Search results for " Prediction"

miR-1207-5p Can Contribute to Dysregulation of Inflammatory Response in COVID-19 via Targeting SARS-CoV-2 RNA

2020

The present study focuses on the role of human miRNAs in SARS-CoV-2 infection. An extensive analysis of human miRNA binding sites on the viral genome led to the identification of miR-1207-5p as potential regulator of the viral Spike protein. It is known that exogenous RNA can compete for miRNA targets of endogenous mRNAs leading to their overexpression. Our results suggest that SARS-CoV-2 virus can act as an exogenous competing RNA, facilitating the over-expression of its endogenous targets. Transcriptomic analysis of human alveolar and bronchial epithelial cells confirmed that the CSF1 gene, a known target of miR-1207-5p, is over-expressed following SARS-CoV-2 infection. CSF1 enhances macr…

Fasciola spp: Mapping of the MF6 epitope and antigenic analysis of the MF6p/HDM family of heme-binding proteins

2017

MF6p/FhHDM-1 is a small cationic heme-binding protein which is recognized by the monoclonal antibody (mAb) MF6, and abundantly present in parenchymal cells and secreted antigens of Fasciola hepatica. Orthologs of this protein (MF6p/HDMs) also exist in other causal agents of important foodborne trematodiasis, such as Clonorchis sinensis, Opisthorchis viverrini and Paragonimus westermani. Considering that MF6p/FhHDM-1 is relevant for heme homeostasis in Fasciola and was reported to have immunomodulatory properties, this protein is expected to be a useful target for vaccination. Thus, in this study we mapped the epitope recognized by mAb MF6 and evaluated its antigenicity in sheep. The sequenc…

Robust Assembly Assistance Using Informed Tree Search with Markov Chains

2022

Manual work accounts for one of the largest workgroups in the European manufacturing sector, and improving the training capacity, quality, and speed brings significant competitive benefits to companies. In this context, this paper presents an informed tree search on top of a Markov chain that suggests possible next assembly steps as a key component of an innovative assembly training station for manual operations. The goal of the next step suggestions is to provide support to inexperienced workers or to assist experienced workers by providing choices for the next assembly step in an automated manner without the involvement of a human trainer on site. Data stemming from 179 experiment partici…

Analyzing Learned Representations of a Deep ASR Performance Prediction Model

2018

This paper addresses a relatively new task: prediction of ASR performance on unseen broadcast programs. In a previous paper, we presented an ASR performance prediction system using CNNs that encode both text (ASR transcript) and speech, in order to predict word error rate. This work is dedicated to the analysis of speech signal embeddings and text embeddings learnt by the CNN while training our prediction model. We try to better understand which information is captured by the deep model and its relation with different conditioning factors. It is shown that hidden layers convey a clear signal about speech style, accent and broadcast type. We then try to leverage these 3 types of information …

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

2019

Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural ne…

Evaluation of Roundabout Safety Performance through Surrogate Safety Measures from Microsimulation

2018

The paper presents a microsimulation-based approach for roundabout safety performance evaluation. Based on a sample of Slovenian roundabouts, the vehicle trajectories exported from AIMSUN and VISSIM were used to estimate traffic conflicts using the Surrogate Safety Assessment Model (SSAM). AIMSUN and VISSIM were calibrated for single-lane, double-lane and turbo roundabouts using the corresponding empirical capacity function which included critical and follow-up headways estimated through meta-analysis. Based on calibration of the microsimulation models, a crash prediction model from simulated peak hour conflicts for a sample of Slovenian roundabouts was developed. A generalized linear model…

Predictive pumping based on sensor data and weather forecast

2019

In energy production, peat extraction has a significant role in Finland. However, protection of nature has become more and more important globally. How do we solve this conflict of interests respecting both views? In peat production, one important phase is to drain peat bog so that peat production becomes available. This means that we have control over how we can lead water away from peat bog to nature without water contamination with solid and other harmful substances. In this paper we describe a novel method how fouling of water bodies from peat bog can be controlled more efficiently by using weather forecast to predict rainfall and thus, minimize the effluents to nature. peerReviewed

ASR performance prediction on unseen broadcast programs using convolutional neural networks

2018

In this paper, we address a relatively new task: prediction of ASR performance on unseen broadcast programs. We first propose an heterogenous French corpus dedicated to this task. Two prediction approaches are compared: a state-of-the-art performance prediction based on regression (engineered features) and a new strategy based on convolutional neural networks (learnt features). We particularly focus on the combination of both textual (ASR transcription) and signal inputs. While the joint use of textual and signal features did not work for the regression baseline, the combination of inputs for CNNs leads to the best WER prediction performance. We also show that our CNN prediction remarkably …

Study and identification of new molecular descriptors, finalized to the development of Virtual Screening techniques through the use of deep neural ne…

2022

The Maudsley Environmental Risk Score for Psychosis

2018

AbstractBackgroundRisk prediction algorithms have long been used in health research and practice (e.g. prediction of cardiovascular disease and diabetes). However, similar tools have not been developed for mental health. For example, for psychotic disorders, attempts to sum environmental risk are rare, unsystematic and dictated by available data. In light of this, we sought to develop a valid, easy to use measure of the aggregate environmental risk score (ERS) for psychotic disorders.MethodsWe reviewed the literature to identify well-replicated and validated environmental risk factors for psychosis that combine a significant effect and large-enough prevalence. Pooled estimates of relative r…