Inferring (Biological) Signal Transduction Networks via Transitive Reductions of Directed Graphs

In this paper we consider the p-ary transitive reduction (TR _p ) problem where p>0 is an integer; for p=2 this problem arises in inferring a sparsest possible (biological) signal transduction network consistent with a set of experimental observations with a goal to minimize false positive inferences even if risking false negatives. Special cases of TR _p have been investigated before in different contexts; the best previous results are as follows:

Dynamic Verification of Memory Consistency in Cache-Coherent Multithreaded Computer Architectures

Multithreaded servers with cache-coherent shared memory are the dominant type of machines used to run critical network services and database management systems. To achieve the high availability required for these tasks, it is necessary to incorporate mechanisms for error detection and recovery. Correct operation of the memory system is defined by the memory consistency model. Errors can therefore be detected by checking if the observed memory system behavior deviates from the specified consistency model. Based on recent work, we design a framework for dynamic verification of memory consistency (DVMC). The framework consists of mechanisms to verify three invariants that are proven to guarantee that a specified memory consistency model is obeyed. We describe an implementation of the framework for the SPARCv9 architecture, and we experimentally evaluate its performance using full-system simulation of commercial workloads.     

A large anisotropic damage theory based on an incremental complementary energy equivalence model

This paper presents a large anisotropic damage theory of continuum damage mechanics. It is developed via a new hypothesis of incremental complementary elastic energy equivalence. This hypothesis is more versatile and accurate if compared to the original hypothesis of total complementary energy equivalence. To model the large damage, we assumed that it occurs as a series of incremental small damage. An expression for the damage effect tensor M(D) for large damage is derived. It is shown that when the damage is small, that is, D _i1, the proposed large damage theory reduces to the small damage model of Chow and Wang [1]. To demonstrate this large damage theory, it is applied to model the following cases: (a) uniaxial tension, (b) pure torsion and (c) elastic perfectly-plastic material behavior. In all three cases, the results clearly show that when the damage is small, Chow and Wang's model is recovered. However, for large damage, there are significant differences in predictions. Since this large damage theory is formulated on the basis of the incremental complementary energy, it is applicable to a wider range of problems.     

Unsupervised tissue classification of brain MR images for voxel‐based morphometry analysis

In this article, a fully unsupervised method for brain tissue segmentation of T1‐weighted MRI 3D volumes is proposed. The method uses the Fuzzy C‐Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro‐radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial‐and‐error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro‐Spinal Fluid in an unsupervised way. The method has been tested on the IBSR dataset, on the BrainWeb Phantom, on the BrainWeb SBD dataset, and on the real dataset “University of Palermo Policlinico Hospital” (UPPH), Italy. Sensitivity, Specificity, Dice and F‐Factor scores have been calculated on the IBSR and BrainWeb datasets segmented using the proposed method, the FCM algorithm, and two state‐of‐the‐art brain segmentation software packages (FSL and SPM) to prove the effectiveness of the proposed approach. A qualitative evaluation involving a group of five expert radiologists has been performed segmenting the real dataset using the proposed approach and the comparison algorithms. Finally, a usability analysis on the proposed method and reference methods has been carried out from the same group of expert radiologists. The achieved results show that the segmentations of the proposed method are comparable or better than the reference methods with a better usability and degree of acceptance.     

Effect of brain edema on the recurrence pattern of malignant gliomas

PURPOSE: To assess the influence of initial preoperative brain edema in malignant gliomas on regrowth patterns. SUBJECTS AND METHODS: 79 patients with histologically verified supratentorial malignant glioma were prospectively studied by magnetic resonance imaging (MRI) before and every 2-3 months after surgery. The median follow-up time was 11 months. We correlated the configuration of the initial vasogenic edema on T2-weighted images with tumor regrowth patterns on contrast-enhanced T1-weighted images. RESULTS: 35/47 tumor regrowths (75%) imitated the initial edema configuration, while 11/47 occurred within the initial tumor bed; in one case tumor recurrence was multilocal. CONCLUSION: In glioblastoma, tumor regrowth patterns correlate positively with the configuration of the initial vasogenic brain edema. The initial, "presurgical" peritumoral edema should thus be considered when planning further treatment.     

Design rules for an integratable low-power amplifier/filter combination

In this paper design rules for a circuit topology in which there is an inseparable combination of an amplifier and a filter characteristic, are presented. By intentionally using the capacitance of an already present input sensor for the filtering, the total required integrated capacitance is much less than that in circuits, which have a separately designed amplifier and filter function. Consequently, it is possible to have the advantage of a better integratability. Moreover, less complexity in the design is achieved. The presented circuit shows a current-to-voltage conversion and an inherently controllable second-order low-pass filter characteristic. A discrete realization has been designed to test the circuit. This circuit operates down to a 1 V supply voltage and the transfer shows a 1.8 M currentto-voltage conversion with a bandwidth of 6 kHz. Measurement results of this circuit show that a 63 dB dynamic range can be achieved with a total required integrated capacitance of only 31 pF.     

Capillary HPLC-NMR Coupling: High-Resolution (1)H NMR Spectroscopy in the Nanoliter Scale

Coupling HPLC and NMR is one of the most powerful techniques for simultaneous separation and structural elucidation of unknown compounds in mixtures. To date, however, minimizing the detection volume, as is required when coupling NMR with miniaturized separation techniques, has been accompanied by a dramatic loss in resolution of the NMR spectra. Here, we report on the coupling of gradient capillary HPLC with on-column, high-resolution NMR detection. On-line stopped-flow and static (1)H NMR spectra were acquired with capillary columns of 75-315 μm i.d. With detection over a length of 1.2 cm, cell volumes cover a range of 50-900 nL. An on-line-detected NMR separation of dansylated amino acids was carried out in a 315 μm i.d. fused silica capillary packed to a length of 12 cm with C(18) stationary phase. The low solvent consumption makes the use of fully deuterated solvents economically feasible. NMR spectra with resolution on the order of 3 Hz were obtained using a 50 nL detection cell to measure 1.1 nmol of dansylated γ-aminobutyric acid under static conditions in a 75 μm i.d. capillary.     

A SIMD-efficient 14 instruction shader program for high-throughput microtriangle rasterization

This paper shows that breaking the barrier of 1 triangle/clock rasterization rate for microtriangles in modern GPU architectures in an efficient way is possible. The fixed throughput of the special purpose culling and triangle setup stages of the classic pipeline limits the GPU scalability to rasterize many triangles in parallel when these cover very few pixels. In contrast, the shader core counts and increasing GFLOPs in modern GPUs clearly suggests parallelizing this computation entirely across multiple shader threads, making use of the powerful wide-ALU instructions. In this paper, we present a very efficient SIMD-like rasterization code targeted at very small triangles that scales very well with the number of shader cores and has higher performance than traditional edge equation based algorithms. We have extended the ATTILA GPU shader ISA (del Barrioet al. in IEEE International Symposium on Performance Analysis of Systems and Software, pp. 231–241, 2006) with two fixed point instructions to meet the rasterization precision requirement. This paper also introduces a novel subpixel Bounding Box size optimization that adjusts the bounds much more finely, which is critical for small triangles, and doubles the 2×2-pixel stamp test efficiency. The proposed shader rasterization program can run on top of the original pixel shader program in such a way that selected fragments are rasterized, attribute interpolated and pixel shaded in the same pass. Our results show that our technique yields better performance than a classic rasterizer at 8 or more shader cores, with speedups as high as 4× for 16 shader cores.