Application of data augmentation techniques towards metabolomics.

Niemann-Pick Class 1 (NPC1) disease is a rare and debilitating neurodegenerative lysosomal storage disease (LSD). Metabolomics datasets of NPC1 patients available to perform this type of analysis are often limited in the number of samples and severely unbalanced. In order to improve the predictive capability and identify new biomarkers in an NPC1 disease urinary dataset, data augmentation (DA) techniques based on computational intelligence have been employed to create synthetic samples, i.

Integration of a Passive Exoskeleton and a Robotic Supernumerary Finger for Grasping Compensation in Chronic Stroke Patients: The SoftPro Wearable System.

Upper-limb impairments are all-pervasive in Activities of Daily Living (ADLs). As a consequence, people affected by a loss of arm function must endure severe limitations. To compensate for the lack of a functional arm and hand, we developed a wearable system that combines different assistive technologies including sensing, haptics, orthotics and robotics.

Treg and TH17 link to immune response in individuals with peri-implantitis: a preliminary report.

Background And Objectives: Treg and TH17 cells influence the inflammatory process in periodontal diseases and could also play in a similar pattern, an essential role in immune-inflammatory mechanisms involved in the destruction of the peri-implant tissues, peri-implantitis. Therefore, this study evaluated the levels of RORγT and FOXP3 gene expression in subjects with peri-implantitis and healthy peri-implant tissues.

Methods: A total of 35 subjects with implant-supported restorations in both diseased and healthy clinical conditions (n = 15 healthy; n = 20 peri-implantitis) were included in this study.

Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.

Precision medicine in oncology aims at obtaining data from heterogeneous sources to have a precise estimation of a given patient's state and prognosis. With the purpose of advancing to personalized medicine framework, accurate diagnoses allow prescription of more effective treatments adapted to the specificities of each individual case. In the last years, next-generation sequencing has impelled cancer research by providing physicians with an overwhelming amount of gene-expression data from RNA-seq high-throughput platforms.

Strontium ranelate improves alveolar bone healing in estrogen-deficient rats.

Background: This study evaluated the impact of strontium ranelate on tooth-extraction wound healing in estrogen-deficient and estrogen-sufficient rats.

Methods: Ninety-six Wistar rats (90 days of age) were allocated into one of the following groups: sham-surgery+water (estrogen-sufficient); ovariectomy+water (estrogen-deficient), sham-surgery+strontium ranelate (625 mg/kg/d) (strontium/estrogen-sufficient); ovariectomy+strontium ranelate (625 mg/kg/d) (strontium/estrogen-deficient). Water or strontium ranelate were administrated from the 14th day post-ovariectomy/sham surgery until euthanasia.

BLASSO: integration of biological knowledge into a regularized linear model.

Background: In RNA-Seq gene expression analysis, a genetic signature or biomarker is defined as a subset of genes that is probably involved in a given complex human trait and usually provide predictive capabilities for that trait. The discovery of new genetic signatures is challenging, as it entails the analysis of complex-nature information encoded at gene level. Moreover, biomarkers selection becomes unstable, since high correlation among the thousands of genes included in each sample usually exists, thus obtaining very low overlapping rates between the genetic signatures proposed by different authors.

Expression Levels of Semaphorins 3A, 3B, 4A, and 4D on Human Peri-implantitis.

Purpose: To evaluate the gene expression levels of semaphorins 3A, 3B, 4A, and 4D in both healthy and diseased implants.

Materials And Methods: Subjects with peri-implantitis presented clinical attachment loss, probing depth ≥ 5 mm, bleeding on probing and/or suppuration, and radiographic bone loss > 4 mm. Peri-implant tissue biopsy specimens were sampled for analysis of the mRNA expression levels for semaphorins 3A, 3B, 4A, and 4D.

Supervised discretization can discover risk groups in cancer survival analysis.

Discretization of continuous variables is a common practice in medical research to identify risk patient groups. This work compares the performance of gold-standard categorization procedures (TNM+A protocol) with that of three supervised discretization methods from Machine Learning (CAIM, ChiM and DTree) in the stratification of patients with breast cancer. The performance for the discretization algorithms was evaluated based on the results obtained after applying standard survival analysis procedures such as Kaplan-Meier curves, Cox regression and predictive modelling.

Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science.

One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis.

Comparative Finite Element Analysis of Short Implants and Lateralization of the Inferior Alveolar Nerve With Different Prosthesis Heights.

The lateralization of the inferior alveolar nerve (LIAN) and short implants are efficient options for rehabilitation of the posterior atrophic mandible. However, the loss of bone leads to prosthesis with greater height and lever effect that in turn can have different impact on treatments. Through the finite element method, the present study tests the hypothesis that conventional implants placed under LIAN and short implants have similar risk of bone loss regarding variable height of the crown and that crown-to-implant ratio is not a reliable resource to evaluate risk in these treatments.

Efficient Implementation of the Backpropagation Algorithm in FPGAs and Microcontrollers.

