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The simulation of engine combustion processes, such as autoignition, an important process in the co-optimization of fuel-engine design, can be computationally expensive due to the large number of thermo-chemical scalars needed to describe the full chemical system. Yet, the inherent correlations between the different chemical species during oxidation can significantly reduce the complexity of representing this system. One strategy is to select a subset of representative species that accurately captures the combustion process at a fraction of the computational cost of the full system. In this study, we compare the performance of four different techniques to select these species. They include the two-step principal component analysis (PCA) approach, directed relation graphs (DRGs), the global pathway selection (GPS) approach, and the manifold-informed species selection method. A parametric study of the representative species selection is carried out on data from the simulation of homogeneous and perfectly stirred reactors by investigating seven cumulative variances and 47 different cut-off percentages for the two-step PCA, and 65 and 51 thresholds for the DRGs and GPS, respectively. Results show that these selection methods capture key important species that can accurately describe the chemical system and track each stage of oxidation. The two-step PCA is sensitive to the cumulative variance, and DRGs and GPS are sensitive to the choice of target variables. By selecting key representative species and reducing the number of thermo-chemical scalars, these three methods can be used to develop computationally efficient hybrid chemistry schemes.

The vast amount of biological knowledge accumulated over the years has allowed researchers to identify various biochemical interactions and define different families of pathways. There is an increased interest in identifying pathways and pathway elements involved in particular biological processes. Drug discovery efforts, for example, are focused on identifying biomarkers as well as pathways related to a disease. We propose a Bayesian model that addresses this question by incorporating information on pathways and gene networks in the analysis of DNA microarray data. Such information is used to define pathway summaries, specify prior distributions, and structure the MCMC moves to fit the model. We illustrate the method with an application to gene expression data with censored survival outcomes. In addition to identifying markers that would have been missed otherwise and improving prediction accuracy, the integration of existing biological knowledge into the analysis provides a better understanding of underlying molecular processes.

Materials science is a fast-evolving area that aims to uncover functional materials with ever more sophisticated properties and functions. For this to happen, new methodologies for materials synthesis, optimization, and preparation are desired. In this context, microfluidic technologies have emerged as a key enabling tool for a low-cost and fast prototyping of materials. Their ability to screen multiple reaction conditions rapidly with a small amount of reagent, together with their unique physico-chemical characteristics, have made microfluidic devices a cornerstone technology in this research field. Among the different microfluidic approaches to materials synthesis, the main contenders can be classified in two categories: continuous-flow and segmented-flow microfluidic devices. These two families of devices present very distinct characteristics, but they are often pooled together in general discussions about the field with seemingly little awareness of the major divide between them. In this perspective, we outline the parallel evolution of those two sub-fields by highlighting the key differences between both approaches, via a discussion of their main achievements. We show how continuous-flow microfluidic approaches, mimicking nature, provide very finely-tuned chemical gradients that yield highly-controlled reaction–diffusion (RD) areas, while segmented-flow microfluidic systems provide, on the contrary, very fast homogenization methods, and therefore well-defined super-saturation regimes inside arrays of micro-droplets that can be manipulated and controlled at the milliseconds scale. Those two classes of microfluidic reactors thus provide unique and complementary advantages over classical batch synthesis, with a drive towards the rational synthesis of out-of-equilibrium states for the former, and the preparation of high-quality and complex nanoparticles with narrow size distributions for the latter.

Here, we report an engineering approach toward multicomponent self-assembly processes by developing a methodology to circumvent spurious, metastable assemblies. The formation of metastable aggregates often hampers self-assembly of molecular building blocks into the desired nanostructures. Strategies are explored to master the pathway complexity and avoid off-pathway aggregates by optimizing the rate of assembly along the correct pathway. We study as a model system the coassembly of two monomers, the R- and S-chiral enantiomers of a π-conjugated oligo(p-phenylene vinylene) derivative. Coassembly kinetics are analyzed by developing a kinetic model, which reveals the initial assembly of metastable structures buffering free monomers and thereby slows the formation of thermodynamically stable assemblies. These metastable assemblies exert greater influence on the thermodynamically favored self-assembly pathway if the ratio between both monomers approaches 1:1, in agreement with experimental results. Moreover, competition by metastable assemblies is highly temperature dependent and hampers the assembly of equilibrium nanostructures most effectively at intermediate temperatures. We demonstrate that the rate of the assembly process may be optimized by tuning the cooling rate. Finally, it is shown by simulation that increasing the driving force for assembly stepwise by changing the solvent composition may circumvent metastable pathways and thereby force the assembly process directly into the correct pathway.

