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‘Candidatus Phytoplasma ulmi’ (Ca. P. ulmi) belongs to the ribosomal subgroup 16SrV-A and is associated with dieback, shoot proliferation and yellows disease on various Ulmus spp. Other plant species, such as Carpinus betulus and Prunus spp. have also been reported infected by the same pathogen. In 2021, in the frame of research activities focused on grapevine’s Flavescence dorée (FD), one specimen of Orientus ishidae - an East Palearctic leafhopper that was identified as an alternative vector of FD phytoplasmas - was found harboring Ca. P. ulmi in southern Switzerland. No phytoplasmas were detected in plant samples taken in the same location. Orientus ishidae has already been reported to be able to acquire diverse phytoplasmas associated with other plant diseases, such as Peach X-disease. This is the first report of Ca. P. ulmi in Switzerland, as well as in O. ishidae. Ca. P. ulmi may potentially be present in the wild compartment of the Swiss Pre-alpine and Alpine range, but no dedicated survey has so far been conducted. In the case of O. ishidae, this finding highlights the broad affinity of such a species for the acquisition of several phytoplasmas. This calls for a further investigation regarding its potential role as a vector on various pathosystems of agronomic importance.

Abstract Snow and ice topography impact and are impacted by fluxes of mass, energy, and momentum in Arctic sea ice. We measured the topography on approximately a 0.5 km2 drifting parcel of Arctic sea ice on 42 separate days from 18 October 2019 to 9 May 2020 via Terrestrial Laser Scanning (TLS). These data are aligned into an ice-fixed, lagrangian reference frame such that topographic changes (e.g., snow accumulation) can be observed for time periods of up to six months. Using in-situ measurements, we have validated the vertical accuracy of the alignment to ± 0.011 m. This data collection and processing workflow is the culmination of several prior measurement campaigns and may be generally applied for repeat TLS measurements on drifting sea ice. We present a description of the data, a software package written to process and align these data, and the philosophy of the data processing. These data can be used to investigate snow accumulation and redistribution, ice dynamics, surface roughness, and they can provide valuable context for co-located measurements.

Abstract This data descriptor assigns the major and minor rock names from worldwide Holocene volcanoes of the Global Volcanism Program (GVP) using the Total Alkali-Silica diagram (TAS) for the chemical classification of volcanic rocks using the Geochemistry of Rocks of the Oceans and Continents (GEOROC) database. The precompiled files of the GEOROC database provide the chemical composition of volcanic rock samples, from which we computed major and minor rocks for global Holocene volcanoes reported in GVP. The combined dataset associates each volcano with the relative abundance of each volcanic sample type (whole rock, glass, melt inclusion) and provides the five major (more than 10% abundance) and minor rock names. In total, over 138,000 GEOROC volcanic rock samples were considered, for ~1000 Holocene volcanoes. The resulting major rock compositions are in general consistent with those given in GVP. The dataset provides a global panorama of rock composition for Holocene volcanoes.

Abstract Snow plays an essential role in the Arctic as the interface between the sea ice and the atmosphere. Optical properties, thermal conductivity and mass distribution are critical to understanding the complex Arctic sea ice system’s energy balance and mass distribution. By conducting measurements from October 2019 to September 2020 on the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have produced a dataset capturing the year-long evolution of the physical properties of the snow and surface scattering layer, a highly porous surface layer on Arctic sea ice that evolves due to preferential melt at the ice grain boundaries. The dataset includes measurements of snow during MOSAiC. Measurements included profiles of depth, density, temperature, snow water equivalent, penetration resistance, stable water isotope, salinity and microcomputer tomography samples. Most snowpit sites were visited and measured weekly to capture the temporal evolution of the physical properties of snow. The compiled dataset includes 576 snowpits and describes snow conditions during the MOSAiC expedition.

Abstract Engineering protein biosensors that sensitively respond to specific biomolecules by triggering precise cellular responses is a major goal of diagnostics and synthetic cell biology. Previous biosensor designs have largely relied on binding structurally well-defined molecules. In contrast, approaches that couple the sensing of flexible compounds to intended cellular responses would greatly expand potential biosensor applications. Here, to address these challenges, we develop a computational strategy for designing signaling complexes between conformationally dynamic proteins and peptides. To demonstrate the power of the approach, we create ultrasensitive chemotactic receptor—peptide pairs capable of eliciting potent signaling responses and strong chemotaxis in primary human T cells. Unlike traditional approaches that engineer static binding complexes, our dynamic structure design strategy optimizes contacts with multiple binding and allosteric sites accessible through dynamic conformational ensembles to achieve strongly enhanced signaling efficacy and potency. Our study suggests that a conformationally adaptable binding interface coupled to a robust allosteric transmission region is a key evolutionary determinant of peptidergic GPCR signaling systems. The approach lays a foundation for designing peptide-sensing receptors and signaling peptide ligands for basic and therapeutic applications.

