Tropical belt width proportionately more sensitive to aerosols than greenhouse gases

The tropical belt has widened during the last several decades, and both internal variability and anthropogenic forcings have contributed. Although greenhouse gases and stratospheric ozone depletion have been implicated as primary anthropogenic drivers of tropical expansion, the possible role of other drivers remains uncertain. Here, we analyze the tropical belt width response to idealized perturbations in multiple models. Our results show that absorbing black carbon (BC) aerosol drives tropical expansion, and scattering sulfate aerosol drives contraction. BC, especially from Asia, is more efficient per unit radiative forcing than greenhouse gases in driving tropical expansion, particularly in the Northern Hemisphere. Tropical belt expansion (contraction) is associated with an increase (decrease) in extratropical static stability induced by absorbing (scattering) aerosol. Although a formal attribution is difficult, scaling the normalized expansion rates to the historical time period suggests that BC is the largest driver of the Northern Hemisphere tropical widening but with relatively large uncertainty

Ensemble of Hankel Matrices for Face Emotion Recognition

In this paper, a face emotion is considered as the result of the composition of multiple concurrent signals, each corresponding to the movements of a specific facial muscle. These concurrent signals are represented by means of a set of multi-scale appearance features that might be correlated with one or more concurrent signals. The extraction of these appearance features from a sequence of face images yields to a set of time series. This paper proposes to use the dynamics regulating each appearance feature time series to recognize among different face emotions. To this purpose, an ensemble of Hankel matrices corresponding to the extracted time series is used for emotion classification within a framework that combines nearest neighbor and a majority vote schema. Experimental results on a public available dataset shows that the adopted representation is promising and yields state-of-the-art accuracy in emotion classification.Comment: Paper to appear in Proc. of ICIAP 2015. arXiv admin note: text overlap with arXiv:1506.0500

Validation of a consumer-grade activity monitor for continuous daily activity monitoring in individuals with multiple sclerosis.

Background:Technological advancements of remote-monitoring used in clinical-care and research require validation of model updates. Objectives:To compare the output of a newer consumer-grade accelerometer to a previous model in people with multiple sclerosis (MS) and to the ActiGraph, a waist-worn device widely used in MS research. Methods:Thirty-one individuals with MS participated in a 7-day validation by the Fitbit Flex (Flex), Fitbit Flex-2 (Flex2) and ActiGraph GT3X. Primary outcome was step count. Valid epochs of 5-min block increments, where there was overlap of ≥1 step/min for both devices were compared and summed to give a daily total for analysis. Results:Bland-Altman plots showed no systematic difference between the Flex and Flex2; mean step-count difference of 25 more steps-per-day more recorded by Flex2 (95% confidence intervals (CI) = 2, 48; p = 0.04),interclass correlation coefficient (ICC) = 1.00. Compared to the ActiGraph, Flex2 (and Flex) tended to record more steps (808 steps-per-day more than the ActiGraph (95% CI= -2380, 765; p < 0.01), although the ICC was high (0.98) indicating that the devices were likely measuring the same kind of activity. Conclusions:Steps from Flex and Flex2 can be used interchangeably. Differences in total step count between ActiGraph and Flex devices can make cross-device comparisons of numerical step-counts challenging particularly for faster walkers

CenTime: Event-conditional modelling of censoring in survival analysis

Survival analysis is a valuable tool for estimating the time until specific events, such as death or cancer recurrence, based on baseline observations. This is particularly useful in healthcare to prognostically predict clinically important events based on patient data. However, existing approaches often have limitations; some focus only on ranking patients by survivability, neglecting to estimate the actual event time, while others treat the problem as a classification task, ignoring the inherent time-ordered structure of the events. Additionally, the effective utilisation of censored samples−data points where the event time is unknown− is essential for enhancing the model's predictive accuracy. In this paper, we introduce CenTime, a novel approach to survival analysis that directly estimates the time to event. Our method features an innovative event-conditional censoring mechanism that performs robustly even when uncensored data is scarce. We demonstrate that our approach forms a consistent estimator for the event model parameters, even in the absence of uncensored data. Furthermore, CenTime is easily integrated with deep learning models with no restrictions on batch size or the number of uncensored samples. We compare our approach to standard survival analysis methods, including the Cox proportional-hazard model and DeepHit. Our results indicate that CenTime offers state-of-the-art performance in predicting time-to-death while maintaining comparable ranking performance. Our implementation is publicly available at https://github.com/ahmedhshahin/CenTime

Exome sequencing followed by large-scale genotyping suggests a limited role for moderately rare risk factors of strong effect in schizophrenia.

