SOSA – a new model to simulate the concentrations of organic vapours and sulphuric acid inside the ABL – Part 1: Model description and initial evaluation

Chemistry in the atmospheric boundary layer (ABL) is controlled by complex processes of surface fluxes, flow, turbulent transport, and chemical reactions. We present a new model SOSA (model to simulate the concentration of organic vapours and sulphuric acid) and attempt to reconstruct the emissions, transport and chemistry in the ABL in and above a vegetation canopy using tower measurements from the SMEAR II at Hyytiälä, Finland and available soundings data from neighbouring meteorological stations. Using the sounding data for upper boundary condition and nudging the model to tower measurements in the surface layer we were able to get a reasonable description of turbulence and other quantities through the ABL. As a first application of the model, we present vertical profiles of organic compounds and discuss their relation to newly formed particles

Particle concentration and flux dynamics in the atmospheric boundary layer as the indicator of formation mechanism

We carried out column model simulations to study particle fluxes and deposition and to evaluate different particle formation mechanisms at a boreal forest site in Finland. We show that kinetic nucleation of sulphuric acid cannot be responsible for new particle formation alone as the simulated vertical profile of particle number concentration does not correspond to observations. Instead organic induced nucleation leads to good agreement confirming the relevance of the aerosol formation mechanism including organic compounds emitted by the biosphere. <br><br> The simulation of aerosol concentration within the atmospheric boundary layer during nucleation event days shows a highly dynamical picture, where particle formation is coupled with chemistry and turbulent transport. We have demonstrated the suitability of our turbulent mixing scheme in reproducing the most important characteristics of particle dynamics within the boundary layer. Deposition and particle flux simulations show that deposition affects noticeably only the smallest particles in the lowest part of the atmospheric boundary layer

HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands

Wetlands are one of the most significant natural sources of methane (CH4) to the atmosphere. They emit CH4 because decomposition of soil organic matter in waterlogged anoxic conditions produces CH4, in addition to carbon dioxide (CO2). Production of CH4 and how much of it escapes to the atmosphere depend on a multitude of environmental drivers. Models simulating the processes leading to CH4 emissions are thus needed for upscaling observations to estimate present CH4 emissions and for producing scenarios of future atmospheric CH4 concentrations. Aiming at a CH4 model that can be added to models describing peatland carbon cycling, we developed a model called HIMMELI that describes CH4 build-up in and emissions from peatland soils. It is not a full peatland carbon cycle model but it requires the rate of anoxic soil respiration as input. Driven by soil temperature, leaf area index (LAI) of aerenchymatous peatland vegetation and water table depth (WTD), it simulates the concentrations and transport of CH4, CO2 and oxygen (O2) in a layered one-dimensional peat column. Here, we present the HIMMELI model structure, results of tests on the model sensitivity to the input data and to the description of the peat column (peat depth and layer thickness), and an intercomparison of the modelled and measured CH4 fluxes at Siikaneva, a peatland flux measurement site in Southern Finland. As HIMMELI describes only the CH4-related processes, not the full carbon cycle, our analysis revealed mechanisms and dependencies that may remain hidden when testing CH4 models connected to complete peatland carbon models, which is usually the case. Our results indicated that 1) the model is flexible and robust and thus suitable for different environments; 2) the simulated CH4 emissions largely depend on the prescribed rate of anoxic respiration; 3) the sensitivity of the total CH4 emission to other input variables, LAI and WTD, is mainly mediated via the O2 concentrations that affect the CH4 production and oxidation rates; 4) with given input respiration, the peat column description does not affect significantly the simulated CH4 emissions

Cell-to-cell diversity in protein levels of a gene driven by a tetracycline inducible promoter

<p>Abstract</p> <p>Background</p> <p>Gene expression in <it>Escherichia coli </it>is regulated by several mechanisms. We measured in single cells the expression level of a single copy gene coding for green fluorescent protein (GFP), integrated into the genome and driven by a tetracycline inducible promoter, for varying induction strengths. Also, we measured the transcriptional activity of a tetracycline inducible promoter controlling the transcription of a RNA with 96 binding sites for MS2-GFP.</p> <p>Results</p> <p>The distribution of GFP levels in single cells is found to change significantly as induction reaches high levels, causing the Fano factor of the cells' protein levels to increase with mean level, beyond what would be expected from a Poisson-like process of RNA transcription. In agreement, the Fano factor of the cells' number of RNA molecules target for MS2-GFP follows a similar trend. The results provide evidence that the dynamics of the promoter complex formation, namely, the variability in its duration from one transcription event to the next, explains the change in the distribution of expression levels in the cell population with induction strength.</p> <p>Conclusions</p> <p>The results suggest that the open complex formation of the tetracycline inducible promoter, in the regime of strong induction, affects significantly the dynamics of RNA production due to the variability of its duration from one event to the next.</p

