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Made available in DSpace on 2020-12-12T01:58:39Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-02-19 Background: Pelvic floor muscles (PFM) and rectus abdominis muscles (RAM) of pregnant diabetic rats exhibit atrophy, co-localization of fast and slow fibers and an increased collagen type I/III ratio. However, the role of similar PFM or RAM hyperglycemic-related myopathy in women with gestational diabetes mellitus (GDM) remains poorly investigated. This study aims to assess the frequency of pelvic floor muscle disorders and pregnancy-specific urinary incontinence (PS-UI) 12 months after the Cesarean (C) section in women with GDM. Specifically, differences in PFM/RAM hyperglycemic myopathy will be evaluated. Methods: The Diamater is an ongoing cohort study of four groups of 59 pregnant women each from the Perinatal Diabetes Research Centre (PDRC), Botucatu Medical School (FMB)-UNESP (São Paulo State University), Brazil. Diagnosis of GDM and PS-UI will be made at 24-26 weeks, with a follow-up at 34-38 weeks of gestation. Inclusion in the study will occur at the time of C-section, and patients will be followed at 24-48 h, 6 weeks and 6 and 12 months postpartum. Study groups will be classified as (1) GDM plus PS-UI; (2) GDM without PS-UI; (3) Non-GDM plus PS-UI; and (4) Non-GDM without PS-UI. We will analyze relationships between GDM, PS-UI and hyperglycemic myopathy at 12 months after C-section. The mediator variables to be evaluated include digital palpation, vaginal squeeze pressure, 3D pelvic floor ultrasound, and 3D RAM ultrasound. RAM samples obtained during C-section will be analyzed for ex-vivo contractility, morphological, molecular and OMICS profiles to further characterize the hyperglycemic myopathy. Additional variables to be evaluated include maternal age, socioeconomic status, educational level, ethnicity, body mass index, weight gain during pregnancy, quality of glycemic control and insulin therapy. Discussion: To our knowledge, this will be the first study to provide data on the prevalence of PS-UI and RAM and PFM physical and biomolecular muscle profiles after C-section in mothers with GDM. The longitudinal design allows for the assessment of cause-effect relationships between GDM, PS-UI, and PFMs and RAMs myopathy. The findings may reveal previously undetermined consequences of GDM. Department of Gynecology and Obstetrics Botucatu Medical School (FMB) São Paulo State University (UNESP) Physics Department Institute of Biosciences Letters and Exact Sciences Multiuser Center for Biomolecular Innovation UNESP-São Paulo State University Physiotherapy Department Faculdades Integradas de Bauru FIB Universidade Do Sagrado Coração (USC), Jardim Brasil Department of Health Research Methods Evidence and Impact McMaster University Biostatistics Unit Father Sean o'Sullivan Research Centre St Joseph's Healthcare-Hamilton Department of Obstetrics and Gynecology Weill Cornell Medicine Institute of Tropical Medicine University of Sao Paulo Medical School School of Philosophy and Sciences Department of Physiotherapy and Occupational Therapy UNESP-São Paulo State University Department of Gynecology and Obstetrics Botucatu Medical School (FMB) São Paulo State University (UNESP) Physics Department Institute of Biosciences Letters and Exact Sciences Multiuser Center for Biomolecular Innovation UNESP-São Paulo State University School of Philosophy and Sciences Department of Physiotherapy and Occupational Therapy UNESP-São Paulo State University

