Charles University in Prague

About: Charles University in Prague is a education organization based out in Prague, Czechia. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 32392 authors who have published 74435 publications receiving 1804208 citations.

Increasing incidence of colorectal cancer in young adults in Europe over the last 25 years

Abstract: Objective The incidence of colorectal cancer (CRC) declines among subjects aged 50 years and above. An opposite trend appears among younger adults. In Europe, data on CRC incidence among younger adults are lacking. We therefore aimed to analyse European trends in CRC incidence and mortality in subjects younger than 50 years. Design Data on age-related CRC incidence and mortality between 1990 and 2016 were retrieved from national and regional cancer registries. Trends were analysed by Joinpoint regression and expressed as annual percent change. Results We retrieved data on 143.7million people aged 20–49 years from 20 European countries. Of them, 187 918 (0.13%) were diagnosed with CRC. On average, CRC incidence increased with 7.9% per year among subjects aged 20–29 years from 2004 to 2016. The increase in the age group of 30–39 years was 4.9% per year from 2005 to 2016, the increase in the age group of 40–49 years was 1.6% per year from 2004 to 2016. This increase started earliest in subjects aged 20–29 years, and 10–20 years later in those aged 30–39 and 40–49 years. This is consistent with an age-cohort phenomenon. Although in most European countries the CRC incidence had risen, some heterogeneity was found between countries. CRC mortality did not significantly change among the youngest adults, but decreased with 1.1%per year between 1990 and 2016 and 2.4% per year between 1990 and 2009 among those aged 30–39 years and 40–49 years, respectively. Conclusion CRC incidence rises among young adults in Europe. The cause for this trend needs to be elucidated. Clinicians should be aware of this trend. If the trend continues, screening guidelines may need to be reconsidered.

The Physics of the B Factories

Abstract: This work is on the Physics of the B Factories. Part A of this book contains a brief description of the SLAC and KEK B Factories as well as their detectors, BaBar and Belle, and data taking related issues. Part B discusses tools and methods used by the experiments in order to obtain results. The results themselves can be found in Part C.

Narcolepsy is strongly associated with the T-cell receptor alpha locus.

Abstract: Narcolepsy with cataplexy, characterized by sleepiness and rapid onset into REM sleep, affects 1 in 2,000 individuals. Narcolepsy was first shown to be tightly associated with HLA-DR2 (ref. 3) and later sublocalized to DQB1*0602 (ref. 4). Following studies in dogs and mice, a 95% loss of hypocretin-producing cells in postmortem hypothalami from narcoleptic individuals was reported. Using genome-wide association (GWA) in Caucasians with replication in three ethnic groups, we found association between narcolepsy and polymorphisms in the TRA@ (T-cell receptor alpha) locus, with highest significance at rs1154155 (average allelic odds ratio 1.69, genotypic odds ratios 1.94 and 2.55, P < 10(-21), 1,830 cases, 2,164 controls). This is the first documented genetic involvement of the TRA@ locus, encoding the major receptor for HLA-peptide presentation, in any disease. It is still unclear how specific HLA alleles confer susceptibility to over 100 HLA-associated disorders; thus, narcolepsy will provide new insights on how HLA-TCR interactions contribute to organ-specific autoimmune targeting and may serve as a model for over 100 other HLA-associated disorders.

A subexponential bound for linear programming

Abstract: We present a simple randomized algorithm which solves linear programs withn constraints andd variables in expected $$\min \{ O(d^2 2^d n),e^{2\sqrt {dIn({n \mathord{\left/ {\vphantom {n {\sqrt d }}} \right. \kern- ulldelimiterspace} {\sqrt d }})} + O(\sqrt d + Inn)} \}$$ time in the unit cost model (where we count the number of arithmetic operations on the numbers in the input); to be precise, the algorithm computes the lexicographically smallest nonnegative point satisfyingn given linear inequalities ind variables. The expectation is over the internal randomizations performed by the algorithm, and holds for any input. In conjunction with Clarkson's linear programming algorithm, this gives an expected bound of $$O(d^2 n + e^{O(\sqrt {dInd} )} ).$$ The algorithm is presented in an abstract framework, which facilitates its application to several other related problems like computing the smallest enclosing ball (smallest volume enclosing ellipsoid) ofn points ind-space, computing the distance of twon-vertex (orn-facet) polytopes ind-space, and others. The subexponential running time can also be established for some of these problems (this relies on some recent results due to Gartner).