Computational Analysis Tools Research Articles

Application of computer vision techniques for 3D matching and retrieval of archaeological objects.

As cultural institutions embark in projects oriented to digitise art and archaeological collections in three dimensions, the need for developing means to access the resulting 3D models has become imperative. Shape recognition techniques developed in the field of computer vision can help in this task. This paper describes the implementation of three shape descriptors, specifically shape distributions, reflective symmetry and spherical harmonics as part of the development of a search engine that retrieves 3D models from an archaeological database without the need of using keywords as query criteria. The usefulness of this system is obvious in the context of cultural heritage museums, where it is essential to provide automatic access to archaeological and art collections. The prototype described in this paper uses, as study case, 3D models of archaeological objects belonging to Museo del Templo Mayor, a Mexican institution that preserves one of the largest collections of Aztec cultural heritage. This work is part of an ongoing project focused on creating generic methodologies and user-friendly computational tools for shape analysis for the benefit of scholars and students interested in describing, interpreting and disseminating new knowledge about the morphology of cultural objects.

Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing.

Spatial transcriptomics (ST) technologies are rapidly becoming the extension of single-cell RNA sequencing (scRNAseq), holding the potential of profiling gene expression at a single-cell resolution while maintaining cellular compositions within a tissue. Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would not be possible with traditional sequencing technologies. Data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools for accurate and robust analysis. However, many ST studies currently utilize traditional scRNAseq tools, which are inadequate for analyzing complex ST datasets. On the other hand, many of the existing ST-specific methods are built upon traditional statistical or machine learning frameworks, which have shown to be sub-optimal in many applications due to the scale, multi-modality, and limitations of spatially resolved data (such as spatial resolution, sensitivity, and gene coverage). Given these intricacies, researchers have developed deep learning (DL)-based models to alleviate ST-specific challenges. These methods include new state-of-the-art models in alignment, spatial reconstruction, and spatial clustering, among others. However, DL models for ST analysis are nascent and remain largely underexplored. In this review, we provide an overview of existing state-of-the-art tools for analyzing spatially resolved transcriptomics while delving deeper into the DL-based approaches. We discuss the new frontiers and the open questions in this field and highlight domains in which we anticipate transformational DL applications.

Social support on Reddit for antiretroviral therapy

PurposeSocial media platforms such as Reddit can be used as a place for people with shared health problems to share knowledge and support. Previous studies have focused on the overall picture of how much social support people who live with HIV/AIDS (PLWHA) receive from online interactions. Yet, only few studies have examined the impact of social support from social media platforms on antiretroviral therapy (ART), which is a necessary lifelong therapy for PLWHA. This study used social support theory to examine related Reddit posts.Design/methodology/approachThis study used content analysis to analyze ART-related Reddit posts. Each Reddit post was manually coded by two coders for social support type. A computational text analysis tool, Linguistic Inquiry and Word Count, was used to generate linguistic features. ANOVA analyses were conducted to compare differences in user engagement and well-being across the types of social support.FindingsResults suggest that most of the posts were informational support posts, followed by emotional support posts and instrumental support posts. Results indicate that there are no significant differences within user engagement variables, but there are significant differences within several well-being variables including analytic score, clout score, health words usage and negative emotional words usage among social support types.Originality/valueThis study contributes to further understanding of social support theory in an online context used predominantly by a younger generation. Practical advice for public health researchers and practitioners is discussed.

Uncovering perturbations in human hematopoiesis associated with healthy aging and myeloid malignancies at single-cell resolution.