To predict patients who would benefit from adaptive radiotherapy (ART) and re-planning intervention based on machine learning from anatomical and dosimetric variations in a retrospective dataset.90 patients (pts) treated for head-neck cancer (HN) formed a multicenter data-set. 41 HN pts (45.6%) were considered for learning; 49 pts (54.4%) were used to test the tool. A homemade machine-learning classifier was developed to analyze volume and dose variations of parotid glands (PG). Using deformable image registration (DIR) and GPU, patients' conditions were analyzed automatically. Support Vector Machines (SVM) was used for time-series evaluation. "Inadequate" class identified patients that might benefit from replanning. Double-blind evaluation by two radiation oncologists (ROs) was carried out to validate day/week selected for re-planning by the classifier.The cohort was affected by PG mean reduction of 23.7±8.8%. During the first 3weeks, 86.7% cases show PG deformation aligned with predefined tolerance, thus not requiring re-planning. From 4th week, an increased number of pts would potentially benefit from re-planning: a mean of 58% of cases, with an inter-center variability of 8.3%, showed "inadequate" conditions. 11% of cases showed "bias" due to DIR and script failure; 6% showed "warning" output due to potential positioning issues. Comparing re-planning suggested by tool with recommended by ROs, the 4th week seems the most favorable time in 70% cases.SVM and decision-making tool was applied to overcome ART challenges. Pts would benefit from ART and ideal time for re-planning intervention was identified in this retrospective analysis.

Smart homes play a strategic role for improving life quality of people, enabling to monitor people at home with numerous intelligent devices. Sensors can be installed to provide a continuous assistance without limiting the resident&rsquo, s daily routine, giving her/him greater comfort, well-being and safety. This paper is based on the development of domestic technological solutions to improve the life quality of citizens and monitor the users and the domestic environment, based on features extracted from the collected data. The proposed smart sensing architecture is based on an integrated sensor network to monitor the user and the environment to derive information about the user&rsquo, s behavior and her/his health status. The proposed platform includes biomedical, wearable, and unobtrusive sensors for monitoring user&rsquo, s physiological parameters and home automation sensors to obtain information about her/his environment. The sensor network stores the heterogeneous data both locally and remotely in Cloud, where machine learning algorithms and data mining strategies are used for user behavior identification, classification of user health conditions, classification of the smart home profile, and data analytics to implement services for the community. The proposed solution has been experimentally tested in a pilot study based on the development of both sensors and services for elderly users at home.

Purpose To investigate the accuracy and robustness of commercial algorithms for Deformable Image Registration (DIR) to propagate regions of interest (ROIs), against image quality typical of Cone Beam CT (CBCT) and Megavoltage Cone-beam CT (MVCT) images, using computational phantoms based on real head and neck (HN) patient images. Methods Eleven institutions joined the study with five commercial DIR solutions. Three real patient data-sets from different on board imaging devices (OBI by Varian, XVI by Elekta, and MVCT by Tomotherapy) were used with different image quality. Two specific Deformation Vector Fields (DVF) with realistic level of deformations, were generated with a dedicated software and then applied to each data-set. The accuracy of the algorithms was assessed by comparing the DIR-mapped ROIs from each center with those of reference, using the Dice Similarity Coefficient (DSC) and Mean Distance to Conformity (MDC) metrics. Prognostic factors of DIR performances were carried out. Results Based on more than 700 DIR-mapped ROIs the 4-way ANOVA analysis states that DIR algorithm, image quality and organ type are significant predictors of DIR performances. A slightly dependence from applied DVF is detected (p = 0.035) with a post hoc test. Putting together all CI data of all institutions for the first DVF, the mean CI was 0.88 ± 0.05 (1 SD), and the mean MDC is 0.38 ± 0.96 (1 SD); for the second DVF, the mean CI was 0.86 ± 0.11 (1 SD), and the mean MDC is 0.43 ± 1.08 (1 SD). The thyroid, located near the FOV border, was the underperformer organ with large errors (unacceptable). All other organs were mapped with an acceptable accuracy (at voxel size level). One imaging system performs statistically better than the others, and one algorithm produced unsatisfactory results respect all the others. Conclusions This work studies the impact of CBCT and MVCT image quality on DIR performance in a ground truth provided scenario. Clinical issues like Adaptive Radiation Therapy (ART) need accurate and robust DIR software. The reported result highlight that in HN district the image quality and the implemented algorithm are strictly related to DIR performances.

