Toward explaining the Holocene carbon dioxide and carbon isotope records: Results from transient ocean carbon cycle-climate simulations

[1] The Bern3D model was applied to quantify the mechanisms of carbon cycle changes during the Holocene (last 11,000 years). We rely on scenarios from the literature to prescribe the evolution of shallow water carbonate deposition and of land carbon inventory changes over the glacial termination (18,000 to 11,000 years ago) and the Holocene and modify these scenarios within uncertainties. Model results are consistent with Holocene records of atmospheric CO2 and δ13C as well as the spatiotemporal evolution of δ13C and carbonate ion concentration in the deep sea. Deposition of shallow water carbonate, carbonate compensation of land uptake during the glacial termination, land carbon uptake and release during the Holocene, and the response of the ocean-sediment system to marine changes during the termination contribute roughly equally to the reconstructed late Holocene pCO2 rise of 20 ppmv. The 5 ppmv early Holocene pCO2 decrease reflects terrestrial uptake largely compensated by carbonate deposition and ocean sediment responses. Additional small contributions arise from Holocene changes in sea surface temperature, ocean circulation, and export productivity. The Holocene pCO2 variations result from the subtle balance of forcings and processes acting on different timescales and partly in opposite direction as well as from memory effects associated with changes occurring during the termination. Different interglacial periods with different forcing histories are thus expected to yield different pCO2 evolutions as documented by ice cores.

Deficiency in parvalbumin, but not in calbindin D-28k upregulates mitochondrial volume and decreases smooth endoplasmic reticulum surface selectively in a peripheral, subplasmalemmal region in the soma of Purkinje cells

The Ca(2+)-binding proteins parvalbumin (PV) and calbindin D-28k (CB) are key players in the intracellular Ca(2+)-buffering in specific cells including neurons and have profound effects on spatiotemporal aspects of Ca(2+) transients. The previously observed increase in mitochondrial volume density in fast-twitch muscle of PV-/- mice is viewed as a specific compensation mechanism to maintain Ca(2+) homeostasis. Since cerebellar Purkinje cells (PC) are characterized by high expression levels of the Ca(2+) buffers PV and CB, the question was raised, whether homeostatic mechanisms are induced in PC lacking these buffers. Mitochondrial volume density, i.e. relative mitochondrial mass was increased by 40% in the soma of PV-/- PC. Upregulation of mitochondrial volume density was not homogenous throughout the soma, but was selectively restricted to a peripheral region of 1.5 microm width underneath the plasma membrane. Accompanied was a decreased surface of subplasmalemmal smooth endoplasmic reticulum (sPL-sER) in a shell of 0.5 microm thickness underneath the plasma membrane. These alterations were specific for the absence of the "slow-onset" buffer PV, since in CB-/- mice neither changes in peripheral mitochondria nor in sPL-sER were observed. This implicates that the morphological alterations are aimed to specifically substitute the function of the slow buffer PV. We propose a novel concept that homeostatic mechanisms of components involved in Ca(2+) homeostasis do not always occur at the level of similar or closely related molecules. Rather the cell attempts to restore spatiotemporal aspects of Ca(2+) signals prevailing in the undisturbed (wildtype) situation by subtly fine tuning existing components involved in the regulation of Ca(2+) fluxes.

Combined voltage and calcium epifluorescence imaging in vitro and in vivo reveals subthreshold and suprathreshold dynamics of mouse barrel cortex

Cortical dynamics can be imaged at high spatiotemporal resolution with voltage-sensitive dyes (VSDs) and calcium-sensitive dyes (CaSDs). We combined these two imaging techniques using epifluorescence optics together with whole cell recordings to measure the spatiotemporal dynamics of activity in the mouse somatosensory barrel cortex in vitro and in the supragranular layers in vivo. The two optical signals reported distinct aspects of cortical function. VSD fluorescence varied linearly with membrane potential and was dominated by subthreshold postsynaptic potentials, whereas the CaSD signal predominantly reflected local action potential firing. Combining VSDs and CaSDs allowed us to monitor the synaptic drive and the spiking activity of a given area at the same time in the same preparation. The spatial extent of the two dye signals was different, with VSD signals spreading further than CaSD signals, reflecting broad subthreshold and narrow suprathreshold receptive fields. Importantly, the signals from the dyes were differentially affected by pharmacological manipulations, stimulation strength, and depth of isoflurane anesthesia. Combined VSD and CaSD measurements can therefore be used to specify the temporal and spatial relationships between subthreshold and suprathreshold activity of the neocortex.

Mechanisms of intrinsic beating variability in cardiac cell cultures and model pacemaker networks

Heart rate variability (HRV) exhibits fluctuations characterized by a power law behavior of its power spectrum. The interpretation of this nonlinear HRV behavior, resulting from interactions between extracardiac regulatory mechanisms, could be clinically useful. However, the involvement of intrinsic variations of pacemaker rate in HRV has scarcely been investigated. We examined beating variability in spontaneously active incubating cultures of neonatal rat ventricular myocytes using microelectrode arrays. In networks of mathematical model pacemaker cells, we evaluated the variability induced by the stochastic gating of transmembrane currents and of calcium release channels and by the dynamic turnover of ion channels. In the cultures, spontaneous activity originated from a mobile focus. Both the beat-to-beat movement of the focus and beat rate variability exhibited a power law behavior. In the model networks, stochastic fluctuations in transmembrane currents and stochastic gating of calcium release channels did not reproduce the spatiotemporal patterns observed in vitro. In contrast, long-term correlations produced by the turnover of ion channels induced variability patterns with a power law behavior similar to those observed experimentally. Therefore, phenomena leading to long-term correlated variations in pacemaker cellular function may, in conjunction with extracardiac regulatory mechanisms, contribute to the nonlinear characteristics of HRV.

Semi-automatic segmentation of fetal cardiac cavities: progress towards an automated fetal echocardiogram

OBJECTIVE: To develop a novel application of a tool for semi-automatic volume segmentation and adapt it for analysis of fetal cardiac cavities and vessels from heart volume datasets. METHODS: We studied retrospectively virtual cardiac volume cycles obtained with spatiotemporal image correlation (STIC) from six fetuses with postnatally confirmed diagnoses: four with normal hearts between 19 and 29 completed gestational weeks, one with d-transposition of the great arteries and one with hypoplastic left heart syndrome. The volumes were analyzed offline using a commercially available segmentation algorithm designed for ovarian folliculometry. Using this software, individual 'cavities' in a static volume are selected and assigned individual colors in cross-sections and in 3D-rendered views, and their dimensions (diameters and volumes) can be calculated. RESULTS: Individual segments of fetal cardiac cavities could be separated, adjacent segments merged and the resulting electronic casts studied in their spatial context. Volume measurements could also be performed. Exemplary images and interactive videoclips showing the segmented digital casts were generated. CONCLUSION: The approach presented here is an important step towards an automated fetal volume echocardiogram. It has the potential both to help in obtaining a correct structural diagnosis, and to generate exemplary visual displays of cardiac anatomy in normal and structurally abnormal cases for consultation and teaching.

Shifted Coupling of EEG Driving Frequencies and fMRI Resting State Networks in Schizophrenia Spectrum Disorders

INTRODUCTION: The cerebral resting state in schizophrenia is altered, as has been demonstrated separately by electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) resting state networks (RSNs). Previous simultaneous EEG/fMRI findings in healthy controls suggest that a consistent spatiotemporal coupling between neural oscillations (EEG frequency correlates) and RSN activity is necessary to organize cognitive processes optimally. We hypothesized that this coupling is disorganized in schizophrenia and related psychotic disorders, in particular regarding higher cognitive RSNs such as the default-mode (DMN) and left-working-memory network (LWMN). METHODS: Resting state was investigated in eleven patients with a schizophrenia spectrum disorder (n = 11) and matched healthy controls (n = 11) using simultaneous EEG/fMRI. The temporal association of each RSN to topographic spectral changes in the EEG was assessed by creating Covariance Maps. Group differences within, and group similarities across frequencies were estimated for the Covariance Maps. RESULTS: The coupling of EEG frequency bands to the DMN and the LWMN respectively, displayed significant similarities that were shifted towards lower EEG frequencies in patients compared to healthy controls. CONCLUSIONS: By combining EEG and fMRI, each measuring different properties of the same pathophysiology, an aberrant relationship between EEG frequencies and altered RSNs was observed in patients. RSNs of patients were related to lower EEG frequencies, indicating functional alterations of the spatiotemporal coupling. SIGNIFICANCE: The finding of a deviant and shifted coupling between RSNs and related EEG frequencies in patients with a schizophrenia spectrum disorder is significant, as it might indicate how failures in the processing of internal and external stimuli, as commonly seen during this symptomatology (i.e. thought disorders, hallucinations), arise.

A satellite-based snow cover climatology (1985–2011) for the European Alps derived from AVHRR data

Seasonal snow cover is of great environmental and socio-economic importance for the European Alps. Therefore a high priority has been assigned to quantifying its temporal and spatial variability. Complementary to land-based monitoring networks, optical satellite observations can be used to derive spatially comprehensive information on snow cover extent. For understanding long-term changes in alpine snow cover extent, the data acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensors mounted onboard the National Oceanic and Atmospheric Association (NOAA) and Meteorological Operational satellite (MetOp) platforms offer a unique source of information. In this paper, we present the first space-borne 1 km snow extent climatology for the Alpine region derived from AVHRR data over the period 1985–2011. The objective of this study is twofold: first, to generate a new set of cloud-free satellite snow products using a specific cloud gap-filling technique and second, to examine the spatiotemporal distribution of snow cover in the European Alps over the last 27 yr from the satellite perspective. For this purpose, snow parameters such as snow onset day, snow cover duration (SCD), melt-out date and the snow cover area percentage (SCA) were employed to analyze spatiotemporal variability of snow cover over the course of three decades. On the regional scale, significant trends were found toward a shorter SCD at lower elevations in the south-east and south-west. However, our results do not show any significant trends in the monthly mean SCA over the last 27 yr. This is in agreement with other research findings and may indicate a deceleration of the decreasing snow trend in the Alpine region. Furthermore, such data may provide spatially and temporally homogeneous snow information for comprehensive use in related research fields (i.e., hydrologic and economic applications) or can serve as a reference for climate models.

Diversification in the South American Pampas: the genetic and morphological variation of the widespread Petunia axillaris complex (Solanaceae)

Understanding the spatiotemporal distribution of genetic variation and the ways in which this distribution is connected to the ecological context of natural populations is fundamental for understanding the nature and mode of intraspecific and, ultimately, interspecific differentiation. The Petunia axillaris complex is endemic to the grasslands of southern South America and includes three subspecies: P.a.axillaris, P.a.parodii and P.a.subandina. These subspecies are traditionally delimited based on both geography and floral morphology, although the latter is highly variable. Here, we determined the patterns of genetic (nuclear and cpDNA), morphological and ecological (bioclimatic) variation of a large number of P.axillaris populations and found that they are mostly coincident with subspecies delimitation. The nuclear data suggest that the subspecies are likely independent evolutionary units, and their morphological differences may be associated with local adaptations to diverse climatic and/or edaphic conditions and population isolation. The demographic dynamics over time estimated by skyline plot analyses showed different patterns for each subspecies in the last 100000years, which is compatible with a divergence time between 35000 and 107000years ago between P.a.axillaris and P.a.parodii, as estimated with the IMa program. Coalescent simulation tests using Approximate Bayesian Computation do not support previous suggestions of extensive gene flow between P.a.axillaris and P.a.parodii in their contact zone.

