Designing artificial two-dimensional landscapes via atomic-layer substitution.

Technology advancements in history have often been propelled by material innovations. In recent years, two-dimensional (2D) materials have attracted substantial interest as an ideal platform to construct atomic-level material architectures. In this work, we design a reaction pathway steered in a very different energy landscape, in contrast to typical thermal chemical vapor deposition method in high temperature, to enable room-temperature atomic-layer substitution (RT-ALS). First-principle calculations elucidate how the RT-ALS process is overall exothermic in energy and only has a small reaction barrier, facilitating the reaction to occur at room temperature. As a result, a variety of Janus monolayer transition metal dichalcogenides with vertical dipole could be universally realized. In particular, the RT-ALS strategy can be combined with lithography and flip-transfer to enable programmable in-plane multiheterostructures with different out-of-plane crystal symmetry and electric polarization. Various characterizations have confirmed the fidelity of the precise single atomic layer conversion. Our approach for designing an artificial 2D landscape at selective locations of a single layer of atoms can lead to unique electronic, photonic, and mechanical properties previously not found in nature. This opens a new paradigm for future material design, enabling structures and properties for unexplored territories.

scMEB: a fast and clustering-independent method for detecting differentially expressed genes in single-cell RNA-seq data.

BACKGROUND: Cell clustering is a prerequisite for identifying differentially expressed genes (DEGs) in single-cell RNA sequencing (scRNA-seq) data. Obtaining a perfect clustering result is of central importance for subsequent analyses, but not easy. Additionally, the increase in cell throughput due to the advancement of scRNA-seq protocols exacerbates many computational issues, especially regarding method runtime. To address these difficulties, a new, accurate, and fast method for detecting DEGs in scRNA-seq data is needed. RESULTS: Here, we propose single-cell minimum enclosing ball (scMEB), a novel and fast method for detecting single-cell DEGs without prior cell clustering results. The proposed method utilizes a small part of known non-DEGs (stably expressed genes) to build a minimum enclosing ball and defines the DEGs based on the distance of a mapped gene to the center of the hypersphere in a feature space. CONCLUSIONS: We compared scMEB to two different approaches that could be used to identify DEGs without cell clustering. The investigation of 11 real datasets revealed that scMEB outperformed rival methods in terms of cell clustering, predicting genes with biological functions, and identifying marker genes. Moreover, scMEB was much faster than the other methods, making it particularly effective for finding DEGs in high-throughput scRNA-seq data. We have developed a package scMEB for the proposed method, which could be available at https://github.com/FocusPaka/scMEB .

A scaling-free minimum enclosing ball method to detect differentially expressed genes for RNA-seq data.

BACKGROUND: Identifying differentially expressed genes between the same or different species is an urgent demand for biological and medical research. For RNA-seq data, systematic technical effects and different sequencing depths are usually encountered when conducting experiments. Normalization is regarded as an essential step in the discovery of biologically important changes in expression. The present methods usually involve normalization of the data with a scaling factor, followed by detection of significant genes. However, more than one scaling factor may exist because of the complexity of real data. Consequently, methods that normalize data by a single scaling factor may deliver suboptimal performance or may not even work.The development of modern machine learning techniques has provided a new perspective regarding discrimination between differentially expressed (DE) and non-DE genes. However, in reality, the non-DE genes comprise only a small set and may contain housekeeping genes (in same species) or conserved orthologous genes (in different species). Therefore, the process of detecting DE genes can be formulated as a one-class classification problem, where only non-DE genes are observed, while DE genes are completely absent from the training data. RESULTS: In this study, we transform the problem to an outlier detection problem by treating DE genes as outliers, and we propose a scaling-free minimum enclosing ball (SFMEB) method to construct a smallest possible ball to contain the known non-DE genes in a feature space. The genes outside the minimum enclosing ball can then be naturally considered to be DE genes. Compared with the existing methods, the proposed SFMEB method does not require data normalization, which is particularly attractive when the RNA-seq data include more than one scaling factor. Furthermore, the SFMEB method could be easily extended to different species without normalization. CONCLUSIONS: Simulation studies demonstrate that the SFMEB method works well in a wide range of settings, especially when the data are heterogeneous or biological replicates. Analysis of the real data also supports the conclusion that the SFMEB method outperforms other existing competitors. The R package of the proposed method is available at https://bioconductor.org/packages/MEB .

Declines in PDE4B activity promote myopia progression through downregulation of scleral collagen expression.

Myopia is the most common cause of a visual refractive error worldwide. Cyclic adenosine monophosphate (cAMP)-linked signaling pathways contribute to the regulation of myopia development, and increases in cAMP accumulation promote myopia progression. To pinpoint the underlying mechanisms by which cAMP modulates myopia progression, we performed scleral transcriptome sequencing analysis in form-deprived mice, a well-established model of myopia development. Form deprivation significantly inhibited the expression levels of genes in the cAMP catabolic pathway. Quantitative real-time polymerase chain reaction analysis validated that the gene expression level of phosphodiesterase 4B (PDE4B), a cAMP hydrolase, was downregulated in form-deprived mouse eyes. Under visually unobstructed conditions, loss of PDE4B function in Pde4b-knockout mice increased the myopic shift in refraction, -3.661 ± 1.071 diopters, more than that in the Pde4b-wildtype littermates (P < 0.05). This suggests that downregulation and inhibition of PDE4B gives rise to myopia. In guinea pigs, subconjunctival injection of rolipram, a selective inhibitor of PDE4, led to myopia in normal eyes, and it also enhanced form-deprivation myopia (FDM). Subconjunctival injection of dibutyryl-cyclic adenosine monophosphate, a cAMP analog, induced only a myopic shift in the normal visually unobstructed eyes, but it did not enhance FDM. As myopia developed, axial elongation occurred during scleral remodeling that was correlated with changes in collagen fibril thickness and distribution. The median collagen fibril diameter in the FDM + rolipram group, 55.09 ± 1.83 nm, was thinner than in the FDM + vehicle group, 59.33 ± 2.06 nm (P = 0.011). Thus, inhibition of PDE4 activity with rolipram thinned the collagen fibril diameter relative to the vehicle treatment in form-deprived eyes. Rolipram also inhibited increases in collagen synthesis induced by TGF-ß2 in cultured human scleral fibroblasts. The current results further support a role for PDE enzymes such as PDE4B in the regulation of normal refractive development and myopia because either loss or inhibition of PDE4B function increased myopia and FDM development through declines in the scleral collagen fibril diameter.

A statistical normalization method and differential expression analysis for RNA-seq data between different species.

BACKGROUND: High-throughput techniques bring novel tools and also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, normalization serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects. RESULTS: In this paper, we propose a scale based normalization (SCBN) method by taking into account the available knowledge of conserved orthologous genes and by using the hypothesis testing framework. Considering the different gene lengths and unmapped genes between different species, we formulate the problem from the perspective of hypothesis testing and search for the optimal scaling factor that minimizes the deviation between the empirical and nominal type I errors. CONCLUSIONS: Simulation studies show that the proposed method performs significantly better than the existing competitor in a wide range of settings. An RNA-seq dataset of different species is also analyzed and it coincides with the conclusion that the proposed method outperforms the existing method. For practical applications, we have also developed an R package named "SCBN", which is freely available at http://www.bioconductor.org/packages/devel/bioc/html/SCBN.html .

Interfacial redox processes in memristive devices based on valence change and electrochemical metallization.

Memristive devices based on electrochemical processes are promising candidates for next-generation memory and neuromorphic applications. The redox processes happening at the interfaces are crucial steps for the ionization as well as generation of counter charges, and are thus indispensable for successful resistive switching, but their detailed mechanism has not been fully clarified. Here, we study the interfacial redox reactions in the forming process of memristive devices based on valence change and electrochemical metallization, using high-resolution electron microscopy and electrostatic force microscopy observations. We show direct evidence for the anodic oxidation of oxygen ions and cathodic reduction of moisture in HfO2- and Ta2O5-based valence change cells, which could take place in different horizontal locations. We further found that the anodic reactions always led to more pronounced structural damage to the electrode, indicating the possibility of additional cathodic reactions without producing gaseous products. When an active electrode is present, oxidation of metal atoms takes place at the anodic interface instead. Further investigations on electrochemical metallization cells have identified Cu ionization and moisture reduction as the anodic and cathodic reactions, respectively, and formation of Cu nuclei at the cathodic interface was directly observed. These findings with microscopic evidence could facilitate future development of memristive devices.

Revealing the anaerobic acclimation of microbial community in a membrane bioreactor for coking wastewater treatment by Illumina Miseq sequencing.

The dynamic change of microbial community during sludge acclimation from aerobic to anaerobic in a MBR for coking wastewater treatment was revealed by Illumina Miseq sequencing in this study. The diversity of both Bacteria and Archaea showed an increase-decrease trajectory during acclimation, and exhibited the highest at the domestication interim. Ignavibacteria changed from a tiny minority (less than 1%) to the dominant bacterial group (54.0%) along with acclimation. The relative abundance of Betaproteobacteria kept relatively steady, as in this class some species increased coupled with some other species decreased during acclimation. The dominant Archaea shifted from Halobacteria in initial aerobic sludge to Methanobacteria in the acclimated anaerobic sludge. The dominant bacterial and archaeal groups in different acclimation stages were indigenous microorganisms in the initial sludge, though some of them were very rare. This study supported that the species in "rare biosphere" might eventually become dominant in response to environmental change.

Highly Confined Hybridized Polaritons in Scalable van der Waals Heterostructure Resonators.

The optimization of nanoscale optical devices and structures will enable the exquisite control of planar optical fields. Polariton manipulation is the primary strategy in play. In two-dimensional heterostructures, the ability to excite mixed optical modes offers an additional control in device design. Phonon polaritons in hexagonal boron nitride have been a common system explored for the control of near-infrared radiation. Their hybridization with graphene plasmons makes these mixed phonon polariton modes in hexagonal boron nitride more appealing in terms of enabling active control of electrodynamic properties with a reduction of propagation losses. Optical resonators can be added to confine these hybridized plasmon-phonon polaritons deeply into the subwavelength regime, with these structures featuring high quality factors. Here, we show a scalable approach for the design and fabrication of heterostructure nanodisc resonators patterned in chemical vapor deposition-grown monolayer graphene and h-BN sheets. Real-space mid-infrared nanoimaging reveals the nature of hybridized polaritons in the heterostructures. We simulate and experimentally demonstrate localized hybridized polariton modes in heterostructure nanodisc resonators and demonstrate that those nanodiscs can collectively couple to the waveguide. High quality factors for the nanodiscs are measured with nanoscale Fourier transform infrared spectroscopy. Our results offer practical strategies to realize scalable nanophotonic devices utilizing low-loss hybridized polaritons for applications such as on-chip optical components.

Imputation for Single-cell RNA-seq Data with Non-negative Matrix Factorization and Transfer Learning.

Single-cell RNA sequencing (scRNA-seq) has been proven to be an effective technology for investigating the heterogeneity and transcriptome dynamics due to the single-cell resolution. However, one of the major problems for data obtained by scRNA-seq is excessive zeros in the count matrix, which hinders the downstream analysis enormously. Here, we present a method that integrates non-negative matrix factorization and transfer learning (NMFTL) to impute the scRNA-seq data. It borrows gene expression information from the additional dataset and adds graph-regularized terms to the decomposed matrices. These strategies not only maintain the intrinsic geometrical structure of the data itself but also further improve the accuracy of estimating the expression values by adding the transfer term in the model. The real data analysis result demonstrates that the proposed method outperforms the existing matrix-factorization-based imputation methods in recovering dropout entries, preserving gene-to-gene and cell-to-cell relationships, and in the downstream analysis, such as cell clustering analysis, the proposed method also has a good performance. For convenience, we have implemented the "NMFTL" method with R scripts, which could be available at https://github.com/FocusPaka/NMFTL.

Low-thermal-budget synthesis of monolayer molybdenum disulfide for silicon back-end-of-line integration on a 200 mm platform.

Two-dimensional (2D) materials are promising candidates for future electronics due to their excellent electrical and photonic properties. Although promising results on the wafer-scale synthesis (≤150 mm diameter) of monolayer molybdenum disulfide (MoS2) have already been reported, the high-quality synthesis of 2D materials on wafers of 200 mm or larger, which are typically used in commercial silicon foundries, remains difficult. The back-end-of-line (BEOL) integration of directly grown 2D materials on silicon complementary metal-oxide-semiconductor (CMOS) circuits is also unavailable due to the high thermal budget required, which far exceeds the limits of silicon BEOL integration (<400 °C). This high temperature forces the use of challenging transfer processes, which tend to introduce defects and contamination to both the 2D materials and the BEOL circuits. Here we report a low-thermal-budget synthesis method (growth temperature < 300 °C, growth time ≤ 60 min) for monolayer MoS2 films, which enables the 2D material to be synthesized at a temperature below the precursor decomposition temperature and grown directly on silicon CMOS circuits without requiring any transfer process. We designed a metal-organic chemical vapour deposition reactor to separate the low-temperature growth region from the high-temperature chalcogenide-precursor-decomposition region. We obtain monolayer MoS2 with electrical uniformity on 200 mm wafers, as well as a high material quality with an electron mobility of ~35.9 cm2 V-1 s-1. Finally, we demonstrate a silicon-CMOS-compatible BEOL fabrication process flow for MoS2 transistors; the performance of these silicon devices shows negligible degradation (current variation < 0.5%, threshold voltage shift < 20 mV). We believe that this is an important step towards monolithic 3D integration for future electronics.

A novel grey power-Markov model for the prediction of China's electricity consumption.

Forecasting the electricity consumption has always played an important role in the management of power system management, which requires higher forecasting technology. Therefore, based on the principle of "new information priority", combined with rolling mechanism and Markov theory, a novel grey power-Markov prediction model with time-varying parameters (RGPMM(λ,1,1)) is designed, which overcomes the inherent defects of fixed structure and poor adaptability to the changes of original data. In addition, in order to prove the validity and applicability of the prediction model, we have used the model to predict China's total electricity consumption, and have compared it with the prediction results by a series of benchmark models. The result shows that the can better adapt to the characteristics of electricity consumption data, and it also shows the advantages of the proposed forecasting model. In this paper, the proposed forecasting model is used to predict China's total electricity consumption in the next six years from 2018 to 2023, so as to provide certain reference value for power system management and distribution.

A distributed nanocluster based multi-agent evolutionary network.

As an important approach of distributed artificial intelligence, multi-agent system provides an efficient way to solve large-scale computational problems through high-parallelism processing with nonlinear interactions between the agents. However, the huge capacity and complex distribution of the individual agents make it difficult for efficient hardware construction. Here, we propose and demonstrate a multi-agent hardware system that deploys distributed Ag nanoclusters as physical agents and their electrochemical dissolution, growth and evolution dynamics under electric field for high-parallelism exploration of the solution space. The collaboration and competition between the Ag nanoclusters allow information to be effectively expressed and processed, which therefore replaces cumbrous exhaustive operations with self-organization of Ag physical network based on the positive feedback of information interaction, leading to significantly reduced computational complexity. The proposed multi-agent network can be scaled up with parallel and serial integration structures, and demonstrates efficient solution of graph and optimization problems. An artificial potential field with superimposed attractive/repulsive components and varied ion velocity is realized, showing gradient descent route planning with self-adaptive obstacle avoidance. This multi-agent network is expected to serve as a physics-empowered parallel computing hardware.

Hypoxia-Induced Scleral HIF-2α Upregulation Contributes to Rises in MMP-2 Expression and Myopia Development in Mice.

Purpose: Scleral hypoxia is a key factor that induces hypoxia-inducible factor-1α (HIF-1α) upregulation, and this response contributes to myopia progression. Currently, we aim to determine if the different HIF subtypes, including HIF-1α and HIF-2α, mediate hypoxia-induced myopia development through promoting scleral MMP-2 expression and collagen degradation. Methods: Our study included: (1) time-course of scleral HIF-2α, MMP-2, and COL1α1 expression during form-deprivation myopia (FDM) development was determined in C57BL/6J mice. (2) The effect of silencing either HIF-1Α or HIF-2A on hypoxia-induced alterations in MMP-2 expression was analyzed in cultured human scleral fibroblasts (HSFs) under a hypoxic condition (i.e. 1% oxygen). (3) To knock-down either HIF-1α or HIF-2α expression in the sclera, we performed Sub-Tenon's capsule injection of an adeno-associated virus (AAV)8-packaged Cre overexpression vector (AAV8-Cre) in HIF-1αfl/fl or HIF-2αfl/fl mice. HIF-1α, HIF-2α, MMP-2, and COL1α1 expression were analyzed by Western blot or quantitative real-time PCR (qRT-PCR). In addition, the effects of scleral HIF-2α knock-down on normal refractive development and FDM development were evaluated. Results: The time-dependent increases in scleral HIF-2α mimicked the HIF-1α expression profiles as we previously described. Hypoxia significantly promoted MMP-2 expression in HSFs, and this upregulation was solely alleviated by HIF-2A rather than HIF-1A silencing. Scleral HIF-2α knockdown significantly inhibited form-deprivation (FD)-induced MMP-2 upregulation and declines in COL1α1 accumulation and myopia development. Although scleral HIF-1α knockdown also significantly suppressed FD-induced declines in COL1α1 accumulation, it did not abrogate scleral MMP-2 upregulation. Conclusions: HIF-2α rather than HIF-1α induces myopia development through upregulating MMP-2 and promoting collagen degradation in the sclera.

Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing.

Two-dimensional materials such as graphene have shown great promise as biosensors, but suffer from large device-to-device variation due to non-uniform material synthesis and device fabrication technologies. Here, we develop a robust bioelectronic sensing platform  composed of  more than 200 integrated sensing units, custom-built high-speed readout electronics, and machine learning inference that overcomes these challenges to achieve rapid, portable, and reliable measurements. The platform demonstrates reconfigurable multi-ion electrolyte sensing capability and provides highly sensitive, reversible, and real-time response for potassium, sodium, and calcium ions in complex solutions despite variations in device performance. A calibration method leveraging the sensor redundancy and device-to-device variation is also proposed, while a machine learning model trained with multi-dimensional information collected through the multiplexed sensor array is used to enhance the sensing system's functionality and accuracy in ion classification.

Soft-lock drawing of super-aligned carbon nanotube bundles for nanometre electrical contacts.

The assembly of single-walled carbon nanotubes (CNTs) into high-density horizontal arrays is strongly desired for practical applications, but challenges remain despite myriads of research efforts. Herein, we developed a non-destructive soft-lock drawing method to achieve ultraclean single-walled CNT arrays with a very high degree of alignment (angle standard deviation of ~0.03°). These arrays contained a large portion of nanometre-sized CNT bundles, yielding a high packing density (~400 µm-1) and high current carrying capacity (∼1.8 × 108 A cm-2). This alignment strategy can be generally extended to diverse substrates or sources of raw single-walled CNTs. Significantly, the assembled CNT bundles were used as nanometre electrical contacts of high-density monolayer molybdenum disulfide (MoS2) transistors, exhibiting high current density (~38 µA µm-1), low contact resistance (~1.6 kΩ µm), excellent device-to-device uniformity and highly reduced device areas (0.06 µm2 per device), demonstrating their potential for future electronic devices and advanced integration technologies.

Selecting Classification Methods for Small Samples of Next-Generation Sequencing Data.

Next-generation sequencing has emerged as an essential technology for the quantitative analysis of gene expression. In medical research, RNA sequencing (RNA-seq) data are commonly used to identify which type of disease a patient has. Because of the discrete nature of RNA-seq data, the existing statistical methods that have been developed for microarray data cannot be directly applied to RNA-seq data. Existing statistical methods usually model RNA-seq data by a discrete distribution, such as the Poisson, the negative binomial, or the mixture distribution with a point mass at zero and a Poisson distribution to further allow for data with an excess of zeros. Consequently, analytic tools corresponding to the above three discrete distributions have been developed: Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). However, it is unclear what the real distributions would be for these classifications when applied to a new and real dataset. Considering that count datasets are frequently characterized by excess zeros and overdispersion, this paper extends the existing distribution to a mixture distribution with a point mass at zero and a negative binomial distribution and proposes a zero-inflated negative binomial logistic discriminant analysis (ZINBLDA) for classification. More importantly, we compare the above four classification methods from the perspective of model parameters, as an understanding of parameters is necessary for selecting the optimal method for RNA-seq data. Furthermore, we determine that the above four methods could transform into each other in some cases. Using simulation studies, we compare and evaluate the performance of these classification methods in a wide range of settings, and we also present a decision tree model created to help us select the optimal classifier for a new RNA-seq dataset. The results of the two real datasets coincide with the theory and simulation analysis results. The methods used in this work are implemented in the open-scource R scripts, with a source code freely available at https://github.com/FocusPaka/ZINBLDA.

