New horizons for building pyrenoid-based CO2-concentrating mechanisms in plants to improve yields.

Many photosynthetic species have evolved CO2-concentrating mechanisms (CCMs) to improve the efficiency of CO2 assimilation by Rubisco and reduce the negative impacts of photorespiration. However, the majority of plants (i.e. C3 plants) lack an active CCM. Thus, engineering a functional heterologous CCM into important C3 crops, such as rice (Oryza sativa) and wheat (Triticum aestivum), has become a key strategic ambition to enhance yield potential. Here, we review recent advances in our understanding of the pyrenoid-based CCM in the model green alga Chlamydomonas reinhardtii and engineering progress in C3 plants. We also discuss recent modeling work that has provided insights into the potential advantages of Rubisco condensation within the pyrenoid and the energetic costs of the Chlamydomonas CCM, which, together, will help to better guide future engineering approaches. Key findings include the potential benefits of Rubisco condensation for carboxylation efficiency and the need for a diffusional barrier around the pyrenoid matrix. We discuss a minimal set of components for the CCM to function and that active bicarbonate import into the chloroplast stroma may not be necessary for a functional pyrenoid-based CCM in planta. Thus, the roadmap for building a pyrenoid-based CCM into plant chloroplasts to enhance the efficiency of photosynthesis now appears clearer with new challenges and opportunities.

The small subunit of Rubisco and its potential as an engineering target.

Rubisco catalyses the first rate-limiting step in CO2 fixation and is responsible for the vast majority of organic carbon present in the biosphere. The function and regulation of Rubisco remain an important research topic and a longstanding engineering target to enhance the efficiency of photosynthesis for agriculture and green biotechnology. The most abundant form of Rubisco (Form I) consists of eight large and eight small subunits, and is found in all plants, algae, cyanobacteria, and most phototrophic and chemolithoautotrophic proteobacteria. Although the active sites of Rubisco are located on the large subunits, expression of the small subunit regulates the size of the Rubisco pool in plants and can influence the overall catalytic efficiency of the Rubisco complex. The small subunit is now receiving increasing attention as a potential engineering target to improve the performance of Rubisco. Here we review our current understanding of the role of the small subunit and our growing capacity to explore its potential to modulate Rubisco catalysis using engineering biology approaches.

MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction.

The applications of artificial intelligence, machine learning, and deep learning techniques in the field of materials science are becoming increasingly common due to their promising abilities to extract and utilize data-driven information from available data and accelerate materials discovery and design for future applications. In an attempt to assist with this process, we deploy predictive models for multiple material properties, given the composition of the material. The deep learning models described here are built using a cross-property deep transfer learning technique, which leverages source models trained on large data sets to build target models on small data sets with different properties. We deploy these models in an online software tool that takes a number of material compositions as input, performs preprocessing to generate composition-based attributes for each material, and feeds them into the predictive models to obtain up to 41 different material property values. The material property predictor is available online at http://ai.eecs.northwestern.edu/MPpredictor.

Mesoscale Battery Science: The Behavior of Electrode Particles Caught on a Multispectral X-ray Camera.

