Search results for "learning"

2020

Human movements are characterized by highly non-linear and multi-dimensional interactions within the motor system. Recently, an increasing emphasis on machine-learning applications has led to a significant contribution to the field of gait analysis, e.g., in increasing the classification performance. In order to ensure the generalizability of the machine-learning models, different data preprocessing steps are usually carried out to process the measured raw data before the classifications. In the past, various methods have been used for each of these preprocessing steps. However, there are hardly any standard procedures or rather systematic comparisons of these different methods and their im…

Evaluation of tumor immune contexture among intrinsic molecular subtypes helps to predict outcome in early breast cancer

2021

BackgroundThe prognosis of early breast cancer is linked to clinic-pathological stage and the molecular characteristics of intrinsic tumor cells. In some patients, the amount and quality of tumor-infiltrating immune cells appear to affect long term outcome. We aimed to propose a new tool to estimate immune infiltrate, and link these factors to patient prognosis according to breast cancer molecular subtypes.MethodsWe performed in silico analyses in more than 2800 early breast cancer transcriptomes with corresponding clinical annotations. We first developed a new gene expression deconvolution algorithm that accurately estimates the quantity of immune cell populations (tumor immune contexture,…

Short telomere length is associated with impaired cognitive performance in European ancestry cohorts

2017

AbstractThe association between telomere length (TL) dynamics on cognitive performance over the life-course is not well understood. This study meta-analyses observational and causal associations between TL and six cognitive traits, with stratifications on APOE genotype, in a Mendelian Randomization (MR) framework. Twelve European cohorts (N=17 052; mean age=59.2±8.8 years) provided results for associations between qPCR-measured TL (T/S-ratio scale) and general cognitive function, mini-mental state exam (MMSE), processing speed by digit symbol substitution test (DSST), visuospatial functioning, memory and executive functioning (STROOP). In addition, a genetic risk score (GRS) for TL includin…

Asynchronous and pathological windows of implantation: two causes of recurrent implantation failure

2018

STUDY QUESTION: Is endometrial recurrent implantation failure (RIF) only a matter of an asynchronous (displaced) window of implantation (WOI), or could it also be a pathological (disrupted) WOI? SUMMARY ANSWER: Our predictive results demonstrate that both displaced and disrupted WOIs exist and can present independently or together in the same RIF patient. WHAT IS KNOWN ALREADY: Since 2002, many gene expression signatures associated with endometrial receptivity and RIF have been described. Endometrial transcriptomics prediction has been applied to the human WOI in two previous studies. One study describes endometrial RIF to be the result of a temporal displacement of the WOI. The other indic…

NF1 microdeletion syndrome: case report of two new patients

2019

Abstract Background 17q11.2 microdeletions, which include the neurofibromatosis type 1 (NF1) gene region, are responsible for the NF1 microdeletion syndrome, observed in 4.2% of all NF1 patients. Large deletions of the NF1 gene and its flanking regions are associated with a more severe NF1 phenotype than the NF1 general population. Case presentation We hereby describe the clinical and molecular features of two girls (aged 2 and 4 years, respectively), with non-mosaic atypical deletions. Patient 1 showed fifteen café-au-lait spots and axillary freckling, as well as a Lisch nodule in the left eye, strabismus, high-arched palate, malocclusion, severe kyphoscoliosis, bilateral calcaneovalgus fo…

Eomes broadens the scope of CD8 T-cell memory by inhibiting apoptosis in cells of low affinity.

2020

The memory CD8 T-cell pool must select for clones that bind immunodominant epitopes with high affinity to efficiently counter reinfection. At the same time, it must retain a level of clonal diversity to allow recognition of pathogens with mutated epitopes. How the level of diversity within the memory pool is controlled is unclear, especially in the context of a selective drive for antigen affinity. We find that preservation of clones that bind the activating antigen with low affinity depends on expression of the transcription factor Eomes in the first days after antigen encounter. Eomes is induced at low activating signal strength and directly drives transcription of the prosurvival protein…

Hippocampal electrical stimulation disrupts associative learning when targeted at dentate spikes

2017

KEY POINTS Dentate spikes are fast fluctuations of hilar local-field potentials that take place during rest and are thought to reflect input arriving from the entorhinal cortex to the hippocampus. During dentate spikes, neuronal firing in hippocampal input (dentate gyrus) and output (CA1/CA3) regions is uncoupled. To date, the behavioural significance of dentate spikes is unknown. Here, we provide evidence that disrupting the dentate spike-related uncoupling of the dentate gyrus and the CA1/CA3 subregions for 1 h after training retards associative learning. We suggest dentate spikes play a significant role in memory consolidation. ABSTRACT Hippocampal electrophysiological oscillations, name…

Automatic sleep scoring: A deep learning architecture for multi-modality time series

2020

Background: Sleep scoring is an essential but time-consuming process, and therefore automatic sleep scoring is crucial and urgent to help address the growing unmet needs for sleep research. This paper aims to develop a versatile deep-learning architecture to automate sleep scoring using raw polysomnography recordings. Method: The model adopts a linear function to address different numbers of inputs, thereby extending model applications. Two-dimensional convolution neural networks are used to learn features from multi-modality polysomnographic signals, a “squeeze and excitation” block to recalibrate channel-wise features, together with a long short-term memory module to exploit long-range co…

On the structural connectivity of large-scale models of brain networks at cellular level

2021

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the …

Evaluating the stability of pharmacophore features using molecular dynamics simulations.

2016

Abstract Molecular dynamics simulations of twelve protein—ligand systems were used to derive a single, structure based pharmacophore model for each system. These merged models combine the information from the initial experimental structure and from all snapshots saved during the simulation. We compared the merged pharmacophore models with the corresponding PDB pharmacophore models, i.e., the static models generated from an experimental structure in the usual manner. The frequency of individual features, of feature types and the occurrence of features not present in the static model derived from the experimental structure were analyzed. We observed both pharmacophore features not visible in …