Probing ultrafast carrier dynamics and nonlinear absorption and refraction in core-shell silicon nanowires

We investigate the relaxation dynamics of photogenerated carriers in silicon nanowires consisting of a crystalline core and a surrounding amorphous shell, using femtosecond time-resolved differential reflectivity and transmission spectroscopy at photon energies of 3.15 eV and 1.57 eV. The complex behavior of the differential transmission and reflectivity transients is the mixed contributions from the crystalline core and the amorphous silicon on the nanowire surface and the substrate where competing effects of state filling and photoinduced absorption govern the carrier dynamics. Faster relaxation rates are observed on increasing the photo-generated carrier density. Independent experimental results on crystalline silicon-on-sapphire help us in separating the contributions from the carrier dynamics in crystalline core and the amorphous regions in the nanowire samples. Further, single beam z-scan nonlinear transmission experiments at 1.57 eV in both open and close aperture configurations yield two-photon absorption coefficient \$beta$ (~3 cm/GW) and nonlinear refraction coefficient \$gamma$ (-2.5x10^-4 cm2/GW).Comment: 6 pages, 6 figure

Do mutual funds have consistency in their performance?

Using a comprehensive data set of 714 Chinese mutual funds from 2004 to 2015, the study investigates these funds’ performance persistence by using the Capital Asset Pricing model, the Fama-French three-factor model and the Carhart Four-factor model. For persistence analysis, we categorize mutual funds into eight octiles based on their one year lagged performance and then observe their performance for the subsequent 12 months. We also apply Cross-Product Ratio technique to assess the performance persistence in these Chinese funds. The study finds no significant evidence of persis- tence in the performance of the mutual funds. Winner (loser) funds do not continue to be winner (loser) funds in the subsequent time period. These findings suggest that future performance of funds cannot be predicted based on their past performance.info:eu-repo/semantics/publishedVersio

Lower cerebrospinal fluid/plasma fibroblast growth factor 21 (FGF21) ratios and placental FGF21 production in gestational diabetes

Objectives: Circulating Fibroblast Growth Factor 21 (FGF21) levels are increased in insulin resistant states such as obesity, type 2 diabetes mellitus and gestational diabetes mellitus (GDM). In addition, GDM is associated with serious maternal and fetal complications. We sought to study human cerebrospinal fluid (CSF) and corresponding circulating FGF21 levels in women with gestational diabetes mellitus (GDM) and in age and BMI matched control subjects. We also assessed FGF21 secretion from GDM and control human placental explants. Design: CSF and corresponding plasma FGF21 levels of 24 women were measured by ELISA [12 GDM (age: 26–47 years, BMI: 24.3–36.3 kg/m2) and 12 controls (age: 22–40 years, BMI: 30.1–37.0 kg/m2)]. FGF21 levels in conditioned media were secretion from GDM and control human placental explants were also measured by ELISA. Results: Glucose, HOMA-IR and circulating NEFA levels were significantly higher in women with GDM compared to control subjects. Plasma FGF21 levels were significantly higher in women with GDM compared to control subjects [234.3 (150.2–352.7) vs. 115.5 (60.5–188.7) pg/ml; P<0.05]. However, there was no significant difference in CSF FGF21 levels in women with GDM compared to control subjects. Interestingly, CSF/Plasma FGF21 ratio was significantly lower in women with GDM compared to control subjects [0.4 (0.3–0.6) vs. 0.8 (0.5–1.6); P<0.05]. FGF21 secretion into conditioned media was significantly lower in human placental explants from women with GDM compared to control subjects (P<0.05). Conclusions: The central actions of FGF21 in GDM subjects maybe pivotal in the pathogenesis of insulin resistance in GDM subjects. The significance of FGF21 produced by the placenta remains uncharted and maybe crucial in our understanding of the patho-physiology of GDM and its associated maternal and fetal complications. Future research should seek to elucidate these points

Chronic digital infection presenting with gross enlargement of the toes: two case reports and review of the literature

There are many conditions ranging from the benign to the malignant, which can present with enlargement of one or more digits. An understanding of the differential diagnosis is important such that the potentially serious aetiologies are not missed and patients can therefore be treated appropriately

