluorinated iodine alkanes (PFIs) are important intermediates in the synthesis of organic fluoride products. Recently, PFIs have been detected in fluoropolymers as residual raw materials, as well as in the ambient environment

Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads Ranking

Dividing ads ranking system into retrieval, early, and final stages is a common practice in large scale ads recommendation to balance the efficiency and accuracy. The early stage ranking often uses efficient models to generate candidates out of a set of retrieved ads. The candidates are then fed into a more computationally intensive but accurate final stage ranking system to produce the final ads recommendation. As the early and final stage ranking use different features and model architectures because of system constraints, a serious ranking consistency issue arises where the early stage has a low ads recall, i.e., top ads in the final stage are ranked low in the early stage. In order to pass better ads from the early to the final stage ranking, we propose a multi-task learning framework for early stage ranking to capture multiple final stage ranking components (i.e. ads clicks and ads quality events) and their task relations. With our multi-task learning framework, we can not only achieve serving cost saving from the model consolidation, but also improve the ads recall and ranking consistency. In the online A/B testing, our framework achieves significantly higher click-through rate (CTR), conversion rate (CVR), total value and better ads-quality (e.g. reduced ads cross-out rate) in a large scale industrial ads ranking system.Comment: Accepted by AdKDD 2

Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale

Neural Architecture Search (NAS) has demonstrated its efficacy in computer vision and potential for ranking systems. However, prior work focused on academic problems, which are evaluated at small scale under well-controlled fixed baselines. In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle. In this paper, we present Rankitect, a NAS software framework for ranking systems at Meta. Rankitect seeks to build brand new architectures by composing low level building blocks from scratch. Rankitect implements and improves state-of-the-art (SOTA) NAS methods for comprehensive and fair comparison under the same search space, including sampling-based NAS, one-shot NAS, and Differentiable NAS (DNAS). We evaluate Rankitect by comparing to multiple production ranking models at Meta. We find that Rankitect can discover new models from scratch achieving competitive tradeoff between Normalized Entropy loss and FLOPs. When utilizing search space designed by engineers, Rankitect can generate better models than engineers, achieving positive offline evaluation and online A/B test at Meta scale.Comment: Wei Wen and Kuang-Hung Liu contribute equall

A novel single nucleotide polymorphism within the NOD2 gene is associated with pulmonary tuberculosis in the Chinese Han, Uygur and Kazak populations

<p>Abstract</p> <p>Background</p> <p>The present study aimed to investigate the genetic polymorphisms in exon 4 of the <it>NOD2 </it>gene in tuberculosis patients and healthy controls, in order to clarify whether polymorphisms in the <it>NOD2 </it>gene is associated with tuberculosis.</p> <p>Methods</p> <p>A case-control study was performed on the Chinese Han, Uygur and Kazak populations. Exon 4 of the <it>NOD2 </it>gene was sequenced in 425 TB patients and 380 healthy controls to identify SNPs.</p> <p>Results</p> <p>The frequency of T/G genotypes for the Arg587Arg (CGT → CGG) single nucleotide polymorphism (SNP) in <it>NOD2 </it>was found to be significantly higher in the Uygur (34.9%) and Kazak (37.1%) populations than the Han population (18.6%). Also, the frequency of G/G genotypes for the Arg587Arg SNP was significantly higher in the Uyghur (8.3%) and Kazak (5.4%) populations than the Han population (0.9%). Meanwhile, no significant difference was found in the Arg587Arg polymorphism between the tuberculosis patients and healthy controls in the Uyghur and Kazak populations (<it>P </it>> 0.05) whereas, a significant difference was observed in the Arg587Arg polymorphism between the tuberculosis patients and healthy controls in the Han population (<it>P </it>< 0.01). The odd ratio of 2.16 (95% CI = 1.31-3.58; <it>P </it>< 0.01) indicated that the Arg587Arg SNP in <it>NOD2 </it>may be associated with susceptibility to tuberculosis in the Chinese Han population.</p> <p>Conclusions</p> <p>Our study is the first to demonstrate that the Arg587Arg SNP in <it>NOD2 </it>is a new possible risk factor for tuberculosis in the Chinese Han population, but not in the Uyghur and Kazak populations. Our results may reflect racial differences in genetic susceptibility to tuberculosis.</p

PrP(Sc)-specific antibodies with the ability to immunodetect prion oligomers.