Differences in Retinal and Choroidal Vasculature and Perfusion Related to Axial Length in Pediatric Anisomyopes.

Purpose: The purpose of this study was to evaluate the interocular differences in choroidal vasculature, choriocapillaris perfusion, and retinal microvascular network, and to explore their associations with interocular asymmetry in axial lengths (ALs) in children with anisomyopia. Methods: Refractive error, AL, and other biometric parameters were measured in 70 children with anisomyopia. Using optical coherence tomography (OCT) and OCT-angiography, we measured the submacular choroidal thickness (ChT), total choroidal area (TCA), luminal area (LA), stromal area (SA), choroidal vascularity index (CVI), choriocapillaris flow deficit (CcFD), retinal vessel density (VD), and foveal avascular zone (FAZ) area. Results: The mean interocular differences in spherical equivalent refraction and AL were -2.26 ± 0.94 diopters and 0.95 ± 0.46 mm, respectively. Submacular ChT, TCA, LA, SA, and CVI were all significantly lower in the more myopic (longer AL) eyes than in the less myopic (shorter AL) fellow eyes. In eyes with longer ALs, both the CcFD and FAZ areas were significantly greater, whereas the superficial and deep retinal VDs were significantly less. After adjusting for corneal power and intraocular pressure, interocular differences in LA (ß = -0.774), SA (ß = -0.991), and CcFD (ß = 0.040) were significantly associated with interocular asymmetry in AL (all P < 0.05). Conclusions: In pediatric anisomyopes, eyes with longer ALs tended to have lower choroidal vascularity and choriocapillaris perfusion than the contralateral eyes with shorter ALs. Longitudinal investigations would be useful follow-ups to test for a causal role of choroidal circulation in human myopia.

Dual-Gated MoS2 Neuristor for Neuromorphic Computing.

The field of neuromorphic computing systems has been through enormous progress in recent years, whereas some issues are still remaining to be solved. One of the biggest challenges in neuromorphic circuit designing is the lack of a robust device with functions comparable to or even better than the metal-oxide-semiconductor field-effect transistor (MOSFET) used in traditional integrated circuits. In this work, we demonstrated a MoS2 neuristor using a dual-gate transistor structure. An ionic top gate is designed to control the migration of ions, while an electronic back gate is used to control electronic migration. By applying different driving signals, the MoS2 neuristor can be programmed as a neuron, a synapse, or an n-type MOSFET, which can be seen as a fundamental building block in the neuromorphic circuit design. The MoS2 neuristor provides viable solutions for future reconfigurable neuromorphic systems and can be a promising candidate for future neuromorphic computing.

Methylation-level inferences and detection of differential methylation with MeDIP-seq data.

DNA methylation is an essential epigenetic modification involved in regulating the expression of mammalian genomes. A variety of experimental approaches to generate genome-wide or whole-genome DNA methylation data have emerged in recent years. Methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq) is one of the major tools used in whole-genome epigenetic studies. However, analyzing this data in terms of accuracy, sensitivity, and speed still remains an important challenge. Existing methods, such as BATMAN and MEDIPS, analyze MeDIP-seq data by dividing the whole genome into equal length windows and assume that each CpG of the same window has the same methylation level. More precise work is necessary to estimate the methylation level of each CpG site in the whole genome. In this paper, we propose a Statistical Inferences with MeDIP-seq Data (SIMD) to infer the methylation level for each CpG site. In addition, we analyze a real dataset for DNA methylation. The results show that our method displays improved precision in detecting differentially methylated CpG sites compared to the existing method. To meet the demands of the application, we have developed an R package called "SIMD", which is freely available in https://github.com/FocusPaka/SIMD.