**Author details**

Mohammed Dauwed1 \* and Ahmed Meri2

1 Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia

2 Department of Medical Instrumentation Techniques Engineering, Al-Hussain University College, Karbala, Iraq

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**63**

*IOT Service Utilisation in Healthcare*

**References**

[2] Downing NL et al. Health information exchange policies of 11 diverse health systems and the associated impact on volume of exchange. Journal of the American Medical Informatics Association.

[3] Vest J.R. Health information exchange: national and international approaches. In Health Information Technology in the International Context. Vol. 12. Statesboro, GA, USA: Emerald Group Publishing Ltd; 2012:3-24

[4] Pagliari C. Health information exchange as a complex and adaptive construct: Scoping review. Journal of Innovation in Health Informatics.

[5] Rosenfeld S, Koss S, Caruth K. Evolution of state health information exchange/A study of vision, strategy, and progress. In: AHRQ Heal. IT Publ.

[6] Miller AR, Tucker C. Health

The value of connected health

Making. 2016;**16**(1):93

information exchange, system size and information silos. Journal of Health Economics. 2014;**33**(1):28-42

[7] WHO. Health Information. World

[8] Tharmalingam S, Hagens S, Zelmer J.

information: Perceptions of electronic health record users in Canada. BMC Medical Informatics and Decision

[9] Ghane K. Healthcare information exchange system based on a hybrid central/federated model. In: 36th

2016;**34**(13):150-160

2016;**23**(4): 633-683

No. 06. 2006

Health; 2008

*DOI: http://dx.doi.org/10.5772/intechopen.86014*

[1] Mitchell S, Villa N, Stewart-Weeks M, Lange A. The Internet of Everything for Cities. Connect. People, Process. Data, Things to Improv. 'Livability'of Cities Communities; Cisco. 2013. pp. 1-21

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2014.

[10] Chatterjee P, Armentano RL. Internet of things for a smart and ubiquitous eHealth system. In: 2015 International Conference on Computational Intelligence and Communication Networks (CICN).

[11] Hassanalieragh M et al. Health monitoring and management using internet-of-things (IoT) sensing with cloud-based processing: Opportunities and challenges. In: Proc. of 2015 IEEE Int. Conf. Serv. Comput. SCC. 2015.

[12] Riazul Islam SM, Kwak D, Humaun Kabir M, Hossain M, Kwak K-S. The internet of things for health care: A comprehensive survey. IEEE Access.

[13] Davis FD, Bagozzi RP, Warshaw PR.

User acceptance of computer technology: A comparison of two theoretical models. Management Science. 1989;**35**(8):982-1003

[14] Turcu CE, Turcu CO. Internet of things as key enabler for sustainable healthcare delivery. Procedia-Social and Behavioral Sciences. 2013;**73**:251-256

[15] Zhang H, Han BT, Tang Z. Constructing a nationwide

2017;**6**(142151):142-151

interoperable health information system in China: The case study of Sichuan Province. Health Policy and Technology.

[16] Serrano KJ et al. Willingness to exchange health information via mobile devices: Findings from a populationbased survey. Annals of Family Medicine. 2016;**14**(1):34-40

pp. 1362-1365

2016. pp. 903-907

pp. 285-292

2015;**3**:678-708

*IOT Service Utilisation in Healthcare DOI: http://dx.doi.org/10.5772/intechopen.86014*


**6. ADC-Based Pickup and Clearing Times**

The block diagram in Figure 8 shows the SSCB configuration that was implemented in this experiment. The snubbing circuit used in this experiment was a simple RC snubber optimized for 10 A at 1.19 μH of line inductance, as described in [22]. Advanced snubbing circuits are a continuing point of research in SSCBs, as inductances are not always known and can be found in [23,24].

**Figure 8.** Generalized block diagram for an auxiliary-powered unidirectional SSCB.


*Edited by Marcelo Saad and Roberta de Medeiros*

Acupuncture may have about 4000 years of history, but it has only been clinically accepted in the West for some 40 years. Acupuncture receives both praise from its users and skepticism from its critics. High-quality scientific studies have advanced, but the technique of acupuncture in health services has stagnated. In this current scenario of contrasts, *Acupuncture—Resolving Old Controversies and Pointing New Pathways* intends to be a modern reference for scholars, without totally exhausting the subject. The editors expect this work to assist with the advancement of the scientific understanding and clinical usage of acupuncture. The authors are well versed in the subject and, along with literature reviews, are able to add their own impressions. In this book, some traditional fundamentals of ancient Asian medicine are translated into the current scientific knowledge of neurophysiology and mechanisms of action. Specific variations of acupuncture, such as the scalp microsystem technique, are discussed and explained. Practical aspects, such as education on acupuncture, are enriched with descriptions of novel treatments. The therapeutic use of acupuncture and related techniques is explored regarding their incorporation into a comprehensive integrative medicine approach. As editors, we thank the contributing authors for their exquisite work, and we congratulate IntechOpen for its efforts in book production. For you, the readers, we hope to match the trust you put in this work, and we hope you find it useful.

ISBN 978-1-78984-079-7

Acupuncture - Resolving Old Controversies and Pointing New Pathways

Published in London, UK © 2019 IntechOpen © filmfoto / iStock


*2.5.1 Mechanisms*

High consumption of animal fats could increase colonic production of primary bile acids, which undergo degradation by anaerobic bacteria in the large bowel, and result in the formation of carcinogenic secondary bile acids including deoxycholic and lithocholic acids. High concentrations of these compounds could lead to increased colonocytes proliferation, through the production of ROS, thereby increasing the risk of mutation and malignant transformation [17].

Low intake or avoidance of red/processed meat, sugar/fat diet, fast foods, and sugar-sweetened drinks, alcohol, smoking, and animal fats is encouraged, as these could reduce the risk of CRC.


**5. References**

[1] Park SM, Chatterjee VKK (2005) Genetics of congenital hypothyroidism. J Med Genet 42: 379-389.

\* Corresponding Author

[2] Grasberger H, Refetoff S (2011) Genetic causes of congenital hypothyroidism due to dyshormonogenesis. Curr Opin Pediatr 23: 421-428.

Ecological Genetics of Thyroid Hormone Physiology in Humans and Wild Animals 45

[20] Hollowell JG, Staehling CA, Flanders WD, Hannon WH, Gunter EW, et al. (2002) Serum TSH, T4, and thyroid antibodies in the United States population (1988 to 1994): National Health and Nutrition Examination Survey (NHANES III). J Clin Endocrinol Metab 87:

[21] Walker JA, Illions EH, Huddleston JF, Smallridge RC (2005) Racial comparisons of thyroid function and autoimmunity during pregnancy and the postpartum period.

[22] Surks MI, Boucai L (2010) Age- and race-based serum thyrotropin reference limits.  Only one of two amino acid

[27] Leonard WR, Sorensen MV, Galloway VA, Spencer GJ, Mosher MJ, et al. (2002) Climatic influences on basal metabolic rates among circumpolar populations. Am J Hum Biol 14:

[28] Laurberg P, Andersen S, Karmisholt J (2005) Cold adaptation and thyroid hormone

[30] Leonard WR, Snodgrass JJ, Sorensen MV (2005) Metaolic adaptation in indigenous

[31] Ravaglia G, Forti P, Maioli F, Nesi B, Pratelli L, et al. (2000) Blood micronutrient and thyroid hormone concentrations in the oldest-old. J Clin Endocrinol Metab 85: 2260-

[32] Atzmon G, Barzilai N, Hollowell JG, Surks MI, Gabriely I (2009) Extreme longevity is associated with increased serum thyrotropin. J Clin Endocrinol Metab 94: 1251-1254. [33] Gussekloo J, van Exel E, de Craen AJ, Meinders AE, Frolich M, et al. (2004) Thyroid status, disability and cognitive function, and survival in old age. JAMA - J Am Med

[34] Murata Y, Refetof S, Same DH, Dick M, Watson F Variant thyroxine-binding globulin in serum of Australian Aborigines-its physical, chemical and biological properties. J

[35] Peeters RP, van der Deure WM, Visser TJ (2006) Genetic variation in thyroid hormone pathway genes; polymorphisms in the TSH receptor and the iodothyronine

[29] Launay J-C, Savourey G (2009) Cold adaptations. Ind Health 47: 221-227.

replacements is responsible for its altered properties. J Clin Invest 83: 1344-1348. [26] Murata Y, Refetoff S, Sarne DH, Dick M, Watson F (1985) Variant thyroxine-binding globulin in serum of Australian aborigines: its physical, chemical and biological

western Australian Aborigines. Med J Australia 1: 115-118.

489–499.

609-620.

2265.

Assoc 292: 2591-2599.

Endocrinol Invest 8: 225-232.

deiodinases. Eur J Endocrinol 155: 655-662.

Obstet Gynecol 106: 1365-1371.

Clin Endocrinol Metab 95: 496-502.

Aborigines. Clin Chim Acta 116: 361-367.

properties. J Endocrinol Invest 8: 225-232.

metabolism. Thieme eJournal 37: 545-549.

Siberian populations. Ann Rev Anthropol 34: 451-471.

[20] Hollowell JG, Staehling CA, Flanders WD, Hannon WH, Gunter EW, et al. (2002) Serum TSH, T4, and thyroid antibodies in the United States population (1988 to 1994): National Health and Nutrition Examination Survey (NHANES III). J Clin Endocrinol Metab 87: 489–499.

44 Thyroid Hormone

164: 1-9.

[2] Grasberger H, Refetoff S (2011) Genetic causes of congenital hypothyroidism due to

[3] Rastogi MV, LaFranchi SH (2010) Congenital hypothyroidism. Orphanet J Rare Dis 5: 17. [4] Grüters A, Krude H, Biebermann H (2004) Molecular genetic defects in congenital

[5] Moreno JC, de Vijlder JJM, Vulsma T, Ris-Stalpers C (2003) Genetic basis of hypothyroidism: recent advances, gaps and strategies for future research. Trends

[6] Hébrant A, van Staveren VCG, Maenhaut C, Dumont JE, Leclère J (2011) Genetic hyperthyroidism: hyperthyroidism due to activating TSHR mutations. Eur J Endocrinol

[7] Prummel MF, Strieder T, Wiersinga WM (2004) The environment and autoimmune

[8] Tomer Y (2010) Genetic susceptibility to autoimmune thyroid disease: past, present, and

[9] Landa I, Robledo M (2011) Association studies in thyroid cancer susceptibility: are we on

[10] Selinus O, Alloway B, Centeno JA, Finkelman RB, Fiuge R, et al. (2005) Essentials of

[11] Koutras DA, Matovinovic J, Vought R (1985) The ecology of iodine. In: Stanbury JB, Hetzel BS, editors. Endemic Goitre and Cretinism, Iodine Nutrition in Health and

[12] Davenport CB (1932) The genetical factor in endemic goiter. Lancaster: Lancaster Press. [13] Böttcher Y, Eszlinger M, Tönjes A, Paschke R (2005) The genetics of euthyroid familial

[14] Singer J, Eszlinger M, Wicht J, Paschke R (2011) Evidence for a more pronounced effect of genetic predisposition than environmental factors on goitrogenesis by a case control

[16] Corral J, Martin C, Pérez R, Sánchez I, González-Sarmiento R, et al. (1993) Thyroglobulin gene point mutation associated with non-endemic simple goitre. Lancet

[17] Matsuda A, Kosugi S (1997) A homozygous missense mutation of the sodium/iodide symporter gene causing iodide transport defect. J Clin Endocrinol Metab 82: 3966-3971. [18] Neumann S, Bayer Y, Reske A, Tajtáková, Paschke R (2003) Further indications for

[19] Neumann S, Willgerodt H, Ackermann F, Reske A, Jung M, et al. (1999) Linkage of familial euthyroid goiter to the multinodular goiter-1 locus and exclusion of the

symporter. J Clin

genetic heterogeneity of euthyroid familial goiter. J Mol Med 81: 736-745.

candidate genes thyroglobulin, thyroperoxidase, and Na+/I−

study in an area with low normal iodine supply. Horm Metab Res 43: 349-354. [15] Perez-Centeno C, Gonzalez-Sarmiento R, Mories MT, Corrales JJ, Miralles-Garcia JM (1996) Thyroglobulin exon 10 gene point mutation in a patient with endemic goiter.

dyshormonogenesis. Curr Opin Pediatr 23: 421-428.

hypothyroidism. Eur J Endocrinol 151: U39-U44.

thyroid diseases. Eur J Endocrinol 150: 605-618.

the right track? J Mol Endocrinol 47: R43-R58

Medical Geology. Burlington: Elsevier.

Disease. New York: Wiley. pp. 185-195.

goiter. Trends Endocrinol Metab 16: 314-319.

Endocrinol Metab 14: 318-326.

future. Thyroid 20: 715-725.

Thyroid 6: 423-427.

Endocrinol Metab 84: 3750-3756

341: 462-464.

[36] Peeters RP, van Toor H, Klootwijk W, de Rijke YB, Kuiper GG, et al. (2003) Polymorphism in thryoid hormone pathway genes are associated with plasma TSH and iodothyronine levels in healthy subjects. J Clin Endocrinol Metab 88: 2880-2888.

