Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units.

PURPOSE: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. METHOD: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). RESULTS: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. CONCLUSION: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables like soil gas radon measurements as well as more detailed geological information.

Multi-institutional validation of the prognostic value of Ki-67 labeling index in patients treated with radical prostatectomy.

OBJECTIVE: Several smaller single-center studies have reported a prognostic role for Ki-67 labeling index in prostate cancer. Our aim was to test whether Ki-67 is an independent prognostic marker of biochemical recurrence (BCR) in a large international cohort of patients treated with radical prostatectomy (RP). METHODS: Ki-67 immunohistochemical staining on prostatectomy specimens from 3,123 patients who underwent RP for prostate cancer was retrospectively performed. Univariable and multivariable Cox regression models were used to assess the association of Ki-67 status with BCR. RESULTS: Ki-67 positive status was observed in 762 (24.4 %) patients and was associated with lymph node involvement (LNI) (p = 0.039). Six hundred and twenty-one (19.9 %) patients experienced BCR. The estimated 3-year biochemical-free survivals were 85 % for patients with negative Ki-67 status and 82.1 % for patients with positive Ki-67 status (log-rank test, p = 0.014). In multivariable analysis that adjusted for the effects of age, preoperative PSA, RP Gleason sum, seminal vesicle invasion, extracapsular extension, positive surgical margins, lymphovascular invasion, and LNI, Ki-67 was significantly associated with BCR (HR = 1.19; p = 0.019). Subgroup analysis revealed that Ki-67 is associated with BCR in patients without LNI (p = 0.004), those with RP Gleason sum 7 (p = 0.015), and those with negative surgical margins (p = 0.047). CONCLUSION: We confirmed Ki-67 as an independent predictor of BCR after RP. Ki-67 could be particularly informative in patients with favorable pathologic characteristics to help in the clinical decision-making regarding adjuvant therapy and optimized follow-up scheduling.

Rockfall detection from LIDAR point clouds: A clustering approach using R

The analysis of rockfall characteristics and spatial distribution is fundamental to understand and model the main factors that predispose to failure. In our study we analysed LiDAR point clouds aiming to: (1) detect and characterise single rockfalls; (2) investigate their spatial distribution. To this end, different cluster algorithms were applied: 1a) Nearest Neighbour Clutter Removal (NNCR) in combination with the Expectation?Maximization (EM) in order to separate feature points from clutter; 1b) a density based algorithm (DBSCAN) was applied to isolate the single clusters (i.e. the rockfall events); 2) finally we computed the Ripley's K-function to investigate the global spatial pattern of the extracted rockfalls. The method allowed proper identification and characterization of more than 600 rockfalls occurred on a cliff located in Puigcercos (Catalonia, Spain) during a time span of six months. The spatial distribution of these events proved that rockfall were clustered distributed at a welldefined distance-range. Computations were carried out using R free software for statistical computing and graphics. The understanding of the spatial distribution of precursory rockfalls may shed light on the forecasting of future failures.

On the use of an appropriate TdT-mediated dUTP-biotin nick end labeling assay to identify apoptotic cells.

Apoptosis is an essential cellular mechanism involved in many processes such as embryogenesis, metamorphosis, and tissue homeostasis. DNA fragmentation is one of the key markers of this form of cell death. DNA fragmentation is executed by endogenous endonucleases such as caspase-activated DNase (CAD) in caspase-dependent apoptosis. The TUNEL (TdT-mediated dUTP-biotin nick end labeling) technique is the most widely used method to identify apoptotic cells in a tissue or culture and to assess drug toxicity. It is based on the detection of 3'-OH termini that are labeled with dUTP by the terminal deoxynucleotidyl transferase. Although the test is very reliable and sensitive in caspase-dependent apoptosis, it is completely useless when cell death is mediated by pathways involving DNA degradation that generates 3'-P ends as in the LEI/L-DNase II pathway. Here, we propose a modification in the TUNEL protocol consisting of a dephosphorylation step prior to the TUNEL labeling. This allows the detection of both types of DNA breaks induced during apoptosis caspase-dependent and independent pathways, avoiding underestimating the cell death induced by the treatment of interest.