JEDi: java essential dynamics inspector — a molecular trajectory analysis toolkit

Abstract Background Principal component analysis (PCA) is commonly applied to the atomic trajectories of biopolymers to extract essential dynamics that describe biologically relevant motions. Although application of PCA is straightforward, specialized software to facilitate workflows and analysis of molecular dynamics simulation data to fully harness the power of PCA is lacking. The Java Essential Dynamics inspector (JEDi) software is a major upgrade from the previous JED software. Results Employing multi-threading, JEDi features a user-friendly interface to control rapid workflows for interrogating conformational motions of biopolymers at various spatial resolutions and within subregions, including multiple chain proteins. JEDi has options for Cartesian-based coordinates (cPCA) and internal distance pair coordinates (dpPCA) to construct covariance (Q), correlation (R), and partial correlation (P) matrices. Shrinkage and outlier thresholding are implemented for the accurate estimation of covariance. The effect of rare events is quantified using outlier and inlier filters. Applying sparsity thresholds in statistical models identifies latent correlated motions. Within a hierarchical approach, small-scale atomic motion is first calculated with a separate local cPCA calculation per residue to obtain eigenresidues. Then PCA on the eigenresidues yields rapid and accurate description of large-scale motions. Local cPCA on all residue pairs creates a map of all residue-residue dynamical couplings. Additionally, kernel PCA is implemented. JEDi output gives high quality PNG images by default, with options for text files that include aligned coordinates, several metrics that quantify mobility, PCA modes with their eigenvalues, and displacement vector projections onto the top principal modes. JEDi provides PyMol scripts together with PDB files to visualize individual cPCA modes and the essential dynamics occurring within user-selected time scales. Subspace comparisons performed on the most relevant eigenvectors using several statistical metrics quantify similarity/overlap of high dimensional vector spaces. Free energy landscapes are available for both cPCA and dpPCA. Conclusion JEDi is a convenient toolkit that applies best practices in multivariate statistics for comparative studies on the essential dynamics of similar biopolymers. JEDi helps identify functional mechanisms through many integrated tools and visual aids for inspecting and quantifying similarity/differences in mobility and dynamic correlations.

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Downstream Changes in Odonate (Insecta: Odonata) Communities along a Suburban to Urban Gradient: Untangling Natural and Anthropogenic Effects

Insects ◽

10.3390/insects12030201 ◽

2021 ◽

Vol 12 (3) ◽

pp. 201

Author(s):

Wade B. Worthen ◽

R. Kile Fravel ◽

Connor P. Horne

Keyword(s):

Large Scale ◽

Spatial Scales ◽

Principal Component ◽

Open Water ◽

Information Criteria ◽

Habitat Characteristics ◽

Small Scale ◽

Larval Abundance ◽

Percent Cover ◽

Surrounding Landscape

The community structure of lotic odonates (Insecta: Odonata) changes downstream, but it is difficult to untangle natural and anthropogenic causes. We surveyed larvae and adults at 15 sites along the Reedy River in Greenville Co., SC, USA, from sites in forested suburban landscapes through the urban core of the city of Greenville. We used principal component analyses and Akaike information criteria models to describe the relationships between larval and adult community descriptors (abundance, richness, and diversity) and habitat characteristics at several spatial scales, including water chemistry, sediment and detritus, aquatic and streamside vegetation, and the percent cover of landforms in the surrounding landscape. At all scales, larval abundance, richness, and diversity correlated with the amount of detritus. At a small scale, adult indices correlated with the amount of sunlight and streamside vegetation. Zygopteran community composition was nested at a large scale; richness and diversity did not correlate with changes in the landscape but increased downstream. Anisopteran composition was also nested, but richness correlated with the percent cover of field, wetland, and open water in the habitat and was unrelated to downstream site position. Landscape transformation affected anisopterans more than zygopterans by opening habitats that facilitate these generalist heliotherms.

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Ancestry-agnostic estimation of DNA sample contamination from sequence reads

10.1101/466268 ◽

2018 ◽

Cited By ~ 1

Author(s):

Fan Zhang ◽

Matthew Flickinger ◽

Keyword(s):

