UBR7 acts as a histone chaperone for post-nucleosomal histone H3.

Histone chaperones modulate the stability of histones beginning from histone synthesis, through incorporation into DNA, and during recycling during transcription and replication. Following histone removal from DNA, chaperones regulate histone storage and degradation. Here, we demonstrate that UBR7 is a histone H3.1 chaperone that modulates the supply of pre-existing post-nucleosomal histone complexes. We demonstrate that UBR7 binds to post-nucleosomal H3K4me3 and H3K9me3 histones via its UBR box and PHD. UBR7 binds to the non-nucleosomal histone chaperone NASP. In the absence of UBR7, the pool of NASP-bound post-nucleosomal histones accumulate and chromatin is depleted of H3K4me3-modified histones. We propose that the interaction of UBR7 with NASP and histones opposes the histone storage functions of NASP and that UBR7 promotes reincorporation of post-nucleosomal H3 complexes.

Non-invasive mid-term electrocardiogram patch monitoring is effective in detecting atrial fibrillation.

BACKGROUND: Atrial fibrillation (AF) is a cause of serious morbidity such as stroke. Early detection and treatment of AF is important. Current guidelines recommend screening via opportunistic pulse taking or 12­lead electrocardiogram. Mid-term ECG patch monitors increases the sensitivity of AF detection. METHODS: The Singapore Atrial Fibrillation Study is a prospective multi-centre study aiming to study the incidence of AF in patients with no prior AF and a CHA2DS2-VASc score of at least 1, with the use of a mid-term continuous ECG monitoring device (Spyder ECG). Consecutive patients from both inpatient and outpatient settings were recruited from 3 major hospitals from May 2016 to December 2019. RESULTS: Three hundred and fifty-five patients were monitored. 6 patients (1.7%) were diagnosed with AF. There were no significant differences in total duration of monitoring between the AF and non-AF group (6.39 ± 3.19 vs 5.42 ± 2.46 days, p = 0.340). Patients with newly detected AF were more likely to have palpitations (50.0% vs 11.8%, p = 0.027). Half of the patients (n = 3, 50.0%) were diagnosed on the first day of monitoring and the rest were diagnosed after 24 h. On univariate analysis, only hyperlipidemia was associated with reduced odds of being diagnosed with AF (OR HR 0.08 CI 0.01-0.74, p = 0.025). In a group of 128 patients who underwent coronary artery bypass grafting and had post-operative ECG monitoring, 9 patients (7.0%) were diagnosed with post-operative AF. CONCLUSIONS: The use of non-invasive mid-term patch-based ECG monitoring is an effective modality for AF screening.

Detecting and Characterizing Inferior Vena Cava Filters on Abdominal Computed Tomography with Data-Driven Computational Frameworks.

Two data-driven algorithms were developed for detecting and characterizing Inferior Vena Cava (IVC) filters on abdominal computed tomography to assist healthcare providers with the appropriate management of these devices to decrease complications: one based on 2-dimensional data and transfer learning (2D + TL) and an augmented version of the same algorithm which accounts for the 3-dimensional information leveraging recurrent convolutional neural networks (3D + RCNN). The study contains 2048 abdominal computed tomography studies obtained from 439 patients who underwent IVC filter placement during the 10-year period from January 1st, 2009, to January 1st, 2019. Among these, 399 patients had retrievable filters, and 40 had non-retrievable filter types. The reference annotations for the filter location were obtained through a custom-developed interface. The ground truth annotations for the filter types were determined based on the electronic medical record and physician review of imaging. The initial stage of the framework returns a list of locations containing metallic objects based on the density of the structure. The second stage processes the candidate locations and determines which one contains an IVC filter. The final stage of the pipeline classifies the filter types as retrievable vs. non-retrievable. The computational models are trained using Tensorflow Keras API on an Nvidia Quadro GV100 system. We utilized a fine-tuning supervised training strategy to conduct our experiments. We find that the system achieves high sensitivity on detecting the filter locations with a high confidence value. The 2D + TL model achieved a sensitivity of 0.911 and a precision of 0.804, and the 3D + RCNN model achieved a sensitivity of 0.923 and a precision of 0.853 for filter detection. The system confidence for the IVC location predictions is high: 0.993 for 2D + TL and 0.996 for 3D + RCNN. The filter type prediction component of the system achieved 0.945 sensitivity, 0.882 specificity, and 0.97 AUC score with 2D + TL and 0. 940 sensitivity, 0.927 specificity, and 0.975 AUC score with 3D + RCNN. With the intent to create tools to improve patient outcomes, this study describes the initial phase of a computational framework to support healthcare providers in detecting patients with retained IVC filters, so an individualized decision can be made to remove these devices when appropriate, to decrease complications. To our knowledge, this is the first study that curates abdominal computed tomography (CT) scans and presents an algorithm for automated detection and characterization of IVC filters.

