Normal ultrasonic myocardial reflectivity in hypertensive patients. A tissue characterization study.

Ultrasonic backscatter of myocardial walls is directly related to the morphometrically evaluated collagen content in humans. The integrated backscatter is also increased in hypertrophic cardiomyopathy, whereas it gives normal values in the physiological hypertrophy of elite athletes. We assessed the quantitatively evaluated myocardial reflectivity in 46 mild to moderate, clinically uncomplicated essential hypertensive patients, with echocardiographically assessed normal regional and global left ventricular function, and 22 age- and sex-matched normotensive control subjects. With an echo prototype implemented in our institute, we performed an on-line radiofrequency analysis to obtain quantitative operator-independent measurements of the integrated backscatter signal of the ventricular septum and posterior wall. The integrated values of the radiofrequency signal of myocardial walls were normalized for those of the pericardial interface and expressed as a percent (integrated backscatter index). Hypertensive patients and control subjects differed in mean blood pressure (119 +/- 11 versus 95 +/- 5 mm Hg, p < 0.001) and left ventricular mass index (134 +/- 31 versus 105 +/- 21 g/m2, p < 0.001). However, integrated backscatter index overlapped for both the septum (28 +/- 17% versus 25 +/- 6%, p = NS) and the posterior wall (13 +/- 7% versus 13 +/- 4%, p = NS). In the hypertensive group, there was no detectable correlation between septal integrated backscatter index and either septal thickness (r = -0.26, p = NS) or mean arterial pressure (r = -0.14, p = NS). Hypertensive patients showed a normal pattern of quantitatively assessed ultrasonic backscatter, even in the presence of left ventricular hypertrophy.(ABSTRACT TRUNCATED AT 250 WORDS)

Comparison between diurnal changes and changes induced by hydrocortisone and epinephrine in circulating myeloid progenitor cells (CFU-GM) in man.

A significant increase from 8 AM to 3 PM was found in both myeloid progenitor cell (CFU-GM) and polymorphonuclear leukocyte (PMN) blood concentration in 45 normal subjects. Diurnal blood (CFU-GM and PMN changes were significantly correlated. Spontaneous diurnal changes in blood CFU-GM levels and in PMN were compared with the changes induced by i. v. administration of hydrocortisone (16 normal volunteers) and of epinephrine (10 normal volunteers). Diurnal changes in CFU-GM and PMN seem to follow a pattern similar to that induced by epinephrine administration. These findings suggest that diurnal changes in CFU-GM reflect mainly a shift of these cells between different blood compartments.

Empirical measure of multiclass generalization performance: the K-winner machine case.

Combining the K-winner machine (KWM) model with empirical measurements of a classifier's Vapnik-Chervonenkis (VC)-dimension gives two major results. First, analytical derivations refine the theory that characterizes the generalization performances of binary classifiers. Second, a straightforward extension of the theoretical framework yields bounds to the generalization error for multiclass problems.

Objective quality assessment of MPEG-2 video streams by using CBP neural networks.

The increasing use of compression standards in broadcasting digital TV has raised the need for established criteria to measure perceived quality. Novel methods must take into account the specific artifacts introduced by digital compression techniques. This paper presents a methodology using circular backpropagation (CBP) neural networks for the objective quality assessment of motion picture expert group (MPEG) video streams. Objective features are continuously extracted from compressed video streams on a frame-by-frame basis; they feed the CBP network estimating the corresponding perceived quality. The resulting adaptive modeling of subjective perception supports a real-time system for monitoring displayed video quality. The overall system mimics perception but does not require an analytical model of the underlying physical phenomenon. The ability to process compressed video streams represents a crucial advantage over existing approaches, as avoiding the decoding process greatly enhances the system's real-time performance. Experimental evidence confirmed the approach validity. The system was tested on real test videos; they included different contents ranging from fiction to sport. The neural model provided a satisfactory, continuous-time approximation for actual scoring curves, which was validated statistically in terms of confidence analysis. As expected, videos with slow-varying contents such as fiction featured the best performances.

Circular backpropagation networks for classification.

The class of mapping networks is a general family of tools to perform a wide variety of tasks. This paper presents a standardized, uniform representation for this class of networks, and introduces a simple modification of the multilayer perceptron with interesting practical properties, especially well suited to cope with pattern classification tasks. The proposed model unifies the two main representation paradigms found in the class of mapping networks for classification, namely, the surface-based and the prototype-based schemes, while retaining the advantage of being trainable by backpropagation. The enhancement in the representation properties and the generalization performance are assessed through results about the worst-case requirement in terms of hidden units and about the Vapnik-Chervonenkis dimension and cover capacity. The theoretical properties of the network also suggest that the proposed modification to the multilayer perceptron is in many senses optimal. A number of experimental verifications also confirm theoretical results about the model's increased performances, as compared with the multilayer perceptron and the Gaussian radial basis functions network.

