scholarly journals TAPAS: an open-source software package for Translational Neuromodeling and Computational Psychiatry

2021 ◽  
Author(s):  
Stefan Frässle ◽  
Eduardo A. Aponte ◽  
Saskia Bollmann ◽  
Kay H. Brodersen ◽  
Cao T. Do ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Open Source ◽  
Software Package ◽  
Clinical Decision Making ◽  
Building Blocks ◽  
Clinical Decision ◽  
Patient Specific ◽  
Computational Psychiatry ◽  
Open Source Software Package ◽  
End To End

ABSTRACTPsychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops “computational assays” for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use.In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

scholarly journals TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry

2021 ◽  
Vol 12 ◽  
Author(s):  
Stefan Frässle ◽  
Eduardo A. Aponte ◽  
Saskia Bollmann ◽  
Kay H. Brodersen ◽  
Cao T. Do ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Open Source ◽  
Software Package ◽  
Clinical Decision Making ◽  
Building Blocks ◽  
Clinical Decision ◽  
Patient Specific ◽  
Computational Psychiatry ◽  
Open Source Software Package ◽  
End To End

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops “computational assays” for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

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 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 Quantifying the Impact of Patient-Specific Factors and Disease Severity on Clinical Decision Making in Cuff Tear Arthropathy: A Case-Based Survey

2019 ◽  
Vol 15 (3) ◽  
pp. 276-285
Author(s):  
Adam P. Schumaier ◽  
Yehia H. Bedeir ◽  
Joshua S. Dines ◽  
Keith Kenter ◽  
Lawrence V. Gulotta ◽  
...  
Keyword(s):  
Decision Making ◽  
Disease Severity ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Patient Specific ◽  
Cuff Tear Arthropathy ◽  
Cuff Tear ◽  
Specific Factors ◽  
The Impact ◽  
Case Based

Complementing sparse vascular imaging data by physiological adaptation rules

2020 ◽  
Author(s):  
Maarten H.G. Heusinkveld ◽  
Robert J. Holtackers ◽  
Bouke P. Adriaans ◽  
Jos Op't Roodt ◽  
Theo Arts ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Mechanical Load ◽  
Wall Stress ◽  
Clinical Decision ◽  
Physiological Adaptation ◽  
Patient Specific ◽  
Data Sets ◽  
Imaging Data ◽  
Artery Segment ◽  
Flow Waveforms

Introduction:Mathematical modeling of pressure and flow waveforms in blood vessels using pulse wave propagation (PWP)-models has tremendous potential to support clinical decision-making. For a personalized model outcome, measurements of all modeled vessel radii and wall thicknesses are required. In clinical practice, however, data sets are often incomplete. To overcome this problem, we hypothesized that the adaptive capacity of vessels in response to mechanical load could be utilized to fill in the gaps of incomplete patient-specific data sets. Methods:We implemented homeostatic feedback loops in a validated PWP model to allow adaptation of vessel geometry to maintain physiological values of wall stress and wall shear stress. To evaluate our approach, we gathered vascular MRI and ultrasound data sets of wall thicknesses and radii of central and arm arterial segments of ten healthy subjects. Reference models (i.e. termed RefModel, n=10) were simulated using complete data, whereas adapted models (AdaptModel, n=10) used data of one carotid artery segment only while the remaining geometries in this model were estimated using adaptation. We evaluated agreement between RefModel and AdaptModel geometries, as well as between pressure and flow waveforms of both models. Results:Limits of agreement (bias±2SD of difference) between AdaptModel and RefModel radii and wall thicknesses were 0.2±2.6 mm and -140±557 μm, respectively. Pressure and flow waveform characteristics of the AdaptModel better resembled those of the RefModels as compared to the model in which the vessels were not adapted.Conclusions:Our adaptation-based PWP-model enables personalization of vascular geometries even when not all required data is available.

scholarly journals Development and Evaluation of an Open-Source Software Package “CGITA” for Quantifying Tumor Heterogeneity with Molecular Images

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yu-Hua Dean Fang ◽  
Chien-Yu Lin ◽  
Meng-Jung Shih ◽  
Hung-Ming Wang ◽  
Tsung-Ying Ho ◽  
...  
Keyword(s):  
Open Source ◽  
Texture Analysis ◽  
Open Source Software ◽  
Software Package ◽  
Tumor Heterogeneity ◽  
Locally Advanced ◽  
Image Texture ◽  
Pixel Intensity ◽  
Open Source Software Package ◽  
Molecular Images

