Carbon sequestration associated to the land-use and land-cover changes in the forestry sector in Southern Brazil.

We studied the Paraíba do Sul river watershed , São Paulo state (PSWSP), Southeastern Brazil, in order to assess the land use and cover (LULC) and their implication s to the amount of carbon (C) stored in the forest cover between the years 1985 and 2015. Th e region covers a n area of 1,395,975 ha . We used images made by the Operational Land Imager (OLI) sensor (OLI/Landsat - 8) to produce mappings , and image segmentation techniques to produce vectors with homogeneous characteristics. The training samples and the samples used for classification and validation were collected from the segmented image. To quantify the C stocked in aboveground live biomass (AGLB) , we used an indirect method and applied literature - based reference values. The recovery of 205,690 ha of a secondary Native Forest (NF) after 1985 sequestered 9.7 Tg (Teragram) of C . Considering the whole NF area (455,232 ha), the amount of C accumulated al ong the whole watershed was 3 5 .5 Tg , and the whole Eucalyptus crop (EU) area (113,600 ha) sequester ed 4. 4 Tg of C. Thus, the total amount of C sequestered in the whole watershed (NF + EU) was 3 9 . 9 Tg of C or 1 45 . 6 Tg of CO 2 , and the NF areas were responsible for the large st C stock at the watershed (8 9 %). Therefore , the increase of the NF cover contribut es positively to the reduction of CO 2 concentration in the atmosphere, and Reducing Emissions from Deforestation and Forest Degradation (REDD + ) may become one of the most promising compensation mechanisms for the farmers who increased forest cover at their farms.

Methods for objective and interpretative evaluation of function and motor control: gait in perturbed condition and in the aquatic therapy

Gait analysis allows to characterize motor function, highlighting deviations from normal motor behavior related to an underlying pathology. The widespread use of wearable inertial sensors has opened the way to the evaluation of ecological gait, and a variety of methodological approaches and algorithms have been proposed for the characterization of gait from inertial measures (e.g. for temporal parameters, motor stability and variability, specific pathological alterations). However, no comparative analysis of their performance (i.e. accuracy, repeatability) was available yet, in particular, analysing how this performance is affected by extrinsic (i.e. sensor location, computational approach, analysed variable, testing environmental constraints) and intrinsic (i.e. functional alterations resulting from pathology) factors. The aim of the present project was to comparatively analyze the influence of intrinsic and extrinsic factors on the performance of the numerous algorithms proposed in the literature for the quantification of specific characteristics (i.e. timing, variability/stability) and alterations (i.e. freezing) of gait. Considering extrinsic factors, the influence of sensor location, analyzed variable, and computational approach on the performance of a selection of gait segmentation algorithms from a literature review was analysed in different environmental conditions (e.g. solid ground, sand, in water). Moreover, the influence of altered environmental conditions (i.e. in water) was analyzed as referred to the minimum number of stride necessary to obtain reliable estimates of gait variability and stability metrics, integrating what already available in the literature for over ground gait in healthy subjects. Considering intrinsic factors, the influence of specific pathological conditions (i.e. Parkinson’s Disease) was analyzed as affecting the performance of segmentation algorithms, with and without freezing. Finally, the analysis of the performance of algorithms for the detection of gait freezing showed how results depend on the domain of implementation and IMU position.

Robotic Sensing and Manipulation of Deformable Linear Objects with Learning-based methods

Nowadays robotic applications are widespread and most of the manipulation tasks are efficiently solved. However, Deformable-Objects (DOs) still represent a huge limitation for robots. The main difficulty in DOs manipulation is dealing with the shape and dynamics uncertainties, which prevents the use of model-based approaches (since they are excessively computationally complex) and makes sensory data difficult to interpret. This thesis reports the research activities aimed to address some applications in robotic manipulation and sensing of Deformable-Linear-Objects (DLOs), with particular focus to electric wires. In all the works, a significant effort was made in the study of an effective strategy for analyzing sensory signals with various machine learning algorithms. In the former part of the document, the main focus concerns the wire terminals, i.e. detection, grasping, and insertion. First, a pipeline that integrates vision and tactile sensing is developed, then further improvements are proposed for each module. A novel procedure is proposed to gather and label massive amounts of training images for object detection with minimal human intervention. Together with this strategy, we extend a generic object detector based on Convolutional-Neural-Networks for orientation prediction. The insertion task is also extended by developing a closed-loop control capable to guide the insertion of a longer and curved segment of wire through a hole, where the contact forces are estimated by means of a Recurrent-Neural-Network. In the latter part of the thesis, the interest shifts to the DLO shape. Robotic reshaping of a DLO is addressed by means of a sequence of pick-and-place primitives, while a decision making process driven by visual data learns the optimal grasping locations exploiting Deep Q-learning and finds the best releasing point. The success of the solution leverages on a reliable interpretation of the DLO shape. For this reason, further developments are made on the visual segmentation.

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

Embedded systems and advanced signal processing for Acousto- Ultrasonic Inspections

Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) are becoming essential in many application contexts, e.g. civil, industrial, aerospace etc., to reduce structures maintenance costs and improve safety. Conventional inspection methods typically exploit bulky and expensive instruments and rely on highly demanding signal processing techniques. The pressing need to overcome these limitations is the common thread that guided the work presented in this Thesis. In the first part, a scalable, low-cost and multi-sensors smart sensor network is introduced. The capability of this technology to carry out accurate modal analysis on structures undergoing flexural vibrations has been validated by means of two experimental campaigns. Then, the suitability of low-cost piezoelectric disks in modal analysis has been demonstrated. To enable the use of this kind of sensing technology in such non conventional applications, ad hoc data merging algorithms have been developed. In the second part, instead, imaging algorithms for Lamb waves inspection (namely DMAS and DS-DMAS) have been implemented and validated. Results show that DMAS outperforms the canonical Delay and Sum (DAS) approach in terms of image resolution and contrast. Similarly, DS-DMAS can achieve better results than both DMAS and DAS by suppressing artefacts and noise. To exploit the full potential of these procedures, accurate group velocity estimations are required. Thus, novel wavefield analysis tools that can address the estimation of the dispersion curves from SLDV acquisitions have been investigated. An image segmentation technique (called DRLSE) was exploited in the k-space to draw out the wavenumber profile. The DRLSE method was compared with compressive sensing methods to extract the group and phase velocity information. The validation, performed on three different carbon fibre plates, showed that the proposed solutions can accurately determine the wavenumber and velocities in polar coordinates at multiple excitation frequencies.

Mathematical methods to analyze and interpret calcium signals of astrocytes

Astrocytes are the most numerous glial cell type in the mammalian brain and permeate the entire CNS interacting with neurons, vasculature, and other glial cells. Astrocytes display intracellular calcium signals that encode information about local synaptic function, distributed network activity, and high-level cognitive functions. Several studies have investigated the calcium dynamics of astrocytes in sensory areas and have shown that these cells can encode sensory stimuli. Nevertheless, only recently the neuro-scientific community has focused its attention on the role and functions of astrocytes in associative areas such as the hippocampus. In our first study, we used the information theory formalism to show that astrocytes in the CA1 area of the hippocampus recorded with 2-photon fluorescence microscopy during spatial navigation encode spatial information that is complementary and synergistic to information encoded by nearby "place cell" neurons. In our second study, we investigated various computational aspects of applying the information theory formalism to astrocytic calcium data. For this reason, we generated realistic simulations of calcium signals in astrocytes to determine optimal hyperparameters and procedures of information measures and applied them to real astrocytic calcium imaging data. Calcium signals of astrocytes are characterized by complex spatiotemporal dynamics occurring in subcellular parcels of the astrocytic domain which makes studying these cells in 2-photon calcium imaging recordings difficult. However, current analytical tools which identify the astrocytic subcellular regions are time consuming and extensively rely on user-defined parameters. Here, we present Rapid Astrocytic calcium Spatio-Temporal Analysis (RASTA), a novel machine learning algorithm for spatiotemporal semantic segmentation of 2-photon calcium imaging recordings of astrocytes which operates without human intervention. We found that RASTA provided fast and accurate identification of astrocytic cell somata, processes, and cellular domains, extracting calcium signals from identified regions of interest across individual cells and populations of hundreds of astrocytes recorded in awake mice.

Sensor networks optimization and software development for Structural Health Monitoring based on ultrasonic guided waves

The Structural Health Monitoring (SHM) research area is increasingly investigated due to its high potential in reducing the maintenance costs and in ensuring the systems safety in several industrial application fields. A growing demand of new SHM systems, permanently embedded into the structures, for savings in weight and cabling, comes from the aeronautical and aerospace application fields. As consequence, the embedded electronic devices are to be wirelessly connected and battery powered. As result, a low power consumption is requested. At the same time, high performance in defects or impacts detection and localization are to be ensured to assess the structural integrity. To achieve these goals, the design paradigms can be changed together with the associate signal processing. The present thesis proposes design strategies and unconventional solutions, suitable both for real-time monitoring and periodic inspections, relying on piezo-transducers and Ultrasonic Guided Waves. In the first context, arrays of closely located sensors were designed, according to appropriate optimality criteria, by exploiting sensors re-shaping and optimal positioning, to achieve improved damages/impacts localisation performance in noisy environments. An additional sensor re-shaping procedure was developed to tackle another well-known issue which arises in realistic scenario, namely the reverberation. A novel sensor, able to filter undesired mechanical boundaries reflections, was validated via simulations based on the Green's functions formalism and FEM. In the active SHM context, a novel design methodology was used to develop a single transducer, called Spectrum-Scanning Acoustic Transducer, to actively inspect a structure. It can estimate the number of defects and their distances with an accuracy of 2[cm]. It can also estimate the damage angular coordinate with an equivalent mainlobe aperture of 8[deg], when a 24[cm] radial gap between two defects is ensured. A suitable signal processing was developed in order to limit the computational cost, allowing its use with embedded electronic devices.

High-resolution stratigraphy and seismic site characterization of late Quaternary paleovalley systems (Adriatic coastal plain, Italy)

In highly urbanized coastal lowlands, effective site characterization is crucial for assessing seismic risk. It requires a comprehensive stratigraphic analysis of the shallow subsurface, coupled with the precise assessment of the geophysical properties of buried deposits. In this context, late Quaternary paleovalley systems, shallowly buried fluvial incisions formed during the Late Pleistocene sea-level fall and filled during the Holocene sea-level rise, are crucial for understanding seismic amplification due to their soft sediment infill and sharp lithologic contrasts. In this research, we conducted high-resolution stratigraphic analyses of two regions, the Pescara and Manfredonia areas along the Adriatic coastline of Italy, to delineate the geometries and facies architecture of two paleovalley systems. Furthermore, we carried out geophysical investigations to characterize the study areas and perform seismic response analyses. We tested the microtremor-based horizontal-to-vertical spectral ratio as a mapping tool to reconstruct the buried paleovalley geometries. We evaluated the relationship between geological and geophysical data and identified the stratigraphic surfaces responsible for the observed resonances. To perform seismic response analysis of the Pescara paleovalley system, we integrated the stratigraphic framework with microtremor and shear wave velocity measurements. The seismic response analysis highlights strong seismic amplifications in frequency ranges that can interact with a wide variety of building types. Additionally, we explored the applicability of artificial intelligence in performing facies analysis from borehole images. We used a robust dataset of high-resolution digital images from continuous sediment cores of Holocene age to outline a novel, deep-learning-based approach for performing automatic semantic segmentation directly on core images, leveraging the power of convolutional neural networks. We propose an automated model to rapidly characterize sediment cores, reproducing the sedimentologist's interpretation, and providing guidance for stratigraphic correlation and subsurface reconstructions.

Pipkiiα Is Widely Expressed In Hematopoietic-derived Cells And May Play A Role In The Expression Of Alpha- And Gamma-globins In K562 Cells.

Characterized for the first time in erythrocytes, phosphatidylinositol phosphate kinases (PIP kinases) belong to a family of enzymes that generate various lipid messengers and participate in several cellular processes, including gene expression regulation. Recently, the PIPKIIα gene was found to be differentially expressed in reticulocytes from two siblings with hemoglobin H disease, suggesting a possible relationship between PIPKIIα and the production of globins. Here, we investigated PIPKIIα gene and protein expression and protein localization in hematopoietic-derived cells during their differentiation, and the effects of PIPKIIα silencing on K562 cells. PIPKIIα silencing resulted in an increase in α and γ globins and a decrease in the proliferation of K562 cells without affecting cell cycle progression and apoptosis. In conclusion, using a cell line model, we showed that PIPKIIα is widely expressed in hematopoietic-derived cells, is localized in their cytoplasm and nucleus, and is upregulated during erythroid differentiation. We also showed that PIPKIIα silencing can induce α and γ globin expression and decrease cell proliferation in K562 cells.

Obesity And Inflammation And The Effect On The Hematopoietic System.

