Immediate brain plasticity after one hour of brain-computer interface (BCI).

KEY POINTS: Two groups of inexperienced brain-computer interface users underwent a purely mental EEG-BCI session that rapidly impacted on their brain. Modulations in structural and functional MRI were found after only 1 h of BCI training. Two different types of BCI (based on motor imagery or visually evoked potentials) were employed and analyses showed that the brain plastic changes are spatially specific for the respective neurofeedback. This spatial specificity promises tailored therapeutic interventions (e.g. for stroke patients). ABSTRACT: A brain-computer-interface (BCI) allows humans to control computational devices using only neural signals. However, it is still an open question, whether performing BCI also impacts on the brain itself, i.e. whether brain plasticity is induced. Here, we show rapid and spatially specific signs of brain plasticity measured with functional and structural MRI after only 1 h of purely mental BCI training in BCI-naive subjects. We employed two BCI approaches with neurofeedback based on (i) modulations of EEG rhythms by motor imagery (MI-BCI) or (ii) event-related potentials elicited by visually targeting flashing letters (ERP-BCI). Before and after the BCI session we performed structural and functional MRI. For both BCI approaches we found increased T1-weighted MR signal in the grey matter of the respective target brain regions, such as occipital/parietal areas after ERP-BCI and precuneus and sensorimotor regions after MI-BCI. The latter also showed increased functional connectivity and higher task-evoked BOLD activity in the same areas. Our results demonstrate for the first time that BCI by means of targeted neurofeedback rapidly impacts on MRI measures of brain structure and function. The spatial specificity of BCI-induced brain plasticity promises therapeutic interventions tailored to individual functional deficits, for example in patients after stroke.

DRCmpVis: Visual Comparison of Physical Targets in Mobile Diminished and Mixed Reality.

Numerous physical objects in our daily lives are grouped or ranked according to a stereotyped presentation style. For example, in a library, books are typically grouped and ranked based on classification numbers. However, for better comparison, we often need to re-group or re-rank the books using additional attributes such as ratings, publishers, comments, publication years, keywords, prices, etc., or a combination of these factors. In this paper, we propose a novel mobile DR/MR-based application framework named DRCmpVis to achieve in-context multi-attribute comparisons of physical objects with text labels or textual information. The physical objects are scanned in the real world using mobile cameras. All scanned objects are then segmented and labeled by a convolutional neural network and replaced (diminished) by their virtual avatars in a DR environment. We formulate three visual comparison strategies, including filtering, re-grouping, and re-ranking, which can be intuitively, flexibly, and seamlessly performed on their avatars. This approach avoids breaking the original layouts of the physical objects. The computation resources in virtual space can be fully utilized to support efficient object searching and multi-attribute visual comparisons. We demonstrate the usability, expressiveness, and efficiency of DRCmpVis through a user study, NASA TLX assessment, quantitative evaluation, and case studies involving different scenarios.

Tele-monitoring reduces exacerbation of COPD in the context of climate change--a randomized controlled trial.

BACKGROUND: A home based tele-monitoring system was developed to assess the effects of heat stress (days > 25°C) on clinical and functional status in patients with chronic obstructive pulmonary disease (COPD). METHODS: Sixty-two COPD patients (GOLD II-IV) were randomized into a tele-monitoring Group (TG, N = 32) or Control Group (CG, N = 30). Tele-monitoring included 1) daily clinical status (COPD Assessment Test-CAT), 2) daily lung function and 3) weekly 6-minute walk test (6MWT). Duration of monitoring lasted a total of nine months (9 M). RESULTS: From June 1st-August 31st 2012, 32 days with heat stress (29.0 ± 2.5°C) were recorded and matched with 32 thermal comfort days (21.0 ± 2.9°C). During heat stress, the TG showed a significant reduction in lung function and exercise capacity (FEV1% predicted: 51.1 ± 7.2 vs. 57.7 ± 5.0%; P <0.001 and 6MWT performance: 452 ± 85 vs. 600 ± 76 steps; P <0.001) and increase in CAT scores (19.2 ± 7.9 vs. 16.2 ± 7.2; P <0.001).Over summer, significantly fewer TG patients suffered exacerbation of COPD compared to CG patients (3 vs. 14; P = 0.006). Over entire 9 M follow-up, the TG group had fewer exacerbations compared to CG (7 vs. 22; P = 0.012), shorter cumulative hospital stay (34 vs. 97 days) and 43% fewer specialist consultations (24. vs. 42; P = 0.04). CONCLUSION: Heat stress affects clinical and functional status in COPD. Tele-monitoring reduces exacerbation frequency and health care utilization during heat stress and other periods of the year. TRIAL REGISTRATION DRKS-ID: DRK00000705.

