A review on EEG-based methods for screening and diagnosing alcohol use disorder

The screening test for alcohol use disorder (AUD) patients has been of subjective nature and could be misleading in particular cases such as a misreporting the actual quantity of alcohol intake. Although the neuroimaging modality such as electroencephalography (EEG) has shown promising research results in achieving objectivity during the screening and diagnosis of AUD patients. However, the translation of these findings for clinical applications has been largely understudied and hence less clear. This study advocates the use of EEG as a diagnostic and screening tool for AUD patients that may help the clinicians during clinical decision making. In this context, a comprehensive review on EEG-based methods is provided including related electrophysiological techniques reported in the literature. More specifically, the EEG abnormalities associated with the conditions of AUD patients are summarized. The aim is to explore the potentials of objective techniques involving quantities/features derived from resting EEG, event-related potentials or event-related oscillations data.     

An integrated machine learning system to computationally screen protein databases for protein binding peptide ligands

A fairly large set of protein interactions is mediated by families of peptide binding domains, such as Src homology 2 (SH2), SH3, PDZ, major histocompatibility complex, etc. To identify their ligands by experimental screening is not only labor-intensive but almost futile in screening low abundance species due to the suppression by high abundance species. An ideal way of studying protein-protein interactions is to use high throughput computational approaches to screen protein sequence databases to direct the validating experiments toward the most promising peptides. Predictors with only good cross-validation were not good enough to screen protein databases. In the current study we built integrated machine learning systems using three novel coding methods and screened the Swiss-Prot and GenBank protein databases for potential ligands of 10 SH3 and three PDZ domains. A large fraction of predictions has already been experimentally confirmed by other independent research groups, indicating a satisfying generalization capability for future applications in identifying protein interactions.     

An information-based machine learning approach to elasticity imaging

An information-based technique is described for applications in mechanical property imaging of soft biological media under quasi-static loads. We adapted the Autoprogressive method that was originally developed for civil engineering applications for this purpose. The Autoprogressive method is a computational technique that combines knowledge of object shape and a sparse distribution of force and displacement measurements with finite-element analyses and artificial neural networks to estimate a complete set of stress and strain vectors. Elasticity imaging parameters are then computed from estimated stresses and strains. We introduce the technique using ultrasonic pulse-echo measurements in simple gelatin imaging phantoms having linear-elastic properties so that conventional finite-element modeling can be used to validate results. The Autoprogressive algorithm does not require any assumptions about the material properties and can, in principle, be used to image media with arbitrary properties. We show that by selecting a few well-chosen force–displacement measurements that are appropriately applied during training and establish convergence, we can estimate all nontrivial stress and strain vectors throughout an object and accurately estimate an elastic modulus at high spatial resolution. This new method of modeling the mechanical properties of tissue-like materials introduces a unique method of solving the inverse problem and is the first technique for imaging stress without assuming the underlying constitutive model.     

An unsupervised machine learning method for assessing quality of tandem mass spectra

Background

In a single proteomic project, tandem mass spectrometers can produce hundreds of millions of tandem mass spectra. However, majority of tandem mass spectra are of poor quality, it wastes time to search them for peptides. Therefore, the quality assessment (before database search) is very useful in the pipeline of protein identification via tandem mass spectra, especially on the reduction of searching time and the decrease of false identifications. Most existing methods for quality assessment are supervised machine learning methods based on a number of features which describe the quality of tandem mass spectra. These methods need the training datasets with knowing the quality of all spectra, which are usually unavailable for the new datasets.

Results

This study proposes an unsupervised machine learning method for quality assessment of tandem mass spectra without any training dataset. This proposed method estimates the conditional probabilities of spectra being high quality from the quality assessments based on individual features. The probabilities are estimated through a constraint optimization problem. An efficient algorithm is developed to solve the constraint optimization problem and is proved to be convergent. Experimental results on two datasets illustrate that if we search only tandem spectra with the high quality determined by the proposed method, we can save about 56 % and 62% of database searching time while losing only a small amount of high-quality spectra.

Conclusions

Results indicate that the proposed method has a good performance for the quality assessment of tandem mass spectra and the way we estimate the conditional probabilities is effective.

     

Homology Induction: the use of machine learning to improve sequence similarity searches

Background  

The inference of homology between proteins is a key problem in molecular biology The current best approaches only identify ~50% of homologies (with a false positive rate set at 1/1000).     