End-to-end 6-DoF Object Pose Estimation through Differentiable Rasterization

Here we introduce an approximated differentiable renderer to refine a 6-DoF pose prediction using only 2D alignment information. To this end, a two-branched convolutional encoder network is employed to jointly estimate the object class and its 6-DoF pose in the scene. We then propose a new formulation of an approximated differentiable renderer to re-project the 3D object on the image according to its predicted pose; in this way the alignment error between the observed and the re-projected object silhouette can be measured. Since the renderer is differentiable, it is possible to back-propagate through it to correct the estimated pose at test time in an online learning fashion. Eventually we show how to leverage the classification branch to profitably re-project a representative model of the predicted class (i.e. a medoid) instead. Each object in the scene is processed independently and novel viewpoints in which both objects arrangement and mutual pose are preserved can be rendered. Differentiable renderer code is available at:https://github.com/ndrplz/tensorflow-mesh-renderer

Coherent coupling between radio frequency, optical, and acoustic waves in piezo-optomechanical circuits

The interaction of optical and mechanical modes in nanoscale optomechanical systems has been widely studied for applications ranging from sensing to quantum information science. Here, we develop a platform for cavity optomechanical circuits in which localized and interacting 1550 nm photons and 2.4 GHz phonons are combined with photonic and phononic waveguides. Working in GaAs facilitates manipulation of the localized mechanical mode either with a radio frequency field through the piezo-electric effect, or optically through the strong photoelastic effect. We use this to demonstrate a novel acoustic wave interference effect, analogous to coherent population trapping in atomic systems, in which the coherent mechanical motion induced by the electrical drive can be completely cancelled out by the optically-driven motion. The ability to manipulate cavity optomechanical systems with equal facility through either photonic or phononic channels enables new device and system architectures for signal transduction between the optical, electrical, and mechanical domains

Epigenetics as a mechanism driving polygenic clinical drug resistance

Aberrant methylation of CpG islands located at or near gene promoters is associated with inactivation of gene expression during tumour development. It is increasingly recognised that such epimutations may occur at a much higher frequency than gene mutation and therefore have a greater impact on selection of subpopulations of cells during tumour progression or acquisition of resistance to anticancer drugs. Although laboratory-based models of acquired resistance to anticancer agents tend to focus on specific genes or biochemical pathways, such 'one gene : one outcome' models may be an oversimplification of acquired resistance to treatment of cancer patients. Instead, clinical drug resistance may be due to changes in expression of a large number of genes that have a cumulative impact on chemosensitivity. Aberrant CpG island methylation of multiple genes occurring in a nonrandom manner during tumour development and during the acquisition of drug resistance provides a mechanism whereby expression of multiple genes could be affected simultaneously resulting in polygenic clinical drug resistance. If simultaneous epigenetic regulation of multiple genes is indeed a major driving force behind acquired resistance of patients' tumour to anticancer agents, this has important implications for biomarker studies of clinical outcome following chemotherapy and for clinical approaches designed to circumvent or modulate drug resistance

Pleiotropic functions of the tumor- and metastasis-suppressing Matrix Metalloproteinase-8 in mammary cancer in MMTV-PyMT transgenic mice

Matrix metalloproteinase-8 (MMP-8; neutrophil collagenase) is an important regulator of innate immunity which has onco-suppressive actions in numerous tumor types

Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction

This document is the Accepted Manuscript of the following article: Mohammed Chalouli, Nasr-eddine Berrached, and Mouloud Denai, ‘Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction’, Journal of Failure Analysis and Prevention, Vol. 17 (5): 1053-1066, October 2017. Under embargo. Embargo end date: 31 August 2018. The final publication is available at Springer via DOI: https://doi.org/10.1007/s11668-017-0343-y.Finding reliable condition monitoring solutions for large-scale complex systems is currently a major challenge in industrial research. Since fault diagnosis is directly related to the features of a system, there have been many research studies aimed to develop methods for the selection of the relevant features. Moreover, there are no universal features for a particular application domain such as machine diagnosis. For example, in machine bearing fault diagnosis, these features are often selected by an expert or based on previous experience. Thus, for each bearing machine type, the relevant features must be selected. This paper attempts to solve the problem of relevant features identification by building an automatic fault diagnosis process based on relevant feature selection using a data-driven approach. The proposed approach starts with the extraction of the time-domain features from the input signals. Then, a feature reduction algorithm based on cross-correlation filter is applied to reduce the time and cost of the processing. Unsupervised learning mechanism using K-means++ selects the relevant fault features based on the squared Euclidian distance between different health states. Finally, the selected features are used as inputs to a self-organizing map producing our health indicator. The proposed method is tested on roller bearing benchmark datasets.Peer reviewe