Considerations for the design and conduct of human gut microbiota intervention studies relating to foods

With the growing appreciation for the influence of the intestinal microbiota on human health, there is increasing motivation to design and refine interventions to promote favorable shifts in the microbiota and their interactions with the host. Technological advances have improved our understanding and ability to measure this indigenous population and the impact of such interventions. However, the rapid growth and evolution of the field, as well as the diversity of methods used, parameters measured and populations studied, make it difficult to interpret the significance of the findings and translate their outcomes to the wider population. This can prevent comparisons across studies and hinder the drawing of appropriate conclusions. This review outlines considerations to facilitate the design, implementation and interpretation of human gut microbiota intervention studies relating to foods based upon our current understanding of the intestinal microbiota, its functionality and interactions with the human host. This includes parameters associated with study design, eligibility criteria, statistical considerations, characterization of products and the measurement of compliance. Methodologies and markers to assess compositional and functional changes in the microbiota, following interventions are discussed in addition to approaches to assess changes in microbiota–host interactions and host responses. Last, EU legislative aspects in relation to foods and health claims are presented. While it is appreciated that the field of gastrointestinal microbiology is rapidly evolving, such guidance will assist in the design and interpretation of human gut microbiota interventional studies relating to foods

Comparison of machine learning algorithms in restaurant revenue prediction

In this paper, we address several aspects of applying classical machine learning algorithms to a regression problem. We compare the predictive power to validate our approach on a data about revenue of a large Russian restaurant chain. We pay special attention to solve two problems: data heterogeneity and a high number of correlated features. We describe methods for considering heterogeneity—observations weighting and estimating models on subsamples. We define a weighting function via Mahalanobis distance in the space of features and show its predictive properties on following methods: ordinary least squares regression, elastic net, support vector regression, and random forest.</p

Estrogen receptor beta expression in prostate adenocarcinoma

<p>Abstract</p> <p>Background</p> <p>Prostate cancer is the most commonly diagnosed cancer in men and the second leading cause of cancer death in men. Estrogen induction of cell proliferation is a crucial step in carcinogenesis of gynecologic target tissues, and there are many studies recently done, showing that prostate cancer growth is also influenced by estrogen. The characterization of estrogen receptor beta (ER-b) brought new insight into the mechanisms underlying estrogen signalling. In the present study, we investigated the expression of estrogen receptor-b (ER-b) in human prostate cancer tissues.</p> <p>Methods</p> <p>We selected 52 paraffin-embedded blocks of prostate needle biopsies in a cross-sectional study to determine frequency and rate of ER-b expression in different grades of prostate adenocarcinoma according to Gleason grading system. Immunohistochemical staining of tissue sections by monoclonal anti ER-b antibody was performed using an Envision method visualising system.</p> <p>Results</p> <p>ER-b expression was seen in tumoral cells of prostatic carcinoma in all 29 cases with low and intermediate tumors (100%) and 19 of 23 cases with high grade tumor (83%). Mean rate of ER-b expression in low & intermediate grade cancers was 68.41% (SD = 25.63) whereas high grade cancers showed 49.48% rate of expression (SD = 28.79).</p> <p>Conclusions</p> <p>ER-b expression is reduced in high grade prostate cancers compared to low & intermediate grade ones (<it>P </it>value 0.027).</p

Dilepton production in heavy ion collisions at intermediate energies

We present a unified description of the vector meson and dilepton production in elementary and in heavy ion reactions. The production of vector mesons ($\rho,\omega$) is described via the excitation of nuclear resonances ($R$). The theoretical framework is an extended vector meson dominance model (eVMD). The treatment of the resonance decays $R\longmapsto NV$ with arbitrary spin is covariant and kinematically complete. The eVMD includes thereby excited vector meson states in the transition form factors. This ensures correct asymptotics and provides a unified description of photonic and mesonic decays. The resonance model is successfully applied to the $\omega$ production in $p+p$ reactions. The same model is applied to the dilepton production in elementary reactions ($p+p, p+d$). Corresponding data are well reproduced. However, when the model is applied to heavy ion reactions in the BEVALAC/SIS energy range the experimental dilepton spectra measured by the DLS Collaboration are significantly underestimated at small invariant masses. As a possible solution of this problem the destruction of quantum interference in a dense medium is discussed. A decoherent emission through vector mesons decays enhances the corresponding dilepton yield in heavy ion reactions. In the vicinity of the $\rho/\omega$-peak the reproduction of the data requires further a substantial collisional broadening of the $\rho$ and in particular of the $\omega$ meson.Comment: 32 pages revtex, 19 figures, to appear in PR

