Single-particle measurements of filamentous influenza virions reveal damage induced by freezing

Clinical isolates of influenza virus produce pleiomorphic virions, ranging from small spheres to elongated filaments. The filaments are seemingly adaptive in natural infections, but their basic functional properties are poorly understood and functional studies of filaments often report contradictory results. This may be due to artefactual damage from routine laboratory handling, an issue which has been noted several times without being explored in detail. To determine whether standard laboratory techniques could damage filaments, we used immunofluorescence microscopy to rapidly and reproducibly quantify and characterize the dimensions of filaments. Most of the techniques we tested had minimal impact on filaments, but freezing to −70 °C, a standard storage step before carrying out functional studies on influenza viruses, severely reduced their concentration, median length and the infectivity of the whole virion population. We noted that damage from freezing is likely to have affected most of the functional studies of filaments performed to date, and to address this we show that it can be mitigated by snap-freezing or incorporating the cryoprotectant DMSO. We recommend that functional studies of filaments characterize virion populations prior to analysis to ensure reproducibility, and that they use unfrozen samples if possible and cryoprotectants if not. These basic measures will support the robust functional characterizations of filaments that are required to understand their roles in natural influenza virus infections

Machine-Learning Dessins d'Enfants: Explorations via Modular and Seiberg-Witten Curves

We apply machine-learning to the study of dessins d'enfants. Specifically, we investigate a class of dessins which reside at the intersection of the investigations of modular subgroups, Seiberg-Witten curves and extremal elliptic K3 surfaces. A deep feed-forward neural network with simple structure and standard activation functions without prior knowledge of the underlying mathematics is established and imposed onto the classification of extension degree over the rationals, known to be a difficult problem. The classifications reached 0.92 accuracy with 0.03 standard error relatively quickly. The Seiberg-Witten curves for those with rational coefficients are also tabulated.Comment: 60 pages, 197 figures. Acknowledgements updated to reflect thanks to the group at UoAugsburg for highlighting a data analysis problem, that lead authors to identify the dessin d'enfant representation subtlety and use the improved cyclic edge list representation, as in version

Neurons on Amoebae

We apply methods of machine-learning, such as neural networks, manifold learning and image processing, in order to study 2-dimensional amoebae in algebraic geometry and string theory. With the help of embedding manifold projection, we recover complicated conditions obtained from so-called lopsidedness. For certain cases it could even reach $\sim99\%$ accuracy, in particular for the lopsided amoeba of $F_0$ with positive coefficients which we place primary focus. Using weights and biases, we also find good approximations to determine the genus for an amoeba at lower computational cost. In general, the models could easily predict the genus with over $90\%$ accuracies. With similar techniques, we also investigate the membership problem, and image processing of the amoebae directly.Comment: 53 page

Altering the Size Distribution of Influenza Virion Populations [Poster]

Harry Smith Vacation Studentship Laboratory-adapted influenza viruses produce predominantly spherical virions. In contrast, clinical and veterinary isolates produce a mixture of virions of different sizes, from 0.1 µm spheres to filaments which can reach tens of microns in length. Filamentous influenza virions were discovered in 1946, but the bulk of influenza research has analysed only spherical forms of the virus and the role of filaments in influenza infections is unclear. Functional studies of filaments require the development of methods to manipulate the ratio of spherical to filamentous virions, and we reasoned that this could be achieved by filtration. To test this, we infected MDCK cells with the filamentous Udorn strain of influenza A virus. We collected virus-containing growth media and passed this through filters with 5 µm, 0.45 µm and 0.2 µm pores. Filtrates and unfiltered virus were compared, using Western blotting to measure their protein composition, plaque assays to measure their infectivity and negative stain transmission electron microscopy to measure individual particle sizes. We found that filtration through a filter with 5 µm pores had little effect on composition, infectivity and the ratio of spherical to filamentous particles. In contrast, sub-micron filters, particularly those with 0.2 µm pores, caused a general depletion of virions but increased the sphere to filament ratio. We therefore concluded that sub-micron pore sizes can be used to preferentially remove filaments from populations of pleomorphic influenza virions, providing a useful tool for subtractive studies of the contribution filaments make to influenza virus infections

