Ianus: an Adpative FPGA Computer

Dedicated machines designed for specific computational algorithms can outperform conventional computers by several orders of magnitude. In this note we describe {\it Ianus}, a new generation FPGA based machine and its basic features: hardware integration and wide reprogrammability. Our goal is to build a machine that can fully exploit the performance potential of new generation FPGA devices. We also plan a software platform which simplifies its programming, in order to extend its intended range of application to a wide class of interesting and computationally demanding problems. The decision to develop a dedicated processor is a complex one, involving careful assessment of its performance lead, during its expected lifetime, over traditional computers, taking into account their performance increase, as predicted by Moore's law. We discuss this point in detail

6D Dyonic String With Active Hyperscalars

We derive the necessary and sufficient conditions for the existence of a Killing spinor in N=(1,0) gauge supergravity in six dimensions coupled to a single tensor multiplet, vector multiplets and hypermultiplets. These are shown to imply most of the field equations and the remaining ones are determined. In this framework, we find a novel 1/8 supersymmetric dyonic string solution with nonvanishing hypermultiplet scalars. The activated scalars parametrize a 4 dimensional submanifold of a quaternionic hyperbolic ball. We employ an identity map between this submanifold and the internal space transverse to the string worldsheet. The internal space forms a 4 dimensional analog of the Gell-Mann-Zwiebach tear-drop which is noncompact with finite volume. While the electric charge carried by the dyonic string is arbitrary, the magnetic charge is fixed in Planckian units, and hence necessarily non-vanishing. The source term needed to balance a delta function type singularity at the origin is determined. The solution is also shown to have 1/4 supersymmetric AdS_3 x S^3 near horizon limit where the radii are proportional to the electric charge.Comment: 28 pages, latex, minor corrections mad

Reasons for hospitalizations in patients with type 2 diabetes mellitus in the CANVAS Program:a secondary analysis

AIMS: To determine the reasons for hospitalizations in the CANagliflozin cardioVascular Assessment Study (CANVAS) Program and the effects of the sodium glucose co-transporter 2 inhibitor canagliflozin on hospitalization. MATERIALS AND METHODS: A secondary analysis was performed on the CANVAS Program that included 10,142 participants with type 2 diabetes mellitus randomized to canagliflozin or placebo. The primary outcome was total (first plus all recurrent) all-cause hospitalization (ACH). Secondary outcomes were total hospitalizations categorized by the Medical Dictionary for Regulatory Activities hierarchy at the system organ class level, reported by investigators at each center. Outcomes were assessed using negative binomial models. RESULTS: Of the 7115 hospitalizations reported, the most common reasons were cardiac disorders (23.7%), infections and infestations (15.0%), and nervous system disorders (9.0%). The rate of total ACH was lower in the canagliflozin group (n=5795) compared to the placebo group (n=4347): 197.9 versus 215.8 participants per 1000 patient-years, respectively (rate ratio [RR] 0.92; 95% confidence interval [CI] 0.86, 0.98). Canagliflozin reduced the rate of total hospitalizations due to cardiac disorders (RR 0.81; 95% CI 0.75, 0.88). There was no significant difference between the canagliflozin and placebo groups in the rates of total hospitalizations due to infections and infestations (RR 0.96; 95% CI 0.86, 1.02) or nervous system disorders (RR 0.96; 95% CI 0.88, 1.05). CONCLUSIONS: In the CANVAS Program, the most common reasons for hospitalization were cardiac disorders, infections and infestations, and nervous system disorders. Canagliflozin, compared with placebo, reduced the rate of total ACH. This article is protected by copyright. All rights reserved

Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology?

Objective: Conventional magnetic resonance imaging (MRI) of the multiple sclerosis spinal cord is limited by low specificity regarding the underlying pathological processes, and new MRI metrics assessing microscopic damage are required. We aim to show for the first time that neurite orientation dispersion (i.e., variability in axon/dendrite orientations) is a new biomarker that uncovers previously undetected layers of complexity of multiple sclerosis spinal cord pathology. Also, we validate against histology a clinically viable MRI technique for dispersion measurement (neurite orientation dispersion and density imaging,NODDI), to demonstrate the strong potential of the new marker. Methods: We related quantitative metrics from histology and MRI in four post mortem spinal cord specimens (two controls; two progressive multiple sclerosis cases). The samples were scanned at high field, obtaining maps of neurite density and orientation dispersion from NODDI and routine diffusion tensor imaging (DTI) indices. Histological procedures provided markers of astrocyte, microglia, myelin and neurofilament density, as well as neurite dispersion. Results: We report from both NODDI and histology a trend toward lower neurite dispersion in demyelinated lesions, indicative of reduced neurite architecture complexity. Also, we provide unequivocal evidence that NODDI-derived dispersion matches its histological counterpart (P < 0.001), while DTI metrics are less specific and influenced by several biophysical substrates. Interpretation: Neurite orientation dispersion detects a previously undescribed and potentially relevant layer of microstructural complexity of multiple sclerosis spinal cord pathology. Clinically feasible techniques such as NODDI may play a key role in clinical trial and practice settings, as they provide histologically meaningful dispersion indices

Feasibility of data-driven, model-free quantitative MRI protocol design: application to brain and prostate diffusion-relaxation imaging

Purpose: We investigate the feasibility of data-driven, model-free quantitative MRI (qMRI) protocol design on in vivo brain and prostate diffusion-relaxation imaging (DRI). Methods: We select subsets of measurements within lengthy pilot scans, without identifying tissue parameters for which to optimise for. We use the “select and retrieve via direct upsampling” (SARDU-Net) algorithm, made of a selector, identifying measurement subsets, and a predictor, estimating fully-sampled signals from the subsets. We implement both using artificial neural networks, which are trained jointly end-to-end. We deploy the algorithm on brain (32 diffusion-/T1-weightings) and prostate (16 diffusion-/T2-weightings) DRI scans acquired on three healthy volunteers on two separate 3T Philips systems each. We used SARDU-Net to identify sub-protocols of fixed size, assessing reproducibility and testing sub-protocols for their potential to inform multi-contrast analyses via the T1-weighted spherical mean diffusion tensor (T1-SMDT, brain) and hybrid multi-dimensional MRI (HM-MRI, prostate) models, for which sub-protocol selection was not optimised explicitly. Results: In both brain and prostate, SARDU-Net identifies sub-protocols that maximise information content in a reproducible manner across training instantiations using a small number of pilot scans. The sub-protocols support T1-SMDT and HM-MRI multi-contrast modelling for which they were not optimised explicitly, providing signal quality-of-fit in the top 5% against extensive sub-protocol comparisons. Conclusions: Identifying economical but informative qMRI protocols from subsets of rich pilot scans is feasible and potentially useful in acquisition-time-sensitive applications in which there is not a qMRI model of choice. SARDU-Net is demonstrated to be a robust algorithm for data-driven, model-free protocol design

Noninvasive diffusion magnetic resonance imaging of brain tumour cell size for the early detection of therapeutic response

Cancer cells differ in size from those of their host tissue and are known to change in size during the processes of cell death. A noninvasive method for monitoring cell size would be highly advantageous as a potential biomarker of malignancy and early therapeutic response. This need is particularly acute in brain tumours where biopsy is a highly invasive procedure. Here, diffusion MRI data were acquired in a GL261 glioma mouse model before and during treatment with Temozolomide. The biophysical model VERDICT (Vascular Extracellular and Restricted Diffusion for Cytometry in Tumours) was applied to the MRI data to quantify multi-compartmental parameters connected to the underlying tissue microstructure, which could potentially be useful clinical biomarkers. These parameters were compared to ADC and kurtosis diffusion models, and, measures from histology and optical projection tomography. MRI data was also acquired in patients to assess the feasibility of applying VERDICT in a range of different glioma subtypes. In the GL261 gliomas, cellular changes were detected according to the VERDICT model in advance of gross tumour volume changes as well as ADC and kurtosis models. VERDICT parameters in glioblastoma patients were most consistent with the GL261 mouse model, whilst displaying additional regions of localised tissue heterogeneity. The present VERDICT model was less appropriate for modelling more diffuse astrocytomas and oligodendrogliomas, but could be tuned to improve the representation of these tumour types. Biophysical modelling of the diffusion MRI signal permits monitoring of brain tumours without invasive intervention. VERDICT responds to microstructural changes induced by chemotherapy, is feasible within clinical scan times and could provide useful biomarkers of treatment response

Fusion of bone-marrow-derived cells with Purkinje neurons, cardiomyocytes and hepatocytes

Recent studies have suggested that bone marrow cells possess a broad differentiation potential, being able to form new liver cells, cardiomyocytes and neurons(1,2). Several groups have attributed this apparent plasticity to 'transdifferentiation'(3-5). Others, however, have suggested that cell fusion could explain these results(6-9). Using a simple method based on Cre/lox recombination to detect cell fusion events, we demonstrate that bone-marrow-derived cells (BMDCs) fuse spontaneously with neural progenitors in vitro. Furthermore, bone marrow transplantation demonstrates that BMDCs fuse in vivo with hepatocytes in liver, Purkinje neurons in the brain and cardiac muscle in the heart, resulting in the formation of multinucleated cells. No evidence of transdifferentiation without fusion was observed in these tissues. These observations provide the first in vivo evidence for cell fusion of BMDCs with neurons and cardiomyocytes, raising the possibility that cell fusion may contribute to the development or maintenance of these key cell types.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62789/1/nature02069.pd

Regional State Aid Control in Europe: A Legal and Economic Assessment

This paper provides a legal and economic analysis of the European rules for regional State aid according to Article 107 (1) and (3) TFEU. It summarizes the historical evolution and the trends of regional aid rules and describes the economic rationale behind them. The main principles are discussed with reference to recent academic research, leading cases and the State Aid Modernization initiative ("SAM"). The current rules for the assessment of compatibility as laid down in the General Block Exemption and the Regional Aid Guidelines 2014 are critically reviewed in light of these principles

Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 2: Ex vivo imaging: Added value and acquisition

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher SNR and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents “Part 2” of a three-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge