Multi-omics insights into the microbiota-gut-brain axis and cognitive improvement post-bariatric surgery Full Text

Mean ENN_MN is a simple indicator of patch context and has been widely used to quantify patch isolation15. Therefore, in order to control the CODMn concentration, we should in one hand slow down the rapid urbanization trend and in another hand prevent it from developing in patches. At administrative scale for composition, forest is negatively correlated with some water quality parameters (Table 4). Previous study reported that forest play significant role to decrease the pollution16, which is similar to the result of our study. In addition, agricultural and built-up land areas are reported to play an important role in influencing water quality9,17,18.

Understanding emergence of function

Thus, a shape granulometry is an ordered set of operators that are anti-extensive, scale-invariant, and idempotent. To exclude sensitivity to size, we add property (54), which is just scale invariance for all ψr. The absorption property (55) is easily achieved by using any scale-invariant attribute combined with a criterion of the form in Eq. Urbach and Wilkinson (2002) and Urbach et al., (2007) extended the theory of granulometries to define shape granulometries.

JOINTLY: interpretable joint clustering of single-cell transcriptomes

The framework forms algorithmic alloys between nonlinear machine learning algorithms and the equation-free approach for modelling complex systems. Learning the effective dynamics deploys autoencoders to formulate a mapping between fine- and coarse-grained representations and evolves the latent space dynamics using recurrent neural networks. The algorithm is validated on benchmark problems, and we find that it outperforms state-of-the-art reduced-order models in terms of predictability, and large-scale simulations in terms of cost. Learning the effective dynamics is applicable to systems ranging from chemistry to fluid mechanics and reduces the computational effort by up to two orders of magnitude while maintaining the prediction accuracy of the full system dynamics. We argue that learning the effective dynamics provides a potent novel modality for accurately predicting complex systems.

• Other promising directions include the hybrid and nanocomposites, materials with imperfect interfaces, boundary effects, multiscale analysis of steady-state and transient phenomena, to mention a few.
• ScPoli was able to perform query-to-reference mapping across different species and yielded an overall label transfer accuracy of 86% (Supplementary Fig. 9c–e).
• To quantify label transfer accuracy we used the weighted averaged and macro-averaged F1 score.
• We have presented scPoli, a generative model for data integration, label transfer and reference mapping.

LC-MS metabolomics data acquisition

• The atlas contains 7,800,850 PBMC cells from 2,375 samples, representing cells from 25 datasets, 1,977 healthy or diseased donors.
• On the contrary, the usual toggle contrast enhances the image uniform.
• A big challenge is to formulate generic frameworks for multiscale modeling and simulation.
• The most efficient solution is to use multiscale FEA to divide and conquer the problem.
• We do not fix a default value for this uncertainty above which a cell should be classified as unknown.
• The blood samples were allowed to settle, then centrifuged at 3,000 rpm for 10 min (room temperature).
• Appropriate increases in the “sink” landscape such as forests and grasslands, can efficiently reduce the risk of non-point source pollution to the water body at lower costs34.

We assessed the quality of this integration by comparing it against that of PeakVI39, a CVAE-based method for scATAC-seq data integration, and found that scPoli achieved comparable performance (Supplementary Fig. 10c). A,b, Uniform manifold approximation and projection (UMAP) of the integrated HLCA core after reference building, cells are color coded by their study of origin (a) and by cell type (b). C, Comparison of integration performance yielded by scPoli and the scANVI.

• Et al. 45 also showed that obesity negatively affects brain function and structure, whereas bariatric surgery positively impacts the cognitive function of obese patients.
• We selected 30 random samples and set their cell type annotations and sample annotations as unknown.
• The use of open source codes and data sharing by the machine learning community is a positive step, but more benchmarks and guidelines are needed for neural networks constrained by physics.
• Most previous studies just simply analyzed correlations between landscape patterns of one certain land use type and water quality.
• The atlas is divided into a core collection of data, which comprises data from 166 samples and 11 datasets, and an extended one that includes the remaining data.
• We then simply traverse the Max-tree once and add the grey-level contribution of each Chk to the appropriate bin Figure 10.
• Water quality including BOD, CODMn, NH3-N, petroleum and DTP concentrations was slightly better in flood than in low water period, especially after the year of 2004 (Fig. 3).

Can an arrhythmia patient trust a neural net controller embedded in a pacemaker that was trained under different environmental conditions than the ones during his own use? Training data come at various scales and different levels of fidelity. Data are typically generated by existing models, experimental assays, historical data, and other surveys, all of which multi-scale analysis come with their own inductive biases. Machine learning algorithms can only be as good as the data they have seen. This implies that proper care needs to be taken to safe-guard against biased datasets.