**4. Conclusions**

128 Chromatography and Its Applications

alkaloidal extract analysis, with some minor modifications, which allowed a good separation of it majority compounds (Figure 9B). The main modification made was the addition of octane sulfonic acid to mobile phase. The presence of this component allowed that the separation to be mediated by more types of interaction, because it acts by an ion

Fig. 9. A: high performance liquid chromatography (HPLC) chromatogram of standards mixture isolated from *Aspidosperma ramiflorum*; B: HPLC chromatogram of alkaloidal extract. Peaks – 1: internal standard (tryptophol); 2: 10-methoxy-geissoschizol; 3: ramiflorine A; 4:

For HPLC analysis, the crude extract of *A. ramiflorum* (Ferreira et al., 2007) was dissolved in CH2Cl2:MeOH (80:20 v/v) and 10 μL were injected into a Waters μ-Bondapak RP-18 (reverse phase, 4.6 mm x 250 mm) column at 40°C. Solvent A was 100 mmol/L ammonium formate in 0.12% octane sulfonic acid (v/v), formic acid and acetonitrile (88:4:8, v/v), while solvent B

**3.4.1 High Performance Liquid Chromatography analysis**

ramiflorine B.

pair formation mechanism, increasing the resolution and efficiency of analysis.

The development of the TLC solvent system for *Aspidosperma ramiflorum* alkaloids at the beginning of this article clearly shows that TLC and PTLC as a routine and classical method appreciated in alkaloid separation techniques, can still be optimized with introduction of new methodologies as desorption electrospray ionization (DESI) which can permit coupled TLC with mass spectrometry (Jackson et al., 2009). This is not necessarily true for alkaloid determination by GC, because decomposition can occur due to the high temperatures applied and however widespread application of this technique is limited by volatility of more polar alkaloids. HPLC is a classical method for alkaloids separations and allowed a good separation of majority compounds in *A. ramiflorum* alkaloids but can still be optimized. However, the pre-purification of crude alkaloid extract will be a crucial step for all analytical procedure described. The applicability of different chromatographic techniques for the separation and identification of crude mixtures of bioactive *A. ramiflorum* indole alkaloids has thus been demonstrated.


**Sin-Yee Yoo , Taehee Kim , Suhan Ham, Sumin Choi and Chan-Ryul Park \***

Urban Forests Research Center, National Institute of Forest Science, 57, Hoegiro, Dongdaemun-gu, Seoul 02455, Korea<EMAIL_ADDRESS>(S.-Y.Y<EMAIL_ADDRESS>(T.K<EMAIL_ADDRESS>(S.H<EMAIL_ADDRESS>(S.C.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +82-2-961-2612

Received: 5 July 2020; Accepted: 12 September 2020; Published: 16 September 2020

**Abstract:** The utilization of urban green areas has increased, but it is unclear whether urban green areas can decrease the concentration of particulate matter at an industrial complex city in Korea. We measured the extent of particulate matter (PM) reduction at a buffer green area in the Sihwa Industrial Complex. PM was measured at the industrial complex, the urban green area, and a nearby residential area from April to October 2019. PM reduction rates were highest at the urban green area in August and October, which is related to increased atmospheric mixing height and the active west wind blowing from the industrial complex to the residential area. Reduction rates of PM10 and PM2.5 at the urban green area showed the lowest values, namely 14.4% and 25.3%, respectively. The air temperature, wind speed, and humidity could affect the PM reduction rate by influencing the movement and dispersion of PM at the micro-spatiotemporal scale. These results indicated that PM concentration could be reduced by the structural change of a forest layer at a micro scale in urban green areas.

**Keywords:** urban green area; Sihwa Industrial Complex; air pollutants; PM reduction; atmospheric mixing height; wind speed


4. Results and discussion

This study provides a novel approach for defining the quantifiable estimation of boundary states and specific points which define changes of phase behavior. The global phase diagram technique is applied for deriving the analytical expressions and Pareto-based approach with fuzzy convolution scheme used for adequate evaluation of model parameters.

There are no rigorous mathematical methods to construct adequate model without subjectively based solution due to a number of uncertainties and conflicts. To reduce the source of unavoidable uncertainty, the different compromise schemes of criteria convolution are considered. It is important to note that the selection of the convolution scheme is a subjective choice of the decision-maker.

Below we provide results and discussion of application of the abovementioned approach for identification of the azeotrope- and double azeotrope-breaking criteria


**Transparent Exopolymer Particle (TEPs) Dynamics and Contribution to Particulate Organic Carbon (POC) in Jaran Bay, Korea**

**Jae Hyung Lee 1, Won Chan Lee 2, Hyung Chul Kim 2, Naeun Jo 1, Hyo Keun Jang 1, Jae Joong Kang 1, Dabin Lee 1, Kwanwoo Kim 1 and Sang Heon Lee 1,\***

Received: 11 March 2020; Accepted: 4 April 2020; Published: 8 April 2020

**Abstract:** Transparent exopolymer particles (TEPs) are defined as acidic polysaccharide particles and they are influenced by various biotic and abiotic processes that play significant roles in marine biogeochemical cycles. However, little information on their monthly variation, relationship and contribution to particulate organic carbon (POC) is currently available particularly in coastal regions. In this study, the water samples were collected monthly to determine TEP concentrations and POC concentrations in a southern coastal region of Korea, Jaran Bay from April 2016 to March 2017. The TEP concentrations varied from 26.5 to 1695.4 μg Xeq L−1 (mean ± standard deviation (S.D.) = 215.9 ± 172.2 μg Xeq L−1) and POC concentrations ranged from 109.9 to 1201.9 μg L−1 (mean ± S.D. = 399.1 ± 186.5 μg L−1) during our observation period. Based on the 13C stable isotope tracer technique, monthly carbon uptake rates of phytoplankton ranged from 3.0 to 274.1 mg C m−2 h−1 (mean ± S.D. = 34.5 ± 45.2 mg C m−2 h−1). The cross-correlation analysis showed a lag-time of 2 months between chlorophyll a and TEP concentrations (*r* = 0.86, *p* < 0.01; Pearson's correlation coefficient). In addition, we observed a 2 month lag-phased correlation between TEP concentrations and primary production (*r* = 0.73, *p* < 0.05; Pearson's correlation coefficient). In Jaran Bay, the TEP contribution was as high as 78.0% of the POC when the TEP-C content was high and declined to 2.4% of the POC when it was low. These results showed that TEP-C could be a significant contributor to the POC pool in Jaran Bay.

**Keywords:** TEP; TEP-C; phytoplankton; chlorophyll a; POC; primary production; Jaran Bay

#### **1. Introduction**

Transparent exopolymer particles (TEPs) are defined as carbon-rich gel particles, mainly consisting of acidic polysaccharides, ubiquitous in the marine environment [1–3]. TEPs are formed naturally from dissolved precursor substances, which contribute considerably to the colloidal dissolved organic matter (DOM) pool in aquatic systems [2]. TEP formation is very important as the major pathway through which DOM is converted to particulate organic matter (POM) [2]. These two different forms of organic matter have specific characteristics and specific roles in the chemistry and biology of the ocean. TEPs are a significant component of particulate organic carbon (POC) in the ocean, with an approximate size range of 0.4 to 100 μm [4–6]. The presence of TEPs is influenced by environmental factors such as temperature, salinity stratification, nutrient conditions, and biological processes that include phytoplankton and bacteria production [2,4,7–9]. Generally, phytoplankton and bacteria

have been considered the major producers of TEPs and precursors in aquatic ecosystems [2,3,10]. TEPs and precursors that produced by these aquatic organisms cannot easily be predicted from their natural abundance because phytoplankton and bacteria do not produce TEPs or precursors equally [2]. For instance, the formation of TEPs by bacteria varies with species composition and growth conditions. Some phytoplankton species produce TEPs during growth, stationary phase and senescence [3]. According to Passow [2], TEP production by phytoplankton varies as a function of the conditions related to light, growth rates, and major or minor nutrient depletion. Therefore, the production of TEPs and precursors depends on the physiological status and growth conditions of the individual organisms.

In recent years, TEPs have received considerable attention in terms of their influence on carbon cycling [5,11–17]. Given their sticky character, TEPs increase particle aggregation forming marine snow and enhancing carbon export to deep waters in pelagic/oceanic ecosystems [11,18]. In coastal regions, coinciding with high biological productivity and phytoplankton biomass, TEP concentrations are also found to be high [10,12,19–22]. The half-lives of TEPs observed in a coastal environment are less than 1 day when large marine snow aggregates are formed, whereas they are more than 2 days during the period without large aggregations [23]. Although TEPs play an important role in carbon cycling and budget in coastal seas [24], little information has been reported on them in coastal seas to date.

