3D Histology, Interactive High-resolution Volume Rendering for Pathology
Histology, the medical analysis at microscopic level of tissue specimen from the human body, is a cornerstone in medical research. Digitization of the microscopy images opens up exciting possibilities for visualization and image analysis.
A Cut Finite Element Method for Incompressible Two-Phase Navier-Stokes Flows
A Cut Finite Element Method (CutFEM) for the time-dependent Navier-Stokes equations involving two immiscible incompressible fluids is developed. The numerical method is able to accurately approximate a discontinuous pressure and a weak discontinuous velocity field across evolving interfaces without requiring the mesh to be fitted to the domain or regularizing the discontinuities.
A key role predicted of for fast synaptic plasticity in working memory in the cortex
We tested whether a cortical spiking neural network model with fast Hebbian synaptic plasticity could learn a multi-item WM task (word list learning). The model could indeed reproduce human cognitive phenomena, while being simultaneously compatible with experimental data on structure, connectivity, and neurophysiology of the underlying cortical tissue.
Accelerated sampling of conformational transitions in DNA
Molecular dynamics is a powerful technique to study the behavior of molecules, and in particular bio-molecules such as proteins and DNA. However, the time scales of biologically important events are often much longer than what can be reached with simulations.
AI-centered visual analytics of histology
This project aims to merge AI techniques with visual exploration.
Analytic Imaging Diagnostics Arena (AIDA) funded
AIDA is a cross-disciplinary and cross-sectoral collaboration aiming for large-scale usefulness from Artificial Intelligence (AI) in healthcare.
Are NVIDIA Tensor cores good for HPC?
The NVIDIA Volta GPU microarchitecture introduces a specialized unit, called Tensor Core that performs one matrix-multiply-and-accumulate on 4×4 matrices per clock cycle.
Atmospheric circulation in a much warmer Earth, simulations using alternative warming scenarios for the Eocene
It is possible to study this period with climate models, but to obtain a reasonable global match between model surface temperature and proxy reconstructions extremely high atmospheric CO2concentrations or a reduction in global cloud albedo is needed. In this work, these two methods are examined.
Automatic workflow, data collection, and development of open-data infrastructure
For DCMD activities in data exploration and visualization, we need to address generation, collection, storing, and organizing data via research on automatic workflow, data collection for materials data, and development of an open-data infrastructure. All activities lead to necessary insights and software to do complex simulations within materials physics and molecular chemistry.
Binding sites for luminescent amyloid biomarkers from non-biased molecular dynamics simulations
A visual analytics environment VIA-MD(visual interactive analysis of molecular dynamics) tailored for large-scale spatio-temporal molecular dynamics simulation data has been developed. A key concept of the environment is to link interactive 3D exploration of geometry and statistical analysis.
Bioinformatics Highlight: Assessing protein mass spectrometry data using Percolator – how to weed out valuable information from the noise
Mass spectrometry (MS) is currently the most effective way to analyze protein on a large scale, and hence one of the most important tools for answering those questions. There are still however difficult challenges in analysing the wealth of data MS-based experiments produce.
Bioinformatics Highlight: Protein structure prediction — state-of-the-art methods proven in contests
Several scientists in the Bioinformatics community study and develop methods for protein structure prediction.
Brain network architecture and dynamics of short- and long-term memory
In this project we intend to study cortical network phenomena accompanying brain plasticity effects relevant to short- and long-term memory processes. The overarching aim is to enhance e-science approaches for studying brain networks developed at KTH and KI, and inject corresponding informatics workflows into the environments at SUBIC. PH plans to advance an existing spiking and non-spiking large-scale neural network models to simulate memory phenomena in close collaboration with AL.
