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Successful synthetic biology efforts rely on conceptual and experimental designs in combination with testing of multi-gene constructs. Despite recent progresses, several limitations still hinder the ability to flexibly assemble and collectively share different types of DNA segments. Here, we describe an advanced system for joining DNA fragments from a universal library that automatically maintains open reading frames (ORFs) and does not require linkers, adaptors, sequence homology, amplification or mutation (domestication) of fragments in order to work properly. This system, which is enhanced by a unique buffer formulation, provides unforeseen capabilities for testing, and sharing, complex multi-gene circuitry assembled from different DNA fragments.

Crassulacean acid metabolism (CAM) is a specialized mode of photosynthesis that features nocturnal CO2 uptake, facilitates increased water-use efficiency (WUE), and enables CAM plants to inhabit water-limited environments such as semi-arid deserts or seasonally dry forests. Human population growth and global climate change now present challenges for agricultural production systems to increase food, feed, forage, fiber, and fuel production. One approach to meet these challenges is to increase reliance on CAM crops, such as Agave and Opuntia, for biomass production on semi-arid, abandoned, marginal, or degraded agricultural lands. Major research efforts are now underway to assess the productivity of CAM crop species and to harness the WUE of CAM by engineering this pathway into existing food, feed, and bioenergy crops. An improved understanding of CAM has potential for high returns on research investment. To exploit the potential of CAM crops and CAM bioengineering, it will be necessary to elucidate the evolution, genomic features, and regulatory mechanisms of CAM. Field trials and predictive models will be required to assess the productivity of CAM crops, while new synthetic biology approaches need to be developed for CAM engineering. Infrastructure will be needed for CAM model systems, field trials, mutant collections, and data management.

The application of organometallic magnesium(II) compounds in the catalytic dihydroboration of nitriles and hydroboration of alkynes has been reported. Five compounds [(L)MgCl(THF)3] (1), [(L)MgBr(THF)3] (2), [(L)2Mg2Cl2(THF)]·0.2Tol (3·0.2Tol), [(L)2Mg2Br2(THF)] (4) and [(L1)2Mg2] (5) (HL = 2-(2-((((1H-indol-2-yl)methylene)amino)methyl)-1H-pyrrol-1-yl)-N,N-dimethylethan-1-amine, H2L1 = N-((1-(2-(dimethylamino)ethyl)-1H-pyrrol-2-yl)methyl)-1-(1H-indol-2-yl)pentan-1-amine) were prepared. Mononuclear complexes 1 and 2 were synthesized by the 1 : 1 reaction of HL with THF solution of MeMgCl and MeMgBr, respectively. When the reactions were conducted in toluene, two dinuclear compounds, 3·0.2Tol and 4, were obtained. When the magnesium source was changed to nBu2Mg, the nucleophilic addition of nBu2Mg to the C＝N bond of the HL ligand occurred, affording the new ligand [L1]2−via the transformation of the C＝N bond into a C–N bond and the generation of a new C–CBu bond. Complexes 1–5 are active precatalysts for the hydroboration of nitriles and alkynes, and 3·0.2Tol and 4 exhibited higher activities. The catalytic reactions possess the features of excellent functional group tolerance and good chemo- and regioselectivity. [ABSTRACT FROM AUTHOR]

An epiretinal membrane (ERM) is a fibrocellular proliferation on the inner surface of the retina causing blurred and distorted central vision. Surgery is the only effective method for ERM removal. This paper investigated the mechanical properties of ERM peeling using the finite element (FE) method. A FE model of ERM formation on the retina surface was constructed. The failure criterion was applied to the attachment pegs to represent the adhesive force between the ERM and retina. The simulation results were consistent with the experimental data in published research. The maximum peeling force was 4.1 mN at a peeling velocity of 2 mm/s and an angle of 30°. The peeling force was minimum at the peeling angle of 45° and increased with the increase in peeling velocity and Young's modulus of the membrane. The outcome of this paper can improve the safety and efficiency of ERM removal. [ABSTRACT FROM AUTHOR]