To present our methods and results regarding the modeling of a carbon fiber couch (Varian Exact IGRT) in the RayStation treatment planning system (TPS).Three geometrical-models (GMs) were implemented in the TPS to represent the three different regions of the couch (thick, medium and thin). The materials and densities of each GM component were tuned to maximize the agreement between measured and calculated attenuations. Moreover, a couch computed-tomography (CT) scan was acquired and dosimetrically compared with the GMs. For validation, plan-specific quality assurance (QA) of VMAT plans (TG-119 cases, 5 prostate and 5 HN clinical cases) was performed by comparing measured dose distributions with doses computed with and without including the GMs in the TPS.Couch attenuations up to 4.3% were measured (energy: 6MV). Compared to couch CT, GMs could be modified to optimize the agreement with measurements and reduce dependence on the dose grid resolution. For both couch CT and GM, absolute deviations between measured and calculated attenuations were within 1.0%. When including the GMs in plan-specific QA, global 2%/2mm γ-pass rates showed an average improvement of 4.8% (p-value0.001, max +18.6%). The couch reduced the mean dose to targets by up to 2.4% of the prescribed dose for prostate cases and up to 1.4% for HN cases.RayStation accurately considers the implemented couch GMs replicating measured attenuations within an uncertainty of 1.0%. Materials and densities are proposed for the Varian Exact IGRT couch. The results obtained justify introducing couch GMs in clinical routine.

Background Diastolic dysfunction is common immediately after coronary artery bypass surgery (CABG). The duration of this phenomenon is unknown. Intravenous calcium is frequently administered during separation from cardiopulmonary bypass (CPB). We sought to determine whether intravenous calcium influences perioperative diastolic function and whether diastolic dysfunction persists into the postoperative period. Methods and results Patients undergoing first-time elective CABG (n = 29) were randomly assigned to receive intravenous calcium chloride (n = 13) or placebo (n = 16) during separation from CPB. Diastolic function was assessed by the pressure-area relation with transesophageal echocardiography and pulmonary capillary wedge pressure (PCWP) measured simultaneously. Left ventricular end-diastolic area (LVEDA) and Doppler indexes were measured at comparable PCWP (within 2 mm Hg) at baseline, after separation from CPB, after sternal closure, and 3 hours after surgery. After CABG, both groups had a significant decrease in LVEDA and mitral E-wave deceleration time that persisted at 3 hours. Because there were no significant differences between the calcium and control groups at any time point, the data for the entire study cohort was analyzed. The LVEDA decreased (stiffness increased) progressively from 16.9 ± 3.4 cm 2 at baseline to 15.8 ± 2.9 cm 2 after CPB, 14.9 ± 2.5 cm 2 after sternal closure, and 14.3 ± 3.1 cm 2 at 3 hours after surgery ( P P = .0001). Conclusions An increase in left ventricular diastolic chamber stiffness is nearly universal after CABG, and it persists for at least 3 hours after surgery. An intravenous bolus of calcium chloride given during separation from CPB has no measurable negative effect on diastolic function. In the setting of increased chamber stiffness, the PCWP alone does not adequately reflect the volume status and effective preload of the left ventricle.

We are organizing a challenge to reverse engineer the structure of neuronal networks from patterns of activity recorded with calcium fluorescence imaging. Unraveling the brain structure at the neuronal level at a large scale is an important step in brain science, with many ramifications in the comprehension of animal and human intelligence and learning capabilities, as well as understanding and curing neuronal diseases and injuries. However, uncovering the anatomy of the brain by disentangling the neural wiring with its very fine and intertwined dendrites and axons, making both local and far reaching synapses, is a very arduous task: traditional methods of axonal tracing are tedious, difficult, and time consuming. This challenge proposes to approach the problem from a different angle, by reconstructing the effective connectivity of a neuronal network from observations of neuronal activity of thousands of neurons, which can be obtained with state-of-the-art fluorescence calcium imaging. To evaluate the effectiveness of proposed algorithms, we will use data obtained with a realistic simulator of real neurons for which we have ground truth of the neuronal connections. We produced simulated calcium imaging data, taking into account a model of fluorescence and light scattering. The task of the participants is to reconstruct a network of 1000 neurons from time series of neuronal activities obtained with this model. This challenge is part of the official selection of the WCCI 2014 competition program.

Effects of diffraction and the size of the target on TR in the context of CTR-based bunch length measurements are studied on the basis of Kirchoff diffraction theory. Spectra of TR from the finite-size target for several schemes of measurements are calculated in the far-infrared region showing strong distortion at low frequencies. Influence of the effect on the accuracy of bunch length measurements is estimated.

