**NOVEL IMPLICATIONS OF EXOSOMES IN DIAGNOSIS AND TREATMENT OF CANCER AND INFECTIOUS DISEASES**

Edited by **Jin Wang**

#### **Novel Implications of Exosomes in Diagnosis and Treatment of Cancer and Infectious Diseases**

http://dx.doi.org/10.5772/65504 Edited by Jin Wang

#### **Contributors**

Sin-Yeang Teow, Suat-Cheng Peh, Mustafa Kotmakçı, Gülşah Erel Akbaba, Jin Wang, Lucia Paolini, Andrea Zendrini, Doris Ricotta, Paolo Bergese, Giuseppe Di Noto, Bárbara Adem, Sonia Melo, Muhammad Nawaz, Farah Fatima, Jeremy Squire, Duc Bach Nguyen, Thuy Ly Thi Bich, Ingolf Bernhardt, Gulfaraz Khan, Waqar Ahmed, Pretty Philip

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Novel Implications of Exosomes in Diagnosis and Treatment of Cancer and Infectious Diseases Edited by Jin Wang p. cm. Print ISBN 978-953-51-3335-3 Online ISBN 978-953-51-3336-0 eBook (PDF) ISBN 978-953-51-4745-9


*2.3.1 Assay for siderophore production*

Siderophore productions by antagonistic bacterial isolates were tested qualitatively as described by Alexander and Zuberer [56]. 5 μl of antagonistic bacterial cell suspension (5 � 108 CFU/mL) was spot inoculated on Chrome azurol S (CAS) agar plate. The plates were then incubated at 30°C for 5 days. Development of yelloworange halo zone around the bacterial growth was considered as positive for siderophore production. Experiment was performed with a completely randomized design with 3 replications. CAS agar was prepared from 4 solutions. Solution 1 (Fe-CAS indicator solution) was prepared by mixing 10 mL of 1 mmol L�1 FeCl3.6H2O *Potential Role of Rice Plant Growth Promoting Phylloplane and Rhizospheric Bacteria… DOI: http://dx.doi.org/10.5772/intechopen.99854*

(in 10 mmol L1 HCl) with 50 mL of an aqueous solution of CAS (1.21 g L1). The resulting dark purple mixture was added slowly with constant stirring to 40 mL of aqueous solution of hexadecyl trimethyl ammonium bromide (1.821 g L1). The yielded of dark blue solution which was autoclaved, then cooled to 50°C. The entire reagent was freshly prepared for each batch CAS agar. Solution 2 (buffer solution) was prepared by dissolving 30.24 g of piperazine-N, N-bis (2-ethane sufonic acid) (PIPES) in 750 mL of salt solution containing 0.3 g K2PO4, 0.5 g NaCl and 1.0 g NH4Cl. The pH was adjusted to 6.8 with 50% (w/v) KOH, and water was added to bring the volume 800 mL. The solution was autoclaved after adding 15 g of agar then cooled to 50°C. Solution 3 contained 2 g glucose, 2 g mannitol, 493 mg MgSO4.7H2O, 11 mg CaCl2, 1.17 mg MnSO4.2H2O, 1.4 mg H3BO3, 0.04 mg CuSO4.5H2O, 1.2 mg ZnSO4.7H2O, 1.0 mg NaMoO4.2H2O in 70 mL water, autoclaved, cooled to 50°C. Solution 4 was 30 mL filter sterilized 10% (w/v) casamino acid. Finally, solution 3 added to solution 2 along with solution 4, solution 1 was added last, with sufficient.

#### *2.3.2 Assay for indole acetic acid (IAA) production*

IAA production of antagonistic bacterial isolates were carried out as per the procedure described by Patten and Glick [57]. Every isolate was grown in LB media supplemented with (0.005%) L-tryptophan and incubated in shaker at 30°C with 160 rpm for 48 h. Then bacterial culture was centrifuged at 8000 rpm for 15 min and 1 mL culture filtrate was mixed with 4 mL salkowski's reagent (1.5 mL FeCl3.6H2O 0.5 M solution in 80 mL 60% H2SO4) and the mixture was incubated at room temperature for 30 min, presence of pink color indicate qualitatively that isolate produced IAA. Formation of pink color indicated the presence of indoles [58].

#### *2.3.3 Phosphate solubilization assay by antagonistic bacterial isolates*

Phosphate solubilization was determined according to the method of Azman et al. [59]. Sterile filter papers (5.0 mm) were soaked in antagonistic bacterial cell suspension (5 108 CFU/mL) was dispensed using pipette onto sterile filter paper (6.0 mm) that was placed on National Botanical Research Institute's phosphate (NBRIP) agar plate (Glucose (10 g/L), Ca3 (PO4)2 (5 g/L), MgCl2.6H2O (5 g/L), MgSO4.H2O (0.25 g/L), KCl (0.2 g/L), (NH4)2SO4 (0.1 g/L), Bacteriological Agar (15 g/L) [60]. The plates were then incubated at 28°C for 7 days. Phosphate solubilization was assessed by observing the clear halo zone. The experiment was performed with a completely randomized design (CRD) with 3 replications.


*4.2. Inter-Device Comparisons for Sleep Stages and Metrics*

Table 2 shows the summary statistics for all device-produced metrics and SRSMs. TSD was reported by all devices and by the participants themselves (i.e., as part of SRSM). Figure 2A shows a correlation matrix of TSD. The correlations were generally medium to weak ( < 0.7 for all pairwise comparisons), although surprisingly the correlations of the SRSM with device estimates were on par with correlations among the devices themselves. Figure 2B shows a REM sleep (in sec) cycle correlations across the Oura, Hexoskin and Withings (Fitbit did not report an estimate of REM sleep). The correlation between Oura and Withings was highest at = 0.44, while Oura and Hexoskin had the lowest correlation ( = 0.22). Figure 2C shows Kendall's rank correlation across overall sleep stages for Withings, Hexoskin, and Oura (see Section 3.9). All of these assessments were statistically significant at the *p* < 0.05 threshold. We report the *p* values from these analyses in Supplemental S5.

**Figure 2.** (**A**) A correlation matrix of total sleep duration (TSD) (in seconds) by device and self-reported estimation (i.e., self-reported sleep metrics (SRSMs)) with *p* value significance indication (\* *p* < 0.1; \*\* *p* < 0.05; \*\*\* *p* < 0.01). Each point represents data from each night for each participant. The plots in the diagonals of A and B reflect the distribution of sleep metric of interest (TSD and REM, respectively). (**B**) A REM sleep (in sec) correlation across the Oura, Hexoskin, and Withings devices with *p* value significance indication (same as above). The Fitbit was excluded, as it does not track REM vs. NREM sleep. for each individual device. The plots in the bottom left of A and B show the trend line with 95% confidence intervals between devices. (**C**) A correlation matrix of overall sleep stages (awake, NREM, and REM) between Oura, Hexoskin, and Withings devices (Fitbit does not differentiate between NREM and REM) with *p* value significance indication (same as above).

**Table 2.** Summary metrics of device data and SRSMs. All units are in hours except wakeups which is in occurrences and efficiency (no units). Sleep efficiency is a metric to track percentage of time in bed while asleep. TSD is total sleep duration which is similar to start-end duration and similar features were utilized that included latency and other measures.


In the TIMESAT program we used the Savitzky-Golay algorithm and extracted 13 attributes describing the spectro-temporal profile of mangroves for each season (Table 3). The filtering process preserved the trend of the original NDVI data and the fitted curve depicted the seasonal variation in forest characteristics; it, therefore, provides reliable data for the extraction of phenological information. For the mid-section of our study region (Rocky Dam Creek/Cape Palmerston National Park, Figure 3) we found the SOS to be December, EOS to be September/October, and the LOS to be 10 months. POS varied through the timeseries (February to July): 1999–2003 in June/July; 2004 in February; 2005–2006 in May; 2007–2008 in June/July; 2009–2010 in May/June; 2011–2012 in May; 2013 in April; 2014–2020 in June/July (Figure 6). There is a marked upward movement in the seasonal trend decomposition of NDVI data from the summer of 2009 to 2020 (Figure 6). Notwithstanding the length of the season gradually becoming shorter, the long integral (total productivity in the annual cycle) increased (Figure S4 in the Supplementary Materials). The trend in the peak NDVI value (maximum recorded NDVI value in the time series) increased from the summer of 2009 (Figure 7 and Figure S5 in the Supplementary Materials). In evergreen areas the small integral (productivity within the growing season) may be small even if the total vegetation production is large. The small integral and amplitude (difference between the maximal value and the base level) both declined from 2009 in our study region due to a positive change in the base value increasing the amount of green vegetation for this period (Figures S6 and S7 in the Supplementary Materials). We found a significantly high correlation (*p* < 0.001) between the phenology metrics from TIMESAT through the time series particularly season length and duration of the peak value (Pearson correlation coefficient r value = −0.91, Adjusted R2 = 0.8182), and season (year) and season length (Pearson correlation coefficient r value = −0.91, Adjusted R2 = 0.8179) indicating a dynamic trend in the cyclical behaviour of tropical mangrove forests (Table 4).

