Qian Liu

Bio: Qian Liu is an academic researcher from Children's Hospital of Philadelphia. The author has contributed to research in topics: Nanopore sequencing & Computer science. The author has an hindex of 23, co-authored 78 publications receiving 2214 citations. Previous affiliations of Qian Liu include Rutgers University & Columbia University.

A library of atomically thin metal chalcogenides

Abstract: Investigations of two-dimensional transition-metal chalcogenides (TMCs) have recently revealed interesting physical phenomena, including the quantum spin Hall effect1,2, valley polarization3,4 and two-dimensional superconductivity 5 , suggesting potential applications for functional devices6–10. However, of the numerous compounds available, only a handful, such as Mo- and W-based TMCs, have been synthesized, typically via sulfurization11–15, selenization16,17 and tellurization 18 of metals and metal compounds. Many TMCs are difficult to produce because of the high melting points of their metal and metal oxide precursors. Molten-salt-assisted methods have been used to produce ceramic powders at relatively low temperature 19 and this approach 20 was recently employed to facilitate the growth of monolayer WS2 and WSe2. Here we demonstrate that molten-salt-assisted chemical vapour deposition can be broadly applied for the synthesis of a wide variety of two-dimensional (atomically thin) TMCs. We synthesized 47 compounds, including 32 binary compounds (based on the transition metals Ti, Zr, Hf, V, Nb, Ta, Mo, W, Re, Pt, Pd and Fe), 13 alloys (including 11 ternary, one quaternary and one quinary), and two heterostructured compounds. We elaborate how the salt decreases the melting point of the reactants and facilitates the formation of intermediate products, increasing the overall reaction rate. Most of the synthesized materials in our library are useful, as supported by evidence of superconductivity in our monolayer NbSe2 and MoTe2 samples21,22 and of high mobilities in MoS2 and ReS2. Although the quality of some of the materials still requires development, our work opens up opportunities for studying the properties and potential application of a wide variety of two-dimensional TMCs.

Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data

Abstract: DNA base modifications, such as C5-methylcytosine (5mC) and N6-methyldeoxyadenosine (6mA), are important types of epigenetic regulations. Short-read bisulfite sequencing and long-read PacBio sequencing have inherent limitations to detect DNA modifications. Here, using raw electric signals of Oxford Nanopore long-read sequencing data, we design DeepMod, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) to detect DNA modifications. We sequence a human genome HX1 and a Chlamydomonas reinhardtii genome using Nanopore sequencing, and then evaluate DeepMod on three types of genomes (Escherichia coli, Chlamydomonas reinhardtii and human genomes). For 5mC detection, DeepMod achieves average precision up to 0.99 for both synthetically introduced and naturally occurring modifications. For 6mA detection, DeepMod achieves ~0.9 average precision on Escherichia coli data, and have improved performance than existing methods on Chlamydomonas reinhardtii data. In conclusion, DeepMod performs well for genome-scale detection of DNA modifications and will facilitate epigenetic analysis on diverse species. DNA modification generates unique electric signals in Oxford Nanopore sequencing data but the signals can be complicated to decipher. Here, the authors develop a deep learning framework, DeepMod, to detect DNA base modifications including 5mC and 6mA using Nanopore sequencing data

Auto-optimizing Hydrogen Evolution Catalytic Activity of ReS2 through Intrinsic Charge Engineering.

Abstract: Optimizing active electronic states responding to catalysis is of paramount importance for developing high-activity catalysts because thermodynamics itself may not favor forming an optimal electronic state. Setting the monolayer transition metal dichalcogenide (TMD) ReS2 as a model for the hydrogen evolution reaction (HER), we uncover that intrinsic charge engineering has an auto-optimizing effect on enhancing catalytic activity through regulating active electronic states. The experimental and theoretical results show that intrinsic charge compensation from S to Re–Re bonds could manipulate the active electronic states, allowing hydrogen to absorb the active sites neither strongly nor weakly. Two types of S sites exhibit the optimal hydrogen adsorption free energies (ΔGH*) of 0.016 and 0.061 eV, which are the closest to zero corresponding to the highest HER activity. This auto-optimization via charge engineering is further demonstrated by higher turnover frequency per sulfur atom of 1–10 s–1 and lower ove...