Automatic Segmentation in Acute Ischemic Stroke: Prognostic Significance of Topological Stroke Volumes on Stroke Outcome.

BACKGROUND: Stroke infarct volume predicts patient disability and has utility for clinical trial outcomes. Accurate infarct volume measurement requires manual segmentation of stroke boundaries in diffusion-weighted magnetic resonance imaging scans which is time-consuming and subject to variability. Automatic infarct segmentation should be robust to rotation and reflection; however, prior work has not encoded this property into deep learning architecture. Here, we use rotation-reflection equivariance and train a deep learning model to segment stroke volumes in a large cohort of well-characterized patients with acute ischemic stroke in different vascular territories. METHODS: In this retrospective study, patients were selected from a stroke registry at Houston Methodist Hospital. Eight hundred seventy-five patients with acute ischemic stroke in any brain area who had magnetic resonance imaging with diffusion-weighted imaging were included for analysis and split 80/20 for training/testing. Infarct volumes were manually segmented by consensus of 3 independent clinical experts and cross-referenced against radiology reports. A rotation-reflection equivariant model was developed based on U-Net and grouped convolutions. Segmentation performance was evaluated using Dice score, precision, and recall. Ninety-day modified Rankin Scale outcome prediction was also evaluated using clinical variables and segmented stroke volumes in different brain regions. RESULTS: Segmentation model Dice scores are 0.88 (95% CI, 0.87-0.89; training) and 0.85 (0.82-0.88; testing). The modified Rankin Scale outcome prediction AUC using stroke volume in 30 refined brain regions based upon modified Rankin Scale-relevance areas adjusted for clinical variables was 0.80 (0.76-0.83) with an accuracy of 0.75 (0.72-0.78). CONCLUSIONS: We trained a deep learning model with encoded rotation-reflection equivariance to segment acute ischemic stroke lesions in diffusion- weighted imaging using a large data set from the Houston Methodist stroke center. The model achieved competitive performance in 175 well-balanced hold-out testing cases that include strokes from different vascular territories. Furthermore, the location specific stroke volume segmentations from the deep learning model combined with clinical factors demonstrated high AUC and accuracy for 90-day modified Rankin Scale in an outcome prediction model.

Cholecystokinin release triggered by NMDA receptors produces LTP and sound-sound associative memory.

Memory is stored in neural networks via changes in synaptic strength mediated in part by NMDA receptor (NMDAR)-dependent long-term potentiation (LTP). Here we show that a cholecystokinin (CCK)-B receptor (CCKBR) antagonist blocks high-frequency stimulation-induced neocortical LTP, whereas local infusion of CCK induces LTP. CCK-/- mice lacked neocortical LTP and showed deficits in a cue-cue associative learning paradigm; and administration of CCK rescued associative learning deficits. High-frequency stimulation-induced neocortical LTP was completely blocked by either the NMDAR antagonist or the CCKBR antagonist, while application of either NMDA or CCK induced LTP after low-frequency stimulation. In the presence of CCK, LTP was still induced even after blockade of NMDARs. Local application of NMDA induced the release of CCK in the neocortex. These findings suggest that NMDARs control the release of CCK, which enables neocortical LTP and the formation of cue-cue associative memory.

Relative Entropy of Correct Proximal Policy Optimization Algorithms with Modified Penalty Factor in Complex Environment.

In the field of reinforcement learning, we propose a Correct Proximal Policy Optimization (CPPO) algorithm based on the modified penalty factor ß and relative entropy in order to solve the robustness and stationarity of traditional algorithms. Firstly, In the process of reinforcement learning, this paper establishes a strategy evaluation mechanism through the policy distribution function. Secondly, the state space function is quantified by introducing entropy, whereby the approximation policy is used to approximate the real policy distribution, and the kernel function estimation and calculation of relative entropy is used to fit the reward function based on complex problem. Finally, through the comparative analysis on the classic test cases, we demonstrated that our proposed algorithm is effective, has a faster convergence speed and better performance than the traditional PPO algorithm, and the measure of the relative entropy can show the differences. In addition, it can more efficiently use the information of complex environment to learn policies. At the same time, not only can our paper explain the rationality of the policy distribution theory, the proposed framework can also balance between iteration steps, computational complexity and convergence speed, and we also introduced an effective measure of performance using the relative entropy concept.

Wide complex tachycardia cycle length shorter than the narrow complex tachycardia. What is the mechanism?

15-Year-old man's cardiac event monitor showed an episode of wide complex tachycardia which transitioned into narrow complex tachycardia. The tachycardia cycle length was shorter during wide complex tachycardia compared with narrow complex tachycardia. He underwent electrophysiology study in which an orthodromic atrioventricular reentrant tachycardia (wide and narrow complex tachycardia) utilizing left posterolateral bypass tract was induced. We propose the mechanism for shorter tachycardia cycle length during wide complex tachycardia compared with narrow complex tachycardia.

Illumination-Invariant Feature Point Detection Based on Neighborhood Information.

Feature point detection is the basis of computer vision, and the detection methods with geometric invariance and illumination invariance are the key and difficult problem in the field of feature detection. This paper proposes an illumination-invariant feature point detection method based on neighborhood information. The method can be summarized into two steps. Firstly, the feature points are divided into eight types according to the number of connected neighbors. Secondly, each type of feature points is classified again according to the position distribution of neighboring pixels. The theoretical deduction proves that the proposed method has lower computational complexity than other methods. The experimental results indicate that, when the photometric variation of the two images is very large, the feature-based detection methods are usually inferior, while the learning-based detection methods performs better. However, our method performs better than the learning-based detection method in terms of the number of feature points, the number of matching points, and the repeatability rate stability. The experimental results demonstrate that the proposed method has the best illumination robustness among state-of-the-art feature detection methods.

Neural evidence for long-term marriage shaping the functional brain network organization between couples.

Long-term married couples have been reported to share personality and behavioural similarities, but whether long-term marriage would shape the brain is hitherto unknown. In this study, 35 pairs of long-term married couples, who have married and living together at least 30 years, were recruited, and resting state functional magnetic resonance imaging was used to examine the neural correlates of long-term marriage between couples. Seven intrinsic connectivity networks were extracted using spatially constrained group independent component analysis, and the spatial similarity of each network as well as functional connectome similarity between couples were investigated respectively. The significant spatial similarities in the salience and frontoparietal networks as well as marginally significant connectome similarity were observed in long-term married couples. In addition, the marital duration showed a significantly positive correlation with the spatial similarity in the frontoparietal network and connectome similarity. The results provide objective evidence that long-term marriage would shape brain network organization, and the combination of initial personality traits and long-term common experience of the couples may be potential factors that account for similar brain network organizations between couples.

An LSTM-Method-Based Availability Prediction for Optimized Offloading in Mobile Edges.

Mobile edge computing (MEC) can augment the computation capabilities of a vehicle terminal (VT) through offloading the computational tasks from the VT to the mobile edge computing-enabled base station (MEC-BS) covering them. However, due to the limited mobility of the vehicle and the capacity of the MEC-BS, the connection between the vehicle and the MEC-BS may be intermittent. If we can expect the availability of MEC-BS through cognitive computing, we can significantly improve the performance in a mobile environment. Based on this idea, we propose a offloading optimization algorithm based on availability prediction. We examine the admission control decision of MEC-BS and the mobility problem, in which we improve the accuracy of availability prediction based on Empirical Mode Decomposition(EMD) and LSTM in deep learning. Firstly, we calculate the availability of MEC, completion time, and energy consumption together to minimize the overall cost. Then, we use a game method to obtain the optimal offloading decision. Finally, the experimental results show that the algorithm can save energy and shorten the completion time more effectively than other existing algorithms in the mobile environment.

Towards understanding promiscuity in multidrug efflux pumps.

Drug export from cells is a major factor in the acquisition of cellular resistance to antimicrobial and cancer chemotherapy, and poses a significant threat to future clinical management of disease. Many of the proteins that catalyse drug efflux do so with remarkably low substrate specificity, a phenomenon known as multidrug transport. For these reasons we need a greater understanding of drug recognition and transport in multidrug pumps to inform research that attempts to circumvent their action. Structural and computational studies have been heralded as being great strides towards a full elucidation of multidrug recognition and transport. In this review we summarise these advances and ask how close we are to a molecular understanding of this remarkable phenomenon.

Magnetic resonance elastography measured shear stiffness as a biomarker of fibrosis in pediatric nonalcoholic fatty liver disease.

Magnetic resonance elastography (MRE) is a promising technique for noninvasive assessment of fibrosis, a major determinant of outcome in nonalcoholic fatty liver disease (NAFLD). However, data in children are limited. The purpose of this study was to determine the accuracy of MRE for the detection of fibrosis and advanced fibrosis in children with NAFLD and to assess agreement between manual and novel automated reading methods. We performed a prospective, multicenter study of two-dimensional (2D) MRE in children with NAFLD. MR elastograms were analyzed manually at two reading centers, and using a new automated technique. Analysis using each approach was done independently. Correlations were determined between MRE analysis methods and fibrosis stage. Thresholds for classifying the presence of fibrosis and of advanced fibrosis were computed and cross-validated. In 90 children with a mean age of 13.1 ± 2.4 years, median hepatic stiffness was 2.35 kPa. Stiffness values derived by each reading center were strongly correlated with each other (r = 0.83). All three analyses were significantly correlated with fibrosis stage (center 1, ρ = 0.53; center 2, ρ = 0.55; and automated analysis, ρ = 0.52; P < 0.001). Overall cross-validated accuracy for detecting any fibrosis was 72.2% for all methods (95% confidence interval [CI], 61.8%-81.1%). Overall cross-validated accuracy for assessing advanced fibrosis was 88.9% (95% CI, 80.5%-94.5%) for center 1, 90.0% (95% CI, 81.9%-95.3%) for center 2, and 86.7% (95% CI, 77.9%-92.9%) for automated analysis. CONCLUSION: 2D MRE can estimate hepatic stiffness in children with NAFLD. Further refinement and validation of automated analysis techniques will be an important step in standardizing MRE. How to best integrate MRE into clinical protocols for the assessment of NAFLD in children will require prospective evaluation. (Hepatology 2017;66:1474-1485).

Biological Weighting Functions for Evaluating the Role of Sunlight-Induced Inactivation of Coliphages at Selected Beaches and Nearby Tributaries.

Coliphages can indicate contamination of recreational waters and previous studies show that sunlight is important in altering densities of coliphages, other indicator microorganisms, and pathogens in aquatic environments. Here, we report on laboratory studies of light-induced inactivation of two coliphage groups-male-specific (F+) and somatic coliphage-under various conditions in phosphate-buffered water (PBW). Strains isolated from wastewater treatment facilities and laboratory strains (MS2 and phiX174 coliphages) were evaluated. Inactivation rates were determined in a series of irradiations using simulated solar radiation passed through light filters that blocked different parts of the ultraviolet spectral region. Inactivation rates and spectral irradiance from these experiments were then analyzed to develop biological weighting functions (BWFs) for the light-induced inactivation. BWFs were used to model the inactivation of coliphages over a range of conditions in aquatic environments that included two beach sites in Lake Michigan and one in Lake Erie. For example, modeled effects of sunlight attenuation, using UV absorption data from the three Great Lakes beach sites, inferred that direct photoinactivation rate constants, averaged over a one-meter water column in swimmable areas, were reduced 2- to 5-fold, compared to near-surface rate constants.