Except for the northern region, Central Queensland is largely decoupled from southern monsoonal influences and exhibits highly variable phenology that is rainfall pulse driven. Monthly rainfall was 0–800 mm at our study site, Rocky Dam Creek/Cape Palmerston National Park (Figure 3). Australia's millennium drought of 2001–2007 is reflected in the rainfall pattern and the reduced mean NDVI trendline (Figure 8). Considerable decreases in NDVI occurred in 2001, 2002, 2004, and 2008 that are consistent with the trend of monthly rainfall. In addition to the general trend, phenological characteristics and rainfall patterns correspond with major climate events such as the EL Niño-Southern Oscillation (ENSO). The increase in precipitation from the summer of 2009 in the mid-section of our study region produced a concomitant increase in photosynthetically active growth and biomass production in mangrove forests at this site. The extreme and extended rainfall event from La Niño that occurred over Queensland in the summer of 2010/2011 was reflected in the net seasonal photosynthetic rates for this period. Further, a strong La Niño event is apparent in the Southern Oscillation Index (SOI, the difference in surface air pressure measured between Tahiti and Darwin, northern Australia) that spikes in the 2010/11 summer (Figure S8 in Supplementary Materials). In addition, a severe tropical cyclone (Tropical Cyclone Yasi, Category 5) brought an intense rainfall event in February 2011 to the entire region [76]. A relatively high rainfall pattern continued until mid-Autumn of 2017 when a severe tropical cyclone (Tropical Cyclone Debbie, Category 4) impacted the coast and destroyed coastal vegetation [77]. Recovery from the cyclone is evident in 2018 when the mean NDVI reached 0.9 at Rocky Dam Creek/Cape Palmerston National Park (Figure 8).

 of our study region, Startt.:

photosynthesis

 in the

**Table 3.**

start of season is the beginning of measurable

Savitsky-Golay

 filter with

phenometrics

 extracted from TIMESAT for the Landsat dense time series of mangrove forest in the mid-section

photosynthesis

 in the mangrove canopy (month of the year); Endt.: end of season is the end of measurable

**Table 4.** Response and explanatory variables used in the linear regression models with Pearson correlation coefficient r values and adjusted R2 at significantly high values (*p* < 0.001).

**Figure 8.** Interpolated, filtered NDVI and rainfall trendlines for the mid-section of our study region, Rocky Dam Creek/Cape Palmerston National Park, rainfall data gathered from Koumala Station (station number: 33038).

> In comparison to the mid-section, the northern section of our study region, Repulse Bay, displays a generally higher level of mean NDVI and less temporal variability (Figure 3 and Figure S9 in the Supplementary Materials). Monthly rainfall was 0–900 mm across the time series. The POS occurred in diverse months (May to August), exemplifying a different

climate pattern at this site. The northern site exhibited a NDVI decrease in 2002 similar to our mid-section site; however, the decrease occurred in the winter of 2002 (at the northern site it was early summer) and was less pronounced (Figure 9). From 2003–2020 a steady increase in NDVI activity was apparent. High rainfall peaks occurred from 2007–2012, followed by 6 years of moderate rainfall, escalating to a monsoonal trough event in January 2019, then falling to moderate rainfall conditions in 2020. A tropical cyclone (Tropical Cyclone Jasmine, Category 2) affected the northern section in February 2012. A substantial decrease in NDVI was apparent in the winter of 2017 that was not reflected in the rainfall regime but possibly occurred from the influence of cloud cover.

**Figure 9.** Interpolated, filtered NDVI and rainfall trendlines for the northern section of our study region, Repulse Bay, rainfall data gathered from Lethebrook Station (station number: 33041).

> The southern section of our study region (Herbert Creek bordering Shoalwater and Corio Bays Area Ramsar Wetland) exhibited drier conditions than the other sections (Figure 3 and Figure S10 in the Supplementary Materials). A shorter season is apparent with POS occurring in May to July. The NDVI trendline occurred at a lower level than the other sites, but with less temporal variability. Monthly rainfall was 0–390 mm across the time series. Substantial rainfall years in 2010–2011 corresponded with the La Niño pattern of precipitation throughout Queensland but with less intensity (Figure 10). A large decrease in NDVI occurred in 2007 after prolonged low rainfall conditions. The NDVI trendline at this site reflects the El Niño year of 2006/2007 with a low rainfall level and the La Niña year of 2007/2008 with a high rainfall level. POS consistent NDVI years over the 3 sections of our study region were in 2010 with POS in May/June, 2015 with POS in June, and 2019 with POS in June/July.

**Figure 10.** Interpolated, filtered NDVI and rainfall trendlines for the southern section of our study region, Herbert Creek bordering Shoalwater and Corio Bays Area Ramsar Wetland, rainfall data gathered from St. Lawrence Station (station number: 33065).

#### **4. Discussion**

#### *4.1. Change Dynamics in Mangrove Forests*

With one of the richest flora in the world comprising thirty-nine species, old-world tropical mangroves found in the Indo-Pacific including Australia are notable for their attributes of species diversity, abundance, and succession and they are therefore considered to be the most dominant and important mangroves globally [78,79]. We examined decadal changes in mangrove forest cover in Great Barrier Reef catchments using a machine learning approach with the online cloud-computing platform, GEE. With GEE, we compiled a dense time series of NDVI-based Landsat imagery, inputted the time series to the TIMESAT program and investigated the phenodynamics of mangroves at different locations in our study region. This work has relevance to the maintenance of biodiversity, conservation of estuarine wetlands and mangroves, and the role of coastal forests in climate change. To our knowledge, this is the first study to examine and compare the phenology of tropical mangroves with remote sensing time series across different sites adjacent to the Great Barrier Reef, on the east coast of Australia.

We demonstrated that the mangrove extent derived from the RF classification in our study area decreased by 1480 hectares (−2.31%) over the three-year decadal window, 2009–2019. Our findings corroborate a previous study of mangrove deforestation and degradation that described a total of 11,285 hectares lost worldwide from episodic disturbance (climatic events such as dieback, disturbance mortality, climatic extremes, sea level variability) between 2000–2016 with 8004 hectares from Australia [80]. Global annual mangrove loss rates through deforestation, determined from remote sensing datasets, are estimated to be between 0.26% and 0.66% [81]. Decoupling human-induced mangrove mortality such as degradation, from declines due to episodic disturbance events is inherently difficult to achieve [82]. Moreover, spatial inconsistencies can be present as tropical coastal zones are affected by clouds and atmospheric contamination due to their proximity to oceans and the climate regime [83]. Remote sensing-based time series approaches for change detection require a series of images captured continuously throughout a time period. Thus, there is a need for substantially extra and regular image acquisitions over the area of interest [84]. As with previous studies on mangrove change dynamics [26,85,86], we found that the GEE

platform provided efficient data preparation and processing capability for classification and land cover mapping with spectral, auxiliary, and phenology variables.

VIs, particularly NDVI are often used as variables in research related to regional remote sensing forest assessments and shown to be linked not only to canopy structure and leaf area index (the area of single sides leaves per area of soil) but also to canopy photosynthesis [29]. The contrast between strong near-infrared reflectance and low red reflectance of green vegetation is the basis for most VIs. NDVI, and related VIs are functional variants of the simple SR index [87]. We considered four VIs as predictor variables in our classification, SR, NDVI, MNDWI, and GCVI. VIs are commonly used in land cover classification as they increase the classification accuracy, for example: [15] selected NDVI and [88] chose SR to quantify mangrove changes over the Western Arabian Gulf and United Arab Emirates, respectively; [26] generated MNDWI for mapping mangrove extent in China. A major limitation of NDVI however, is that it saturates when mapping high amounts of biomass and cannot distinguish dense vegetation [87]. Another essential point is that NDVI is sensitive to soil, atmospheric effects, and aerosols [29,59]. Furthermore, in open mangrove canopies tidal stage can produce NDVI variations when there is no change in the vegetation amount or condition. Whereas GCVI has been developed as a proxy for chlorophyll content with Landsat surface reflectance data in crops [89], to our knowledge no previous authors have incorporated GCVI for mapping tropical forests or wetlands.

#### *4.2. Mangrove Phenology*

Our multitemporal analysis based on mean-NDVI Landsat time series (from a representative region in the study area, that is, the mid-section for approximately each month in the study period 1999–2019) revealed a strong seasonality in mangrove forests. Specifically, we found a high growth in winter and a trough of growth in late spring to summer. Such cyclical effect in seasonality has been proposed for mangroves in other tropical regions [31] and can be explained by the fact that local climate properties affect the resource availability of water through seasonal influences of tides, rainfall, and river flows and with variation in the strength of ENSO cycles [90]. Considering all available Landsat 8 imagery we demonstrated that the Fourier-style harmonic regression method provides an explicit depiction of temporal shifts in a region with substantial climate variability. Although rarely met in practice, a stringent requirement of investigating continuous intra-annual dynamics of vegetation phenology, is to acquire error-free remotely sensed data at regular time instants [91], and we successfully achieved this goal. The start, length, peak, and end of season are phenological markers that indicate the photosynthetic activity over the growing season. Specifically, we found SOS (start of the season) to be December, EOS (end of the season) to be September/October, and the LOS (length of the season) to be 10 months, regulated by the precipitation pattern in the study region. We suggest that synchronization of new leaf growth with dry season (winter) shedding, shifts canopy composition toward younger, higher radiometric green-efficient leaves, explaining obvious seasonal increases in ecosystem photosynthesis in our study region [92,93]. For instance, studies conducted on *Xylocarpus* spp. phenology-related litter fall in Australia demonstrate that leaf shedding occurred in the mid to late dry season on the tropical east coast [94] and *Avicennia marina* displayed low leafing and shedding rates throughout the year with a seasonal increase in winter [49]. The phenology of *Rhizophora stylosa* in both Northern and Eastern Australia was documented by [95] who detected lower photosynthetic values during the wet season and higher values during the dry season with Landsat imagery, in accordance with our study. Tropical regions experience variable climate conditions with frequent cloud cover and high temperatures during spring and summer months that may limit the capacity of mangroves to photosynthesize. Consequently, greater NDVI values occur in the winter months under cooler temperatures and more radiance. Conversely, a few studies have reported lower spectral index values during the dry season and higher values during the wet season [96,97].

The mismatch that we uncovered between the gradual contraction in the season length, and the increase in seasonal productivity can be explained by mangrove response to the resource availability of water and perhaps nutrients in tropical, coastal soils. Higher rainfall conditions as occurred from the summer of 2009 following six years of drought, produced an associated increase in photosynthetically active growth and biomass production in mangrove forests. High rainfall increases mangrove growth rates by helping to supply sediments and nutrients thereby stimulating productivity [97]. According to [98] water availability influences several ecological processes including vegetative cover, species composition, and community biomass of tidal wetlands. Freshwater is essential for mangroves to maintain the turgidity of cells and tissues, mechanisms of cell augmentation, and stomata regulation [99]. Consequently, in drought years resource-use strategies become geared towards maximum survival as opposed to rapid biomass gain.

#### TIMESAT Analysis

Our TIMESAT-based approach with the adaptive Savitzky-Golay filter generated smoothed data for each time step, and seasonality parameters for each identified growing season over the time series. To provide a clear and interpretable vegetation signal we chose to implement the Savitzky-Golay filter as it iteratively tightens the search window in order to capture very rapid increase or decrease in the data and follows local variations in the seasonal curve more closely [100]. Our findings that a disproportionately bimodal pattern is evident in the time series e g., late summer and winter peaks, confirm studies in phenologybased litterfall conducted on the east coast of Australia with *Avicennia marina* [101], which postulated that bimodality in leaf production is interrelated with position in the inter-tidal zone and frequent tidal inundation. More importantly, abiotic factors such as hydrology and photoperiod and biotic factors such as the timing of the reproductive cycle, appear to be linked to mangrove vegetative phenology [102].

The seasonal dynamics derived using TIMESAT, with mangrove NDVI time series as input, showed differences in the spatio-temporal patterns of growth dynamics. While this is the case, the three different sections of our study region are analogous in that all sections are tropical estuarine ecosystems with the same mangrove species such as the stilted mangrove *Rhizophora stylosa*, the grey mangrove *Avicennia marina*, the yellow mangrove *Ceriops tagal*, and the river mangrove *Aegiceras corniculatum*. Macroclimatic drivers such as rainfall and temperature regimes are recognized for their ability to limit and influence processes within coastal-estuarine wetlands [103]. The premise that rainfall has the greater importance in affecting biodiversity, biomass, and phenology of tropical mangrove vegetation [49] is supported by our study. For example, our observation that peaks and troughs in the NDVI trendline correspond with the trend of monthly rainfall and are magnified by climate events occurred at all three study sections, albeit with differential affects. Accordingly, we found the northern site that was also the wettest had the highest NDVI values and the southern site that was the driest had the lowest NDVI values. The site with the greatest variability in the trendline was the mid-section (Rocky Dam Creek/Cape Palmerston National Park). The mid-section was also the site with moderate monthly rainfall indicating that mangrove growth variability responds to localised patterns.

We also found that the La Niña event of 2010–2011 and its associated rainfall changes impacted vegetation growth, increasing NDVI values at all section sites. Another essential point is that the Southern Oscillation Index spikes in La Niño years, particularly 2010–2011. Indeed, a study conducted at Corio Bay in the Ramsar wetland (a generally euhaline site) in the El Niño year of 2006–2007 reported salinity reached exceedingly high values (up to 40 parts per thousand) towards the mouth. Conversely, at Corio Bay in the La Niña year of 2007–2008 salinities down to inordinately low values (23 parts per thousand) were recorded, which were associated with flooding of the adjacent Fitzoy River and local catchment run off [43]. Notably, our analysis shows that extreme climate-related salinity values such as these, are reflected in the mangrove NDVI trendline where a trough occurs in 2006–2007 followed by rise in 2007–2008 at our southern section (Herbert Creek bordering Shoalwater

and Corio Bays Area Ramsar Wetland). Our results are particularly concerning for tropical mangrove forests as they highlight contemporary research on the intensification of ENSO in the coming decades with climate change [10,82].

Episodic disturbance events such as tropical cyclones and climatic extremes are purported to be responsible for approximately 80% of global mangrove loss from natural causes since 1990 [82]. In addition to coastal damage from strong winds [104], impacts from tropical cyclones include high rainfall that exacerbates the hydrological and hydrodynamic conditions [105]. Moreover, the anomalies in rainfall induce lower salinity, greater turbidity, as well as higher dissolved nutrients and phytoplankton biomass in the coastal waters when compared with typical conditions [106]. The frequency and intensity of cyclones and storms has increased as a consequence of greater sea-surface temperature, with further escalation predicted under most climate change scenarios [107,108]. Greater cyclone frequency has compounding effects on mangrove forests and may extend or halt mangrove recovery [107]. Our findings show decreases in the NDVI values associated with the occurrence of tropical cyclones in each section of our study region. Tropical Cyclone Yasi impacted all three sites in 2011, Tropical Cyclone Jasmine destroyed the northern section site in 2012, and Tropical Cyclone Debbie devastated the mid-section and southern section sites in 2017, which was the highest precipitation event over the time series.

#### *4.3. Limitations of the Study*

Remote sensing data and tools are fundamental methods for measuring land cover, but there are key drawbacks in the change detection of wetlands such as mangrove forests. The need for assessing accuracy of a map generated from any remotely sensed product has become a universal requirement and an integral part of any classification project. The user community needs to know the accuracy of the classified image data being presented [109] otherwise the classification lacks robust validation which could have serious implications for some users and may lessen their confidence in remote sensing as a source of land cover data. Accuracy assessment (1) allows self-evaluation and to learn from mistakes in the classification process; (2) provides quantitative comparison of the classified maps; (3) ensures greater reliability of the resulting maps/spatial information. Therefore, we presented robust validation methods that detail the user's and producer's accuracies of change with Kappa coefficient. Furthermore, we compared the mangrove forest classified maps with Digital Earth Australia high-resolution imagery (at the continental-scale) for visual refinement and to evaluate the performance of our method [56]. The second key drawback is that there can be a similarity of spectral reflectance in the NIR and SWIR regions between mangrove forest and the mixture of other natural vegetation and water at high tide compared to that obtained at low tide [26,86,110]. The third drawback is that quantification is difficult in long-term studies because inconsistencies and gaps in data collection due to observation error are common [111]. The fourth drawback is that temperate climates use simple spring indicators and measures related to 1 January that are not appropriate for tropical phenology because of the circularity of the data. Thus, tropical phenological approaches necessitate taking account of data circularity, be flexible, quantitative, and attribute confidence to conclusions [112]. Finally, a key drawback is that in similar climate conditions, genotypes of mangrove species spread across a study region may vary enough to exemplify different phenophases. In addition, although mangroves are highly specialized and occupy unique and heterogeneous suites of habitats within Australia, they are not a genetic entity but an ecological one [98,112,113]. Consequently, our study has accessed nearby weather stations to our section sites such that localised and subtle climate variations (known to influence phenophases) in location are recorded, and this has reflected in the timeline signals.

#### *4.4. Implications for the Conservation of Mangrove Forests*

Growing evidence of mangrove's ability to sequester and store carbon has brought attention to their conservation value from international climate change policy and decision makers [114]. Although estuarine monitoring is almost completely absent at Shoalwater and Corio Bay Areas Ramsar Wetland, it is critical to establish a monitoring program to provide baseline assessments of vegetation seasonality in relation to weather patterns and detect responses to extreme events. The Ramsar Convention on Wetlands is an intergovernmental treaty with Parties to the Convention from 171 countries. The purpose of the Ramsar Convention is to promote wetland conservation and wise use to ensure that the benefits of wetlands contribute towards meeting the UN Sustainable Development goals, Aichi Biodiversity Targets, Paris Agreement on Climate Change, and other related international commitments [115]. The United Nations' 2030 Agenda for Sustainable Development together with SDGs require the monitoring and mapping of mangroves through the sustainable development indicator 6.6.1: "change in the extent of water-related ecosystems over time" [11]. Monitoring of mangrove forests is critical to preserve the ecosystem services they provide such as pollution control, detoxification, and regulation of natural hazards. Hydrologic conditions, land use, and management are important drivers of coastal vegetation communities with many land use changes developing over decades. So far, studies on tropical forests and mangrove cover with phenodynamics have been limited by the unavailability of suitable remote sensing imagery. Dense time series of satellite images has allowed a comprehensive understanding of mangrove phenology and its relation to climate factors across different sites. Our results are of relevance for conservation strategies of tropical coastal wetlands in Australia that account for foundation species and their seasonal responses to climate drivers.

