Free ureteral replacement in rats: regeneration of ureteral wall components in the acellular matrix graft.

ObjectivesTo evaluate ureteral replacement by a free homologous graft of acellular matrix in a rat model.MethodsIn 30 male Sprague-Dawley rats, a 0.3 to 0.8-cm midsegment of the left ureter was resected and replaced with an acellular matrix graft of equal length placed on a polyethylene stent. The animals were killed at varying intervals, and the grafted specimens were prepared for light and electron microscopy.ResultsIn all animals, the acellular matrix graft remained in its original position without evidence of incrustation or infection, and histologic examination showed complete epithelialization and progressive infiltration by vessels. At 10 weeks, smooth muscle fibers were observed; at 12 weeks, nerve fibers were first detected; at 4 months, smooth muscle cells had assumed regular configuration.ConclusionsThe ureteral acellular matrix graft appears to promote the regeneration of all ureteral wall components

Module identification in bipartite and directed networks

Modularity is one of the most prominent properties of real-world complex networks. Here, we address the issue of module identification in two important classes of networks: bipartite networks and directed unipartite networks. Nodes in bipartite networks are divided into two non-overlapping sets, and the links must have one end node from each set. Directed unipartite networks only have one type of nodes, but links have an origin and an end. We show that directed unipartite networks can be conviniently represented as bipartite networks for module identification purposes. We report a novel approach especially suited for module detection in bipartite networks, and define a set of random networks that enable us to validate the new approach

Classification of public administration complaints

Complaint management is a problem faced by many organizations that is both vital to customer image and highly dependent on human resources. This work attempts to tackle a part of the problem, by classifying summaries of complaints using machine learning models in order to better redirect these to the appropriate responders. The main challenges of this task is that training datasets are often small and highly imbalanced. This can can have a big impact on the performance of classification models. The dataset analyzed in this work suffers from both of these problems, being relatively small and having labels in different proportions. In this work, two different techniques are analyzed: combining classes together to increase the number of elements of the new class; and, providing new artificial examples for some classes via translation into other languages. The classification models explored were the following: k-NN, SVM, Naïve Bayes, boosting, and Deep Learning approaches, including transformers. The paper concludes that although, as expected, the classes with little representation are hard to classify, the techniques explored helped to boost the performance, especially in the classes with a low number of elements. SVM and BERT-based models outperformed their peers.info:eu-repo/semantics/publishedVersio

Temperature effects on dislocation core energies in silicon and germanium

Temperature effects on the energetics of the 90-degree partial dislocation in silicon and germanium are investigated, using non-equilibrium methods to estimate free energies, coupled with Monte Carlo simulations. Atomic interactions are described by Tersoff and EDIP interatomic potentials. Our results indicate that the vibrational entropy has the effect of increasing the difference in free energy between the two possible reconstructions of the 90-degree partial, namely, the single-period and the double-period geometries. This effect further increases the energetic stability of the double-period reconstruction at high temperatures. The results also indicate that anharmonic effects may play an important role in determining the structural properties of these defects in the high-temperature regime.Comment: 8 pages in two-column physical-review format with six figure

Runoff at the micro-plot and slope scale following wildfire, central Portugal

Through their effects on soil properties and vegetation/litter cover, wildfires can strongly enhance overland flow generation and accelerate soil erosion [1] and, thereby, negatively affect land-use sustainability as well as downstream aquatic and flood zones. Wildfires are a common phenomenon in present-day Portugal, devastating in an average year some 100.000 ha of forest and woodlands and in an exceptional year like 2003 over 400.000 ha. There therefore exists a clear need in Portugal for a tool that can provide guidance to post-fire land management by predicting soil erosion risk, on the one hand, and, on the other, the mitigation effectiveness of soil conservation measures. Such a tool has recently been developed for the Western U.S.A. [3: ERMiT] but its suitability for Portuguese forests will need to be corroborated by field observations. Testing the suitability of existing erosion models in recently burned forest areas in Portugal is, in a nutshell, the aim of the EROSFIRE projects. In the first EROSFIRE project the emphasis was on the prediction of erosion at the scale of individual hill slopes. In the ongoing EROSFIRE-II project the spatial scope is extended to include the catchment scale, so that also the connectivity between hill slopes as well as channel and road processes are being addressed. Besides ERMiT, the principal models under evaluation for slope-scale erosion prediction are: (i) the variant of USLE [4] applied by the Portuguese Water Institute after the wildfires of 2003; (ii) the Morgan–Morgan–Finney model (MMF) [5]; (iii) MEFIDIS [6]. From these models, MEFIDIS and perhaps MMF will, after successful calibration at the slope scale, also be applied for predicting catchment-scale sediment yields of extreme events