Now showing items 21-40 of 53347

    • Constraining families of dynamic models using geological, geodetic and strong ground motion data: the Mw 6.5, October 30th, 2016, Norcia earthquake, Italy

      Tinti, Elisa; Casarotti, Emanuele; Ulrich, Thomas; Li, D.; Taufiqurrahman, Taufiqurrahman; Gabriel, Alice-Agnes (California Digital Library (CDL), 2021-06-14) [Preprint]
      The 2016 Central Italy earthquake sequence is characterized by remarkable rupture complexity, including highly heterogeneous slip across multiple faults in an extensional tectonic regime. The dense coverage and high quality of geodetic and seismic data allow to image intriguing details of the rupture kinematics of the largest earthquake of the sequence, the Mw 6.5 October 30th, 2016 Norcia earthquake, such as an energetically weak nucleation phase. Several kinematic models suggest multiple fault planes rupturing simultaneously, however, the mechanical viability of such models is not guaranteed.Using 3D dynamic rupture and seismic wave propagation simulations accounting for two fault planes, we constrain 'families' of spontaneous dynamic models informed by a high-resolution kinematic rupture model of the earthquake. These families differ in their parameterization of initial heterogeneous shear stress and strength in the framework of linear slip weakening friction.First, we dynamically validate the kinematically inferred two-fault geometry and rake inferences with models based on only depth-dependent stress and constant friction coefficients. Then, more complex models with spatially heterogeneous dynamic parameters allow us to retrieve slip distributions similar to the target kinematic model and yield good agreement with seismic and geodetic observations. We discuss the consistency of the assumed constant or heterogeneous static and dynamic friction coefficients with mechanical properties of rocks at 3-10 km depth characterizing the Italian Central Apennines and their local geological and lithological implications. We suggest that suites of well-fitting dynamic rupture models belonging to the same family generally exist and can be derived by exploiting the trade-offs between dynamic parameters.Our approach will be applicable to validate the viability of kinematic models and classify spontaneous dynamic rupture scenarios that match seismic and geodetic observations at the same time as geological constraints.
    • Training Graph Neural Networks with 1000 Layers

      Li, Guohao; Müller, Matthias; Ghanem, Bernard; Koltun, Vladlen (arXiv, 2021-06-14) [Preprint]
      Deep graph neural networks (GNNs) have achieved excellent results on various tasks on increasingly large graph datasets with millions of nodes and edges. However, memory complexity has become a major obstacle when training deep GNNs for practical applications due to the immense number of nodes, edges, and intermediate activations. To improve the scalability of GNNs, prior works propose smart graph sampling or partitioning strategies to train GNNs with a smaller set of nodes or sub-graphs. In this work, we study reversible connections, group convolutions, weight tying, and equilibrium models to advance the memory and parameter efficiency of GNNs. We find that reversible connections in combination with deep network architectures enable the training of overparameterized GNNs that significantly outperform existing methods on multiple datasets. Our models RevGNN-Deep (1001 layers with 80 channels each) and RevGNN-Wide (448 layers with 224 channels each) were both trained on a single commodity GPU and achieve an ROC-AUC of $87.74 \pm 0.13$ and $88.14 \pm 0.15$ on the ogbn-proteins dataset. To the best of our knowledge, RevGNN-Deep is the deepest GNN in the literature by one order of magnitude. Please visit our project website https://www.deepgcns.org/arch/gnn1000 for more information.
    • Smart Gradient -- An Adaptive Technique for Improving Gradient Estimation

      Fattah, Esmail Abdul; Niekerk, Janet Van; Rue, Haavard (arXiv, 2021-06-14) [Preprint]
      Computing the gradient of a function provides fundamental information about its behavior. This information is essential for several applications and algorithms across various fields. One common application that require gradients are optimization techniques such as stochastic gradient descent, Newton's method and trust region methods. However, these methods usually requires a numerical computation of the gradient at every iteration of the method which is prone to numerical errors. We propose a simple limited-memory technique for improving the accuracy of a numerically computed gradient in this gradient-based optimization framework by exploiting (1) a coordinate transformation of the gradient and (2) the history of previously taken descent directions. The method is verified empirically by extensive experimentation on both test functions and on real data applications. The proposed method is implemented in the R package smartGrad and in C++.
    • Lagrangian Spatio-Temporal Covariance Functions for Multivariate Nonstationary Random Fields

