Nanotechnology and LSTM machine learning algorithms in advanced fuel spray dynamics in CI engines with different bowl geometries.

This study explores the integration of nanotechnology and Long Short-Term Memory (LSTM) machine learning algorithms to enhance the understanding and optimization of fuel spray dynamics in compression ignition (CI) engines with varying bowl geometries. The incorporation of nanotechnology, through the addition of nanoparticles to conventional fuels, improves fuel atomization, combustion efficiency, and emission control. Simultaneously, LSTM models are employed to analyze and predict the complex spray behavior under diverse operational and geometric conditions.

Effect of femtosecond laser induced surface patterns on the flexural strength of monolithic zirconia.

To investigate how patterns generated by femtosecond (fs) laser and femtosecond laser power affect the surface roughness (Ra) and biaxial flexural strength (BFS) of monolithic zirconia. Eighty disk-shaped zirconia specimens were divided into eight subgroups (n = 10): Control (C), airborne-particle abrasion (APA), 400 mW fs laser (spiral [SP], square [SQ], circular [CI]), and 700 mW fs laser ([SP], [SQ], [CI]). Ra values were calculated by using a surface profilometer.

Classification of Influenza Hemagglutinin Protein Sequences using Convolutional Neural Networks.

The Influenza virus can be considered as one of the most severe viruses that can infect multiple species with often fatal consequences to the hosts. The Hemagglutinin (HA) gene of the virus can be a target for antiviral drug development realised through accurate identification of its sub-types and possible the targeted hosts. This paper focuses on accurately predicting if an Influenza type A virus can infect specific hosts, and more specifically, Human, Avian and Swine hosts, using only the protein sequence of the HA gene.

Investigation of the expression levels of CPEB4, APC, TRIP13, EIF2S3, EIF4A1, IFNg, PIK3CA and CTNNB1 genes in different stage colorectal tumors.

Background/aim: The aim of the study is to assess expression levels of CPEB4, APC, TRIP13, EIF2S3, EIF4A1, IFNg, PIK3CA and CTNNB1 genes in tumors and peripheral bloods of colorectal cancer patients in stages I–IV.

Materials And Methods: The mRNA levels of the genes were determined in tumor tissues and peripheral blood samples of 45 colorectal cancer patients and colon tissues and peripheral blood samples of 5 healthy individuals. Real-time polymerase chain reaction method was used for the analysis.

Exploration of unsupervised feature selection methods to predict chronological age of individuals by utilising CpG dinucleotics from whole blood.

Identification of the age of individuals from epigenetic biomarkers can reveal vital information for criminal investigation, disease prevention, and extension of life. DNA methylation changes are highly associated with chronological age and the process of disease development. Computational methods such as clustering, feature selection and regression can be utilised to construct quantitative model of aging.

Recognition of protozoan parasites from microscopic images: Eimeria species in chickens and rabbits as a case study.

Automated diagnosis and identification of diseases and conditions such as parasites from microscopic images have been mainly carried out by utilizing the object morphological characteristics. The extraction of morphometric features needs the use of highly complex techniques that require computational power. Therefore, in order to reduce this complexity, this paper presents an automated identification based on analyzing three groups of pixel-based feature sets: column features (CF), row features (RF), and the third one (CRF) obtained by merging CF and RF together.

Prediction of Influenza A virus infections in humans using an Artificial Neural Network learning approach.

The Influenza type A virus can be considered as one of the most severe viruses that can infect multiple species with often fatal consequences to the hosts. The Haemagglutinin (HA) gene of the virus has the potential to be a target for antiviral drug development realised through accurate identification of its sub-types and possible the targeted hosts. In this paper, to accurately predict if an Influenza type A virus has the capability to infect human hosts, by using only the HA gene, is therefore developed and tested.

Automated lesion segmentation and dermoscopic feature segmentation for skin cancer analysis.

Segmentation is the first and most important task in computer-based diagnosis of skin cancer since other tasks are relied mainly on accurately segmented lesions. Recently, deep learning as a mainstream method in machine learning has shown promising results on semantic image segmentation. In this paper, we demonstrate applying deep convolutional networks to two main segmentation tasks in melanoma diagnosis, a lesion segmentation task followed by a lesion dermoscopic feature segmentation task.

Binding affinity prediction of S. cerevisiae 14-3-3 and GYF peptide-recognition domains using support vector regression.

Proteins interact with other proteins and bio-molecules to carry out biological processes in a cell. Computational models help understanding complex biochemical processes that happens throughout the life of a cell. Domain-mediated protein interaction to peptides one such complex problem in bioinformatics that requires computational predictive models to identify meaningful bindings.

Structural classification of protein sequences based on signal processing and support vector machines.

The function of any protein depends directly on its secondary and tertiary structure. Proteins can fold into a three-dimensional shape, which is primarily depended on the arrangement of amino acids in the primary structure. In recent years, with the explosive sequencing of proteins, it is unfeasible to perform detailed experimental studies, as these methodologies are very expensive and time consuming.

Unsupervised selection of RV144 HIV vaccine-induced antibody features correlated to natural killer cell-mediated cytotoxic reactions.

HIV-1 vaccine injection has been shown less effective due to the diversity of antigens. Increasing the knowledge of the associations between immune system and virus would ultimately result in producing effective vaccines against HIV-1 virus. To increase the understanding of immunological information, computational models can be utilised to construct predictive models.

Novel protein weight matrix generated from amino acid indices.