We organized a Challenge to unravel the connectivity of simulated neuronal networks. The provided data was solely based on fluorescence time series of spontaneous activity in a network constituted by 1000 neurons. The task of the participants was to compute the effective connectivity between neurons, with the goal to reconstruct as accurately as possible the ground truth topology of the network. The procured dataset is similar to the one measured in in vivo and in vitro recordings of calcium fluorescence imaging, and therefore the algorithms developed by the participants may largely contribute in the future to unravel major topological features of living neuronal networks from just the analysis of recorded data, and without the need of slow, painstaking experimental connectivity labeling methods. Among 143 entrants, 16 teams participated in the final round of the challenge to compete for prizes. The winners significantly outperformed the baseline method provided by the organizers. To measure influences between neurons the participants used an array of diverse methods, including transfer entropy, regression algorithms, correlation, deep learning, and network deconvolution. The development of "connectivity reconstruction" techniques is a major step in brain science, with many ramifications in the comprehension of neuronal computation, as well as the understanding of network dysfunctions in neuropathologies.

In a multicenter evaluation the precision and stability of single slice peripheral Quantitative Computed Tomography (pQCT) systems were assessed. Compared to central Europe the peripheral QCT device is also widely used in the Italian health care system. Phantoms were measured frequently on each machine in four pQCT scanners over a two-year period using an identical automated software program. Intrasite and intersite phantom mineral densities, expressed in units of mg/cm^3, were analyzed and examined. The short and long term precision were estimated from the consistency of these measurements using the root mean square error and expressed as coefficient of variation in percent (% CV). Short term precision was good (mean intrasite coefficient of variation (CV) 0.21\pm 0.056 standard deviation (SD)). Intersite measurements of a single phantom revealed differences between machines of the same type of not more than 2.9 mg/cm^3. At four sites frequent phantom measurements revealed a mean CV of 0.18% (range 0.12-0.28). No significant changes in the regression slopes (drift of the machine versus time) were observed. We conclude that the in vitro precision and stability of the single slice pQCT systems are sufficiently high and unlikely to negatively affect the in vivo precision.

It is known that RNS VLSI processors can parallelize fixed-point addition and multiplication operations by the use of the Chinese remainder theorem (CRT). The required modular operations, however, must use specialized hardware whose design and implementation can create several problems. In this paper a modified residue arithmetic, called pseudo-RNS is introduced in order to alleviate some of the RNS problems when digital signal processing (DSP) structures are implemented. Pseudo-RNS requires only the use of modified binary processors and exhibits a speed performance comparable with other RNS traditional approaches. Some applications of the pseudo-RNS to common DSP architectures, such as multipliers and filters, are also presented in this paper. They are compared in terms of the area-time square product versus other RNS and weighted binary structures. It is proven that existing combinatorial or look-up table approaches for RNS are tailored to small designs or special applications, while the pseudo-RNS approach remains competitive also for complex systems. >

Cultured neurons in vitro quickly connect to one another to establish a spontaneously active network within a week. The resulting neuronal network is char- acterized by a combination of excitatory and inhibitory integrate–and–fire units coupled through synaptic connec- tions, and that interact in a highly nonlinear manner. The nonlinear behavior emerges from the dynamics of both the neurons’ membrane potential and synaptic transmission, together with intrinsic biological noise. These ingredients give rise to a rich repertoire of phenomena that are still poorly understood, including periodic spontaneous activ- ity, avalanches, propagation of activity fronts, and synchro- nization. Here we describe some experimental results on spontaneous activity in cultures, and elaborate on theoreti- cal models that describe its initiation and maintenance

Multilayer perceptrons (MLPs) with weight values restricted to powers of two or sums of powers of two are introduced. In a digital implementation, these neural networks do not need multipliers but only shift registers when computing in forward mode, thus saving chip area and computation time. A learning procedure, based on backpropagation, is presented for such neural networks. This learning procedure requires full real arithmetic and therefore must be performed offline. Some test cases are presented, concerning MLPs with hidden layers of different sizes, on pattern recognition problems. Such tests demonstrate the validity and the generalization capability of the method and give some insight into the behavior of the learning algorithm. >

To the Editor: We read with great interest the study by Patti and colleagues1 that suggests a beneficial effect of atorvastatin in preventing postoperative atrial fibrillation in cardiac surgery patients. The placebo arm in this study had fewer patients on β-blockers (60% versus 72%, P =0.08), more patients with left atrial …

The study of the energy stability along the macropulse of the TESLA Test Facility Linac (TTFL) was obtained by the measurement of the angular distribution of the optical transition radiation (OTR). This technique does not require a dispersive section and can be performed at any point of the beam line. Measurements have been performed with different settings of the RF low level control and at different values of the beam current. An energy variation along the macropulse was observed in a good agreement with the measured energy spread of the whole macrobunch. The analysis of the OTR angular distribution pattern allows also, to some extent, to evaluate the beam angular spread.

Coherent transition radiation (CTR) is now well established as one of basic tools to measure electron bunches on a subpicosecond scale. However, a series of experiments has demonstrated that suppression of CTR spectra at low frequencies, occurring in practice, leads to a great uncertainty in the bunch length determination. In addition to known sources of suppression, it was recently found that the size of a screen used to produce transition radiation (TR) can be a factor strongly affecting the spectrum. In this paper we calculate TR spectra emitted by a relativistic electron from a finite-size metallic screen and influenced by diffraction on apertures in environment and collecting optics.

The discrete Hartley transform (DHT) is a real-valued transform closely related to the discrete Fourier transform (DFT) of a real-valued sequence. Several fast algorithms for the computation of the DHT (FHTs) have been proposed. A new cascade circuit for computing the DHT is presented in two versions, which implement, respectively, the radix-2 decimation-in-time and decimation-in-frequency FHT algorithms. The circuit makes use of CORDIC processors and allows an easy computation of the DFT, the amplitude and phase spectra, and the cyclic convolution and correlation of two sequences. Some architectures to perform such operations are also presented. >

Using some observations on biological network plasticity, an idea is introduced to develop a procedure for varying network topology by simply pruning off connections in an initial overdimensioned fully connected network. The topology of a neural network is dynamically adapted using only the information of the learning set. The method, although it simply eliminates connections from an initial fully connected without creating new synapses, presents characteristics which can resemble certain kinds of biological behavior, such as the so-called critical period. >

Several DSP (digital signal processor) structures based on residual number systems (RNSs) have been proposed in the technical literature. Most of them use a lookup-table approach, which consumes space on the chip and lacks flexibility and reprogrammability. Small binary structures based on pseudoresidue odd-moduli RNS are presented and shown to be highly efficient in terms of speed, area, and reprogrammability, especially when large structures are to be built. The proposed method allows easy estimation of the overall complexity of an RNS algorithm with respect to other possible implementations. A mixed radix reconstruction cell and two examples of FIR (finite impulse response) filter structures are developed using this approach. These examples demonstrate all the advantages of the proposed scheme when used with the highly concurrent and repetitive architectures typical of VLSI/VHSIC design. >

The problem of registration of displaced images is considered. A well known method for this problem is the phase-correlation algorithm, which provides superior performance when rigid plane translation may be expected, even if the images are noisy and subject to varying conditions of illumination. However, the computational cost of this approach is high since it requires complex arithmetics. The computational cost can be reduced by using the correlation function between the binary boundary maps of the images, computed by Fermat number transforms. Based on this approach, an enhanced method which uses the Burg filter to whiten the image sequences before the contour extraction is proposed. The experimental results prove the superior performance of the proposed method with respect to direct correlation. >

The possibility of using the multilayer perceptron (MLP) neural network for the processing of EEG evoked potentials (EPs) is examined. A structure composed of the cascade of a MLP and a linear combiner is proposed. Experimental results, both on synthetic and real data, show that the method provides good results with very few EP ensembles and without the necessity of prior knowledge of the signal characteristics. >

In this paper we describe an experimental system for the recognition of Italian-style car license plates. Images are usually taken from a camera at a toll gate and preprocessed by a fast and robust 1-D DFT scheme to find the plate and character positions. Characters are classified by a multilayer neural network trained by the recently developed BRLS learning algorithm. The same neural network replaces both the traditional feature extractor and the classifier. The percentage of correctly recognized characters reaches the best scores obtained in literature, being highly insensitive to the environment variability, while the architecture appears best suited for parallel implementation on programmable DSP processors.

