**2.5 Conclusions**

Nile water treated with NALCO inhibitors caused fouling problems inside CO2 coolers in different Egyptian ammonia plants. Technical solutions including redesigning of the PHEs and new plate geometries were investigated. Reducing the surface area of the CO2 coolers by 34% increased the gap velocity from 0.30 to 0.42 m/s (shear stress from 5.31 to 10.84 Pa) and hence decreased fouling. The operation time for the cooler was increased from 30 days to 43 days. NT-plates with asymmetric flow distribution over the channels decreased the rate of deposition on the surface of the plates.


**4. Discussion**

This study addresses the potential of sampling data for estimating some landscape metrics in remote sensing data (aerial photo). Sample based approach appears to be a very promising alternative to complete mapping approach both in terms of time needed (cost) and data quality (Kleinn and Traub, 2003; Corona et al., 2004; Esseen et al., 2006). However, some metrics may not be estimated from sample data regardless of chosen sampling method since currently used landscape metrics are defined based on mapped data. To describe landscape patterns accurately, a set of landscape metrics is needed since all aspect of landscape composition and configuration cannot be captured through a single metric. On the other hand, all metrics cannot be extracted using a single sampling method. Thus, in a sample based approach a combination of different sampling methods is needed, for instance, a combination of point and line intersect sampling. In such combined design, the

Landscape Environmental Monitoring:

**5. Conclusion**

ecological surveys.

**6. References**

p. 32-42.

Sample Based Versus Complete Mapping Approaches in Aerial Photographs 217

A sample based approach can be used complementary to complete mapping approach, and adds a number of advantages, including 1) the possibility to extract metrics at low cost 2) applicable in case of lacking categorical map of entire landscape 3) the possibility in some case to obtain more reliable information and 4) the possibility of estimating some metrics from ongoing field-based inventory such as national forest inventories (NFI). In some cases, there is a need to slightly redefine currently used landscape metrics or develop new metrics to meet sample data. There is obviously plenty of room for further studies into this topic since sample based assessment of landscape metrics is a new approach in landscape

Blaschke, T., (2004). Object-based contextual image classification built on image segmentation: IEEE Transactions on Geoscience and Remote Sensing, p. 113-119. Bunce, R.G.H., Metzger, M.J., Jongman, R.H.G., Brandt, J., de Blust, G., and Elena-Rossello,

Corona, P., Chirici, G., and Travaglini, D., (2004). Forest ecotone survey by line intersect

Esseen, P.A., Jansson, K.U., and Nilsson, M., (2006). Forest edge quantification by line

Fischer, J., and Lindenmayer, D.B., (2007). Landscape modification and habitat fragmentation: a synthesis: Global Ecology and Biogeography, v. 16, p. 265-280. Gregoire, T.G., and Valentine, H.T., (2008). *Sampling Strategies for Natural Resources and the*

Gustafson, J.E., (1998). Quantifying landscape spatial pattern: What is the state of the art?:

Hanski, I., (2005). Landscape fragmentation, biodiversity loss and the societal response - The

Jansson, K.U., Nilsson, M., and Esseen, P.-A., (2011). Length and classification of natural and

Kleinn, C., and Traub, B., (2003). Describing landscape pattern by sampling methods, in

Matérn, B., (1964). A method of estimating the total length of roads by means of line survey:

McGarigal, K., and Cushman, S.A., (2005). The gradient concept of landscape structure, in

*Environment* Boca Raton, Fla. London, Chapman & Hall/CRC.

Cochran, G., (1977). *Sampling techniques*: New York, Wiley, xvi, 428 p.

Forestiere, v. 34, p. 1776-1783.

Ecosystems, v. 1, p. 143-156.

unpleasant: Embo Reports, v. 6, p. 388-392.

Ecology and Management.v.262,P.461-469

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landscapes: Landscape Ecology, v. 8, p. 155-162.

R., et al., (2008). A standardized procedure for surveillance and monitoring European habitats and provision of spatial data: landscape Ecology, v. 23, p. 11-25.

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intersect sampling in aerial photographs: Forest Ecology and Management, v. 230,

longterm consequences of our use of natural resources may be surprising and

created forest edges in boreal landscapes throughout northern Sweden: Forest

Corona, P., Köhl, M., and Marchetti, M., eds., *Advances in forest inventory for sustainable forest management and biodiversity monitoring.*, Volume 76, p. 175-189. Li, H., and Reynolds, J., (1993). A new contagion index to quantify spatial patterns of

Wiens, J., and Moss, M., eds., *Issues and perspectives in landscape ecology*: Cambrideg,

start, mid and end points of line transects can be treated as grid of points which is preferred for estimating area proportions of different land cover classes within a landscape and thus Shannon's diversity. It would also be effective in terms of cost if several metrics could simultaneously be derived from a single sampling method.

From a statistical point of view unbiasedness is a desirable property of an estimator. In sample based assessment of landscape metrics, attributes (metrics components) such as the number, size, and edge length of patches must unbiasedly be estimated (Traub and Kleinn, 1999) if an unbiased estimate is needed. However, this is a necessary but not sufficient conditions (Ramezani, 2010). For instance, in the case of Shannon' diversity, there is still bias despite its component i.e., area proportions of land cover classes can be estimated without bias through both point and line intersect sampling methods (Ramezani et al., 2010; Ramezani and Holm, 2011c). The bias is due to non–linear transformation, which also generally is the case for other metrics with non–linear expression such as contagion. Bias of selected metric estimators is very small if the sample size is large and the magnitude of bias depends jointly on type of selected metric, the sampling method, and the complexity of the landscape structure. To achieve an acceptable precision in a complex landscape there is a need for a larger sample size compared to the homogenous landscape.

