Dense Temporal Sampling Research Articles

Strengthening of alpha synchronization is a neural correlate of cognitive transfer.

Cognitive training can lead to improvements in both task-specific strategies and general capacities, such as visuo-spatial working memory (VSWM). The latter emerge slowly and linearly throughout training, in contrast to strategy where changes typically occur within the first days of training. Changes in strategy and capacity have not been separated in prior neuroimaging studies. Here, we used a within-participants design with dense temporal sampling to capture the time dynamics of neural mechanisms associated with change in capacity. In four participants, neural activity was recorded with magnetoencephalography on seven occasions over two months of visuo-spatial working memory training. During scanning, the participants performed a trained visuo-spatial working memory task, a transfer task, and a control task. First, we extracted an individual visuo-spatial working memory-load-dependent synchronization network for each participant. Next, we identified linear changes over time in the network, congruent with the temporal dynamics of capacity change. Three out of four participants showed a gradual strengthening of alpha synchronization. Strengthening of the same connections was also found in the transfer task but not in the control task. This suggests that cognitive transfer occurs through slow, gradual strengthening of alpha synchronization between cortical regions that are vital for both the trained task and the transfer task.

An extremum-guided interpolation for sparsely sampled photoacoustic imaging

In photoacoustic (PA) reconstruction, spatial constraints or real-time system requirements often result to sparse PA sampling data. For sparse PA sensor data, the sparse spatial and dense temporal sampling often leads to poor signal continuity. To address the structural characteristics of sparse PA signals, a data interpolation algorithm based on extremum-guided interpolation is proposed. This algorithm is based on the continuity of the signal, and can complete the estimation of high sampling rate signals without complex mathematical calculations. PA signal data is interpolated and reconstructed, and the results are evaluated using image quality assessment methods. The simulation and experimental results show that the proposed method performs better than several typical algorithms, effectively restoring image details, suppressing the generation of artifacts and noise, and improving the quality of PA reconstruction under sparse sampling.

Learning HDR video reconstruction for dual-exposure sensors with temporally-alternating exposures

High-dynamic range (HDR) video reconstruction using conventional single-exposure sensors can be achieved by temporally alternating exposures. This, in turn, requires computing exposure alignment which is difficult to achieve due to the exposure differences that notoriously creates problems for moving content, in particular, in larger saturated and dis-occluded regions. An attractive alternative are dual-exposure sensors that capture, in a single-shot, differently exposed and spatially interleaved half-frames, so that they are perfectly spatially and temporally (up to varying motion blur) aligned by construction. In this work, we demonstrate that we successfully compensate for reduced spatial resolution and aliasing in such sensors, and we improve overall the quality and dynamic range of reconstructed HDR video with respect to single-exposure sensors for a given number of alternating exposures. Specifically, we consider low, mid, and high exposures, and we propose that the mid exposure is captured for every frame, and serves as a spatial and temporal reference. We capitalize here on neural networks for denoising, deblurring, and upsampling tasks, so that effectively we obtain two clean, sharp, and full-resolution exposures for every frame, which are then complemented by warping a missing third exposure. High-quality warping is achieved by learning optical flow that merges the individual flows found for each specific exposure. Such flow merging is instrumental in handling saturated/dis-occluded image regions, while dense temporal sampling of mid exposure improves motion quality reproduction between more sparsely sampled exposures. We demonstrate that by capturing only a limited amount of sensor-specific data and a novel use of histograms, instead of common parametric noise statistics, we are able to generate synthetic training data that lead to a better denoising and deblurring quality than can be achieved by existing state-of-the-art methods. As there is not enough high-quality HDR video available, we devise a method to learn from LDR video instead. Our approach compares favorably to several strong baselines, and can boost existing HDR image and video methods when they are re-trained on our data.

The effects of granulation and supergranulation on Earth-mass planet detectability in the habitable zone around F6-K4 stars

Context. The detectability of exoplanets and the determination of their projected mass in radial velocity are affected by stellar magnetic activity and photospheric dynamics. Among those processes, the effect of granulation, and even more so of supergranulation, has been shown to be significant in the solar case. The impact for other spectral types has not yet been characterised. Aims. Our study is aimed at quantifying the impact of these flows for other stars and estimating how such contributions affect their performance. Methods. We analysed a broad array of extended synthetic time series that model these processes to characterise the impact of these flows on exoplanet detection for main sequence stars with spectral types from F6 to K4. We focussed on Earth-mass planets orbiting within the habitable zone around those stars. We estimated the expected detection rates and detection limits, tested the tools that are typically applied to such observations, and performed blind tests. Results. We find that both granulation and supergranulation on these stars significantly affect planet mass characterisation in radial velocity when performing a follow-up of a transit detection: the uncertainties on these masses are sometimes below 20% for a 1 MEarth (for granulation alone or for low-mass stars), but they are much larger in other configurations (supergranulation, high-mass stars). For granulation and low levels of supergranulation, the detection rates are good for K and late G stars (if the number of points is large enough), but poor for more massive stars. The highest level of supergranulation leads to a very poor performance, even for K stars; this is both due to low detection rates and to high levels of false positives, even for a very dense temporal sampling over 10 yr. False positive levels estimated from standard false alarm probabilities sometimes significantly overestimate or underestimate the true level, depending on the number of points: it is, therefore, crucial to take this effect into account when analysing observations. Conclusions. We conclude that granulation and supergranulation significantly affect the performance of exoplanet detectability. Future works will focus on improving the following three aspects: decreasing the number of false positives, increasing detection rates, and improving the false alarm probability estimations from observations.

CNN SEMANTIC SEGMENTATION TO RETRIEVE PAST LAND COVER OUT OF HISTORICAL ORTHOIMAGES AND DSM: FIRST EXPERIMENTS

Abstract. Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes occurred since the mid 20th century. They provide a relatively dense temporal sampling of the territories with a very high spatial resolution. Thus, they offer time series data which can answer a large variety of long-term environmental monitoring studies. Besides, they are generally stereoscopic surveys, making it possible to derive 3D information (Digital Surface Models). In recent years, they have often been digitized, making them more suitable to be considered in automatic analyses processes. Some photogrammetric softwares make it possible to retrieve their geometry (pose and camera calibration) and to generate corresponding DSM and orthophotomosaic. Thus, archival aerial photogrammetric surveys appear as being a powerful remote sensing data source to study land use/cover evolution over the last century. However, several difficulties have to be faced to be able to use them in automatic analysis processes. Indeed, surveys available on a study area can exhibit very different characteristics: survey pattern, focal, spatial resolution, modality (panchromatic, colour, infrared…). Planimetric and altimetric accuracies of derived products strongly depend on these characteristics. Thus, analysis processes have to cope with these uncertainties. Another important gap states in the lack of training data. Deep learning methods and especially Convolutional Neural Networks (CNN) are at present the most efficient semantic segmentation methods as long as a sufficient training dataset is available. However, temporal gaps can be very important between existing available databases and archival data. In this study, two custom variants of simple yet effective U-net - Deconv-Net inspired DL architectures are developed to process ortho-image and DSM based information. They are then trained out of a groundtruth derived out of a recent database to process archival datasets.

Retrieval of land surface properties from an annual time series of Landsat TOA radiances during a drought episode using coupled radiative transfer models

The accurate retrieval of land surface vegetation properties under varying environmental conditions from time series of moderately high spatial resolution satellite observations is challenging. By coupling various Radiative Transfer (RT) models one can describe the soil, vegetation and atmosphere contributions in a “bottom-up” approach and, thereby, simulate top-of-atmosphere (TOA) spectral radiance data comparable to satellite-observed TOA radiances. This makes it possible to retrieve vegetation properties directly from TOA radiances rather than from atmospherically corrected top-of-canopy (TOC) reflectance data. The advantages of this approach are that a separate atmospheric correction of the satellite images is not necessary, and that the anisotropic surface reflection can also be taken into account effectively. In this study, we coupled various RT models, including the brightness – shape – moisture (BSM) reflectance model of the soil, the optical radiative transfer (RTMo) model of vegetation and the ‘MODerate resolution atmospheric TRANsmission’ (MODTRAN) model of the atmosphere, to simulate an annual time series of Landsat satellite TOA radiances observed during a drought episode in California Mediterranean grasslands in 2004. The inversion of this coupled system through an optimization technique against Landsat TOA radiances resulted in direct retrieval of vegetation properties. We accommodated the surface anisotropic reflection in our coupled modeling and also defined a novel anisotropy index to quantitatively express the importance of this phenomenon in satellite image analysis for the first time. Our study showed that the coupled use of RT models was able to accurately reproduce the time series of observed TOA radiances collected under varying soil moisture contents during an extended drought episode. The proposed coupling approach is useful for successful retrieval of vegetation properties from time series of satellite TOA radiance data to produce maps of land surface properties and to monitor vegetation properties variations in an operational straightforward way. The approach can also be easily adapted for conducting multi-sensor time series studies, creating a much denser temporal sampling than would be possible for separate single sensors.