Localization of acoustic sources in reverberant environments by microphone arrays remains a challenging task in audio signal processing. As a matter of fact, most assumptions of commonly adopted models are not met in real applications. Moreover, in practical systems it is not convenient or possible to employ sophisticated and costly architectures, that require precise synchronization and fast data shuffling among sensors. In this paper, a new robust multi-step procedure for speaker localization in reverberant rooms is introduced and described. The new approach is based on a disturbed harmonics model of time delays in the frequency domain and employs the well-known ROOT-MUSIC algorithm, after a preliminary distributed processing of the received signals. Candidate source positions are then estimated by clustering of raw TDOA estimates. Main features of the proposed approach, compared to previous solutions, are the capability of tracking multiple speakers and the high accuracy of the closed form TDOA estimator.

Objective To analyze the Intracranial Hypertension (IH) development related to jugular bulb oxygen saturation (SjO2) disturbances in severe traumatic brain injury patients (sTBI).

Measurements of coherent diffraction radiation from a slit of a variable width generated by short electron bunches were performed in millimeter and submillimeter range. Experimental data are compared with transition radiation case and theoretical predictions. More realistic description, than the conventional theory does, is necessary to correctly account for the data. No noticeable difference in the bunch length value was observed between the diffraction radiation mode in a wide range of the slit widths and transition radiation one in the bunch length evaluation.

Neural networks add flexibility to the design of equalizers for digital communications. In this work novel decision-feedback (DF) neural equalizers (DFNE) are introduced and compared with classical DF equalizers and Viterbi demodulators. It is shown that the choice of a cost functional based on the discriminative learning, coupled with a fast training paradigm, can provide neural equalizers that out perform the standard DF equalizer at practical signal to noise ratio. Resulting architectures are competitive with the Viterbi solution from cost performance aspects.

Localization of acoustic sources in the presence of reverberation is still a challenging task in audio signal processing. As a matter of fact, commonly adopted models are not adequate to describe real scenarios. Moreover, practical systems should not employ sophisticated and expensive architectures, that require precise synchronization and fast data shuffling among sensors. This work describes a new robust multi-step procedure for speaker localization in reverberant rooms. The proposed approach is based on a disturbed harmonics model of time delays in the frequency domain and employs the well-known ROOT-MUSIC algorithm, after a proper pre-processing of the received signals. Final clustering of raw TDOA estimates gives candidate source positions. Among the appealing features of the proposed approach are the capability of tracking multiple speakers simultaneously and the high accuracy of the closed form TDOA estimator.

In this work a novel approach to the training of recurrent neural nets is presented. The algorithm exploits the separability of each neuron into its linear and nonlinear part. Each Iteration of the learning consists of two steps: first the descent of the error functional in the space of the linear outputs of the neurons is performed (descent in the neuron space);then the weights are updated by solving a linear system with a Recursive Least Squares technique.

In this paper a novel approach to learning in Recurrent Neural Networks (RNN) is introduced and applied to the problem of digital adaptive equalization. The proposed method extends to RNN a technique applied with success to feedforward NN and is based on the descent of the error functional in the space of the linear combinations of the neurons (the neuron space); it exploits the principle of discriminative learning, based on the minimization of an error functional which is a direct measure of the classification error considered in equalization problems. Main features of the new approach are higher speed of convergence and better numerical conditioning w.r.t. gradient-based approaches, while numerical stability is assured by the use of robust Least Squares solvers. Preliminary experiments regarding the equalization of PAM signals in different transmission channels are described, which demonstrate the effectiveness of the proposed approach.

