Statistics for Spatial Data.

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Land use and land cover data play a central role in climate change assessments. These data originate from different sources and inventory techniques. Each source of land use/cover data has its own domain of applicability and quality standards. Often data are selected without explicitly considering the suitability of the data for the specific application, the bias originating from data inventory and aggregation, and the effects of the uncertainty in the data on the results of the assessment. Uncertainties due to data selection and handling can be in the same order of magnitude as uncertainties related to the representation of the processes under investigation. While acknowledging the differences in data sources and the causes of inconsistencies, several methods have been developed to optimally extract information from the data and document the uncertainties. These methods include data integration, improved validation techniques and harmonization of classification systems. Based on the data needs of global change studies and the data availability, recommendations are formulated aimed at optimal use of current data and focused efforts for additional data collection. These include: improved documentation using classification systems for land use/cover data; careful selection of data given the specific application and the use of appropriate scaling and aggregation methods. In addition, the data availability may be improved by the combination of different data sources to optimize information content while collection of additional data must focus on validation of available data sets and improved coverage of regions and land cover types with a high level of uncertainty. Specific attention in data collection should be given to the representation of land management (systems) and mosaic landscapes.

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Challenges in using land use and land cover data for global change studies

The purpose of this paper is to survey and assess the state-of-the-art in automatic target recognition for synthetic aperture radar imagery (SAR-ATR). The aim is not to develop an exhaustive survey of the voluminous literature, but rather to capture in one place the various approaches for implementing the SAR-ATR system. This paper is meant to be as self-contained as possible, and it approaches the SAR-ATR problem from a holistic end-to-end perspective. A brief overview for the breadth of the SAR-ATR challenges is conducted. This is couched in terms of a single-channel SAR, and it is extendable to multi-channel SAR systems. Stages pertinent to the basic SAR-ATR system structure are defined, and the motivations of the requirements and constraints on the system constituents are addressed. For each stage in the SAR-ATR processing chain, a taxonomization methodology for surveying the numerous methods published in the open literature is proposed. Carefully selected works from the literature are presented under the taxa proposed. Novel comparisons, discussions, and comments are pinpointed throughout this paper. A two-fold benchmarking scheme for evaluating existing SAR-ATR systems and motivating new system designs is proposed. The scheme is applied to the works surveyed in this paper. Finally, a discussion is presented in which various interrelated issues, such as standard operating conditions, extended operating conditions, and target-model design, are addressed. This paper is a contribution toward fulfilling an objective of end-to-end SAR-ATR system design.

Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review

A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data

In September 2000, an airborne synthetic aperture radar (SAR) mission acquired unprecedented full polarimetric P-band data over the Tapajos National Forest (Para State), which is an area in the Brazilian Amazon which has been continuously monitored in the last three decades. Eight land use/cover classes were identified, namely, primary forest, regeneration older than 25 years, regeneration between 12 and 25 years, regeneration between 6 and 12 years, regeneration younger than six years, crops/pasture, bare soil, and floodplain (FP). The objective of this paper is to analyze the potential of full polarimetric P-band data in distinguishing different land use/cover classes with a minimum established Kappa value of 75%, using the latest development on SAR statistical characterization. The iterated conditional mode (ICM) contextual classifier was applied to amplitude, intensity images, biomass index, and some polarimetric parameters (entropy, alpha angle, and anisotropy) extracted from the polarimetric P-band data. As the accuracy obtained for eight classes was not acceptable, another two sets, with five and four classes, were formed by the combination of the previous ones. They were defined by confusion matrix analysis and by the graphical analysis of average backscatter values, entropy, [alpha] angle, and anisotropy images and by the H/alpha plans of the land use samples. The classification accuracy with four classes (three levels of biomass plus FP) was then considered acceptable with a Kappa value of 76.81%, using the ICM classification with the adequate bivariate distribution for the HV and VV channels.

Land Use and Land Cover Mapping in the Brazilian Amazon Using Polarimetric Airborne P-Band SAR Data

The temporal evolution of L-band microwave backscatter for several land cover types in the Tapajos National Forest is analysed using JERS-1. Five images were acquired from 1993 to 1997. Samples of the studied land cover types were identified after analysis of a Landsat Thematic Mapper (TM) dataset of 12 images obtained from 1984 to 1999. The following land cover types were investigated: bare soil, agriculture, pastures, forest regrowth from 1 to 23-year-old and primary forest. Backscatter behaviour of regrowth stands is influenced by the previous land use and by human impacts such as fire and selective logging. This selective logging can be mechanized or not, because people who live in the Amazon usually cut trees to build houses, bridges, fences, etc. Backscatter of young regrowth stands on sites immediately abandoned after slash and burn increases at a faster rate than young regrowth on sites abandoned after some years of agriculture. Backscatter of old regrowth stands also behaves differently where the...

