Common fragile sites are characterized by histone hypoacetylation

Common fragile sites (CFSs) represent large, highly unstable regions of the human genome. CFS sequences are sensitive to perturbation of replication; however, the molecular basis for the instability at CFSs is poorly understood. We hypothesized that a unique epigenetic pattern may underlie the unusual sensitivity of CFSs to replication interference. To examine this hypothesis, we analyzed chromatin modification patterns within the six human CFSs with the highest levels of breakage, and their surrounding non-fragile regions (NCFSs). Chromatin at most of the CFSs analyzed has significantly less histone acetylation than that of their surrounding NCFSs. Trichostatin A and/or 5-azadeoxycytidine treatment reduced chromosome breakage at CFSs. Furthermore, chromatin at the most commonly expressed CFS, the FRA3B, is more resistant to micrococcal nuclease than that of the flanking non-fragile sequences. These results demonstrate that histone hypoacetylation is a characteristic epigenetic pattern of CFSs, and chromatin within CFSs might be relatively more compact than that of the NCFSs, indicating a role for chromatin conformation in genomic instability at CFSs. Moreover, lack of histone acetylation at CFSs may contribute to the defective response to replication stress characteristic of CFSs, leading to the genetic instability characteristic of this regions

Sensitivity and Insensitivity of Galaxy Cluster Surveys to New Physics

We study the implications and limitations of galaxy cluster surveys for constraining models of particle physics and gravity beyond the Standard Model. Flux limited cluster counts probe the history of large scale structure formation in the universe, and as such provide useful constraints on cosmological parameters. As a result of uncertainties in some aspects of cluster dynamics, cluster surveys are currently more useful for analyzing physics that would affect the formation of structure than physics that would modify the appearance of clusters. As an example we consider the Lambda-CDM cosmology and dimming mechanisms, such as photon-axion mixing.Comment: 24 pages, 8 eps figures. References added, discussion of scatter in relations between cluster observables lengthene

Elevated basal serum tryptase identifies a multisystem disorder associated with increased TPSAB1 copy number

Elevated basal serum tryptase levels are present in 4-6% of the general population, but the cause and relevance of such increases are unknown. Previously, we described subjects with dominantly inherited elevated basal serum tryptase levels associated with multisystem complaints including cutaneous flushing and pruritus, dysautonomia, functional gastrointestinal symptoms, chronic pain, and connective tissue abnormalities, including joint hypermobility. Here we report the identification of germline duplications and triplications in the TPSAB1 gene encoding α-tryptase that segregate with inherited increases in basal serum tryptase levels in 35 families presenting with associated multisystem complaints. Individuals harboring alleles encoding three copies of α-tryptase had higher basal serum levels of tryptase and were more symptomatic than those with alleles encoding two copies, suggesting a gene-dose effect. Further, we found in two additional cohorts (172 individuals) that elevated basal serum tryptase levels were exclusively associated with duplication of α-tryptase-encoding sequence in TPSAB1, and affected individuals reported symptom complexes seen in our initial familial cohort. Thus, our findings link duplications in TPSAB1 with irritable bowel syndrome, cutaneous complaints, connective tissue abnormalities, and dysautonomia

A longitudinal survey of African animal trypanosomiasis in domestic cattle on the Jos Plateau, Nigeria:prevalence, distribution and risk factors

BACKGROUND: Trypanosomiasis is a widespread disease of livestock in Nigeria and a major constraint to the rural economy. The Jos Plateau, Nigeria was free from tsetse flies and the trypanosomes they transmit due to its high altitude and the absence of animal trypanosomiasis attracted large numbers of cattle-keeping pastoralists to inhabit the plateau. The Jos Plateau now plays a significant role in the national cattle industry, accommodating approximately 7% of the national herd and supporting 300,000 pastoralists and over one million cattle. However, during the past two decades tsetse flies have invaded the Jos Plateau and animal trypanosomiasis has become a significant problem for livestock keepers. METHODS: In 2008 a longitudinal two-stage cluster survey on the Jos Plateau. Cattle were sampled in the dry, early wet and late wet seasons. Parasite identification was undertaken using species-specific polymerase chain reactions to determine the prevalence and distribution bovine trypanosomiasis. Logistic regression was performed to determine risk factors for disease. RESULTS: The prevalence of bovine trypanosomiasis (Trypanosoma brucei brucei, Trypanosoma congolense savannah, Trypanosoma vivax) across the Jos Plateau was found to be high at 46.8% (39.0 – 54.5%) and significant, seasonal variation was observed between the dry season and the end of the wet season. T. b. brucei was observed at a prevalence of 3.2% (1% – 5.5%); T. congolense at 27.7% (21.8% - 33.6%) and T. vivax at 26.7% (18.2% - 35.3%). High individual variation was observed in trypanosomiasis prevalence between individual villages on the Plateau, ranging from 8.8% to 95.6%. Altitude was found to be a significant risk factor for trypanosomiasis whilst migration also influenced risk for animal trypanosomiasis. CONCLUSIONS: Trypanosomiasis is now endemic on the Jos Plateau showing high prevalence in cattle and is influenced by seasonality, altitude and migration practices. Attempts to successfully control animal trypanosomiasis on the Plateau will need to take into account the large variability in trypanosomiasis infection rates between villages, the influence of land use, and husbandry and management practices of the pastoralists, all of which affect the epidemiology of the disease

Event generators for high-energy physics experiments

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments

Event generators for high-energy physics experiments

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments

Mindfulness-based cognitive therapy for psychological distress in pregnancy: study protocol for a randomized controlled trial

BACKGROUND: Clinically significant psychological distress in pregnancy is common, with epidemiological research suggesting that between 15 and 25 % of pregnant women experience elevated symptoms of stress, anxiety, and depression. Untreated psychological distress in pregnancy is associated with poor obstetrical outcomes, changes in maternal physiology, elevated incidence of child physical and psychological disorders, and is predictive of maternal postpartum mood disorders. Despite the wide-ranging impact of antenatal psychological distress on mothers and their children, there is a gap in our knowledge about the most effective treatments that are available for psychological distress experienced in pregnancy. Additionally, no trials have focused on potential physiological changes that may occur as a result of receiving mindfulness training in pregnancy. The proposed trial will determine the effectiveness of an 8-week modified Mindfulness-based Cognitive Therapy (MBCT) intervention delivered during pregnancy. METHODS: A randomized controlled trial (RCT) design with repeated measures will be used to evaluate the effectiveness of MBCT to treat psychological distress in pregnancy. A sample of 60 consenting pregnant women aged 18 years and above will be enrolled and randomized to the experimental (MBCT) or control (treatment as usual) condition. Primary (e.g., symptoms of stress, depression, and anxiety), secondary (cortisol, blood pressure (BP), heart rate variability (HRV), and sleep) and other outcome data (e.g., psychological diagnoses) will be collected via a combination of laboratory visits and at-home assessments from both groups at baseline (T(1)), immediately following the intervention (T(2)), and at 3 months postpartum (T(3)). Descriptive statistics will be used to describe sample characteristics. Data will be analyzed using an intention-to-treat approach. Hierarchical linear models will be used to test intervention effects on primary and secondary outcomes. DISCUSSION: The trial is expected to improve knowledge about evidence-based treatments for psychological distress experienced in pregnancy and to evaluate the potential impact of mindfulness-based interventions on maternal physiology. TRIAL REGISTRATION: ClinicalTrials.gov: NCT02214732, registered on 7 August 2014. Protocol Version 2.0., 5 September 2016. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13063-016-1601-0) contains supplementary material, which is available to authorized users

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages