Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases

and demographic characteristics of people living within small geographic areas. They have hitherto been regarded as products, which are the final “best” outcome that can be achieved using available data and algorithms. However, reduction in computational cost, increased network bandwidths and increasingly accessible spatial data infrastructures have together created the potential for the creation of classifications in near real time within distributed online environments. Yet paramount to the creation of truly real time geodemographic classifications is the ability for software to process and efficiency cluster large multidimensional spatial databases within a timescale that is consistent with online user interaction. To this end,this article evaluates the computational efficiency of a number of clustering algorithms with a view to creating geodemographic classifications “on the fly” at a range of different geographic scales.tgis_1197 283..29

Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases

and demographic characteristics of people living within small geographic areas. They have hitherto been regarded as products, which are the final “best” outcome that can be achieved using available data and algorithms. However, reduction in computational cost, increased network bandwidths and increasingly accessible spatial data infrastructures have together created the potential for the creation of classifications in near real time within distributed online environments. Yet paramount to the creation of truly real time geodemographic classifications is the ability for software to process and efficiency cluster large multidimensional spatial databases within a timescale that is consistent with online user interaction. To this end,this article evaluates the computational efficiency of a number of clustering algorithms with a view to creating geodemographic classifications “on the fly” at a range of different geographic scales.tgis_1197 283..29

Mass spectrometry of B. subtilis CopZ: Cu(I)-binding and interactions with bacillithiol

CopZ from Bacillus subtilis is a well-studied member of the highly conserved family of Atx1-like copper chaperones. It was previously shown via solution and crystallographic studies to undergo Cu(I)-mediated dimerisation, where the CopZ dimer can bind between one and four Cu(I) ions. However, these studies could not provide information about the changing distribution of species at increasing Cu(I) levels. To address this, electrospray ionisation mass spectrometry using soft ionisation was applied to CopZ under native conditions. Data revealed folded, monomeric CopZ in apo- and Cu(I)-bound forms, along with Cu(I)-bound dimeric forms of CopZ at higher Cu(I) loading. Cu4(CopZ)2 was the major dimeric species at loadings >1 Cu(I)/CopZ, indicating the cooperative formation of the tetranuclear Cu(I)-bound species. As the principal low molecular weight thiol in B. subtilis, bacillithiol (BSH) may play a role in copper homeostasis. Mass spectrometry showed that increasing BSH led to a reduction in Cu(I)-bound dimeric forms, and the formation of S-bacillithiolated apo-CopZ and BSH adducts of Cu(I)-bound forms of CopZ, where BSH likely acts as a Cu(I) ligand. These data, along with the high affinity of BSH for Cu(I), determined here to be β2(BSH) = ∼4 × 1017 M−2, are consistent with a role for BSH alongside CopZ in buffering cellular Cu(I) levels. Here, mass spectrometry provides a high resolution overview of CopZ–Cu(I) speciation that cannot be obtained from less discriminating solution-phase methods, thus illustrating the potential for the wider application of this technique to studies of metal–protein interactions

Measuring portfolio performance using a modified measure of risk

This paper reports the results of an investigation into the properties of a theoretical modification of beta proposed by Leland (1999) and based on earlier work of Rubinstein (1976). It is shown that when returns are elliptically symmetric, beta is the appropriate measure of risk and that there are other situations in which the modified beta will be similar to the traditional measure based on the capital asset pricing model. For the case where returns have a normal distribution, it is shown that the criterion either does not exist or reduces exactly to the conventional beta. It is therefore conjectured that the modified measure will only be useful for portfolios that have nonstandard return distributions which incorporate skewness. For such situations, it is shown how to estimate the measure using regression and how to compare the resulting statistic with a traditional estimated beta using Hotelling's test. An empirical study based on stocks from the FTSE350 does not find evidence to support the use of the new measure even in the presence of skewness.Journal of Asset Management (2007) 7, 388-403. doi:10.1057/palgrave.jam.225005

Project Daedalus: An Additive Manufacturing Vending Machine

The project was a research endeavor focused on designing and building a vending machine for 3D-printed parts. It also had the secondary objective of catalyzing leadership qualities among its membership by emphasizing individual responsibility and forward thinking. The project began in the spring of 2015, when the topic of autonomous 3D-printing was chosen, funding was secured, and the majority of the leadership was brought on. Over the summer and into the fall semester the team developed project requirements and infrastructure, and gathered members from the parent organization. By December of 2015 most of the machine design had been completed, and the parts were en route so that building could be started in the spring semester. Due to a combination of time constraints, underestimated difficulty, and unforeseen logistical circumstances, the project was not able to achieve its primary goal of having a working prototype by May 2016. However, it is the belief of the project’s leadership and many of the members that it succeeded in its secondary goal of creating competent and confident leaders, several of whom went on to lead projects of their own

