scholarly journals multiSyncPy: A Python Package for Assessing Multivariate Coordination Dynamics

2021 ◽  
Author(s):  
Dan Hudson ◽  
Travis J. Wiltshire ◽  
Martin Atzmueller
Keyword(s):  
Open Source Software ◽  
State Of The Art ◽  
Theoretical Background ◽  
Coordination Dynamics ◽  
Multiple Modalities ◽  
Interpersonal Synchrony ◽  
Open Source Software Package ◽  
Cluster Phase ◽  
Study Designs ◽  
Python Package

In order to support the burgeoning field of research into interpersonal synchrony, we present an open-source software package: multiSyncPy. Multivariate synchrony goes beyond the bivariate case and can be useful for quantifying how groups, teams, and families coordinate their behaviors, or estimating the degree to which multiple modalities from an individual become synchronized. Our package includes state-of-the-art multivariate methods including symbolic entropy, multidimensional recurrence quantification, coherence, the cluster-phase ‘Rho’ metric, and a statistical test based on the Kuramoto order parameter. We also include functions for two surrogation techniques to compare the observed coordination dynamics with chance levels. Taken together, our collation and presentation of these make the study of interpersonal synchronization and coordination dynamics applicable to larger, more complex and often more ecologically valid study designs. In this work, we summarize the relevant theoretical background and present illustrative practical examples as well as guidance for the usage of our package - using synthetic as well as empirical data. Furthermore, we provide a discussion of our work and software and outline interesting further directions and perspectives. multiSyncPy is freely available under the LGPL license at: https://github.com/cslab-hub/multiSyncPy, and also available at the Python package index.

scholarly journals Predicting Peptide-MHC Binding Affinities with Imputed Training Data

2016 ◽  
Author(s):  
Alex Rubinsteyn ◽  
Timothy O'Donnell ◽  
Nandita Damaraju ◽  
Jeff Hammerbacher
Keyword(s):  
Open Source Software ◽  
Software Package ◽  
State Of The Art ◽  
Peptide Ligands ◽  
Training Data ◽  
Binding Affinities ◽  
Affinity Matrix ◽  
Open Source Software Package ◽  
Allele Specific ◽  
Computational Immunology

AbstractPredicting the binding affinity between MHC proteins and their peptide ligands is a key problem in computational immunology. State of the art performance is currently achieved by the allele-specific predictor NetMHC and the pan-allele predictor NetMHCpan, both of which are ensembles of shallow neural networks. We explore an intermediate between allele-specific and pan-allele prediction: training allele-specific predictors with synthetic samples generated by imputation of the peptide-MHC affinity matrix. We find that the imputation strategy is useful on alleles with very little training data. We have implemented our predictor as an open-source software package called MHCflurry and show that MHCflurry achieves competitive performance to NetMHC and NetMHCpan.

scholarly journals FlowScatt: enabling volume-independent flow cytometry data by decoupling fluorescence from scattering

2020 ◽  
Author(s):  
Ruud Stoof ◽  
Lewis Grozinger ◽  
Huseyin Tas ◽  
Ángel Goñi-Moreno
Keyword(s):  
Experimental Data ◽  
Flow Cytometry ◽  
Single Cell ◽  
Cell Volume ◽  
Open Source Software ◽  
Software Package ◽  
Flow Cytometry Data ◽  
Open Source Software Package ◽  
The Impact ◽  
Python Package

AbstractMotivationMeasuring fluorescence by flow cytometry is fundamental for characterising single-cell performance. While it is known that fluorescence and scattering values tend to positively correlate, the impact of cell volume on fluorescence is typically overlooked. This makes of fluorescence values alone an inaccurate measurement for high-precision characterisations.ResultsWe developed FlowScatt, an open-source software package that removes volume-dependency in the fluorescence channel. Using FlowScatt, flourescence values are re-calculated based on the unified volume per cell that arises from scattering decomposition.AvailabilityFlowScatt is openly available as a Python package on https://github.com/rstoof/FlowScatt. Experimental data for validation is available [email protected]