Brain-IT Highlight: D1R-Golf signaling modules segregate into compartments
The development of a large signaling model that takes into consideration the existence of at least two D1R-Golf signaling compartments explains the data pattern. …
Brain-IT Highlight: D1R-Golf signaling modules segregate into compartments
The development of a large signaling model that takes into consideration the existence of at least two D1R-Golf signaling compartments explains the data pattern. …
Brain-IT Highlight: Workflows for the estimation of model parameters
When modeling subcellular signaling pathways, experimental data are integrated into a precise and structured framework from which it is possible to make predictions that could be tested experimentally. …
Brain-like approach to Machine Learning
The main aim of this project is to advance the development of hierarchical brain-like network architectures for holistic pattern discovery drawing from the computational insights into neural information processing in the brain in the context of sensory perception, multi-modal sensory fusion, sequence learning and memory association among others
Breakthrough in Big Data: 16X performance gains for Hadoop, delivering over 1.2 million operations per second
At USENIX FAST 2017, researchers from RISE SICS and KTH, in collaboration with Spotify and Oracle, presented a next-generation distribution of Apache Hadoop File System, called HopsFS, that delivers a quantum leap in both the cluster-size and throughput compared to Hadoop clusters.
Cancer screening – natural history, prediction and microsimulation
We will continue work on natural history modelling for cervical, breast and prostate cancer. Methods include HPC-intensive calibrations of simulation likelihoods using Bayesian methods and optimisation procedures for expensive or imprecise objective functions (Laure, Jauhiainen at AstraZenenca, Uncertainty Quantification with SeRC-Brain-IT). We will investigate a computational framework for storage and analysis of m icro-simulation experiments for calibration and prediction (Laure, Dowling).
Canonical quantum observables for molecular systems approximated by ab initio molecular dynamics
It is known that ab initio molecular dynamics based on the electron ground state eigenvalue can be used to approximate quantum observables in the canonical ensemble when the temperature is low compared to the first electron eigenvalue gap.
In this project, we aim to develop machine learning tools for causal discovery and causal inference, along with tools for visualizing large causal structures to a human to increase the interpretability of the structure.
Characterization of molecular motion in Cryo-EM image reconstruction
In collaboration with the Laboratory of Molecular Biology (Cambridge, UK), the SeRC Molecular Simulation community has continued their work on new reconstruction algorithms for cryo-electron microscopy.
Data exploration and visualization
Method development is needed into data exploration and visualization of the generated materials data. This is a new and exciting field of multidisciplinary research where data science meets computational materials physics. Specific activities in this group include:
Data-driven brain modeling highlights the importance of the activation patterns of single inhibitory interneurons in the brain
Dendritic plateau potentials generated by the activation of clustered excitatory inputs play a crucial role in neuronal computation and are involved in sensory perception, learning, and memory.
In climate science, we will use datasets collected from existing external projects as well as public datasets to build prediction models that out-perform existing analytical and simulation models.
In this project, we will develop deep learning methods using biological data. In particular, we will address the protein structure prediction problem, which involves predicting the structure from the amino acid sequence, predict interactions with other proteins and peptides, evaluating model qualities and predict amino acid contacts.
In this project, we propose to develop and use generic Deep Learning techniques that are able to model physical (simulated and/or measured) dynamics.
Development of novel modeling techniques
A need for high quality large volume materials data requires basic research into thedevelopment of novel modeling techniques. This work concerns method development with increased accuracy and efficiency, including dynamical mean-field theory (DMFT), spin- dynamics, time-dependent response theory (TDRSP), and molecular dynamics (MD).
Direct Numerical Simulations of cloud droplet – turbulence interactions
The possible enhancement of droplet growth by turbulence is investigated. The first step in this project is to include droplet microphysics, condensation and collection processes, in a DNS code.
e-Spect: In silica spectroscopy of complex molecular systems
Theoretical simulations are essential for the microscopic understanding of spectroscopic data that enable the design of biomarkers and materials. Modeling these complex systems requires a combination of molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) approaches.
Electronic Highlight: Development of the Dalton program
Two powerful molecular electronic structure programs, Dalton and lsDalton, provide an extensive functionality for the calculation of molecular properties. …
Electronic Highlight: Hyperfine Splitting and Room-Temperature Ferromagnetism of Ni at Multimegabar Pressure
Observed magnetic hyperfine splitting confirms the ferromagnetic state of Ni up to 260 GPa, the highest pressure where magnetism in any material has been observed so far. …
Electronic Highlight: Machine Learning Energies of 2 Million Elpasolite (ABC2D6) Crystals
Elpasolite is a common crystal structure. We have developed and trained a machine learning model to predict formation energies of all 2M pristine ABC2D6 elpasolite crystals that can be made up from main-group elements (up to bismuth). …
Electronic Highlight: On the Influence of Water on the Electronic Structure of Firefly Oxyluciferin Anions
Combining molecular dynamics simulations and time-dependent density functional theory calculations indicate that the preferred binding site for the water molecule is the phenolate oxygen of the anion. …
Enhanced skyrmion stability due to exchange frustration
We show that energy barriers and critical fields of skyrmion collapse as well as skyrmion lifetimes are drastically enhanced due to frustrated exchange and that antiskyrmions are metastable.
