Yi Chen

Bio: Yi Chen is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Large Hadron Collider & Medicine. The author has an hindex of 217, co-authored 4342 publications receiving 293080 citations. Previous affiliations of Yi Chen include Rochester Institute of Technology & National Institutes of Health.

Overview of KAGRA: Calibration, detector characterization, physical environmental monitors, and the geophysics interferometer

Abstract: KAGRA is a newly built gravitational wave observatory, a laser interferometer with a 3 km arm length, located at Kamioka, Gifu, Japan. In this series of articles we present an overview of the baseline KAGRA, for which we finished installing the designed configuration in 2019. This article describes the method of calibration (CAL) used for reconstructing gravitational wave signals from the detector outputs, as well as the characterization of the detector (DET). We also review the physical environmental monitoring (PEM) system and the geophysics interferometer (GIF). Both are used for characterizing and evaluating the data quality of the gravitational wave channel. They play important roles in utilizing the detector output for gravitational wave searches. These characterization investigations will be even more important in the near future, once gravitational wave detection has been achieved, and in using KAGRA in the gravitational wave astronomy era.

First All-Sky Search for Continuous Gravitational Waves from Unknown Sources in Binary Systems

Abstract: We present the first results of an all-sky search for continuous gravitational waves from unknown spinning neutron stars in binary systems using LIGO and Virgo data. Using a specially developed analysis program, the TwoSpect algorithm, the search was carried out on data from the sixth LIGO Science Run and the second and third Virgo Science Runs. The search covers a range of frequencies from 20 Hz to 520 Hz, a range of orbital periods from 2 to ~2,254 h and a frequency- and period-dependent range of frequency modulation depths from 0.277 to 100 mHz. This corresponds to a range of projected semi-major axes of the orbit from ~0.6e-3 ls to ~6,500 ls assuming the orbit of the binary is circular. While no plausible candidate gravitational wave events survive the pipeline, upper limits are set on the analyzed data. The most sensitive 95% confidence upper limit obtained on gravitational wave strain is 2.3e-24 at 217 Hz, assuming the source waves are circularly polarized. Although this search has been optimized for circular binary orbits, the upper limits obtained remain valid for orbital eccentricities as large as 0.9. In addition, upper limits are placed on continuous gravitational wave emission from the low-mass x-ray binary Scorpius X-1 between 20 Hz and 57.25 Hz.

Exploiting Sparsity in Hyperspectral Image Classification via Graphical Models

Abstract: A significant recent advance in hyperspectral image (HSI) classification relies on the observation that the spectral signature of a pixel can be represented by a sparse linear combination of training spectra from an overcomplete dictionary. A spatiospectral notion of sparsity is further captured by developing a joint sparsity model, wherein spectral signatures of pixels in a local spatial neighborhood (of the pixel of interest) are constrained to be represented by a common collection of training spectra, albeit with different weights. A challenging open problem is to effectively capture the class conditional correlations between these multiple sparse representations corresponding to different pixels in the spatial neighborhood. We propose a probabilistic graphical model framework to explicitly mine the conditional dependences between these distinct sparse features. Our graphical models are synthesized using simple tree structures which can be discriminatively learnt (even with limited training samples) for classification. Experiments on benchmark HSI data sets reveal significant improvements over existing approaches in classification rates as well as robustness to choice of training.

CRISPR/Cas9-Engineered Universal CD19/CD22 Dual-Targeted CAR-T Cell Therapy for Relapsed/Refractory B-cell Acute Lymphoblastic Leukemia.

Abstract: Purpose: Autologous chimeric antigen receptor T (CAR-T) cell therapy is an effective treatment for relapsed/refractory acute lymphoblastic leukemia (r/r ALL). However, certain characteristics of autologous CAR-T cells can delay treatment availability. Relapse caused by antigen escape after single-targeted CAR-T therapy is another issue. Therefore, we aim to develop CRISPR-edited universal off-the-shelf CD19/CD22 dual-targeted CAR-T cells as a novel therapy for r/r ALL. Patients and Methods: In this open-label dose-escalation phase I study, universal CD19/CD22-targeting CAR-T cells (CTA101) with a CRISPR/Cas9-disrupted TRAC region and CD52 gene to avoid host immune-mediated rejection were infused in patients with r/r ALL. Safety, efficacy, and CTA101 cellular kinetics were evaluated. Results: CRISPR/Cas9 technology mediated highly efficient, high-fidelity gene editing and production of universal CAR-T cells. No gene editing–associated genotoxicity or chromosomal translocation was observed. Six patients received CTA101 infusions at doses of 1 (3 patients) and 3 (3 patients) × 106 CAR+ T cells/kg body weight. Cytokine release syndrome occurred in all patients. No dose-limiting toxicity, GvHD, neurotoxicity, or genome editing–associated adverse events have occurred to date. The complete remission (CR) rate was 83.3% on day 28 after CTA101 infusion. With a median follow-up of 4.3 months, 3 of the 5 patients who achieved CR or CR with incomplete hematologic recovery (CR/CRi) remained minimal residual disease (MRD) negative. Conclusions: CRISPR/Cas9-engineered universal CD19/CD22 CAR-T cells exhibited a manageable safety profile and prominent antileukemia activity. Universal dual-targeted CAR-T cell therapy may offer an alternative therapy for patients with r/r ALL.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

KEGG: Kyoto Encyclopedia of Genes and Genomes

Abstract: Kyoto Encyclopedia of Genes and Genomes (KEGG) is a knowledge base for systematic analysis of gene functions in terms of the networks of genes and molecules. The major component of KEGG is the PATHWAY database that consists of graphical diagrams of biochemical pathways including most of the known metabolic pathways and some of the known regulatory pathways. The pathway information is also represented by the ortholog group tables summarizing orthologous and paralogous gene groups among different organisms. KEGG maintains the GENES database for the gene catalogs of all organisms with complete genomes and selected organisms with partial genomes, which are continuously re-annotated, as well as the LIGAND database for chemical compounds and enzymes. Each gene catalog is associated with the graphical genome map for chromosomal locations that is represented by Java applet. In addition to the data collection efforts, KEGG develops and provides various computational tools, such as for reconstructing biochemical pathways from the complete genome sequence and for predicting gene regulatory networks from the gene expression profiles. The KEGG databases are daily updated and made freely available (http://www.genome.ad.jp/kegg/).

limma powers differential expression analyses for RNA-sequencing and microarray studies

Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

Atherosclerosis — An Inflammatory Disease

Abstract: Atherosclerosis is an inflammatory disease. Because high plasma concentrations of cholesterol, in particular those of low-density lipoprotein (LDL) cholesterol, are one of the principal risk factors for atherosclerosis,1 the process of atherogenesis has been considered by many to consist largely of the accumulation of lipids within the artery wall; however, it is much more than that. Despite changes in lifestyle and the use of new pharmacologic approaches to lower plasma cholesterol concentrations,2,3 cardiovascular disease continues to be the principal cause of death in the United States, Europe, and much of Asia.4,5 In fact, the lesions of atherosclerosis represent . . .