Ecological Genetics of Thyroid Hormone Physiology in Humans and Wild Animals 47

[52] Sonstegard RA (1976) Studies of the etiology and epizootiology of lymphosarcoma in

[53] Moccia RD, Leatherland JF, Sonstegard RA (1981) Quantitative interlake comparison of thyroid pathology in Great Lakes coho (*Oncorhynchus kisutch*) and chinook

[54] Moccia RD, Fox GA, Britton A (1986) A quantitative assessment of thyroid histopathology of herring gulls (*Larus argentatus*) from the Great Lakes and a hypothesis

[55] Sonstegard RA, Leatherland JF (1979) Hypothyroidism in rats fed Great Lakes coho

[56] Schlumberger HG (1955) Spontaneous goiter and cancer of the thyroid in animals. Ohio

[57] Honma Y, Shioda S, Yoshie S (1977) Changes in the thyroid gland associated with the diadromous migration of the threespine stickleback, *Gasterosteus aculeatus*. Jpn J

[58] Hamada K (1975) Excessively enlarged thyroid follicles of the threespine stickleback, *Gasterosteus aculeatus aculeatus*, reared in freshwater. Jpn J Ichthyol 21: 183–190 [59] Cabello G, Vilaxa A, Spotorno AE, Valladares JP, Pickard M, et al. (2003) Evolutionary adaptation of a mammalian species to an environment severely depleted of iodide.

[60] Dunlap KL, Reynolds AJ, Refsal KR, Kerr WW, Duffy LK (2008) Cross-latitudinal, seasonal and diurnal comparisons in thyroid hormone concentrations in sled dogs. Am

[61] Burger MF, Denver RJ (2001) Plasma thyroid hormone concentrations in a wintering passerine bird: their relationship to geographic variation, environmental factors,

[62] Fair PA, Montie E, Balthis L, Reif JS, Bossart GD (2011) Influences of biological variables and geographic location on circulating concentrations of thyroid hormones in wild

[63] Buchholz DR, Hayes TB (2005) Variation in thyroid hormone action and tissue content underlies species differences in the timing of metamorphosis in desert frogs. Evol Dev

[64] Bragg AN (1945) The spadefoot toads in Oklahoma with a summary of our knowledge

[65] Newman RA (1988) Genetic variation for larval anuran (*Scaphiopus couchii*)

[66] Pickard MR, Sinha AK, Ogilvie LM, Leonard AJ, Edwards PR, et al. (1999) Maternal hypothyroxinemia influences glucose transporter expression in fetal brain and placenta.

[67] Wilson CM, McNabb FM (1997) Maternal thyroid hormones in Japanese quail eggs and their influence on embryonic development. Gen Comp Endocrinol 107: 153-165. [68] Brown DD (1997) The role of thyroid hormone in zebrafish and axolotl development.

development time in an uncertain environment. Evolution 42: 763-773.

bottlenose dolphins (*Tursiops truncatus*). Gen Comp Endocrinol 174: 184-194.

metabolic rate, and body fat. Physiol Biochem Zool 75: 187-199.

Esox (*Esox lucius* L. and *Esox masquinongy*). Prog Exp Tumor Res 20: 141-155.

on the causal role of environmental contaminants. J Wildlife Dis 22: 60-70.

(*Oncorhynchus tschawytscha*) salmon. Cancer Res 41: 2200-2210.

salmon. Bull Environ Contam Toxicol 22: 779-784.

J Sci 55: 23-43.

Ichthyol 24: 17-25.

Pflug Arch Eur J Phy 446: 42-45.

of the group. II. Am Nat 79: 52-72.

Proc Natl Acad Sci USA 94: 13011-13016.

J Endocrinol 163: 385-394.

J Anim Vet Sci 3: 96-103.

7: 458-467.

[52] Sonstegard RA (1976) Studies of the etiology and epizootiology of lymphosarcoma in Esox (*Esox lucius* L. and *Esox masquinongy*). Prog Exp Tumor Res 20: 141-155.

46 Thyroid Hormone

[36] Peeters RP, van Toor H, Klootwijk W, de Rijke YB, Kuiper GG, et al. (2003) Polymorphism in thryoid hormone pathway genes are associated with plasma TSH and

[38] Alberobello AT, Congedo V, Liu H, Cochran C, Skarulis MC, et al. (2011) An intronic SNP in the thyroid hormone receptor beta gene is associated with pituitary cell-specific over-expression of a mutant thyroid hormone receptor beta2 (R338W) in the index case

[39] Dayan CM, Panicker V (2009) Novel insights into thyroid hormones from the study of

[40] Horvath A, Faucz F, Finkielstain GP, Nikita ME, Rothenbuhler A, et al. (2010) Haplotypeanalysis of the promoter region of phosphodiesterase type 8B (PDE8B) in correlation with inactivating PDE8B mutation and the serum thyroid-stimulating

[41] Arnaud-Lopez L, Usala G, Ceresini G, Mitchell BD, Pilia MG, et al. (2008) Phosphodiesterase 8B gene variants are associated with serum TSH levels and thyroid

[42] Sabeti PC, Schaffner SF, Fry B, Lohmueller J, Varilly P, et al. (2006) Positive natural

