Artificial neural networks and machine learning have now reached a new era after several decades of improvement where applications are to explode in many fields of science, industry, and technology. Here, we use artificial neural networks to study an intriguing phenomenon in quantum physics-the topological phases of matter. We find that certain topological states, either symmetry-protected or with intrinsic topological order, can be represented with classical artificial neural networks. This is demonstrated by using three concrete spin systems, the one-dimensional (1D) symmetry-protected topological cluster state and the 2D and 3D toric code states with intrinsic topological orders. For all three cases we show rigorously that the topological ground states can be represented by shortrange neural networks in an exact and efficient fashion-the required number of hidden neurons is as small as the number of physical spins and the number of parameters scales only linearly with the system size. For the 2D toric-code model, we find that the proposed short-range neural networks can describe the excited states with abelain anyons and their nontrivial mutual statistics as well. In addition, by using reinforcement learning we show that neural networks are capable of finding the topological ground states of non-integrable Hamiltonians with strong interactions and studying their topological phase transitions. Our results demonstrate explicitly the exceptional power of neural networks in describing topological quantum states, and at the same time provide valuable guidance to machine learning of topological phases in generic lattice models.

Binary code analysis allows analyzing binary code without having access to the corresponding source code. A binary, after disassembly, is expressed in an assembly language. This inspires us to approach binary analysis by leveraging ideas and techniques from Natural Language Processing (NLP), a fruitful area focused on processing text of various natural languages. We notice that binary code analysis and NLP share many analogical topics, such as semantics extraction, classification, and code/text comparison. This work thus borrows ideas from NLP to address two important code similarity comparison problems. (I) Given a pair of basic blocks of different instruction set architectures (ISAs), determining whether their semantics is similar; and (II) given a piece of code of interest, determining if it is contained in another piece of code of a different ISA. The solutions to these two problems have many applications, such as cross-architecture vulnerability discovery and code plagiarism detection.Despite the evident importance of Problem I, existing solutions are either inefficient or imprecise. Inspired by Neural Machine Translation (NMT), which is a new approach that tackles text across natural languages very well, we regard instructions as words and basic blocks as sentences, and propose a novel cross-(assembly)-lingual deep learning approach to solving Problem I, attaining high efficiency and precision. Many solutions have been proposed to determine whether two pieces of code, e.g., functions, are equivalent (called the equivalence problem), which is different from Problem II (called the containment problem). Resolving the cross-architecture code containment problem is a new and more challenging endeavor. Employing our technique for crossarchitecture basic-block comparison, we propose the first solution to Problem II. We implement a prototype system INNEREYE and perform a comprehensive evaluation. A comparison between our approach and existing approaches to Problem I shows that our system outperforms them in terms of accuracy, efficiency and scalability. The case studies applying the system demonstrate that our solution to Problem II is effective. Moreover, this research showcases how to apply ideas and techniques from NLP to largescale binary code analysis.

Purpose N6-methyladenosine (m6A), the most abundant mRNA modification in mammals, is involved in various biological processes. KIAA1429 is an important methyltransferase participating in m6A modification. However, the role of KIAA1429 in hepatocellular carcinoma (HCC) is still not well understood. Here, we aimed to investigate the function of KIAA1429 and its corresponding regulation mechanisms in HCC. Patients and methods HCC-related genes were analyzed by clinical and expression data of HCC patients in The Cancer Genome Atlas (TCGA) database. Expression of KIAA1429 was verified by quantitative reverse-transcription PCR, and interference efficiency was obtained using small interfering RNA (siRNA). Cell proliferation, migration, and invasion were assessed by cell counting kit-8 and transwell assays, and the m6A modification was detected by methylated RNA immunoprecipitation-PCR (MeRIP-PCR). Results We found a difference in the expression of KIAA1429 between HCC and normal hepatic tissues by analyzing data from the TCGA database. Comparing HCC cell lines (HepG2, Huh-7, HepG2.2.15) with normal hepatic cells (HL-7702), we observed an identically significant difference in KIAA1429 expression. KIAA1429 significantly enhanced proliferation, migration, and invasion of HepG2 cells. Moreover, Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis and correlation analysis revealed a significant negative correlation between KIAA1429 and ID2. In the subsequent MeRIP-PCR assay, downregulation of KIAA1429 inhibited m6A modification of ID2 mRNA. Conclusion KIAA1429 facilitated migration and invasion of HCC by inhibiting ID2 via upregulating m6A modification of ID2 mRNA.

