Trifluoroacetate in Precipitation: Deriving a Benchmark Data Set.

Although precipitation is considered to be the most important diffuse source of trifluoroacetate (TFA) to the nonmarine environment, information regarding the wet deposition of TFA as well as general data on the spatial and temporal variations in TFA concentration in precipitation is scarce. This is the first study to provide a comprehensive overview of the occurrence of TFA in precipitation by a systematic and nation-wide field monitoring campaign. In total, 1187 precipitation samples, which were collected over the course of 12 consecutive months at eight locations across Germany, were analyzed. The median, the estimated average, and the precipitation-weighted average TFA concentration of all analyzed wet deposition samples were 0.210, 0.703, and 0.335 µg/L, respectively. For Germany, an annual wet deposition flux of 190 µg/m2 or approximately 68 t was calculated for the sampling period from February 2018 to January 2019. The campaign revealed a pronounced seasonality of the TFA concentration and wet deposition flux of collected samples. Correlation analysis suggested an enhanced transformation of TFA precursors in the troposphere in the summertime due to higher concentrations of photochemically generated oxidants such as hydroxyl radicals, ultimately leading to an enhanced atmospheric deposition of TFA during summer.

Monitoring plant drought stress response using terahertz time-domain spectroscopy.

We present a novel measurement setup for monitoring changes in leaf water status using nondestructive terahertz time-domain spectroscopy (THz-TDS). Previous studies on a variety of plants showed the principal applicability of THz-TDS. In such setups, decreasing leaf water content directly correlates with increasing THz transmission. Our new system allows for continuous, nondestructive monitoring of the water status of multiple individual plants each at the same constant leaf position. It overcomes previous drawbacks, which were mainly due to the necessity of relocating the plants. Using needles of silver fir (Abies alba) seedlings as test subjects, we show that the transmission varies along the main axis of a single needle due to a variation in thickness. Therefore, the relocation of plants during the measuring period, which was necessary in the previous THz-TDS setups, should be avoided. Furthermore, we show a highly significant correlation between gravimetric water content and respective THz transmission. By monitoring the relative change in transmission, we were able to narrow down the permanent wilting point of the seedlings. Thus, we established groups of plants with well-defined levels of water stress that could not be detected visually. This opens up the possibility for a broad range of genetic and physiological experiments.

Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations.

A neural network (NN) technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites and in situ physical observations. Satellite-derived "ocean color" (OC) data are used in this study because OC variability is primarily driven by biological processes related and correlated in complex, nonlinear relationships with the physical processes of the upper ocean. Specifically, ocean color chlorophyll-a fields from NOAA's operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as NOAA and NASA ocean surface and upper-ocean observations employed--signatures of upper-ocean dynamics. An NN transfer function is trained, using global data for two years (2012 and 2013), and tested on independent data for 2014. To reduce the impact of noise in the data and to calculate a stable NN Jacobian for sensitivity studies, an ensemble of NNs with different weights is constructed and compared with a single NN. The impact of the NN training period on the NN's generalization ability is evaluated. The NN technique provides an accurate and computationally cheap method for filling in gaps in satellite ocean color observation fields and time series.

Differential Gene Expression Reveals Candidate Genes for Drought Stress Response in Abies alba (Pinaceae).

Increasing drought periods as a result of global climate change pose a threat to many tree species by possibly outpacing their adaptive capabilities. Revealing the genetic basis of drought stress response is therefore implemental for future conservation strategies and risk assessment. Access to informative genomic regions is however challenging, especially for conifers, partially due to their large genomes, which puts constraints on the feasibility of whole genome scans. Candidate genes offer a valuable tool to reduce the complexity of the analysis and the amount of sequencing work and costs. For this study we combined an improved drought stress phenotyping of needles via a novel terahertz water monitoring technique with Massive Analysis of cDNA Ends to identify candidate genes for drought stress response in European silver fir (Abies alba Mill.). A pooled cDNA library was constructed from the cotyledons of six drought stressed and six well-watered silver fir seedlings, respectively. Differential expression analyses of these libraries revealed 296 candidate genes for drought stress response in silver fir (247 up- and 49 down-regulated) of which a subset was validated by RT-qPCR of the twelve individual cotyledons. A majority of these genes code for currently uncharacterized proteins and hint on new genomic resources to be explored in conifers. Furthermore, we could show that some traditional reference genes from model plant species (GAPDH and eIF4A2) are not suitable for differential analysis and we propose a new reference gene, TPC1, for drought stress expression profiling in needles of conifer seedlings.