National evaluation of the neighbourhood nurseries: integrated report

Report description: The NNI was launched in 2001 to provide high quality childcare in the most disadvantaged neighbourhoods of England, to help parents into employment, reduce child poverty and boost children’s development. By 2005 45,000 new childcare places had been created in approximately 1,400 neighbourhood nurseries. This report brings together the findings of the four individual strands of the National Evaluation of Neighbourhood Nurseries Initiative as shown above and makes a number of recommendations. The report shows the rationale for the government’s strategy in targeting disadvantaged neighbourhoods and in focusing on high quality childcare to provide the link between raising parental employment and income and improving children’s life chances

Engineered Tumor-Targeted T Cells Mediate Enhanced Anti-Tumor Efficacy Both Directly and through Activation of the Endogenous Immune System.

Chimeric antigen receptor (CAR) T cell therapy has proven clinically beneficial against B cell acute lymphoblastic leukemia and non-Hodgkin's lymphoma. However, suboptimal clinical outcomes have been associated with decreased expansion and persistence of adoptively transferred CAR T cells, antigen-negative relapses, and impairment by an immunosuppressive tumor microenvironment. Improvements in CAR T cell design are required to enhance clinical efficacy, as well as broaden the applicability of this technology. Here, we demonstrate that interleukin-18 (IL-18)-secreting CAR T cells exhibit enhanced in vivo expansion and persistence and significantly increase long-term survival in syngeneic mouse models of both hematological and solid malignancies. In addition, we demonstrate that IL-18-secreting CAR T cells are capable of modulating the tumor microenvironment, as well as enhancing an effective endogenous anti-tumor immune response. IL-18-secreting CAR T cells represent a promising strategy to enhance the clinical outcomes of adoptive T cell therapy

Dynamic Assessment of Baroreflex Control of Heart Rate During Induction of Propofol Anesthesia Using a Point Process Method

In this article, we present a point process method to assess dynamic baroreflex sensitivity (BRS) by estimating the baroreflex gain as focal component of a simplified closed-loop model of the cardiovascular system. Specifically, an inverse Gaussian probability distribution is used to model the heartbeat interval, whereas the instantaneous mean is identified by linear and bilinear bivariate regressions on both the previous R−R intervals (RR) and blood pressure (BP) beat-to-beat measures. The instantaneous baroreflex gain is estimated as the feedback branch of the loop with a point-process filter, while the RRBP feedforward transfer function representing heart contractility and vasculature effects is simultaneously estimated by a recursive least-squares filter. These two closed-loop gains provide a direct assessment of baroreflex control of heart rate (HR). In addition, the dynamic coherence, cross bispectrum, and their power ratio can also be estimated. All statistical indices provide a valuable quantitative assessment of the interaction between heartbeat dynamics and hemodynamics. To illustrate the application, we have applied the proposed point process model to experimental recordings from 11 healthy subjects in order to monitor cardiovascular regulation under propofol anesthesia. We present quantitative results during transient periods, as well as statistical analyses on steady-state epochs before and after propofol administration. Our findings validate the ability of the algorithm to provide a reliable and fast-tracking assessment of BRS, and show a clear overall reduction in baroreflex gain from the baseline period to the start of propofol anesthesia, confirming that instantaneous evaluation of arterial baroreflex control of HR may yield important implications in clinical practice, particularly during anesthesia and in postoperative care.National Institutes of Health (U.S.) (Grant R01-HL084502)National Institutes of Health (U.S.) (Grant K25-NS05758)National Institutes of Health (U.S.) (Grant DP2- OD006454)National Institutes of Health (U.S.) (Grant T32NS048005)National Institutes of Health (U.S.) (Grant T32NS048005)National Institutes of Health (U.S.) (Grant R01-DA015644)Massachusetts General Hospital (Clinical Research Center, UL1 Grant RR025758

Which facets of mindfulness protect Individuals from the negative experiences of obsessive intrusive thoughts?

Obsessive intrusive thoughts (OITs) are experienced by the majority of the general population, and in their more extreme forms are characteristic of obsessive–compulsive disorder (OCD). These cognitions are said to exist on a continuum that includes differences in their frequency and associated distress. The key factors that contribute to an increased frequency and distress are how the individual appraises and responds to the OIT. Facets of mindfulness, such as nonjudgment and nonreactivity, offer an alternative approach to OITs than the negative appraisals and commonly utilised control strategies that often contribute to distress. Clarifying the role of facets of mindfulness in relation to these cognitions offers a means to elucidate individual characteristics that may offer protection from distress associated with OITs. A sample of nonclinical individuals (n = 583) completed an online survey that assessed their experiences of OITs, including frequency, emotional reaction and appraisals, and trait mindfulness. The findings from a series of multiple regression analyses confirmed that specific facets of mindfulness relating to acting with awareness and acceptance (nonjudgment and nonreactivity) consistently predicted less frequent and distressing experiences of OITs. In contrast, the observe facet emerged as a consistent predictor of negative experiences of OITs. These findings suggest that acting with awareness and acceptance may confer protective characteristics in relation to OITs, but that the observe facet may reflect a hypervigilance to OITs. Mindfulness-based prevention and intervention for OCD should be tailored to take account of the potential differential effects of increasing specific facets of mindfulness