Plant height (PH) and ear height (EH) are two very important agronomic traits related to the population density and lodging in maize. In order to better understand of the genetic basis of nature variation in PH and EH, two bi-parental populations and one genome-wide association study (GWAS) population were used to map quantitative trait loci (QTL) for both traits. Phenotypic data analysis revealed a wide normal distribution and high heritability for PH and EH in the three populations, which indicated that maize height is a highly polygenic trait. A total of 21 QTL for PH and EH in three common genomic regions (bin 1.05, 5.04/05, and 6.04/05) were identified by QTL mapping in the two bi-parental populations under multiple environments. Additionally, 41 single nucleotide polymorphisms (SNPs) were identified for PH and EH by GWAS, of which 29 SNPs were located in 19 unique candidate gene regions. Most of the candidate genes were related to plant growth and development. One QTL on Chromosome 1 was further verified in a near-isogenic line (NIL) population, and GWAS identified a C2H2 zinc finger family protein that maybe the candidate gene for this QTL. These results revealed that nature variation of PH and EH are strongly controlled by multiple genes with low effect and facilitated a better understanding of the underlying mechanism of height in maize.

The physiological components that contribute to cystic fibrosis (CF) lung disease are steadily being elucidated. Gene therapy could potentially correct these defects. CFTR-null pigs provide a relevant model to test gene therapy vectors. Using an in vivo selection strategy that amplifies successful capsids by replicating their genomes with helper adenovirus coinfection, we selected an adeno-associated virus (AAV) with tropism for pig airway epithelia. The evolved capsid, termed AAV2H22, is based on AAV2 with 5 point mutations that result in a 240-fold increased infection efficiency. In contrast to AAV2, AAV2H22 binds specifically to pig airway epithelia and is less reliant on heparan sulfate for transduction. We administer AAV2H22-CFTR expressing the CF transmembrane conductance regulator (CFTR) cDNA to the airways of CF pigs. The transduced airways expressed CFTR on ciliated and nonciliated cells, induced anion transport, and improved the airway surface liquid pH and bacterial killing. Most gene therapy studies to date focus solely on Cl− transport as the primary metric of phenotypic correction. Here, we describe a gene therapy experiment where we not only correct defective anion transport, but also restore bacterial killing in CFTR-null pig airways.

α-synuclein (α-Syn) is a presynaptic enriched protein involved in the pathogenesis of Parkinson’s disease. However, the physiological roles of α-Syn remain poorly understood. Recent studies have indicated a critical role of α-Syn in the sensing and generation of membrane curvature during vesicular exocytosis and endocytosis. It has been known to modulate the assembly of SNARE complex during exocytosis including vesicle docking, priming and fusion steps. Growing evidence suggests that α-Syn also plays critical roles in the endocytosis of synaptic vesicles. It also modulates the availability of releasable vesicles by promoting synaptic vesicles clustering. Here, we provide an overview of recent progresses in understanding the function of α-Syn in the regulation of exocytosis, endocytosis, and vesicle recycling under physiological and pathological conditions.

Sucrose synthase (SS) is widely considered as the key enzyme involved in the plant sugar metabolism that is critical to plant growth and development, especially quality of the fruit. The members of SS gene family have been identified and characterized in multiple plant genomes. However, detailed information about this gene family is lacking in grapevine (Vitis vinifera L.). In this study, we performed a systematic analysis of the grape (V. vinifera) genome and reported that there are five SS genes (VvSS1–5) in the grape genome. Comparison of the structures of grape SS genes showed high structural conservation of grape SS genes, resulting from the selection pressures during the evolutionary process. The segmental duplication of grape SS genes contributed to this gene family expansion. The syntenic analyses between grape and soybean (Glycine max) demonstrated that these genes located in corresponding syntenic blocks arose before the divergence of grape and soybean. Phylogenetic analysis revealed distinct evolutionary paths for the grape SS genes. VvSS1/VvSS5, VvSS2/VvSS3 and VvSS4 originated from three ancient SS genes, which were generated by duplication events before the split of monocots and eudicots. Bioinformatics analysis of publicly available microarray data, which was validated by quantitative real-time reverse transcription PCR (qRT-PCR), revealed distinct temporal and spatial expression patterns of VvSS genes in various tissues, organs and developmental stages, as well as in response to biotic and abiotic stresses. Taken together, our results will be beneficial for further investigations into the functions of SS gene in the processes of grape resistance to environmental stresses.

Real-time engineering of elastic rays in solid materials is crucial for several applications relevant to active noise and vibration cancellation and to inverse methods aiming to either reveal or dissimulate the presence of foreign bodies. Here, we introduce a programmable elastic metasurface for the first time with sensing-and-actuating units, allowing to adapt and reprogram its wave control functionalities in real time. The active units behave following decoupled ‘feedforward’ sensor-to-actuator control loops governed by local transfer functions encoded into a digital circuit and offering highly flexible phase and amplitude engineering of transmitted and/or scattered waves. The proposed metasurface is concretized numerically and experimentally by achieving, for the first time, real-time tunable ray steering of flexural waves in a host plate. Various other significant demonstrations have been included to strongly illustrate the multifunctional adaptability of the design. In particular, one-way non-reciprocal blocking of waves is observed experimentally whereas skin cloaking of voids is tested numerically. Finally, operability across broad wave frequency ranges is demonstrated (5–45 kHz). The design will pave a new efficient way in the field of sensing and actuation of elastic waves.