Aquaporin 2 expression increased by glucagon in normal rat inner medullary collecting ducts

Yano Y, Cesar KR, Araujo M, Rodrigues Jr. AC, Andrade LC, Magaldi AJ. Aquaporin 2 expression increased by glucagon in normal rat inner medullary collecting ducts. Am J Physiol Renal Physiol 296: F54-F59, 2009. First published October 1, 2008; doi: 10.1152/ajprenal.90367.2008.-It is well known that Glucagon (Gl) is released after a high protein diet and participates in water excretion by the kidney, principally after a protein meal. To study this effect in in vitro perfused inner medullary collecting ducts (IMCD), the osmotic water permeability (Pf; mu m/s) at 37 degrees C and pH 7.4 in normal rat IMCDs (n = 36) perfused with Ringer/HCO(3) was determined. Gl (10(-7) M) in absence of Vasopressin (AVP) enhanced the Pf from 4.38 +/- 1.40 to 11.16 +/- 1.44 mu m/s (P < 0.01). Adding 10(-8), 10(-7), and 10(-6) M Gl, the Pf responded in a dose-dependent manner. The protein kinase A inhibitor H8 blocked the Gl effect. The specific Gl inhibitor, des-His(1)-[Glu(9)] glucagon (10(-7) M), blocked the Gl-stimulated Pf but not the AVP-stimulated Pf. There occurred a partial additional effect between Gl and AVP. The cAMP level was enhanced from the control 1.24 +/- 0.39 to 59.70 +/- 15.18 fm/mg prot after Gl 10(-7) M in an IMCD cell suspension. The immunoblotting studies indicated an increase in AQP2 protein abundance of 27% (cont 100.0 +/- 3.9 vs. Gl 127.53; P = 0.0035) in membrane fractions extracted from IMCD tubule suspension, incubated with 10(-6) M Gl. Our data showed that 1) Gl increased water absorption in a dose-dependent manner; 2) the anti-Gl blocked the action of Gl but not the action of AVP; 3) Gl stimulated the cAMP generation; 4) Gl increased the AQP2 water channel protein expression, leading us to conclude that Gl controls water absorption by utilizing a Gl receptor, rather than a AVP receptor, increasing the AQP2 protein expression.

Comparison of two methods for collecting free-living ticks in the Amazonian forest

In the present study, we compared 2 methods for collecting ixodid ticks on the verges of animal trails in a primary Amazon forest area in northern Brazil. (i) Dragging: This method was based on passing a 1-m(2) white flannel over the vegetation and checking the flannel for the presence of caught ticks every 5-10 m. (ii) Visual search: This method consisted of looking for guesting ticks on the tips of leaves of the vegetation bordering animal trails in the forest. A total of 103 adult ticks belonging to 4 Amblyomma species were collected by the visual search method on 5 collecting dates, while only 44 adult ticks belonging to 3 Amblyomma species were collected by dragging on 5 other collecting dates. These values were statistically different (Mann-Whitney Test, P = 0.0472). On the other hand, dragging was more efficient for subadult ticks, since no larva or nymph was collected by visual search, whereas 18 nymphs and 7 larvae were collected by dragging. The visual search method proved to be suitable for collecting adult ticks in the Amazon forest: however, field studies should include a second method, such as dragging in order to maximize the collection of subadult ticks. Indeed, these 2 methods can be performed by a single investigator at the same time, while he/she walks on an animal trail in the forest. (C) 2010 Elsevier GmbH. All rights reserved.

Comparing machine learning classifiers in potential distribution modelling

Species` potential distribution modelling consists of building a representation of the fundamental ecological requirements of a species from biotic and abiotic conditions where the species is known to occur. Such models can be valuable tools to understand the biogeography of species and to support the prediction of its presence/absence considering a particular environment scenario. This paper investigates the use of different supervised machine learning techniques to model the potential distribution of 35 plant species from Latin America. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species. The experimental results highlight the good performance of random trees classifiers, indicating this particular technique as a promising candidate for modelling species` potential distribution. (C) 2010 Elsevier Ltd. All rights reserved.