Early hematopoiesis is a continuous process in which hematopoietic stem and progenitor cells (HSPCs) gradually differentiate toward specific lineages. Aging and myeloid malignant transformation are characterized by changes in the composition and regulation of HSPCs. In this study, we used single-cell RNA sequencing (scRNA-seq) to characterize an enriched population of human HSPCs obtained from young and elderly healthy individuals.Based on their transcriptional profile, we identified changes in the proportions of progenitor compartments during aging, and differences in their functionality, as evidenced by gene set enrichment analysis. Trajectory inference revealed that altered gene expression dynamics accompanied cell differentiation, which could explain aging-associated changes in hematopoiesis. Next, we focused on key regulators of transcription by constructing gene regulatory networks (GRNs) and detected regulons that were specifically active in elderly individuals. Using previous findings in healthy cells as a reference, we analyzed scRNA-seq data obtained from patients with myelodysplastic syndrome (MDS) and detected specific alterations of the expression dynamics of genes involved in erythroid differentiation in all patients with MDS such as TRIB2. In addition, the comparison between transcriptional programs and GRNs regulating normal HSPCs and MDS HSPCs allowed identification of regulons that were specifically active in MDS cases such as SMAD1, HOXA6, POU2F2, and RUNX1 suggesting a role of these transcription factors (TFs) in the pathogenesis of the disease.In summary, we demonstrate that the combination of single-cell technologies with computational analysis tools enable the study of a variety of cellular mechanisms involved in complex biological systems such as early hematopoiesis and can be used to dissect perturbed differentiation trajectories associated with perturbations such as aging and malignant transformation. Furthermore, the identification of abnormal regulatory mechanisms associated with myeloid malignancies could be exploited for personalized therapeutic approaches in individual patients.

De-novo reconstruction and identification of transcriptional gene regulatory network modules differentiating single-cell clusters.

Single-cell RNA sequencing (scRNA-seq) technology provides an unprecedented opportunity to understand gene functions and interactions at single-cell resolution. While computational tools for scRNA-seq data analysis to decipher differential gene expression profiles and differential pathway expression exist, we still lack methods to learn differential regulatory disease mechanisms directly from the single-cell data. Here, we provide a new methodology, named DiNiro, to unravel such mechanisms de novo and report them as small, easily interpretable transcriptional regulatory network modules. We demonstrate that DiNiro is able to uncover novel, relevant, and deep mechanistic models that not just predict but explain differential cellular gene expression programs. DiNiro is available at https://exbio.wzw.tum.de/diniro/.

MethylC-analyzer: a comprehensive downstream pipeline for the analysis of genome-wide DNA methylation

DNA methylation is a crucial epigenetic modification involved in multiple biological processes and diseases. Current approaches for measuring genome-wide DNA methylation via bisulfite sequencing (BS-seq) include whole-genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS), and enzymatic methyl-seq (EM-seq). The computational analysis tools available for BS-seq data include customized aligners for mapping bisulfite-converted reads and computational pipelines for downstream data analysis. Current post-alignment methylation tools are specialized for the interpretation of CG methylation, which is known to dominate mammalian genomes, however, non-CG methylation (CHG and CHH, where H refers to A, C, or T) is commonly observed in plants and fungi and is closely associated with gene regulation, transposon silencing, and plant development. Thus, we have developed a MethylC-analyzer to analyze and visualize post-alignment WGBS, RRBS, and EM-seq data focusing on CG. The tool is able to also analyze non-CG sites to enhance deciphering genomes of plants and fungi. By processing aligned data and gene location files, MethylC-analyzer generates a genome-wide view of methylation levels and methylation in user-specified genomic regions. The meta-plot, for example, allows the investigation of DNA methylation within specific genomic elements. Moreover, our tool identifies differentially methylated regions (DMRs) and investigates the enrichment of genomic features associated with variable methylation. MethylC-analyzer functionality is not limited to specific genomes, and we demonstrated its performance on both plant and human BS-seq data. MethylC-analyzer is a Python- and R-based program designed to perform comprehensive downstream analyses of methylation data, providing an intuitive analysis platform for scientists unfamiliar with DNA methylation analysis. It is available as either a standalone version for command-line uses or a graphical user interface (GUI) and is publicly accessible at https://github.com/RitataLU/MethylC-analyzer.

Applications of Metabolomics for the Elucidation of Abiotic Stress Tolerance in Plants: A Special Focus on Osmotic Stress and Heavy Metal Toxicity.

