The Static Failure of Adhesively Bonded Metal Laminate Structures: A Cohesive Zone Approach

Data on distribution, ecology, biomass, recruitment, growth, mortality and productivity of the West African bloody cockle Anadara senilis were collected at the Banc d'Aguuin, Mauritania, in early 1985 and 1986. Ash-free dry weight appeared to be correlated best with shell height. A. senilis was abundant on the tidal flats of landlocked coastal bays, but nearly absent on the tidal flats bordering the open sea. The average biomass for the entire area of tidal flats was estimated at 5.5 g·m−2 ash-free dry weight. The A. senilis population appeared to consist mainly of 10 to 20-year-old individuals, showing a very slow growth and a production: biomass ratio of about 0.02 y−1. Recruitment appeared negligible and mortality was estimated to be about 10% per year. Oystercatchers (Haematopus ostralegus), the gastropod Cymbium cymbium and unknown fish species were responsible for a large share of this. The distinction of annual growth marks permitted the assessment of year-class strength, which appeared to be correlated with the average discharge of the river Senegal. This may be explained by assuming that year-class strength and river discharge both are correlated with rainfall at the Banc d'Arguin.

DETECTION OF CLOUDS IN MEDIUM-RESOLUTION SATELLITE IMAGERY USING DEEP CONVOLUTIONAL NEURAL NETS

Cloud detection is an inextricable pre-processing step in remote sensing image analysis workflows. Most of the traditional rule-based and machine-learning-based algorithms utilize low-level features of the clouds and classify individual cloud pixels based on their spectral signatures. Cloud detection using such approaches can be challenging due to a multitude of factors including harsh lighting conditions, the presence of thin clouds, the context of surrounding pixels, and complex spatial patterns. In recent studies, deep convolutional neural networks (CNNs) have shown outstanding results in the computer vision domain. These methods are practiced for better capturing the texture, shape as well as context of images. In this study, we propose a deep learning CNN approach to detect cloud pixels from medium-resolution satellite imagery. The proposed CNN accounts for both the low-level features, such as color and texture information as well as high-level features extracted from successive convolutions of the input image. We prepared a cloud-pixel dataset of approximately 7273 randomly sampled 320 by 320 pixels image patches taken from a total of 121 Landsat-8 (30m) and Sentinel-2 (20m) image scenes. These satellite images come with cloud masks. From the available data channels, only blue, green, red, and NIR bands are fed into the model. The CNN model was trained on 5300 image patches and validated on 1973 independent image patches. As the final output from our model, we extract a binary mask of cloud pixels and non-cloud pixels. The results are benchmarked against established cloud detection methods using standard accuracy metrics

Fluorescence Signal Enhancement in Antibody Microarrays Using Lightguiding Nanowires

Fluorescence-based detection assays play an essential role in the life sciences and medicine. To offer better detection sensitivity and lower limits of detection (LOD), there is a growing need for novel platforms with an improved readout capacity. In this context, substrates containing semiconductor nanowires may offer significant advantages, due to their proven light-emission enhancing, waveguiding properties, and increased surface area. To demonstrate and evaluate the potential of such nanowires in the context of diagnostic assays, we have in this work adopted a well-established single-chain fragment antibody-based assay, based on a protocol previously designed for biomarker detection using planar microarrays, to freestanding, SiO2-coated gallium phosphide nanowires. The assay was used for the detection of protein biomarkers in highly complex human serum at high dilution. The signal quality was quantified and compared with results obtained on conventional flat silicon and plastic substrates used in the established microarray applications. Our results show that using the nanowire-sensor platform in combination with conventional readout methods, improves the signal intensity, contrast, and signal-to-noise by more than one order of magnitude compared to flat surfaces. The results confirm the potential of lightguiding nanowires for signal enhancement and their capacity to improve the LOD of standard diagnostic assays

Identification of a serum biomarker signature associated with metastatic prostate cancer

Purpose: Improved early diagnosis and determination of aggressiveness of prostate cancer (PC) is important to select suitable treatment options and to decrease over-treatment. The conventional marker is total prostate specific antigen (PSA) levels in blood, but lacks specificity and ability to accurately discriminate indolent from aggressive disease. Experimental design: In this study, we sought to identify a serum biomarker signature associated with metastatic PC. We measured 157 analytes in 363 serum samples from healthy subjects, patients with non-metastatic PC and patients with metastatic PC, using a recombinant antibody microarray. Results: A signature consisting of 69 proteins differentiating metastatic PC patients from healthy controls was identified. Conclusions and clinical relevance: The clinical value of this biomarker signature requires validation in larger independent patient cohorts before providing a new prospect for detection of metastatic PC

Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes and estimated using ground-penetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS). We found that GPR data provided high-precision estimates of snow depth (RMSE  =  2.9 cm), with a spatial sampling of 10 cm along transects. Phodar-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing, while yielding a high precision (RMSE  =  6.0 cm) and a fine spatial sampling (4 cm × 4 cm). We then investigated the spatial variability of snow depth and its correlation to micro- and macrotopography using the snow-free lidar digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free lidar DEM and multiscale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high-precision estimates of snow depth (RMSE  =  6.0 cm), at 0.5 m resolution and over the lidar domain (750 m × 700 m)

Affect in mathematics education

There are two different uses for the word “affect” in behavioral sciences. Often it is used as an overarching umbrella concept that covers attitudes, beliefs, motivation, emotions, and all other noncognitive aspects of human mind. In this article, however, the word affect is used in a more narrow sense, referring to emotional states and traits. A more technical definition of emotions, states, and traits will follow later.Peer reviewe

Towards the minimal amount of exercise for improving metabolic health: beneficial effects of reduced-exertion high-intensity interval training

High-intensity interval training (HIT) has been proposed as a time-efficient alternative to traditional cardiorespiratory exercise training, but is very fatiguing. In this study, we investigated the effects of a reduced-exertion HIT (REHIT) exercise intervention on insulin sensitivity and aerobic capacity. Twenty-nine healthy but sedentary young men and women were randomly assigned to the REHIT intervention (men, n = 7; women, n = 8) or a control group (men, n = 6; women, n = 8). Subjects assigned to the control groups maintained their normal sedentary lifestyle, whilst subjects in the training groups completed three exercise sessions per week for 6 weeks. The 10-min exercise sessions consisted of low-intensity cycling (60 W) and one (first session) or two (all other sessions) brief ‘all-out’ sprints (10 s in week 1, 15 s in weeks 2–3 and 20 s in the final 3 weeks). Aerobic capacity ( V˙O2peakV˙O2peak ) and the glucose and insulin response to a 75-g glucose load (OGTT) were determined before and 3 days after the exercise program. Despite relatively low ratings of perceived exertion (RPE 13 ± 1), insulin sensitivity significantly increased by 28% in the male training group following the REHIT intervention (P < 0.05). V˙O2peakV˙O2peak increased in the male training (+15%) and female training (+12%) groups (P < 0.01). In conclusion we show that a novel, feasible exercise intervention can improve metabolic health and aerobic capacity. REHIT may offer a genuinely time-efficient alternative to HIT and conventional cardiorespiratory exercise training for improving risk factors of T2D