Domain Composition Research Articles

Analysing the glaze of a medieval ceramic fragment from the Durres Amphitheater in Albania

The paper analyses the glaze of a ceramic sherd found in the southern sector of the Durres amphitheatre. Specifically, the sherd was found in a layer datable to the late 12th to early 13th century, which can be interpreted as a dismissal layer of a pottery kiln in use between the early and second half of the 12th century. The glaze was analysed using SEM–EDS and Total XRF techniques. The green-ocean glaze with a blue-sky decoration of the fragment has As-Co and Pb–Sn-Si compounds as pigments and phosphorous as a modifying agent and a flux. The glaze composition is SiO2 47.6 wt.%, TiO2 0.22 wt.%, Al2O3 4.08 wt.%, FeOtotal 0.22 wt.%, MnO 0.08 wt.%, MgO 0.23 wt.%, CaO 2.51 wt.%, Na2O 1.55 wt.%, K2O 5.16 wt.%, P2O5 3.01 wt.%, SnO2 4.13 wt.%, As2O5 4.13 wt.%, PbO 25.4 wt%. Fe is expressed as FeOtotal. The trace elements composition (ppm) is Co 3684, Ni 1023, Cu 819, Zn 3070, Bi 3172, and Sr 205. We introduced a robust glaze classification scheme based on chemistry. This scheme categories the glaze as alkaline-lead SnO2-opacified. We examined uncommon compounds formed in various textural contexts to establish the production origin and technique peculiarity. The glaze glasses form three different compositional domains: one represents the parental high-temperature initial glass composition, and two are related to immiscible segregations forming at lower temperatures. Five phases of the apatite supergroup were identified, along with other phases distributed throughout the glaze. The compounds present, such as Pb and Sn silicates, leucite, and k-feldspar and their balances, constrain the firing temperature to 720 ℃ and 900 ℃, respectively.

MBB-MOGWO: Modified Boltzmann-Based Multi-Objective Grey Wolf Optimizer.

The primary objective of multi-objective optimization techniques is to identify optimal solutions within the context of conflicting objective functions. While the multi-objective gray wolf optimization (MOGWO) algorithm has been widely adopted for its superior performance in solving multi-objective optimization problems, it tends to encounter challenges such as local optima and slow convergence in the later stages of optimization. To address these issues, we propose a Modified Boltzmann-Based MOGWO, referred to as MBB-MOGWO. The performance of the proposed algorithm is evaluated on multiple multi-objective test functions. Experimental results demonstrate that MBB-MOGWO exhibits rapid convergence and a reduced likelihood of being trapped in local optima. Furthermore, in the context of the Internet of Things (IoT), the quality of web service composition significantly impacts complexities related to sensor resource scheduling. To showcase the optimization capabilities of MBB-MOGWO in real-world scenarios, the algorithm is applied to address a Multi-Objective Problem (MOP) within the domain of web service composition, utilizing real data records from the QWS dataset. Comparative analyses with four representative algorithms reveal distinct advantages of our MBB-MOGWO-based method, particularly in terms of solution precision for web service composition. The solutions obtained through our method demonstrate higher fitness and improved service quality.

Genome-Wide Identification and Characterization of the Salvia miltiorrhiza Histone Deacetylase (HDAC) Family in Response to Multiple Abiotic Stresses.

Salvia miltiorrhiza is a plant commonly used in traditional Chinese medicine. Its material bases for treating diseases are tanshinones and phenolic acids, including salvianolic acids. Histone deacetylase proteins (HDACs) are a class of specific functional enzymes that interact with acetylation groups on the N-terminal lysine of histone proteins further regulate gene transcription through structural changes at the chromatin level. HDACs involved in the growth and development of various plants, and induced by plant hormones to regulate the internal environment of plants to resist stress, at the same time affect the accumulation of some secondary metabolites. However, the role of SmHDACs on the accumulation of salvianolic acid in S. miltiorrhiza remains unclear. In this study, 16 SmHDACs genes were identified from the high-quality S. miltiorrhiza genome, their physicochemical properties were predicted. In phylogenetic trees co-constructed with HDACs proteins from other plants, SmHDACs was divided into three subfamilies, each with similar motif and conserved domain composition. The distribution of the three subfamilies is similar to that of dicotyledonous plants. Chromosome localization analysis showed that SmHDACs genes were randomly located. Cis-acting element analysis predicted that SmHDACs gene expression may be related to and induced by various phytohormones, such as MeJA and ABA. By combining the expression pattern and co-expression network induced by phytohormones, we speculate that SmHDACs may further influence the synthesis of salvianolic acid, and identified SmHDA5, a potential functional gene, then speculate its downstream target based on the co-expression network. In summary, we analyzed the SmHDACs gene family of S. miltiorrhiza and screened out the potential functional gene SmHDA5. From the perspective of epigenetics, we proposed the molecular mechanism of plant hormone promoting salvianolic acid synthesis, which filled the gap in the subdivision of histone deacetylase in S. miltiorrhiza research, provided a theoretical basis for the culture and transformation of S. miltiorrhiza germplasm resources.

