Liquid nitrogen for the treatment of actinic keratosis: A longitudinal assessment

Cryosurgery with liquid nitrogen is one of the most used treatments for actinic keratosis. We aimed to study the effectiveness of two consecutive sessions of cryosurgery for actinic keratosis and investigate factors associated with its therapeutic success. Hence, we conducted a longitudinal study including 92 patients of both sexes, aged 50–75 years with 5–50 actinic keratosis on the face and forearms, who underwent cryosurgery and treatment with sunscreen SPF 30, at baseline and after 120 days. The lesions were counted in duplicate by the same examiner before the start of treatment and after 120 (N = 92) and 300 days (N = 33), represented by their medians and quartiles and compared using the generalized linear mixed effects model (negative binomial). Treatment behavior was investigated in relation to sex, age, education, skin type, smoking, sun exposure at work and the use of aspirin, anti-inflammatory and angiotensin-converting enzyme inhibitors. There was a significant reduction in the actinic keratosis count on the face and forearms (p < 0.05). Our results confirmed the effectiveness of cryosurgery for actinic keratosis, with a 57% reduction in the number, and size of the lesions. Higher education levels (p = 0.02) and less sun exposure at work (p = 0.02) independently promoted a significant reduction in the actinic keratosis count. Different population groups showed characteristic responses to the treatment, which may be explained by the degree of adherence to the use of photoprotection. In two sessions, cryosurgery with liquid nitrogen reduced the actinic keratosis count.     

Random Partition Models and Exchangeability for Bayesian Identification of Population Structure

We introduce a Bayesian theoretical formulation of the statistical learning problem concerning the genetic structure of populations. The two key concepts in our derivation are exchangeability in its various forms and random allocation models. Implications of our results to empirical investigation of the population structure are discussed.     

A Statistical Approach to Identify Ancient Template DNA

One of the key problems in the study of ancient DNA is that of authenticating sequences obtained from PCR amplifications of highly degraded samples. Contamination of ancient samples and postmortem damage to endogenous DNA templates are the major obstacles facing researchers in this task. In particular, the authentication of sequences obtained from ancient human remains is thought by many to be rather challenging. We propose a novel approach, based on the c statistic, that can be employed to help identify the sequence motif of an endogenous template, based on a sample of sequences that reflect the nucleotide composition of individual template molecules obtained from ancient tissues (such as cloned products from a PCR amplification). The c statistic exploits as information the most common form of postmortem damage observed among clone sequences in ancient DNA studies, namely, lesion-induced substitutions caused by cytosine deamination events. Analyses of simulated sets of templates with miscoding lesions and real sets of clone sequences from the literature indicate that the c-based approach is highly effective in identifying endogenous sequence motifs, even when they are not present among the sampled clones. The proposed approach is likely to be of general use to researchers working with DNA from ancient tissues, particularly from human remains, where authentication of results has been most challenging. [Reviewing Editor: Dr. Magnus Nordborg]     

Comparison between principal component analysis and independent component analysis in electroencephalograms modelling

Principal Component Analysis (PCA) is a classical technique in statistical data analysis, feature extraction and data reduction, aiming at explaining observed signals as a linear combination of orthogonal principal components. Independent Component Analysis (ICA) is a technique of array processing and data analysis, aiming at recovering unobserved signals or 'sources' from observed mixtures, exploiting only the assumption of mutual independence between the signals. The separation of the sources by ICA has great potential in applications such as the separation of sound signals (like voices mixed in simultaneous multiple records, for example), in telecommunication or in the treatment of medical signals. However, ICA is not yet often used by statisticians. In this paper, we shall present ICA in a statistical framework and compare this method with PCA for electroencephalograms (EEG) analysis.We shall see that ICA provides a more useful data representation than PCA, for instance, for the representation of a particular characteristic of the EEG named event-related potential (ERP).     

Statistical optimization of medium components for avilamycin production by <Emphasis Type="Italic">Streptomyces viridochromogenes</Emphasis> Tü57-1 using response surface methodology

A fermentation medium for avilamycin production by Streptomyces viridochromogenes Tü57-1 has been optimized. Important components and their concentrations were investigated using fractional factorial design and Box–Behnken Design. The results showed that soybean flour, soluble starch, MgSO₄·7H₂O and CaCl₂·2H₂O are important for avilamycin production. A polynomial model related to medium components and avilamycin yield had been established. A high coefficient of determination (R ² = 0.92) was obtained that indicated good agreement between the experimental and predicted values of avilamycin yield. Student’s T-test of each coefficient showed that all the linear and quadratic terms had significant effect (P > |T| < 0.05) on avilamycin yield. The significance of tested components was related to MgSO₄·7H₂O (0.37 g/L), CaCl₂·2H₂O (0.39 g/L), soybean flour (21.97 g/L) and soluble starch (37.22 g/L). The yield of avilamycin reached 88.33 ± 0.94 mg/L (p < 0.05) that was 2.8-fold the initial yield.