Multitemporal analysis of land use/land cover JERS-1 backscatter in the Brazilian tropical rainforest

Power spectrum analysis was used for the analysis of spatial forest features from airborne X‐band synthetic aperture radar (SAR) data in the Brazilian Amazon. Spectral estimates were arrived at empirically by periodograms and correlograms, and from autoregressive moving‐average (ARMA) models. The spectral estimates derived from SAR data were validated by those derived from ground data with locational match. The results obtained by ARMA modelling revealed particularly good correspondence between remote sensing and reference data: repeating patterns at pixel level could be detected in the images. These patterns were shown to arise from canopy structure and distances between major tree individuals; and thus allowed the extraction of parameters of spatial forest structure, particularly of forest density. The method was applied to an example area of primary tropical forest, and its spatial patterns were modelled.

Power spectrum analysis of SAR data for spatial forest characterization in Amazonia

The impact of dialysis modality on posttransplant outcomes remains controversial. The authors have compared primary failure, delayed graft function (DGF), acute rejection episodes as well as patient and allograft survivals among patients undergoing renal transplantation between 2004 and 2009, according to the modality of hemodialysis (HD) versus peritoneal dialysis (PD). We studied 306 patients (268 HD and 38 PD) with a mean follow-up of 29 ± 16 months. The PD cohort included a predominance of females (68.4% vs 36.2%; P = .001), lower age at transplantation (38 ± 14 vs 46 ± 12 years; P = .004), shorter time on dialysis (33 ± 49 vs 59 ± 157 months; P = .043), and higher rate of living donor grafts (PD 31.6% vs HD 13.1%; P = .003). Donor age (PD 43 ± 13 vs HD 45 ± 14 years; P = .30), human leukocyte antigen mismatch (P = .17), panel reactive antibody values (HD 11 ± 22 vs PD 13 ± 26; P = .55), and hyperimunized patients (HD 3.73%; PD 7.89%; P = .23) were not different. Primary graft failure (3.4% vs 0%; P = .025) and DGF (37.1% vs 13.1%; P = .037) were more frequent among HD patients, but incidences of acute rejection episodes were similar (HD 10.5% vs PD 5.3%; P = 0.19). Neither recipient survival at 1 (97% in PD and HD) or 3 years (HD 90% vs PD 94%; P = .657) nor allograft survival at 1 year (HD 94% vs PD 95%; P = .80) or 3 years: (HD 70%, vs PD 81%; P = .73) were different. Graft function was similar at 1 (HD 64.2 ± 25 vs PD 56.4 ± 24 mL/min; P = .17) and 3 years (HD 62.3 ± 21 vs PD 46 ± 23 mL/min; P = .16). In our study, HD patients showed an higher incidence of DGF and primary allograft failure, but there was no difference in acute rejection episodes, long-term survivals, or renal function.

Posttransplant outcomes of peritoneal dialysis versus hemodialysis patients.

Over 9 years, we have performed 93 simultaneous pancreas-kidney transplants (SPKT). The morbidity of this procedure is high compared with kidney transplantation alone; readmissions are frequent and costs are higher. Herein we have presented the complications during follow-up of these 93 patients. Their mean age was 34 +/- 6 years and prior dialysis time was 32 +/- 25 months. The median hospital stay on the first admission for the transplant procedure was 22 days, including 2 days in the intensive care unit. Bleeding, thrombosis, and infection were the most frequent reasons for prolonged hospitalization. Thirty patients underwent >or=1 surgical reinterventions. Incidence of acute rejection episodes was 11.8%. After discharge, 74.2% of the patients had 197 readmission episodes with infection being the main cause, urinary tract infections, the most frequent; however, systemic viral and fungal infections required the longest readmission periods. The need for surgical interventions, graft dysfunction, and vascular problems were the remaining causes of readmission. At the end of follow-up, 87 patients were alive, 86 with well-functioning kidneys and 74 with normal functioning pancreata. Global survival rates for patient, kidney, and pancreas were 96%, 95%, and 81% at 1-year; 93%, 90%, and 79% at 5-years; and 93%, 90% and 79% at 9-years. Although pancreas-kidney transplant patients are complex presenting many management difficulties, our overall results represent a positive stimulus for diabetic patients.

https://repositorio.chporto.pt/bitstream/10400.16/277/1/Pancreas%20kidney%20transplantation%5b1%5d.pdf

Cristina Freitas

Papers

Land Use and Land Cover Mapping in the Brazilian Amazon Using Polarimetric Airborne P-Band SAR Data

Multitemporal analysis of land use/land cover JERS-1 backscatter in the Brazilian tropical rainforest

Power spectrum analysis of SAR data for spatial forest characterization in Amazonia

Posttransplant outcomes of peritoneal dialysis versus hemodialysis patients.

Pancreas-Kidney Transplantation: Complications and Readmissions in 9-Years of Follow-up