Pressure-induced structural modulations in coesite

Silica phases, SiO2, have attracted significant attention as important phases in the fields of condensed-matter physics, materials science, and (in view of their abundance in the Earth's crust) geoscience. Here, we experimentally and theoretically demonstrate that coesite undergoes structural modulations under high pressure. Coesite transforms to a distorted modulated structure, coesite-II, at 22–25 GPa with modulation wave vector q=0.5b∗. Coesite-II displays further commensurate modulation along the y axis at 36–40 GPa and the long-range ordered crystalline structure collapses beyond ∼40GPa and starts amorphizing. First-principles calculations illuminate the nature of the modulated phase transitions of coesite and elucidate the modulated structures of coesite caused by modulations along the y-axis direction. The structural modulations are demonstrated to result from phonon instability, preceding pressured-induced amorphization. The recovered sample after decompression develops a rim of crystalline coesite structure, but its interior remains low crystalline or partially amorphous. Our results not only clarify that the pressure-induced reversible phase transitions and amorphization in coesite originate from structural modulations along the y-axis direction, but also shed light on the densification mechanism of silica under high pressure

EMBA: Efficient memory bandwidth allocation to improve performance on intel commodity processor

On multi-core processors, contention on shared resources such as the last level cache (LLC) and memory bandwidth may cause serious performance degradation, which makes efficient resource allocation a critical issue in data centers. Intel recently introduces Memory Bandwidth Allocation (MBA) technology on its Xeon scalable processors, which makes it possible to allocate memory bandwidth in a real system. However, how to make the most of MBA to improve system performance remains an open question. In this work, (1) we formulate a quantitative relationship between a program\u27s performance and its LLC occupancy and memory request rate on commodity processors. (2) Guided by the performance formula, we propose a heuristic bound-aware throttling algorithm to improve system performance and (3) we further develop a hierarchical clustering method to improve the algorithm\u27s efficiency. (4) We implement these algorithms in EMBA, a low-overhead dynamic memory bandwidth scheduling system to improve performance on Intel commodity processors. The results show that, when multiple programs run simultaneously on a multi-core processor whose memory bandwidth is saturated, the programs with high memory bandwidth demand usually use bandwidth inefficiently compared with programs with medium memory bandwidth demand from the perspective of CPU performance. By slightly throttling the former\u27s bandwidth, we can significantly improve the performance of the latter. On average, we improve system performance by 36.9% at the expense of 8.6% bandwidth utilization rate

Exploring the Applicability of Self-Organizing Maps for Ecosystem Service Zoning of the Guangdong-Hong Kong-Macao Greater Bay Area

Sustainability is one of the major challenges in the 21st century for humanity. Spatial zoning of ecosystem services is proposed in this study as a solution to meet the demands for the sustainable use of ecosystem services. This study presented a workflow and performed an exploratory analysis using self-organizing maps (SOM) for visualizing the spatial patterns of the ecosystem service value (ESV) of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). The zoning was performed based on 11 types of ecosystem services, resulting in 11 ecosystem service zones. Each of the zones derived has its unique characteristics in terms of the dominating ecosystem service types, ESV, land use/land cover patterns, and associated human activity levels. It is recommended that reasonable and effective utilization of the ecosystem services in the GBA should be based on its zonal characteristics rather than haphazard exploitations, which can contribute to the sustainable economy and environment of the region. The applicability of SOM for the GBA ecosystem service zoning has been demonstrated in this study. However, it should be stressed that the method and workflow presented in this study should mainly be used for supporting decision-making rather than used for deriving gold-standard zoning maps

KATZNCP: a miRNA–disease association prediction model integrating KATZ algorithm and network consistency projection

Abstract Background Clinical studies have shown that miRNAs are closely related to human health. The study of potential associations between miRNAs and diseases will contribute to a profound understanding of the mechanism of disease development, as well as human disease prevention and treatment. MiRNA–disease associations predicted by computational methods are the best complement to biological experiments. Results In this research, a federated computational model KATZNCP was proposed on the basis of the KATZ algorithm and network consistency projection to infer the potential miRNA–disease associations. In KATZNCP, a heterogeneous network was initially constructed by integrating the known miRNA–disease association, integrated miRNA similarities, and integrated disease similarities; then, the KATZ algorithm was implemented in the heterogeneous network to obtain the estimated miRNA–disease prediction scores. Finally, the precise scores were obtained by the network consistency projection method as the final prediction results. KATZNCP achieved the reliable predictive performance in leave-one-out cross-validation (LOOCV) with an AUC value of 0.9325, which was better than the state-of-the-art comparable algorithms. Furthermore, case studies of lung neoplasms and esophageal neoplasms demonstrated the excellent predictive performance of KATZNCP. Conclusion A new computational model KATZNCP was proposed for predicting potential miRNA–drug associations based on KATZ and network consistency projections, which can effectively predict the potential miRNA–disease interactions. Therefore, KATZNCP can be used to provide guidance for future experiments

