The spatial pattern of child growth in Papua New Guinea

Child growth in PNG shows strong regional differences, with highlands children being generally shorter but stockier than those from lowland areas. Differences in diet, socioeconomic status and local subsistence agriculture were found to be important predictors of child growth. All variables indicating higher socioeconomic status were correlated with better growth, as was a high consumption of imported and local high quality foods such as cereals, legumes, tinned fish or meat and fresh fish. Differences in subsistence explained between 25% and 50% of the geographical variation in growth. Child growth was better in systems based on cassava and sweet potato, and worse in those where banana, sago and taro are staples. The cultivation of all major cash crops and sales of fish and food crops improved child growth. Birth weights show similar patterns to those observed in child growth. The implications of these findings for possible interventions are discussed.

Matrix-free mass spectrometric imaging using laser desorption ionisation Fourier transform ion cyclotron resonance mass spectrometry

Mass spectrometry imaging (MSI) is a powerful tool in metabolomics and proteomics for the spatial localization and identification of pharmaceuticals, metabolites, lipids, peptides and proteins in biological tissues. However, sample preparation remains a crucial variable in obtaining the most accurate distributions. Common washing steps used to remove salts, and solvent-based matrix application, allow analyte spreading to occur. Solvent-free matrix applications can reduce this risk, but increase the possibility of ionisation bias due to matrix adhesion to tissue sections. We report here the use of matrix-free MSI using laser desorption ionisation performed on a 12 T Fourier transform ion cyclotron resonance (FTICR) mass spectrometer. We used unprocessed tissue with no post-processing following thaw-mounting on matrix-assisted laser desorption ionisation (MALDI) indium-tin oxide (ITO) target plates. The identification and distribution of a range of phospholipids in mouse brain and kidney sections are presented and compared with previously published MALDI time-of-flight (TOF) MSI distributions.

Development of methods for spatial modeling of sediments quality in Lake Okeechobee, Florida

The major objectives of this dissertation were to develop optimal spatial techniques to model the spatial-temporal changes of the lake sediments and their nutrients from 1988 to 2006, and evaluate the impacts of the hurricanes occurred during 1998–2006. Mud zone reduced about 10.5% from 1988 to 1998, and increased about 6.2% from 1998 to 2006. Mud areas, volumes and weight were calculated using validated Kriging models. From 1988 to 1998, mud thicknesses increased up to 26 cm in the central lake area. The mud area and volume decreased about 13.78% and 10.26%, respectively. From 1998 to 2006, mud depths declined by up to 41 cm in the central lake area, mud volume reduced about 27%. Mud weight increased up to 29.32% from 1988 to 1998, but reduced over 20% from 1998 to 2006. The reduction of mud sediments is likely due to re-suspension and redistribution by waves and currents produced by large storm events, particularly Hurricanes Frances and Jeanne in 2004 and Wilma in 2005. Regression, kriging, geographically weighted regression (GWR) and regression-kriging models have been calibrated and validated for the spatial analysis of the sediments TP and TN of the lake. GWR models provide the most accurate predictions for TP and TN based on model performance and error analysis. TP values declined from an average of 651 to 593 mg/kg from 1998 to 2006, especially in the lake’s western and southern regions. From 1988 to 1998, TP declined in the northern and southern areas, and increased in the central-western part of the lake. The TP weights increased about 37.99%–43.68% from 1988 to 1998 and decreased about 29.72%–34.42% from 1998 to 2006. From 1988 to 1998, TN decreased in most areas, especially in the northern and southern lake regions; western littoral zone had the biggest increase, up to 40,000 mg/kg. From 1998 to 2006, TN declined from an average of 9,363 to 8,926 mg/kg, especially in the central and southern regions. The biggest increases occurred in the northern lake and southern edge areas. TN weights increased about 15%–16.2% from 1988 to 1998, and decreased about 7%–11% from 1998 to 2006.

Spatial and Temporal Variability of Dissolved Organic Matter Quantity and Composition in an Oilgotrophic Subtropical Coastal Wetland

Dissolved organic matter (DOM) is an essential component of the carbon cycle and a critical driver in controlling variety of biogeochemical and ecological processes in wetlands. The quality of this DOM as it relates to composition and reactivity is directly related to its sources and may vary on temporal and spatial scales. However, large scale, long-term studies of DOM dynamics in wetlands are still scarce in the literature. Here we present a multi-year DOM characterization study for monthly surface water samples collected at 14 sampling stations along two transects within the greater Everglades, a subtropical, oligotrophic, coastal freshwater wetland-mangrove-estuarine ecosystem. In an attempt to assess quantitative and qualitative variations of DOM on both spatial and temporal scales, we determined dissolved organic carbon (DOC) values and DOM optical properties, respectively. DOM quality was assessed using, excitation emission matrix (EEM) fluorescence coupled with parallel factor analysis (PARAFAC). Variations of the PARAFAC components abundance and composition were clearly observed on spatial and seasonal scales. Dry versus wet season DOC concentrations were affected by dry-down and re-wetting processes in the freshwater marshes, while DOM compositional features were controlled by soil and higher plant versus periphyton sources respectively. Peat-soil based freshwater marsh sites could be clearly differentiated from marl-soil based sites based on EEM–PARAFAC data. Freshwater marsh DOM was enriched in higher plant and soil-derived humic-like compounds, compared to estuarine sites which were more controlled by algae- and microbial-derived inputs. DOM from fringe mangrove sites could be differentiated between tidally influenced sites and sites exposed to long inundation periods. As such coastal estuarine sites were significantly controlled by hydrology, while DOM dynamics in Florida Bay were seasonally driven by both primary productivity and hydrology. This study exemplifies the application of long term optical properties monitoring as an effective technique to investigate DOM dynamics in aquatic ecosystems. The work presented here also serves as a pre-restoration condition dataset for DOM in the context of the Comprehensive Everglades Restoration Plan (CERP).

Data to Decision in a Dynamic Ocean: Robust Species Distribution Models and Spatial Decision Frameworks

Human use of the oceans is increasingly in conflict with conservation of endangered species. Methods for managing the spatial and temporal placement of industries such as military, fishing, transportation and offshore energy, have historically been post hoc; i.e. the time and place of human activity is often already determined before assessment of environmental impacts. In this dissertation, I build robust species distribution models in two case study areas, US Atlantic (Best et al. 2012) and British Columbia (Best et al. 2015), predicting presence and abundance respectively, from scientific surveys. These models are then applied to novel decision frameworks for preemptively suggesting optimal placement of human activities in space and time to minimize ecological impacts: siting for offshore wind energy development, and routing ships to minimize risk of striking whales. Both decision frameworks relate the tradeoff between conservation risk and industry profit with synchronized variable and map views as online spatial decision support systems.

For siting offshore wind energy development (OWED) in the U.S. Atlantic (chapter 4), bird density maps are combined across species with weights of OWED sensitivity to collision and displacement and 10 km2 sites are compared against OWED profitability based on average annual wind speed at 90m hub heights and distance to transmission grid. A spatial decision support system enables toggling between the map and tradeoff plot views by site. A selected site can be inspected for sensitivity to a cetaceans throughout the year, so as to capture months of the year which minimize episodic impacts of pre-operational activities such as seismic airgun surveying and pile driving.

Routing ships to avoid whale strikes (chapter 5) can be similarly viewed as a tradeoff, but is a different problem spatially. A cumulative cost surface is generated from density surface maps and conservation status of cetaceans, before applying as a resistance surface to calculate least-cost routes between start and end locations, i.e. ports and entrance locations to study areas. Varying a multiplier to the cost surface enables calculation of multiple routes with different costs to conservation of cetaceans versus cost to transportation industry, measured as distance. Similar to the siting chapter, a spatial decisions support system enables toggling between the map and tradeoff plot view of proposed routes. The user can also input arbitrary start and end locations to calculate the tradeoff on the fly.

Essential to the input of these decision frameworks are distributions of the species. The two preceding chapters comprise species distribution models from two case study areas, U.S. Atlantic (chapter 2) and British Columbia (chapter 3), predicting presence and density, respectively. Although density is preferred to estimate potential biological removal, per Marine Mammal Protection Act requirements in the U.S., all the necessary parameters, especially distance and angle of observation, are less readily available across publicly mined datasets.

In the case of predicting cetacean presence in the U.S. Atlantic (chapter 2), I extracted datasets from the online OBIS-SEAMAP geo-database, and integrated scientific surveys conducted by ship (n=36) and aircraft (n=16), weighting a Generalized Additive Model by minutes surveyed within space-time grid cells to harmonize effort between the two survey platforms. For each of 16 cetacean species guilds, I predicted the probability of occurrence from static environmental variables (water depth, distance to shore, distance to continental shelf break) and time-varying conditions (monthly sea-surface temperature). To generate maps of presence vs. absence, Receiver Operator Characteristic (ROC) curves were used to define the optimal threshold that minimizes false positive and false negative error rates. I integrated model outputs, including tables (species in guilds, input surveys) and plots (fit of environmental variables, ROC curve), into an online spatial decision support system, allowing for easy navigation of models by taxon, region, season, and data provider.