TOWARD AUTOMATIC GEOREFERENCING OF ARCHIVAL AERIAL PHOTOGRAMMETRIC SURVEYS

Abstract. Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes over the past 100 years. They provide a relatively dense temporal sampling of the territories with very high spatial resolution. Such time series image analysis is a mandatory baseline for a large variety of long-term environmental monitoring studies. The current bottleneck for accurate comparison between epochs is their fine georeferencing step. No fully automatic method has been proposed yet and existing studies are rather limited in terms of area and number of dates. State-of-the art shows that the major challenge is the identification of ground references: cartographic coordinates and their position in the archival images. This task is manually performed, and extremely time-consuming. This paper proposes to use a photogrammetric approach, and states that the 3D information that can be computed is the key to full automation. Its original idea lies in a 2-step approach: (i) the computation of a coarse absolute image orientation; (ii) the use of the coarse Digital Surface Model (DSM) information for automatic absolute image orientation. It only relies on a recent orthoimage+DSM, used as master reference for all epochs. The coarse orthoimage, compared with such a reference, allows the identification of dense ground references and the coarse DSM provides their position in the archival images. Results on two areas and 5 dates show that this method is compatible with long and dense archival aerial image series. Satisfactory planimetric and altimetric accuracies are reported, with variations depending on the ground sampling distance of the images and the location of the Ground Control Points.

Compositional shifts in root-associated bacterial and archaeal microbiota track the plant life cycle in field-grown rice.

Bacterial communities associated with roots impact the health and nutrition of the host plant. The dynamics of these microbial assemblies over the plant life cycle are, however, not well understood. Here, we use dense temporal sampling of 1,510 samples from root spatial compartments to characterize the bacterial and archaeal components of the root-associated microbiota of field grown rice (Oryza sativa) over the course of 3 consecutive growing seasons, as well as 2 sites in diverse geographic regions. The root microbiota was found to be highly dynamic during the vegetative phase of plant growth and then stabilized compositionally for the remainder of the life cycle. Bacterial and archaeal taxa conserved between field sites were defined as predictive features of rice plant age by modeling using a random forest approach. The age-prediction models revealed that drought-stressed plants have developmentally immature microbiota compared to unstressed plants. Further, by using genotypes with varying developmental rates, we show that shifts in the microbiome are correlated with rates of developmental transitions rather than age alone, such that different microbiota compositions reflect juvenile and adult life stages. These results suggest a model for successional dynamics of the root-associated microbiota over the plant life cycle.

Temporal probabilistic modeling of bacterial compositions derived from 16S rRNA sequencing.

MotivationThe number of microbial and metagenomic studies has increased drastically due to advancements in next-generation sequencing-based measurement techniques. Statistical analysis and the validity of conclusions drawn from (time series) 16S rRNA and other metagenomic sequencing data is hampered by the presence of significant amount of noise and missing data (sampling zeros). Accounting uncertainty in microbiome data is often challenging due to the difficulty of obtaining biological replicates. Additionally, the compositional nature of current amplicon and metagenomic data differs from many other biological data types adding another challenge to the data analysis.ResultsTo address these challenges in human microbiome research, we introduce a novel probabilistic approach to explicitly model overdispersion and sampling zeros by considering the temporal correlation between nearby time points using Gaussian Processes. The proposed Temporal Gaussian Process Model for Compositional Data Analysis (TGP-CODA) shows superior modeling performance compared to commonly used Dirichlet-multinomial, multinomial and non-parametric regression models on real and synthetic data. We demonstrate that the nonreplicative nature of human gut microbiota studies can be partially overcome by our method with proper experimental design of dense temporal sampling. We also show that different modeling approaches have a strong impact on ecological interpretation of the data, such as stationarity, persistence and environmental noise models.Availability and implementationA Stan implementation of the proposed method is available under MIT license at https://github.com/tare/GPMicrobiome.Supplementary information Supplementary data are available at Bioinformatics online.

Quantitative ultrashort echo time imaging for assessment of massive iron overload at 1.5 and 3 Tesla.