Functional materials and devices are usually morphologically complex and chemically heterogeneous. Their structures are often designed to be hierarchical because of the desired functionalities, which usually require many different components to work together in a coherent manner. The lithium ion battery, as an energy storage device, is a very typical example of this kind of structure. In a lithium ion battery, the cathode, anode, and separator are soaked in a liquid electrolyte, facilitating the back and forward shuttling of the lithium ions for energy storage and release. The desired performance of a lithium ion battery has many different aspects that need to be engineered and balanced depending on the targeted applications. In most cases, the cathode material has become the limiting factor for further improvements and, thus, has attracted intense attention from the research community. While the improvement in the overall performance of the lithium ion battery is the ultimate goal of the research in this field, understanding the relationship between the microscopic properties and the macroscopic behaviors of the materials/devices can inform the design of better battery chemistries for practical applications. As a result, it is of great fundamental and practical importance to investigate the electrode materials using experimental probes that can provide good chemical sensitivity and sufficient spatial resolution, ideally, under operating conditions. With this motivation, our group has been focusing on the development of the nanoscale full-field X-ray spectro-microscopy, which has now become a well-recognized tool for imaging battery electrode materials at the particle level. With nanoscale spatial resolution, this technique can effectively and efficiently tackle the intrinsically complicated mesoscale chemistry. It allows us to monitor the particles' morphological and chemical evolution upon battery operation, providing valuable insights that can be incorporated into the design of new battery chemistries. In this Account, we review a series of our recent studies of battery electrode materials using nanoscale full-field X-ray spectro-microscopy. The materials that are the subjects of our studies, including layer-structured and spinel-structured oxide cathodes, are technically very important as they not only play an important role in today's devices but also possess promising potential for future developments. We discuss how the subparticle level compositional and state-of-charge heterogeneity can be visualized and linked to the bulk performance through systematic quantification of the imaging data. Subsequently, we highlight recent ex situ and in situ observations of the cathode particles' response to different reaction conditions, including the spontaneously adjusted reaction pathways and the morphological changes for the mechanical strain release. The important role of surface chemistry in the system is also discussed. While the microscopic investigation at the particle level provides useful insights, the degree to which this represents the overall properties of the battery is always a question for further generalizing the conclusions. In order to address this concern, we finally discuss a high throughput experimental approach, in which a large number of cathode particles are scanned. We discuss a case study that demonstrates the identification and analysis of functionally important minority phases in an operating battery cell through big data mining methods. With an emphasis on the data/information mining aspect of the nanoscale X-ray spectro-microscopic study of battery cathode particles, we anticipate that this Account will attract more research to this field.

Automatic projection image registration for nanoscale X-ray tomographic reconstruction.

Novel developments in X-ray sources, optics and detectors have significantly advanced the capability of X-ray microscopy at the nanoscale. Depending on the imaging modality and the photon energy, state-of-the-art X-ray microscopes are routinely operated at a spatial resolution of tens of nanometres for hard X-rays or ∼10 nm for soft X-rays. The improvement in spatial resolution, however, has led to challenges in the tomographic reconstruction due to the fact that the imperfections of the mechanical system become clearly detectable in the projection images. Without proper registration of the projection images, a severe point spread function will be introduced into the tomographic reconstructions, causing the reduction of the three-dimensional (3D) spatial resolution as well as the enhancement of image artifacts. Here the development of a method that iteratively performs registration of the experimentally measured projection images to those that are numerically calculated by reprojecting the 3D matrix in the corresponding viewing angles is shown. Multiple algorithms are implemented to conduct the registration, which corrects the translational and/or the rotational errors. A sequence that offers a superior performance is presented and discussed. Going beyond the visual assessment of the reconstruction results, the morphological quantification of a battery electrode particle that has gone through substantial cycling is investigated. The results show that the presented method has led to a better quality tomographic reconstruction, which, subsequently, promotes the fidelity in the quantification of the sample morphology.

[Preparation and characterization of salvianolic acids-tanshinones composite powder for inhalation via co-micronized method].

Salvianolic acids and tanshinones are main hydrophilic and lipophilic extracts from Salvia Miltiorrhiza with significant anti-pulmonary fibrosis effects. The aim of this study was to prepare a co-micronized salvianolic acids-tanshinones composite powder for inhalation using a planetary ball mill. The micronization process parameters were optimized by central composite design (CCD) and response surface methodology (RSM). Treatment time, rotation speed and the ball/sample weight ratio were selected as the independent variables, and the volume fraction of particle size in 1-5 µm was taken as the dependent variable. The powder properties were evaluated by scanning electron microscopy (SEM), laser diffraction and X-ray powder diffraction (XRPD). The powder flow and hygroscopicity were determined with repose angle, compressibility index and critical relative humidity(CRH). According to the results, the salvianolic acids-tanshinones composite powder produced in optimal conditions had a narrow and unimodal particle size distribution and a smaller D50 of 2.33 µm. The volume fraction of particle size in 1-5 µm was 80.82%. The repose angle was (50.60±1.13) °, and the critical relative humidity is about 77%. After being micronized, the particle size significantly reduced, and the number of amorphous substances slightly increased, with no significant changes in powder flow and hygroscopicity. These findings indicate that the grinding method with a planetary ball mill can be used to co-micronize various components with different properties and prepare composite drug powders for dry powder inhalation.

