Intramolecular vibronic dynamics in molecular solids: C60

Vibronic coupling in solid C60 has been investigated with a combination of resonant photoemission spectroscopy (RPES) and resonant inelastic x-ray scattering (RIXS). Excitation as a function of energy within the lowest unoccupied molecular orbital resonance yielded strong oscillations in intensity and dispersion in RPES, and a strong inelastic component in RIXS. Reconciling these two observations establishes that vibronic coupling in this core hole excitation leads to predominantly inelastic scattering and localization of the excited vibrations on the molecule on a femtosecond time scale. The coupling extends throughout the widths of the frontier valence bands.

Hazardous materials management using a Cradle-to-Grave Tracking and Information System (CGTIS)

Hazardous materials management includes interactions among materials, personnel, facilities, hazards, and processes of various groups within a DOE site`s environmental, safety & health (ES&H) and line organizations. Although each group is charged with addressing a particular aspect of these properties and interactions, the information it requires must be gathered into a coherent set of common data for accurate and consistent hazardous material management and regulatory reporting. It is these common data requirements which the Cradle-to-Grave Tracking and Information System (CGTIS) is designed to satisfy. CGTIS collects information at the point at which a process begins or a material enters a facility, and maintains that information, for hazards management and regulatory reporting, throughout the entire life-cycle by providing direct on-line links to a site`s multitude of data bases to bring information together into one common data model

Hazardous materials management using a Cradle-to-Grave Tracking and Information System (CGTIS)

Hazardous materials management includes interactions among materials, personnel, facilities, hazards, and processes of various groups within a DOE site`s environmental, safety & health (ES&H) and line organizations. Although each group is charged with addressing a particular aspect of these properties and interactions, the information it requires must be gathered into a coherent set of common data for accurate and consistent hazardous material management and regulatory reporting. It is these common data requirements which the Cradle-to-Grave Tracking and Information System (CGTIS) is designed to satisfy. CGTIS collects information at the point at which a process begins or a material enters a facility, and maintains that information, for hazards management and regulatory reporting, throughout the entire life-cycle by providing direct on-line links to a site`s multitude of data bases to bring information together into one common data model

A systems approach to model natural variation in reactive properties of bacterial ribosomes

<p>Abstract</p> <p>Background</p> <p>Natural variation in protein output from translation in bacteria and archaea may be an organism-specific property of the ribosome. This paper adopts a systems approach to model the protein output as a measure of specific ribosome reactive properties in a ribosome-mediated translation apparatus. We use the steady-state assumption to define a transition state complex for the ribosome, coupled with mRNA, tRNA, amino acids and reaction factors, as a subsystem that allows a focus on the completed translational output as a measure of specific properties of the ribosome.</p> <p>Results</p> <p>In analogy to the steady-state reaction of an enzyme complex, we propose a steady-state translation complex for mRNA from any gene, and derive a maximum specific translation activity, <it>T</it>_{<it>a</it>(max)}, as a property of the ribosomal reaction complex. <it>T</it>_{<it>a</it>(max)}has units of <it>a</it>-protein output per time per <it>a</it>-specific mRNA. A related property of the ribosome, <inline-formula><m:math name="1752-0509-2-62-i1" xmlns:m="http://www.w3.org/1998/Math/MathML"><m:semantics><m:mrow><m:msub><m:mover accent="true"><m:mi>T</m:mi><m:mo>˜</m:mo></m:mover><m:mrow><m:mi>a</m:mi><m:mo stretchy="false">(</m:mo><m:mi>max</m:mi><m:mo>⁡</m:mo><m:mo stretchy="false">)</m:mo></m:mrow></m:msub></m:mrow><m:annotation encoding="MathType-MTEF"> MathType@MTEF@5@5@+=feaagaart1ev2aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGaciGaaiaabeqaaeqabiWaaaGcbaGafmivaqLbaGaadaWgaaWcbaGaemyyaeMaeiikaGIagiyBa0MaeiyyaeMaeiiEaGNaeiykaKcabeaaaaa@3464@</m:annotation></m:semantics></m:math></inline-formula>, has units of <it>a</it>-protein per time per total RNA with the relationship <inline-formula><m:math name="1752-0509-2-62-i1" xmlns:m="http://www.w3.org/1998/Math/MathML"><m:semantics><m:mrow><m:msub><m:mover accent="true"><m:mi>T</m:mi><m:mo>˜</m:mo></m:mover><m:mrow><m:mi>a</m:mi><m:mo stretchy="false">(</m:mo><m:mi>max</m:mi><m:mo>⁡</m:mo><m:mo stretchy="false">)</m:mo></m:mrow></m:msub></m:mrow><m:annotation encoding="MathType-MTEF"> MathType@MTEF@5@5@+=feaagaart1ev2aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGaciGaaiaabeqaaeqabiWaaaGcbaGafmivaqLbaGaadaWgaaWcbaGaemyyaeMaeiikaGIagiyBa0MaeiyyaeMaeiiEaGNaeiykaKcabeaaaaa@3464@</m:annotation></m:semantics></m:math></inline-formula> = <it>ρ</it>_{<it>a </it>}<it>T</it>_{<it>a</it>(max)}, where <it>ρ</it>_{<it>a </it>}represents the fraction of total RNA committed to translation output of <it>P</it>_{<it>a </it>}from gene <it>a </it>message. <it>T</it>_{<it>a</it>(max)}as a ribosome property is analogous to <it>k</it>_catfor a purified enzyme, and <inline-formula><m:math name="1752-0509-2-62-i1" xmlns:m="http://www.w3.org/1998/Math/MathML"><m:semantics><m:mrow><m:msub><m:mover accent="true"><m:mi>T</m:mi><m:mo>˜</m:mo></m:mover><m:mrow><m:mi>a</m:mi><m:mo stretchy="false">(</m:mo><m:mi>max</m:mi><m:mo>⁡</m:mo><m:mo stretchy="false">)</m:mo></m:mrow></m:msub></m:mrow><m:annotation encoding="MathType-MTEF"> MathType@MTEF@5@5@+=feaagaart1ev2aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGaciGaaiaabeqaaeqabiWaaaGcbaGafmivaqLbaGaadaWgaaWcbaGaemyyaeMaeiikaGIagiyBa0MaeiyyaeMaeiiEaGNaeiykaKcabeaaaaa@3464@</m:annotation></m:semantics></m:math></inline-formula> is analogous to enzyme specific activity in a crude extract.</p> <p>Conclusion</p> <p>Analogy to an enzyme reaction complex led us to a ribosome reaction model for measuring specific translation activity of a bacterial ribosome. We propose to use this model to design experimental tests of our hypothesis that specific translation activity is a ribosomal property that is subject to natural variation and natural selection much like <it>V</it>_maxand <it>K</it>_mfor any specific enzyme.</p