      Salvaña, Mary Lai O. (2021-06-14) [Thesis]
      Advisor: Genton, Marc G.
      Committee members: Ombao, Hernando; Sang, Huiyan; Stenchikov, Georgiy L.
      In geostatistical analysis, we are faced with the formidable challenge of specifying a valid spatio-temporal covariance function, either directly or through the construction of processes. This task is di cult as these functions should yield positive de nite covariance matrices. In recent years, we have seen a ourishing of methods and theories on constructing spatiotemporal covariance functions satisfying the positive de niteness requirement. The current state-of-the-art when modeling environmental processes are those that embed the associated physical laws of the system. The class of Lagrangian spatio-temporal covariance functions ful lls this requirement. Moreover, this class possesses the allure that they turn already established purely spatial covariance functions into spatio-temporal covariance functions by a direct application of the concept of Lagrangian reference frame. In the three main chapters that comprise this dissertation, several developments are proposed and new features are provided to this special class. First, the application of the Lagrangian reference frame on transported purely spatial random elds with second-order nonstationarity is explored, an appropriate estimation methodology is proposed, and the consequences of model misspeci cation is tackled. Furthermore, the new Lagrangian models and the new estimation technique are used to analyze particulate matter concentrations over Saudi Arabia. Second, a multivariate version of the Lagrangian framework is established, catering to both secondorder stationary and nonstationary spatio-temporal random elds. The capabilities of the Lagrangian spatio-temporal cross-covariance functions are demonstrated on a bivariate reanalysis climate model output dataset previously analyzed using purely spatial covariance functions. Lastly, the class of Lagrangian spatio-temporal cross-covariance functions with multiple transport behaviors is presented, its properties are explored, and its use is demonstrated on a bivariate pollutant dataset of particulate matter in Saudi Arabia. Moreover, the importance of accounting for multiple transport behaviors is discussed and validated via numerical experiments. Together, these three extensions to the Lagrangian framework makes it a more viable geostatistical approach in modeling realistic transport scenarios.
    • Cu boosting the collaborative effect of Ni and H+ in alloyed NiCu/saponite catalysts for hydrogenolysis of glycidol

      Gebretsadik, Fiseha Bogale; Ruiz-Martinez, Javier; González, María Dolores; Salagre, Pilar; Cesteros, Yolanda (Dalton transactions (Cambridge, England : 2003), Royal Society of Chemistry (RSC), 2021-06-14) [Article]
      The effect of copper on various acid saponite supported Ni-Cu bimetallic catalysts, prepared with different Ni : Cu ratios, was studied for the liquid phase hydrogenolysis of glycidol on a batch reactor at 393 and 453 K. Characterization of the catalysts showed that Ni and Cu are in close contact as the XRD measurements evidenced the formation of an alloy. H2 chemisorption results revealed that the measured metallic area progressively decreased with an increase in the wt% of copper. In the presence of high metal activity (higher Ni wt%), the formation of 1,2-propanediol (1,2-PD) outweighed, while acid activity led to the formation of dimerization and oligomerization products. The addition of Cu and the increase of the reaction temperature decreased the diol formation but boosted the 1,3-PD/1,2-PD ratio. This could be explained by an improvement of the collaborative effect between the metal Ni and the H+ of the saponite. Therefore, the presence of an appropriate amount of Cu allowed the control of the hydrogenation capacity of Ni and enhanced the collaborative effect of Ni and H+ favouring the formation of 1,3-propanediol with respect to 1,2-propanediol.
    • Aromatics Production via Methanol-Mediated Transformation Routes

      Li, Teng; Shoinkhorova, Tuiana; Gascon, Jorge; Ruiz-Martinez, Javier (ACS Catalysis, American Chemical Society (ACS), 2021-06-13) [Article]
      The methanol-to-aromatics (MTA) process is regarded as a promising route to produce aromatic commodities through non-petroleum carbon resources, such as biomass, waste, coal, natural gas, and CO2. In contrast with the industrially implemented methanol-to-olefin (MTO) process, most MTA studies are still in the laboratory-scale stage. Recently, a few demonstration plants of MTA have been successfully launched, indicating the importance and the gradual industrial maturity of this technology. However, there are still many fundamental questions and technological challenges that must be addressed. In this Review, we summarize the recent advances in mechanistic understanding on the reaction and catalyst deactivation during MTA, elaborate the available strategies to improve the catalytic performance, and correlate MTA studies with other important catalytic aromatization processes. With this knowledge in hand, we share our views on future research directions in this field.
    • Prediction of protein-protein interaction sites through eXtreme gradient boosting with kernel principal component analysis.

