Oxygen and carbon isotope composition of Globoquadrina venezuelana and Oridorsalis umbonatus at DSDP Leg 85 Holes

Oxygen and carbon isotope stratigraphies are given for the planktonic foraminifer Globoquadrina venezuelana (a deep-dwelling species) at three DSDP sites located along a north-south transect at approximately 133°W across the Pacific equatorial high-productivity zone. The records obtained at Sites 573 and 574 encompass the lower Miocene. At Site 575 the record includes the middle Miocene and extends into the lowermost lower Miocene. The time resolution of the planktonic foraminifer isotope record varies from 50,000 to 500,000 yr. The benthic foraminifer Oridorsalis umbonatus was analyzed for isotope composition at a few levels of Site 575. Isotope stratigraphies for all three sites are compared with carbonate, foraminifer preservation, and grain size records. We identified a number of chemostratigraphic signals that appear to be synchronous with previously recognized signals in the western equatorial Pacific and the tropical Indian Ocean, and thus provide useful tools for chronostratigraphic correlations. The sedimentary sequence at Site 573 is incomplete and condensed, whereas the sequences from Sites 574 and 575 together provide a complete lower Miocene record. The expanded nature of this record, which was recovered with minimum disturbance and provides excellent calcareous and siliceous biostratigraphic control, offers a unique opportunity to determine the precise timing of early Miocene events. Paleomagnetic data from the hydraulic piston cores at Site 575 for the first time allow late early Miocene paleoceanographic events to be tied directly to the paleomagnetic time scale. The multiple-signal stratigraphies provide clues for paleoceanographic reconstruction during the period of preconditioning before the major middle Miocene cooling. In the lowermost lower Miocene there is a pronounced shift toward greater d13C values (by -1%) within magnetic Chron 16 (between approximately 17.5 and 16.5 Ma). The "Chron 16 Carbon Shift" coincides with the cessation of an early Miocene warming trend visible in the d18O signals. Values of d13C remain high until approximately 15 Ma, then decrease toward initial (early Miocene) values near 13.5 Ma. The broad lower to middle Miocene d13C maximum appears to correlate with the deposition of organic-carbon-rich sediments around the margin of the northern Pacific in the Monterey Formation of California and its lateral equivalents. The sediments rimming the Pacific were probably deposited under coastal upwelling conditions that may have resulted from the development of a strong permanent thermocline. Deposition in the upwelling areas occurred partly under anaerobic conditions, which led to the excess extraction of organic carbon from the ocean. The timing of the middle Miocene cooling, which began after the Chron 16 Carbon Shift, suggests that the extraction of organic carbon preconditioned the ocean-atmosphere system for subsequent cooling. A major carbonate dissolution event in the late early Miocene, starting at approximately 18.7 Ma, is associated with the enrichment in 13C. The maximum dissolution is coeval with the Chron 16 Carbon Shift. It corresponds to a prominent acoustic horizon that can be traced throughout the equatorial Pacific.

Molecular basis for CD40 signaling mediated by TRAF3

Tumor necrosis factor receptors (TNFR) are single transmembrane-spanning glycoproteins that bind cytokines and trigger multiple signal transduction pathways. Many of these TNFRs rely on interactions with TRAF proteins that bind to the intracellular domain of the receptors. CD40 is a member of the TNFR family that binds to several different TRAF proteins. We have determined the crystal structure of a 20-residue fragment from the cytoplasmic domain of CD40 in complex with the TRAF domain of TRAF3. The CD40 fragment binds as a hairpin loop across the surface of the TRAF domain. Residues shown by mutagenesis and deletion analysis to be critical for TRAF3 binding are involved either in direct contact with TRAF3 or in intramolecular interactions that stabilize the hairpin. Comparison of the interactions of CD40 with TRAF3 vs. TRAF2 suggests that CD40 may assume different conformations when bound to different TRAF family members. This molecular adaptation may influence binding affinity and specific cellular triggers.

p300 is a component of an estrogen receptor coactivator complex.

The estrogen receptor (ER) is a ligand-dependent transcription factor that regulates expression of target genes in response to estrogen in concert with other cellular signaling pathways. This suggests that the mechanism by which ER transmits an activating signal to the general transcription machinery may include factors that integrate these diverse signals. We have previously characterized the estrogen receptor-associated protein, ERAP160, as a factor that complexes with ER in an agonist-dependent manner. We have now found that the transcriptional coactivator p300 associates with agonist bound ER and augments ligand-dependent activation by ER. Our studies show that an ER coactivator complex involves a direct hormone-dependent interaction between ER and ERAP160, resulting in the recruitment of p300. In addition, antibodies directed against the cloned steroid receptor coactivator 1 (SRC1) recognize ERAP160. The known role of p300 in multiple signal transduction pathways, including those involving the second messenger cAMP, suggests p300 functions as a point of integration between ER and these other pathways.