Extreme learning machine classification method for lower limb movement recognition

In order to identify the lower limb movements accurately and quickly, a recognition method based on extreme learning machine (ELM) is proposed. The recognizing target set is constructed by decomposing the daily actions into different segments. To get the recognition accuracy of seven movements based on the surface electromyography, the recognition feature vector space is established by integrating short-time statistical characteristics under time domain, and locally linear embedding algorithm is used to reduce the computational complexity and improve robustness of algorithm. Compared with BP, the overall recognition accuracy for each subject in the best dimension with ELM is above 95%.     

An examination of on-line machine learning approaches for pseudo-random generated data

A pseudo-random generator is an algorithm to generate a sequence of objects determined by a truly random seed which is not truly random. It has been widely used in many applications, such as cryptography and simulations. In this article, we examine current popular machine learning algorithms with various on-line algorithms for pseudo-random generated data in order to find out which machine learning approach is more suitable for this kind of data for prediction based on on-line algorithms. To further improve the prediction performance, we propose a novel sample weighted algorithm that takes generalization errors in each iteration into account. We perform intensive evaluation on real Baccarat data generated by Casino machines and random number generated by a popular Java program, which are two typical examples of pseudo-random generated data. The experimental results show that support vector machine and k-nearest neighbors have better performance than others with and without sample weighted algorithm in the evaluation data set.     

Using mobile phone technology to treat alcohol use disorder: study protocol for a randomized controlled trial

Background

A primary concern within the healthcare system is to make treatment more accessible as well as attractive for the great majority of alcohol-dependent people who feel reluctant to participate in the treatment programs available. This paper presents the protocol for a randomized controlled trial (RCT) to test the efficacy of two different technical devices (mobile phone application and breathalyzer) on alcohol consumption.

Methods

The study is a three-armed RCT with follow-ups 3 and 6 months after randomization. In total, 375 adults (age 18+ years) diagnosed with alcohol use disorder (AUD) will be invited to participate in a 3-month intervention. The primary outcome is the number of days with heavy drinking, defined as four or more standard drinks (12 g alcohol/drink) and measured by the timeline follow back (TLFB) and Alcohol Use Disorder Identification Test (AUDIT) instruments at 3-month and 6-month follow-up. Secondary outcome measures include weekly alcohol consumption, measured by the TLFB, AUDIT, and phosphatidylethanol in blood values at 3-month and 6-month follow-up (number of days with blood alcohol concentration levels exceeding 60 mg/100 ml).

Discussion

Improving ways of collecting data on alcohol consumption, as well as the treatment system with regards to AUD, is of vital importance. Mobile phone technology, with associated applications, is widely recognized as a potentially powerful tool in the prevention and management of disease. This study will provide unique knowledge regarding the use of new technology as instruments for measuring alcohol consumption and, also, as a possible way to decrease it.

Trial registration

ISRCTN, ISRCTN14515753. Registered on 31 May 2018.

     

A new method to screen polysaccharide cleavage enzymes

The activity of polysaccharide cleavage enzymes has usually been evaluated by qualitative plate screening methods and quantitative colorimetric or chromatographic assays. The recent development of protein engineering has shown the limits of these techniques when applied to high throughput screening. Here we propose a microplate method to measure the activity of polysaccharide cleavage enzymes through small variations in viscosity. Polysaccharide solutions are co-incubated with magnetic particles in enzyme buffers. The cleavage action of polymer-degrading enzymes increases the mobility of the particles in a magnetic field, even at low levels of enzyme activities. This reproducible, sensitive technique was used to evaluate enzymatic specificity towards substrates. BioFilm indices (BFI) determined by associated software were used to follow enzyme kinetics and measure the usual variables.     

TMB-Hunt: An amino acid composition based method to screen proteomes for beta-barrel transmembrane proteins

Background  

Beta-barrel transmembrane (bbtm) proteins are a functionally important and diverse group of proteins expressed in the outer membranes of bacteria (both gram negative and acid fast gram positive), mitochondria and chloroplasts. Despite recent publications describing reasonable levels of accuracy for discriminating between bbtm proteins and other proteins, screening of entire genomes remains troublesome as these molecules only constitute a small fraction of the sequences screened. Therefore, novel methods are still required capable of detecting new families of bbtm protein in diverse genomes.     

Improved machine learning method for analysis of gas phase chemistry of peptides

Background  

Accurate peptide identification is important to high-throughput proteomics analyses that use mass spectrometry. Search programs compare fragmentation spectra (MS/MS) of peptides from complex digests with theoretically derived spectra from a database of protein sequences. Improved discrimination is achieved with theoretical spectra that are based on simulating gas phase chemistry of the peptides, but the limited understanding of those processes affects the accuracy of predictions from theoretical spectra.     

An integrated machine learning approach for predicting DosR-regulated genes in Mycobacterium tuberculosis

Background

Most cellular signal transduction mechanisms depend on a few molecular partners whose roles depend on their position and movement in relation to the input signal. This movement can follow various rules and take place in different compartments. Additionally, the molecules can form transient complexes. Complexation and signal transduction depend on the specific states partners and complexes adopt. Several spatial simulator have been developed to date, but none are able to model reaction-diffusion of realistic multi-state transient complexes.

Results

Meredys allows for the simulation of multi-component, multi-feature state molecular species in two and three dimensions. Several compartments can be defined with different diffusion and boundary properties. The software employs a Brownian dynamics engine to simulate reaction-diffusion systems at the reactive particle level, based on compartment properties, complex structure, and hydro-dynamic radii. Zeroth-, first-, and second order reactions are supported. The molecular complexes have realistic geometries. Reactive species can contain user-defined feature states which can modify reaction rates and outcome. Models are defined in a versatile NeuroML input file. The simulation volume can be split in subvolumes to speed up run-time.

Conclusions

Meredys provides a powerful and versatile way to run accurate simulations of molecular and sub-cellular systems, that complement existing multi-agent simulation systems. Meredys is a Free Software and the source code is available at http://meredys.sourceforge.net/.     

An improvement of extreme learning machine for compact single-hidden-layer feedforward neural networks

Recently, a novel learning algorithm called extreme learning machine (ELM) was proposed for efficiently training single-hidden-layer feedforward neural networks (SLFNs). It was much faster than the traditional gradient-descent-based learning algorithms due to the analytical determination of output weights with the random choice of input weights and hidden layer biases. However, this algorithm often requires a large number of hidden units and thus slowly responds to new observations. Evolutionary extreme learning machine (E-ELM) was proposed to overcome this problem; it used the differential evolution algorithm to select the input weights and hidden layer biases. However, this algorithm required much time for searching optimal parameters with iterative processes and was not suitable for data sets with a large number of input features. In this paper, a new approach for training SLFNs is proposed, in which the input weights and biases of hidden units are determined based on a fast regularized least-squares scheme. Experimental results for many real applications with both small and large number of input features show that our proposed approach can achieve good generalization performance with much more compact networks and extremely high speed for both learning and testing.     

Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method

ABSTRACT

Monoclonal antibodies (mAbs) have become a major class of protein therapeutics that target a spectrum of diseases ranging from cancers to infectious diseases. Similar to any protein molecule, mAbs are susceptible to chemical modifications during the manufacturing process, long-term storage, and in vivo circulation that can impair their potency. One such modification is the oxidation of methionine residues. Chemical modifications that occur in the complementarity-determining regions (CDRs) of mAbs can lead to the abrogation of antigen binding and reduce the drug’s potency and efficacy. Thus, it is highly desirable to identify and eliminate any chemically unstable residues in the CDRs during the therapeutic antibody discovery process. To provide increased throughput over experimental methods, we extracted features from the mAbs’ sequences, structures, and dynamics, used random forests to identify important features and develop a quantitative and highly predictive in silico methionine oxidation model.     

The use of lymphocytes to screen for oxidative phosphorylation disorders

Biochemical analysis of oxidative phosphorylation (OXPHOS) disorders is traditionally carried out on muscle biopsies, cultured fibroblasts, and transformed lymphocytes. Here we present a new screening technique using lymphocytes to identify OXPHOS dysfunction and initially avoid an invasive diagnostic procedure. Lymphocytes represent an easily obtainable source of tissue that presents advantages over the use of fibroblasts or lymphoblast cell lines. The time delay in culturing skin fibroblasts and the interactions between cell transformation and mitochondrial activity are avoided in this methodology. The method requires a small amount of blood (<5 mL); can be completed in a few hours, and allows for repeated measurements. Our assay has been adapted from published methods utilizing cultured fibroblasts and transformed lymphocytes, and our data suggest that measurement of ATP synthesis in lymphocytes is an effective screening tool for diagnosing OXPHOS disorders. This method may also provide an objective tool for monitoring response to treatment and evaluating progression of disease.