Genomics Data Analysis via Spectral Shape and Topology

Mapper, a topological algorithm, is frequently used as an exploratory tool to build a graphical representation of data. This representation can help to gain a better understanding of the intrinsic shape of high-dimensional genomic data and to retain information that may be lost using standard dimension-reduction algorithms. We propose a novel workflow to process and analyze RNA-seq data from tumor and healthy subjects integrating Mapper and differential gene expression. Precisely, we show that a Gaussian mixture approximation method can be used to produce graphical structures that successfully separate tumor and healthy subjects, and produce two subgroups of tumor subjects. A further analysis using DESeq2, a popular tool for the detection of differentially expressed genes, shows that these two subgroups of tumor cells bear two distinct gene regulations, suggesting two discrete paths for forming lung cancer, which could not be highlighted by other popular clustering methods, including t-SNE. Although Mapper shows promise in analyzing high-dimensional data, building tools to statistically analyze Mapper graphical structures is limited in the existing literature. In this paper, we develop a scoring method using heat kernel signatures that provides an empirical setting for statistical inferences such as hypothesis testing, sensitivity analysis, and correlation analysis.Comment: 21 pages and 10 figure

Measuring hidden phenotype:Quantifying the shape of barley seeds using the Euler characteristic transform

Shape plays a fundamental role in biology. Traditional phenotypic analysis methods measure some features but fail to measure the information embedded in shape comprehensively. To extract, compare and analyse this information embedded in a robust and concise way, we turn to topological data analysis (TDA), specifically the Euler characteristic transform. TDA measures shape comprehensively using mathematical representations based on algebraic topology features. To study its use, we compute both traditional and topological shape descriptors to quantify the morphology of 3121 barley seeds scanned with X-ray computed tomography (CT) technology at 127 μm resolution. The Euler characteristic transform measures shape by analysing topological features of an object at thresholds across a number of directional axes. A Kruskal-Wallis analysis of the information encoded by the topological signature reveals that the Euler characteristic transform picks up successfully the shape of the crease and bottom of the seeds. Moreover, while traditional shape descriptors can cluster the seeds based on their accession, topological shape descriptors can cluster them further based on their panicle. We then successfully train a support vector machine to classify 28 different accessions of barley based exclusively on the shape of their grains. We observe that combining both traditional and topological descriptors classifies barley seeds better than using just traditional descriptors alone. This improvement suggests that TDA is thus a powerful complement to traditional morphometrics to comprehensively describe a multitude of 'hidden' shape nuances which are otherwise not detected.</p

To which world regions does the valence–dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world

Comportamiento innovador de las PYMES y grandes empresas en la industria química de Cartagena

Este documento aborda un análisis del comportamiento en materia de innovación en las pequeñas, medianas y grandes empresas del sector químico de la ciudad de Cartagena, entre el período 2000 y 2003. En él se concluye, que las empresas en estudio son innovadoras en sentido amplio, ya que han alcanzado en ese período, innovaciones tecnológicas en productos y en procesos, y en menor medida, también han tenido ciertas innovaciones en lo organizacional y en comercialización. Las fuentes de tales innovaciones han sido tanto internas como externas a las empresas, aunque se observó que la relación de éstas con el entorno científico y tecnológico es calificada como bajo, no sólo con su ciudad y región sino con el país.Revista Panorama Económico. No.13 (2005). Pág. 130 - 15

Comportamiento innovador de las PYMES y grandes empresas en la industria química de Cartagena

Este documento aborda un análisis del comportamiento en materia de innovación en las pequeñas, medianas y grandes empresas del sector químico de la ciudad de Cartagena, entre el período 2000 y 2003. En él se concluye, que las empresas en estudio son innovadoras en sentido amplio, ya que han alcanzado en ese período, innovaciones tecnológicas en productos y en procesos, y en menor medida, también han tenido ciertas innovaciones en lo organizacional y en comercialización. Las fuentes de tales innovaciones han sido tanto internas como externas a las empresas, aunque se observó que la relación de éstas con el entorno científico y tecnológico es calificada como bajo, no sólo con su ciudad y región sino con el país.Revista Panorama Económico. No.13 (2005). Pág. 130 - 15

Comportamiento innovador de las PYMES y grandes empresas en la industria química de Cartagena

Este documento aborda un análisis del comportamiento en materia de innovación en las pequeñas, medianas y grandes empresas del sector químico de la ciudad de Cartagena, entre el período 2000 y 2003. En él se concluye, que las empresas en estudio son innovadoras en sentido amplio, ya que han alcanzado en ese período, innovaciones tecnológicas en productos y en procesos, y en menor medida, también han tenido ciertas innovaciones en lo organizacional y en comercialización. Las fuentes de tales innovaciones han sido tanto internas como externas a las empresas, aunque se observó que la relación de éstas con el entorno científico y tecnológico es calificada como bajo, no sólo con su ciudad y región sino con el país.Palabras Claves: Innovación tecnológica, Industria química, competitividad, I&amp;D

The shape of things to come: Topological Data Analysis and biology, from molecules to organisms

Shape is data and data is shape. Biologists are accustomed to thinking about how the shape of biomolecules, cells, tissues, and organisms arise from the effects of genetics, development, and the environment. Less often do we consider that data itself has shape and structure, or that it is possible to measure the shape of data and analyze it. Here, we review applications of topological data analysis (TDA) to biology in a way accessible to biologists and applied mathematicians alike. TDA uses principles from algebraic topology to comprehensively measure shape in data sets. Using a function that relates the similarity of data points to each other, we can monitor the evolution of topological features—connected components, loops, and voids. This evolution, a topological signature, concisely summarizes large, complex data sets. We first provide a TDA primer for biologists before exploring the use of TDA across biological sub‐disciplines, spanning structural biology, molecular biology, evolution, and development. We end by comparing and contrasting different TDA approaches and the potential for their use in biology. The vision of TDA, that data are shape and shape is data, will be relevant as biology transitions into a data‐driven era where the meaningful interpretation of large data sets is a limiting factor

Fig 10 -

95% confidence intervals (CIs) of graphical subject scores (GSSs) are shown for 814 subjects with (a) ϵ = 700 and p = 80, (b) ϵ = 800 and p = 80, (c) ϵ = 900 and p = 80, and (d) ϵ = 1000 and p = 80. The CIs were estimated by considering all possible values of the parameter b. The horizontal axis represents the subject index and vertical axis represents the GSSs. The red line is for the mean of the observations, black line is for the upper confidence limit, and green line is lower confidence limit. The first 314 subjects are healthy subjects, and we observe a distinguishable pattern in their GSSs with respect to the other 500 subjects.</p

GO term analysis and KEGG pathway analysis.

The .xlsx file includes an archive of total six .xlsx files. The first three present GO term analysis results with unique genes in PI = +1, shared genes in PI = +1 and PI = -1, and unique genes in PI = -1, respectively. The next three consist of KEGG pathway analysis results with unique genes in PI = +1, shared genes in PI = +1 and PI = -1, and unique genes in PI = -1, respectively. (XLSX)</p