Food environment research in Canada: a rapid review of methodologies and measures deployed between 2010 and 2021.

Numerous research methodologies have been used to examine food environments. Existing reviews synthesizing food environment measures have examined a limited number of domains or settings and none have specifically targeted Canada. This rapid review aimed to 1) map research methodologies and measures that have been used to assess food environments; 2) examine what food environment dimensions and equity related-factors have been assessed; and 3) identify research gaps and priorities to guide future research. A systematic search of primary articles evaluating the Canadian food environment in a real-world setting was conducted. Publications in English or French published in peer-reviewed journals between January 1 2010 and June 17 2021 and indexed in Web of Science, CAB Abstracts and Ovid MEDLINE were considered. The search strategy adapted an internationally-adopted food environment monitoring framework covering 7 domains (Food Marketing; Labelling; Prices; Provision; Composition; Retail; and Trade and Investment). The final sample included 220 articles. Overall, Trade and Investment (1%, n = 2), Labelling (7%, n = 15) and, to a lesser extent, Prices (14%, n = 30) were the least studied domains in Canada. Among Provision articles, healthcare (2%, n = 1) settings were underrepresented compared to school (67%, n = 28) and recreation and sport (24%, n = 10) settings, as was the food service industry (14%, n = 6) compared to grocery stores (86%, n = 36) in the Composition domain. The study identified a vast selection of measures employed in Canada overall and within single domains. Equity-related factors were only examined in half of articles (n = 108), mostly related to Retail (n = 81). A number of gaps remain that prevent a holistic and systems-level analysis of food environments in Canada. As Canada continues to implement policies to improve the quality of food environments in order to improve dietary patterns, targeted research to address identified gaps and harmonize methods across studies will help evaluate policy impact over time.

Insights into gut microbiomes in stem cell transplantation by comprehensive shotgun long-read sequencing

The gut microbiome is a diverse ecosystem, dominated by bacteria; however, fungi, phages/viruses, archaea, and protozoa are also important members of the gut microbiota. Exploration of taxonomic compositions beyond bacteria as well as an understanding of the interaction between the bacteriome with the other members is limited using 16S rDNA sequencing. Here, we developed a pipeline enabling the simultaneous interrogation of the gut microbiome (bacteriome, mycobiome, archaeome, eukaryome, DNA virome) and of antibiotic resistance genes based on optimized long-read shotgun metagenomics protocols and custom bioinformatics. Using our pipeline we investigated the longitudinal composition of the gut microbiome in an exploratory clinical study in patients undergoing allogeneic hematopoietic stem cell transplantation (alloHSCT; n = 31). Pre-transplantation microbiomes exhibited a 3-cluster structure, characterized by Bacteroides spp. /Phocaeicola spp., mixed composition and Enterococcus abundances. We revealed substantial inter-individual and temporal variabilities of microbial domain compositions, human DNA, and antibiotic resistance genes during the course of alloHSCT. Interestingly, viruses and fungi accounted for substantial proportions of microbiome content in individual samples. In the course of HSCT, bacterial strains were stable or newly acquired. Our results demonstrate the disruptive potential of alloHSCTon the gut microbiome and pave the way for future comprehensive microbiome studies based on long-read metagenomics.

Unearthing a Cryptic Biosynthetic Gene Cluster for the Piperazic Acid-Bearing Depsipeptide Diperamycin in the Ant-Dweller Streptomyces sp. CS113.

Piperazic acid is a cyclic nonproteinogenic amino acid that contains a hydrazine N-N bond formed by a piperazate synthase (KtzT-like). This amino acid, found in bioactive natural products synthesized by non-ribosomal peptide synthetases (NRPSs), confers conformational constraint to peptides, an important feature for their biological activities. Genome mining of Streptomyces strains has been revealed as a strategy to identify biosynthetic gene clusters (BGCs) for potentially active compounds. Moreover, the isolation of new strains from underexplored habitats or associated with other organisms has allowed to uncover new BGCs for unknown compounds. The in-house "Carlos Sialer (CS)" strain collection consists of seventy-one Streptomyces strains isolated from the cuticle of leaf-cutting ants of the tribe Attini. Genomes from twelve of these strains have been sequenced and mined using bioinformatics tools, highlighting their potential to encode secondary metabolites. In this work, we have screened in silico those genomes, using KtzT as a hook to identify BGCs encoding piperazic acid-containing compounds. This resulted in uncovering the new BGC dpn in Streptomyces sp. CS113, which encodes the biosynthesis of the hybrid polyketide-depsipeptide diperamycin. Analysis of the diperamycin polyketide synthase (PKS) and NRPS reveals their functional similarity to those from the aurantimycin A biosynthetic pathway. Experimental proof linking the dpn BGC to its encoded compound was achieved by determining the growth conditions for the expression of the cluster and by inactivating the NRPS encoding gene dpnS2 and the piperazate synthase gene dpnZ. The identity of diperamycin was confirmed by High-Resolution Mass Spectrometry (HRMS) and Nuclear Magnetic Resonance (NMR) and by analysis of the domain composition of modules from the DpnP PKS and DpnS NRPS. The identification of the dpn BGC expands the number of BGCs that have been confirmed to encode the relatively scarcely represented BGCs for depsipeptides of the azinothricin family of compounds and will facilitate the generation of new-to-nature analogues by combinatorial biosynthesis.

Second-Order Three-Scale Asymptotic Analysis and Algorithms for Steklov Eigenvalue Problems in Composite Domain with Hierarchical Cavities

Second-Order Three-Scale Asymptotic Analysis and Algorithms for Steklov Eigenvalue Problems in Composite Domain with Hierarchical Cavities

Change in Cognitive Function over the Course of Major Depressive Disorder: A Systematic Review and Meta-analysis.

Major depressive disorder (MDD) is associated with significant cognitive deficits during the acute and remitted stages. The aim of this systematic review and meta-analysis was to examine the course of cognitive function whilst considering demographic, treatment, or clinical features of MDD that could moderate the extent of cognitive change. Databases were searched to identify studies that reported on cognitive function in MDD with a ≥12-week test-retest interval. Relevant studies were pooled using random effects modelling to generate an inverse-variance, weighted, mean effect size estimate (Hedges' g) of cognitive change for each cognitive variable and for an overall composite cognitive domain. Of 6898 records, 99 eligible studies were identified from which 69 were meta-analysed, consisting of 4639 MDD patients (agemean = 40.25years, female% = 64.62%) across 44 cognitive variables. In over 95% of cognitive variables, improvements were either of non-significant, negligible, or of a small magnitude, and when compared to matched healthy controls, the possibility of practice effects could not be precluded. Depressive symptom improvement and the number of previous depressive episodes moderated the extent of cognitive change, demonstrating state- and scar-like features for one-quarter of the cognitive domains. Further longitudinal studies are required to elucidate the MDD cognitive trajectory from initial onset. Findings nonetheless suggest that following pharmacological and non-pharmacological treatment, cognitive change in MDD is typically small, but the capacity for change may be less with episode recurrence. Targeting cognition early in the course of illness may facilitate better prognosis and support a more complete functional recovery.