      Wang, Xue; Zhang, Yaqun; Yu, Bin; Salhi, Adil; Chen, Ruixin; Wang, Lin; Liu, Zengfeng (Computers in biology and medicine, Elsevier BV, 2021-06-13) [Article]
      Predicting protein-protein interaction sites (PPI sites) can provide important clues for understanding biological activity. Using machine learning to predict PPI sites can mitigate the cost of running expensive and time-consuming biological experiments. Here we propose PPISP-XGBoost, a novel PPI sites prediction method based on eXtreme gradient boosting (XGBoost). First, the characteristic information of protein is extracted through the pseudo-position specific scoring matrix (PsePSSM), pseudo-amino acid composition (PseAAC), hydropathy index and solvent accessible surface area (ASA) under the sliding window. Next, these raw features are preprocessed to obtain more optimal representations in order to achieve better prediction. In particular, the synthetic minority oversampling technique (SMOTE) is used to circumvent class imbalance, and the kernel principal component analysis (KPCA) is applied to remove redundant characteristics. Finally, these optimal features are fed to the XGBoost classifier to identify PPI sites. Using PPISP-XGBoost, the prediction accuracy on the training dataset Dset186 reaches 85.4%, and the accuracy on the independent validation datasets Dtestset72, PDBtestset164, Dset_448 and Dset_355 reaches 85.3%, 83.9%, 85.8% and 85.4%, respectively, which all show an increase in accuracy against existing PPI sites prediction methods. These results demonstrate that the PPISP-XGBoost method can further enhance the prediction of PPI sites.
    • Plateau–Rayleigh Instability Induced Self-Assembly of Nano-Cubes in Stretched DNA Molecules

      Zhang, Peng; Yang, Zi Qiang; Thoroddsen, Sigurdur T; Di Fabrizio, Enzo (Submitted to MNE2021 - 47th international conference on Micro and Nano Engineering, 2021-06-13) [Preprint]
    • Development of the myzozoan aquatic parasite Perkinsus marinus as a versatile experimental genetic model organism

      Einarsson, Elin; Lassadi, Imen; Zielinski, Jana; Guan, Qingtian; Wyler, Tobias; Pain, Arnab; Gornik, Sebastian G; Waller, Ross F (Cold Spring Harbor Laboratory, 2021-06-12) [Preprint]
      The phylum Perkinsozoa is an aquatic parasite lineage that has devastating effects on commercial and natural mollusc populations, and also comprises parasites of algae, fish and amphibians. They are related to, and share much of their biology with, dinoflagellates and apicomplexans and thus offer excellent genetic models for both parasitological and evolutionary studies. Genetic transformation has been previously achieved for select Perkinsus spp. but with few tools for transgene expression and only limited selection efficacy. We thus sought to expand the power of experimental genetic tools for Perkinsus marinus — the principal perkinsozoan model to date. We constructed a modular plasmid assembly system that enables expression of multiple genes simultaneously. We developed an efficient selection system for three drugs, puromycin, bleomycin and blasticidin, that achieves transformed cell populations in as little as three weeks. We developed and quantified eleven new promoters of variable expression strength. Furthermore, we identified that genomic integration of transgenes is predominantly via non-homologous recombination and often involves transgene fragmentation including deletion of some introduced elements. To counter these dynamic processes, we show that bi-cistronic transcripts using the viral 2A peptides can couple selection systems to the maintenance of the expression of a transgene of interest. Collectively, these new tools and insights provide new capacity to efficiently genetically modify and study Perkinsus as an aquatic parasite and evolutionary model.
    • An integrative investigation of sensory organ development and orientation behavior throughout the larval phase of a coral reef fish.