Jaran Bay, located on the coast of the South Sea in Korea, is an area of productive bivalve farming in Korea [25,26]. There is no doubt that the roles of TEPs could be significant, but nothing has been studied in Jaran Bay. Thus, the present study aimed to (1) investigate the monthly variations in TEP concentrations for one year for the first time in Jaran Bay on the southern coast of Korea, (2) compare TEP with POC stocks to understand how much TEPs contribute to POC as a kind of carbon budget and (3) determine the relationship between the phytoplankton (biomass and productivity) and TEPs in coastal environmental conditions in Jaran Bay.

#### **2. Materials and Methods**

The study site was located in Jaran Bay on the southern coast of Korea, which borders the South Sea (Figure 1). Water samples were obtained at seven different stations in Jaran Bay monthly from April 2016 to March 2017 (Figure 1). The study area has a shallow area within 20 m depth. Usinga5L Niskin water sampler (General Oceanics Inc., Miami, FL, USA), water samples were collected from two *in-situ* different light depths (100 and 1%) to include the depth range of the euphotic zone. The light depths were calculated by a Secchi disk using a vertical attenuation coefficient (*K*d = 1.7/Secchi depth) from Poole and Atkins [27]. The water temperature and salinity were measured with a YSI-30 (YSI Incorporated, Yellow Spring, OH, USA). The large zooplankton were removed by 333 μm mesh when the water samples were collected.

The chlorophyll a concentration was determined by filtering 200 mL of seawater on 0.7 μm pore sized Whatman glass fiber filters (GF/F; 25 mm, Whatman, Maidstone, UK) and the filters were kept frozen until analysis. Chlorophyll a pigments were extracted from the filters with 10 mL acetone (90% v:v) in the dark at 4 ◦C for 24 h and fluorescence was measured with a Turner Designs model 10-AU fluorometer (Turner Designs, San Jose, CA, USA). The method for the chlorophyll a calculation followed that of Parson et al. [28].

TEP concentrations were estimated following the method of Passow and Alldredge [29]. Fifty milliliters of water was filtered onto 0.4 μm pore sized Nuclepore polycarbonate membrane filters (ADVANTEC; 25 mm, Toyo Roshi Kaisha, Tokyo, Japan) at a low and constant vacuum (<10 cm Hg), because the live cells could be damaged during strong vacuum filtration [29]. The filters were stained with 0.5 mL of precalibrated (with a xanthan gum solution) Alcian Blue (8GX, Sigma) for 5 s, rinsed with ultrapure water, frozen immediately and returned to the laboratory at Pusan National University in South Korea for further analysis. The filters for TEP concentration were soaked in 80% sulfuric acid for 3 h, and absorbance was read at 787 nm in a spectrophotometer (Hitachi-UH 5300, Hitachi, Tokyo, Japan). TEPs were quantified by a standard curve prepared with xanthan gum particles, as described in Passow and Alldredge [29]. The detection limit of the measurements was 0.025 absorbance units

and the coefficient of variation of the replicates was 12.4%. Three blanks (empty filters stained with Alcian Blue) were also prepared with every batch of filtered samples every day. TEP concentration was expressed in terms of xanthan gum equivalents (μg Xeq L−1). Based on the conversion factor ranging from 0.51 to 0.88 μg Xeq L−1 for a comparison of TEP with POC stocks [12,21], we used the lowest conversion factor to estimate TEP-C conservatively, since it corresponds to diatom-dominated ecosystems [12]. The conversion factor of 0.51 μg Xeq L−1 was used to convert from micrograms of xanthan gum to micrograms of carbon (TEP-C). The water samples (300 mL) were obtained from 100% and 1% light depths and filtered on pre-combusted (4 h, 450 ◦C) GF/F filters (25 mm) for POC concentrations. The filters were immediately frozen at −20 ◦C until further mass spectrometric analysis within 3 months.

The estimation of the carbon uptake rates was performed using 13C stable isotope labeling experiments. Seawater samples at 6 different light depths (100, 50, 30, 12, 5, and 1%) determined by a Secchi disk were transferred from the Niskin bottles to 1 L polycarbonate incubation bottles (NALGENE, Rochester, NY, USA), wrapped with neutral density light filters (LEE filters, Andover, England), to match the desired light levels. Then, the water samples were inoculated with a labeled carbon (NaH13CO3) solution, and the bottles were incubated in large polycarbonate incubators under natural light conditions for at least 4 h, or up to 6 h when it was cloudy. To maintain in situ environments, ambient surface seawater was continuously run through the incubators during incubation. The 4–6 h incubations were terminated by filtration. The incubated waters were well mixed, and the measured volume (0.3 L) was filtered through precombusted (450 ◦C) GF/F filters (25 mm).

All filter samples for POC concentration and the abundance of 13C/ 15N were kept frozen (−20 ◦C) until the mass spectrometric analysis in a Finnigan Delta+XL mass spectrometer in the stable isotope laboratory (University of Alaska Fairbanks, Fairbanks, AL, USA) after overnight HCl fuming to remove carbonate. The measurement uncertainties for δ13C and δ15N were ± 0.06‰ and ± 0.02‰, respectively. Finally, the carbon uptake rates were calculated based on Hama et al. [30].

Statistical tests were performed using the statistical software IBM SPSS Statistics (*t*-test, one-way ANOVA and Pearson's correlation coefficient).

**Figure 1.** Study area map with the sampling stations in Jaran Bay, Korea.

#### **3. Results**

The monthly average water temperature and salinity were 6.4–28.7 ◦C and 28.7–34.0 during our sampling period, respectively (Table 1). The water temperature substantially varied, whereas salinity showed a relatively lower variation. The temporal variations in temperature and salinity showed patterns typical of those in mid-latitude regions. Water transparency was measured using Secchi disk depths, ranging between 2 and 10 m in Jaran Bay (Table 1). During the study period, the average euphotic depth was 8 ± 2 m, ranging from 5 to 18 m.

**Table 1.** Station-averaged environmental variables observed at the sampling stations in Jaran Bay. N.D. means no data.

The chlorophyll a concentration at the euphotic depth ranged from 0.2 to 8.4 μg L−1 (mean ± S.D. = 1.8 ± 1.6 μg L−1) in this study (Figure 2a). The chlorophyll a concentration was averaged from the two light depths at each station, because they were not significantly different (one-way ANOVA test, *p* > 0.05). The monthly chlorophyll a concentration averaged from the two light depths at the 7 stations ranged from 0.3 ± 0.1 μg L−1 in March 2017 to 4.9 ± 1.6 μg L−1 in October 2016 (Figure 2a). Distinct temporal variations in chlorophyll a concentrations at 100% and 1% light depths were observed in this study. Two bloom peaks were found during the study period. The first bloom was initiated at the 100% light depth in July 2016, followed by the bloom at the 1% light depth in August 2016. The second blooms were at both the 100% and 1% light depths in October 2016, which were relatively higher than the former blooms (Figure 2a).

**Figure 2.** Monthly variations in the (**a**) transparent exopolymer particles (TEP) (μg Xeq L−1), (**b**) chlorophyll a (μg L−1) and (**c**) particulate organic carbon (POC) (μg L−1) concentrations at the 100% and 1% light depths averaged from 7 different stations in Jaran Bay.

The overall TEP concentrations ranged from 26.5 to 1695.4 μg Xeq L−1 (mean ± S.D. = 215.9 ± 172.2 μg Xeq L−1) in Jaran Bay from April 2016 to March 2017 in this study (Figure 2b). The TEP concentrations from the two light depths (100 and 1%) were different during the observation period (one-way ANOVA test, *p* < 0.05); the mean concentrations of TEP from the two light depths were 192.0 ± 119.3 μg Xeq L−1 and 239.9 ± 210.4 μg Xeq L−1, respectively. The highest TEP concentrations monthly averaged from the 7 stations were observed in December 2016 (100% = 332.1 ± 105.9 μg Xeq L−1; 1% = 581.9 ± 527.4 μg Xeq L−1). After the winter peak in December, TEP concentrations decreased rapidly throughout the month (Figure 2b). TEP-C values were calculated by multiplying the TEP concentrations by the lowest conversion factor (0.51 μg Xeq L−1), ranging from 13.5 to 864.7 μgCL−1 (mean ± S.D. = 110.1 ± 87.8 μgCL−1) during the study period. The average values of TEP-C at the two light depths (100 and 1%) were 97.9 ± 60.8 μgCL−1 and 122.3 ± 107.3 μgCL−1, respectively.

