•Rainfall data to be used in the hydrological practice is available in aggregated form.•Aggregated form produce the underestimate of annual maximum rainfall depth (Hd).•Errors in the Hd evaluation ...from data with coarse time aggregations are investigated.•Relationships to overcome the underestimate of Hd are presented.
For a few decades the local rainfall measurements are generally obtained by tipping bucket sensors, that allow to record each tipping time corresponding to a well-known rain depth. However, a considerable part of rainfall data to be used in the hydrological practice is available in aggregated form within constant time intervals. This can produce undesirable effects, like the underestimation of the annual maximum rainfall depth, Hd, associated with a given duration, d, that is the basic quantity in the development of rainfall depth-duration-frequency relationships. The errors in the evaluation of Hd from data characterized by a coarse temporal aggregation, ta, and a procedure to reduce the non-homogeneity of the Hd series are here investigated. Our results show that for ta = 1 min the underestimation is practically negligible, whereas for larger temporal aggregations with d = ta the error in a single Hd can reach values up to 50% and in a series of Hd in the average up to 17%. Relationships between the non-dimensional ratio ta/d and the average underestimation of Hd, derived through continuous rainfall data observed in many stations of Central Italy, are presented to overcome this issue. These equations allow to improve the Hd estimates and the associated depth-duration-frequency curves at least in areas with similar climatic conditions. The effect of the correction of the Hd series on the rainfall depth-duration-frequency curves is quantified. Our results indicate that the improvements obtained by the proposed procedure are of the order of 10%.
► We evaluated different maturity stages of the biomixture of biobed. ► Effect of the maturity stages of the biomixture on chlorpyrifos degradation and biological activity were evaluated. ► TCP ...formation was also evaluated. ► In all time of maturity stages evaluated pesticide was degraded, but decreased with increasing concentrations. ► When the time of maturity increased, greater accumulation of TCP in the biomix occurred.
The biomixture is a principal element controlling the degradation efficacy of the biobed. The maturity of the biomixture used in the biobed affects its overall performance of the biobed, but this is not well studied yet. The aim of this research was to evaluate the effect of using a typical composition of Swedish biomixture at different maturity stages on the degradation of chlorpyrifos. Tests were made using biomixture at three maturity stages: 0d (BC0), 15d (BC15) and 30d (BC30); chlorpyrifos was added to the biobeds at final concentration of 200, 320 and 480mgkg−1. Chlorpyrifos degradation in the biomixture was monitored over time. Formation of TCP (3,5,6-trichloro-2-pyrinidol) was also quantified, and hydrolytic and phenoloxidase activities measured. The biomixture efficiently degraded chlorpyrifos (degradation efficiency >50%) in all the evaluated maturity stages. However, chlorpyrifos degradation decreased with increasing concentrations of the pesticide. TCP formation occurred in all biomixtures, but a major accumulation was observed in BC30. Significant differences were found in both phenoloxidase and hydrolytic activities in the three maturity stages of biomixture evaluated. Also, these two biological activities were affected by the increase in pesticide concentration. In conclusion, our results demonstrated that chlorpyrifos can be degraded efficiently in all the evaluated maturity stages.
Significant prostate carcinoma (sPCa) classification based on MRI using radiomics or deep learning approaches has gained much interest, due to the potential application in assisting in clinical ...decision-making. Objective: To systematically review the literature (i) to determine which algorithms are most frequently used for sPCa classification, (ii) to investigate whether there exists a relation between the performance and the method or the MRI sequences used, (iii) to assess what study design factors affect the performance on sPCa classification, and (iv) to research whether performance had been evaluated in a clinical setting Methods: The databases Embase and Ovid MEDLINE were searched for studies describing machine learning or deep learning classification methods discriminating between significant and nonsignificant PCa on multiparametric MRI that performed a valid validation procedure. Quality was assessed by the modified radiomics quality score. We computed the median area under the receiver operating curve (AUC) from overall methods and the interquartile range. Results: From 2846 potentially relevant publications, 27 were included. The most frequent algorithms used in the literature for PCa classification are logistic regression (22%) and convolutional neural networks (CNNs) (22%). The median AUC was 0.79 (interquartile range: 0.77–0.87). No significant effect of number of included patients, image sequences, or reference standard on the reported performance was found. Three studies described an external validation and none of the papers described a validation in a prospective clinical trial. Conclusions: To unlock the promising potential of machine and deep learning approaches, validation studies and clinical prospective studies should be performed with an established protocol to assess the added value in decision-making.
•Microbiome/microbiota is a fundamental component of the ocular surface system.•Their composition may include both saprophytes and potentially pathogenic bacteria.•They regulate the ...tolerance/inflammation response at the ocular surface.•Bacteria on the ocular surface are mainly organized in biofilm.•Geography, age, food, and other factors may influence microbiota composition.
The ocular surface flora perform an important role in the defense mechanisms of the ocular surface system. Its regulation of the immunological activity and the barrier effect against pathogen invasion are remarkable. Composition of the flora differs according to the methods of investigation, because the microbiome, composed of the genetic material of bacteria, fungi, viruses, protozoa, and eukaryotes on the ocular surface, differs from the microbiota, which are the community of microorganisms that colonize the ocular surface. The observed composition of the ocular surface flora depends on harvesting and examining methods, whether with traditional culture or with more refined genetic analysis based on rRNA and DNA sequencing. Environment, diet, sex, and age influence the microbial flora composition, thus complicating the analysis of the baseline status. Moreover, potentially pathogenic organisms can affect its composition, as do various disorders, including chronic inflammation, and therapies applied to the ocular surface.
A better understanding of the composition and function of microbial communities at the ocular surface could bring new insights and clarify the epidemiology and pathology of ocular surface dynamics in health and disease. The purpose of this review is to provide an up-to-date overview of knowledge about this topic.
Purpose
With the expansion of research utilizing electronic healthcare data to identify transgender (TG) population health trends, the validity of computational phenotype (CP) algorithms to identify ...TG patients is not well understood. We aim to identify the current state of the literature that has utilized CPs to identify TG people within electronic healthcare data and their validity, potential gaps, and a synthesis of future recommendations based on past studies.
Methods
Authors searched the National Library of Medicine's PubMed, Scopus, and the American Psychological Association PsycInfo's databases to identify studies published in the United States that applied CPs to identify TG people within electronic healthcare data.
Results
Twelve studies were able to validate or enhance the positive predictive value (PPV) of their CP through manual chart reviews (n = 5), hierarchy of code mechanisms (n = 4), key text‐strings (n = 2), or self‐surveys (n = 1). CPs with the highest PPV to identify TG patients within their study population contained diagnosis codes and other components such as key text‐strings. However, if key text‐strings were not available, researchers have been able to find most TG patients within their electronic healthcare databases through diagnosis codes alone.
Conclusion
CPs with the highest accuracy to identify TG patients contained diagnosis codes along with components such as procedural codes or key text‐strings. CPs with high validity are essential to identifying TG patients when self‐reported gender identity is not available. Still, self‐reported gender identity information should be collected within electronic healthcare data as it is the gold standard method to better understand TG population health patterns.
PDF