OnPoint: A package for online experiments in motor control and motor learning

2020 ◽  
Author(s):  
Jonathan Sanching Tsay ◽  
Alan S. Lee ◽  
Guy Avraham ◽  
Darius E. Parvin ◽  
Jeremy Ho ◽  
...  
Keyword(s):  
Motor Control ◽  
Motor Learning ◽  
Open Source Software ◽  
Research Progress ◽  
Global Pandemic ◽  
Large N ◽  
Open Source Software Package ◽  
Potential Applications ◽  
Temporal And Spatial ◽  
The One

Motor learning experiments are typically run in-person, exploiting finely calibrated setups (digitizing tablets, robotic manipulandum, full VR displays) that provide high temporal and spatial resolution. However, these experiments come at a cost, not limited to the one-time expense of purchasing equipment but also the substantial time devoted to recruiting participants and administering the experiment. Moreover, exceptional circumstances that limit in-person testing, such as a global pandemic, may halt research progress. These limitations of in-person motor learning research have motivated the design of OnPoint, an open-source software package for motor control and motor learning researchers. As with all online studies, OnPoint offers an opportunity to conduct large-N motor learning studies, with potential applications to do faster pilot testing, replicate previous findings, and conduct longitudinal studies (GitHub repository: https://github.com/alan-s-lee/OnPoint).

scholarly journals SynBiopython: an open-source software library for Synthetic Biology

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Jing Wui Yeoh ◽  
Neil Swainston ◽  
Peter Vegh ◽  
Valentin Zulkower ◽  
Pablo Carbonell ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Open Source ◽  
Open Source Software ◽  
Development Projects ◽  
Software Library ◽  
Current State ◽  
Starting Point ◽  
Common Problems ◽  
Data Tracking ◽  
Python Package

Abstract Advances in hardware automation in synthetic biology laboratories are not yet fully matched by those of their software counterparts. Such automated laboratories, now commonly called biofoundries, require software solutions that would help with many specialized tasks such as batch DNA design, sample and data tracking, and data analysis, among others. Typically, many of the challenges facing biofoundries are shared, yet there is frequent wheel-reinvention where many labs develop similar software solutions in parallel. In this article, we present the first attempt at creating a standardized, open-source Python package. A number of tools will be integrated and developed that we envisage will become the obvious starting point for software development projects within biofoundries globally. Specifically, we describe the current state of available software, present usage scenarios and case studies for common problems, and finally describe plans for future development. SynBiopython is publicly available at the following address: http://synbiopython.org.

scholarly journals Efficient Rank-Based Diffusion Process with Assured Convergence

2021 ◽  
Vol 7 (3) ◽  
pp. 49
Author(s):  
Daniel Carlos Guimarães Pedronette ◽  
Lucas Pascotti Valem ◽  
Longin Jan Latecki
Keyword(s):  
Diffusion Process ◽  
Learning Strategies ◽  
State Of The Art ◽  
Representation Learning ◽  
Theoretical Background ◽  
High Dimensional ◽  
Visual Features ◽  
Learning Approaches ◽  
Previous Decade ◽  
Asymptotic Complexity

Visual features and representation learning strategies experienced huge advances in the previous decade, mainly supported by deep learning approaches. However, retrieval tasks are still performed mainly based on traditional pairwise dissimilarity measures, while the learned representations lie on high dimensional manifolds. With the aim of going beyond pairwise analysis, post-processing methods have been proposed to replace pairwise measures by globally defined measures, capable of analyzing collections in terms of the underlying data manifold. The most representative approaches are diffusion and ranked-based methods. While the diffusion approaches can be computationally expensive, the rank-based methods lack theoretical background. In this paper, we propose an efficient Rank-based Diffusion Process which combines both approaches and avoids the drawbacks of each one. The obtained method is capable of efficiently approximating a diffusion process by exploiting rank-based information, while assuring its convergence. The algorithm exhibits very low asymptotic complexity and can be computed regionally, being suitable to outside of dataset queries. An experimental evaluation conducted for image retrieval and person re-ID tasks on diverse datasets demonstrates the effectiveness of the proposed approach with results comparable to the state-of-the-art.

scholarly journals Integration of enabling methods for the automated flow preparation of piperazine-2-carboxamide

2014 ◽  
Vol 10 ◽  
pp. 641-652 ◽  
Author(s):  
Richard J Ingham ◽  
Claudio Battilocchio ◽  
Joel M Hawkins ◽  
Steven V Ley
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Package ◽  
Flow Chemistry ◽  
Raspberry Pi ◽  
Step Flow ◽  
Simultaneous Control ◽  
Open Source Software Package

