692 research outputs found
A statistical model for epigenetic control of miRNAs
microRNAs are small, non-coding RNAs involved in post-transcriptional gene regulation. Since the dysregulation of only a few miRNAs can affect many biological pathways, miRNAs are thought to play a key role in cancer development and can be used as biomarkers for cancer diagnosis and prognosis. In order to understand how miRNA dysregulation leads to a cancer phenotype it is important to determine the basic regulatory mechanisms that drive miRNA expression. Although much is known about miRNA-mediated post-transcriptional regulation, little is known about the epigenetic control of miRNAs. Here, we performed cell-line specific miRNA promoter predictions and built a classification model for expressed and non-expressed miRNAs based on several epigenetic features, e.g. histone marks and DNA methylation at both, miRNA promoters and around miRNA hairpins. We were able to classify intragenic and intergenic miRNAs with an accuracy of 79% and 85%, respectively, and identified the most important features for classification via feature selection. Surprisingly, we found that DNA methylation seems to have a dual role in regulating miRNA expression at transcriptional level: at promoters, high levels of DNA methylation correlate with transcriptional repression, while around miRNA hairpins high levels of DNA methylation have a positive impact on the expression level of the mature miRNA
deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks. Our implementation encompasses (1) a modular neural network building system based on the deep learning library TensorFlow for the fusion of various statistical and deep learning approaches, (2) an orthogonalization cell to allow for an interpretable combination of different subnetworks, as well as (3) pre-processing steps necessary to set up such models. The software package allows to define models in a user-friendly manner via a formula interface that is inspired by classical statistical model frameworks such as mgcv. The package's modular design and functionality provides a unique resource for both scalable estimation of complex statistical models and the combination of approaches from deep learning and statistics. This allows for state-of-the-art predictive performance while simultaneously retaining the indispensable interpretability of classical statistical models
Processos clĂnicos em NĂșcleos de Apoio Ă SaĂșde da FamĂlia / NASF: estĂĄgio supervisionado
Rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART): study protocol for a randomized controlled trial
BACKGROUND:
Acute respiratory distress syndrome (ARDS) is associated with high in-hospital mortality. Alveolar recruitment followed by ventilation at optimal titrated PEEP may reduce ventilator-induced lung injury and improve oxygenation in patients with ARDS, but the effects on mortality and other clinical outcomes remain unknown. This article reports the rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART).
METHODS/DESIGN:
ART is a pragmatic, multicenter, randomized (concealed), controlled trial, which aims to determine if maximum stepwise alveolar recruitment associated with PEEP titration is able to increase 28-day survival in patients with ARDS compared to conventional treatment (ARDSNet strategy). We will enroll adult patients with ARDS of less than 72 h duration. The intervention group will receive an alveolar recruitment maneuver, with stepwise increases of PEEP achieving 45 cmH2O and peak pressure of 60 cmH2O, followed by ventilation with optimal PEEP titrated according to the static compliance of the respiratory system. In the control group, mechanical ventilation will follow a conventional protocol (ARDSNet). In both groups, we will use controlled volume mode with low tidal volumes (4 to 6 mL/kg of predicted body weight) and targeting plateau pressure 6430 cmH2O. The primary outcome is 28-day survival, and the secondary outcomes are: length of ICU stay; length of hospital stay; pneumothorax requiring chest tube during first 7 days; barotrauma during first 7 days; mechanical ventilation-free days from days 1 to 28; ICU, in-hospital, and 6-month survival. ART is an event-guided trial planned to last until 520 events (deaths within 28 days) are observed. These events allow detection of a hazard ratio of 0.75, with 90% power and two-tailed type I error of 5%. All analysis will follow the intention-to-treat principle.
