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Stereoselective ring-opening polymerization catalysts are used to produce degradable stereoregular poly(lactic acids) with thermal and mechanical properties that are superior to those of atactic polymers. However, the process of discovering highly stereoselective catalysts is still largely empirical. We aim to develop an integrated computational and experimental framework for efficient, predictive catalyst selection and optimization. As a proof of principle, we have developed a Bayesian optimization workflow on a subset of literature results for stereoselective lactide ring-opening polymerization, and using the algorithm, we identify multiple new Al complexes that catalyze either isoselective or heteroselective polymerization. In addition, feature attribution analysis uncovers mechanistically meaningful ligand descriptors, such as percent buried volume (%V_bur) and the highest occupied molecular orbital energy (E_HOMO), that can access quantitative and predictive models for catalyst development.

 

As countries look toward re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging. While contact tracing only aims to track past activities of infected users, one path to safe reopening is to develop reliable spatiotemporal risk scores to indicate the propensity of the disease. Existing works which aim at developing risk scores either rely on compartmental model-based reproduction numbers (which assume uniform population mixing) or develop coarse-grain spatial scores based on reproduction number (R0) and macro-level density-based mobility statistics. Instead, in this article, we develop a Hawkes process-based technique to assign relatively fine-grain spatial and temporal risk scores by leveraging high-resolution mobility data based on cell-phone originated location signals. While COVID-19 risk scores also depend on a number of factors specific to an individual, including demography and existing medical conditions, the primary mode of disease transmission is via physical proximity and contact. Therefore, we focus on developing risk scores based on location density and mobility behaviour. We demonstrate the efficacy of the developed risk scores via simulation based on real-world mobility data. Our results show that fine-grain spatiotemporal risk scores based on high-resolution mobility data can provide useful insights and facilitate safe re-opening. 

Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. We show that Restricted Boltzmann Machines (RBMs), an unsupervised two-layer neural network architecture, can successfully be trained on sequence ensembles from single rounds of SELEX experiments for thrombin aptamers. RBMs assign scores to sequences that can be directly related to their fitnesses estimated through experimental enrichment ratios. Hence, RBMs trained from sequence data at a given round can be used to predict the effects of selection at later rounds. Moreover, the parameters of the trained RBMs are interpretable and identify functional features contributing most to sequence fitness. To exploit the generative capabilities of RBMs, we introduce two different training protocols: one taking into account sequence counts, capable of identifying the few best binders, and another based on unique sequences only, generating more diverse binders. We then use RBMs model to generate novel aptamers with putative disruptive mutations or good binding properties, and validate the generated sequences with gel shift assay experiments. Finally, we compare the RBM’s performance with different supervised learning approaches that include random forests and several deep neural network architectures. 

Rice resistance (R) genes have been effectively deployed to prevent blast disease caused by the fungal pathogen Magnaporthe oryzae, one of the most serious threats for stable rice production worldwide. Weedy rice competing with cultivated rice may carry novel or lost R genes. The quantitative trait locus qBR12.3b was previously mapped between two single nucleotide polymorphism markers at the 10,633,942-bp and 10,820,033-bp genomic positions in a black-hull-awned (BHA) weed strain using a weed-crop-mapping population under greenhouse conditions. In this study, we found a portion of the known resistance gene Ptr encoding a protein with four armadillo repeats and confers a broad spectrum of blast resistance. We then analyzed the sequences of the Ptr gene from weedy rice, Ptr^BHA, and identified a unique amino acid glutamine at protein position 874. Minor changes of protein conformation of the Ptr^BHAgene were predicted through structural analysis of Ptr^BHA, suggesting that the product of Ptr^BHAis involved in disease resistance. A gene-specific codominant marker HJ17-13 from Ptr^BHAwas then developed to distinguish alleles in weeds and crops. The Ptr^BHAgene existed in 207 individuals of the same mapping population, where qBR12.3b was mapped using this gene-specific marker. Disease reactions of 207 individuals and their parents to IB-33 were evaluated. The resistant individuals had Ptr^BHAwhereas the susceptible individuals did not, suggesting that HJ17-13 is reliable to predict qBR12.3b. Taken together, this newly developed marker, and weedy rice genotypes carrying qBR12.3b, are useful for blast improvement using marker assisted selection.

 

Abstract Investigating the length scales of granules could help understand the dynamics of granules in the photosphere. In this work, we detected and identified granules in an active region near disk center observed at wavelength of TiO (7057 Å) by the 1.6 m Goode Solar Telescope (GST). By a detailed analysis of the size distribution and flatness of granules, we found a critical size that divides the granules in motions into two regimes: convection and turbulence. The length scales of granules with sizes larger than 600 km follow Gauss function and demonstrate “flat” in flatness, which reveal that these granules are dominated by convection. Those with sizes smaller than 600 km follow power-law function and behave power-law tendency in flatness, which indicate that the small granules are dominated by turbulence. Hence, for the granules in active regions, they are originally convective in large length scale, and directly become turbulent once their sizes turn to small, likely below the critical size of 600 km. Comparing with the granules in quiet regions, they evolve with the absence of the mixing motions of convection and turbulence. Such a difference is probably caused by the interaction between fluid motions and strong magnetic fields in active regions. The strong magnetic fields make high magnetic pressure which creates pressure walls and slows down the evolution of convective granules. Such walls cause convective granules extending to smaller sizes on one hand, and cause wide intergranular lanes on the other hand. The small granules isolated in such wide intergranular lanes are continually sheared, rotated by strong downflows in surroundings and hereby become turbulent.