Reemergence of dengue in Cuba: a 1997 epidemic in Santiago de Cuba.

After 15 years of absence, dengue reemerged in the municipality of Santiago de Cuba because of increasing migration to the area by people from disease-endemic regions, a high level of vector infestation, and the breakdown of eradication measures. The 1997 epidemic was detected early through an active surveillance system. Of 2,946 laboratory-confirmed cases, 205 were dengue hemorrhagic fever, and 12 were fatal. No deaths were reported in persons under 16 years of age. Now the epidemic is fully controlled

Consumer credit in comparative perspective

We review the literature in sociology and related fields on the fast global growth of consumer credit and debt and the possible explanations for this expansion. We describe the ways people interact with the strongly segmented consumer credit system around the world—more specifically, the way they access credit and the way they are held accountable for their debt. We then report on research on two areas in which consumer credit is consequential: its effects on social relations and on physical and mental health. Throughout the article, we point out national variations and discuss explanations for these differences. We conclude with a brief discussion of the future tasks and challenges of comparative research on consumer credit.Accepted manuscrip

Cyclophilin C-associated protein (CyCAP) knock-out mice spontaneously develop colonic mucosal hyperplasia and exaggerated tumorigenesis after treatment with carcinogen azoxymethane1

<p>Abstract</p> <p>Background</p> <p>The discovery of a "serrated neoplasia pathway" has highlighted the role of hyperplastic lesions of the colon as the significant precursor of colorectal adenocarcinoma. In mice, hyperplasia of the colonic mucosa is a regular phenomenon after a challenge with colonic carcinogens indicating that mucosal hyperproliferation and thickening, even without cytological dysplasia, represents an early pre-malignant change. Cyclophilin C-associated protein (CyCAP) has been described to down-modulate endotoxin signaling in colorectal murine mucosa and is a murine orthologue of the tumor-associated antigen 90 K (TAA90K)/mac-2-binding protein.</p> <p>Methods</p> <p>Female Balb/c wild-type (WT) and CyCAP knock-out (KO) mice (6–8 weeks old) were administered 2 or 6 weekly subcutaneous injections of azoxymethane. The animals were evaluated post-injection at six weeks for aberrant crypt foci (ACF) study and at five months for colon tumor measurement. The thickness of the colon crypts was measured in microns and the number of colonocytes per crypt was also determined in well-oriented crypts. Morphometric analyses of the colon mucosa were also performed in untreated 6–8 weeks old KO and WT animals. Formalin-fixed/paraffin-embedded colon sections were also studied by immunohistochemistry to determine the Ki-67 proliferation fraction of the colon mucosa, β-catenin cellular localization, cyclin D1, c-myc, and lysozyme in Paneth cells.</p> <p>Results</p> <p>Cyclophilin C-associated protein (CyCAP)^-/-mice, spontaneously developed colonic mucosal hyperplasia early in life compared to wild-type mice (WT) (p < 0.0001, T-test) and crypts of colonic mucosa of the (CyCAP)^-/-mice show higher proliferation rate (p = 0.039, Mann-Whitney Test) and larger number of cyclin D1-positive cells (p < 0.0001, Mann-Whitney Test). Proliferation fraction and cyclin D1 expression showed positive linear association (p = 0.019, Linear-by-Linear Association). The hyperplasia was even more pronounced in CyCAP^-/-mice than in WT after challenge with azoxymethane (p = 0.005, T-test). The length of the crypts (r = 0.723, p = 0.018, Spearman Correlation) and the number of colonocytes per crypt (r = 0.863, p = 0.001, Spearman Correlation) in non-tumorous areas were positively associated with azoxymethane-induced number of tumors. CyCAP^-/-developed larger numbers of tumors than WT animals (p = 0.003, T-Test) as well as overall larger tumor mass (p = 0.016, T-Test). Membranous β-catenin was focally overexpressed in KO mice including proliferative zone of the crypts.</p> <p>Conclusion</p> <p>CyCAP^-/-represent the first described model of spontaneous colonic mucosal hyperplasia. We conclude that CyCAP-deficient mice spontaneously and after challenge with carcinogen develop significantly more colorectal mucosal hyperplasia, an early stage in murine colonic carcinogenesis.</p

Precise Regulation of Gene Expression Dynamics Favors Complex Promoter Architectures

