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A redox-active metal-organic composite material shows improved and selective O-2 adsorption over N-2 with respect to individual components (MIL-101 and ferrocene). The O-2 sensitivity of the composite material arises due to the formation of maghemite nanoparticles with the pore of the metal-organic framework material.
We have recently identified the enzyme NMN deamidase (PncC), which plays a key role in the regeneration of NAD in bacteria by recycling back to the coenzyme the pyridine by-products of its non redox consumption. In several bacterial species, PncC is fused to a COG1058 domain of unknown function, highly conserved and widely distributed in all living organisms. Here, we demonstrate that the PncC-fused domain is endowed with a novel Co +2 - and K + -dependent ADP-ribose pyrophosphatase activity, and discuss the functional connection of such an activity with NAD recycling. An in-depth phylogenetic analysis of the COG1058 domain evidenced that in most bacterial species it is fused to PncC, while in α- and some δ-proteobacteria, as well as in archaea and fungi, it occurs as a stand-alone protein. Notably, in mammals and plants it is fused to FAD synthase. We extended the enzymatic characterization to a representative bacterial single-domain protein, which resulted to be a more versatile ADP-ribose pyrophosphatase, active also towards diadenosine 5'-diphosphate and FAD. Multiple sequence alignment analysis, and superposition of the available three-dimensional structure of an archaeal COG1058 member with the structure of the enzyme MoeA of the molybdenum cofactor biosynthesis, allowed identification of residues likely involved in catalysis. Their role has been confirmed by site-directed mutagenesis.
The entry of the SARS-CoV-2, a causative agent of COVID-19, into human host cells is mediated by the SARS-CoV-2 spike (S) glycoprotein, which critically depends on the formation of complexes involving the spike protein receptor-binding domain (RBD) and the human cellular membrane receptor angiotensin-converting enzyme 2 (hACE2). Using classical site density functional theory (SDFT) and structural bioinformatics methods, we investigate binding and conformational properties of these complexes and study the overlooked role of water-mediated interactions. Analysis of the three-dimensional reference interaction site model (3DRISM) of SDFT indicates that water mediated interactions in the form of additional water bridges strongly increases the binding between SARS-CoV-2 spike protein and hACE2 compared to SARS-CoV-1-hACE2 complex. By analyzing structures of SARS-CoV-2 and SARS-CoV-1, we find that the homotrimer SARS-CoV-2 S receptor-binding domain (RBD) has expanded in size, indicating large conformational change relative to SARS-CoV-1 S protein. Protomer with the up-conformational form of RBD, which binds with hACE2, exhibits stronger intermolecular interactions at the RBD-ACE2 interface, with differential distributions and the inclusion of specific H-bonds in the CoV-2 complex. Further interface analysis has shown that interfacial water promotes and stabilizes the formation of CoV-2/hACE2 complex. This interaction causes a significant structural rigidification of the spike protein, favoring proteolytic processing of the S protein for the fusion of the viral and cellular membrane. Moreover, conformational dynamics simulations of RBD motions in SARS-CoV-2 and SARS-CoV-1 point to the role in modification of the RBD dynamics and their impact on infectivity.
Over the past decade, a variety of innovative methodologies have been developed to better characterize the relationships between processing conditions and the physical, morphological, and chemical features of special nuclear material (SNM). Different processing conditions generate SNM products with different features, which are known as “signatures” because they are indicative of the processing conditions used to produce the material. These signatures can potentially allow a forensic analyst to determine which processes were used to produce the SNM and make inferences about where the material originated. This article investigates a statistical technique for relating processing conditions to the morphological features of PuO 2 particles. We develop a Bayesian implementation of seemingly unrelated regression (SUR) to inverse-predict unknown PuO 2 processing conditions from known PuO 2 features. Model results from simulated data demonstrate the usefulness of the technique. Applied to empirical data from a bench-scale experiment specifically designed with inverse prediction in mind, our model successfully predicts nitric acid concentration, while results for Pu concentration and precipitation temperature were equivalent to a simple mean model. Our technique compliments other recent methodologies developed for forensic analysis of nuclear material and can be generalized across the field of chemometrics for application to other materials.
