A Systematic Framework and Nanoperiodic Concept for Unifying Nanoscience: Hard/Soft Nanoelements, Superatoms, Meta-Atoms, New Emerging Properties, Periodic Property Patterns, and Predictive Mendeleev-like Nanoperiodic Tables

2016 ◽  
Vol 116 (4) ◽  
pp. 2705-2774 ◽  
Author(s):  
Donald A. Tomalia ◽  
Shiv N. Khanna
2014 ◽  
Vol 28 (03) ◽  
pp. 1430002 ◽  
Author(s):  
DONALD A. TOMALIA ◽  
SHIV N. KHANNA

This is an invited overview of a lecture presented at the American Physical Society (APS) Meeting, Boston, USA (March 1, 2012). The primary focus of this APS lecture was to trace the historical emergence of Hard and Soft nanoscale superatoms (i.e. nano-element categories) as well as a recent merging of these concepts/entities by chemists/physicists into a unified system and framework for defining nanoscience. The convergence of these quantized, organic/inorganic superatom entities involved the application of traditional "first principles" and their nanoscale "atom mimicry" features as a criteria for evolving a roadmap of quantized nano-elemental categories, nano-compound/assemblies and nano-periodic patterns, etc., much as was observed in traditional chemistry. This simple perspective was used to define a nanoscale taxonomy of hard/soft superatom/nano-element categories, as well as to explain the dependency of a broad range of nano-periodic properties/features on one or more of six Critical Nanoscale Design Parameters (CNDPs) associated with these nano-building blocks, namely: (1) size, (2) shape, (3) surface chemistry, (4) rigidity/flexibility, (5) architecture and (6) elemental composition. Validation and support of this systematic nano-periodic perspective has appeared in many recent publications describing CNDP dependent nano-periodic property patterns/trends, rules and Mendeleev-like nano-periodic tables which may unify and provide first steps toward a "central paradigm" for nanoscience.


2019 ◽  
Vol 13 (2) ◽  
pp. 144-152 ◽  
Author(s):  
Roni Reiter-Palmon ◽  
Boris Forthmann ◽  
Baptiste Barbot

Author(s):  
Chung-Kuan Cheng ◽  
Andrew B. Kahng ◽  
Hayoung Kim ◽  
Minsoo Kim ◽  
Daeyeal Lee ◽  
...  
Keyword(s):  

2016 ◽  
Vol 25 (4) ◽  
pp. 49-61
Author(s):  
Herlander Mata-Lima ◽  
Fernando Morgado-Dias ◽  
Marina Carrato Galuzzi da Silva ◽  
Kelly Alcântara ◽  
José António Almeida

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Erkhembayar Jadamba ◽  
Miyoung Shin

Drug repositioning offers new clinical indications for old drugs. Recently, many computational approaches have been developed to repurpose marketed drugs in human diseases by mining various of biological data including disease expression profiles, pathways, drug phenotype expression profiles, and chemical structure data. However, despite encouraging results, a comprehensive and efficient computational drug repositioning approach is needed that includes the high-level integration of available resources. In this study, we propose a systematic framework employing experimental genomic knowledge and pharmaceutical knowledge to reposition drugs for a specific disease. Specifically, we first obtain experimental genomic knowledge from disease gene expression profiles and pharmaceutical knowledge from drug phenotype expression profiles and construct a pathway-drug network representing a priori known associations between drugs and pathways. To discover promising candidates for drug repositioning, we initialize node labels for the pathway-drug network using identified disease pathways and known drugs associated with the phenotype of interest and perform network propagation in a semisupervised manner. To evaluate our method, we conducted some experiments to reposition 1309 drugs based on four different breast cancer datasets and verified the results of promising candidate drugs for breast cancer by a two-step validation procedure. Consequently, our experimental results showed that the proposed framework is quite useful approach to discover promising candidates for breast cancer treatment.


2019 ◽  
Vol 46 (6) ◽  
pp. 511-521
Author(s):  
Lian Gu ◽  
Tae J. Kwon ◽  
Tony Z. Qiu

In winter, it is critical for cold regions to have a full understanding of the spatial variation of road surface conditions such that hot spots (e.g., black ice) can be identified for an effective mobilization of winter road maintenance operations. Acknowledging the limitations in present study, this paper proposes a systematic framework to estimate road surface temperature (RST) via the geographic information system (GIS). The proposed method uses a robust regression kriging method to take account for various geographical factors that may affect the variation of RST. A case study of highway segments in Alberta, Canada is used to demonstrate the feasibility and applicability of the method proposed herein. The findings of this study suggest that the geostatistical modelling framework proposed in this paper can accurately estimate RST with help of various covariates included in the model and further promote the possibility of continuous monitoring and visualization of road surface conditions.


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