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Abstract. Wetlands and freshwater bodies (mainly lakes) are the largestnatural sources of the greenhouse gas CH4 to the atmosphere. Great effortshave been made to quantify these source emissions and their uncertainties.Previous research suggests that there might be significant uncertaintiescoming from “double accounting” emissions from freshwater bodies andwetlands. Here we quantify the methane emissions from both land andfreshwater bodies in the pan-Arctic with two process-based biogeochemistrymodels by minimizing the double accounting at the landscape scale. Twonon-overlapping dynamic areal change datasets are used to drive the models.We estimate that the total methane emissions from the pan-Arctic are 36.46 ± 1.02 Tg CH4 yr−1 during 2000–2015, of which wetlands andfreshwater bodies are 21.69 ± 0.59 Tg CH4 yr−1 and 14.76 ± 0.44 Tg CH4 yr−1, respectively. Our estimation narrows thedifference between previous bottom-up (53.9 Tg CH4 yr−1) andtop-down (29 Tg CH4 yr−1) estimates. Our correlation analysisshows that air temperature is the most important driver for methane emissionsof inland water systems. Wetland emissions are also significantly affected byvapor pressure, while lake emissions are more influenced by precipitation andlandscape areal changes. Sensitivity tests indicate that pan-Arctic lakeCH4 emissions were highly influenced by air temperature but less bylake sediment carbon increase. 

Abstract Motivation Biclustering has emerged as a powerful approach to identifying functional patterns in complex biological data. However, existing tools are limited by their accuracy and efficiency to recognize various kinds of complex biclusters submerged in ever large datasets. We introduce a novel fast and highly accurate algorithm RecBic to identify various forms of complex biclusters in gene expression datasets. Results We designed RecBic to identify various trend-preserving biclusters, particularly, those with narrow shapes, i.e. clusters where the number of genes is larger than the number of conditions/samples. Given a gene expression matrix, RecBic starts with a column seed, and grows it into a full-sized bicluster by simply repetitively comparing real numbers. When tested on simulated datasets in which the elements of implanted trend-preserving biclusters and those of the background matrix have the same distribution, RecBic was able to identify the implanted biclusters in a nearly perfect manner, outperforming all the compared salient tools in terms of accuracy and robustness to noise and overlaps between the clusters. Moreover, RecBic also showed superiority in identifying functionally related genes in real gene expression datasets. Availability and implementation Code, sample input data and usage instructions are available at the following websites. Code: https://github.com/holyzews/RecBic/tree/master/RecBic/. Data: http://doi.org/10.5281/zenodo.3842717. Supplementary information Supplementary data are available at Bioinformatics online. 

Over the past decade, Ag 2 Se has attracted increasing attention due to its potentially excellent thermoelectric (TE) performance as an n-type semiconductor. It has been considered a promising alternative to Bi–Te alloys and other commonly used yet toxic and/or expensive TE materials. To optimize the TE performance of Ag 2 Se, recent research has focused on fabricating nanosized Ag 2 Se. However, synthesizing Ag 2 Se nanoparticles involves energy-intensive and time-consuming techniques with poor yield of final product. In this work, we report a low-cost, solution-processed approach that enables the formation of Ag 2 Se thin films from Cu 2−x Se template films via cation exchange at room temperature. Our simple two-step method involves fabricating Cu 2−x Se thin films by the thiol-amine dissolution of bulk Cu 2 Se, followed by soaking Cu 2−x Se films in AgNO 3 solution and annealing to form Ag 2 Se. We report an average power factor (PF) of 617 ± 82 μW m −1 K −2 and a corresponding ZT value of 0.35 at room temperature. We obtained a maximum PF of 825 μW m −1 K −2 and a ZT value of 0.46 at room temperature for our best-performing Ag 2 Se thin-film after soaking for 5 minutes. These high PFs have been achieved via full solution processing without hot-pressing.