Matching Recall and Storage in Sequence Learning with Spiking Neural Networks

Storing and recalling spiking sequences is a general problem the brain needs to solve. It is, however, unclear what type of biologically plausible learning rule is suited to learn a wide class of spatiotemporal activity patterns in a robust way. Here we consider a recurrent network of stochastic spiking neurons composed of both visible and hidden neurons. We derive a generic learning rule that is matched to the neural dynamics by minimizing an upper bound on the Kullback–Leibler divergence from the target distribution to the model distribution. The derived learning rule is consistent with spike-timing dependent plasticity in that a presynaptic spike preceding a postsynaptic spike elicits potentiation while otherwise depression emerges. Furthermore, the learning rule for synapses that target visible neurons can be matched to the recently proposed voltage-triplet rule. The learning rule for synapses that target hidden neurons is modulated by a global factor, which shares properties with astrocytes and gives rise to testable predictions.

Empirical chemosensitivity testing in a spheroid model of ovarian cancer using a microfluidics-based multiplex platform.

The use of biomarkers to infer drug response in patients is being actively pursued, yet significant challenges with this approach, including the complicated interconnection of pathways, have limited its application. Direct empirical testing of tumor sensitivity would arguably provide a more reliable predictive value, although it has garnered little attention largely due to the technical difficulties associated with this approach. We hypothesize that the application of recently developed microtechnologies, coupled to more complex 3-dimensional cell cultures, could provide a model to address some of these issues. As a proof of concept, we developed a microfluidic device where spheroids of the serous epithelial ovarian cancer cell line TOV112D are entrapped and assayed for their chemoresponse to carboplatin and paclitaxel, two therapeutic agents routinely used for the treatment of ovarian cancer. In order to index the chemoresponse, we analyzed the spatiotemporal evolution of the mortality fraction, as judged by vital dyes and confocal microscopy, within spheroids subjected to different drug concentrations and treatment durations inside the microfluidic device. To reflect microenvironment effects, we tested the effect of exogenous extracellular matrix and serum supplementation during spheroid formation on their chemotherapeutic response. Spheroids displayed augmented chemoresistance in comparison to monolayer culturing. This resistance was further increased by the simultaneous presence of both extracellular matrix and high serum concentration during spheroid formation. Following exposure to chemotherapeutics, cell death profiles were not uniform throughout the spheroid. The highest cell death fraction was found at the center of the spheroid and the lowest at the periphery. Collectively, the results demonstrate the validity of the approach, and provide the basis for further investigation of chemotherapeutic responses in ovarian cancer using microfluidics technology. In the future, such microdevices could provide the framework to assay drug sensitivity in a timeframe suitable for clinical decision making.

Low rank subspace clustering (LRSC)

We consider the problem of fitting a union of subspaces to a collection of data points drawn from one or more subspaces and corrupted by noise and/or gross errors. We pose this problem as a non-convex optimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise and/or gross errors. By self-expressive we mean a dictionary whose atoms can be expressed as linear combinations of themselves with low-rank coefficients. In the case of noisy data, our key contribution is to show that this non-convex matrix decomposition problem can be solved in closed form from the SVD of the noisy data matrix. The solution involves a novel polynomial thresholding operator on the singular values of the data matrix, which requires minimal shrinkage. For one subspace, a particular case of our framework leads to classical PCA, which requires no shrinkage. For multiple subspaces, the low-rank coefficients obtained by our framework can be used to construct a data affinity matrix from which the clustering of the data according to the subspaces can be obtained by spectral clustering. In the case of data corrupted by gross errors, we solve the problem using an alternating minimization approach, which combines our polynomial thresholding operator with the more traditional shrinkage-thresholding operator. Experiments on motion segmentation and face clustering show that our framework performs on par with state-of-the-art techniques at a reduced computational cost.