Hepatic iron content (HIC) quantification via transverse relaxation rate (R2*)-MRI using multi-gradient echo (mGRE) imaging is compromised toward high HIC or at higher fields due to the rapid signal decay. Our study aims at presenting an optimized 2D ultrashort echo time (UTE) sequence for R2* quantification to overcome these limitations. Two-dimensional UTE imaging was realized via half-pulse excitation and radial center-out sampling. The sequence includes chemically selective saturation pulses to reduce streaking artifacts from subcutaneous fat, and spatial saturation (sSAT) bands to suppress out-of-slice signals. The sequence employs interleaved multi-echo readout trains to achieve dense temporal sampling of rapid signal decays. Evaluation was done at 1.5 Tesla (T) and 3T in phantoms, and clinical applicability was demonstrated in five patients with biopsy-confirmed massively high HIC levels (>25 mg Fe/g dry weight liver tissue). In phantoms, the sSAT pulses were found to remove out-of-slice contamination, and R2* results were in excellent agreement to reference mGRE R2* results (slope of linear regression: 1.02/1.00 for 1.5/3T). UTE-based R2* quantification in patients with massive iron overload proved successful at both field strengths and was consistent with biopsy HIC values. The UTE sequence provides a means to measure R2* in patients with massive iron overload, both at 1.5T and 3T. Magn Reson Med 78:1839-1851, 2017. © 2017 Wiley Periodicals, Inc.

The Landscape of Dynamic Genetic Changes in Ibrutinib-Treated CLL

Cancer evolution poses a significant challenge to the ability of therapy to eliminate malignant cells. In chronic lymphocytic leukemia (CLL), clonal evolution was reported as a key feature of disease relapse after standard chemoimmunotherapy and after targeted therapy with the BTK inhibitor ibrutinib. To study potential selection of pre-existing sub-clones, we analyzed changes in clonal architecture at early time points in patients receiving ibrutinib therapy. We therefore performed in-depth longitudinal analysis of CLL samples collected from 59 patients treated with ibrutinib alone (n=44) or in combination with rituximab (n=15).Across this cohort, we performed whole-exome sequencing (WES) on a median of 3 samples per patient with median coverage of 105x (range 50-225x). Pretreatment samples were available for all patients, with post-treatment samples at 1 and 6 months for more than 85% of patients. Additional samples were available at 2, 3, or 12 months for 22 patients. The median age of the cohort was 65 (range 33-85) years. Thirty-one patients (53%) had del(17p) by FISH, 37 patients (63%) had IGHV unmutated CLL and 32 patients (54%) had relapsed or refractory disease. We observed a median of 56 (range 17-201) somatic silent and non-silent single nucleotide variants (SNVs) and insertions and deletions (Indels) per patient. The number of SNVs and indels did not differ between patients receiving ibrutinib alone or with rituximab (Wilcoxon, P=0.2). Thirteen patients had somatic mutations of BCR pathway genes prior to treatment, including one with CARD11-L251P, previously reported to confer resistance to ibrutinib in diffuse large B-cell lymphoma.Comparing pre-treatment and the latest samples within the first year (range 56-357 days, median 168), 26 of 59 (44%) CLLs showed significant changes in cancer cell fraction (CCF) over time, defined as a shift > 5% CCF of the clone with the greatest change (BH-FDR <0.1). These included 11 CLLs with a clone that significantly increased in size, 9 with a clone that significantly decreased in size, and 6 with significant reciprocal rises and falls of different clones consistent with a branched evolutionary pattern. The median CCF change during the observation period was 24% (range 7-89%). Thus, even within a short period of treatment exposure, dense temporal sampling of CLLs reveals that ibrutinib therapy commonly leads to clonal shifts.The likelihood for clonal shifts was not impacted by whether patients were treated with ibrutinib alone or with rituximab (45% vs. 40%, P=0.77), nor by exposure to prior therapy (47% vs. 41% with and without prior therapy, P=0.79). We observed trends towards higher likelihood of early clonal evolution in CLLs with unmutated IGHV status, with TP53 somatic aberrations, and those with a pre-treatment subclonal driver (subclones containing CHD2, BIRC3, SF3B1, MAP2K1, CARD11, XPO1, TP53, PTPN11, KRAS, HIST1H1E, ATM). Finally, patients with a decrease in ALC at 6 months compared to pre-treatment were more likely to exhibit significant CCF shifts (P=0.01). This last observation suggests that effective treatment activity and resulting therapeutic bottlenecks indeed contribute to the shift in clonal proportion within the first year of ibrutinib therapy.Relapse samples were available for 10 CLL cases, 6 of whom showed early clonal shifts during the first year of therapy. These included 2 cases of transformation to prolymphocytic leukemia (PLL), 2 with Richter transformation (RT) and 6 cases of progressive CLL disease. All relapse cases were clonally related to the pre-treatment samples. Mutations in BTK or PLCg2 were found in 5 cases by either WES or deep targeted sequencing of known resistance hotspots (both PLL, one RT, two progressive CLL). The remaining 5 cases showed marked clonal shifts suggestive of selection of other resistant genotypes.Altogether, targeted therapy with ibrutinib is associated with overall clonal stability in more than half of CLLs over the first year of treatment. Nevertheless, in many patients, clonal selection occurs even during the first months of therapy and may provide clues regarding the future evolutionary and clinical outcome of the disease. DisclosuresFarooqui:Merck: Employment. Burger:Pharmacyclics, LLC, an AbbVie Company: Research Funding; Gilead: Research Funding; Janssen: Consultancy, Other: Travel, Accommodations, Expenses; Roche: Other: Travel, Accommodations, Expenses; Portola: Consultancy. Wiestner:Acerta Pharma: Research Funding; Pharmacyclics: Research Funding. Wu:Neon Therapeutics: Membership on an entity's Board of Directors or advisory committees, Other: Co-founder.

A comprehensive transcriptional map of primate brain development.

The transcriptional underpinnings of brain development remain poorly understood, particularly in humans and closely related non-human primates. We describe a high resolution transcriptional atlas of rhesus monkey brain development that combines dense temporal sampling of prenatal and postnatal periods with fine anatomical parcellation of cortical and subcortical regions associated with human neuropsychiatric disease. Gene expression changes more rapidly before birth, both in progenitor cells and maturing neurons, and cortical layers and areas acquire adult-like molecular profiles surprisingly late postnatally. Disparate cell populations exhibit distinct developmental timing but also unexpected synchrony of processes underlying neural circuit construction including cell projection and adhesion. Candidate risk genes for neurodevelopmental disorders including primary microcephaly, autism spectrum disorder, intellectual disability, and schizophrenia show disease-specific spatiotemporal enrichment within developing neocortex. Human developmental expression trajectories are more similar to monkey than rodent, and approximately 9% of genes show human-specific regulation with evidence for prolonged maturation or neoteny.

Temporal patterns of gene expression during calyx of held development

Relating changes in gene expression to discrete developmental events remains an elusive challenge in neuroscience, in part because most neural territories are comprised of multiple cell types that mature over extended periods of time. The medial nucleus of the trapezoid body (MNTB) is an attractive vertebrate model system that contains a nearly homogeneous population of neurons, which are innervated by large glutamatergic nerve terminals called calyces of Held (CH). Key steps in maturation of CHs and MNTB neurons, including CH growth and competition, occur very quickly for most cells between postnatal days (P)2 and P6. Therefore, we characterized genome-wide changes in this system, with dense temporal sampling during the first postnatal week. We identified 541 genes whose expression changed significantly between P0-6 and clustered them into eight groups based on temporal expression profiles. Candidate genes from each of the eight profile groups were validated in separate samples by qPCR. Our tissue sample permitted comparison of known glial and neuronal transcripts and revealed that monotonically increasing or decreasing expression profiles tended to be associated with glia and neurons, respectively. Gene ontology revealed enrichment of genes involved in axon pathfinding, cell differentiation, cell adhesion and extracellular matrix. The latter category included elements of perineuronal nets, a prominent feature of MNTB neurons that is morphologically distinct by P6, when CH growth and competition are resolved onto nearly all MNTB neurons. These results provide a genetic framework for investigation of general mechanisms responsible for nerve terminal growth and maturation.

