Evidence for the Protective and Compensatory Functions of Resilience in Children with Intellectual and Developmental Disabilities

Children with intellectual and developmental disabilities (IDD) are more likely to engage in behavior problems than children without IDD. In the present study, we explored whether adverse life experiences and events were related to child behavioral and emotional problems. We also examined whether child resilience would act as a protective factor in this putative association between adverse experiences and child behavioral and emotional problems. Mothers of 310 children with IDD aged between 4 and 15 years old completed a cross-sectional online survey including measures of exposure to adverse life experiences, child resilience, and behavior and emotional problems. In moderated multiple regression models, we found that exposure to adverse life experiences had a positive association with child behavior problems and peer problems and that these associations were moderated by child resilience. Resilience served a protective function—lowering risk of problems for children exposed to adversity. Child resilience also served a compensatory function, being directly associated with fewer conduct and emotional problems and increased pro-social behavior. Child resilience may be an important factor in understanding the behavior and emotional problems of children with IDD. Further, especially longitudinal, research is needed. Interventions designed to increase children’s resilience may be beneficial for children with IDD

The ErbB2ΔEx16 splice variant is a major oncogenic driver in breast cancer that promotes a pro-metastatic tumor microenvironment.

Amplification and overexpression of erbB2/neu proto-oncogene is observed in 20-30% human breast cancer and is inversely correlated with the survival of the patient. Despite this, somatic activating mutations within erbB2 in human breast cancers are rare. However, we have previously reported that a splice isoform of erbB2, containing an in-frame deletion of exon 16 (herein referred to as ErbB2ΔEx16), results in oncogenic activation of erbB2 because of constitutive dimerization of the ErbB2 receptor. Here, we demonstrate that the ErbB2ΔEx16 is a major oncogenic driver in breast cancer that constitutively signals from the cell surface. We further show that inducible expression of the ErbB2ΔEx16 variant in mammary gland of transgenic mice results in the rapid development of metastatic multifocal mammary tumors. Genetic and biochemical characterization of the ErbB2ΔEx16-derived mammary tumors exhibit several unique features that distinguish this model from the conventional ErbB2 ones expressing the erbB2 proto-oncogene in mammary epithelium. Unlike the wild-type ErbB2-derived tumors that express luminal keratins, ErbB2ΔEx16-derived tumors exhibit high degree of intratumoral heterogeneity co-expressing both basal and luminal keratins. Consistent with these distinct pathological features, the ErbB2ΔEx16 tumors exhibit distinct signaling and gene expression profiles that correlate with activation of number of key transcription factors implicated in breast cancer metastasis and cancer stem cell renewal

Artificial Odour-Vision Syneasthesia via Olfactory Sensory Argumentation

The phenomenology of synaesthesia provides numerous cognitive benefits, which could be used towards augmenting interactive experiences with more refined multisensorial capabilities leading to more engaging and enriched experiences, better designs, and more transparent human-machine interfaces. In this study, we report a novel framework for the transformation of odours into the visual domain by applying the ideology from synaesthesia, to a low cost, portable, augmented reality/virtual reality system. The benefits of generating an artificial form of synesthesia are outlined and implemented using a custom made electronic nose to gather information about odour sources which is then sent to a mobile computing engine for characterisation, classification, and visualisation. The odours are visualised in the form of coloured 2D abstract shapes in real-time. Our results show that our affordable system has the potential to increase human odour discrimination comparable to that of natural syneasthesia highlighting the prospects for augmenting human-machine interfaces with an artificial form of this phenomenon

A heteroskedastic error covariance matrix estimator using a first-order conditional autoregressive Markov simulation for deriving asympotical efficient estimates from ecological sampled Anopheles arabiensis aquatic habitat covariates

<p>Abstract</p> <p>Background</p> <p>Autoregressive regression coefficients for <it>Anopheles arabiensis </it>aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes it difficult to identify where there are clusters of <it>An. arabiensis </it>aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of <it>An. arabiensis </it>aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled <it>Anopheles </it>aquatic habitat covariates. A test for diagnostic checking error residuals in an <it>An. arabiensis </it>aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature.</p> <p>Methods</p> <p>Field and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4^®was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices) in a SAS/GIS^®database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression). The eigenfunction values from the spatial configuration matrices were then used to define expectations for prior distributions using a Markov chain Monte Carlo (MCMC) algorithm. A set of posterior means were defined in WinBUGS 1.4.3^®. After the model had converged, samples from the conditional distributions were used to summarize the posterior distribution of the parameters. Thereafter, a spatial residual trend analyses was used to evaluate variance uncertainty propagation in the model using an autocovariance error matrix.</p> <p>Results</p> <p>By specifying coefficient estimates in a Bayesian framework, the covariate number of tillers was found to be a significant predictor, positively associated with <it>An. arabiensis </it>aquatic habitats. The spatial filter models accounted for approximately 19% redundant locational information in the ecological sampled <it>An. arabiensis </it>aquatic habitat data. In the residual error estimation model there was significant positive autocorrelation (i.e., clustering of habitats in geographic space) based on log-transformed larval/pupal data and the sampled covariate depth of habitat.</p> <p>Conclusion</p> <p>An autocorrelation error covariance matrix and a spatial filter analyses can prioritize mosquito control strategies by providing a computationally attractive and feasible description of variance uncertainty estimates for correctly identifying clusters of prolific <it>An. arabiensis </it>aquatic habitats based on larval/pupal productivity.</p