The carotid body (CB) is the key sensing organ for physiological oxygen levels in the body. Under conditions of low oxygen (hypoxia), the CB plays crucial roles in signaling to the cardiorespiratory center in the medulla oblongata for the restoration of oxygen homeostasis. How hypoxia regulates gene expression in the human CB remains poorly understood. While limited information on transcriptional regulation in animal CBs is available, the identity and impact of important post-transcriptional regulators such as non-coding RNAs, and in particular miRNAs are not known. Here we show using ex vivo experiments that indeed a number of miRNAs are differentially regulated in surgically removed human CB slices when acute hypoxic conditions were applied. Analysis of the hypoxia-regulated miRNAs shows that they target biological pathways with upregulation of functions related to cell proliferation and immune response and downregulation of cell differentiation and cell death functions. Comparative analysis of the human CB miRNAome with the global miRNA expression patterns of a large number of different human tissues showed that the CB miRNAome had a unique profile which reflects its highly specialized functional status. Nevertheless, the human CB miRNAome is most closely related to the miRNA expression pattern of brain tissues indicating that they may have the most similar developmental origins. - Highlights: • Hypoxia triggers differential expression of many miRNAs in the human carotid body. • This can lead to the upregulation of proliferation and immune response functions. • CB expression profile in the carotid body resembles the miRNA expression pattern in the brain. • miRNAs are involved in the regulation of carotid body functions including oxygen sensing.
Secondary batteries based on earth-abundant sodium metal anodes are desirable for both stationary and portable electrical energy storage. Room-temperature sodium metal batteries are impractical today because morphological instability during recharge drives rough, dendritic electrodeposition. Chemical instability of liquid electrolytes also leads to premature cell failure as a result of parasitic reactions with the anode. Here we use joint density-functional theoretical analysis to show that the surface diffusion barrier for sodium ion transport is a sensitive function of the chemistry of solid–electrolyte interphase. In particular, we find that a sodium bromide interphase presents an exceptionally low energy barrier to ion transport, comparable to that of metallic magnesium. We evaluate this prediction by means of electrochemical measurements and direct visualization studies. These experiments reveal an approximately three-fold reduction in activation energy for ion transport at a sodium bromide interphase. Direct visualization of sodium electrodeposition confirms large improvements in stability of sodium deposition at sodium bromide-rich interphases.
Given the wide-ranging potential applications of metal organic frameworks (MOFs), an emerging imperative is to understand their formation with atomic scale precision. This will aid in designing syntheses for next-generation MOFs with enhanced properties and functionalities. Major challenges are to characterize the early-stage seeds, and the pathways to framework growth, which require synthesis coupled with in situ structural characterization sensitive to nanoscale structures in solution. Here we report measurements of an in situ synthesis of a prototypical MOF, ZIF-8, utilizing synchrotron X-ray atomic pair distribution function (PDF) analysis optimized for sensitivity to dilute species, complemented by mass spectrometry, electron microscopy, and density functional theory calculations. We observe that despite rapid formation of the crystalline product, a high concentration of Zn(2-MeIm) 4 (2-MeIm=2-methylimidazolate) initially forms and persists as stable clusters over long times. A secondary, amorphous phase also pervades during the synthesis, which has a structural similarity to the final ZIF-8 and may act as an intermediate to the final product.
