Aims/hypothesis
Increasing evidence suggests that environmental factors changing the normal colonisation pattern in the gut strongly influence the risk of developing autoimmune diabetes. The aim of ...this study was to investigate, both during infancy and adulthood, whether treatment with vancomycin, a glycopeptide antibiotic specifically directed against Gram-positive bacteria, could influence immune homeostasis and the development of diabetic symptoms in the NOD mouse model for diabetes.
Methods
Accordingly, one group of mice received vancomycin from birth until weaning (day 28), while another group received vancomycin from 8 weeks of age until onset of diabetes. Pyrosequencing of the gut microbiota and flow cytometry of intestinal immune cells was used to investigate the effect of vancomycin treatment.
Results
At the end of the study, the cumulative diabetes incidence was found to be significantly lower for the neonatally treated group compared with the untreated group, whereas the insulitis score and blood glucose levels were significantly lower for the mice treated as adults compared with the other groups. Mucosal inflammation was investigated by intracellular cytokine staining of the small intestinal lymphocytes, which displayed an increase in cluster of differentiation (CD)4
+
T cells producing pro-inflammatory cytokines in the neonatally treated mice. Furthermore, bacteriological examination of the gut microbiota composition by pyrosequencing revealed that vancomycin depleted many major genera of Gram-positive and Gram-negative microbes while, interestingly, one single species,
Akkermansia muciniphila
, became dominant.
Conclusions/interpretation
The early postnatal period is a critical time for microbial protection from type 1 diabetes and it is suggested that the mucolytic bacterium
A. muciniphila
plays a protective role in autoimmune diabetes development, particularly during infancy.
Deep learning, due to its unprecedented success in tasks such as image classification, has emerged as a new tool in image reconstruction with potential to change the field. In this paper, we ...demonstrate a crucial phenomenon: Deep learning typically yields unstable methods for image reconstruction. The instabilities usually occur in several forms: 1) Certain tiny, almost undetectable perturbations, both in the image and sampling domain, may result in severe artefacts in the reconstruction; 2) a small structural change, for example, a tumor, may not be captured in the reconstructed image; and 3) (a counterintuitive type of instability) more samples may yield poorer performance. Our stability test with algorithms and easy-to-use software detects the instability phenomena. The test is aimed at researchers, to test their networks for instabilities, and for government agencies, such as the Food and Drug Administration (FDA), to secure safe use of deep learning methods.
Water ice exists in hugely different environments, artificially or naturally occurring ones across the universe. The phase diagram of crystalline phases of ice is still under construction: a ...high-pressure phase, ice XIX, has just been reported but its structure remains ambiguous.
We propose robust methods for inference about the effect of a treatment variable on a scalar outcome in the presence of very many regressors in a model with possibly non-Gaussian and heteroscedastic ...disturbances. We allow for the number of regressors to be larger than the sample size. To make informative inference feasible, we require the model to be approximately sparse; that is, we require that the effect of confounding factors can be controlled for up to a small approximation error by including a relatively small number of variables whose identities are unknown. The latter condition makes it possible to estimate the treatment effect by selecting approximately the right set of regressors. We develop a novel estimation and uniformly valid inference method for the treatment effect in this setting, called the "post-double-selection" method. The main attractive feature of our method is that it allows for imperfect selection of the controls and provides confidence intervals that are valid uniformly across a large class of models. In contrast, standard post-model selection estimators fail to provide uniform inference even in simple cases with a small, fixed number of controls. Thus, our method resolves the problem of uniform inference after model selection for a large, interesting class of models. We also present a generalization of our method to a fully heterogeneous model with a binary treatment variable. We illustrate the use of the developed methods with numerical simulations and an application that considers the effect of abortion on crime rates.
Classifying drivers of global forest loss Curtis, Philip G; Slay, Christy M; Harris, Nancy L ...
Science (American Association for the Advancement of Science),
2018-Sep-14, 2018-09-14, 20180914, Letnik:
361, Številka:
6407
Journal Article
Recenzirano
Global maps of forest loss depict the scale and magnitude of forest disturbance, yet companies, governments, and nongovernmental organizations need to distinguish permanent conversion (i.e., ...deforestation) from temporary loss from forestry or wildfire. Using satellite imagery, we developed a forest loss classification model to determine a spatial attribution of forest disturbance to the dominant drivers of land cover and land use change over the period 2001 to 2015. Our results indicate that 27% of global forest loss can be attributed to deforestation through permanent land use change for commodity production. The remaining areas maintained the same land use over 15 years; in those areas, loss was attributed to forestry (26%), shifting agriculture (24%), and wildfire (23%). Despite corporate commitments, the rate of commodity-driven deforestation has not declined. To end deforestation, companies must eliminate 5 million hectares of conversion from supply chains each year.
Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest ...loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil's well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change.
Consistent, large-scale operational monitoring of forest height is essential for estimating forest-related carbon emissions, analyzing forest degradation, and quantifying the effectiveness of forest ...restoration initiatives. The Global Ecosystem Dynamics Investigation (GEDI) lidar instrument onboard the International Space Station has been collecting unique data on vegetation structure since April 2019. Here, we employed global Landsat analysis-ready data to extrapolate GEDI footprint-level forest canopy height measurements, creating a 30 m spatial resolution global forest canopy height map for the year 2019. The global forest height map was compared to the GEDI validation data (RMSE = 6.6 m; MAE = 4.45 m, R2 = 0.62) and available airborne lidar data (RMSE = 9.07 m; MAE = 6.36 m, R2 = 0.61). The demonstrated integration of GEDI data with time-series optical imagery is expected to enable multidecadal historic analysis and operational forward monitoring of forest height and its dynamics. Such capability is important to support global climate and sustainable development initiatives.
Gas hydrates are ice-like solids, in which guest molecules or atoms are trapped inside cages formed within a crystalline host framework (clathrate) of hydrogen-bonded water molecules. They are ...naturally present in large quantities on the deep ocean floor and as permafrost, can form in and block gas pipelines, and are thought to occur widely on Earth and beyond. A natural point of reference for this large and ubiquitous family of inclusion compounds is the empty hydrate lattice, which is usually regarded as experimentally inaccessible because the guest species stabilize the host framework. However, it has been suggested that sufficiently small guests may be removed to leave behind metastable empty clathrates, and guest-free Si- and Ge-clathrates have indeed been obtained. Here we show that this strategy can also be applied to water-based clathrates: five days of continuous vacuum pumping on small particles of neon hydrate (of structure sII) removes all guests, allowing us to determine the crystal structure, thermal expansivity and limit of metastability of the empty hydrate. It is the seventeenth experimentally established crystalline ice phase, ice XVI according to the current ice nomenclature, has a density of 0.81 grams per cubic centimetre (making it the least dense of all known crystalline water phases) and is expected to be the stable low-temperature phase of water at negative pressures (that is, under tension). We find that the empty hydrate structure exhibits negative thermal expansion below about 55 kelvin, and that it is mechanically more stable and has at low temperatures larger lattice constants than the filled hydrate. These observations attest to the importance of kinetic effects and host-guest interactions in clathrate hydrates, with further characterization of the empty hydrate expected to improve our understanding of the structure, properties and behaviour of these unique materials.
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