DISCUSSION:
If the ART strategy with maximum recruitment and PEEP titration improves 28-day survival, this will represent a notable advance to the care of ARDS patients. Conversely, if the ART strategy is similar or inferior to the current evidence-based strategy (ARDSNet), this should also change current practice as many institutions routinely employ recruitment maneuvers and set PEEP levels according to some titration metho
The democracy of Green Infrastructure
With the understanding of nature in terms of ecosystem services and the recognition of the vital role these play for human wellbeing (Millennium Assessment, 2005), the value of the natural realm is scientifically and socially defined while at the same time institutionalised. Within this frame of interpretation, nature is a supplier of provision-ing, regulating, supporting welfare and cultural services, thus becoming not only a life-enabling factor for humanity but also a conceptual construct comparable to cornerstones of democracy, such as equality, freedom and citizenship. The idea of green infrastructure is another recently coined term envisioning nature in cities in the form of a net-work and enabling a broad life-furthering vision of society. Standards for green open spaces embedded in some planning frameworks further state the right for all to a common good. Yet, evidence shows that this common right is not always met. Within the current context of advanced and neoliberal capitalism, green areas are sometimes used as an added financial value for real estate, thus increasing restrictions to their free access and full utilization. In developing countries with young democracies, such as Brazil, this process implies another significant factor of social inequality insofar the restricted access to nature by the poorest people means also diminished food safety, and the jeopardizing of certain cultural practices. In developed countries, loss of land for food production and movements reclaiming the right to the city by squatting unoccupied open spaces to initiate community gar-dens, demonstrates that the access to green spaces is also problematic, although in different ways if compared to developing countries. This chapter contributes to this topic by discussing the inequality in provision of green spaces in informal settlements and social housing development in Brazil, as well as in the globalised north. The chapter concludes with recommendations to enhance democracy through a just provision of nature in cities
Pervasive gaps in Amazonian ecological research
Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4
While the increasing availability of global databases on ecological communities has advanced our knowledge
of biodiversity sensitivity to environmental changes,5â7 vast areas of the tropics remain understudied.8â11 In
the American tropics, Amazonia stands out as the worldâs most diverse rainforest and the primary source of
Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13â15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazonâs biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus
crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced
environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian
Amazonia, while identifying the regionâs vulnerability to environmental change. 15%â18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by
2050. This means that unless we take immediate action, we will not be able to establish their current status,
much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio
NEOTROPICAL XENARTHRANS: a data set of occurrence of xenarthran species in the Neotropics
Xenarthrans â anteaters, sloths, and armadillos â have essential functions for ecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosystem engineers. Because of habitat loss and fragmentation, hunting pressure, and conflicts with 24 domestic dogs, these species have been threatened locally, regionally, or even across their full distribution ranges. The Neotropics harbor 21 species of armadillos, ten anteaters, and six sloths. Our dataset includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae (3), Bradypodidae (4), and Megalonychidae (2). We have no occurrence data on Dasypus pilosus (Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized, but new genetic studies have revealed that the group is represented by seven species. In this data-paper, we compiled a total of 42,528 records of 31 species, represented by occurrence and quantitative data, totaling 24,847 unique georeferenced records. The geographic range is from the south of the USA, Mexico, and Caribbean countries at the northern portion of the Neotropics, to its austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regarding anteaters, Myrmecophaga tridactyla has the most records (n=5,941), and Cyclopes sp. has the fewest (n=240). The armadillo species with the most data is Dasypus novemcinctus (n=11,588), and the least recorded for Calyptophractus retusus (n=33). With regards to sloth species, Bradypus variegatus has the most records (n=962), and Bradypus pygmaeus has the fewest (n=12). Our main objective with Neotropical Xenarthrans is to make occurrence and quantitative data available to facilitate more ecological research, particularly if we integrate the xenarthran data with other datasets of Neotropical Series which will become available very soon (i.e. Neotropical Carnivores, Neotropical Invasive Mammals, and Neotropical Hunters and Dogs). Therefore, studies on trophic cascades, hunting pressure, habitat loss, fragmentation effects, species invasion, and climate change effects will be possible with the Neotropical Xenarthrans dataset
Identification and reconstruction of low-energy electrons in the ProtoDUNE-SP detector
Measurements of electrons from interactions are crucial for the Deep
Underground Neutrino Experiment (DUNE) neutrino oscillation program, as well as
searches for physics beyond the standard model, supernova neutrino detection,
and solar neutrino measurements. This article describes the selection and
reconstruction of low-energy (Michel) electrons in the ProtoDUNE-SP detector.
ProtoDUNE-SP is one of the prototypes for the DUNE far detector, built and
operated at CERN as a charged particle test beam experiment. A sample of
low-energy electrons produced by the decay of cosmic muons is selected with a
purity of 95%. This sample is used to calibrate the low-energy electron energy
scale with two techniques. An electron energy calibration based on a cosmic ray
muon sample uses calibration constants derived from measured and simulated
cosmic ray muon events. Another calibration technique makes use of the
theoretically well-understood Michel electron energy spectrum to convert
reconstructed charge to electron energy. In addition, the effects of detector
response to low-energy electron energy scale and its resolution including
readout electronics threshold effects are quantified. Finally, the relation
between the theoretical and reconstructed low-energy electron energy spectrum
is derived and the energy resolution is characterized. The low-energy electron
selection presented here accounts for about 75% of the total electron deposited
energy. After the addition of lost energy using a Monte Carlo simulation, the
energy resolution improves from about 40% to 25% at 50~MeV. These results are
used to validate the expected capabilities of the DUNE far detector to
reconstruct low-energy electrons.Comment: 19 pages, 10 figure
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