The emergent field of data science is a critical driver for innovation in all sectors, a focus of tremendous workforce development, and an area of increasing importance within science, technology, engineering, and math (STEM). In all of its aspects, data science has the potential to narrow the gender gap and set a new bar for inclusion. To evolve data science in a way that promotes gender diversity, we must address two challenges: (1) how to increase the number of women acquiring skills and working in data science and (2) how to evolve organizations and professional cultures to better retain and advance women in data science. Everyone can contribute.
During language processing, humans form complex embedded representations from sequential inputs. Here, we ask whether a “geometrical language” with recursive embedding also underlies the human ability to encode sequences of spatial locations. We introduce a novel paradigm in which subjects are exposed to a sequence of spatial locations on an octagon, and are asked to predict future locations. The sequences vary in complexity according to a well-defined language comprising elementary primitives and recursive rules. A detailed analysis of error patterns indicates that primitives of symmetry and rotation are spontaneously detected and used by adults, preschoolers, and adult members of an indigene group in the Amazon, the Munduruku, who have a restricted numerical and geometrical lexicon and limited access to schooling. Furthermore, subjects readily combine these geometrical primitives into hierarchically organized expressions. By evaluating a large set of such combinations, we obtained a first view of the language needed to account for the representation of visuospatial sequences in humans, and conclude that they encode visuospatial sequences by minimizing the complexity of the structured expressions that capture them.
A new sildenafil analogue was found to be added illegally to a energy drink marketed for the enhancement of sexual function. The structure was determined as 1-[4-propoxy-3-(6,7-dihydro-1-methyl-7-oxo-3-propyl-1H-pyrazolo[4,3-d]pyrimidin-5-yl)phenylsulfonyl]-4-methylpiperazine. Owing to the inclusion of one more methyl group to sildenafil (on C-21), the detected compound was called “propoxyphenyl sildenafil”. The sample was purified with column chromatography. The UV, IR, LC/MS (ESI) and completely assigned NMR data of propoxyphenyl sildenafil is reported. Having compared the structure with sildenafil, the results showed that the 2-ethoxy group (at the position on C-19) has been replaced by propoxy group.
In this paper, we propose an improved variational level set approach to correct the bias and to segment the magnetic resonance (MR) images with inhomogeneous intensity. First, we use a Gaussian distribution with bias field as a local region descriptor in two-phase level set formulation for segmentation and bias field correction of the images with inhomogeneous intensities. By using the information of the local variance in this descriptor, our method is able to obtain accurate segmentation results. Furthermore, we extend this method to three-phase level set formulation for brain MR image segmentation and bias field correction. By using this three-phase level set function to replace the four-phase level set function, we can reduce the number of convolution operations in each iteration and improve the efficiency. Compared with other approaches, this algorithm demonstrates a superior performance.
A new strategy for the synthesis of tetrahydroquinolines (THQs) via the sequential functionalizations of remote C-H bonds is reported. This method uses a single picolinamide directing/protecting group to effect Pd-catalyzed γ-C(sp(3))-H arylation, metal-free ε-C(sp(2))-H iodination, and Cu-catalyzed intramolecular C-N cross-coupling. The overall sequence is efficient and versatile, and offers a streamlined synthesis of THQs with complex substitution patterns from readily available aryl iodide and aliphatic amine precursors.
We report a detailed binding study addressing both the thermodynamics and kinetics of binding of a large set of guest molecules - with widely varying properties - to a water-soluble metal-organic M4L6 host. The effects of different guest properties upon binding strength and kinetics are elucidated by a systematic analysis of the binding data through principal component analysis, thus allowing for structure-property relationships to be determined. These insights allowed us to design more complex encapsulation sequences, in which multiple guests, added simultaneously, are bound and released by the host in a time-dependent manner, thus allowing multiple states of the system to be accessed sequentially. Moreover, by inclusion of the pH-sensitive guest pyridine we were able to further extend our control over binding by creating a reversible pH-controlled three-guest sequential binding cycle.
