Experimental and Theoretical Advances on Single Atom and Atomic Cluster-Decorated Low-Dimensional Platforms towards Superior Electrocatalysts

The fundamental relationship between structure and properties, which is called “structure-property”, plays a vital role in the rational designing of high-performance catalysts for diverse electrocatalytic applications. Low-dimensional (LD) nanomaterials, including 0D, 1D, 2D materials, combined with low-nuclearity metal atoms, ranging from single atoms to subnanometer clusters, are currently emerging as rising star nanoarchitectures for heterogeneous catalysis due to their well-defined active sites and unbeatable metal utilization efficiencies. In this work, a comprehensive experimental and theoretical review is provided on the recent development of single atom and atomic cluster-decorated LD platforms towards some typical clean energy reactions, such as water-splitting, nitrogen fixation, and carbon dioxide reduction reactions. The upmost attractive structural properties, advanced characterization techniques, and theoretical principles of these low-nuclearity electrocatalysts as well as their applications in key electrochemical energy devices are also elegantly discussed

Immediate Breast Reconstruction (IBR) With Direct, Anatomic, Extra-Projection Prosthesis 102 Cases

Abstract: There are different methods described until now for immediate breast reconstruction. Despite the use of autologous flaps considered by many authors, implants are considered as an option by others. A prospective study of 102 clinical cases was designed, including a 1-year follow-up in which glands were reconstructed by immediate breast reconstruction (IBR) with direct, extra projection, anatomic prostheses located in a submuscular pocket after a skin-sparing mastectomy. The prosthesis coverage was made by the muscle in its upper two thirds and by using the skin from the mastectomy in its lower third. The cosmetic results obtained were evaluated according to the volume, form, and symmetry achieved using a linear numeric analogical score. This evaluation had an averaged value of 2.79 Ϯ 0.8 in our scale from poor (0) to excellent result (4). The overall rate of complications was 15.7% of the cases, with seroma being the most frequent. In conclusion, this preliminary study demonstrates that immediate breast reconstruction with a direct, extra projection, anatomic prosthesis is a good alternative. Nevertheless, more long-term studies with a higher number of patients and using an SF-36 for patient satisfaction are needed to confirm these results

Breast tumor segmentation in ultrasound images using contextual-information-aware deep adversarial learning framework.

Automatic tumor segmentation in breast ultrasound (BUS) images is still a challenging task because of many sources of uncertainty, such as speckle noise, very low signal-to-noise ratio, shadows that make the anatomical boundaries of tumors ambiguous, as well as the highly variable tumor sizes and shapes. This article proposes an efficient automated method for tumor segmentation in BUS images based on a contextual information-aware conditional generative adversarial learning framework. Specifically, we exploit several enhancements on a deep adversarial learning framework to capture both texture features and contextual dependencies in the BUS images that facilitate beating the challenges mentioned above. First, we adopt atrous convolution (AC) to capture spatial and scale context (i.e., position and size of tumors) to handle very different tumor sizes and shapes. Second, we propose the use of channel attention along with channel weighting (CAW) mechanisms to promote the tumor-relevant features (without extra supervision) and mitigate the effects of artifacts. Third, we propose to integrate the structural similarity index metric (SSIM) and L1-norm in the loss function of the adversarial learning framework to capture the local context information derived from the area surrounding the tumors. We used two BUS image datasets to assess the efficiency of the proposed model. The experimental results show that the proposed model achieves competitive results compared with state-of-the-art segmentation models in terms of Dice and IoU metrics. The source code of the proposed model is publicly available at https://github.com/vivek231/Breast-US-project

Mexican World Heritage information on the web: Institutional presence and visibility

This study offers a global overview of the presence and visibility of web information on UNESCO World Heritage located in Mexico, via the analysis of official websites and Web 2.0 information. Cultural heritage is a determining factor in linking people to their history, and contributes to increasing cultural tourism and economic development. The study starts from the hypothesis that the design of these has an influence on the dissemination and popularity of the aforementioned heritage. The relationships between the administrative organization of the country and Internet protocols are compared. A webometric study of the official Mexican websites was carried out. An evaluation sheet was designed to allow the assessment of aspects relating to identification, presence, accessibility and content. The multilingual nature of this information and its presence on social networks and Wikipedia was analysed. The analysis of URLs confirms that the domain .mx is used in 84% of cases. The results indicate a noticeable use of Web 2.0 dissemination of the heritage assets on YouTube (51%) and Facebook (40%), followed by 23% on Twitter. The Web Accessibility Initiative (WAI) guidelines are not yet frequently applied. Finally, the results obtained make it possible to identify variables that can contribute to improvements in the visibility and dissemination of official web information.This paper was supported by the RD & I Project, HAR2012-38562 (Spanish Ministry of Economy and Competitiveness)