Stroke and cognitive decline, due to periprocedural embolic and/or haemodynamic events, are the most feared neurological complications of Transfemoral Aortic Valve Implantation (TAVI). Transcranial Doppler (TCD) monitoring allows real time detection of such abnormalities, but their exact correlation with new-onset brain lesions and clinical findings has not been fully elucidated yet. Consecutive candidates to TAVI will be enrolled; patients with contraindications to MRI or inadequate transtemporal acoustic window will be excluded. Pre-procedurally, at discharge and at 3 months follow-up, neurological examination (through NIHSS, mRS), cognitive profile (through MMSE and MoCA test) and brain parenchyma status (through DWI and FLAIR sequences on MRI) will be assessed. During TAVI, a continuous TCD monitoring on middle cerebral arteries (with 2-MHz flat-probes) will be performed; a dedicated software for recording of hemodynamic changes and discrimination of microembolic events will be used, too. Off-line, TCD alterations will be analysed and the potential correlation with phase, neuroradiological and clinical findings will be evaluated. The study could shed new light into pathophysiology of neurological complications after TAVI, helping to assess the most dangerous phases of the procedure and suggesting potential technical modifications to increase its safety.

Fibromuscular dysplasia (FMD), is a nonatherosclerotic, noninflammatory vascular disease commonly affecting the renal, extracranial carotid and vertebral arteries (VA). Etiology remains unknown. Clinical presentation is determined by distribution of arteries affected. Carotid artery involvement is associated with headache, TIA, stroke, pulsatile tinnitus, or may be asymptomatic. Treatment is being recommended in symptomatic cases. A 49-year-old Caucasian woman, was presented to our emergency department for transient phosphenes in right eye, followed by front-temporal headache. Cranial CT was normal, cervical duplex ultrasound and transcranial Doppler were suggestive of a tandem stenosis of the distal tract of left carotid internal artery (ICA) and middle cerebral artery. MR-angiography documented a fibrodisplasic aspect of the middle and distal ICA, hypoplasia of the right VA, an hypoplasic A1 segment of the left anterior cerebral artery and a small aneurysm (

The association of ischemic cerebrovascular lesions with livedo reticularis is known as Sneddon syndrome. It affects young subjects, primarily women, and its neurological manifestations are TIAs, ischemic stroke, progressive dementia and epileptic seizures. Its etiopathogenesis has still to be clarified. Some authors have associated it with an antiphospholipid antibody syndrome. Recently it has been assumed that a defect in blood coagulation may be involved in its pathogenesis. Here we report a case in which both an increase in coagulation factor VII activity and a deficiency in free protein S were documented.

Recently, it has been proposed that biological networks change not only the synaptic strengths of connection but also partially their internal topologies, according to either the received external stimuli or the pre-existent connection layouts. Following this idea, a method is presented to dynamically adapt the topology of neural networks with supervised learning, using only the information of the training set. The method eliminates connections from an initial fully connected network, concurrently with the learning algorithm. Several experimental results obtained with multilayered networks are also reported to demonstrate the capabilities of the proposed method. >

It is generally agreed that an adequate calcium intake is necessary for the maintenance of bone health and that calcium supplementation reduces the rate of bone loss in postmenopausal women. Mineral waters are calorie free, and some, with relatively high calcium levels, might be significant sources of calcium. We studied the effect of mineral water in 45 early postmenopausal women randomly assigned to receive a high-calcium (Ferrarelle, Italy) or a low-calcium mineral water. On the basis of the dietary regimen, women were divided in two clusters (A = 23 subjects, B = 22 subjects) significantly different only for calcium intake (CI) and for dietary consumption of calories (energy). At the end of the study period (13 +/- 1 months), bone mineral density at the distal radius showed a significant decrease (P < 0.001) only in cluster with low CI. The difference between the clusters was significant (P < 0.05). Furthermore, the cluster with high CI showed a significant (P < 0.05) reduction in osteocalcin serum levels after 3 months. This study provides further evidence to support the use of a high calcium mineral water as an effective prophylaxis against postmenopausal bone loss.