Large Scale ◽

Early Stage ◽

Principal Component ◽

Allele Frequencies ◽

Genetic Ancestry ◽

Accurate Estimation ◽

Sample Contamination ◽

Dna Contamination ◽

Robust Statistical Method ◽

Specific Allele

AbstractDetecting and estimating DNA sample contamination are important steps to ensure high quality genotype calls and reliable downstream analysis. Existing methods rely on population allele frequency information for accurate estimation of contamination rates. Correctly specifying population allele frequencies for each individual in early stage of sequence analysis is impractical or even impossible for large-scale sequencing centers that simultaneously process samples from multiple studies across diverse populations. On the other hand, incorrectly specified allele frequencies may result in substantial bias in estimated contamination rates. For example, we observed that existing methods often fail to identify 10% contaminated samples at a typical 3% contamination exclusion threshold when genetic ancestry is misspecified. Such an incomplete screening of contaminated samples substantially inflates the estimated rate of genotyping errors even in deeply sequenced genomes and exomes.We propose a robust statistical method that accurately estimates DNA contamination and is agnostic to genetic ancestry of the intended or contaminating sample. Our method integrates the estimation of genetic ancestry and DNA contamination in a unified likelihood framework by leveraging individual-specific allele-frequencies projected from reference genotypes onto principal component coordinates. We demonstrate this method robustly and accurately estimates contamination rates across different populations and contamination rates. We further demonstrate that in the presence contamination, quantitative estimates of genetic ancestry (e.g. principal component coordinates) can be substantially biased if contamination is ignored, and that our proposed method corrects for this bias. Our method is publicly available at http://github.com/Griffan/verifyBamID

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Stable spike clusters for the one-dimensional Gierer–Meinhardt system

European Journal of Applied Mathematics ◽

10.1017/s0956792516000450 ◽

2016 ◽

Vol 28 (4) ◽

pp. 576-635 ◽

Cited By ~ 10

Author(s):

JUNCHENG WEI ◽

MATTHIAS WINTER

Keyword(s):

Large Scale ◽

Spatial Scales ◽

Small Scale ◽

Biologically Relevant ◽

Formula Group ◽

Group A ◽

Scale Inhomogeneity ◽

The One ◽

Image Position ◽

Small Scale Structures

We consider the Gierer–Meinhardt system with precursor inhomogeneity and two small diffusivities in an interval $$\begin{equation*} \left\{ \begin{array}{ll} A_t=\epsilon^2 A''- \mu(x) A+\frac{A^2}{H}, &x\in(-1, 1),\,t>0,\\[3mm] \tau H_t=D H'' -H+ A^2, & x\in (-1, 1),\,t>0,\\[3mm] A' (-1)= A' (1)= H' (-1) = H' (1) =0, \end{array} \right. \end{equation*}$$$$\begin{equation*}\mbox{where } \quad 0<\epsilon \ll\sqrt{D}\ll 1, \quad \end{equation*}$$$$\begin{equation*} \tau\geq 0 \mbox{ and $\tau$ is independent of $\epsilon$. } \end{equation*}$$ A spike cluster is the combination of several spikes which all approach the same point in the singular limit. We rigorously prove the existence of a steady-state spike cluster consisting of N spikes near a non-degenerate local minimum point t0 of the smooth positive inhomogeneity μ(x), i.e. we assume that μ′(t0) = 0, μ″(t0) > 0 and we have μ(t0) > 0. Here, N is an arbitrary positive integer. Further, we show that this solution is linearly stable. We explicitly compute all eigenvalues, both large (of order O(1)) and small (of order o(1)). The main features of studying the Gierer–Meinhardt system in this setting are as follows: (i) it is biologically relevant since it models a hierarchical process (pattern formation of small-scale structures induced by a pre-existing large-scale inhomogeneity); (ii) it contains three different spatial scales two of which are small: the O(1) scale of the precursor inhomogeneity μ(x), the $O(\sqrt{D})$ scale of the inhibitor diffusivity and the O(ε) scale of the activator diffusivity; (iii) the expressions can be made explicit and often have a particularly simple form.

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Nonparametric CAT for CD in Educational Settings With Small Samples

Applied Psychological Measurement ◽

10.1177/0146621618813113 ◽

2018 ◽

Vol 43 (7) ◽

pp. 543-561 ◽

Cited By ~ 3

Author(s):

Yuan-Pei Chang ◽

Chia-Yi Chiu ◽

Rung-Ching Tsai

Keyword(s):

Large Scale ◽

Computerized Adaptive Testing ◽

Small Samples ◽

Accurate Estimation ◽

Small Scale ◽

Large Scale Assessment ◽

Scale Assessment ◽

Item Parameters ◽

Item Responses ◽

Nonparametric Classification

Cognitive diagnostic computerized adaptive testing (CD-CAT) has been suggested by researchers as a diagnostic tool for assessment and evaluation. Although model-based CD-CAT is relatively well researched in the context of large-scale assessment systems, this type of system has not received the same degree of research and development in small-scale settings, such as at the course-based level, where this system would be the most useful. The main obstacle is that the statistical estimation techniques that are successfully applied within the context of a large-scale assessment require large samples to guarantee reliable calibration of the item parameters and an accurate estimation of the examinees’ proficiency class membership. Such samples are simply not obtainable in course-based settings. Therefore, the nonparametric item selection (NPS) method that does not require any parameter calibration, and thus, can be used in small educational programs is proposed in the study. The proposed nonparametric CD-CAT uses the nonparametric classification (NPC) method to estimate an examinee’s attribute profile and based on the examinee’s item responses, the item that can best discriminate the estimated attribute profile and the other attribute profiles is then selected. The simulation results show that the NPS method outperformed the compared parametric CD-CAT algorithms and the differences were substantial when the calibration samples were small.

