Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation.

PurposeWith the advent of MR guided radiotherapy, internal organ motion can be imaged simultaneously during treatment. In this study, we evaluate the feasibility of pancreas MRI segmentation using state-of-the-art segmentation methods.Methods and materialT2 weighted HASTE and T1 weighted VIBE images were acquired on 3 patients and 2 healthy volunteers for a total of 12 imaging volumes. A novel dictionary learning (DL) method was used to segment the pancreas and compared to t mean-shift merging (MSM), distance regularized level set (DRLS), graph cuts (GC) and the segmentation results were compared to manual contours using Dice's index (DI), Hausdorff distance and shift of the-center-of-the-organ (SHIFT).ResultsAll VIBE images were successfully segmented by at least one of the auto-segmentation method with DI >0.83 and SHIFT ≤2 mm using the best automated segmentation method. The automated segmentation error of HASTE images was significantly greater. DL is statistically superior to the other methods in Dice's overlapping index. For the Hausdorff distance and SHIFT measurement, DRLS and DL performed slightly superior to the GC method, and substantially superior to MSM. DL required least human supervision and was faster to compute.ConclusionOur study demonstrated potential feasibility of automated segmentation of the pancreas on MRI images with minimal human supervision at the beginning of imaging acquisition. The achieved accuracy is promising for organ localization

Analysis of potential helicopter vibration reduction concepts

Results of analytical investigations to develop, understand, and evaluate potential helicopter vibration reduction concepts are presented in the following areas: identification of the fundamental sources of vibratory loads, blade design for low vibration, application of design optimization techniques, active higher harmonic control, blade appended aeromechanical devices, and the prediction of vibratory airloads. Primary sources of vibration are identified for a selected four-bladed articulated rotor operating in high speed level flight. The application of analytical design procedures and optimization techniques are shown to have the potential for establishing reduced vibration blade designs through variations in blade mass and stiffness distributions, and chordwise center-of-gravity location

Antennal transcriptome profiles of anopheline mosquitoes reveal human host olfactory specialization in Anopheles gambiae

BACKGROUND: Two sibling members of the Anopheles gambiae species complex display notable differences in female blood meal preferences. An. gambiae s.s. has a well-documented preference for feeding upon human hosts, whereas An. quadriannulatus feeds on vertebrate/mammalian hosts, with only opportunistic feeding upon humans. Because mosquito host-seeking behaviors are largely driven by the sensory modality of olfaction, we hypothesized that hallmarks of these divergent host seeking phenotypes will be in evidence within the transcriptome profiles of the antennae, the mosquito's principal chemosensory appendage. RESULTS: To test this hypothesis, we have sequenced antennal mRNA of non-bloodfed females from each species and observed a number of distinct quantitative and qualitative differences in their chemosensory gene repertoires. In both species, these gene families show higher rates of sequence polymorphisms than the overall rates in their respective transcriptomes, with potentially important divergences between the two species. Moreover, quantitative differences in odorant receptor transcript abundances have been used to model potential distinctions in volatile odor receptivity between the two sibling species of anophelines. CONCLUSION: This analysis suggests that the anthropophagic behavior of An. gambiae s.s. reflects the differential distribution of olfactory receptors in the antenna, likely resulting from a co-option and refinement of molecular components common to both species. This study improves our understanding of the molecular evolution of chemoreceptors in closely related anophelines and suggests possible mechanisms that underlie the behavioral distinctions in host seeking that, in part, account for the differential vectorial capacity of these mosquitoes

A methodology for producing reliable software, volume 1

An investigation into the areas having an impact on producing reliable software including automated verification tools, software modeling, testing techniques, structured programming, and management techniques is presented. This final report contains the results of this investigation, analysis of each technique, and the definition of a methodology for producing reliable software

Generation of Efficient High-Level Hardware Code from Dataflow Programs

High-level synthesis (HLS) aims at reducing the time-to-market by providing an automated design process that interprets and compiles high-level abstraction programs into hardware. However, HLS tools still face limitations regarding the performance of the generated code, due to the difficulties of compiling input imperative languages into efficient hardware code. Moreover the hardware code generated by the HLS tools is usually target-dependant and at a low level of abstraction (i.e. gate-level). A generated code at a high-level of abstraction (i.e. chip-level) is better suited to the needs of systems' architects because they can understand and control all of the design processes. We propose in this paper a new approach to HLS to generate efficient, high-level hardware code from Dataflow Programs. Implementation results (from two dynamic dataflow programs) on Xilinx, Altera and Latice FPGAs and on ASIC targeting 90nm CMOS technology are also presented

Domestication and divergence of Saccharomyces cerevisiae beer yeasts

Whereas domestication of livestock, pets, and crops is well documented, it is still unclear to what extent microbes associated with the production of food have also undergone human selection and where the plethora of industrial strains originates from. Here, we present the genomes and phenomes of 157 industrial Saccharomyces cerevisiae yeasts. Our analyses reveal that today's industrial yeasts can be divided into five sublineages that are genetically and phenotypically separated from wild strains and originate from only a few ancestors through complex patterns of domestication and local divergence. Large-scale phenotyping and genome analysis further show strong industry-specific selection for stress tolerance, sugar utilization, and flavor production, while the sexual cycle and other phenotypes related to survival in nature show decay, particularly in beer yeasts. Together, these results shed light on the origins, evolutionary history, and phenotypic diversity of industrial yeasts and provide a resource for further selection of superior strains

