The Potential of Asiatic Acid in the Reversion of Cyclophosphamide-Induced Hemorrhagic Cystitis in Rats

The purpose of this study was to determine if asiatic acid may act efficiently in the model of cyclophosphamide (CYP)-induced cystitis in rats. We performed experiments after administration of CYP (single dose 200 mg/kg, intraperitoneally), asiatic acid (30 mg/kg/day for 14 consecutive days, by oral gavage), or CYP plus asiatic acid, during which conscious cystometry, measurements of urothelium thickness and bladder edema, as well as selected biomarkers analyses were conducted. In rats that received asiatic acid together with CYP, a drop in bladder basal pressure, detrusor overactivity index, non-voiding contraction amplitude, non-voiding contraction frequency, and the area under the pressure curve were observed, when compared to the CYP group. Furthermore, a significant increase in threshold pressure, voided volume, intercontraction interval, bladder compliance, and volume threshold to elicit NVC were found in that group accordingly. Administration of the asiatic acid successfully restored concentrations of biomarkers both in bladder urothelium (BDNF, CGRP, OCT-3, IL-1β, IL-6, NGF, nitrotyrosine, malondialdehyde, TNF-α, SV2A, SNAP23, SNAP25, PAC-1, ORM1, occludin, IGFBP-3, HB-EGF, T–H protein, Z01, and HPX) and detrusor muscle (Rho kinase and VAChT) in CYP-treated rats. Finally, asiatic acid significantly decreased urothelium thickness and bladder oedema. Asiatic acid proved to be a potent and effective drug in the rat model of CYP-induced cystitis.     

Torrefied biomass‐polypropylene composites

Torrefied almond shells and wood chips were incorporated into polypropylene as fillers to produce torrefied biomass‐polymer composites. The composites were prepared by extrusion and injection molding. Response surface methodology was used to examine the effects of filler concentration, filler size, and lignin factor (relative lignin to cellulose concentration) on the material properties of the composites. The heat distortion temperatures, thermal properties, and tensile properties of the composites were characterized by thermomechanical analysis, differential scanning calorimetry, and tensile tests, respectively. The torrefied biomass composites had heat distortion temperatures of 8–24°C higher than that of neat polypropylene. This was due to the torrefied biomass restricting mobility of polypropylene chains, leading to higher temperatures for deformation. The incorporation of torrefied biomass generally resulted in an increase in glass transition temperature, but did not affect melting temperature. Also, the composites had lower tensile strength and elongation at break values than those of neat polypropylene, indicating weak adhesion between torrefied biomass and polypropylene. However, scanning electron microscopy results did indicate some adhesion between torrefied biomass and polypropylene. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41582.     

The Tumor Microenvironment in Liver Metastases from Colorectal Carcinoma in the Context of the Histologic Growth Patterns

Colorectal carcinoma (CRC) is the third most common cancer. Likewise, it is a disease that has a long survival if it is prematurely detected. However, more than 50% of patients will develop metastases, mainly in the liver (LM-CRC), throughout the evolution of their disease, which accounts for most CRC-related deaths. Treatment it is certainly a controversial issue, since it has not been shown to increase overall survival in the adjuvant setting, although it does improve disease free survival (DFS). Moreover, current chemotherapy combinations are administered based on data extrapolated from primary tumors (PT), not considering that LM-CRC present a very particular tumor microenvironment that can radically condition the effectiveness of treatments designed for a PT. The liver has a particular histology and microenvironment that can determine tumor growth and response to treatments: double blood supply, vascularization through fenestrated sinusoids and the presence of different mesenchymal cell types, among other particularities. Likewise, the liver presents a peculiar immune response against tumor cells, a fact that correlates with the poor response to immunotherapy. All these aspects will be addressed in this review, putting them in the context of the histological growth patterns of LM-CRC, a particular pathologic feature with both prognostic and predictive repercussions.     

The Complexity of Compressed Membership Problems for Finite Automata

In this paper, a compressed membership problem for finite automata, both deterministic (DFAs) and non-deterministic (NFAs), with compressed transition labels is studied. The compression is represented by straight-line programs (SLPs), i.e. context-free grammars generating exactly one string. A novel technique of dealing with SLPs is employed: the SLPs are recompressed, so that substrings of the input word are encoded in SLPs labelling the transitions of the NFA (DFA) in the same way, as in the SLP representing the input text. To this end, the SLPs are locally decompressed and then recompressed in a uniform way. Furthermore, in order to reflect the recompression in the NFA, we need to modify it only a little, in particular its size stays polynomial in the input size. Using this technique it is shown that the compressed membership for NFA with compressed labels is in NP, thus confirming the conjecture of Plandowski and Rytter (Jewels Are Forever, pp. 262–272, Springer, Berlin, 1999) and extending the partial result of Lohrey and Mathissen (in CSR, LNCS, vol. 6651, pp. 275–288, Springer, Berlin, 2011); as this problem is known to be NP-hard (in Plandowski and Rytter, Jewels Are Forever, pp. 262–272, Springer, Berlin, 1999), we settle its exact computational complexity. Moreover, the same technique applied to the compressed membership for DFA with compressed labels yields that this problem is in P, and this problem is known to be P-hard (in Markey and Schnoebelen, Inf. Process. Lett. 90(1):3–6, 2004; Beaudry et al., SIAM J. Comput. 26(1):138–152, 1997).     

Understanding the IT/business partnership: A business process perspective

From a business process perspective, the business value of information technologies (IT) stems from how they improve or enable business processes. At the same time, in the field of strategic IT/business alignment, the locus of discussion has been how IT/business partnerships enhance the value of IT. Despite this apparent relationship, the business process perspective has been absent from the IT/business alignment discussion. In this paper, we use the case of an industrial company to develop a model for understanding IT/business partnerships in business process terms. Based on our findings, we define these partnerships by allocating responsibilities between central IT and the local business during two stages of a process lifecycle: formation and standardization. The significance of the findings lies in how the model’s configuration leads to different types of IT units’ process centricity. This in turn affects the ability of the company as a whole to transform its operations with IT.     

Improved likelihood inference in Birnbaum-Saunders regressions

The Birnbaum-Saunders regression model is commonly used in reliability studies. We address the issue of performing inference in this class of models when the number of observations is small. Our simulation results suggest that the likelihood ratio test tends to be liberal when the sample size is small. We obtain a correction factor which reduces the size distortion of the test. Also, we consider a parametric bootstrap scheme to obtain improved critical values and improved p-values for the likelihood ratio test. The numerical results show that the modified tests are more reliable in finite samples than the usual likelihood ratio test. We also present an empirical application.     

A Closed-Loop Automatic Data-Mining Approach for Preventive Network Monitoring

As telecommunication networks grow in size and complexity, monitoring systems need to scale up accordingly. Alarm data generated in a large network are often highly correlated. These correlations can be explored to simplify the process of network fault management, by reducing the number of alarms presented to the network-monitoring operator. This makes it easier to react to network failures. But in some scenarios, it is highly desired to prevent the occurrence of these failures by predicting the occurrence of alarms before hand. This work investigates the usage of data mining methods to generate knowledge from historical alarm data, and using such knowledge to train a machine learning system, in order to predict the occurrence of the most relevant alarms in the network. The learning system was designed to be retrained periodically in order to keep an updated knowledge base.