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Tertiary phosphines are ubiquitous in inorganic chemistry. They play important roles as ligands in coordination chemistry and catalysis. Furthermore, they act as surface acidity probes for oxide surfaces. However, only volatile phosphines, such as PH3 have been applied in this function so far. Here we demonstrate for the first time that the triaryl- and trialkylphosphines PPh3 and PCy3 with high melting points self-adsorb readily onto a silica surface even in the absence of a solvent. The self-adsorption takes place within days when both solid components are mixed and then left undisturbed. The phosphines form well-defined monolayers on the surface and the transition from monolayer to left-over polycrystalline phosphine is abrupt. Therefore, the maximal surface coverage with a monolayer can be easily determined. When the phosphines are adsorbed from solutions, the same maximal surface coverage is found. Solid-state NMR spectroscopy provides a unique analytical tool for studying the structure and dynamics of phosphines in different environments. 31P and 2H solid-state NMR measurements are successfully applied for characterizing the adsorption process and the mobilities of the adsorbed phosphines across the silica surface. Furthermore, using (Ph3P)2Ni(CO)2 as a representative, it is demonstrated that the silica surface has a hitherto unrecognized impact on immobilized and surface-residing catalysts because it competes for phosphine ligands coordinated to a metal center. This competition manifests as one more factor leading to the loss of phosphine ligands and ultimately leaching of immobilized metal complexes or nanoparticle formation. Besides the increase of fundamental knowledge about adsorption processes, the presented results have implications for chromatographic separations of metal complexes and for the lifetime of immobilized and other types of surface-residing catalysts. 

Methylphosphate Capping Enzyme (MePCE) monomethylates the gamma phosphate at the 5′ end of the 7SK noncoding RNA, a modification thought to protect 7SK from degradation. 7SK serves as a scaffold for assembly of a snRNP complex that inhibits transcription by sequestering the positive elongation factor P-TEFb. While much is known about the biochemical activity of MePCE in vitro, little is known about its functions in vivo, or what roles—if any—there are for regions outside the conserved methyltransferase domain. Here, we investigated the role of Bin3, the Drosophila ortholog of MePCE, and its conserved functional domains in Drosophila development. We found that bin3 mutant females had strongly reduced rates of egg-laying, which was rescued by genetic reduction of P-TEFb activity, suggesting that Bin3 promotes fecundity by repressing P-TEFb. bin3 mutants also exhibited neuromuscular defects, analogous to a patient with MePCE haploinsufficiency. These defects were also rescued by genetic reduction of P-TEFb activity, suggesting that Bin3 and MePCE have conserved roles in promoting neuromuscular function by repressing P-TEFb. Unexpectedly, we found that a Bin3 catalytic mutant (Bin3Y795A) could still bind and stabilize 7SK and rescue all bin3 mutant phenotypes, indicating that Bin3 catalytic activity is dispensable for 7SK stability and snRNP function in vivo. Finally, we identified a metazoan-specific motif (MSM) outside of the methyltransferase domain and generated mutant flies lacking this motif (Bin3ΔMSM). Bin3ΔMSM mutant flies exhibited some—but not all—bin3 mutant phenotypes, suggesting that the MSM is required for a 7SK-independent, tissue-specific function of Bin3.

 

Varying coefficient models have been used to explore dynamic effects in many scientific areas, such as in medicine, finance, and epidemiology. As most existing models ignore the existence of zero regions, we propose a new soft-thresholded varying coefficient model, where the coefficient functions are piecewise smooth with zero regions. Our new modeling approach enables us to perform variable selection, detect the zero regions of selected variables, obtain point estimates of the varying coefficients with zero regions, and construct a new type of sparse confidence intervals that accommodate zero regions. We prove the asymptotic properties of the estimator, based on which we draw statistical inference. Our simulation study reveals that the proposed sparse confidence intervals achieve the desired coverage probability. We apply the proposed method to analyze a large-scale preoperative opioid study.