Long-read sequencing identified intronic repeat expansions in SAMD12 from Chinese pedigrees affected with familial cortical myoclonic tremor with epilepsy

Abstract: Background The locus for familial cortical myoclonic tremor with epilepsy (FCMTE) has long been mapped to 8q24 in linkage studies, but the causative mutations remain unclear. Recently, expansions of intronic TTTCA and TTTTA repeat motifs within SAMD12 were found to be involved in the pathogenesis of FCMTE in Japanese pedigrees. We aim to identify the causative mutations of FCMTE in Chinese pedigrees. Methods We performed genetic linkage analysis by microsatellite markers in a five-generation Chinese pedigree with 55 members. We also used array-comparative genomic hybridisation (CGH) and next-generation sequencing (NGS) technologies (whole-exome sequencing, capture region deep sequencing and whole-genome sequencing) to identify the causative mutations in the disease locus. Recently, we used low-coverage (~10×) long-read genome sequencing (LRS) on the PacBio Sequel and Oxford Nanopore platforms to identify the causative mutations, and used repeat-primed PCR for validation of the repeat expansions. Results Linkage analysis mapped the disease locus to 8q23.3–24.23. Array-CGH and NGS failed to identify causative mutations in this locus. LRS identified the intronic TTTCA and TTTTA repeat expansions in SAMD12 as the causative mutations, thus corroborating the recently published results in Japanese pedigrees. Conclusions We identified the pentanucleotide repeat expansion in SAMD12 as the causative mutation in Chinese FCMTE pedigrees. Our study also suggested that LRS is an effective tool for molecular diagnosis of genetic disorders, especially for neurological diseases that cannot be positively diagnosed by conventional clinical microarray and NGS technologies.

Hierarchical N-Doped Porous Carbons for Zn–Air Batteries and Supercapacitors

Abstract: Nitrogen-doped carbon materials with a large specific surface area, high conductivity, and adjustable microstructures have many prospects for energy-related applications. This is especially true for N-doped nanocarbons used in the electrocatalytic oxygen reduction reaction (ORR) and supercapacitors. Here, we report a low-cost, environmentally friendly, large-scale mechanochemical method of preparing N-doped porous carbons (NPCs) with hierarchical micro-mesopores and a large surface area via ball-milling polymerization followed by pyrolysis. The optimized NPC prepared at 1000 °C (NPC-1000) offers excellent ORR activity with an onset potential (Eonset) and half-wave potential (E1/2) of 0.9 and 0.82 V, respectively (vs. a reversible hydrogen electrode), which are only approximately 30 mV lower than that of Pt/C. The rechargeable Zn–air battery assembled using NPC-1000 and the NiFe-layered double hydroxide as bifunctional ORR and oxygen evolution reaction electrodes offered superior cycling stability and comparable discharge performance to RuO2 and Pt/C. Moreover, the supercapacitor electrode equipped with NPC prepared at 800 °C exhibited a high specific capacity (431 F g−1 at 10 mV s−1), outstanding rate, performance, and excellent cycling stability in an aqueous 6-M KOH solution. This work demonstrates the potential of the mechanochemical preparation method of porous carbons, which are important for energy conversion and storage.

Machine learning

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.).

Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields

Abstract: : The unpolarized absorption and circular dichroism spectra of the fundamental vibrational transitions of the chiral molecule, 4-methyl-2-oxetanone, are calculated ab initio. Harmonic force fields are obtained using Density Functional Theory (DFT), MP2, and SCF methodologies and a 5S4P2D/3S2P (TZ2P) basis set. DFT calculations use the Local Spin Density Approximation (LSDA), BLYP, and Becke3LYP (B3LYP) density functionals. Mid-IR spectra predicted using LSDA, BLYP, and B3LYP force fields are of significantly different quality, the B3LYP force field yielding spectra in clearly superior, and overall excellent, agreement with experiment. The MP2 force field yields spectra in slightly worse agreement with experiment than the B3LYP force field. The SCF force field yields spectra in poor agreement with experiment.The basis set dependence of B3LYP force fields is also explored: the 6-31G* and TZ2P basis sets give very similar results while the 3-21G basis set yields spectra in substantially worse agreements with experiment. jg