The rapid development of additive technologies in recent years is accompanied by their intensive introduction into various fields of science and related technologies, including analytical chemistry. ...The use of 3D printing in analytical instrumentation, in particular, for making prototypes of new equipment and manufacturing parts having complex internal spatial configuration, has been proved as exceptionally effective. Additional opportunities for the widespread introduction of 3D printing technologies are associated with the development of new optically transparent, current- and thermo-conductive materials, various composite materials with desired properties, as well as possibilities for printing with the simultaneous combination of several materials in one product. This review will focus on the application of 3D printing for production of new advanced analytical devices, such as compact chromatographic columns for high performance liquid chromatography, flow reactors and flow cells for detectors, devices for passive concentration of toxic compounds and various integrated devices that allow significant improvements in chemical analysis. A special attention is paid to the complexity and functionality of 3D-printed devices.
separable model for dynamic networks Krivitsky, Pavel N; Handcock, Mark S
Journal of the Royal Statistical Society. Series B, Statistical methodology,
January 2014, Letnik:
76, Številka:
1
Journal Article
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Models of dynamic networks—networks that evolve over time—have manifold applications. We develop a discrete time generative model for social network evolution that inherits the richness and ...flexibility of the class of exponential family random‐graph models. The model—a separable temporal exponential family random‐graph model—facilitates separable modelling of the tie duration distributions and the structural dynamics of tie formation. We develop likelihood‐based inference for the model and provide computational algorithms for maximum likelihood estimation. We illustrate the interpretability of the model in analysing a longitudinal network of friendship ties within a school.
3D printing technology is now frequently employed in many areas of research and development. However, a relatively narrow range of 3D printable materials with a limited spectrum of physico-chemical ...properties still restricts the true potential of this potentially disruptive technology. There is rapidly increasing interest in the improvement and diversification of properties of generic printing materials
via
the introduction of fillers with unique properties, and/or by blending materials exhibiting different properties to generate high performance composites. 3D printed composites have already been utilised in a wide range of applications, including biomedical, mechanical, electrical, thermal and optically enhanced products. The increasing popularity of 3D printed composites can be attributed to the ability to fabricate complex geometries, low cost production, and other advantages associated with rapid prototyping. This review covers all the recent reports in which the properties of generic 3D printable materials have been modified either by adding nanoparticles, fibers, other polymers, or by a chemical reaction for fabrication of composites with enhanced biomaterial, mechanical, electrical, thermal, optical and other properties.
The formulation of new composite materials compatible with additive fabrication techniques is driving a revolution in the field of applied materials science.
Tissue-specific autoimmune diseases are driven by activation of diverse immune cells in the target organs. However, the molecular signatures of immune cell populations over time in an autoimmune ...process remain poorly defined. Using single-cell RNA sequencing, we performed an unbiased examination of diverse islet-infiltrating cells during autoimmune diabetes in the nonobese diabetic mouse. The data revealed a landscape of transcriptional heterogeneity across the lymphoid and myeloid compartments. Memory CD4 and cytotoxic CD8 T cells appeared early in islets, accompanied by regulatory cells with distinct phenotypes. Surprisingly, we observed a dramatic remodeling in the islet microenvironment, in which the resident macrophages underwent a stepwise activation program. This process resulted in polarization of the macrophage subpopulations into a terminal proinflammatory state. This study provides a single-cell atlas defining the staging of autoimmune diabetes and reveals that diabetic autoimmunity is driven by transcriptionally distinct cell populations specialized in divergent biological functions.
The last two decades have seen considerable progress in foundational aspects of statistical network analysis, but the path from theory to application is not straightforward. Two large, heterogeneous ...samples of small networks of within-household contacts in Belgium were collected using two different but complementary sampling designs: one smaller but with all contacts in each household observed, the other larger and more representative but recording contacts of only one person per household. We wish to combine their strengths to learn the social forces that shape household contact formation and facilitate simulation for prediction of disease spread, while generalising to the population of households in the region. To accomplish this, we describe a flexible framework for specifying multi-network models in the exponential family class and identify the requirements for inference and prediction under this framework to be consistent, identifiable, and generalisable, even when data are incomplete; explore how these requirements may be violated in practice; and develop a suite of quantitative and graphical diagnostics for detecting violations and suggesting improvements to candidate models. We report on the effects of network size, geography, and household roles on household contact patterns (activity, heterogeneity in activity, and triadic closure). Supplementary materials for this article are available online.