Quantitative neuropathology:data collection and statistical analysis

The use of quantitative methods has become increasingly important in the study of neuropathology and especially in neurodegenerative disease. Disorders such as Alzheimer's disease (AD) and the frontotemporal dementias (FTD) are characterized by the formation of discrete, microscopic, pathological lesions which play an important role in pathological diagnosis. This chapter reviews the advantages and limitations of the different methods of quantifying pathological lesions in histological sections including estimates of density, frequency, coverage, and the use of semi-quantitative scores. The sampling strategies by which these quantitative measures can be obtained from histological sections, including plot or quadrat sampling, transect sampling, and point-quarter sampling, are described. In addition, data analysis methods commonly used to analysis quantitative data in neuropathology, including analysis of variance (ANOVA), polynomial curve fitting, multiple regression, classification trees, and principal components analysis (PCA), are discussed. These methods are illustrated with reference to quantitative studies of a variety of neurodegenerative disorders.

Multi-wavelength regeneration of phase encoded signals based on phase sensitive amplifiers

Future high capacity optical links will have to make use of frequent signal regeneration to enable long distance transmission. In this respect, the role of all-optical signal processing becomes increasingly important because of its potential to mitigate signal impairments at low cost and power consumption. More substantial benefits are expected if regeneration is achieved simultaneously on a multiple signal band. Until recently, this had been achieved only for on-off keying modulation formats. However, as in future transmission links the information will be encoded also in the phase for enhancing the spectral efficiency, novel subsystem concepts will be needed for multichannel processing of such advanced signal formats. In this paper we show that phase sensitive amplifiers can be an ideal technology platform for developing such regenerators and we discuss our recent demonstration of the first multi-channel regenerator for phase encoded signals.

Multi-wavelength regeneration of phase encoded signals based on phase sensitive amplifiers

Future high capacity optical links will have to make use of frequent signal regeneration to enable long distance transmission. In this respect, the role of all-optical signal processing becomes increasingly important because of its potential to mitigate signal impairments at low cost and power consumption. More substantial benefits are expected if regeneration is achieved simultaneously on a multiple signal band. Until recently, this had been achieved only for on-off keying modulation formats. However, as in future transmission links the information will be encoded also in the phase for enhancing the spectral efficiency, novel subsystem concepts will be needed for multichannel processing of such advanced signal formats. In this paper we show that phase sensitive amplifiers can be an ideal technology platform for developing such regenerators and we discuss our recent demonstration of the first multi-channel regenerator for phase encoded signals.

Parkinsonian tremor identification with multiple local field potential feature classification

This paper explores the development of multi-feature classification techniques used to identify tremor-related characteristics in the Parkinsonian patient. Local field potentials were recorded from the subthalamic nucleus and the globus pallidus internus of eight Parkinsonian patients through the implanted electrodes of a Deep brain stimulation (DBS) device prior to device internalization. A range of signal processing techniques were evaluated with respect to their tremor detection capability and used as inputs in a multi-feature neural network classifier to identify the activity of Parkinsonian tremor. The results of this study show that a trained multi-feature neural network is able, under certain conditions, to achieve excellent detection accuracy on patients unseen during training. Overall the tremor detection accuracy was mixed, although an accuracy of over 86% was achieved in four out of the eight patients.

3D MPRAGE improves classification of cortical lesions in multiple sclerosis.

BACKGROUND: Gray matter lesions are known to be common in multiple sclerosis (MS) and are suspected to play an important role in disease progression and clinical disability. A combination of magnetic resonance imaging (MRI) techniques, double-inversion recovery (DIR), and phase-sensitive inversion recovery (PSIR), has been used for detection and classification of cortical lesions. This study shows that high-resolution three-dimensional (3D) magnetization-prepared rapid acquisition with gradient echo (MPRAGE) improves the classification of cortical lesions by allowing more accurate anatomic localization of lesion morphology. METHODS: 11 patients with MS with previously identified cortical lesions were scanned using DIR, PSIR, and 3D MPRAGE. Lesions were identified on DIR and PSIR and classified as purely intracortical or mixed. MPRAGE images were then examined, and lesions were re-classified based on the new information. RESULTS: The high signal-to-noise ratio, fine anatomic detail, and clear gray-white matter tissue contrast seen in the MPRAGE images provided superior delineation of lesion borders and surrounding gray-white matter junction, improving classification accuracy. 119 lesions were identified as either intracortical or mixed on DIR/PSIR. In 89 cases, MPRAGE confirmed the classification by DIR/PSIR. In 30 cases, MPRAGE overturned the original classification. CONCLUSION: Improved classification of cortical lesions was realized by inclusion of high-spatial resolution 3D MPRAGE. This sequence provides unique detail on lesion morphology that is necessary for accurate classification.

Void fraction measurement and signal analysis from multiple-electrode impedance sensors

Void fraction sensors are important instruments not only for monitoring two-phase flow, but for furnishing an important parameter for obtaining flow map pattern and two-phase flow heat transfer coefficient as well. This work presents the experimental results obtained with the analysis of two axially spaced multiple-electrode impedance sensors tested in an upward air-water two-phase flow in a vertical tube for void fraction measurements. An electronic circuit was developed for signal generation and post-treatment of each sensor signal. By phase shifting the electrodes supplying the signal, it was possible to establish a rotating electric field sweeping across the test section. The fundamental principle of using a multiple-electrode configuration is based on reducing signal sensitivity to the non-uniform cross-section void fraction distribution problem. Static calibration curves were obtained for both sensors, and dynamic signal analyses for bubbly, slug, and turbulent churn flows were carried out. Flow parameters such as Taylor bubble velocity and length were obtained by using cross-correlation techniques. As an application of the void fraction tested, vertical flow pattern identification could be established by using the probability density function technique for void fractions ranging from 0% to nearly 70%.

Paper 5: Surveillance of multiple congenital anomalies: implementation of a computer algorithm in European registers for classification of cases.

BACKGROUND: Surveillance of multiple congenital anomalies is considered to be more sensitive for the detection of new teratogens than surveillance of all or isolated congenital anomalies. Current literature proposes the manual review of all cases for classification into isolated or multiple congenital anomalies. METHODS: Multiple anomalies were defined as two or more major congenital anomalies, excluding sequences and syndromes. A computer algorithm for classification of major congenital anomaly cases in the EUROCAT database according to International Classification of Diseases (ICD)v10 codes was programmed, further developed, and implemented for 1 year's data (2004) from 25 registries. The group of cases classified with potential multiple congenital anomalies were manually reviewed by three geneticists to reach a final agreement of classification as "multiple congenital anomaly" cases. RESULTS: A total of 17,733 cases with major congenital anomalies were reported giving an overall prevalence of major congenital anomalies at 2.17%. The computer algorithm classified 10.5% of all cases as "potentially multiple congenital anomalies". After manual review of these cases, 7% were agreed to have true multiple congenital anomalies. Furthermore, the algorithm classified 15% of all cases as having chromosomal anomalies, 2% as monogenic syndromes, and 76% as isolated congenital anomalies. The proportion of multiple anomalies varies by congenital anomaly subgroup with up to 35% of cases with bilateral renal agenesis. CONCLUSIONS: The implementation of the EUROCAT computer algorithm is a feasible, efficient, and transparent way to improve classification of congenital anomalies for surveillance and research.

Sparse Representations of Multiple Signals

We discuss the problem of finding sparse representations of a class of signals. We formalize the problem and prove it is NP-complete both in the case of a single signal and that of multiple ones. Next we develop a simple approximation method to the problem and we show experimental results using artificially generated signals. Furthermore,we use our approximation method to find sparse representations of classes of real signals, specifically of images of pedestrians. We discuss the relation between our formulation of the sparsity problem and the problem of finding representations of objects that are compact and appropriate for detection and classification.

Application of complex extreme learning machine to multiclass classification problems with high dimensionality: a THz spectra classification problem

We extend extreme learning machine (ELM) classifiers to complex Reproducing Kernel Hilbert Spaces (RKHS) where the input/output variables as well as the optimization variables are complex-valued. A new family of classifiers, called complex-valued ELM (CELM) suitable for complex-valued multiple-input–multiple-output processing is introduced. In the proposed method, the associated Lagrangian is computed using induced RKHS kernels, adopting a Wirtinger calculus approach formulated as a constrained optimization problem similarly to the conventional ELM classifier formulation. When training the CELM, the Karush–Khun–Tuker (KKT) theorem is used to solve the dual optimization problem that consists of satisfying simultaneously smallest training error as well as smallest norm of output weights criteria. The proposed formulation also addresses aspects of quaternary classification within a Clifford algebra context. For 2D complex-valued inputs, user-defined complex-coupled hyper-planes divide the classifier input space into four partitions. For 3D complex-valued inputs, the formulation generates three pairs of complex-coupled hyper-planes through orthogonal projections. The six hyper-planes then divide the 3D space into eight partitions. It is shown that the CELM problem formulation is equivalent to solving six real-valued ELM tasks, which are induced by projecting the chosen complex kernel across the different user-defined coordinate planes. A classification example of powdered samples on the basis of their terahertz spectral signatures is used to demonstrate the advantages of the CELM classifiers compared to their SVM counterparts. The proposed classifiers retain the advantages of their ELM counterparts, in that they can perform multiclass classification with lower computational complexity than SVM classifiers. Furthermore, because of their ability to perform classification tasks fast, the proposed formulations are of interest to real-time applications.