Management of bilateral idiopathic renal hematuria in a dog with silver nitrate

Renal hematuria has limited treatment options. This report describes management of bilateral idiopathic renal hematuria in a dog with surgically assisted installation of 0.5% silver nitrate solution. Initial treatment resulted in freedom from clinical signs or recurrent anemia for 10 months; however, recurrence of bleeding following a nephrectomy resulted in euthanasia

Short-range order of compressed amorphous GeSe2

The structure of amorphous GeSe2 (a-GeSe2) has been studied by means of a combination of two-edges X-ray absorption spectroscopy (XAS) and angle-dispersive X-ray diffraction under pressures up to about 30 GPa. Multiple-edge XAS data-analysis of a-GeSe2 at ambient conditions allowed us to reconstruct and compare the first-neighbor distribution function with previous results obtained by neutron diffraction with isotopic substitution. GeSe2 is found to remain amorphous up to the highest pressures attained, and a reversible 1.5 eV red-shift of the Ge K-edge energy indicating metallization, occurs between 10 GPa and 15 GPa. Two compression stages are identified by XAS structure refinement. First, a decrease of the first-neighbor distances up to about 10 GPa, in the same pressure region of a previously observed breakdown of the intermediate-range order. Second, an increase of the Ge-Se distances, bond disorder, and of the coordination number. This stage is related to a reversible non-isostructural transition involving a gradual conversion from tetra- to octa-hedral geometry which is not yet fully completed at 30 GPa

Deep infiltrating endometriosis of the colon causing cyclic bleeding

[Description] Endometriosis, the presence of functional endometrial tissue outside the uterus, occurs in about 3–10% of women of reproductive age and is a cause of chronic pelvic pain and infertility for some.1 Bowel involvement may be present in about 5–10% of these women, mostly affecting the rectum and distal sigmoid (over 80% of cases), and, more infrequently, the appendix, ileum and caecum. The most common lesions involve only the serosa (endometriotic implants) but they can penetrate the muscular layers of the wall, in which case they are called deep infiltrating endometriosis. (...)(undefined

Integration of biocontrol agents and food-grade additives for enhancing protection of stored apples from Penicillium expansum.

Forty-nine compounds currently used as additives in foods were tested in combination with three biocontrol agents, the yeasts Rhodotorula glutinis, Cryptococcus laurentii, and the yeastlike fungus Aureobasidium pullulans, to increase their antagonistic activity against Penicillium expansum, the causal agent of blue mold on apples. Twelve additives dramatically improved the antagonistic activity of one or more of the tested biocontrol agents. In a two-way factorial experiment with these selected additives the percentage of P. expansum rots on apples was significantly influenced by the antagonist and the additive as well as by their interaction. The combination of the biocontrol agents and some additives resulted in a significantly higher activity with respect to the single treatments applied separately, producing additive or synergistic effects. Some of the selected additives combined with a low yeast concentration (106 cells per ml) had comparable or higher efficacy than the biocontrol agents applied alone at a 100-fold higher concentration (10(8) cells per ml). Some organic and inorganic calcium salts, natural gums, and some antioxidants displayed the best results. In general, the effect of each additive was specific to the biocontrol isolate used in the experiments. Possible mechanisms involved in the activity of these beneficial additives and their potential application in effective formulations of postharvest biofungicides are discussed

Depth profiling of melting and metallization in Si(111) and Si(001) surfaces

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Management training for hospital administrators: sentinel lymph-node biopsy under local anaesthetic for carcinoma of the breast–organizational and economic impact

This study compares sentinel lymph-node biopsy carried out at the time of removal of the primary breast tumour, under general anaesthetic, with sentinel lymph-node biopsy carried out under local anaesthetic prior to the main operation. It compares the total cost of the two treatment approaches, in terms of average income and of their impact on the subsequent programming of operations and hence on waiting lists and income

Optically variable active galactic nuclei in the 3 yr VST survey of the COSMOS field

The analysis of the variability of active galactic nuclei (AGNs) at different wavelengths and the study of possible correlations among different spectral windows are nowadays a major field of inquiry. Optical variability has been largely used to identify AGNs in multivisit surveys. The strength of a selection based on optical variability lies in the chance to analyze data from surveys of large sky areas by ground-based telescopes. However the effectiveness of optical variability selection, with respect to other multiwavelength techniques, has been poorly studied down to the depth expected from next generation surveys. Here we present the results of our r-band analysis of a sample of 299 optically variable AGN candidates in the VST survey of the COSMOS field, counting 54 visits spread over three observing seasons spanning > 3 yr. This dataset is > 3 times larger in size than the one presented in our previous analysis (De Cicco et al. 2015), and the observing baseline is ~8 times longer. We push towards deeper magnitudes (r(AB) ~23.5 mag) compared to past studies; we make wide use of ancillary multiwavelength catalogs in order to confirm the nature of our AGN candidates, and constrain the accuracy of the method based on spectroscopic and photometric diagnostics. We also perform tests aimed at assessing the relevance of dense sampling in view of future wide-field surveys. We demonstrate that the method allows the selection of high-purity (> 86%) samples. We take advantage of the longer observing baseline to achieve great improvement in the completeness of our sample with respect to X-ray and spectroscopically confirmed samples of AGNs (59%, vs. ~15% in our previous work), as well as in the completeness of unobscured and obscured AGNs. The effectiveness of the method confirms the importance to develop future, more refined techniques for the automated analysis of larger datasets.Comment: 21 pages, 10 figures; accepted for publication in A&

Explainable Artificial Intelligence in communication networks: A use case for failure identification in microwave networks

Artificial Intelligence (AI) has demonstrated superhuman capabilities in solving a significant number of tasks, leading to widespread industrial adoption. For in-field network-management application, AI-based solutions, however, have often risen skepticism among practitioners as their internal reasoning is not exposed and their decisions cannot be easily explained, preventing humans from trusting and even understanding them. To address this shortcoming, a new area in AI, called Explainable AI (XAI), is attracting the attention of both academic and industrial researchers. XAI is concerned with explaining and interpreting the internal reasoning and the outcome of AI-based models to achieve more trustable and practical deployment. In this work, we investigate the application of XAI for network management, focusing on the problem of automated failure-cause identification in microwave networks. We first introduce the concept of XAI, highlighting its advantages in the context of network management, and we discuss in detail the concept behind Shapley Additive Explanations (SHAP), the XAI framework considered in our analysis. Then, we propose a framework for a XAI-assisted ML-based automated failure-cause identification in microwave networks, spanning model's development and deployment phases. For the development phase, we show how to exploit SHAP for feature selection and how to leverage SHAP to inspect misclassified instances during model's development process, and how to describe model's global behavior based on SHAP's global explanations. For the deployment phase, we propose a framework based on predictions uncertainty to detect possibly wrong predictions that will be inspected through XAI