Additional file 1: of Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study

Table S1. Risk of breast cancer for BMI and mammographic measures by detection mode, excluding HRT users. Table S2 Risk of breast cancer for BMI and mammographic measures by detection mode and tumor size, excluding HRT users. Table S3 Risk of interval versus screen-detected cancer for BMI and mammographic measures, excluding HRT users. Table S4 Risk of breast cancer for BMI and mammographic measures by detection mode, excluding cases (and matched controls) diagnosed within 2 years of mammogram. Table S5 Risk of breast cancer for BMI and mammographic measures by detection mode and tumor size, excluding cases (and matched controls) diagnosed within 2 years from mammogram. Table S6 Risk of interval versus screen-detected cancer for BMI and mammographic measures, excluding cases (and matched controls) diagnosed within 2 years from mammogram. Table S7 Risk of breast cancer for BMI and mammographic measures by detection mode, excluding cases diagnosed between 1 and 2 years after negative screening, and their matching controls. Table S8 Risk of breast cancer for BMI and mammographic measures by detection mode and tumor size, excluding cases diagnosed between 1 and 2 years after negative screening, and their matching controls. Table S9 Risk of interval versus screen-detected cancer for BMI and mammographic measures, excluding cases diagnosed between 1 and 2 years after negative screening, and their matching controls. (DOCX 61 kb

Causes of blood methylomic variation for middle-aged women measured by the HumanMethylation450 array

<p>To address the limitations in current classic twin/family research on the genetic and/or environmental causes of human methylomic variation, we measured blood DNA methylation for 479 women (mean age 56 years) including 66 monozygotic (MZ), 66 dizygotic (DZ) twin pairs and 215 sisters of twins, and 11 random technical duplicates using the HumanMethylation450 array. For each methylation site, we estimated the correlation for pairs of duplicates, MZ twins, DZ twins, and siblings, fitted variance component models by assuming the variation is explained by genetic factors, by shared and individual environmental factors, and by independent measurement error, and assessed the best fitting model. We found that the average (standard deviation) correlations for duplicate, MZ, DZ, and sibling pairs were 0.10 (0.35), 0.07 (0.21), -0.01 (0.14) and -0.04 (0.07). At the genome-wide significance level of 10⁻⁷, 93.3% of sites had no familial correlation, and 5.6%, 0.1%, and 0.2% of sites were correlated for MZ, DZ, and sibling pairs. For 86.4%, 6.9%, and 7.1% of sites, the best fitting model included measurement error only, a genetic component, and at least one environmental component. For the 13.6% of sites influenced by genetic and/or environmental factors, the average proportion of variance explained by environmental factors was greater than that explained by genetic factors (0.41 vs. 0.37, <i>P</i> value <10⁻¹⁵). Our results are consistent with, for middle-aged woman, blood methylomic variation measured by the HumanMethylation450 array being largely explained by measurement error, and more influenced by environmental factors than by genetic factors.</p

Table_1_Development, testing and validation of a SARS-CoV-2 multiplex panel for detection of the five major variants of concern on a portable PCR platform.XLS

Many SARS-CoV-2 variants have emerged during the course of the COVID-19 pandemic. These variants have acquired mutations conferring phenotypes such as increased transmissibility or virulence, or causing diagnostic, therapeutic, or immune escape. Detection of Alpha and the majority of Omicron sublineages by PCR relied on the so-called S gene target failure due to the deletion of six nucleotides coding for amino acids 69–70 in the spike (S) protein. Detection of hallmark mutations in other variants present in samples relied on whole genome sequencing. However, whole genome sequencing as a diagnostic tool is still in its infancy due to geographic inequities in sequencing capabilities, higher cost compared to other molecular assays, longer turnaround time from sample to result, and technical challenges associated with producing complete genome sequences from samples that have low viral load and/or high background. Hence, there is a need for rapid genotyping assays. In order to rapidly generate information on the presence of a variant in a given sample, we have created a panel of four triplex RT-qPCR assays targeting 12 mutations to detect and differentiate all five variants of concern: Alpha, Beta, Gamma, Delta, and Omicron. We also developed an expanded pentaplex assay that can reliably distinguish among the major sublineages (BA.1–BA.5) of Omicron. In silico, analytical and clinical testing of the variant panel indicate that the assays exhibit high sensitivity and specificity. This panel can help fulfill the need for rapid identification of variants in samples, leading to quick decision making with respect to public health measures, as well as treatment options for individuals. Compared to sequencing, these genotyping PCR assays allow much faster turn-around time from sample to results—just a couple hours instead of days or weeks.</p

