Time-variable gravity signal in Greenland revealed by high-low satellite-to-satellite tracking

[1] In the event of a termination of the Gravity Recovery and Climate Experiment (GRACE) mission before the launch of GRACE Follow-On (due for launch in 2017), high-low satellite-to-satellite tracking (hl-SST) will be the only dedicated observing system with global coverage available to measure the time-variable gravity field (TVG) on a monthly or even shorter time scale. Until recently, hl-SST TVG observations were of poor quality and hardly improved the performance of Satellite Laser Ranging observations. To date, they have been of only very limited usefulness to geophysical or environmental investigations. In this paper, we apply a thorough reprocessing strategy and a dedicated Kalman filter to Challenging Minisatellite Payload (CHAMP) data to demonstrate that it is possible to derive the very long-wavelength TVG features down to spatial scales of approximately 2000 km at the annual frequency and for multi-year trends. The results are validated against GRACE data and surface height changes from long-term GPS ground stations in Greenland. We find that the quality of the CHAMP solutions is sufficient to derive long-term trends and annual amplitudes of mass change over Greenland. We conclude that hl-SST is a viable source of information for TVG and can serve to some extent to bridge a possible gap between the end-of-life of GRACE and the availability of GRACE Follow-On.

Earth gravity field recovery using GPS, GLONASS, and SLR satellites

The time variable Earth’s gravity field provides the information about mass transport within the system Earth, i.e., the relationship of mass transport between atmosphere, oceans, and land hydrology. We recover the low-degree parameters of the time variable gravity field using microwave observations from GPS and GLONASS satellites and from SLR data to five geodetic satellites, namely LAGEOS-1/2, Starlette, Stella, and AJISAI. GPS satellites are particularly sensitive to specific coefficients of the Earth's gravity field, because of the deep 2:1 orbital resonance with Earth rotation (two revolutions of the GPS satellites per sidereal day). The resonant coefficients cause, among other, a “secular” drift (actually periodic variations of very long periods) of the semi-major axes of up to 5.3 m/day of GPS satellites. We processed 10 years of GPS and GLONASS data using the standard orbit models from the Center of Orbit Determination in Europe (CODE) with a simultaneous estimation of the Earth gravity field coefficients and other parameters, e.g., satellite orbit parameters, station coordinates, Earth rotation parameters, troposphere delays, etc. The weekly GNSS gravity solutions up to degree and order 4/4 are compared to the weekly SLR gravity field solutions. The SLR-derived geopotential coefficients are compared to monthly GRACE and CHAMP results.

Robust Proximal Femur Segmentation in Conventional X-Ray Images via Random Forest Regression on Multi-resolution Gradient Features

In this paper, we propose a fully automatic, robust approach for segmenting proximal femur in conventional X-ray images. Our method is based on hierarchical landmark detection by random forest regression, where the detection results of 22 global landmarks are used to do the spatial normalization, and the detection results of the 59 local landmarks serve as the image cue for instantiation of a statistical shape model of the proximal femur. To detect landmarks in both levels, we use multi-resolution HoG (Histogram of Oriented Gradients) as features which can achieve better accuracy and robustness. The efficacy of the present method is demonstrated by experiments conducted on 150 clinical x-ray images. It was found that the present method could achieve an average point-to-curve error of 2.0 mm and that the present method was robust to low image contrast, noise and occlusions caused by implants.

Identification of the Causative Bacteria in Musculoskeletal Infections Using 16s rDNA - Denaturing Gradient Gel Electrophoresis Analysis

Musculoskeletal infections are infections of the bone and surrounding tissues. They are currently diagnosed based on culture analysis, which is the gold standard for pathogen identification. However, these clinical laboratory methods are frequently inadequate for the identification of the causative agents, because a large percentage (25-50%) of confirmed musculoskeletal infections are false negatives in which no pathogen is identified in culture. My data supports these results. The goal of this project was to use PCR amplification of a portion of the 16S rRNA gene to test an alternative approach for the identification of these pathogens and to assess the diversity of the bacteria involved. The advantages of this alternative method are that it should increase sample sensitivity and the speed of detection. In addition, bacteria that are non-culturable or in low abundance can be detected using this molecular technique. However, a complication of this approach is that the majority of musculoskeletal infections are polymicrobial, which prohibits direct identification from the infected tissue by DNA sequencing of the initial 16S rDNA amplification products. One way to solve this problem is to use denaturing gradient gel electrophoresis (DGGE) to separate the PCR products before DNA sequencing. Denaturing gradient gel electrophoresis (DGGE) separates DNA molecules based on their melting point, which is determined by their DNA sequence. This analytical technique allows a mixture of PCR products of the same length that electrophoreses through agarose gels as one band, to be separated into different bands and then used for DNA sequence analysis. In this way, the DGGE allows for the identification of individual bacterial species in polymicrobial-infected tissue, which is critical for improving clinical outcomes. By combining the 16S rDNA amplification and the DGGE techniques together, an alternative approach for identification has been used. The 16S rRNA gene PCR-DGGE method includes several critical steps: DNA extraction from tissue biopsies, amplification of the bacterial DNA, PCR product separation by DGGE, amplification of the gel-extracted DNA, and DNA sequencing and analysis. Each step of the method was optimized to increase its sensitivity and for rapid detection of the bacteria present in human tissue samples. The limit of detection for the DNA extraction from tissue was at least 20 Staphylococcus aureus cells and the limit of detection for PCR was at least 0.05 pg of template DNA. The conditions for DGGE electrophoreses were optimized by using a double gradient of acrylamide (6 – 10%) and denaturant (30-70%), which increased the separation between distinct PCR products. The use of GelRed (Biotium) improved the DNA visualization in the DGGE gel. To recover the DNA from the DGGE gels the gel slices were excised, shredded in a bead beater, and the DNA was allowed to diffuse into sterile water overnight. The use of primers containing specific linkers allowed the entire amplified PCR product to be sequenced and then analyzed. The optimized 16S rRNA gene PCR-DGGE method was used to analyze 50 tissue biopsy samples chosen randomly from our collection. The results were compared to those of the Memorial Hermann Hospital Clinical Microbiology Laboratory for the same samples. The molecular method was congruent for 10 of the 17 (59%) culture negative tissue samples. In 7 of the 17 (41%) culture negative the molecular method identified a bacterium. The molecular method was congruent with the culture identification for 7 of the 33 (21%) positive cultured tissue samples. However, in 8 of the 33 (24%) the molecular method identified more organisms. In 13 of the 15 (87%) polymicrobial cultured tissue samples the molecular method identified at least one organism that was also identified by culture techniques. Overall, the DGGE analysis of 16S rDNA is an effective method to identify bacteria not identified by culture analysis.

