Dynamically-Coupled Oscillators -- Cooperative Behavior via Dynamical Interaction --

We propose a theoretical framework to study the cooperative behavior of dynamically coupled oscillators (DCOs) that possess dynamical interactions. Then, to understand synchronization phenomena in networks of interneurons which possess inhibitory interactions, we propose a DCO model with dynamics of interactions that tend to cause 180-degree phase lags. Employing an approach developed here, we demonstrate that although our model displays synchronization at high frequencies, it does not exhibit synchronization at low frequencies because this dynamical interaction does not cause a phase lag sufficiently large to cancel the effect of the inhibition. We interpret the disappearance of synchronization in our model with decreasing frequency as describing the breakdown of synchronization in the interneuron network of the CA1 area below the critical frequency of 20 Hz.Comment: 10 pages, 3 figure

Single electron charging of impurity sites visualized by scanning gate experiments on a quantum point contact

A quantum point contact (QPC) patterned on a two-dimensional electron gas is investigated with a scanning gate setup operated at a temperature of 300 mK. The conductance of the point contact is recorded while the local potential is modified by scanning the tip. Single electron charging of impurities induced by the local potential is observed as a stepwise conductance change of the constriction. By selectively changing the state of some of these impurities, it is possible to observe changes in transmission resonances of the QPC. The location of such impurities is determined, and their density is estimated to be below 50 per \mu m^2, corresponding to less than 1 % of the doping concentration

Groups of Galaxies in the Two Micron All-Sky Redshift Survey

We present the results of applying a percolation algorithm to the initial release of the Two Micron All-Sky Survey Extended Source Catalog, using subsequently measured redshifts for almost all of the galaxies with K < 11.25 mag. This group catalog is based on the first near-IR all-sky flux-limited survey that is complete to |b| = 5 deg. We explore the dependence of the clustering on the length and velocity scales involved. The paper describes a group catalog, complete to a limiting redshift of 10,000 km/s, created by maximizing the number of groups containing 3 or more members. A second catalog is also presented, created by requiring a minimum density contrast of 80 to identify groups. We identify known nearby clusters in the catalogs and contrast the groups identified in the two catalogs. We examine and compare the properties of the determined groups and verify that the results are consistent with the UZC-SSRS2 and northern CfA redshift survey group catalogs. The all-sky nature of the catalog will allow the development of a flow-field model based on the density field inferred from the estimated cluster masses.Comment: Accepted for publication in ApJ (29 pages including 13 figures). A version with high-resolution figures is available at http://www.cfa.harvard.edu/~acrook/preprints

External and internal noise surveys of London primary schools

Internal and external noise surveys have been carried out around schools in London, UK, to provide information on typical levels and sources to which children are exposed while at school. Noise levels were measured outside 142 schools, in areas away from flightpaths into major airports. 86% of the schools surveyed were exposed to noise from road traffic, the average external noise level outside a school being 57 dB LAeq. Detailed internal noise surveys have been carried out in 140 classrooms in 16 schools, together with classroom observations. It was found that noise levels inside classrooms depend upon the activities in which the children are engaged, with a difference of 20 dB LAeq between the 'quietest' and 'noisiest' activities. The average background noise level in classrooms exceeds the level recommended in current standards. The number of children in the classroom was found to affect noise levels. External noise influenced internal noise levels only when children were engaged in the quietest classroom activities. The effects of the age of the school buildings and types of window upon internal noise were examined but results were inconclusive

Clinical investigation of an outbreak of alveolitis and asthma in a car engine manufacturing plant

Background Exposure to metal working fluid (MWF) has been associated with outbreaks of EAA in the US, with bacterial contamination of MWF being a possible cause, but was uncommon in the UK. Twelve workers developed extrinsic allergic alveolitis (EAA) in a car engine manufacturing plant in the UK, presenting clinically between December 2003 and May 2004. This paper reports the subsequent epidemiological investigation of the whole workforce. This had three aims:- • To measure the extent of the outbreak by identifying other workers who may have developed EAA or other work-related respiratory diseases. • To provide case-detection so that those affected can be treated. • To provide epidemiological data to identify the cause of the outbreak. Methods The outbreak was investigated in a three-phase cross-sectional survey of the workforce. Phase I A respiratory screening questionnaire was completed by 808/836 workers (96.7%) in May 2004. Phase II 481 employees with at least one respiratory symptom on screening and 50 asymptomatic controls were invited for investigation at the factory in June 2004. This included a questionnaire, spirometry and clinical opinion. 454/481(94.4%) responded along with 48/50(96%) controls. Workers were identified who needed further investigation and serial measurements of peak expiratory flow (PEF). Phase III 162 employees were seen at the Birmingham Occupational Lung Disease clinic. 198 employees returned PEF records, including 141 of the 162 who attended for clinical investigation. Case definitions for diagnoses were agreed. Results 87 workers (10.4% of workforce) met case definitions for occupational lung disease, comprising EAA(19), occupational asthma(74) and humidifier fever(7). 12 workers had more than one diagnosis. The peak onset of work-related breathlessness was Spring 2003. The proportion of workers affected was higher for those using metal working fluid (MWF) from a large sump(27.3%) compared with working all over the manufacturing area (7.9%) (OR=4.39,p<0.001). Two workers had positive specific provocation tests to the used but not the unused MWF solution. Conclusions Extensive investigation of the outbreak of EAA detected a large number of affected workers, not only with EAA but also occupational asthma. This is the largest reported outbreak in Europe. Mist from used MWF is the likely cause. In workplaces using MWF, there is a need to carry out risk assessments, to monitor and maintain fluid quality, to control mist and to carry out respiratory health surveillance

Optimizing DNA Extraction Methods for Nanopore Sequencing of Neisseria gonorrhoeae Directly from Urine Samples

Empirical gonorrhea treatment at initial diagnosis reduces onward transmission. However, increasing resistance to multiple antibiotics may necessitate waiting for culture-based diagnostics to select an effective treatment. There is a need for same-day culture-free diagnostics that identify infection and detect antimicrobial resistance. We investigated if Nanopore sequencing can detect sufficient Neisseria gonorrhoeae DNA to reconstruct whole genomes directly from urine samples. We used N. gonorrhoeae-spiked urine samples and samples from gonorrhea infections to determine optimal DNA extraction methods that maximize the amount of N. gonorrhoeae DNA sequenced while minimizing contaminating host DNA. In simulated infections, the Qiagen UCP pathogen mini kit provided the highest ratio of N. gonorrhoeae to human DNA and the most consistent results. Depletion of human DNA with saponin increased N. gonorrhoeae yields in simulated infections but decreased yields in clinical samples. In 10 urine samples from men with symptomatic urethral gonorrhea, ≥92.8% coverage of an N. gonorrhoeae reference genome was achieved in all samples, with ≥93.8% coverage breath at ≥10-fold depth in 7 (70%) samples. In simulated infections, if ≥104 CFU/ml of N. gonorrhoeae was present, sequencing of the large majority of the genome was frequently achieved. N. gonorrhoeae could also be detected from urine in cobas PCR medium tubes and from urethral swabs and in the presence of simulated Chlamydia coinfection. Using Nanopore sequencing of urine samples from men with urethral gonorrhea, sufficient data can be obtained to reconstruct whole genomes in the majority of samples without the need for culture

A Bayesian mixture modelling approach for spatial proteomics.

Analysis of the spatial sub-cellular distribution of proteins is of vital importance to fully understand context specific protein function. Some proteins can be found with a single location within a cell, but up to half of proteins may reside in multiple locations, can dynamically re-localise, or reside within an unknown functional compartment. These considerations lead to uncertainty in associating a protein to a single location. Currently, mass spectrometry (MS) based spatial proteomics relies on supervised machine learning algorithms to assign proteins to sub-cellular locations based on common gradient profiles. However, such methods fail to quantify uncertainty associated with sub-cellular class assignment. Here we reformulate the framework on which we perform statistical analysis. We propose a Bayesian generative classifier based on Gaussian mixture models to assign proteins probabilistically to sub-cellular niches, thus proteins have a probability distribution over sub-cellular locations, with Bayesian computation performed using the expectation-maximisation (EM) algorithm, as well as Markov-chain Monte-Carlo (MCMC). Our methodology allows proteome-wide uncertainty quantification, thus adding a further layer to the analysis of spatial proteomics. Our framework is flexible, allowing many different systems to be analysed and reveals new modelling opportunities for spatial proteomics. We find our methods perform competitively with current state-of-the art machine learning methods, whilst simultaneously providing more information. We highlight several examples where classification based on the support vector machine is unable to make any conclusions, while uncertainty quantification using our approach provides biologically intriguing results. To our knowledge this is the first Bayesian model of MS-based spatial proteomics data.LG was supported by the BBSRC Strategic Longer and Larger grant (Award BB/L002817/1) and the Wellcome Trust Senior Investigator Award 110170/Z/15/Z awarded to KSL. PDWK was supported by the MRC (project reference MC_UP_0801/1). CMM was supported by a Wellcome Trust Technology Development Grant (Grant number 108467/Z/15/Z). OMC is a Wellcome Trust Mathematical Genomics and Medicine student supported financially by the School of Clinical Medicine, University of Cambridge. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

A Bayesian semi-parametric model for thermal proteome profiling.

Funder: Wellcome TrustThe thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a highly multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on the thermal stability of a protein. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current robust statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets

COVID-19 pandemic impact on adolescent mental health: a reassessment accounting for development

Current prospective reports suggest a pandemic-related increase in adolescent mental health problems. We examine whether age-related change over 11-14 years accounts for this increase. Mothers and adolescents in a UK-based birth cohort (Wirral Child Health and Development Study; WCHADS; N = 737) reported on adolescent depression and behavioural problems pre-pandemic (December 2019-March 2020), mid-pandemic (June 2020-March 2021) and late pandemic (July 2021-March 2022). Analysis used repeated measures models for over-dispersed Poisson counts with an adolescent-specific intercept with age as a time-varying covariate. Maturational curves for girls, but not for boys, showed a significant increase in self-reported depression symptoms over ages 11-14 years. Behavioural problems decreased for both. After adjusting for age-related change, girls' depression increased by only 13% at mid-pandemic and returned to near pre-pandemic level at late pandemic (mid versus late - 12%), whereas boys' depression increased by 31% and remained elevated (mid versus late 1%). Age-adjusted behavioural problems increased for both (girls 40%, boys 41%) and worsened from mid- to late pandemic (girls 33%, boys 18%). Initial reports of a pandemic-related increase in depression in young adolescent girls could be explained by a natural maturational rise. In contrast, maturational decreases in boys' depression and both boys' and girls' behavioural problems may mask an effect of the pandemic