In recent years, numerous protein weight matrices have been developed that include physical characteristics of proteins, such as local sequence-structure information, alpha-helix information, secondary structure information and solvent accessibility states. These protein weight matrices are shown to have generally improved protein sequence alignments over classical protein weight matrices, like Point Accepted Mutation (PAM), Blocks of Amino Acid Substitution (BLOSUM), and GONNET matrices, where important limitations have been observe in recent works. In this paper, a novel protein weight matrix is constructed and presented.

Inference of nonlinear gene regulatory networks through optimized ensemble of support vector regression and dynamic Bayesian networks.

Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in systems biology. Most methods for modeling and inferring the dynamics of GRNs, such as those based on state space models, vector autoregressive models and G1DBN algorithm, assume linear dependencies among genes. However, this strong assumption does not make for true representation of time-course relationships across the genes, which are inherently nonlinear.

Comparison of unsupervised feature selection methods for high-dimensional regression problems in prediction of peptide binding affinity.

Identification of robust set of predictive features is one of the most important steps in the construction of clustering, classification and regression models from many thousands of features. Although there have been various attempts to select predictive feature sets from high-dimensional data sets in classification and clustering, there is a limited attempt to study it in regression problems. As semi-supervised and supervised feature selection methods tend to identify noisy features in addition to discriminative variables, unsupervised feature selection methods (USFSMs) are generally regarded as more unbiased approach.

Dynamic partial reconfiguration implementation of the SVM/KNN multi-classifier on FPGA for bioinformatics application.

Bioinformatics data tend to be highly dimensional in nature thus impose significant computational demands. To resolve limitations of conventional computing methods, several alternative high performance computing solutions have been proposed by scientists such as Graphical Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs). The latter have shown to be efficient and high in performance.

Pigment network-based skin cancer detection.

Diagnosing skin cancer in its early stages is a challenging task for dermatologists given the fact that the chance for a patient's survival is higher and hence the process of analyzing skin images and making decisions should be time efficient. Therefore, diagnosing the disease using automated and computerized systems has nowadays become essential. This paper proposes an efficient system for skin cancer detection on dermoscopic images.

In silico discovery of significant pathways in colorectal cancer metastasis using a two-stage optimisation approach.

Accurate and reliable modelling of protein-protein interaction networks for complex diseases such as colorectal cancer can help better understand mechanism of diseases and potentially discover new drugs. Different machine learning methods such as empirical mode decomposition combined with least square support vector machine, and discrete Fourier transform have been widely utilised as a classifier and for automatic discovery of biomarkers for the diagnosis of the disease. The existing methods are, however, less efficient as they tend to ignore interaction with the classifier.

CISAPS: Complex Informational Spectrum for the Analysis of Protein Sequences.

Complex informational spectrum analysis for protein sequences (CISAPS) and its web-based server are developed and presented. As recent studies show, only the use of the absolute spectrum in the analysis of protein sequences using the informational spectrum analysis is proven to be insufficient. Therefore, CISAPS is developed to consider and provide results in three forms including absolute, real, and imaginary spectrum.

Wavelet denoising and reconstruction of a microneedle embedded in human skin ex-vivo using terahertz pulsed reflectance.

Biological tissue can show promising features in the terahertz region of the electro-magnetic spectrum but face the problem that the signal to noise ratio can be poor due to the low energy output from the measurement instrument coupled with the high absorbance of water in biological tissue. Wavelet denoising and reconstruction are known to be suitable digital signal processing filters for reflected terahertz energy when appropriate thresholds, scales and mother-wavelets are chosen. In this article, we therefore describe a Wavelet transform-based method for denoising reflections of THz energy from ex-vivo human skin with an embedded microneedle.

Construction of protein dendrograms based on amino acid indices and Discrete Fourier Transform.

From the literature, existing methods use pairwise percent identity to identify the percentage of similarity between two protein sequences, in order to create a dendrogram. As this is a parametric method of measuring the similarities between proteins, and different parameter may yield different results, this method does not guarantee that the global optimal similarity values will be found. As protein dendrogram construction is used in other areas, such as multiple protein sequence alignments, it is very important that the most related protein sequences to be identified and align first.

Hybrid imbalanced data classifier models for computational discovery of antibiotic drug targets.

Identification of drug candidates is an important but also difficult process. Given drug resistance bacteria that we face, this process has become more important to identify protein candidates that demonstrate antibacterial activity. The aim of this study is therefore to develop a bioinformatics approach that is more capable of identifying a small but effective set of proteins that are expected to show antibacterial activity, subsequently to be used as antibiotic drug targets.

Prediction of protein allergenicity based on signal-processing bioinformatics approach.

Current bioinformatics tools accomplish high accuracies in classifying allergenic protein sequences with high homology and generally perform poorly with low homology protein sequences. Although some homologous regions explained Immunoglobulin E (IgE) cross-reactivity in groups of allergens, no universal molecular structure could be associated with allergenicity. In addition, studies have showed that cross-reactivity is not directly linked to the homology between protein sequences.

A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis.

Computational and machine learning techniques have been applied in identifying biomarkers and constructing predictive models for diagnosis of hypertension. Strategies such as improved classification rules based on decision trees have been proposed. Other techniques such as Fuzzy Expert Systems (FES) and Neuro-Fuzzy Systems (NFS) have recently been applied.

Combining multiple clusterings for protein structure prediction.

Computational annotation and prediction of protein structure is very important in the post-genome era due to existence of many different proteins, most of which are yet to be verified. Mutual information based feature selection methods can be used in selecting such minimal yet predictive subsets of features. However, as protein features are organised into natural partitions, individual feature selection that ignores the presence of these views, dismantles them, and treats their variables intermixed along with those of others at best results in a complex un-interpretable predictive system for such multi-view datasets.