      Majoris, John E.; Foretich, Matthew A; Hu, Yinan; Nickles, Katie R; Di Persia, Camilla L; Chaput, Romain; Schlatter, E; Webb, Jacqueline F; Paris, Claire B; Buston, Peter M (Scientific reports, Springer Science and Business Media LLC, 2021-06-12) [Article]
      The dispersal of marine larvae determines the level of connectivity among populations, influences population dynamics, and affects evolutionary processes. Patterns of dispersal are influenced by both ocean currents and larval behavior, yet the role of behavior remains poorly understood. Here we report the first integrated study of the ontogeny of multiple sensory systems and orientation behavior throughout the larval phase of a coral reef fish-the neon goby, Elacatinus lori. We document the developmental morphology of all major sensory organs (lateral line, visual, auditory, olfactory, gustatory) together with the development of larval swimming and orientation behaviors observed in a circular arena set adrift at sea. We show that all sensory organs are present at hatch and increase in size (or number) and complexity throughout the larval phase. Further, we demonstrate that most larvae can orient as early as 2 days post-hatch, and they swim faster and straighter as they develop. We conclude that sensory organs and swimming abilities are sufficiently developed to allow E. lori larvae to orient soon after hatch, suggesting that early orientation behavior may be common among coral reef fishes. Finally, we provide a framework for testing alternative hypotheses for the orientation strategies used by fish larvae, laying a foundation for a deeper understanding of the role of behavior in shaping dispersal patterns in the sea.
    • Nutrient and temperature constraints on primary production and net phytoplankton growth in a tropical ecosystem

      López-Sandoval, Daffne C.; Duarte, Carlos M.; Agusti, Susana (Limnology and Oceanography, Wiley, 2021-06-12) [Article]
      The Red Sea depicts a north–south gradient of positively correlated temperature and nutrient concentration. Despite its overall oligotrophic characteristics, primary production rates in the Red Sea vary considerably. In this study, based on five cruises and a 2-year time series (2016–2018) sampling in the Central Red Sea, we determined phytoplankton photosynthetic rates (PP) by using 13C as a tracer and derived phytoplankton net growth rates (μ) and chlorophyll a (Chl a)-normalized photosynthesis (PB). Our results indicate a ninefold variation (14–125 mgC m−2 h−1) in depth-integrated primary production and reveal a marked seasonality in PP, PB, and μ. Depth-integrated PP remained <30 mg C m−2 h−1 during spring and summer, and peaked in autumn–winter, particularly in the southernmost stations (~17°N). In surface waters, phytoplankton grew at a slow rate (0.2 ± 0.02 d−1), with the population doubling every 3.5 days, on average. However, during the autumn–winter period, when Chl a concentrations peaked in the central and southern regions, μ increased to values between 0.60 and 0.84 d−1, while PB reached its maximum rate (7.8 mgC [mg Chl a]−1 h−1). We used path analysis to resolve direct vs. indirect components between correlations. Our results show that nutrient availability modulates the photosynthetic performance and growth of phytoplankton communities and that PB and μ fluctuations are not directly associated with temperature changes. Our study suggests that similarly to other oligotrophic warm seas, phosphorus concentration exerts a key role in defining photosynthetic rates and the biomass levels of phytoplankton communities in the region.
    • Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups

      Spencer, Daniel; Yu (Ryan) Yue; Bolin, David; Ryan, Sarah; Mejia, Amanda F. (arXiv, 2021-06-12) [Preprint]
      The general linear model (GLM) is a popular and convenient tool for estimating the functional brain response and identifying areas of significant activation during a task or stimulus. However, the classical GLM is based on a massive univariate approach that does not leverage the similarity of activation patterns among neighboring brain locations. As a result, it tends to produce noisy estimates and be underpowered to detect significant activations, particularly in individual subjects and small groups. The recently proposed cortical surface-based spatial Bayesian GLM leverages spatial dependencies among neighboring cortical vertices to produce smoother and more accurate estimates and areas of functional activation. The spatial Bayesian GLM can be applied to individual and group-level analysis. In this study, we assess the reliability and power of individual and group-average measures of task activation produced via the surface-based spatial Bayesian GLM. We analyze motor task data from 45 subjects in the Human Connectome Project (HCP) and HCP Retest datasets. We also extend the model to multi-session analysis and employ subject-specific cortical surfaces rather than surfaces inflated to a sphere for more accurate distance-based modeling. Results show that the surface-based spatial Bayesian GLM produces highly reliable activations in individual subjects and is powerful enough to detect trait-like functional topologies. Additionally, spatial Bayesian modeling enhances reliability of group-level analysis even in moderately sized samples (n=45). Notably, the power of the spatial Bayesian GLM to detect activations above a scientifically meaningful effect size is nearly invariant to sample size, exhibiting high power even in small samples (n=10). The spatial Bayesian GLM is computationally efficient in individuals and groups and is convenient to use with the \texttt{BayesfMRI} R package.
    • Energy Spotlight