In silico molecular characterization of TGF-β gene family in Bufo bufo: genome-wide analysis

Bufo bufo is a living example of evolutionary processes due to its numerous physiological and ecological adaptations. This is the first study to genetically characterize the TGF-β gene family in B. bufo at the genome-wide level, and a total of 28 TGF-β gene family homologs are identified. Physicochemical characteristics of TGF-β homologs exhibit a basic nature except for BMP1, BMP4, BMP10, BMP15, AMH, INHA, NODAL Modulator and TGFB1. Phylogenetic analysis divided the TGF-β gene family homologs into 2 major clades along with other vertebrate species. In domain and motif composition analysis, the gene structure for all TGF-β homologs exhibited homogeneity except BMP1. We have identified the TGF-β propeptide domain together with the TGF-β in all family homologs of TGF-β superfamily. Gene structure comparisons indicated that the TGF-β gene family have arisen by gene duplications. We also identified 10 duplicated gene pairs, all of which were detected to be segmental duplications. The Ka/Ks test ratio findings for every pair of genes revealed that none of the ratios surpassed 1 except for one gene pair (INHA/BMP1), indicating that these proteins are under positive selection. Circos analysis showed that TGF-β gene family homologs are arranged in 11 dispersed clusters and all were segmentally arrayed in the genome. This study provides a molecular basis for TGF-β ligand protein functional analysis and may serve as a reference for in-depth phylogenomics and may promote the development of novel strategies. Communicated by Ramaswamy H. Sarma

PredictEFC: a fast and efficient multi-label classifier for predicting enzyme family classes

BackgroundEnzymes play an irreplaceable and important role in maintaining the lives of living organisms. The Enzyme Commission (EC) number of an enzyme indicates its essential functions. Correct identification of the first digit (family class) of the EC number for a given enzyme is a hot topic in the past twenty years. Several previous methods adopted functional domain composition to represent enzymes. However, it would lead to dimension disaster, thereby reducing the efficiency of the methods. On the other hand, most previous methods can only deal with enzymes belonging to one family class. In fact, several enzymes belong to two or more family classes.ResultsIn this study, a fast and efficient multi-label classifier, named PredictEFC, was designed. To construct this classifier, a novel feature extraction scheme was designed for processing functional domain information of enzymes, which counting the distribution of each functional domain entry across seven family classes in the training dataset. Based on this scheme, each training or test enzyme was encoded into a 7-dimenion vector by fusing its functional domain information and above statistical results. Random k-labelsets (RAKEL) was adopted to build the classifier, where random forest was selected as the base classification algorithm. The two tenfold cross-validation results on the training dataset shown that the accuracy of PredictEFC can reach 0.8493 and 0.8370. The independent test on two datasets indicated the accuracy values of 0.9118 and 0.8777.ConclusionThe performance of PredictEFC was slightly lower than the classifier directly using functional domain composition. However, its efficiency was sharply improved. The running time was less than one-tenth of the time of the classifier directly using functional domain composition. In additional, the utility of PredictEFC was superior to the classifiers using traditional dimensionality reduction methods and some previous methods, and this classifier can be transplanted for predicting enzyme family classes of other species. Finally, a web-server available at http://124.221.158.221/ was set up for easy usage.

Identification and expression analysis of citrate synthase 3 gene family members in apple

As one of the key enzymes in cell metabolism, the activity of citrate synthase 3 (CS3) regulates the substance and energy metabolism of organisms. The protein members of CS3 family were identified from the whole genome of apple, and bioinformatics analysis was performed and expression patterns were analyzed to provide a theoretical basis for studying the potential function of CS3 gene in apple. BLASTp was used to identify members of the apple CS3 family based on the GDR database, and the basic information of CS3 protein sequence, subcellular localization, domain composition, phylogenetic relationship and chromosome localization were analyzed by Pfam, SMART, MEGA5.0, clustalx.exe, ExPASy Proteomics Server, MEGAX, SOPMA, MEME, WoLF PSORT and other software. The tissue expression and inducible expression characteristics of 6 CS3 genes in apple were determined by acid content and real-time fluorescence quantitative polymerase chain reaction (qRT-PCR). Apple CS3 gene family contains 6 members, and these CS3 proteins contain 473-608 amino acid residues, with isoelectric point distribution between 7.21 and 8.82. Subcellular localization results showed that CS3 protein was located in mitochondria and chloroplasts, respectively. Phylogenetic analysis divided them into 3 categories, and the number of genes in each subfamily was 2. Chromosome localization analysis showed that CS3 gene was distributed on different chromosomes of apple. The secondary structure of protein is mainly α-helix, followed by random curling, and the proportion of β-angle is the smallest. The 6 members were all expressed in different apple tissues. The overall expression trend from high to low was the highest relative expression content of MdCS3.4, followed by MdCS3.6, and the relative expression level of other members was in the order of MdCS3.3 > MdCS3.2 > MdCS3.1 > MdCS3.5. qRT-PCR results showed that MdCS3.1 and MdCS3.3 genes had the highest relative expression in the pulp of 'Chengji No. 1' with low acid content, and MdCS3.2 and MdCS3.3 genes in the pulp of 'Asda' with higher acid content had the highest relative expression. Therefore, in this study, the relative expression of CS3 gene in apple cultivars with different acid content in different apple varieties was detected, and its role in apple fruit acid synthesis was analyzed. The experimental results showed that the relative expression of CS3 gene in different apple varieties was different, which provided a reference for the subsequent study of the quality formation mechanism of apple.

