Combining xanthan and chitosan membranes to multipotent mesenchymal stromal cells as bioactive dressings for dermo-epidermal wounds

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Combining xanthan and chitosan membranes to multipotent mesenchymal stromal cells as bioactive dressings for dermo-epidermal wounds

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Monitoring the Prey-Field of Marine Predators: Combining Digital Imaging With Datalogging Tags

There is increasing interest in the diving behavior of marine mammals. However, identifying foraging among recorded dives often requires several assumptions. The simultaneous acquisition of images of the prey encountered, together with records of diving behavior will allow researchers to more fully investigate the nature of subsurface behavior. We tested a novel digital camera linked to a time-depth recorder on Antarctic fur seals (Arctocephalus gazella). During the austral summer 2000-2001, this system was deployed on six lactating female fur seals at Bird Island, South Georgia, each for a single foraging trip. The camera was triggered at depths greater than 10 m. Five deployments recorded still images (640 x 480 pixels) at 3-sec intervals (total 8,288 images), the other recorded movie images at 0.2-sec intervals (total 7,598 frames). Memory limitation (64 MB) restricted sampling to approximately 1.5 d of 5-7 d foraging trips. An average of 8.5% of still pictures (2.4%-11.6%) showed krill (Euphausia superba) distinctly, while at least half the images in each deployment were empty, the remainder containing blurred or indistinct prey. In one deployment krill images were recorded within 2.5 h (16 km, assuming 1.8 m/sec travel speed) of leaving the beach. Five of the six deployments also showed other fur seals foraging in conjunction with the study animal. This system is likely to generate exciting new avenues for interpretation of diving behavior.

Student Writing Performance: Identifying the Effects when Combining Planning and Revising Instructional Strategies

The purpose of the current study is to identify the impact of teaching students to revise their stories on writing production (Total Words Written; TWW), writing accuracy (Percent Correct Writing Sequences; %CWS), number of critical story elements included in stories, and quality of writing. Three third-grade and one fourth-grade student who were experiencing difficulties in the area of writing were involved in the study. The students were first taught to plan their stories using the evidence-based program, Self-Regulated Strategy Development (SRSD), which has frequently been implemented to teach students to plan their stories. Students were then taught to revise their stories using SRSD procedures modified for instruction in revision strategies. Student progress was evaluated through a multiple-probe design across tasks and a multiple-probe design across participants, which allowed for experimental control over time and across story probes. In addition to the previously mentioned variables, student’s acceptability of the intervention and their attitudes toward writing were also assessed. Results indicated that instruction in revising increased student writing accuracy beyond the effects of instruction in planning. Additionally, although instruction in planning was shown to increase writing production, number of critical story elements, and quality of writing, instruction in revising produced additional improvement in these variables as well. Finally, results indicated that students liked the intervention and their attitudes toward writing generally increased. Implications for practice and future research directions will be discussed. Advisor: Merilee McCurdy

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.

Combining face and facial feature detectors for face detection performance improvement

[EN]In this paper, we experimentally study the combination of face and facial feature detectors to improve face detection performance. The face detection problem, as suggeted by recent face detection challenges, is still not solved. Face detectors traditionally fail in large-scale problems and/or when the face is occluded or di erent head rotations are present. The combination of face and facial feature detectors is evaluated with a public database. The obtained results evidence an improvement in the positive detection rate while reducing the false detection rate. Additionally, we prove that the integration of facial feature detectors provides useful information for pose estimation and face alignment.

Retrieval of trace gases vertical profile in the lower atmosphere combining. Differential Optical Absorption Spectroscopy with radiative transfer models

