A remark on the dimension of the Bergman space of some Hartogs domains

Let D be a Hartogs domain of the form D={(z,w) \in CxC^N : |w| < e^{-u(z)}} where u is a subharmonic function on C. We prove that the Bergman space of holomorphic and square integrable functions on D is either trivial or infinite dimensional.Comment: 12 page

FindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysis

Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci) to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ

High resolution and contrast 7 tesla MR brain imaging of the neonate

IntroductionUltra-high field MR imaging offers marked gains in signal-to-noise ratio, spatial resolution, and contrast which translate to improved pathological and anatomical sensitivity. These benefits are particularly relevant for the neonatal brain which is rapidly developing and sensitive to injury. However, experience of imaging neonates at 7T has been limited due to regulatory, safety, and practical considerations. We aimed to establish a program for safely acquiring high resolution and contrast brain images from neonates on a 7T system.MethodsImages were acquired from 35 neonates on 44 occasions (median age 39 + 6 postmenstrual weeks, range 33 + 4 to 52 + 6; median body weight 2.93 kg, range 1.57 to 5.3 kg) over a median time of 49 mins 30 s. Peripheral body temperature and physiological measures were recorded throughout scanning. Acquired sequences included T2 weighted (TSE), Actual Flip angle Imaging (AFI), functional MRI (BOLD EPI), susceptibility weighted imaging (SWI), and MR spectroscopy (STEAM).ResultsThere was no significant difference between temperature before and after scanning (p = 0.76) and image quality assessment compared favorably to state-of-the-art 3T acquisitions. Anatomical imaging demonstrated excellent sensitivity to structures which are typically hard to visualize at lower field strengths including the hippocampus, cerebellum, and vasculature. Images were also acquired with contrast mechanisms which are enhanced at ultra-high field including susceptibility weighted imaging, functional MRI, and MR spectroscopy.DiscussionWe demonstrate safety and feasibility of imaging vulnerable neonates at ultra-high field and highlight the untapped potential for providing important new insights into brain development and pathological processes during this critical phase of early life

Model of the management of the budgeting and controlling process in the organizational entities of academic institutions

In this article, the tendencies of the development of financing and functioning of academic institutions on the present-day educational market are presented. The required changes are characterized in the way finances are managed with a particular consideration of public academic institutions. Proposals are provided in relation to the determination of standard revenues of the organizational entities of academic institutions, analysis methods of the costs of the functioning of these entities and the budgeting of their operation. Prospects are presented of the development of the controlling of the realization of the budgets of the organizational entities of academic institutions

Calculation model of teaching costs in a university

A calculation model of teaching costs in a university is a system of guidelines, notions and relations to facilitate an assessment of the costs generated by individual university departments, majors, subjects etc. The existing calculation models based on the assessment of costs with the use of precision data prove to be ineffective in practice. The major drawback of these systems is the fact that it is not possible to take into account non-precision data in relation to cost generating factors (e.g. the number of didactic groups, hourly rates etc.). This article presents the author’s own proposal of a cost calculation model based on the formalism of fuzzy logics (with the use of the L-R representation). On the basis of the model proposed, it is possible to assess the costs of an academic subject with imprecise information concerning cost generating factors, or the values of those factors are assessed which imply the values set of the cost of a subject