Toward smart and sustainable cement manufacturing process: Analysis and optimization of cement clinker quality using thermodynamic and data-informed approaches

Cement manufacturing is widely recognized for its harmful impacts on the natural environment. In recent years, efforts have been made to improve the sustainability of cement manufacturing through the use of renewable energy, the capture of CO2 emissions, and partial replacement of cement with supplementary cementitious materials. To further enhance sustainability, optimizing the cement manufacturing process is essential. This can be achieved through the prediction and optimization of clinker phases in relation to chemical compositions of raw materials and manufacturing conditions. Cement clinkers are produced by heating raw materials in kilns, where both raw material compositions and processing conditions dictate the final chemical makeup of the clinkers. This study uses thermodynamic simulations to analyze phase assemblages of alite- and belite-enriched clinkers based on chemical compositions of raw materials and to create a database. The thermodynamic simulations can accurately reproduce clinker phases in comparison with experimental results. Subsequently, the simulated database is employed to train a data-informed model, and the predictions are used to determine the optimal composition domains that produce high quality clinker (C3S>50 %) at different calcination temperatures. Additionally, optimal lime saturation factor and alumina modulus are investigated to achieve target clinker phases. Overall, this study demonstrates the potential of using a data-informed approach to achieve smart and sustainable cement manufacturing process.

Obsessive-Compulsive Disorder: A Medical School Curriculum and Textbook Review.

We conducted a curriculum review of Canadian undergraduate medical programs to identify why aggressive obsessions (among those with obsessive-compulsive disorder [OCD]) are so often misidentified by primary care physicians and professional students. This study involved standardized interviews with representatives from Canadian medical schools regarding the content, time, and teaching styles used to deliver curricula related to OCD. Further, we utilized a set of standardized criteria to assess the OCD content of recommended textbooks from these schools. Canadian medical curricula failed to provide a comprehensive picture of OCD. One-third of medical programs did not provide an example of aggressive obsessions to students, with textbook case examples centered heavily (70%) on contamination or symmetry. Only 25% of programs (and 60% of textbooks) discussed the composition of the Unacceptable Thought Domain to include aggressive, sexual, and religious obsessions. Finally, over half of medical programs failed to indicate that aggressive obsessions are ego-dystonic and do not lead people to harm themselves or others. A series of recommendations are provided for medical schools intended to improve the comprehensiveness of OCD-related training.

Harnessing prion-inspired amyloid self-assembly for sustainable and biocompatible proton conductivity.