Evaluation of sampling density on the accuracy of aortic pulse wave velocity from velocity‐encoded MRI in patients with Marfan syndrome

To evaluate the effect of spatial (ie, number of sampling locations along the aorta) and temporal sampling density on aortic pulse wave velocity (PWV) assessment from velocity-encoded MRI in patients with Marfan syndrome (MFS). Twenty-three MFS patients (12 men, mean age 36 ± 14 years) were included. Three PWV-methods were evaluated: 1) reference PWV(i.p.) from in-plane velocity-encoded MRI with dense temporal and spatial sampling; 2) conventional PWV(t.p.) from through-plane velocity-encoded MRI with dense temporal but sparse spatial sampling at three aortic locations; 3) EPI-accelerated PWV(t.p.) with sparse temporal but improved spatial sampling at five aortic locations with acceleration by echo-planar imaging (EPI). Despite inferior temporal resolution, EPI-accelerated PWV(t.p.) showed stronger correlation (r = 0.92 vs. r = 0.65, P = 0.03) with reference PWV(i.p.) in the total aorta, with less error (8% vs. 16%) and variation (11% vs. 27%) as compared to conventional PWV(t.p.) . In the aortic arch, correlation was comparable for both EPI-accelerated and conventional PWV(t.p.) with reference PWV(i.p.) (r = 0.66 vs. r = 0.67, P = 0.46), albeit 92% scan-time reduction by EPI-acceleration. Improving spatial sampling density by adding two acquisition planes along the aorta results in more accurate PWV assessment, even when temporal resolution decreases. For regional PWV assessment in the aortic arch, EPI-accelerated and conventional PWV assessment are comparably accurate. Scan-time reduction makes EPI-accelerated PWV assessment the preferred method of choice.

Fast characterization of aluminum plates with TV-holography measurements of the frequency spectrum of multimode, quasi- monochromatic lamb waves

We introduce a novel approach for measuring the frequency spectrum of Lamb waves and, subsequently, for obtaining the thickness and the bulk wave velocities of isotropic, homogeneous plates. It is based on Fourier transforming a set of spatial and temporal samples of the acoustic displacement but, in contrast to the traditional approach that employs dense temporal sampling and a reduced set of spatial sampling locations, our data set is a sequence of 2-D high-resolution maps of the instantaneous out-of-plane displacement obtained with TV holography. We have devised three variants to obtain a set of points of the wavenumber-frequency space, based, respectively, on the spatial (1-D or 2-D) and on the spatio-temporal (3-D) Fourier transforms. The whole process to obtain these points can be easily automated and substantial time savings can be achieved, compared with other full-field techniques that require human intervention or with pointwise scanned probes. Experimental demonstration of the three variants with quasimonochromatic multimode Lamb waves in aluminum plates is presented. The characteristic parameters of the plates are calculated by fitting the theoretical model to the experimental points of the frequency spectrum. The analysis of the uncertainties shows that the accuracy of the method is only slightly lower than the accuracy of a previously reported method based on measuring the wavelength of single-modes, for which the data acquisition procedure is much slower.

High-resolution millimeter-interferometer observations of the solar chromosphere

The use of millimeter-interferometer data for the study of chromospheric structure and dynamics is tested using 85 GHz observations with the 10-element Berkeley-Illinois-Maryland Array (BIMA). Interferometer data have the advantage over single-dish data that they allow both high spatial resolution and dense temporal sampling simultaneously. However, snapshot imaging of the quiet solar atmosphere with a small number of dishes is challenging. We demonstrate that techniques are available to carry out this task successfully using maximum entropy deconvolution from a default image constructed from the entire observation: one of our results is that the solar chromosphere at millimeter wavelengths exhibits features that are long-lasting and the map of the entire observation is significant provided that atmospheric phase errors do not prevent deconvolution. We compare observations of quiet Sun, active region and coronal hole targets. The interferometer is not sensitive to the disk emission and the positivity constraint of the maximum entropy algorithm used forces the zero level in the images to be at the temperature of the coolest feature in each field. The brightest features in the images are typically 1000–1500 K above the zero level, with a snapshot noise level of order 100 K. We use extensive tests to determine whether oscillation power can be recovered from sequences of snapshot images and show that individual sources can be down to quite weak levels at locations in the image where significant flux is present; oscillation power located in cool regions of the image is not well recovered due to the deconvolution method used and may be redistributed to brighter regions of the millimeter image. We then investigate whether the data do show oscillation power using uninterrupted 30-min scans of the target regions. Intensity oscillations with significant power in the frequency range 1.5–8.0 mHz are found in the quiet-Sun and active region targets. For the quiet-Sun region we compare the oscillation properties of network boundaries and cell interiors (internetwork) in the spatially-resolved time series. In agreement with investigations at other wavelengths, in the millimeter data the power in the network tends to be at periods of 5 min and longer while power in the internetwork is present also at shorter (3-min) periods.