A better understanding of the processes in atmospheric boundary layers (ABL) and efficient simulations are required e.g. to improve climate predictions, and simulations of wind parks.
Extended spin model in atomistic simulations of alloys
The proposed model strives to find the right compromise between accuracy and computational feasibility for accurate modeling, even for complex magnetic alloys and compounds.
Feature based exploration of large-scale turbulent flow simulations
This project will enable in-situ detection and tracking of flow structures, which will be used as focus regions to build a multi-resolution description of the data. Interactive visualization methods from volume and flow visualization will be adapted to the new multi- resolution scheme.
FLOW Highlight: Fully resolved simulations of fibers with a significant size in turbulent flows
The interaction between fibers and carrier fluid is modeled through an external boundary force method. …
FLOW Highlight: Natural Laminar Flow
Direct numerical simulations of flow over wings for understanding the physics of modern methods of transition control. …
FLOW Highlight: Recurring intense bursts of turbulence in rotating channel flow simulations
Carrying out extensive simulations and observing for the first time intense recurrent bursts of turbulence in rotating channel flow. Rotating channel flow is therefore suggested as a prototype for future studies. …
FLOW Highlight: Simulations of turbulent boundary layers at high Reynolds numbers
The study of simplified canonical flows allows for deducing important properties of the physics. Therefore, a number of canonical flow cases have emerged as standard model problems to study wall-bounded turbulence. …
FLOW Highlight: TRITOS: Transitions and Turbulence Of complex Suspensions
Investigating the mechanisms by which the system microstructure determines the macroscopic flow properties provides valuable insight into the nature of flowing suspensions, and also leads to new ways of modelling and controlling it.
FLOW Highlight: Universal Scaling Laws or Dense Particle Suspensions in Turbulent Wall-Bounded Flows
We examine by means of large-scale interface-resolved numerical simulations the macroscopic behavior of dense suspensions of neutrally-buoyant spheres in turbulent plane channel flow. …
GROMACS & RELION
Molecular simulation has evolved into a standard technique employed in virtually all high-impact publications e.g. on new protein structures. The main bottleneck for scaling in GROMACS is the 3D-FFT used in the particle-mesh Ewald electrostatics (PME). Since PME is very fast, and used by MD codes world wide, it is worth investigating if the communication overhead can be lowered. This will be done in collaboration with PDSE (see the 3D-FFT sub-project). For extreme scaling, we will also investigate the fast multipole method (FMM) since it has better scaling complexity. A problem was always energy conservation, which is now solved in collaboration with the numerical analysis community, and we will integrate the ExaFMM code of Rio Yokota (Tokyo Tech) into GROMACS.
Integrated analysis of cardiac function
Recent developments within image-based simulation and imaging of cardiac function can potentially advance diagnosis and treatment optimization of a range of cardiac diseases.
Iterative eigenvalue algorithms in unbounded domains
In this project, we take an approach where the domain is treated as an infinite domain, leading to a nonlinear eigenvalue problems. New approaches are developed from a numerical linear algebra perspective, e.g. using Arnoldi’s method and modern iterative methods for linear systems, as well as a perspective of numerical methods for partial differential equations.
Large scale neural network models of the basal ganglia – an in silico tool for understanding the healthy and diseased system
We built a BG network model consisting of 80 000 spiking neurons (Lindahl and Hellgren Kotaleski, 2017) and used it to better understand how network parameters contribute to function as well as network dynamics, and how functionality can be recovered in the disease state.
Within the scope of the DataScience MCP, we aim to work on a radical software platform architecture able to close the gap between recording data and acting on it.