For predicting cetacean density within the inner waters of British Columbia (chapter 3), I calculated density from systematic, line-transect marine mammal surveys over multiple years and seasons (summer 2004, 2005, 2008, and spring/autumn 2007) conducted by Raincoast Conservation Foundation. Abundance estimates were calculated using two different methods: Conventional Distance Sampling (CDS) and Density Surface Modelling (DSM). CDS generates a single density estimate for each stratum, whereas DSM explicitly models spatial variation and offers potential for greater precision by incorporating environmental predictors. Although DSM yields a more relevant product for the purposes of marine spatial planning, CDS has proven to be useful in cases where there are fewer observations available for seasonal and inter-annual comparison, particularly for the scarcely observed elephant seal. Abundance estimates are provided on a stratum-specific basis. Steller sea lions and harbour seals are further differentiated by ‘hauled out’ and ‘in water’. This analysis updates previous estimates (Williams & Thomas 2007) by including additional years of effort, providing greater spatial precision with the DSM method over CDS, novel reporting for spring and autumn seasons (rather than summer alone), and providing new abundance estimates for Steller sea lion and northern elephant seal. In addition to providing a baseline of marine mammal abundance and distribution, against which future changes can be compared, this information offers the opportunity to assess the risks posed to marine mammals by existing and emerging threats, such as fisheries bycatch, ship strikes, and increased oil spill and ocean noise issues associated with increases of container ship and oil tanker traffic in British Columbia’s continental shelf waters.

Starting with marine animal observations at specific coordinates and times, I combine these data with environmental data, often satellite derived, to produce seascape predictions generalizable in space and time. These habitat-based models enable prediction of encounter rates and, in the case of density surface models, abundance that can then be applied to management scenarios. Specific human activities, OWED and shipping, are then compared within a tradeoff decision support framework, enabling interchangeable map and tradeoff plot views. These products make complex processes transparent for gaming conservation, industry and stakeholders towards optimal marine spatial management, fundamental to the tenets of marine spatial planning, ecosystem-based management and dynamic ocean management.

Assessment of the Spatial and Temporal Distribution of Functional Connectivity in Resting-State BOLD fMRI

Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate the spatial and temporal functional network information found within resting-state data, and aims to investigate the feasibility of extracting functional connectivity networks using different methods as well as the dynamic variability within some of the methods. Furthermore, this work looks into producing valid networks using a sparsely-sampled sub-set of the original data.

In this work we utilize four main methods: independent component analysis (ICA), principal component analysis (PCA), correlation, and a point-processing technique. Each method comes with unique assumptions, as well as strengths and limitations into exploring how the resting state components interact in space and time.

Correlation is perhaps the simplest technique. Using this technique, resting-state patterns can be identified based on how similar the time profile is to a seed region’s time profile. However, this method requires a seed region and can only identify one resting state network at a time. This simple correlation technique is able to reproduce the resting state network using subject data from one subject’s scan session as well as with 16 subjects.

Independent component analysis, the second technique, has established software programs that can be used to implement this technique. ICA can extract multiple components from a data set in a single analysis. The disadvantage is that the resting state networks it produces are all independent of each other, making the assumption that the spatial pattern of functional connectivity is the same across all the time points. ICA is successfully able to reproduce resting state connectivity patterns for both one subject and a 16 subject concatenated data set.

Using principal component analysis, the dimensionality of the data is compressed to find the directions in which the variance of the data is most significant. This method utilizes the same basic matrix math as ICA with a few important differences that will be outlined later in this text. Using this method, sometimes different functional connectivity patterns are identifiable but with a large amount of noise and variability.

To begin to investigate the dynamics of the functional connectivity, the correlation technique is used to compare the first and second halves of a scan session. Minor differences are discernable between the correlation results of the scan session halves. Further, a sliding window technique is implemented to study the correlation coefficients through different sizes of correlation windows throughout time. From this technique it is apparent that the correlation level with the seed region is not static throughout the scan length.

