Wetland classification based on depth-adaptive convolutional neural networks using leaf-off SAR imagery

The recent development of deep learning (DL) techniques has created opportunities for classifying wetlands from remote sensing data (mainly optical data). However, the methods for accurately and efficiently classifying large-scale wetlands using DL and radar data that can be more effective than optical data still needs evaluation. In this study, we developed an end-to-end depth-adaptive convolutional neural network (CNN) for mapping wetlands using leaf-off time-series Sentinel-1 Synthetic Aperture Radar (SAR) imagery along with ancillary data. We examined the inclusion of multi-land cover proximity information and a CNN-based self-supervised SAR denoising procedure for enhancing wetland classification accuracy. The depth-adaptive CNN based on U-Net architecture was designed to classify wetland classes (emergent wetland, scrub-shrub wetland, forested wetland, and open water) in Delaware, U.S. while achieving optimization between model complexity (network depths) and accuracy. Results show that our proposed DL method (OA = 0.93, MIoU = 0.60) not only produced a higher classification accuracy than the traditional RF method (OA = 0.89, MIoU = 0.18) but also had a significantly reduced computational cost compared to established state-of-the-art CNNs (e.g., DeepLabV3+ and DANet) without loss of accuracy. The inclusion of multi-land cover proximity information (especially distances to forest and water) and the CNN-based self-supervised SAR denoising procedure can both enhance wetland classification accuracy, especially for forested wetland using traditional RF methods. These results demonstrated the novelty and efficiency of our proposed DL method for classifying wetlands by combing denoised SAR imagery and ancillary information, which provides insights on integration of DL approach and radar data for supporting operational wetland mapping at large spatial scales

The multifunctional solute carrier 3A2 (SLC3A2) confers a poor prognosis in the highly proliferative breast cancer subtypes

Background: Breast cancer (BC) is a heterogeneous disease characterised by variant biology, metabolic activity and patient outcome. This study aimed to evaluate the biological and prognostic value of the membrane solute carrier, SLC3A2 in BC with emphasis on the intrinsic molecular subtypes. Methods: SLC3A2 was assessed at the genomic level, using METABRIC data (n=1,980), and proteomic level, using immunohistochemistry on TMA sections constructed from a large well-characterised primary BC cohort (n=2,500). SLC3A2 expression was correlated with clinicopathological parameters, molecular subtypes, and patient outcome. Results: SLC3A2 mRNA and protein expression were strongly correlated with higher tumour grade and poor Nottingham prognostic index (NPI). High expression of SLC3A2 was observed in triple negative (TN), HER2+, and ER+ high proliferation subtypes. SLC3A2 mRNA and protein expression were significantly associated with the expression of c-MYC in all BC subtypes (p<0.001). High expression of SLC3A2 protein was associated with poor patient outcome (p<0.001)), but only in the ER+ high proliferation (p=0.01) and triple negative (p=0.04) subtypes. In multivariate analysis SLC3A2 protein was an independent risk factor for shorter breast cancer specific survival (p<0.001). Conclusions: SLC3A2 appears to play a role in the aggressive BC subtypes driven by MYC and could act as a potential prognostic marker. Functional assessment is necessary to reveal its potential therapeutic value in the different BC subtypes

Drainage ditch network extraction from lidar data using deep convolutional neural networks in a low relief landscape

Drainage networks composed of small, channelized ditches are very common in the eastern United States. These are human-made features commonly constructed for wetland drainage and constitute the headwater portion of permanent hydrographic networks. Accurate information on the drainage ditch location can help define where wetlands have been drained and evaluate impacts of artificial drainage patterns on hydrologic changes. Traditional water channel extraction approaches often cannot accurately identify small ditches especially in low-relief agricultural landscapes. In this study, we employed a state-of-the-art deep learning (DL) approach to extract drainage ditches using light detection and ranging (lidar) data in a low-relief agricultural landscape within the Delmarva area. First, we adopted a deep convolutional neural network based on U-Net architecture to classify ditches from different combinations of aerial optical and lidar derived (i.e., topographic and return intensity) features. The classification results were compared with a typical pixel-oriented machine learning classifier, random forest (RF). Next, we improved the connectivity of ditch networks through a minimum-cost approach and a further incorporation of FA to connect with natural drainage networks. Finally, we evaluated the connected drainage networks against flowlines derived from typical flow routing method (D8), an open-source channel network extraction tool (GeoNet), and the U.S. Geological Survey National Hydrography Dataset High Resolution data at 1:24,000 scale. Our results show that the DL model significantly outperformed the RF model, and the lidar derived topographic features were the most important input for ditch classification. The connected drainage networks extracted with DL exhibited pronouncedly higher precision (0.88) and recall (0.89) and a higher positional accuracy (within one pixel) than other flowline products. Overall, this study demonstrates the utility of DL approaches for automated extraction of ditch networks and the important contribution of lidar-derived topographic data for operational drainage network mapping at local and regional scales

Temporal and Individual Variation in Offspring Provisioning by Tree Swallows: A New Method of Automated Nest Attendance Monitoring

Studies of the ecology and evolution of avian nesting behavior have been limited by the difficulty and expense of sampling nest attendance behavior across entire days or throughout a substantial portion of the nestling period. Direct observation of nesting birds using human observers and most automated devices requires sub-sampling of the nestling period, which does not allow for the quantification of the duration of chick-feeding by parents within a day, and may also inadequately capture temporal variation in the rate at which chicks are fed. Here I describe an inexpensive device, the Automated Perch Recorder (APR) system, which collects accurate, long-term data on hourly rates of nest visitation, the duration of a pair's workday, and the total number of visits the pair makes to their nest across the entire period for which it is deployed. I also describe methods for verifying the accuracy of the system in the field, and several examples of how these data can be used to explore the causes of variation in and tradeoffs between the rate at which birds feed their chicks and the total length of time birds spend feeding chicks in a day