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design.

There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure-property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.

Condensation of Rubisco into a proto-pyrenoid in higher plant chloroplasts.

Photosynthetic CO2 fixation in plants is limited by the inefficiency of the CO2-assimilating enzyme Rubisco. In most eukaryotic algae, Rubisco aggregates within a microcompartment known as the pyrenoid, in association with a CO2-concentrating mechanism that improves photosynthetic operating efficiency under conditions of low inorganic carbon. Recent work has shown that the pyrenoid matrix is a phase-separated, liquid-like condensate. In the alga Chlamydomonas reinhardtii, condensation is mediated by two components: Rubisco and the linker protein EPYC1 (Essential Pyrenoid Component 1). Here, we show that expression of mature EPYC1 and a plant-algal hybrid Rubisco leads to spontaneous condensation of Rubisco into a single phase-separated compartment in Arabidopsis chloroplasts, with liquid-like properties similar to a pyrenoid matrix. This work represents a significant initial step towards enhancing photosynthesis in higher plants by introducing an algal CO2-concentrating mechanism, which is predicted to significantly increase the efficiency of photosynthetic CO2 uptake.

CRISPR-Cas9-Mediated Mutagenesis of the Rubisco Small Subunit Family in Nicotiana tabacum.

Engineering the small subunit of the key CO2-fixing enzyme Rubisco (SSU, encoded by rbcS) in plants currently poses a significant challenge, as many plants have polyploid genomes and SSUs are encoded by large multigene families. Here, we used CRISPR-Cas9-mediated genome editing approach to simultaneously knock-out multiple rbcS homologs in the model tetraploid crop tobacco (Nicotiana tabacum cv. Petit Havana). The three rbcS homologs rbcS_S1a, rbcS_S1b and rbcS_T1 account for at least 80% of total rbcS expression in tobacco. In this study, two multiplexing guide RNAs (gRNAs) were designed to target homologous regions in these three genes. We generated tobacco mutant lines with indel mutations in all three genes, including one line with a 670 bp deletion in rbcS-T1. The Rubisco content of three selected mutant lines in the T1 generation was reduced by ca. 93% and mutant plants accumulated only 10% of the total biomass of wild-type plants. As a second goal, we developed a proof-of-principle approach to simultaneously introduce a non-native rbcS gene while generating the triple SSU knockout by co-transformation into a wild-type tobacco background. Our results show that CRISPR-Cas9 is a viable tool for the targeted mutagenesis of rbcS families in polyploid species and will contribute to efforts aimed at improving photosynthetic efficiency through expression of superior non-native Rubisco enzymes in plants.

ECG R-wave peaks marking with simultaneously recorded continuous blood pressure.

BACKGROUND AND OBJECTIVES: ECG signal is relatively weak and vulnerable to various noise interferences, such as electromyography. There will be robustness problems when detecting the instantaneous heart rate independently. In some cases, multiple human physiologic parameters are monitored to help in heart rate detection. METHODS: In this paper, an algorithm that marks the R-wave peaks with the help of simultaneously recorded continuous blood pressure is proposed and tested on two databases. One database, called the challenge database, is provided by the PhysioNet/Computing in Cardiology Challenge 2014, and the other is the MGH/MF waveform database. RESULTS: The final scores of the proposed algorithm are 97.3% for the challenge database and 96.6% for the MGH/MF waveform database. CONCLUSIONS: The experimental results show that this algorithm has high detection accuracy and a relatively strong robustness.