      Dasgupta, Neil P.; Berry, Joseph J.; Bakr, Osman; Christopher, Phillip (ACS Energy Letters, American Chemical Society (ACS), 2021-06-11) [Article]
      Three papers recently published in ACS Energy Letters are featured in this month’s Energy Spotlight. These highlights include the design rules for optimizing Li metal morphology and composition through co-electrodeposition (highlighted by Neil P. Dasgupta), the use of aromatic formamidine variants to create a 2D/3D active layer for boosting the efficiency of 2D perovskite solar cells (highlighted by Joseph J. Berry and Osman M. Bakr), and inelastic neutron scattering to probe surface-bound hydrides during plasma-driven catalytic ammonia synthesis (highlighted by Phillip Christopher). We also encourage you to take a look at the latest Virtual Issue, Advances in Solid State Batteries, which will present key papers on this topic published in ACS Energy Letters. These and other papers included in this issue provide mechanistic insights into the energy conversion and storage processes.
    • Living with the enemy: from protein-misfolding pathologies we know, to those we want to know

      Emwas, Abdul-Hamid; Alghrably, Mawadda; Dhahri, Manel; Sharfalddin, Abeer; Alsiary, Rawiah; Jaremko, Mariusz; Faa, Gavino; Campagna, Marcello; Congiu, Terenzio; Piras, Monica; Piludu, Marco; Pichiri, Giuseppina; Coni, Pierpaolo; lachowicz, joanna izabela (Ageing Research Reviews, Elsevier BV, 2021-06-11) [Article]
      Conformational diseases are caused by the aggregation of misfolded proteins. The risk for such pathologies develops years before clinical symptoms appear, and is higher in people with alpha-1 antitrypsin (AAT) polymorphisms. Thousands of people with alpha-1 antitrypsin deficiency (AATD) are underdiagnosed. Enemy-aggregating proteins may reside in these underdiagnosed AATD patients for many years before a pathology for AATD fully develops. In this perspective review, we hypothesize that the AAT protein could exert a new and previously unconsidered biological effect as an endogenous metal ion chelator that plays a significant role in essential metal ion homeostasis. In this respect, AAT polymorphism may cause an imbalance of metal ions, which could be correlated with the aggregation of amylin, tau, amyloid beta, and alpha synuclein proteins in type 2 diabetes mellitus (T2DM), Alzheimer’s and Parkinson’s diseases, respectively.
    • Antiviral activities of flavonoids

      Badshah, Syed Lal; Faisal, Shah; Muhammad, Akhtar; Poulson, Benjamin Gabriel; Emwas, Abdul-Hamid M.; Jaremko, Mariusz (Biomedicine & Pharmacotherapy, Elsevier BV, 2021-06-11) [Article]
      Flavonoids are natural phytochemicals known for their antiviral activity. The flavonoids acts at different stages of viral infection, such as viral entrance, replication and translation of proteins. Viruses cause various diseases such as SARS, Hepatitis, AIDS, Flu, Herpes, etc. These, and many more viral diseases, are prevalent in the world, and some (i.e. SARS-CoV-2) are causing global chaos. Despite much struggle, effective treatments for these viral diseases are not available. The flavonoid class of phytochemicals has a vast number of medicinally active compounds, many of which are studied for their potential antiviral activity against different DNA and RNA viruses. Here, we reviewed many flavonoids that showed antiviral activities in different testing environments such as in vitro, in vivo (mice model) and in silico. Some flavonoids had stronger inhibitory activities, showed no toxicity & the cell proliferation at the tested doses are not affected. Some of the flavonoids used in the in vivo studies also protected the tested mice prophylactically from lethal doses of virus, and effectively prevented viral infection. The glycosides of some of the flavonoids increased the solubility of some flavonoids, and therefore showed increased antiviral activity as compared to the non-glycoside form of that flavonoid. These phytochemicals are active against different disease-causing viruses, and inhibited the viruses by targeting the viral infections at multiple stages. Some of the flavonoids showed more potent antiviral activity than the market available drugs used to treat viral infections.
    • Moderate Seasonal Dynamics Indicate an Important Role for Lysogeny in the Red Sea

