Influence of basement rock lithology on soil hydraulic conductivity and grain size distribution in lateritic soils of western Uganda: Implications for estuarine management

101 Influence of basement rock lithology on soil hydraulic conductivity and grain size distribution in lateritic soils of western Uganda: Implications for estuarine management Haftay Hailu1 1 Department of Land Resource Management and Environmental Protection, Water Resources and Irrigation Management, Mekelle University, P.O. Box: 231, Mekelle, Ethiopia Corresponding author: Haftay Hailu (haftay[email protected], [email protected]) Copyright: © Haftay Hailu. This is an open access article distributed under terms of the Creative Commons Attribution License (Attribution 4.0 International – CC BY 4.0). Research Article Abstract The main focus of this study is to assess the control of grain size distribution, lithology, and depth on hydraulic conductivity (K_Hazen) within five weathered basement rocks (gneiss, gneiss-volcanics, quartzite, and granite) by means of statistical, regression, and mixed-effects modeling methods underpinned by hydrogeological interpretation. For this purpose, a total of 24 soil samples were analyzed using the GGU-SIEVE program, focusing on the determination of uniformity coefficient (Cu), coefficient of curvature (Cc), and hydraulic conductivity (K_Hazen). The investigation also included analysis of median-grain distribution, gradation, permeability, and soil type classifications, which are vital for sustainable soil management in regions with variable geological bedrocks, like in western Uganda. The results for Cu values range from 15 to 439, Cc ranges from 0.2 to 1.8, and K ranges from 5.8 × 10-8 to 1.3 × 10-4 m/s. These results demonstrate that the soils exhibit a wide range of textural variations and depth-dependent permeability trends. Gneiss-derived soils are permeable and exhibit moderate gradation. The quartzite and granite soils are more broadly graded but less porous, resulting from denser packing and finer matrix content. On the other hand, the gneiss-volcanics profile presents an intermediate characteristic due to mixed lithological influence. Based on statistical analysis, K decreases systematically with depth and is positively correlated with Cu and negatively with the percentage of fines. This is also confirmed by the model diagnostics, which suggest that K is jointly controlled by such factors as lithology, grain-size heterogeneity, and depth-dependent structure, with Cu emerging as a strong predictor, with the mixed-effects framework proving effective to model subsurface variability. These results underline the role of the controlling variables related to the parent rock and the intensity of weathering in influencing soil texture, gradation, shape (structure), and hydrologic response, which should be considered for erosion risk management, geotechnical study, and subsurface flow modeling in basement terrains. Key words: Texture, Depth, Grading coefficients, Hydraulic behavior, Parent rock Introduction Estuarine environs are extremely dynamic, sensitive ecosystems, really influenced by land inputs and outputs such as direct surface runoff, sediment, and groundwater discharge and recharge. Within these hydrological catchments, Academic editor: Tri Soeprobowati Received: 22 October 2025 Accepted: 22 November 2025 Published: 8 December 2025 Citation: Hailu H (2025) Influence of basement rock lithology on soil hydraulic conductivity and grain size distribution in lateritic soils of western Uganda: Implications for estuarine management. Estuarine Management and Technologies 2: 101–118. https:// doi.org/10.3897/emt.2.175774 Estuarine Management and Technologies 2: 101–118 (2025) DOI: 10.3897/emt.2.175774 102 Estuarine Management and Technologies 2: 101–118 (2025), DOI: 10.3897/emt.2.175774 Haftay Hailu: Influence of basement rock on hydraulic conductivity and grain size parameters soil hydraulic properties play a vital role in governing contaminant transport, water transport, and nutrient leaching (Li et al. 2019; Fukushima et al. 2021). Lateritic soils are dominant in WU, which form under intense tropical weathering regimes further modified by the underlying bedrock. These soils meaningfully influence runoff formation, soil erosion processes, and sediment transportation rate, especially under high-intensity rainfall events. Soil physical properties, especially grain size distribution, grading parameters such as uniformity coefficient (Cu) and coefficient of curvature (Cc), as well as the hydraulic conductivity (K_Hazen), are largely determined by the parent material, as Fajobi et al. (2014) and Etim et al. (2025) reported. Basement rocks of WU, for example, granite, gneiss, quartzite, and gneiss-volcanic rocks, have distinctive mineralogical and textural contents producing heterogeneous soils with varying hydraulic behavior (Omara et al. 2018). For example, fine-textured parent materials, such as