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Modulations of Synoptic Weather Patterns on Warm-Sector Heavy Rainfall in South China: Insights From High-Density Observations With Principal Component Analysis

Frontiers in Earth Science ◽

10.3389/feart.2021.678230 ◽

2021 ◽

Vol 9 ◽

Author(s):

Wanju Li ◽

Xueyan Bi ◽

Lifang Sheng ◽

Yali Luo ◽

Jianhua Sun

Keyword(s):

Principal Component Analysis ◽

Heavy Rainfall ◽

Large Scale ◽

Principal Component ◽

Component Analysis ◽

High Density ◽

Small Scale ◽

Weather Patterns ◽

Warm Sector ◽

Synoptic Weather

Based on hourly high-density precipitation data in Guangdong Province, China, 134 warm-sector heavy rainfall (WSHR) events were selected from 2016 to 2018. The synoptic weather patterns of these WSHR events were objectively classified using T-mode principal component analysis. Six WSHR weather patterns were identified, as follows: Type 1-southwest (T1-SW), Type 2-southeast (T2-SE), Type 3-coastal jets I (T3-CJI), Type 4-coastal jets II (T4-CJ II), Type 5-western low vortex (T5-WL), and Type 6-high-pressure (T6-HP). Three high-occurrence WSHR centers were finally extracted: the areas of Yangjiang and Shanwei, and the urban agglomeration of Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Compared with the other five patterns, T6-HP is a newly identified WSHR weather pattern, which is related to a local/small-scale weather system in the context of anomalous northward movement of the western Pacific subtropical high. Notably, the precipitation area of the T6-HP type of WSHR event is smaller, which can only be captured by high-density observations. In addition, the occurrence locations of six large-scale extreme precipitation events were closely associated with the urban agglomerations in GBA, implying that urbanization plays an important role in extreme magnitudes of large-scale WSHR events and their occurrence centers.

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Dark energy survey year 1 results: Constraining baryonic physics in the Universe

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stab357 ◽

2021 ◽

Vol 502 (4) ◽

pp. 6010-6031

Author(s):

Hung-Jin Huang ◽

Tim Eifler ◽

Rachel Mandelbaum ◽

Gary M Bernstein ◽

Anqi Chen ◽

...

Keyword(s):

Dark Energy ◽

Large Scale ◽

Principal Component ◽

External Information ◽

Small Scale ◽

Acoustic Oscillations ◽

Dark Energy Survey ◽

Theoretical Predictions ◽

Hydrodynamical Simulations ◽

Energy Survey

ABSTRACT Measurements of large-scale structure are interpreted using theoretical predictions for the matter distribution, including potential impacts of baryonic physics. We constrain the feedback strength of baryons jointly with cosmology using weak lensing and galaxy clustering observables (3 × 2pt) of Dark Energy Survey (DES) Year 1 data in combination with external information from baryon acoustic oscillations (BAO) and Planck cosmic microwave background polarization. Our baryon modelling is informed by a set of hydrodynamical simulations that span a variety of baryon scenarios; we span this space via a Principal Component (PC) analysis of the summary statistics extracted from these simulations. We show that at the level of DES Y1 constraining power, one PC is sufficient to describe the variation of baryonic effects in the observables, and the first PC amplitude (Q1) generally reflects the strength of baryon feedback. With the upper limit of Q1 prior being bound by the Illustris feedback scenarios, we reach $\sim 20{{\ \rm per\ cent}}$ improvement in the constraint of $S_8=\sigma _8(\Omega _{\rm m}/0.3)^{0.5}=0.788^{+0.018}_{-0.021}$ compared to the original DES 3 × 2pt analysis. This gain is driven by the inclusion of small-scale cosmic shear information down to 2.5 arcmin, which was excluded in previous DES analyses that did not model baryonic physics. We obtain $S_8=0.781^{+0.014}_{-0.015}$ for the combined DES Y1+Planck EE+BAO analysis with a non-informative Q1 prior. In terms of the baryon constraints, we measure $Q_1=1.14^{+2.20}_{-2.80}$ for DES Y1 only and $Q_1=1.42^{+1.63}_{-1.48}$ for DESY1+Planck EE+BAO, allowing us to exclude one of the most extreme AGN feedback hydrodynamical scenario at more than 2σ.