Background: Macrophomina phaseolina is an emerging and devastating fungal pathogen that causes significant losses in crop production under high temperatures and drought stress. An increasing number of disease incidence reports highlight the wide prevalence of the pathogen around the world and its contribution toward crop yield suppression. In cowpea [Vigna unguiculata (L) Walp.], limited sources of low-level host resistance have been identified, the genetic basis of which is unknown. In this study we report on the identification of strong sources of host resistance to M. phaseolina and the genetic mapping of putative resistance loci on a cowpea genetic map comprised of gene-derived single nucleotide polymorphisms (SNPs) and amplified fragment length polymorphisms (AFLPs). Results: Nine quantitative trait loci (QTLs), accounting for between 6.1 and 40.0% of the phenotypic variance (R2 ), were identified using plant mortality data taken over three years in field experiments and disease severity scores taken from two greenhouse experiments. Based on annotated genic SNPs as well as synteny with soybean (Glycine max) and Medicago truncatula, candidate resistance genes were found within mapped QTL intervals. QTL Mac-2 explained the largest percent R2 and was identified in three field and one greenhouse experiments where the QTL peak co-located with a SNP marker derived from a pectin esterase inhibitor encoding gene. Maturity effects on the expression of resistance were indicated by the co-location of Mac-6 and Mac-7 QTLs with maturity-related senescence QTLs Mat-2 and Mat-1, respectively. Homologs of the ELF4 and FLK flowering genes were found in corresponding syntenic soybean regions. Only three Macrophomina resistance QTLs co-located with delayed drought-induced premature senescence QTLs previously mapped in the same population, suggesting that largely different genetic mechanisms mediate cowpea response to drought stress and Macrophomina infection. Conclusion: Effective sources of host resistance were identified in this study. QTL mapping and synteny analysis identified genomic loci harboring resistance factors and revealed candidate genes with potential for further functional genomics analysis.
The bright future of particle physics at the Energy and Intensity frontiers poses exciting challenges to the scientific software community. The traditional strategies for processing and analyzing data are evolving in order to (i) offer higher-level programming model approaches and (ii) exploit parallelism to cope with the ever increasing complexity and size of the datasets. This contribution describes how the ROOT framework, a cornerstone of software stacks dedicated to particle physics, is preparing to provide adequate solutions for the analysis of large amount of scientific data on parallel architectures. The functional approach to parallel data analysis provided with the ROOT TDataFrame interface is then characterized. The design choices behind this new interface are described also comparing with other widely adopted tools such as Pandas and Apache Spark. The programming model is illustrated highlighting the reduction of boilerplate code, composability of the actions and data transformations as well as the capabilities of dealing with different data sources such as ROOT, JSON, CSV or databases. Details are given about how the functional approach allows transparent implicit parallelization of the chain of operations specified by the user. The progress done in the field of distributed analysis is examined. In particular, the power of the integration of ROOT with Apache Spark via the PyROOT interface is shown. In addition, the building blocks for the expression of parallelism in ROOT are briefly characterized together with the structural changes applied in the building and testing infrastructure which were necessary to put them in production.
Regeneration and functional reconstruction of peripheral nerve defects remained a significant clinical challenge. Nerve guide conduits, with seed cells or neurotrophic factors (NTFs), had been widely used to improve the repair and regeneration of injured peripheral nerve. Pyrroloquinoline quinone (PQQ) was an antioxidant that can stimulate nerve growth factors (NGFs) synthesis and accelerate the Schwann cells (SCs) proliferation and growth. In present study, three kinds of nerve guide conduits were constructed: one from cellulose/SPI hollow tube (CSC), another from CSC combined with SCs (CSSC), and the third one from CSSC combined with PQQ (CSSPC), respectively. And then they were applied to bridge and repair the sciatic nerve defect in rats, using autograft as control. Effects of different nerve guide conduits on the nerve regeneration were comparatively evaluated by general analysis, sciatic function index (SFI) and histological analysis (HE and TEM). Newly-formed regenerative nerve fibers were observed and running through the transparent nerve guide conduits 12 weeks after surgery. SFI results indicated that the reconstruction of motor function in CSSPC group was better than that in CSSC and CSC groups. HE images from the cross-sections and longitudinal-sections of the harvested regenerative nerve indicated that regenerative nerve fibers had been formed and accompanied with new blood vessels and matrix materials in the conduits. TEM images also showed that lots of fresh myelinated and non-myelinated nerve fibers had been formed. Parts of vacuolar, swollen and abnormal axons occurred in CSC and CSSC groups, while the vacuolization and swell of axons was the least serious in CSSPC group. These results indicated that CSSPC group had the most ability to repair and reconstruct the nerve structure and functions due to the comprehensive contributions from hollow CSC tube, SCs and PQQ. As a result, the CSSPC may have the potential for the applications as nerve guide conduits in the field of nerve tissue engineering. - Highlights: • A novel nerve conduit was constructed and applied to repair nerve defect in rats. • Transparent hollow cellulose/soy protein isolate tube was used as conduit matrix. • Pyrroloquinoline quinine was adsorbed into the hollow tube as nerve growth factor. • Schwann cells were cultured into the hollow tube as seed cells. • The new nerve conduit could repair and reconstruct the peripheral nerve defects.
