Characterization of Aura TES carbonyl sulfide retrievals over ocean

We present a description of the NASA Aura Tropospheric Emission Spectrometer (TES) carbonyl sulfide (OCS) retrieval algorithm for oceanic observations, along with evaluation of the biases and uncertainties using aircraft profiles from the HIPPO (HIAPER Pole-to-Pole Observations) campaign and data from the NOAA Mauna Loa site. In general, the OCS retrievals (1) have less than 1.0 degree of freedom for signals (DOFs), (2) are sensitive in the mid-troposphere with a peak sensitivity typically between 300 and 500 hPa, (3) but have much smaller systematic errors from temperature, CO₂ and H₂O calibrations relative to random errors from measurement noise. We estimate the monthly means from TES measurements averaged over multiple years so that random errors are reduced and useful information about OCS seasonal and latitudinal variability can be derived. With this averaging, TES OCS data are found to be consistent (within the calculated uncertainties) with NOAA ground observations and HIPPO aircraft measurements. TES OCS data also captures the seasonal and latitudinal variations observed by these in situ data

Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss

An effective framework for learning 3D representations for perception tasks is distilling rich self-supervised image features via contrastive learning. However, image-to point representation learning for autonomous driving datasets faces two main challenges: 1) the abundance of self-similarity, which results in the contrastive losses pushing away semantically similar point and image regions and thus disturbing the local semantic structure of the learned representations, and 2) severe class imbalance as pretraining gets dominated by over-represented classes. We propose to alleviate the self-similarity problem through a novel semantically tolerant image-to-point contrastive loss that takes into consideration the semantic distance between positive and negative image regions to minimize contrasting semantically similar point and image regions. Additionally, we address class imbalance by designing a class-agnostic balanced loss that approximates the degree of class imbalance through an aggregate sample-to-samples semantic similarity measure. We demonstrate that our semantically-tolerant contrastive loss with class balancing improves state-of-the art 2D-to-3D representation learning in all evaluation settings on 3D semantic segmentation. Our method consistently outperforms state-of-the-art 2D-to-3D representation learning frameworks across a wide range of 2D self-supervised pretrained models.Comment: Accepted in CVPR 202

Profiling tropospheric CO_2 using Aura TES and TCCON instruments

Monitoring the global distribution and long-term variations of CO_2 sources and sinks is required for characterizing the global carbon budget. Total column measurements are useful for estimating regional-scale fluxes; however, model transport remains a significant error source, particularly for quantifying local sources and sinks. To improve the capability of estimating regional fluxes, we estimate lower tropospheric CO_2 concentrations from ground-based near-infrared (NIR) measurements with space-based thermal infrared (TIR) measurements. The NIR measurements are obtained from the Total Carbon Column Observing Network (TCCON) of solar measurements, which provide an estimate of the total CO_2 column amount. Estimates of tropospheric CO_2 that are co-located with TCCON are obtained by assimilating Tropospheric Emission Spectrometer (TES) free tropospheric CO_2 estimates into the GEOS-Chem model. We find that quantifying lower tropospheric CO_2 by subtracting free tropospheric CO_2 estimates from total column estimates is a linear problem, because the calculated random uncertainties in total column and lower tropospheric estimates are consistent with actual uncertainties as compared to aircraft data. For the total column estimates, the random uncertainty is about 0.55 ppm with a bias of −5.66 ppm, consistent with previously published results. After accounting for the total column bias, the bias in the lower tropospheric CO_2 estimates is 0.26 ppm with a precision (one standard deviation) of 1.02 ppm. This precision is sufficient for capturing the winter to summer variability of approximately 12 ppm in the lower troposphere; double the variability of the total column. This work shows that a combination of NIR and TIR measurements can profile CO_2 with the precision and accuracy needed to quantify lower tropospheric CO_2 variability

