Seniority Number in Valence Bond Theory

In this work, a hierarchy of valence bond (VB) methods based on the concept of seniority number, defined as the number of singly occupied orbitals in a determinant or an orbital configuration, is proposed and applied to the studies of the potential energy curves (PECs) of H₈, N₂, and C₂ molecules. It is found that the seniority-based VB expansion converges more rapidly toward the full configuration interaction (FCI) or complete active space self-consistent field (CASSCF) limit and produces more accurate PECs with smaller nonparallelity errors than its molecular orbital (MO) theory-based analogue. Test results reveal that the nonorthogonal orbital-based VB theory provides a reverse but more efficient way to truncate the complete active Hilbert space by seniority numbers

Thermal-mechanical optimization of folded core sandwich panels for thermal protection systems of space vehicles

The integrated thermal protection system (ITPS) is a complicated system that addresses both mechanical and thermal considerations. An M-pattern folded core sandwich panel packedwith low-density insulation material provides inherently lowmass for a potential ITPS panel. Herein, we identify the most influential geometric parameters and establish a viable, computationally efficient optimization procedure. Variables considered for optimization are geometric dimensions of the ITPS, while temperature and deflection are taken as constraints. A one-dimensional (1D) thermal model based on a modified form of the rule of mixtures was established, while a three-dimensional (3D) model was adopted for linear static analyses. Parametric models were generated to facilitate a design of experiment (DOE) study, and approximate models using radial basis functions were obtained to carry out the optimization process. Sensitivity studies were first conducted to investigate the effect of geometric parameters on the ITPS responses. Then optimizations were performed for both thermal and thermal-mechanical constraints. The results show that the simplified 1D thermal model is able to predict temperature through the ITPS thickness satisfactorily. The combined optimization strategy evidently improves the computational efficiency of the design process showing it can be used for initial design of folded core ITPS

Additional file 1: Table S1. of Improvement of betulinic acid biosynthesis in yeast employing multiple strategies

Primers used in this study. Figure S1. The mass fragmented patterns of the products lupeol (LUP), betulin (BN) and betulinic acid (BA) as well as their corresponding chemical standards. Figure S2. The mass spectrums of the unknown peaks 1–3 shown in the Fig. 1 of the main text. Figure S3. GC-MS analysis of the products extracted from the lupeol C28-oxidase (LO) alone-expressed yeast cultures fed with lupeol. Figure S4. The production of betulinic acid (BA) was mostly found in the yeast culture mediums of both CEN.PK-ATR1-LB and WAT11-406-LB strains with relatively much less BA being detected inside the cells. Figure S5. Confirmation of the Gal80p gene disruption by diagnostic PCRs. Figure S6. Comparison of the BPLO transcripts between the wild type strain WAT11-LB and the mutant WAT11-LB-△Gal80 under 2 % galactose as the carbon source. (DOCX 831 kb

Nanofibrous Adhesion: The Twin of Gecko Adhesion

Inspired by dusty spider dragline silk, we studied the adhesive interaction between artificial nanofibers and their aerosol surroundings. The nanofibers are found to be able to actively capture particulate matters from the environment, exactly as the spider dragline silk does. Examinations prove that such nanofibrous adhesion is insensitive to the chemical nature of the fibers and the physical states of the particulate matter and depends only on the fiber diameters. Such facts indicate that nanofibrous adhesion is a case of dry adhesion, mainly governed by van der Waals force, sharing the same mechanism to gecko adhesion. Nanofibrous adhesion is of great importance and has promising potential. For instance, in this work, nanofibers are fabricated into a thin and translucent filter, which has a filtration performance, as high as 95%, that easily outperformed ordinary ones. We believe that this adhesive property of nanofibers will open up broader applications in both scientific and industrial fields

Statistic of sequencing and <i>de novo</i> assembling of transcriptome in <i>W</i>. <i>ugandensis</i>.

<p>Statistic of sequencing and <i>de novo</i> assembling of transcriptome in <i>W</i>. <i>ugandensis</i>.</p

Histogram of the gene ontology classifications of annotated unigenes from the <i>W</i>. <i>ugandensis</i> transcriptome.