Declining pine growth in Central Spain coincides with increasing diurnal temperature range since the 1970s

Growing evidence suggests environmental change to be most severe across the semi-arid subtropics, with past, present and projected drying of the Mediterranean Basin posing a key multidisciplinary challenge. Consideration of a single climatic factor, however, often fails to explain spatiotemporal growth dynamics of drought-prone ecosystems. Here, we present annually resolved and absolutely dated ring width measurements of 871 Scots pines (Pinus sylvestris) from 18 individual plot sites in the Central Spanish Pinar Grande forest reserve. Although comprising tree ages from 6 to 175 years, this network correlates surprisingly well with the inverse May–July diurnal temperature range (r = 0.84; p < 0.00011956–2011). Ring width extremes were triggered by pressure anomalies of the North Atlantic Oscillation, and the long-term growth decline coincided with Iberian-wide drying since the mid-1970s. Climate model simulations not only confirm this negative trend over the last decades but also project drought to continuously increase over the 21st century. Associated ecological effects and socio-economic consequences should be considered to improve adaptation strategies of agricultural and forest management, as well as biodiversity conservation and ecosystem service.

Site- and species-specific responses of forest growth to climate across the European continent

Aim To evaluate the climate sensitivity of model-based forest productivity estimates using a continental-scale tree-ring network. Location Europe and North Africa (30–70° N, 10° W–40° E). Methods We compiled close to 1000 annually resolved records of radial tree growth for all major European tree species and quantified changes in growth as a function of historical climatic variation. Sites were grouped using a neural network clustering technique to isolate spatiotemporal and species-specific climate response patterns. The resulting empirical climate sensitivities were compared with the sensitivities of net primary production (NPP) estimates derived from the ORCHIDEE-FM and LPJ-wsl dynamic global vegetation models (DGVMs). Results We found coherent biogeographic patterns in climate response that depend upon (1) phylogenetic controls and (2) ambient environmental conditions delineated by latitudinal/elevational location. Temperature controls dominate forest productivity in high-elevation and high-latitude areas whereas moisture sensitive sites are widespread at low elevation in central and southern Europe. DGVM simulations broadly reproduce the empirical patterns, but show less temperature sensitivity in the boreal zone and stronger precipitation sensitivity towards the mid-latitudes. Main conclusions Large-scale forest productivity is driven by monthly to seasonal climate controls, but our results emphasize species-specific growth patterns under comparable environmental conditions. Furthermore, we demonstrate that carry-over effects from the previous growing season can significantly influence tree growth, particularly in areas with harsh climatic conditions – an element not considered in most current-state DGVMs. Model–data discrepancies suggest that the simulated climate sensitivity of NPP will need refinement before carbon-cycle climate feedbacks can be accurately quantified.

Tracing the origin of Arctic driftwood

Arctic environments, where surface temperatures increase and sea ice cover and permafrost depth decrease, are very sensitive to even slight climatic variations. Placing recent environmental change of the high-northern latitudes in a long-term context is, however, complicated by too short meteorological observations and too few proxy records. Driftwood may represent a unique cross-disciplinary archive at the interface of marine and terrestrial processes. Here, we introduce 1445 driftwood remains from coastal East Greenland and Svalbard. Macroscopy and microscopy were applied for wood anatomical classification; a multi-species subset was used for detecting fungi; and information on boreal vegetation patterns, circumpolar river systems, and ocean current dynamics was reviewed and evaluated. Four conifer (Pinus, Larix, Picea, and Abies) and three deciduous (Populus, Salix, and Betula) genera were differentiated. Species-specific identification also separated Pinus sylvestris and Pinus sibirica, which account for ~40% of all driftwood and predominantly originate from western and central Siberia. Larch and spruce from Siberia or North America represents ~26% and ~18% of all materials, respectively. Fungal colonization caused different levels of driftwood staining and/or decay. Our results demonstrate the importance of combining wood anatomical knowledge with insight on boreal forest composition for successfully tracing the origin of Arctic driftwood. To ultimately reconstruct spatiotemporal variations in ocean currents, and to better quantify postglacial uplift rates, we recommend consideration of dendrochronologically dated material from many more circumpolar sites.

Tuning curve of type-0 spontaneous parametric down-conversion

We study the tuning curve of entangled photons generated by type-0 spontaneous parametric down-conversion in a periodically poled potassium titanyl phosphate crystal. We demonstrate the X-shaped spatiotemporal structure of the spectrum by means of measurements and numerical simulations. Experiments for different pump waists, crystal temperatures, and crystal lengths are in good agreement with numerical simulations.