#### **5. Conclusions**

In this paper, we demonstrated an operational method to (1) map mangrove forests in Central Queensland, Australia using an NDVI time series, which was built in GEE from Landsat imagery, and (2) examine seasonal and inter-annual changes in mangrove phenology extracted with the TIMESAT program, and linked to natural and extreme climate variability. Surprisingly, estuarine monitoring has been absent in the coastal ecosystems of Shoalwater and Corio Bay Ramsar Wetland. Mangrove forests in this southern section of our study region are bounded by an internationally recognized Ramsar Wetland Treaty and therefore changes in their ecology are important for Australia's reporting commitments to the Ramsar Convention. Policy guidelines of the Convention underline that urgent action is needed to raise awareness of the benefits of wetlands, with greater safeguards for their survival. We propose that a regulated monitoring program be instigated so that information can be gathered on the change dynamics and seasonality of estuarine communities and mangroves under threat from episodic disturbances.

Monitoring is essential, ideally before and particularly after an extreme event, to improve our knowledge of the connections among climate, weather, and biodiversity. Our objective to acquire long-term and spatially extensive data was due to the highly variable nature of biological data, and the need to examine periodical events at different locations. The detection of responses to extreme events is important to improve understanding of the connections between climate, extreme weather events, and biodiversity. Such knowledge is essential to inform conservation management attempts to mitigate the impacts of extreme events with ongoing climate change.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/rs13153032/s1, Figure S1: Annual sea surface temperature anomaly for the Great Barrier Reef (1999–2020) from Australian Bureau of Meteorology using NOAA ERSST V5 gridded data, Figure S2: Annual rainfall anomaly for Queensland (1900–2020) from Australian Bureau of Meteorology, Figure S3: Linear regression detrended time series of all available Landsat 8 imagery with NDVI for the study region, Bowen to Shoalwater and Corio Bays Area Ramsar Wetland, Central Queensland, Figure S4: Length of season and long integral (total productivity in the annual cycle) in the time series for our study region, Figure S5: The peak NDVI value (maximum recorded NDVI value in the time series) and short integral (the difference between the function describing the season and the base level from season start to end) in our study region, Figure S6: Amplitude (the range between the

maximum NDVI and the base level or the maximum increase in canopy photosynthetic activity above the baseline) and the short integral (the difference between the function describing the season and the base level from season start to end) in our study region, Figure S7: Season year (1999–2020) with seasonal metrics from the Savitzky-Golay filter in TIMESAT, Ampl. (amplitude), L.deriv. (growth rate). R.deriv. (rate of senescence), Figure S8: Southern Oscillation Index and rainfall trendline for the mid-section of our study region, rainfall data gathered from Koumala Station (station number: 33038), Figure S9: Mangrove forest seasonal trend decomposition in TIMESAT with Landsat NDVI data and dense time-series 1999–2020, northern section of our study region, Repulse Bay, black line indicates the seasonal trend, grey dots display the start and end of the growing season, Central Queensland, Figure S10: Mangrove forest seasonal trend decomposition in TIMESAT with Landsat NDVI data and dense time-series 1999–2020, southern section of our study region, Herbert Creek bordering Shoalwater and Corio Bays Area Ramsar Wetland, black line indicates the seasonal trend, grey dots display the start and end of the growing season, Central Queensland.

**Author Contributions:** S.R.P. and D.A.C. jointly conceived and designed this study. H.P.P. provided input into the contents of the paper, suggestions for analysis and editing of the draft and final versions. D.A.C. processed and analysed the data and led the writing of the paper with all authors making contributions. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


*3.5. Anthocyanins*

The major anthocyanin, cyanidin-3-*O*-rutinoside (c-3-rut), was 2.27 mg g−1 DW at harvest (Table 2). In the controls, it decreased gradually but significantly during storage, reaching the levels of approximately 1.13 mg g−1 DW on day 21, and remained almost stable thereafter. The two-way analysis from day 7 showed the significant effect of treatment and days, but not their interaction, with controls exhibiting the lowest values. Values of G25-treated fruit decreased from 7 d up to 14 d, reaching 1.15 mg c-3-rut g−1 DW, and then increased to 1.76 mg c-3-rut g−1 DW on day 21 and to 2.82 mg c-3-rut g−1 DW at the end of storage. C-3-rut in chitosan-treated fruit showed decreases after day 7, while it increased afterwards up to 1.86 mg c-3-rut g−1 DW on day 28. Partial analysis (data on 21 and 28 d) showed that cherries coated with G25, G50 and Polys averaged 2.95 mg g−1 DW, being 1.3-fold higher than the initial value, in contrast to the chitosan-coated and control fruits, being 0.82- and 0.66-fold lower, respectively.

Cyanidin-3-*O*-glucoside (c-3-glc) was found at 0.06 mg g−1 DW at harvest (Table 2), being at the highest level of the whole experiment, whereas the lowest level during storage was 0.01 mg g−1 DW on day 14 in the controls. In all treatments, c-3-glc decreased in the middle of storage, being lower in controls, G25 and chitosan than Polys and G50 on day 14, and then increased, averaging 0.2 mg g−1 DW in controls and chitosan and 0.5 mg g- 1 DW in the remaining treatments on day 28. The effect of days was significant in controls. The two-way analysis showed that the effect of treatment and days were significant, but not their interaction.

Peonodin-3-*O*-glucoside (p-3-glc) was 0.2 mg g- 1 DW at harvest (Table 2). In the controls, it decreased significantly to 0.02 mg g−1 DW on day 21 and showed an insignificant increase on day 28. When all data were analyzed, the treatment effect and storage days were also both significant, but not their interaction. During storage, c-3-glc averaged 0.06 mg g−1 DW in controls compared to the significantly higher values of the remaining treatments, averaging 0.13 mg g−1 DW, being approximately two-fold higher than the controls. On day 28, c-3-glc in all treatments also showed an increase, averaging 0.17 mg g−1 DW in comparison to the respective value of 0.07 mg g−1 DW on day 21. On day 28, the values in the controls were significantly lower than those in G25, G50 and Polys, which reached the levels at harvest, whereas those in chitosan were intermediate and close to those at harvest.

Malvidin-3-*O*-gluoside (m-3-glc) was 0.037 mg g−1 DW at harvest and remained almost stable in controls during storage. In the two-way analysis, the treatment effect and storage days were significant, but not their interaction. Values fluctuated from 0.02 to 0.072 mgg−1 DW, with the average values in the controls and G25 being 0.029 mg g−1 DW compared to 0.052 g- 1 DW in the other treatments. On day 28, in treated cherries, m-3-glc averaged 0.05 mg g−1 DW compared to 0.03 on day 21 mg g−1 DW, with controls and G25 being significantly lower than the remaining treatments on day 21.


**3. Models**

Typically, the stress increases gradually during transformation, and this transformation hardening is enhanced by increasing the strain rate. In those models that did not directly consider the strain rate effect [90–93], the enhanced transformation hardening was modeled by a phenomenological hardening function with parameters fitted for different strain rates rather than a physical model that inherently considers the hardening mechanism. The hardening function provided extra resistance to transformation, and was generally described as a function of internal state variables with temperature-dependent parameters. These parameters were calibrated by experiments under a specific strain rate and needed to be updated when the strain rate changed. As a result, these models could only give accurate predictions in cases of limited change of the strain rate with a specific group of parameters.

To directly account for the strain rate effects discussed in Section 2, thermal source models are proposed for modeling the NiTi SMA behaviors at low and medium strain rates, and as extended versions, thermal kinetic models are proposed for high strain rates to consider the kinetic effect in shock conditions. These models that take account of the selfheating mechanism and represent the strain rate effect by means of thermal sources can be termed as thermal source models. This is because the strain rate influences the martensitic transformation rate, in turn influences the heat production rate and temperature field. In shock conditions, the velocity of dislocations and phase interfaces set in, so the thermal source model has been extended to thermal kinetic models to consider these kinetic effects.


**6. Macrophage and T-cell differentiation**

In pathogen infection, dendritic cells (DCs) and macrophages primarily act as phagocytotic antigen-presenting cells (APCs) that degrade infected pathogens into fragments, and then move those fragments to the nearby lymphoid organs. The pathogen fragments combine with cell surface histocompatibility complex (major histocompatibility complex) to activate and differentiate T cells. **Figure 3** displays the cooperation of the antigen-presenting cells, costimulatory molecules and cytokines.