[43] López Herráez D, Bauchet M, Tang K, Theunert C, Pugach I, et al. (2010) Genetic variation and recent positive selection in worldwide human populations: evidence from

[44] Dormitzer PR, Ellison PT, Bode HH (1989) Anomalously low endemic goiter prevalence

[45] Hancock AM, Witonsky DB, Alkorta-Aranburu G, Beall CM, Gebremedhin A, et al. (2011) Adaptations to climate-mediated selective pressures in humans. PLoS Genet 7:

[46] Nielsen R, Bustamante C, Clark AG, Glanowski S, Sackton TB, et al. (2005) A scan for positively selected genes in the genomes of humans and chimpanzees. PLoS Biol 3:

[47] Gagneux P, Amess B, Diaz S, Moore S, Patel T, et al. (2001) Proteomic comparison of human and great ape blood plasma reveals conserved glycosylation and differences in

[49] Iwasawa H (1960) On the local variation of the thyroid gland in the Japanese pond frog,

[50] Woodhead AD, Scully PM (1977) A comparative study of the Pretumorous thyroid gland of the gynogenetic teleost, *Poecilia formosa*, and that of other Poeciliid fishes.

[51] Moccia RD, Leatherland JF, Sonstegard RA (1977) Increasing frequency of thyroid goiters in Coho salmon (*Oncorhynchus kisutch*) in the Great Lakes. Science 198: 425-426.

of pituitary-selective resistance to thyroid hormone. J Transl Med 9: 144.

iodothyronine levels in healthy subjects. J Clin Endocrinol Metab 88: 2880-2888. [37] Panicker V, Cluett C, Shields B, Murray A, Parnell KS, et al. (2008) A common variation in deiodinase 1 gene DIO1 is associated with the relative levels of free thyroxine and

triiodothyronine. J Clin Endocrinol Metab 93: 3075-3081.

common genetic variation. Nat Rev Endocrinol 5: 211-218.

selection in the human lineage. Science 312: 1614-1620.

among Efe pygmies. Am J Phys Anthropol 78: 527–531.

thyroid hormone metabolism. Am J Phys Anthropol 115: 99-109. [48] Newton P (1996) Hypothyroid nails and evolution. Lancet 347: 1832-1833.

*Rana nigromaculata*. Doubutsugaku zasshi 69: 125-128.

hormone Levels. Thyroid 20.

e1001375.

Cancer Res 37: 3751-3755.

e170.

function. Am J Hum Genet 82: 1270-1280.

nearly 1 million SNPs. PLoS One 4: e7888.

[69] Tagawa M, Hirano T (1987) Presence of thyroxine in eggs and changes in its content during early development of chum salmon, *Oncorhynchus keta*. Gen Comp Endocrinol 68: 129-135.

Ecological Genetics of Thyroid Hormone Physiology in Humans and Wild Animals 49

[85] Hauck SJ, Hunter WS, Danilovich N, Kopchick JJ, Bartke A (2001) Reduced levels of thyroid hormones, insulin, and glucose, and lower body core temperature in the growth

[87] Boura D, Castric V, Bernatchez L, Audet C (2002) Physiological, endocrine, and genetic bases of anadromy in the brook charr, *Salvelinus fontinalis*, of the Laval River (Que ́bec,

[88] Rubin CJ, Zody MC, Eriksson J, Meadows JR, Sherwood E, et al. (2010) Whole-genome resequencing reveals loci under selection during chicken domestication. Nature 464:

[89] Kijas JW, Lenstra JA, Hayes B, Boitard S, Porto Neto LR, et al. (2012) Genome-wide analysis of the world's sheep breeds reveals high levels of historic mixture and strong

[90] Nakao N, Ono H, Yamamura T, Anraku T, Takagi T, et al. (2008) Thyrotrophin in the

[91] Ono H, Hoshino Y, Yasuo S, Watanabe M, Nakane Y, et al. (2008) Involvement of thyrotropin in photoperiodic signal transduction in mice. Proc Natl Acad Sci U S A 105:

[92] Hanon EA, Lincoln GA, Fustin JM, Dardente H, Masson-Pevet M, et al. (2008) Ancestral TSH mechanism signals summer in a photoperiodic mammal. Curr Biol 18: 1147-1152. [93] Masumoto K, Ukai-Tadenuma M, Kasukawa T, Nagano M, Uno KD, et al. (2010) Acute induction of Eya3 by late-night light stimulation triggers TSHβ expression in

[94] Dardente H, Wyse CA, Birnie MJ, Dupré SM, Loudon ASI, et al. (2010) A molecular

[95] Schluter D (2000) The Ecology of Adaptive Radiation. New York: Oxford University

[96] Barrett RDH, Hoekstra HE (2011) Molecular spandrels: tests of adaptation at the genetic

[97] Sonstegard R, Leatherland JF (1976) The epizootiology and pathogenesis of thyroid hyperplasia in coho salmon (*Oncorhynchus kisutch*) in Lake Ontario. Cancer Res 36: 4467-

[98] Hewitt EA, Crain DA, Gunderson MP, Guillette LJJ (2002) Thyroid status in juvenile alligators (*Alligator mississippiensis*) from contaminated and reference sites on Lake

[99] Gorbman A, Gordon M (1951) Spontaneous thyroidal tumors in the swordtail

[100] Berg O, Gordon M, Gorbman A (1954) Comparative effects of thyroidal stimulants and inhibitors of normal and tumorous thyroids in Xiphophorin fishes. Cancer Res 14: 527-

switch for photoperiod responsiveness in mammals. Cur Biol 20: 1193 - 2198

pars tuberalis triggers photoperiodic response. Nature 452: 317-322.

hormone receptor/binding protein knockout mouse. Exp Biol Med 226: 552-558. [86] Kitano J, Lema SC, Luckenbach JA, Mori S, Kawagishi Y, et al. (2010) Adaptive divergence in the thyroid hormone signaling pathway in the stickleback radiation. Curr

Biol 20: 2124-2130.

587–591.

18238-18242.

Press.

4475.

533.

Canada). Environ Biol Fish 64: 229-242.

recent selection. PLoS Biol 10: e1001258.

photoperiodism. Curr Biol 20: 2199 - 2206

Okeechobee, Florida, USA. Chemosphere 47: 1129-1135.

*Xiphophorus montezumae*. Cancer Res 11: 184-187.

level. Nat Rev Genet 12: 767-780.

[85] Hauck SJ, Hunter WS, Danilovich N, Kopchick JJ, Bartke A (2001) Reduced levels of thyroid hormones, insulin, and glucose, and lower body core temperature in the growth hormone receptor/binding protein knockout mouse. Exp Biol Med 226: 552-558.

48 Thyroid Hormone

68: 129-135.

167-173.

72: 149-177.

6: 28-45.

81: 687-693.

Fish Soc 119: 416-430.

Physiol-Reg I 280: R149-155.

Mech Ageing Dev 33: 275-282.

Development 120: 515-522.

dwarfism. Nature 384: 327-333.

A Biol Sci Med Sci 59: 1244-1250.

[69] Tagawa M, Hirano T (1987) Presence of thyroxine in eggs and changes in its content during early development of chum salmon, *Oncorhynchus keta*. Gen Comp Endocrinol

[70] McComb DM, Gelsleichter J, Manire CA, Brinn R, Brown CL (2005) Comparative thyroid hormone concentration in maternal serum and yolk of the bonnethead shark (*Sphyrna tiburo*) from two sites along the coast of Florida. Gen Comp Endocrinol 144:

[71] Parsons GR (1993) Geographic variation in reproduction between two populations of

[72] Arendt JD (1997) Adaptive intrinsic growth rates: an integration across taxa. Q Rev Biol

[73] Conover DO (1990) The relationship between capacity for growth and length of growing season: evidence for and implications of countergradient variation. Trans Am

[76] Hulbert AJ, Hinds DS, MacMillen RE (1985) Minimal metabolism, summit metabolism and plasma thyroxine in rodents from different environments.  Am J

[80] Ooka H, Shinkai T (1986) Effects of chronic hyperthyroidism on the lifespan of the rat.

[81] Lin SC, Li S, Drolet DW, Rosenfeld MG (1994) Pituitary ontogeny of the Snell dwarf mouse reveals Pit-1-independent and Pit-1-dependent origins of the thyrotrope.

[82] Sornson MW, Wu W, Dasen JS, Flynn SE, Norman DJ, et al. (1996) Pituitary lineage determination by the Prophet of Pit-1 homeodomain factor defective in Ames

[83] Boylston WH, DeFord JH, Papaconstantinou J (2006) Identification of longevity-

[84] Vergara M, Smith-Wheelock M, Harper JM, Sigler R, Miller RA (2004) Hormone-treated snell dwarf mice regain fertility but remain long lived and disease resistant. J Gerontol

associated genes in long-lived Snell and Ames dwarf mice. Age 28: 125-144.

induction of plasma SBP in immature males. J Endocrinol 110: 271-278.

research. J Gerontol A Biol Sci Med Sci 60: 1369-1377.

[74] Habra M, Sarlis NJ (2005) Thyroid and aging. Rev Endocr Metab Disord 6: 145-154. [75] Brown-Borg HM (2007) Hormonal regulation of longevity in mammals. Ageing Res Rev

the bonnethead shark, *Sphyma tiburo*. Environ Biol Fish 38: 25-35.