The development of antibodies with binding capacity towards soluble oligomeric forms of PrPSc recognised in the aggregation process in early stage of the disease would be of paramount importance in diagnosing prion diseases before extensive neuropathology has ensued. As blood transfusion appears to be efficient in the transmission of the infectious prion agent, there is an urgent need to develop reagents that would specifically recognize oligomeric forms of the abnormally folded prion protein, PrPSc.To that end, we show that anti-PrP monoclonal antibodies (called PRIOC mAbs) derived from mice immunised with native PrP-coated microbeads are able to immunodetect oligomers/multimers of PrPSc. Oligomer-specific immunoreactivity displayed by these PRIOC mAbs was demonstrated as large aggregates of immunoreactive deposits in prion-permissive neuroblastoma cell lines but not in equivalent non-infected or prn-p(0/0) cell lines. In contrast, an anti-monomer PrP antibody displayed diffuse immunoreactivity restricted to the cell membrane. Furthermore, our PRIOC mAbs did not display any binding with monomeric recombinant and cellular prion proteins but strongly detected PrPSc oligomers as shown by a newly developed sensitive and specific ELISA. Finally, PrioC antibodies were also able to bind soluble oligomers formed of Aβ and α-synuclein. These findings demonstrate the potential use of anti-prion antibodies that bind PrPSc oligomers, recognised in early stage of the disease, for the diagnosis of prion diseases in blood and other body fluids

Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models

Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand in its data-centers. In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of Zion platform, namely ZionEX. We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40X speedup in terms of time to solution over previous systems. We achieve this by (i) designing the ZionEX platform with dedicated scale-out network, provisioned with high bandwidth, optimal topology and efficient transport (ii) implementing an optimized PyTorch-based training stack supporting both model and data parallelism (iii) developing sharding algorithms capable of hierarchical partitioning of the embedding tables along row, column dimensions and load balancing them across multiple workers; (iv) adding high-performance core operators while retaining flexibility to support optimizers with fully deterministic updates (v) leveraging reduced precision communications, multi-level memory hierarchy (HBM+DDR+SSD) and pipelining. Furthermore, we develop and briefly comment on distributed data ingestion and other supporting services that are required for the robust and efficient end-to-end training in production environments

Low temperature and temperature decline increase acute aortic dissection risk and burden: A nationwide case crossover analysis at hourly level among 40,270 patients.

Background: Acute aortic dissection (AAD) is a life-threatening cardiovascular emergency with high mortality, so identifying modifiable risk factors of AAD is of great public health significance. The associations of non-optimal temperature and temperature variability with AAD onset and the disease burden have not been fully understood. Methods: We conducted a time-stratified case-crossover study using a nationwide registry dataset from 1,868 hospitals in 313 Chinese cities. Conditional logistic regression and distributed lag models were used to investigate associations of temperature and temperature changes between neighboring days (TCN) with the hourly AAD onset and calculate the attributable fractions. We also evaluated the heterogeneity of the associations. Findings: A total of 40,270 eligible AAD cases were included. The exposure-response curves for temperature and TCN with AAD onset risk were both inverse and approximately linear. The risks were present on the concurrent hour (for temperature) or day (for TCN) and lasted for almost 1 day. The cumulative relative risks of AAD were 1.027 and 1.026 per 1°C lower temperature and temperature decline between neighboring days, respectively. The associations were significant during the non-heating period, but were not present during the heating period in cities with central heating. 23.13% of AAD cases nationwide were attributable to low temperature and 1.58% were attributable to temperature decline from the previous day. Interpretation: This is the largest nationwide study demonstrating robust associations of low temperature and temperature decline with AAD onset. We, for the first time, calculated the corresponding disease burden and further showed that central heating may be a modifier for temperature-related AAD risk and burden. Funding: This work was supported by the National Natural Science Foundation of China (92043301 and 92143301), Shanghai International Science and Technology Partnership Project (No. 21230780200), the Medical Research Council-UK (MR/R013349/1), and the Natural Environment Research Council UK (NE/R009384/1)

Postnatal Proteasome Inhibition Induces Neurodegeneration and Cognitive Deficiencies in Adult Mice: A New Model of Neurodevelopment Syndrome

Defects in the ubiquitin-proteasome system have been related to aging and the development of neurodegenerative disease, although the effects of deficient proteasome activity during early postnatal development are poorly understood. Accordingly, we have assessed how proteasome dysfunction during early postnatal development, induced by administering proteasome inhibitors daily during the first 10 days of life, affects the behaviour of adult mice. We found that this regime of exposure to the proteasome inhibitors MG132 or lactacystin did not produce significant behavioural or morphological changes in the first 15 days of life. However, towards the end of the treatment with proteasome inhibitors, there was a loss of mitochondrial markers and activity, and an increase in DNA oxidation. On reaching adulthood, the memory of mice that were injected with proteasome inhibitors postnatally was impaired in hippocampal and amygdala-dependent tasks, and they suffered motor dysfunction and imbalance. These behavioural deficiencies were correlated with neuronal loss in the hippocampus, amygdala and brainstem, and with diminished adult neurogenesis. Accordingly, impairing proteasome activity at early postnatal ages appears to cause morphological and behavioural alterations in adult mice that resemble those associated with certain neurodegenerative diseases and/or syndromes of mental retardation