The Gender Context of HIV Risk and Pregnancy Goals in Western Kenya

Background: Intentional childbearing may place heterosexual couples at risk of HIV infection in resource-limited settings with high HIV prevalence areas where society places great value on having children.Objective: To explore cognitive, cultural, and spatial mapping of sexual and reproductive health domains and services in western Kenya among men and women.Design: Community-based formative qualitative study design.Setting: Five administrative/geographical divisions of Nyando District, western Kenya.Subjects: Adult male 18 years and older and female who were of reproductive-potential ages (15 to 49 years for females)(n=90).Results:Men and women have disparate goals for number of children and engage in gendered patterns of protective method use (contraceptives used by women often in secret, condoms by men but rarely).Conclusion: HIV infection was still seen as stigmatising. These study results are relevant to design of effective integrated delivery for reproductive and HIV services in high-burden sub-Saharan African countries

Using network theory to identify the causes of disease outbreaks of unknown origin.

The identification of undiagnosed disease outbreaks is critical for mobilizing efforts to prevent widespread transmission of novel virulent pathogens. Recent developments in online surveillance systems allow for the rapid communication of the earliest reports of emerging infectious diseases and tracking of their spread. The efficacy of these programs, however, is inhibited by the anecdotal nature of informal reporting and uncertainty of pathogen identity in the early stages of emergence. We developed theory to connect disease outbreaks of known aetiology in a network using an array of properties including symptoms, seasonality and case-fatality ratio. We tested the method with 125 reports of outbreaks of 10 known infectious diseases causing encephalitis in South Asia, and showed that different diseases frequently form distinct clusters within the networks. The approach correctly identified unknown disease outbreaks with an average sensitivity of 76 per cent and specificity of 88 per cent. Outbreaks of some diseases, such as Nipah virus encephalitis, were well identified (sensitivity = 100%, positive predictive values = 80%), whereas others (e.g. Chandipura encephalitis) were more difficult to distinguish. These results suggest that unknown outbreaks in resource-poor settings could be evaluated in real time, potentially leading to more rapid responses and reducing the risk of an outbreak becoming a pandemic

Rice early flowering1, a CKI, phosphorylates DELLA protein SLR1 to negatively regulate gibberellin signalling

The plant hormone gibberellin (GA) is crucial for multiple aspects of plant growth and development. To study the relevant regulatory mechanisms, we isolated a rice mutant earlier flowering1, el1, which is deficient in a casein kinase I that has critical roles in both plants and animals. el1 had an enhanced GA response, consistent with the suppression of EL1 expression by exogenous GA3. Biochemical characterization showed that EL1 specifically phosphorylates the rice DELLA protein SLR1, proving a direct evidence for SLR1 phosphorylation. Overexpression of SLR1 in wild-type plants caused a severe dwarf phenotype, which was significantly suppressed by EL1 deficiency, indicating the negative effect of SLR1 on GA signalling requires the EL1 function. Further studies showed that the phosphorylation of SLR1 is important for maintaining its activity and stability, and mutation of the candidate phosphorylation site of SLR1 results in the altered GA signalling. This study shows EL1 a novel and key regulator of the GA response and provided important clues on casein kinase I activities in GA signalling and plant development

DynaMorph: self-supervised learning of morphodynamic states of live cells

A cell's shape and motion represent fundamental aspects of cell identity and can be highly predictive of function and pathology. However, automated analysis of the morphodynamic states remains challenging for most cell types, especially primary human cells where genetic labeling may not be feasible. To enable automated and quantitative analysis of morphodynamic states, we developed DynaMorph-a computational framework that combines quantitative live cell imaging with self-supervised learning. To demonstrate the robustness and utility of this approach, we used DynaMorph to annotate morphodynamic states observed with label-free measurements of optical density and anisotropy of live microglia isolated from human brain tissue. These cells show complex behavior and have varied responses to disease-relevant perturbations. DynaMorph generates quantitative morphodynamic representations that can be used to compare the effects of the perturbations. Using DynaMorph, we identify distinct morphodynamic states of microglia polarization and detect rare transition events between states. The concepts and the methods presented here can facilitate automated discovery of functional states of diverse cellular systems

In situ post etch treatment as solution to improve defect density for porous Low-k integration using metallic hard mask

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