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Language ability is put into the big picture of cognitive ability in our model, thus more attention is given to the language processing stage, although it has proved to be difficult to operationalize this model.
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The CLM-APSIM model is thus more suitable than its predecessor models in terms of simulating abiotic environmental stresses on maize yield.
This study believed more attention should be paid to the rule-based machine learning approach because the machine learning approach does not require to build a predictive model, thus are more flexible than conventional regressions.
An appropriate choice of a mixing distribution allows its parameter to vary and acts as a means of "loosening" the structure of the initial model, thus offering more realistic interpretations of the mechanisms that generated the data.
The significant overlap between our computational predictions and RNAi screening results suggests that RNAi screen results can be improved by using computational predictions to guide experiments, performing RNAi screens in size-reduced, prioritized subspaces predicted by our model, thus allowing more tissue types or experimental conditions to be tested with the same resources.
Our model thus complements more commonly used population-based longevity assays.
The heterogeneous stem cell state in our model thus enables more efficient transitions towards different mature cell types.
Human primary cells were chosen because they provide conditions that closely simulate a living model, thus yielding more physiologically significant results than animal-derived cells or cell lines.
Finally, the strengths of the current study were: (1) the use of a new decellularization method on allogenic human dermis that provided a reliable 3D structural and biological scaffold for chondrocytes and MSCs; (2) the use of primary non-transformed human cells that closely simulate a living model, thus yielding more physiologically significant results than animal derived cells.
It has the added value of incorporating the spatial model and thus more reliably depicting the shapes of geologic features.
This new model is thus more directly applicable across the broad range of concentrations observed around the world, and further allows flexibility for the investigators to adjust the dose response curve to the air pollutant and health outcomes under study.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com