The last method introduced, a point processing method, is one of the more novel techniques because it does not require analysis of the continuous time points. Here, network information is extracted based on brief occurrences of high or low amplitude signals within a seed region. Because point processing utilizes less time points from the data, the statistical power of the results is lower. There are also larger variations in DMN patterns between subjects. In addition to boosted computational efficiency, the benefit of using a point-process method is that the patterns produced for different seed regions do not have to be independent of one another.

This work compares four unique methods of identifying functional connectivity patterns. ICA is a technique that is currently used by many scientists studying functional connectivity patterns. The PCA technique is not optimal for the level of noise and the distribution of the data sets. The correlation technique is simple and obtains good results, however a seed region is needed and the method assumes that the DMN regions is correlated throughout the entire scan. Looking at the more dynamic aspects of correlation changing patterns of correlation were evident. The last point-processing method produces a promising results of identifying functional connectivity networks using only low and high amplitude BOLD signals.

UTILIZATION OF GIS AND SPATIAL ANALYSIS TECHNIQUES IN ARTISANAL AND SMALL SCALE MINING TO LOCATE A CENTRALIZED PROCESSING CENTRE

One of the global phenomena with threats to environmental health and safety is artisanal mining. There are ambiguities in the manner in which an ore-processing facility operates which hinders the mining capacity of these miners in Ghana. These problems are reviewed on the basis of current socio-economic, health and safety, environmental, and use of rudimentary technologies which limits fair-trade deals to miners. This research sought to use an established data-driven, geographic information (GIS)-based system employing the spatial analysis approach for locating a centralized processing facility within the Wassa Amenfi-Prestea Mining Area (WAPMA) in the Western region of Ghana. A spatial analysis technique that utilizes ModelBuilder within the ArcGIS geoprocessing environment through suitability modeling will systematically and simultaneously analyze a geographical dataset of selected criteria. The spatial overlay analysis methodology and the multi-criteria decision analysis approach were selected to identify the most preferred locations to site a processing facility. For an optimal site selection, seven major criteria including proximity to settlements, water resources, artisanal mining sites, roads, railways, tectonic zones, and slopes were considered to establish a suitable location for a processing facility. Site characterizations and environmental considerations, incorporating identified constraints such as proximity to large scale mines, forest reserves and state lands to site an appropriate position were selected. The analysis was limited to criteria that were selected and relevant to the area under investigation. Saaty’s analytical hierarchy process was utilized to derive relative importance weights of the criteria and then a weighted linear combination technique was applied to combine the factors for determination of the degree of potential site suitability. The final map output indicates estimated potential sites identified for the establishment of a facility centre. The results obtained provide intuitive areas suitable for consideration

Continuum tensor network field states, path integral representations and spatial symmetries

A natural way to generalize tensor network variational classes to quantum field systems is via a continuous tensor contraction. This approach is first illustrated for the class of quantum field states known as continuous matrix-product states (cMPS). As a simple example of the path-integral representation we show that the state of a dynamically evolving quantum field admits a natural representation as a cMPS. A completeness argument is also provided that shows that all states in Fock space admit a cMPS representation when the number of variational parameters tends to infinity. Beyond this, we obtain a well-behaved field limit of projected entangled-pair states (PEPS) in two dimensions that provide an abstract class of quantum field states with natural symmetries. We demonstrate how symmetries of the physical field state are encoded within the dynamics of an auxiliary field system of one dimension less. In particular, the imposition of Euclidean symmetries on the physical system requires that the auxiliary system involved in the class' definition must be Lorentz-invariant. The physical field states automatically inherit entropy area laws from the PEPS class, and are fully described by the dissipative dynamics of a lower dimensional virtual field system. Our results lie at the intersection many-body physics, quantum field theory and quantum information theory, and facilitate future exchanges of ideas and insights between these disciplines.

Comparing global land cover datasets through the Eagle matrix land cover components for continental Portugal

