52 research outputs found
Online QSAR Modelling Hackathon by Easy Access to Jaqpot
No full textOn Tuesday 13th April 2021, the H2020 projects NanoCommons and NanoSolveIT, in a joint initiative with the NanoSafety Cluster (NSC), organised an entry-level workshop on in silico nanotoxicology, providing users with easy (no installations required) access to Jaqpot, a powerful and versatile nanotoxicological in silico prediction platform, enabled through powerful Google Colab notebooks.
Users received training from the expert team from the National Technical University of Athens, who developed the platform, on how to develop a Quantitative Structure-Activity Relationship (QSAR) model and publish it as a web application through the Jaqpot platform with minimal programming skills requirements. Models built on Jaqpot can be used over the graphical user interface or across platforms over the API and can be shared to groups with controlled access and rights.
Under the subtitle “Deploy your model as a web service in a few minutes”, >30 participants having in silico modelling background were guided through the Google Colab Notebook-driven Jaqpot interface deploying a linear nanoQSAR model of C60 solubility in various solvents.
At the start a short introduction was given by Martin Himly, Chair of the WG-A “Education, Training and Communication” of the NSC, on the brand new NanoCommons User Guidance Handbook, where to find the different training materials offered by NanoCommons, and the forthcoming events being organised by NanoCommons.
Following Martin’s intro, Harry gave an introductory overview on the capacity of the Jaqpot QSAR predictive modelling suite highlighting its intentional use for model sharing, discussion, and deployment as web service. Thereafter, Philip took over the virtual podium and, in an interactive manner, run the example. Pantelis, Periklis, and Jason supported the attendees in break-out sessions to warrant a smooth progress of the online training session.
Attached here you find the handout in pdf-format covering the entire webinar slide sets, the preparation instructions and the turoiral for the hands-on session. The webinar recording is amongst others available online at the NanoCommons YouTube channel, for a direct link to the recording of this event click here.
Furthermore, you find additional information on related trainings at the NanoCommons Infrastructure, in the NanoCommons Customer Guidance Handbook, and at the ELIXIR TeSS channel of NanoCommons
The design of a high speed CMOS image sensor: featuring global shutter, high dynamic range and flexible exposure control in 110nm technology
No full textHigh speed imagers find applications in many fields such as scientific and medical imaging, automotive applications, machine vision and much more. In this thesis, the design of a high speed, high dynamic range (HDR) CMOS sensor with electronic global shutter (GS) and flexible exposure control is presented. The sensor is designed in the 0.11μm CIS process, features 1k(H) x 1k(V) pixels and achieves frame rates greater that 10.000 fps.A review of the architecture of the sensor is given, along with functional illustrations for each comprising block. The quadrant-based approach is described, along with the selectable region-of-interest capability. The pixel design is a eleven-transistor (11T) pinned photodiode global shutter pixel, implementing HDR by means of two in-pixel capacitors. The design of the pipelined Sample & Hold, column gain and column-level Correlated Double Sampling (CDS) circuits are shown.Electrical Engineerin
Material for the 2nd online Jaqpot Hackathon
No full textContains training material and datasets that are used during the hackatho
PBPK modelling on the Jaqpot web platform - a PAA-peg nanoparticles case study
No full textJaqpot is a computational platform developed by NTUA, that facilitates in silico modelling, by enabling the systematic production, collection, organization, validation, storage and sharing of predictive models, with emphasis on predictive toxicology. A particular type of models that can be hosted in the Jaqpot environment are the so called Physiologically-based pharmacokinetic (PBPK) models which are used for describing and predicting the biokinetics of chemicals and pharmaceutical drugs. PBPK modelling of Nanomaterials (NMs) is more challenging due to their complicated in vivo disposition properties compared to conventional chemicals. The scientific community has addressed this problem, mainly by augmenting the system of differential equations for describing the concentration of NPs in different tissues and organs as a function of time. One such PBPK model has been developed by Li et al. (2014) for modelling the biokinetics of polyethylene glycol-coated polyacrylamide (PAA-peg) NPs in the rat. In this work we present the implementation of this PBPK model as a web service in the Jaqpot modelling platform. This development is part of the transnational access (TA) activities of the NanoCommons EU Horizon 2020 project, aiming to increase the visibility of the model and allow simulation and testing of different biodistribution scenarios by users
Width-parameterized SAT: time-space tradeoffs,
No full textAlekhnovich and Razborov (2002) presented an algorithm that solves SAT on instances ϕ of size n and tree-width TW(ϕ), using time and space bounded by 2O(TW(ϕ))nO(1). Although several follow-up works appeared over the last decade, the first open question of Alekhnovich and Razborov remained essentially unresolved: Can one check satisfiability of formulas with small tree-width in polynomial space and time as above? We essentially resolve this question, by (1) giving a polynomial space algorithm with a slightly worse run-time, (2) providing a complexity-theoretic characterization of bounded tree-width SAT, which strongly suggests that no polynomial-space algorithm can run significantly faster, and (3) presenting a spectrum of algorithms trading off time for space, between our PSPACE algorithm and the fastest known algorithm.
