2D electrophoresis image brightness correction based on gradient interval histogram.

BACKGROUND: Two-dimensional electrophoresis (2DE) is one of the most widely applied techniques in comparative proteomics. The basic task of 2DE is to identify differential protein expression by quantitative analysis of 2DE images. To reduce the errors of spot quantification in 2DE images, a novel brightness correction method based on gradient interval histogram (GIH) is proposed in this paper. RESULTS: First, GIH equalization is proposed to enhance the protein spot edges, especially the weak protein spots in the 2DE image. Second, to eliminate the overall brightness shift, GIH matching is applied to the 2DE images that need to be compared. Finally, the proposed method is verified by subjective quality evaluation and quantitative analysis of protein spots in real 2DE images. CONCLUSIONS: The experimental results show that the average error of the quantification of corresponding protein spots in the resulting image pairs is less than 3%, which is significantly superior to that of the existing methods.

Nanoscale thermal cross-talk effect on phase-change probe memory.

Phase-change probe memory is considered as one of the most promising means for next-generation mass storage devices. However, the achievable storage density of phase-change probe memory is drastically affected by the resulting thermal cross-talk effect while previously lacking detailed study. Therefore, a three dimensional model that couples electrical, thermal, and phase-change processes of the Ge2Sb2Te5 media is developed, and subsequently deployed to assess the thermal cross-talk effect based on a Si/TiN/ Ge2Sb2Te5/diamond-like carbon (DLC) structure by appropriately tailoring the electro-thermal and geometrical properties of the storage media stack for a variety of external excitations. The modeling results show that the DLC capping with a thin thickness, a high electrical conductivity, and a low thermal conductivity is desired to minimize the thermal cross-talk, while the TiN underlayer has a slight impact on the thermal cross-talk. Combining the modeling findings with the previous film deposition experience, an optimized phase-change probe memory architecture is presented, and its capability of providing ultra-high recording density simultaneously with a sufficiently low thermal cross-talk is demonstrated.

Potential of ITO thin film for electrical probe memory applications.

Electrical probe memory has received considerable attention during the last decade due to its prospective potential for the future mass storage device. However, the electrical probe device with conventional diamond-like carbon capping and bottom layers encounters with large interfacial contact resistance and difficulty to match the experimentally measured properties, while its analog with titanium nitride capping and bottom layers also faces serious heat dissipation through either probe and silicon substrate. Therefore, the feasibility of using indium tin oxide (ITO) media for the capping and bottom layers of the electrical probe device is investigated by tailoring the thickness and electrothermal properties of the ITO capping and bottom layers within experimentally established range and subsequently calculating the resultant temperature at several predefined points based on a previously developed three-dimensional model. To meet the required temperature and to fit the experimentally reported values, the thickness, electrical conductivity, and thermal conductivity of the ITO capping and bottom layers are found to be 5 nm, 103 Ω-1 m-1, 0.84 W m-1 K-1, and 200 nm, 1.25 × 106 Ω-1 m-1, 0.84 W m-1 K-1, respectively. The practicality of using this optimized device to achieve ultrahigh density, ultralow energy consumption, ultrafast switching speed, low interfacial contact resistance, and high thermal reliability has also been demonstrated.

[Fast Implementation Method of Protein Spots Detection Based on CUDA].

In order to improve the efficiency of protein spots detection, a fast detection method based on CUDA was proposed. Firstly, the parallel algorithms of the three most time-consuming parts in the protein spots detection algorithm: image preprocessing, coarse protein point detection and overlapping point segmentation were studied. Then, according to single instruction multiple threads executive model of CUDA to adopted data space strategy of separating two-dimensional (2D) images into blocks, various optimizing measures such as shared memory and 2D texture memory are adopted in this study. The results show that the operative efficiency of this method is obviously improved compared to CPU calculation. As the image size increased, this method makes more improvement in efficiency, such as for the image with the size of 2,048 x 2,048, the method of CPU needs 52,641 ms, but the GPU needs only 4,384 ms.

[An algorithm for separating overlapped protein spots based on valley characteristics].

To separate the overlapped protein spots in two-dimensional gel electrophoresis (2-DE) images, we proposed an auto-separating algorithm based on valley characteristics. Firstly, the marker-controlled watershed algorithm was used to detect the initial outlines of the object regions. Secondly, medial axis transform and hierarchical branch pruning method were applied to the main skeletons of the object regions, and each main skeleton was fitted into line segments to describe the overlap directions. Then, the 3-dimensional model of the object region was scanned on the normal planes of the line segments to find the valley locations. And finally, a validation model was adopted to construct separation lines. The experiments on 2 real scanned 2-DE images showed that the true overlap separate (TOSs) were 78.95% and 85.71%, respectively. The results indicated that the proposed algorithm was better than the existing algorithms and could be used in engineering practice.

[New spot matching algorithm for protein 2-DE images based on geometric blocking and gray hierarchical].

To reduce the mismatching and non-matching in the protein two-dimension electrophoresis (2-DE) images, we proposed an auto-matching algorithm based on gray hierarchical and geometric blocking in this study. Firstly, protein spots in the gel images were divided into groups by gray level and geometric position, and then a method based on shape context and normalized correlation was used for coarse matching in protein spots. Secondly, matched pairs in coarse matching were set as feature points, and the precise matching in the rest of not matched protein spots was accomplished by the method of geometric correlation and similarity criterion. Finally, local affine transformation was used in the verification of matching results to remove non-matching and mis-matching points. The algorithm was applied to different 2-DE gel images. The results showed that the new matching algorithm could reduce the non-matching and mis-matching spots, and increase the matching accuracy.

