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Artificial photosynthesis, specifically H2O dissociation for CO2 reduction with solar energy, is regarded as one of the most promising methods for sustainable energy and utilisation of environmental resources. However, a highly efficient conversion still remains extremely challenging. The hydrogenation of CO2 is regarded as the most commercially feasible method, but this method requires either exotic catalysts or high-purity hydrogen and hydrogen storage, which are regarded as an energy-intensive process. Here we report a highly efficient method of H2O dissociation for reducing CO2 into chemicals with Zn powder that produces formic acid with a high yield of approximately 80%, and this reaction is revealed for the first time as an autocatalytic process in which an active intermediate, ZnH− complex, serves as the active hydrogen. The proposed process can assist in developing a new concept for improving artificial photosynthetic efficiency by coupling geochemistry, specifically the metal-based reduction of H2O and CO2, with solar-driven thermochemistry for reducing metal oxide into metal.

Reproductive mode can impact population genetic dynamics and evolutionary landscape of plant pathogens as well as on disease epidemiology and management. In this study, we monitored the spatial dynamics and mating type idiomorphs in ~700 Alternaria alternata isolates sampled from the main potato production areas in China to infer the mating system of potato early blight. Consistent with the expectation of asexual species, identical genotypes were recovered from different locations separated by hundreds of kilometers of geographic distance and spanned across many years. However, high genotype diversity, equal MAT1-1 and MAT1-2 frequencies within and among populations, no genetic differentiation and phylogenetic association between two mating types, combined with random association amongst neutral markers in some field populations, suggested that sexual reproduction may also play an important role in the epidemics and evolution of the pathogen in at least half of the populations assayed despite the fact that no teleomorphs have been observed yet naturally or artificially. Our results indicated that A. alternata may adopt an epidemic mode of reproduction by combining many cycles of asexual propagation with fewer cycles of sexual reproduction, facilitating its adaptation to changing environments and making the disease management on potato fields even more difficult.

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The emergence of new pathogens, such as severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Ebola virus, poses serious challenges to global public health and highlights the urgent need for novel antiviral approaches. Monoclonal antibodies (mAbs) have been successfully used to treat various diseases, particularly cancer and immunological disorders. Antigen-specific mAbs have been isolated using several different approaches, including hybridoma, transgenic mice, phage display, yeast display, and single B-cell isolation. Consequently, an increasing number of mAbs, which exhibit high potency against emerging viruses in vitro and in animal models of infection, have been developed. In this paper, we summarize historical trends and recent developments in mAb discovery, compare the advantages and disadvantages of various approaches to mAb production, and discuss the potential use of such strategies for the development of antivirals against emerging diseases. We also review the application of recently developed human mAbs against SARS-CoV, MERS-CoV, and Ebola virus and discuss prospects for the development of mAbs as therapeutic agents against emerging viral diseases.

We design algorithms for minimizing max i∈[n] f i (x) over a d-dimensional Euclidean or simplex domain. When each f i is 1-Lipschitz and 1-smooth, our method computes an ϵ-approximate solution using O(nϵ −1/3 + ϵ −2 ) gradient and function evaluations, and O(nϵ −4/3 ) additional runtime. For large n, our evaluation complexity is optimal up to polylogarithmic factors. In the special case where each f i is linear-which corresponds to finding a near-optimal primal strategy in a matrix game-our method finds an ϵ-approximate solution in runtime O(n(d/ϵ) 2/3 + nd + dϵ −2 ). For n > d and ϵ = 1/ √ n this improves over all existing first-order methods. When additionally d = ω(n 8/11 ) our runtime also improves over all known interior point methods.Our algorithm combines three novel primitives: (1) A dynamic data structure which enables efficient stochastic gradient estimation in small ℓ 2 or ℓ 1 balls. (2) A mirror descent algorithm tailored to our data structure implementing an oracle which minimizes the objective over these balls. (3) A simple ball oracle acceleration framework suitable for non-Euclidean geometry.

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FPGA-based soft multiprocessors are viable system solutions for high performance applications. They provide a software abstraction to enable quick implementations on the FPGA. The multiprocessor can be customized for a target application to achieve high performance. Modern FPGAs provide the capacity to build a variety of micro-architectures composed of 20-50 processors, complex memory hierarchies, heterogeneous interconnection schemes and custom co-processors for performance critical operations. However, the diversity in the architectural design space makes it difficult to realize the performance potential of these systems. In this paper we develop an exploration framework to build efficient FPGA multiprocessors for a target application. Our main contribution is a tool based on Integer Linear Programming to explore micro-architectures and allocate application tasks to maximize throughput. Using this tool, we implement a soft multiprocessor for IPv4 packet forwarding that achieves a throughput of 2 Gbps, surpassing the performance of a carefully tuned hand design.