granite and gneiss, create soils with more clay and silt, resulting in reduced permeability and increased surface runoff, which increases erosion susceptibility (Obiefuna and Nur 2022; Amadi et al. 2023). Conversely, coarse-textured soils, as more permeable, may cause sediment to be quickly transported into estuarine environments when vegetation is removed or land is disturbed (Tang et al. 2022). The degree of particle sorting, as indicated by Cu and Cc values, also affects soil structure and infiltration rate; the poorly graded soils (low Cu) tend to support low water infiltration and are highly susceptible to surface runoff (Lee et al. 2024; Etim et al. 2025; Waleed et al. 2025). This is particularly relevant in areas where land use changes, such as deforestation and agricultural expansion, restrict vegetative cover and increase erosion intensity (Amadi et al. 2023; Biru et al. 2025). Despite the recognized importance of grain size parameters (Cu and Cc) and hydraulic characteristics to estuary and watershed management, a large knowledge gap still exists as far as understanding the effects of basement lithology on these characteristics in the study area. This gap needs to be filled in order to design effective soil conservation and water management techniques for river basins throughout the region. Therefore, the main focus of this study is to determine the controls of grain size parameter distribution, lithology, and depth on hydraulic conductivity within five weathered basement parent materials, using statistical, regression, and mixed-effects modeling approaches supported by hydrogeological interpretation. This includes the following specific objectives: 1) Quantification of the relationships among grain-size parameters (Cu and Cc), depth, and hydraulic conductivity (K_Hazen), by using correlation analysis along with exploratory plotting. 2) Using of grain-size metrics and depth to assess the predictive capability by fitting linear regression models and comparing their performance; 3) Testing the influence of lithology and profile-level variability by means of mixed-effects modeling, including random effects that take into account hierarchical geological structures as well as to compare different models with Akaike information criterion (AIC), Bayesian information criterion (BIC), log-likelihood ((LLF), and diagnostic plots to find the best modeling framework that fits this set of data; and 4) Explaining the observed patterns in grain-size variability and K across the five basement lithologies through integrated geological and hydrogeothecnical interpretation. 103 Estuarine Management and Technologies 2: 101–118 (2025), DOI: 10.3897/emt.2.175774 Haftay Hailu: Influence of basement rock on hydraulic conductivity and grain size parameters Materials and methods Study area description and sample locations The study site is in WU, which is geographically located astride the equator between 1°209'S and 2°209'N and 29°309'E and 32°059'E. The region forms part of the western boundary of the broad plateau of central Africa, which was uplifted between the eastern and western arms of the rift valley system during the mid-Tertiary (Nyende et al. 2014). The Rwenzori Range and lakes of the Western rift valley form a topographic periphery to the west and north, while the Victoria Nile, a major tributary of the White Nile, delimits the eastern boundary. The southern border is marked by the shoreline of Lake Victoria and by the international border with Tanzania and Rwanda. The Basement Complex, in western Uganda, comprises of an extensive groups of schists, marbles, gneisses and granulites with granitic and basic intrusions (Schlüter 2006). In addition to the predominant acid to intermediate gneisses and granulites, hornblende and pyroxene varieties are common. Though ultra-basic rocks are rare (Schlüter 2006), the dolerites and amphiboles are wide spread (Fig. 1). Lateralization processes have led to the development of weathering mantles tens of meters thick, often capped with ferricretes (Taylar and Howard 1999) as a result of intense weathering of ferruginous shale. According to him, weathering can dissolved and washed away all the soluble minerals leaving behind hydrated Iron (III) oxide (laterites); and the remnants of laterites, certainly of Miocene age and possibly even older, cap many hill, and it is presumed that it has been formed on many surfaces until the present day. Five weathering soil profiles exposed to major roads through construction activities and/or gully erosion were selected during a field campaign in western Uganda. Each profile was freshly cleaned before sampling, and samples were taken in such a manner as to cover all stratigraphic sequences. Figure 1. Location of soil samples on the geological map and topographic map (right) of western Uganda (Modified after Schlüter 2006). 