2517 Background: The main objective in the intent to “cure” chronic myeloid leukemia (CML) is to obtain complete cytogenetic (CCR) and molecular remission (MR). Interferon-A and Imatinib have only partially achieved this goal. An alternative approach to target the residual disease is specific immunotherapy. We associated Imatinib and immunogenic peptides derived form the p210 b3a2 fusion protein in patients on Imatinib with stable disease at cytogenetic level. Methods: Ten patients with CML in chronic phase on Imatinib for a median time of 15.5 months (range 12–24) were enrolled. All patients were on their +2nd line treatment being resistant or refractory to IFN-based previous therapies. They presented with an unchanged residual disease from a median of 10 months (range 6–18) consisting of bone marrow Ph+ metaphases ranging from 2–43%. Vaccination included GM-CSF pre treatment and administration of 500 mcg of 5 different length(8–25 aa)peptides mixture of restricted for HLA class I and class II molecules,...

An important problem in real-time DSP (digital signal-processing) systems with highly integrated components is the capability of automatic error detection and correction. The use of residue number arithmetic allows error detection and correction because of its unweighted nature. A single-error-correction procedure is proposed which is based on the use of redundant residue number systems (RRNS) and the base extension operation. The proposed method uses a small decision table and works in parallel mode; therefore it is suitable for high-speed VLSI circuit realization. A parallel architecture which realizes the method is also introduced. >

The possibility of using the multilayer perceptron (MLP) neural network for the processing of the evoked potentials (EPs) is analyzed. In this case, the process can be conceived as deterministic low amplitude signals (damped sine waves), corresponding to the brain's response to stimuli, embedded in strongly colored noise, the EEG background activity. Typical values of the signal-to-noise ratio are less than 0 dB. The network, used as a nonlinear filter, is trained using iteratively as the input signal one of a set of available EP ensembles and as the target signal another ensemble of the same set. Experimental results, both on synthetic and real data, show that the method provides good results with very few EP ensembles. Therefore, it allows a noteworthy reduction of the signal nonstationarity and the patient's annoyance

A fast and versatile method for classifying dust particles dispersed in the air is presented. The method uses images captured by a simple imaging system composed of a photographic sensor array and of an illuminating source. Such a device is exposed to free particulate deposition from the environment, and its accumulation is measured by observing the shadows of the particles the air casts onto the photographic sensor. Particles are detected and classified in order to measure their density and to analyse their composition. To this purpose, the contour paths of particle shadows are traced. Then, distinctive features of single particles, such as dimension and morphology, are extracted by looking at corresponding features of the sequence of local orientation changes of contours. Discrimination between dust and fibre particles is efficiently done using the varimax norm of these orientation changes. It is shown through field examples that such a technique is very well suited for quantitative and qualitative dust analysis in real environments.

Neural networks have been successfully applied to the equalization of digital communication channels. Decision feedback is a common technique to enhance the performance of linear equalizers. The two concepts can be effectively merged, generating a wide set of possible architectures. In this work several decision-feedback (DF) neural equalizers (DFNE) are compared with classical DF equalizers and Viterbi demodulators. In particular, it is shown that the choice of a cost functional based on Discriminative Learning (DL), coupled with a fast training paradigm, can provide neural equalizers that outperform the standard DF equalizer (DFE) at a practical signal to noise ratio (SNR). Resulting architectures are competitive with the Viterbi solution as for cost-performance aspects.

Management of air quality is an important task in many human activities. It is carried out mainly by installing ventilation and filtering facilities. In order to ensure efficiency, these systems must be designed after the knowledge of key environmental parameters, such as size and type of particles and fibres present in the air. In this paper, we propose a new method for the classification of dust particles and fibres based on a minimal set of geometric features extracted from binary images of dust elements, captured by a very cheap imaging system. The proposed technique is discussed and tested. Experimental results obtained by real- measured data are presented, showing satisfactory performance by using several well-known classifiers.

The feasibility of restricting the weight values to powers-of-two or sums of powers-of-two in multilayer neural networks is discussed. A learning procedure based on back-propagation to obtain a neural network with such weights is presented. This learning procedure requires full real arithmetic, and therefore must be performed offline. These neural networks do not require multipliers, and are well suited for high-speed and high-integration digital neural circuits. To show the effectiveness of the approach, tests on a pattern recognition problem are presented. >