D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias.

With the rise of AI, algorithms have become better at learning underlying patterns from the training data including ingrained social biases based on gender, race, etc. Deployment of such algorithms to domains such as hiring, healthcare, law enforcement, etc. has raised serious concerns about fairness, accountability, trust and interpretability in machine learning algorithms. To alleviate this problem, we propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases from tabular datasets. It uses a graphical causal model to represent causal relationships among different features in the dataset and as a medium to inject domain knowledge. A user can detect the presence of bias against a group, say females, or a subgroup, say black females, by identifying unfair causal relationships in the causal network and using an array of fairness metrics. Thereafter, the user can mitigate bias by refining the causal model and acting on the unfair causal edges. For each interaction, say weakening/deleting a biased causal edge, the system uses a novel method to simulate a new (debiased) dataset based on the current causal model while ensuring a minimal change from the original dataset. Users can visually assess the impact of their interactions on different fairness metrics, utility metrics, data distortion, and the underlying data distribution. Once satisfied, they can download the debiased dataset and use it for any downstream application for fairer predictions. We evaluate D-BIAS by conducting experiments on 3 datasets and also a formal user study. We found that D-BIAS helps reduce bias significantly compared to the baseline debiasing approach across different fairness metrics while incurring little data distortion and a small loss in utility. Moreover, our human-in-the-loop based approach significantly outperforms an automated approach on trust, interpretability and accountability.

DOMINO: Visual Causal Reasoning With Time-Dependent Phenomena.

Current work on using visual analytics to determine causal relations among variables has mostly been based on the concept of counterfactuals. As such the derived static causal networks do not take into account the effect of time as an indicator. However, knowing the time delay of a causal relation can be crucial as it instructs how and when actions should be taken. Yet, similar to static causality, deriving causal relations from observational time-series data, as opposed to designed experiments, is not a straightforward process. It can greatly benefit from human insight to break ties and resolve errors. We hence propose a set of visual analytics methods that allow humans to participate in the discovery of causal relations associated with windows of time delay. Specifically, we leverage a well-established method, logic-based causality, to enable analysts to test the significance of potential causes and measure their influences toward a certain effect. Furthermore, since an effect can be a cause of other effects, we allow users to aggregate different temporal cause-effect relations found with our method into a visual flow diagram to enable the discovery of temporal causal networks. To demonstrate the effectiveness of our methods we constructed a prototype system named DOMINO and showcase it via a number of case studies using real-world datasets. Finally, we also used DOMINO to conduct several evaluations with human analysts from different science domains in order to gain feedback on the utility of our system in practical scenarios.

Interactive Subspace Cluster Analysis Guided by Semantic Attribute Associations.

Multivariate datasets with many variables are increasingly common in many application areas. Most methods approach multivariate data from a singular perspective. Subspace analysis techniques, on the other hand. provide the user a set of subspaces which can be used to view the data from multiple perspectives. However, many subspace analysis methods produce a huge amount of subspaces, a number of which are usually redundant. The enormity of the number of subspaces can be overwhelming to analysts, making it difficult for them to find informative patterns in the data. In this paper, we propose a new paradigm that constructs semantically consistent subspaces. These subspaces can then be expanded into more general subspaces by ways of conventional techniques. Our framework uses the labels/meta-data of a dataset to learn the semantic meanings and associations of the attributes. We employ a neural network to learn a semantic word embedding of the attributes and then divide this attribute space into semantically consistent subspaces. The user is provided with a visual analytics interface that guides the analysis process. We show via various examples that these semantic subspaces can help organize the data and guide the user in finding interesting patterns in the dataset.

Graphical Enhancements for Effective Exemplar Identification in Contextual Data Visualizations.

An exemplar is an entity that represents a desirable instance in a multi-attribute configuration space. It offers certain strengths in some of its attributes without unduly compromising the strengths in other attributes. Exemplars are frequently sought after in real life applications, such as systems engineering, investment banking, drug advisory, product marketing and many others. We study a specific method for the visualization of multi-attribute configuration spaces, the Data Context Map (DCM), for its capacity in enabling users to identify proper exemplars. The DCM produces a 2D embedding where users can view the data objects in the context of the data attributes. We ask whether certain graphical enhancements can aid users to gain a better understanding of the attribute-wise tradeoffs and so select better exemplar sets. We conducted several user studies for three different graphical designs, namely iso-contour, value-shaded topographic rendering and terrain topographic rendering, and compare these with a baseline DCM display. As a benchmark we use an exemplar set generated via Pareto optimization which has similar goals but unlike humans can operate in the native high-dimensional data space. Our study finds that the two topographic maps are statistically superior to both the iso-contour and the DCM baseline display.

NAS-Navigator: Visual Steering for Explainable One-Shot Deep Neural Network Synthesis.

The success of DL can be attributed to hours of parameter and architecture tuning by human experts. Neural Architecture Search (NAS) techniques aim to solve this problem by automating the search procedure for DNN architectures making it possible for non-experts to work with DNNs. Specifically, One-shot NAS techniques have recently gained popularity as they are known to reduce the search time for NAS techniques. One-Shot NAS works by training a large template network through parameter sharing which includes all the candidate NNs. This is followed by applying a procedure to rank its components through evaluating the possible candidate architectures chosen randomly. However, as these search models become increasingly powerful and diverse, they become harder to understand. Consequently, even though the search results work well, it is hard to identify search biases and control the search progression, hence a need for explainability and human-in-the-loop (HIL) One-Shot NAS. To alleviate these problems, we present NAS-Navigator, a visual analytics (VA) system aiming to solve three problems with One-Shot NAS; explainability, HIL design, and performance improvements compared to existing state-of-the-art (SOTA) techniques. NAS-Navigator gives full control of NAS back in the hands of the users while still keeping the perks of automated search, thus assisting non-expert users. Analysts can use their domain knowledge aided by cues from the interface to guide the search. Evaluation results confirm the performance of our improved One-Shot NAS algorithm is comparable to other SOTA techniques. While adding Visual Analytics (VA) using NAS-Navigator shows further improvements in search time and performance. We designed our interface in collaboration with several deep learning researchers and evaluated NAS-Navigator through a control experiment and expert interviews.

Image factory: A method for synthesizing novel CT images with anatomical guidance.

BACKGROUND: Deep learning in medical applications is limited due to the low availability of large labeled, annotated, or segmented training datasets. With the insufficient data available for model training comes the inability of these networks to learn the fine nuances of the space of possible images in a given medical domain, leading to the possible suppression of important diagnostic features hence making these deep learning systems suboptimal in their performance and vulnerable to adversarial attacks. PURPOSE: We formulate a framework to address this lack of labeled data problem. We test this formulation in computed tomographic images domain and present an approach that can synthesize large sets of novel CT images at high resolution across the full Hounsfield (HU) range. METHODS: Our method only requires a small annotated dataset of lung CT from 30 patients (available online at the TCIA) and a large nonannotated dataset with high resolution CT images from 14k patients (received from NIH, not publicly available). It then converts the small annotated dataset into a large annotated dataset, using a sequence of steps including texture learning via StyleGAN, label learning via U-Net and semi-supervised learning via CycleGAN/Pixel-to-Pixel (P2P) architectures. The large annotated dataset so generated can then be used for the training of deep learning networks for medical applications. It can also be put to use for the synthesis of CT images with varied anatomies that were nonexistent within either of the input datasets, enriching the dataset even further. RESULTS: We demonstrate our framework via lung CT-Scan synthesis along with their novel generated annotations and compared it with other state of the art generative models that only produce images without annotations. We evaluate our framework effectiveness via a visual turing test with help of a few doctors and radiologists. CONCLUSIONS: We gain the capability of generating an unlimited amount of annotated CT images. Our approach works for all HU windows with minimal depreciation in anatomical plausibility and hence could be used as a general purpose framework for annotated data augmentation for deep learning applications in medical imaging.

PC-Expo: A Metrics-Based Interactive Axes Reordering Method for Parallel Coordinate Displays.

Parallel coordinate plots (PCPs) have been widely used for high-dimensional (HD) data storytelling because they allow for presenting a large number of dimensions without distortions. The axes ordering in PCP presents a particular story from the data based on the user perception of PCP polylines. Existing works focus on directly optimizing for PCP axes ordering based on some common analysis tasks like clustering, neighborhood, and correlation. However, direct optimization for PCP axes based on these common properties is restrictive because it does not account for multiple properties occurring between the axes, and for local properties that occur in small regions in the data. Also, many of these techniques do not support the human-in-the-loop (HIL) paradigm, which is crucial (i) for explainability and (ii) in cases where no single reordering scheme fits the users' goals. To alleviate these problems, we present PC-Expo, a real-time visual analytics framework for all-in-one PCP line pattern detection and axes reordering. We studied the connection of line patterns in PCPs with different data analysis tasks and datasets. PC-Expo expands prior work on PCP axes reordering by developing real-time, local detection schemes for the 12 most common analysis tasks (properties). Users can choose the story they want to present with PCPs by optimizing directly over their choice of properties. These properties can be ranked, or combined using individual weights, creating a custom optimization scheme for axes reordering. Users can control the granularity at which they want to work with their detection scheme in the data, allowing exploration of local regions. PC-Expo also supports HIL axes reordering via local-property visualization, which shows the regions of granular activity for every axis pair. Local-property visualization is helpful for PCP axes reordering based on multiple properties, when no single reordering scheme fits the user goals. A comprehensive evaluation was done with real users and diverse datasets confirm the efficacy of PC-Expo in data storytelling with PCPs.

RadVolViz: An Information Display-Inspired Transfer Function Editor for Multivariate Volume Visualization.

In volume visualization transfer functions are widely used for mapping voxel properties to color and opacity. Typically, volume density data are scalars which require simple 1D transfer functions to achieve this mapping. If the volume densities are vectors of three channels, one can straightforwardly map each channel to either red, green or blue, which requires a trivial extension of the 1D transfer function editor. We devise a new method that applies to volume data with more than three channels. These types of data often arise in scientific scanning applications, where the data are separated into spectral bands or chemical elements. Our method expands on prior work in which a multivariate information display, RadViz, was fused with a radial color map, in order to visualize multi-band 2D images. In this work, we extend this joint interface to blended volume rendering. The information display allows users to recognize the presence and value distribution of the multivariate voxels and the joint volume rendering display visualizes their spatial distribution. We design a set of operators and lenses that allow users to interactively control the mapping of the multivariate voxels to opacity and color. This enables users to isolate or emphasize volumetric structures with desired multivariate properties. Furthermore, it turns out that our method also enables more insightful displays even for RGB data. We demonstrate our method with three datasets obtained from spectral electron microscopy, high energy X-ray scanning, and atmospheric science.

Efficient low-dose CT artifact mitigation using an artifact-matched prior scan.