Assessing the dipolar signal in stacked paleointensity records using a statistical error model and geodynamo simulations

Stacks of globally distributed relative paleointensity records from sediment cores are used to study temporal variations in the strength of the geomagnetic dipole. We assess the intrinsic accuracy and resolution of such stacks, which may be limited by errors in paleointensity, non-dipole field contributions, and the age scales assigned to each sediment core. Our approach employs two types of simulations. Numerical geodynamo models generate accurate predictions of time series of magnetic variations anywhere in the world. The predicted variations are then degraded using an appropriate statistical model to simulate expected age and paleointensity errors. A series of experiments identify the major contributors to error and loss of resolution in the resulting stacks. The statistical model simulates rock magnetic and measurement errors in paleointensity, and age errors due to finite sampling and approximations inherent in interpolation, incomplete or inaccurate tie point information, and sedimentation rate variations. Data sampling and interpolation to a designated age scale cause substantial decorrelation, and control the maximum level of agreement attainable between completely accurate records. The particular method of interpolation appears to have little effect on the coherence between accurate records, but denser tie point data improve the agreement. Age errors decorrelate geomagnetic signals, usually at shorter periods, although they can destroy coherence over a broad range of periods. The poor correlation between neighboring paleomagnetic records often observed in real data can be accounted for by age errors of moderate magnitude. In a global dataset of 20 records, modeled after the SINT800 compilation and spanning 300 kyr, our results show that dipole variations with periods longer than about 20 kyr can be recovered by the stacking process. Reasonable contributions to error in the paleointensity itself have a modest influence on the result, as do non-dipole field contributions whose effect is minor at periods longer than 10 kyr. Modest errors in the ages of tie points probably account for most of the degradation in geomagnetic signal. Stacked sedimentary paleomagnetic records can be improved by denser temporal sampling and careful selection of independent high-quality tie points.

Distribution and Evolution of CH 4, N 2, and CO Ices on Pluto's Surface: 1995 to 1998

We present new near-infrared spectra of the planet Pluto obtained at Lowell Observatory on 83 nights during 1995–1998. The dense temporal sampling of our observations enables us to measure with unprecedented detail cyclical changes in the depths of methane, carbon monoxide, and nitrogen ice absorption bands, modulated by Pluto's diurnal rotation. We show that CO, N 2, and weak CH 4 absorption band depths exhibit rotational patterns very different from those of Pluto's visible lightcurve, unlike the strong CH 4 absorption bands which are closely correlated with the visible lightcurve. Our observations are used to constrain the longitudinal distributions of the three ice species on Pluto's surface. The data also reveal a subtle, longer term strengthening of Pluto's strong near-infrared CH 4 bands, which is used to constrain the latitudinal distribution of CH 4 ice. We simulate the observed diurnal and seasonal spectral and photometric behavior of Pluto by means of model distributions of three terrain types. We see no evidence for changes in the distributions of Pluto's surface ices during the 1995–1998 interval.

GPO moving forward in the electronic age: A strategic outlook

The spontaneous dynamics of the brain modulate its function from moment to moment, shaping neural computation and cognition. Functional MRI (fMRI), while classically used as a tool for spatial localization, is increasingly being used to identify the temporal dynamics of brain activity. fMRI analyses focused on the temporal domain have revealed important new information about the dynamics underlying states such as arousal, attention, and sleep. Dense temporal sampling – either by using fast fMRI acquisition, or multiple repeated scan sessions within individuals – can further enrich the information present in these studies. This review focuses on recent developments in using fMRI to identify dynamics across brain states, particularly vigilance and sleep states, and the potential for highly temporally sampled fMRI to answer these questions.

On generating altimetric sea level time series from crossover differences

Abstract The essential features in generating altimetric sea level time series from crossover differences (as originally proposed by Fu and Chelton, and followed by several other adaptations) are reviewed here. New results are also presented in an effort to answer questions raised in the review. All procedures involve explicit or implicit spatial and temporal smoothing, which are necessary for error reduction and for ensuring reasonably dense temporal sampling rates. One crucial step in the procedures involves the designation of a spatial sampling box, where a larger box results in better error reduction and denser temporal sampling, but may cause excessive signal attenuation. These and various other issues are discussed here by comparing the island tide gauge data to GEOSAT (first 17 months, the nonrepeat era) sea level time series in the tropical Pacific. It is found that the optimal box size varies from island to island because of the geographical dependence of the error as well as the signal. Furtherm...