[Separation algorithm of overlapped protein spots based on shape characteristics].

In order to detect the overlapped and clustered protein spots in protein gel images, we suggest an auto-separating algorithm based on the shape characteristics in this paper. Firstly, we used a marker-controlled watershed algorithm to detect the initial outline of the protein spots in application of the method. Secondly, we extracted shape markers adaptively by combining local minima depth and shape characteristics information, and calculated the shape distance image according to these shape markers. Then, we imposed these shape markers on the shape distance image as its local minima. Finally, we applied the watershed algorithm again on the modified shape distance image to separate the overlapped spots accurately. The experiments on the synthetic gel image and the different types of real gel images showed that compared with other improved watershed algorithm, the proposed one had a higher correct separation rate with an error less than 6%, and better separation results of overlapped spots.

In-Memory Logic Operations and Neuromorphic Computing in Non-Volatile Random Access Memory.

Recent progress in the development of artificial intelligence technologies, aided by deep learning algorithms, has led to an unprecedented revolution in neuromorphic circuits, bringing us ever closer to brain-like computers. However, the vast majority of advanced algorithms still have to run on conventional computers. Thus, their capacities are limited by what is known as the von-Neumann bottleneck, where the central processing unit for data computation and the main memory for data storage are separated. Emerging forms of non-volatile random access memory, such as ferroelectric random access memory, phase-change random access memory, magnetic random access memory, and resistive random access memory, are widely considered to offer the best prospect of circumventing the von-Neumann bottleneck. This is due to their ability to merge storage and computational operations, such as Boolean logic. This paper reviews the most common kinds of non-volatile random access memory and their physical principles, together with their relative pros and cons when compared with conventional CMOS-based circuits (Complementary Metal Oxide Semiconductor). Their potential application to Boolean logic computation is then considered in terms of their working mechanism, circuit design and performance metrics. The paper concludes by envisaging the prospects offered by non-volatile devices for future brain-inspired and neuromorphic computation.

Electrical Conduction Characteristic of a 2D MXene Device with Cu/Cr2C/TiN Structure Based on Density Functional Theory.

The electronic structure and the corresponding electrical conductive behavior of the Cu/Cr2C/TiN stack were assessed according to a newly developed first-principle model based on density functional theory. Using an additional Cr2C layer provides the metal-like characteristic of the Cu/Cr2C/TiN stack with much larger electrical conduction coefficients (i.e., mobility, diffusivity, and electrical conductivity) than the conventional Ag/Ti3C2/Pt stack due to the lower activation energy. This device is therefore capable of offering faster switching speeds, lower programming voltage, and better stability and durability than the memristor device with conventional Ti3C2 MXene.

Overview of Phase-Change Electrical Probe Memory.

Phase-change electrical probe memory has recently attained considerable attention owing to its profound potential for next-generation mass and archival storage devices. To encourage more talented researchers to enter this field and thereby advance this technology, this paper first introduces approaches to induce the phase transformation of chalcogenide alloy by probe tip, considered as the root of phase-change electrical probe memory. Subsequently the design rule of an optimized architecture of phase-change electrical probe memory is proposed based on a previously developed electrothermal and phase kinetic model, followed by a summary of the state-of-the-art phase-change electrical probe memory and an outlook for its future prospects.

Amorphization Optimization of Ge2Sb2Te5 Media for Electrical Probe Memory Applications.

Electrical probe memory using Ge2Sb2Te5 media has been considered a promising candidate in the future archival storage market due to its potential for ultra-high density and long data retention time. However, most current research efforts have been devoted to the writing of crystalline bits using electrical probe memory while ignoring the viability of writing amorphous bits. Therefore, this paper proposes a physical, realistic, full three-dimensional model to optimize the practicable media stack by spatially and temporally calculating temperature distributions inside the active media during the writing of amorphous bits. It demonstrates the feasibility of using an optimized device that follows a Silicon/Titanium Nitride/Ge2Sb2Te5/Diamond-Like Carbon design with appropriate electro-thermal properties and thickness to achieve ultra-high density, low energy consumption, and a high data rate without inducing excessive temperature. The ability to realize multi-bit recording and rewritability using the designed device is also proven, making it attractive and suitable for practicable applications.

PhosphoRice: a meta-predictor of rice-specific phosphorylation sites.

BACKGROUND: As a result of the growing body of protein phosphorylation sites data, the number of phosphoprotein databases is constantly increasing, and dozens of tools are available for predicting protein phosphorylation sites to achieve fast automatic results. However, none of the existing tools has been developed to predict protein phosphorylation sites in rice. RESULTS: In this paper, the phosphorylation site predictors, NetPhos 2.0, NetPhosK, Kinasephos, Scansite, Disphos and Predphosphos, were integrated to construct meta-predictors of rice-specific phosphorylation sites using several methods, including unweighted voting, unreduced weighted voting, reduced unweighted voting and weighted voting strategies. PhosphoRice, the meta-predictor produced by using weighted voting strategy with parameters selected by restricted grid search and conditional random search, performed the best at predicting phosphorylation sites in rice. Its Matthew's Correlation Coefficient (MCC) and Accuracy (ACC) reached to 0.474 and 73.8%, respectively. Compared to the best individual element predictor (Disphos_default), PhosphoRice archieved a significant increase in MCC of 0.071 (P < 0.01), and an increase in ACC of 4.6%. CONCLUSIONS: PhosphoRice is a powerful tool for predicting unidentified phosphorylation sites in rice. Compared to the existing methods, we found that our tool showed greater robustness in ACC and MCC. PhosphoRice is available to the public at http://bioinformatics.fafu.edu.cn/PhosphoRice.