Data_Sheet_1_Development, testing and validation of a SARS-CoV-2 multiplex panel for detection of the five major variants of concern on a portable PCR platform.PDF

Many SARS-CoV-2 variants have emerged during the course of the COVID-19 pandemic. These variants have acquired mutations conferring phenotypes such as increased transmissibility or virulence, or causing diagnostic, therapeutic, or immune escape. Detection of Alpha and the majority of Omicron sublineages by PCR relied on the so-called S gene target failure due to the deletion of six nucleotides coding for amino acids 69–70 in the spike (S) protein. Detection of hallmark mutations in other variants present in samples relied on whole genome sequencing. However, whole genome sequencing as a diagnostic tool is still in its infancy due to geographic inequities in sequencing capabilities, higher cost compared to other molecular assays, longer turnaround time from sample to result, and technical challenges associated with producing complete genome sequences from samples that have low viral load and/or high background. Hence, there is a need for rapid genotyping assays. In order to rapidly generate information on the presence of a variant in a given sample, we have created a panel of four triplex RT-qPCR assays targeting 12 mutations to detect and differentiate all five variants of concern: Alpha, Beta, Gamma, Delta, and Omicron. We also developed an expanded pentaplex assay that can reliably distinguish among the major sublineages (BA.1–BA.5) of Omicron. In silico, analytical and clinical testing of the variant panel indicate that the assays exhibit high sensitivity and specificity. This panel can help fulfill the need for rapid identification of variants in samples, leading to quick decision making with respect to public health measures, as well as treatment options for individuals. Compared to sequencing, these genotyping PCR assays allow much faster turn-around time from sample to results—just a couple hours instead of days or weeks.</p

Pedagogical and psychological factors that influence player performance in youth football

Title: Psychological and pedagogical factors that influence player performance in youth football Objectives: The objective of the theoretical part is to analyze the psychological development of the player and the pedagogical aspects influencing the performance from the point of view of the educational activities of the coaches and parents. The selected theoretical starting points are then linked to the player's game play and its sporting development. The content of the research section is to examine the emotional reactions of the players of the preparatory and pupil categories in the clubs AC Sparta Praha and FK Dukla Praha. We analyzed the results of the emotional experience of the players and then we analyzed the differences of factors in the developmental stages of the sporting development and the results of the differences of emotional reactions of the players of both clubs. Methods: In this work we used a questioning method, namely a standardized DEMOR emotional reaction questionnaire examining emotional reactions of pupils. We have adapted it to the sport training environment and piloted. The research was conducted in seven teams of the AC Sparta Prague Football Club and seven teams of FK Dukla Prague in the U9-U15 category. Results: The results showed high values of positive emotional..

Difference in square-root mammographic density measures in postmenopausal compared to premenopausal women and by time since menopause: Overall and in subgroups (pooled analyses).

<p>Difference in square-root mammographic density measures in postmenopausal compared to premenopausal women and by time since menopause: Overall and in subgroups (pooled analyses).</p

Characteristics of ICMD participants by age: Menopausal status, BMI, and measures of mammographic density.

<p>Characteristics of ICMD participants by age: Menopausal status, BMI, and measures of mammographic density.</p

Polynomial smoothed curves of the crude association of percent mammographic density with age, for each population group within broad ethnic groups.

<p>The broad ethnic groups are organised from largest (black women) to smallest (East Asian women) average breast area for BMI. Full names and details of studies/population groups presented in this figure are provided in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002335#pmed.1002335.s011" target="_blank">S1 Text</a>). Adjustments: none. PD, percent mammographic density.</p

Difference in square-root mammographic density measures with a 10-year difference in age, in pre- and postmenopausal women.

<p>Difference in square-root mammographic density measures with a 10-year difference in age, in pre- and postmenopausal women.</p

Additional file 2: of Mammographic density assessed on paired raw and processed digital images and on paired screen-film and digital images across three mammography systems

is Table S2 presenting mean MD measures of inter-reader repeats, by reader and image type. (DOC 29 kb