Emulsion technology is a suitable way of encapsulating, protecting and releasing hydrophobic bioactive compounds for application in food industries, but they are thermodynamically unstable systems. Good results have been achieved for emulsions stabilized by protein-polysaccharide complexes subjected to high-pressure homogenization. Improved stabilization of oil-in-water emulsions results from electrostatic complexes formed between proteins and polysaccharides at pH lower than the protein isoelectric point, which adsorb at the oil-water interface. In addition, polysaccharides contribute to emulsion stability by increasing viscosity of the continuous phase. The aim of this work is to investigate the production of carotenoid-rich buriti oil emulsions using soy protein isolate and high-methoxyl pectin as stabilizers.Using a rotatable central composite experimental design, we assessed the effects of oil content, soy protein isolate/high-methoxyl pectin ratio and homogenization pressure on the stability, droplet size, electrical conductivity, electrical charge, microstructure and rheological behaviour of the emulsions.5 Pa. This emulsion was stable for at least seven days, presenting reduced average droplet size, low electrical conductivity and high modulus of negative charges. The mechanical spectra showed that the emulsion behaved as a viscoelastic gel under oscillatory, non-destructive shearing, whereas shear-thinning behaviour took place under steady shear conditions.An optimized emulsion was produced with 28% buriti oil, 55% soy protein isolate, and homogenization pressure of 380·10The optimized buriti oil emulsions stabilized by soy protein isolate and high-methoxyl pectin could be suitable for fat substitution, energy reduction and carotenoid enrichment in food products, such as dairy and bakery products, ice cream, salad sauces and vegetable-based cream. Emulsion technology is suitable for encapsulation, protection and modulation of the release of hydrophobic bioactive compounds for use in food and pharmaceutical industries. However, emulsions are thermodynamically unstable systems and require addition of emulsifiers and/or stabilizers, as well as the use of energy (homogenization process) to attain kinetic stability (Soy protein isolate (SPI) and high-methoxyl pectin (HMP) are examples of a protein and anionic polysaccharide that may form electrostatic complexes when pH is lower than the protein isoelectric point  emulsions requires the use of energy to reduce droplet sizes and thus prevent creaming, which is a consequence of the difference in density between disperse and continuous phases. The homogenization step is also of crucial importance to modulate physicochemical and organoleptic properties, such as texture, taste, appearance and stability of emulsified systems  are among the most useful vegetable resources in the Amazon region. Buriti oil has a high concentration of monounsaturated fatty acids, with values higher than those found in olive oil or Brazil nuts, foods known to have high nutritional quality oil due to the properties of reducing blood cholesterol , soy protein isolate (SPI)  and high-methoxyl pectin (HMP) . Preparation of samples required deionized water, 1 M solutions of sodium azide, hydrochloric acid or sodium hydroxide  and McIlvaine buffer (pH=3.5) prepared using disodium phosphate and citric acid (DinâmicaTM).The raw materials used were buriti oil (Amazon Oil Industry5-400·105 Pa). These were the three independent variables. The HMP content was not included as an independent variable in the experimental design since it was added in the amount necessary to attain a fixed total mass fractions of biopolymers (SPI+HMP) in the continuous phase of the emulsion.Oil-in-water emulsions were prepared with different mass fractions of oil (10-30%) and SPI in the continuous phase (50-80%), and different applied homogenization pressures  to analyze the effects of three independent variables (The assays were carried out according to a 2ariables . The fol1 is the oil mass fraction (m(oil)/m(emulsion))/%, x2 is the SPI mass fraction (m(SPI)/m(SPI+HMP))/%, x3 is the homogenization pressure (Pa), and βi and βij are the fitted coefficients.where y is the response variable, xThe results were subjected to analysis of variance (ANOVA) and the fitted model was tested regarding the lack of fit and coefficient of regression using the software STATISTICA v. 7.0  and rheological behaviour. Confocal laser scanning microscopy (CLSM) was performed on the same day of emulsion preparation with fluorescent dyes to avoid fluorescence losses. For samples that show phase separation after 7 days, the analyses were performed using the samples from the creamed phase. The responses for the experimental RCCD were stability, droplet size, electrical conductivity and electrical charge. The analytical procedures are described as follows.h) and the emulsion initial height (h0) in the tube according to the following equation  was formed. The creaming index (CI), indicative of emulsion stability, was determined as the ratio of the lower phase height  with a 40× magnification objective coupled with a digital camera  and software Image-Pro Plus 6.0 . The samples were dispersed in deionized water at 10% obscuration and kept under stirring during analysis. The refractive indices used for droplets and dispersant phase were 1.46 (buriti oil) and 1.33 (deionized water), respectively. Considering that some samples showed bimodal particle size distribution, the mode of the cumulative frequency distribution was adopted as the most representative particle size  at room temperature . The emulsion or cream phase samples (0.05%) were dispersed in ultrapure water followed by pH=3.5 adjustment using HCl solution to maintain the dispersing medium as close as possible to the emulsion continuous phase, according to the proposed method  rheometer with geometry of serrated parallel plates of 40 mm diameter and gap of 300 μm at 25 °C. To avoid droplet crushing, the distance between parallel plates (gap) was defined as being at least 10 times larger than the maximum droplet size (-1) and upward (0.1 to 100 s-1) shear rate ramps to obtain apparent viscosity versus shear rate curves. The Sisko viscosity model was fitted to the experimental data:Steady shear tests were performed following downward (100 to 0.1 sηap is the apparent viscosity (Pa·s), η∞ is the infinite-shear viscosity (Pa·s), γ̇ is the shear rate (s-1), is the consistency index (Pa·sn), and is the flow behaviour index (dimensionless) (Where Κ) and flow behaviour index (n), the infinite shear viscosity (η∞) of the samples. The infinite shear viscosity provides information on how the system behaves when exposed to high shear conditions, such as spraying or extrusion, which is relevant for food processing industries.The Sisko model is interesting because it presents, in addition to the consistency index (ω) dependence on the storage (G’) and loss (G”) moduli, according to the following equations:The viscoelastic properties were evaluated along frequency sweeps between 0.03 and 30 rad/s at maximum deformation of 0.001, which was in the linear viscoelastic region. A power law model was applied to describe the frequency  and power law models (Eqs. 4 and 5) were evaluated by the coefficient of determination, Ret al.  with 40× magnification and the laser was adjusted to green/red fluorescence mode, at wavelengths of 488 and 543 nm to visualize FITC and Rhodamine B, respectively. Green and red fluorescent images were obtained from the two separate channels and, then, overlaid and analyzed by Zen 2010 software  were carried out according to Lamprecht Scanning electron microscopy (SEM) images were obtained in a TM 3000  microscope with a 600× magnification. The samples were inserted into the chamber and immobilized by vacuum application during image capture.Experimental validation of the models