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Dr. Paddock spent the year completing a book and revising an article.

Two term sabbatical to create "Baroque Float", an extensive number of paintings investigating the visual and conceptual correlation between the structures of life as depicted by scientific molecular/cell imaging and structures of art as depicted by still life and Baroque ceiling painting.

"Coastal zones represent a frontline in the battle for sustainability, as coastal communities face unprecedented economic challenges. Coastal ecosystems are subject to overuse, loss of resilience and increased vulnerability. This book aims to interrogate the multi-scalar complexities in creating a more sustainable coastal zone. Sustainability transitions are geographical processes, which happen in situated, particular places. However, much contemporary discussion of transition is either aspatial or based on implicit assumptions about spatial homogeneity. This book addresses these limitations through an examination of socio-technological transitions with an explicitly spatial focus in the context of the coastal zone. The book begins by focusing on theoretical understandings of transition processes specific to the coastal zone and includes detailed empirical case studies. The second half of the book appraises governance initiatives in coastal zones and their efficacy. The authors conclude with an implicit theme of social and environmental justice in coastal sustainability transitions. Research will be of interest to practitioners, academics and decision-makers active in the sphere of coastal sustainability. The multi-disciplinary nature encourages accessibility for individuals working in the fields of Economic Geography, Regional Development, Public Policy and Planning, Environmental Studies, Social Geography and Sociology" -- Provided by publisher's website.

A collection of empirical research published by Dr. yan Quan Liu and his reasearch teams in the field of information and library science for the 21st-century readers. - Back cover.

In Psychoanalysis and Repetition, Juan-David Nasio, one of the leading contemporary Lacanian psychoanalysts in France, argues that unconcious repetition represents the core of psychoanalysis as well as no less than the fundamental constitution of the human being--back cover.

Promoter regions of long non-coding RNA (lncRNA) genes are crucial to understand their transcriptional regulatory pattern. LncRNA genes, being more cryptic than protein-coding genes in terms of their functionality and biogenesis divergence, are lacking in number of existing studies to elucidate the roles of their promoters compared to their counterparts. Based on the overlap between epigenetic marks and transcription start sites, human lncRNAs were categorized into two broad categories: enhancer-originated lncRNAs (e-lncRNAs) and promoter-originated lncRNAs (p-lncRNAs) and hence these two groups are subject to distinct transcriptional regulatory programs. To understand the difference in the transcriptional regulatory mechanisms that governs p- and e-lncRNAs, we studied the promoter sequences of these two groups of lncRNAs including distinct transcription factor (TF) proteins that favor p-over e-lncRNA (and vice versa). In addition, we developed a convolution neural network (CNN) based deep learning (DL) framework DeePEL (deep p-, e-lncRNA promoter recognizer), to classify the promoter of p- and e-lncRNAs. To the best of our knowledge, this is the first attempt to classify these two groups of lncRNA promoters, using sequence and TF information, based on DL framework. We report several sequence specific signatures in the promoter regions as well as several distinct TFs specific to groups of lncRNAs that will help in understanding the promoter-proximal transcriptional regulation of p-lncRNAs and e-lncRNAs. © 2019 IEEE.

Deep learning is a promising approach for fine- grained disease severity classification for smart agriculture, as it avoids the labor-intensive feature engineering and segmentation-based threshold. In this work, we first propose a Densely Connected Convolutional Networks (DenseNet) based transfer learning method to detect the plant diseases, which expects to run on edge servers with augmented computing resources. Then, we propose a lightweight Deep Neural Networks (DNN) approach that can run on Internet of Things (IoT) devices with constrained resources. To reduce the size and computation cost of the model, we further simplify the DNN model and reduce the size of input sizes. The proposed models are trained with different image sizes to find the appropriate size of the input images. Experiment results are provided to evaluate the performance of the proposed models based on real- world dataset, which demonstrate the proposed models can accurately detect plant disease using low computational resources. © 2019 IEEE.

Testate amoebae diversity from 28 surface (0-3 cm depth) soil samples found near Cuzco (6 samples), in Machu Piсchu (17 samples), in Aguas Calientes (5 samples), and one bottom sediment sample from the Lake Titicaca near Puno were collected during March of 2016 were analyzed. The 144 testate amoebae species and infra-specific taxa belonging to 27 genera were identified. Nineteen amoebae have not been identified to species level and likely represent new taxa. Species richness varied from one to 54 taxa per sample. The highest diversity was found in rainforests followed by those in meadows and agave habitats. The only bottom sample from Lake Titicaca has yielded two hydrobiont species from the genus Difflugia. In the course of the study, several rare species with limited geographical distribution were observed, namely Centropyxis castaneus, C. compressa, C. deflandriana, C. latideflandriana, C. cf. ohridensis, C. cf. ovoides, C. cf. pannosus, C. stenodeflandriana, Cyclopyxis plana, C. profundistoma, Apodera vas, Argynnia retorta, A. spicata, Certesella certesi, Trachelcorythion pulchellum. Our study fills a geographical gap in the distribution of some flagship species with restricted geographic distribution, e.g. Apodera vas and Certesella certesi in Peru. The results illustrate the continuity of expansion species along the Pacific coast. © 2019 by Revista de Biologia Tropical. All rights reserved.

Many students in higher education have undiagnosed reading disabilities (RDs), but there are few measures to screen for RD in this population. The aim of this study was to determine the ability of tasks that are sensitive to RDs—such as measures of phonemic awareness and working memory—to differentiate university students previously diagnosed with RDs from controls. Participants were university students with an RD (n = 26), a clinical control group diagnosed with attention-deficit/hyperactivity disorder (n = 24), and neurotypical controls (n = 44). Participants completed brief phonological processing and working memory tasks. The RD group scored significantly lower on all tasks than both control groups. The phonological processing tasks alone—without the working memory task—discriminated participants with RDs from controls with excellent sensitivity and specificity. A brief battery of phonemic tasks could be an effective screening instrument for persons with RDs on university campuses. © 2019 John Wiley & Sons, Ltd.

Let D be any of the 10 digraphs obtained by orienting the edges of K4 - e. We establish necessary and sufficient conditions for the existence of a (K∗ n,D)-design for 8 of these digraphs. Partial results as well as some nonexistence results are established for the remaining 2 digraphs. © 2019 Ryan C. Bunge et al., published by Sciendo 2019.

This paper presents highly robust, novel approaches to solving the forward and inverse problems of an Electrical Capacitance Tomography (ECT) system for imaging conductive materials. ECT is one of the standard tomography techniques for industrial imaging. An ECT technique is nonintrusive and rapid and requires a low burden cost. However, the ECT system still suffers from a soft-field problem which adversely affects the quality of the reconstructed images. Although many image reconstruction algorithms have been developed, still the generated images are inaccurate and poor. In this work, the Capacitance Artificial Neural Network (CANN) system is presented as a solver for the forward problem to calculate the estimated capacitance measurements. Moreover, the Metal Filled Fuzzy System (MFFS) is proposed as a solver for the inverse problem to construct the metal images. To assess the proposed approaches, we conducted extensive experiments on image metal distributions in the lost foam casting (LFC) process to light the reliability of the system and its efficiency. The experimental results showed that the system is sensible and superior. © 2019 Wael Deabes et al.

ZnO and Fe-doped ZnO nanoparticles were analyzed in ethanol solution and dry powder form using fluorescence spectroscopy. Near-band-edge emission (NBE) and defect emission (DE) peaks were studied. A blue-shift was observed with the NBE emission peak. © OSA 2019. The Author(s).