Plants undergo metabolic perturbations under various abiotic stress conditions; due to their sessile nature, the metabolic network of plants requires continuous reconfigurations in response to environmental stimuli to maintain homeostasis and combat stress. The comprehensive analysis of these metabolic features will thus give an overview of plant metabolic responses and strategies applied to mitigate the deleterious effects of stress conditions at a biochemical level. In recent years, the adoption of metabolomics studies has gained significant attention due to the growing technological advances in analytical biochemistry (plant metabolomics). The complexity of the plant biochemical landscape requires sophisticated, advanced analytical methods. As such, technological advancements in the field of metabolomics have been realized, aided much by the development and refinement of separatory techniques, including liquid and gas chromatography (LC and GC), often hyphenated to state-of-the-art detection instruments such as mass spectrometry (MS) or nuclear resonance magnetic (NMR) spectroscopy. Significant advances and developments in these techniques are briefly highlighted in this review. The enormous progress made thus far also comes with the dawn of the Internet of Things (IoT) and technology housed in machine learning (ML)-based computational tools for data acquisition, mining, and analysis in the 4IR era allowing for broader metabolic coverage and biological interpretation of the cellular status of plants under varying environmental conditions. Thus, scientists can paint a holistic and comprehensive roadmap and predictive models for metabolite-guided crop improvement. The current review outlines the application of metabolomics and related technological advances in elucidating plant responses to abiotic stress, mainly focusing on heavy metal toxicity and subsequent osmotic stress tolerance.

Exploring the path of network security and student privacy protection in smart campus based on Markov model

Abstract With the advent of technological society, data and intelligence have become the directional trend of development, and the network security of smart campus has become the focus of public attention day by day. The personalization of college students and the development of intelligent analytics have brought about a whole new change in privacy protection. The intertwining of campus networks and privacy protection is a complex and very real issue, and research based on the privacy protection of college students has become more urgent. Using Markov model as a computational analysis tool, this paper deeply investigates the security and usage degree of smart campus network security technology, and it propose a complete Markov- network security technology system. The calculation shows that the intrusion detection system has the highest security of 51% and the widest usage of 63%. This is followed by firewall technology with 19% security and 26% usage. Based on the above techniques, the system security of the proposed Markov-network security technique is 57%, which is much better than the 43% of the traditional network model.

A focus on aromaticity: fuzzier than ever before?

The field of aromaticity has grown five-fold in the last two decades as revealed by Merino et al. in their Perspective "Aromaticity: Quo Vadis" where they ask where the field is heading (Chem. Sci., 2023, https://doi.org/10.1039/D2SC04998H). Numerous computational tools for aromaticity analysis have been introduced and novel classes of molecules that exhibit aromatic (or antiaromatic) features have been explored experimentally. Hence, the aromaticity concept is broader and possibly fuzzier than ever. Yet, earlier it also triggered vigorous debates after periods when new analysis tools emerged, and it survived. Today's debate reveals that the field is vital and that new knowledge is produced. Yet, as much as we ask where the field is moving, we should ask "Aromaticity: Cui Bono?"; who utilizes the aromaticity concept and who benefits from it? Especially, who benefits from it being overly fuzzy and who does the opposite? It is an exciting debate. We should get out of it with a better understanding of the chemical-bonding phenomenon labelled aromaticity.

Pharmacokinetic and toxicological in silco profiling of pluchine from the medicinal plant – Pluchea lanceolata

Background: Pluchea lanceolata belongs to the genus Pluchea (family: Asteraceae). The medicinal herb has been used for various ailments to prevent joint swelling in arthritic disorders, rheumatism, and other neurological-inflammatory disorders. Pluchine, which in our review was found to be a compound from P. Lanceolata, is explored for possible pharmacokinetic and toxicological properties by in silico computational predictive tools. Aims and Objectives: The aim of the study is to profile the pharmacokinetic, toxicological, and drug likeness scores of pluchine’s active principle using computational analysis and prediction tools. Materials and Methods: The authenticated and validated chemical structure of pluchine with its chemical formula will be extracted from standard web-based tools PubChem and ChemSpider. The two-dimensional to three-dimensional (3D) structure conversion will be processed with ChemSketchonline software; the 3D structure output will be processed further to be screened for molecular and pharmacokinetic properties for absorption, distribution, metabolism, and elimination, followed by toxicity profiling through AdmetSAR software. Results: Pluchine active principle from P. lanceolata had a predictive pharmacokinetic profile of human intestinal absorption of <30%, Human skin permeability coefficients (log k p) of pluchine were −6.75 cm/s, and the blood–brain barrier were predicted to be positive with a probability score of 0.65. Drug likeness lipinskiscore is positive with -0- violations. Acute oral toxicity testing for pluchine predicts it to be placed in category III with LD50 values >500 mg/kg but <5000 mg/kg. Conclusion: The pluchineactive principle from P. lanceolata is predicted to have a favorable pharmacokinetic and drug-like property profile for lead compound development.