First, we give a simple algorithm that runs in polynomial space and achieves run-time 3TW(ϕ)lognnO(1), which approaches the run-time of Alekhnovich and Razborov (2002), but has an additional log n factor in the exponent. Then, we conjecture that this annoying log n factor is in general unavoidable.
Our negative results show our conjecture true if one believes a well-known complexity assumption, which is the SC ≠ NC conjecture and its scaled variants. Technically, we base our result on the following lemma. For arbitrary k, SAT of tree-width logkn is complete for the class of problems computed by circuits of logarithmic depth, semi-unbounded fan-in and size 2O(logkn) (SAC1 when k=1). Problems in this class can be solved simultaneously in time-space (2O(logk+1n),O(logk+1n)), and also in (2O(logkn), 2O(logkn)). Then, we show that our conjecture (for SAT instances with poly-log tree-width) is equivalent to the question of whether the small-space simulation of semi-unbounded circuit classes can be sped up without incurring a large space penalty. This is a recasting of the conjecture that SAC1 (and even its subclass NL) is not contained in SC.
Although we cannot hope for an improvement asymptotically in the exponent of time and space, we introduce a new algorithmic technique which trades constants in the exponents: for each ε with 0<ε<1, we give an algorithm in time-space
(31.441(1−ε)TW(ϕ)log|ϕ||ϕ|O(1),22εTW(ϕ)|ϕ|O(1)).
We systematically study the limitations of our technique for trading off time and space, and we show that our bounds are the best achievable using this technique.Licensed under a Creative Commons Attribution License (CC-BY) http://creativecommons.org/licenses/by/3.0/Peer reviewe
PBPK modelling on the Jaqpot web platform - a PAA-peg nanoparticles case study
No full textJaqpot is a computational platform developed by NTUA, that facilitates in silico modelling, by enabling the systematic production, collection, organization, validation, storage and sharing of predictive models, with emphasis on predictive toxicology. A particular type of models that can be hosted in the Jaqpot environment are the so called Physiologically-based pharmacokinetic (PBPK) models which are used for describing and predicting the biokinetics of chemicals and pharmaceutical drugs. PBPK modelling of Nanomaterials (NMs) is more challenging due to their complicated in vivo disposition properties compared to conventional chemicals. The scientific community has addressed this problem, mainly by augmenting the system of differential equations for describing the concentration of NPs in different tissues and organs as a function of time. One such PBPK model has been developed by Li et al. (2014) for modelling the biokinetics of polyethylene glycol-coated polyacrylamide (PAA-peg) NPs in the rat. In this work we present the implementation of this PBPK model as a web service in the Jaqpot modelling platform. This development is part of the transnational access (TA) activities of the NanoCommons EU Horizon 2020 project, aiming to increase the visibility of the model and allow simulation and testing of different biodistribution scenarios by users.</p
Population pharmacokinetic reanalysis of a Diazepam PBPK model: a comparison of Stan and GNU MCSim
No full textThe aim of this study is to benchmark two Bayesian software tools, namely Stan and GNU MCSim, that use different Markov chain Monte Carlo (MCMC) methods for the estimation of physiologically based pharmacokinetic (PBPK) model parameters. The software tools were applied and compared on the problem of updating the parameters of a Diazepam PBPK model, using time-concentration human data. Both tools produced very good fits at the individual and population levels, despite the fact that GNU MCSim is not able to consider multivariate distributions. Stan outperformed GNU MCSim in sampling efficiency, due to its almost uncorrelated sampling. However, GNU MCSim exhibited much faster convergence and performed better in terms of effective samples produced per unit of time
Minimizing deep submergence mixed gas speech distortion
No full textMan's deeper exploration of the ocean depths has presented interesting physiological problems. In addition to nitrogen narcosis, decompression sickness, and helium tremors; speech communication is impaired. The use of exotic diving gas mixtures and the greater density of the mixtures in the human vocal tract disrupt the spacing of the basic frequency components of speech- formants. Thus, intelligibility of diver-to-diver and diver-to-tender communications is reduced and already time-critical underwater tasks are unnecessarily extended. Improvements in general communications equipment, electronic unscramblers, and special word lists have reduced but not eliminated the problem. The author examines two facets of the underwater speech distortion problem. First, will an optimum diving mixture for each depth minimize the distortion? Second, are there significant inter-formant spacing variations in speech during a full exhalation? Calculations revealed that the optimum gas mixture using helium oxygen (heliox) violated minimum oxygen partial pressure requirements before a practical working depth was reached. A similar restriction was encountered for hydrogen-oxygen (hydrox) mixtures. Also, keeping track of the amount of inert gas absorption for a continuously-varying diving gas mixture is beyond state-of-the-art in decompression diving at this time. Measurable variations in the inter-formant spacing for speech during a complete exhalation were noted. Speech and gas samples were collected on a 200 foot heliox dive in the Texas A&M University hyperbaric chamber. Spectral analysis revealed that a linear and non-linear formant shifting mechanism were operating in opposition and distortion was generally greater during the initial exhalation and decreased with extended exhalation. An empirical equation was derived from the experimental data to characterize the amount of formant shifting as a function of frequency and depth for a 79.32 helium/20.68[percent] oxygen mixture used in the experimentElectrical and Computer Engineering, Department o
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