Moses: Planning for the Next Generation

The study of population changes has always been at the centre of public policy and planning. People’s movements, interactions and behaviors will inevitably have an important impact on the society and environment that they are living in. At the same time, such changes will also lead to an evolution of the population itself over time. Advances in technologies and new tools often bring new visions to such studies. To facilitate strategic decision making and to plan developments for a more sustainable future, it is vital to study and understand the changes in our population. This paper introduces Moses, an individual based model that simulates the UK population through discrete demographic processes at a fine spatial scale for 30 years from 2001 to 2031. The modeling method is grounded in a dynamic spatial MicroSimulation Model (MSM), but also introduced Agent Based Model (ABM) insights to strengthen the modeling of movements, interactions and behaviors of distinctively different sub-populations. The MSM can not only produce projections of baseline population with rich information on individuals to facilitate various studies, it can be also useful in providing an assessment of multiple scenarios for different planning applications. In this paper, we will demonstrate three spatial planning applications in the areas of residential land use planning, public health planning and public transport planning. Whilst the demonstrations are deliberately made simple, the contribution of intelligent agents in the modeling of interaction, behavior and the impact of personal histories on demographic changes is clearly shown. Within this framework, it enables researchers to effectively model the heterogeneous decision making units on a large scale, as well as provide the flexibility to introduce different modeling techniques to strengthen various aspects of the model

Identifying tweets from Syria refugees using a Random Forest classifier

A social unrest and violent atmosphere can force a vast number of people to flee their country. While governments and international aid organizations need migration data to inform their decisions, the availability of this data is often delayed due to the tediousness to collect and publish this data. Recent studies recognized the increasing usage of social networking platforms amongst refugees to seek help and express their hardship during their journeys. This paper investigates the feasibility of accurately extracting and identifying tweets from Syria refugees. A robust framework has been developed to find, retrieve, clean and classify tweets from Syria. This includes the development of a Random Forest classifier, which automatically determines which tweets are from Syria refugees. Testing the classifier with samples of historical Twitter data produced promising result of 81% correct classification rate. This preliminary study demonstrates the potential that refugees’ messages can be accurately identified and extracted from social media data mixed with many unwanted messages, and this enables further works for studying refugee issues and predicting their migration patterns

Metabolic Health Has Greater Impact on Diabetes than Simple Overweight/Obesity in Mexican Americans

To compare the risk for diabetes in each of 4 categories of metabolic health and BMI. Methods. Participants were drawn from the Cameron County Hispanic Cohort, a randomly selected Mexican American cohort in Texas on the US-Mexico border. Subjects were divided into 4 phenotypes according to metabolic health and BMI: metabolically healthy normal weight, metabolically healthy overweight/obese, metabolically unhealthy normal weight, and metabolically unhealthy overweight/obese. Metabolic health was defined as having less than 2 metabolic abnormalities. Overweight/obese status was assessed by BMI higher than 25 kg/m2. Diabetes was defined by the 2010 ADA definition or by being on a diabetic medication. Results. The odds ratio for diabetes risk was 2.25 in the metabolically healthy overweight/obese phenotype (95% CI 1.34, 3.79), 3.78 (1.57, 9.09) in the metabolically unhealthy normal weight phenotype, and 5.39 (3.16, 9.20) in metabolically unhealthy overweight/obese phenotype after adjusting for confounding factors compared with the metabolically healthy normal weight phenotype. Conclusions. Metabolic health had a greater effect on the increased risk for diabetes than overweight/obesity. Greater focus on metabolic health might be a more effective target for prevention and control of diabetes than emphasis on weight loss alone

Density Profiles of Collapsed Rotating Massive Stars Favor Long Gamma-Ray Bursts

Long-duration gamma-ray bursts (lGRBs) originate in relativistic collimated outflows -- jets -- that drill their way out of collapsing massive stars. Accurately modeling this process requires realistic stellar profiles for the jets to propagate through and break out of. Most previous studies have used simple power laws or pre-collapse models for massive stars. However, the relevant stellar profile for lGRB models is in fact that of a star after its core has collapsed to form a compact object. To self-consistently compute such a stellar profile, we use the open-source code GR1D to simulate the core-collapse process for a suite of low-metallicity, rotating, massive stellar progenitors that have undergone chemically homogeneous evolution. Our models span a range of zero-age main sequence (ZAMS) masses: $M_\mathrm{ZAMS} = 13, 18, 21, 25, 35, 40$, and $45 M_\odot$. All of these models, at the onset of core-collapse, feature steep density profiles, $\rho \propto r^{-\alpha}$ with $\alpha\approx 2.5$, which would result in jets that are inconsistent with lGRB observables. We follow the collapse of four out of our seven models until they form BHs and the other three proto-neutron stars (PNSs). We find, across all models, that the density profile outside of the newly-formed BH or PNS is well-represented by a flatter power law with $\alpha \approx 1.35{-}1.55$. Such flat density profiles are conducive to successful formation and breakout of BH-powered jets and, in fact, required to reproduce observable properties of lGRBs. Future models of lGRBs should be initialized with shallower \textit{post-collapse} stellar profiles like those presented here instead of the much steeper pre-collapse profiles that are typically used.Comment: 9 pages, 4 figures+1 table, submitted to ApJL, comments welcom

limma powers differential expression analyses for RNA-sequencing and microarray studies

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously describe

Web-based physiotherapy for people affected by multiple sclerosis: a single blind, randomized controlled feasibility study

Objective: To examine the feasibility of a trial to evaluate web-based physiotherapy compared to a standard home exercise programme in people with multiple sclerosis. Design: Multi-centre, randomized controlled, feasibility study. Setting: Three multiple sclerosis out-patient centres. Participants: A total of 90 people with multiple sclerosis (Expanded Disability Status Scale 4–6.5). Interventions: Participants were randomized to a six-month individualized, home exercise programme delivered via web-based physiotherapy (n = 45; intervention) or a sheet of exercises (n = 45; active comparator). Outcome measures: Outcome measures (0, three, six and nine months) included adherence, two-minute walk test, 25 foot walk, Berg Balance Scale, physical activity and healthcare resource use. Interviews were undertaken with 24 participants and 3 physiotherapists. Results: Almost 25% of people approached agreed to take part. No intervention-related adverse events were recorded. Adherence was 40%–63% and 53%–71% in the intervention and comparator groups. There was no difference in the two-minute walk test between groups at baseline (Intervention-80.4(33.91)m, Comparator-70.6(31.20)m) and no change over time (at six-month Intervention-81.6(32.75)m, Comparator-74.8(36.16)m. There were no significant changes over time in other outcome measures except the EuroQol-5 Dimension at six months which decreased in the active comparator group. For a difference of 8(17.4)m in two-minute walk test between groups, 76 participants/group would be required (80% power, P > 0.05) for a future randomized controlled trial. Conclusion: No changes were found in the majority of outcome measures over time. This study was acceptable and feasible by participants and physiotherapists. An adequately powered study needs 160 participants

Maintaining Well-Being During the COVID-19 Pandemic: A Network Analysis of Well-Being Responses from British Youth

COVID-19 has significant impacts on young peoples’ lives and emotions. Understanding how young people maintain well-being in the face of challenges can inform future mental health intervention development. Here we applied network analysis to well-being data gathered from 2532 young people (12-25 years) residing in the UK during the COVID-19 pandemic to identify the structure across well-being and crucially, its central defining features. Gender and age differences in networks were also investigated. Across all participants, items emerged in two clusters: 1) optimism, positive self-perception, and social connectedness, and 2) processing problems and ideas. The two central features of well-being were: “I’ve been dealing with problems well” and “I’ve been thinking clearly”. There were minimal age and gender differences. Our findings suggest that the perception of being able to process problems and ideas efficiently could be a hallmark of well-being, particularly in the face of challenging circumstances. These findings contrast with pre-pandemic studies that point to positive affect as central aspects of well-being networks. Future interventions that encourage problem-solving and mental flexibility could be useful in helping young people maintain well-being during times of stress and uncertainty

Radio Jet Feedback and Star Formation in Heavily Obscured Quasars at Redshifts ~0.3-3, I: ALMA Observations

We present ALMA 870 micron (345 GHz) data for 49 high redshift (0.47<z<2.85), luminous (11.7 < log L(bol) (Lsun) < 14.2) radio-powerful AGN, obtained to constrain cool dust emission from starbursts concurrent with highly obscured radiative-mode black hole (BH) accretion in massive galaxies which possess a small radio jet. The sample was selected from WISE with extremely steep (red) mid-infrared (MIR) colors and with compact radio emission from NVSS/FIRST. Twenty-six sources are detected at 870 microns, and we find that the sample has large mid- to far-infrared luminosity ratios consistent with a dominant and highly obscured quasar. The rest-frame 3 GHz radio powers are 24.7 < log P3.0 GHz (W/Hz) < 27.3, and all sources are radio-intermediate or radio-loud. BH mass estimates are 7.7 < log M(BH) (Msun) < 10.2. The rest frame 1-5 um SEDs are very similar to the "Hot DOGs" (Hot Dust Obscured Galaxies), and steeper (redder) than almost any other known extragalactic sources. ISM masses estimated for the ALMA detected sources are 9.9 < log M(ISM) (Msun) < 11.75 assuming a dust temperature of 30K. The cool dust emission is consistent with star formation rates (SFRs) reaching several thousand Msun/yr, depending on the assumed dust temperature, however we cannot rule out the alternative that the AGN powers all the emission in some cases. Our best constrained source has radiative transfer solutions with ~ equal contributions from an obscured AGN and a young (10-15 Myr) compact starburst.Comment: 29 pages, 8 figures. To appear in Astrophysical Journal. Update on Sept 14 to correct the ALMA proposal id. to ADS/JAO.ALMA#2011.0.00397.S and to add a missing acknowledgemen

β2 Adrenergic receptor activation induces microglial NADPH oxidase activation and dopaminergic neurotoxicity through an ERK-dependent/protein kinase A-independent pathway

Activation of the β2 adrenergic receptor (β2AR) on immune cells has been reported to possess anti-inflammatory properties, however, the pro-inflammatory properties of β2AR activation remain unclear. In this study, using rat primary mesencephalic neuron-glia cultures, we report that salmeterol, a long-acting β2AR agonist, selectively induces dopaminergic (DA) neurotoxicity through its ability to activate microglia. Salmeterol selectively increased the production of reactive oxygen species (ROS) by NADPH oxidase (PHOX), the superoxide-producing enzyme in microglia. A key role of PHOX in mediating salmeterol-induced neurotoxicity was demonstrated by the inhibition of DA neurotoxicity in cultures pretreated with diphenylene-iodonium (DPI), an inhibitor of PHOX activity. Mechanistic studies revealed the activation of microglia by salmeterol results in the selective phosphorylation of ERK, a signaling pathway required for the translocation of the PHOX cytosolic subunit p47phox to the cell membrane. Furthermore, we found ERK inhibition, but not protein kinase A (PKA) inhibition, significantly abolished salmeterol-induced superoxide production, p47phox translocation, and its ability to mediate neurotoxicity. Together, these findings indicate that β2AR activation induces microglial PHOX activation and DA neurotoxicity through an ERK-dependent/PKA-independent pathway

Discovery of a Redox Thiol Switch: Implications for Cellular Energy Metabolism

The redox-based modifications of cysteine residues in proteins regulate their function in many biological processes. The gas molecule H2S has been shown to persulfidate redox sensitive cysteine residues resulting in an H2S-modified proteome known as the sulfhydrome. Tandem Mass Tags (TMT) multiplexing strategies for large-scale proteomic analyses have become increasingly prevalent in detecting cysteine modifications. Here we developed a TMT-based proteomics approach for selectively trapping and tagging cysteine persulfides in the cellular proteomes. We revealed the natural protein sulfhydrome of two human cell lines, and identified insulin as a novel substrate in pancreatic beta cells. Moreover, we showed that under oxidative stress conditions, increased H2S can target enzymes involved in energy metabolism by switching specific cysteine modifications to persulfides. Specifically, we discovered a Redox Thiol Switch, from protein S-glutathioinylation to S-persulfidation (RTSGS). We propose that the RTSGS from S-glutathioinylation to S-persulfidation is a potential mechanism to fine tune cellular energy metabolism in response to different levels of oxidative stress