Influence of precipitation on the Portevin-Le Chatelier effect in Al-Mg alloys

AbstractIn the alloy with solute content higher than the limiting solubility, the solute atoms that have failed to dissolve will precipitate from the solid solution and form precipitations. In this study, the Portevin-Le Chatelier (PLC) effects in annealed 5456 and 5052 aluminum alloys with different precipitation contents have been investigated under different applied strain rates. The results suggest that precipitations have significant effect on the PLC effect and the more the precipitations are, the greater the influence is. Furthermore, the solute diffusion is pipe diffusion in 5052 alloy with lower precipitation content. However, for 5456 alloy with higher precipitation content, the diffusion is no longer the case but more complex

ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease

Alzheimer's Disease (AD) and related dementia are a growing global health challenge due to the aging population. In this paper, we present ADMarker, the first end-to-end system that integrates multi-modal sensors and new federated learning algorithms for detecting multidimensional AD digital biomarkers in natural living environments. ADMarker features a novel three-stage multi-modal federated learning architecture that can accurately detect digital biomarkers in a privacy-preserving manner. Our approach collectively addresses several major real-world challenges, such as limited data labels, data heterogeneity, and limited computing resources. We built a compact multi-modality hardware system and deployed it in a four-week clinical trial involving 91 elderly participants. The results indicate that ADMarker can accurately detect a comprehensive set of digital biomarkers with up to 93.8% accuracy and identify early AD with an average of 88.9% accuracy. ADMarker offers a new platform that can allow AD clinicians to characterize and track the complex correlation between multidimensional interpretable digital biomarkers, demographic factors of patients, and AD diagnosis in a longitudinal manner

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Analysis of community chronic disease health management mode under the background of big data

In recent years, the “Internet + medical” exploration and the country’s vigorously promoted hierarchical diagnosis and treatment system have provided an opportunity to improve the status quo of diabetes. Some scholars have proposed “one-to-one binding community nurses” (Wang Li et al., 2016) and personalized treatment based on big data (He Ting et al., 2016). New chronic disease management concepts such as an integrated chronic disease management model for the elderly based on mobile medical technology (Che Fengyuan et al., 2016). Although different names are used, the core point of view is that patients and community doctors complete the contract, the community doctors will take care of the patients, and the hospital doctors will take care of the patients. The patient’s blood glucose data can be shared with relatives and friends, community doctors, and hospital doctors in real time with the help of platform tools such as blood glucose meters, mobile apps, and cloud medical platforms. And community and hospital doctors’ feedback on patients can also be sent to patients and relatives and friends in real time, thereby realizing hierarchical diagnosis and treatment of diabetic patients when medical resources are scarce and unevenly distributed. This article refers to this model as the “family-style chronic disease management model”. The interaction between patients, relatives and friends, community doctors, and hospital doctors is shown in Figure 1

Breeding strategies for increasing yield potential in super hybrid rice

Super hybrid rice breeding is a new breeding method combining semi-dwarf breeding and heterosis breeding using germplasm and gene-environment interactions. This paper reviews the breeding strategies of super hybrid rice breeding in China, focusing on the utilization of heterosis of indica and japonica subspecies, construction of ideal plant architecture and pyramiding of disease resistant genes in restorer lines. To develop super hybrid rice, considerable effort should be made to explore genes related with high yield, good quality, resistance to pests and diseases, tolerance to stresses. Molecular breeding methods in combination with crossing techniques should be adopted in super hybrid rice breeding

Blockchain-Enhanced Fair Task Scheduling for Cloud-Fog-Edge Coordination Environments: Model and Algorithm

The cloud-fog-edge hybrid system is the evolution of the traditional centralized cloud computing model. Through the combination of different levels of resources, it is able to handle service requests from terminal users with a lower latency. However, it is accompanied by greater uncertainty, unreliability, and instability due to the decentralization and regionalization of service processing, as well as the unreasonable and unfairness in resource allocation, task scheduling, and coordination, caused by the autonomy of node distribution. Therefore, this paper introduces blockchain technology to construct a trust-enabled interaction framework in a cloud-fog-edge environment, and through a double-chain structure, it improves the reliability and verifiability of task processing without a big management overhead. Furthermore, in order to fully consider the reasonability and load balance in service coordination and task scheduling, Berger’s model and the conception of service justice are introduced to perform reasonable matching of tasks and resources. We have developed a trust-based cloud-fog-edge service simulation system based on iFogsim, and through a large number of experiments, the performance of the proposed model is verified in terms of makespan, scheduling success rate, latency, and user satisfaction with some classical scheduling models

Interactive and individual effects of multi-factor controls on water use efficiency in Central Asian ecosystems

Water use efficiency (WUE) characterizes the relationship between water dissipation and carbon sequestration. Knowledge of WUE dynamics and its responses to complex climate controls are prerequisites for addressing the challenges of future climate change and human disturbance of wild lands. Owing to a lack of experimental observations and the complexity of quantifying the individual and interactive effects of different environmental factors, the mechanism of WUE dynamics and the spatiotemporal characteristics of WUE in Central Asian ecosystems remain unclear. Here, a specific Arid Ecosystem Model was used to assess WUE dynamics under environmental stresses, specifically isolating and identifying proprietary features from complex coupling effects, across different ecosystems in Central Asia from 1980 to 2014. WUE declined in southern Xinjiang but exhibited an upward trend in the Tianshan Mountains and northern Kazakhstan. Precipitation and CO _2 controlled WUE of 39% and 54% of Central Asia, respectively. The factor analysis showed that the negative effects of climate change were largely compensated by the CO _2 fertilization effect, and their interaction produced negative feedback to WUE. This resulted in inhibition of the CO _2 fertilization effect during long droughts. The negative effects of warming included increased water stress and enhanced evapotranspiration from vegetation. Based on variations in precipitation and net primary production, we determined that southern Xinjiang and the Turgay Plateau were environmentally vulnerable areas. Our study provides guidance regarding how ecologically fragile regions in Central Asia might cope with environmental pressures under extreme climate change in the future