- Journal of physics. Condensed matter : an Institute of Physics journal
- Published almost 7 years ago
We study the properties of wavefunctions and the wavepacket dynamics in quasiperiodic tight-binding models in one, two, and three dimensions. The atoms in the one-dimensional quasiperiodic chains are coupled by weak and strong bonds aligned according to the Fibonacci sequence. The associated d-dimensional quasiperiodic tilings are constructed from the direct product of d such chains, which yields either the hypercubic tiling or the labyrinth tiling. This approach allows us to consider fairly large systems numerically. We show that the wavefunctions of the system are multifractal and that their properties can be related to the structure of the system in the regime of strong quasiperiodic modulation by a renormalization group (RG) approach. We also study the dynamics of wavepackets to get information about the electronic transport properties. In particular, we investigate the scaling behaviour of the return probability of the wavepacket with time. Applying again the RG approach we show that in the regime of strong quasiperiodic modulation the return probability is governed by the underlying quasiperiodic structure. Further, we also discuss lower bounds for the scaling exponent of the width of the wavepacket and propose a modified lower bound for the absolute continuous regime.
Contaminated site remediation is generally difficult, time consuming, and expensive. As a result ranking may aid in efficient allocation of resources. In order to rank the priorities of contaminated sites, input parameters relevant to contaminant fate and transport, and exposure assessment should be as accurate as possible. Yet, in most cases these parameters are vague or not precise. Most of the current remediation priority ranking methodologies overlook the vagueness in parameter values or do not go beyond assigning a contaminated site to a risk class. The main objective of this study is to develop an alternative remedial priority ranking system (RPRS) for contaminated sites in which vagueness in parameter values is considered. RPRS aims to evaluate potential human health risks due to contamination using sufficiently comprehensive and readily available parameters in describing the fate and transport of contaminants in air, soil, and groundwater. Vagueness in parameter values is considered by means of fuzzy set theory. A fuzzy expert system is proposed for the evaluation of contaminated sites and a software (ConSiteRPRS) is developed in Microsoft Office Excel 2007 platform. Rankings are employed for hypothetical and real sites. Results show that RPRS is successful in distinguishing between the higher and lower risk cases.
Among internal fertilizers, typically fewer than 1% sperm survive the journey through the oviduct. Several studies suggest that the sperm reaching the ovum-the ‘fertilizing set’-comprise a non-random sub-population, but the characteristics of this group remain unclear. We tested whether oviductal selection in birds results in a morphologically distinct subset of sperm, by exploiting the fact that the fertilizing set are trapped by the perivitelline layer of the ovum. We show that these sperm have remarkably low morphological variation, as well as smaller head size and greater tail length, compared with those inseminated. Our study shows that the morphological composition of sperm-rather than length alone-influences success in reaching the ovum.
- IEEE transactions on visualization and computer graphics
- Published about 2 years ago
Recent advances in data acquisition produce volume data of very high resolution and large size, such as terabyte-sized microscopy volumes. These data often contain many fine and intricate structures, which pose huge challenges for volume rendering, and make it particularly important to efficiently skip empty space. This paper addresses two major challenges: (1) The complexity of large volumes containing fine structures often leads to highly fragmented space subdivisions that make empty regions hard to skip efficiently. (2) The classification of space into empty and non-empty regions changes frequently, because the user or the evaluation of an interactive query activate a different set of objects, which makes it unfeasible to pre-compute a well-adapted space subdivision. We describe the novel SparseLeap method for efficient empty space skipping in very large volumes, even around fine structures. The main performance characteristic of SparseLeap is that it moves the major cost of empty space skipping out of the ray-casting stage. We achieve this via a hybrid strategy that balances the computational load between determining empty ray segments in a rasterization (object-order) stage, and sampling non-empty volume data in the ray-casting (image-order) stage. Before ray-casting, we exploit the fast hardware rasterization of GPUs to create a ray segment list for each pixel, which identifies non-empty regions along the ray. The ray-casting stage then leaps over empty space without hierarchy traversal. Ray segment lists are created by rasterizing a set of fine-grained, view-independent bounding boxes. Frame coherence is exploited by re-using the same bounding boxes unless the set of active objects changes. We show that SparseLeap scales better to large, sparse data than standard octree empty space skipping.