resulting from the RCCD (Eq. 1) was carried out based on the results of stability, droplet size, electrical conductivity and electrical charge. Selected values for buriti oil and SPI mass fractions, and homogenization pressure were applied to produce an emulsion and the corresponding experimental results were compared with those predicted by the fitted models. In addition, this emulsion was characterized by rheological and morphological analyses (as described above).5 Pa.The emulsions prepared with different buriti oil and SPI contents and subjected to various homogenization pressures, according to the RCCD in 2), for each one of the dependent variables given in The obtained results  were use1) and SPI (β2), and homogenization pressure (β3). In addition, there were significant interactions between the oil and SPI mass fractions (β12), as well as between SPI mass fraction and homogenization pressure (β23) (The creaming index was significantly affected (p≤0.10) by the mass fractions of oil (βre (β23) . The oilThe increase of emulsion stability with increasing concentration of the dispersed phase is probably related to the higher viscosity associated with the increased concentration of emulsion droplets , which s1, and quadratic, β11), homogenization pressure (β3 and β33), the interactions between the oil and SPI mass fractions (β12) and between the SPI mass fraction and homogenization pressure (β23). The oil mass fraction and the interaction between mass fractions of oil and SPI had positive effects on droplet size, while the homogenization pressure and the interaction between the SPI mass fractions and homogenization pressure had negative effects. This suggests that high HMP levels helped to stabilize the emulsions, although the smaller amount of SPI was not sufficient to produce emulsions with small droplets, since a larger amount of proteins would be needed to cover the larger interfacial area.With respect to droplet size measured by OM, the variables with significant effects (p≤ 0.10) were the oil mass fraction  and SPI (β2), homogenization pressure (β3 and β33) and by the interaction between the oil and SPI mass fractions (β12). The SPI content and homogenization pressure had positive effects, whereas the oil content, homogenization pressure, and the interaction between the oil and SPI mass fractions had negative effects. It is interesting to note the large difference between the results for droplet size determined by OM and LD. Differences could also be observed in the variables that exerted significant effects on these results. The laser diffraction technique applied requires sample dilution, which does not happen in the optical microscopy technique. This fact led to the hypothesis that the dilution was not enough to completely disperse the oil droplets, which remained arranged in clusters. This hypothesis was confirmed by observing the emulsions at the same dilution in the optical microscope. Despite the observed differences, the results from LD are relevant, since this is the standard technique used for droplet size analysis in emulsions (The droplet sizes determined by LD were represented by the distribution model and were significantly affected (p≤0.10) by the mass fractions of oil (β1) and SPI (β2) and homogenization pressure (β3). The oil and SPI mass fractions had negative effects and the homogenization pressure had positive effect on the electrical conductivity of emulsions or on cream phases. These results can be explained considering that the systems with higher SPI mass fractions had lower HMP mass fraction and, that pectin helps to maintain oil droplets apart from each other. Therefore, in these systems there was a great possibility that oil droplets were flocculated, or even merged  in the creamed phase, leading to lower electrical conductivity values. On the other hand, in emulsions subjected to higher homogenization pressures, smaller oil droplets and greater homogeneity are expected, thus, resulting in higher electrical conductivity values.The electrical conductivity was significantly affected (p≤0.10) by the mass fractions of oil , characterizing a metastable system . At pH=32) and homogenization pressure (β33), both having positive effects on the zeta potential, i.e. increasing SPI mass fraction and homogenization pressure caused lower charge modulus.Stable emulsions  showed high chargemodulus . HoweverBuriti oil emulsion stability), as well as the results observed in previous works  and O26S74P360 (stars) produced with the same oil mass fraction and homogenization pressure, slightly higher viscosity values were observed in the emulsion with the lower SPI mass fraction and, consequently, higher HMP mass fraction. On the other hand, comparing the emulsions O26S74P240 (triangles) and O26S74P360 (stars) that are of the same composition but homogenized at different pressures, it was noted that the higher homogenization pressure slightly reduced the apparent viscosity, probably because of the smaller oil droplets produced, which is in accordance with the discussion on the creaming index (see η∞) provides information on how the system behaves when submitted to high shear conditions, such as spraying or extrusion (The Sisko model (Eq. 3) presented satisfactory fit to the apparent viscosity curves , resultiThe apparent viscosity curves corresponding to the downward and upward shear rate ramps practically overlap, suggesting that the rheological behaviour of the emulsions was independent of the shearing time, which could be an interesting characteristic for industrial processing such as pumping and pipe flow.G') and loss (G") moduli as functions of frequency for the emulsions O14S74P360, O26S56P360, O26S74P240 and O26S74P360 are observable in G' showed values greater than G" in the entire studied frequency range, showing that the emulsions and creamed phases were elastic, a behavior characteristic of weak gel. Weak or physical gels are frequency dependent, with G’>G” but with no crossing-over, whereas true or elastic gels show constant G’ along frequency sweeps  had the lowest modulus. The O14S74P360 (squares) emulsion was the most unstable one, and despite this, its creamed phase showed similar viscoelastic results to O26S56P360 (circles) assay, which was a stable emulsion.G’ and G” versus frequency data. The fitting parameters obtained for the studied emulsions can be found in 2>0.87) and RMSE<0.43. For biopolymer systems that behave as true gels, the loss and storage moduli are congruent for several decades of frequency and this behaviour results in a constant value of the loss tangent, tan (δ), in which δ is the phase angle between stress (σ) and strain (γ). These relations can be expressed as follows  to andnr is the relaxation exponent and Eq. 7 predicts that G”>G’ for nr>0.5, G’>G” for nr<0.5, and G”=G’ for nr=0.5. A gel with nr approaching 1 is a purely viscous gel, whereas nr approaching 0 suggests a purely elastic gel  were included in The parameter <n”<0.24  and the Optical microscopy was used to observe the emulsion or creamed phase morphology and it was also possible to analyze and compare oil droplet arrangement among different assays. Emulsions O14S74P360  and O26SThe images corresponding to emulsions O14S74P360, O26S56P360, O26S74P240 and O26S74P360  were obtIt is observable that the SPI covered the oil droplets, which are seen as green spheres when observed individually . High-meEmulsion O26S56P360  was stabThe microstructure of emulsions or creamed phases was also analyzed by scanning electron microscopy. It is possible to observe a dense continuous phase, probably due to the excess of high-methoxyl pectin and buriti oil droplets embedded into this biopolymer matrix. Comparing the images, once more a difference in morphology is observable comparing different formulations. The droplets of O14S74P360 emulsion , for exa5 Pa.In order to produce a stable emulsion with high carotenoid and tocopherol contents, which corresponds to a higher buriti oil content, with reduced size droplets, low electrical conductivity, and high electrical charge, the optimal values for the independent variables were fixed at 28% buriti oil, 55% SPI, and homogenization pressure of 380·10The observed results, as well as those predicted by the regression models  for the The images obtained by optical microscopy, SEM and CLSM allowed to observe that the optimized buriti oil emulsion morphology is small and less flocculated.The pseudoplastic behaviour of this system was confirmed by the good fitting of the Sisko viscosity model. The mechanical spectra revealed that the sample was located in the viscoelastic plateau region and its microstructure revealed weak gel characteristics.et al. , low electrical conductivity (2.23 mS/cm), and high modulus of negative charges (-17.65). This emulsion behaved typically for a weak gel and an excess of pectin in the continuous phase was observed to contribute to the emulsion stability. Emulsions containing the carotenoid-rich buriti oil stabilized by SPI/HMP could be valuable structured systems applied for fat substitution, energy reduction, and carotenoid enrichment in foods such as dairy and bakery products, ice cream, salad sauces, and vegetable-based cream.The rotatable central composite design (RCCD) was suitable for investigating the formation of buriti oil emulsions stabilized by soy protein isolate (SPI) and high-methoxyl pectin (HMP) electrostatic interactions. Thus we obtained an optimized emulsion produced with 28% buriti oil, 55% SPI in the continuous phase, and homogenized at 380·10


The gross forest gain also increased from ~6000 km2 (1985–1995) to ~7440 km2 (1995–2005). Overall, India experienced a net decline in forest by ~18,000 km2 (gross loss–gross gain) consistently during both decades. We show that the major source of forest loss was cropland expansion in areas of low cropland productivity (due to soil degradation and lack of irrigation), followed by industrial development and mining/quarrying activities, and excessive economic dependence of villages on forest resources.We examine the dynamics and spatial determinants of land change in India by integrating decadal land cover maps (1985–1995–2005) from a wall-to-wall analysis of Landsat images with spatiotemporal socioeconomic database for ~630,000 villages in India. We reinforce our results through collective evidence from synthesis of 102 case studies that incorporate field knowledge of the causes of land change in India. We focus on cropland–fallow land conversions, and forest area changes . We show that cropland to fallow conversions are prominently associated with lack of irrigation and capital, male agricultural labor shortage, and fragmentation of land holdings. We find gross forest loss is substantial and increased from ~23,810 kmThe online version of this article (doi:10.1007/s10113-016-1068-2) contains supplementary material, which is available to authorized users. Importantly, our study period (1985–2005) includes the period of economic liberalization in India (1991 onwards) following which the pressure on land resources intensified. We report LULCC estimates at national (Tables S2-S4) and state level , and by agro-ecological zones (AEZs)  considering their policy relevance to forest and agriculture . AEZs are regions delineated by similar climatic and soil conditions  that incorporate field knowledge of the causes of LULCC mainly through social surveys and local expertise. While ground studies  offer crucial qualitative insights, data collection is typically expensive and therefore covers small regions. It is hard to generalize and quantify the causal relations of LULCC by studying few villages in a country of over 600,000 villages with diverse agro-ecological and sociocultural environment. Our synthesis helps to identify accumulated effects that are statistically stronger than any individual case study due to increased sample size and greater diversity. It is important to note that the case studies often relate to the triggers of the change . Thus far, no geospatial socioeconomic database exists for complete India at village level; our data are a significant improvement in spatial resolution compared to existing national datasets . Overall, we compiled spatial data on over 200 socioeconomic variables for two consecutive census years  (Text S1). The use of village-level data is crucial for two reasons. First, it captures the high granularity in socioeconomics  that is crucial to explain the spatial variations in high-resolution Landsat data. The granularity gets masked at coarser administrative levels . Second, we use over forty village-specific categorical/qualitative variables (Table S12) that cannot be represented at coarser administrative levels . We also include key biophysical factors (Text S1) hypothesized to affect the spatiotemporal patterns of land change in India (Table S12).Here, we describe our methods and data briefly. See Text S1 for further details.Table S13 summarizes key datasets used with references. We highlight socioeconomic and LULUC data, both of which are central to our analysis.We created the spatial socioeconomic database by combining tabular information from the Indian census  with seamless village- and town-level administrative boundaries of India corresponding to 2001 census specifically prepared for this study, sourced from Survey of India topographic sheets . Both the tabular data and administrative boundaries required substantial amount of organization, data cleaning, and quality checks prior to being linked together.We have a dedicated article describing the technical details and validation of the LULCC database, with basic land cover area statistics . We preferred this method for two reasons. We quantify the  determinants by estimating spatial logistic regressions (Text S1) between land cover conversion estimates (dependent variable) and hypothesized socioeconomic and biophysical factors (or their proxies) grounded through local case studies (Table S12). We estimate regression models (Table S14) specific to land cover conversion and decadal time period, at both national scale and for sub-national hot spots identified by AEZs (Table S6). Our regression analysis is carried out at 1 km × 1 km resolution (see Text S1 for data preprocessing). The 1-km resolution was mainly a tradeoff between the 30-m LULCC data and relatively coarser socioeconomic data (~2 km × 2 km per village on average). To minimize loss of information, while aggregating the 30-m LULUC data, we calculated the fraction of 1-km grid cell undergoing various land cover conversions, as opposed to approximating the entire grid cell area to undergo one (dominant) land cover conversion.Our statistical modeling technique draws on our recent work . We performed a systematic literature search on ISI Web of Science and Google Scholar for studies on LULCC covering India and our study period. We additionally included key (sub-) national reports, not indexed in either literature database. In total, we reviewed 643 articles, of which we discarded 177 as irrelevant (38 of which discussed causes of LULCC processes not a focus of our study). Of the remaining 466 articles, over three-fourth focused only on land change detection, highlighting the relatively less attention on understanding the causes of change. The 102 articles in our synthesis provide information on the causes of land change typically by combining one or more of: household surveys, field transects, and regional/local expertise of authors. Often, studies also included remote sensing component. The studies are summarized in Tables S8-S11, and the study locations are visualized in Fig. S3. To quantify the results of our synthesis, we analyzed the frequency of causes across case studies. We grouped the studies by LULCC processes and into broad clusters of causes (see Dataset S3 for study-wise grouping details and Text S1 for detailed methods); the clusters being specific to LULCC process.We present the LULCC conversion estimates and spatial determinants in the first three subsections. LULCC conversion estimates are based on analysis of satellite data. All our estimates pertain to the sum of urban, peri-urban, and rural areas within the region of quantification . Our results on spatial determinants are based on regression analysis of satellite data with hypothesized biophysical and village socioeconomic variables. We present the results of synthesis from 102 case studies in the fourth subsection.We find major shifts between cropland and fallow land during the period of study Fig. . About 3Land can be kept under fallow temporarily to restore and maintain soil fertility in multiple cropping systems. However, as our maps are decadal, we cannot identify whether the cropland–fallow conversions observed are a part of land restoration process or not. Therefore, for further insights, we complied annual (1984–2012) district-level ground statistics data on fallow land from the Government of India (Dataset S4). The statistic indicates that in both AEZ2 and AEZ8  the area of long fallows (land not cultivated for 1–5 years) exceeds that of temporary fallows (<1 year). Furthermore, 3.5% of India’s land area was consistently under long fallows over the past decade (Dataset S4).Our regression analysis at national scale Fig. a, S5a inFactors prominent in explaining conversion from cropland to fallow land Fig. b, S5b: aIn AEZ2, 6, and 8 , knowledge to reclaim land is an important factor to reduce fallow land . We find contrasting relationships between farm size (average size within each grid cell) and fallow land across sub-national hot spots. Cropland to fallow land conversion is positively associated with larger farm size in AEZ2 , and positively with smaller farm size in semiarid hot spots  . In AEZ2, resources are a limiting factor to fuller land utilization, as also indicated by negative relationship between fallow land and availability of labor, capital, and irrigation . The massive reclamation of fallow land to cropland in AEZ2 during 1995–2005 Fig. .2 of gross forest loss, i.e., sum of all forest area loss) of the forest area that existed in 1985 , and the rate increased to ~3.5% during 1995–2005  . Cropland was the major source of forest conversion during both decades, contributing to ~39% of gross forest loss in 1985–1995, and ~35% during 1995–2005. The relative area of gross forest loss to shrubland increased from ~31% in 1985–1995 to ~32% in 1995–2005. Expansion of commercial plantations into forests accounted for ~7% of gross forest loss during both decades. These trends are in stark contrast with the 1988 National Forest Policy that regards forest as a national asset and imposed strict rules to protect them  by 1995 . In 1995–2005, the region’s gross forest area loss declined to 1630 km2. In AEZ5, 4.9% of the regions forest area in 1985 was converted to other land use by 1995, and the rate increased to 7.9% in subsequent decade. Nonetheless, Eastern Plateau and Eastern Ghats (AEZ12), Central Highlands (AEZ10), and Western Himalayas (AEZ14) emerged as persistent hot spots for both decades. AEZ5, 10, 12, and 17 collectively accounted for ~59% (1985–1995) and ~56% (1995–2005) of the national total of gross forest area lost to cropland. AEZ4, 5, 10, 12, and 19 collectively accounted for ~84% (1985–1995) and ~80% (1995–2005) of the national total of forest area converted to shrubland. AEZ12 alone accounted for 40% (1985–1995) and 35% (1995–2005) of the national total of gross forest area lost to shrubland.A regional breakdown indicates that gross forest loss is widespread across India, and forest loss hot spots change over time , and protected areas Fig. a, S15. WAcross AEZ hot spots, the following agriculture-related variables show prominent negative association with gross forest area loss: proportion of irrigated areas Figs. S17-S19), higher fertility of agricultural soils , average farm size , availability of power supply for agriculture , proportion of main  agricultural laborers , and proximity to agricultural credit institutions . These relationships broadly indicate that higher agricultural productivity tends to reduce the pressure on adjoining forests. Most diversion of forest to cropland is encroachment, because national forest policy does not favor diversion of forest to non-forest, which requires prior approval from central government MoEF . Further7-S19, hibeedi, leaf plates, baskets, brooms, match sticks, paper pulp) ; colder temperatures , wooden furniture/timber, and making of woolen blankets (indicating sheep over-browsing) .A regional analysis indicates that in AEZ19 that encloses the Western Ghats, mining activities, manufacturing of wooden agricultural implements, and villages dependent on coconut and coffee plantations (encroachment) show positive association with forest loss . Across all hot spots in central India , mining/quarrying activities, industrial development, and factors associated with low agricultural productivity  show positive association with forest loss . Other factors prominently associated with forest loss are wooden furniture/timber extraction and cattle overgrazing ; villages making bamboo products ; villages making forest products , gross forest area gains were positively associated with state administrative divisions, mined-out areas, density of forestry workers, and density of community workers . The identified state administrative divisions typically have larger amount of forest inundated to water bodies (irrigation projects), and forest diverted to built-up land  . Both state administrative divisions and greening of mined-out areas indicate compensatory afforestation by respective state governments to partly compensate for forest loss. The forestry workers are employed by forest department and are a proxy for level of protection and control. These workers are typically involved in forest maintenance, wildlife protection, fire observations, and interface with tourism, among others. Community workers help with restoration efforts  by involving forest department and local communities.N = 37) labor shortage/migration driven by new income opportunities (N = 14), lack of infrastructure , lack of access to capital (N = 7), and cropland fragmentation . Reclamation of fallow land depends mainly on (based on 16 studies) critical support services , level of education , and village infrastructure . Illegal forest encroachment , wood extraction for subsistence (N = 23), expansion of man-made structures (N = 21), industrial exploitation (N = 15), and cattle overgrazing (N = 12) are common causes of forest loss. Unlike cropland fragmentation that drives fallow land, no case studies (N = 42) suggested that forest fragmentation drives forest loss. Regarding forest area gains, only three case studies  were designed to consider passive forces (regrowth following land abandonment), with other studies focusing on factors that influence the effectiveness of participatory forest management programs . Our study finds passive forces to be a major factor for forest area increase. The prominent socioeconomic factors of forest area gain identified from our regression analysis are echoed in our synthesis .Our synthesis indicates that the three LULCC  are driven by different combinations of factors. Nonetheless, the accumulated effects  report wood extraction for construction materials as a main determinant of forest loss in AEZ14. Some factors can also behave differently in individual cases. For example, different case studies  stemming from same AEZ show opposing effects on how education affects fallow land. Education  causes a shift to off-farm jobs, thus increasing fallow land. In contrast, with education farmers perceive higher returns to investment on