Reservists and veterans: Viewed from within and without

This chapter describes two important groups relative to military service – reservists and veterans. Definitions are provided regarding who is a member of each group. A summary of past and current research findings for each group is provided. The summary is organized by investigative topics or themes, which provide the current scope of the field for reservists and for veterans. Finally, approaches to the study of reservists and veterans are described, along with challenges – both substantively and methodologically – for future research studies. These serve as fertile areas for improvements and investigations in future research studies

FluNet: An AI-Enabled Influenza-like Warning System

Influenza is an acute viral respiratory disease that is currently causing severe financial and resource strains worldwide. With the COVID-19 pandemic exceeding 153 million cases worldwide, there is a need for a low-cost and contactless surveillance system to detect symptomatic individuals. The objective of this study was to develop FluNet, a novel, proof-of-concept, low-cost and contactless device for the detection of high-risk individuals. The system conducts face detection in the LWIR with a precision rating of 0.98, a recall of 0.91, an F-score of 0.96, and a mean intersection over union of 0.74 while sequentially taking the temperature trend of faces with a thermal accuracy of &#x00B1; 1 K. While in parallel determining if someone is coughing by using a custom lightweight deep convolutional neural network with a precision rating of 0.95, a recall of 0.92, an F score of 0.94 and an AUC of 0.98. We concluded this study by testing the accuracy of the direction of arrival estimation for the cough detection revealing an error of &#x00B1; 4.78&#x00B0;. If a subject is symptomatic, a photo is taken with a specified region of interest using a visible light camera. Two datasets have been constructed, one for face detection in the LWIR consisting of 250 images of 20 participants&#x2019; faces at various rotations and coverings, including face masks. The other for the real-time detection of coughs comprised of 40,482 cough / not cough sounds. These findings could be helpful for future low-cost edge computing applications for influenza-like monitoring

The relationship between the systemic inflammatory response, tumour proliferative activity, T-lymphocytic and macrophage infiltration, microvessel density and survival in patients with primary operable breast cancer

The significance of the inter-relationship between tumour and host local/systemic inflammatory responses in primary operable invasive breast cancer is limited. The inter-relationship between the systemic inflammatory response (pre-operative white cell count, C-reactive protein and albumin concentrations), standard clinicopathological factors, tumour T-lymphocytic (CD4+ and CD8+) and macrophage (CD68+) infiltration, proliferative (Ki-67) index and microvessel density (CD34+) was examined using immunohistochemistry and slide-counting techniques, and their prognostic values were examined in 168 patients with potentially curative resection of early-stage invasive breast cancer. Increased tumour grade and proliferative activity were associated with greater tumour T-lymphocyte (P&lt;0.05) and macrophage (P&lt;0.05) infiltration and microvessel density (P&lt;0.01). The median follow-up of survivors was 72 months. During this period, 31 patients died; 18 died of their cancer. On univariate analysis, increased lymph-node involvement (P&lt;0.01), negative hormonal receptor (P&lt;0.10), lower albumin concentrations (P&lt;0.01), increased tumour proliferation (P&lt;0.05), increased tumour microvessel density (P&lt;0.05), the extent of locoregional control (P&lt;0.0001) and limited systemic treatment (Pless than or equal to0.01) were associated with cancer-specific survival. On multivariate analysis of these significant covariates, albumin (HR 4.77, 95% CI 1.35–16.85, P=0.015), locoregional treatment (HR 3.64, 95% CI 1.04–12.72, P=0.043) and systemic treatment (HR 2.29, 95% CI 1.23–4.27, P=0.009) were significant independent predictors of cancer-specific survival. Among tumour-based inflammatory factors, only tumour microvessel density (P&lt;0.05) was independently associated with poorer cancer-specific survival. The host inflammatory responses are closely associated with poor tumour differentiation, proliferation and malignant disease progression in breast cancer