POC concentrations ranged from 109.9 to 1201.9 μg L−1, with an average of 399.1 ± 186.5 μg L−1 in this study (Figure 2c). The mean concentration of POC was statistically higher at the 1% light depth than at the 100% light depth (one-way ANOVA test, *p* < 0.05). The average concentrations of POC were 368.0 ± 167.7 μg L−1 at the 100% light depth and 430.2 ± 199.8 μg L−1 at the 1% light depth. In this study, the highest POC concentrations at the euphotic depth were observed in the summer season (100% light

depth: July 2016; 1% light depth: August 2016) in Jaran Bay (Figure 2c). Monthly TEP, chlorophyll a and POC concentrations were not significantly correlated (Pearson's correlation coefficient).

The average TEP-C contributions to POC were 21.4% ± 11.7% at the 100% light depth and 22.0% ± 11.2% at the 1% light depth (Figure 3). The TEP-C contributions to POC were not constant throughout the different months in this study (one-way ANOVA test, *p* < 0.05). The monthly average contribution of TEP-C to POC was relatively high in September (100% light depth: 35.3 ± 23.8%; 1% light depth: 36.2% ± 19.9%). In contrast, relatively lower TEP-C contributions to POC were found in May (12.8% ± 6.7%; 100% light depth) and July (10.1% ± 3.9%; 1% light depth) (Figure 3).

**Figure 3.** Monthly TEP-C contributions to POC at different light depths (100 and 1%) in Jaran Bay.

The monthly mean specific and absolute carbon uptake rates of the phytoplankton in the water column during this study are presented in Table 2. The specific carbon uptake rates were 0.001–0.112 h−1 at the 100% light depth and 0–0.004 h−1 at the 1% light depth. The absolute carbon uptake rates were 0.2–137.4 μgCL−1 h−1 and 0.0–1.3 μgCL−1 h−1 at the 100% light depth and 1% light depth, respectively (Table 2). The hourly specific and absolute carbon uptake rates were relatively higher in the surface waters than at other light depths. The monthly carbon uptake rates of the phytoplankton integrated from the surface to a 1% light depth (primary production of phytoplankton from the euphotic layer) ranged from 3.0 to 274.1 mg C m−2 h−1 (mean ± S.D. = 34.5 ± 45.2 mg C m−2 h−1). The highest monthly primary production was recorded in the early summer (mean ± S.D. = 115.4 ± 97.3 mg C m−2 h−1; July 2016) and the lowest value was measured in the summer (mean ± S.D. = 5.7 ± 2.3 mg C m−2 h−1; August 2016) (Table 2).

**Table 2.** Station-averaged specific and absolute carbon uptake rates and primary production of phytoplankton at the 100% and 1% light depths in Jaran Bay.

#### **4. Discussion**

The TEP concentrationsmeasuredin Jaran Bay during this study ranging from 26.5 to 1695.4μg Xeq L−1 generally fall within the range observed in other studies from various coastal seas and bays to open oceans around the world (Table 3). However, our TEP concentrations were relatively higher than those observed in open ocean environments [6,31–33]. In general, TEP concentrations are higher in coastal waters than in adjacent open ocean waters [2,34]. Since TEPs are mainly formed by phytoplankton, the general pattern of higher TEP concentrations in coastal oceans are closely related to the phytoplankton biomass [18]. Similarly, the relatively higher chlorophyll a concentration in this study (0.3 to 4.9 μg L−1) could be explained by the higher TEP concentrations compared to those of the open oceans (Figure 2a) [6,9]. Previous studies have shown that TEP production rates are largely involved in the physiological status of phytoplankton [2,10,23]. To investigate this relationship in this study, the specific and absolute carbon uptake rates of phytoplankton were used for their physiological conditions during our research period. Generally, the monthly mean specific and absolute carbon uptake rates were statistically higher at the 100% light depth than at the 1% light depth (one-way ANOVA test, *p* < 0.05). However, neither specific nor absolute carbon uptake rates were significantly correlated with the TEP concentrations in our study. One of the reasons for the higher TEP concentrations observed in our study compared with those in previous studies could be related to the phytoplankton community. Previous studies have reported that extracellular organic matter created from phytoplankton serves as a precursor for TEPs [18,35,36]. In particular, TEPs are considerably correlated with the abundance of diatoms, which are considered major producers of TEPs [2,23]. In this study, we found relatively high chlorophyll a concentrations and carbon uptake rates of phytoplankton. In addition, the dominant species were diatoms during this study based on the relative contributions of the major phytoplankton classes in a parallel study (unpublished data). The monthly contributions of diatoms to the total phytoplankton classes ranged from 19.8% to 96.7%, with an average of 66.4% ± 24.3% in Jaran Bay

(unpublished data) [26,37]. It was also reported that the predominant species are diatoms in this area. Specific to these diatom-dominated ecosystems, the overall dominant macromolecular compositions of POC are carbohydrates (51.8 ± 8.7%) in Jaran Bay [38]. The carbohydrate-dominant phytoplankton could be a potential reason for the higher TEP concentrations observed in this study, since TEPs can be produced from dissolved carbohydrate polymers exuded by phytoplankton [2]. Another potential reason for the higher TEP concentration in this study is that large shellfish aquaculture farms were intensely implemented in our study areas. In coastal waters, mucus nets of filter feeders and anthropogenic sources, such as river inputs (urban-industrial release) and aquaculture farms (macroalgae and shellfish) could be major sources of TEPs [39,40]. There are no major river inputs into Jaran Bay, so no significant influence of urban-industrial release would be expected. Based on previous studies, cation availability might be one such important controlling factor on the TEP formation system [34]. The formation and sustenance of TEP are dependent on cation availability, as cations (particularly Ca2+ and Mg2+) stabilize the structural integrity of TEP through cation bonding [34,41]. In Jaran Bay, cation availability (Ca2+) might be high because of intensive shellfish (oysters and scallops) aquaculture conditions. Jaran Bay is a relatively shallow coastal bay with an average water depth of 10 m, which might sustain a high concentration of cations from the relatively easy resuspension of biodeposition on the top of the sediments underneath aquaculture farms. In fact, oyster soft tissues are generally reported for high mineral elements such as Ca2+ and Mg2+ [42]. However, this should be verified in further studies.

During the study period, the TEP/chlorophyll a ratio value ranged from 8 to 1233 μg Xeq μg Chl *a*−1, averaging 221 ± 217 μg Xeq μg Chl *a*−1. High levels of variability in TEP/chlorophyll a ratio values were observed in this study. These results were similar to those in former studies. The mean TEP/chlorophyll a ratio values were 206.8 and 281.5 in Otsuchi Bay and the Gulf of Cadiz, respectively [43–45].

TEPs often make up a large fraction of the POC in the water column [4]. Although TEPs play a key role in the sequestration of excess carbon to deeper waters in open oceans, TEP-C is also an important part in understanding the carbon budget as an additional carbon source in coastal marine food webs [2]. Previous studies have reported significant relationships between TEP and POC concentrations [9,17]. However, no significant correlation was found between TEP and POC concentrations in this study, which is similar to the result of Ortega-Retuerta et al. [22] in the coastal NW Mediterranean Sea. Considering the TEP-C and POC values reported herein, we estimated the TEP-C contribution to the POC pool in Jaran Bay during our study period. In the present study, we used the lowest conversion factor (0.51 μg Xeq L−1), which corresponds to diatom-dominated phytoplankton ecosystems, to estimate TEP-C. The same conversion factor has been used to investigate the TEP-C contribution to the total organic carbon pool in the upper surface water column in open deep oceans [6,21]. In the present study, the estimated TEP-C contribution to the total POC ranged from 2.4 to 67.3%, with an average of 21.7% ± 11.4% (Table 4). In comparison, the TEP-C contribution to the total POC based on the highest conversion factor (0.88 μg Xeq L−1) ranged from 4.1% to 78.0% (31.4% ± 13.8%). The contribution of TEP-C to the total POC in Jaran Bay during our research period was within the range observed in previous studies in different bays, ranging from 7% to 32%. Furthermore, Bhaskar and Bhosle [46] reported that TEP-C could constitute 7% of the total POC in Dona Paula Bay, and Malpezzi et al. [4] found that TEP-C averaged 32% of the total POC in the Chesapeake Bay. In contrast to the values in bays, the TEP-C contributions can be higher in relatively deeper environmental conditions [9,17,23]. Moreover, Parinos et al. [17] observed that TEP-C represented 70% of the POC, on average, in the NE Aegean Sea. Notably, Zamanillo et al. [9] reported that the monthly mean TEP contribution to POC was 73% of the POC pool in the southwestern Atlantic Shelf, and Ortega-Retuerta et al. [22] found that TEP-C averaged 77% of the POC in coastal NW Mediterranean Sea in early summer. Higher TEP concentrations, as discussed previously, but lower TEP-C contributions to the total POC pools, indicate relatively more POC-based carbon available under coastal or bay environmental conditions, compared to that under open ocean environmental conditions. TEP production and formation rates are largely dependent on physiological conditions, especially nutrient stress, of phytoplankton and bacteria [23]. In general, relatively better phytoplankton nutrient conditions in bay systems than in deeper ocean could be a major reason for the lower contribution of TEP-C to the total POC. Another potential explanation might be different degrees of overestimation from different pore sized filters for TEP and POC measurements. Typically, 0.4 μm pore-sized filters are used for TEP concentration, while 0.7 μm pore-sized filters are used for POC. Therefore, the TEP-C contribution to the total POC could potentially be overestimated by methodological sampling approaches. However, TEPs smaller than 0.7 μm might be more abundant in open ocean conditions than in coastal or bay environments with active TEP formation conditions, as discussed previously, which could result in increased overestimations of TEP-C in open oceans. Verification for this concept could be conducted in future studies.

**Table 3.** Comparison of the TEP concentrations between this and previous studies from various regions.

In this study, monthly TEP and chlorophyll a concentrations were not significantly coupled (Figure 2a,b). Previous studies reported strong interrelationships between TEP and chlorophyll a concentrations for some time series [17,24,53,54]. In contrast, no strong relationships were found [46,55], or some correlations were observed for certain periods of the year [22,56]. Ortega-Retuerta et al. [22] observed a 4-month lag phase between chlorophyll a and TEP concentration peaks in the coastal NW Mediterranean Sea (*r* = 0.48, *p* < 0.04). In the present study, the cross-correlation analysis indicated a lag-time of two months between chlorophyll a and TEP concentration peaks to a significant degree (*r* = 0.86, *p* < 0.01; Pearson's correlation coefficient) (Figure 4a). Moreover, we observed a two-month lag-phased correlation between TEP concentrations and primary production (*r* = 0.73, *p* < 0.05; Pearson's correlation coefficient) (Figure 4b). Previous studies reported that the lag-phase between chlorophyll a and TEP concentration peaks could be caused by an enhancement in the TEP production rate under nutrient-limiting conditions of phytoplankton after phytoplankton blooms [23]. Based on the concentrations and molar ratios of nutrients [57–59]; no major nutrient limiting conditions were found in this study area except in May 2016 [26]. In coastal areas, TEP production and removal/consumption processes are influenced by various biotic factors (e.g., phytoplankton biomass and community structure, growth, grazing, and heterotrophy) and abiotic (e.g., stratification, current, tidal, land run-off, and sorption to sediments) processes [43,46]. In addition, nutrient limitations to phytoplankton growth enhance TEP production rates [23,60]. Therefore, the lag-phase patterns between chlorophyll a and TEP concentration and primary production and TEP concentration could not be controlled by any single factor.

**Figure 4.** Lag-phased correlations between (**a**) chlorophyll a and TEP concentration and (**b**) primary production and TEP concentration.

**Table 4.** Comparison of the TEP-C contribution to the POC (%) between this and previous studies.

#### **5. Conclusions**

This study reported the spatiotemporal dynamics and relative POC contributions of TEP in Jaran Bay, South Korea, based on monthly field measurement data. This study clearly showed that the large monthly variation in TEP is mainly driven by phytoplankton biomass, such as chlorophyll a concentration and their photosynthetic productivity, with 2-month lag phases in Jaran Bay. Although the TEP carbon contribution to the POC pool can be as high as up to approximately 70% at some sampling sites, the overall TEP contribution was 21.7% (± 11.4%) in Jaran Bay, which is consistent with the results of previous studies. Generally, high TEP concentrations but relatively lower TEP carbon contributions in coastal or bay environments suggest that POC-based carbon could be more available in shallow waters than in deep open oceans. However, some of the different sampling methods that we discussed should not be ignored because of the potential possibilities for the discrepancy between TEP concentrations and their carbon contributions. In this study, relatively higher TEP concentrations were observed in Jaran Bay than in other bays. This could be due to the diatom-dominant coastal environments in Jaran Bay, with overall monthly contributions of diatoms > 60% based on a parallel study. The relatively higher TEP concentration in Jaran Bay could be due to the many shellfish aquaculture farms with potentially high cation availability (Ca2+), which should be verified in further studies. This result contributes to a comprehensive understanding of the seasonal dynamics of TEPs and their potential roles in the organic carbon pool in coastal or bay environments. In particular, this study contributes to providing the background for using TEPs to evaluate phytoplankton responses to ongoing changes in coastal ecosystems associated with global climate change.

**Author Contributions:** Conceptualization, J.H.L., W.C.L., H.C.K. and S.H.L.; Data curation, J.H.L.; Formal analysis, H.K.J.; Investigation, J.H.L., N.J., J.J.K., D.L. and K.K.; Methodology, H.K.J.; Validation, J.H.L., W.C.L. and H.C.K.; Visualization, J.H.L.; Writing—original draft, J.H.L.; Writing—review and editing, J.H.L., N.J. and S.H.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the grant (R2020050) from the National Institute of Fisheries Science (NIFS) and "Improvements of ocean prediction accuracy using numerical modeling and artificial intelligence technology", funded by the Ministry of Oceans and Fisheries, Republic of Korea.

**Acknowledgments:** We thank the anonymous reviewers who greatly improved an earlier version of manuscript.

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

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**1. Introduction**

Global Geoparks authorized by the United Nations Educational, Scientific and Cultural Organization (UNESCO) have expanded in number since inception in 2000 to more than 160 units in 45 different countries. Geology in one form or another is the educational focus of such parks, but with an emphasis on geotourism in support of conservation and the socioeconomic development of rural areas [1]. Remote by nature, many islands possess features of extraordinary significance worthy of development as geoparks. As an example, Iceland is iconic for its status as an island that straddles an expanding oceanic ridge. The island boasts of two UNESCO geoparks, but the potential for several additional parks is anticipated by local planners [2]. In Norway, the Trollfjell (Troll Mountain) Geopark is an example of a well-organized geopark in a remote part of that country near the Arctic Circle that includes many coastal islands with small communities. Stunning coastal scenery that enfolds world-class aspects of geology in the Trollfjell Geopark also includes elements of regional folklore that relate the landscape to its human occupation. Established in 2010, the National Norwegian Geological Monument on Leka Island within the greater geopark boosts the program beyond that envisioned by UNESCO [3]. The monument not only abets an increase in commerce through geotourism but also attracts working geologists and geomorphologists who otherwise may not have known about the

place. The dynamics foster a feed-back loop, through which a steady increase in knowledge adds to the overall significance of geoheritage. The present contribution on a unique boulder beach and the interpretation of its hydrodynamics is offered in that spirit.

The aim of this paper is to review unusual physical traits along one of the monument's well-marked trails at Støypet near the north shore of Leka Island. In Norwegian, the word *støypet* may be translated as "the foundry" in reference to the rare igneous rocks and chromite ore concentrated at that place [4,5], although there is no evidence that mineral extraction and smelting took place any time since human occupation began in the earliest Holocene. The unique aspect of Støypet as a geomorphologic and cultural site is the accumulations of a boulder beach dominated by "rolling stones" eroded from adjacent rocky shores composed of dunite and chromite. These ultramafic rocks originated in the deepest part of the earth's crust near the discontinuity with the upper mantle and register high values of specific gravity that give them greater mass. The implication is that wave heights affecting coastal surge were sufficiently powerful to remove joint-bound blocks from sea cliffs that resulted in unusually dense boulders. The same mathematical equations for estimation of wave heights as applied previously to other shores with boulders derived from more common source rocks of lesser mass, including limestone, rhyolite, andesite, and basalt [6–9], are newly applied at Støypet. In addition, the present-day steering winds and wave dynamics characteristic of Norway's Arctic Circle region [10] are reviewed in the context of prominent storms during recent decades in order to appraise the likely direction of wave impact responsible for the boulder beach at Støypet.

#### **2. Geographical and Geological Setting**

Open to the Norwegian Sea off the coast of northern Norway, Leka Island lies south of the Arctic Circle within the Trollfjell Geopark (Figure 1a). Together with smaller Madsøya, the two islands combine for a total area of 57 km2 (Figure 1b), and support a population of about 500 inhabitants. From the mainland, the island is accessible by ferryboat across a 4-km wide strait. Norway's National Geological Monument offers an extensive system of hiking trails that are well laid out and include educational trail-side markers with texts in Norwegian, English, and German. Several themes combine to make the park an attractive experience, including local folklore. The island's most prominent landmark is a monolith at the side of Lekamøyhammaren Mountain (Figure 1b), said to embody a troll maiden. The monolith (Figure 2a) is a sea stack composed of the igneous rock gabbro, now isolated inland by postglacial rebound at an elevation 100 m above present-day sea level. Other effects of coastal erosion are evident around the sea stack, where former sea cliffs show deep wear in the form of surge channels cut and polished in gabbro basement rocks (Figure 2b). Detailed work by Høgaas and Sveia (2015) that covers the inner (southeastern) part of Leka Island outlines slightly earlier shore erosion by ice scour during the Younger Dryas interval of 12.8 to 11.5 thousand years ago [11].

Geologically, Leka Island is renowned for exposures of the igneous rocks dunite and harzburgite. These are attributed to formation around the boundary between the Earth's lower crust and upper mantle, known as the Mohorovicic Discontinuity [4]. Such rocks are accessible in few other places around the world where their occurrence is justly celebrated, most notably in Canada's Gros Morne National Park in western Newfoundland [12]. Enrichment in minerals belonging to the platinum group, including gold, platinum, and chromium, also occurs as a dense chromite ore associated with dunite on Leka Island [5]. Brought to the surface by tectonic events, these parent rocks were locally subject to Holocene coastal erosion. Thus, today's Leka Island represents a rare convergence of factors related to the geology of deep ocean crust and the geomorphology of recent marine coastal erosion.

On the island's north shore (Figure 1c), access to a Holocene valley crosses a seam of banded chromite framed by outcrops of dunite (Figure 3) that is described by geopark signage. It is noticeable that the chromite bands at this locality are dissected by joints and fractures typical of exfoliation under subaerial conditions comparable to those in former sea cliffs elsewhere in Støypet valley.

**Figure 1.** The west coast of Norway and Leka Island: (**a**) Norway, showing latitude of the Arctic Circle and region of the Trollfjell Geopark, (**b**) Leka Island and Madsøya with details on highland peaks and location of the Leka maiden monolith (asterisk), and (**c**) inset showing location the Støypet study site.

**Figure 2.** The raised shoreline now about 100 m above sea level at the base of Lekamøyhammaren Mountain (see Figure 1b): (**a**) Gabbro sea stack attributed by legend to the stony embodiment of the Leka troll maiden (author for scale) and (**b**) wave-polished surge channel eroded in gabbro close by the sea stack (figure for scale).

**Figure 3.** Rough exfoliation of chromite bands (dark rocks) and dunite (pale rocks) at the northeast (NE) end of Støypet valley 250 m from the island ring road on the access trail.


*The Unique Approaches to Water Management for Transforming Bangladesh's Drought-Prone… DOI: http://dx.doi.org/10.5772/intechopen.105840*

(BADC) in 1986 and, thus the development of the area was started. The project area included three districts: Rajshahi, Nagaon, and C. Nawabganj [20]. The major goal of the project was to boost crop production through the creation of irrigation facilities and to contribute to food security. However, due to administrative difficulties and a delay in the release of funds by BADC to the project authority, the project's progress was hampered. In 1992, Barind Multipurpose Development Authority (BMDA) was established under the Ministry of Agriculture to address this issue and ensure the project's seamless operation. The government approved BIADP-II, and the BMDA successfully implemented it on time. In 2003, the Gov. handed over 1217 unused deep tube wells (DTW) from then north Bengal DTW project of the Bangladesh Water Development Board (BWDB) under Thakurgaon, Dinajpur, and Panchogar districts, recognizing the BMDA's achievement. Within a year, BMDA had successfully put all of the DTWs into operation [21]. Finally, in 2018, the Bangladesh government enacted an act that expanded the BMDA's jurisdiction to include all 16 districts of the Rajshahi and Rangpur Division. **Figure 1** depicts the sequential expansion of BMDA's jurisdiction.

The main goal of BMDA is to turn its land into a granary by creating irrigation facilities. So, the aim of this study is to discuss the water resources development and management activities of BMDA and evaluate them.

### **Figure 1.**

*The map of the sequential expansion of BMDA's jurisdiction into 16 districts of Rajshahi and Rangur division of Bangladesh.*


**Preface to "Plant Embryogenesis"**

Embryogenesis is a fundamental process in plant ontogeny. The fusion of a paternal sperm and a maternal egg generates a zygote and initiates the series of developmental events to set the basic body plan of the future plant. Therefore, embryogenesis is a dynamic procedure, involving a shift from the haploid gametophytic to the diploid sporophytic generation, metabolic activation, pattern formation, and dormancy in seed maturation. Furthermore, successful embryogenesis is essential for plant fertility and reproductive fitness. Thus, embryonic regulations are important not only for understanding both plant evolution and the diverse survival strategies of various plant species but also for bioengineering to increase plant productivity in agriculture.

> **Minako Ueda, Daisuke Kurihara** *Editors*

## *Review* **A Quarter Century History of** *ATML1* **Gene Research**

**Hiroyuki Iida 1 and Shinobu Takada 2,\***

**Abstract:** The cloning of the *ATML1* gene, encoding an HD-ZIP class IV transcription factor, was first reported in 1996. Because *ATML1* mRNA was preferentially detected in the shoot epidermis, cis-regulatory sequences of *ATML1* have been used to drive gene expression in the outermost cells of the shoot apical meristem and leaves, even before the function of *ATML1* was understood. Later studies revealed that *ATML1* is required for developmental processes related to shoot epidermal specification and differentiation. Consistent with its central role in epidermal development, *ATML1* activity has been revealed to be restricted to the outermost cells via several regulatory mechanisms. In this review, we look back on the history of *ATML1* research and provide a perspective for future studies.

**Keywords:** embryogenesis; epidermal development; *ATML1*; transcriptional regulation; posttranscriptional regulation

#### -- **-**

**Citation:** Iida, H.; Takada, S. A Quarter Century History of *ATML1* Gene Research. *Plants* **2021**, *10* , 290 . https://doi.org/10.3390/plants10020290

Academic Editor: Minako Ueda Received: 5 January 2021 Accepted: 29 January 2021 Published: 3 February 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Epidermis Formation and Its Role in Plant Development**

*ATML1* was first reported ~25 years ago as a gene that is expressed in the epidermis of *Arabidopsis thaliana*. The epidermis is the outermost cell layer of land plants, first formed during early embryogenesis. In *Arabidopsis thaliana*, embryo proper cells during the eightcell stage undergo tangential cell divisions, generating outer and inner daughter cells. The outer daughter cells exhibit mostly anticlinal cell divisions to help maintain the single cell layer and are eventually differentiated into the epidermal cells.

The epidermis is positioned at the interface between plants and the environment; therefore, specialized cell types and structures in the epidermis facilitate abiotic and biotic stress responses. For example, stomatal guard cells and trichomes enable efficient gas exchange and prevent insect herbivory, respectively [1,2]. Additionally, the cuticle, a hydrophobic lipid layer, is deposited on the outermost surface of the shoot epidermal cell walls, in order to prevent water loss due to the dry terrestrial environment [3].

In addition to its protective functions, the epidermis plays an important role in controlling plant growth. Brassinosteroid (BR) is a plant hormone promoting cell division and expansion and the BR response and biosynthetic mutants show a severe dwarf phenotype. Activation of BR signaling in the outermost layer of those mutants rescued their dwarf phenotypes, whereas inactivation of BR signaling in the wild-type epidermal layer made plants small. These observations suggest that the epidermal layer restricts the extent of shoot growth depending on the activity of BR signaling [4]. It has also been reported that production of very-long-chain fatty acids (VLCFAs) in the epidermis, which are components of the cuticle wax, is required for repressing cell proliferation in the vascular tissue [5,6]. Observed increase in cytokinin, a plant hormone that promotes cell proliferation, in VLCFA-deficient seedlings suggests that VLCFAs or their derivatives play a role in repressing cytokinin biosynthesis in the vasculature [5].

The epidermis also influences pattern formation in the shoot apical meristem (SAM), as several reports have suggested that certain microRNAs (miRNAs), generated in the

outermost cell layer of the SAM, form an inhibitory gradient that contributes to the positioning of the stem cell niche at away from the epidermis [7,8]. Consistently, epidermal deficient mutants often show ectopic SAM activity in the leaves [9].

Molecular genetic analysis has revealed many genes that are required for epidermal cell differentiation. Among those genes, *ARABIDOPSIS THALIANA MERISTEM L1 LAYER* (*ATML1*), which encodes an HD-ZIP class IV transcription factor, has been shown to be a master regulator for shoot epidermal cell identity in *Arabidopsis thaliana* (Figure 1). This review summarizes *ATML1* research of the last 25 years and provides a future perspective in the field.

**Figure 1.** Timeline of the *ATML1* research. The history of *ATML1* gene research is summarized. Each box shows important findings in the indicated year.

