פרסומים

Summary Natural landscapes are both fragmented and heterogeneous, affecting the distribution of organisms, and their interactions. While predation in homogeneous environments increases the probability of population extinction, fragmentation/heterogeneity promotes coexistence and enhances community stability as shown by experimentation with animals and microorganisms, and supported by theory. Patch connectivity can modulate such effects but how microbial predatory interactions are affected by water-driven connectivity is unknown. In soil, patch habitability by microorganisms, and their connectivity depend upon the water saturation degree (SD). Here, using the obligate bacterial predator Bdellovibrio bacteriovorus, and a Burkholderia prey, we show that soil spatial heterogeneity profoundly affects predatory dynamics, enhancing long-term co-existence of predator and prey in a SD-threshold dependent-manner. However, as patches and connectors cannot be distinguished in these soil matrices, metapopulations cannot be invoked to explain the dynamics of increased persistence. Using a set of experiments combined with statistical and physical models we demonstrate and quantify how under full connectivity, predation is independent of water content but depends on soil microstructure characteristics. In contrast, the SD below which predation is largely impaired corresponds to a threshold below which the water network collapses and water connectivity breaks down, preventing the bacteria to move within the soil matrix.

The recognition of treated wastewater (TWW) as an alternative water resource is expanding in areas with a shortage of freshwater (FW). While many studies have been devoted to the effects of long-term irrigation with TWW on soil wettability and spatial flow variations in the soil profile, much less attention has been given to the spatial distribution of soil water repellency in the soil surface layer. This is the objective of the current study. Undisturbed soil samples (5 cm thick) were taken at 15-cm intervals parallel to a drip lateral in two adjacent plots of a commercial citrus orchard in central Israel. Each soil sample was sectioned into five consecutive 1-cm layers for which soil water repellency was determined by water drop penetration time method, and soil organic matter by loss-on-ignition method. Geostatistics and multivariate empirical mode decomposition were used to investigate the overall and scale-specific spatial variation of soil water repellency and its dependence on dripper intervals along the lateral. A high degree of soil water repellency with strong spatial variation was found in the surface soil after 4–6 years of TWW irrigation. Weak to moderate spatial dependence of soil water repellency with maximum autocorrelation distance of around 30 cm was discovered by geostatistical analysis. The spatial distribution of soil water repellency was considered to be greatly affected by the location of the drippers, being higher between adjacent drippers and lower underneath them. This soil water repellency distribution is presumed to result from ongoing lateral displacement of the amphiphilic substances in the TWW toward the outer edge of the wetted plume periphery. Multivariate empirical mode decomposition of the overall spatial variation of soil water repellency yielded three scale-specific variations with corresponding characteristic scales of 30 cm, 110 cm and 200 cm. Most of the soil water repellency variation was separated into the 30 cm and 110 cm spatial scales, which were correlated to processes related to the drippers and trees. Replacing TWW with FW for the reclamation of water-repellent soils partially alleviated the intensity of TWW irrigation-induced soil water repellency. Moreover, an inconsistency between the hot spots of water-repellency development between adjacent drippers and the areas that are effectively ameliorated by FW irrigation below the drippers could be developed and affect the spatial distribution of flow pattern in an a priori unpredictable way.

RNA localization in eukaryotes is a mechanism to regulate transcripts fate. Conversely, bacterial transcripts were not assumed to be specifically localized. We previously demonstrated that E. coli mRNAs may localize to where their products localize in a translation-independent manner, thus challenging the transcription-translation coupling extent. However, the scope of RNA localization in bacteria remained unknown. Here, we report the distribution of the E. coli transcriptome between the membrane, cytoplasm, and poles by combining cell fractionation with deep-sequencing (Rloc-seq). Our results reveal asymmetric RNA distribution on a transcriptome-wide scale, significantly correlating with proteome localization and prevalence of translation-independent RNA localization. The poles are enriched with stress-related mRNAs and small RNAs, the latter becoming further enriched upon stress in an Hfq-dependent manner. Genome organization may play a role in localizing membrane protein-encoding transcripts. Our results show an unexpected level of intricacy in bacterial transcriptome organization and highlight the poles as hubs for regulation.

RNA localization in eukaryotes is a mechanism to regulate transcripts fate. Conversely, bacterial transcripts were not assumed to be specifically localized. We previously demonstrated that E. coli mRNAs may localize to where their products localize in a translation-independent manner, thus challenging the transcription-translation coupling extent. However, the scope of RNA localization in bacteria remained unknown. Here, we report the distribution of the E. coli transcriptome between the membrane, cytoplasm, and poles by combining cell fractionation with deep-sequencing (Rloc-seq). Our results reveal asymmetric RNA distribution on a transcriptome-wide scale, significantly correlating with proteome localization and prevalence of translation-independent RNA localization. The poles are enriched with stress-related mRNAs and small RNAs, the latter becoming further enriched upon stress in an Hfq-dependent manner. Genome organization may play a role in localizing membrane protein-encoding transcripts. Our results show an unexpected level of intricacy in bacterial transcriptome organization and highlight the poles as hubs for regulation.

