Resilience and well-being among children of migrant parents in South-East Asia

There has been little systematic empirical research on the well-being of children in transnational households in South-East Asia—a major sending region for contract migrants. This study uses survey data collected in 2008 from children aged 9, 10 and 11 and their caregivers in Indonesia, the Philippines, and Vietnam (N=1,498). Results indicate that while children of migrant parents, especially migrant mothers, are less likely to be happy compared to children in non-migrant households, greater resilience in child well-being is associated with longer durations of maternal absence. There is no evidence for a direct parental migration effect on school enjoyment and performance. The analyses highlight the sensitivity of results to the dimension of child well-being measured and who makes the assessment.Publisher PDFPeer reviewe

Determination of an Interaction Network between an Extracellular Bacterial Pathogen and the Human Host

A major gap in understanding infectious diseases is the lack of information about molecular interaction networks between pathogens and the human host. Haemophilus ducreyi causes the genital ulcer disease chancroid in adults and is a leading cause of cutaneous ulcers in children in the tropics. We developed a model in which human volunteers are infected on the upper arm with H. ducreyi until they develop pustules. To define the H. ducreyi and human interactome, we determined bacterial and host transcriptomic and host metabolomic changes in pustules. We found that in vivo H. ducreyi transcripts were distinct from those in the inocula, as were host transcripts in pustule and wounded control sites. Many of the upregulated H. ducreyi genes were found to be involved in ascorbic acid and anaerobic metabolism and inorganic ion/nutrient transport. The top 20 significantly expressed human pathways showed that all were involved in immune responses. We generated a bipartite network for interactions between host and bacterial gene transcription; multiple positively correlated networks contained H. ducreyi genes involved in anaerobic metabolism and host genes involved with the immune response. Metabolomic studies showed that pustule and wounded samples had different metabolite compositions; the top ion pathway involved ascorbate and aldarate metabolism, which correlated with the H. ducreyi transcriptional response and upregulation of host genes involved in ascorbic acid recycling. These data show that an interactome exists between H. ducreyi and the human host and suggest that H. ducreyi exploits the metabolic niche created by the host immune response.IMPORTANCE Dual RNA sequencing (RNA-seq) offers the promise of determining an interactome at a transcriptional level between a bacterium and the host but has yet to be done on any bacterial infection in human tissue. We performed dual RNA-seq and metabolomics analyses on wounded and infected sites following experimental infection of the arm with H. ducreyi Our results suggest that H. ducreyi survives in an abscess by utilizing l-ascorbate as an alternative carbon source, possibly taking advantage of host ascorbic acid recycling, and that H. ducreyi also adapts by upregulating genes involved in anaerobic metabolism and inorganic ion and nutrient transport. To our knowledge, this is the first description of an interaction network between a bacterium and the human host at a site of infection

White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain Magnetic Resonance Imaging Using Adaptive U-Net and Local Convolutional Neural Network

According to the World Alzheimer Report 2015, 46 million people are living with dementia in the world. The diagnosis of diseases helps doctors treating patients better. One of the signs of diseases is related to white matter, grey matter and cerebrospinal fluid. Therefore, the automatic segmentation of three tissues in brain imaging especially from magnetic resonance imaging (MRI) plays an important role in medical analysis. In this research, we proposed an effective approach to segment automatically these tissues in three-dimensional (3D) brain MRI. First, a deep learning model is used to segment the sure and unsure regions. In the unsure region, another deep learning model is used to classify each pixel. In the experiments, an adaptive U-net model is used to segment the sure and unsure regions, and the Local Convolutional Neural Network (CNN) model with multiple inputs is used to classify each pixel only in the unsure region. Our method was evaluated with a real image database, Internet Brain Segmentation Repository database, with 18 persons (IBSR 18) (https://www.nitrc.org/projects/ibsr) and compared with state of art methods being the results very promising

A High-Throughput Mechanical Activator for Cartilage Engineering Enables Rapid Screening of in vitro Response of Tissue Models to Physiological and Supra-Physiological Loads

Articular cartilage is crucially influenced by loading during development, health, and disease. However, our knowledge of the mechanical conditions that promote engineered cartilage maturation or tissue repair is still incomplete. Current in vitro models that allow precise control of the local mechanical environment have been dramatically limited by very low throughput, usually just a few specimens per experiment. To overcome this constraint, we have developed a new device for the high throughput compressive loading of tissue constructs: the High Throughput Mechanical Activator for Cartilage Engineering (HiT-MACE), which allows the mechanoactivation of 6 times more samples than current technologies. With HiT-MACE we were able to apply cyclic loads in the physiological (e.g., equivalent to walking and normal daily activity) and supra-physiological range (e.g., injurious impacts or extensive overloading) to up to 24 samples in one single run. In this report, we compared the early response of cartilage to physiological and supra-physiological mechanical loading to the response to IL-1β exposure, a common but rudimentary in vitro model of cartilage osteoarthritis. Physiological loading rapidly upregulated gene expression of anabolic markers along the TGF-β1 pathway. Notably, TGF-β1 or serum was not included in the medium. Supra-physiological loading caused a mild catabolic response while IL-1β exposure drove a rapid anabolic shift. This aligns well with recent findings suggesting that overloading is a more realistic and biomimetic model of cartilage degeneration. Taken together, these findings showed that the application of HiT-MACE allowed the use of larger number of samples to generate higher volume of data to effectively explore cartilage mechanobiology, which will enable the design of more effective repair and rehabilitation strategies for degenerative cartilage pathologies

Solvability of singular integral equations with rotations and degenerate kernels in the vanishing coefficient case

By means of Riemann boundary value problems and of certain convenient systems of linear algebraic equations, this paper deals with the solvability of a class of singular integral equations with rotations and degenerate kernel within the case of a coefficient vanishing on the unit circle. All the possibilities about the index of the coefficients in the corresponding equations are considered and described in detail, and explicit formulas for their solutions are obtained. An example of application of the method is shown at the end of the last section