Promoters process signals through recruitment of transcription factors and RNA polymerase, and dynamic changes in promoter activity constitute a major noise source in gene expression. However, it is barely understood how complex promoter architectures determine key features of promoter dynamics. Here, we employ prototypical promoters of yeast ribosomal protein genes as well as simplified versions thereof to analyze the relations among promoter design, complexity, and function. These promoters combine the action of a general regulatory factor with that of specific transcription factors, a common motif of many eukaryotic promoters. By comprehensively analyzing stationary and dynamic promoter properties, this model-based approach enables us to pinpoint the structural characteristics underlying the observed behavior. Functional tradeoffs impose constraints on the promoter architecture of ribosomal protein genes. We find that a stable scaffold in the natural design results in low transcriptional noise and strong co-regulation of target genes in the presence of gene silencing. This configuration also exhibits superior shut-off properties, and it can serve as a tunable switch in living cells. Model validation with independent experimental data suggests that the models are sufficiently realistic. When combined, our results offer a mechanistic explanation for why specific factors are associated with low protein noise in vivo. Many of these findings hold for a broad range of model parameters and likely apply to other eukaryotic promoters of similar structure

A multiresolution EM algorithm for unsupervised image classification

We take benefit from a causal Markov model defined on a quadtree to derive a multiresolution EM algorithm for unsupervised image classification. This algorithm is an efficient alternative to expensive or approximate EM algorithms associated with Markov Random Fields. We show on synthetic and real images that our algorithm also provides good or even better results than those obtained by spatial MRF models

Autolabel : Improving Petri Dish Automatic Labels with AI Algorithms

International audienceThe present research aims at improving the accuracy of labels on Petri dish images containing Colony Forming Units using Artificial Intelligence algorithms. Indeed, the labeling methods proposed by classical computer vision software such as ScanStation for example, are prone to errors and the manual correction of these errors is a difficult task. We propose a methodology based on AI models. At first, a YOLO model is trained on the existing labels given by ScanStation. The bounding boxes provided by ScanStation and YOLO are then binarized using the OTSU algorithm to generate semantic labels that are used to train a U-Net. Then, a Xception model is trained to classify all the segments generated by the U-Net as either outlier or colony. For new data, the trained U-Net and Xception models are used to improve the labeling. The results indicate that the proposed approach improves the accuracy of the labeling process without human correction

Older adults empowerment and well-being: Fall detection and activity monitoring

International audienceAim The goal of this project is to contribute effectively to the overall objective of preventing loss of autonomy, by providing home care for older adults, that is efficient, useful and acceptable.Addressing the major public health problem of the fall of older adults, an interdisciplinary projectfounded by the French Research Agency has been set up in order to propose a low-cost system thatcan be implemented in the homes of the elderly. This device, based on depth and/or low costthermal sensors is aimed, on the one hand, to detect falls and, on the other hand, to prevent the riskof falling by analyzing the activity of individuals. MethodsTwo passive acquisition systems for fall detection and activity monitoring have been explored: 1) apair of depth and thermal cameras with processing methods for people tracking using particle filtersand activity recognition using Deep Learning and; 2) a stereo pair of low resolution (80x60 pixels)thermal images with several processes including machine learning-based fall detection and activityrecognition. The sequence of activities are then analyzed in order to establish the routine life of theelderly. Modification of this routine can be a sign of frailty. ResultsLearning datasets have been created in 3 different locations with several people using bothacquisition modalities. On these datasets, the machine learning-based fall detection from a stereopair of thermal images reached an accuracy higher than 0.9 for fall detection. Concerning theactivity recognition, we reached an accuracy higher than 0.95 on depth/thermal acquisitions andthan 0.75 using only one low-resolution thermal image. For the activity analysis phase, since datawas insufficient at the beginning, we created a model which simulates the routine (normal) or non-routine (abnormal) day, according to the variance of frailty indexes over a six-month perio

Older adults empowerment and well-being: Fall detection and activity monitoring

International audienceAim The goal of this project is to contribute effectively to the overall objective of preventing loss of autonomy, by providing home care for older adults, that is efficient, useful and acceptable.Addressing the major public health problem of the fall of older adults, an interdisciplinary projectfounded by the French Research Agency has been set up in order to propose a low-cost system thatcan be implemented in the homes of the elderly. This device, based on depth and/or low costthermal sensors is aimed, on the one hand, to detect falls and, on the other hand, to prevent the riskof falling by analyzing the activity of individuals. MethodsTwo passive acquisition systems for fall detection and activity monitoring have been explored: 1) apair of depth and thermal cameras with processing methods for people tracking using particle filtersand activity recognition using Deep Learning and; 2) a stereo pair of low resolution (80x60 pixels)thermal images with several processes including machine learning-based fall detection and activityrecognition. The sequence of activities are then analyzed in order to establish the routine life of theelderly. Modification of this routine can be a sign of frailty. ResultsLearning datasets have been created in 3 different locations with several people using bothacquisition modalities. On these datasets, the machine learning-based fall detection from a stereopair of thermal images reached an accuracy higher than 0.9 for fall detection. Concerning theactivity recognition, we reached an accuracy higher than 0.95 on depth/thermal acquisitions andthan 0.75 using only one low-resolution thermal image. For the activity analysis phase, since datawas insufficient at the beginning, we created a model which simulates the routine (normal) or non-routine (abnormal) day, according to the variance of frailty indexes over a six-month perio