Background. The quantification of tumor heterogeneity with molecular images, by analyzing the local or global variation in the spatial arrangements of pixel intensity with texture analysis, possesses a great clinical potential for treatment planning and prognosis. To address the lack of available software for computing the tumor heterogeneity on the public domain, we develop a software package, namely, Chang-Gung Image Texture Analysis (CGITA) toolbox, and provide it to the research community as a free, open-source project.Methods. With a user-friendly graphical interface, CGITA provides users with an easy way to compute more than seventy heterogeneity indices. To test and demonstrate the usefulness of CGITA, we used a small cohort of eighteen locally advanced oral cavity (ORC) cancer patients treated with definitive radiotherapies.Results. In our case study of ORC data, we found that more than ten of the current implemented heterogeneity indices outperformed SUVmeanfor outcome prediction in the ROC analysis with a higher area under curve (AUC). Heterogeneity indices provide a better area under the curve up to 0.9 than the SUVmeanand TLG (0.6 and 0.52, resp.).Conclusions. CGITA is a free and open-source software package to quantify tumor heterogeneity from molecular images. CGITA is available for free for academic use athttp://code.google.com/p/cgita.

Computer-based ICU data acquisition as an aid to clinical decision-making

1982 ◽  
Vol 10 (12) ◽  
pp. 823-830 ◽  
Author(s):  
REED M. GARDNER ◽  
BLAIR J. WEST ◽  
T. ALLAN PRYOR ◽  
KEITH G. LARSEN ◽  
HOMER R. WARNER ◽  
...  
Keyword(s):  
Decision Making ◽  
Data Acquisition ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Computer Based

Combination chemotherapy versus temozolomide (TMZ) for patients with MGMT methylated (m-MGMT) glioblastoma (GBM): Results of cellworks omics biosimulation—MyCare-015.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14501-e14501
Author(s):  
Michael Castro ◽  
Nirjhar Mundkur ◽  
Anusha Pampana ◽  
Aftab Alam ◽  
Aktar Alam ◽  
...  
Keyword(s):  
Combination Therapy ◽  
Clinical Decision Making ◽  
Drug Effects ◽  
Standard Of Care ◽  
Clinical Decision ◽  
Phase Iii ◽  
Patient Specific ◽  
Experimental Therapy ◽  
Newly Diagnosed ◽  
Current Standard

e14501 Background: UKT-03 evaluated TMZ plus Lomustine in a single arm phase II trial in newly diagnosed GBM patients. An overall survival of 23 months was a substantial improvement over historical experience. Patients with m-MGMT v. unmethylated tumors had a 2-yr survival of 75% and median survival not reached compared to 20% and 12.5 months, respectively. These data formed the basis for NOA-9, a randomized phase III trial in newly diagnosed, m-MGMT GBM which randomized 141 patients to standard therapy or experimental therapy with Lomustine and TMZ every 6 weeks. A superiority for the combination was observed: 48.1 v. 31.4 months for the standard arm in the ITT analysis. Nevertheless, many neurooncologists are reluctant to adopt this approach. The current standard of care uses single biomarker, m-MGMT, in contrast to comprehensive pathway analysis (CPA). We sought to determine if CPA could discriminate more effectively among each patient’s likelihood of benefiting from combination treatment. Methods: Cellworks Singula employs a novel Cellworks Omics Biology Model (CBM) to predict patient-specific biomarker and phenotype response of personalized GBM avatars to drug agents, radiation, and targeted therapies. The CBM was developed and validated using PubMed to generate protein network maps of patient-specific activated and inactivated disease pathways. CBM was used to simulate the TMZ and TMZ-Lomustine therapies for each patient in a TCGA cohort of 368 GBM patients. Omics data including methylation, whole exome sequencing, and copy number alterations were input into CBM. The Singula Composite Inhibition Score (CIS) was calculated based on the measured quantitative drug effects. Results: Though incremental gain from the combination was seen in all patients, CIS varied across the population with relative scores ranging from 32-82, with best responders have more than twice the benefit. Conclusions: CPA shows that m-MGMT is an excellent biomarker for determining the likelihood of benefit from TMZ and lomustine, with the caveat that CBM identifies 18% could be spared from TMZ exposure and would benefit from Lomustine alone. Otherwise, these data lend support for evolving the standard of care with combination therapy for patients with m-MGMT GBM and should help overcome a reluctance to employing combination therapy. Additionally, CBM has utility to individualize clinical decision making. [Table: see text]

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