Here we describe the use of a new open-source software package and a Raspberry Pi® computer for the simultaneous control of multiple flow chemistry devices and its application to a machine-assisted, multi-step flow preparation of pyrazine-2-carboxamide – a component of Rifater®, used in the treatment of tuberculosis – and its reduced derivative piperazine-2-carboxamide.

scholarly journals ogttMetrics: Data structures and algorithms for oral glucose tolerance tests

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 684
Author(s):  
Benjamin J. Stubbs ◽  
Keith Frankston ◽  
Marcel Ramos ◽  
Nancy Laranjo ◽  
Frank M. Sacks ◽  
...  
Keyword(s):  
Glucose Tolerance ◽  
Open Source Software ◽  
Oral Glucose ◽  
Oral Glucose Tolerance ◽  
Estimation Of Parameters ◽  
Large Clinical Trial ◽  
Oral Glucose Tolerance Tests ◽  
Open Source Software Package ◽  
Glucose Tolerance Tests ◽  
Data Structures And Algorithms

We describe an open source software package, ogttMetrics, to compute diverse measures of glucose metabolism derived from oral glucose tolerance tests (OGTTs). Tools are provided to organize, visualize and compare OGTT data from large cohorts. Numerical difficulties in estimation of parameters of the Bergman minimal model are described, and in one large clinical trial, the simpler closed form index of Matsuda is observed to lead to similar rankings of individuals with respect to insulin sensitivity, and similar inferences concerning effects of modifications to carbohydrate content and glycemic index of experimental diets.

scholarly journals An open source software package for primality testing of numbers of the form p2^n+1, with no constraints on the relative sizes of p and 2^n

2018 ◽  
Author(s):  
Tejas R. Rao
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Package ◽  
Primality Testing ◽  
Fermat Number ◽  
Open Source Software Package ◽  
Sierpinski Numbers ◽  
Efficient Software

We develop an efficient software package to test for the primality of p2^n+1, p prime and p>2^n. This aids in the determination of large, non-Sierpinski numbers p, for prime p, and in cryptography. It furthermore uniquely allows for the computation of the smallest n such that p2^n+1 is prime when p is large. We compute primes of this form for the first one million primes p and find four primes of the form above 1000 digits. The software may also be used to test whether p2^n+1 divides a generalized fermat number base 3.

scholarly journals Swarm-CG: Automatic Parametrization of Bonded Terms in Coarse-Grained Models of Simple to Complex Molecules via Fuzzy Self-Tuning Particle Swarm Optimization

2020 ◽  
Author(s):  
Charly Empereur-mot ◽  
Luca Pesce ◽  
Davide Bochicchio ◽  
Claudio Perego ◽  
Giovanni M. Pavan
Keyword(s):  
Particle Swarm Optimization ◽  
State Of The Art ◽  
Structural Complexity ◽  
Coarse Grained ◽  
Complex Molecules ◽  
Swarm Optimization ◽  
Molecular Systems ◽  
Self Tuning ◽  
User Friendly ◽  
Python Package

We present Swarm-CG, a versatile software for the automatic parametrization of bonded parameters in coarse-grained (CG) models. By coupling state-of-the-art metaheuristics to Boltzmann inversion, Swarm-CG performs accurate parametrization of bonded terms in CG models composed of up to 200 pseudoatoms within 4h-24h on standard desktop machines, using an AA trajectory as reference and default<br>settings of the software. The software benefits from a user-friendly interface and two different usage modes (default and advanced). We particularly expect Swarm-CG to support and facilitate the development of new CG models for the study of molecular systems interesting for bio- and nanotechnology.<br>Excellent performances are demonstrated using a benchmark of 9 molecules of diverse nature, structural complexity and size. Swarm-CG usage is ideal in combination with popular CG force<br>fields, such as e.g. MARTINI. However, we anticipate that in principle its versatility makes it well suited for the optimization of models built based also on other CG schemes. Swarm-CG is available with all its dependencies via the Python Package Index (PIP package: swarm-cg). Tutorials and demonstration data are available at: www.github.com/GMPavanLab/SwarmCG.

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