[101] Buffernstein R, Pinto M (2009) Endocrine function in naturally long-living small mammals. Mol Cell Endocrinol 299: 101-111.

**Chapter 3**

© 2012 Martín-Lacave et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**Paracrine Regulation of Thyroid-Hormone**

Rocío García-Marín, José Carmelo Utrilla and Inés Martín-Lacave

Thyroid hormone secretion by follicular cells is mainly regulated by thyroid-stimulating hormone (TSH) from the pituitary gland, which in turn is controlled by thyrotropin releasing hormone (TRH) of hypothalamic origin. However, there are a number of additional endocrine mediators, such as estrogens and corticosteroids, which are also involved in the regulation of the biosynthesis of T3 and T4. Besides this extrathyroidal main regulation, thyroid function and growth are under the influence of intrathyroidal peptides. Thus, there is evidence of the role played by some local factors released by nerve endings, such as vasointestinal peptide (VIP) or neuropeptide Y [1], by follicular cells themselves, such as insulin-like growth factors (IGFs) [2], and, specifically, by C cells through a paracrine mechanism, acting locally upon neighboring follicular cells [1]. This last intrathyroidal mechanism of regulation of follicular-cell activity by C cells is the main

The thyroid gland consists of two lobes connected by a narrow band of thyroid tissue called the isthmus, located in the midportion of the neck, immediately in front of the larynx and trachea. Each lobe of the thyroid gland consists of numerous follicles, the **thyroid follicles,**  which constitute the structural and functional unit of the gland. Each follicle consists of a simple layer of cuboidal epithelial cells, the **follicular epithelium,** enclosing a central lumen containing a **colloid** substance rich in **thyroglobulin,** an iodinated glycoprotein, yielding a periodic-Schiff (PAS)-positive reaction. The shape of the normal follicles varies from round to oval and they show a considerable size variation depending on the degree of gland

and reproduction in any medium, provided the original work is properly cited.

José María Fernández-Santos, Jesús Morillo-Bernal,

**2. Histological organization of the thyroid gland**

Additional information is available at the end of the chapter

**Synthesis by C Cells**

http://dx.doi.org/10.5772/46178

subject of the present review.

activity (Figure 1).

**1. Introduction**


*4.1. Determination of the Value of Parameter fC*

The value of the uniaxial compressive strength parameter *fC* was obtained through a uniaxial compression test. Five uniaxial compression tests were carried out, and three sets of effective data were obtained. Based on the obtained data, the stress–strain curves of briquette samples under uniaxial compression were plotted. The curves are shown in Figure 5.

**Figure 5.** Uniaxial compression stress–strain curve.

As shown in Table 4, the value of the uniaxial compressive strength parameter *fC* of the sample was obtained from the peak point of the stress–strain curve.

**Table 4.** Uniaxial compression test results.

According to the experimental results presented in Table 4, the mean value of the three samples was considered as the value of uniaxial compressive strength parameter *fC* for the briquette sample, which was 2.33 MPa.

### *4.2. Determination of the Value of Parameter T*

A splitting test was required to obtain the value of the uniaxial tensile strength parameter *T*. The test equipment used for the splitting of coal samples was the same as that used for the uniaxial compression test. Samples were disc-shaped with a diameter of 50 mm and length of 25 mm. The axial load was applied by displacement control at a rate of 0.05 mm/min. When a crack appeared on the surface of the coal sample, the experiment was finished. Sample damage resulting from the four splitting experiments is shown in Figure 6.

**Figure 6.** Splitting test specimen failure morphologies.

The load *P* at the failure point of the sample was obtained from the splitting test, and the tensile strength *T* of the sample was calculated using the equation *T* = 2*P*/π*dh*, where *d* and *h* are the diameter and length of the sample, respectively. The values of load *P* and tensile strength *T* at the fracture points of different samples are summarized in Table 5.

**Table 5.** Sample tensile strength values.

According to experimental results presented in Table 5, the mean value of the four samples was considered as the tensile strength of briquette sample, which was 0.55 MPa.