#### **2. Cloning of the** *ATML1* **Gene and Analysis of Its Expression Pattern**

*ATML1* was first identified in a screen searching for cDNA clones that have a sequence homology with an ovule-enriched homeobox gene, O39 in orchid, by plaque hybridization [10,11]. '*ATML1* was initially named as an acronym of *ARABIDOPSIS THALIANA MERISTEM L1 LAYER* (the outermost layer is called L1) although it is mistakenly written down as *AtML1* or has been described with the acronym of *ARABIDOPSIS THALIANA MERISTEM LAYER1*. *ATML1* belongs to the HD-ZIP class IV family transcription factors, including 16 members in *Arabidopsis thaliana*. The first identified member of the HD-ZIP class IV genes, *GLABRA2* (*GL2*), was involved in trichome and root hair patterning [12,13]. The first report showed that *ATML1* mRNA was detected specifically in the outermost cells of embryos, the SAM, and floral organs; *ATML1* is the first gene shown to be expressed in a specific cell layer of plant tissues [10] (Figure 2a,c).

In the first report, in situ hybridization experiments showed preferential accumulation of *ATML1* mRNA in the outermost cells of the SAM, leaf primordia, and floral organs. During embryogenesis, *ATML1* mRNA was detected in the apical cell of the one-cell stage embryo and its localization was restricted to the outermost cells of the embryo-proper from the early globular stage onwards [10].

After the first report, *ATML1* expression pattern has been re-examined in more details by several researchers [14–16]. *ATML1* promoter activity was also detected in the outermost cells of the root meristem [15] (Figure 2c). Detailed expression analysis in the embryos, with the aid of a sensitive reporter gene, revealed that *ATML1* promoter was also active in the basal cell of the one-cell stage embryo and in the suspensor cells (Figure 2a). Furthermore, when the reporter was fused with a destruction-box motif, which degrades the reporter protein at anaphase and enables detection of only newly synthesized protein after cell division, *ATML1* promoter activity was hardly detected in the inner cells of the 16-cell stage embryos, suggesting that *ATML1* transcription is restricted to the outermost cells from the 16-cell stage [16]. Recently, we reported that although *ATML1* was strongly expressed in the outermost cells, weak expression was also detected in the inner cells of the embryos, especially in the subepidermal cells [14] (Figure 2a).

**Figure 2.** Schematic of *ATML1* transcript and ATML1 protein localization. (**a**) *ATML1* is transcribed strongly in the outermost cells and weakly in the inner cells of the embryos (**b**) ATML1 protein accumulation is observed only in the surface cells from the 16-cell stage. ATML1 protein is exclusively localized in the nuclei until the 8-cell stage. From the 16-cell stage onwards, ATML1 protein is present in both nuclei and cytosol of the outermost cells of the embryo-proper, while it only remains to accumulate in the nuclei of the suspensor cells. When the epidermal cells undergo aberrant periclinal divisions, ATML1 nuclear accumulation becomes weaker in the inner daughter cells compared with the outer daughter cells. (**c**) *ATML1* activity during post-embryonic development in the shoot apex (left) and the root tip (right). (**d**) When accumulated above the threshold level during the G2 phase, ATML1 promotes giant cell formation in the sepal epidermis. (**e**) Feedback regulation of *ATML1* via the L1 box in the promoter (*proATML1*). Orange: *ATML1* transcription, green in the cell: ATML1 protein, red: giant cells, yellow box in the *ATML1* promoter: an L1 box.

In situ localization of *ATML1* transcripts reported in 1996 implied its role in epidermis development, which was later proved in 2003.

#### **3.** *ATML1* **Functions in Plant Development**

*ATML1* loss-of-function mutant phenotypes were first described in 2003 and its role in plant development was explored. The *atml1-1* single mutant, which has a T-DNA insertion

near the 3 end of the ORF, did not show a clear abnormality [17]. *PROTODERMAL FAC-TOR2* (*PDF2*), an *ATML1* paralog expressed in the epidermis, functions redundantly with *ATML1*; the *atml1-1;pdf2-1* double mutant is seedling-lethal harboring reduced cotyledons and narrow leaves lacking an epidermis [17]. Transcript levels of some epidermal genes were reduced in the *atml1-1;pdf2-1* mutant background, suggesting that *ATML1* and *PDF2* are required for epidermal differentiation. After 11 years, *atml1-1* was shown to be a weak allele mutant and a new *ATML1* null mutation, caused by a T-DNA insertion in the homeobox, combined with a *PDF2* mutation turned out to cause developmental arrest at the globular stage. The outermost cells of the arrested *atml1;pdf2* embryos were swollen and often underwent unusual periclinal cell divisions, suggesting that epidermal cell identity was lost in those embryos [9,18]. These results revealed that *ATML1* and *PDF2* are required for proper embryo development as well as epidermal specification. Considering that the epidermis is the first tissue formed during embryogenesis, the epidermis formation might be a prerequisite for the proper progression of embryogenesis.

Gain-of-function experiments have also been performed to assess the ability of *ATML1* to promote epidermal cell differentiation. When *ATML1* expression was constitutively induced in whole seedlings, epidermal traits such as stomatal guard cells and trichomes were found ectopically in the inner tissues of cotyledons or leaves [19,20]. Given that ectopic expression of a guard-cell marker was detected in *ATML1*-overexpressing roots, ATML1 should promote shoot but not root epidermal cell identity [20]. Furthermore, expression of *ATML1* and *PDF2* fused to a transcriptional repressor sequence (called EAR or SRDX motif) caused organ fusions and high permeability to a hydrophobic dye, which are typical phenotypes related to cuticle formation defects [9,21]. Expression of *ATML1-SRDX*, which is supposed to downregulate target genes of ATML1, caused de-differentiation of the epidermis even at late stages of seedling development, suggesting that ATML1 and/or its target genes are required for epidermal cell fate maintenance [21]. Additionally, *ATML1* promotes the differentiation of giant cells during flower development; in *Arabidopsis thaliana*, sepal epidermal cells come in different sizes: large 'giant cells' and small cells. The *atml1-2* mutation lacked giant cell formation, while *ATML1* overexpression resulted in sepals consisting of mostly giant cells [22,23]. Fluctuations of ATML1 protein levels occurred in sepal epidermal cells and when its level was above a certain threshold during the G2 phase, these cells would undergo endoreduplication and became giant cells [22] (Figure 2d). Therefore, *ATML1* is a key regulator not only for epidermal specification during embryogenesis but also for maintaining and specializing epidermal cells even at later development stages.

#### **4. Transcriptional Regulation of the** *ATML1* **Gene**

Multiple studies address how *ATML1* transcription is preferentially activated in the outermost cells, for example, by analyzing cis-regulatory sequences of *ATML1* [15,16,24]. Sequence comparison of L1-specific promoters, including *ATML1*, has identified a conserved cis-regulatory sequence, called L1 box. Direct binding of ATML1 and PDF2 to the L1 box has been shown in vitro and mutations in the L1 box abolished the expression of an epidermal gene, *PDF1* [17,24]. This has provided a molecular basis for epidermal gene activation by ATML1: *ATML1* activates epidermis-specific gene expression through its direct binding to the L1 box. Consistently, expression of epidermal genes that contain the L1 box in their regulatory regions was increased and decreased in *ATML1*-overexpressing and *atml1;pdf2* mutant plants, respectively [20]. Binding of ATML1 to the L1 box region was confirmed also in vivo by chromatin immunoprecipitation assays [18]. Because the *ATML1* promoter contains an L1 box sequence, it has been proposed that positive feedback regulation may be important for epidermis-specific expression of *ATML1* [24] (Figure 2e).

The whole genomic fragment of *ATML1* and the 3.4-kb promoter sequence, which has been widely used as a driver for epidermis-specific expression, drove the same expression pattern, suggesting that the 3.4-kb promoter sequence contains all the required cis-elements for proper *ATML1* expression [14,16]. For more precise understanding of

*ATML1* transcriptional regulation, effects of deletions in the 3.4-kb *ATML1* promoter on the outermost-cell specific expression were examined in the embryos. Detailed promoter analysis has shown that a 101-bp *ATML1* promoter fragment, located upstream of the translational start site and contains an L1 box and a WUS-binding site, was sufficient for the expression in the outermost cells. Unexpectedly, even when mutating both the L1 box and WUS-binding site, reporter gene expression was reduced but still detected in the outermost cells, indicating that the expression in the epidermal cells can be achieved independently of these cis-elements. Consistently, *ATML1* expression was still detected in the outermost cells of *atml1-1;pdf2-1* embryos and leaves, suggesting that positive feedback regulation is not required for the outermost-cell specific promoter activity [16,20].

There are two scenarios that can explain the outermost cell-specific expression of *ATML1*: transcriptional activation in the outermost cells or transcriptional repression in the inner cells. Although it is difficult to distinguish these two possibilities, reporter gene expression was not activated in the inner cells of any promoter-deletion lines tested, implying that there is no transcriptional repressors that directly bind to the *ATML1* promoter and repress its expression in the inner cells of the embryos [16]. Therefore, only positive regulators appear to act on the *ATML1* promoter to activate expression in the outermost cells, suggesting that transcriptional activation in the outermost cells is more plausible. However, we cannot exclude the possibility that these positive regulators are repressed or inactivated in the inner cells of the embryos.

