Buchmogensen3093

The focus of this review is to analyze exosomes and their miRNA cargo to modulate TBI neuroinflammation providing new strategies for prevent long-term progression of disease.Mantle cell lymphoma (MCL) is a rare but aggressive B-cell hemopathy characterized by the translocation t(11;14)(q13;q32) that leads to the overexpression of the cell cycle regulatory protein cyclin D1. This translocation is the initial event of the lymphomagenesis, but tumor cells can acquire additional alterations allowing the progression of the disease with a more aggressive phenotype and a tight dependency on microenvironment signaling. To date, the chemotherapeutic-based standard care is largely inefficient and despite the recent advent of different targeted therapies including proteasome inhibitors, immunomodulatory drugs, tyrosine kinase inhibitors, relapses are frequent and are generally related to a dismal prognosis. As a result, MCL remains an incurable disease. In this review, we will present the molecular mechanisms of drug resistance learned from both preclinical and clinical experiences in MCL, detailing the main tumor intrinsic processes and signaling pathways associated to therapeutic drug escape. We will also discuss the possibility to counteract the acquisition of drug refractoriness through the design of more efficient strategies, with an emphasis on the most recent combination approaches.The hybridization of Prosopis burkartii, a critically endangered endemic species, and the identification of its paternal species has not been genetically studied before. In this study we aimed to genetically confirm the origin of this species. To resolve the parental status of P. burkartii, inter-simple sequence repeat (ISSR), simple sequence repeats (SSR) and intron trnL molecular markers were used, and compared with Chilean species from the Algarobia and Strombocarpa sections. Out of seven ISSRs, a total of 70 polymorphic bands were produced in four species of the Strombocarpa section. An Multi-dimensional scaling (MDS) and Bayasian (STRUCTURE) analysis showed signs of introgression of genetic material in P. burkartii. Unweighted pair group method with arithmetic average (UPGMA) cluster analysis showed three clusters, and placed the P. burkartii cluster nested within the P. tamarugo group. Sequencing of the trnL intron showed a fragment of 535 bp and 529 bp in the species of the Algarobia and Strombocarpa sections, respectively. Using maximum parsimony (MP) and maximum likelihood (ML) trees with the trnL intron, revealed four clusters. A species-specific diagnostic method was performed, using the trnL intron Single Nucleotide Polymorphism (SNP). This method identified if individuals of P. burkartii inherited their maternal DNA from P. tamarugo or from P. strombulifera. We deduced that P. tamarugo and P. strombulifera are involved in the formation of P. burkartii.Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research in the handwriting recognition field is focused around deep learning techniques and has achieved breakthrough performance in the last few years. Still, the rapid growth in the amount of handwritten data and the availability of massive processing power demands improvement in recognition accuracy and deserves further investigation. Convolutional neural networks (CNNs) are very effective in perceiving the structure of handwritten characters/words in ways that help in automatic extraction of distinct features and make CNN the most suitable approach for solving handwriting recognition problems. Our aim in the proposed work is to explore the various design options like number of layers, stride size, receptive field, kernel size, padding and dilution for CNN-based handwritten digit recognition. In addition, we aim to evaluate various SGD optimization algorithms in improving the performance of handwritten digit recognition. A network's recognition accuracy increases by incorporating ensemble architecture. Here, our objective is to achieve comparable accuracy by using a pure CNN architecture without ensemble architecture, as ensemble architectures introduce increased computational cost and high testing complexity. Thus, a CNN architecture is proposed in order to achieve accuracy even better than that of ensemble architectures, along with reduced operational complexity and cost. Moreover, we also present an appropriate combination of learning parameters in designing a CNN that leads us to reach a new absolute record in classifying MNIST handwritten digits. We carried out extensive experiments and achieved a recognition accuracy of 99.87% for a MNIST dataset.In recent decades, the living conditions of young breast cancer (BC) survivors have garnered increasing attention. This population-based study aimed to identify the clinical, social and economic determinants of Health-Related Quality of Life (HRQoL), and to describe other living conditions of young long-term BC survivors. Women with non-metastatic BC diagnosed between 2006 and 2015, aged 45 years and younger at the time of diagnosis, were identified through the Breast and Gynecologic Cancer Registry of the Côte d'Or, France. Participants completed self-report questionnaires including standardized measures of HRQoL, anxiety, depression, social deprivation, social support and sexuality. Fertility and professional reintegration issues were also assessed. The determinants of HRQoL were identified using mixed regression model. In total, 218 BC survivors participated in the survey. The main determinants of poor HRQoL were