Mitigating bias in estimating epidemic severity due to heterogeneity of epidemic onset and data aggregation

Outbreaks of infectious diseases, such as influenza, are a major societal burden. Mitigation policies during an outbreak or pandemic are guided by the analysis of data of ongoing or preceding epidemics. The reproduction number, , defined as the expected number of secondary infections arising from a single individual in a population of susceptibles is critical to epidemiology. For typical compartmental models such as the Susceptible-Infected-Recovered (SIR) represents the severity of an epidemic. It is an estimate of the early-stage growth rate of an epidemic and is an important threshold parameter used to gain insights into the spread or decay of an outbreak. Models typically use incidence counts as indicators of cases within a single large population; however, epidemic data are the result of a hierarchical aggregation, where incidence counts from spatially separated monitoring sites (or sub-regions) are pooled and used to infer . Is this aggregation approach valid when the epidemic has different dynamics across the regions monitored? We characterize bias in the estimation of from a merged data set when the epidemics of the sub-regions, used in the merger, exhibit delays in onset. We propose a method to mitigate this bias, and study its efficacy on synthetic data as well as real-world influenza and COVID-19 data

Four-qubit entangled symmetric states with positive partial transpositions

We solve the open question of the existence of four-qubit entangled symmetric states with positive partial transpositions (PPT states). We reach this goal with two different approaches. First, we propose a half-analytical-half-numerical method that allows to construct multipartite PPT entangled symmetric states (PPTESS) from the qubit-qudit PPT entangled states. Second, we adapt the algorithm allowing to search for extremal elements in the convex set of bipartite PPT states [J. M. Leinaas, J. Myrheim, and E. Ovrum, Phys. Rev. A 76, 034304 (2007)] to the multipartite scenario. With its aid we search for extremal four-qubit PPTESS and show that generically they have ranks (5,7,8). Finally, we provide an exhaustive characterization of these states with respect to their separability properties.Comment: 5+4 pages, improved version, title slightly modifie

The pgip family in soybean and three other legume species: evidence for a birth-and-death model of evolution

Polygalacturonase-inhibiting proteins (PGIPs) are leucine-rich repeat (LRR) plant cell wall glycoproteins involved in plant immunity. They are typically encoded by gene families with a small number of gene copies whose evolutionary origin has been poorly investigated. Here we report the complete characterization of the full complement of the pgip family in soybean (Glycine max [L.] Merr.) and the characterization of the genomic region surrounding the pgip family in four legume species. Results: BAC clone and genome sequence analyses showed that the soybean genome contains two pgip loci. Each locus is composed of three clustered genes that are induced following infection with the fungal pathogen Sclerotinia sclerotiorum (Lib.) de Bary, and remnant sequences of pgip genes. The analyzed homeologous soybean genomic regions (about 126 Kb) that include the pgip loci are strongly conserved and this conservation extends also to the genomes of the legume species Phaseolus vulgaris L., Medicago truncatula Gaertn. and Cicer arietinum L., each containing a single pgip locus. Maximum likelihood-based gene trees suggest that the genes within the pgip clusters have independently undergone tandem duplication in each species. Conclusions: The paleopolyploid soybean genome contains two pgip loci comprised in large and highly conserved duplicated regions, which are also conserved in bean, M. truncatula and C. arietinum. The genomic features of these legume pgip families suggest that the forces driving the evolution of pgip genes follow the birth-and-death model, similar to that proposed for the evolution of resistance (R) genes of NBS-LRR-type

Targeting angiogenesis in advanced non-small-cell lung cancer: are biomarkers needed?

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Monitoring freeze-thaw state by means of GNSS reflectometry. An analysis of TechDemoSat-1 data

The article of the freeze/thaw dynamic of high-latitude Earth surfaces is extremely important and informative for monitoring the carbon cycle, the climate change, and the security of infrastructures. Current methodologies mainly rely on the use of active and passive microwave sensors, while very few efforts have been devoted to the assessment of the potential of observations based on signals of opportunity. This article aims at assessing the performance of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) for high-spatial and highoral resolution monitoring of the Earth-surface freeze/thaw state. To this aim, reflectivity values derived from the TechDemoSat-1 (TDS-1) data have been collected and elaborated, and thus compared against the soil moisture active passive (SMAP) freeze/thaw information. Shallow subsurface soil temperature values recorded by a network of in situ stations have been considered as well. Even if an extensive and timeliness cross availability of both types of experimental data is limited by the spatial coverage and density of TDS-1 observations, the proposed analysis clearly indicates a significant seasonal cycle in the calibrated reflectivity. This opens new perspectives for the bistatic L-band high-resolution satellite monitoring of the freeze/thaw state, as well as to support the development of next-generation of GNSS-R satellite missions designed to provide enhanced performance and improved temporal and spatial coverage over high latitude areas

Geomatics for Integrated Coastal Zone Management: multitemporal shoreline analysis and future regional perspective for the Portuguese Central Region

Shoreline mapping and change detection are critical for Integrated Coastal Zone Management (ICZM) and all that it represents. This research utilized previous studies that combined both Remote Sensing and Geographical Information System (GIS) techniques to assess, map and forecast shoreline evolution from short-term perspectives. The study area is located in the central region of Portugal, between the counties of Ovar and Marinha Grande (circa 140 km) and the time period assessed was from 1984 to 2011. Historical data were used to calculate advance and retreat rates in order to support environmental scenarios for the Portuguese Central Region’s Coastal Management Plan. To ensure accuracy, a repeatable procedure was validated using Landsat TM and ETM+ satellite images, which were subsequently enhanced and elaborated by Remote Sensing analyses to detect and extract shorelines. They were subsequently integrated within an Esri ArcGIS software application (DSAS - Digital Shoreline Analysis System) to determine and predict rates of coastline change. Graphical DSAS plots identified coastline phases and shifts and were used to simulate the 2022 coastline scenario. These results will be integrated into the Coastal Zone Management Plan (Horizon – 2022). Importantly this methodological planning approach provides visual coastline change information for regional decision-makers and stakeholders

A cloud-based healthcare infrastructure for neonatal intensive-care units

Intensive medical attention of preterm babies is crucial to avoid short-term and long- term complications. Within neonatal intensive care units (NICUs), cribs are equipped with electronic devices aimed at: monitoring, administering drugs and supporting clinician in making diagnosis and offer treatments. To manage this huge data flux, a cloud-based healthcare infrastructure that allows data collection from different devices (i.e., patient monitors, bilirubinometers, and transcutaneous bilirubinometers), storage, processing and transferring will be presented. Communication protocols were designed to enable the communication and data transfer between the three different devices and a unique database and an easy to use graphical user interface (GUI) was implemented. The infrastructure is currently used in the “Women’s and Children’s Hospital G.Salesi” in Ancona (Italy), supporting clinicians and health opertators in their daily activities