SCR형 탈질 시스템의 암모니아 분사 노즐의 유량 제어에 대한 CFD기법 적용에 관한 연구

In the present study, computational analysis tools have been applied to improving the performance of the denitrification process by flow control of the ammonia injection nozzles adjusting to the flow distribution of the nitrogen oxides in the inlet flue gas of the selective catalytic reduction system. The uniformity of NH3/NO molar ratio is selected as the performance parameter. Design Xplorer was used to minimize the root mean square of NH₃/NO. Four types of inlet flow pattern were chosen as the simulated parameters, i. e. uniform, parabolic, upper-skewed and random. The flow rate of the sixteen nozzles installed in the ammonia injection grid was adjusted to the inlet conditions. In order to solve the three-dimensional steady, incompressible and viscous flow fields, the commercial software named as ANSYS-Fluent were used with the turbulence model. From the present computational analysis, the denitrification performance was improved between 1.47% and 88.6% according to flow pattern of the inlet flue gas.

An exploratory study of social media's role in facilitating public participation in e‐rulemaking using computational text analysis tools

AbstractGovernment agencies struggle to use social media effectively to engage and involve the public. However, we know little about how individuals and groups on the other side of these efforts actually use social media to participate in government decision‐making processes, independent of government‐led engagement efforts. This exploratory study aims to address this by looking closer at how Twitter users self‐mobilize after a particular event in which they may be motivated to participate. Specifically, the study examines Twitter activity before and after the United States Department of the Interior proposed changes to its Freedom of Information Act (FOIA) regulations that would limit public access to agency records. Acomputational text analysis of 5000 tweets using #FOIA over a 99‐day period showed significant changes in the sentiment of the dialog before and after the proposed rule was announced. Furthermore, structural topic models show evidence of a diverse set of stakeholders using Twitter to exercise voice and share information about the proposed rule. Findings suggest that citizens and interest groups are taking to Twitter to mobilize participation in the rulemaking process prompting the need for more research on how the public uses social media to influence government decision‐making, absent of government‐led engagement efforts.

TACI: An ImageJ Plugin for 3D Calcium Imaging Analysis.

Research in neuroscience has evolved to use complex imaging and computational tools to extract comprehensive information from data sets. Calcium imaging is a widely used technique that requires sophisticated software to obtain reliable results, but many laboratories struggle to adopt computational methods when updating protocols to meet modern standards. Difficulties arise due to a lack of programming knowledge and paywalls for software. In addition, cells of interest display movements in all directions during calcium imaging. Many approaches have been developed to correct the motion in the lateral (x/y) direction. This paper describes a workflow using a new ImageJ plugin, TrackMate Analysis of Calcium Imaging (TACI), to examine motion on the z-axis in 3D calcium imaging. This software identifies the maximum fluorescence value from all the z-positions a neuron appears in and uses it to represent the neuron's intensity at the corresponding t-position. Therefore, this tool can separate neurons overlapping in the lateral (x/y) direction but appearingon distinct z-planes. As an ImageJ plugin, TACI is a user-friendly, open-source computational tool for 3D calcium imaging analysis. We validated this workflow using fly larval thermosensitive neurons that displayed movements in all directions during temperature fluctuation and a 3D calcium imaging dataset acquired from the fly brain.