Tree growth response along an elevational gradient: climate or genetics?

Environment and genetics combine to influence tree growth and should therefore be jointly considered when evaluating forest responses in a warming climate. Here, we combine dendroclimatology and population genetic approaches with the aim of attributing climatic influences on growth of European larch (Larix decidua) and Norway spruce (Picea abies). Increment cores and genomic DNA samples were collected from populations along a ~900-m elevational transect where the air temperature gradient encompasses a ~4 °C temperature difference. We found that low genetic differentiation among populations indicates gene flow is high, suggesting that migration rate is high enough to counteract the selective pressures of local environmental variation. We observed lower growth rates towards higher elevations and a transition from negative to positive correlations with growing season temperature upward along the elevational transect. With increasing elevation there was also a clear increase in the explained variance of growth due to summer temperatures. Comparisons between climate sensitivity patterns observed along this elevational transect with those from Larix and Picea sites distributed across the Alps reveal good agreement, and suggest that tree-ring width (TRW) variations are more climate-driven than genetics-driven at regional and larger scales. We conclude that elevational transects are an extremely valuable platform for understanding climatic-driven changes over time and can be especially powerful when working within an assessed genetic framework.

Climatic drivers of hourly to yearly tree radius variations along a 6°C natural warming gradient

Climate affects the timing, rate and dynamics of tree growth, over time scales ranging from seconds to centuries. Monitoring how a tree's stem radius varies over these time scales can provide insight into intra-annual stem dynamics and improve our understanding of climate impacts on tree physiology and growth processes. Here, we quantify the response of radial conifer stem size to environmental fluctuations via a novel assessment of tree circadian cycles. We analyze four years of sub-hourly data collected from 56 larch and spruce trees growing along a natural temperature gradient of ∼6 °C in the central Swiss Alps. During the growing season, tree stem diameters were greatest at mid-morning and smallest in the late evening, reflecting the daily cycle of water uptake and loss. Along the gradient, amplitudes calculated from the stem radius cycle were ∼50% smaller at the upper site (∼2200 m a.s.l.) relative to the lower site (∼800 m a.s.l.). We show changes in precipitation, temperature and cloud cover have a substantial effect on typical growing season diurnal cycles; amplitudes were nine times smaller on rainy days (>10 mm), and daily amplitudes are approximately 40% larger when the mean daily temperature is 15–20 °C than when it is 5–10 °C. We find that over the growing season in the sub-alpine forests, spruce show greater daily stem water movement than larch. However, under projected future warming, larch could experience up to 50% greater stem water use, which may severely affect future growth on already dry sites. Our data further indicate that because of the confounding influences of radial growth and short-term water dynamics on stem size, conventional methodology probably overstates the effect of water-linked meteorological variables (i.e. precipitation and relative humidity) on intra-annual tree growth. We suggest future studies use intra-seasonal measurements of cell development and consider whether climatic factors produce reversible changes in stem diameter. These study design elements may help researchers more accurately quantify and attribute changes in forest productivity in response to future warming.

The importance of biotic interactions and local adaptation for plant response to environmental changes: field evidence along an elevational gradient

Predicting the response of species to environmental changes is a great and on-going challenge for ecologists, and this requires a more in-depth understanding of the importance of biotic interactions and the population structuration in the landscape. Using a reciprocal transplantation experiment, we tested the response of five species to an elevational gradient. This was combined to a neighbour removal treatment to test the importance of local adaptation and biotic interactions. The trait studied was performance measured as survival and biomass. Species response varied along the elevational gradient, but with no consistent pattern. Performance of species was influenced by environmental conditions occurring locally at each site, as well as by positive or negative effects of the surrounding vegetation. Indeed, we observed a shift from competition for biomass to facilitation for survival as a response to the increase in environmental stress occurring in the different sites. Unlike previous studies pointing out an increase of stress along the elevation gradient, our results supported a stress gradient related to water availability, which was not strictly parallel to the elevational gradient. For three of our species, we observed a greater biomass production for the population coming from the site where the species was dominant (central population) compared to population sampled at the limit of the distribution (marginal population). Nevertheless, we did not observe any pattern of local adaptation that could indicate adaptation of populations to a particular habitat. Altogether, our results highlighted the great ability of plant species to cope with environmental changes, with no local adaptation and great variability in response to local conditions. Our study confirms the importance of taking into account biotic interactions and population structure occurring at local scale in the prediction of communities’ responses to global environmental changes.