Representation and generalization properties of class-entropy networks.

Using conditional class entropy (CCE) as a cost function allows feedforward networks to fully exploit classification-relevant information. CCE-based networks arrange the data space into partitions, which are assigned unambiguous symbols and are labeled by class information. By this labeling mechanism the network can model the empirical data distribution at the local level. Region labeling evolves with the network-training process, which follows a plastic algorithm. The paper proves several theoretical properties about the performance of CCE-based networks, and considers both convergence during training and generalization ability at run-time. In addition, analytical criteria and practical procedures are proposed to enhance the generalization performance of the trained networks. Experiments on artificial and real-world domains confirm the accuracy of this class of networks and witness the validity of the described methods.

Circular backpropagation networks embed vector quantization.

This letter proves the equivalence between vector quantization (VQ) classifiers and circular backpropagation (CBP) networks. The calibrated prototypes for a VQ schema can be plugged in a CBP feedforward structure having the same number of hidden neurons and featuring the same mapping. The letter describes how to exploit such equivalence by using VQ prototypes to perform a meaningful initialization for BP optimization. The approach effectiveness was tested considering a real classification problem (NIST handwritten digits).

K-winner machines for pattern classification.

The paper describes the K-winner machine (KWM) model for classification. KWM training uses unsupervised vector quantization and subsequent calibration to label data-space partitions. A K-winner classifier seeks the largest set of best-matching prototypes agreeing on a test pattern, and provides a local-level measure of confidence. A theoretical analysis characterizes the growth function of a K-winner classifier, and the result leads to tight bounds to generalization performance. The method proves suitable for high-dimensional multiclass problems with large amounts of data. Experimental results on both a synthetic and a real domain (NIST handwritten numerals) confirm the approach effectiveness and the consistency of the theoretical framework.

Digital implementation of hierarchical vector quantization.

A formal methodology drives the design and realization of a digital very large-scale integration (VLSI) device supporting hierarchical vector quantization (HVQ) in computation-intensive coding applications. The hardware-oriented model-selection approach enhances the Minimum Description Length criterion with circuit-related aspects that allow consistent and efficient design. The resulting model parameters drive the subsequent realization in digital circuitry, which has first been implemented in field-programmable gate array (FPGA) technology to verify its correctness. The eventual VLSI realization results in an HVQ chip providing cost-effective, computationally efficient real-time performances. Real-world applications support the consistency of the vector quantization approach and the effectiveness of the HVQ device.

Imaging artifacts in MRI simulators.

Artifactual images are created in the process of imaging. They are images that distort reality, i.e. they do not correspond to the image but they arise from them and are false. This paper describes image artifacts in MR that can lead to misdiagnosis, analyzes their causes and reviews the most common imaging artifacts.

Expression of scinderin in megakaryoblastic leukemia cells induces differentiation, maturation, and apoptosis with release of plateletlike particles and inhibits proliferation and tumorigenesis.

Rapid proliferation of atypical megakaryoblasts is a characteristic of megakaryoblastic leukemia. Cells from patients with this disorder and cell lines established from this type of leukemia showed the presence of gelsolin but the absence of scinderin expression, 2 filamentous actin-severing proteins present in normal megakaryocytes and platelets. Vector-mediated expression of scinderin in the megakaryoblastic cell line MEG-01 induced a decrease in both F-actin and gelsolin. This was accompanied by increased Rac2 expression and by activation of the PAK/MEKK.SEK/JNK/c-jun, c-fos transduction pathway. The Raf/MEK/ERK pathway was also activated in these cells. Transduction pathway activation was followed by cell differentiation, polyploidization, maturation, and apoptosis with release of platelet-like particles. Particles expressed surface CD41a antigen (glycoprotein IIb/IIIa or fibrinogen receptor), had dense bodies, high-affinity serotonin transport, and circular array of microtubules. Treatment of particles with thrombin induced serotonin release and aggregation that was blocked by CD41a antibodies. PAC-1 antibodies also blocked aggregation. Exposure of cells to PD98059, a blocker of MEK, inhibited antigen CD41a expression, increases in cell volume, and number of protoplasmic extensions. Cell proliferation and cell ability to form tumors in nude mice were also inhibited by the expression of scinderin. MEG-01 cells expressing scinderin had the same fate in vivo as in culture. Thus, when injected into nude mice, they entered apoptosis and released platelet-like particles. The lack of scinderin expression in megakaryoblastic leukemia cells seems to be responsible for their inability to enter into differentiation and maturation pathways characteristic of their normal counterparts.