Perspective on Coarse-Graining, Cognitive Load, and Materials Simulation

The predictive capabilities of computational materials science today derive from overlapping advances in simulation tools, modeling techniques, and best practices. We outline this ecosystem of molecular simulations by explaining how important contributions in each of these areas have fed into each other. The combined output of these tools, techniques, and practices is the ability for researchers to advance understanding by efficiently combining simple models with powerful software. As specific examples, we show how the prediction of organic photovoltaic morphologies have improved by orders of magnitude over the last decade, and how the processing of reacting epoxy thermosets can now be investigated with million-particle models. We discuss these two materials systems and the training of materials simulators through the lens of cognitive load theory. For students, the broad view of ecosystem components should facilitate understanding how the key parts relate to each other first, followed by targeted exploration. In this way, the paper is organized in loose analogy to a coarse-grained model: The main components provide basic framing and accelerated sampling from which deeper research is better contextualized. For mentors, this paper is organized to provide a snapshot in time of the current simulation ecosystem and an on-ramp for simulation experts into the literature on pedagogical practice

Phenotypic and molecular analyses of primary lateral sclerosis

Objective: To understand phenotypic and molecular characteristics of patients with clinically “definite” primary lateral sclerosis (PLS) in a prospective study. Methods: Six sites enrolled 41 patients who had pure upper motor neuron dysfunction, bulbar symptoms, a normal EMG done within 12 months of enrollment, and onset of symptoms ≥5 years before enrollment. For phenotypic analyses, 27 demographic, clinical, and cognitive variables were analyzed using the k-means clustering method. For molecular studies, 34 available DNA samples were tested for the C9ORF72 mutation, and exome sequencing was performed to exclude other neurologic diseases with known genetic cause. Results: K-means clustering using the 25 patients with complete datasets suggested that patients with PLS can be classified into 2 groups based on clinical variables, namely dysphagia, objective bulbar signs, and urinary urgency. Secondary analyses performed in all 41 patients and including only variables with complete data corroborated the results from the primary analysis. We found no evidence that neurocognitive variables are important in classifying patients with PLS. Molecular studies identified C9ORF72 expansion in one patient. Well-characterized pathogenic mutations were identified in SPG7, DCTN1, and PARK2. Most cases showed no known relevant mutations. Conclusions: Cluster analyses based on clinical variables indicated at least 2 subgroups of clinically definite PLS. Molecular analyses further identified 4 cases with mutations associated with amyotrophic lateral sclerosis, Parkinson disease, and possibly hereditary spastic paraplegia. Phenotypic and molecular characterization is the first step in investigating biological clues toward the definition of PLS. Further studies with larger numbers of patients are essential

Identifying how COVID-19-related misinformation reacts to the announcement of the UK national lockdown: An interrupted time-series study

COVID-19 is unique in that it is the first global pandemic occurring amidst a crowded information environment that has facilitated the proliferation of misinformation on social media. Dangerous misleading narratives have the potential to disrupt ‘official’ information sharing at major government announcements. Using an interrupted time-series design, we test the impact of the announcement of the first UK lockdown (8–8.30 p.m. 23 March 2020) on short-term trends of misinformation on Twitter. We utilise a novel dataset of all COVID-19-related social media posts on Twitter from the UK 48 hours before and 48 hours after the announcement (n = 2,531,888). We find that while the number of tweets increased immediately post announcement, there was no evidence of an increase in misinformation-related tweets. We found an increase in COVID-19-related bot activity post-announcement. Topic modelling of misinformation tweets revealed four distinct clusters: ‘government and policy’, ‘symptoms’, ‘pushing back against misinformation’ and ‘cures and treatments’

Women, resettlement and desistance

With the numbers of women imprisoned increasing across Western jurisdictions over the last 15 or so years, so too have the numbers of women returning to the community following a period in custody. Despite increasing policy attention in the UK and elsewhere to prisoner resettlement, women’s experiences on release from prison have received limited empirical and policy attention. Drawing upon interviews with women leaving prison in Victoria, Australia, this article discusses the resettlement challenges faced by the women and highlights their similarity to the experiences of women leaving prison in other jurisdictions. Women had mixed (and predominantly negative) experiences and views of accessing services and supports following release, though experiences of parole supervision by community corrections officers were often positive, especially if women felt valued and supported by workers who demonstrated genuine concern. Analysis of factors associated with further offending and with desistance, points to the critical role of flexible, tailored and women-centred post-release support building, and, where possible, upon relationships established with women while they are still in prison