The metabolic organs, such as the liver, pancreas and adipose tissue, are composed of parenchymal and stromal cells, which include macrophages to maintain metabolic homeostasis. Bacterial infection innately activates macrophages, causing the secretion of proinflammatory cytokines, such as TNF-α, IL-6 and IL-1β. This promotes peripheral insulin resistance and reduces nutrient storage during the metabolic reaction. Furthermore, some additional physiological mechanism can lead to the activation of macrophages. For these latest, the regulatory T cells (Treg), the Fcγ receptors, the apoptotic cells and the prostaglandins are increasing the number of macrophages involved in the regulation of inflammation and antitumour defences [84]. These inflammatory mediators are involved in activating anti-microbial defence mechanisms, including oxidative processes that contribute to killing pathogens and the secreted IL-12 and IL-23. These direct the differentiation and expansion of anti-microbial TH1 and TH17 cells that help to drive inflammatory responses [85]. Recent research shows that intestinal antigen-presenting cells can be divided into CD11c+ CD11b− , CD11c+ CD11b+ and CD11cdullCD11b+ categories. Particularly, the CD11cdullCD11b+ cells are CD103− F4/80+ macrophages, with efficient role in inducing the Foxp3+ regulatory T (Treg) cells [86]. Tumour cells affect the surrounding cellular environment by promoting tumour growth and metastasis by establishing a tumour microenvironment that is conducive to tumour development [87–90]. In the tumour microenvironment, tumour cells secrete inflammatory cytokines, such as TGFβ and IL-10. These cytokines stimulate differentiation of regulatory T and Treg cells [91, 92] as well as differentiation of tumour-associated macrophages (TAMs) into M2 macrophages. This causes the host immune system to locate and attack cancer cells, which generates subsequent tumour cell evasion of this immune surveillance and attack, which enhances tumour growth and metastasis [87, 93–98]. Various cytokines, chemokines and growth factors in the tumour microenvironment are the primary elements that affect the host's anti-tumour ability and evasion of tumour cells [89, 99]. Tumour microenvironments are complicated cellular microcosms [89, 97], and numerous immune cells are located throughout tumour microenvironments. Macrophages are the most crucial and abundant immune cells in the tumour microenvironment. The two most critical types of macrophages, based on function and differentiation, are M1 and M2 macrophages. M1 macrophages are characterised by tumour

**Figure 3.** The cooperation of the antigen-presenting cells, costimulatory molecules, and cytokines. Bacterial infection innately activates macrophages, causing the secretion of pro-inflammatory cytokines, such as TNF-α, IL-6, and IL-1β. This promotes peripheral insulin resistance and reduces nutrient storage during the metabolic reaction. Furthermore, several additional mechanisms can also contribute to the activation of macrophages for immune-regulatory activity.

resistance, whereas M2 macrophages are characterised by tumour promotion [98, 100]. In mouse models, macrophages present CD11b, F4/80, CSF-1R and F4/80 as the surface proteins for M1 and M2 macrophages [93, 101]. Recent studies have noted large quantities of TAMs in tumour tissue. TAMs are the most abundant and critical immune cells in the tumour microenvironment [102–104] and are the main factors that enable the tumour microenvironment to exert immune inhibitory effects [101, 102]. In the tumour microenvironment, tumour cells and the surrounding stoma cells secrete cytokines and growth factors that stimulate TAMs and activate the various expression, function, receptor regulation and secretion types of chemokines [103, 105], including anti-tumour M1 macrophages and pro-tumour M2 macrophages [98, 106–108]. In the tumour microenvironment, the proportions of M1 and M2 macrophages are unequal. Tumour microenvironments contain large amounts of transmitters, such as M-CSF, IL-6, IL-10, TGF-β and COX-2, that induce transformation of TAMs into M2 macrophages that secrete immune inhibitory chemokines and have poor antigen-presenting and cytotoxic abilities, which generates tumour growth and metastasis [49, 98, 102–104, 109–114]. M2 macrophages and TAMs have protumour and immune inhibitory effects, secrete large amounts of IL-10, TGF-β, CCL17 and CCL22, attract non-cytotoxic Treg and TH2 cells to aggregate in tumour tissue, inhibit T-cell differentiation and function, lower cytotoxic T-cell function, induce T-cell apoptosis, secrete CCL18 and attract naïve T cells [49, 98, 115]. NADPH oxidase is a major enzymatic source of cellular ROS. NADPH plays a fundamental role in maintaining normal cell functions. Recent research has focussed on this enzyme's role in cellular oxidative stress, which may eventually contribute to various pathophysiological conditions and diseases [27, 28]. Studies have found that NADPH oxidase modulates multiple redox-sensitive intra-cellular signalling pathways by generating ROS molecules. This modulation includes inhibition of protein tyrosine phosphatases and activation of certain redoxsensitive transcription factors [116, 117]. ROS consist of numerous molecular species, including H2O2, oxide ions (O2 − ) and OH−29, that act as signalling molecules involved in the migration of hepatic profibrogenic cells [118] and the functioning of peripheral blood monocytes [119]. ROS and RNS, generated endogenously or in response to environmental stress, have long been implicated in tissue injury for a variety of disease states [120, 121]. Stimulation of the mitochondrial apoptotic pathway through ROS and mitochondrial DNA damage promotes outer membrane permeabilisation, which triggers caspase-dependent or caspaseindependent cytosolic signalling events [122]. Activated inflammatory cells serve as sources of ROS and RNS that can initiate the alteration of the cell function, gathering specific cellular signalling, transcription factor activation, physiological factors release, the apoptosis process and compensatory cell proliferation. However, it remains unclear whether the ROS or the RNS production and release through neutrophils or macrophages enhance sufficient diffusion into the intra-cellular cytoplasm as to affect the cellular response [123, 124].

### **7. Wound healing**

physiological mechanism can lead to the activation of macrophages. For these latest, the regulatory T cells (Treg), the Fcγ receptors, the apoptotic cells and the prostaglandins are increasing the number of macrophages involved in the regulation of inflammation and antitumour defences [84]. These inflammatory mediators are involved in activating anti-microbial defence mechanisms, including oxidative processes that contribute to killing pathogens and the secreted IL-12 and IL-23. These direct the differentiation and expansion of anti-microbial TH1 and TH17 cells that help to drive inflammatory responses [85]. Recent research shows that

affect the surrounding cellular environment by promoting tumour growth and metastasis by establishing a tumour microenvironment that is conducive to tumour development [87–90]. In the tumour microenvironment, tumour cells secrete inflammatory cytokines, such as TGFβ and IL-10. These cytokines stimulate differentiation of regulatory T and Treg cells [91, 92] as well as differentiation of tumour-associated macrophages (TAMs) into M2 macrophages. This causes the host immune system to locate and attack cancer cells, which generates subsequent tumour cell evasion of this immune surveillance and attack, which enhances tumour growth and metastasis [87, 93–98]. Various cytokines, chemokines and growth factors in the tumour microenvironment are the primary elements that affect the host's anti-tumour ability and evasion of tumour cells [89, 99]. Tumour microenvironments are complicated cellular microcosms [89, 97], and numerous immune cells are located throughout tumour microenvironments. Macrophages are the most crucial and abundant immune cells in the tumour microenvironment. The two most critical types of macrophages, based on function and differentiation, are M1 and M2 macrophages. M1 macrophages are characterised by tumour

**Figure 3.** The cooperation of the antigen-presenting cells, costimulatory molecules, and cytokines. Bacterial infection innately activates macrophages, causing the secretion of pro-inflammatory cytokines, such as TNF-α, IL-6, and IL-1β. This promotes peripheral insulin resistance and reduces nutrient storage during the metabolic reaction. Furthermore, several additional mechanisms can also contribute to the activation of macrophages for immune-regulatory activity.

CD11b−

cells are CD103−

regulatory T (Treg) cells [86]. Tumour cells

, CD11c+

CD11b+

F4/80+

and

macro-

intestinal antigen-presenting cells can be divided into CD11c+

phages, with efficient role in inducing the Foxp3+

174 Wound Healing - New insights into Ancient Challenges

categories. Particularly, the CD11cdullCD11b+

CD11cdullCD11b+

Immune cells are involved in virtually every aspect of the wound repair process, from the initial stages, where they participate in haemostasis and work to prevent infection, to later stages where they drive scar formation [125, 126]. T lymphocytes exercise crucial in vivo effects on various parameters of healing [127–129]. Neutrophils help control infection during wound healing, but they also release harmful enzymes that damage healthy tissue surrounding the wound site [130–132]. Recent researchers have noted that several specific proteins produced by wound macrophages at the site of injury are involved: (1) in the recruitment and activation of additional macrophages infiltrating in the wound; (2) in the production of growth factors that promote cellular proliferation and tissue recovery synthesis; (3) in stimulating proteases and extra-cellular matrix growth and (4) in the process of tissue remodelling [133]. β-catenindependent Wnt pathways, which are classified according to their ability to promote stabilisation of β-catenin in the cytoplasm, act as cellular signals through cytoplasmic stabilisation and accumulation of β-catenin in the nucleus to activate gene transcription [134]. This could enhance wound healing by lymphocytes [135, 136]. Nicotinamide adenine dinucleotide phosphate (NADPH) oxidase modulates multiple redox-sensitive intra-cellular signalling pathways by generating ROS molecules. This includes inhibiting protein tyrosine phosphatases and activating certain redox-sensitive transcription factors [116, 137, 138]. This shows that ROS regulate the expression of key chemical mediators that further modulate the inflammatory response in animal models; it has also been reported that these redox-sensitive processes may include cytokine action, angiogenesis, cell motility and extra-cellular matrix formation [139–141]; this can enable reliable estimates of wound-healing capacity, which is altered by various conditions, such as inflammation. Furthermore, research on one of the ROS has indicated that H2O2 plays a critical role in wound repair, inflammation and anti-inflammation mechanisms [142, 143]. Our published research also showed that the production of ROS (i.e., H2O2 after an injury has occurred) may cause healing to generate inflammation through the apoptosis of the cell. Over-inhibition of NADPH oxidase activity may reduce the normal progress of apoptosis under the wound and might delay healing [29].

