Composition Vector Research Articles

On the Leonardo quaternions sequence

In the present work, a new sequence of quaternions related to the Leonardo numbers -- named the Leonardo quaternions sequence -- is defined and studied. Binet's formula and certain sum and binomial-sum identities, some of which derived from the mentioned formula, are established. Tagiuri-Vajda's identity and, as consequences, Catalan's identity, d'Ocagne's identity and Cassini's identity are presented. Furthermore, applying Catalan's identity, and the connection between composition algebras and vector cross product algebras, Gelin-Cesàro's identity is also stated and proved. Finally, the generating function, the exponential generating function and the Poisson generating function are deduced. In addition to the results on Leonardo quaternions, known results on Leonardo numbers and on Fibonacci quaternions are extended.

Adapting moving‐window metrics to vector datasets for the characterization and comparison of simulated urban scenarios

AbstractDescriptive scenarios about the possible evolution of land use in our cities are essential instruments in urban planning. Although the simulation of these scenarios has enormous potential, further characterization is needed in order to be able to evaluate and compare them so as to provide more effective support for public policy. One of the most commonly used tools for assessing these scenarios is spatial moving‐window metrics, a useful mechanism for extracting accurate information from simulated land‐use maps on urban diversity and urban growth patterns. This article seeks to explore this question further and has two main aims. First, to develop and implement vSHEI and vLEI, two multiscale composition and configuration vector moving‐window metrics for calculating urban diversity and urban growth patterns. Second, to test these metrics using the spatially explicit simulation of three prospective scenarios in the Henares Corridor (Spain), comparing the results and analyzing how well the scenario narratives match their spatial configuration, as measured using vSHEI and vLEI. Via the implementation of vSHEI and vLEI, we obtained urban diversity and urban expansion values at a local level, offering more precise and more realistic, mappable information on the composition and configuration of urban land use than that provided by raster metrics or by vector Patch‐Matrix model metrics. We also used these metrics to test whether the simulated scenarios matched their description in the narrative storylines. Our results demonstrate the potential of vector moving‐window metrics for characterizing the urban patterns that might develop under different scenarios at the parcel level.

Comparative Phylogenetic Analysis and Protein Prediction Reveal the Taxonomy and Diverse Distribution of Virulence Factors in Foodborne Clostridium Strains.

Clostridium botulinum and Clostridium perfringens, 2 major foodborne pathogenic fusobacteria, have a variety of virulent protein types with nervous and enterotoxic pathogenic potential, respectively. The relationship between the molecular evolution of the 2 Clostridium genomes and virulence proteins was studied via a bioinformatics prediction method. The genetic stability, main features of gene coding and structural characteristics of virulence proteins were compared and analyzed to reveal the phylogenetic characteristics, diversity, and distribution of virulence factors of foodborne Clostridium strains. The phylogenetic analysis was performed via composition vector and average nucleotide identity based methods. Evolutionary distances of virulence genes relative to those of housekeeping genes were calculated via multilocus sequence analysis. Bioinformatics software and tools were used to predict and compare the main functional features of genes encoding virulence proteins, and the structures of virulence proteins were predicted and analyzed through homology modeling and a deep learning algorithm. According to the diversity of toxins, genome evolution tended to cluster based on the protein-coding virulence genes. The evolutionary transfer distances of virulence genes relative to those of housekeeping genes in C. botulinum strains were greater than those in C. perfringens strains, and BoNTs and alpha toxin proteins were located extracellularly. The BoNTs have highly similar structures, but BoNT/A/B and BoNT/E/F have significantly different conformations. The beta2 toxin monomer structure is similar to but simpler than the alpha toxin monomer structure, which has 2 mobile loops in the N-terminal domain. The C-terminal domain of the CPE trimer forms a "claudin-binding pocket" shape, which suggests biological relevance, such as in pore formation. According to the genotype of protein-coding virulence genes, the evolution of Clostridium showed a clustering trend. The genetic stability, functional and structural characteristics of foodborne Clostridium virulence proteins reveal the taxonomy and diverse distribution of virulence factors.

Changes in species compositions of fish in the bays of the Northwestern Gulf of Mexico

IntroductionLocal biodiversity is increasing in many temperate and subtropical waters due to climate change. It is often caused by shifting fish distributions, thus the biodiversity gradient, from lower to higher latitudes. However, these shifts in distributions do not occur uniformly across all species. Consequently, communities are not only shifting their spatial distributions, but species compositions are also changing. We investigated spatiotemporal differences in the compositions of fish species in the bays of the northeastern Gulf of Mexico and identified species that contribute to the temporal changes.MethodsWe used fish count data collected using gillnets in eight major bays, encompassing over 600 km of coastline, during spring and fall seasons from 1982 to 2019. The nonmetric multidimensional scaling (NMDS) on the Bray-Curtis dissimilarity index among species composition vectors was used to detect the differences in species composition, and the similarity percentages (SIMPER) were used to determine the contribution of species to the differences. ResultsThe result shows there was a gradual change in species composition in all bays over the years, and the composition was different among bays and seasons. The species contributing to the temporal changes included those that are expanding (e.g., Common snook, Centropomus undecimalis, and Smallscale fat snook, C. parallelus) as well as retracting (e.g., Southern flounder, Paralichthys lethostigma, and Spanish mackerel, Scomberomorus maculatus) their distributions toward the north. The species observed only in recent years in these bays tended to have a preference for warmer water (e.g., Gulf pipefish, Syngnathus scovelli, and Chain pipefish, S. louisiana). DiscussionThe results are consistent with the potential effects of climate change. However, the salinity of the bays in the study area generally exhibits an increasing trend from the northern to southern bays. The spatial salinity gradient has a substantial impact on species compositions, indicating that species distributions are influenced by multiple environmental conditions. This complexity makes our ability to accurately predict future species compositions under changing environmental conditions challenging.

Systematic comparison of genome information processing and boundary recognition tools used for genomic island detection

Genomic islands are fragments of foreign DNA that are found in bacterial and archaeal genomes, and are typically associated with symbiosis or pathogenesis. While numerous genomic island detection methods have been proposed, there has been limited evaluation of the efficiency of the genome information processing and boundary recognition tools. In this study, we conducted a review of the statistical methods involved in genomic signatures, host signature extraction, informative signature selection, divergence measures, and boundary detection steps in genomic island prediction. We compared the performances of these methods on simulated experiments using alien fragments obtained from both artificial and real genomes. Our results indicate that among the nine genomic signatures evaluated, genomic signature frequency and full probability performed the best. However, their performance declined when normalized to their expectations and variances, such as Z-score and composition vector. Based on our experiments of the E. coli genome, we found that the confidence intervals of the window variances achieved the best performance in the signature extraction of the host, with the best confidence interval being 1.5–2 times the standard error. Ordered kurtosis was most effective in selecting informative signatures from a single genome, without requiring prior knowledge from other datasets. Among the three divergence measures evaluated, the two-sample t-test was the most successful, and a non-overlapping window with a small eye window (size 2) was best suited for identifying compositionally distinct regions. Finally, the maximum of the Markovian Jensen-Shannon divergence score, in terms of GC-content bias, was found to make boundary detection faster while maintaining a similar error rate.

Development of an Online Genome Sequence Comparison Resource for Bacillus cereus sensu lato Strains Using the Efficient Composition Vector Method.

An automated method was developed for differentiating closely related B. cereus sensu lato (s.l.) species, especially biopesticide Bacillus thuringiensis, from other human pathogens, B. anthracis and B. cereus sensu stricto (s.s.). In the current research, four typing methods were initially compared, including multi-locus sequence typing (MLST), single-copy core genes phylogenetic analysis (SCCGPA), dispensable genes content pattern analysis (DGCPA) and composition vector tree (CVTree), to analyze the genomic variability of 23 B. thuringiensis strains from aizawai, kurstaki, israelensis, thuringiensis and morrisoni serovars. The CVTree method was the best option to be used for typing B. thuringiensis strains since it proved to be the fastest method, whilst giving high-resolution data about the strains. In addition, CVTree agrees well with ANI-based method, revealing the relationship between B. thuringiensis and other B. cereus s.l. species. Based on these data, an online genome sequence comparison resource was built for Bacillus strains called the Bacillus Typing Bioinformatics Database to facilitate strain identification and characterization.

Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition.

Introduction: Apoptosis proteins play an important role in the process of cell apoptosis, which makes the rate of cell proliferation and death reach a relative balance. The function of apoptosis protein is closely related to its subcellular location, it is of great significance to study the subcellular locations of apoptosis proteins. Many efforts in bioinformatics research have been aimed at predicting their subcellular location. However, the subcellular localization of apoptotic proteins needs to be carefully studied. Methods: In this paper, based on amphiphilic pseudo amino acid composition and support vector machine algorithm, a new method was proposed for the prediction of apoptosis proteins\x{2019} subcellular location. Results and Discussion: The method achieved good performance on three data sets. The Jackknife test accuracy of the three data sets reached 90.5%, 93.9% and 84.0%, respectively. Compared with previous methods, the prediction accuracies of APACC_SVM were improved.

Bioelectrical impedance vectors in the Colombian university population

Introduction: body composition (BC) analysis is an essential element in clinical nutritional practice. Bioelectrical impedance is one of the most widely used techniques for estimating BC. Several approaches have been proposed to reduce the margin of error it presents, ensuring its application in all populations. One of them is the use of impedance vectors, using tolerance ellipses. Objective: to compare the sample vectors with the Italian population and to determine specific tolerance ellipses for the Colombian university population. Materials and methods: observational cross-sectional study in 608 university students. Participation was at convenience and voluntary, between February 2022 and March 2023. BIVA 2002 software was used to calculate the tolerance ellipses and BIVA confidence to compare the vectors of the ellipses with the Italian population using Hotelling's T2 test, which was considered as significant at p < 0.05. Results: R/H and Xc/H measurements were higher in females (420.75 ± 56.012 Ω/m vs 308.7508 ± 41.81 Ω/m) and (46.15 ± 5.79 Ω/m vs 39.44 ± 5.01 Ω/m), respectively. The impedance vectors were positioned over the upper quadrants of the RXc plot, evidencing significant differences in the distribution of the composition vectors between samples. Conclusions: the vectors of the Colombian university students were different from the reference population, so it was necessary to determine the specific ellipses.

CD-MAWS: An Alignment-Free Phylogeny Estimation Method Using Cosine Distance on Minimal Absent Word Sets.

Multiple sequence alignment has been the traditional and well established approach of sequence analysis and comparison, though it is time and memory consuming. As the scale of sequencing data is increasing day by day, the importance of faster yet accurate alignment-free methods is on the rise. Several alignment-free sequence analysis methods have been established in the literature in recent years, which extract numerical features from genomic data to analyze sequences and also to estimate phylogenetic relationship among genes and species. Minimal Absent Word (MAW) is an effective concept for representing characteristics of a sequence in an alignment-free manner. In this study, we present CD-MAWS, a distance measure based on cosine of the angle between composition vectors constructed using minimal absent words, for sequence analysis in a computationally inexpensive manner. We have benchmarked CD-MAWS using several AFProject datasets, such as Fish mtDNA, E.coli, Plants, Shigella and Yersinia datasets, and found it to perform quite well. Applied on several other biological datasets such as mammal mtDNA, bacterial genomes and viral genomes, CD-MAWS resolved phylogenetic relationships similar to or better than state-of-the-art alignment-free methods such as Mash, Skmer, Co-phylog and kSNP3.

Case-study of a flowsheet simulation using deep-learning process models for multi-objective optimization of petrochemical production plants

This work demonstrates a case-study to create machine-learning based process models from process data, which are connected into an overall process flowsheet and provide a high level of numerical stability for further multi-objective optimization. The used models are black-box and gray-box models, which are further compared to the proven LP approach. The proposed methodology is demonstrated using real-world measurement data from a refinery, involving a distillation unit, a hydrotreater, a reformer and an ethylene plant. The developed unit models are connected into an overall process flowsheet, which is solved by a sequential-modular approach and optimized in view of maximizing production margin and minimizing CO2-emissions. This work points out that the combination of engineering knowledge with data-driven techniques enables the incorporation of indirect information for process units, e.g., the crude oil composition vector for downstream units, leveraging the prediction performance of the unit models, compared to models not involving this information.

Stochastic parcel tracking in an Euler-Lagrange compartment model for fast simulation of fermentation processes.

The compartment model (CM) is a well‐known approach for computationally affordable, spatially resolved hydrodynamic modeling of unit operations. Recent implementations use flow profiles based on Computational Fluid Dynamics (CFD) simulations, and several authors included microbial kinetics to simulate gradients in bioreactors. However, these studies relied on black‐box kinetics that do not account for intracellular changes and cell population dynamics in response to heterogeneous environments. In this paper, we report the implementation of a Lagrangian reaction model, where the microbial phase is tracked as a set of biomass‐parcels, each linked with an intracellular composition vector and a structured reaction model describing their intracellular response to extracellular variations. A stochastic parcel tracking approach is adopted, in contrast to the resolved trajectories used in CFD implementations. A penicillin production process is used as a case study. We show good performance of the model compared with full CFD simulations, both regarding the extracellular gradients and intracellular pool response, using the mixing time as a matching criterion and taking into account that the mixing time is sensitive to the number of compartments. The sensitivity of the model output towards some of the inputs is explored. The coarsest representative CM requires a few minutes to solve 80 h of flow time, compared with approximately 2 weeks for a full Euler–Lagrange CFD simulation of the same case. This alleviates one of the major bottlenecks for the application of such CFD simulations towards the analysis and optimization of industrial fermentation processes.

The Influence of Menstrual Cycle on Bioimpedance Vector Patterns, Performance, and Flexibility in Elite Soccer Players.

To examine whether menstrual cycle affects body composition and bioimpedance vector analysis (BIVA) patterns, jumping and sprinting ability, and flexibility in elite soccer players. A total of 20 elite female soccer players (age: 23.8 [3.4]y, height: 1.63 [0.04]m, body mass: 61.4 [5.9]kg, and body mass index: 22.5 [2.4]kg/m2) were monitored during the early follicular and ovulatory phase across 2 consecutive menstrual cycles. Bioimpedance analysis was performed using foot-to-hand technology, and total body water and fat mass were determined by specific equations developed for athletes. Bioelectrical resistance and reactance were adjusted according to the BIVA procedures and plotted as a vector within the resistance-reactance graph. In addition, countermovement jump, 20-m sprint, and sit and reach were assessed. A time effect (P < .05) was found for body mass, total body water, bioelectrical resistance and reactance, and flexibility. Specifically, body mass increased (P = .021) along with a gain in total body water (P = .001) from the ovulatory to the early follicular phase, while it decreased from the early follicular to the ovulatory phase during the second menstrual cycle. The BIVA vector shortened during the early follicular phases (P < .001). No change in jumping and sprinting capacity was observed (P > .05). Flexibility was impaired during the early follicular phases (P < .05). Specific bioelectrical impedance analysis and BIVA procedures are able to detect menstrual cycle-induced changes in body composition in elite soccer players. The early follicular phase resulted in fluid accumulations and BIVA vector shortening. In addition, while menstrual cycle did not affect performance, a fluctuation in flexibility was observed.

