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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.

Background: Cell clustering is a prerequisite for identifying differentiallyexpressed genes (DEGs) in single-cell RNA sequencing (scRNA-seq) data.Obtaining a perfect clustering result is of central importance for subsequentanalyses, but doing so is not easy. Additionally, the increase in cell throughputdue to the advancement of scRNA-seq protocols exacerbates many computationalissues, 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 noveland fast method for detecting single-cell DEGs without prior cell clusteringresults. The proposed method utilizes a small part of known non-DEGs (stablyexpressed genes) to build a minimum enclosing ball and defines the DEGs basedon the distance of a mapped gene to the center of the sphere in a feature space. Conclusions: We benchmarked scMEB against two other methods that haveimplications for DEG identification without clustering the cells. The analysisresults of 11 real datasets demonstrated that scMEB had a better or at leastcomparable performance with the competing methods in terms of identifyingmarker genes, cell clustering, and predicting genes with biological functions.Moreover, scMEB ran much faster than the other methods, which is especiallyuseful for detecting DEGs in high-throughput scRNA-seq data. Our method“scMEB” has been incorporated into the R package (MEB) and could beaccessed at https://github.com/FocusPaka/MEB.

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.

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.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.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.HIF-2α rather than HIF-1α induces myopia development through upregulating MMP-2 and promoting collagen degradation in the sclera.

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.

The grey prediction models of time series are widely used in demand forecasting because only limited data can be used to build the models and no statistical hypothesis is needed. In this paper, a grey power Markov prediction model (RGPMM(λ,1,1)) with time-varying parameters is proposed. This model is based on the principle of “new information priority”, combined with rolling mechanism and Markov theory, and the prediction residual error is modified to further improve the prediction accuracy. Compared with the classic grey models, the new model not only overcomes the inherent defect of poor adaptability to the original data, but also uses real-time information to better reflect the nonlinear characteristics of the original data, so it can be used to describe and predict the nonlinear development trend of things. In order to verify the validity and applicability of the model, the proposed model is used to forecast the total electric consumption in China. The experimental results show that the proposed model has a better prediction effect than other grey models. The proposed model is used to forecast China’s total electricity consumption in the next six years from 2018 to 2023.

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.

Here, we demonstrate a novel physically transient true random number generator based on magnesium oxide volatile threshold switches, using the intrinsic variation in resistive switching as a natural source of randomness. Taking advantage of the volatile nature in switching, a reset operation is not required in each cycle and the operation is largely simplified. It is demonstrated that such random number generators can be applied for complex numerical calculations, and the value of ${ \pi }$ was successfully calculated in Monte Carlo method based on the random number generators. Such random number generators based on physically transient threshold switches can have a great prospect in Monte Carlo computing and secure electronics.

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.

Van der Waals heterostructures composed of two-dimensional (2D) transition metal dichalcogenides (TMD) materials have stimulated tremendous research interest in various device applications, especially in energy-efficient future-generation electronics. Such ultra-thin stacks as tunnel junction theoretically present unprecedented possibilities of tunable relative band alignment and pristine interfaces, which enable significant performance enhancement for steep-slope tunneling transistors. In this work, the optimal 2D-2D heterostructure for tunneling transistors is presented and elaborately engineered, taking into consideration both electric properties and material stability. The key challenges, including band alignment and metal-to-2D semiconductor contact resistances, are optimized separately for integration. By using a new dry transfer technique for the vertical stack, the selected WS2/SnS2 heterostructure-based tunneling transistor is fabricated for the first time, and exhibits superior performance with comparable on-state current and steeper subthreshold slope than conventional FET, as well as on-off current ratio over 106 which is among the highest value of 2D-2D tunneling transistors. A visible negative differential resistance feature is also observed. This work shows the great potential of 2D layered semiconductors for new heterostructure devices and can guide possible development of energy-efficient future-generation electronics.

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-β2 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.

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.

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

Resistive random-access memory is a promising candidate for high-density, low-power neuromorphic computing. Here, we demonstrate a thermally oxidized TaO x RRAM array with built-in oxygen concentration gradient, which ensures good linearity and symmetry and consequently better inference accuracy on both MNIST and CIFAR10 datasets after training with perceptron, LeNet-5 and ResNet-18 networks, thus illustrating the potentiation of applying RRAMs for high-density, high-accuracy, large-scale neuromorphic computing.

Energy efficiency has become the main concern of IC technology. Tunnel FET (TFET) is recognized to possess the superiority of energy efficiency over MOSFET at ultralow voltage, while variability might limit the voltage reduction and thus the energy saving. Recently, device source doping gradient (SDG) is found to be the dominant variation source in TFET. In this paper, for the first time, with the awareness of SDG variation, the energy efficiency of TFET-based circuits with different activity factors ( $\alpha $ ) is comprehensively reassessed and the device optimization strategy is provided. It is found that the superiority of energy efficiency of TFET will significantly decrease especially for the more active circuit due to the inherent tradeoff between SDG variation and nominal SDG. Based on the quantitative relationship between SDG variation and nominal SDG extracted from the experimental devices, the optimal design of tunnel junction abruptness to maximize the circuit energy efficiency is obtained, leading to significant energy efficiency improvements of TFET-based circuit over MOSFET even for circuit with high $\alpha $ . This paper provides helpful device design guidelines for TFET with device variability awareness from the perspective of circuit energy efficiency.

