Intra-Ring Wood Density and Dynamic Modulus of Elasticity Profiles for Black Spruce and Jack Pine from X-ray Densitometry and Ultrasonic Wave Velocity Measurement

Currently, ultrasonic measurement is a widely used nondestructive approach to determine wood elastic properties, including the dynamic modulus of elasticity (DMOE). DMOE is determined based on wood density and ultrasonic wave velocity measurement. The use of wood average density to estimate DMOE introduces significant imprecision: Density varies due to intra-tree and intra-ring differences and differing silvicultural treatments. To ensure accurateDMOEassessment, we developed a prototype device to measure ultrasonic wave velocity with the same resolution as that provided by the X-ray densitometer for measuring wood density. A nondestructive method based on X-ray densitometry and the developed prototype was applied to determine radial and intra-ring wood DMOE profiles. This method provides accurate information on wood mechanical properties and their sources of variation. High-order polynomials were used to model intra-ring wood density and DMOE profiles in black spruce and jack pine wood. The transition from earlywood to latewood was defined as the inflection point. High and highly significant correlations were obtained between predicted and measured wood density and DMOE. An examination of the correlations between wood radial growth, density, and DMOE revealed close correlations between density and DMOE in rings, earlywood, and latewood

Association analysis of polymorphisms in EGFR, HER2, ESR1 and THRA genes with coronary artery diseases

Background: Research in the genetic basis of coronary artery diseases (CAD) has identified some genes and pathways associated with diseases that would not be considered to underlie conventional risk factors. Among these genes there are the EGFR (epidermal growth factor receptor) receptor family genes and the regulation factor genes (such as thyroid hormone receptor a (THRA) and estrogen receptor a (ESR1)).Aim: In this study we investigated the relation between 4 polymorphisms within EGFR, HER2 (human epidermal growth factor receptor 2), ESR1 and THRA genes and CAD.Subjects and methods: The association analysis was performed with 151 healthy individuals and 151 CAD patients documented by angiography.Results: No significant difference was found in the allelic and genotypic frequency distribution of the four variants studied between the control and patient groups. We have also investigated the relationship of these polymorphic sites with clinical and biochemical parameters such as smoking habit, diabetes mellitus, hypertension, dyslipidemia, CAD severity, glucose, triglyceride, total cholesterol and urea levels. The EGFR and THRA variants were associated with glycemia and triglyceride levels, respectively. Also a significant correlation was found between the ESR1 polymorphism and the levels of urea and triglyceride.Conclusion: Our results suggest the absence of any significant association between the four polymorphisms analyzed and CAD risk as well as disease severity

Non-classical human leukocyte antigen class I in Tunisian children with autism

Autism spectrum disorders (ASD) are one of the most common childhood morbidities characterized by deficits in communication and social skills. Increasing evidence has suggested associations between immune genes located in the human leukocyte antigen (HLA) complex and etiology of autism.In this study, we investigated whether the non-classical class I HLA-G, -E, and -F polymorphisms are associated with genetic predisposition to autism in Tunisia. We aimed to find a correlation between HLA-G genotypes and soluble HLA-G (sHLA-G) levels. We have analyzed the HLA-G, -E, and -F genotypes of 15 autistic children and their parents. DNA typing of HLA class I genes was performed using PCR-SSP and PCR-RFLP methods. Also, we evaluated the serum levels of HLA-G (1 and 5) by a validated ELISA technique in autistic probands and their parents.No association was found between any polymorphism and autism in the study subjects. Additionally, we found no correlation between sHIA-G1 and sHLA-G5 and autism. Also, no significant difference in sHIA-G testing in parents and offspring was found. However, parents carrying [GG] genotype presented a higher sHLA-G levels than those carrying ([CC]+[GC]) genotypes (p = 0.037).From this preliminary study, we conclude that the investigated polymorphisms of HLA-G, -E, and -F genes did not lead to autism susceptibility in Tunisian children. However, the CGTIGA haplotype was found to be associated with the disease