Composition Vector Spaces as a New Type of Tri-Operational Algebras

The aim of this paper is to define and study the composition vector spaces as a type of tri-operational algebras. In this regard, by presenting nontrivial examples, it is emphasized that they are a proper generalization of vector spaces and their structure can be characterized by using linear operators. Additionally, some related properties about foundations, composition subspaces and residual elements are investigated. Moreover, it is shown how to endow a vector space with a composition structure by using bijective linear operators. Finally, more properties of the composition vector spaces are presented in connection with linear transformations.

Deep Learning-Based Prediction of Material Properties Using Chemical Compositions and Diffraction Patterns as Experimentally Accessible Inputs.

We report a deep learning (DL) model that predicts various material properties while accepting directly accessible inputs from routine experimental platforms: chemical compositions and diffraction data, which can be obtained from the X-ray or electron-beam diffraction and energy-dispersive spectroscopy, respectively. These heterogeneous forms of inputs are treated simultaneously in our DL model, where the novel chemical composition vector is proposed by developing element embedding with the normalized composition matrix. With 1524 binary samples available in the Materials Project database, the model predicts formation energies and band gaps with mean absolute errors of 0.29 eV/atom and 0.66 eV, respectively. According to the weighing test between these two inputs, the properties tend to be more influenced by the chemical composition than the crystal structure. This work intentionally avoids using inputs that are not directly accessible (e.g., atomic coordinates) in experimental platforms, and thus is expected to substantially improve the practical use of DL models.

CVTree: A Parallel Alignment-free Phylogeny and Taxonomy Tool Based on Composition Vectors of Genomes

Composition Vector Tree (CVTree) is an alignment-free algorithm to infer phylogenetic relationships from genome sequences. It has been successfully applied to study phylogeny and taxonomy of viruses, prokaryotes, and fungi based on the whole genomes, as well as chloroplast genomes, mitochondrial genomes, and metagenomes. Here we presented the standalone software for the CVTree algorithm. In the software, an extensible parallel workflow for the CVTree algorithm was designed. Based on the workflow, new alignment-free methods were also implemented. And by examining the phylogeny and taxonomy of 13,903 prokaryotes based on 16S rRNA sequences, we showed that CVTree software is an efficient and effective tool for studying phylogeny and taxonomy based on genome sequences. The code of CVTree software can be available at https://github.com/ghzuo/cvtree.

Novel Interdiffusion Analysis in Multicomponent Alloys - Part 2: Application to Quaternary, Quinary and Higher Alloys

A novel study of analysis interdiffusion in multicomponent alloys is investigated by means of closed form solutions and numerical simulations. Quaternary as well as selected CoCrFeMnNi (HEAs) quinary metallic systems are analysed using one, two and three diffusion couples with the full set of interdiffusion coefficients being calculated. A custom written Matlab fitting program (MFP) is used as the main tool for the simultaneous fitting into multiple composition profiles in both systems. The retrieved interdiffusion matrices are obtained using a newly developed approach that is interlinked with composition vectors, eigenvalues and eigenvector. On average, it can be concluded that the accuracy of the obtained matrices steadily improves with the increase of the number of couples used in the analysis.

The Insights of Genomic Synteny and Codon Usage Preference on Genera Demarcation of Iridoviridae Family.

The members of the family Iridoviridae are large, double-stranded DNA viruses that infect various hosts, including both vertebrates and invertebrates. Although great progress has been made in genomic and phylogenetic analyses, the adequacy of the existing criteria for classification within the Iridoviridae family remains unknown. In this study, we redetermined 23 Iridoviridae core genes by re-annotation, core-pan analysis and local BLASTN search. The phylogenetic tree based on the 23 re-annotated core genes (Maximum Likelihood, ML-Tree) and amino acid sequences (composition vector, CV-Tree) were found to be consistent with previous reports. Furthermore, the information provided by synteny analysis and codon usage preference (relative synonymous codon usage, correspondence analysis, ENC-plot and Neutrality plot) also supports the phylogenetic relationship. Collectively, our results will be conducive to understanding the genera demarcation within the Iridoviridae family based on genomic synteny and component (codon usage preference) and contribute to the existing taxonomy methods for the Iridoviridae family.