RNA localization in eukaryotes is a mechanism to regulate transcripts fate. Conversely, bacterial transcripts were not assumed to be specifically localized. We previously demonstrated that E. coli mRNAs may localize to where their products localize in a translation-independent manner, thus challenging the transcription-translation coupling extent. However, the scope of RNA localization in bacteria remained unknown. Here, we report the distribution of the E. coli transcriptome between the membrane, cytoplasm, and poles by combining cell fractionation with deep-sequencing (Rloc-seq). Our results reveal asymmetric RNA distribution on a transcriptome-wide scale, significantly correlating with proteome localization and prevalence of translation-independent RNA localization. The poles are enriched with stress-related mRNAs and small RNAs, the latter becoming further enriched upon stress in an Hfq-dependent manner. Genome organization may play a role in localizing membrane protein-encoding transcripts. Our results show an unexpected level of intricacy in bacterial transcriptome organization and highlight the poles as hubs for regulation.

There is an increasing interest in using hyperspectral data for phenotyping and crop management while overcoming the challenge of changing atmospheric conditions. The Piccolo dual field-of-view system collects up- and downwelling radiation nearly simultaneously with one spectrometer. Such systems offer great promise for crop monitoring under highly variable atmospheric conditions. Here, the system’s utility from a tractor-mounted boom was demonstrated for a case study of estimating soybean plant populations in early vegetative stages. The Piccolo system is described and its performance under changing sky conditions are assessed for two replicates of the same experiment. Plant population assessment was estimated by partial least squares regression (PLSR) resulting in stable estimations by models calibrated and validated under sunny and cloudy or cloudy and sunny conditions, respectively. We conclude that the Piccolo system is effective for data collection under variable atmospheric conditions, and we show its feasibility of operation for precision agriculture research and potential commercial applications. © 2019, MDPI AG. All rights reserved.

Monitoring plant root within the subsurface is important but challenging, due to the opacity of the soil. Recently, it was demonstrated that the spectral induced polarization (SIP) method has the potential to image roots, but the mechanisms governing the SIP signal of roots remain poorly understood. Here, we present a numerical model and experimental setup that was designed to establish relationships between root properties and the SIP response and to enhance our understanding of the polarization mechanisms of roots. Our preliminary results show a positive correlation between root mass and quadrature conductivity in nutrient solution. Surprisingly, a negative relation was found in soil. Overall, the results from this study further demonstrate the potential of the SIP method to monitor roots. © 2018 SEG.

Grasses accumulate silicon in the form of silicic acid, which is precipitated as amorphous silica in microscopic particles termed phytoliths. These particles comprise a variety of morphologies according to the cell type in which the silica was deposited. Despite the evident morphological differences, phytolith chemistry has mostly been analysed in bulk samples, neglecting differences between the varied types formed in the same species. In this work, we extracted leaf phytoliths from mature plants of (L.) Moench. Using solid state NMR and thermogravimetric analysis, we show that the extraction methods alter greatly the silica molecular structure, its condensation degree and the trapped organic matter. Measurements of individual phytoliths by Raman and synchrotron FTIR microspectroscopies in combination with multivariate analysis separated bilobate silica cells from prickles and long cells, based on the silica molecular structures and the fraction and composition of occluded organic matter. The variations in structure and composition of sorghum phytoliths suggest that the biological pathways leading to silica deposition vary between these cell types.

Abstract In this work, the electronic structure and spectroscopic properties of lumiflavin are calculated using various quantum chemical methods. The excitation energies for ten singlet and triplet states as well as the analysis of the electron density difference are assessed using various wave function-based methods and density functionals. The relative order of singlet and triplet excited states is established on the basis of the coupled cluster method CC2. We find that at least seven singlet excited states are required to assign all peaks in the UV/Vis spectrum. In addition, we have studied the solvatochromic effect on the excitation energies and found differential effects except for the first bright excited state. Vibrational frequencies as well as IR, Raman and resonance Raman intensities are simulated and compared to their experimental counterparts. We have assigned peaks, assessed the effect of anharmonicity, and confirmed the previous assignments in case of the most intense transitions. Finally, we have studied the NMR shieldings and established the effect of the solvent polarity. The present study provides data for lumiflavin in the gas phase and in implicit solvent model that can be used as a reference for the protein-embedded flavin simulations and assignment of experimental spectra.

Background:Staphylococcus aureus (SA) is a major cause of bacteremia in children. Methicillin-resistant SA (MRSA) is considered a public health threat; however, the differences in the prognosis of children with methicillin-susceptible SA (MSSA) versus MRSA bacteremia are not well defined.Methods:Dat

Often, large, high-dimensional datasets collected across multiple modalities can be organized as a higher-order tensor. Low-rank tensor decomposition then arises as a powerful and widely used tool to discover simple low-dimensional structures underlying such data. However, we currently lack a theoretical understanding of the algorithmic behavior of low-rank tensor decompositions. We derive Bayesian approximate message passing (AMP) algorithms for recovering arbitrarily shaped low-rank tensors buried within noise, and we employ dynamic mean field theory to precisely characterize their performance. Our theory reveals the existence of phase transitions between easy, hard and impossible inference regimes, and displays an excellent match with simulations. Moreover it reveals several qualitative surprises compared to the behavior of symmetric, cubic tensor decomposition. Finally, we compare our AMP algorithm to the most commonly used algorithm, alternating least squares (ALS), and demonstrate that AMP significantly outperforms ALS in the presence of noise.