Construction of a novel S-scheme AgVO/CaInS heterostructure for efficient photodegradation of tetracycline hydrochloride.

Constructing an S-scheme system with highly active catalysts is a significant approach for improving the separation of photoinduced carriers to solve the related environmental aggravation. In this study, a well-designed S-scheme AgVO/CaInS photocatalyst was synthesized for water purification by growing CaInS nanocrystals on AgVO nanorod surfaces. The optimized AgVO/CaInS heterostructure demonstrates an enhanced photocatalytic efficiency (94.

A 3D hierarchical TiO/CaInS/CNarrays photoanode with dual-heterojunction for enhanced photoelectrochemical performance.

A novel 3D hierarchical TiO/CaInS/CNarrays with dual heterojunctions photoanode is constructed by stepwise deposition of CaInSnanosheets and ultrathin CNonto the well-aligned TiOnanorods arrays. Integrating the merit of the superior ability of CaInSand CNto harvest visible light, dual type-Ⅱ heterojunction band structure and one-dimensional ordered nanostructures, the TiO/CaInS/CNphotoanode exhibits simultaneous significant improvements in visible-light harvesting, charge separation and electron transfer capability. At 1.

Self-Attention-Based Deep Convolution LSTM Framework for Sensor-Based Badminton Activity Recognition.

Sensor-based human activity recognition aims to classify human activities or behaviors according to the data from wearable or embedded sensors, leading to a new direction in the field of Artificial Intelligence. When the activities become high-level and sophisticated, such as in the multiple technical skills of playing badminton, it is usually a challenging task due to the difficulty of feature extraction from the sensor data. As a kind of end-to-end approach, deep neural networks have the capacity of automatic feature learning and extracting.

Distributed search and fusion for wine label image retrieval.

With the popularity of wine culture and the development of artificial intelligence (AI) technology, wine label image retrieval becomes more and more important. Taking an wine label image as an input, the goal of this task is to return the wine information that the user hopes to know, such as the main brand and sub-brand of the wine. The main challenge in wine label image retrieval task is that there are a large number of wine brands with the imbalance of their sample images which strongly affects the training of the retrieval system based on deep learning.

Fabrication of 3D hierarchical FeO/SnOphotoanode for enhanced photoelectrochemical performance.

Exploring and fabricating a suitable photoanode with high catalytic activity is critical for enhancing photoelectrochemical (PEC) performance. Herein, a novel 3D hierarchical FeO/SnOphotoanode was fabricated by a hydrothermal route, combining with an annealing process. The morphology, crystal structure were studied by scanning electron microscopy, transmission electron microscopy, x-ray photon spectroscopy, and x-ray diffraction, respectively.

Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images.

As an analytic tool in medicine, deep learning has gained great attention and opened new ways for disease diagnosis. Recent studies validate the effectiveness of deep learning algorithms for binary classification of skin lesions (i.e.

Deep Learning-Based Morphological Classification of Human Sperm Heads.

Human infertility is considered as a serious disease of the reproductive system that affects more than 10% of couples across the globe and over 30% of the reported cases are related to men. The crucial step in the assessment of male infertility and subfertility is semen analysis that strongly depends on the sperm head morphology, i.e.

A Two-Layer Mixture Model of Gaussian Process Functional Regressions and Its MCMC EM Algorithm.

The mixture of Gaussian processes (GPs) is capable of learning any general stochastic process based on a given set of (sample) curves for the regression and prediction problems. However, it is ineffective for curve clustering and prediction, when the sample curves are derived from different stochastic processes as independent sources linearly mixed together. In this paper, we propose a two-layer mixture model of GP functional regressions (GPFRs) to describe such a mixture of general stochastic processes or independent sources, especially for curve clustering and prediction.

Compound signature detection on LINCS L1000 big data.

The Library of Integrated Network-based Cellular Signatures (LINCS) L1000 big data provide gene expression profiles induced by over 10 000 compounds, shRNAs, and kinase inhibitors using the L1000 platform. We developed csNMF, a systematic compound signature discovery pipeline covering from raw L1000 data processing to drug screening and mechanism generation. The csNMF pipeline demonstrated better performance than the original L1000 pipeline.

Effect of sintering temperature on the luminescence properties of Ca0.8Ba0.2TiO3:Pr(3+) red-emitting phosphor.

Nanoparticles with the nominal composition Ca0.8Ba0.2Ti03:Pr(3+) were prepared using the sol-gel process.

Texture classification and retrieval using shearlets and linear regression.

Statistical modeling of wavelet subbands has frequently been used for image recognition and retrieval. However, traditional wavelets are unsuitable for use with images containing distributed discontinuities, such as edges. Shearlets are a newly developed extension of wavelets that are better suited to image characterization.

DrugComboRanker: drug combination discovery based on target network analysis.

Motivation: Currently there are no curative anticancer drugs, and drug resistance is often acquired after drug treatment. One of the reasons is that cancers are complex diseases, regulated by multiple signaling pathways and cross talks among the pathways. It is expected that drug combinations can reduce drug resistance and improve patients' outcomes.

A 3D multiscale model of cancer stem cell in tumor development.