Inflammation enhances vascular permeability, active migration of blood cells and the passage of plasma constituents into the injured tissue [144]. Blood leukocytes actively participate in the defence and inflammation responses, being activated since the earliest phases of atherosclerosis process. Inflammation and atherosclerosis shelter intricate mechanisms relied to leukocytes recruitment [145]. Neuro-inflammation mediators are described to be closely related to brain cells functioning (such as microglia and astrocytes), to the complement system activation and to cytokines, and chemokines production [146]. Regarding cancer development [147], proinflammatory cytokines, including IL-1α, IL-1β, IL-6, IL-8, IL-18, chemokines, matrix metallopeptidase-9 and vascular endothelial growth factor, are primarily regulated by the transcription NF-κB, which is active in most tumours and is induced by carcinogens [148]. Cutaneous wound repair is a tightly regulated and dynamic process involving blood clotting, inflammation, formation of new tissue and tissue remodelling [149]. Thrombin is the protease involved in blood coagulation. Its deregulation can cause haemostatic abnormalities, which range from subtle subclinical problems to serious life-threatening coagulopathies (i.e., during septicaemia) [150]. Inflammation and coagulation are both parts of the natural mechanism that protects the organism against infection. The endothelial cells and the platelets are capable to react in the acute, also in the chronic inflammatory environment. They release pro-inflammatory mediators that produce adhesion of molecules, proteases and clotting factors associated to leukocytes recruitment [151]. The elements of the PAR family serve as sensors that detect blood-clotting serine proteinases in the inflamed target cells. Activation of PAR-1 by thrombin and of PAR-2 by other factors on the membrane of endothelial cells generates rapid expression and exposure of adhesive proteins that mediate an acute inflammatory reaction and of the tissue factor that initiates the blood coagulation cascade [152] as presented as **Figure 4**.

stages where they drive scar formation [125, 126]. T lymphocytes exercise crucial in vivo effects on various parameters of healing [127–129]. Neutrophils help control infection during wound healing, but they also release harmful enzymes that damage healthy tissue surrounding the wound site [130–132]. Recent researchers have noted that several specific proteins produced by wound macrophages at the site of injury are involved: (1) in the recruitment and activation of additional macrophages infiltrating in the wound; (2) in the production of growth factors that promote cellular proliferation and tissue recovery synthesis; (3) in stimulating proteases and extra-cellular matrix growth and (4) in the process of tissue remodelling [133]. β-catenindependent Wnt pathways, which are classified according to their ability to promote stabilisation of β-catenin in the cytoplasm, act as cellular signals through cytoplasmic stabilisation and accumulation of β-catenin in the nucleus to activate gene transcription [134]. This could enhance wound healing by lymphocytes [135, 136]. Nicotinamide adenine dinucleotide phosphate (NADPH) oxidase modulates multiple redox-sensitive intra-cellular signalling pathways by generating ROS molecules. This includes inhibiting protein tyrosine phosphatases and activating certain redox-sensitive transcription factors [116, 137, 138]. This shows that ROS regulate the expression of key chemical mediators that further modulate the inflammatory response in animal models; it has also been reported that these redox-sensitive processes may include cytokine action, angiogenesis, cell motility and extra-cellular matrix formation [139–141]; this can enable reliable estimates of wound-healing capacity, which is altered by various conditions, such as inflammation. Furthermore, research on one of the ROS has indicated that H2O2 plays a critical role in wound repair, inflammation and anti-inflammation mechanisms [142, 143]. Our published research also showed that the production of ROS (i.e., H2O2 after an injury has occurred) may cause healing to generate inflammation through the apoptosis of the cell. Over-inhibition of NADPH oxidase activity may reduce the

176 Wound Healing - New insights into Ancient Challenges

normal progress of apoptosis under the wound and might delay healing [29].

Inflammation enhances vascular permeability, active migration of blood cells and the passage of plasma constituents into the injured tissue [144]. Blood leukocytes actively participate in the defence and inflammation responses, being activated since the earliest phases of atherosclerosis process. Inflammation and atherosclerosis shelter intricate mechanisms relied to leukocytes recruitment [145]. Neuro-inflammation mediators are described to be closely related to brain cells functioning (such as microglia and astrocytes), to the complement system activation and to cytokines, and chemokines production [146]. Regarding cancer development [147], proinflammatory cytokines, including IL-1α, IL-1β, IL-6, IL-8, IL-18, chemokines, matrix metallopeptidase-9 and vascular endothelial growth factor, are primarily regulated by the transcription NF-κB, which is active in most tumours and is induced by carcinogens [148]. Cutaneous wound repair is a tightly regulated and dynamic process involving blood clotting, inflammation, formation of new tissue and tissue remodelling [149]. Thrombin is the protease involved in blood coagulation. Its deregulation can cause haemostatic abnormalities, which range from subtle subclinical problems to serious life-threatening coagulopathies (i.e., during septicaemia) [150]. Inflammation and coagulation are both parts of the natural mechanism that protects the organism against infection. The endothelial cells and the platelets are capable to react in the acute, also in the chronic inflammatory environment. They release pro-inflammatory mediators that produce adhesion of molecules, proteases and clotting factors associated

**Figure 4.** Wound healing was initiated after the injury of the cell, tissue even the organ. In the early stage of the healing, the damaged tissue producing a lot of ROS leading to neighbour cells into the apoptosis, following the apoptotic cells collapsed and released caspases were able to induce the tissue repair. However, the imbalance inflammatory may induce over-production of blood glucose that is leading to decrease the EGF receptor expression further to impair the wound healing.


**Author details**

Tien M. Nguyen1,2

1 California State University, Fullerton, USA

2 The Aerospace Corporation, El Segundo, USA

\*Address all correspondence to<EMAIL_ADDRESS><EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Future Satellite System Architectures and Practical Design Issues: An Overview DOI: http://dx.doi.org/10.5772/intechopen.92308*


**2. Materials and methods**

#### **2.1 Materials**

Bovine sodium caseinate powder, CMC low viscosity (50-200 cP, 4 % in H2O, 25 °C), GDL, tris(hydroxymethyl)aminomethane (Tris), 8-anilino-1-naphthalenesulfonate (ANS) as an ammonium salt, and sodium azide were purchased from Sigma-Aldrich Co. (Steinheim, Germany). HCl, and NaOH were provided by Cicarelli SRL (San Lorenzo, Argentina). The CMC stock solutions were also prepared in water and stored at 4 °C.

Acid-Induced Aggregation and Gelation of

vs. NaCAS concentration (wt%) curve.

**2.5 Acid aggregation**

Bovine Sodium Caseinate-Carboxymethylcellulose Mixtures 79

The surface hydrophobicity (S0) was estimated according to the method of Kato and Nakai (Haskard & Li-Chan, 1998; Kato & Nakai, 1980), using ANS as an hydrophobic fluorescent marker. The measurements were carried out in an Aminco Bowman Series 2 spectrofluorometer, using an λex of 396 nm and an emission wavelength (λem) of 489 nm, previously determined from excitation and emission spectra of protein-ANS complex, at a constant temperature of 35ºC. The FI was measured in samples containing ANS 0.04 mM and consecutive aggregates of 0.1 wt% NaCAS with or without CMC (FIb). The FI was also determined in samples containing only protein (i.e. without the addition of the fluorescent probe) in the presence or absence of CMC at the same concentrations (FIp). The difference between FIb and FIp (F) was calculated and S0 was determined as the initial slope in the F

Kinetics of NaCAS aggregation induced by the acidification with GDL, in the presence or absence of CMC, was analyzed by measuring turbidity (τ) in the range of 450 to 650 nm, in a Spekol 1200 spectrophotometer with a diode arrangement and a thermostatized cell. The

wt% GDL R

R used for this experiment was 0.5, at a temperature of 35 °C. Acidification was initiated by

Changes in the protein average size were followed by the dependence of τ on wavelength

d log 4.2+ d log

 is a parameter that has a direct relationship with the average size of the particles, can be used to easily detect and follow rapid size changes, and was obtained from the slope of log τ versus log λ plots, in the 450 to 650 nm range, where the absorption due to the protein chromophores is negligible allowing the estimation of τ as absorbance (Camerini-Otero & Day, 1978; Risso et al., 2007). Absorption spectra and absorbance at 650 nm (A650) were registered as a function of time until a maximum and constant value of A650 was reached; simultaneously the pH decrease was measured. The measurements of pH were carried out on digital pH meter Orion SA 720, equipped with proton-selective glass membrane electrode combined with saturated calomel reference electrode. On the other hand, it has been shown that , for a system of aggregating particles of the characteristics of caseinates, tends, upon aggregation, toward an asymptotic value that can be considered as a fractal

To verify if β was actually related to the average size of the particles, the size distribution functions and the hydrodynamic diameters of NaCAS particles were determined by dynamic light scattering (DLS) using a Brookhaven 9863 Model equipment with a He–Ne laser (λ0 = 632.8 nm) with a maximal power of 15 mV, and using 90° as the measuring angle. Hydrodynamic diameters were calculated using the BI9000AT 6,5 Version software

wt% NaCAS (1)

(2)

amount of GDL added was calculated using the following relation:

the addition of solid GDL to 10 g of NaCAS suspension (0.5 wt%).

dimension (Df) of the aggregates (Horne, 1987; Risso et al., 2007).

**2.6 Changes in size and compaction of particles**

(λ) of the suspensions, determined according to:

NaCAS suspensions were prepared from dissolution of commercial drug in distilled water (isoionic pH) at room temperature. After concentration measurements, 0.15 g.L-1 sodium azide was added as a bacteriostatic agent, and the solutions were stored at 4 °C. Protein concentration was determined by the Kuaye's method which is based on the ability of strong alkaline solutions to shift the spectrum of the amino acid tyrosine to higher wavelength values in the UV region (Kuaye, 1994).

Stock solutions nearly 6 mM for ANS were prepared in distilled water and stored in the dark at 4 °C; the concentration was determined by absorbance measurements, using a molar extinction coefficient (of 4,950 M-1 cm-1 at 350 nm.

#### **2.2 NaCAS–CMC phase diagram**

Phase diagram was established at pH 6.8 (buffer Tris-HCl 10 mM) and 35 °C, and it was constructed by determining the transition from single to two-phase systems. The thermodynamic compatibility study was carried out using the method proposed by Spyropoulos et al. They propose to carefully prepare a series of polysaccharide/protein aqueous solutions to give binary systems and incubate them a certain time at a given temperature, and then evaluate a single or two-phase systems formation (Spyropoulos et al., 2010).

These binary systems were prepared with the same CMC concentration but with NaCAS concentrations ranging from 0 to 4 wt% in one case, and with the same protein concentration but with polysaccharide concentrations ranging from 0 to 4.5 wt% in the other. A total of three samples were taken from each of these binary solutions and kept in sealed tubes at 35 °C for at least 24 h, after which the occurrence of phase separation (or not) was verified by visual inspection.

#### **2.3 Thermal stability of NACAS in the presence or absence of CMC**

The effects of thermal treatments on NaCAS, CMC and their mixtures were monitored through spectrophotometry with the aim of evaluating the biopolymer aggregation by heating at different temperatures. Measurements at increasing temperature were made at 650 nm from 10 to 100 °C with a heating rate of 0.5 °C per minute. The equipment used was a Jasco V-550 doublebean spectrophotometer equipped with a cell holder heated by Peltier effect and controlled by a programmable unit. The cell was filled with a 0.02 wt% NaCAS solution in buffer Tris-HCl 10mM pH 6.8 up to a final volume of 2.5 mL and sealed with a teflon stopper to avoid evaporation during each experiment. The spectrophotometer compartment was continuously purged with nitrogen to prevent the condensation of water vapor on the cell walls. This method was also performed in the presence of CMC at NaCAS:CMC proportion of 8:1, 4:1, 2:1, 1:1 and 1:1.5.

#### **2.4 Spectrofluorimetric determinations**

Fluorescence excitation and emission spectra of the NaCAS (0.1 wt%) were obtained using a spectrofluorometer Aminco Bowman Series 2. Measurements were carried out in the presence and absence of CMC, in order to detect any spectral shifts and/or changes in the relative intensity of fluorescence (FI). Previously, the excitation wavelength (λex) and the range of concentration with a non significant internal filter effect were determined. The samples (3 mL) used for the spectral analysis and FI measures were transferred into a fluorescence cell with a light path length of 1 cm and placed into a cell holder keeping the temperature constant at the fixed values desired. Values of FI were registered within the range of 300-400 nm at 35ºC using a λex of 286 nm.

The surface hydrophobicity (S0) was estimated according to the method of Kato and Nakai (Haskard & Li-Chan, 1998; Kato & Nakai, 1980), using ANS as an hydrophobic fluorescent marker. The measurements were carried out in an Aminco Bowman Series 2 spectrofluorometer, using an λex of 396 nm and an emission wavelength (λem) of 489 nm, previously determined from excitation and emission spectra of protein-ANS complex, at a constant temperature of 35ºC. The FI was measured in samples containing ANS 0.04 mM and consecutive aggregates of 0.1 wt% NaCAS with or without CMC (FIb). The FI was also determined in samples containing only protein (i.e. without the addition of the fluorescent probe) in the presence or absence of CMC at the same concentrations (FIp). The difference between FIb and FIp (F) was calculated and S0 was determined as the initial slope in the F vs. NaCAS concentration (wt%) curve.

#### **2.5 Acid aggregation**

78 Food Industrial Processes – Methods and Equipment

NaCAS suspensions were prepared from dissolution of commercial drug in distilled water (isoionic pH) at room temperature. After concentration measurements, 0.15 g.L-1 sodium azide was added as a bacteriostatic agent, and the solutions were stored at 4 °C. Protein concentration was determined by the Kuaye's method which is based on the ability of strong alkaline solutions to shift the spectrum of the amino acid tyrosine to higher

Stock solutions nearly 6 mM for ANS were prepared in distilled water and stored in the dark at 4 °C; the concentration was determined by absorbance measurements, using a molar

Phase diagram was established at pH 6.8 (buffer Tris-HCl 10 mM) and 35 °C, and it was constructed by determining the transition from single to two-phase systems. The thermodynamic compatibility study was carried out using the method proposed by Spyropoulos et al. They propose to carefully prepare a series of polysaccharide/protein aqueous solutions to give binary systems and incubate them a certain time at a given temperature, and then evaluate a single or two-phase systems formation (Spyropoulos et al.,

These binary systems were prepared with the same CMC concentration but with NaCAS concentrations ranging from 0 to 4 wt% in one case, and with the same protein concentration but with polysaccharide concentrations ranging from 0 to 4.5 wt% in the other. A total of three samples were taken from each of these binary solutions and kept in sealed tubes at 35 °C for at least 24 h, after which the occurrence of phase separation (or not) was verified by

The effects of thermal treatments on NaCAS, CMC and their mixtures were monitored through spectrophotometry with the aim of evaluating the biopolymer aggregation by heating at different temperatures. Measurements at increasing temperature were made at 650 nm from 10 to 100 °C with a heating rate of 0.5 °C per minute. The equipment used was a Jasco V-550 doublebean spectrophotometer equipped with a cell holder heated by Peltier effect and controlled by a programmable unit. The cell was filled with a 0.02 wt% NaCAS solution in buffer Tris-HCl 10mM pH 6.8 up to a final volume of 2.5 mL and sealed with a teflon stopper to avoid evaporation during each experiment. The spectrophotometer compartment was continuously purged with nitrogen to prevent the condensation of water vapor on the cell walls. This method was also performed in the presence of CMC at

Fluorescence excitation and emission spectra of the NaCAS (0.1 wt%) were obtained using a spectrofluorometer Aminco Bowman Series 2. Measurements were carried out in the presence and absence of CMC, in order to detect any spectral shifts and/or changes in the relative intensity of fluorescence (FI). Previously, the excitation wavelength (λex) and the range of concentration with a non significant internal filter effect were determined. The samples (3 mL) used for the spectral analysis and FI measures were transferred into a fluorescence cell with a light path length of 1 cm and placed into a cell holder keeping the temperature constant at the fixed values desired. Values of FI were registered within the

**2.3 Thermal stability of NACAS in the presence or absence of CMC**

NaCAS:CMC proportion of 8:1, 4:1, 2:1, 1:1 and 1:1.5.

range of 300-400 nm at 35ºC using a λex of 286 nm.

**2.4 Spectrofluorimetric determinations**

wavelength values in the UV region (Kuaye, 1994).

extinction coefficient (of 4,950 M-1 cm-1 at 350 nm.

**2.2 NaCAS–CMC phase diagram**

2010).

visual inspection.

Kinetics of NaCAS aggregation induced by the acidification with GDL, in the presence or absence of CMC, was analyzed by measuring turbidity (τ) in the range of 450 to 650 nm, in a Spekol 1200 spectrophotometer with a diode arrangement and a thermostatized cell. The amount of GDL added was calculated using the following relation:

$$\text{IR} = \frac{\text{wt\%} \text{GDL}}{\text{wt\%} \text{NaCAS}} \tag{1}$$

R used for this experiment was 0.5, at a temperature of 35 °C. Acidification was initiated by the addition of solid GDL to 10 g of NaCAS suspension (0.5 wt%).

#### **2.6 Changes in size and compaction of particles**

Changes in the protein average size were followed by the dependence of τ on wavelength (λ) of the suspensions, determined according to:

$$\beta = 4.2 + \frac{\mathrm{d}(\log \tau)}{\mathrm{d}(\log \lambda)}\tag{2}$$

 is a parameter that has a direct relationship with the average size of the particles, can be used to easily detect and follow rapid size changes, and was obtained from the slope of log τ versus log λ plots, in the 450 to 650 nm range, where the absorption due to the protein chromophores is negligible allowing the estimation of τ as absorbance (Camerini-Otero & Day, 1978; Risso et al., 2007). Absorption spectra and absorbance at 650 nm (A650) were registered as a function of time until a maximum and constant value of A650 was reached; simultaneously the pH decrease was measured. The measurements of pH were carried out on digital pH meter Orion SA 720, equipped with proton-selective glass membrane electrode combined with saturated calomel reference electrode. On the other hand, it has been shown that , for a system of aggregating particles of the characteristics of caseinates, tends, upon aggregation, toward an asymptotic value that can be considered as a fractal dimension (Df) of the aggregates (Horne, 1987; Risso et al., 2007).

To verify if β was actually related to the average size of the particles, the size distribution functions and the hydrodynamic diameters of NaCAS particles were determined by dynamic light scattering (DLS) using a Brookhaven 9863 Model equipment with a He–Ne laser (λ0 = 632.8 nm) with a maximal power of 15 mV, and using 90° as the measuring angle. Hydrodynamic diameters were calculated using the BI9000AT 6,5 Version software

Acid-Induced Aggregation and Gelation of

**2.8 Statistical analysis**

**3. Results and discussions**

phase samples, (●) two-phase gel-like systems

absence of the polysaccharide.

