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Liang Ding

Bio: Liang Ding is an academic researcher from National University of Defense Technology. The author has contributed to research in topics: Ion implantation & Thin film. The author has an hindex of 22, co-authored 152 publications receiving 1782 citations. Previous affiliations of Liang Ding include University of Texas–Pan American & Nanyang Technological University.


Papers
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Journal ArticleDOI
12 Sep 2018-Nature
TL;DR: A large-scale genomics study shows that the cell of origin and founding mutations determine disease subtype and lead to the expression of multiple haematopoietic lineage-defining antigens in mixed phenotype acute leukaemia.
Abstract: Mixed phenotype acute leukaemia (MPAL) is a high-risk subtype of leukaemia with myeloid and lymphoid features, limited genetic characterization, and a lack of consensus regarding appropriate therapy Here we show that the two principal subtypes of MPAL, T/myeloid (T/M) and B/myeloid (B/M), are genetically distinct Rearrangement of ZNF384 is common in B/M MPAL, and biallelic WT1 alterations are common in T/M MPAL, which shares genomic features with early T-cell precursor acute lymphoblastic leukaemia We show that the intratumoral immunophenotypic heterogeneity characteristic of MPAL is independent of somatic genetic variation, that founding lesions arise in primitive haematopoietic progenitors, and that individual phenotypic subpopulations can reconstitute the immunophenotypic diversity in vivo These findings indicate that the cell of origin and founding lesions, rather than an accumulation of distinct genomic alterations, prime tumour cells for lineage promiscuity Moreover, these findings position MPAL in the spectrum of immature leukaemias and provide a genetically informed framework for future clinical trials of potential treatments for MPAL

202 citations

Journal ArticleDOI
TL;DR: Fiber-to-chip grating couplers with aligned silicon nitride (Si(3)N(4)) and silicon (Si) grating teeth for wide bandwidths and high coupling efficiencies without the use of bottom reflectors are proposed and experimentally demonstrated.
Abstract: We propose and experimentally demonstrate fiber-to-chip grating couplers with aligned silicon nitride (Si(3)N(4)) and silicon (Si) grating teeth for wide bandwidths and high coupling efficiencies without the use of bottom reflectors. The measured 1-dB bandwidth is a record 80 nm, and the measured peak coupling efficiency is -1.3 dB, which is competitive with the best Si-only grating couplers. The grating couplers are integrated in a Si(3)N(4) on silicon-on-insulator (SOI) integrated optics platform with aligned waveguides in both the Si(3)N(4) and Si, and we demonstrate a 1 × 4 tunable multiplexer/demultiplexer using the Si(3)N(4)-on-SOI dual-level grating couplers and thermally-tuned Si microring resonators.

120 citations

Journal ArticleDOI
TL;DR: In this article, the optical properties of isolated silicon nanocrystals nc-Si with a mean size of 4 nm embedded in a SiO2 matrix that was synthesized with an ion beam technique have been determined with spectroscopic ellipsometry in the photon energy range of 1.1-5.0 eV.
Abstract: Optical properties of isolated silicon nanocrystals nc-Si with a mean size of 4 nm embedded in a SiO2 matrix that was synthesized with an ion beam technique have been determined with spectroscopic ellipsometry in the photon energy range of 1.1–5.0 eV. The optical properties of the nc-Si are found to be well described by both the Lorentz oscillator model and the Forouhi-Bloomer FB model. The nc-Si exhibits a significant reduction in the dielectric functions and optical constants and a large blueshift 0.6 eV in the absorption spectrum as compared with bulk crystalline silicon. The band gap of the nc-Si obtained from the FB model is 1.7 eV, showing a large band gap expansion of 0.6 eV relative to the bulk value. The band gap expansion is in very good agreement with the first-principles calculation of the nc-Si optical gap based on quantum confinement.