Prognostic factors for patients with hepatic metastases from breast cancer

Median survival from liver metastases secondary to breast cancer is only a few months, with very rare 5-year survival. This study reviewed 145 patients with liver metastases from breast cancer to determine factors that may influence survival. Data were analysed using Kaplan–Meier survival curves, univariate and multivariate analysis. Median survival was 4.23 months (range 0.16–51), with a 27.6% 1-year survival. Factors that significantly predicted a poor prognosis on univariate analysis included symptomatic liver disease, deranged liver function tests, the presence of ascites, histological grade 3 disease at primary presentation, advanced age, oestrogen receptor (ER) negative tumours, carcinoembryonic antigen of over 1000 ng ml−1 and multiple vs single liver metastases. Response to treatment was also a significant predictor of survival with patients responding to chemo- or endocrine therapy surviving for a median of 13 and 13.9 months, respectively. Multivariate analysis of pretreatment variables identified a low albumin, advanced age and ER negativity as independent predictors of poor survival. The time interval between primary and metastatic disease, metastases at extrahepatic sites, histological subtype and nodal stage at primary presentation did not predict prognosis. Awareness of the prognostic implications of the above factors may assist in selecting the most appropriate treatment for these patients

Water quality and planktonic microbial assemblages of isolated wetlands in an agricultural landscape

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Wetlands 31 (2011): 885-894, doi:10.1007/s13157-011-0203-6.Wetlands provide ecosystem services including flood protection, water quality enhancement, food chain support, carbon sequestration, and support regional biodiversity. Wetlands occur in human-altered landscapes, and the ongoing ability of these wetlands to provide ecosystem services is lacking. Additionally, the apparent lack of connection of some wetlands, termed geographically isolated, to permanent waters has resulted in little regulatory recognition. We examined the influence of intensive agriculture on water quality and planktonic microbial assemblages of intermittently inundated wetlands. We sampled 10 reference and 10 agriculturally altered wetlands in the Gulf Coastal Plain of Georgia. Water quality measures included pH, alkalinity, dissolved organic carbon, nutrients (nitrate, ammonium, and phosphate), and filterable solids (dry mass and ash-free dry mass). We measured abundance and relative size distribution of the planktonic microbial assemblage (< 45 μm) using flow cytometry. Water quality in agricultural wetlands was characterized by elevated nutrients, pH, and suspended solids. Autotrophic microbial cells were largely absent from both wetland types. Heterotrophic microbial abundance was influenced by nutrients and suspended matter concentration. Agriculture caused changes in microbial assemblages forming the base of wetland food webs. Yet, these wetlands potentially support important ecological services in a highly altered landscape.Funding was provided by the Joseph W. Jones Ecological Research Center.2012-07-2

Community profiling and gene expression of fungal assimilatory nitrate reductases in agricultural soil

Although fungi contribute significantly to the microbial biomass in terrestrial ecosystems, little is known about their contribution to biogeochemical nitrogen cycles. Agricultural soils usually contain comparably high amounts of inorganic nitrogen, mainly in the form of nitrate. Many studies focused on bacterial and archaeal turnover of nitrate by nitrification, denitrification and assimilation, whereas the fungal role remained largely neglected. To enable research on the fungal contribution to the biogeochemical nitrogen cycle tools for monitoring the presence and expression of fungal assimilatory nitrate reductase genes were developed. To the ∼100 currently available fungal full-length gene sequences, another 109 partial sequences were added by amplification from individual culture isolates, representing all major orders occurring in agricultural soils. The extended database led to the discovery of new horizontal gene transfer events within the fungal kingdom. The newly developed PCR primers were used to study gene pools and gene expression of fungal nitrate reductases in agricultural soils. The availability of the extended database allowed affiliation of many sequences to known species, genera or families. Energy supply by a carbon source seems to be the major regulator of nitrate reductase gene expression for fungi in agricultural soils, which is in good agreement with the high energy demand of complete reduction of nitrate to ammonium

Function of SSA Subfamily of Hsp70 Within and Across Species Varies Widely in Complementing Saccharomyces cerevisiae Cell Growth and Prion Propagation

BACKGROUND:The cytosol of most eukaryotic cells contains multiple highly conserved Hsp70 orthologs that differ mainly by their spatio-temporal expression patterns. Hsp70s play essential roles in protein folding, transport or degradation, and are major players of cellular quality control processes. However, while several reports suggest that specialized functions of Hsp70 orthologs were selected through evolution, few studies addressed systematically this issue. METHODOLOGY/PRINCIPAL FINDINGS:We compared the ability of Ssa1p-Ssa4p from Saccharomyces cerevisiae and Ssa5p-Ssa8p from the evolutionary distant yeast Yarrowia lipolytica to perform Hsp70-dependent tasks when expressed as the sole Hsp70 for S. cerevisiae in vivo. We show that Hsp70 isoforms (i) supported yeast viability yet with markedly different growth rates, (ii) influenced the propagation and stability of the [PSI(+)] and [URE3] prions, but iii) did not significantly affect the proteasomal degradation rate of CFTR. Additionally, we show that individual Hsp70 orthologs did not induce the formation of different prion strains, but rather influenced the aggregation properties of Sup35 in vivo. Finally, we show that [URE3] curing by the overexpression of Ydj1p is Hsp70-isoform dependent. CONCLUSION/SIGNIFICANCE:Despite very high homology and overlapping functions, the different Hsp70 orthologs have evolved to possess distinct activities that are required to cope with different types of substrates or stress situations. Yeast prions provide a very sensitive model to uncover this functional specialization and to explore the intricate network of chaperone/co-chaperone/substrates interactions