land, invest more on resource conservation, and have better access to information leading to fuller land utilization. Such heterogeneity is concurrent and important to recognize; in such cases, our statistical analysis covering the entire region helps identify the dominant effect.Our analysis provides a comprehensive spatial coverage of the dynamics and spatial determinants of LULCC in India by integrating remote sensing data with rich and uniform socioeconomic data collected from each village and town at national scale. The analysis is important because a general understanding of the spatiotemporal dynamics and determinants of LULCC over larger regions of India is limited, hindering effective national-level planning and policy making.Our analysis of spatial determinants is useful because it adds a quantitative component to our study. Determinants help identify biophysical and socioeconomic variables that contribute to the statistical explanation of (the location of) observed LULCC. However, determinants do not necessarily imply causality; they only provide some empirical support for causal relations. On the other hand, local case studies often identify causality. Our synthesis of case studies (Tables S8-S11) helps to identify causes that are common across case studies Fig. . The synFirst, as we estimated LULCC from decadal satellite images, they capture only the decadal changes in LULCC, and can mask within-decade variations including intermediary land uses. Especially, inter-annual climate variability causes fluctuations in fallow land (Dataset S4). However, the conversions between cropland and fallow inferred between decadal end points reflect only the climate effect of end point. Our decadal data also cannot identify land fallowed as a part of multiple cropping systems to restore and maintain soil fertility. Except cropland–fallow systems, other land cover conversions  tend to be unidirectional at decadal timescale due to high cost of land reversion . The resulting bias is likely minimal because: (1) persistent modification of forest would likely manifest as a change in land cover within a decade, and (2) our statistical estimation weighs each observation (grid cell) by the magnitude of land change; thus, small changes have less influence in our model.Third, our analysis does not extend beyond 2005 due to data limitations. Wall-to-wall analysis of Landsat scenes is laborious, and efforts are underway to extend our decadal land cover conversion estimates to 2015. Furthermore, while India has conducted the 2011 socioeconomic census, tabular data on village profiles is on hold, pending consistency and quality checks. Nonetheless, our analysis already covers two decades and offers key insights on the non-stationary of factors associated with LULCC in India.Our results highlight the dichotomy where on the one hand, large amounts of India’s cropland area are converted fallow, thereby not contributing to agricultural production. On the other, forest area is being encroached for agriculture. We show that both land conversions occur in areas of low agricultural productivity as broadly indicated by factors related to deficits in infrastructure (irrigation and markets), knowledge and critical support services. Our results imply that strategies to improve agricultural productivity can have a positive effect by enhancing food production and simultaneously help reduce the pressure on forest . This is crucial for sustainable land use planning in India because India is among the world’s fastest growing economy and population, with constant land area. Henceforth, we discuss specific implications of our results for land use planning in India.Our results indicate that labor shortage; land fragmentation; and deficits in infrastructure, knowledge, and access to capital are key factors associated with crop to fallow conversions. There are threefold implications of our results. First, with the National Rural Employment Guarantee Scheme . Importantly, our reported process of fallowing small, less productive farms combined with job opportunities from an industrializing economy show striking similarity to the path outlined in forest transition theory , with support from both public and private initiatives, e.g., through building Indira Gandhi Canal in AEZ2 and Integrated Wasteland Development Programs . Our geospatial village-level socioeconomic database (covering 1991 and 2001) will be made available for download for free from NASA Socioeconomic Data and Applications Center . Contact the first author for more information.Our satellite LULC data for three decades can be downloaded for free from Supplementary material 1 (XLS 458 kb)Supplementary material 2 (XLS 541 kb)Supplementary material 3 (XLS 49 kb)Supplementary material 4 (XLS 79 kb)Supplementary material 5 (DOCX 7671 kb)Supplementary material 6 (DOCX 615 kb)Supplementary material 7 (PDF 852 kb)Below is the link to the electronic supplementary material.


For many pathological states, microparticles are supposed to be one of the causes of hypercoagulation. Although there are some indirect data about microparticles participation in coagulation activation and propagation, the integral hemostasis test Thrombodynamics allows to measure micropaticles participation in these two coagulation phases directly. Demonstrates microparticles participation in coagulation activation by influence on the appearance of coagulation centres in the plasma volume and the rate of clot growth from the surface with immobilized tissue factor.Methods: Microparticles were obtained from platelets and erythrocytes by stimulation with thrombin receptor-activating peptide (SFLLRN) and calcium ionophore (A23187), respectively, from monocytes, endothelial HUVEC culture and monocytic THP cell culture by stimulation with lipopolysaccharides. Microparticles were counted by flow cytometry and titrated in microparticle-depleted normal plasma in the Thrombodynamics test.3/ul range of microparticles concentrations. However, at concentrations greater than 500·103/ul, erythrocyte microparticles increased the stationary clot growth rate to significantly higher levels than do platelet microparticles or artificial phospholipid vesicles consisting of phosphatidylcholine and phosphatidylserine.Monocyte microparticles induced the appearance of clotting centres through the TF pathway at concentrations approximately 100-fold lower than platelet and erythrocyte microparticles, which activated plasma by the contact pathway. For endothelial microparticles, both activation pathways were essential, and their activity was intermediate. Monocyte microparticles induced plasma clotting by the appearance of hundreds of clots with an extremely slow growth rate, while erythrocyte microparticles induced the appearance of a few clots with a growth rate similar to that from surface covered with high-density tissue factor. Patterns of clotting induced by platelet and endothelial microparticles were intermediate. Platelet, erythrocyte and endothelial microparticles impacts on the rate of clot growth from the surface with tissue factor did not differ significantly within the 0–200·10Microparticles of different origins demonstrated qualitatively different characteristics related to coagulation activation and propagation. Cell destruction or activation leads to microparticles (MPs) shedding. In the blood of normal donors, more than 80% of MPs are derived from platelets ,2. In paThere are a number of clinical works where the MPs concentration is shown to increase in pathological states associated with elevated thrombotic risk. Data are represented in corresponding reviews –8. Many Thus, although there is a considerable amount of data on the involvement of MPs in hypercoagulation, MPs concentration does not always reflect the risk of thrombotic complications. Therefore, the detailed study of what type of MPs and in what way influence on coagulation