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BioacPepFinder: Discovery of bioactive peptides from protein digestion.
No full textInternational audienc
Impact of a multidisciplinary team meeting on patient-reported outcomes at 2 years after lumbar surgery: A prospective comparative exploratory study
No full textInternational audienceFailed back surgery syndrome is a challenge. We hypothesized that a multidisciplinary team meeting (MTM) may be useful to select patients who are the most likely to benefit from lumbar surgery. We conducted an observational, prospective, comparative, exploratory study. We aimed to compare core clinical patient-reported outcomes at 2 years after lumbar surgery between patients who attended a MTM and those who did not. Patients who underwent lumbar surgery for a degenerative disease, in a single academic orthopedic department, between January and September 2018, were consecutively screened. Eligible patients were surveyed between April and June 2020. Patient-reported outcomes included lumbar and radicular pain, spine-specific activity limitations and health-related quality of life assessed via self-administered questionnaires. Outcomes were compared between respondents who attended the MTM and those who did not. Overall, 211 patients underwent lumbar surgery, 108 were eligible and 44 included: 11 attended the MTM and 33 did not. Mean participants' age was 57.4 (15.4) years, symptom duration was 14.8 (15.3) months, lumbar pain was 51.3 (18.2) and radicular pain was 53.4 (18.6). At 2 years, we found no evidence that lumbar and radicular pain, activity limitations and health-related quality of life differed between the 2 groups. The decrease was −26.8 (41.1) versus −20.8 (30.4) in lumbar pain and −25.5 (43.0) versus −19.5 (27.5) in radicular pain, in participants who attended the MTM versus those who did not, respectively. We found no evidence that core clinical patient-reported outcomes at 2 years after lumbar surgery differed between participants who attended the MTM and those who did not. However, the exploratory design of our study does not allow concluding that MTMs do not have an impact
Rirha : bilan des recherches pluridisciplinaires d’une ville antique et médiévale
No full textInternational audienc
Industrial legacy and hotel pricing: An application of spatial hedonic pricing analysis in Nord-Pas-de-Calais, France.
No full textInternational audienceAn old industrial region's legacy can be a serious impediment to the development of tourism and other activities by generating negative externalities and disamenities. The aim of this article is to examine whether the cost of this industrial past as valued by tourists is reflected in hotel rates of the Nord-Pas de Calais region, a forerunner of the Industrial Revolution in France. An analysis based on the hedonic price method is undertaken using geolocalized data, to decompose hotel prices into the implicit prices of a set of attributes, both private and public, including the adverse public attributes inherited from the industrial past (brownfield sites, slag heaps, industrial seaports). By comparing the importance of each factor, our analysis provides useful information for public policy and hotel management strategies. In particular, our estimations reveal a significant negative effect of these adverse inherited public attributes on hotel rates for the Nord-Pas-de-Calais, but in the same order of magnitude as the effect of a tourist attraction, suggesting the potential power of public policy and local regeneration initiatives. Furthermore, our results show that hotel managers can obtain valuable information relative to the choice of a location for initial development, their investment strategy, and their communication strategy
Bacterial colibactin-induced lipid accumulation and loss of a c-type lectin cooperates for supporting an immune-suppressive microenvironment in right-sided colon cancer
No full textInternational audienceRight-sided colon cancer (RCC) patients exhibit difference in the microbiota organization in relation to left-sided colon cancer and has a worse prognosis. Among several species of bacteria associated with colon cancer, colibactin-producing by Escherichia coli (CoPEC) are attracting attention. However, if CoPEC contributes to tumor lipid metabolism remains incompletely understood. Herein, we revealed that CoPEC is negatively correlated with human regenerating family member 3 alpha gene (REG3A) expression and trigger reprogramming lipid metabolism, which exacerbates accumulation of glycerophospholipids. Notably, APC mutation and metabolic consensus molecular subtype (CMS3) are predominant in RCC, especially in patients colonized by CoPEC. While SFRP2 expression is increased in these tumors, CD8+ T cells were reduced. In addition, human tumors have similarities to mice tumors. In particular, mRNAs encoding immunoglobulins heavy chains were clearly increased in both models with low Reg3A expression. Taken together, CoPEC associated with REG3A is promising as a biomarker in cancer therapy
2D versus 3D Convolutional Spiking Neural Networks Trained with Unsupervised STDP for Human Action Recognition
No full textInternational audienceCurrent advances in technology have highlighted the importance of video analysis in the domain of computer vision. However, video analysis has considerably high computational costs with traditional artificial neural networks (ANNs). Spiking neural networks (SNNs) are third generation biologically plausible models that process the information in the form of spikes. Unsupervised learning with SNNs using the spike timing dependent plasticity (STDP) rule has the potential to overcome some bottlenecks of regular artificial neural networks, but STDP-based SNNs are still immature and their performance is far behind that of ANNs. In this work, we study the performance of SNNs when challenged with the task of human action recognition, because this task has many real-time applications in computer vision, such as video surveillance. In this paper we introduce a multilayered 3D convolutional SNN model trained with unsupervised STDP. We compare the performance of this model to those of a 2D STDP-based SNN when challenged with the KTH and Weizmann datasets. We also compare single-layer and multi-layer versions of these models in order to get an accurate assessment of their performance. We show that STDP-based convolutional SNNs can learn motion patterns using 3D kernels, thus enabling motionbased recognition from videos. Finally, we give evidence that 3D convolution is superior to 2D convolution with STDP-based SNNs, especially when dealing with long video sequences
Prenatal exposure to Cannabis smoke induces early and lasting damage to the brain
No full textInternational audienc
Dynamic Facial Expression Recognition under Partial Occlusion with Optical Flow Reconstruction
No full textInternational audienc
Spiking Neural Networks Trained withUnsupervised STDP for Video Analysis
No full textCurrent advances in technology have highlighted the importance of video analysis in the domain of computer vision. Traditional artificial neural networks have considerably high computational costs with video analysis, and many modern applications such as autonomous vehicles have limited computational resources. Spiking neural networks (SNNs) are third generation, biologically plausible models that are seen as hypothetical solutions for the bottlenecks of ANNs, such as energy efficiency. However, current SNN-specific methods that achieve good classification rates, such as ANN-to-SNN conversion and back-propagation, depend on labeled data, which requires costly human intervention. Meanwhile, unsupervised learning with SNNs using the spike timing-dependent plasticity (STDP) rule has the potential to overcome some bottlenecks of regular artificial neural networks. However, STDP-based SNNs are still immature. SNNs trained in an unsupervised manner with STDP can hypothetically surpass ANNs in energy efficiency, and thus must be studied and improved. In this work, we study the performance of these networks with human action recognition tasks. Moreover, we focus on the motion found in videos in order to recognise the actions. In this paper, we focus on studying the effects that different motion modeling techniques can have on the spatio-temporal features extracted by a spiking neural network trained with unsupervised STDP