The landscape metrics used in this study are based on a patch-mosaic model where sharp borders are assumed between patches. In such procedure, as noted by Gustafson (1998) the patch definition is subjective and depends on criterion such as the smallest unit that will be mapped (minimum mapping units, MMU). This becomes more challenging in a highly fragmented landscape where smaller patches than predefined MMU are neglected. Even though these patches constitute a small proportion (area) of the landscape, they contribute significantly to the overall diversity of that landscape; including biodiversity where other type organisms may occupy these patches habitats. However, in sample based approach which can be conducted in non–delineated aerial photos, there is no need to predefine minimum patch size and even very small patches can be included in the monitoring system. Furthermore, point sampling appears to be in consistent with gradient based model of landscape (McGarigal and Cushman, 2005) where landscape properties change gradually and continuously in space and where no subjective sharp border need to be assumed between patches.

Polygon delineation errors are common in manual mapping process. It can be assumed that this error can be eliminated when sampling methods are used for estimating some landscape metrics. As a result, obtained information and subsequent analysis is more reliable than for traditional manual polygon delineation. As an example, for estimating the metrics Shannon's diversity and contagion using point sampling, no mapped data are needed and assessment is only concentrated on sampling locations. This is also true for the LIS, for instance, the total length estimation of linear features within a landscape is to be based on simply counting the interactions between lines transect and a potential patch border. Consequently, assessment is conducted along line transect which, thus, considerable reduce the polygon delineation error.

It is clear, however, that a sample based approach cannot compete with a complete mapping approach, in particular when high quality mapped data is available. With the mapping approach a suite of metrics can be calculated for patch, class, and landscape levels whereas in sample based approach a limited number of metrics on landscape level can often be estimated.


**Abstract**

Pancreatic α-amylase binds to the *N*-glycan of glycoproteins. Here, I will show that pancreatic α-amylase has regulatory functions in the small intestine other than starch digestion. These new functions were revealed by identification of α-amylasebinding proteins in the intestinal brush border membrane (BBM). This topic will include the following four parts: 1) identification of glycoproteins that bind pancreatic α-amylase in the small intestinal BBM; 2) interactions between pancreatic α-amylase and the binding glycoproteins, sucrose-isomaltase (α-glucosidase), and sodium/glucose co-transporter 1 (SGLT1), in which pancreatic α-amylase enhanced maltose degradation of sucrose-isomaltase under conditions including calcium and sodium, and inhibited glucose uptake of SGLT1; 3) localization of pancreatic α-amylase in the small intestine by binding to the BBM and being internalized into lysosomes through the endocytic pathway; and 4) expression of endogenous α-amylase in the duodenum: *AMY2B*, a pancreatic type α-amylase, is highly expressed in the human duodenum next to the pancreas. The α-amylase expression in the duodenum is required for proliferation and differentiation of human small intestinal epithelial cells.

**Keywords:** pancreatic α-amylase, small intestine, sucrase-isomaltase, SGLT1, cell proliferation, cell differentiation, endocytosis


**Author details**

Guillermo Vilalta\* and Félix Nieto *Mechanical Engineering Division, CARTIF Centro Tecnológico, Boecillo (Valladolid), Spain*

Enrique San Norberto and Carlos Vaquero *Angiology and Vascular Surgery Service, University and Clinic Hospital of Valladolid, Valladolid, Spain*

María Ángeles Pérez *ITAP Institute, University of Valladolid, Valladolid, Spain*

José A. Vilalta *Industrial Engineering Department, Polytechnical University of Havana, Havana, 19340, Cuba*


*3.5. Genetic Diagnosis of Lymph Node Metastasis in Patients with Multiple Lung Cancers*

Lymph node metastasis was detected in five patients with double primary lung cancers (Table 2). It occurred approximately at the time of surgery in three patients and was identified as postoperative lymph node recurrence in two patients (Table 2). In some patients, the route of lymph node metastasis was apparent from the timing of the metastasis as well as the location and pathological findings of the metastatic lesions (cases 4 and 5 in Table 2). In contrast, it was difficult to identify the clonal origin of lymph node metastasis on the basis of the clinical and pathological findings in the other patients, especially in those in whom the primary lesions were both squamous cell carcinomas (cases 1–3 in Table 2). However, even in these patients, a comparison of the mutation profiles of the primary and lymph node metastatic lesions revealed the route of lymph node metastasis (Figure 6).

**Figure 6.** Schema of lymphatic metastasis and mutation profiles in multiple lung cancers. On the basis of the coincidence and differences in the mutation profiles, the clonality of each tumor and the pathway of lymphatic progression are clearly elucidated in each case. The arrows indicate the lymphatic routes of the cancer invasion. Tumor 1 is shown in black, tumor 2 in red and lymph node metastasis in blue. #4, tracheobronchial lymph node; #7, subcarinal lymph node; #11, interlobar lymph node; and #12, hilar lymph node. S, segment; LN, lymph node; AF, allele fraction

**Table 2.** LN metastasis in patients with multiple lung cancers.