Genomic analysis of sewage from 101 countries reveals global landscape of antimicrobial resistance

Antimicrobial resistance (AMR) is a major threat to global health. Understanding the emergence, evolution, and transmission of individual antibiotic resistance genes (ARGs) is essential to develop sustainable strategies combatting this threat. Here, we use metagenomic sequencing to analyse ARGs in 757 sewage samples from 243 cities in 101 countries, collected from 2016 to 2019. We find regional patterns in resistomes, and these differ between subsets corresponding to drug classes and are partly driven by taxonomic variation. The genetic environments of 49 common ARGs are highly diverse, with most common ARGs carried by multiple distinct genomic contexts globally and sometimes on plasmids. Analysis of flanking sequence revealed ARG-specific patterns of dispersal limitation and global transmission. Our data furthermore suggest certain geographies are more prone to transmission events and should receive additional attention

Genomic analysis of sewage from 101 countries reveals global landscape of antimicrobial resistance

Antimicrobial resistance (AMR) is a major threat to global health. Understanding the emergence, evolution, and transmission of individual antibiotic resistance genes (ARGs) is essential to develop sustainable strategies combatting this threat. Here, we use metagenomic sequencing to analyse ARGs in 757 sewage samples from 243 cities in 101 countries, collected from 2016 to 2019. We find regional patterns in resistomes, and these differ between subsets corresponding to drug classes and are partly driven by taxonomic variation. The genetic environments of 49 common ARGs are highly diverse, with most common ARGs carried by multiple distinct genomic contexts globally and sometimes on plasmids. Analysis of flanking sequence revealed ARG-specific patterns of dispersal limitation and global transmission. Our data furthermore suggest certain geographies are more prone to transmission events and should receive additional attention.info:eu-repo/semantics/publishedVersio

Northern Fennoscandia via the British Isles: evidence for a novel postglacial recolonization route by winter moth (Operophtera brumata)

The frequency and severity of outbreaks by pestiferous insects is increasing globally, likely as a result of human-mediated introductions of non-native organisms. However, it is not always apparent whether an outbreak is the result of a recent introduction of an evolutionarily naïve population, or of recent disturbance acting on an existing population that arrived previously during natural range expansion. Here we use approximate Bayesian computation to infer the colonization history of a pestiferous insect, the winter moth, Operophtera brumata L. (Lepidoptera: Geometridae), which has caused widespread defoliation in northern Fennoscandia. We generated genotypes using a suite of 24 microsatellite loci and find that populations of winter moth in northern Europe can be assigned to five genetically distinct clusters that correspond with 1) Iceland, 2) the British Isles, 3) Central Europe and southern Fennoscandia, 4) Eastern Europe, and 5) northern Fennoscandia. We find that the northern Fennoscandia winter moth cluster is most closely related to a population presently found in the British Isles, and that these populations likely diverged around 2,900 years ago. This result suggests that current outbreaks are not the result of a recent introduction, but rather that recent climate or habitat disturbance is acting on existing populations that may have arrived to northern Fennoscandia via pre-Roman traders from the British Isles, and/or by natural dispersal across the North Sea likely using the Orkney Islands of northern Scotland as a stepping-stone before dispersing up the Norwegian coast. © 2021. The authors, CC-BY 4.0 license.</p