      Abdulrahman Ashy, Ruba; Suttle, Curtis A.; Agusti, Susana (Microorganisms, MDPI AG, 2021-06-11) [Article]
      Viruses are the most abundant microorganisms in marine environments and viral infections can be either lytic (virulent) or lysogenic (temperate phage) within the host cell. The aim of this study was to quantify viral dynamics (abundance and infection) in the coastal Red Sea, a narrow oligotrophic basin with high surface water temperatures (22–32 °C degrees), high salinity (37.5–41) and continuous high insolation, thus making it a stable and relatively unexplored environment. We quantified viral and environmental changes in the Red Sea (two years) and the occurrence of lysogenic bacteria (induced by mitomycin C) on the second year. Water temperatures ranged from 24.0 to 32.5 °C, and total viral and bacterial abundances ranged from 1.5 to 8.7 × 106 viruses mL−1 and 1.9 to 3.2 × 105 bacteria mL−1, respectively. On average, 12.24% ± 4.8 (SE) of the prophage bacteria could be induced by mitomycin C, with the highest percentage of 55.8% observed in January 2018 when bacterial abundances were low; whereas no induction was measurable in spring when bacterial abundances were highest. Thus, despite the fact that the Red Sea might be perceived as stable, warm and saline, relatively modest changes in seasonal conditions were associated with large swings in the prevalence of lysogeny.
    • Integration of Droplet Microfluidic Tools for Single-cell Functional Metagenomics: An Engineering Head Start

      Conchouso Gonzalez, David; Alma’abadi, Amani D.; Behzad, Hayedeh; Alarawi, Mohammed; Hosokawa, Masahito; Nishikawa, Yohei; Takeyama, Haruko; Mineta, Katsuhiko; Gojobori, Takashi (Institute of Electrical and Electronics Engineers (IEEE), 2021-06-11) [Preprint]
      <p>Droplet microfluidics techniques have shown promising results to study single-cells at high throughput. However, their adoption in laboratories studying “-omics” sciences is still irrelevant because of the field’s complex and multidisciplinary nature. To facilitate their use, here we provide engineering details and organized protocols for integrating three droplet-based microfluidic technologies into the metagenomic pipeline to enable functional screening of bioproducts at high throughput. First, a device encapsulating single-cells in droplets at a rate of ~ 250 Hz is described considering droplet size and cell growth. Then, we expand on previously reported fluorescent activated droplet sorting (FADS) systems to integrate the use of 4 independent fluorescence-exciting lasers (e.g., 405, 488, 561, 637 nm) in a single platform to make it compatible with different fluorescence-emitting biosensors. For this sorter, both hardware and software are provided and optimized for effortlessly sorting droplets at 60 Hz. Then, a passive droplet merger was also integrated into our method to enable adding new reagents to already made droplets at a rate of 200 Hz. Finally, we provide an optimized recipe for manufacturing these chips using silicon dry-etching tools. Because of the overall integration and the technical details presented here, our approach allows biologists to quickly use microfluidic technologies and achieve both single-cell resolution and high-throughput (> 50,000 cells/day) capabilities to mining and bioprospecting metagenomic data.</p>
    • Proposal of Carbonactinosporaceae fam. nov. within the class Actinomycetia. Reclassification of Streptomyces thermoautotrophicus as Carbonactinospora thermoautotrophica gen. nov., comb. nov