We present DAGSENS, a new theory for parametric transient sensitivity analysis of Differential Algebraic Equation systems (DAEs), such as SPICE-level circuits. The key ideas behind DAGSENS are, (1) to represent the entire sequence of computations, starting from DAE parameters, all the way up to the objective function whose sensitivity is needed, as a Directed Acyclic Graph (DAG) called the "sensitivity DAG", and (2) to compute the required sensitivities efficiently (with time complexity linear in the size of the sensitivity DAG) by leveraging dynamic programming techniques to traverse the DAG. DAGSENS is simple, elegant, and easy-to-understand compared to existing sensitivity analysis approaches; for example, in DAGSENS, one can switch between direct and adjoint transient sensitivities just by changing the direction of DAG traversal (i.e., topological order vs. reverse topological order). Also, DAGSENS is significantly more powerful than existing sensitivity analysis approaches because it allows one to compute the sensitivities of a much more general class of objective functions, including those defined based on "events" that occur during a transient simulation (e.g., a node voltage crossing a particular threshold, a phase-locked loop (PLL) achieving lock, a signal reaching its maximum/minimum value during a transient run, etc.). In this paper, we apply DAGSENS to compute the sensitivities of important event-driven performance metrics in several real-world electronic and biological applications, including high-speed communication (featuring sub-systems such as I/0 links and PLLs), statistical cell library characterization, and gene expression in Drosophila embryos.
In this work, real space pair-distribution function (PDF) analysis in conjunction with molecular dynamics (MD) simulations was applied to characterize crystal structure, shape and surface structure of diamond nanoparticles annealed in air up to 880 °C. MD simulated models of individual nanodiamond particles of various shapes and sizes were used to calculate theoretical radial distribution functions G(r) for powders with different grain size distributions. The calculated curves, showing distinct differences for the explored parameter space, were compared to the experimental G(r) functions obtained from neutron scattering for annealed detonated nanodiamond samples in a search for a model best describing the actual powders. It was found that as-synthesized nanodiamond grains are terminated by equally abundant (100), (110) and (111) crystallographic faces and their structure does not change upon annealing in air up to 280 °C. The shape and size of grains annealed above 480 °C changes due to surface etching. The etching rate differs between crystals faces and depends on temperature, resulting in the observed differences in grain shape. Below 680 °C the share of the (110) facets gradually increases and at 680 °C the grains become rhombic dodecahedra terminated solely by (110) surfaces. Further increase of the annealing temperature promotes formation of (111) surfaces and at 880 °C the preferred grain shape becomes an octahedron terminated by a variant of (111) surfaces possessing three dangling sp3 bonds per atom. At lower temperatures all grains become smaller and the size distribution moves towards the lower values. Furthermore, as the annealing temperature increases, the smallest grains disappear entirely and the average grain size grows back.
Abstract Motivation High-throughput sequencing technologies have greatly facilitated microbiome research and have generated a large volume of microbiome data with the potential to answer key questions regarding microbiome assembly, structure and function. Cluster analysis aims to group features that behave similarly across treatments, and such grouping helps to highlight the functional relationships among features and may provide biological insights into microbiome networks. However, clustering microbiome data are challenging due to the sparsity and high dimensionality. Results We propose a model-based clustering method based on Poisson hurdle models for sparse microbiome count data. We describe an expectation–maximization algorithm and a modified version using simulated annealing to conduct the cluster analysis. Moreover, we provide algorithms for initialization and choosing the number of clusters. Simulation results demonstrate that our proposed methods provide better clustering results than alternative methods under a variety of settings. We also apply the proposed method to a sorghum rhizosphere microbiome dataset that results in interesting biological findings. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PHclust. Supplementary information Supplementary data are available at Bioinformatics online.