Single-particle Measurements Reveal Damage to Filamentous Influenza Virions During Laboratory Handling [Poster]

Most laboratory strains of influenza virus produce near-spherical virions, but clinical isolates also produce extended filaments whose biophysical properties are understudied. Most functional studies of filamentous influenza viruses do not include data on the concentration or lengths of the virions, making it hard to interpret their sometimes contradictory results. Furthermore, anecdotal reports suggest that filaments are damaged during routine laboratory handling. Therefore, to understand filament function we require a tool to assess the number and dimensions of filaments in a sample and an assessment of how filaments respond to standard handling procedures. We initially sought to analyse filament populations using negative stain particle counting, but found that this was low-throughput and could not detect particles longer than 10 µm. Instead, we used confocal microscopy with semi-automated image analysis. This allowed a high-throughput, quantitative analysis of length distributions in filament populations. Using this, we assessed the effects of pipetting, vortexing, sonicating, clarification and freezing on filaments. Most procedures did not appreciably alter filament dimensions. Pipetting and vortexing both slightly reduced filament numbers, but their effects were only appreciable after extended treatment. In contrast, freezing substantially reduced the number and median length of filaments, as well as creating ‘kinks’ in filaments which suggest damage to the capsid. We conclude that confocal microscopy can provide the basic measurements needed to interpret functional studies of filamentous strains. Using this approach, we found that freezing filaments causes previously unappreciated damage, which should be considered when planning further research

Cluster Algebras: Network Science and Machine Learning

Cluster algebras have recently become an important player in mathematics and physics. In this work, we investigate them through the lens of modern data science, specifically with techniques from network science and machine-learning. Network analysis methods are applied to the exchange graphs for cluster algebras of varying mutation types. The analysis indicates that when the graphs are represented without identifying by permutation equivalence between clusters an elegant symmetry emerges in the quiver exchange graph embedding. The ratio between number of seeds and number of quivers associated to this symmetry is computed for finite Dynkin type algebras up to rank 5, and conjectured for higher ranks. Simple machine learning techniques successfully learn to differentiate cluster algebras from their seeds. The learning performance exceeds 0.9 accuracies between algebras of the same mutation type and between types, as well as relative to artificially generated data.Comment: 38 pages, 27 figure

Mahler Measuring the Genetic Code of Amoebae

Amoebae from tropical geometry and the Mahler measure from number theory play important roles in quiver gauge theories and dimer models. Their dependencies on the coefficients of the Newton polynomial closely resemble each other, and they are connected via the Ronkin function. Genetic symbolic regression methods are employed to extract the numerical relationships between the 2d and 3d amoebae components and the Mahler measure. We find that the volume of the bounded complement of a d-dimensional amoeba is related to the gas phase contribution to the Mahler measure by a degree-d polynomial, with d = 2 and 3. These methods are then further extended to numerical analyses of the non-reflexive Mahler measure. Furthermore, machine learning methods are used to directly learn the topology of 3d amoebae, with strong performance. Additionally, analytic expressions for boundaries of certain amoebae are given.Comment: 45 pages; 33 Figure

Strontium isotopes trace the dissolution and precipitation of mineral organic carbon interactions in thawing permafrost