104 Estuarine Management and Technologies 2: 101–118 (2025), DOI: 10.3897/emt.2.175774 Haftay Hailu: Influence of basement rock on hydraulic conductivity and grain size parameters Description of sampled soil profiles The soils from gneisses (both S1 and S5) are more complex in texture, fineto coarse-grained, composed of quartz, feldspars, amphiboles (mainly hornblende), and biotite. Their colour ranges from grey to light brown and in some places pink depending on the type of feldspar present (Fig. 2). This textural and colour due to drainage variation of the basement rock gneiss may cause the textural and colour variations in the soil. The soils sampled from profiles S1 and S5 are from the same basement of gneiss rock. However, profiles S1 and S5 were taken from nearly flat and near the top of the hill, respectively. Soil Profile S5 (near the hilltop) was dominated by red to orange colour, whereas soil profile S1 was dominated by gray/ brown with yellowish subsurface. This colour difference depends on how well drained (how wet or dry) the profile is. The redder the soil, generally the better drained it is, as the water content controls the types of iron oxides that are present (Brown et al. 2004). This is why soils at or near the top the top of hill are quite red or orange, and progressively get more yellow and then brown towards the downhill (from soil profile S5 to S1 in the same basement gneiss rock). As Brown et al. (2004) explained, this transition is known as a ‘toposequence’. There was a typical lateral (from higher in the landscape to lower in the landscape) difference between profiles S5 and S1 of gneiss basement rock in colour, permeability, texture, and porosity. This was due to the soil–landscape Figure 2. Showing the distinct soil profiles developed on gneiss (S1 and S5), gneiss-volcanics (S2), quartzite (S3), and on granite (S4) in soils of western Uganda. 105 Estuarine Management and Technologies 2: 101–118 (2025), DOI: 10.3897/emt.2.175774 Haftay Hailu: Influence of basement rock on hydraulic conductivity and grain size parameters patterns: (1) regular topography–geology relationships; (2) poor soil drainage on lower (S1) landscape positions; and (3) soil–landscape processes including erosion–deposition and hill-slope solute transport (Brown et al. 2004). Some profiles show irregular, sometimes steeply dipping linear formations of gravel in profile S5 (Fig. 2), whose origin, according to different researchers such as Muller et al. (1981) and Colin et al. (1992), is not yet clear. The soil S1-1: 0.9 m depth is greyish-brown in colour, sandy with fine gravels, weak and friable, highly bioturbated with many medium and fine roots. This top profile S1 (S1-1) is stripped of much of its clay into S1-2 and S1-3 layers, and is thus lighter in colour than S1-2 and S1-3, which are beneath it (Fig. 2, S1). S1-1 is (eluviation) lower in clay content because of leaching. S1-3 is a zone of illuviation (accumulated substances-clay, iron, and aluminium compounds) that have been leached from overlaying layers. At S1-4 (4.5 m), the clay composition was reduced. This was due to the effect of the impermeable clay layer of illuviation overlaying it. The gneiss-volcanic profile (S2) is about 5 m thick with four recognisable sequences (Fig. 2). About 3 m from the bottom, the sequence is highly crumbled with highly altered minerals, but still visible, and it is red in colour above, with fineness and clay content increasing towards the topmost layer with progressive changing of colour from red to darker shades of grey. The residual horizon, about 20 cm thick, is highly bioturbated. The sequences of soil horizons in this profile did not depend on the origin of the basement rock because of the volcanic activities and their depositions. The top horizon (S2-1) is characterized by very dark greyish-brown, gravely clayey sand, highly bioturbated, with medium and fine roots, very low porosity, and low hydraulic conductivity. The soil profile S3 is mainly dominated by red colour (Fig. 2), which indicates the presence of hematite (Fe2O3). This profile is mainly dominated by sand and acidic soil. The soil profile (S4) also has a basement rock with interesting and beautiful light-colored rocks of granite. Quartz feldspars are the main components of this stone, with minor amounts of mica and amphibole minerals. The stage of weathering or alteration of the granites depends strongly on the structure and location of the rock. The massive, un-banded granites tend to be very fresh, and generally, the rock is less altered with distance from the escarpment. In other word, it is acidic rock that has a profound effect on the soil to make it acidic. The soil profile from the granite basement is about 8 m thick and typically red in colour. The profile S4 is cross-cut by quartz mineral veins (Fig. 2) at several points. The bottom sequence is strongly mottled and the minerals are greatly altered but still visible. The particle-size distribution (PSD), gradation (using Cu, Cc), and hydraulic conductivity are important soil parameters for detailed characterization