Two types of S phase precipitates in Al-Cu-Mg alloys

Transmission electron microscopy (TEM) and differential scanning calorimetry (DSC) have been used to study S phase precipitation in an Al-4.2Cu-1.5Mg-0.6Mn-0.5Si (AA2024) and an Al-4.2Cu-1.5Mg-0.6Mn-0.08Si (AA2324) (wt-%) alloy. In DSC experiments on as solution treated samples two distinct exothermic peaks are observed in the range 250 to 350°C, whereas only one peak is observed in solution treated and subsequently stretched or cold worked samples. Samples heated to 270°C and 400°C at a rate of 10°C/min in the DSC have been studied by TEM. The selected area diffraction patterns show that S phase precipitates with the classic orientation relationship form during the lower temperature peak, and for the solution treated samples, the higher temperature peak is caused by the formation of a second type of S phase precipitates which have an orientation relationship that is rotated by ~4 degrees to the classic one. The effects of Si and cold work on the formation of second type of S precipitates have been discussed

Model-based object recognition from a complex binary imagery using genetic algorithm

This paper describes a technique for model-based object recognition in a noisy and cluttered environment, by extending the work presented in an earlier study by the authors. In order to accurately model small irregularly shaped objects, the model and the image are represented by their binary edge maps, rather then approximating them with straight line segments. The problem is then formulated as that of finding the best describing match between a hypothesized object and the image. A special form of template matching is used to deal with the noisy environment, where the templates are generated on-line by a Genetic Algorithm. For experiments, two complex test images have been considered and the results when compared with standard techniques indicate the scope for further research in this direction

Real-Time Hand Shape Classification

The problem of hand shape classification is challenging since a hand is characterized by a large number of degrees of freedom. Numerous shape descriptors have been proposed and applied over the years to estimate and classify hand poses in reasonable time. In this paper we discuss our parallel framework for real-time hand shape classification applicable in real-time applications. We show how the number of gallery images influences the classification accuracy and execution time of the parallel algorithm. We present the speedup and efficiency analyses that prove the efficacy of the parallel implementation. Noteworthy, different methods can be used at each step of our parallel framework. Here, we combine the shape contexts with the appearance-based techniques to enhance the robustness of the algorithm and to increase the classification score. An extensive experimental study proves the superiority of the proposed approach over existing state-of-the-art methods.Comment: 11 page

More individual differences in language attainment: How much do adult native speakers of English know about passives and quantifiers?

This paper provides experimental evidence suggesting that there are considerable differences in native language attainment, and that these are at least partially attributable to individual speakers’ experience. Experiment 1 tested high academic attainment (hereafter, HAA) and low academic attainment (LAA) participants’ comprehension using a picture selection task. Test sentences comprised passives and two variants of the universal quantification construction. Active constructions were used as a control condition. HAA participants performed at ceiling in all conditions; LAA participants performed at ceiling only on actives. As predicted by usage-based accounts, the order of difficulty of the four sentence types mirrored their frequency. Experiment 2 tested whether the less-educated participants’ difficulties with these constructions are attributable to insufficient experience. After a screening test, low scoring participants were randomly assigned to two training groups. The passive training group were given a short training session on the passive construction; and the quantifier training group were trained on sentences with quantifiers. A series of post-training tests show that performance on the trained construction improved dramatically, and that the effect was long-lasting

iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects

We address the task of 6D pose estimation of known rigid objects from single input images in scenarios where the objects are partly occluded. Recent RGB-D-based methods are robust to moderate degrees of occlusion. For RGB inputs, no previous method works well for partly occluded objects. Our main contribution is to present the first deep learning-based system that estimates accurate poses for partly occluded objects from RGB-D and RGB input. We achieve this with a new instance-aware pipeline that decomposes 6D object pose estimation into a sequence of simpler steps, where each step removes specific aspects of the problem. The first step localizes all known objects in the image using an instance segmentation network, and hence eliminates surrounding clutter and occluders. The second step densely maps pixels to 3D object surface positions, so called object coordinates, using an encoder-decoder network, and hence eliminates object appearance. The third, and final, step predicts the 6D pose using geometric optimization. We demonstrate that we significantly outperform the state-of-the-art for pose estimation of partly occluded objects for both RGB and RGB-D input

Anomalous diffusion and asymmetric tempering memory in neutrophil chemotaxis.

