Hierarchical Bayesian modeling of random and residual variance–covariance matrices in bivariate mixed effects models

Bivariate mixed effects models are often used to jointly infer upon covariance matrices for both random effects ( u ) and residuals ( e ) between two different phenotypes in order to investigate the architecture of their relationship. However, these (co)variances themselves may additionally depend upon covariates as well as additional sets of exchangeable random effects that facilitate borrowing of strength across a large number of clusters. We propose a hierarchical Bayesian extension of the classical bivariate mixed effects model by embedding additional levels of mixed effects modeling of reparameterizations of u‐ level and e ‐level (co)variances between two traits. These parameters are based upon a recently popularized square‐root‐free Cholesky decomposition and are readily interpretable, each conveniently facilitating a generalized linear model characterization. Using Markov Chain Monte Carlo methods, we validate our model based on a simulation study and apply it to a joint analysis of milk yield and calving interval phenotypes in Michigan dairy cows. This analysis indicates that the e ‐level relationship between the two traits is highly heterogeneous across herds and depends upon systematic herd management factors.     

Boosting Superior Lithium Storage Performance of Alloy‐Based Anode Materials via Ultraconformal Sb Coating–Derived Favorable Solid‐Electrolyte Interphase

Alloy materials such as Si and Ge are attractive as high‐capacity anodes for rechargeable batteries, but such anodes undergo severe capacity degradation during discharge–charge processes. Compared to the over‐emphasized efforts on the electrode structure design to mitigate the volume changes, understanding and engineering of the solid‐electrolyte interphase (SEI) are significantly lacking. This work demonstrates that modifying the surface of alloy‐based anode materials by building an ultraconformal layer of Sb can significantly enhance their structural and interfacial stability during cycling. Combined experimental and theoretical studies consistently reveal that the ultraconformal Sb layer is dynamically converted to Li₃Sb during cycling, which can selectively adsorb and catalytically decompose electrolyte additives to form a robust, thin, and dense LiF‐dominated SEI, and simultaneously restrain the decomposition of electrolyte solvents. Hence, the Sb‐coated porous Ge electrode delivers much higher initial Coulombic efficiency of 85% and higher reversible capacity of 1046 mAh g^?1 after 200 cycles at 500 mA g^?1, compared to only 72% and 170 mAh g^?1 for bare porous Ge. The present finding has indicated that tailoring surface structures of electrode materials is an appealing approach to construct a robust SEI and achieve long‐term cycling stability for alloy‐based anode materials.     

A method for editing motor unit potential trains obtained by decomposition of surface electromyographic signals

Rather than discarding motor unit potential trains (MUPTs) because they do not meet 100% validity criteria, we describe and evaluate a novel editing routine that preserves valid discharge times, based on decreasing shape variability (variance ratio, VR) within a MUPT. The error filtered estimation (EFE) algorithm is then applied to the remaining ‘high confidence’ discharge times to estimate inter-discharge interval (IDI) statistics. Decomposed surface EMG data from the flexor carpi radialis recorded from 20 participants during 60% MVC wrist flexion was used. There were two levels of denoising criteria (relaxed and strict) criteria for removing MUPs to decrease the VR and increase the signal-to-noise ratio (SNR) of a MUPT. In total, VR decreased 24.88% and SNR increased 6.0% (p’s < 0.05). The MUP template peak-to-peak (P-P) amplitude and P-P duration were dependent on the level of denoising (p’s < 0.05). The standard error of the estimate (SEE) of the mean IDI before and after editing using the relaxed criteria (3.2% versus 3.69%), was very similar (p > 0.05). The same was true for the SEE between denoising criteria, which increased only to 5.14% for the strict criteria (p > 0.05). Editing the MUPTs resulted in a significant decrease in MUP shape variability and in the measures extracted from the MUP templates, with trivial differences between the SEE of the mean IDI between the edited and unedited MUPTs.     

