Adding Vector and Matrix Support to SimSQL

In this thesis, I consider the problem of making linear algebra simple to use and efficient to run in a relational database management system. Relational database systems are widely used, and much of the data in the world is stored within them. Having linear algebra integrated into a relational database would provide great support for tasks such as in-database analytics and in-database machine learning. Currently, when it is necessary to perform such analyses, one must either extract the data from a database, and use an external tool such as MATLAB, or else use awkward, existing within-the-database linear algebra facilities. In this thesis, I will focus on my four main contributions: (1) I add vector and matrix types to SQL, the most commonly-used database programming language; (2) I design a few simple SQL language extensions to accommodate vectors and matrices; (3) I consider the problem of making vector and matrix operations efficient via integration with the database query optimizer; and (4) I conduct some experiments to show the efficacy of my language extensions

Extinction by plasmonic nanoparticles in dispersive and dissipative media

Extinction of small metallic spheres has been well understood through the classical Mie theory when the host medium is dispersive and transparent. However, the role of host dissipation on the particulate extinction remains a competition between the enhancing and reducing effects on the localized surface plasmonic resonance (LSPR). Here, using a generalized Mie theory, we elaborate on the specific influence mechanisms of host dissipation on the extinction efficiency factors of a plasmonic nanosphere. To this end, we isolate the dissipative effects by comparing the dispersive and dissipative host with its transparent counterpart. As a result, we identify the damping effects of host dissipation on the LSPR including the resonance widening and amplitude reducing. The resonance positions are shifted by host dissipation, which cannot be predicted by the classical Fr\"ohlich condition. Finally, we demonstrate that a wide-band extinction enhancement due to host dissipation can be realized away from the positions of LSPR.Comment: 5 pages, 3 figure

PlinyCompute: A Platform for High-Performance, Distributed, Data-Intensive Tool Development

This paper describes PlinyCompute, a system for development of high-performance, data-intensive, distributed computing tools and libraries. In the large, PlinyCompute presents the programmer with a very high-level, declarative interface, relying on automatic, relational-database style optimization to figure out how to stage distributed computations. However, in the small, PlinyCompute presents the capable systems programmer with a persistent object data model and API (the "PC object model") and associated memory management system that has been designed from the ground-up for high performance, distributed, data-intensive computing. This contrasts with most other Big Data systems, which are constructed on top of the Java Virtual Machine (JVM), and hence must at least partially cede performance-critical concerns such as memory management (including layout and de/allocation) and virtual method/function dispatch to the JVM. This hybrid approach---declarative in the large, trusting the programmer's ability to utilize PC object model efficiently in the small---results in a system that is ideal for the development of reusable, data-intensive tools and libraries. Through extensive benchmarking, we show that implementing complex objects manipulation and non-trivial, library-style computations on top of PlinyCompute can result in a speedup of 2x to more than 50x or more compared to equivalent implementations on Spark.Comment: 48 pages, including references and Appendi

A novel molecular signature for predicting prognosis and immunotherapy response in osteosarcoma based on tumor-infiltrating cell marker genes

BackgroundTumor infiltrating lymphocytes (TILs), the main component in the tumor microenvironment, play a critical role in the antitumor immune response. Few studies have developed a prognostic model based on TILs in osteosarcoma.MethodsScRNA-seq data was obtained from our previous research and bulk RNA transcriptome data was from TARGET database. WGCNA was used to obtain the immune-related gene modules. Subsequently, we applied LASSO regression analysis and SVM algorithm to construct a prognostic model based on TILs marker genes. What’s more, the prognostic model was verified by external datasets and experiment in vitro. ResultsEleven cell clusters and 2044 TILs marker genes were identified. WGCNA results showed that 545 TILs marker genes were the most strongly related with immune. Subsequently, a risk model including 5 genes was developed. We found that the survival rate was higher in the low-risk group and the risk model could be used as an independent prognostic factor. Meanwhile, high-risk patients had a lower abundance of immune cell infiltration and many immune checkpoint genes were highly expressed in the low-risk group. The prognostic model was also demonstrated to be a good predictive capacity in external datasets. The result of RT-qPCR indicated that these 5 genes have differential expression which accorded with the predicting outcomes.ConclusionsThis study developed a new molecular signature based on TILs marker genes, which is very effective in predicting OS prognosis and immunotherapy response

Temperature-mediated structural evolution of vapor–phase deposited cyclosiloxane polymer thin films for enhanced mechanical properties and thermal conductivity

Polymer-derived ceramic (PDC) thin films are promising wear-resistant coatings for protecting metals and carbon–carbon composites from corrosion and oxidation. However, the high pyrolysis temperature hinders the applications on substrate materials with low melting points. We report a new synthesis route for PDC coatings using initiated chemical vapor deposited poly(1,3,5-trivinyl-1,3,5-trimethylcyclotrisiloxane) (pV _3 D _3 ) as the precursor. We investigated the changes in siloxane moieties and the network topology, and proposed a three-stage mechanism for the thermal annealing process. The rise of the connectivity number for the structures obtained at increased annealing temperatures was found with strong correlation to the enhanced mechanical properties and thermal conductivity. Our PDC films obtained via annealing at 850 °C exhibit at least 14.6% higher hardness than prior reports for PDCs synthesized below 1100 °C. Furthermore, thermal conductivity up to 1.02 W (mK) ^−1 was achieved at the annealing temperature as low as 700 °C, which is on the same order of magnitude as PDCs obtained above 1100 °C. Using minimum thermal conductivity models, we found that the thermal transport is dominated by diffusons in the films below the percolation of rigidity, while ultra-short mean-free path phonons contribute to the thermal conductivity of the films above the percolation threshold. The findings of this work provide new insights for the development of wear-resistant and thermally conductive PDC thin films for durable protection coatings

Long non-coding RNA and circular RNA and coding RNA profiling of plasma exosomes of osteosarcoma by RNA seq

Abstract Osteosarcoma (OS) is a primary bone tumor with high malignancy and the mechanism of hematogenous metastasis in OS is still not clear. The plasma exosomes derived from osteosarcoma play a key role in the process of tumor metastasis. Here, we established RNA-seq dataset for lncRNAs, circRNAs and mRNAs in plasma exosomes from 10 OS patients and 5 healthy donors. A total of 329.52 Gb of clean data was obtained. Besides, 1754 lincRNAs, 7096 known and 1935 new circRNA was identified. Finally, gene expression profiles and differentially expressed genes (DEGs) were analyzed among these 15 samples. There were 331 DEGs of mRNA, 132 of lincRNA and 489 of circRNA was obtained, respectively. This data set provides a significant resource for relevant researchers to excavate potential dysregulated lncRNAs, circRNAs and mRNAs of plasma exosomes in OS versus normal conditions

Structural insights into broadly neutralizing antibodies elicited by hybrid immunity against SARS-CoV-2

ABSTRACTIncreasing spread by SARS-CoV-2 Omicron variants challenges existing vaccines and broadly reactive neutralizing antibodies (bNAbs) against COVID-19. Here we determine the diversity, potency, breadth and structural insights of bNAbs derived from memory B cells of BNT162b2-vaccinee after homogeneous Omicron BA.1 breakthrough infection. The infection activates diverse memory B cell clonotypes for generating potent class I/II and III bNAbs with new epitopes mapped to the receptor-binding domain (RBD). The top eight bNAbs neutralize wildtype and BA.1 potently but display divergent IgH/IgL sequences and neuralization profiles against other variants of concern (VOCs). Two of them (P2D9 and P3E6) belonging to class III NAbs display comparable potency against BA.4/BA.5, although structural analysis reveals distinct modes of action. P3E6 neutralizes all variants tested through a unique bivalent interaction with two RBDs. Our findings provide new insights into hybrid immunity on BNT162b2-induced diverse memory B cells in response to Omicron breakthrough infection for generating diverse bNAbs with distinct structural basis