**3.2 Thermal stability of NACAS: CMC mixtures**

system resolution for the protein gels images was:

Bovine Sodium Caseinate-Carboxymethylcellulose Mixtures 81

the pixel width in m, linear calibration was carried out using a micrometer rule. The final

The data are reported as the average values ± their standard deviations. Statistical analysis was performed with Sigma Plot 10.0 and Image J softwares. Relationship between variables was statistically analyzed by correlation analysis using Pearson correlation coefficient (r).

The results obtained for mixtures of NACAS and CMC are shown in Fig. 1. The polysaccharide and protein concentrations, in each of the prepared binary solutions, correspond to a single point on the phase diagram. This approach provides a "map'' of the

Fig. 1. Approach used for the determination of the phase diagrams for NaCAS:CMC systems after 24 h at 35 °C. Key: (○) one-phase clear solution, (∆) one-phase turbid solution, (▲) two-

Both the CMC as all mixtures NaCAS:CMC in all relations tested were not affected by rising temperature within the temperature range studied (10-100 °C). This would indicate that the polysaccharide is thermally stable in this range, and that the addition of CMC to NACAS increases its thermal stability, since the NaCAS starts aggregating at about 60 °C in the

*m Resolution* 15.5 *pixel m*

(4)

1pixel width 0.0645 0.0005

The differences were considered statistically significant at p < 0.05 values.

transition from the single-phase to the two-phase region of the phase diagram.

**3.1 Thermodynamic compatibility of NaCAS:CMC mixtures**

processing. To carry out this determination an amount of solid GDL was added to 8 mL of a 0.5 wt% NaCAS solution in order to obtain a GDL/protein relation of 0.5. Measurements at different times were performed until the maximum of allowed by the instrument was reached while pH was simultaneously monitored.

#### **2.6.1 Effect of CMC on the viscosity of media**

The aggregation process is limited by diffusion, which depends on the medium viscosity (). Therefore, is important to determine the effect that the presence of the polysaccharide exerts on that property. The was measured in triplicate, using a rotational viscosimeter Brookfield LV Master (LVDV-III) with cone/plate geometry and thermostatically controlled at a temperature of 35.00 ± 0.05ºC. The relative viscosity (r) was calculated as:

$$
\eta\_{\mathbf{r}} = \eta\_{\text{sol}} / \eta\_0 \tag{3}
$$

where sol is the solution viscosity and 0 is the water ones.

#### **2.7 Acid gelation**

Above a certain protein concentration, the loss of electrostatic stability by acidification results in the formation of a three-dimensional gel network. Effect of CMC concentration on the kinetic of gelation, rheological properties and microstructure of gels were investigated.

#### **2.7.1 Rheological properties of acid gels**

Rheological properties of NaCAS samples (3 wt%), in the absence or presence of CMC, were determined in a stress and strain controlled rheometer AR G2 model using a cone geometry (diameter: 40 mm, cone angle: 2°, cone truncation: 55 mm) and a system of temperature control with a recirculating bath (Julabo model ACW 100) connected to a Peltier plate. An amount of solid GDL according to a certain R was added to initiate the acid gelation.

Measurements were performed each 20 sec with a constant oscillation stress of 0.1 Pa and a frequency of 0.1 Hz. The Lissajous figures at various times were plotted to ensure that the measurements of storage or elastic modulus (G') and loss or viscous modulus (G'') were always obtained within the linear viscoelastic region.

The G'-G'' crossover times (tg) of acidified caseinate systems were considered here as the gel times, since most studies of milk/caseinate gelation have adopted this criterion (Braga et al., 2006; Curcio et al., 2001). pH at tg was also determined considering the pH value at the G'- G'' crossover (pHg).

#### **2.7.2 Conventional inverted microscopy**

The degree of compactness of gels was evaluated through digital image analysis. For this, bottom surface image of gel were obtained by conventional inverted microscopy. To obtain the microscopic images, 90 μL of each sample were placed in compartments of the LAB-TEK II cells. The samples were obtained by duplicate under a constant temperature set at 35ºC. Transmission images of gels were obtained using a conventional inverted microscopy (Union Optical) with an objective 100x and a digital camera (Canon PowershotA640) with a zoom 7.1x and microscope adapter of 52 mm.

The average pore diameters of gels were determined using the program Image J. To do this, straight lines were drawn on the digital images and values of pore size were measured in pixels. These values were averaged (n=5) and obtained the average pore size. To determine the pixel width in m, linear calibration was carried out using a micrometer rule. The final system resolution for the protein gels images was:

$$1\,\text{pixel width} = \left(0.0645 \pm 0.0005\right)\mu m \Rightarrow \text{Resolution} = 15.5\,\text{pixel}/\mu m \tag{4}$$

### **2.8 Statistical analysis**

80 Food Industrial Processes – Methods and Equipment

processing. To carry out this determination an amount of solid GDL was added to 8 mL of a 0.5 wt% NaCAS solution in order to obtain a GDL/protein relation of 0.5. Measurements at different times were performed until the maximum of allowed by the instrument was

The aggregation process is limited by diffusion, which depends on the medium viscosity (). Therefore, is important to determine the effect that the presence of the polysaccharide exerts on that property. The was measured in triplicate, using a rotational viscosimeter Brookfield LV Master (LVDV-III) with cone/plate geometry and thermostatically controlled

> r 0

Above a certain protein concentration, the loss of electrostatic stability by acidification results in the formation of a three-dimensional gel network. Effect of CMC concentration on the kinetic of gelation, rheological properties and microstructure of gels were investigated.

Rheological properties of NaCAS samples (3 wt%), in the absence or presence of CMC, were determined in a stress and strain controlled rheometer AR G2 model using a cone geometry (diameter: 40 mm, cone angle: 2°, cone truncation: 55 mm) and a system of temperature control with a recirculating bath (Julabo model ACW 100) connected to a Peltier plate. An

Measurements were performed each 20 sec with a constant oscillation stress of 0.1 Pa and a frequency of 0.1 Hz. The Lissajous figures at various times were plotted to ensure that the measurements of storage or elastic modulus (G') and loss or viscous modulus (G'') were

The G'-G'' crossover times (tg) of acidified caseinate systems were considered here as the gel times, since most studies of milk/caseinate gelation have adopted this criterion (Braga et al., 2006; Curcio et al., 2001). pH at tg was also determined considering the pH value at the G'-

The degree of compactness of gels was evaluated through digital image analysis. For this, bottom surface image of gel were obtained by conventional inverted microscopy. To obtain the microscopic images, 90 μL of each sample were placed in compartments of the LAB-TEK II cells. The samples were obtained by duplicate under a constant temperature set at 35ºC. Transmission images of gels were obtained using a conventional inverted microscopy (Union Optical) with an objective 100x and a digital camera (Canon PowershotA640) with a

The average pore diameters of gels were determined using the program Image J. To do this, straight lines were drawn on the digital images and values of pore size were measured in pixels. These values were averaged (n=5) and obtained the average pore size. To determine

amount of solid GDL according to a certain R was added to initiate the acid gelation.

*sol* (3)

at a temperature of 35.00 ± 0.05ºC. The relative viscosity (r) was calculated as:

reached while pH was simultaneously monitored.

**2.6.1 Effect of CMC on the viscosity of media**

**2.7.1 Rheological properties of acid gels**

always obtained within the linear viscoelastic region.

**2.7.2 Conventional inverted microscopy**

zoom 7.1x and microscope adapter of 52 mm.

**2.7 Acid gelation**

G'' crossover (pHg).

where sol is the solution viscosity and 0 is the water ones.

The data are reported as the average values ± their standard deviations. Statistical analysis was performed with Sigma Plot 10.0 and Image J softwares. Relationship between variables was statistically analyzed by correlation analysis using Pearson correlation coefficient (r). The differences were considered statistically significant at p < 0.05 values.


**Author details**

At the ESCA, microscopy beamline of the Elettra synchrotron, a dynamic high pressure (DHP) system, has been recently developed. This setup is a solution adaptable to existing synchrotron‐based XPS spectroscopes and microscopes, which allows to overcome the pressure constraints of photoemission technique (high vacuum or ultra‐high vacuum) and to operate up to mbar range. The success of the first near ambient pressure scanning photoelectron microscopy experiment manifests that the developed novel solutions can pave the road to ambient pressure photoelectron spectromicroscopy, allowing for instance the characterization of cata-

**Figure 2.** Schematic representation of some X‐ray–based techniques due to the interaction of synchrotron radiation with

In this book, five chapters on synchrotron‐based characterization of nanostructured materials

lytic systems near real condition [4].

matter (Courtesy of Elettra Sincrotrone Trieste).

4 X-ray Characterization of Nanostructured Energy Materials by Synchrotron Radiation

related to energy applications have been collected.

**Figure 3.** Schematic of beamline BL28XU at SPring‐8 in Japan [3].

Mehdi Khodaei1 \* and Luca Petaccia2

\*Address all correspondence to<EMAIL_ADDRESS><EMAIL_ADDRESS>
1 Faculty of Materials Science and Engineering, K. N. Toosi University of Technology, Tehran, Iran

2 Elettra Sincrotrone Trieste, Trieste, Italy