Improving software fault injection

Tanenbaum, A.S. [Promotor

Core binding factor leukemia hijacks T-cell prone PU.1 antisense promoter [preprint]

The blood system serves as a key model for cell differentiation and cancer. It is orchestrated by precise spatiotemporal expression of the hematopoietic master regulator PU.11–4. PU.1 gene expression is regulated through enhancer-promoter interactions within a topologically associated domain (TAD)5,6. PU.1 levels increase during myeloid differentiation while failure to do so results in myeloid leukemia7. In contrast, T-cell differentiation requires PU.1 to be completely switched off8–10. Little is known about the precise mechanisms of PU.1 repression, physiological as in T-cell differentiation, or pathological as in leukemia. Here we demonstrate that the down-regulation of PU.1 mRNA is a dynamic process involving an alternative promoter11 in intron 3 that is induced by RUNX transcription factors driving noncoding antisense transcription. Core binding factor (CBF) fusions, RUNX1-ETO and CBFβ-MYH11 in t(8;21) and inv(16) acute myeloid leukemia (AML)12, activate the PU.1 antisense promoter, thus shifting from sense towards antisense transcription and blocking myeloid differentiation. In patients with CBF-AML, we found that an elevated antisense/sense ratio represents a hallmark compared to normal karyotype AML or healthy CD34+ cells. Competitive interaction of the enhancer with the proximal or the antisense promoter are at the heart of differential PU.1 expression during myeloid and T-cell development. Leukemic CBF fusions thus utilize a physiologic mechanism employed by T-cells to decrease sense PU.1 transcription. Our results identify the first example of a sense/antisense promoter competition as a crucial functional switch for gene expression perturbation by oncogenes. This novel basic disease mechanism reveals a previously unknown Achilles heel for future precise therapeutic targeting of oncogene-induced chromatin remodeling

Benchmarking Crimes: An Emerging Threat in Systems Security

Properly benchmarking a system is a difficult and intricate task. Unfortunately, even a seemingly innocuous benchmarking mistake can compromise the guarantees provided by a given systems security defense and also put its reproducibility and comparability at risk. This threat is particularly insidious as it is generally not a result of malice and can easily go undetected by both authors and reviewers. Moreover, as modern defenses often trade off security for performance in an attempt to find an ideal design point in the performance-security space, the damage caused by benchmarking mistakes is increasingly worrisome. To analyze the magnitude of the phenomenon, we identify a set of 22 "benchmarking crimes" that threaten the validity of systems security evaluations and perform a survey of 50 defense papers published in top venues. To ensure the validity of our results, we perform the complete survey twice, with two independent readers. We find only a very small number of disagreements between readers, showing that our assessment of benchmarking crimes is highly reproducible. We show that benchmarking crimes are widespread even in papers published at tier-1 venues. We find that tier-1 papers commit an average of five benchmarking crimes and we find only a single paper in our sample that committed no benchmarking crimes. Moreover, we find that the scale of the problem is constant over time, suggesting that the community is not yet addressing it despite the problem being now more relevant than ever. This threatens the scientific process, which relies on reproducibility and comparability to ensure that published research advances the state of the art. We hope to raise awareness of these issues and provide recommendations to improve benchmarking quality and safeguard the scientific process in our community. Computer Systems, Imagery and Medi

Fault-Tolerant Nanosatellite Computing on a Budget

Computer Systems, Imagery and Medi

Real‐time motion and retrospective coil sensitivity correction for CEST using volumetric navigators (vNavs) at 7T

PURPOSE: To explore the impact of temporal motion-induced coil sensitivity changes on CEST-MRI at 7T and its correction using interleaved volumetric EPI navigators, which are applied for real-time motion correction. METHODS: Five healthy volunteers were scanned via CEST. A 4-fold correction pipeline allowed the mitigation of (1) motion, (2) motion-induced coil sensitivity variations, Δ B 1 - , (3) motion-induced static magnetic field inhomogeneities, ΔB0 , and (4) spatially varying transmit RF field fluctuations, ΔB 1 + . Four CEST measurements were performed per session. For the first 2, motion correction was turned OFF and then ON in absence of voluntary motion, whereas in the other 2 controlled head rotations were performed. During post-processing Δ B 1 - was removed additionally for the motion-corrected cases, resulting in a total of 6 scenarios to be compared. In all cases, retrospective ∆B0 and - ΔB 1 + corrections were performed to compute artifact-free magnetization transfer ratio maps with asymmetric analysis (MTRasym ). RESULTS: Dynamic Δ B 1 - correction successfully mitigated signal deviations caused by head motion. In 2 frontal lobe regions of volunteer 4, induced relative signal errors of 10.9% and 3.9% were reduced to 1.1% and 1.0% after correction. In the right frontal lobe, the motion-corrected MTRasym contrast deviated 0.92%, 1.21%, and 2.97% relative to the static case for Δω = 1, 2, 3 ± 0.25 ppm. The additional application of Δ B 1 - correction reduced these deviations to 0.10%, 0.14%, and 0.42%. The fully corrected MTRasym values were highly consistent between measurements with and without intended head rotations. CONCLUSION: Temporal Δ B 1 - cause significant CEST quantification bias. The presented correction pipeline including the proposed retrospective Δ B 1 - correction significantly reduced motion-related artifacts on CEST-MRI