Uncertainty Analysis of WWER-1000 Core Macroscopic Cross Sections due to Spectral Effects

The paper presents the results of uncertainty analysis of WWER‑1000 core macroscopic cross sections due to spectral effects during WWER‑1000 fuel burnup and the analysis of cross section sensitivity from thermophysical parameters of the calculated cell, which affect energy spectrum of neutron flux density.&#x0D; The calculation of changes in the isotopic composition during burnup and the preparation of macroscopic cross sections used the developed HELIOS computer model [1] for TVSA, which is currently operated at most Ukrainian WWER‑1000 units.&#x0D; The GRS approach applying Software for Uncertainty and Sensitivity Analyses (SUSA) [2] was chosen to assess the uncertainty of the macroscopic cross sections due to spectral effects and analysis of cross section sensitivity from thermophysical parameters. The spectral effect on macroscopic cross sections was taken into account by calculating the fuel burnup for variational sets of thermophysical parameters (fuel temperature, coolant temperature and density, boric acid concentration) prepared in advance by the SUSA program, as a result of which fuel isotopic composition vectors were obtained. After that, neutronic constants for the reference state were developed for each of the sets of isotopic composition, which corresponded to a certain set of thermophysical parameters.&#x0D; At the next stage, the uncertainty of macroscopic cross sections of the interaction due to the spectral effects on the isotopic composition of the fuel was analyzed using SUSA 4, followed by the analysis of cross section sensitivity from thermophysical parameters of the calculated cell affecting energy spectrum of neutron flux density. In the future, the uncertainty of two-group macroscopic diffusion constants can be used to estimate the overall uncertainty of neutronic characteristics in large-grid core calculations, in particular, in the safety analysis.

Genomic analysis of Ranavirus and exploring alternative genes for phylogenetics.

Ranaviruses can infect both captive and wild cold-blooded vertebrates, leading to significant economic and environmental losses. With the cases of ranavirus infection increasing, many ranavirus genomic sequences were published, but little is known about ranavirus taxonomy on a whole-genome level. In this study, 44 ranaviruses core genes were identified in 32 ranaviruses genome sequences by using PanX. The neighbour-joining phylogenetic trees (NJ-tree) based on 44 ranaviruses core genes and 24 iridoviridae core genes and composition vector phylogenetic tree (CV-Tree) based on whole genome were constructed. The three of phylogenetic trees showed that 32 ranavirus isolates can be divided into 4 different subgroups including SGIV-like, EHNV-like, FV3-like and CMTV-like, and subgroups taxonomic position of three phylogenetic trees were consistent. However, the phylogenetic position of ToRV could not be determined if it belongs to FV3-like or CMTV-like group. Subsequently, we carried out dot plot analysis and confirmed that ToRV should belong to CMTV-like group. Based on dot plot analysis and phylogenetic trees, the taxonomic classification of ranaviruses was confirmed. Finally, four genes which are suitable for the construction of phylogenetic tree were selected from ranavirus core genes by recombination analysis, substitution saturation analysis and single-gene phylogenetic analysis. Phylogenetic tree based on concatenated sequences of the four selected genes showed that the classification of subgroups was identical with three of the phylogenetic trees. Conclusion: Our results confirmed taxonomic identification of ranaviruses; the four selected genes used in phylogenetic analysis will make taxonomic identification more convenient and accurate.

Is Domain Knowledge Necessary for Machine Learning Materials Properties?

New featurization schemes for describing materials as composition vectors in order to predict their properties using machine learning are common in the field of Materials Informatics. However, little is known about the comparative efficacy of these methods. This work sets out to make clear which featurization methods should be used across various circumstances. Our findings include, surprisingly, that simple fractional and random-noise representations of elements can be as effective as traditional and new descriptors when using large amounts of data. However, in the absence of large datasets or for data that is not fully representative, we show that the integration of domain knowledge offers advantages in predictive ability.