Automatic Physiological Waveform Processing for fMRI Noise Correction and Analysis

Functional MRI resting state and connectivity studies of brain focus on neural fluctuations at low frequencies which share power with physiological fluctuations originating from lung and heart. Due to the lack of automated software to process physiological signals collected at high magnetic fields, a gap exists in the processing pathway between the acquisition of physiological data and its use in fMRI software for both physiological noise correction and functional analyses of brain activation and connectivity. To fill this gap, we developed an open source, physiological signal processing program, called PhysioNoise, in the python language. We tested its automated processing algorithms and dynamic signal visualization on resting monkey cardiac and respiratory waveforms. PhysioNoise consistently identifies physiological fluctuations for fMRI noise correction and also generates covariates for subsequent analyses of brain activation and connectivity

Understanding Climate Change: A primer for local government officials in the Philippines

Understanding the impacts of climate change on agriculture, associated landscapes and natural resources in general is crucial if local development efforts are to be tailored towards addressing the impacts of climate change. Simplification of scientific concepts can help local planners to introduce and then mainstream strategies that have factored in the impacts from climate change

Quetiapine in the treatment of schizophrenia and related disorders

Quetiapine was developed in 1985 by scientists at AstraZeneca (formerly Zeneca) Pharmaceuticals. It received official US Food and Drug Administration approval in September 1997 and approval in Germany in 2000. Since then, quetiapine has been used in the treatment of severe mental illness in approximately 70 countries including Canada, most Western European countries, and Japan. Quetiapine is a dibenzothiazepine derivative with a relatively broad receptor binding profile. It has major affinity to cerebral serotonergic (5HT2A), histaminergic (H1), and dopaminergic D1 and D2 receptors, moderate affinity to α1- und α2-adrenergic receptors, and minor affinity to muscarinergic M1 receptors; it demonstrates a substantial selectivity for the limbic system. This receptor occupancy profile with relatively higher affinity for the 5HT2A receptor compared with the D2 receptor is in part responsible for the antipsychotic characteristics and low incidence of extrapyramidal side-effects of quetiapine. The efficacy of quetiapine in reducing positive and negative symptoms of schizophrenia has been proven in several clinical trials with placebo-controlled comparators. Quetiapine has also demonstrated robust efficacy for treatment of cognitive, anxious-depressive, and aggressive symptoms in schizophrenia. Long-term trials show sustained tolerability for a broad spectrum of symptoms. Quetiapine has also proven efficacy and tolerability in the treatment of moderate to severe manic episodes, and in the treatment of juveniles with oppositional-defiant or conduct disorders, and in the geriatric dementia population. Recent data indicate that quetiapine may also be effective in the treatment of bipolar depressive symptoms without increasing the risk of triggering manic episodes, and in borderline personality disorder. In comparison with other antipsychotics, quetiapine has a favorable side-effect profile. In clinical trials only small insignificant prolongations of the QT interval were observed. Weight-gain liabilities and new-onset metabolic side-effects occupy a middle-ground among newer antipsychotics. As a result of its good efficacy and tolerability profile quetiapine has become well established in the treatment of schizophrenia and manic episodes

An Empirical Comparison of Information-Theoretic Criteria in Estimating the Number of Independent Components of fMRI Data

BACKGROUND: Independent Component Analysis (ICA) has been widely applied to the analysis of fMRI data. Accurate estimation of the number of independent components of fMRI data is critical to reduce over/under fitting. Although various methods based on Information Theoretic Criteria (ITC) have been used to estimate the intrinsic dimension of fMRI data, the relative performance of different ITC in the context of the ICA model hasn't been fully investigated, especially considering the properties of fMRI data. The present study explores and evaluates the performance of various ITC for the fMRI data with varied white noise levels, colored noise levels, temporal data sizes and spatial smoothness degrees. METHODOLOGY: Both simulated data and real fMRI data with varied Gaussian white noise levels, first-order auto-regressive (AR(1)) noise levels, temporal data sizes and spatial smoothness degrees were carried out to deeply explore and evaluate the performance of different traditional ITC. PRINCIPAL FINDINGS: Results indicate that the performance of ITCs depends on the noise level, temporal data size and spatial smoothness of fMRI data. 1) High white noise levels may lead to underestimation of all criteria and MDL/BIC has the severest underestimation at the higher Gaussian white noise level. 2) Colored noise may result in overestimation that can be intensified by the increase of AR(1) coefficient rather than the SD of AR(1) noise and MDL/BIC shows the least overestimation. 3) Larger temporal data size will be better for estimation for the model of white noise but tends to cause severer overestimation for the model of AR(1) noise. 4) Spatial smoothing will result in overestimation in both noise models. CONCLUSIONS: 1) None of ITC is perfect for all fMRI data due to its complicated noise structure. 2) If there is only white noise in data, AIC is preferred when the noise level is high and otherwise, Laplace approximation is a better choice. 3) When colored noise exists in data, MDL/BIC outperforms the other criteria