High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas

Available online 4 May 2017The amygdala is composed of multiple nuclei with unique functions and connections in the limbic system and to the rest of the brain. However, standard in vivo neuroimaging tools to automatically delineate the amygdala into its multiple nuclei are still rare. By scanning postmortem specimens at high resolution (100–150 µm) at 7 T field strength (n = 10), we were able to visualize and label nine amygdala nuclei (anterior amygdaloid, cortico-amygdaloid transition area; basal, lateral, accessory basal, central, cortical medial, paralaminar nuclei). We created an atlas from these labels using a recently developed atlas building algorithm based on Bayesian inference. This atlas, which will be released as part of FreeSurfer, can be used to automatically segment nine amygdala nuclei from a standard resolution structural MR image. We applied this atlas to two publicly available datasets (ADNI and ABIDE) with standard resolution T1 data, used individual volumetric data of the amygdala nuclei as the measure and found that our atlas i) discriminates between Alzheimer's disease participants and age-matched control participants with 84% accuracy (AUC=0.915), and ii) discriminates between individuals with autism and age-, sex- and IQ-matched neurotypically developed control participants with 59.5% accuracy (AUC=0.59). For both datasets, the new ex vivo atlas significantly outperformed (all p < .05) estimations of the whole amygdala derived from the segmentation in FreeSurfer 5.1 (ADNI: 75%, ABIDE: 54% accuracy), as well as classification based on whole amygdala volume (using the sum of all amygdala nuclei volumes; ADNI: 81%, ABIDE: 55% accuracy). This new atlas and the segmentation tools that utilize it will provide neuroimaging researchers with the ability to explore the function and connectivity of the human amygdala nuclei with unprecedented detail in healthy adults as well as those with neurodevelopmental and neurodegenerative disorders.This work was supported by the PHS grant DA023427 and NICHD/ NIH grant F32HD079169 (Z.M.S); Feodor Lynen Postdoctoral Fellowship of the Alexander von Humboldt Foundation (D.K.); R21(MH106796), R21 (AG046657) and K01AG28521 (J.C.A.), the National Cancer Institute (1K25CA181632-01) as well as the Genentech Foundation (M.R.); the European Union's Horizon 2020 Marie Sklodowska-Curie grant agreement No 654911 (project ”THALAMODEL”) and ERC Starting Grant agreement No 677697 (project “BUNGEE-TOOLS”); and the Spanish Ministry of Economy and Competitiveness (MINECO) reference TEC2014-51882-P (J.E.I.); and the NVIDIA hardware award (M.R. and J.E.I.). Further support for this research was provided in part by the National Institute for Biomedical Imaging and Bioengineering (P41EB015896, R01EB006758, R21EB018907, R01EB019956, R01- EB013565), the National Institute on Aging (5R01AG008122, R01AG016495), the National Institute of Diabetes and Digestive and Kidney Diseases (1-R21-DK-108277-01), the National Institute for Neurological Disorders and Stroke (R01NS0525851, R21NS072652, R01NS070963, R01NS083534, 5U01NS086625), the Massachusetts ADRC (P50AG005134) and was made possible by the resources provided by Shared Instrumentation Grants 1S10RR023401, 1S10RR019307, and 1S10RR023043. Additional support was provided by the NIH Blueprint for Neuroscience Research (5U01-MH093765), part of the multi-institutional Human Connectome Project. In addition, BF has a financial interest in CorticoMetrics, a company whose medical pursuits focus on brain imaging and measurement technologies. BF's interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies. The collection and sharing of the ADNI MRI data used in the evaluation was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2- 0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer's Association; Alzheimer's Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www. fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California

A comparison of static and dynamic ∆B0 mapping methods for correction of CEST MRI in the presence of temporal B0 field variations