and inter-profile comparisons of these profiles (S1 to S5). Study site selection and sampling techniques Five weathering soil profiles exposed to major roads through construction activities and/or gully erosion were selected during a field campaign in Uganda. Each profile lithologic (gneiss, granite, quartzite, volcanics) was freshly cleaned before sampling, and samples were taken in such a manner as to cover all stratigraphic sequences of the common basement rocks. The main purpose of choosing sampling sites was to obtain weathering profiles that 106 Estuarine Management and Technologies 2: 101–118 (2025), DOI: 10.3897/emt.2.175774 Haftay Hailu: Influence of basement rock on hydraulic conductivity and grain size parameters develop on these different basement rocks, including Archean gneisses of the Ugandan basement complex, granites and quartzite of the Proterozoic Buganda-Toro mobile belt, and Miocene highly mafic volcanics (Schlüter 2006), to characterize the soil property. Twenty-four soil samples were collected from each distinct lithologic site (labeled S1 to S5), i.e., in each profile, 3 to 6 samples were taken depending on the soil layering (horizonation) and colour, texture (particle size), structure, and other characteristics. These samples were obtained and labeled as S1-1, S1-2, to S5-5 at different depths (Table 1). Profiles were located and spaced at representative sites of each basement rack to ensure spatial independence. During the analysis of a soil profile, more attention is mainly focused on the subsoil horizons compared to the surface horizons, since surface horizons are often modified by human activities and thus may not reflect natural soil-forming processes. Therefore, the soils are composed of a succession of horizons, morphologically distinct layers that differ in appearance, thickness, structure, and properties. These differences are due to a variety of soil-forming processes. These horizons are roughly parallel to the land surface and form the soil profile. A soil profile, therefore, is a vertical section of the soil, a three-dimensional view that displays its layered nature. Grain size distribution determination through sieve and sedimentation One of the most important methods to characterize soil samples is the sieve analysis. The soil specimen was washed to separate fine grains from coarse grains. The samples were oven dried at 105 °C, weighed onto a 0.1% precision scale, after being sieved using a series of sieves by an agitating machine. The residue masses from each sieve after drying at 105 °C to their constant mass were determined. The shape of the grain size distribution was determined as per the standards of DIN 4022, which was supplemented by DIN ISO 11277, as highlighted in Buß (2025). It was conducted by separating the soil samples into their particle size fractions, using dry sieve analysis (for sizes > 0.063 mm) as well as sedimentation (for soil particle sizes < 0.03 mm) techniques (Fig. 3). Figure 3. Illustrating the outlined methods for determining grain size distribution of soil samples 107 Estuarine Management and Technologies 2: 101–118 (2025), DOI: 10.3897/emt.2.175774 Haftay Hailu: Influence of basement rock on hydraulic conductivity and grain size parameters Combined sieving (dry and wet sieving) and sedimentation analysis were used to drive a continuous grain size distribution curve that is useful for soil classification and construction applications. Dry sieve analysis was conducted by passing the dried soil sample through a series of progressively finer sieves (usually ranging from 63 to 0.063 mm). The count weight of retained soil for every soil sample was computed to attain the proportion of coarse-grained grading (Fig. 4b). During the sedimentation test (hydrometer method), the suspension of soil was dispersed with a dispersing agent to prevent flocculation and was permitted to settle in a cylinder (Fig. 4d). These hydrometer observations at definite intervals of time were employed to determine the concentration of particles remaining in suspension. The decreasing density of the suspension is measured at time intervals. Sizes were determined from the settling velocity and times recorded. Percentages between sizes were determined from density differences. About 30 to 50 g of the <0.063 mm sample that was collected in the buckets was put into a beaker with 25 cm³ of the stock solution and thinned with 400 cm³ purified water. The result was mixed up for about 20 minutes with the Figure 4. Illustrating the dry sieving (a), wet sieving (c), percent coarser than the sieve diameter (b), and hydrometer (d) methods for the determination of grain size distribution for a soil sample in western Uganda. 