is of current interest. This may help to distinguish the conditions when MPs add risk of thrombosis. It is well known that coagulation can be activated by two pathways: external, when coagulation is triggered by tissue factor (TF), and internal, when factor XII is activated. This phase is called coagulation initiation. Activation of either pathway leads to the same reactions in the coagulation cascade–the clot growth phase. The process finishes with the termination phase–the protein C reactions. Phospholipid surfaces containing phosphatidylserine (PS) play an important role in these phases, especially in the propagation phase. MPs are able to increase the clot growth rate by providing such surfaces and by binding coagulation factors at higher concentrations than platelets –23. MPs Thrombodynamics test allows us to observe the activation and propagation phases independently. The time of appearance and number of spontaneous activation centres characterize the ability of MPs to initiate coagulation. The MPs influence on the growth rate of clot triggered by surface with immobilized TF characterizes their impact on coagulation propagation. In this work, we compared MPs derived from almost all cells that are the major sources of MPs in blood  in Thrombodynamics test. The comparison revealed the predominant impact of TF-bearing MMPs and EMPs on coagulation activation and ErMPs on coagulation propagation.Blood of healthy volunteers from the laboratories staff from Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology and from National Medical Research Center for Cardiology was collected for subsequent plasma or cell isolation with the approval of the ethics committees of the Center for Theoretical Problems of Physicochemical Pharmacology in time period from 03.2015 to 12.2019. Written informed consent was obtained from all donors.Microparticles (MPs) produced by activated platelets, monocytes, human monocytic THP-1 cells and endothelial cells were prepared as described earlier in detail ,31.8 platelets/ml in Tyrode/HEPES solution  were activated by 10 μM thrombin receptor activating peptide (SFLLRN) for 10 min at 37°C.Platelets were isolated from the blood of healthy volunteers. Washed platelets suspended at a concentration of 5 x 10Monocytes were isolated from the blood of healthy volunteers. Citrated blood was centrifuged at 180g, 10 min to obtain platelet rich plasma, platelets were removed by centrifugation , and plasma was returned to the blood. Starch solution was added to agglutinate erythrocytes and after their precipitation, the supernatant was layered over Histopaque1077 solution and centrifuged at 400 g, 30 min. Mononuclear leukocytes were washed from the «buffy coat» formed after this centrifugation, resuspended in RPMI 1640 medium, containing 20 mM HEPES, 2 mM L-glutamine, 1 mM sodium pyruvate, penicillin (50 U/ml), streptomycin (100 μg/ml), and 10% fetal bovine serum, seeded into untreated 100 mm Petri dishes (25·x 106 cells/dish) and cultivated for 18 hours. Not attached mononuclear cells were washed off and adherent monocytes were activated by 1 μg/ml bacterial lipopolysaccharide (LPS)  for 6 hours. Activation of monocytes by LPS  was performed in a medium containing not-inactivated fetal bovine serum (instead of inactivated) as a source of LPS-binding protein.THP-1 human monocytic cell line was obtained from American Type Culture Collection (ATCC TIB-202)  and stored in cell bank of National Medical Research Center for Cardiology. After thawing, cells were cultured under standard conditions  in RPMI 1640 medium containing 20 mM Hepes, 2 mM L-glutamine, 1 mM sodium pyruvate, penicillin (50 U/ml), streptomycin (100 μg/ml), and 10% heat inactivated FBS. Monocytic origin was confirmed by staining of CD45 and CD14 antigens. Cultured cells reach stable growth within 1–2 weeks and those cells were used for activation and further isolations of MPs. THP-1 cells were activated with 1 μg/ml LPS for 6 hours.Endothelial cells were isolated from human umbilical vein as described earlier . Informe9/ml, supplemented with 3 mM CaCl2 and treated with 10 μM A23187 calcium ionophore  for 60 min at room temperature.Erythrocyte-derived MPs were prepared as described by Van Der Meijden et al.  with som8 platelets, 1 x 106 monocytes, 1 or 3 x 106 THP-1 cells, 1 x 106 ECs, and 1 x 109 erythrocytes.Activated platelets were spun down by double centrifugation at 2500 g for 15 min. Culture medium from activated monocytes and endothelial cells and suspension of activated THP-1 cells were centrifuged at 400 g for 10 min and then at 2500 g for 15 min. Erythrocytes and their large fragments were removed by centrifugation at 2000 g for 15 min and at 2500 g. MPs were sedimented from obtained supernatants at 20,000 g for 30 min at 4°C and resuspended in filtered  HEPES-buffered saline  containing 1% BSA (HBS/BSA). Suspensions contained in 1 ml MPs from 5 x 10All MPs were frozen in liquid nitrogen, stored at -70°C for no longer than 6 months and thawed at 37°C just before use. Repeat freezing/thawing cycles were avoided. For procoagulant activity measurement, MPs were thawed, centrifuged for 30 min at 16,000 g, resuspended in buffer A  and concentrated to the necessary extent.MPs were thawed at 37°C, and 5 to 45 μl of suspension was added to 300 μl of annexin V binding buffer . After the addition of 2.5 μl of annexin V-FITC , the suspension was incubated in the dark for 30 min at room temperature. Control probes contained filtered HBS buffer without MPs. Standard beads with a diameter of 3 μm and known concentration  were used for counting calibration. Fifteen microliters of these beads was added to the analysed probes. Standard beads with a diameter of 1 μm  were used for sizing calibration. MPs were analysed and counted in a FACS Canto II flow cytometer . Events were acquired at a low rate mode (less than 3000 events per sec). Data acquisition and analysis were performed using CELL Quest TM software . The noise threshold was set up in the FITC fluorescence channel . In preliminary experiments, we noticed that a much lower number of MPs were counted when the noise threshold was set up in the SSC or FSC channel, which was presumably due to the elimination of a significant fraction of small MPs from the analysis. Events above fluorescence threshold were counted in the SSC/FSC (side scattering/forward scattering) window in the size gate < 1 μm . Analysis was performed until 1000 events were acquired in the gate for 3-μm counting beads. The concentration of MPs in probes was calculated as follows: number of MPs in 1 μl = (number of events in the gate < 1 μm) х (number of counting beads in 1 μl) x (dilution coefficient) / 1000. The dilution coefficient was applied upon the addition of different volumes of analysed MPs (from 5 to 45 μl) and counting beads (15 μl). The number of events (< 1 μm) in 1 μl of negative control probes (HBS buffer without MPs) was subtracted from the number of MPs in 1 μl. For all types of MPs, we observed a linear relationship between the number of counted MPs and the volume of analysed probe (5–45 μl).Artificial phospholipid vesicles composed of 80% phosphatidylcholine (PC) and 20% phosphatidylserine (PS) or 15% PS and 85% PS or 10% PS and 90% PC were prepared according to the protocol recommended by Avanti Polar Lipids with minor changes. Phospholipids dissolved in chloroform were transferred into a round-bottomed flask, dried for 30 minutes under a nitrogen current to eliminate chloroform, and hydrated in 20 mM HEPES, 140 mM NaCl (pH 7.5) buffer for 30 minutes at 55°C on a shaker. The resulting solution was treated with three freeze-thaw cycles, heated to 55°C, and forced through the extruder membrane. The pore diameter was 100 nm.Blood from healthy donors was collected into Greiner Bio-One Vacuette or Sarstedt Monovette citrate tubes. The first tube collected after the venipuncture was discarded. Platelet-poor plasma was obtained by blood centrifugation 15 min at 1600 g and additionally centrifuged 5 min at 10,000 g. The upper 90% of the volume were collected and regarded as platelet-free plasma. MPs were removed by centrifugation for 30 min at 16,000 g. Experiments were carried out on unfrozen plasma of individual donors.The procoagulant activity of MPs was studied using the Thrombodynamics test. The assay sensitivity to pre-analytical conditions and method error when different protocols was used is described in . ThromboThe appearance of clotting centres within a distant from the activator (spontaneous clots) was characterized by Tsp, the time when the plasma volume excluding clot growing from the activator clotted to 5% .2 was defined as the time of spontaneous clot appearance.To calculate a number of spontaneous clots dependence on time, the photos were converted into binary format. Black colour corresponded to regions with a liquid state of plasma, and white colour corresponded to regions with fibrin clots. The transition occurred when the light scattering intensity at a point became higher than half of the maximal light scattering intensity of a clot growing from the activator. The time point when a spontaneous clot area exceeded an arbitrarily chosen value of 0,1 mmTo characterize the growth of individual clots, we introduced the following parameters: lag-time (t lag), maximal rate of intensity increase (VImax), and the rate of coagulation front propagation from a spontaneous clot centre (Vsc). Spontaneous clots induced by low concentrations of MMPs and EMPs terminated growing early, and their light scattering intensity often did not reach half of the maximal light scattering intensity of a clot growing from activator. Therefore, we defined the lag-time as the time when the light scattering intensity in the centre of a spontaneous clot reached 2000 arbitrary units. VImax was defined as the maximum of the time derivative of light scattering intensity in a spontaneous clot centre. The size of the spontaneous clot was defined as the distance between the spontaneous clot centre and the coordinate, where the light scattering intensity from the clot was half the intensity at the centre of the clot. The clot size dependence on time was calculated until fusion of the clot with a neighbouring clot or until the end of the test. Vsc was calculated as the linear approximation of the last 10 min of clot size dependence on time.2+ solution were incubated for 5 min with TF, mixed with fX and incubated for 15 min. The reaction was stopped by EDTA addition, and the amount of fXa gained was evaluated by the cleavage rate of the substrate S 2765. PPACK removal was checked by the influence of fVIIai solution on fluorogenic substrate cleavage by thrombin.The fVIIa inactivation method was carried out as described in  with modTo determine the activation pathway from MPs, the time of spontaneous clots appearance (Tsp) in plasma supplemented with MPs of different types was measured without any inhibitors, with the addition of 200 μg/ml CTI, 100 nM VIIai (TF pathway inhibitor) or both inhibitors.The results represented as means ± standard deviation unless otherwise described. For analysis of significant differences, the Mann-Whitney test was used with significance level p = 0.05. Spearman correlation coefficient was used to evaluate association between parameters.MPs were characterized using flow cytometry as described in methods. The percentage of annexin V-positive MPs (above the threshold of the negative control) significantly varied in MPs of different cellular origins  or the TF pathway were added. The test showed that the most active MMPs, THP MPs and EMPs bear TF on their surfaces . In addiAs described above, the distribution of fibrin clots in space in this method is reflected by the light scattering intensity profile. Analysis of the time dependences of light scattering intensity profiles allows the introduction of quantitative characteristics of spontaneous clots. The corresponding dependences when MPs of different origins were supplemented to plasma are represented in VI max is by some approximation proportional to the rate of fibrin formation and consequently to the thrombin concentration . Therefore, VI max indirectly indicates activation strength. VI max in the centres of spontaneous clots induced by any type of MPs was considerably less than VI max at the surface with immobilized TF (activator) . At the If the increment in light scattering intensity in the centre of a clot depends on thrombin formed in the immediate vicinity of the activator, the clot propagation depends on thrombin formed at a distance from the activator as a result of positive feedbacks work. In this way, the parameters VI max and Vsc are largely determined by different reactions. The first characterizes the activation phase, and the second characterizes the propagation phase. The spontaneous clots size in most cases depended linearly on time . In someGrowth rates were, on average, increased with concentrations  and it w−15) for ErMPs, 0.78 (p = 5·10−23) for EMPs, 0.61 (p = 3·10−10) for MMPs, and 0.62 for THP MPs.It was previously shown that in the presence of a sufficient amount of phospholipid surface in the plasma and activation higher than the threshold, the steady-state clot growth rate over a wide range does not depend on the TF concentration , type of−9) for EMPs, 0.32 (p = 0.004) for MMPs, and 0.41 (p = 1·10−5) for THP MPs (−4) for ErMPs, -0.35 (p = 0.025) for EMPs, -0.5 (p = 0.0015) for MMPs, and 0.01 (p = 0.92) for THP MPs  for PMPs, 0.58  and stationary (Vs) growth rates of clot growing from the activator in the Thrombodynamics test at different MPs concentrations . Clot gris range . Howeveris range .7/μl of vesicles, which is approximately 20 times higher than the concentration of ErMPs causing spontaneous coagulation. Artificial vesicles did not induce spontaneous clotting at any of the tested concentrations. Both Vi and Vs reached saturation for artificial vesicles. The PS content increment from 10% to 20% led to a sharp increase in the slope of the initial linear part of the Vs dependence on the vesicle concentration from 0.24 μm/(min·μM) to 5.1 μm/(min·μM). Moreover, with an increase in PS content, the concentration at which the rates reached saturation decreased, but the saturation level did not change  because platelet membranes contain, according to various sources, from 6.7 to 12% PS –51, whilt change . The maxIt was previously shown that MPs in the plasma of patients cause the formation of spontaneous clots . In the Our method of MPs counting with flow cytometry had some differences with the standard one: instead of counting annexin-positive MPs only, we took into account all the objects less than 1 μm and higher than the fluorescence threshold (the number of objects in stained buffer was subtracted). This was done because the percentage of annexin V-positive MPs (above the threshold of the negative control) significantly varied in MPs of different cellular origins: lower than 10%, 15–20%, approximately 30%, and up to 40% for ErMPs, PMPs, MMPs and THP MPs, and EMPs, respectively. Variations in the percentage of annexin V-positive MPs could be at least partially explained by the difference in MPs size. Earlier, using the dynamic light scattering method, for the MPs sizing, we have shown in direct comparative studies that ErMPs have an average diameter of 200–250 nm, PMPs 350–400 nm, EMPs and THP MPs 400–500 nm, and EMPs 550–600 nm ,30,53. W6/μl [3/μl. The same concentration of PS + MPs is contained in approximately 300·103/μl PMPs, 530·103/μl ErMPs, 180·103/μl MMPs and 130·103/μl EMPs. Since ErMPs, EMPs, and MMPs normally make up less than 20% of the total MPs concentration [3/μl for ErMPs and 200·103/μl for EMPs and MMPs can serve as a rough estimate of the upper physiologically achievable concentrations.It is difficult to evaluate the physiological range of MPs concentrations by comparing the absolute values of concentrations with literature data, since even when measuring concentrations using flow cytometry, depending on the measurement protocol and the cytometer used, normal MPs concentrations may vary from hundreds to 106/μl ,54–57. TThe MPs activity in coagulation activation, estimated from the minimal concentration causing spontaneous clotting, was determined primarily by TF on the surface of the MPs, which corresponds to the results from comparing the same MPs in the recalcification test in our previous works –31 and i3−107 MPs. This led to the assumption that coagulation centres may be caused not by MPs themselves but by some larger residual fragments of cells or MPs aggregates. In addition to the fact that the ratio of the number of clots to the number of MPs is greater for TF-bearing MMPs and EMPs, coagulation begins over the whole volume of the chamber, not just from the individual centres, when certain concentrations of MMPs and EMPs are reached. This is probably due to the participation of a much larger fraction of TF+MPs than 1/103 in coagulation activation due to the effect described by Kastrup et al., when the convergence of several centres with individual subthreshold activation leads to overcoming the activation threshold [The ratio of the number of coagulation centres to the number of MPs in a chamber is on the order of one clot per 10hreshold .3/μl, there were no significant differences in the effect of PMPs, ErMPs, and EMPs on the clot growth rate. Perhaps we could not identify it due to deviations between the MPs samples isolated from the cells of different donors and between the donor plasmas in which the titrations were performed. However, in view of the fact that the differences in the effects of MPs on the rates were weak and the protein composition in MPs originating from different cells is also different, the effect of MPs on rates is probably determined by the lipid composition of membranes to a larger extent than by the protein composition. Rather inactive at low concentrations, ErMPs at 500·103/μl increased the clot growth rate to a significantly higher level in comparison with 500·103/μl PMPs. The growth rate of spontaneous clots induced by ErMPs also significantly exceeds the growth rate of clots induced by PMPs, EMPs and MMPs. The high ErMPs activity in coagulation propagation is consistent with the data of van der Meijden, where the same conditional PS activity, which was measured by the prothrombinase activity on MPs samples, corresponded to a 3-fold higher activity of PMPs compared with that of ErMPs [3/μl, and an increase in the PS content in artificial vesicles did not change the saturation level. Other components of phospholipid composition may determine ErMPs activity. Notably, ErMP concentrations could be underestimated due to their small size. Thus, the difference between ErMPs and PMPs can partially be accounted for by higher ErMPs concentrations, and the attainability of 500·103/μl ErMPs in physiological conditions is questionable.In the range up to 200·10of ErMPs . ErMPs aA significant distinction of TF-bearing MPs is that there are concentrations at which MPs are able to induce some separate clots, but the growth of these clots stops in the first 10–15 min of a test. Previously, Oliver et al. concluded, by means of the thrombin generation test, that TF-bearing MPs participate in coagulation activation but not in coagulation propagation . It is lSummarizing the results, we can say that MPs derived from different cells play a qualitatively different role in coagulation activation and propagation: TF+ MMPs have a strong activating ability and have a very weak effect on coagulation propagation; on the contrary, contact activation from PMPs and ErMPs in normal plasma is weak, and these MPs, firstly, contribute coagulation propagation. Although an increased concentration of MPs is usually regarded as a risk of thrombosis, MPs that have weak activating capacity but support coagulation propagation in some cases can play a positive role, for example, by reducing blood loss during surgery or mitigating the clinical manifestations of haemophilia . EndotheS1 Movie(PPTX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.S1 FigThe number of clotting centres was recalculated to represent what it would have been if the plasma volume had not been decreased by clots that appeared earlier (N corrected). Clotting was induced in normal MP-depleted plasma by supplementation with (A) platelet MPs, (B) erythrocyte MPs, (C) endothelial MPs, and (D) monocyte MPs. Different curves correspond to different MPs samples and different concentrations. Legends identify labels of MPs samples and the concentration used in the experiment.(TIF)Click here for additional data file.S2 FigClotting was induced in normal MP-depleted plasma by supplementation with (A) platelet MPs, (B) erythrocyte MPs, (C) endothelial MPs, and (D) monocyte MPs. Legends identify labels of MPs samples.(TIF)Click here for additional data file.S3 FigN = 5 at different concentrations are connected with lines, and symbols of different types and colours correspond to different MPs samples.Data are represented for (A) platelet MPs, (B) erythrocyte MPs, (C) endothelial MPs, and (D) monocyte MPs. Dots correspond to individual tests, the mean values of t(TIF)Click here for additional data file.S4 FigClotting was induced in normal MP-depleted plasma by supplementation of (A) platelet MPs, (B) erythrocyte MPs, (C), (E), (G) endothelial MPs, monocyte MPs, THP MPs respectively in conditionally “low” concentrations, (D), (F), (H) endothelial MPs, monocyte MPs, THP MPs respectively in conditionally “high” concentrations. Time dependence of the light scattering intensity of clots growing from activator are drawn with dashed lines, and those of the light scattering intensity in the centre of spontaneous clots are drawn with solid lines.(TIF)Click here for additional data file.S5 FigThe maximal light scattering intensity dependence on the concentration of (A) platelet MPs, (B) erythrocyte MPs, (C) endothelial MPs, (D) monocyte MPs and (E) THP MPs. The maximal light scattering intensity in the centres of spontaneous clots is denoted with opened symbols, and that of clots growing from activator is denoted with filled symbols.(TIF)Click here for additional data file.S6 FigData represent (A) platelet MPs, (B) erythrocyte MPs, (C) endothelial MPs, (D) monocyte MPs and (E) THP MPs. Dots correspond to individual tests, the mean values of VI at different concentrations are connected with lines, and symbols of different types and colours correspond to different MP samples.(TIF)Click here for additional data file.S7 FigClotting was induced in normal MP-depleted plasma by supplementation (A) platelet MPs, (B) erythrocyte MPs, (C), (E), (G) endothelial MPs, monocyte MPs, THP MPs respectively in conditionally “low” concentrations, (D), (F), (H) endothelial MPs, monocyte MPs, THP MPs respectively in conditionally “high” concentrations. Time dependences of clots growing from activator sizes are drawn with dashed lines, and those of spontaneous clot sizes are drawn with solid lines.(TIF)Click here for additional data file.S8 FigData represent (A) platelet MPs, (B) erythrocyte MPs, (C) endothelial MPs, and (D) monocyte MPs. Dots correspond to individual tests, the mean values of VI at different concentrations are connected with lines, and symbols of different types and colours correspond to different MP samples.(TIF)Click here for additional data file.S9 Fig(A) Data are represented for platelet MPs, (B) erythrocyte MPs, (C) endothelial MPs, (D) monocyte MPs and (E) THP MPs.(TIF)Click here for additional data file.S10 Fig(A) Data are represented for platelet MPs, (B) erythrocyte MPs, (C) endothelial MPs, (D) monocyte MPs and (E) THP MPs.(TIF)Click here for additional data file.