Global land cover maps play an important role in the understanding of the Earth's ecosystem dynamic. Several global land cover maps have been produced recently namely, Global Land Cover Share (GLC-Share) and GlobeLand30. These datasets are very useful sources of land cover information and potential users and producers are many times interested in comparing these datasets. However these global land cover maps are produced based on different techniques and using different classification schemes making their interoperability in a standardized way a challenge. The Environmental Information and Observation Network (EIONET) Action Group on Land Monitoring in Europe (EAGLE) concept was developed in order to translate the differences in the classification schemes into a standardized format which allows a comparison between class definitions. This is done by elaborating an EAGLE matrix for each classification scheme, where a bar code is assigned to each class definition that compose a certain land cover class. Ahlqvist (2005) developed an overlap metric to cope with semantic uncertainty of geographical concepts, providing this way a measure of how geographical concepts are more related to each other. In this paper, the comparison of global land cover datasets is done by translating each land cover legend into the EAGLE bar coding for the Land Cover Components of the EAGLE matrix. The bar coding values assigned to each class definition are transformed in a fuzzy function that is used to compute the overlap metric proposed by Ahlqvist (2005) and overlap matrices between land cover legends are elaborated. The overlap matrices allow the semantic comparison between the classification schemes of each global land cover map. The proposed methodology is tested on a case study where the overlap metric proposed by Ahlqvist (2005) is computed in the comparison of two global land cover maps for Continental Portugal. The study resulted with the overlap spatial distribution among the two global land cover maps, Globeland30 and GLC-Share. These results shows that Globeland30 product overlap with a degree of 77% with GLC-Share product in Continental Portugal.

Quantitative Proteomic Analysis Reveals Metabolic Alterations, Calcium Dysregulation, And Increased Expression Of Extracellular Matrix Proteins In Laminin α2 Chain-deficient Muscle.

Congenital muscular dystrophy with laminin α2 chain deficiency (MDC1A) is one of the most severe forms of muscular disease and is characterized by severe muscle weakness and delayed motor milestones. The genetic basis of MDC1A is well known, yet the secondary mechanisms ultimately leading to muscle degeneration and subsequent connective tissue infiltration are not fully understood. In order to obtain new insights into the molecular mechanisms underlying MDC1A, we performed a comparative proteomic analysis of affected muscles (diaphragm and gastrocnemius) from laminin α2 chain-deficient dy(3K)/dy(3K) mice, using multidimensional protein identification technology combined with tandem mass tags. Out of the approximately 700 identified proteins, 113 and 101 proteins, respectively, were differentially expressed in the diseased gastrocnemius and diaphragm muscles compared with normal muscles. A large portion of these proteins are involved in different metabolic processes, bind calcium, or are expressed in the extracellular matrix. Our findings suggest that metabolic alterations and calcium dysregulation could be novel mechanisms that underlie MDC1A and might be targets that should be explored for therapy. Also, detailed knowledge of the composition of fibrotic tissue, rich in extracellular matrix proteins, in laminin α2 chain-deficient muscle might help in the design of future anti-fibrotic treatments. All MS data have been deposited in the ProteomeXchange with identifier PXD000978 (http://proteomecentral.proteomexchange.org/dataset/PXD000978).

Matrix Metalloproteinases And Left Ventricular Function And Structure In Spinal Cord Injured Subjects.

Subjects with spinal cord injury (SCI) exhibit impaired left ventricular (LV) diastolic function, which has been reported to be attenuated by regular physical activity. This study investigated the relationship between circulating matrix metalloproteinases (MMPs) and tissue inhibitors of MMPs (TIMPs) and echocardiographic parameters in SCI subjects and the role of physical activity in this regard. Forty-two men with SCI [19 sedentary (S-SCI) and 23 physically-active (PA-SCI)] were evaluated by clinical, anthropometric, laboratory, and echocardiographic analysis. Plasmatic pro-MMP-2, MMP-2, MMP-8, pro-MMP-9, MMP-9, TIMP-1 and TIMP-2 levels were determined by enzyme-linked immunosorbent assay and zymography. PA-SCI subjects presented lower pro-MMP-2 and pro-MMP-2/TIMP-2 levels and improved markers of LV diastolic function (lower E/Em and higher Em and E/A values) than S-SCI ones. Bivariate analysis showed that pro-MMP-2 correlated inversely with Em and directly with E/Em, while MMP-9 correlated directly with LV mass index and LV end-diastolic diameter in the whole sample. Following multiple regression analysis, pro-MMP-2, but not physical activity, remained associated with Em, while MMP-9 was associated with LV mass index in the whole sample. These findings suggest differing roles for MMPs in LV structure and function regulation and an interaction among pro-MMP-2, diastolic function and physical activity in SCI subjects.

Identification Of Corynebacterium Spp. Isolated From Bovine Intramammary Infections By Matrix-assisted Laser Desorption Ionization-time Of Flight Mass Spectrometry.