Classification of MRI brain tumors based on registration preprocessing and deep belief networks

In recent years, augmented reality has emerged as an emerging technology with huge potential in image-guided surgery, and in particular, its application in brain tumor surgery seems promising. Augmented reality can be divided into two parts: hardware and software. Further, artificial intelligence, and deep learning in particular, have attracted great interest from researchers in the medical field, especially for the diagnosis of brain tumors. In this paper, we focus on the software part of an augmented reality scenario. The main objective of this study was to develop a classification technique based on a deep belief network (DBN) and a softmax classifier to (1) distinguish a benign brain tumor from a malignant one by exploiting the spatial heterogeneity of cancer tumors and homologous anatomical structures, and (2) extract the brain tumor features. In this work, we developed three steps to explain our classification method. In the first step, a global affine transformation is preprocessed for registration to obtain the same or similar results for different locations (voxels, ROI). In the next step, an unsupervised DBN with unlabeled features is used for the learning process. The discriminative subsets of features obtained in the first two steps serve as input to the classifier and are used in the third step for evaluation by a hybrid system combining the DBN and a softmax classifier. For the evaluation, we used data from Harvard Medical School to train the DBN with softmax regression. The model performed well in the classification phase, achieving an improved accuracy of 97.2%

A year of genomic surveillance reveals how the SARS-CoV-2 pandemic unfolded in Africa

[Figure: see text]

A year of genomic surveillance reveals how the SARS-CoV-2 pandemic unfolded in Africa.

The progression of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in Africa has so far been heterogeneous, and the full impact is not yet well understood. In this study, we describe the genomic epidemiology using a dataset of 8746 genomes from 33 African countries and two overseas territories. We show that the epidemics in most countries were initiated by importations predominantly from Europe, which diminished after the early introduction of international travel restrictions. As the pandemic progressed, ongoing transmission in many countries and increasing mobility led to the emergence and spread within the continent of many variants of concern and interest, such as B.1.351, B.1.525, A.23.1, and C.1.1. Although distorted by low sampling numbers and blind spots, the findings highlight that Africa must not be left behind in the global pandemic response, otherwise it could become a source for new variants

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Fouille de données spatio-temporelles appliquée aux trajectoires dans un réseau

Ces dernières années ont vu le développement des techniques de fouille de données dans de nombreux domaines d applications dans le but d analyser des données volumineuses et complexes. Parallèlement, le déploiement croissant de systèmes de localisation, tels que le GPS, amène à produire des masses de données relatives aux traces de géolocalisation. C est dans ce contexte que se situent nos travaux. Nous sommes partis du constat que le grand volume des données de géolocalisation rend leur exploitation et leur analyse fastidieuse pour les utilisateurs et les analystes. Nous nous sommes intéressés à la fouille de trajectoires d objets mobiles et plus particulièrement ceux évoluant dans un réseau, comme les véhicules dans un réseau routier. Cette thèse a abouti aux contributions suivantes : une méthode originale de clustering de trajectoires dans un contexte contraint par le réseau, une méthode de caractérisation de l'évolution de la densité sur le réseau routier, la définition et la découverte de patrons de trajectoires et une méthode de généralisation de trajectoires basée sur ces patrons.Recent years have seen the development of data mining techniques for many application areas in order to analyze large and complex data. At the same time, the increasing deployment of location-acquisition technologies such as GPS, leads to produce a large datasets of geolocation traces. In this thesis, we are interested in mining trajectories of moving objects, such as vehicles in the road network. We propose a method for discovering dense routes by clustering similar road sections according to both traffic and location in each time period. The traffic estimation is based on the collected spatio-temporal trajectories. We also propose a characterization approach of the temporal evolution of dense routes by a graph connecting dense routes over consecutive time periods. This graph is labelled by a degree of evolution. Our last proposal concerns the discovery of mobility patterns and using these patterns to define a new representation of generalised trajectories.VERSAILLES-BU Sciences et IUT (786462101) / SudocSudocFranceF