3,040 research outputs found
A geometric condition implying energy equality for solutions of 3D Navier-Stokes equation
We prove that every weak solution to the 3D Navier-Stokes equation that
belongs to the class and \n u belongs to localy
away from a 1/2-H\"{o}lder continuous curve in time satisfies the generalized
energy equality. In particular every such solution is suitable.Comment: 10 page
Interior regularity criteria for suitable weak solutions of the Navier-Stokes equations
We present new interior regularity criteria for suitable weak solutions of
the 3-D Navier-Stokes equations: a suitable weak solution is regular near an
interior point if either the scaled -norm of the velocity
with , , or the -norm of the
vorticity with , , or the
-norm of the gradient of the vorticity with , , , is sufficiently small near
Intention of preserving forest remnants among landowners in the Atlantic Forest: The role of the ecological context via ecosystem services
Unravelling the psychological processes determining landowners' support towards forest conservation is crucial, particularly in rural areas of the tropics, where most forest remnants are within private lands. As humanânature connections are known to shape proâenvironmental behaviours, the intention of preserving forest remnants should ultimately be determined by the ecological context people live in. Here, we investigate the pathways through which the ecological context (forest cover), via direct contact with forests and ecosystem services and disservices, influence the psychological antecedents of conservation behaviour (beliefs, attitude and intention of preserving forest remnants). We conceptualized a model based on the Reasoned Action Approach, using the ecological context and these three forest experiences as background factors, and tested the model using Piecewise Structural Equation Modelling. Data were collected through an interviewâbased protocol applied to 106 landowners across 13 landscapes varying in forest cover in a consolidated rural region in the Brazilian Atlantic Forest. Our results indicate that: (a) ecosystem services are more important than disservices for shaping intention of preserving forests, particularly nonâprovisioning services; (b) contact with forest has an indirect effect on intention, by positively influencing the frequency of receiving ecosystem services; (c) people living in more forested ecological contexts have more contact with forests, receive ecosystem services more frequently and, ultimately, have stronger intention of preserving forests. Hence, our study suggests a dangerous positive feedback loop between deforestation, the extinction of forest experiences and impairment of humanânature connections. Local demands across the full range of ecosystem services, the balance between services and disservices and the ecological context people live in should be considered when developing conservation initiatives in tropical rural areas
Pre-Training on In Vitro and Fine-Tuning on Patient-Derived Data Improves Deep Neural Networks for Anti-Cancer Drug-Sensitivity Prediction
Large-scale databases that report the inhibitory capacities of many combinations of candidate drug compounds and cultivated cancer cell lines have driven the development of preclinical drug-sensitivity models based on machine learning. However, cultivated cell lines have devolved from human cancer cells over years or even decades under selective pressure in culture conditions. Moreover, models that have been trained on in vitro data cannot account for interactions with other types of cells. Drug-response data that are based on patient-derived cell cultures, xenografts, and organoids, on the other hand, are not available in the quantities that are needed to train high-capacity machine-learning models. We found that pre-training deep neural network models of drug sensitivity on in vitro drug-sensitivity databases before fine-tuning the model parameters on patient-derived data improves the modelsâ accuracy and improves the biological plausibility of the features, compared to training only on patient-derived data. From our experiments, we can conclude that pre-trained models outperform models that have been trained on the target domains in the vast majority of cases
Matching anticancer compounds and tumor cell lines by neural networks with ranking loss
Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drugâs inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the modelâs capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data
Large-scale literature mining to assess the relation between anti-cancer drugs and cancer types
Background:There is a huge body of scientific literature describing the relation between tumor types and anti-cancer drugs. The vast amount of scientific literature makes it impossible for researchers and physicians to extract all relevant information manually.Methods:In order to cope with the large amount of literature we applied an automated text mining approach to assess the relations between 30 most frequent cancer types and 270 anti-cancer drugs. We applied two different approaches, a classical text mining based on named entity recognition and an AI-based approach employing word embeddings. The consistency of literature mining results was validated with 3 independent methods: first, using data from FDA approvals, second, using experimentally measured IC-50 cell line data and third, using clinical patient survival data.Results:We demonstrated that the automated text mining was able to successfully assess the relation between cancer types and anti-cancer drugs. All validation methods showed a good correspondence between the results from literature mining and independent confirmatory approaches. The relation between most frequent cancer types and drugs employed for their treatment were visualized in a large heatmap. All results are accessible in an interactive web-based knowledge base using the following link: https://knowledgebase.microdiscovery.de/heatmap.Conclusions:Our approach is able to assess the relations between compounds and cancer types in an automated manner. Both, cancer types and compounds could be grouped into different clusters. Researchers can use the inter-active knowledge base to inspect the presented results and follow their own research questions, for example the identification of novel indication areas for known drugs
Instability of the expression of morphological and phenological descriptors to environmental variation in white oat.
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Local ecosystem feedbacks and critical transitions in the climate
Global and regional climate models, such as those used in IPCC assessments, are the best tools available for climate predictions. Such models typically account for large-scale land-atmosphere feedbacks. However, these models omit local vegetationenvironment 5 feedbacks that are crucial for critical transitions in ecosystems. Here, we reveal the hypothesis that, if the balance of feedbacks is positive at all scales, local vegetation-environment feedbacks may trigger a cascade of amplifying effects, propagating from local to large scale, possibly leading to critical transitions in the largescale climate. We call for linking local ecosystem feedbacks with large-scale land10 atmosphere feedbacks in global and regional climate models in order to yield climate predictions that we are more confident about
Singular and regular solutions of a non-linear parabolic system
We study a dissipative nonlinear equation modelling certain features of the
Navier-Stokes equations. We prove that the evolution of radially symmetric
compactly supported initial data does not lead to singularities in dimensions
. For dimensions we present strong numerical evidence supporting
existence of blow-up solutions. Moreover, using the same techniques we
numerically confirm a conjecture of Lepin regarding existence of self-similar
singular solutions to a semi-linear heat equation.Comment: 16 page
The Clumping Transition in Niche Competition: a Robust Critical Phenomenon
We show analytically and numerically that the appearance of lumps and gaps in
the distribution of n competing species along a niche axis is a robust
phenomenon whenever the finiteness of the niche space is taken into account. In
this case depending if the niche width of the species is above or
below a threshold , which for large n coincides with 2/n, there are
two different regimes. For the lumpy pattern emerges
directly from the dominant eigenvector of the competition matrix because its
corresponding eigenvalue becomes negative. For the lumpy
pattern disappears. Furthermore, this clumping transition exhibits critical
slowing down as is approached from above. We also find that the number
of lumps of species vs. displays a stair-step structure. The positions
of these steps are distributed according to a power-law. It is thus
straightforward to predict the number of groups that can be packed along a
niche axis and it coincides with field measurements for a wide range of the
model parameters.Comment: 16 pages, 7 figures;
http://iopscience.iop.org/1742-5468/2010/05/P0500
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