Schizophrenia is a severe psychiatric disorder with strong heritability and marked heterogeneity in symptoms, course, and treatment response. There is strong interest in identifying genetic risk factors that can help to elucidate the pathophysiology and that might result in the development of improved treatments. Linkage and genome-wide association studies (GWASs) suggest that the genetic basis of schizophrenia is heterogeneous. However, it remains unclear whether the underlying genetic variants are mostly moderately rare and can be identified by the genotyping of variants observed in sequenced cases in large follow-up cohorts or whether they will typically be much rarer and therefore more effectively identified by gene-based methods that seek to combine candidate variants. Here, we consider 166 persons who have schizophrenia or schizoaffective disorder and who have had either their genomes or their exomes sequenced to high coverage. From these data, we selected 5,155 variants that were further evaluated in an independent cohort of 2,617 cases and 1,800 controls. No single variant showed a study-wide significant association in the initial or follow-up cohorts. However, we identified a number of case-specific variants, some of which might be real risk factors for schizophrenia, and these can be readily interrogated in other data sets. Our results indicate that schizophrenia risk is unlikely to be predominantly influenced by variants just outside the range detectable by GWASs. Rather, multiple rarer genetic variants must contribute substantially to the predisposition to schizophrenia, suggesting that both very large sample sizes and gene-based association tests will be required for securely identifying genetic risk factors. © 2012 The American Society of Human Genetics

A stable, power scaling, graphene-mode-locked all-fiber oscillator

This is the final version. Available from AIP Publishing via the DOI in this record.We report power tunability in a fiber laser mode-locked with a solution-processed filtered graphene film on a fiber connector. 370 fs pulses are generated with output power continuously tunable from 4 up to 52 mW. This is a simple, low-cost, compact, portable, all-fiber ultrafast source for applications requiring environmentally stable, portable sources, such as imaging.European Research Council (ERC)Engineering and Physical Sciences Research Council (EPSRC)Emmanuel College, CambridgeIsaac Newton Trust, Trinity College Cambridg

A stable, power scaling, graphene-mode-locked all-fiber oscillator

We report power tunability in a fiber laser mode-locked with a solution-processed filtered graphene film on a fiber connector. ∼370 fs pulses are generated with output power continuously tunable from ∼4 up to ∼52 mW. This is a simple, low-cost, compact, portable, all-fiber ultrafast source for applications requiring environmentally stable, portable sources, such as imaging.</jats:p

Small RNA Profile in Moso Bamboo Root and Leaf Obtained by High Definition Adapters

Moso bamboo (Phyllostachy heterocycla cv. pubescens L.) is an economically important fast-growing tree. In order to gain better understanding of gene expression regulation in this important species we used next generation sequencing to profile small RNAs in leaf and roots of young seedlings. Since standard kits to produce cDNA of small RNAs are biased for certain small RNAs, we used High Definition adapters that reduce ligation bias. We identified and experimentally validated five new microRNAs and a few other small non-coding RNAs that were not microRNAs. The biological implication of microRNA expression levels and targets of microRNAs are discussed

3D multi-agent models for protein release from PLGA spherical particles with complex inner morphologies

In order to better understand and predict the release of proteins from bioerodible micro- or nanospheres, it is important to know the influences of different initial factors on the release mechanisms. Often though it is difficult to assess what exactly is at the origin of a certain dissolution profile. We propose here a new class of fine-grained multi-agent models built to incorporate increasing complexity, permitting the exploration of the role of different parameters, especially that of the internal morphology of the spheres, in the exhibited release profile. This approach, based on Monte-Carlo (MC) and Cellular Automata (CA) techniques, has permitted the testing of various assumptions and hypotheses about several experimental systems of nanospheres encapsulating proteins. Results have confirmed that this modelling approach has increased the resolution over the complexity involved, opening promising perspectives for future developments, especially complementing in vitro experimentation

Graphene-based mid-infrared room-temperature pyroelectric bolometers with ultrahigh temperature coefficient of resistance.

There is a growing number of applications demanding highly sensitive photodetectors in the mid-infrared. Thermal photodetectors, such as bolometers, have emerged as the technology of choice, because they do not need cooling. The performance of a bolometer is linked to its temperature coefficient of resistance (TCR, ∼2-4% K-1 for state-of-the-art materials). Graphene is ideally suited for optoelectronic applications, with a variety of reported photodetectors ranging from visible to THz frequencies. For the mid-infrared, graphene-based detectors with TCRs ∼4-11% K-1 have been demonstrated. Here we present an uncooled, mid-infrared photodetector, where the pyroelectric response of a LiNbO3 crystal is transduced with high gain (up to 200) into resistivity modulation for graphene. This is achieved by fabricating a floating metallic structure that concentrates the pyroelectric charge on the top-gate capacitor of the graphene channel, leading to TCRs up to 900% K-1, and the ability to resolve temperature variations down to 15 μK