Pro-MAP: a robust pipeline for the pre-processing of single channel protein microarray data

The central role of proteins in diseases has made them increasingly attractive as therapeutic targets and indicators of cellular processes. Protein microarrays are emerging as an important means of characterising protein activity. Their accurate downstream analysis to produce biologically significant conclusions is largely dependent on proper pre-processing of extracted signal intensities. However, existing computational tools are not specifically tailored to the nature of these data and lack unanimity. Here, we present the single-channel Protein Microarray Analysis Pipeline, a tailored computational tool for analysis of single-channel protein microarrays enabling biomarker identification, implemented in R, and as an interactive web application. We compared four existing background correction and normalization methods as well as three array filtering techniques, applied to four real datasets with two microarray designs, extracted using two software programs. The normexp, cyclic loess, and array weighting methods were most effective for background correction, normalization, and filtering respectively. Thus, here we provided a versatile and effective pre-processing and differential analysis workflow for single-channel protein microarray data in form of an R script and web application ( https://metaomics.uct.ac.za/shinyapps/Pro-MAP/ .) for those not well versed in the R programming language.

On Fitness Landscape Analysis of Permutation Problems: From Distance Metrics to Mutation Operator Selection

In this paper, we explore the theory and expand upon the practice of fitness landscape analysis for optimization problems over the space of permutations. Many of the computational and analytical tools for fitness landscape analysis, such as fitness distance correlation, require identifying a distance metric for measuring the similarity of different solutions to the problem. We begin with a survey of the available distance metrics for permutations, and then use principal component analysis to classify these metrics. The result of this analysis aligns with existing classifications of permutation problem types produced through less formal means, including the A-permutation, R-permutation, and P-permutation types, which classifies problems by whether absolute position of permutation elements, relative positions of elements, or general precedence of pairs of elements, is the dominant influence over solution fitness. Additionally, the formal analysis identifies subtypes within these problem categories. We see that the classification can assist in identifying appropriate metrics based on optimization problem feature for use in fitness landscape analysis. Using optimization problems of each class, we also demonstrate how the classification scheme can subsequently inform the choice of mutation operator within an evolutionary algorithm. From this, we present a classification of a variety of mutation operators as a counterpart to that of the metrics. Our implementations of the permutation metrics, permutation mutation operators, and associated evolutionary algorithm, are available in a pair of open source Java libraries. All of the code necessary to recreate our analysis and experimental results are also available as open source.

The cityseer Python package for pedestrian-scale network-based urban analysis

cityseer-api is a Python package consisting of computational tools for fine-grained street-network and land-use analysis, helpful in assessing the morphological precursors to vibrant neighbourhoods. It is underpinned by network-based methods developed specifically for urban analysis at the pedestrian scale. cityseer-api computes a variety of node and segment-based network centrality methods, land-use accessibility and mixed-use measures, and statistical aggregations. Accessibilities and aggregations are computed dynamically over the street-network while taking walking distance thresholds and the direction of approach into account, and can optionally incorporate spatial impedances and network decomposition to increase spatial precision. The use of Python facilitates compatibility with popular computational tools for network manipulation (NetworkX), geospatial topology (shapely), geospatial data state management (GeoPandas), and the NumPy stack of scientific packages. The provision of robust network cleaning tools aids the use of OpenStreetMap data for network analysis. Underlying loop-intensive algorithms are implemented in Numba JIT compiled code so that the methods scale efficiently to larger cities and regions. Online documentation is available from cityseer.benchmarkurbanism.com, and the Github repository is available at github.com/benchmark-urbanism/cityseer. Example notebooks are available at cityseer.benchmarkurbanism.com/examples/

Closed-form expansion for option price under stochastic volatility model with concurrent jumps

We propose and implement a novel path-perturbation-based closed-form expansion for approximating option prices under a general class of models featuring stochastic volatility and jumps in both asset return and volatility. The expansion naturally employs formulas reported in the literature for pricing options under jump-diffusions with constant volatility as the leading term and provides corrections up to an arbitrary order. It offers an efficient computational tool for empirical analysis on the models through, e.g., calibration or estimation based on option data, in particular for flexible yet analytically intractable cases.

Emerging Computational Methods in Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.