99 citations

Journal ArticleDOI
TL;DR: 2.5D Through-Si-Interposer (TSI) is a strong candidate to deliver improved performance while consuming lower power than in previous generations of servers/data centers and mobile devices.
Abstract: Driven by the need to reduce the power consumption of mobile devices, and servers/data centers, and yet continue to deliver improved performance and experience by the end consumer of digital data, the semiconductor industry is looking for new technologies for manufacturing integrated circuits (ICs). In this quest, power consumed in transferring data over copper interconnects is a sizeable portion that needs to be addressed now and continuing over the next few decades. 2.5D Through-Si-Interposer (TSI) is a strong candidate to deliver improved performance while consuming lower power than in previous generations of servers/data centers and mobile devices. These low-power/high-performance advantages are realized through achievement of high interconnect densities on the TSI (higher than ever seen on Printed Circuit Boards (PCBs) or organic substrates), and enabling heterogeneous integration on the TSI platform where individual ICs are assembled at close proximity (<1 mm separation) compared with several centim...

98 citations

Journal ArticleDOI
TL;DR: Novel polarization management devices in a custom-designed silicon nitride (Si(3)N(4)) waveguides on silicon-on-insulator (SOI) integrated photonics platform are demonstrated.
Abstract: We demonstrate novel polarization management devices in a custom-designed silicon nitride (Si(3)N(4)) on silicon-on-insulator (SOI) integrated photonics platform. In the platform, Si(3)N(4) waveguides are defined atop silicon waveguides. A broadband polarization rotator-splitter using a TM0-TE1 mode converter in a composite Si(3)N(4)-silicon waveguide is demonstrated. The polarization crosstalk, insertion loss, and polarization dependent loss are less than -19 dB, 1.5 dB, and 1.0 dB, respectively, over a bandwidth of 80 nm. A polarization controller composed of polarization rotator-splitters, multimode interference couplers, and thin film heaters is also demonstrated.

92 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

01 Feb 2015
TL;DR: In this article, the authors describe the integrative analysis of 111 reference human epigenomes generated as part of the NIH Roadmap Epigenomics Consortium, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression.
Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

4,409 citations

Journal Article
TL;DR: Schulz et al. as discussed by the authors investigated whether adult macrophages all share a common developmental origin and found that a population of yolk-sac-derived, tissue-resident macophages was able to develop and persist in adult mice in the absence of hematopoietic stem cells.
Abstract: Macrophage Development Rewritten Macrophages provide protection against a wide variety of infections and critically shape the inflammatory environment in many tissues. These cells come in many flavors, as determined by differences in gene expression, cell surface phenotype and specific function. Schulz et al. (p. 86, published online 22 March) investigated whether adult macrophages all share a common developmental origin. Immune cells, including most macrophages, are widely thought to arise from hematopoietic stem cells (HSCs), which require the transcription factor Myb for their development. Analysis of Myb-deficient mice revealed that a population of yolk-sac–derived, tissue-resident macrophages was able to develop and persist in adult mice in the absence of HSCs. Importantly, yolk sac–derived macrophages also contributed substantially to the tissue macrophage pool even when HSCs were present. In mice, a population of tissue-resident macrophages arises independently of bone marrow–derived stem cells. Macrophages and dendritic cells (DCs) are key components of cellular immunity and are thought to originate and renew from hematopoietic stem cells (HSCs). However, some macrophages develop in the embryo before the appearance of definitive HSCs. We thus reinvestigated macrophage development. We found that the transcription factor Myb was required for development of HSCs and all CD11bhigh monocytes and macrophages, but was dispensable for yolk sac (YS) macrophages and for the development of YS-derived F4/80bright macrophages in several tissues, such as liver Kupffer cells, epidermal Langerhans cells, and microglia—cell populations that all can persist in adult mice independently of HSCs. These results define a lineage of tissue macrophages that derive from the YS and are genetically distinct from HSC progeny.

1,673 citations

01 Apr 2016
TL;DR: Tirosh et al. as discussed by the authors applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells.
Abstract: Single-cell expression profiles of melanoma Tumors harbor multiple cell types that are thought to play a role in the development of resistance to drug treatments. Tirosh et al. used single-cell sequencing to investigate the distribution of these differing genetic profiles within melanomas. Many cells harbored heterogeneous genetic programs that reflected two different states of genetic expression, one of which was linked to resistance development. Following drug treatment, the resistance-linked expression state was found at a much higher level. Furthermore, the environment of the melanoma cells affected their gene expression programs. Science, this issue p. 189 Melanoma cells show transcriptional heterogeneity. To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.

823 citations