The motivation for the work presented in this thesis is to retrieve profile information for the atmospheric trace constituents nitrogen dioxide (NO2) and ozone (O3) in the lower troposphere from remote sensing measurements. The remote sensing technique used, referred to as Multiple AXis Differential Optical Absorption Spectroscopy (MAX-DOAS), is a recent technique that represents a significant advance on the well-established DOAS, especially for what it concerns the study of tropospheric trace consituents. NO2 is an important trace gas in the lower troposphere due to the fact that it is involved in the production of tropospheric ozone; ozone and nitrogen dioxide are key factors in determining the quality of air with consequences, for example, on human health and the growth of vegetation. To understand the NO2 and ozone chemistry in more detail not only the concentrations at ground but also the acquisition of the vertical distribution is necessary. In fact, the budget of nitrogen oxides and ozone in the atmosphere is determined both by local emissions and non-local chemical and dynamical processes (i.e. diffusion and transport at various scales) that greatly impact on their vertical and temporal distribution: thus a tool to resolve the vertical profile information is really important. Useful measurement techniques for atmospheric trace species should fulfill at least two main requirements. First, they must be sufficiently sensitive to detect the species under consideration at their ambient concentration levels. Second, they must be specific, which means that the results of the measurement of a particular species must be neither positively nor negatively influenced by any other trace species simultaneously present in the probed volume of air. Air monitoring by spectroscopic techniques has proven to be a very useful tool to fulfill these desirable requirements as well as a number of other important properties. During the last decades, many such instruments have been developed which are based on the absorption properties of the constituents in various regions of the electromagnetic spectrum, ranging from the far infrared to the ultraviolet. Among them, Differential Optical Absorption Spectroscopy (DOAS) has played an important role. DOAS is an established remote sensing technique for atmospheric trace gases probing, which identifies and quantifies the trace gases in the atmosphere taking advantage of their molecular absorption structures in the near UV and visible wavelengths of the electromagnetic spectrum (from 0.25 μm to 0.75 μm). Passive DOAS, in particular, can detect the presence of a trace gas in terms of its integrated concentration over the atmospheric path from the sun to the receiver (the so called slant column density). The receiver can be located at ground, as well as on board an aircraft or a satellite platform. Passive DOAS has, therefore, a flexible measurement configuration that allows multiple applications. The ability to properly interpret passive DOAS measurements of atmospheric constituents depends crucially on how well the optical path of light collected by the system is understood. This is because the final product of DOAS is the concentration of a particular species integrated along the path that radiation covers in the atmosphere. This path is not known a priori and can only be evaluated by Radiative Transfer Models (RTMs). These models are used to calculate the so called vertical column density of a given trace gas, which is obtained by dividing the measured slant column density to the so called air mass factor, which is used to quantify the enhancement of the light path length within the absorber layers. In the case of the standard DOAS set-up, in which radiation is collected along the vertical direction (zenith-sky DOAS), calculations of the air mass factor have been made using “simple” single scattering radiative transfer models. This configuration has its highest sensitivity in the stratosphere, in particular during twilight. This is the result of the large enhancement in stratospheric light path at dawn and dusk combined with a relatively short tropospheric path. In order to increase the sensitivity of the instrument towards tropospheric signals, measurements with the telescope pointing the horizon (offaxis DOAS) have to be performed. In this circumstances, the light path in the lower layers can become very long and necessitate the use of radiative transfer models including multiple scattering, the full treatment of atmospheric sphericity and refraction. In this thesis, a recent development in the well-established DOAS technique is described, referred to as Multiple AXis Differential Optical Absorption Spectroscopy (MAX-DOAS). The MAX-DOAS consists in the simultaneous use of several off-axis directions near the horizon: using this configuration, not only the sensitivity to tropospheric trace gases is greatly improved, but vertical profile information can also be retrieved by combining the simultaneous off-axis measurements with sophisticated RTM calculations and inversion techniques. In particular there is a need for a RTM which is capable of dealing with all the processes intervening along the light path, supporting all DOAS geometries used, and treating multiple scattering events with varying phase functions involved. To achieve these multiple goals a statistical approach based on the Monte Carlo technique should be used. A Monte Carlo RTM generates an ensemble of random photon paths between the light source and the detector, and uses these paths to reconstruct a remote sensing measurement. Within the present study, the Monte Carlo radiative transfer model PROMSAR (PROcessing of Multi-Scattered Atmospheric Radiation) has been developed and used to correctly interpret the slant column densities obtained from MAX-DOAS measurements. In order to derive the vertical concentration profile of a trace gas from its slant column measurement, the AMF is only one part in the quantitative retrieval process. One indispensable requirement is a robust approach to invert the measurements and obtain the unknown concentrations, the air mass factors being known. For this purpose, in the present thesis, we have used the Chahine relaxation method. Ground-based Multiple AXis DOAS, combined with appropriate radiative transfer models and inversion techniques, is a promising tool for atmospheric studies in the lower troposphere and boundary layer, including the retrieval of profile information with a good degree of vertical resolution. This thesis has presented an application of this powerful comprehensive tool for the study of a preserved natural Mediterranean area (the Castel Porziano Estate, located 20 km South-West of Rome) where pollution is transported from remote sources. Application of this tool in densely populated or industrial areas is beginning to look particularly fruitful and represents an important subject for future studies.