Abstract Snow depth on sea ice is an Essential Climate Variable and a major source of uncertainty in satellite altimetry‐derived sea ice thickness. During winter of the MOSAiC Expedition, the “KuKa” dual‐frequency, fully polarized Ku‐ and Ka‐band radar was deployed in “stare” nadir‐looking mode to investigate the possibility of combining these two frequencies to retrieve snow depth. Three approaches were investigated: dual‐frequency, dual‐polarization and waveform shape, and compared to independent snow depth measurements. Novel dual‐polarization approaches yielded r2 values up to 0.77. Mean snow depths agreed within 1 cm, even for data sub‐banded to CryoSat‐2 SIRAL and SARAL AltiKa bandwidths. Snow depths from co‐polarized dual‐frequency approaches were at least a factor of four too small and had a r2 0.15 or lower. r2 for waveform shape techniques reached 0.72 but depths were underestimated. Snow depth retrievals using polarimetric information or waveform shape may therefore be possible from airborne/satellite radar altimeters.

Sea-ice pore microstructure constrains ice transport properties, affecting fluid flow relevant to oil-in-ice transport and biogeochemical processes. Motivated by a lack of pore microstructural data, in particular for granular ice and across the seasonal cycle, throat size, tortuosity, connectivity, and other microstructural variables were derived from X-ray computed tomography for brine-filled pores in seasonal landfast ice off northern Alaska. Data were obtained for granular and columnar ice during the ice growth, transition, and melt season. While granular ice exhibits a more heterogeneous pore space than columnar ice, pore and throat size distributions are comparable. The greater tortuosity of pores in granular (1.2 < τg < 1.7) compared to columnar ice (1.0 < τc < 1.1) compounded with a less interconnected pore space translates into lower permeability for granular ice during the growth season for a given porosity. The microstructural data explain findings of granular ice hindering vertical oil-in-ice transport during ice growth and transition stage. With granular ice more frequent in the changing Arctic, data from studies such as this are needed to inform improved modeling of porosity-permeability relationships.

The sub-kilometre scale distribution of snow depth on Arctic sea ice impacts atmosphere-ice fluxes of energy and mass, and is of importance for satellite estimates of sea-ice thickness from both radar and lidar altimeters. While information about the mean of this distribution is increasingly available from modelling and remote sensing, the full distribution cannot yet be resolved. We analyse 33 539 snow depth measurements from 499 transects taken at Soviet drifting stations between 1955 and 1991 and derive a simple statistical distribution for snow depth over multi-year ice as a function of only the mean snow depth. We then evaluate this snow depth distribution against snow depth transects that span first-year ice to multiyear ice from the MOSAiC, SHEBA and AMSR-Ice field campaigns. Because the distribution can be generated using only the mean snow depth, it can be used in the downscaling of several existing snow depth products for use in flux modelling and altimetry studies.

Este trabajo propone un nuevo funcionamiento en modo tolerante a fallas aplicado a convertidores CC-CC con Puentes Duales Activos Trifasicos (CPDA3) cuando ocurre una falla de circuito abierto de transistor en el puente del lado de la carga. La propuesta consiste en modificar las senales de activación de los transistores, para lograr mantener la transferencia de potencia dentro de determinados limites despues de la reconfiguración. Este enfoque no introduce costos computacionales adicionales y no requiere la incorporacion de componentes o circuitos adicionales a la topología original. La potencia maxima que se puede transferir es mayor comparado conlas propuestas anteriores. Ademas, la ondulación de la tensión de salida se reduce significativamente, lo que permite aumentar la vida util esperada de los capacitores del filtro. Se presentan los resultados de la simulación y experimentales de un prototipo de 1.5 kW para validar el analisis teórico y la viabilidad de la propuesta.

Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth and density retrievals from a SnowMicroPen and approximately weekly measured snow depths along fixed transect paths. We used the derived SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were also compared with ERA5 reanalysis snowfall rates for the drift track. We found an accumulated snow mass of 38 mm SWE between the end of October 2019 and end of April 2020. The initial SWE over first-year ice relative to second-year ice increased from 50 % to 90 % by end of the investigation period. Further, we found that the Vaisala Present Weather Detector 22, an optical precipitation sensor, and installed on a railing on the top deck of research vessel Polarstern, was least affected by blowing snow and showed good agreements with SWE retrievals along the transect. On the contrary, the OTT Pluvio2 pluviometer and the OTT Parsivel2 laser disdrometer were largely affected by wind and blowing snow, leading to too high measured precipitation rates. These are largely reduced when eliminating drifting snow periods in the comparison. ERA5 reveals good timing of the snowfall events and good agreement with ground measurements with an overestimation tendency. Retrieved snowfall from the ship-based Ka-band ARM zenith radar shows good agreements with SWE of the snow cover and differences comparable to those of ERA5. Based on the results, we suggest the Ka-band radar-derived snowfall as an upper limit and the present weather detector on RV Polarstern as a lower limit of a cumulative snowfall range. Based on these findings, we suggest a cumulative snowfall of 72 to 107 mm and a precipitation mass loss of the snow cover due to erosion and sublimation as between 47 % and 68 %, for the time period between 31 October 2019 and 26 April 2020. Extending this period beyond available snow cover measurements, we suggest a cumulative snowfall of 98–114 mm.

This work is motivated by applications of parsimonious cladograms for the purpose of analyzing non-biological data. Parsimonious cladograms were introduced as a means to help understanding the tree of life, and are now used in fields related to biological sciences at large, e.g., to analyze viruses or to predict the structure of proteins. We revisit parsimonious cladograms through the lens of clustering and compare cladograms optimized for parsimony with dendograms obtained from single linkage hierarchical clustering. We show that despite similarities in both approaches, there exist datasets whose clustering dendogram is incompatible with parsimony optimization. Furthermore, we provide numerical examples to compare via F-scores the clustering obtained through both parsimonious cladograms and single linkage hierarchical dendograms.

The major element composition of volcanic rocks carries important information about the source and differentiation processes affecting the magma, the physical properties that allow it to erupt, and its eruptive style. Although global rock geochemical databases exist, these are not linked to volcanic eruption history which hampers our global understanding of the relationship between magma composition and eruption dynamics. Here, we integrate two global databases, the Geochemistry of Rocks of the Oceans and Continents (GEOROC) and the Holocene volcanoes of the world of the Global Volcanism Program (VOTW-GVP). The integration is based on matching the location name, geographic position and eruption time, which is automated by a tool called DashVolcano. The tool is open-source, accessible at https://github.com/feog/DashVolcano, and gives access to the integrated datasets via an interactive dashboard. DashVolcano is based on more than 138,000 volcanic rock samples and provides the basis for the identification of global relationships between eruption styles, volcano types, and rock composition for more than 700 volcanoes and their eruptions for the last 10,000 years. The combined record of the eruptive history and its corresponding geochemical rock composition that DashVolcano provides can be used for characterizing global geochemical differences between volcanoes, and should also prove useful for improved long-term hazard and risk evaluations.

For over a decade, erasure codes have become an integral part of large-scale data storage solutions and data-centers. However, in commercial systems, they are, so far, used predominantly for static data. In the meanwhile, there has also been almost a decade and a half of research on mutable erasure coded data, looking at various associated issues, including update computation, concurrency control and consistency, which has led to a variety of reasonably mature techniques. In this work we aim at curating and systematizing this knowledge on managing mutable erasure coded data. We believe the time is right, both because of the richness and maturity of the literature itself, and also, given the pervasiveness of erasure codes in data-centers, because it is natural to expect a transition to accommodate mutable content using erasure coded redundancy in order to support more diverse and versatile overlying applications, while benefiting from the advantages (particularly, that of significantly lower storage overhead) of erasure codes.

The structure of many complex networks includes edge directionality and weights on top of their topology. Network analysis that can seamlessly consider combination of these properties are desirable. In this paper, we study two important such network analysis techniques, namely, centrality and clustering. An information-flow based model is adopted for clustering, which itself builds upon an information theoretic measure for computing centrality. Our principal contributions include (1) a generalized model of Markov entropic centrality with the flexibility to tune the importance of node degrees, edge weights and directions, with a closed-form asymptotic analysis, which (2) leads to a novel two-stage graph clustering algorithm. The centrality analysis helps reason about the suitability of our approach to cluster a given graph, and determine ‘query’ nodes, around which to explore local community structures, leading to an agglomerative clustering mechanism. Our clustering algorithm naturally inherits the flexibility to accommodate edge directionality, as well as different interpretations and interplay between edge weights and node degrees. Extensive benchmarking experiments are provided, using both real-world networks with ground truth and synthetic networks.

