149 research outputs found

    Association of maternal risk factors with infant maltreatment: an administrative data cohort study

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    Objective: We aimed to evaluate the risk of infant maltreatment associated with commonly used criteria for home visiting programmes: young maternal age, maternal adversity (homelessness, substance abuse, intimate partner violence), newcomer status and mental health concerns in Ontario, Canada. Design: This retrospective cohort study included infants born in hospital in Ontario from 1 April 2005 to 31 March 2017 captured in linked health administrative and demographic databases. Infants were followed from newborn hospitalisation until 1 year of age for child maltreatment captured in healthcare or death records. The association between type and number of maternal risk factors, and maltreatment, was analysed using multivariable logistic regression modelling, controlling for infant characteristics and material deprivation. Further modelling explored the association of each year of maternal age with maltreatment. Results: Of 989 586 infants, 434 (0.04%) had recorded maltreatment. Maternal age <22 years conferred higher risk of infant maltreatment (adjusted OR (aOR) 5.5, 95% CI 4.5 to 6.8) compared with age ≥22 years. Maternal mental health diagnoses (aOR 2.0, 95% CI 1.6 to 2.5) were also associated with maltreatment, while refugee status appeared protective (aOR 0.6, 95% CI 0.4 to 1.0). The odds of maltreatment increased with higher numbers of maternal risk factors. Maternal age was associated with maltreatment until age 28 years. Conclusion: Infants born to young mothers are at greater risk of infant maltreatment, and this association remained until age 28 years. These findings are important for ensuring public health interventions are supporting populations experiencing structural vulnerabilities with the aim of preventing maltreatment

    Increased Light Extraction of Thin-Film Flip-Chip UVB LEDs by Surface Texturing

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    Ultraviolet light-emitting diodes (LEDs) suffer from a low wall-plug efficiency, which is to a large extent limited by the poor light extraction efficiency (LEE). A thin-film flip-chip (TFFC) design with a roughened N-polar AlGaN surface can substantially improve this. We here demonstrate an enabling technology to realize TFFC LEDs emitting in the UVB range (280-320 nm), which includes standard LED processing in combination with electrochemical etching to remove the substrate. The integration of the electrochemical etching is achieved by epitaxial sacrificial and etch block layers in combination with encapsulation of the LED. The LEE was enhanced by around 25% when the N-polar AlGaN side of the TFFC LEDs was chemically roughened, reaching an external quantum efficiency of 2.25%. By further optimizing the surface structure, our ray-tracing simulations predict a higher LEE from the TFFC LEDs than flip-chip LEDs and a resulting higher wall-plug efficiency

    Thermodynamic stability of folded proteins against mutations

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    By balancing the average energy gap with its typical change due to mutations for protein-like heteropolymers with M residues, we show that native states are unstable to mutations on a scale M* ~ (lambda/sigma_mu)^(1/zeta_s), where lambda is the dispersion in the interaction free energies and sigma_mu their typical change. Theoretical bounds and numerical estimates (based on complete enumeration on four lattices) of the instability exponent zeta_s are given. Our analysis suggests that a limiting size of single-domain proteins should exist, and leads to the prediction that small proteins are insensitive to random mutations.Comment: 5 pages, 3 figures, to be published in Physical Review Letter

    Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data

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    Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials

    Some New Results on Complex-Temperature Singularities in Potts Models on the Square Lattice

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    We report some new results on the complex-temperature (CT) singularities of qq-state Potts models on the square lattice. We concentrate on the problematic region Re(a)<0Re(a) < 0 (where a=eKa=e^K) in which CT zeros of the partition function are sensitive to finite lattice artifacts. From analyses of low-temperature series expansions for 3≤q≤83 \le q \le 8, we establish the existence, in this region, of complex-conjugate CT singularities at which the magnetization and susceptibility diverge. From calculations of zeros of the partition function, we obtain evidence consistent with the inference that these singularities occur at endpoints ae, ae∗a_e, \ a_e^* of arcs protruding into the (complex-temperature extension of the) FM phase. Exponents for these singularities are determined; e.g., for q=3q=3, we find βe=−0.125(1)\beta_e=-0.125(1), consistent with βe=−1/8\beta_e=-1/8. By duality, these results also imply associated arcs extending to the (CT extension of the) symmetric PM phase. Analytic expressions are suggested for the positions of some of these singularities; e.g., for q=5q=5, our finding is consistent with the exact value ae,ae∗=2(−1∓i)a_e,a_e^*=2(-1 \mp i). Further discussions of complex-temperature phase diagrams are given.Comment: 26 pages, latex, with eight epsf figure

    Disconnection Mechanism and Regional Cortical Atrophy Contribute to Impaired Processing of Facial Expressions and Theory of Mind in Multiple Sclerosis: A Structural MRI Study

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    Successful socialization requires the ability of understanding of others' mental states. This ability called as mentalization (Theory of Mind) may become deficient and contribute to everyday life difficulties in multiple sclerosis. We aimed to explore the impact of brain pathology on mentalization performance in multiple sclerosis. Mentalization performance of 49 patients with multiple sclerosis was compared to 24 age- and gender matched healthy controls. T1- and T2-weighted three-dimensional brain MRI images were acquired at 3Tesla from patients with multiple sclerosis and 18 gender- and age matched healthy controls. We assessed overall brain cortical thickness in patients with multiple sclerosis and the scanned healthy controls, and measured the total and regional T1 and T2 white matter lesion volumes in patients with multiple sclerosis. Performances in tests of recognition of mental states and emotions from facial expressions and eye gazes correlated with both total T1-lesion load and regional T1-lesion load of association fiber tracts interconnecting cortical regions related to visual and emotion processing (genu and splenium of corpus callosum, right inferior longitudinal fasciculus, right inferior fronto-occipital fasciculus, uncinate fasciculus). Both of these tests showed correlations with specific cortical areas involved in emotion recognition from facial expressions (right and left fusiform face area, frontal eye filed), processing of emotions (right entorhinal cortex) and socially relevant information (left temporal pole). Thus, both disconnection mechanism due to white matter lesions and cortical thinning of specific brain areas may result in cognitive deficit in multiple sclerosis affecting emotion and mental state processing from facial expressions and contributing to everyday and social life difficulties of these patients

    A Switch from a Gradient to a Threshold Mode in the Regulation of a Transcriptional Cascade Promotes Robust Execution of Meiosis in Budding Yeast

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    Tight regulation of developmental pathways is of critical importance to all organisms, and is achieved by a transcriptional cascade ensuring the coordinated expression of sets of genes. We aimed to explore whether a strong signal is required to enter and complete a developmental pathway, by using meiosis in budding yeast as a model. We demonstrate that meiosis in budding yeast is insensitive to drastic changes in the levels of its consecutive positive regulators (Ime1, Ime2, and Ndt80). Entry into DNA replication is not correlated with the time of transcription of the early genes that regulate this event. Entry into nuclear division is directly regulated by the time of transcription of the middle genes, as premature transcription of their activator NDT80, leads to a premature entry into the first meiotic division, and loss of coordination between DNA replication and nuclear division. We demonstrate that Cdk1/Cln3 functions as a negative regulator of Ime2, and that ectopic expression of Cln3 delays entry into nuclear division as well as NDT80 transcription. Because Ime2 functions as a positive regulator for premeiotic DNA replication and NDT80 transcription, as well as a negative regulator of Cdk/Cln, we suggest that a double negative feedback loop between Ime2 and Cdk1/Cln3 promotes a bistable switch from the cell cycle to meiosis. Moreover, our results suggest a regulatory mode switch that ensures robust meiosis as the transcription of the early meiosis-specific genes responds in a graded mode to Ime1 levels, whereas that of the middle and late genes as well as initiation of DNA replication, are regulated in a threshold mode
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