PURPOSE: Low-dose CT has attracted increasing attention due to growing concerns about radiation exposure in medical scans. However, the frugal use of x-ray radiation inevitably reduces the quality of the CT images, introducing artifacts such as noise and streaks which make the reconstructed images difficult to read in clinical routine. For follow-up CT exams a prior scan is often available. It typically contains the same anatomical structures, just somewhat deformed and not aligned. This work describes a two-step technique that utilizes this prior scan to achieve high-quality low-dose CT imaging, overcoming difficulties arising from noise artifacts and misalignment. We specifically focus on reducing the dose by lowering the number of projections. This gives rise to severe streak artifacts which possibly lower the readability of CT images to a larger extent than the fine-grained noise that results from lowering the mA or kV settings. METHODS: A common approach is to apply image filtering to reduce the noise artifacts. These techniques typically utilize pixel neighborhoods in the degraded image to estimate the true value of a pixel at the center of this neighborhood. However, this can lead to poor results when the image is severely contaminated under very low low-dose situations. We propose a method that utilizes the nondegraded, clean prior to determine higher quality pixel statistics to form the pixel estimates, supported by the matching scheme of the non-local means filter. To make this matching reliable, a good registration of prior and low-dose image is required. For this, we employ a state-of-the-art registration method, called SIFT-flow, which can tolerate the high amount of streak noise. But even for properly registered images, using an artifact free prior for the matching yields inferior results. We hence describe a scheme that first constructs a tandem-prior with streak artifacts resembling those in the low-dose image, and then employs this image for the matching, but uses the corresponding high-quality prior to determine the pixel estimates. RESULTS: Two experimental studies are carried out, using a head phantom and a human lung with projections gathered via simulation. We assess the quality of the processed reconstruction with various metrics: mathematical and perceptual. We find that the quality that can be obtained with the artifact-matched prior-based scheme significantly exceeds that of all competing schemes. Even though the general prior-based approach is able to eliminate the streak artifacts, only the artifact-matched scheme can restore small detail and feature sharpness. CONCLUSIONS: The reduced-projection low-dose image reconstruction algorithm we present outperforms traditional image restoration algorithms when a prior scan is available. Our method is quite efficient and as such it is well suited for fast-paced clinical applications such as image-assisted interventions, orthopedic alignment scans, and follow-ups.

Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making.

The widespread adoption of algorithmic decision-making systems has brought about the necessity to interpret the reasoning behind these decisions. The majority of these systems are complex black box models, and auxiliary models are often used to approximate and then explain their behavior. However, recent research suggests that such explanations are not overly accessible to lay users with no specific expertise in machine learning and this can lead to an incorrect interpretation of the underlying model. In this article, we show that a predictive and interactive model based on causality is inherently interpretable, does not require any auxiliary model, and allows both expert and non-expert users to understand the model comprehensively. To demonstrate our method we developed Outcome Explorer, a causality guided interactive interface, and evaluated it by conducting think-aloud sessions with three expert users and a user study with 18 non-expert users. All three expert users found our tool to be comprehensive in supporting their explanation needs while the non-expert users were able to understand the inner workings of a model easily.

Industrial Processing Induces Pericardial Patch Degeneration.

Background: Autologous pericardium is considered gold standard for various reconstructive surgical procedures in children. However, processed bovine, equine, and porcine pericardial tissue are also widely used. We investigated structural differences and analyzed alterations caused by industrial processing. Additionally human and equine pericardium explants, used during aortic valve reconstruction were analyzed. Methods: Pericardial tissues (native, processed and explanted) were gathered and stained with HE and EvG to visualize collagen as well as elastic fibers. Fiber structures were visualized by light and polarization microscopy. Antibody staining against CD 3, CD 20, and CD 68 was performed to identify inflammation. Results: Native pericardium of different species showed small differences in thickness, with bovine pericardium being the thickest [bovine: 390 µm (± 40.6 µm); porcine: 223 µm (± 30.1 µm); equine: 260 µm (± 28.4 µm)]. Juvenile pericardium was 277 µm (± 26.7 µm). Single collagen bundle diameter displayed variations (~3-20 µm). Parallel collagen fibers were densely packed with small inter-fibrillary space. After industrial tissue processing, loosening of collagen network with inter-fibrillary gapping was observed. Pericardium appeared thicker (mean values ranging from 257-670 µm). Processed tissue showed less birefringence under polarized light. All analyzed tissues showed a small number of elastic fibers. Fibrosis, calcification and inflammatory processes of autologous and equine pericardium were observed in patient explants. Conclusion: None of the analyzed tissues resembled the exact structure of the autologous pericardial explant. Degeneration of pericardium starts during industrial processing, suggesting a potential harm on graft longevity in children. A careful surgical approach prior to the implantation of xenografts is therefore needed.

Development of metaverse for intelligent healthcare.

The metaverse integrates physical and virtual realities, enabling humans and their avatars to interact in an environment supported by technologies such as high-speed internet, virtual reality, augmented reality, mixed and extended reality, blockchain, digital twins and artificial intelligence (AI), all enriched by effectively unlimited data. The metaverse recently emerged as social media and entertainment platforms, but extension to healthcare could have a profound impact on clinical practice and human health. As a group of academic, industrial, clinical and regulatory researchers, we identify unique opportunities for metaverse approaches in the healthcare domain. A metaverse of 'medical technology and AI' (MeTAI) can facilitate the development, prototyping, evaluation, regulation, translation and refinement of AI-based medical practice, especially medical imaging-guided diagnosis and therapy. Here, we present metaverse use cases, including virtual comparative scanning, raw data sharing, augmented regulatory science and metaversed medical intervention. We discuss relevant issues on the ecosystem of the MeTAI metaverse including privacy, security and disparity. We also identify specific action items for coordinated efforts to build the MeTAI metaverse for improved healthcare quality, accessibility, cost-effectiveness and patient satisfaction.

Impact of cardiac fibrosis and collagens on right ventricular failure and acute kidney injury in patients after continuous-flow left ventricular assist devices.

OBJECTIVES: We aim to investigate the impact of cardiac fibrosis and collagens on right ventricular failure (RVF) and acute kidney injury (AKI) in patients receiving continuous flow left ventricular assist devices. METHODS: Heart tissues from 34 patients were obtained from continuous flow left ventricular assist device insertion sites and corresponding clinical data were collected. The participants were divided into 2 groups according to the extent of the cardiac fibrosis or collagens. RESULTS: Overall, 18 patients developed RVF with 14 receiving right ventricular assist device (RVAD), and 22 patients developed AKI with 12 needing new-onset renal replacement therapy. Higher collagen I (Col1) was significantly associated with increased incidences of RVF (76.5% vs 29.4%, P = 0.015), RVAD support (64.7% vs 17.6%, P = 0.013) and stage 3 AKI (58.8% vs 17.6%, P = 0.032), and patients with higher Col1 were more prone to renal replacement therapy (52.9% vs 17.6%, P = 0.071). Receiver operating characteristic curves showed that Col1 had good predictive effects on RVF [area under the curve (AUC) = 0.806, P = 0.002], RVAD support (AUC = 0.789, P = 0.005), stage 3 AKI (AUC = 0.740, P = 0.020) and renal replacement therapy (AUC = 0.731, P = 0.028) after continuous-flow left ventricular assist device. Moreover, patients with higher Col1 had significantly longer postoperative duration of mechanical ventilation, duration of intensive care unit stay and hospital length of stay (all P < 0.05). Cardiac fibrosis, collagen III (Col3) and Col1/Col3 shared similar results or trends with Col1. CONCLUSIONS: Cardiac fibrosis and related collagens in the apical left ventricular tissue are associated with increased risks of RVF, RVAD use and worse renal function. Further study is warranted owing to the small sample size.

High-performance iterative electron tomography reconstruction with long-object compensation using graphics processing units (GPUs).

Iterative reconstruction algorithms pose tremendous computational challenges for 3D Electron Tomography (ET). Similar to X-ray Computed Tomography (CT), graphics processing units (GPUs) offer an affordable platform to meet these demands. In this paper, we outline a CT reconstruction approach for ET that is optimized for the special demands and application setting of ET. It exploits the fact that ET is typically cast as a parallel-beam configuration, which allows the design of an efficient data management scheme, using a holistic sinogram-based representation. Our method produces speedups of about an order of magnitude over a previously proposed GPU-based ET implementation, on similar hardware, and completes an iterative 3D reconstruction of practical problem size within minutes. We also describe a novel GPU-amenable approach that effectively compensates for reconstruction errors resulting from the TEM data acquisition on (long) samples which extend the width of the parallel TEM beam. We show that the vignetting artifacts typically arising at the periphery of non-compensated ET reconstructions are completely eliminated when our method is employed.

