HUMAN URINARY BLADDER GLANDS AND THERE MACRO-MICROSCOPIC PECULIARITIES

Purpose of the investigation is to learn the morphological peculiarities (quantity parameters, age, individual and regional characteristics) of the glands of urinary bladder in the different age stages of the postnatal ontogenesis in the norm. A macro-microscopy method on total preparations of a wall of the bladder 54 received from corpses. Victims from the casual reasons at the age from the period newborn to senile age and we investigated variants of the form of a bladder glands, feature of its change in different sites of a wall of organ (proximal, average, distal thirds), taking into account age. Glands have preliminary been painted 0.05 % by a solution methylene dark blue with Sinelnicov’s method. The glands were investigated with the application of stereomicroscopic-binocular microscope MBS-9. Statistical data processing included calculation of arithmeticmean values, their errors, confidential intervals (excel). In quantity of the parameters of the urinary bladder, glands have individual changeability in the investigation. The boundary of variation of the parameters of the urinary bladder glands is rather wider in the maturity and senile stages. Connected with biological activity of the people in the definite degree, the quantity of the glands does not change in comparison with elderly period in old stage. In the stages of the first maturity and majority, the parameters of the measure and quantity of the urinary bladder glands in the women have difference from men. In this microscopic investigation, there is very important anatomical scientific information about the human urinary bladder glands that in the different stages, their quantity, age, form and regional changeability, proximal-distal gradient as well as other anatomical facts were established.

Adaptive Proximal Average Based Variance Reducing Stochastic Methods for Optimization with Composite Regularization

We focus on empirical risk minimization with a composite regulariser, which has been widely applied in various machine learning tasks to introduce important structural information regarding the problem or data. In general, it is challenging to calculate the proximal operator with the composite regulariser. Recently, proximal average (PA) which involves a feasible proximal operator calculation is proposed to approximate composite regularisers. Augmented with the prevailing variance reducing (VR) stochastic methods (e.g. SVRG, SAGA), PA based algorithms would achieve a better performance. However, existing works require a fixed stepsize, which needs to be rather small to ensure that the PA approximation is sufficiently accurate. In the meantime, the smaller stepsize would incur many more iterations for convergence. In this paper, we propose two fast PA based VR stochastic methods – APA-SVRG and APA-SAGA. By initializing the stepsize with a much larger value and adaptively decreasing it, both of the proposed methods are proved to enjoy the (ô n log 1/ε + mo 1/ε) iteration complexity to achieve the accurate solutions, where m0 is the initial number of inner iterations and n is the number of samples. Moreover, experimental results demonstrate the superiority of the proposed algorithms.