Auditory cortex activation is modulated nonlinearly by stimulation duration: A functional near-infrared spectroscopy (fNIRS) study

Functional near-infrared spectroscopy (fNIRS) is an increasingly popular method in hearing research. However, few studies have considered efficient stimulation parameters for fNIRS auditory experimental design. The objectives of our study are (1) to characterize the auditory hemodynamic responses to trains of white noise with increasing stimulation durations (8s, 10s, 15s, 20s) in terms of amplitude and response linearity; (2) to identify the most-efficient stimulation duration using fNIRS; and (3) to generalize results to more ecological environmental stimuli. We found that cortical activity is augmented following the increments in stimulation durations and reaches a plateau after about 15s of stimulation. The linearity analysis showed that this augmentation due to stimulation duration is not linear in the auditory cortex, the non-linearity being more pronounced for longer durations (15s and 20s). The 15s block duration that we propose as optimal precludes signal saturation, is associated with a high response amplitude and a relatively short total experimental duration. Moreover, the 15s duration remains optimal independently of the nature of presented sounds. The sum of these findings suggests that 15s stimulation duration used in the appropriate experimental setup allows researchers to acquire optimal fNIRS signal quality.

Highly Local Model Calibration with a New GEDI LiDAR Asset on Google Earth Engine Reduces Landsat Forest Height Signal Saturation

While Landsat has proved to be effective for monitoring many elements of forest condition and change, the platform has well-documented limitations in measuring forest structure, the vertical distribution of the canopy. This is important because structure determines several key ecosystem functions, including: carbon storage; habitat suitability; and timber volume. Canopy structure is directly measured by LiDAR, and it should be possible to train Landsat structure models at a highly local scale with the dense, global sample of full waveform LiDAR observations collected by NASA’s Global Ecosystem Dynamics Investigation (GEDI). Local models are expected to perform better because: (a) such models may take advantage of localized correlations between structure and canopy surface reflectance; and (b) to the extent that models revert to the mean of the calibration data due to a lack of discrimination, local models will revert to a more representative mean. We tested Landsat-based relative height predictions using a new GEDI asset on Google Earth Engine, described here. Mean prediction error declined by 23% and important prediction biases at the extremes of the range of canopy height dropped as model calibration became more local, minimizing forest structure signal saturation commonly associated with Landsat and other passive optical sensors. Our results suggest that Landsat-based maps of structural variables such as height and biomass may substantially benefit from the kind of local calibration that GEDI’s dense sample of LiDAR data supports.

Portiera gives new clues on the evolutionary history of whiteflies

Whiteflies (Hemiptera: Sternorrhyncha: Aleyrodidae) are a superfamily of small phloem-feeding insects. Their taxonomy is currently based on the morphology of nymphal stages that display phenotypic plasticity, which produces inconsistencies. To overcome this limitation, we developed a new phylogenetic framework that targets five genes of Candidatus Portiera aleyrodidarum, the primary endosymbiont of whiteflies. Portiera lineages have been co-diverging with whiteflies since their origin and therefore reflect their host evolutionary history. We also studied the origin of stability and instability in Portiera genomes by testing for the presence of two alternative gene rearrangements and the loss of a functional polymerase proofreading subunit (dnaQ), previously associated with genome instability. We present two phylogenetic reconstructions. One using the sequences of all five target genes from 22 whitefly species belonging to 17 genera. The second uses only two genes to include additional published Portiera sequences of 21 whitefly species, increasing our sampling size to 42 species from 25 genera. The developed framework showed low signal saturation, specificity to whitefly samples, and efficiency in solving inter-genera relationships and standing inconsistencies in the current taxonomy of the superfamily. Genome instability was found to be present only in the Aleurolobini tribe containing the Singhiella, Aleurolobus and Bemisia genera. This suggests that Portiera genome instability likely arose in the Aleurolobini tribe’s common ancestor, around 70 Mya. We propose a link between the switch from multi-bacteriocyte to a single-bacteriocyte mode of inheritance in the Aleurolobini tribe and the appearance of genome instability in Portiera.

Snow Cover Monitoring with Chinese Gaofen-4 PMS Imagery and the Restored Snow Index (RSI) Method: Case Studies

Snow cover is an essential climate variable of the Global Climate Observing System. Gaofen-4 (GF-4) is the first Chinese geostationary satellite to obtain optical imagery with high spatial and temporal resolution, which presents unique advantages in snow cover monitoring. However, the panchromatic and multispectral sensor (PMS) onboard GF-4 lacks the shortwave infrared (SWIR) band, which is crucial for snow cover detection. To reach the potential of GF-4 PMS in snow cover monitoring, this study developed a novel method termed the restored snow index (RSI). The SWIR reflectance of snow cover is restored firstly, and then the RSI is calculated with the restored reflectance. The distribution of snow cover can be mapped with a threshold, which should be adjusted according to actual situations. The RSI was validated using two pairs of GF-4 PMS and Landsat-8 Operational Land Imager images. The validation results show that the RSI can effectively map the distribution of snow cover in these cases, and all of the classification accuracies are above 95%. Signal saturation slightly affects PMS images, but cloud contamination is an important limiting factor. Therefore, we propose that the RSI is an efficient method for monitoring snow cover from GF-4 PMS imagery without requiring the SWIR reflectance.

Calibration of a 35-GHz Airborne Cloud Radar: Lessons Learned and Intercomparisons with 94-GHz Cloud Radars

In this study, we will give an overview of lessons learned during the radiometric calibration of the airborne, high-power Ka-band cloud radar on board the German research aircraft HALO. Within this context, a number of flight experiments over Europe and over the tropical and extra-tropical North-Atlantic have been conducted, where the ocean surface backscatter was used as an external reference reflector. Measurements of signal linearity and signal saturation complement this characterization. To validate the external calibration, joint flights of HALO and the French Falcon 20 aircraft, which was equipped with the RASTA cloud radar at 94 GHz and underflights of the spaceborne CloudSat at 94 GHz have been conducted. Finally, the influence of different radar wavelengths was explored with numerical studies.