The well-known backpropagation learning algorithm is implemented in a field-programmable gate array (FPGA) board and a microcontroller, focusing in obtaining efficient implementations in terms of a resource usage and computational speed. The algorithm was implemented in both cases using a training/validation/testing scheme in order to avoid overfitting problems. For the case of the FPGA implementation, a new neuron representation that reduces drastically the resource usage was introduced by combining the input and first hidden layer units in a single module.

Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data.

Background: Extracting relevant information from microarray data is a very complex task due to the characteristics of the data sets, as they comprise a large number of features while few samples are generally available. In this sense, feature selection is a very important aspect of the analysis helping in the tasks of identifying relevant genes and also for maximizing predictive information.

Methods: Due to its simplicity and speed, Stepwise Forward Selection (SFS) is a widely used feature selection technique.

The generalization complexity measure for continuous input data.

We introduce in this work an extension for the generalization complexity measure to continuous input data. The measure, originally defined in Boolean space, quantifies the complexity of data in relationship to the prediction accuracy that can be expected when using a supervised classifier like a neural network, SVM, and so forth. We first extend the original measure for its use with continuous functions to later on, using an approach based on the use of the set of Walsh functions, consider the case of having a finite number of data points (inputs/outputs pairs), that is, usually the practical case.

Serum protein levels following surgery in breast cancer patients: a protein microarray approach.

Surgery is the primary treatment for non-metastatic breast cancer. However, the risk of early recurrence remains after surgical removal of the primary tumor. Recurrence is suggested to result from hidden micrometastatic foci, which are triggered to escape from dormancy by surgical resection of the primary tumor.

C-Mantec: a novel constructive neural network algorithm incorporating competition between neurons.

C-Mantec is a novel neural network constructive algorithm that combines competition between neurons with a stable modified perceptron learning rule. The neuron learning is governed by the thermal perceptron rule that ensures stability of the acquired knowledge while the architecture grows and while the neurons compete for new incoming information. Competition makes it possible that even after new units have been added to the network, existing neurons still can learn if the incoming information is similar to their stored knowledge, and this constitutes a major difference with existing constructing algorithms.

Missing data imputation using statistical and machine learning methods in a real breast cancer problem.

Objectives: Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. This work evaluates the performance of several statistical and machine learning imputation methods that were used to predict recurrence in patients in an extensive real breast cancer data set.

Materials And Methods: Imputation methods based on statistical techniques, e.

Prediction of subjective affective state from brain activations.

Decoding and information theoretic techniques were used to analyze the predictions that can be made from functional magnetic resonance neuroimaging data on individual trials. The subjective pleasantness produced by warm and cold applied to the hand could be predicted on single trials with typically in the range 60-80% correct from the activations of groups of voxels in the orbitofrontal and medial prefrontal cortex and pregenual cingulate cortex, and the information available was typically in the range 0.1-0.

Neuronal selectivity, population sparseness, and ergodicity in the inferior temporal visual cortex.

The sparseness of the encoding of stimuli by single neurons and by populations of neurons is fundamental to understanding the efficiency and capacity of representations in the brain, and was addressed as follows. The selectivity and sparseness of firing to visual stimuli of single neurons in the primate inferior temporal visual cortex were measured to a set of 20 visual stimuli including objects and faces in macaques performing a visual fixation task. Neurons were analysed with significantly different responses to the stimuli.

Information in the first spike, the order of spikes, and the number of spikes provided by neurons in the inferior temporal visual cortex.

Information theoretic analyses showed that for single inferior temporal neurons and neuronal populations, more information was encoded in 20 or more ms by all the spikes available than just by the first spike in the same time window about which of 20 objects or faces was shown. Further, the temporal order in which the first spike arrived from different simultaneously recorded neurons did not encode more information than was present in the first spike or the spike counts. Thus information transmission in the inferior temporal cortex by the number of spikes in even short time windows is fast, and provides more information than only the first spike, or the spike order from different neurons.

The influence of oppositely classified examples on the generalization complexity of Boolean functions.

In this paper, we analyze Boolean functions using a recently proposed measure of their complexity. This complexity measure, motivated by the aim of relating the complexity of the functions with the generalization ability that can be obtained when the functions are implemented in feed-forward neural networks, is the sum of a number of components. We concentrate on the case in which we use the first two of these components.

Informed prognosis [corrected] after abdominal aortic aneurysm repair using predictive modeling techniques [corrected].

Objective: To identify the best method for the prediction of postoperative mortality in individual abdominal aortic aneurysm surgery (AAA) patients by comparing statistical modelling with artificial neural networks' (ANN) and clinicians' estimates.

Methods: An observational multicenter study was conducted of prospectively collected postoperative Acute Physiology and Chronic Health Evaluation II data for a 9-year period from 24 intensive care units (ICU) in the Thames region of the United Kingdom. The study cohort consisted of 1205 elective and 546 emergency AAA patients.