To develop a selection pathway to facilitate the use of multifocal intraocular lenses (mfIOLs) in cataract surgery in a public hospital setting. A single-surgeon prospective cohort study in an Australian tertiary referral public hospital was conducted. A mfIOL selection pathway was designed and assessed. Outcomes measured included unaided distance (UDVA), intermediate (UIVA) and near visual acuity (UNVA), dysphotopsia, spectacle dependence and satisfaction. Patient-reported outcome measures (PROMs) were assessed using Catquest-9SF (CQ) and Near Visual Acuity Questionnaire (NAVQ). A cost-analysis was performed. Fifty-four eyes from 27 patients underwent cataract surgery with mfIOL implantation. The monocular UDVA (mean ± standard deviation) was 0.05 ± 0.12 logMAR; UIVA 0.19 ± 0.05 logMAR; UNVA 0.28 ± 0.14 logMAR; 87% and 98% of eyes achieved within 0.5D and 1.0D of target refraction respectively. Spectacle independence was 85% at distance, 81% at intermediate, 59% at near vision. High satisfaction was reported with CQ (>85%) and NAVQ (100%). The cost difference between bilateral monofocal and mfIOLs is comparable to a pair of spectacles. Projected annual cost to the health system for a 5%-10% eligibility rate is 1.1-2.3 million Australian dollars. The selection pathway presented overcomes the challenges in patient selection inherent to a public hospital setting and was implemented by a senior trainee with excellent vision and PROMs. The pathway ensures the cost-effectiveness of mfOL implantation. There are several funding models that can be applied to support equitable access and improved visual outcomes with mfIOLs within the government funded health system.

The leucine-rich repeat domain of PP32 is composed of five β-strand-containing repeats anchored by terminal caps. These repeats differ in sequence but are similar in structure, providing a means to connect topology, sequence, and folding pathway selection. Through kinetic studies of PP32, we find folding to be rate-limited by the formation of an on-pathway intermediate. Destabilizing core substitutions reveal a transition state ensemble that is highly polarized toward the C-terminal repeat and cap. To determine if this nucleus for folding corresponds to the most stable region of PP32, we monitored amide hydrogen exchange by NMR spectroscopy. Indeed, we find the highest protection to be biased toward the C terminus. Sequence manipulations that destabilize the C terminus spread out the transition state toward the middle of the protein. Consistent with results for helical ankyrin repeat proteins, these results suggest that local stabilities determine folding pathways.

Discovering important genes that account for the phenotype of interest has long been a challenge in genome-wide expression analysis. Analyses such as gene set enrichment analysis (GSEA) that incorporate pathway information have become widespread in hypothesis testing, but pathway-based approaches have been largely absent from regression methods due to the challenges of dealing with overlapping pathways and the resulting lack of available software. The R package grpreg is widely used to fit group lasso and other group-penalized regression models; in this study, we develop an extension, grpregOverlap, to allow for overlapping group structure using a latent variable approach. We compare this approach to the ordinary lasso and to GSEA using both simulated and real data. We find that incorporation of prior pathway information can substantially improve the accuracy of gene expression classifiers, and we shed light on several ways in which hypothesis-testing approaches such as GSEA differ from regression approaches with respect to the analysis of pathway data.

This study explores the multifactorial synergistic effects and configurational pathways for enhancing corporate social responsibility (CSR) performance among Chinese professional sports clubs. Drawing on 188 valid questionnaires from Chinese professional football and basketball clubs, the research employs fuzzy-set qualitative comparative analysis to examine the influence of seven antecedent conditions, commercial environment, government regulation, expectancy pressure, economic interests, internal emotional traits, moral quality, and information disclosure, on CSR performance. The findings reveal that CSR performance results from the interplay of multiple factors, identifying two equivalent pathways for enhancement: the coupling of government pressure with internal autonomy, and the coordination of commercial environment with internal moral qualities. These insights clarify the complex causal mechanisms underlying CSR implementation in professional sports clubs and propose two strategic approaches for promoting CSR: optimizing external institutional frameworks and activating internal endogenous motivation. The study offers configurationally grounded pathway options and managerial implications for improving CSR practices in Chinese professional sports clubs.

Artificial intelligence in healthcare can potentially identify the probability of contracting a particular disease more accurately. There are five common molecular subtypes of breast cancer: luminal A, luminal B, basal, ERBB2, and normal-like. Previous investigations showed that pathway-based microarray analysis could help in the identification of prognostic markers from gene expressions. For example, directed random walk (DRW) can infer a greater reproducibility power of the pathway activity between two classes of samples with a higher classification accuracy. However, most of the existing methods (including DRW) ignored the characteristics of different cancer subtypes and considered all of the pathways to contribute equally to the analysis. Therefore, an enhanced DRW (eDRW+) is proposed to identify breast cancer prognostic markers from multiclass expression data. An improved weight strategy using one-way ANOVA (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+. The experimental results show that the eDRW+ exceeds other methods in terms of AUC. Besides this, the eDRW+ identifies 294 gene markers and 45 pathway markers from the breast cancer datasets with better AUC. Therefore, the prognostic markers (pathway markers and gene markers) can identify drug targets and look for cancer subtypes with clinically distinct outcomes.

The objectives of this study were two-fold. Firstly, to estimate the likely correlated responses in milk urea nitrogen (MUN) concentration, lactation yields of milk (MY), fat (FY) and crude protein (CPY) and mature cow liveweight (LWT) under three selection scenarios which varied in relative emphasis for MUN; 0% relative emphasis (MUN0%: equivalent to current New Zealand breeding worth index), and sign of the economic value; 20% relative emphasis positive selection (MUN+20%), and 20% relative emphasis negative selection (MUN−20%). Secondly, to estimate for these three scenarios the likely change in urinary nitrogen (UN) excretion under pasture based grazing conditions. The predicted genetic responses per cow per year for the current index were 16.4 kg MY, 2.0 kg FY, 1.4 kg CPY, −0.4 kg LWT and −0.05 mg/dL MUN. Positive selection on MUN in the index resulted in annual responses of 23.7 kg MY, 2.0 kg FY, 1.4 kg CPY, 0.6 kg LWT and 0.10 mg/dL MUN, while negative selection on MUN in the index resulted in annual responses of 5.4 kg MY, 1.6 kg FY, 1.0 kg CPY, −1.1 kg LWT and −0.17 mg/dL MUN. The MUN−20% reduced both MUN and cow productivity, whereas the MUN+20% increased MUN, milk production and LWT per cow. Per cow dry matter intake (DMI) was increased in all three scenarios as milk production increased compared to base year, therefore stocking rate (SR) was adjusted to control pasture cover. Paradoxically, ten years of selection with SR adjusted to maintain annual feed demand under the MUN+20% actually reduced per ha UN excretion by 3.54 kg, along with increases of 63 kg MY, 26 kg FY and 16 kg CPY compared to the base year. Ten years of selection on the MUN0% index generated a greater reductions of 10.45 kg UN and 30 kg MY, and increases of 32 kg FY and 21 kg CPY per ha, whereas the MUN−20% index reduced 14.06 kg UN and 136 kg MY with increases of 32 kg FY and 18 kg CPY compared to base year. All three scenarios increased partitioning of nitrogen excreted as feces. The selection index that excluded MUN was economically beneficial in the current economic circumstances over selection indices including MUN regardless of whether selection was either for or against MUN. There was no substantial benefit from an environmental point of view from including MUN in the Breeding Worth index, because N leaching is more a function of SR rather than of individual cow UN excretion. This study demonstrates that attention needs to be paid to the whole system consequences of selection for environmental outcomes in pastoral grazing circumstances.