Interactions between minerals and organic carbon (OC) in soils are key to stabilize OC and mitigate greenhouse gas emissions upon permafrost thaw. However, changes in soil water pathways upon permafrost thaw are likely to affect the stability of mineral OC interactions by inducing their dissolution and precipitation. This study aims to assess and quantify how mineral OC interactions are affected by dissolution and precipitation in thawed relative to unthawed layers. We hypothesize that a change in the radiogenic strontium (Sr) isotopic ratio (87Sr/86Sr) involved in mineral OC interactions upon changing water saturation conditions implies a destabilization of the mineral OC interaction. We quantified mineral OC interactions using selective extractions in soils facing gradual thaw (Eight Mile Lake, AK, USA) and in sediments with a thawing history of abrupt thaw (Duvanny Yar, Russia), and we measured the 87Sr/86Sr ratio of the selective extracts targeting the Sr associated to mineral OC interactions. Firstly, for water saturated layers with a higher proportion of mineral OC interactions, we found a difference in the 87Sr/86Sr ratio relative to the surrounding layers, and this supports the preservation of a Sr “stable” pool in these mineral OC interactions. We estimated that a portion of these mineral OC interactions have remained undissociated since their formation (between 4% and 64% by Sr isotope mass balance). Secondly, we found no difference in 87Sr/86Sr ratio between layers accumulating Fe oxides at redox interfaces regularly affected by water table changes (or upon thermokarst processes) relative to surrounding layers. This supports the dominance of a Sr “labile” pool inherited from processes of dissolution and precipitation of the mineral OC interactions. Thirdly, our estimations based on a Sr isotope mass balance support that, as a consequence of permafrost thaw, a larger proportion of Sr from primary mineral weathering (>80%) controls the Sr in mineral OC interactions in the saturated zone of deeply thawed soils relative to poorly thawed soils (∼50%). In conclusion, we found that the radiogenic Sr isotope method, applied for the first time in this context, is promising to trace dissolution-precipitation processes of mineral OC interaction in thawing permafrost

Brain Webs for Brane Webs

We propose a new technique for classifying 5d Superconformal Field Theories arising from brane webs in Type IIB String Theory, using technology from Machine Learning to identify different webs giving rise to the same theory. We concentrate on webs with three external legs, for which the problem is analogous to that of classifying sets of 7-branes. Training a Siamese Neural Network to determine equivalence between any two brane webs shows an improved performance when webs are considered equivalent under a weaker set of conditions. Thus, Machine Learning teaches us that the conjectured classification of 7-brane sets is not complete, which we confirm with explicit examples.Comment: 12 pages, 12 figure

Gaming the FTSE 100 Index

TesisLima NorteEscuela de PosgradoReforma y Modernización del EstadoLa presente investigación titulada: Calidad del servicio en la satisfacción del usuario de consulta externa en una Red del Ministerio Salud, en el año 2019, tuvo como objetivo general determinar la incidencia de la calidad del servicio en la satisfacción del usuario de consulta externa en una Red del Ministerio Salud, en el año 2019. Los instrumentos que se utilizaron fueron cuestionarios en escala de Likert para las variables. Estos instrumentos fueron sometidos a los análisis respectivos de confiabilidad y validez, que determinaron que los cuestionarios tienen validez y confiabilidad. El método empleado fue el hipotético deductivo, el tipo de investigación fue básica, nivel correlacional causal, de enfoque cuantitativo; diseño no experimental de corte transversal. La población estuvo formada por 650 usuarios atendidos en el área de consulta externa en una Red del Ministerio Salud y el muestreo fue de tipo probabilístico. La técnica empleada para recolectar información fue la encuesta y los instrumentos de recolección de datos fueron cuestionarios que fueron debidamente validados a través de juicios de expertos y su confiabilidad a través del estadístico de fiabilidad Alfa de Cronbach el resultado fue de ,823 para la variable calidad del servicio, se evidencia que tiene una fuerte confiabilidad y para la variable satisfacción del usuario tuvo un resultado de ,918, por lo que, se puede manifestar que dicha variable tiene una alta confiabilidad. Los coeficientes presentan valores significativos, mayores al 17.2% de incidencia de la calidad de servicio que es explicada por la variable incluida en la satisfacción; habiéndose obtenido un p-value igual a 0,000