The prevalence of peripheral intravenous cannulae and pattern of use: A point prevalence in a private hospital setting.

AIMS AND OBJECTIVES: To determine the prevalence and pattern of use of peripheral intravenous cannulae in hospital wards. BACKGROUND: Peripheral intravenous cannulae are commonly used in acute health care to directly access the bloodstream for the administration of medications, intravenous fluids and blood products. Peripheral intravenous cannulae are associated with multiple adverse events including hospital-acquired bloodstream infection, thrombophlebitis and pain/discomfort. Administration of intravenous fluids is associated with impaired mobility and nocturia which may increase falls risk in the older people. DESIGN: Observational, point prevalence study. METHODS: Three private hospitals comprising a total of 1,230 beds participated in the study. Nurses recorded the presence of a peripheral intravenous cannulae, duration of insertion, state of the dressing and whether the peripheral intravenous cannulae was accessed in the previous 24 hr and for what purpose. Nurses were also asked whether they would replace the peripheral intravenous cannulae should it fail. RESULTS: Approximately one-quarter of patients had a peripheral intravenous cannulae, the majority of which had been present for <24 hr. The major use of the peripheral intravenous cannulae was antibiotic administration. Administration of intravenous fluids occurred in the presence of normal oral fluid intake. Nurses would not replace one-third of peripheral intravenous cannulae in the event of failure. A majority of patients were at increased falls risk, and one-third of these were receiving intravenous fluids. CONCLUSIONS: There is room for improvement in the utilisation of peripheral intravenous cannulae, particularly in removal and associated use of intravenous fluids. Alternative strategies for medication administration and timely switch to the oral route may reduce the risks associated with intravenous fluids. RELEVANCE TO CLINICAL PRACTICE: Vigilance is required in the use of peripheral intravenous cannulae. Consider transition of medication administration to oral intake where possible to minimise risks associated with the use of invasive devices and increased fluid intake.

A Survey of Data Mining and Deep Learning in Bioinformatics.

The fields of medicine science and health informatics have made great progress recently and have led to in-depth analytics that is demanded by generation, collection and accumulation of massive data. Meanwhile, we are entering a new period where novel technologies are starting to analyze and explore knowledge from tremendous amount of data, bringing limitless potential for information growth. One fact that cannot be ignored is that the techniques of machine learning and deep learning applications play a more significant role in the success of bioinformatics exploration from biological data point of view, and a linkage is emphasized and established to bridge these two data analytics techniques and bioinformatics in both industry and academia. This survey concentrates on the review of recent researches using data mining and deep learning approaches for analyzing the specific domain knowledge of bioinformatics. The authors give a brief but pithy summarization of numerous data mining algorithms used for preprocessing, classification and clustering as well as various optimized neural network architectures in deep learning methods, and their advantages and disadvantages in the practical applications are also discussed and compared in terms of their industrial usage. It is believed that in this review paper, valuable insights are provided for those who are dedicated to start using data analytics methods in bioinformatics.

Plasma membrane dynamics and tetrameric organisation of ABCG2 transporters in mammalian cells revealed by single particle imaging techniques.

ABCG2 is one of three human ATP binding cassette (ABC) transporters involved in the export from cells of a chemically and structurally diverse range of compounds. This multidrug efflux capability, together with a broad tissue distribution in the body, means that ABCG2 exerts a range of effects on normal physiology such as kidney urate transport, as well as contributing towards the pharmacokinetic profiles of many exogenous drugs. The primary sequence of ABCG2 contains only half the number of domains required for a functioning ABC transporter and so it must oligomerise in order to function, yet its oligomeric state in intact cell membranes remains uncharacterized. We have analysed ABCG2 in living cell membranes using a combination of fluorescence correlation spectroscopy, photon counting histogram analysis, and stepwise photobleaching to demonstrate a predominantly tetrameric structure for ABCG2 in the presence or absence of transport substrates. These results provide the essential basis for exploring pharmacological manipulation of oligomeric state as a strategy to modulate ABCG2 activity in future selective therapeutics.

Computational medical imaging and hemodynamics framework for functional analysis and assessment of cardiovascular structures.