In order to identify possible general relationships between changes in the framework topology and changes in the extra framework ion properties, a set consisting of 2202 crystal structures of ionic ...coordination polymers was extracted from the Cambridge Structural Database. Changes in ion properties served as independent variables for several machine learning models trained to predict the changes in framework dimensionality, topological density, and average ring size of the framework’s tiling. The trained classifiers showed acceptable predictive performance with F1 score in the range 0.4 ÷ 0.6 and were subjected to the validation tests, which confirmed that they fit the data significantly better than by chance. Subsequent feature importance analysis of the classifiers revealed a set of the ion properties being important for prediction of the changes in corresponding framework characteristics in the extracted set of crystal structures. It is shown that in general changes in molecular surface area and molecular flexibility of the guest ions are essential for predicting changes in selected topological characteristics of a framework. Case studies were conducted for several sets of crystal structures with frameworks that are observed to host significant variety of counter ions. The decision tree classifiers allowed us to discover the ion properties determining topological characteristics in particular frameworks.
Exponential-family random graph models (ERGMs) provide a principled and flexible way to model and simulate features common in social networks, such as propensities for homophily, mutuality, and ...friend-of-a-friend triad closure, through choice of model terms (sufficient statistics). However, those ERGMs modeling the more complex features have, to date, been limited to binary data: presence or absence of ties. Thus, analysis of valued networks, such as those where counts, measurements, or ranks are observed, has necessitated dichotomizing them, losing information and introducing biases. In this work, we generalize ERGMs to valued networks. Focusing on modeling counts, we formulate an ERGM for networks whose ties are counts and discuss issues that arise when moving beyond the binary case. We introduce model terms that generalize and model common social network features for such data and apply these methods to a network dataset whose values are counts of interactions.
Complexes (dpp-BIAN)
Co
I
·MeCN (
) and (Py)
CoI
(
) were synthesized by the reaction between cobalt(II) iodide and 1,2-bis(2,6-diisopropylphenylimino)acenaphthene (dpp-BIAN) or pyridine (Py), ...respectively. The molecular structures of the complexes were determined by X-ray diffraction. The Co(II) ions in both compounds are in a distorted tetrahedral environment (CoN
I
). The electrochemical behavior of complex
was studied by cyclic voltammetry. Magnetochemical measurements revealed that when an external magnetic field is applied, both compounds exhibit the properties of field-induced single ion magnets.
•Rigorous inferential framework for egocentrically-sampled network data is discussed.•Validity and comparability requirements for survey questions about attributes of egos and alters, ego–alter ties, ...and alter–alter ties are formulated.•Strategies for optimal stratified sampling for estimating homophily parameters are described.•Analytical and simulation-based results are presented discussing advantages and disadvantages of fixed-choice design (FCD) and augmented fixed-choice design (AFCD).
Egocentric sampling of networks selects a subset of nodes (“egos”) and collects information from them on themselves and their immediate network neighbours (“alters”), leaving the rest of the nodes in the network unobserved. This design is popular because it is relatively inexpensive to implement and can be integrated into standard sample surveys. Recent methodological developments now make it possible to statistically analyse this type of network data with exponential-family random graph models (ERGMs). This provides a framework for principled statistical inference, and the fitted models can in turn be used to simulate complete networks of arbitrary size that are consistent with the observed sample data, allowing one to infer the distribution of whole-network properties generated by the observed egocentric network statistics. In this paper, we discuss how design choices for egocentric network studies impact statistical estimation and inference for ERGMs. The design choices include both measurement strategies (for ego and alter attributes, and for ego–alter and alter–alter ties) and sampling strategies (for egos and alters). We discuss the importance of harmonising measurement specifications across egos and alters, and conduct simulation studies to demonstrate the impact of sampling design on statistical inference, specifically stratified sampling and degree censoring.
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