Disordered Databases and Ordered Explanations

entropy, contextual infor-mation. 'Learning begins with organized knowledge which grows and becomes better organized '- Charnick and McDermont. The paper adopts an 'information theoretic' approach to an area of Machine Learning that is known as unsupervised, conceptual clustering. The approach is developed in the 'Vision Domain', databases of iipto 1000 entries being analysed con-taining information derived from digitised colour images. Typically, the field of supervised Machine Learning attempts to develop programs which learn from example and counter-example, failure or even instruction. Unsupervised Machine Learn ing algorithms adopt a different approach. The programs simply try to discover patterns, trends and hierachies of relationships which, although not explicitly stated, are never the less present within a database. In terms of the Vision problem, this corresponds to searching for significant classes or clusterings implicit in the attributes and relationships of im-age regions or features. The paper develops further the Knowledge rep-resentation structure and transformation rules presented at AVC87 [2]. A Hyperdigraph struc-ture for encoding relational knowledge is used, these being suitable for treatment from an in-formation theoretic viewpoint. By allowing non-disjoint classes to exist in this structure, it is shown that the transformation rules reduce the database to a minimal state of complexity. The uniqueness of this state allows classes of features, formed by the clustering process, to be ranked in importance by the increase in complexity within the hyperdigraph structure that their destruction would invoke. As regards implementation, the clustering pro-cess may be regarded as a "many-pattern / many-object " matching problem and the efficiency of such algorithms is discussed. Results based on the analysis of real images are presented.

Cancer3D: understanding cancer mutations through protein structures.

The new era of cancer genomics is providing us with extensive knowledge of mutations and other alterations in cancer. The Cancer3D database at http://www.cancer3d.org gives an open and user-friendly way to analyze cancer missense mutations in the context of structures of proteins in which they are found. The database also helps users analyze the distribution patterns of the mutations as well as their relationship to changes in drug activity through two algorithms: e-Driver and e-Drug. These algorithms use knowledge of modular structure of genes and proteins to separately study each region. This approach allows users to find novel candidate driver regions or drug biomarkers that cannot be found when similar analyses are done on the whole-gene level. The Cancer3D database provides access to the results of such analyses based on data from The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE). In addition, it displays mutations from over 14,700 proteins mapped to more than 24,300 structures from PDB. This helps users visualize the distribution of mutations and identify novel three-dimensional patterns in their distribution

Assessment techniques, database design and software facilities for thermodynamics and diffusion

The purpose of this article is to give a set of recommendations to producers of assessed thermodynamic data, who may be involved in either the critical evaluation of limited chemical systems or the creation and dissemination of larger thermodynamic databases. Also, it is hoped that reviewers and editors of scientific publications in this field will find some of the information useful. Good practice in the assessment process is essential, particularly as datasets from many different sources may be combined together into a single database. With this in mind, we highlight some problems that can arise during the assessment process and we propose a quality assurance procedure. It is worth mentioning at this point, that the provision of reliable assessed thermodynamic data relies heavily on the availability of high quality experimental information. The different software packages for thermodynamics and diffusion are described here only briefly

The crystal structure of perdeuterated methanol hemiammoniate (CD3OD center dot 0.5ND(3)) determined from neutron powder diffraction data at 4.2 and 180 K

The crystal structure of perdeuterated methanol hemiammoniate, CD3OD center dot 0.5ND(3), has been solved from neutron powder diffraction data collected at 4.2 and 180 K. The structure is orthorhombic, space group Pn2(1)a (Z = 4), with unit-cell dimensions a = 12.70615 (16), b = 8.84589 (9), c = 4.73876 (4) angstrom, V = 532.623 (8) angstrom(3) [rho(calc) = 1149.57 (2) kg m(-3)] at 4.2 K, and a = 12.90413 (16), b = 8.96975 (8), c = 4.79198 (4) angstrom, V = 554.656 (7) angstrom(3) [rho(calc) = 1103.90 (1) kg m(-3)] at 180 K. The crystal structure was determined by ab initio methods from the powder data; atomic coordinates and isotropic displacement parameters were subsequently refined by the Rietveld method to R-p similar or equal to 2% at both temperatures. The crystal structure comprises a three-dimensionally hydrogen-bonded network in which the ND3 molecules are tetrahedrally coordinated by the hydroxy moieties of the methanol molecule. This connectivity leads to the formation of zigzag chains of ammonia-hydroxy groups extending along the c axis, formed via N-D center dot center dot center dot O hydrogen bonds; these chains are cross-linked along the a axis through the hydroxy moiety of the second methanol molecule via N-D center dot center dot center dot O and O-D center dot center dot center dot O hydrogen bonds. This 'bridging' hydroxy group in turn donates an O-D center dot center dot center dot N hydrogen bond to ammonia in adjacent chains stacked along the b axis. The methyl deuterons in methanol hemiammoniate, unlike those in methanol monoammoniate, do not participate in hydrogen bonding and reveal evidence of orientational disorder at 180 K. The relative volume change on warming from 4.2 to 180 K, Delta V/V, is + 4.14%, which is comparable to, but more nearly isotropic (as determined from the relative change in axial lengths, e. g. Delta a/a) than, that observed in deuterated methanol monohydrate, and very similar to what is observed in methanol monoammoniate