Master Thesis project #1
Master thesis project #1 short description
Medical image analysis and deep learning, with applications to prostate biopsies and mammograms
The ability to digitise large quantities of medical images together with recent progress in the area of deep learning and stochastic modelling of highly structured systems offers an opportunity to change and improve diagnostic procedures for screening.
Molecular Highlight: Accelerating cryo electron microscopy refinement
Over the past years cryo electron microscopy has become a dominant technique for determining the structure of large biomolecules. …
Molecular Highlight: Accurate predictions of GPCR-drug complexes
SeRC researchers have developed methods that enable accurate prediction of receptor-drug interactions at the atomic level. …
Molecular Highlight: The world’s fastest software for Molecular Dynamics on CPUs & GPUs
The increase in computing power allows part of (bio) molecular experimental work to be replaced by simulations. However, designing software that leverages the full power of modern (super) computing is becoming more and more challenging due to the fact that hardware is continuously becoming more parallel and heterogeneous. …
Molecular Highlight: Unraveling the strokes of ion transporters in computers
As highlighted by the 2013 Nobel Prize for chemistry, life science and biomolecular modeling are some of the most important applications for molecular simulation. By simulating the motions of membrane protein transporters in close connection with experiments, we have been able to explain fundamental mechanisms of nerve signal propagation both inside and between cells. …
Molecular models of the skin from cemovis data and simulations of permeation
In a collaboration between SeRC researchers at SU, KTH, and KI as well as ERCO Pharma AB, we have developed new computational methods to fit molecular data to cryo-EM image data of vitreous sections (CEMOVIS) of skin.
Multi-scale simulations of synaptic plasticity
In this project, models of subcellular signalling cascades important for synaptic plasticity (see e.g. Nair et al, 2015) will be further developed and then challenged in co-simulations. We will explore how to extract phenomenological and simplified activity- dependent synaptic plasticity rules and neuromodulatory effects by considering multi-scale models of a neural network.
NA Highlight: Multi-scale methods for wave propagation
A new type of multi-scale numerical method for simulation of wave problems has been developed and analyzed. …
NA Highlight: Numerical methods for molecular dynamics
Langevin dynamics has been derived from the more fundamental Ehrenfest dynamics. …
NA Highlight: Spectrally accurate fast Ewald summation
A new highly accurate method for the evaluation of forces resulting from electrostatic interactions has been developed. …
NA Highlight: Spectrally accurate fast Ewald summation
A new highly accurate method for the evaluation of forces resulting from electrostatic interactions has been developed.
NA Highlight: Structured iterative methods for waveguide eigenvalue problems
In the field of electromagnetics, certain types of wave propagation can be modeled with waveguides, and can be analyzed by discretizing an associated partial differential equation. …
We will focus on two aspects in further developing the Nek5000 code within SESSI. First, we will porting Nek5000 to accelerators using OpenACC and CUDA. We will continue the effort of programming Nek5000 for using accelerators to perform batched small matrix matrix multiplication that this the main computational kernel affecting Nek5000 performance. This work will also include optimization with the possibility of using CUDA in combination with OpenACC and improve the efficiency of data movement between host and GPU memories in the GS operator code. This work will be done in collaboration with the EC-funded exascale EPiGRAM-HS project that is led by PDC, and researchers at the Argonne National Laboratory. Second, we will consider new formulations of the compute and communication intensive kernels of Nek5000, including the main communication library gslib. We continue our work on one-sided communication primitives into this kernel via UPC, a PGAS programming system taking advantage of modern network hardware support for efficient one-sided communication. This includes the expertise of Niclas Jansson who developed an initial proof-of-concept of such new software. Features of new languages will also be used to overlap computation and communications by re-organising the flow of the communication.
Non-conformal simulations in the high-order code Nek5000
Our continuing work on extending the open-source code Nek5000, based on the high-order spectral element method, has now reached a level of maturity such that we can – for the first time – perform simulations of turbulence in complex geometries.
We aim to develop new better numerical algorithms for certain problems stemming from data science and machine learning, by using state-of-the-art numerical linear algebra techniques and software and solve the corresponding computationally demanding core problems.
Open Space: A tool for space research and communication
OpenSpace is an international open source software development project, with its seat in Norrköping. It is designed to visualize data in astronomy-related research and development.
OpenSpace research results in best paper award at IEEE vis conference 2017: Globe browsing: Contextualized spatio-temporal planetary surface visualization
Results of planetary mapping are often shared openly for use in scientific research and mission planning.In its raw format, however, the data is not accessible to non-experts due to the difficulty in grasping the context and the intricate acquisition process. The OpenSpace software enables interactive contextualization of geospatial surface data of celestial bodies for use in science communication.
Organic materials for the future
Atomistic MD simulations of polymer- and cellulose-based materials are performed to investigate the impact of ionic liquid on the morphology of systems. We will focus on the dynamics of the capacitive charging, which will provide us with the information of the mechanism of doping/dedopinng on the atomistic scale of the intrinsic capacitance in the presence of ionic liquid.
Origin of DNA-Induced Circular Dichroism in a Minor-Groove Binder
With a combination of molecular dynamics simulation, CD response calculations, and experiments on an AT-sequence, we show that the ICD originates from an intricate interplay between the chiral imprint of DNA, off-resonant excitonic coupling to nucleobases, charge-transfer, and resonant excitonic coupling between DAPIs.
PDSE/3D-FFT on Emerging Architectures
One of the most promising techniques to accelerate FFTs and other computational kernels, is the design and programming of specialized logics in reconfigurable hardware (FPGA). By configuring hardware logics so that each core performs small 3D Discrete Fourier Transform, it is possible to specialize hardware logics for fast computation of 3D FFTs.
Promiscuous and selective calmodulin
Calmodulin is a ubiquitous calcium sensor that confers calcium sensitivity to many cellular partners. How calmodulin can bind a large number of targets while retaining some selectivity, a phenomenon called promiscuous selectivity, is a fascinating question that we address with molecular dynamics simulations conducted with GROMACS.
PSDE Highlight: An atlas of combinatorial transcriptional regulation in mice and man
Availability of large TF combinatorial networks in both humans and mice will provide many opportunities to study gene regulation, tissue differentiation, and mammalian evolution. …
PSDE Highlight: Decentralized Graph Partitioning for Social Networks and Classification Systems
SeRC researchers have developed a novel decentralized method for partitioning of graphs, with applications in areas such as social networks and classification systems, that was awarded with best paper in the IEEE International Conference on Self-Adaptive and Self-Organizing Systems, 2013.
PSDE Highlight: Enabling efficient and future-proof HPC applications: High-level component-based programming frameworks for heterogeneous parallel systems
Recent disruptive changes in computer hardware (in particular, the transition to multi-/manycore and heterogeneous architectures) have led to a crisis on the application software side: efficient programming for modern parallel and heterogeneous systems has become more tedious, error-prone and hardware specific than ever. In particular, this holds for GPU-based systems that are increasingly popular in high performance computing, with GPU architecture evolving quickly – but which application writer has the time to rewrite and/or re-optimize his/her code for each new hardware generation? …
PSDE Highlight: Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation
Glycine consumption and expression of the mitochondrial glycine biosynthetic pathway seem to be strongly correlated with rates of growth across cancer cells. …
PSDE Highlight: Object-Oriented Modeling and Simulation with Modelica
The modeling language Modelica is bringing about a revolution in the area of simulating complex cyber-physical systems for e.g. robotics, aircrafts, satellites or power plants. …
PSDE Highlight: Pediatric systems medicine: evaluating needs and opportunities using congenital heart block as a case study
Executing a systems medicine programme in pediatrics creates potential for collaboration between clinicians and families who are keen to prevent and predict diseases and nurture wellness in the families’ children. …
PSDE Highlight: Scalable Performance Monitoring
Understanding how parallel applications behave is crucial for using HPC resources efficiently. Particularly, exascale systems will be composed by heterogeneous architectures with multiple levels of concurrency and energy constraints. In such complex scenarios, performance monitoring and runtime systems will play a major role to obtain good application performance and scalability. SeRC researchers have developed techniques for online access to performance data and efficient data formats for performance data.