Super-resolution imaging and quantitative analysis of microtubule arrays in model neurons show that epothilone D increases the density but decreases the length and straightness of microtubules in axon-like processes

Microtubules are essential for the development of neurons and the regulation of their structural plasticity. Microtubules also provide the structural basis for the long-distance transport of cargo. Various factors influence the organization and dynamics of neuronal microtubules, and disturbance of microtubule regulation is thought to play a central role in neurodegenerative diseases. However, imaging and quantitative assessment of the microtubule organization in the densely packed neuronal processes is challenging. The development of super-resolution techniques combined with the use of nanobodies offers new possibilities to visualize microtubules in neurites in high resolution. In combination with recently developed computational analysis tools, this allows automated quantification of neuronal microtubule organization with high precision. Here we have implemented three-dimensional DNA-PAINT (Point Accumulation in Nanoscale Topography), a single-molecule localization microscopy (SMLM) technique, which allows us to acquire 3D arrays of the microtubule lattice in axons of model neurons (neuronally differentiated PC12 cells) and dendrites of primary neurons. For the quantitative analysis of the microtubule organization, we used the open-source software package SMLM image filament extractor (SIFNE). We found that treatment with nanomolar concentrations of the microtubule-targeting drug epothilone D (EpoD) increased microtubule density in axon-like processes of model neurons and shifted the microtubule length distribution to shorter ones, with a mean microtubule length of 2.39 µm (without EpoD) and 1.98 µm (with EpoD). We also observed a significant decrease in microtubule straightness after EpoD treatment. The changes in microtubule density were consistent with live-cell imaging measurements of ensemble microtubule dynamics using a previously established Fluorescence Decay After Photoactivation (FDAP) assay. For comparison, we determined the organization of the microtubule array in dendrites of primary hippocampal neurons. We observed that dendritic microtubules have a very similar length distribution and straightness compared to microtubules in axon-like processes of a neuronal cell line. Our data show that super-resolution imaging of microtubules followed by algorithm-based image analysis represents a powerful tool to quantitatively assess changes in microtubule organization in neuronal processes, useful to determine the effect of microtubule-modulating conditions. We also provide evidence that the approach is robust and can be applied to neuronal cell lines or primary neurons, both after incorporation of labeled tubulin and by anti-tubulin antibody staining.

PENGARUH KUALITAS PRODUK DAN EKUITAS MEREK TERHADAP LOYALITAS PELANGGAN PT. LOTTE INDONESIA

he food and beverage industry is growing rapidly, the food and beverage industry demands the implementing parties of the food and beverage industry to improve the quality of their services. With good service, it is hoped that satisfaction will be achieved. Therefore, it is necessary to analyze the resulting quality. The quality or quality of a good product will lead to satisfaction. PT. Lotte Indonesia is a company engaged in the supervision of snacks that is well known by the people in Indonesia, especially in the Greater Jakarta area. The sample used in this study were 80 customers. The analysis used in this study includes descriptive techniques used to describe the object of research, how the profile of PT. Lotte Indonesia describes the tabulation results, and the technique of inferential statistical analysis is carried out using Multiple Linear Regression Analysis. Multiple Linear Regression Analysis is useful in estimating the value of the dependent variable by using more than one independent variable and using the help of computer analysis tools through the IBM SPSS 21 program. Simultaneously, the variables of product quality and brand equity have a significant positive effect on customer loyalty. This is evidenced by the F-count (F-Statistics) of 5.860 with a significance level (probability number) of 0.032 <0.05. while the F-Table with df1 = 2 and df2 = 7 at the 0.05 level is 4.74, thus F-Calculate (5,860) > F-Table (4.74) means that all independent variables jointly have a significant effect on the dependent variable, so it can be concluded that there is a significant positive influence there is customer loyalty PT. Lotte Indonesia (xylitol). While brand equity partially has a negative effect on customer loyalty PT. Lotte Indonesia (xylitol). Because from the results of the analysis that has been carried out, namely tcount -0.463 < ttable 2.6403, it is not in the area of ??acceptance of Ho (accepted), so the brand equity variable has no effect on customer loyalty with a 95% confidence level.