Finite population properties of predictors based on spatial patterns

When statistical inference is used for spatial prediction, the model-based framework known as kriging is commonly used. The predictor for an unsampled element of a population is a weighted combination of sampled values, in which weights are obtained by estimating the spatial covariance function. This solution can be affected by model misspecification and can be influenced by sampling design properties. In classical design-based finite population inference, these problems can be overcome; nevertheless, spatial solutions are still seldom used for this purpose. Through the efficient use of spatial information, a conceptual framework for design-based estimation has been developed in this study. We propose a standardized weighted predictor for unsampled spatial data, using the population information regarding spatial locations directly in the weighting system. Our procedure does not require model estimation of the spatial pattern because the spatial relationship is captured exclusively based on the Euclidean distances between locations (which are fixed and do not require assessment after sample selection). The individual predictor is a design-based ratio estimator, and we illustrate its properties for simple random sampling.spatial sampling; ratio estimator; design-based inference; model-based inference; spatial information in finite population inference campionamento spaziale, stimatore del rapporto, inferenza da disegno, inferenza da modello; informazione spaziale nell’inferenza da popolazioni finite

Finite population properties of predictors based on spatial patterns

When statistical inference is used for spatial prediction, the model-based framework known as kriging is commonly used. The predictor for an unsampled element of a population is a weighted combination of sampled values, in which weights are obtained by estimating the spatial covariance function. This solution can be affected by model misspecification and can be influenced by sampling design properties. In classical design-based finite population inference, these problems can be overcome; nevertheless, spatial solutions are still seldom used for this purpose. Through the efficient use of spatial information, a conceptual framework for design-based estimation has been developed in this study. We propose a standardized weighted predictor for unsampled spatial data, using the population information regarding spatial locations directly in the weighting system. Our procedure does not require model estimation of the spatial pattern because the spatial relationship is captured exclusively based on the Euclidean distances between locations (which are fixed and do not require assessment after sample selection). The individual predictor is a design-based ratio estimator, and we illustrate its properties for simple random sampling

Cauliflower Mosaic Virus TAV, a Plant Virus Protein That Functions like Ribonuclease H1 and is Cytotoxic to Glioma Cells

Recent comparisons between plant and animal viruses reveal many common principles that underlie how all viruses express their genetic material, amplify their genomes, and link virion assembly with replication. Cauliflower mosaic virus (CaMV) is not infectious for human beings. Here, we show that CaMV transactivator/viroplasmin protein (TAV) shares sequence similarity with and behaves like the human ribonuclease H1 (RNase H1) in reducing DNA/RNA hybrids detected with S9.6 antibody in HEK293T cells. We showed that TAV is clearly expressed in the cytosol and in the nuclei of transiently transfected human cells, similar to its distribution in plants. TAV also showed remarkable cytotoxic effects in U251 human glioma cells in vitro. *ese characteristics pave the way for future analysis on the use of the plant virus protein TAV, as an alternative to human RNAse H1 during gene therapy in human cells

Ki67 and PR in Patients Treated with CDK4/6 Inhibitors: A Real-World Experience

CDK4/6 inhibitors (CDK4/6i) are recommended in patients with estrogen receptor (ER)-positive, HER2-negative advanced breast cancer (ABC). Up to now, no prognostic biomarkers have been identified in this setting. We retrospectively analyzed the expression of progesterone receptor (PR) and Ki67, assessed by immunohistochemistry, in 71 ABC patients treated with CDK4/6i and analyzed the impact of these markers on progression-free survival (PFS). The majority of patients 63/71 (88.7%) received palbociclib, 4 (5.6%) received ribociclib, and 4 (5.6%) received abemaciclib. A higher median value of Ki67 was observed in cases undergoing second-line treatment (p= 0.047), whereas the luminal B subtype was more prevalent (p= 0.005). In the univariate analysis of the first-line setting, luminal A subtype showed a trend towards a correlation with a longer PFS (p= 0.053). A higher continuous Ki67 value led to a significantly shorter PFS. When the interaction between pathological characteristics and line of treatment was considered, luminal B subtype showed a significantly (p= 0.043) worse outcome (Hazard Ratio (HR) 2.84; 1.03-7.82 95% Confidence Interval (CI)). PFS in patients undergoing endocrine therapy plus CDK4/6i was inversely correlated with Ki67 expression but not with PR, suggesting that tumor proliferation has a greater impact on cell cycle inhibitors combined with endocrine therapy than PR expression

Employing Distances in Design-based Spatial Estimation

In the last couple of decades we assisted to a reappraisal of spatial design-based techniques. Usually the spatial information regarding the spatial location of the individuals of a population has been used to develop efficient sampling designs. This thesis aims at offering a new technique for both inference on individual values and global population values able to employ the spatial information available before sampling at estimation level by rewriting a deterministic interpolator under a design-based framework. The achieved point estimator of the individual values is treated both in the case of finite spatial populations and continuous spatial domains, while the theory on the estimator of the population global value covers the finite population case only. A fairly broad simulation study compares the results of the point estimator with the simple random sampling without replacement estimator in predictive form and the kriging, which is the benchmark technique for inference on spatial data. The Monte Carlo experiment is carried out on populations generated according to different superpopulation methods in order to manage different aspects of the spatial structure. The simulation outcomes point out that the proposed point estimator has almost the same behaviour as the kriging predictor regardless of the parameters adopted for generating the populations, especially for low sampling fractions. Moreover, the use of the spatial information improves substantially design-based spatial inference on individual values

A competitive design-based spatial predictor

Under the finite population design-based framework, locations' spatial information coordinates of a population have traditionally been used to develop efficient sampling designs rather than for estimation or prediction. We propose to enhance design-based individual prediction by exploiting the spatial information derived from geography, which is available for each population element before sampling. Individual predictors are obtained by reinterpreting deterministic interpolators under the finite population design-based framework, making it possible to derive their statistical properties. Monte Carlo experiments on real and simulated data help to appreciate the performances of the proposed approach in comparison both with estimators that do not employ spatial information and with kriging. We found that under the most favorable conditions for kriging, the proposed predictor shows at least the same performances, while outperforming kriging for small sample sizes