PSDE Highlight: World Record Hadoop Performance
Led by Dr Jim Dowling, in 2016, SeRC researchers from the PSDE community announced world-record performance for the Hadoop platform, with their next-generation distribution of Apache Hadoop File System, HopsFS. …
Representation of Arctic moist intrusions in global coupled climate models
Events with warm and moist air entering the Arctic have been shown to have a substantial effect on the surface temperature climate in winter. Here, the coupled global climate models participating in the Coupled Model Intercomparison project Phase 5 (CMIP5) are evaluated with respect to these events.
Simulation of turbulent wings at various Reynolds numbers.
Continuing with our efforts on understanding the effect of pressure gradient and curvature on turbulent wings, we have now constructed a database consisting of time and spatially resolved turbulence around wings at 4 Reynolds numbers, starting at the low Re=100k up to the (computationally high) 1M.
Software development for exploration and design of complex molecular systems
The dominating software for quantum molecular simulations is an American commercial product (Gaussian). In an undertaking together with PDC, we will develop a full-fledged DFT program with all the standard capabilities as well as non-standard functionalities developed in the Scandinavian Dalton program community and which provides state-of-the-art scaling on contemporary and future HPC hardware platforms based on Intel, ARM, and Power CPUs as well as NVIDIA GPUs.
The aim of this project is to develop stochastic simulation methods in Machine Learning along with a corresponding rigorous efficiency analysis.
Theoretical Characterization of Point Defects in Silicon Carbide and Other Materials
Silicon carbide (SiC) is a large bandgap semiconductor that is in focus for its potential for applications in quantum information processing. It appears possible to engineer defects in SiC with optical and spin properties that are suitable as single photon sources, and states with long enough lifetime to act as qubit memory.
Topology optimization for natural convection flows.
Together with colleagues from Umeå University, we implemented in Nek5000 the possibility to perform high-order accurate topology optimization, useful for flows driven by natural convection.
Translational bioinformatics: Statistical learning for patient stratification
Translational bioinformatics was introduced into eCPC during 2014, when formal collaborations with eSSENCE and the SciLifeLab Clinical Diagnostics facility in Uppsala were also initiated.
Trial design for prediction
Experimental design is an under-appreciated aspect of data science, where better design leads to more efficient parameter estimation and possibilities to address causality. We will contribute to two new studies:
- the STHLM3-MRI study to assess the combination of the S3M test with magnetic resonance imaging (MRI), and
- the ProBio randomised treatment trial for men with metastatic prostate cancer.
Uncertainty quantification and sensitivity analysis in brain modelling
Uncertainty quantification and sensitivity analysis are important aspects of computational modelling, due to the need to assess the validity and precision of model predictions.
Unifying Memory and Storage with MPI Windows
Computing nodes of next-generation supercomputers will include different memory and storage technologies. We propose a novel use of MPI windows to hide the heterogeneity of the memory and storage subsystems by providing a single common programming interface for data movement across these layers.
Variational approximations in the medical sciences
In this new sub-project we will develop machine learning models and tools for Gaussian variational approximations (GVAs) and apply those models to health applications.
Visualization Highlight: Cover story: Interactive Visualization of 3D Scanned Mummies at Public Venues
By combining visualization techniques with interactive multi-touch tables and intuitive user interfaces, visitors to museums and science centers can conduct self-guided tours of large volumetric image data. …
Visualization Highlight: ERC Starting Grant: HEART4FLOW
The objective of the HEART4FLOW project is to develop the next generation of methods for the non-invasive quantitative assessment of cardiac diseases and therapies. …
Visualization Highlight: KAW Research Grant: Seeing Organ Function
More recent medical imaging modalities support acquisition of patient-specific functional data embedded in a high-resolution spatial context. …
Visualization Highlight: Vinnova Framework Grant: Digital Pathology
Within pathology there is an urgent need for substantially increased efficiency in parallel with continued improvements in quality of care. …
Workshop on Arctic airmass transformations
A workshop organized by Gunilla Svensson, SU, and Felix Pithan, Alfred Wegener Institute, Germany, was held at MISU 6-9 November 2017. The theme of the workshop was how to improve the understanding of air mass transformations in the Arctic by observational and modelling strategies.