Climate–ecosystem modelling made easy: The Land Sites Platform

Dynamic Global Vegetation Models (DGVMs) provide a state-of-the-art process-based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single-site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version-controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site-level simulations. We provide input data for 20 established geo-ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines.     

Selected factors limiting the microbial degradation of recalcitrant compounds

Summary The focus of this review is to examine some of the reasons biodegradation may not take place in the environment even though its occurrence in the laboratory has been demonstrated. Some approaches for dealing with chemical persistence will be discussed. In addition, the potential of bioremediation as an in situ clean-up technology will be considered.     

Callionymus leucopoecilus,a new dragonet (Callionymidae) from the Yellow Sea

Callionymus leucopoecilus, a new species of dragonet from the Yellow Sea, is described and illustrated. The new species is characterized by 4 spines in the first dorsal fin, 9 rays each in the second dorsal and anal fins, ii, 5, ii principal caudal fin rays, a preopercular spine formula of      

Evaluation of microbial surfactants for recovery of hydrophobic pollutants from soil

Summary Several microbially produced biosurfactants were evaluated for their ability to remove hydrophobic compounds from soil. The biosurfactants produced byPseudomonas aeruginosa UG2 andAcinetobacter calcoaceticus RAG-1 displayed the best results, with recovery of [¹⁴C]hexachlorobiphenyl from soil slurries of 48.0 and 41.9%, respectively.P. aeruginosa UG2 produced higher levels of extracellular biosurfactants than four otherP. aeruginosa strains.P. aeruginosa UG2 culture filtrate containing biosurfactants enhanced the recovery of several other individual hydrocarbons and polychlorinated biphenyl compounds, as well as several hydrocarbons in a mixture, from soil. The results, suggest that biosurfactants produced byP. aeruginosa UG2 have the potential for remediation of hydrophobic pollutants in soil environments.     

Three major mesoplanktonic communities resolved by in situ imaging in the upper 500 m of the global ocean

Aim

The distribution of mesoplankton communities has been poorly studied at global scale, especially from in situ instruments. This study aims to (1) describe the global distribution of mesoplankton communities in relation to their environment and (2) assess the ability of various environmental-based ocean regionalizations to explain the distribution of these communities.

Location

Global ocean, 0–500 m depth.

Time Period

2008–2019.

Major Taxa Studied

Twenty-eight groups of large mesoplanktonic and macroplanktonic organisms, covering Metazoa, Rhizaria and Cyanobacteria.

Methods

From a global data set of 2500 vertical profiles making use of the Underwater Vision Profiler 5 (UVP5), an in situ imaging instrument, we studied the global distribution of large (>600 μm) mesoplanktonic organisms. Among the 6.8 million imaged objects, 330,000 were large zooplanktonic organisms and phytoplankton colonies, the rest consisting of marine snow particles. Multivariate ordination (PCA) and clustering were used to describe patterns in community composition, while comparison with existing regionalizations was performed with regression methods (RDA).

Results

Within the observed size range, epipelagic plankton communities were Trichodesmium-enriched in the intertropical Atlantic, Copepoda-enriched at high latitudes and in upwelling areas, and Rhizaria-enriched in oligotrophic areas. In the mesopelagic layer, Copepoda-enriched communities were also found at high latitudes and in the Atlantic Ocean, while Rhizaria-enriched communities prevailed in the Peruvian upwelling system and a few mixed communities were found elsewhere. The comparison between the distribution of these communities and a set of existing regionalizations of the ocean suggested that the structure of plankton communities described above is mostly driven by basin-level environmental conditions.

Main Conclusions

In both layers, three types of plankton communities emerged and seemed to be mostly driven by regional environmental conditions. This work sheds light on the role not only of metazoans, but also of unexpected large protists and cyanobacteria in structuring large mesoplankton communities.     