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Deep neural learning based protein function prediction

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2022114 ◽

2022 ◽

Vol 19 (3) ◽

pp. 2471-2488

Author(s):

Wenjun Xu ◽

◽

Zihao Zhao ◽

Hongwei Zhang ◽

Minglei Hu ◽

...

Keyword(s):

Protein Function ◽

Large Scale ◽

Protein Function Prediction ◽

Principal Component ◽

Function Prediction ◽

Kernel Principal Component Analysis ◽

Small Scale ◽

Grasshopper Optimization Algorithm ◽

Fuzzy C Means Clustering ◽

Prediction Approach

<abstract> <p>It is vital for the annotation of uncharacterized proteins by protein function prediction. At present, Deep Neural Network based protein function prediction is mainly carried out for dataset of small scale proteins or Gene Ontology, and usually explore the relationships between single protein feature and function tags. The practical methods for large-scale multi-features protein prediction still need to be studied in depth. This paper proposes a DNN based protein function prediction approach IGP-DNN. This method uses Grasshopper Optimization Algorithm (GOA) and Intuitionistic Fuzzy c-Means clustering (IFCM) based protein function modules extracting algorithm to extract the features of protein modules, utilizing Kernel Principal Component Analysis (KPCA) method to reduce the dimensionality of the protein attribute information, and integrating module features and attribute features. Inputting integrated data into DNN through multiple hidden layers to classify proteins and predict protein functions. In the experiments, the F-measure value of IGP-DNN on the DIP dataset reaches 0.4436, which shows better performance.</p> </abstract>

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Small-Scale Science to Large-Scale Profession

Contemporary Psychology ◽

10.1037/002246 ◽

2000 ◽

Vol 45 (4) ◽

pp. 396-398

Author(s):

Roger Smith

Keyword(s):

Large Scale ◽

Small Scale

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Development of Physics Learning Module Based on Guided Inquiry with Light Wave Material in Class XI of Senior High School

Bulletin of Educational Science and Technology ◽

10.33292/best.v1i1.1 ◽

2020 ◽

Vol 1 (1) ◽

pp. 1-10

Author(s):

Evi Rahmawati ◽

Irnin Agustina Dwi Astuti ◽

N Nurhayati

Keyword(s):

Large Scale ◽

Learning Strategy ◽

Student Response ◽

Average Score ◽

Small Scale ◽

Design Development ◽

Teacher Response ◽

Student Responses ◽

Analysis Design ◽

Scientific Skills

IPA Integrated is a place for students to study themselves and the surrounding environment applied in daily life. Integrated IPA Learning provides a direct experience to students through the use and development of scientific skills and attitudes. The importance of integrated IPA requires to pack learning well, integrated IPA integration with the preparation of modules combined with learning strategy can maximize the learning process in school. In SMP 209 Jakarta, the value of the integrated IPA is obtained from 34 students there are 10 students completed and 24 students are not complete because they get the value below the KKM of 68. This research is a development study with the development model of ADDIE (Analysis, Design, Development, Implementation, and Evaluation). The use of KPS-based integrated IPA modules (Science Process sSkills) on the theme of rainbow phenomenon obtained by media expert validation results with an average score of 84.38%, average material expert 82.18%, average linguist 75.37%. So the average of all aspects obtained by 80.55% is worth using and tested to students. The results of the teacher response obtained 88.69% value with excellent criteria. Student responses on a small scale acquired an average score of 85.19% with highly agreed criteria and on the large-scale student response gained a yield of 86.44% with very agreed criteria. So the module can be concluded receiving a good response by the teacher and students.

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Planetary gentrification and urban (re)development

Urban Development Issues ◽

10.2478/udi-2019-0001 ◽

2019 ◽

Vol 61 (1) ◽

pp. 5-13 ◽

Cited By ~ 4

Author(s):

Loretta Lees

Keyword(s):

Low Income ◽

Policy Making ◽

Large Scale ◽

Urban Redevelopment ◽

Urban Transformation ◽

Small Scale ◽

Income Groups ◽

Low Income Groups

Abstract Gentrification is no-longer, if it ever was, a small scale process of urban transformation. Gentrification globally is more often practised as large scale urban redevelopment. It is state-led or state-induced. The results are clear – the displacement and disenfranchisement of low income groups in favour of wealthier in-movers. So, why has gentrification come to dominate policy making worldwide and what can be done about it?

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