What could be the candidate factors that activate *ATML1* transcription in the outer cells? First, full epidermal cell identity seems not to be required for *ATML1* expression because *ATML1* promoter activity was still detected in the outermost mesophyll-looking cells in the *atml1-1;pdf2-1* mutant [20]. Secondly, *ATML1* expression in the epidermis was detected in mutants defective in auxin signaling, suggesting that auxin, a plant hormone known to direct many cell fate decisions, is not involved in the transcriptional control of *ATML1* [16]. Considering that cell fate decisions in plants largely depend on the position of the cell, *ATML1* expression could be influenced by the position of the cell; namely, the "surface" position [25]. In agreement with this idea, when the epidermal cells underwent aberrant periclinal divisions, only the outer daughter cells remained as epidermal cells while the inner daughter cells were differentiated into mesophyll cells [26]. In addition, *ATML1* was still transcribed in the outermost cells in a mutant defective in the control of cell division orientation (unpublished results from the authors). These results suggest that cell position but not cell lineage is required for *ATML1* transcription, as well for epidermal/mesophyll cell fate decisions. To date, positional cues required for epidermal/mesophyll cell fate changes have still remained unknown, however. Several mutants, in which *ATML1* transcription was decreased or lost, have been reported, implying that the causal genes of these mutants are positive regulators of *ATML1* transcription [27–29]. However, *ATML1* transcription could still be detected at early stages in these mutant embryos, suggesting that these causal genes may be involved, either directly or indirectly, in the maintenance of *ATML1* expression rather than its initiation [3]. Characterization of mutants that completely abolish or alter the *ATML1* expression pattern or direct purification of *ATML1* transcription-promoting molecules should be needed for further progress of *ATML1* research.

#### **5. Post-transcriptional and Post-translational Regulation of** *ATML1*

Several studies have implied post-transcriptional regulation of *ATML1*. In 2010, mutations in the *DICER-LIKE1* (*DCL1*) gene, which is required for correct processing of miRNAs, were shown to increase *ATML1* mRNA levels in suspensor cells whereas normally *ATML1* mRNA is under the detection limit in the wild-type suspensor cells [10,30]. This implies that *ATML1* mRNA is degraded in suspensor cells by unidentified miRNAs. However, the epidermis-specific *ATML1* mRNA accumulation was not affected in *dcl1* embryos, suggesting that miRNAs are not used as positional cues for epidermal specification [30]. Moreover, *ATML1* transcription and ATML1 protein accumulation were detected in both embryo proper

and suspensor cells of wild-type embryos at similar levels using sensitive reporter lines, implying that protein accumulation is not affected despite the apparent differences in mRNA levels [14,16].

The idea of *ATML1* regulation at the protein level came from domain analysis. ATML1 protein is characterized by a homeodomain (HD), a zipper-loop-zipper (ZLZ) motif, a StAR-related lipid-transfer (START) domain and a START-associated domain (SAD) [31]. The HD domain is a DNA binding domain and ZLZ motif is required for dimerization [32]. The START domain is homologous to a lipid-sterol binding domain [33]. The function of SAD domain has not been elucidated. The START domains from ATML1, PDF2 and GL2 were implied to bind with lipids in a yeast system. Although transcriptional activity increased upon its binding with lipids, the START domain alone was not sufficient to confer transcriptional activation, suggesting that it influences the transcription in combination with other domains or co-factors [34]. Therefore, it is plausible that ATML1 is activated by interaction with signaling molecules including lipids through its START domain.

Recently, we have reported post-transcriptional regulation of *ATML1* in the embryos. We found that ATML1 protein was not detected in the inner cells of the embryos whereas *ATML1* was weakly transcribed in these cells (Figure 2a,b). In addition, when epidermal cells underwent periclinal cell divisions, nuclear localization of ATML1 was weaker in the inner daughter cells compared with the outer daughter cells (Figure 2b). Constitutive expression of *ATML1* also confirmed that nuclear localization was reduced in the inner tissues of the embryos. These observations suggest that ATML1 protein accumulation and nuclear localization are negatively regulated in the inner cells to restrict *ATML1* activity to the outermost cells. Treatment with a nuclear export inhibitor or domain deletions increased protein accumulation and nuclear localization of ATML1 in both the outermost and inner cells of the embryos, suggesting that *ATML1* activity was negatively regulated also in the outermost cells [14]. Because *ATML1* can potentially activate and maintain its own expression through positive feedback, post-transcriptional repression might be a reasonable mechanism to suppress excessive *ATML1* activity.

Domain deletion analysis revealed that the *ZLZ*-encoding sequence is required and partially sufficient for the post-transcriptional regulation. How the dimerization motif controls ATML1 protein accumulation and subcellular localization is unclear. One possibility is that protein-protein interaction is required for degradation or for nuclear export of ATML1 protein. The START domain was not necessary for the post-transcriptional repression in the outermost cells but was required for the repression of nuclear localization in the inner cells [14]. The START domain may, therefore, play a role in negatively regulating ATML1 activity in the inner cells by physical interaction with other molecules present in the inner tissues. To understand the mechanisms regulating *ATML1* activity at the post-transcriptional level, future work should focus on the identification of ATML1 interacting factors. DELLA proteins, which are central repressors of gibberellin (GA) signaling, were shown to interact with ATML1 and PDF2, probably to enable GA-dependent ATML1/PDF2 activation during seed germination [35]. In cotton, Gossypium barbadense Meristem Layer 1 (GbML1), an *ATML1* ortholog, has been shown to interact with a MYB domain transcription factor, GbMYB25 [32]. However, as far as we know, interaction of ATML1 or its orthologous protein with molecules that reduce nuclear accumulation or stability has not been reported yet.

#### **6. Future Perspective**

*ATML1* has become an indispensable tool and research target for elucidating epidermal differentiation. Despite being studied for more than two decades, several important questions remain to be answered. Firstly, molecular mechanisms that restrict *ATML1* activity in the outermost cells have not been identified. Molecules directly interacting with ATML1 protein or *ATML1* cis-regulatory sequences may convey "surface" positional cues. Secondly, although *ATML1* orthologues have been identified and shown to be preferentially expressed in the outermost cells in other plant species, loss-of-function mutant phenotypes and biological roles of those genes have yet to be examined [36–38]. From an evolutionary point of view, an *ATML1* ancestral gene should have acquired the function to promote the expression of genes required for epidermal features in the outermost cells during or before plant terrestrialization, as the epidermis is an essential tissue for land plants to live on dry lands. It would be interesting to identify the *ATML1* ancestral gene and study how its regulation has been evolved during the transition from aquatic to land plants. Further *ATML1* research would shed light on developmental and evolutionary processes of land plants.

**Author Contributions:** All authors wrote the manuscript and prepared Figures. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by grants from the Japan Society for the Promotion of Science to S.T. (JP20657012, JP22687003, JP23657036, JP26440142 and JP18K06286).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We thank Brecht Wybouw (University of Helsinki) for helpful comments on this manuscript.

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

#### **References**


**2. Results**

#### *2.1. Modulation of Blood Concentration Levels of IL10 after Pharmacological Stimulation with IL10 and Anti-IL10*

To investigate whether blood concentration levels of endogenous IL10 were a ffected by an intravenous administration of IL10 or anti-IL10 via tail vein injection, a Mouse Magnetic Luminex Assay was used to determine the blood concentration levels of IL10 prior to (baseline, BL) and 24 h (24 h) after the respective treatments. A control group received NaCl 0.9%. At BL, blood concentration levels of IL10 were below the detection limit of 1.59 pg/mL in all groups. At 24 h, elevated blood concentration levels of IL10 were found only in the IL10 treatment group with 6.35 ± 2.20 pg/mL (*p* < 0.05), indicating that an external administration of IL10 leads to a sustainable change in endogenous blood concentration levels of IL10 for at least 24 h when applied via tail vein injection. Blood concentration levels of IL10 in mice treated with anti-IL10 or NaCl 0.9% remained below the detection limit (Figure 1).

**Figure 1.** Modulation of blood concentration levels of IL10 after pharmacological stimulation with IL10 and anti-IL10. IL10, anti-IL10, and NaCl were administered via tail vein injection. Blood concentration levels of IL10 were measured before (baseline, BL) and 24 h after pharmacological stimulation. At BL endogenous IL10 levels were below the detection limit of 1.59 pg/mL in all subjects. After an external administration of IL10, blood concentration levels remained significantly increased after 24 h. An effect of anti-IL10 could not be detected, as baseline levels of IL10 remained below the detection limit. \* indicates *p* < 0.05; *n* = 3 in each group.