Background: Recent reports indicate that a subgroup of tumor cells named cancer stem cells (CSCs) or tumor initiating cells (TICs) are responsible for tumor initiation, growth and drug resistance. This subgroup of tumor cells has self-renewal capacity and could differentiate into heterogeneous tumor cell populations through asymmetric proliferation. The idea of CSC provides informative insights into tumor initiation, metastasis and treatment.

Three-dimensional vasculature reconstruction of tumour microenvironment via local clustering and classification.

The vasculature inside breast cancers is one important component of the tumour microenvironment. The investigation of its spatial morphology, distribution and interactions with cancer cells, including cancer stem cells, is essential for elucidating mechanisms of tumour development and treatment response. Using confocal microscopy and fluorescent markers, we have acquired three-dimensional images of vasculature within mammary tumours and normal mammary gland of mouse models.

FusionQ: a novel approach for gene fusion detection and quantification from paired-end RNA-Seq.

Background: Gene fusions, which result from abnormal chromosome rearrangements, are a pathogenic factor in cancer development. The emerging RNA-Seq technology enables us to detect gene fusions and profile their features.

Results: In this paper, we proposed a novel fusion detection tool, FusionQ, based on paired-end RNA-Seq data.

Bayesian texture classification based on contourlet transform and BYY harmony learning of Poisson mixtures.

As a newly developed 2-D extension of the wavelet transform using multiscale and directional filter banks, the contourlet transform can effectively capture the intrinsic geometric structures and smooth contours of a texture image that are the dominant features for texture classification. In this paper, we propose a novel Bayesian texture classifier based on the adaptive model-selection learning of Poisson mixtures on the contourlet features of texture images. The adaptive model-selection learning of Poisson mixtures is carried out by the recently established adaptive gradient Bayesian Ying-Yang harmony learning algorithm for Poisson mixtures.

Multiple nuclei tracking using integer programming for quantitative cancer cell cycle analysis.

Automated cell segmentation and tracking are critical for quantitative analysis of cell cycle behavior using time-lapse fluorescence microscopy. However, the complex, dynamic cell cycle behavior poses new challenges to the existing image segmentation and tracking methods. This paper presents a fully automated tracking method for quantitative cell cycle analysis.

A DSRPCL-SVM approach to informative gene analysis.

Microarray data based tumor diagnosis is a very interesting topic in bioinformatics. One of the key problems is the discovery and analysis of informative genes of a tumor. Although there are many elaborate approaches to this problem, it is still difficult to select a reasonable set of informative genes for tumor diagnosis only with microarray data.

Workflow and methods of high-content time-lapse analysis for quantifying intracellular calcium signals.

Calcium ions (Ca2+) play a fundamental role in a variety of physiological functions in many cell types by acting as a secondary messenger. Variation of intracellular Ca2+ concentration ([Ca2+]i) is often observed when the cell is stimulated. However, it is a challenging task to automatically quantify intracellular [Ca2+]i in a population of cells.

High content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles.

Background: High content screening (HCS)-based image analysis is becoming an important and widely used research tool. Capitalizing this technology, ample cellular information can be extracted from the high content cellular images. In this study, an automated, reliable and quantitative cellular image analysis system developed in house has been employed to quantify the toxic responses of human H4 neuroglioma cells exposed to metal oxide nanoparticles.

A cost-function approach to rival penalized competitive learning (RPCL).

Rival penalized competitive learning (RPCL) has been shown to be a useful tool for clustering on a set of sample data in which the number of clusters is unknown. However, the RPCL algorithm was proposed heuristically and is still in lack of a mathematical theory to describe its convergence behavior. In order to solve the convergence problem, we investigate it via a cost-function approach.

A hybrid neural network of addressable and content-addressable memory.

We investigate the memory structure and retrieval of the brain and propose a hybrid neural network of addressable and content-addressable memory which is a special database model and can memorize and retrieve any piece of information (a binary pattern) both addressably and content-addressably. The architecture of this hybrid neural network is hierarchical and takes the form of a tree of slabs which consist of binary neurons with the same array. Simplex memory neural networks are considered as the slabs of basic memory units, being distributed on the terminal vertexes of the tree.

The asymptotic memory capacity of the generalized Hopfield network.

This paper presents a theoretical analysis on the asymptotic memory capacity of the generalized Hopfield network. The perceptron learning scheme is proposed to store sample patterns as the stable states in a generalized Hopfield network. We have obtained that (n-1) and 2n are a lower and an upper bound of the asymptotic memory capacity of the network of n neurons, respectively, which shows that the generalized Hopfield network can store the larger number of sample patterns than Hopfield network.

The Stability of the Generalized Hopfield Networks in Randomly Asynchronous Mode.

We present a study on the stability of the generalized Hopfield network in randomly asynchronous mode. First, the stability is investigated from the state space of the network. We introduce a concept of hole and define two kinds of stability in randomly asynchronous mode.

Simplex Memory Neural Networks.

The biological neural network-simplex memory neural network-is proposed to describe the mechanisms of pattern memory in the brain. A mathematical model of the simplex memory neural network is constructed to memorize any binary pattern with content-addressable memory function. Under Hebbian learning rule, the new network has some important functions in accord with the learning and memory behaviors of the brain.