Clinical relevance of genetic instability in prostatic cells obtained by prostatic massage in early prostate cancer

We investigated whether genetic lesions such as loss of heterozygosity (LOH) are detected in prostatic cells obtained by prostatic massage during early diagnosis of prostate cancer (CaP) and discussed their clinical relevance. Blood and first urine voided after prostatic massage were collected in 99 patients with total prostate-specific antigen (PSA) between 4 and 10 ng ml−1, prior to prostate biopsies. Presence of prostatic cells was confirmed by quantitative RT–PCR analysis of PSA mRNA. Genomic DNA was analysed for LOH on six chromosomal regions. One or more allelic deletions were found in prostatic fluid from 57 patients analysed, of whom 33 (58%) had CaP. Sensitivity and specificity of LOH detection and PSA free to total ratio <15% for positive biopsy were respectively 86.7 and 44% (P=0.002) for LOH, and 55 and 74% (P=0.006) for PSA ratio <15%. Analysis of LOH obtained from prostatic tumours revealed similar patterns compared to prostatic fluid cells in 86% of cases, confirming its accuracy. The presence of LOH of urinary prostatic cells obtained after prostatic massage is significantly associated with CaP on biopsy and may potentially help to identify a set of patients who are candidates for further prostate biopsies

Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer

Objectives This retrospective cohort study developed a prognostic model incorporating PET texture analysis in patients with oesophageal cancer (OC). Internal validation of the model was performed. Methods Consecutive OC patients (n = 403) were chronologically separated into development (n = 302, September 2010-September 2014, median age = 67.0, males = 227, adenocarcinomas = 237) and validation cohorts (n = 101, September 2014-July 2015, median age = 69.0, males = 78, adenocarcinomas = 79). Texture metrics were obtained using a machine-learning algorithm for automatic PET segmentation. A Cox regression model including age, radiological stage, treatment and 16 texture metrics was developed. Patients were stratified into quartiles according to a prognostic score derived from the model. A p-value < 0.05 was considered statistically significant. Primary outcome was overall survival (OS). Results Six variables were significantly and independently associated with OS: age [HR =1.02 (95% CI 1.01-1.04), p < 0.001], radiological stage [1.49 (1.20-1.84), p < 0.001], treatment [0.34 (0.24–0.47), p < 0.001], log(TLG) [5.74 (1.44–22.83), p = 0.013], log(Histogram Energy) [0.27 (0.10–0.74), p = 0.011] and Histogram Kurtosis [1.22 (1.04–1.44), p = 0.017]. The prognostic score demonstrated significant differences in OS between quartiles in both the development (X2 143.14, df 3, p < 0.001) and validation cohorts (X2 20.621, df 3, p < 0.001). Conclusions This prognostic model can risk stratify patients and demonstrates the additional benefit of PET texture analysis in OC staging

Intercalibration of the barrel electromagnetic calorimeter of the CMS experiment at start-up

Calibration of the relative response of the individual channels of the barrel electromagnetic calorimeter of the CMS detector was accomplished, before installation, with cosmic ray muons and test beams. One fourth of the calorimeter was exposed to a beam of high energy electrons and the relative calibration of the channels, the intercalibration, was found to be reproducible to a precision of about 0.3%. Additionally, data were collected with cosmic rays for the entire ECAL barrel during the commissioning phase. By comparing the intercalibration constants obtained with the electron beam data with those from the cosmic ray data, it is demonstrated that the latter provide an intercalibration precision of 1.5% over most of the barrel ECAL. The best intercalibration precision is expected to come from the analysis of events collected in situ during the LHC operation. Using data collected with both electrons and pion beams, several aspects of the intercalibration procedures based on electrons or neutral pions were investigated