Staffing & Scheduling

Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning.

RNA binding protein (RBP) plays an important role in cellular processes. Identifying RBPs by computation and experiment are both essential. Recently, an RBP predictor, RBPPred, is proposed in our group to predict RBPs. However, RBPPred is too slow for that it needs to generate PSSM matrix as its feature. Herein, based on the protein feature of RBPPred and Convolutional Neural Network (CNN), we dev...

Protocol-based state estimation for delayed Markovian jumping neural networks.

This paper is concerned with the state estimation problem for a class of Markovian jumping neural networks (MJNNs) with sensor nonlinearities, mode-dependent time delays and stochastic disturbances subject to the Round-Robin (RR) scheduling mechanism. The system parameters experience switches among finite modes according to a Markov chain. As an equal allocation scheme, the RR communication protoc...

Determination of Curdlan Oligosaccharides with High-Performance Anion Exchange Chromatography with Pulsed Amperometric Detection.

The increasing interest of curdlan oligosaccharides (COS) in medicine and plant protection fields implies a necessity to identify and quantify this pr...

Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning.

The accuracy of peptide retention time (RT) prediction model in liquid chromatography (LC) is still not sufficient for wider implementation in proteom...

Study on screening potential allergenic proteins from infant milk powders based on human mast cell membrane chromatography and histamine release assays.

Cow's milk allergy is mainly observed in infants and young children. Most allergic reactions affect the skin, followed by the gastrointestinal and res...

Nanocomplexes composed of chitosan derivatives and β-Lactoglobulin as a carrier for anthocyanins: Preparation, stability and bioavailability in vitro.

To improve sustained-release property, stability and bioavailability of anthocyanins (ACNs) in vitro, we fabricated the nanocomplexes with chitosan hy...

Using a deep learning network to recognise low back pain in static standing.

Low back pain (LBP) remains one of the most prevalent musculoskeletal disorders, while algorithms that able to recognise LBP patients from healthy pop...

Analysis of the adsorption and retention models for Cd, Cr, Cu, Ni, Pb, and Zn through neural networks: selection of variables and competitive model.

In this study, the neural networks are used to predict and explain the behavior of different edaphological variables in the adsorption and retention o...

Current Applications and Future Impact of Machine Learning in Radiology.

Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the poten...

Bayesian Approximate Kernel Regression with Variable Selection.

Nonlinear kernel regression models are often used in statistics and machine learning because they are more accurate than linear models. Variable selec...

m-Health 2.0: New perspectives on mobile health, machine learning and big data analytics.

Mobile health (m-Health) has been repeatedly called the biggest technological breakthrough of our modern times. Similarly, the concept of big data in ...

Prediction of radiographic abnormalities by the use of bag-of-features and convolutional neural networks.

This study evaluated the feasibility of bag-of-features (BOF) and convolutional neural networks (CNN) for computer-aided detection in distinguishing n...

Class-specific extreme learning machine for handling binary class imbalance problem.

Imbalance problem occurs when the majority class instances outnumber the minority class instances. Conventional extreme learning machine (ELM) treats ...

Deconvolution of subcellular protrusion heterogeneity and the underlying actin regulator dynamics from live cell imaging.

Cell protrusion is morphodynamically heterogeneous at the subcellular level. However, the mechanism of cell protrusion has been understood based on th...

Feasibility of robot-based perturbed-balance training during treadmill walking in a high-functioning chronic stroke subject: a case-control study.

BACKGROUND: For stroke survivors, balance deficits that persist after the completion of the rehabilitation process lead to a significant risk of falls...

A novel strategy for retention prediction of nucleic acids with their sequence information in ion-pair reversed phase liquid chromatography.

In this work, retention behaviors of oligonucleotides and double-stranded deoxyribonucleic acids (dsDNAs) have been investigated in ion-pair reversed-...

Bone mineral density in midlife long-term users of hormonal contraception in South Africa: relationship with obesity and menopausal status.

BACKGROUND: In South Africa, hormonal contraception is widely used in women over the age of 40 years. One of these methods and the most commonly used ...

Too Much of a Good Thing: How Novelty Biases and Vocabulary Influence Known and Novel Referent Selection in 18-Month-Old Children and Associative Learning Models.

Identifying the referent of novel words is a complex process that young children do with relative ease. When given multiple objects along with a novel...

A hybrid approach to increase the informedness of CE-based data using locus-specific thresholding and machine learning.

The interpretation of genetic profiles require a robust and reliable method to discriminate true allelic information from noise, regardless of the ins...

Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks.

Network oscillations across and within brain areas are critical for learning and performance of memory tasks. While a large amount of work has focused...