Due to software and hardware differentiation, the problem of false positives mistakenly identified as network attack behavior caused by inconsistent mimic decision results frequently occurs. Therefore, a mimic decision method based on deep learning was proposed. By constructing an unsupervised autoencoder-decoder deep learning model, the deep semantic features of diverse normal response data were explored from different executions and its statistical rules were analyzed and summarized. Additionally, the offline learning-online decision-making mechanism and the feedback optimization mechanism were designed to solve false positive problem, thereby accurately detecting network attacks and improving target system security resilience. Since statistical rules of normal response data was understood and mastered by deep learning model, the mimic decision results among different executions could remain consistent, indicating that the target system was in a secure state. However, once the target system was subjected to a network attacks, the response data outputted by the different executions was deviated from statistical distribution of deep learning model. Therefore, inconsistent mimic decision results were presented, indicating that the affected execution was under attack and the target system was exposed to potential security threats. The experiments show that the performance of the proposed method is significantly superior to the popular mimic decision methods, and the average prediction accuracy is improved by 14.89%, which is conducive to integrating the method into the mimic transformation of real application to enhance the system's defensive capability. [ABSTRACT FROM AUTHOR]

Small proteins (10 200 amino acids aa in length) encoded by short open reading frames (sORF) play important regulatory roles in various biological processes, including tumor progression, stress response, flowering, and hormone signaling. However, ab initio discovery of small proteins has been relatively overlooked. Recent advances in deep transcriptome sequencing make it possible to efficiently identify sORFs at the genome level. In this study, we obtained 2.6 million expressed sequence tag (EST) reads from Populus deltoides leaf transcriptome and reconstructed full-length transcripts fromthe EST sequences. We identified an initial set of 12,852 sORFs encoding proteins of 10 200 aa in length. Three computational approaches were then used to enrich for bona fide protein-coding sORFs from the initial sORF set: (1) codingpotential prediction, (2) evolutionary conservation between P. deltoides and other plant species, and (3) gene familyclustering within P. deltoides. As a result, a high-confidence sORF candidate set containing 1469 genes was obtained. Analysis of the protein domains, non-protein-coding RNA motifs, sequence length distribution, and protein mass spectrometry data supported this high-confidence sORF set. In the high-confidence sORF candidate set, known protein domains were identified in 1282 genes (higher-confidence sORF candidate set), out of which 611 genes, designated as highest-confidence candidate sORF set, were supported by proteomics data. Of the 611 highest-confidence candidate sORF genes, 56 were newto the current Populus genome annotation. This study not only demonstrates that there are potential sORF candidates to be annotated in sequenced genomes, but also presents an efficient strategy for discovery of sORFs in species with no genome annotation yet available.

Abstract Background Glycoside hydrolases cleave the bond between a carbohydrate and another carbohydrate, a protein, lipid or other moiety. Genes encoding glycoside hydrolases are found in a wide range of organisms, from archea to animals, and are relatively abundant in plant genomes. In plants, these enzymes are involved in diverse processes, including starch metabolism, defense, and cell-wall remodeling. Glycoside hydrolase genes have been previously cataloged for Oryza sativa (rice), the model dicotyledonous plant Arabidopsis thaliana, and the fast-growing tree Populus trichocarpa (poplar). To improve our understanding of glycoside hydrolases in plants generally and in grasses specifically, we annotated the glycoside hydrolase genes in the grasses Brachypodium distachyon (an emerging monocotyledonous model) and Sorghum bicolor (sorghum). We then compared the glycoside hydrolases across species, at the levels of the whole genome and individual glycoside hydrolase families. Results We identified 356 glycoside hydrolase genes in Brachypodium and 404 in sorghum. The corresponding proteins fell into the same 34 families that are represented in rice, Arabidopsis, and poplar, helping to define a glycoside hydrolase family profile which may be common to flowering plants. For several glycoside hydrolase familes (GH5, GH13, GH18, GH19, GH28, and GH51), we present a detailed literature review together with an examination of the family structures. This analysis of individual families revealed both similarities and distinctions between monocots and eudicots, as well as between species. Shared evolutionary histories appear to be modified by lineage-specific expansions or deletions. Within GH families, the Brachypodium and sorghum proteins generally cluster with those from other monocots. Conclusions This work provides the foundation for further comparative and functional analyses of plant glycoside hydrolases. Defining the Brachypodium glycoside hydrolases sets the stage for Brachypodium to be a grass model for investigations of these enzymes and their diverse roles in planta. Insights gained from Brachypodium will inform translational research studies, with applications for the improvement of cereal crops and bioenergy grasses.