      Volpiano, Camila Gazolla; Sant'Anna, Fernando Hayashi; da Mota, Fábio Faria; Sangal, Vartul; Sutcliffe, Iain; Munusamy, Madhaiyan; Saravanan, Venkatakrishnan Sivaraj; See-Too, Wah-Seng; Passaglia, Luciane Maria Pereira; Rosado, Alexandre Soares (Systematic and Applied Microbiology, Elsevier BV, 2021-06-10) [Article]
      Streptomyces thermoautotrophicus UBT1T has been suggested to merit generic status due to its phylogenetic placement and distinctive phenotypes among Actinomycetia. To evaluate whether ‘S. thermoautotrophicus’ represents a higher taxonomic rank, ‘S. thermoautotrophicus’ strains UBT1T and H1 were compared to Actinomycetia using 16S rRNA gene sequences and comparative genome analyses. The UBT1T and H1 genomes each contain at least two different 16S rRNA sequences, which are closely related to those of Acidothermus cellulolyticus (order Acidothermales). In multigene-based phylogenomic trees, UBT1T and H1 typically formed a sister group to the Streptosporangiales-Acidothermales clade. The Average Amino Acid Identity, Percentage of Conserved Proteins, and whole-genome Average Nucleotide Identity (Alignment Fraction) values were ≤58.5%, ≤48%, ≤75.5% (0.3) between ‘S. thermoautotrophicus’ and Streptosporangiales members, all below the respective thresholds for delineating genera. The values for genomics comparisons between strains UBT1T and H1 with Acidothermales, as well as members of the genus Streptomyces, were even lower. A review of the ‘S. thermoautotrophicus’ proteomic profiles and KEGG orthology demonstrated that UBT1T and H1 present pronounced differences, both tested and predicted, in phenotypic and chemotaxonomic characteristics compared to its sister clades and Streptomyces. The distinct phylogenetic position and the combination of genotypic and phenotypic characteristics justify the proposal of Carbonactinospora gen. nov., with the type species Carbonactinospora thermoautotrophica comb. nov. (type strain UBT1T, = DSM 100163T = KCTC 49540T) belonging to Carbonactinosporaceae fam. nov. within Actinomycetia.
    • Snapshot Space–Time Holographic 3D Particle Tracking Velocimetry

      Chen, Ni; Wang, Congli; Heidrich, Wolfgang (Laser & Photonics Reviews, Wiley, 2021-06-10) [Article]
      Digital inline holography is an amazingly simple and effective approach for 3D imaging, to which particle tracking velocimetry is of particular interest. Conventional digital holographic particle tracking velocimetry techniques are computationally separated in particle and flow reconstruction, plus the expensive computations. Usually, the particle volumes are recovered first, from which fluid flows are computed. Without iterative reconstructions, This sequential space–time process lacks accuracy. This paper presents a joint optimization framework for digital holographic particle tracking velocimetry: particle volumes and fluid flows are reconstructed jointly in a higher space–time dimension, enabling faster convergence and better reconstruction quality of both fluid flow and particle volumes within a few minutes on modern GPUs. Synthetic and experimental results are presented to show the efficiency of the proposed technique.
    • Functional analysis of colonization factor antigen I positive enterotoxigenic Escherichia coli identifies genes implicated in survival in water and host colonization

      Abd El Ghany, Moataz; Barquist, Lars; Clare, Simon; Brandt, Cordelia; Mayho, Matthew; Joffre´, Enrique; Sjöling, Åsa; Turner, A. Keith; Klena, John D.; Kingsley, Robert A.; Hill-Cawthorne, Grant A.; Dougan, Gordon; Pickard, Derek (Microbial Genomics, Microbiology Society, 2021-06-10) [Article]
      Enterotoxigenic Escherichia coli (ETEC) expressing the colonization pili CFA/I are common causes of diarrhoeal infections in humans. Here, we use a combination of transposon mutagenesis and transcriptomic analysis to identify genes and pathways that contribute to ETEC persistence in water environments and colonization of a mammalian host. ETEC persisting in water exhibit a distinct RNA expression profile from those growing in richer media. Multiple pathways were identified that contribute to water survival, including lipopolysaccharide biosynthesis and stress response regulons. The analysis also indicated that ETEC growing in vivo in mice encounter a bottleneck driving down the diversity of colonizing ETEC populations.