anxiety, depression, comorbidities, social deprivation and menopausal status. Among 72% of women who did not receive information about fertility preservation, 38% of them would have liked to have been informed. CD38 inhibitor 1 mouse Finally, 39% of survivors reported a negative impact of BC on their professional activity. This study showed that BC stage or treatments did not have an impact on HRQOL of young long-term BC survivors. Fertility, sexuality and professional reintegration remained the main concerns for survivors. Specific interventions in these population should focus on these issues.With the advent of the Fourth Industrial Revolution, Internet of Things (IoT) and robotic systems are closely cooperating, reshaping their relations and managing to develop new-generation devices. Such disruptive technology corresponds to the backbone of the so-called Industry 4.0. The integration of robotic agents and IoT leads to the concept of the Internet of Robotic Things, in which innovation in digital systems is drawing new possibilities in both industrial and research fields, covering several domains such as manufacturing, agriculture, health, surveillance, and education, to name but a few. In this manuscript, the state-of-the-art of IoRT applications is outlined, aiming to mark their impact on several research fields, and focusing on the main open challenges of the integration of robotic technologies into smart spaces. IoRT technologies and applications are also discussed to underline their influence in everyday life, inducing the need for more research into remote and automated applications.Mucopolysaccharidoses (MPSs) are inherited disorders of the glycosaminoglycan (GAG) metabolism. The defective digestion of GAGs within the intralysosomal compartment of affected patients leads to a broad spectrum of clinical manifestations ranging from cardiovascular disease to neurological impairment. The molecular mechanisms underlying the progression of the disease downstream of the genetic mutation of genes encoding for lysosomal enzymes still remain unclear. Here, we applied a targeted metabolomic approach to a mouse model of PS IIIB, using a platform dedicated to the diagnosis of inherited metabolic disorders, in order to identify amino acid and fatty acid metabolic pathway alterations or the manifestations of other metabolic phenotypes. Our analysis highlighted an increase in the levels of branched-chain amino acids (BCAAs Val, Ile, and Leu), aromatic amino acids (Tyr and Phe), free carnitine, and acylcarnitines in the liver and heart tissues of MPS IIIB mice as compared to the wild type (WT). Moreover, Ala, Met, Glu, Gly, Arg, Orn, and Cit amino acids were also found upregulated in the liver of MPS IIIB mice. These findings show a specific impairment of the BCAA and fatty acid catabolism in the heart of MPS IIIB mice. In the liver of affected mice, the glucose-alanine cycle and urea cycle resulted in being altered alongside a deregulation of the BCAA metabolism. Thus, our data demonstrate that an accumulation of BCAAs occurs secondary to lysosomal GAG storage, in both the liver and the heart of MPS IIIB mice. Since BCAAs regulate the biogenesis of lysosomes and autophagy mechanisms through mTOR signaling, impacting on lipid metabolism, this condition might contribute to the progression of the MPS IIIB disease.Travel time prediction is critical for advanced traveler information systems (ATISs), which provides valuable information for enhancing the efficiency and effectiveness of the urban transportation systems. However, in the area of bus trips, existing studies have focused on directly using the structured data to predict travel time for a single bus trip. For state-of-the-art public transportation information systems, a bus journey generally has multiple bus trips. Additionally, due to the lack of study on data fusion, it is even inadequate for the development of underlying intelligent transportation systems. In this paper, we propose a novel framework for a hybrid data-driven travel time prediction model for bus journeys based on open data. We explore a convolutional long short-term memory (ConvLSTM) model with a self-attention mechanism that accurately predicts the running time of each segment of the trips and the waiting time at each station. The model is more robust to capture long-range dependence in time series data as well.By controlling the hydrothermal time, porous In2O3 nanosheet-assembled micro-flowers were successfully synthesized by a one-step method. The crystal structure, microstructure, and internal structure of the prepared samples were represented by an x-ray structure diffractometry, scanning electron microscopy, and transmission electron microscopy, respectively. The characterization results showed that when the hydrothermal time was 8 h, the In2O3 nano materials presented a flower-like structure assembled by In2O3 porous nanosheets. After successfully preparing the In2O3 gas sensor, the gas sensing was fully studied. The results show that the In2O3 gas sensor had an excellent gas sensing response to ethanol, and the material prepared under 8 h hydrothermal conditions had the best gas sensing property. At the optimum working temperature of 270 °C, the highest response value could reach 66, with a response time of 12.4 s and recovery time of 10.4 s, respectively. In addition, the prepared In2O3 gas sensor had a wide detection range for ethanol concentration, and still had obvious response for 500 ppb ethanol. Furthermore, the gas sensing mechanism of In2O3 micro-flowers was also studied in detail.