49 research outputs found

    TFTs as photodetectors for optical interconnects

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    In this work we are looking at the prospect of using poly-silicon based Thin Film Transistors (TFTs) as photodetectors for optical interconnects that can detect light effectively at 1100nm wavelength from silicon based Light Emitting Diodes (LEDs). These TFTs were fabricated from laser crystallized silicon and were characterized under darkness and illumination. The photosensitivities of these devices were limited due to the presence of aluminium as their gate electrode but have shown us the possibility of a new approach to photodetection

    Association of dairy consumption with metabolic syndrome, hypertension and diabetes in 147 812 individuals from 21 countries

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    Objective: Our aims were to assess the association of dairy intake with prevalence of metabolic syndrome (MetS) (cross-sectionally) and with incident hypertension and incident diabetes (prospectively) in a large multinational cohort study.Methods: The Prospective Urban Rural Epidemiology (PURE) study is a prospective epidemiological study of individuals aged 35 and 70 years from 21 countries on five continents, with a median follow-up of 9.1 years. In the cross-sectional analyses, we assessed the association of dairy intake with prevalent MetS and its components among individuals with information on the five MetS components (n=112 922). For the prospective analyses, we examined the association of dairy with incident hypertension (in 57 547 individuals free of hypertension) and diabetes (in 131 481 individuals free of diabetes).Results: In cross-sectional analysis, higher intake of total dairy (at least two servings/day compared with zero intake; OR 0.76, 95% CI 0.71 to 0.80, p-trend\u3c0.0001) was associated with a lower prevalence of MetS after multivariable adjustment. Higher intakes of whole fat dairy consumed alone (OR 0.72, 95% CI 0.66 to 0.78, p-trend\u3c0.0001), or consumed jointly with low fat dairy (OR 0.89, 95% CI 0.80 to 0.98, p-trend=0.0005), were associated with a lower MetS prevalence. Low fat dairy consumed alone was not associated with MetS (OR 1.03, 95% CI 0.77 to 1.38, p-trend=0.13). In prospective analysis, 13 640 people with incident hypertension and 5351 people with incident diabetes were recorded. Higher intake of total dairy (at least two servings/day vs zero serving/day) was associated with a lower incidence of hypertension (HR 0.89, 95% CI 0.82 to 0.97, p-trend=0.02) and diabetes (HR 0.88, 95% CI 0.76 to 1.02, p-trend=0.01). Directionally similar associations were found for whole fat dairy versus each outcome.Conclusions: Higher intake of whole fat (but not low fat) dairy was associated with a lower prevalence of MetS and most of its component factors, and with a lower incidence of hypertension and diabetes. Our findings should be evaluated in large randomized trials of the effects of whole fat dairy on the risks of MetS, hypertension, and diabetes

    Dynamical CP Violation in the Early Universe

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    Following earlier ideas of Dolgov, we show that the asymmetrical dynamical evolution of fields in the early Universe provides a new source for CP violation. This can lead to baryogenesis without any additional CP-violating interactions. The magnitude of this CP violation is time-dependent. In particular, it vanishes (or is very small) in the late Universe after the fields have relaxed (or are in their final approach) to their vacuum values. We provide an explicit example in which our mechanism is realized.Comment: 9 pages, latex, 1 figure (enclosed). The idea of the previous version was correct, but there were errors in its implementation. This has now been corrected -- some text modified, references added. Also, one author has been adde

    Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain

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    Grading of gliomas is a piece of critical information related to prognosis and survival. Classifying glioma grade by semantic radiological features is subjective, requires multiple MRI sequences, is quite complex and clinically demanding, and can very often result in erroneous radiological diagnosis. We used a radiomics approach with machine learning classifiers to determine the grade of gliomas. Eighty-three patients with histopathologically proven gliomas underwent MRI of the brain. Whenever available, immunohistochemistry was additionally used to augment the histopathological diagnosis. Segmentation was performed manually on the T2W MR sequence using the TexRad texture analysis softwareTM, Version 3.10. Forty-two radiomics features, which included first-order features and shape features, were derived and compared between high-grade and low-grade gliomas. Features were selected by recursive feature elimination using a random forest algorithm method. The classification performance of the models was measured using accuracy, precision, recall, f1 score, and area under the curve (AUC) of the receiver operating characteristic curve. A 10-fold cross-validation was adopted to separate the training and the test data. The selected features were used to build five classifier models: support vector machine, random forest, gradient boost, naive Bayes, and AdaBoost classifiers. The random forest model performed the best, achieving an AUC of 0.81, an accuracy of 0.83, f1 score of 0.88, a recall of 0.93, and a precision of 0.85 for the test cohort. The results suggest that machine-learning-based radiomics features extracted from multiparametric MRI images can provide a non-invasive method for predicting glioma grades preoperatively. In the present study, we extracted the radiomics features from a single cross-sectional image of the T2W MRI sequence and utilized these features to build a fairly robust model to classify low-grade gliomas from high-grade gliomas (grade 4 gliomas)

    Household, community, sub-national and country-level predictors of primary cooking fuel switching in nine countries from the PURE study

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    Introduction. Switchingfrom polluting (e.g. wood, crop waste, coal)to clean (e.g. gas, electricity) cooking fuels can reduce household air pollution exposures and climate-forcing emissions.While studies have evaluated specific interventions and assessed fuel-switching in repeated cross-sectional surveys, the role of different multilevel factors in household fuel switching, outside of interventions and across diverse community settings, is not well understood. Methods.We examined longitudinal survey data from 24 172 households in 177 rural communities across nine countries within the Prospective Urban and Rural Epidemiology study.We assessed household-level primary cooking fuel switching during a median of 10 years offollow up (∌2005–2015).We used hierarchical logistic regression models to examine the relative importance of household, community, sub-national and national-level factors contributing to primary fuel switching. Results. One-half of study households(12 369)reported changing their primary cookingfuels between baseline andfollow up surveys. Of these, 61% (7582) switchedfrom polluting (wood, dung, agricultural waste, charcoal, coal, kerosene)to clean (gas, electricity)fuels, 26% (3109)switched between different polluting fuels, 10% (1164)switched from clean to polluting fuels and 3% (522)switched between different clean fuels

    Household, community, sub-national and country-level predictors of primary cooking fuel switching in nine countries from the PURE study

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    Materials and integration schemes for above-IC integrated optics

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    A study is presented on silicon oxynitride material for waveguides and germanium-silicon alloys for p-i-n diodes. The materials are manufactured at low, CMOS-backend compatible temperatures, targeting the integration of optical functions on top of CMOS chips. Low-temperature germanium-silicon deposition, crystallization and doping are studied for integrated photodetection up to ~1500 nm wavelength. An inductivelycoupled-plasma chemical vapor deposition process is presented for silicon oxynitride manufacturing at 150 °C wafer temperature, yielding low-loss material in a wide optical spectral range. Integration schemes for an optical plane on top of CMOS are discussed
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