The Gamma--Ray Burst catalog obtained with the Gamma Ray Burst Monitor aboard BeppoSAX

We report on the catalog of Gamma--Ray Bursts (GRBs) detected with the Gamma Ray Burst Monitor aboard the BeppoSAX satellite. It includes 1082 GRBs with 40--700 keV fluences in the range from $1.3\times 10^{-7}$ to $4.5\times 10^{-4}$ erg cm$^{-2}$, and with 40--700 keV peak fluxes from $3.7\times 10^{-8}$ to $7.0\times 10^{-5}$ erg cm$^{-2}$s$^{-1}$. We report in the catalog some relevant parameters of each GRB and discuss the derived statistical properties.Comment: 48 pages, 14 figures, 4 Tables. Accepted for publication in The Astrophysical Journal Supplemen

On the nature of XTE J0421+560/CI Cam

We present the results of the analysis of RXTE, BATSE and optical/infrared data of the 1998 outburst of the X-ray transient system XTE J0421+560 (CI Cam). The X-ray outburst shows a very fast decay (initial e-folding time ~0.5 days, slowing down to ~2.3 days). The X-ray spectrum in the 2-25 keV band is complex, softening considerably during decay and with strongly variable intrinsic absorption. A strong iron emission line is observed. No fast time variability is detected (<0.5 % rms in the 1-4096 Hz band at the outburst peak). The analysis of the optical/IR data suggests that the secondary is a B[e] star surrounded by cool dust and places the system at a distance of >~ 2 kpc. At this distance the peak 2-25 keV luminosity is ~4 x 10^37 erg/s. We compare the properties of this peculiar system with those of the Be/NS LMC transient A 0538-66 and suggest that CI Cam is of similar nature. The presence of strong radio emission during outburst indicates that the compact object is likely to be a black hole or a weakly magnetized neutron star.Comment: Accepted for publication on The Astrophysical Journal, July 199

The Interplanetary Network Supplement to the BeppoSAX Gamma-Ray Burst Catalogs

Between 1996 July and 2002 April, one or more spacecraft of the interplanetary network detected 787 cosmic gamma-ray bursts that were also detected by the Gamma-Ray Burst Monitor and/or Wide-Field X-Ray Camera experiments aboard the BeppoSAX spacecraft. During this period, the network consisted of up to six spacecraft, and using triangulation, the localizations of 475 bursts were obtained. We present the localization data for these events.Comment: 89 pages, 3 figures. Submitted to the Astrophysical Journal Supplement Serie

Influências da densidade de plantas e espaçamento entre linhas no desenvolvimento fenológico e produtividade de grãos de dois genótipos de canola.

A canola (Brassica napus L. var. oleifera) é uma espécie anual, oleaginosa, pertencente à família Brassicaceae, sendo uma das poucas espécies oleaginosas que se adapta ao frio (KOVALESKI, 2015). A cultura é uma alternativa para a ocupação de áreas ociosas no inverno no Rio Grande do Sul (DALMAGO et al., 2013). Em 2018 a área destinada ao cultivo no RS foi de 34,8 mil hectares, com produtividade 1.398 kg ha-1 (CONAB, 2018). Na canola de primavera cultivada no sul do Brasil, as variações na duração do ciclo das cultivares são determinadas pela temperatura do ar, sendo a soma térmica a variável que determina a alteração da duração das fases de desenvolvimento (KRÜGER et al., 2009). Com isso, a emperatura do ar é o fator mais importante na regulação do crescimento e desenvolvimento das plantas de canola

Produtividade de grãos e seus componentes por diferentes arranjos de plantas de canola.

A canola é uma planta da família das crucíferas, destinada principalmente para a produção de óleos. No Brasil cultiva-se apenas canola de primavera, da espécie Brassica napus L. var. oleifera, que foi desenvolvida por melhoramento genético convencional de colza (DOTTO, 2014). Nos últimos anos a espécie tem sido uma importante alternativa para a produção de grãos no período de estação fria nas condições do sul do Brasil

A redshift - observation-time relation for gamma-ray bursts: evidence of a distinct sub-luminous population

We show how the redshift and peak-flux distributions of gamma-ray bursts (GRBs) have an observation time dependence that can be used to discriminate between different burst populations. We demonstrate how observation time relations can be derived from the standard integral distributions and that they can differentiate between GRB populations detected by both the BATSE and \emph{Swift} satellites. Using \emph{Swift} data we show that a redshift--observation-time relation (log\,$Z$\,--\,log\,$T$) is consistent with both a peak-flux\,--\,observation time relation (log\,$P$\,--\,log\,$T$) and a standard log\,$N$\,--\,log\,$P$ brightness distribution. As the method depends only on rarer small-$z$ events, it is invariant to high-$z$ selection effects. We use the log\,$Z$\,--\,log\,$T$ relation to show that sub-luminous GRBs are a distinct population occurring at a higher rate of order $150^{+180}_{-90} \mathrm{Gpc}^{-3}\mathrm{yr}^{-1}$. Our analysis suggests that GRB 060505 -- a relatively nearby GRB observed without any associated supernova -- is consistent with a sub-luminous population of bursts. Finally, we suggest that our relations can be used as a consistency test for some of the proposed GRB spectral energy correlations.Comment: Accepted by MNRA

Glioblastoma surgery imaging—reporting and data system: Standardized reporting of tumor volume, location, and resectability based on automated segmentations

Treatment decisions for patients with presumed glioblastoma are based on tumor characteristics available from a preoperative MR scan. Tumor characteristics, including volume, location, and resectability, are often estimated or manually delineated. This process is time consuming and subjective. Hence, comparison across cohorts, trials, or registries are subject to assessment bias. In this study, we propose a standardized Glioblastoma Surgery Imaging Reporting and Data System (GSI-RADS) based on an automated method of tumor segmentation that provides standard reports on tumor features that are potentially relevant for glioblastoma surgery. As clinical validation, we determine the agreement in extracted tumor features between the automated method and the current standard of manual segmentations from routine clinical MR scans before treatment. In an observational consecutive cohort of 1596 adult patients with a first time surgery of a glioblastoma from 13 institutions, we segmented gadolinium-enhanced tumor parts both by a human rater and by an automated algorithm. Tumor features were extracted from segmentations of both methods and compared to assess differences, concordance, and equivalence. The laterality, contralateral infiltration, and the laterality indices were in excellent agreement. The native and normalized tumor volumes had excellent agreement, consistency, and equivalence. Multifocality, but not the number of foci, had good agreement and equivalence. The location profiles of cortical and subcortical structures were in excellent agreement. The expected residual tumor volumes and resectability indices had excellent agreement, consistency, and equivalence. Tumor probability maps were in good agreement. In conclusion, automated segmentations are in excellent agreement with manual segmentations and practically equivalent regarding tumor features that are potentially relevant for neurosurgical purposes. Standard GSI-RADS reports can be generated by open access software

Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task

For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4.0 mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime