Population-level integration of single-cell datasets enables multi-scale analysis across samples

NH3-N is correlated with AREA_MN of orchard at 500 m scale and with landscape metrics of forest, water and farmland at 1000 m scale, showing that built-up land has no significant impacts on this water quality parameter. Both the PLAND of farmland at 500 m scale and 2000 m scale multi-scale analysis are negatively correlated with BOD which can explain more than 50% of the correlations. Petroleum are correlated with landscape metrics of farmland, orchard, built-up land and water at local scale with R2 more than 0.4. Table 5 also shows that metrics including IJI, ENN, FRAC, SHAPE, and AREA are more correlated with water quality parameters instead of the traditional metrics such as LPI, NP, PLAND, ED and LSI. Composition of different land use types show different influences at different scales, in which effects of built-up land shows the most significant (Table 4). It should be especially noted that NH3-N is influenced by built-up land at all different scales, and the R2 increases with the scale increases.

Fibroblasts: Origins, definitions, and functions in health and disease

This encoding does not allow a downstream interpretation of the effect of each sample on the mapping. Additionally, in the presence of many unique condition categories, the number of conditional inputs can become close or equal to the number of gene expression measurements leading the model to produce inaccurate data representation25. Among current reference-building methods10,12,24,26 only scANVI and Seurat v3 offer cell type classification coupled with a reference mapping algorithm19,21. Yet, while they can integrate annotated data to extend the reference, this requires retraining, which is time-consuming and can sometimes be not possible due to data sharing restrictions. In this paper, a multi-scale finite element analysis method of the plain woven C/C composite finger seal was proposed and conducted by the multi-scale structure analysis. The fifth challenge is to know the limitations of machine learning and multiscale modeling.

Three-dimensional volume of fluid simulations on bubble formation and dynamics in bubble columns

• It was discovered that the static stiffness of the finger beams distributed circumferentially was not uniform due to the differences in the woven structures and fibers distribution inside the finger beams.
• From the three-dimensional simulations, the three-dimensional flow structure exists due to the viscous effects near the span edge.
• We showcase the data integration capability and quality of label transfer yielded by scPoli on the Human Lung Cell Atlas (HLCA)4, a curated collection of 46 datasets of the human lung, with samples from 444 individuals.
• Specifically, Firmicutes composition was higher in the postoperative group (49.30%) than in the preoperative group (34.52%).
• Our study demonstrated that water quality parameters were significantly influenced by landscape composition and configuration.
• Different levels of cell type annotation and sample and patient metadata are curated and available.

In this work, we focus on the applications of scPoli on scRNA-seq data; nonetheless, with the appropriate adaptations, scPoli can be applied to other modalities. To demonstrate this, we used scPoli to integrate a set of single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) samples by modeling the likelihood of the input data using a Poisson distribution, as proposed in ref. 37. We tested this on the NeurIPS 2021 multimodal data integration dataset38, from which we used the scATAC-seq modality. ScPoli integrated data from different samples (Supplementary Fig. 10a) and yielded condition embeddings that captured similarities between samples generated at the same site (Supplementary Fig. 10b).

Machine learning seeks to infer the dynamics of biological, biomedical, and behavioral systems

With the rapid developments in gene sequencing and wearable electronics, the personalized biomedical data has become as accessible and inexpensive as never before. However, efficiently analyzing big datasets within massive design spaces remains a logistic and computational challenge. Multiscale modeling allows us to integrate physics-based knowledge to bridge the scales and efficiently pass information across temporal and spatial scales.

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

In addition, the material pairs of C/C composites-Cr3C2 have excellent frictional wear and hysteresis leakage properties, which provided an important reference for the selection of material pairs for finger seals27. The first challenge is to create robust predictive mechanistic models when dealing with sparse data. The lack of sufficient data is a common problem in modeling biological, biomedical, and behavioral systems. For example, it can result from an inadequate experimental resolution or an incomplete medical history. A critical first step is to systematically identify the missing information.

True Multiscale

This prior information is then leveraged by optimizing the prototype loss on each set of labeled prototypes. To simulate such a scenario, we integrated the HLCA, but this time using both a coarse and a fine annotation (Supplementary Fig. 4a–c). Additionally, for one dataset in the reference (Krasnow2020), we kept only the coarse annotation. We then used scPoli to propagate high-resolution labels to these cells obtaining an overall accuracy of 84.4% (Supplementary Fig. 4d).

The dual-bubble-size (DBS) model

Here, the multipole expansion approach is combined with the theory of complex potentials known to be a powerful tool for the study of 2D problems. A special attention has been paid to the solids with cracks and interface damage and an attempt is made to formulate the micromechanical strength theory of a composite in terms of the peak local stress statistics in disordered fibrous composites. The book is complemented by an Appendix containing five sample Fortran codes. Multi-scale analysis of non-equilibrium hypersonic rarefied diatomic gas flow was presented by using a parallel DSMC method with the DMC model for a diatomic gas molecular collision and with the MS model for a gas-surface interaction model. The parallel implementation of the DSMC code shows to have linear scalability using the dynamic load balancing technique.

• Then, the mechanical performances of C/C composite were still homogenized and equivalent, so the influence of the microstructure such as weaving form and direction on the stiffness variation of finger beams was not considered.
• The process is applied to a real image acquired on the retina of a human eye the Lena image (Figure 21) and (Figure 24).
• We fixed the width of the hidden layer to be the square root of the number of features in the input data, as is done in15.
• Meanwhile, most stations in Wuxing District present high petroleum concentrations, at the IV level.
• Check if you have access through your login credentials or your institution to get full access on this article.
• A real example in the field of image segmentation is illustrated on an image of metallurgic grain boundaries (Figure 24).

The obtained stepwise regression model can be used by managers to predict the effects of landscape metrics where are not sampled or monitored. They can further deepen our understanding of water quality pollution phenomenon for the governments to develop new innovative planning management. Besides, it can provide effective ways for setting water quality criteria and water pollution protection plan. Moreover, the obtained regression models can be integrated with GIS platform, assisting the managers to develop new plans for land use control.

The significant contributions are collated and classified in accordance to their purpose and approach so that potential researcher and practitioners, interested in this subject, can be benefited. Future research possibilities in the direction of “agency cost mitigation” and “synergy between econophysics and behavioral finance in stock market forecasting” are also suggested in the paper. Similarly, the cognition-related neuroactive metabolite DL-Dopa, as a precursor to dopamine, plays a critical role in cognitive processes, including attention, learning, and memory 77.