This manuscript extends the exact-to-precision generalized perturbation theory (E{sub P}GPT), introduced previously, to neutron transport calculation whereby previous developments focused on neutron diffusion calculation only. The E{sub P}GPT collectively denotes new developments in generalized perturbation theory (GPT) that place premium on computational efficiency and defendable accuracy in order to render GPT a standard analysis tool in routine design and safety reactor calculations. EPGPT constructs a surrogate model with quantifiable accuracy which can replace the original neutron transport model for subsequent engineering analysis, e.g. functionalization of the homogenized few-group cross sections in terms of various core conditions, sensitivity analysis and uncertainty quantification. This is achieved by reducing the effective dimensionality of the state variable (i.e. neutron angular flux) by projection onto an active subspace. Confining the state variations to the active subspace allows one to construct a small number of what is referred to as the 'active' responses which are solely dependent on the physics model rather than on the responses of interest, the number of input parameters, or the number of points in the state phase space. (authors)
We perform a joint analysis of the auto and cross-correlations between three cosmic fields: the galaxy density field, the galaxy weak lensing shear field, and the cosmic microwave background (CMB) weak lensing convergence field. These three fields are measured using roughly 1300 sq. deg. of overlapping optical imaging data from first year observations of the Dark Energy Survey and millimeter-wave observations of the CMB from both the South Pole Telescope Sunyaev-Zel'dovich survey and Planck. We present cosmological constraints from the joint analysis of the two-point correlation functions between galaxy density and galaxy shear with CMB lensing. We test for consistency between these measurements and the DES-only two-point function measurements, finding no evidence for inconsistency in the context of flat $\Lambda$CDM cosmological models. Performing a joint analysis of five of the possible correlation functions between these fields (excluding only the CMB lensing autospectrum) yields $S_{8}\equiv \sigma_8\sqrt{\Omega_{\rm m}/0.3} = 0.782^{+0.019}_{-0.025}$ and $\Omega_{\rm m}=0.260^{+0.029}_{-0.019}$. We test for consistency between these five correlation function measurements and the Planck-only measurement of the CMB lensing autospectrum, again finding no evidence for inconsistency in the context of flat $\Lambda$CDM models. Combining constraints from all six two-point functions yields $S_{8}=0.776^{+0.014}_{-0.021}$ and $\Omega_{\rm m}= 0.271^{+0.022}_{-0.016}$. These results provide a powerful test and confirmation of the results from the first year DES joint-probes analysis.
MATK provides basic functionality to facilitate model analysis within the Python computational environment. Model analysis setup within MATK includes: - define parameters - define observations - define model (python function) - define samplesets (sets of parameter combinations) Currently supported functionality includes: - forward model runs - Latin-Hypercube sampling of parameters - multi-dimensional parameter studies - parallel execution of parameter samples - model calibration using internal Levenberg-Marquardt algorithm - model calibration using lmfit package - model calibration using levmar package - Markov Chain Monte Carlo using pymc package MATK facilitates model analysis using: - scipy - calibration (scipy.optimize) - rpy2 - Python interface to R
MATK provides basic functionality to facilitate model analysis within the Python computational environment. Model analysis setup within MATK includes: - define parameters - define observations - define model (python function) - define samplesets (sets of parameter combinations) Currently supported functionality includes: - forward model runs - Latin-Hypercube sampling of parameters - multi-dimensional parameter studies - parallel execution of parameter samples - model calibration using internal Levenberg-Marquardt algorithm - model calibration using lmfit package - model calibration using levmar package - Markov Chain Monte Carlo using pymc package MATK facilitates model analysis using: - scipy - calibration (scipy.optimize) - rpy2 - Python interface to R
In this work we describe how the window method, which has been used with ideal Kramers-Kroning (K-K) consistent dielectric functions, is affected by real optical data where some degree of K-K inconsistency is always present. Analysis using measured data of the frequency-dependent dielectric function for gold and phase-change materials shows that the window method can in some cases amplify the errors introduced in the calculation of the Casimir force due to K-K inconsistency instead of minimizing them. The latter indicates that caution is required for the proper choice of the window function for dielectric data analysis and Casmir force calculations.