Cardiac dysfunction constitutes common cardiovascular health issues in the society, and has been an investigation topic of strong focus by researchers in the medical imaging community. Diagnostic modalities based on echocardiography, magnetic resonance imaging, chest radiography and computed tomography are common techniques that provide cardiovascular structural information to diagnose heart defects. However, functional information of cardiovascular flow, which can in fact be used to support the diagnosis of many cardiovascular diseases with a myriad of hemodynamics performance indicators, remains unexplored to its full potential. Some of these indicators constitute important cardiac functional parameters affecting the cardiovascular abnormalities. With the advancement of computer technology that facilitates high speed computational fluid dynamics, the realization of a support diagnostic platform of hemodynamics quantification and analysis can be achieved. This article reviews the state-of-the-art medical imaging and high fidelity multi-physics computational analyses that together enable reconstruction of cardiovascular structures and hemodynamic flow patterns within them, such as of the left ventricle (LV) and carotid bifurcations. The combined medical imaging and hemodynamic analysis enables us to study the mechanisms of cardiovascular disease-causing dysfunctions, such as how (1) cardiomyopathy causes left ventricular remodeling and loss of contractility leading to heart failure, and (2) modeling of LV construction and simulation of intra-LV hemodynamics can enable us to determine the optimum procedure of surgical ventriculation to restore its contractility and health This combined medical imaging and hemodynamics framework can potentially extend medical knowledge of cardiovascular defects and associated hemodynamic behavior and their surgical restoration, by means of an integrated medical image diagnostics and hemodynamic performance analysis framework.

Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition.

In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called 'shadow features' are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.

Computational evaluation of smoothed particle hydrodynamics for implementing blood flow modelling through CT reconstructed arteries.

Simulation of blood flow in a stenosed artery using Smoothed Particle Hydrodynamics (SPH) is a new research field, which is a particle-based method and different from the traditional continuum modelling technique such as Computational Fluid Dynamics (CFD). Both techniques harness parallel computing to process hemodynamics of cardiovascular structures. The objective of this study is to develop and test a new robust method for comparison of arterial flow velocity contours by SPH with the well-established CFD technique, and the implementation of SPH in computed tomography (CT) reconstructed arteries. The new method was developed based on three-dimensional (3D) straight and curved arterial models of millimeter range with a 25% stenosis in the middle section. In this study, we employed 1,000 to 13,000 particles to study how the number of particles influences SPH versus CFD deviation for blood-flow velocity distribution. Because further increasing the particle density has a diminishing effect on this deviation, we have determined a critical particle density of 1.45 particles/mm2 based on Reynolds number (Re = 200) at the inlet for an arterial flow simulation. Using this critical value of particle density can avoid unnecessarily big computational expenses that have no further effect on simulation accuracy. We have particularly shown that the SPH method has a big potential to be used in the virtual surgery system, such as to simulate the interaction between blood flow and the CT reconstructed vessels, especially those with stenosis or plaque when encountering vasculopathy, and for employing the simulation results output in clinical surgical procedures.

Discrete Element Framework for Modelling Extracellular Matrix, Deformable Cells and Subcellular Components.

This paper presents a framework for modelling biological tissues based on discrete particles. Cell components (e.g. cell membranes, cell cytoskeleton, cell nucleus) and extracellular matrix (e.g. collagen) are represented using collections of particles. Simple particle to particle interaction laws are used to simulate and control complex physical interaction types (e.g. cell-cell adhesion via cadherins, integrin basement membrane attachment, cytoskeletal mechanical properties). Particles may be given the capacity to change their properties and behaviours in response to changes in the cellular microenvironment (e.g., in response to cell-cell signalling or mechanical loadings). Each particle is in effect an 'agent', meaning that the agent can sense local environmental information and respond according to pre-determined or stochastic events. The behaviour of the proposed framework is exemplified through several biological problems of ongoing interest. These examples illustrate how the modelling framework allows enormous flexibility for representing the mechanical behaviour of different tissues, and we argue this is a more intuitive approach than perhaps offered by traditional continuum methods. Because of this flexibility, we believe the discrete modelling framework provides an avenue for biologists and bioengineers to explore the behaviour of tissue systems in a computational laboratory.