Reactive oxygen species abrogate the anticarcinogenic effect of eicosapentaenoic acid in Atm-deficient mice.

Recent studies have demonstrated that n-3 polyunsaturated fatty acids such as eicosapentaenoic acid (EPA) are able to suppress cell proliferation and inhibit tumor growth. The objective of our study was to investigate the influence of a high dose EPA on the development of the tumor phenotype in ataxia-telangiectasia mutated (Atm)-deficient mice, a genetic cancer model that is associated with increased levels of oxidative stress. We analyzed toxicity, proliferation, cell-cycle progression, and apoptosis of EPA in vitro and latency to tumorigenesis in vivo. Because of the impact of reactive oxygen species (ROS) on the tumor incidence in ataxia telangiectasia (AT), we further analyzed the effect of EPA on the generation of ROS and oxidative DNA damage (ODD). EPA effectively inhibited proliferation, altered cell-cycle progression, and induced apoptosis of tumor cells (AT-4). EPA showed no effect on the latency to tumorigenesis in Atm-deficient mice. EPA treatment was accompanied by a significant increase of ROS and ODD. Our results demonstrate the antiproliferative effect of EPA on tumor cells by alteration of cell-cycle progression and induction of apoptosis in vitro. On the other hand, EPA treatment of Atm-deficient mice led to the formation of ROS and accumulation of ODD that might have abrogated the anticarcinogenic effect caused by EPA.

Refraction corrected transmission ultrasound computed tomography for application in breast imaging.

PURPOSE: We present an iterative framework for CT reconstruction from transmission ultrasound data which accurately and efficiently models the strong refraction effects that occur in our target application: Imaging the female breast. METHODS: Our refractive ray tracing framework has its foundation in the fast marching method (FNMM) and it allows an accurate as well as efficient modeling of curved rays. We also describe a novel regularization scheme that yields further significant reconstruction quality improvements. A final contribution is the development of a realistic anthropomorphic digital breast phantom based on the NIH Visible Female data set. RESULTS: Our system is able to resolve very fine details even in the presence of significant noise, and it reconstructs both sound speed and attenuation data. Excellent correspondence with a traditional, but significantly more computationally expensive wave equation solver is achieved. CONCLUSIONS: Apart from the accurate modeling of curved rays, decisive factors have also been our regularization scheme and the high-quality interpolation filter we have used. An added benefit of our framework is that it accelerates well on GPUs where we have shown that clinical 3D reconstruction speeds on the order of minutes are possible.

Influence of fish oil or folate supplementation on the time course of plasma redox markers during pregnancy.

Maternal supplementation with long-chain PUFA, to improve infant neurological development, might cause additional increase of oxidative stress. Pregnant women aged 18-41 years were randomised into one of four supplementation groups. From week 22 on, they received supplements containing either modified fish oil (n 69), 5-methyl-tetrahydro-folate (n 65), both (n 64), or placebo (n 72). Plasma Trolox-equivalent antioxidative capacity (TEAC), concentrations of alpha-tocopherol, retinol, beta-carotene, free thiol groups, uric acid and thiobarbituric acid-reactive substances (TBARS) were determined at weeks 20 and 30 and at delivery. The studied antioxidants showed no significant differences between the four supplementation groups. At week 30 plasma TBARS levels were found to be significantly higher in the fish oil group (0.80 (sem 0.04) micromol/l) than in the folate (0.67 (sem 0.03) micromol/l; P = 0.024) and control (0.69 (sem 0.04) micromol/l; P = 0.01) groups. Concentrations of retinol and free thiol groups decreased during pregnancy, whereas uric acid increased and beta-carotene as well as TEAC showed only minor changes. Fish oil supplementation during the second half of pregnancy appears not to decrease antioxidant status. The increased TBARS levels at week 30 may indicate a period of increased oxidative stress in plasma at this time.