### *2.2. Alteration of Macrophage Polarization in the Perivascular Bed of Growing Collateral Vessels after Pharmacological Stimulation with IL10 and Anti-IL10*

Adductor muscle samples were harvested 3 days (3 d) and 7 d after FAL to analyze the effect of a treatment with IL10 and anti-IL10 on the polarization of macrophages in the perivascular bed of growing collateral vessels. The samples were sectioned and stained using antibodies targeting known macrophage markers CD68 and CD163 [11,13] (Figure 2a). The two largest collateral vessels of each section were selected and the ratio of macrophages of the alternatively activated phenotype CD163+/CD68+ to the classically activated phenotype CD163−/CD68+ per visual field was calculated. Mice treated with NaCl had a median ratio of CD163+/CD68+ to CD163−/CD68+ macrophages of 0.46 (IQR: 0.37–1.20) on day 3 (3 d) and 0.40 (IQR: 0.37–0.55) 7 d after FAL. When treated with IL10 the ratio is skewed towards the alternatively activated phenotype on both 3 d and 7 d after FAL with a ratio of 1.00 (IQR: 0.45–1.44) and 1.19 (IQR: 0.52–1.69). Contrariwise, the ratio is skewed towards the classically activated phenotype after application of anti-IL10 on both 3 d and 7 d after FAL with a ratio of 0.25 (IQR: 0.18–0.35) and 0.27 (IQR: 0.00–0.53), differing significantly from that of the IL10 treatment group (*p* < 0.05) (Figure 2b).

**Figure 2.** Alteration of macrophage polarization in the perivascular bed of growing collateral vessels after pharmacological stimulation with IL10 and anti-IL10. (**a**) Confocal micrographs of macrophage differentiation subtypes 3 d and 7 d after FAL. Sections of adductor muscles segments containing growing collateral vessels (V) were stained using DAPI and macrophage differentiation markers CD68 and CD163. The ratio of the alternatively (CD163+/CD68+) activated phenotype, indicated by white arrows, and classically (CD163−/CD68+) activated phenotype varies with indicated application. Scale bar: 25μm. (**b**) Quantification of macrophage polarization in the perivascular bed of growing collateral vessels after pharmacological stimulation with IL10 and anti-IL10 3 d and 7 d after FAL. The distribution of macrophage subtypes was skewed towards the alternatively activated phenotype after IL10 application. When anti-IL10 was injected, the opposite effect was observed, and the distribution was skewed towards the classically activated phenotype. \* indicates *p* < 0.05; 3 d: *n* = 6 in each group; d7: NaCl and IL10 *n* = 4, anti-IL10: *n* = 6.

### *2.3. Evaluation of Hind-Limb Perfusion Recovery after FAL and Pharmacological Stimulation with IL10 and Anti-IL10*

To assess the effect of varying blood concentration levels of IL10 on growing collateral vessels IL10 and anti-IL10 were externally applied in mice after FAL. Hind-limb perfusion was assessed using Laser-Doppler-Imaging before and shortly after FAL, on 3 d, 7 d, and 14 d and compared to a control group receiving NaCl. Immediately after FAL an acute reduction of hind-limb perfusion was observed in all groups (NaCl: 0.13 ± 0.01, IL10: 0.16 ± 0.03, anti-IL10: 0.13 ± 0.01). Hind-limb reperfusion in the NaCl (*n* = 6) group showed an adequate increase to 0.34 ± 0.04, 0.62 ± 0.07, and 0.62 ± 0.07 on 3 d, 7 d, and 14 d respectively. Elevated blood concentration levels of IL10 led to a significantly higher hind-limb perfusion on 3 d and 14 d (*n* = 5, 3 d: 0.54 ± 0.09, *p* < 0.05, 14 d: 0.83 ± 0.7, *p* < 0.05). Although hind-limb perfusion on 7 d was elevated compared to the control group, the difference was not significant (0.77 ± 0.09, *p* = 0.07) (Figure 3a). Contrariwise, application of anti-IL10 showed a significant impairment of hind-limb perfusion on7d(*n* = 5, 0.42 ± 0.4, *p* < 0.05). On 3 d and 14 d, however, no significant difference was observed (3 d: 0.35 ± 0.05, *p* = 0.86, 14d: 0.61 ± 0.07, *p* = 0.89) (Figure 3b).

**Figure 3.** Evaluation of hind-limb perfusion recovery after femoral artery ligation (FAL) and application of IL10 and anti-IL10 (L/R ratio). Hind-limb perfusion was measured prior to and immediately after FAL, on 3 d, 7 d and 14 d. (**a**) IL10 led to a significant acceleration of hind-limb perfusion recovery on 3 d (IL10: 0.54 ± 0.09 vs. NaCl: 0.34 ± 0.04, *p* < 0.05) and 14 d (IL10: 0.83 ± 0.07 vs. NaCl: 0.62 ± 0.07, *p* < 0.05) while (**b**) anti-IL10 led to a brief but significant impairment of hind-limb perfusion recovery on 7 d (anti-IL10: 0.42 ± 0.04 vs. NaCl: 0.62 ± 0.07, *p* < 0.05). (**c**) Representative Laser-Doppler-Images of ligated and non-ligated hind-limbs. \*indicates *p* < 0.05; NaCl: *n* = 6, IL10 and anti-IL10: *n* = 5.

#### *2.4. Macroscopic Observations on Ligated Hind-Limbs after FAL and Application of IL10 and Anti-IL10*

Ligated hind-limbs were inspected prior to FAL, 3 d and 7 d after FAL. Macroscopic observations associated with an acute ischemia of the affected hind-limb, termed critical ischemic events (CIE), were recorded after FAL and treatment with IL10 and anti-IL10. CIE were categorized as follows: inflamed hind-limb, necrotic digits, necrotic hind-limb and amputation. In total (*n* = 115) CIE occurred in 20%. Necrotic digits were observed most frequently in 65%, followed by a necrotic hind-limb in 17%, an amputation in 13% and an inflamed hind-limb in 4% of CIE. The highest incidence of CIE was observed after application of anti-IL10 (anti-IL10: 37.1% vs. NaCl: 11.1% *p* < 0.01). The incidence of CIE when treated with IL10 was similar to the control group (IL10: 13.6% vs. NaCl: 11.1% *p* = 0.73) (Figure 4).

**Figure 4.** After FAL and pharmacological stimulation with IL10 or anti-IL10 the onset of critical ischemic events (CIE) were recorded. The highest incidence of CIE was observed after application of anti-IL10 (anti-IL10: 37.1% vs. NaCl: 11.1% *p* < 0.01, IL10: 13.6%). \*\*indicates *p* < 0.01; NaCl: *n* = 36, IL10: *n* = 44, anti-IL10: *n* = 35


*2.2. Clustering Model Implementation*

The SWMM model was run six times, once with each of the rainfall scenarios described above. We collected the simulated time-series water depth from each node in the stormwater drainage network for cluster analysis. As there are 60 junctions in the SWMM model, this results in a matrix where each column represents a single time step with a 5-min interval, and each row (60 rows) stands for a junction or node in the network. We then used the principal component analysis (PCA) to reduce the dimensionality of this matrix. PCA uses the eigendecomposition of the correlation matrix to identify a small set of principal components that represent the majority of variance in the original data [50]. Here, we used correlations between the time-series at different nodes to reduce the column of matrix to 2, which means the number of timesteps is compressed to 2 principal components. Finally, the dataset matrix is configured with 60 rows and 2 columns under each modeling scenarios. The datasets used in this work are not large, and for computational costs are limited. While other techniques for data reduction exist (e.g., correspondence analysis, factor analysis, or non-metric multi-dimensional scaling), we used PCA due to the assumed linear response of the water depth values. Although the reduction of dimensionality might cause data loss or an undesirable relationship between score axes, PCA indeed helps reduce computation time and remove redundant data features in the following cluster analysis.

All clustering algorithms were then run using this set of two principal components shown in Figure 2, with the following set up:

(1) K-means: We initially set the number of clusters (*k*) to 2 for each modeling scenarios. The algorithm was repeated ten times with different random initialization, and a maximum of 5 iterations was used to converge the algorithm.

**Figure 2.** Principal component scores for the two components (x\_pca means the first component score; y\_pca means the second component score) by K-mean under varying rainfall scenarios: (**a**) 3 h' duration rainfall, (**b**) 12 h' duration rainfall, (**c**) 48 h' duration rainfall. The principal component scores are used to examine if these two clusters are reasonably distinguished from each other clustering (gray circles the blue and red dots assigned to the closest cluster).

In Figure 2 below, there is no sample marginal overlapping, which indicates the cluster classification is reasonable with respect to grouping the time-series water level data. Additionally, the isolated dots in the subplots of Figure 2 present the dissimilarity of the water depth datasets under this event, indicating these isolated dots might be the potential flooded junctions, which help the decision-makers to pre-screen the vulnerable sites in the drainage networks.