Rectal hemangiopericytoma in a 37-year-old woman: a case report and review of the literature

<p>Abstract</p> <p>Introduction</p> <p>Hemangiopericytoma is an uncommon perivascular tumor. Rectal Hemangiopericytomas are extremely rare. To the best of our knowledge, only two cases have been reported in the literature.</p> <p>Case presentation</p> <p>We report the case of a 37-year-old Asian woman with an Hemangiopericytoma rising from the anterior wall of her rectum. Abdominopelvic computed tomography showed a 7.4 cm solid mass between her uterus and her rectum. Heterogeneous gradual enhancement after intravenous injection of contrast material was noted with several tortuous vessels around her tumor. Intra-operative findings indicated a capsule and well-circumscribed solid tumor connecting with the anterior wall of her rectum by a small pedicle. With immunohistochemical stains, her tumor cells reacted positive for Bcl-2, CD34, and ki67 and negative for CD10, CD117, S100, and Desmin. Follow-up computed tomography scans have shown no tumor recurrence or metastasis signs.</p> <p>Conclusions</p> <p>Rectal Hemangiopericytoma is a rare tumor with non-specific imaging findings. Hemangiopericytomas should be included in the differential list when a massive tumor with heterogeneously gradual enhancement in the regions of the rectum is encountered.</p

Exploiting Symmetric Temporally Sparse BPTT for Efficient RNN Training

Recurrent Neural Networks (RNNs) are useful in temporal sequence tasks. However, training RNNs involves dense matrix multiplications which require hardware that can support a large number of arithmetic operations and memory accesses. Implementing online training of RNNs on the edge calls for optimized algorithms for an efficient deployment on hardware. Inspired by the spiking neuron model, the Delta RNN exploits temporal sparsity during inference by skipping over the update of hidden states from those inactivated neurons whose change of activation across two timesteps is below a defined threshold. This work describes a training algorithm for Delta RNNs that exploits temporal sparsity in the backward propagation phase to reduce computational requirements for training on the edge. Due to the symmetric computation graphs of forward and backward propagation during training, the gradient computation of inactivated neurons can be skipped. Results show a reduction of ∼80% in matrix operations for training a 56k parameter Delta LSTM on the Fluent Speech Commands dataset with negligible accuracy loss. Logic simulations of a hardware accelerator designed for the training algorithm show 2-10X speedup in matrix computations for an activation sparsity range of 50%-90%. Additionally, we show that our training algorithm will be useful for online incremental learning on edge devices with limited computing resources

Poly[[diaqua-μ6-succinato-di-μ5-succinato-didysprosium(III)] mono­hydrate]

The title compound, {[Dy2(C4H4O4)3(H2O)2]·H2O}n, is isostructural with other lanthanide succinates of the same formula. The DyIII atom is nine-coordinated in a tricapped trigonal–prismatic environment by eight O atoms, derived from six carboxyl­ate groups and a water mol­ecule. One of the independent succinate anions is located about a crystallographic inversion center and the uncoordinated water mol­ecule lies on a twofold axis. The crystal structure comprises edge-shared DyO9 polyhedra linked by succinate bridges, forming a three-dimensional network architecture. Intra- and inter­molecular O—H⋯O hydrogen bonds are present in the crystal structure

Topological surface electronic states in candidate nodal-line semimetal CaAgAs

We investigate systematically the bulk and surface electronic structure of the candidate nodal-line semimetal CaAgAs by angle resolved photoemission spectroscopy and density functional calculations. We observed a metallic, linear, non-$k_z$-dispersive surface band that coincides with the high-binding-energy part of the theoretical topological surface state, proving the topological nontriviality of the system. An overall downshift of the experimental Fermi level points to a rigid-band-like $p$-doping of the samples, due possibly to Ag vacancies in the as-grown crystals.Comment: 6 pages, 5 figure

Enhacement in the dymanic response of a viscoelastic fluid flowing through a longitudinally vibrating tube

We analyzed effects of elasticity on the dynamics of fluids in porous media by studying a flow of a Maxwell fluid in a tube, which oscillates longitudinally and is subject to oscillatory pressure gradient. The present study investigates novelties brought about into the classic Biot's theory of propagation of elastic waves in a fluid-saturated porous solid by inclusion of non-Newtonian effects that are important, for example, for hydrocarbons. Using the time Fourier transform and transforming the problem into the frequency domain, we calculated: (A) the dynamic permeability and (B) the function $F(\kappa)$ that measures the deviation from Poiseuille flow friction as a function of frequency parameter $\kappa$. This provides a more complete theory of flow of Maxwell fluid through the longitudinally oscillating cylindrical tube with the oscillating pressure gradient, which has important practical applications. This study has clearly shown transition from dissipative to elastic regime in which sharp enhancements (resonances) of the flow are found