Protein-based materials have emerged as promising candidates for proton-conducting biomaterials. Therefore, drawing inspiration from the amino acid composition of prion-like domains, we designed short self-assembling peptides incorporating the (X-Tyr) motif, with X representing Asn, Gly and Ser, which form fibrillar structures capable of conducting protons. In this study, we conducted an analysis of the conductivity capacity of these fibers, with a focus on temperature and frequency dependence of conductivity. The loss tangent curves data and the electrode polarization model with the Debye approximation were employed to calculate transport properties, including conductivity, diffusivity, and density of charge carriers. Results revealed the prion-like fibers can transport protons more efficiently than biomaterials and other synthetic proton conducting materials, and that a significant increase in conductivity is observed with fibrillar orientations. The temperature dependence of conductivity of the peptides, measured in wet conditions, showed conductivities following the trend σ(NY7) < σ(GY7) < σ(SY7), in all the range of temperatures studied. The Arrhenius behavior, and the activation energy associated with conductivity followed the trend: Eact (SY7) = 8.2 ± 0.6 kJ mol-1 < Eact (GY7) < 13 ± 5 kJ mol-1 < Eact (NY7) = 31 ± 7 kJ mol-1, in different range of temperatures depending of the peptide. Furthermore, the diffusion coefficient correlated with increasing temperature in GY7 and SY7 fibers for temperatures compress between 20 °C and 80 °C, while NY7 only below 60 °C. However, it is noteworthy that the diffusivity observed in the SY7 peptide is lower, compared to GY7 and NY7 presumably due to its enlarged length. This observation can be attributed to two factors: firstly, the higher conductivity values observed in SY7 compared to GY7 and NY7, and secondly, to the value of relation observed of cations present in the peptide SY7 compared with GY7 and NY7, which in turn is dependent on temperature. In light of these findings, we envision our prion-inspired nanofibers as highly efficient proton-conducting natural biopolymers that are both biocompatible and biodegradable. These properties provide the opportunity for the development of next-generation bioelectrical interfaces and protonic devices.

Synergistic advances in natural fibre composites: a comprehensive review of the eco-friendly bio-composite development, its characterization and diverse applications.

In recent years, there has been enhanced interest in the domain of natural fibre composites (NFCs) because of their capacity to provide eco-compatible solutions to ever-increasing ecological concerns. This review provides an intensive assessment of the current situation with examination, progress, and applications concerning NFCs. Natural fibres, viz., jute, kenaf, ramie, banana, coir, wheat grass, etc., and their scope in the development of sustainable composites, techniques involved in the fabrication of the composites, characterization techniques, viz., thermo-mechanical and morphological, biodegradability, dampness retention attributes, and potential applications have been extensively reviewed and reported. Besides, this review encompasses the deterrents and conceivable outcomes connected to NFCs, alongside their environmental implications and monetary feasibility. Through a critical evaluation of the existing literature, this article provides a detailed summary of NFCs for real-time engineering applications. It also provides insights into sustainability practices through NFCs.

Hysteresis in phase volumes, compositions and interfacial roughness in model OPV-small-molecule/polymer thin-films.

Domain morphology and composition, and the structure of interfaces between domains are key factors in the performance and stability of organic photovoltaics (OPVs) fabricated from polymer/small-molecule blends. In this study, we investigate the evolution of composition, phase-volume and interfacial roughness in model polymer/small-molecule bilayers, in response to thermal annealing. Polystyrene/fullerene mixing is studied as a function of annealing temperature, using in situ neutron reflectivity, in thin-film bilayer samples comprising pure component or mixed layers. Remarkably, we discover that thermal annealing at temperatures around or above the reported glass transition temperatures, Tg, of the components can result in extensive mass-transfer between layers, that has the superficial appearance of equilibration, but leaves the layer compositions, thicknesses, and/or the interfacial composition profile in a non-equilibrium state. This is not merely a case of slow kinetics near Tg, as subsequent heating to higher temperatures, followed by cooling, reveals pronounced hysteresis in these systems. This has important implications for the measurement of equilibrium compositions in polymer/small-molecule mixtures for OPV applications, and for device stability during operation.

UAV search coverage under priority of important targets based on multi-location domain decomposition

&lt;abstract&gt; &lt;p&gt;In recent years, the coverage path planning (CPP) of unmanned aerial vehicles (UAVs) has attracted attention in reconnaissance, patrol, and search and rescue efforts, aiming to plan the paths for UAVs to cover a specified area as efficiently as possible. This paper proposes a UAV path fast coverage model to prioritize important targets with domain composition based on the starting point and location of the targets, combined with the domain decomposition strategy of important targets. Considering the constraints of the number of UAVs, the number of operators, and the flight time, the parallel search strategy can plan the coverage scheme with the shortest search time for the search range, and further obtain the coordinate points and path coordinates of the UAV turning. Finally, through multiple simulation experiments in four maps of various islands, the proposed method is verified to have an improved performance compare to the two track path coverage algorithms methods in terms of the coverage efficiency and the time complexity, thus providing a more scientific basis for the path coverage research of multi-target searches.&lt;/p&gt; &lt;/abstract&gt;

The Exact Stochastic Process of the Haploid Multi-Allelic Wright-Fisher Mutation Model.