Corynebacterium species (spp.) are among the most frequently isolated pathogens associated with subclinical mastitis in dairy cows. However, simple, fast, and reliable methods for the identification of species of the genus Corynebacterium are not currently available. This study aimed to evaluate the usefulness of matrix-assisted laser desorption ionization/mass spectrometry (MALDI-TOF MS) for identifying Corynebacterium spp. isolated from the mammary glands of dairy cows. Corynebacterium spp. were isolated from milk samples via microbiological culture (n=180) and were analyzed by MALDI-TOF MS and 16S rRNA gene sequencing. Using MALDI-TOF MS methodology, 161 Corynebacterium spp. isolates (89.4%) were correctly identified at the species level, whereas 12 isolates (6.7%) were identified at the genus level. Most isolates that were identified at the species level with 16 S rRNA gene sequencing were identified as Corynebacterium bovis (n=156; 86.7%) were also identified as C. bovis with MALDI-TOF MS. Five Corynebacterium spp. isolates (2.8%) were not correctly identified at the species level with MALDI-TOF MS and 2 isolates (1.1%) were considered unidentified because despite having MALDI-TOF MS scores >2, only the genus level was correctly identified. Therefore, MALDI-TOF MS could serve as an alternative method for species-level diagnoses of bovine intramammary infections caused by Corynebacterium spp.

Inactivation Of Ampk Mediates High Phosphate-induced Extracellular Matrix Accumulation Via Nox4/tgfß-1 Signaling In Human Mesangial Cells.

High phosphate (Pi) levels and extracellular matrix (ECM) accumulation are associated with chronic kidney disease progression. However, how high Pi levels contribute to ECM accumulation in mesangial cells is unknown. The present study investigated the role and mechanism of high Pi levels in ECM accumulation in immortalized human mesangial cells (iHMCs). iHMCs were exposed to normal (0.9 mM) or increasing Pi concentrations (2.5 and 5 mM) with or without diferent blockers or activators. NOX4, phosphorylated AMPK (p-AMPK), phosphorylated SMAD3 (p-SMAD3), fibronectin (F/N), collagen IV (C-IV) and alpha-smooth muscle actin (α-SMA) expression was assessed via western blot and immunofluorescence. Lucigenin-enhanced chemiluminescence, and dihydroethidium (DHE) assessed NADPH oxidase activity and superoxide (SO), respectively. In iHMCs, a Pi transporter blocker (PFA) abrogated high Pi-induced AMPK inactivation, increase in NADPH oxidase-induced reactive oxygen species (ROS) levels, NOX4, p-SMAD3, α-SMA and C-IV expression. AMPK activation by AICAR, NOX4 silencing or NADPH oxidase blocker prevented high Pi-induced DHE levels, p-SMAD3, F/N, C-IV and α-SMA expression. AMPK inactivation with NOX4-induced ROS formation and transforming growth factor ß-1 (TGFß-1) signaling activation mediates high Pi-induced ECM accumulation in iHMCs. Maneuvers increasing AMPK or reducing NOX4 activity may contribute to renal protection under hyperphosphatemia.

A Metabolomic Protocol For Plant Systematics By Matrix-assisted Laser-desorption/ionization Time-of Flight Mass Spectrometry.

Matrix-assisted laser desorption/ionization time-of flight mass spectrometry (MALDI-TOF MS) has been widely used for the identification and classification of microorganisms based on their proteomic fingerprints. However, the use of MALDI-TOF MS in plant research has been very limited. In the present study, a first protocol is proposed for metabolic fingerprinting by MALDI-TOF MS using three different MALDI matrices with subsequent multivariate data analysis by in-house algorithms implemented in the R environment for the taxonomic classification of plants from different genera, families and orders. By merging the data acquired with different matrices, different ionization modes and using careful algorithms and parameter selection, we demonstrate that a close taxonomic classification can be achieved based on plant metabolic fingerprints, with 92% similarity to the taxonomic classifications found in literature. The present work therefore highlights the great potential of applying MALDI-TOF MS for the taxonomic classification of plants and, furthermore, provides a preliminary foundation for future research.

Matrix Support And Institutional Support: Analyzing Their Construction.

This is an analysis of the theoretical and practical construction of the methodology of Matrix Support by means of studies on Paideia Support (Institutional and Matrix Support), which is an inter-professional work of joint care in recent literature and official documents of the Unified Health System (SUS). An attempt was made to describe methodological concepts and strategies. A comparative analysis of Institutional Support and Matrix Support was also conducted using the epistemological framework of Field and Core Knowledge and Practices.