Comparing finite state machine test coverage criteria

To plan testing activities, testers face the challenge of determining a strategy, including a test coverage criterion that offers an acceptable compromise between the available resources and test goals. Known theoretical properties of coverage criteria do not always help and, thus, empirical data are needed. The results of an experimental evaluation of several coverage criteria for finite state machines (FSMs) are presented, namely, state and transition coverage; initialisation fault and transition fault coverage. The first two criteria focus on FSM structure, whereas the other two on potential faults in FSM implementations. The authors elaborate a comparison approach that includes random generation of FSM, construction of an adequate test suite and test minimisation for each criterion to ensure that tests are obtained in a uniform way. The last step uses an improved greedy algorithm.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.

Complex networks analysis of manual and machine translations

Complex networks have been increasingly used in text analysis, including in connection with natural language processing tools, as important text features appear to be captured by the topology and dynamics of the networks. Following previous works that apply complex networks concepts to text quality measurement, summary evaluation, and author characterization, we now focus on machine translation (MT). In this paper we assess the possible representation of texts as complex networks to evaluate cross-linguistic issues inherent in manual and machine translation. We show that different quality translations generated by NIT tools can be distinguished from their manual counterparts by means of metrics such as in-(ID) and out-degrees (OD), clustering coefficient (CC), and shortest paths (SP). For instance, we demonstrate that the average OD in networks of automatic translations consistently exceeds the values obtained for manual ones, and that the CC values of source texts are not preserved for manual translations, but are for good automatic translations. This probably reflects the text rearrangements humans perform during manual translation. We envisage that such findings could lead to better NIT tools and automatic evaluation metrics.

Using metrics from complex networks to evaluate machine translation

Establishing metrics to assess machine translation (MT) systems automatically is now crucial owing to the widespread use of MT over the web. In this study we show that such evaluation can be done by modeling text as complex networks. Specifically, we extend our previous work by employing additional metrics of complex networks, whose results were used as input for machine learning methods and allowed MT texts of distinct qualities to be distinguished. Also shown is that the node-to-node mapping between source and target texts (English-Portuguese and Spanish-Portuguese pairs) can be improved by adding further hierarchical levels for the metrics out-degree, in-degree, hierarchical common degree, cluster coefficient, inter-ring degree, intra-ring degree and convergence ratio. The results presented here amount to a proof-of-principle that the possible capturing of a wider context with the hierarchical levels may be combined with machine learning methods to yield an approach for assessing the quality of MT systems. (C) 2010 Elsevier B.V. All rights reserved.

Improved voice activity detection algorithm using wavelet and support vector machine

This paper proposes an improved voice activity detection (VAD) algorithm using wavelet and support vector machine (SVM) for European Telecommunication Standards Institution (ETS1) adaptive multi-rate (AMR) narrow-band (NB) and wide-band (WB) speech codecs. First, based on the wavelet transform, the original IIR filter bank and pitch/tone detector are implemented, respectively, via the wavelet filter bank and the wavelet-based pitch/tone detection algorithm. The wavelet filter bank can divide input speech signal into several frequency bands so that the signal power level at each sub-band can be calculated. In addition, the background noise level can be estimated in each sub-band by using the wavelet de-noising method. The wavelet filter bank is also derived to detect correlated complex signals like music. Then the proposed algorithm can apply SVM to train an optimized non-linear VAD decision rule involving the sub-band power, noise level, pitch period, tone flag, and complex signals warning flag of input speech signals. By the use of the trained SVM, the proposed VAD algorithm can produce more accurate detection results. Various experimental results carried out from the Aurora speech database with different noise conditions show that the proposed algorithm gives considerable VAD performances superior to the AMR-NB VAD Options 1 and 2, and AMR-WB VAD. (C) 2009 Elsevier Ltd. All rights reserved.