Exploring the Applicability of Self-Organizing Maps for Ecosystem Service Zoning of the Guangdong-Hong Kong-Macao Greater Bay Area

Sustainability is one of the major challenges in the 21st century for humanity. Spatial zoning of ecosystem services is proposed in this study as a solution to meet the demands for the sustainable use of ecosystem services. This study presented a workflow and performed an exploratory analysis using self-organizing maps (SOM) for visualizing the spatial patterns of the ecosystem service value (ESV) of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). The zoning was performed based on 11 types of ecosystem services, resulting in 11 ecosystem service zones. Each of the zones derived has its unique characteristics in terms of the dominating ecosystem service types, ESV, land use/land cover patterns, and associated human activity levels. It is recommended that reasonable and effective utilization of the ecosystem services in the GBA should be based on its zonal characteristics rather than haphazard exploitations, which can contribute to the sustainable economy and environment of the region. The applicability of SOM for the GBA ecosystem service zoning has been demonstrated in this study. However, it should be stressed that the method and workflow presented in this study should mainly be used for supporting decision-making rather than used for deriving gold-standard zoning maps

Fast miss ratio curve modeling for storage cache

The reuse distance (least recently used (LRU) stack distance) is an essential metric for performance prediction and optimization of storage cache. Over the past four decades, there have been steady improvements in the algorithmic efficiency of reuse distance measurement. This progress is accelerating in recent years, both in theory and practical implementation. In this article, we present a kinetic model of LRU cache memory, based on the average eviction time (AET) of the cached data. The AET model enables fast measurement and use of low-cost sampling. It can produce the miss ratio curve in linear time with extremely low space costs. On storage trace benchmarks, AET reduces the time and space costs compared to former techniques. Furthermore, AET is a composable model that can characterize shared cache behavior through sampling and modeling individual programs or traces

Tear Up the Bubble Boom: Lessons Learned From a Deep Learning Research and Development Cluster

With the proliferation of deep learning, there exists a strong need to efficiently operate GPU clusters for deep learning production in giant AI companies, as well as for research and development (R&D) in small-sized research institutes and universities. Existing works have performed thorough trace analysis on large-scale production-level clusters in giant companies, which discloses the characteristics of deep learning production jobs and motivates the design of scheduling frameworks. However, R&D clusters significantly differ from production-level clusters in both job properties and user behaviors, calling for a different scheduling mechanism. In this paper, we present a detailed workload characterization of an R&D cluster, CloudBrain-I, in a research institute, Peng Cheng Laboratory. After analyzing the fine-grained resource utilization, we discover a severe problem for R&D clusters, resource underutilization, which is especially important in R&D clusters while not characterised by existing works. We further investigate two specific underutilization phenomena and conclude several implications and lessons on R&D cluster scheduling. The traces will be open-sourced to motivate further studies in the community

Clinical and Biological Significances of FBLN5 in Gastric Cancer

Abnormal FBLN5 expression levels are related to various cancer types. This study is the first to explore its clinical and biological significances in gastric cancer (GC). We used The Cancer Genome Atlas-GC (TCGA-GC) and Gene Expression Omnibus (GEO) databases to identify the differential expression of FBLN5, and its association with clinical pathological characteristics was analyzed. A Kaplan–Meier plotter was used to calculate the impact of FBLN5 on GC patient prognosis, and the biological functions of FBLN5 were analyzed. In addition, we constructed a GC tissue microarray, and performed an immunohistochemical staining of FBLN5 to verify our findings. Western blotting was conducted simultaneously to confirm that FBLN5 was overexpressed in GC. We found that the high level of FBLN5 mRNA in GC was associated with a poor prognosis. High FBLN5 expression levels were significantly correlated with INFc and N3 lymph node metastasis. Univariate and multivariate analyses showed that FBLN5 expression levels and lymph node metastasis rate were independent risk factors related to GC patient prognosis, which can be combined to construct a nomogram to serve patients. Therefore, we believe that FBLN5 is significantly related to the poor prognosis of GC patients. FBLN5 is a valuable prognostic indicator to evaluate the prognosis of GC