Gradient microfluidics enables rapid bacterial growth inhibition testing

Bacterial growth inhibition tests have become a standard measure of the adverse effects of inhibitors for a wide range of applications, such as toxicity testing in the medical and environmental sciences. However, conventional well-plate formats for these tests are laborious and provide limited information (often being restricted to an end-point assay). In this study, we have developed a microfluidic system that enables fast quantification of the effect of an inhibitor on bacteria growth and survival, within a single experiment. This format offers a unique combination of advantages, including long-term continuous flow culture, generation of concentration gradients, and single cell morphology tracking. Using Escherichia coli and the inhibitor amoxicillin as one model system, we show excellent agreement between an on-chip single cell-based assay and conventional methods to obtain quantitative measures of antibiotic inhibition (for example, minimum inhibition concentration). Furthermore, we show that our methods can provide additional information, over and above that of the standard well-plate assay, including kinetic information on growth inhibition and measurements of bacterial morphological dynamics over a wide range of inhibitor concentrations. Finally, using a second model system, we show that this chip-based systems does not require the bacteria to be labeled and is well suited for the study of naturally occurring species. We illustrate this using Nitrosomonas europaea, an environmentally important bacteria, and show that the chip system can lead to a significant reduction in the period required for growth and inhibition measurements (<4 days, compared to weeks in a culture flask)

Estimate of carbonyl sulfide tropical oceanic surface fluxes using Aura Tropospheric Emission Spectrometer observations

Quantifying the carbonyl sulfide (OCS) land/ocean fluxes contributes to the understanding of both the sulfur and carbon cycles. The primary sources and sinks of OCS are very likely in a steady state because there is no significant observed trend or interannual variability in atmospheric OCS measurements. However, the magnitude and spatial distribution of the dominant ocean source are highly uncertain due to the lack of observations. In particular, estimates of the oceanic fluxes range from approximately 280 Gg S yr^(−1) to greater than 800 Gg S yr^(−1), with the larger flux needed to balance a similarly sized terrestrial sink that is inferred from NOAA continental sites. Here we estimate summer tropical oceanic fluxes of OCS in 2006 using a linear flux inversion algorithm and new OCS data acquired by the Aura Tropospheric Emissions Spectrometer (TES). Modeled OCS concentrations based on these updated fluxes are consistent with HIAPER Pole‐to‐Pole Observations during 4th airborne campaign and improve significantly over the a priori model concentrations. The TES tropical ocean estimate of 70 ± 16 Gg S in June, when extrapolated over the whole year (about 840 ± 192 Gg S yr^(−1), supports the hypothesis proposed by Berry et al. (2013) that the ocean flux is in the higher range of approximately 800 Gg S yr^(−1)

High spatial resolution imaging of methane and other trace gases with the airborne Hyperspectral Thermal Emission Spectrometer (HyTES)

Currently large uncertainties exist associated with the attribution and quantification of fugitive emissions of criteria pollutants and greenhouse gases such as methane across large regions and key economic sectors. In this study, data from the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) have been used to develop robust and reliable techniques for the detection and wide-area mapping of emission plumes of methane and other atmospheric trace gas species over challenging and diverse environmental conditions with high spatial resolution that permits direct attribution to sources. HyTES is a pushbroom imaging spectrometer with high spectral resolution (256 bands from 7.5 to 12 µm), wide swath (1–2 km), and high spatial resolution (∼ 2 m at 1 km altitude) that incorporates new thermal infrared (TIR) remote sensing technologies. In this study we introduce a hybrid clutter matched filter (CMF) and plume dilation algorithm applied to HyTES observations to efficiently detect and characterize the spatial structures of individual plumes of CH4, H2S, NH3, NO2, and SO2 emitters. The sensitivity and field of regard of HyTES allows rapid and frequent airborne surveys of large areas including facilities not readily accessible from the surface. The HyTES CMF algorithm produces plume intensity images of methane and other gases from strong emission sources. The combination of high spatial resolution and multi-species imaging capability provides source attribution in complex environments. The CMF-based detection of strong emission sources over large areas is a fast and powerful tool needed to focus on more computationally intensive retrieval algorithms to quantify emissions with error estimates, and is useful for expediting mitigation efforts and addressing critical science questions

Genome-wide search for breast cancer linkage in large Icelandic non-BRCA1/2 families