 

Source and sink interactions play a critical but mechanistically poorly understood role in the regulation of senescence. To disentangle the genetic and molecular mechanisms underlying source–sink-regulated senescence (SSRS), we performed a phenotypic, transcriptomic, and systems genetics analysis of senescence induced by the lack of a strong sink in maize (Zea mays). Comparative analysis of genotypes with contrasting SSRS phenotypes revealed that feedback inhibition of photosynthesis, a surge in reactive oxygen species, and the resulting endoplasmic reticulum (ER) stress were the earliest outcomes of weakened sink demand. Multienvironmental evaluation of a biparental population and a diversity panel identified 12 quantitative trait loci and 24 candidate genes, respectively, underlying SSRS. Combining the natural diversity and coexpression networks analyses identified 7 high-confidence candidate genes involved in proteolysis, photosynthesis, stress response, and protein folding. The role of a cathepsin B like protease 4 (ccp4), a candidate gene supported by systems genetic analysis, was validated by analysis of natural alleles in maize and heterologous analyses in Arabidopsis (Arabidopsis thaliana). Analysis of natural alleles suggested that a 700-bp polymorphic promoter region harboring multiple ABA-responsive elements is responsible for differential transcriptional regulation of ccp4 by ABA and the resulting variation in SSRS phenotype. We propose a model for SSRS wherein feedback inhibition of photosynthesis, ABA signaling, and oxidative stress converge to induce ER stress manifested as programed cell death and senescence. These findings provide a deeper understanding of signals emerging from loss of sink strength and offer opportunities to modify these signals to alter senescence program and enhance crop productivity.

 

In this work, we develop a novel Bayesian regression framework that can be used to complete variable selection in high dimensional settings. Unlike existing techniques, the proposed approach can leverage side information to inform about the sparsity structure of the regression coefficients. This is accomplished by replacing the usual inclusion probability in the spike and slab prior with a binary regression model which assimilates this extra source of information. To facilitate model fitting, a computationally efficient and easy to implement Markov chain Monte Carlo posterior sampling algorithm is developed via carefully chosen priors and data augmentation steps. The finite sample performance of our methodology is assessed through numerical simulations, and we further illustrate our approach by using it to identify genetic markers associated with the nicotine metabolite ratio; a key biological marker associated with nicotine dependence and smoking cessation treatment.

 

There are currently no effective biomarkers for diagnosing Parkinson’s disease (PD) or tracking its progression. Here, we developed an artificial intelligence (AI) model to detect PD and track its progression from nocturnal breathing signals. The model was evaluated on a large dataset comprising 7,671 individuals, using data from several hospitals in the United States, as well as multiple public datasets. The AI model can detect PD with an area-under-the-curve of 0.90 and 0.85 on held-out and external test sets, respectively. The AI model can also estimate PD severity and progression in accordance with the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (R = 0.94, P = 3.6 × 10–25). The AI model uses an attention layer that allows for interpreting its predictions with respect to sleep and electroencephalogram. Moreover, the model can assess PD in the home setting in a touchless manner, by extracting breathing from radio waves that bounce off a person’s body during sleep. Our study demonstrates the feasibility of objective, noninvasive, at-home assessment of PD, and also provides initial evidence that this AI model may be useful for risk assessment before clinical diagnosis. 

A major goal of linguistics and cognitive science is to understand what class of learning systems can acquire natural language. Until recently, the computational requirements of language have been used to argue that learning is impossible without a highly constrained hypothesis space. Here, we describe a learning system that is maximally unconstrained, operating over the space of all computations, and is able to acquire many of the key structures present in natural language from positive evidence alone. We demonstrate this by providing the same learning model with data from 74 distinct formal languages which have been argued to capture key features of language, have been studied in experimental work, or come from an interesting complexity class. The model is able to successfully induce the latent system generating the observed strings from small amounts of evidence in almost all cases, including for regular (e.g., a n , ( a b ) n , and { a , b } + ), context-free (e.g., a n b n ,   a n b n + m , and x x R ), and context-sensitive (e.g., a n b n c n ,   a n b m c n d m , and xx ) languages, as well as for many languages studied in learning experiments. These results show that relatively small amounts of positive evidence can support learning of rich classes of generative computations over structures. The model provides an idealized learning setup upon which additional cognitive constraints and biases can be formalized.