Cladosporium tenuissimum URM 7803: a promising new β-galactosidase producer

The Cladosporium genus, defined by Link in 1816, is one of the largest and most heterogeneous Hyphomycetes genus. It comprises more than 189 species still rarely explored biotechnologically. One of the most studied microbial enzymes, -galactosidase is a glycoside hydrolase enzyme that catalyzes the hydrolysis of -galactosides into monosaccharides through the breaking of a glycosidic bond. Recently, new studies comprising new microbial sources of -galactosidase, presenting biotechnologically interesting characteristics, have been encouraged. In this context, the present study evaluated the production of -galactosidase by a new isolate of Cladosporium tenuissimum. A C. tenuissimum inoculum was prepared adding 107 spore/mL in sterile saline solution 0.85% (w/v) NaCl containing 0.01% (w/v) Tween 80 and added to fermentation medium for enzyme production. The fermentation medium, composed of (% w/v): lactose (2), peptone (0.4), yeast extract (0.4) and salts (KH2PO4 (0.2), Na2HPO4.12H2O (0.8) and MgSO4.7H2O (0.025), pH 6.5, was maintained at 28° C and 180 rpm for 13 days. One sample (50 mL erlenmeyer) was removed every 24 hours and -galactosidase activity was evaluated using ONPG (ortho-Nitrophenyl--galactoside) method. The results showed maximum -galactosidase production by C. tenuissimum URM 7803 on thirteenth day, displayed enzymatic activity of 462.13 U/mL. The C. tenuissimum URM 7803 isolate proved to be a powerful new -galactosidase producer with potential application for food processing.info:eu-repo/semantics/publishedVersio

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.CNPqCAPESFAPESPFACEPEFCT [PTDC/EIA/81178 /2006

NKX2.5 is expressed in papillary thyroid carcinomas and regulates differentiation in thyroid cells

Abstract Background NKX2.5 is a transcription factor transiently expressed during thyroid organogenesis. Recently, several works have pointed out the oncogenic role of NKX2.5 in a variety of tumors. We therefore hypothesized that NKX2.5 could also play a role in thyroid cancer. Methods The validation of NKX2.5 expression was assessed by immunohistochemistry analysis in a Brazilian case series of 10 papillary thyroid carcinoma (PTC) patients. Then, the long-term prognostic value of NKX2.5 and its correlation with clinicopathologic features of 51 PTC patients was evaluated in a cohort with 10-years follow-up (1990–1999). Besides, the effect of NKX2.5 overexpression on thyroid differentiation markers and function was also investigated in a non-tumor thyroid cell line (PCCL3). Results NKX2.5 was shown to be expressed in most PTC samples (8/10, case series; 27/51, cohort). Patients who had tumors expressing NKX2.5 showed lower rates of persistence/recurrence (p = 0.013). Overexpression of NKX2.5 in PCCL3 cells led to: 1) downregulation of thyroid differentiation markers (thyrotropin receptor, thyroperoxidase and sodium-iodide symporter); 2) reduced iodide uptake; 3) increased extracellular H2O2 generation, dual oxidase 1 mRNA levels and activity of DuOx1 promoter. Conclusions In summary, NKX2.5 is expressed in most PTC samples analyzed and its presence correlates to better prognosis of PTC. In vitro, NKX2.5 overexpression reduces the expression of thyroid differentiation markers and increases ROS production. Thus, our data suggests that NKX2.5 could play a role in thyroid carcinogenesis