Diffusion models are widely applied in population genetics, but their approximate solutions may not accurately capture the exact stochastic process. Nevertheless, this practice was necessary due to computing limitations, particularly for large populations. In this article, we develop the exact Markov chain algebra (MCA) for a discrete haploid multi-allelic Wright-Fisher model (MA-WFM) with a full mutation matrix to address this challenge. A special case of nonzero mutations between multiple alleles have not been captured by previous bi-allelic models. We formulated the mean allele frequencies for asymptotic equilibrium analytically for the tri- and quad-allelic case. We also evaluated the exact time-dependent Markov model numerically, presenting it concisely in terms of diffusion variables. The convergence with increasing population size to a diffusion limit is demonstrated for the population composition distribution. Our model shows that there will never be exact irreversible extinction when there are nonzero mutation rates into each allele and never be an exact irreversible fixation when there are nonzero mutation rates out of each allele. We only present results where there is no complete extinction and no complete fixation. Finally, we present detailed computations for the full Markov process, exposing the behavior near the boundaries for the compositional domains, which are non-singular boundaries according to diffusion theory.

Starch-fibers composites, a study of all-polysaccharide foams from microwave foaming to biodegradation

Sustainable composite foams based on rice starch and cellulosic long fibers were successfully fabricated using microwave irradiation. They were presented as a promising method to recycle some of the textile industry waste. A deep study of the processability and functionality of the composites revealed the performance improvement of starch with the addition of long cellulosic fibers, especially with 6 wt% of Arbocel®, in terms of foamability, water, and mechanical resistance features. Moreover, sodium bicarbonate, which acted as a blowing and pulping agent, led to a lower density and better fiber distribution that resulted in an improvement of the foams' functionalities. The range of the study is new in the domain of long fiber foam composites in terms of the foaming capability, and mechanical, thermal, and water resistance properties. Furthermore, all foams showed excellent biodegradability properties against a fungus commonly found in the environment; for example, values around 60 % weight loss after 33 days. Finally, the assessment of the CO2 emission during the process underlines the environmental benefits of the method employed.

Genome-Wide Identification and Expression Analysis of Auxin Response Factor (ARF) Gene Family in Betula pendula

As the key transcription factors regulating auxin responsive genes expression, auxin response factors (ARFs) play critical roles in diverse aspects of plant growth and development. Betula pendula is a valuable ornamental tree, and the information on ARF gene family of B. pendula is needed for better understanding. The publication of the genome sequence of B. pendula enable to analyze the bioinformatics information and expression pattern of BpeARF gene family on the genome-wide basis. In this study, physical and chemical properties, chromosome location, phylogenetic relationship, gene structure, conserved domain, motif composition, and cis-acting element of BpeARF gene family were analyzed, and expression patterns of BpeARF genes were investigated using completely random design in different tissues and under exogenous NAA and drought treatments. A total of 17 BpeARF genes was identified from B. pendula genome, which were unevenly distributed on 13 chromosomes and encoded adequate proteins ranging from 613 to 1135 amino acids in length. Three BpeARF gene pairs were formed by segmental duplication, and the Ka/Ks values of these BpeARF gene pairs were less than 1. According to the phylogenetic relationship among B. pendula, Betula platyphylla, Populus trichocarpa, and Arabidopsis thaliana, the BpeARF genes were divided into four classes, and the intron/exon structure, conserved domain, and motif composition showed high similarity among the BpeARF genes within the same class. The cis-acting elements in the promoter regions of BpeARF genes were related to tissue development, hormone response, and stress resistance. Quantitative real-time PCR exhibited diverse expression patterns of BpeARF genes in different tissues and in response to exogenous auxin treatment and drought stress. The expressions of one, ten, seven, and three BpeARF genes were the high levels in buds, young leaves, stems, and roots, respectively. Under exogenous NAA treatment, six BpeARF genes in stems and roots were upregulated expression at all timepoints. Under drought stress, BpeARF7 and BpeARF15 were upregulated in stems and roots, and BpeARF5 and BpeARF6 were downregulated in leaves, stems, and roots. Our results provided valuable information for the classification and putative functions of BpeARF gene family, which may be helpful for selecting candidate genes and verifying gene function in the genetic engineering of birch trees in further research.