Dapsone Form V: A Late Appearing Thermodynamic Polymorph of a Pharmaceutical

Five anhydrate polymorphs (forms I–V) and the isomorphic dehydrate (Hy_{dehy}) of dapsone (4,4′-diaminodiphenyl sulfone or DDS) were prepared and characterized in an interdisciplinary experimental and computational study, elucidating the kinetic and thermodynamic stabilities, solid form interrelationships, and structural features of the known forms I–IV, the novel polymorph form V, and Hy_{dehy}. Calorimetric measurements, solubility experiments, and lattice energy calculations revealed that form V is the thermodynamically stable polymorph from absolute zero to at least 90 °C. At higher temperatures, form II, and then form I, becomes the most stable DDS solid form. The computed 0 K stability order (lattice energy calculations) was confirmed with calorimetric measurements as follows, V (most stable) > III > Hy_{dehy} > II > I > IV (least stable). The discovery of form V was complicated by the fact that the metastable but kinetically stabilized form III shows a higher nucleation and growth rate. By combining laboratory powder X-ray diffraction data and ab initio calculations, the crystal structure of form V (P2_{1} /c, Z′ = 4) was solved, with a high energy DDS conformation allowing a denser packing and more stable intermolecular interactions, rationalizing the formation of a high Z′ structure. The structures of the forms I and IV, only observed from the melt and showing distinct packing features compared to the forms II, III, and V, were derived from the computed crystal energy landscapes. Dehydration modeling of the DDS hydrate led to the Hydehy structure. This study expands our understanding about the complex crystallization behavior of pharmaceuticals and highlights the big challenge in solid form screening, especially that there is no clear end point

Design of asynchronous microprocessor for power proportionality

PhD ThesisMicroprocessors continue to get exponentially cheaper for end users following Moore’s law, while the costs involved in their design keep growing, also at an exponential rate. The reason is the ever increasing complexity of processors, which modern EDA tools struggle to keep up with. This makes further scaling for performance subject to a high risk in the reliability of the system. To keep this risk low, yet improve the performance, CPU designers try to optimise various parts of the processor. Instruction Set Architecture (ISA) is a significant part of the whole processor design flow, whose optimal design for a particular combination of available hardware resources and software requirements is crucial for building processors with high performance and efficient energy utilisation. This is a challenging task involving a lot of heuristics and high-level design decisions. Another issue impacting CPU reliability is continuous scaling for power consumption. For the last decades CPU designers have been mainly focused on improving performance, but “keeping energy and power consumption in mind”. The consequence of this was a development of energy-efficient systems, where energy was considered as a resource whose consumption should be optimised. As CMOS technology was progressing, with feature size decreasing and power delivered to circuit components becoming less stable, the energy resource turned from an optimisation criterion into a constraint, sometimes a critical one. At this point power proportionality becomes one of the most important aspects in system design. Developing methods and techniques which will address the problem of designing a power-proportional microprocessor, capable to adapt to varying operating conditions (such as low or even unstable voltage levels) and application requirements in the runtime, is one of today’s grand challenges. In this thesis this challenge is addressed by proposing a new design flow for the development of an ISA for microprocessors, which can be altered to suit a particular hardware platform or a specific operating mode. This flow uses an expressive and powerful formalism for the specification of processor instruction sets called the Conditional Partial Order Graph (CPOG). The CPOG model captures large sets of behavioural scenarios for a microarchitectural level in a computationally efficient form amenable to formal transformations for synthesis, verification and automated derivation of asynchronous hardware for the CPU microcontrol. The feasibility of the methodology, novel design flow and a number of optimisation techniques was proven in a full size asynchronous Intel 8051 microprocessor and its demonstrator silicon. The chip showed the ability to work in a wide range of operating voltage and environmental conditions. Depending on application requirements and power budget our ASIC supports several operating modes: one optimised for energy consumption and the other one for performance. This was achieved by extending a traditional datapath structure with an auxiliary control layer for adaptable and fault tolerant operation. These and other optimisations resulted in a reconfigurable and adaptable implementation, which was proven by measurements, analysis and evaluation of the chip.EPSR

Brain MR Image Segmentation: From Multi-Atlas Method To Deep Learning Models

Quantitative analysis of the brain structures on magnetic resonance (MR) images plays a crucial role in examining brain development and abnormality, as well as in aiding the treatment planning. Although manual delineation is commonly considered as the gold standard, it suffers from the shortcomings in terms of low efficiency and inter-rater variability. Therefore, developing automatic anatomical segmentation of human brain is of importance in providing a tool for quantitative analysis (e.g., volume measurement, shape analysis, cortical surface mapping). Despite a large number of existing techniques, the automatic segmentation of brain MR images remains a challenging task due to the complexity of the brain anatomical structures and the great inter- and intra-individual variability among these anatomical structures. To address the existing challenges, four methods are proposed in this thesis. The first work proposes a novel label fusion scheme for the multi-atlas segmentation. A two-stage majority voting scheme is developed to address the over-segmentation problem in the hippocampus segmentation of brain MR images. The second work of the thesis develops a supervoxel graphical model for the whole brain segmentation, in order to relieve the dependencies on complicated pairwise registration for the multi-atlas segmentation methods. Based on the assumption that pixels within a supervoxel are supposed to have the same label, the proposed method converts the voxel labeling problem to a supervoxel labeling problem which is solved by a maximum-a-posteriori (MAP) inference in Markov random field (MRF) defined on supervoxels. The third work incorporates attention mechanism into convolutional neural networks (CNN), aiming at learning the spatial dependencies between the shallow layers and the deep layers in CNN and producing an aggregation of the attended local feature and high-level features to obtain more precise segmentation results. The fourth method takes advantage of the success of CNN in computer vision, combines the strength of the graphical model with CNN, and integrates them into an end-to-end training network. The proposed methods are evaluated on public MR image datasets, such as MICCAI2012, LPBA40, and IBSR. Extensive experiments demonstrate the effectiveness and superior performance of the three proposed methods compared with the other state-of-the-art methods