Progressive search in tandem mass spectrometry

A cell exhibits a variety of responses to internal and external cues. These responses are possible, in part, due to the presence of an elaborate gene regulatory network (GRN) in every single cell. In the past 20 years, many groups worked on reconstructing the topological structure of GRNs from large-scale gene expression data using a variety of inference algorithms. Insights gained about participating players in GRNs may ultimately lead to therapeutic benefits. Mutual information (MI) is a widely used metric within this inference/reconstruction pipeline as it can detect any correlation (linear and non-linear) between any number of variables (n-dimensions). However, the use of MI with continuous data (for example, normalized fluorescence intensity measurement of gene expression levels) is sensitive to data size, correlation strength and underlying distributions, and often requires laborious and, at times, ad hoc optimization. In this work, we first show that estimating MI of a bi- and tri-variate Gaussian distribution using k-nearest neighbor (kNN) MI estimation results in significant error reduction as compared to commonly used methods based on fixed binning. Second, we demonstrate that implementing the MI-based kNN Kraskov–Stoögbauer–Grassberger (KSG) algorithm leads to a significant improvement in GRN reconstruction for popular inference algorithms, such as Context Likelihood of Relatedness (CLR). Finally, through extensive in-silico benchmarking we show that a new inference algorithm CMIA (Conditional Mutual Information Augmentation), inspired by CLR, in combination with the KSG-MI estimator, outperforms commonly used methods. Using three canonical datasets containing 15 synthetic networks, the newly developed method for GRN reconstruction—which combines CMIA, and the KSG-MI estimator—achieves an improvement of 20–35% in precision-recall measures over the current gold standard in the field. This new method will enable researchers to discover new gene interactions or better choose gene candidates for experimental validations.     

Model-based control of feed rate and illuminance in a photosynthetic fed-batch reactor for H2S removal

Traditional application of computer to fermentation processes has focused on the measurement and control of parameters such as temperature, pH, vessel pressure, sparge rate, dissolved oxygen, substrate concentration, and product concentration. In a fed-batch reactor with the photosynthetic green sulfur bacterium Chlorobium thiosulfatophilum which converts hydrogen sulfide to elementary sulfur or sulfate, separate measurement of cell mass concentration and sulfur particle concentration turbidimetrically was difficult due to their combined contributions to the total turbidity. Instead of on-line measurement of many process variables, a model-based control of feed rate and illuminance was designed. Optimal operation condition relating feed rate vs. light intensity was obtained to suppress the accumulation of sulfate and sulfide, and to save light energy in a 4-1 photosynthetic fed-batch reactor. This relation was correlated with the inreasing cell mass concentration. A model which describes the cell growth by considering the light attenuation effects due to scattering and absorption, and to crowding effect of the cells, was established beforehand with the results from the experiments. Based on these optimal operating conditions and the cell growth model, automatic controls of feed rate and illuminance were carried out alternatively to the traditional application of computer to fermentation with on-line measurement, realtime response and adjustment of process variables.List of Symbols F ml/min Flow rate of gas mixture - hV lux Average illuminance - Q mmol/(l h) Removal rate of hydrogen sulfide - X mg protein/l Cell mass concentration as protein - X ₀ mg protein/l Initial cell mass concentration - X _m mg protein/l Maximum cell mass concentration - _a h^–1 Apparent specific growth rate     

Atypical lipomatous tumour/well-differentiated liposarcoma found in the buccal mucosa: A rare case report

Oral liposarcomas are uncommon diseases, the most predominant histopathological subtype being atypical lipomatous tumour/well-differentiated liposarcoma. In regard to its clinical aspects in the oral cavity, it is challenging to confirm a diagnosis and develop a treatment plan. In this case report, we present a rare case of atypical lipomatous tumour/well-differentiated liposarcoma in the right cheek of a 77-year-old male patient. Conservative surgery was performed considering the histopathological subtype of the neoplasm. Knowledge of the clinical and histopathological characteristics of this rare disease is essential to maintaining function and aesthetics through conservative treatment in older patients.     

The synthesis of three 4-substitutedbenzo[b]thiophene-2-carboxamidines as potent and selective inhibitors of urokinase

Efficient syntheses of structurally novel 4-substituted benzo[b]thiophene-2-carboxamidmes 1–3, which selectively inhibit urokinase-type plasminogen activator (uPA) with IC₅₀ values of 70–320 nM, are described. The key intermediate, methyl 4-iodobenzo[b]thiophene-2-carboxylate (7), is prepared from 3-fluoroiodobenzene in two steps in 70% overall yield via fluorine-directed metalation/formylation and subsequent thiophene annulation. Amidination of ester 7 gives the 320 nM inhibitor 1. Palladium-catalyzed arylacetylene and vinyl stannane couplings with ester 7 or 4-iodobenzo[b]thiophene-2-carbonitrile (16, derived from 7), respectively, followed by amidination leads to the more potent inhibitors 2 (IC₅₀ = 133 nM) and 3 (IC₅₀ = 70 nM). These compounds represent an important new class of synthetic uPA inhibitors, with carboxamidine 3 being the most potent selective inhibitor currently described in the literature.