scShaper: an ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq data

MotivationComputational models are needed to infer a representation of the cells, i.e. a trajectory, from single-cell RNA-sequencing data that model cell differentiation during a dynamic process. Although many trajectory inference methods exist, their performance varies greatly depending on the dataset and hence there is a need to establish more accurate, better generalizable methods.ResultsWe introduce scShaper, a new trajectory inference method that enables accurate linear trajectory inference. The ensemble approach of scShaper generates a continuous smooth pseudotime based on a set of discrete pseudotimes. We demonstrate that scShaper is able to infer accurate trajectories for a variety of trigonometric trajectories, including many for which the commonly used principal curves method fails. A comprehensive benchmarking with state-of-the-art methods revealed that scShaper achieved superior accuracy of the cell ordering and, in particular, the differentially expressed genes. Moreover, scShaper is a fast method with few hyperparameters, making it a promising alternative to the principal curves method for linear pseudotemporal ordering.Availability and implementationscShaper is available as an R package at https://github.com/elolab/scshaper. The test data are available at https://doi.org/10.5281/zenodo.5734488.</p

ILoReg: a tool for high-resolution cell population identification from single-cell RNA-seq data

Single-cell RNA-seq allows researchers to identify cell populations based on unsupervised clustering of the transcriptome. However, subpopulations can have only subtle transcriptomic differences and the high dimensionality of the data makes their identification challenging.\nWe introduce ILoReg, an R package implementing a new cell population identification method that improves identification of cell populations with subtle differences through a probabilistic feature extraction step that is applied before clustering and visualization. The feature extraction is performed using a novel machine learning algorithm, called iterative clustering projection (ICP), that uses logistic regression and clustering similarity comparison to iteratively cluster data. Remarkably, ICP also manages to integrate feature selection with the clustering through L1-regularization, enabling the identification of genes that are differentially expressed between cell populations. By combining solutions of multiple ICP runs into a single consensus solution, ILoReg creates a representation that enables investigating cell populations with a high resolution. In particular, we show that the visualization of ILoReg allows segregation of immune and pancreatic cell populations in a more pronounced manner compared with current state-of-the-art methods.\nILoReg is available as an R package at https://bioconductor.org/packages/ILoReg.\nSupplementary data are available at Supplementary Information and Supplementary Files 1 and 2.\nMOTIVATION\nRESULTS\nAVAILABILITY\nSUPPLEMENTARY INFORMATIO

Global emissions of terpenoid VOCs from terrestrial vegetation in the last millennium

Peer reviewe

Large methane releases lead to strong aerosol forcing and reduced cloudiness

The release of vast quantities of methane into the atmosphere as a result of clathrate destabilization is a potential mechanism for rapid amplification of global warming. Previous studies have calculated the enhanced warming based mainly on the radiative effect of the methane itself, with smaller contributions from the associated carbon dioxide or ozone increases. Here, we study the effect of strongly elevated methane (CH4) levels on oxidant and aerosol particle concentrations using a combination of chemistry-transport and general circulation models. A 10-fold increase in methane concentrations is predicted to significantly decrease hydroxyl radical (OH) concentrations, while moderately increasing ozone (O3). These changes lead to a 70 % increase in the atmospheric lifetime of methane, and an 18 % decrease in global mean cloud droplet number concentrations (CDNC). The CDNC change causes a radiative forcing that is comparable in magnitude to the longwave radiative forcing ("enhanced greenhouse effect") of the added methane. Together, the indirect CH4-O3 and CH4-OH-aerosol forcings could more than double the warming effect of large methane increases. Our findings may help explain the anomalously large temperature changes associated with historic methane releases

Anode ink formulation for a fully printed flexible fuel cell stack

In fuel cells the underlying reactions take place at the catalyst layers composed of materials favoring the desired electrochemical reactions. This paper introduces a formulation process for a catalyst inkjet ink used as an anode for a fully printed flexible fuel cell stack. The optimal ink formulation was 2.5 wt% of carbon–platinum–ruthenium mixture with 0.5% Nafion concentration in a diacetone alcohol solvent vehicle. The best jetting performance was achieved when 1 wt% binder was included in the ink formulation. Anodes with resistivity of approximately 0.1 Ω cm were inkjet printed, which is close to the commercial anode resistivity of 0.05 Ω cm. The anodes were used in fuel cell stacks that were prepared by utilizing only printing methods. The best five-cell-air-breathing stack showed an open circuit potential under H2/air conditions of 3.4 V. The peak power of this stack was 120 µW cm−2 at 1.75 V, with a resistance obtained from potentiostatic impedance analysis of 295 Ohm cm2. The printed electrodes showed a performance suitable for low-performance solutions, such as powering single-use sensors