Abstract Introduction: A significant proportion of high-risk breast cancer families are not explained by mutations in known genes. Recent genome-wide searches (GWS) have not revealed any single major locus reminiscent of BRCA1 and BRCA2, indicating that still unidentified genes may explain relatively few families each or interact in a way obscure to linkage analyses. This has drawn attention to possible benefits of studying populations where genetic heterogeneity might be reduced. We thus performed a GWS for linkage on nine Icelandic multiple-case non-BRCA1/2 families of desirable size for mapping highly penetrant loci. To follow up suggestive loci, an additional 13 families from other Nordic countries were genotyped for selected markers. Methods: GWS was performed using 811 microsatellite markers providing about five centiMorgan (cM) resolution. Multipoint logarithm of odds (LOD) scores were calculated using parametric and nonparametric methods. For selected markers and cases, tumour tissue was compared to normal tissue to look for allelic loss indicative of a tumour suppressor gene. Results: The three highest signals were located at chromosomes 6q, 2p and 14q. One family contributed suggestive LOD scores (LOD 2.63 to 3.03, dominant model) at all these regions, without consistent evidence of a tumour suppressor gene. Haplotypes in nine affected family members mapped the loci to 2p23.2 to p21, 6q14.2 to q23.2 and 14q21.3 to q24.3. No evidence of a highly penetrant locus was found among the remaining families. The heterogeneity LOD (HLOD) at the 6q, 2p and 14q loci in all families was 3.27, 1.66 and 1.24, respectively. The subset of 13 Nordic families showed supportive HLODs at chromosome 6q (ranging from 0.34 to 1.37 by country subset). The 2p and 14q loci overlap with regions indicated by large families in previous GWS studies of breast cancer. Conclusions: Chromosomes 2p, 6q and 14q are candidate sites for genes contributing together to high breast cancer risk. A polygenic model is supported, suggesting the joint effect of genes in contributing to breast cancer risk to be rather common in non-BRCA1/2 families. For genetic counselling it would seem important to resolve the mode of genetic interaction

Widespread Occurrence of Dosage Compensation in Candida albicans

The important human pathogen Candida albicans possesses an unusual form of gene regulation, in which the copy number of an entire specific chromosome or a large portion of a specific chromosome changes in response to a specific adverse environment, thus, insuring survival. In the absence of the adverse environment, the altered portion of the genome can be restored to its normal condition. One major question is how C. albicans copes with gene imbalance arising by transitory aneuploid states. Here, we compared transcriptomes from cells with either two copies or one copy of chromosome 5 (Ch5) in, respectively, a diploid strain 3153A and its representative derivative Sor55. Statistical analyses revealed that at least 40% of transcripts from the monosomic Ch5 are fully compensated to a disomic level, thus, indicating the existence of a genome-wide mechanism maintaining cellular homeostasis. Only approximately 15% of transcripts were diminished twofold in accordance with what would be expected for Ch5 monosomy. Another minor portion of approximately 6% of transcripts, unexpectedly, increased up to twofold and higher than the disomic level, demonstrating indirect control by monosomy. Array comparative genome hybridization revealed that only few out of approximately 500 genes on the monosomic Ch5b were duplicated, thus, not causing a global up regulation. Dosage compensation was confirmed with several representative genes from another monosomic Ch5a in the mutant Sor60. We suggest that C. albicans's unusual regulation of gene expression by the loss and gain of entire chromosomes is coupled with widespread compensation of gene dosage at the transcriptional level

Embedding mRNA Stability in Correlation Analysis of Time-Series Gene Expression Data

Current methods for the identification of putatively co-regulated genes directly from gene expression time profiles are based on the similarity of the time profile. Such association metrics, despite their central role in gene network inference and machine learning, have largely ignored the impact of dynamics or variation in mRNA stability. Here we introduce a simple, but powerful, new similarity metric called lead-lag R2 that successfully accounts for the properties of gene dynamics, including varying mRNA degradation and delays. Using yeast cell-cycle time-series gene expression data, we demonstrate that the predictive power of lead-lag R2 for the identification of co-regulated genes is significantly higher than that of standard similarity measures, thus allowing the selection of a large number of entirely new putatively co-regulated genes. Furthermore, the lead-lag metric can also be used to uncover the relationship between gene expression time-series and the dynamics of formation of multiple protein complexes. Remarkably, we found a high lead-lag R2 value among genes coding for a transient complex