Regeneration and characterization of somatic hybrids combining sweet orange and mandarin/mandarin hybrid cultivars for citrus scion improvement

Protoplast fusion between sweet orange and mandarin/mandarin hybrids scion cultivars was performed following the model "diploid embryogenic callus protoplast + diploid mesophyll-derived protoplast". Protoplasts were isolated from embryogenic calli of 'Pera' and 'Westin' sweet orange cultivars (Citrus sinensis) and from young leaves of 'Fremont', Nules', and 'Thomas' mandarins (C. reticulata), and 'Nova' tangelo [C. reticulata x (C. paradisi x C. reticulata)]. The regenerated plants were characterized based on their leaf morphology (thickness), ploidy level, and simple sequence repeat (SSR) molecular markers. Plants were successfully generated only when 'Pera' sweet orange was used as the embryogenic parent. Fifteen plants were regenerated being 7 tetraploid and 8 diploid. Based on SSR molecular markers analyses all 7 tetraploid regenerated plants revealed to be allotetraploids (somatic hybrids), including 2 from the combination of 'Pera' sweet orange + 'Fremont' mandarin, 3 'Pera' sweet orange + 'Nules' mandarin, and 2 'Pera' sweet orange + 'Nova' tangelo, and all the diploid regenerated plants showed the 'Pera' sweet orange marker profile. Somatic hybrids were inoculated with Alternaria alternata and no disease symptoms were detected 96 h post-inoculation. This hybrid material has the potential to be used as a tetraploid parent in interploid crosses for citrus scion breeding.

Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

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.

Combining accelerator and reactor measurements of theta(13): the first result

The lepton mixing angle theta(13), the only unknown angle in the standard three-flavor neutrino mixing scheme, is finally measured by the recent reactor and accelerator neutrino experiments. We perform a combined analysis of the data coming from T2K, MINOS, Double Chooz, Daya Bay and RENO experiments and find sin(2)2 theta(13) = 0.096 +/- 0.013(+/- 0.040) at 1 sigma (3 sigma) CL and that the hypothesis theta(13) = 0 is now rejected at a significance level of 7.7 sigma. We also discuss the near future expectation on the precision of the theta(13) determination by using expected data from these ongoing experiments.

Bagging k-dependence probabilistic networks: An alternative powerful fraud detection tool

Fraud is a global problem that has required more attention due to an accentuated expansion of modern technology and communication. When statistical techniques are used to detect fraud, whether a fraud detection model is accurate enough in order to provide correct classification of the case as a fraudulent or legitimate is a critical factor. In this context, the concept of bootstrap aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the adjusted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper, for the first time, we aim to present a pioneer study of the performance of the discrete and continuous k-dependence probabilistic networks within the context of bagging predictors classification. Via a large simulation study and various real datasets, we discovered that the probabilistic networks are a strong modeling option with high predictive capacity and with a high increment using the bagging procedure when compared to traditional techniques. (C) 2012 Elsevier Ltd. All rights reserved.

Facilitation of sodium intake by combining noradrenaline into the lateral parabrachial nucleus with prazosin peripherally

Injections of noradrenaline into the lateral parabrachial nucleus (LPBN) increase arterial pressure and 1.8% NaCl intake and decrease water intake in rats treated with the diuretic furosemide (FURO) combined with a low dose of the angiotensin converting enzyme inhibitor captopril (CAP). In the present study, we investigated the influence of the pressor response elicited by noradrenaline injected into the LPBN on FURO+CAP-induced water and 1.8% NaCl intake. Male Holtzman rats with bilateral stainless steel guide-cannulas implanted into LPBN were used. Bilateral injections of noradrenaline (40 nmol/0.2 μl) into the LPBN increased FURO+CAP-induced 1.8% NaCl intake (12.2±3.5, vs., saline: 4.2±0.8 ml/180 min), reduced water intake and strongly increased arterial pressure (50±7, vs. saline: 1±1 mmHg). The blockade of the α1 adrenoceptors with the prazosin injected intraperitoneally abolished the pressor response and increased 1.8% NaCl and water intake in rats treated with FURO+CAP combined with noradrenaline injected into the LPBN. The deactivation of baro and perhaps volume receptors due to the cardiovascular effects of prazosin is a mechanism that may facilitate water and NaCl intake in rats treated with FURO+CAP combined with noradrenaline injected into the LPBN. Therefore, the activation of α2 adrenoceptors with noradrenaline injected into the LPBN, at least in dose tested, may not completely remove the inhibitory signals produced by the activation of the cardiovascular receptors, particularly the signals that result from the extra activation of these receptors with the increase of arterial pressure.

Combining fractal and deterministic walkers for texture analysis and classification

In this paper,we present a novel texture analysis method based on deterministic partially self-avoiding walks and fractal dimension theory. After finding the attractors of the image (set of pixels) using deterministic partially self-avoiding walks, they are dilated in direction to the whole image by adding pixels according to their relevance. The relevance of each pixel is calculated as the shortest path between the pixel and the pixels that belongs to the attractors. The proposed texture analysis method is demonstrated to outperform popular and state-of-the-art methods (e.g. Fourier descriptors, occurrence matrix, Gabor filter and local binary patterns) as well as deterministic tourist walk method and recent fractal methods using well-known texture image datasets.