Bone marrow is organized in specialized microenvironments known as 'marrow niches'. These are important for the maintenance of stem cells and their hematopoietic progenitors whose homeostasis also depends on other cell types present in the tissue. Extrinsic factors, such as infection and inflammatory states, may affect this system by causing cytokine dysregulation (imbalance in cytokine production) and changes in cell proliferation and self-renewal rates, and may also induce changes in the metabolism and cell cycle. Known to relate to chronic inflammation, obesity is responsible for systemic changes that are best studied in the cardiovascular system. Little is known regarding the changes in the hematopoietic system induced by the inflammatory state carried by obesity or the cell and molecular mechanisms involved. The understanding of the biological behavior of hematopoietic stem cells under obesity-induced chronic inflammation could help elucidate the pathophysiological mechanisms involved in other inflammatory processes, such as neoplastic diseases and bone marrow failure syndromes.

Antiangiogenic And Finasteride Therapies: Responses Of The Prostate Microenvironment In Elderly Mice.

The aim of this study was to evaluate the structural and molecular effects of antiangiogenic therapies and finasteride on the ventral prostate of senile mice. 90 male FVB mice were divided into: Young (18 weeks old) and senile (52 weeks old) groups; finasteride group: finasteride (20mg/kg); SU5416 group: SU5416 (6 mg/kg); TNP-470 group: TNP-470 (15 mg/kg,) and SU5416+TNP-470 group: similar to the SU5416 and TNP-470 groups. After 21 days, prostate ventral lobes were collected for morphological, immunohistochemical and Western blotting analyses. The results demonstrated atrophy, occasional proliferative lesions and inflammatory cells in the prostate during senescence, which were interrupted and/or blocked by treatment with antiangiogenic drugs and finasteride. Decreased AR and endostatin reactivities, and an increase for ER-α, ER-β and VEGF, were seen in the senile group. Decreased VEGF and ER-α reactivities and increased ER-β reactivity were verified in the finasteride, SU5416 groups and especially in SU5416+TNP-470 group. The TNP-470 group showed reduced AR and ER-β protein levels. The senescence favored the occurrence of structural and/or molecular alterations suggesting the onset of malignant lesions, due to the imbalance in the signaling between the epithelium and stroma. The SU5416+TNP-470 treatment was more effective in maintaining the structural, hormonal and angiogenic factor balance in the prostate during senescence, highlighting the signaling of antiproliferation via ER-β.

Egfr Activating Mutations And Their Association With Response To Platinum-doublet Chemotherapy In Brazilian Non-small Cell Lung Cancer Patients.

The aim of the study was to analyze the frequency of epidermal growth factor receptor (EGFR) mutations in Brazilian non-small cell lung cancer patients and to correlate these mutations with response to benefit of platinum-based chemotherapy in non-small cell lung cancer (NSCLC). Our cohort consisted of prospective patients with NSCLCs who received chemotherapy (platinum derivates plus paclitaxel) at the [UNICAMP], Brazil. EGFR exons 18-21 were analyzed in tumor-derived DNA. Fifty patients were included in the study (25 with adenocarcinoma). EGFR mutations were identified in 6/50 (12 %) NSCLCs and in 6/25 (24 %) adenocarcinomas; representing the frequency of EGFR mutations in a mostly self-reported White (82.0 %) southeastern Brazilian population of NSCLCs. Patients with NSCLCs harboring EGFR exon 19 deletions or the exon 21 L858R mutation were found to have a higher chance of response to platinum-paclitaxel (OR 9.67 [95 % CI 1.03-90.41], p = 0.047). We report the frequency of EGFR activating mutations in a typical southeastern Brazilian population with NSCLC, which are similar to that of other countries with Western European ethnicity. EGFR mutations seem to be predictive of a response to platinum-paclitaxel, and additional studies are needed to confirm or refute this relationship.

Intermittent Fasting Induces Hypothalamic Modifications Resulting In Low Feeding Efficiency, Low Body Mass And Overeating.

Intermittent fasting (IF) is an often-used intervention to decrease body mass. In male Sprague-Dawley rats, 24 hour cycles of IF result in light caloric restriction, reduced body mass gain, and significant decreases in the efficiency of energy conversion. Here, we study the metabolic effects of IF in order to uncover mechanisms involved in this lower energy conversion efficiency. After 3 weeks, IF animals displayed overeating during fed periods and lower body mass, accompanied by alterations in energy-related tissue mass. The lower efficiency of energy use was not due to uncoupling of muscle mitochondria. Enhanced lipid oxidation was observed during fasting days, whereas fed days were accompanied by higher metabolic rates. Furthermore, an increased expression of orexigenic neurotransmitters AGRP and NPY in the hypothalamus of IF animals was found, even on feeding days, which could explain the overeating pattern. Together, these effects provide a mechanistic explanation for the lower efficiency of energy conversion observed. Overall, we find that IF promotes changes in hypothalamic function that explain differences in body mass and caloric intake.

The Cratylia Mollis Seed Lectin Induces Membrane Permeability Transition In Isolated Rat Liver Mitochondria And A Cyclosporine A-insensitive Permeability Transition In Trypanosoma Cruzi Mitochondria.

Previous results provided evidence that Cratylia mollis seed lectin (Cramoll 1,4) promotes Trypanosoma cruzi epimastigotes death by necrosis via a mechanism involving plasma membrane permeabilization to Ca(2+) and mitochondrial dysfunction due to matrix Ca(2+) overload. In order to investigate the mechanism of Ca(2+) -induced mitochondrial impairment, experiments were performed analyzing the effects of this lectin on T. cruzi mitochondrial fraction and in isolated rat liver mitochondria (RLM), as a control. Confocal microscopy of T. cruzi whole cell revealed that Cramoll 1,4 binding to the plasma membrane glycoconjugates is followed by its internalization and binding to the mitochondrion. Electrical membrane potential (∆Ψm ) of T. cruzi mitochondrial fraction suspended in a reaction medium containing 10 μM Ca(2+) was significantly decreased by 50 μg/ml Cramoll 1,4 via a mechanism insensitive to cyclosporine A (CsA, membrane permeability transition (MPT) inhibitor), but sensitive to catalase or 125 mM glucose. In RLM suspended in a medium containing 10 μM Ca(2+) this lectin, at 50 μg/ml, induced increase in the rate of hydrogen peroxide release, mitochondrial swelling, and ∆Ψm disruption. All these mitochondrial alterations were sensitive to CsA, catalase, and EGTA. These results indicate that Cramoll 1, 4 leads to inner mitochondrial membrane permeabilization through Ca(2+) dependent mechanisms in both mitochondria. The sensitivity to CsA in RLM characterizes this lectin as a MPT inducer and the lack of CsA effect identifies a CsA-insensitive MPT in T. cruzi mitochondria.

An Isoflavone From Dipteryx Alata Vogel Is Active Against The In Vitro Neuromuscular Paralysis Of Bothrops Jararacussu Snake Venom And Bothropstoxin I, And Prevents Venom-induced Myonecrosis.

Snakebite is a neglected disease and serious health problem in Brazil, with most bites being caused by snakes of the genus Bothrops. Although serum therapy is the primary treatment for systemic envenomation, it is generally ineffective in neutralizing the local effects of these venoms. In this work, we examined the ability of 7,8,3'-trihydroxy-4'-methoxyisoflavone (TM), an isoflavone from Dipteryx alata, to neutralize the neurotoxicity (in mouse phrenic nerve-diaphragm preparations) and myotoxicity (assessed by light microscopy) of Bothrops jararacussu snake venom in vitro. The toxicity of TM was assessed using the Salmonella microsome assay (Ames test). Incubation with TM alone (200 μg/mL) did not alter the muscle twitch tension whereas incubation with venom (40 μg/mL) caused irreversible paralysis. Preincubation of TM (200 μg/mL) with venom attenuated the venom-induced neuromuscular blockade by 84% ± 5% (mean ± SEM; n = 4). The neuromuscular blockade caused by bothropstoxin-I (BthTX-I), the major myotoxic PLA2 of this venom, was also attenuated by TM. Histological analysis of diaphragm muscle incubated with TM showed that most fibers were preserved (only 9.2% ± 1.7% were damaged; n = 4) compared to venom alone (50.3% ± 5.4% of fibers damaged; n = 3), and preincubation of TM with venom significantly attenuated the venom-induced damage (only 17% ± 3.4% of fibers damaged; n = 3; p < 0.05 compared to venom alone). TM showed no mutagenicity in the Ames test using Salmonella strains TA98 and TA97a with (+S9) and without (-S9) metabolic activation. These findings indicate that TM is a potentially useful compound for antagonizing the neuromuscular effects (neurotoxicity and myotoxicity) of B. jararacussu venom.