<p>BP, Biological process; CC, Cell component; MF, Molecular function.</p

A topology-preserving polygon rasterization algorithm

<p>Conventional algorithms for polygon rasterization are typically designed to maintain non-topological characteristics. Consequently, topological relationships, such as the adjacency between polygons, may also be lost or altered, creating topological errors. This paper proposes a topology-preserving polygon rasterization algorithm to avoid topological errors. Four types of topological error may occur during polygon rasterization. The algorithm starts from an initial polygon rasterization and uses a set of preserving strategies to increase topological accuracy. The count of the four types of error measures the topological errors of the conversion. Topological accuracy is summarized as 1 minus the ratio of actual topological errors to the total number of possible error cases. When applied to a land-use dataset with a data volume of 128 MB, 127,836 polygons, and extending 1352 km², the algorithm achieves a topological accuracy of more than 99% when raster cell size is 30 m or smaller (100% for 5 and 10 m). The effects of cell size, polygon shape, and number of iterations on topological accuracy are also examined.</p

Biogenic Melanin-Modified Graphene as a Cathode Catalyst Yields Greater Bioelectrochemical Performances by Stimulating Oxygen–Reduction and Microbial Electron Transfer

Bioelectrochemical systems (BES) can recover energy from organic-bearing waste streams, but their use has been stymied by poor electron transfer from the cathode. Redox-active electron shuttles could stimulate electron transfer provided that they are compatible with the exoelectrogenic bacteria. This work evaluated melanin-modified carboxylated graphene (M/CG) as a novel cathode catalyst in a microbial fuel cell. Biogenic melanin catalysts (i.e., bio-M/CG) significantly increased bioelectricity production due to its abundant pyrrole N, which lowered charge-transfer resistance and, thus, promoted the cathodic oxygen–reduction reaction (ORR). The high content of pyrrole N in the bio-M/CG catalyst also enriched exoelectrogens, such as Azospirillum, Chryseobacterium, and Azoarcus, which accounted for over 50% of the total abundance of bacteria in biofilms on the anode. Moreover, the functional genes of key enzymes involved in microbial electron transfer (MET) were increased by the bio-M/CG catalyst. These data confirm that the bio-M/CG catalyst improved the bioelectrochemical performance via synergetic promotion of cathodic ORR and microbial electron transfer, thus providing a new alternative for advancing BES technology. This work highlights the potential application of melanin in enhancing cathodic oxygen–reduction reaction kinetics and improving microbial electron transfer in BES. This study emphasizes the promising application of melanin in enhancing the ORR kinetics and improving MET in BES, offering exciting prospects for future sustainable and environmentally friendly applications

Phylogenetic tree of terpene synthases.