Purpose: To assess the performance, in the presence of scanner instabilities, of three dynamic correction methods which integrate ∆B 0 mapping into the chemical exchange saturation transfer (CEST) measurement and three established static ∆B 0 -correction approaches. Methods: A homogeneous phantom and five healthy volunteers were scanned with a CEST sequence at 7 T. The in vivo measurements were performed twice: first with unaltered system frequency and again applying frequency shifts during the CEST acquisition. In all cases, retrospective voxel-wise ∆B 0 -correction was performed using one intrinsic and two extrinsic [prescans with dual-echo gradient-echo and water saturation shift referencing (WASSR)] static approaches. These were compared with two intrinsic [using phase data directly generated by single-echo or double-echo GRE (gradient-echo) CEST readout (CEST-GRE-2TE)] and one extrinsic [phase from interleaved dual-echo EPI (echo planar imaging) navigator (NAV-EPI-2TE)] dynamic ∆B 0 -correction approaches [allowing correction of each Z-spectral point before magnetization transfer ratio asymmetry (MTR asym) analysis]. Results: All three dynamic methods successfully mapped the induced drift. The intrinsic approaches were affected by the CEST labeling near water (∆ω < |0.3| ppm). The MTR asym contrast was distorted by the frequency drift in the brain by up to 0.21%/Hz when static ∆B 0 -corrections were applied, whereas the dynamic ∆B 0 corrections reduced this to <0.01%/Hz without the need of external scans. The CEST-GRE-2TE and NAV-EPI-2TE resulted in highly consistent MTR asym values with/without drift for all subjects. Conclusion: Reliable correction of scanner instabilities is essential to establish clinical CEST MRI. The three dynamic approaches presented improved the ∆B 0 -correction performance significantly in the presence of frequency drift compared to established static methods. Among them, the self-corrected CEST-GRE-2TE was the most accurate and straightforward to implement

High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas

The amygdala is composed of multiple nuclei with unique functions and connections in the limbic system and to the rest of the brain. However, standard in vivo neuroimaging tools to automatically delineate the amygdala into its multiple nuclei are still rare. By scanning postmortem specimens at high resolution (100-150µm) at 7T field strength (n = 10), we were able to visualize and label nine amygdala nuclei (anterior amygdaloid, cortico-amygdaloid transition area; basal, lateral, accessory basal, central, cortical medial, paralaminar nuclei). We created an atlas from these labels using a recently developed atlas building algorithm based on Bayesian inference. This atlas, which will be released as part of FreeSurfer, can be used to automatically segment nine amygdala nuclei from a standard resolution structural MR image. We applied this atlas to two publicly available datasets (ADNI and ABIDE) with standard resolution T1 data, used individual volumetric data of the amygdala nuclei as the measure and found that our atlas i) discriminates between Alzheimer's disease participants and age-matched control participants with 84% accuracy (AUC=0.915), and ii) discriminates between individuals with autism and age-, sex- and IQ-matched neurotypically developed control participants with 59.5% accuracy (AUC=0.59). For both datasets, the new ex vivo atlas significantly outperformed (all p < .05) estimations of the whole amygdala derived from the segmentation in FreeSurfer 5.1 (ADNI: 75%, ABIDE: 54% accuracy), as well as classification based on whole amygdala volume (using the sum of all amygdala nuclei volumes; ADNI: 81%, ABIDE: 55% accuracy). This new atlas and the segmentation tools that utilize it will provide neuroimaging researchers with the ability to explore the function and connectivity of the human amygdala nuclei with unprecedented detail in healthy adults as well as those with neurodevelopmental and neurodegenerative disorders

Multi-modal characterization of rapid anterior hippocampal volume increase associated with aerobic exercise.

The hippocampus has been shown to demonstrate a remarkable degree of plasticity in response to a variety of tasks and experiences. For example, the size of the human hippocampus has been shown to increase in response to aerobic exercise. However, it is currently unknown what underlies these changes. Here we scanned sedentary, young to middle-aged human adults before and after a six-week exercise intervention using nine different neuroimaging measures of brain structure, vasculature, and diffusion. We then tested two different hypotheses regarding the nature of the underlying changes in the tissue. Surprisingly, we found no evidence of a vascular change as has been previously reported. Rather, the pattern of changes is better explained by an increase in myelination. Finally, we show hippocampal volume increase is temporary, returning to baseline after an additional six weeks without aerobic exercise. This is the first demonstration of a change in hippocampal volume in early to middle adulthood suggesting that hippocampal volume is modulated by aerobic exercise throughout the lifespan rather than only in the presence of age related atrophy. It is also the first demonstration of hippocampal volume change over a period of only six weeks, suggesting gross morphometric hippocampal plasticity occurs faster than previously thought