First-principles thermodynamic modeling of atomic ordering in yttria-stabilized zirconia

Yttria-stabilized zirconia YSZ is modeled using a cluster expansion statistical thermodynamics method built upon a density-functional theory database. The reliability of cluster expansions in predicting atomic ordering is explored by comparing with the extensive experimental database. The cluster expansion of YSZ is utilized in lattice Monte Carlo simulations to compute the ordering of dopant and oxygen vacancies as a function of concentration. Cation dopants show a strong tendency to aggregate and vacate significantly sized domains below 9 mol % Y_2O_3, which is likely important for YSZ aging processes in ionic conductivity. Evolution of vibrational and underlying electronic properties as a function of Y doping is explored

A data-driven framework for structure-property correlation in ordered and disordered cellular metamaterials

Cellular solids and micro-lattices are a class of lightweight architected materials that have been established for their unique mechanical, thermal, and acoustic properties. It has been shown that by tuning material architecture, a combination of topology and solid(s) distribution, one can design new material systems, also known as metamaterials, with superior performance compared to conventional monolithic solids. Despite the continuously growing complexity of synthesized microstructures, mainly enabled by developments in additive manufacturing, correlating their morphological characteristics to the resulting material properties has not advanced equally. This work aims to develop a systematic data-driven framework that is capable of identifying all key microstructural characteristics and evaluating their effect on a target material property. The framework relies on integrating virtual structure generation and quantification algorithms with interpretable surrogate models. The effectiveness of the proposed approach is demonstrated by analyzing the effective stiffness of a broad class of two-dimensional (2D) cellular metamaterials with varying topological disorder. The results reveal the complex manner in which well-known stiffness contributors, including nodal connectivity, cooperate with often-overlooked microstructural features such as strut orientation, to determine macroscopic material behavior. We further re-examine Maxwell's criteria regarding the rigidity of frame structures, as they pertain to the effective stiffness of cellular solids and showcase microstructures that violate them. This framework can be used for structure-property correlation in different classes of metamaterials as well as the discovery of novel architectures with tailored combinations of material properties

Parental food communication and child eating behaviours : A systematic literature review

Aim To review current evidence for parental food communication practices and their association with child eating behaviours. Methods The PRISMA framework guided the reporting of the review; registered with Prospero in July 2020. Eligible studies were critically appraised using the Joanna Briggs Institute tools. Only quantitative studies that included a parental measure of food communication and a child measure of eating behaviour were included. Results From 11 063 articles 23 were eligible for synthesis. The vast majority (82%) of studies used observational cross-sectional designs. Three involved observing parent–child dyads, with the remainder using questionnaires. Two quasi-experimental designs tested interventions and two randomised control trial were reported. The majority of measures assessing parental food communication were subscales of larger questionnaires. The Caregiver's Feeding Style Questionnaire (CFSQ) was the most direct and relevant measure of parental food communication. Findings of reviewed studies highlighted that “how” parents communicate about food appears to impact child eating behaviours. Using child-centred communication provided promising outcomes for positive child eating behaviours, while parental “diet” communication was found to be associated with poorer dietary outcomes in children. Conclusions Food communication research is in its infancy. However, evidence for the importance of parents' child-focused food communication is emerging, providing a focus for future research and interventions. So What? Given the gaps in our understanding about prevention of disordered eating, there is a significant opportunity to explore what food communication strategies may assist parents to communicate about food in a positive way

A study of intrinsic disorder and its role in functional proteomics

Thesis (Ph.D.) - Indiana University, Informatics, 2009The last decade has witnessed the emergence of an alternate view on how protein function arises. This view attributes the functionality of many proteins to the presence of an ensemble of flexible regions popularly as `intrinsically disordered' or `unstructured'. Several proteomic studies have corroborated the existence of either wholly disordered proteins or proteins that contain regions of disorder in them. The purpose of this dissertation was to investigate the consistency of such regions across experiments, their mechanism of facilitating function via disorder-to-order transitions, their presence and significance in pathogenic versus non-pathogenic organisms and their promise of applicability towards the computational prediction of peptides involved in the most common class of post-translational modifications, phosphorylation. Besides these, a new algorithm exploiting the strong correlation between phosphorylation and intrinsic disorder has also been proposed to improve the detection of phosphorylated peptides via high-throughput methods such as tandem mass-spectrometry (LC-MS/MS). Results presented in this study, guide us in understanding the robustness of unstructured regions in proteins to sequence changes and environment, their role in facilitating molecular recognition as well as improving currently available methods for identification of post-translationally modified peptides. The findings and conclusions of this dissertation have the potential to impact ongoing structural genomics initiatives by suggesting alternative methods for determining structure for targets containing regions of disorder. Additional ramifications of results from this work include directing attention towards the possible use of regions of intrinsic disorder by pathogenic organisms for host cell invasion. We believe that unlike the traditional reductionist approach in a scientific method, this study gathers strength and utility by investigating the role of intrinsic disorder on more than one front in order to provide a novel perspective to the understanding of complex interactions within biological systems. Concluding arguments presented in this study pique one's curiosity regarding the evolution of disordered regions and proteins in general. On a technological side, the findings from this study unequivocally support the viable use of informatics methods in gaining new insights about a relatively young class of proteins known as intrinsically disordered proteins and its applicability to improve our present knowledge of cellular physiology