108 Estuarine Management and Technologies 2: 101–118 (2025), DOI: 10.3897/emt.2.175774 Haftay Hailu: Influence of basement rock on hydraulic conductivity and grain size parameters mixer. After that, it was filled into the measuring cylinder and filled with purified water up to the 1000 cm³ mark. The suspension inside the measuring cylinder was homogenized by completely overturning the cylinder repeatedly for several minutes, but a run off was prevented. After shaking, the cylinder with its contents was placed on a desk, and at the same time, the stopwatch was started. The hydrometer was immersed carefully inside the suspension in a way that it swam without contacting the cylinder sides (Fig. 4d). After 30 seconds, 1, 2, 5, and 15 minutes, the reading above the meniscus was taken. After this time, the hydrometer was carefully taken out, rinsed with distilled water in the standing cylinder until the next reading. This was done to prevent soil particles from settling upon it, which can distort the readings. The next readings were purposefully taken after minutes and after 1, 2, 4, 6, and 24 hours. The suspension’s temperature was measured once within the first 15 minutes, then directly after every reading according to the GGU-SIEVE program manual (Buß 2023). Stokes’ Law was applied to calculate the particle sizes from sediment speeds. Having the results from both methods, which were then used to construct a grain size distribution curve, resulted in a comprehensive particle size composition profile in the soils of the study area. It was the GGU-SIEVE program, which provides grain size curves and calculates soil classification parameters based on the German abbreviations for the soil type (Sand, silt, gravel, etc.). Significant soil properties such as D10, D20, D30, D50, and D60 diameters can be read from the curve for soil classifications. A grain size distribution curve is established by summing up the fractions (by weight), starting with the smallest fraction. A steep grain size distribution curve indicates a well-sorted sample; a flat curve indicates the opposite. After determining the D10, D30, and D60 from the semi-log grain size distribution curve, the GGU-SIEVE program determines the hydraulic conductivity of a soil sample using Hazen (1892), depending on the rules for how to use for soil texture, and clay content of the soil sample. After intensive sieving and sedimentation analysis in the laboratory, the estimation of the uniformity coefficient (Cu), coefficient of curvature (Cc), and the K_Hazen of the soils were carried out using the GGU-SIEVE program. Grain size parameters determination using GGU-SIEVE Each of the four methods in GGU-SIEVE relies directly or indirectly on grain size distribution parameters, particularly the uniformity coefficient (Cu) and coefficient of curvature (Cc), which the program automatically calculates from the inserted input data (for example, cumulative percent finer vs. grain diameter) that was obtained from the sieve analysis (Eqs. 1 and 2). These equations were also applied by Das (2013) and Davarpanah et al. (2025). (1) (2) The D10 is the effective diameter (diameter at 10% finer), D60 and D30 are the grain diameters at which 60% and 30% soil samples are finer (by weight), respectively. Hazen primarily used D10 for estimating K, whereas he used Cu and 109 Estuarine Management and Technologies 2: 101–118 (2025), DOI: 10.3897/emt.2.175774 Haftay Hailu: Influence of basement rock on hydraulic conductivity and grain size parameters Cc only for checking the validity of the soil gradation. Soils with Cu < 5 and 1 ≤ Cc ≤ 3 were categorized as well-graded, whereas deviations from such ranges showed poorly graded, as highlighted by Cai et al. (2023). Hydraulic conductivity determination using GGU-SIEVE The hydraulic conductivity could be determined using hydraulic methods (Darcy’s law) or correlation methods (pore size distribution, grain size distribution, soil texture, and soil mapping unit). As Cirpka (2003) pointed out, the characterization of the pore size diameters will be more appropriate than being concerned about the size of the grains. The determination of pore-size distribution is a complex task; therefore, hydraulic properties are usually approximated using grain size distribution, which is comparatively easier to measure. Hence, researchers have been trying to develop a relationship between permeability and particle size for many decades. Several formulae have been proposed over time to describe this relationship. The direct permeability testing or pedotransfer functions may be required to obtain more accurate hydraulic conductivity values for the fine-rich soils of lateritic materials. According to Vukovic and Soro (1992), the use of different empirical formulas on the same soil type gives permeability values that may differ by a factor of 10 or even 20 (Justine 2008). Hydraulic conductivity values derived in this study were obtained from the GGU-SIEVE program using its optional hydraulic conductivity estimation tool. GGU-SIEVE offers multiple empirical formulas for estimating hydraulic conductivity from particle size distributions; however, unless otherwise specified, the software applies the Hazen (1892) relation using the D10 (10% of fines) grainsize as input. Because this estimation was requested during the initial data processing, our soils are predominantly sandy to silty sand with variable fine gravel content; the resulting hydraulic conductivity values correspond to the Hazen formulation (i.e., K_Hazen). It is vital to note that Hazen’s method is only valid for clean, uniformly graded sands with minimal fines (0.1 to 3 mm). Seven of the lateritic soil samples analyzed in this study contain substantial fine fractions and do not strictly meet the applicability criteria. For this reason, all hydraulic conductivity values are treated as index values (K_Hazen) rather than absolute measurements, and are interpreted qualitatively. After inserting required inputs and activating the ‘’K values determination check-pox… the “Hazen method” setting is helpful, as the program then searches for this appropriate method. All required methods applied to determine grain size distribution, including sedimentation, sieve (wet and dry) analysis, grain size parameters (Cu, Cc), and estimation of hydraulic conductivities, are based on the GGU-software user’s manual reported by Buß (2023). Therefore, the GGU-SIEVE program effectively uses the Hazen’s empirical equation (Eq. 1) to estimate hydraulic conductivity depending on the requirements, soil characteristics, and rules described by the GGU-Software developer. Hazen method (1892): (3) Where K is hydraulic conductivity (coefficient of permeability) (m/s), D10 is the effective grain diameter (10% finer size on grain size curve [mm or cm, depend- 116 Estuarine Management and Technologies 2: 101–118 (2025), DOI: 10.3897/emt.2.175774 Haftay Hailu: Influence of basement rock on hydraulic conductivity and grain size parameters basement-derived profiles differ systematically with lithologic and depth. These gneissic-derived soils, S1 and S5, are moderately to well-graded (Cu ranges from 15 to 228), showing the highest surface hydraulic conductivity (K_Hazen) (up to 10-4 m/s), reducing abruptly with depth. The soils derived from gneiss-volcanics (S2) showed heterogeneous grading uniformity coefficient (Cu) up to 91 and moderate K_Hazen between 10-7–10-6 m/s, reflecting their mixed mineralogical and weathering influences. Quartziteand granite-derived soils, S3 and S4, show extreme gradation, with Cu > 200, but hydraulic conductivity (K_Hazen) is low in the range of 10-8–10-7 m/s, because of dense packing or compaction and fine infilling. The statistical analysis and modeling diagnostics datasets reveal that parent rock composition and the degree of weathering have a dominant control on soil gradation, structure, and K_Hazen. Hydraulic conductivity (K_Hazen) generally declines with increasing depth, fines, and compaction, whereas erosion vulnerability rises with coarser horizon textures. Integration of Cu, Cc, and K results offers a reliable framework for evaluating soil permeability and erosion intensity in weathered basement terrains. These findings are useful in soil conservation planning, slope stability analysis, foundation design, and groundwater recharge modeling within the basement complex region. Consequently, future research will be directed at coupling these sieveand hydrometric-derived soil parameters with in situ infiltration tests, grain-size distribution analysis, and mineralogical characterization to refine empirical correlations between uniformity coefficient, coefficient of curvature, and coefficient of permeability. An advanced geostatistical mapping or numerical flow model of K across lithological limits could improve the geospatial prediction of subsurface hydrological behavior. The long-term monitoring of soil hydraulic changes under varying land use and climatic conditions would also help evaluate erosion response and permeability evolution in weathered basement environments. Acknowledgements The author wishes to express his thanks to Mekelle University and TU Darmstadt for providing a free soil laboratory during my MSc and scientific training work, respectively. And similar thanks for the laboratory workers as well as to the university authority for their support during my work. Additional information Conflict of interest The author has declared that no competing interests exist. Ethical statement No ethical statement was reported. Use of AI No use of AI was reported. Funding No funding was reported. 117 Estuarine Management and Technologies 2: 101–118 (2025), DOI: 10.3897/emt.2.175774 Haftay Hailu: Influence of basement rock on hydraulic conductivity and grain size parameters Author contributions The author solely contributed to this work. Data availability All of the data that support the findings of this study are available in the main text. References Ali M, Sterk G, Seeger M, Boersema M, Peters P (2012) Effect of hydraulic parameters on sediment transport capacity in overland flow over erodible beds. 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