Microbial carbon limitation: The need for integrating microorganisms into our understanding of ecosystem carbon cycling

Numerous studies have demonstrated that fertilization with nutrients such as nitrogen, phosphorus, and potassium increases plant productivity in both natural and managed ecosystems, demonstrating that primary productivity is nutrient limited in most terrestrial ecosystems. In contrast, it has been demonstrated that heterotrophic microbial communities in soil are primarily limited by organic carbon or energy. While this concept of contrasting limitations, that is, microbial carbon and plant nutrient limitation, is based on strong evidence that we review in this paper, it is often ignored in discussions of ecosystem response to global environment changes. The plant‐centric perspective has equated plant nutrient limitations with those of whole ecosystems, thereby ignoring the important role of the heterotrophs responsible for soil decomposition in driving ecosystem carbon storage. To truly integrate carbon and nutrient cycles in ecosystem science, we must account for the fact that while plant productivity may be nutrient limited, the secondary productivity by heterotrophic communities is inherently carbon limited. Ecosystem carbon cycling integrates the independent physiological responses of its individual components, as well as tightly coupled exchanges between autotrophs and heterotrophs. To the extent that the interacting autotrophic and heterotrophic processes are controlled by organisms that are limited by nutrient versus carbon accessibility, respectively, we propose that ecosystems by definition cannot be ‘limited’ by nutrients or carbon alone. Here, we outline how models aimed at predicting non‐steady state ecosystem responses over time can benefit from dissecting ecosystems into the organismal components and their inherent limitations to better represent plant–microbe interactions in coupled carbon and nutrient models.     

Traits mediate drought effects on wood carbon fluxes

CO₂ fluxes from wood decomposition represent an important source of carbon from forest ecosystems to the atmosphere, which are determined by both wood traits and climate influencing the metabolic rates of decomposers. Previous studies have quantified the effects of moisture and temperature on wood decomposition, but these effects were not separated from the potential influence of wood traits. Indeed, it is not well understood how traits and climate interact to influence wood CO₂ fluxes. Here, we examined the responses of CO₂ fluxes from dead wood with different traits (angiosperm and gymnosperm) to 0%, 35%, and 70% rainfall reduction across seasonal temperature gradients. Our results showed that drought significantly decreased wood CO₂ fluxes, but its effects varied with both taxonomical group and drought intensity. Drought‐induced reduction in wood CO₂ fluxes was larger in angiosperms than gymnosperms for the 35% rainfall reduction treatment, but there was no significant difference between these groups for the 70% reduction treatment. This is because wood nitrogen density and carbon quality were significantly higher in angiosperms than gymnosperms, yielding a higher moisture sensitivity of wood decomposition. These findings were demonstrated by a significant positive interaction effect between wood nitrogen and moisture on CO₂ fluxes in a structural equation model. Additionally, we ascertained that a constant temperature sensitivity of CO₂ fluxes was independent of wood traits and consistent with previous estimates for extracellular enzyme kinetics. Our results highlight the key role of wood traits in regulating drought responses of wood carbon fluxes. Given that both climate and forest management might extensively modify taxonomic compositions in the future, it is critical for carbon cycle models to account for such interactions between wood traits and climate in driving dynamics of wood decomposition.     

Modeling of dynamical systems through deep learning

This review presents a modern perspective on dynamical systems in the context of current goals and open challenges. In particular, our review focuses on the key challenges of discovering dynamics from data and finding data-driven representations that make nonlinear systems amenable to linear analysis. We explore various challenges in modern dynamical systems, along with emerging techniques in data science and machine learning to tackle them. The two chief challenges are (1) nonlinear dynamics and (2) unknown or partially known dynamics. Machine learning is providing new and powerful techniques for both challenges. Dimensionality reduction methods are used for projecting dynamical methods in reduced form, and these methods perform computational efficiency on real-world data. Data-driven models drive to discover the governing equations and give laws of physics. The identification of dynamical systems through deep learning techniques succeeds in inferring physical systems. Machine learning provides advanced new and powerful algorithms for nonlinear dynamics. Advanced deep learning methods like autoencoders, recurrent neural networks, convolutional neural networks, and reinforcement learning are used in modeling of dynamical systems.     

Chiral Imidazolidin-4-one with catalytic amount of Dicationic ionic liquid act as a recoverable and reusable Organocatalyst for asymmetric Diels-Alder reaction

Imidazolidin-4-one is used as a recoverable organocatalyst for the asymmetric Diels-Alder reaction in the presence of catalytic amount of dicationic ionic liquid and trifluoroacetic acid as a co-catalyst. The Diels-Alder reaction between model substrate cyclopentadiene and crotonaldehyde gave the product in 95% conversion and 87% ee of the endo-product. The catalyst was shown better reusability when the 20 mol% of dicationic ionic liquid was used and catalyst was reused upto 5 cycles, conversion remains upto 3 recycles but ee of endo- 9 was slightly droped.