The Prediction of Depression in the Postpartum Period

Eight thousand five hundred and fifty-six women enrolled in the Mater-University of Queensland Study of Pregnancy were surveyed to investigate the relationship between potential risk factors for depression and mood states in the postpartum period. Participants were followed from their first antenatal interview until 6 months postpartum. Level of depression was measured at the first interview using the Delusions Signs and Symptoms Inventory (DSSI) and again at 6 months postpartum. A second measure of depression, which was prototypical in nature and related to the maternity blues, was administered retrospectively to cover the 6 month period following parturition. the assessment of independent variables during pregnancy and/or the postpartum period included demographic data and several measures of: neonatal risk, stress and social adjustment. Having excluded from analysis, participants who were depressed at the initial interview a point prevalence for depression of 2.6% (measured by the DSSI) and a prevalence for severe and prolonged postpartum stress of 12.8% (using the prototype measure) was recorded. Data was analysed using categorical modelling techniques and multiple regression analysis. Linear models were constructed to predict, both, DSSI and prototype measures of depression. Predictor variables accounted for 17% of the variance in DSSI scores and only 7% in the prototype measure. Whilst stress and social support formed the core of both models, irrespective of the analysis used, neonatal risk variables were more strongly predictive of 'blues' related depression. Inconsistencies in results are discussed and the need to differentiate between postpartum depression and depression (unrelated to childbirth) occurring in the postpartum period is proposed. Implications for primary prevention are discussed

On the equivalence between hierarchical segmentations and ultrametric watersheds

We study hierarchical segmentation in the framework of edge-weighted graphs. We define ultrametric watersheds as topological watersheds null on the minima. We prove that there exists a bijection between the set of ultrametric watersheds and the set of hierarchical segmentations. We end this paper by showing how to use the proposed framework in practice in the example of constrained connectivity; in particular it allows to compute such a hierarchy following a classical watershed-based morphological scheme, which provides an efficient algorithm to compute the whole hierarchy.Comment: 19 pages, double-colum

Impulsive noise removal from color images with morphological filtering

This paper deals with impulse noise removal from color images. The proposed noise removal algorithm employs a novel approach with morphological filtering for color image denoising; that is, detection of corrupted pixels and removal of the detected noise by means of morphological filtering. With the help of computer simulation we show that the proposed algorithm can effectively remove impulse noise. The performance of the proposed algorithm is compared in terms of image restoration metrics and processing speed with that of common successful algorithms.Comment: The 6th international conference on analysis of images, social networks, and texts (AIST 2017), 27-29 July, 2017, Moscow, Russi

Iterated Watersheds, A Connected Variation of K-Means for Clustering GIS Data

International audienceIn digital age new approaches for effective and efficient governance strategies can be established by exploiting the vast computing and data resources at our disposal. In several cases, the problem of efficient governance translates to finding a solution to an optimization problem. A typical example is where several cases are framed in terms of clustering problem-Given a set of data objects, partition them into clusters such that elements belonging to the same cluster are similar and elements belonging to different clusters are dissimilar. For example, problems such as zonation, river linking, facility allocation and visualizing spatial data can all be framed as clustering problems. However, all these problems come with an additional constraint that the clusters must be connected. In this article, we propose a suitable solution to the clustering problem with a constraint that the clusters must be connected. This is achieved by suitably modifying K-Means algorithm to include connectivity constraints. The modified algorithm involves repeated application of watershed transform, and hence is referred to as iterated watersheds. This algorithm is analyzed in detail using toy examples and the domain of image segmentation due to wide availability of labelled datasets. It has been shown that iterated watersheds perform better than methods such as spectral clustering, isoperimetric partitioning, and K-Means on various measures. To illustrate the applicability of iterated watersheds-a simple problem of placing emergency stations and suitable cost function is considered. Using real world road networks of various cities, iterated watersheds is compared with K-Means and greedy K-center methods. It has been shown that iterated watersheds result in very good improvements over these methods across various experiments

Dating of the oldest continental sediments from the Himalayan foreland basin

A detailed knowledge of Himalayan development is important for our wider understanding of several global processes, ranging from models of plateau uplift to changes in oceanic chemistry and climate(1-4). Continental sediments 55 Myr old found in a foreland basin in Pakistan(5) are, by more than 20 Myr, the oldest deposits thought to have been eroded from the Himalayan metamorphic mountain belt. This constraint on when erosion began has influenced models of the timing and diachrony of the India-Eurasia collision(6-8), timing and mechanisms of exhumation(9,10) and uplift(11), as well as our general understanding of foreland basin dynamics(12). But the depositional age of these basin sediments was based on biostratigraphy from four intercalated marl units(5). Here we present dates of 257 detrital grains of white mica from this succession, using the Ar-40-(39) Ar method, and find that the largest concentration of ages are at 36-40 Myr. These dates are incompatible with the biostratigraphy unless the mineral ages have been reset, a possibility that we reject on the basis of a number of lines of evidence. A more detailed mapping of this formation suggests that the marl units are structurally intercalated with the continental sediments and accordingly that biostratigraphy cannot be used to date the clastic succession. The oldest continental foreland basin sediments containing metamorphic detritus eroded from the Himalaya orogeny therefore seem to be at least 15-20 Myr younger than previously believed, and models based on the older age must be re-evaluated

Early adversity and health outcomes in young adulthood: the role of ongoing stress.

ObjectiveThe current study examined the prospective effects of exposure to stressful conditions in early childhood on physical health in young adulthood, and explored continuing exposure to stressors, as well as depression, in adolescence as possible mechanisms of this relationship.MethodA prospective longitudinal design was used to examine 705 mother-child pairs from a community-based sample, followed from offspring birth through age 20 years. Mothers provided contemporaneous assessments of early adverse conditions from offspring birth through age 5. Offspring responses to the UCLA Life Stress Interview, Structured Clinical Interview for DSM Disorders, Physical Functioning subscale of the SF-36 Health Survey, and questions about the presence of chronic disease were used to assess youth stress at age 15, depression from ages 15-20, and physical health at age 20.ResultsEarly adversity conferred risk for elevated levels of social and nonsocial stress at youth age 15, as well as depression between ages 15 and 20. Social and nonsocial stress, in turn, had effects on physical health at age 20, directly and indirectly via depression.ConclusionFindings suggest that early adverse conditions have lasting implications for physical health, and that continued exposure to increased levels of both social and nonsocial stress in adolescence, as well as the presence of depression, might be important mechanisms by which early adversity impacts later physical health

Automating the measurement of physiological parameters: a case study in the image analysis of cilia motion

International audienceAs image processing and analysis techniques improve, an increasing number of procedures in bio-medical analyses can be automated. This brings many benefits, e.g improved speed and accuracy, leading to more reliable diagnoses and follow-up, ultimately improving patients outcome. Many automated procedures in bio-medical imaging are well established and typically consist of detecting and counting various types of cells (e.g. blood cells, abnormal cells in Pap smears, and so on). In this article we propose to automate a different and difficult set of measurements, which is conducted on the cilia of people suffering from a variety of respiratory tract diseases. Cilia are slender, microscopic, hair-like structures or organelles that extend from the surface of nearly all mammalian cells. Motile cilia, such as those found in the lungs and respiratory tract, present a periodic beating motion that keep the airways clear of mucus and dirt. In this paper, we propose a fully automated method that computes various measurements regarding the motion of cilia, taken with high-speed video-microscopy. The advantage of our approach is its capacity to automatically compute robust, adaptive and regionalized measurements, i.e. associated with different regions in the image. We validate the robustness of our approach, and illustrate its performance in comparison to the state-of-the-art