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It is estimated that 150 people die per hour due to the insufficient therapeutic treatments to combat fungal infections. A major hurdle to developing antifungal therapies is the scarce knowledge on the fungal metabolic pathways and mechanisms of virulence.
Soil microbes are major players in the global carbon cycle, yet we still have little understanding of how the carbon cycle operates in soil communities. A major limitation is that carbon metabolism lacks discrete functional genes that define carbon transformations.
Genome-wide binding sites of 44 putative transcription factors (TFs) from Novosphingobium aromaticivorans DSM12444 were analyzed using DNA affinity purification sequencing. We report that 32 of these TFs have at least one area of enrichment. These data will help better understand aromatic metabolism and other features of N. aromaticivorans biology.
While numerous computational frameworks and workflows are available for recovering prokaryote and eukaryote genomes from metagenome data, only a limited number of pipelines are designed specifically for viromics analysis. With many viromics tools developed in the last few years alone, it can be challenging for scientists with limited bioinformatics experience to easily recover, evaluate quality, annotate genes, dereplicate, assign taxonomy, and calculate relative abundance and coverage of viral genomes using state-of-the-art methods and standards. Here, we describe Modular Viromics Pipeline (MVP) v.1.0, a user-friendly pipeline written in Python and providing a simple framework to perform standard viromics analyses. MVP combines multiple tools to enable viral genome identification, characterization of genome quality, filtering, clustering, taxonomic and functional annotation, genome binning, and comprehensive summaries of results that can be used for downstream ecological analyses. Overall, MVP provides a standardized and reproducible pipeline for both extensive and robust characterization of viruses from large-scale sequencing data including metagenomes, metatranscriptomes, viromes, and isolate genomes. As a typical use case, we show how the entire MVP pipeline can be applied to a set of 20 metagenomes from wetland sediments using only 10 modules executed via command lines, leading to the identification of 11,656 viral contigs and 8,145 viral operational taxonomic units (vOTUs) displaying a clear beta-diversity pattern. Further, acting as a dynamic wrapper, MVP is designed to continuously incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field of viromics. MVP is available at https://gitlab.com/ccoclet/mvp and as versioned packages in PyPi and Conda.
The ability to simultaneously and more directly correlate genes with metabolite levels on a global level would provide novel information for many biological platforms yet has thus far been challenging. Here, we describe a method to help address this problem, which we dub “Met-Seq” ( met abolite-coupled Tn seq uencing).
Presented by NETL at the Joint 52nd Northeastern Annual Section / 51st North-Central Annual Section Meeting - 2017; Pittsburgh, PA; March 19-21, 2017
The Gazestan magnetite-apatite deposit is one of Kiruna-type deposits located 78 km from Bafgh in the province of Yazd, in the Bafgh Posht-e-Badam district, southeast of Iran. In the mining region of Bafgh, volcanic rocks and Precambrian volcano-sedimentary are the main host of iron-apatite deposits. Iron deposits are essentially located in Bafgh region inside the green acidic metamorphic rocks. Volcanic rocks are basically rhyolite and rhyodacitic and the sedimentary units are mostly made of dolomite. Rock units of the region belong to the Rizzo series. The Bafgh apatite compounds are fluoride-type and seen in the monazite mineral. The similarity of the rare earth elements pattern in all samples indicates the magma source. The most amount of rare earth elements in the Gazestan deposit include 7766.25 ppm and the most amount of Th is equal to 65.1 ppm and U is equal to 8.33 ppm. The most amount of P2O5 s equal to 26.5 wt% and Fe is equal to 61.3 wt%. Also, it can be concluded that due to their high correlation with rare earth elements, the deposit is of Uranium Thorium-bearing type.
The spatial arrangement of single linear defects in a Si single crystal (input surface (111)) has been investigated by X-ray topo-tomography using laboratory X-ray sources. The experimental technique and the procedure of reconstructing a 3D image of dislocation half-loops near the Si crystal surface are described. The sizes of observed linear defects with a spatial resolution of about 10 μm are estimated.
Here, a recent theoretical prediction of a breaking of axial symmetry in quasi all heavy nuclei is confronted to a new critical analysis of photon strength functions of nuclei in the valley of stability. For the photon strength in the isovector giant dipole resonance (IVGDR) regime a parameterization of GDR shapes by the sum of three Lorentzians (TLO) is extrapolated to energies below and above the IVGDR. The impact of non-GDR modes adding to the low energy slope of photon strength is discussed including recent data on photon scattering and other radiative processes. These are shown to be concentrated in energy regions where various model calculations predict intermediate collective strength; thus they are obviously separate from the IVGDR tail. The triple Lorentzian (TLO) ansatz for giant dipole resonances is normalized in accordance to the dipole sum rule. The nuclear droplet model with surface dissipation accounts well for positions and widths without local, nuclide specific, parameters. Very few and only global parameters are needed when a breaking of axial symmetry already in the valley of stability is admitted and hence a reliable prediction for electric dipole strength functions also outside of it is expected.
Not Available
We measured two-particle angular correlations in pp collisions at √s=7 TeV for pions, kaons, protons, and lambdas, for all particle/anti-particle combinations in the pair. Data for mesons exhibit an expected peak dominated by effects associated with mini-jets and are well reproduced by general purpose Monte Carlo generators. However, for baryon–baryon and anti-baryon–anti-baryon pairs, where both particles have the same baryon number, a near-side anti-correlation structure is observed instead of a peak. This effect is interpreted in the context of baryon production mechanisms in the fragmentation process. It currently presents a challenge to Monte Carlo models and its origin remains an open question.