<p>Phylogenetic analysis of 14 putative <i>W</i>. <i>ugandensis</i> WuTPS protein sequences with their homologs from other plants indicates that they are clustered into three main clades including: monoterpenoid synthase (WuMts), sesquiterpenoid synthase (WuSps), and diterpenoid synthase (WuDts). WarbTPS-c (ACJ46047.1, putative sesquiterpene synthase, <i>W</i>. <i>ugandensis</i>); WarbTPS-g (ACJ46048.1, putative sesquiterpene synthase, <i>W</i>. <i>ugandensis</i>); WuSps1, CL29873Contig1; WuSps2, CL29511Contig1; WuSps3, CL3178Contig1; WuSps4, CL4160Contig1; WuSps5, comp68897_c0_seq1; WuSps6, CL24969Contig1; WuSps7, CL30258Contig1; WuMts1, CL27268Contig1; WuMts2, CL27339Contig1; WuMts3, CL30385Contig1; WuMts4, CL276Contig2; WuMts5, CL1Contig9269; WuMts6, CL29966Contig1; WuMts7, CL14869Contig1; WuDts1, CL9128Contig1; WuDts2, CL28648Contig1; Citsi_Germacrene_D (XP_006494713.1, (-)-germacrene D synthase-like isoform X2, <i>Citrus sinensis</i>); Popeu_Valencene (XP_011015484.1, valencene synthase-like, <i>Populus euphratica</i>); Nelnu_Germacrene_D (XP_010258444.1, (-)-germacrene D synthase-like, <i>Nelumbo nucifera</i>); Eletr_Copaene (ADK94034.1, alpha-copaene synthase, <i>Eleutherococcus trifoliatus</i>); Gosar_Germacrene_D (KHG04103.1, (-)-germacrene D synthase, <i>Gossypium arboretum</i>); Vitvi_Germacrene_D (XP_010644711.1, (-)-germacrene D synthase, <i>Vitis vinifera</i>); Vitvi_Germacrene_A (ADR66821.1, Germacrene A synthase, <i>Vitis vinifera</i>); Citja_Elemene (BAP74389.1, delta-elemene synthase, <i>Citrus jambhiri</i>); Theca_Cadinene (EOY12648.1, Delta-cadinene synthase isozyme A, <i>Theobroma cacao</i>); Ricco_Cadinene (EEF38721.1, (+)-delta-cadinene synthase isozyme A, <i>Ricinus communis</i>); Aqusi_Guaiene (AIT75875.1, putative delta-guaiene synthase, <i>Aquilaria sinensis</i>); Vitvi_Caryophyllene (AEP17005.1, (E)-beta-caryophyllene synthase, <i>Vitis vinifera</i>); Maggr_Cubebene (ACC66281.1, beta-cubebene synthase, <i>Magnolia grandiflora</i>); Cinos_Linalool (AFK09265.1, S-(+)-linalool synthase, <i>Cinnamomum osmophloeum</i>); Nelnu_Nerolidol (XP_010248179.1, (3S,6E)-nerolidol synthase 1-like, <i>Nelumbo nucifera</i>); Vitvi_Nerolidol (XP_010646919.1, (3S,6E)-nerolidol synthase 1, chloroplastic-like isoform X1, <i>Vitis vinifera</i>); Vitvi_Linalool (ADR74212.1, (3S)-linalool/(E)-nerolidol synthase, <i>Vitis vinifera</i>); Actpo_Linalool (ADD81295.1, linalool synthase, <i>Actinidia polygama</i>); Nelnu_Ent-copalyl (XP_010277558.1, ent-copalyl diphosphate synthase, chloroplastic-like, <i>Nelumbo nucifera</i>); Theca_Ent-copalyl (XP_007050589.1, Copalyl diphosphate synthase, <i>Theobroma cacao</i>); Morno_Ent-copalyl (XP_010090409.1, Ent-copalyl diphosphate synthase, <i>Morus notabilis</i>); Gosar_Ent-copalyl (KHG01750.1, Ent-copalyl diphosphate synthase, chloroplastic, <i>Gossypium arboreum</i>); Nelnu_Ent-kaurene (XP_010260722.1, ent-kaur-16-ene synthase, chloroplastic isoform X1, <i>Nelumbo nucifera</i>); Phoda_Ent-kaurene (XP_008809130.1, ent-kaur-16-ene synthase, chloroplastic, <i>Phoenix dactylifera</i>); Ricico_Ent-kaurene (XP_002533694.1, Ent-kaurene synthase B, chloroplast precursor, <i>Ricinus communis</i>); Popeu_Ent-kaurene (XP_011014299.1, ent-kaur-16-ene synthase, chloroplastic, <i>Populus euphratica</i>); Nicta_Epi-Aristolochene (3M02.A, 5-Epi-Aristolochene Synthase, <i>Nicotiana tabacum</i>); Soltu_Vetispiradiene (Q9XJ32.1, vetispiradiene synthase 1, <i>Solanum tuberosum</i>); Litcu_Ocimene (AEJ91554.1, trans-ocimene synthase, <i>Litsea cubeba</i>); Litcu_Thujene (AEJ91555.1, alpha-thujene synthase, <i>Litsea cubeba</i>); Citli_Limonene (AAM53946.1, (+)-limonene synthase 2, <i>Citrus limon</i>); Vitvi_Ocimene/Myrcene (ADR74206.1, (E)-beta-ocimene/myrcene synthase, <i>Vitis vinifera</i>); Queil_Pinene (CAK55186.1, pinene synthase, <i>Quercus ilex</i>).</p

Summary of unigenes related to lipid and terpenoid metabolism.

<p>Summary of unigenes related to lipid and terpenoid metabolism.</p