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.

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 (β = −0.774), SA (β = −0.991), and CcFD (β = 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.

In this letter, a novel graded-channel heterojunction tunnel field-effect transistor (GCH-TFET) is proposed and studied by simulation. The novel TFET adopts a near broken-gap heterojunction at the source/channel interface to enhance the tunnel efficiency. Besides, it employs a graded component channel which works as an electron barrier to block up the leakage current at the OFF-state and can be removed with the increased gate voltage at the ON-state for high ON/ OFF-current ( ${I}_{\scriptscriptstyle\text {ON}}/{I}_{\scriptscriptstyle\text {OFF}}$ ) ratio. Such graded channel also allows a sudden turn-on of band-to-band tunneling, resulting in a reduced sub-threshold swing (SS) compared with conventional heterojunction TFETs. The GCH-TFET demonstrates an ${I}_{\scriptscriptstyle\text {ON}}/{I}_{\scriptscriptstyle\text {OFF}}$ ratio of more than seven decades with ${I}_{\scriptscriptstyle\text {ON}}$ up to 225 $\mu \text{A}/\mu \text{m}$ at ${V}_{\textsf {DD}} = \textsf {0.3}$ V, ${I}_{\scriptscriptstyle\text {OFF}}$ more than two decades lower than a near broken-gap heterojunction TFET and SS lower than 30 mV/decade for more than five decades, exhibiting excellent potential for ultra-low power applications.

As one of the important sources of low-frequency noise, random telegraph noise (RTN) in tunnel FET (TFET) has attracted growing attention recently. However, there is still lack of explanation for the high-amplitude RTN, which may cause serious variability and reliability problems to TFET-based ultralow-power circuits. In this paper, we experimentally investigate the RTN amplitude characteristics in TFETs, revealing the mechanism of high-amplitude RTN. It is found that the nonuniform distribution of band-to-band tunneling (BTBT) generation rate along device width direction is responsible for the high-amplitude RTN. Locations with relatively higher BTBT generation rate may act as critical paths dominating the device current. “Lucky” trap located at a critical path can induce the high-amplitude RTN. With device width shrinking, the number of critical paths may be reduced, resulting in much higher maximum RTN amplitude. In addition, the impacts of process parameters on RTN amplitude are discussed. Both higher source doping concentration ( $\text{N}_{\text {S}})$ and higher thermal budget can effectively mitigate the nonuniformity of BTBT generation rate along device width direction, causing suppressed RTN amplitudes. Considering that the higher thermal budget may lead to degraded device performance, the annealing process should compromisingly be designed in terms of both variation and device performance.

In this letter, a novel hetero-stacked TFET (HS-TFET) is experimentally demonstrated and optimized for the first time, which can effectively suppress the sub-threshold slope (SS) degradation without leakage current increase through self-adaptively current replenishing with bandgap engineering, greatly alleviating the critical issue of high average SS in conventional TFETs. Based on CMOS-compatible technology, the fabricated HS-TFETs of vertically stacked Si-Si1–xGex source can experimentally achieve superior performance with nearly constant SS over five decades of drain current, high $I_{{{\mathrm{\scriptscriptstyle ON}}}}$ ( $\sim 14\mu \text{A}/\mu \text{m}$ @ $V_{\mathrm {DS}}=-1$ V), and large $I_{{{\mathrm{\scriptscriptstyle ON}}}}/I_{{{\mathrm{\scriptscriptstyle OFF}}}}$ ratio ( $\sim 10^{7})$ . The proposed device with high process compatibility shows great potentials for future ultra-low power applications.

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 MoS

Oxide-based resistive switching devices have tremendous potential in next-generation nonvolatile memory and neuromorphic applications. Here, the emergence of quantized conductance is investigated in resistive switching devices based on Ta 2 O 5 or HfO 2 . By applying sweeping voltages with different current compliances or using consecutive voltage pulses, quantized conductance states including integer and half integer multiples of quantum conductance (G 0 ) were observed, suggesting well-controlled formation of atomic point contacts. Compared with Pt/Ta/Ta 2 O 5 /Pt devices, a larger number of quantized conductance states were obtained in the Pt/Ta/HfO 2 /Pt devices. Such quantized conductance states are inherently discrete and multilevel, which could be promising for applications as multilevel nonvolatile memory and artificial synapses in hardware neural networks.

The histogram of the length difference of the orthologous transcripts between human and mouse. (PDF 5 KB)

The length difference of the orthologous transcripts between human and mouse. (PDF 34 KB)

The scaling factors with different p-value cutoffs. (PDF 5 KB)

The false discovery number at the rates of noise in selected conserved genes being 0, 0.1, 0.2, 0.3, 0.4 and 0.5, respectively. (PDF 180 KB)

M versus A plots of two normalization methods. (PDF 2457 KB)

This article provides a review of current development and challenges in brain-inspired computing with memristors. We review the mechanisms of various memristive devices that can mimic synaptic and neuronal functionalities and survey the progress of memristive spiking and artificial neural networks. Different architectures are compared, including spiking neural networks, fully connected artificial neural networks, convolutional neural networks, and Hopfield recurrent neural networks. Challenges and strategies for nanoelectronic brain-inspired computing systems, including device variations, training, and testing algorithms, are also discussed.

The rapid development of information technology has led to urgent requirements for high efficiency and ultralow power consumption. In the past few decades, neuromorphic computing has drawn extensive attention due to its promising capability in processing massive data with extremely low power consumption. Here, we offer a comprehensive review on emerging artificial neuromorphic devices and their applications. In light of the inner physical processes, we classify the devices into nine major categories and discuss their respective strengths and weaknesses. We will show that anion/cation migration-based memristive devices, phase change, and spintronic synapses have been quite mature and possess excellent stability as a memory device, yet they still suffer from challenges in weight updating linearity and symmetry. Meanwhile, the recently developed electrolyte-gated synaptic transistors have demonstrated outstanding energy efficiency, linearity, and symmetry, but their stability and scalability still need to be optimized. Other emerging synaptic structures, such as ferroelectric, metal–insulator transition based, photonic, and purely electronic devices also have limitations in some aspects, therefore leading to the need for further developing high-performance synaptic devices. Additional efforts are also demanded to enhance the functionality of artificial neurons while maintaining a relatively low cost in area and power, and it will be of significance to explore the intrinsic neuronal stochasticity in computing and optimize their driving capability, etc. Finally, by looking into the correlations between the operation mechanisms, material systems, device structures, and performance, we provide clues to future material selections, device designs, and integrations for artificial synapses and neurons.

Magneto-electronic logic is an innovative approach to performing high-efficiency computations. Additionally, the ultra-large scale integration requirement for computation strongly suggests exploiting magnetoresistance effects in non-magnetic semiconductor materials. Here, we demonstrate the magnetoresistance effect in a silicon nanowire field effect transistor (SNWT) fabricated by complementary metal-oxide-semiconductor (CMOS)-compatible technology. Our experimental results show that the sign and the magnitude of the magnetoresistance in SNWTs can be effectively controlled by the drain-source voltage and the gate-source voltage, respectively, playing the role of a multi-terminal tunable magnetoresistance device. Various current models are established and in good agreement with the experimental results that describe the impact of electrical voltage and magnetic field on magnetoresistance, which provides design feasibility for the high-density magneto-electronic circuit. Such findings will further pave the way for nanoscale silicon-based magneto-electronics logic devices and show a possible path beyond the developmental limits of CMOS logic.

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. Electronic supplementary material The online version of this article (10.1186/s12859-019-2745-1) contains supplementary material, which is available to authorized users.

Van der Waals heterostructures composed of two-dimensional (2D) transition metal dichalcogenides (TMD) materials have stimulated tremendous research interest in various device applications, especially in energy-efficient future-generation electronics. Such ultra-thin stacks as tunnel junction theoretically present unprecedented possibilities of tunable relative band alignment and pristine interfaces, which enable significant performance enhancement for steep-slope tunneling transistors. In this work, the optimal 2D-2D heterostructure for tunneling transistors is presented and elaborately engineered, taking into consideration both electric properties and material stability. The key challenges, including band alignment and metal-to-2D semiconductor contact resistances, are optimized separately for integration. By using a new dry transfer technique for the vertical stack, the selected WS2/SnS2 heterostructure-based tunneling transistor is fabricated for the first time, and exhibits superior performance with comparable on-state current and steeper subthreshold slope than conventional FET, as well as on-off current ratio over 106 which is among the highest value of 2D-2D tunneling transistors. A visible negative differential resistance feature is also observed. This work shows the great potential of 2D layered semiconductors for new heterostructure devices and can guide possible development of energy-efficient future-generation electronics.

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.

Here we report a device size and structure dependent study on the resisitive switching and synaptic plasticity in HfO2-based memristive devices. We found that the on/off ratio of resistive switching constantly increases as device size decreases, and bilayer Pt/TaO x /HfO 2 /Pt devices generally exhibit more uniform switching characteristics and lower threshold voltages compared with single-layer Pt/HfO 2 /Pt devices. Long-term potentiation and depression behaviors have been emulated using such bilayer devices, suggesting potential applications for neuromorphic hardware.

In this work, the power, performance and area of novel multi-Finger Schottky-Barrier Tunnel FET (mFSB-TFET) are benchmarked with standard MOSFET and low-power MOSFET from device and circuit level. Under the same area penalty, mFSB-TFET circuits could obtain comparable delay with significantly lower static and total power consumption by orders of magnitude than standard MOSFET circuits. The advantages are more significant at ultra-low working voltage, even compared with low-power MOSFET circuits, indicating great practical potential of mFSB-TFET for ultra-low power application.