The rapid acceleration of microbial genome sequencing increases opportunities to understand bacterial gene function. Unfortunately, only a small proportion of genes have been studied. Recently, TnSeq has been proposed as a cost-effective, highly reliable approach to predict gene functions as a response to changes in a cell’s fitness before-after genomic changes. However, major questions remain about how to best determine whether an observed quantitative change in fitness represents a meaningful change. To address the limitation, we develop a Gaussian mixture model framework for classifying gene function from TnSeq experiments. In order to implement the mixture model, we present the Expectation-Maximization algorithm and a hierarchical Bayesian model sampled using Stan’s Hamiltonian Monte-Carlo sampler. We compare these implementations against the frequentist method used in current TnSeq literature. From simulations and real data produced by E.coli TnSeq experiments, we show that the Bayesian implementation of the Gaussian mixture framework provides the most consistent classification results.
Convolutional Neural Networks (CNNs) are one of the most extensively used tools in machine learning, but they are still not well understood and in many cases they are over-parameterized, leading to slow inference and impeding their deployment on low-power devices. In the last few years, many methods for decreasing the number of parameters in a network by pruning its output channels have been suggested, but a very recent work has argued that random pruning of channels performs on-par with state-of-the-art pruning methods. While random and other pruning methods might be effectively used for lowering the number of parameters in a CNN, none of these methods can be used to gain any further understanding of the model that the CNN has built. In this work, we propose a novel method for pruning a network, that at the same time can lead to a better understanding of what the individual filters of the network learn about the data. The method proposed aims to keep only the filters that are "important" for a class. We define a filter as important for a class if its removal has the highest negative impact on the accuracy for that class. We demonstrate that our method is better than random pruning on two networks used on the EMNIST and CIFAR10 datasets. By analyzing the important filters, we find that the important filters in the pruned networks learn features which are more general across classes. We demonstrate the importance and applicability of that observation in two transfer-learning tasks.
A greater understanding of human dynamics as they play out in both physical space and through inter-personal communication is vital for the design and development of intelligent and resilient cities. Physical context provides insight in to the space-time distribution of population and their activity patterns, while inter-personal communication can, for the first time, be measured at scale through virtual interactions. The confluence of these two enables spatial social network analysis in the built environment for edge computing, knowledge discovery, and artificial intelligence activities. In this work, we propose a novel method to discover such dynamics. We use a dataset of 72 million tweets to develop a spatially embedded network of communication, and then use community detection algorithms to explore regional and urban delineation in the United States. We find that the broad spatial delineation of communities and community sub-structure is consistent with major United States socio-cultural regions, states, and major metropolitan areas. We describe how these methods could be extended to generate a measure of socially cohesive regions that can be consistently applied anywhere there is a sufficiently rich data source. We hope the proposed methods will generate new perspectives on smart and resilient cities.
Abstract not provided.
Low-temperature electrochemical CO 2 reduction (CO 2 R) in zero-gap membrane electrode assembly (MEA) reactors presents a scalable route to fuels and carbon utilization. However, performance at industrially relevant current densities hinges on mesoscale catalyst layer integration, particularly at the ionomer|catalyst interface. Here, we demonstrate a generalizable in situ electrochemical impedance spectroscopy (EIS) method. We utilize this technique to decouple electrode-level parameters that are correlated to the overall MEA performance. By performing this ex situ EIS method on CO 2 -to-CO catalyst-coated membranes with systematically varied ionomer-to-catalyst (I:C) ratios, we reveal a pronounced dependence of performance, ion transport resistance, and catalyst utilization on the I:C ratio as well as the electrode conditioning. We demonstrate that an optimal I:C ratio exists at which ion transport resistance is minimized and Faradaic efficiency for CO production is maximized. Beyond the electrodes examined, here we compare ion transport resistance to MEA selectivity/Faradaic efficiency obtained in prior studies, revealing a clear correlation between the two. These results suggest that ion transport resistance within the catalyst layer may be a quantitative predictor of MEA performance which underscores the importance of mesoscale integration in achieving scalable CO 2 R technologies.
Multivalent-ion batteries have been under investigation as a technology that can provide energy densities beyond that provided by lithium ion batteries. An emerging cation of interest is calcium, as it has similar electrochemical properties and size to sodium, and systems incorporating it have high theoretical density. Common techniques used to investigate intercalation, such as NMR, are currently unable to distinguish calcium's role in energy storage materials. In this work, we investigated the ability of calcium XANES to provide fingerprint identification of intercalated species using Ca ion exchanged Na x CoO 2 and a Ca(PF 6 ) 2 -based electrolyte as a case study. Ca XANES was able to detect intercalation down to concentrations of ~1 Ca per 950 Å 3 , even with residual electrolyte on the surface. The ability to observe the structure of the intercalated ion will enable the discovery of new electrolytes and intercalation cathodes by helping to confirm theoretical predictions, allowing multivalent batteries to become a viable high energy density storage technology.
The MSE cyber sensor offers a novel approach for scalable network monitoring using heterogeneous computing. Packet data is collected, analyzed, and stored by a stand alone cyber sensor. Machine learning algorithms are run to discover patterns in the communication without the need for deep packet inspection. Tuning of the detection algorithms happens automatically, without need for an engineer to manually create detection rules.