El convertidor CC-CC de tres puertos aislados es de interés para los sistemas de almacenamiento de energía híbridos por su capacidad para controlar los flujos de energía de manera bidireccional, aumentar y disminuir la tensión y la operación con conmutación suave. El control convencional del flujo de energía se realiza aplicando un desfase entre las tensiones a bornes del transformador, lo que puede generar una elevada potencia reactiva debida a la corriente de circulación en el enlace de CA del convertidor, consiguiéndose rendimientos elevados sólo en un rango de operación limitado. Para aumentar el rendimiento en todo el rango de operación, este trabajo propone una estrategia de modulación que extiende la región de conmutación suave y minimiza la potencia reactiva. Esta estrategia aplica un ancho de pulso en el puerto de mayor tensión de CC, manteniendo la modulación tradicional de onda cuadrada en el puerto opuesto. Para validar la estrategia, se presentan resultados para diferentes escenarios de transferencia de potencia.

Este trabajo presenta el modelado de un convertidor modular CC-CC basado en la conexión entrada serie - salida serie de dos celdas con convertidores con puentes duales activos. Esta configuración resulta interesante en aplicaciones en las cuales ambos puertos del convertidor deben soportar tensiones elevadas. A partir del análisis de las formas de onda de las principales variables eléctricas de cada una de las celdas, se obtienen las ecuaciones promediadas que permiten describir la dinámica del convertidor ante cambios significativos en las entradas de control del convertidor. El modelo promediado es linealizado entorno a un punto de funcionamiento del convertidor, obteniéndose dos sistemas de ecuaciones que permiten analizar la influencia de los parámetros constructivos de las celdas en el comportamiento estático y dinámico del convertidor. Los resultados obtenidos permiten validar en el dominio temporal los modelos obtenidos, tanto en régimen permanente como transitorio, en todo el rango de transferencia de potencia del convertidor.

The increased fraction of first year ice (FYI) at the expense of old ice (second-year ice (SYI) and multi-year ice (MYI)) likely affects the permeability of the Arctic ice cover. This in turn influences the pathways of gases circulating therein and the exchange at interfaces with the atmosphere and ocean. We present sea ice temperature and salinity time series from different ice types relevant to temporal development of sea ice permeability and brine drainage efficiency from freeze-up in October to the onset of spring warming in May. Our study is based on a dataset collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition in 2019 and 2020. These physical properties were used to derive sea ice permeability and Rayleigh numbers. The main sites included FYI and SYI. The latter was composed of an upper layer of residual ice that had desalinated but survived the previous summer melt and became SYI. Below this ice a layer of new first-year ice formed. As the layer of new first-year ice has no direct contact with the atmosphere, we call it insulated first-year ice (IFYI). The residual/SYI-layer also contained refrozen melt ponds in some areas. During the freezing season, the residual/SYI-layer was consistently impermeable, acting as barrier for gas exchange between the atmosphere and ocean. While both FYI and SYI temperatures responded similarly to atmospheric warming events, SYI was more resilient to brine volume fraction changes because of its low salinity (< 2). Furthermore, later bottom ice growth during spring warming was observed for SYI in comparison to FYI. The projected increase in the fraction of more permeable FYI in autumn and spring in the coming decades may favor gas exchange at the atmosphere-ice interface when sea ice acts as a source relative to the atmosphere. While the areal extent of old ice is decreasing, so is its thickness at the onset of freeze-up. Our study sets the foundation for studies on gas dynamics within the ice column and the gas exchange at both ice interfaces, i.e. with the atmosphere and the ocean.

In this paper we study the problem of consistency in distributed storage systems relying on erasure coding for storage efficient fault-tolerance. We propose QLOC - a flexible framework for supporting the storage of warm data, i.e., data which, while not being very frequently in use, nevertheless continues to be accessed for reads or writes regularly. QLOC builds upon (1) a generic family of local reconstruction codes with guarantees in terms of fault-tolerance, efficient recovery from failures and degraded mode operations, and can be instantiated with parameters customized to requirements such as storage overhead and reliability dictated by user needs and operational environments, and (2) quorum-based consistency mechanisms with support for read-modify-write operations without any underlying atomic primitives, providing deployment choices trading-off fault-tolerance, consistency and concurrency requirements. We carry out a theoretical analysis of the code properties, and experimentally benchmark the performance of the consistency enforcement mechanisms, demonstrating the practicality of the proposed approach.