The motility of neutrophils and their ability to sense and to react to chemoattractants in their environment are of central importance for the innate immunity. Neutrophils are guided towards sites of inflammation following the activation of G-protein coupled chemoattractant receptors such as CXCR2 whose signaling strongly depends on the activity of Ca2+ permeable TRPC6 channels. It is the aim of this study to analyze data sets obtained in vitro (murine neutrophils) and in vivo (zebrafish neutrophils) with a stochastic mathematical model to gain deeper insight into the underlying mechanisms. The model is based on the analysis of trajectories of individual neutrophils. Bayesian data analysis, including the covariances of positions for fractional Brownian motion as well as for exponentially and power-law tempered model variants, allows the estimation of parameters and model selection. Our model-based analysis reveals that wildtype neutrophils show pure superdiffusive fractional Brownian motion. This so-called anomalous dynamics is characterized by temporal long-range correlations for the movement into the direction of the chemotactic CXCL1 gradient. Pure superdiffusion is absent vertically to this gradient. This points to an asymmetric 'memory' of the migratory machinery, which is found both in vitro and in vivo. CXCR2 blockade and TRPC6-knockout cause tempering of temporal correlations in the chemotactic gradient. This can be interpreted as a progressive loss of memory, which leads to a marked reduction of chemotaxis and search efficiency of neutrophils. In summary, our findings indicate that spatially differential regulation of anomalous dynamics appears to play a central role in guiding efficient chemotactic behavior

What counts in the development of young children's number knowledge?

Prior studies indicate that children vary widely in their mathematical knowledge by the time they enter preschool and that this variation predicts levels of achievement in elementary school. In a longitudinal study of a diverse sample of 44 preschool children, we examined the extent to which their understanding of the cardinal meanings of the number words (e.g., knowing that the word “four” refers to sets with 4 items) is predicted by the “number talk” they hear from their primary caregiver in the early home environment. Results from 5 visits showed substantial variation in parents’ number talk to children between the ages of 14 and 30 months. Moreover, this variation predicted children’s knowledge of the cardinal meanings of number words at 46 months, even when socioeconomic status and other measures of parent and child talk were controlled. These findings suggest that encouraging parents to talk about number with their toddlers, and providing them with effective ways to do so, may positively impact children’s school achievement

Calpain 4 Is Not Necessary for LFA-1-Mediated Function in CD4+ T Cells

T cell activation and immune synapse formation require the appropriate activation and clustering of the integrin, LFA-1. Previous work has reported that the calpain family of calcium-dependent proteases are important regulators of integrin activation and modulate T cell adhesion and migration. However, these studies have been limited by the use of calpain inhibitors, which have known off-target effects.Here, we used a LoxP/CRE system to specifically deplete calpain 4, a small regulatory calpain subunit required for expression and activity of ubiquitously expressed calpains 1 and 2, in CD4+ T cells. CD4+ and CD8+ T cells developed normally in Capn4(F/F):CD4-CRE mice and had severely diminished expression of Calpain 1 and 2, diminished talin proteolysis and impaired casein degradation. Calpain 4-deficient T cells showed no difference in adhesion or migration on the LFA-1 ligand ICAM-1 compared to control T cells. Moreover, there was no impairment in conjugation between Capn4(F/F):CD4-CRE T cells and antigen presenting cells, and the conjugates were still capable of polarizing LFA-1, PKC-theta and actin to the immune synapse. Furthermore, T cells from Capn4(F/F):CD4-CRE mice showed normal proliferation in response to either anti-CD3/CD28 coated beads or cognate antigen-loaded splenocytes. Finally, there were no differences in the rates of apoptosis following extrinsic and intrinsic apoptotic stimuli.Our findings demonstrate that calpain 4 is not necessary for LFA-1-mediated adhesion, conjugation or migration. These results challenge previous reports that implicate a central role for calpains in the regulation of T cell LFA-1 function

Superpixel quality in microscopy images: the impact of noise & denoising

Microscopy is a valuable imaging tool in various biomedical research areas. Recent developments have made high resolution acquisition possible within a relatively short time. State-of-the-art imaging equipment such as serial block-face electron microscopes acquire gigabytes of data in a matter of hours. In order to make these amounts of data manageable, a more data-efficient representation is required. A popular approach for such data efficiency are superpixels which are designed to cluster homogeneous regions without crossing object boundaries. The use of superpixels as a pre-processing step has shown significant improvements in making computationally intensive computer vision analysis algorithms more tractable on large amounts of data. However, microscopy datasets in particular can be degraded by noise and most superpixel algorithms do not take this artifact into account. In this paper, we give a quantitative and qualitative comparison of superpixels generated on original and denoised images. We show that several advanced superpixel techniques are hampered by noise artifacts and require denoising and parameter tuning as a pre-processing step. The evaluation is performed on the Berkeley segmentation dataset as well as on fluorescence and scanning electron microscopy data