**2. Materials and Methods**

### *2.1. Study Area and Geological Settings*

The study area, Bagnoli, is located within the Bay of Pozzuoli, in the western sector of the urban territory of Naples. Close to the Bagnoli site, the already mentioned Phlegraean fields is a large "super volcano" situated to the west of Naples, Italy. Declared a regional park in 2003, the area of the caldera consists of 24 craters and volcanic edifices, most of them submerged under the bay with very high hydrothermal activity and effusive gaseous manifestations [19] (Figure 2). From a geological point of view, this large caldera is in a state of quiescence. The Phlegraean fields experienced extreme volcanic activity in the last 39,000 years. The two main events occurred 35,000 years ago (the 'Campanian ignimbrite eruption'), followed by caldera collapse, and 15,000 years ago (the 'Neapolitan Yellow Tuff', NYT eruption). The NYT eruption formed the central and eastern part of the Bagnoli–Fuorigrotta plain, which is constituted by a sequence of pyroclastic and volcanic material that thickens along the Agnano crater. Agnano volcanic products dominates the western part of the plain. The last eruptive

event occurred in A.D. 1538 and gave rise to the Mt. Nuovo cone. From then on, only bradyseismic and hydrothermal activity (in the Solfatara and Pisciarelli area) are present in the area. The ground deformation in this area was recognized as caused mainly by the actions of long periods of uplift, eruptive activity, and possible deep magma movements [36–38]. According to some studies [39], the Phlegraean fields activity has been predominantly explosive and characterized by an interaction between magma and water. There is evidence of both interaction of the magma with sea water as well as intra-caldera lake water and deep-seated aquifers in some Plinian volcanic events. High concentrations of metals and metalloids, such as As, Mn and Fe are found in groundwater and in the sea sediments near the main tectonic and hydrothermal activity [3,18,31,40,41].

**Figure 2.** Location of the study area, Bagnoli bay, Naples, Italy.

Since the early 1900's Bagnoli has been affected by the presence of various industries. The most important industrial activity was through ILVA steelworks, formerly Italsider. The ILVA steel plant was located along the Bagnoli coastline and was characterized by two long piers (still present now) which served as a berth for large ships that carried raw materials such as coal, iron ore and limestone to the steelworks. In the early 1960's, in order to enlarge the plant, the area between the two piers was filled with industrial waste coming from the steelworks. A new coastline was designed, giving more space for the industrial activities, but causing consistent damage to the marine ecosystems. Furthermore, the presence of nine discharge points along the coastline, combining both industrial and civil sewage, may have contributed to the deterioration of the quality of the environment. Some of these collectors are no longer in use since the steelworks stopped its production in 1990.

The Bagnoli Bay is also characterized by the presence of some submarine thermal springs, mainly located close to the former industrial area. Some offshore thermal springs are also present in the GoP.

#### *2.2. Data Availability: Sampling, Sewage Discharge, and Wave Information*

In this study, a total of 126 sampling points were considered as shown in Figure 3 (black dot points). These samples were part of an explorative campaign carried out in 2017 in the GoP by the Stazione Zoologica Anton Dohrn [42,43]. On the sea bottom, 94 sediment samples were collected using drilling (0-50 cm) and 32 sediment samples were collected by bucket (surface layer). In each point, the concentrations of heavy metals and PAHs were measured. A total of 11 heavy metals (i.e., Al, As, Cd, Cr, Cu, Fe, Hg, Ni, Pb, V, and Zn) and 18 PAHs (i.e., naphthalene, anthracene, phenanthrene, acenaphthylene, acenaphthene, fluorene, fluoranthene, pyrene, benzo(a)anthracene, chrysene, benzo(b)fluoranthene, benzo(a)pyrene, benzo(k)fluoranthene, indeno(1,2,3-cd)pyrene, benzo(ghi)perylene, dibenz(ah)anthracene, benz(j)fluoranthene, benz(e)pyrene) indicated by Environmental Protection Agency (EPA) as important toxicological contaminants were considered. All the samples and the analytical determination derive from the series of studies conducted under the framework of the ABBaCo project [4,33]. Contaminants general statistics are reported in Table 1.

**Table 1.** General statistics of the sampled contaminants in Bagnoli sea sediments. Heavy metals (in mg/kg) and polycyclic aromatic hydrocarbons (PAHs, in μg/kg) are divided by bold line.

A total of 14 sewage discharge points were mapped and selected in the study area (from 1 to 9C, in Figure 3). Some of these discharge points were supposed to be inactive (e.g., Points 4, 7 and 8). However, it is well recognized that due to poor maintenance and some illegal discharges in sewage disposal these points, especially when heavy rains occur, release water carrying waste and sewage to the coastline. For this reason, all the discharge points shown in the figure have been considered as active. It should also be noted that the discharge points labeled as "C" in Figure 3 are control points chosen as the nearest to discharge point (i.e., 9C is the control point of discharge point 9).

**Figure 3.** Location and typology of sewage discharges located along the Bagnoli coastline. The capital letter "C" indicates the discharges which control the nearest discharge point (i.e., 9C is the control point of Discharge Point 9).

Representing natural contaminant sources spread along the Bagnoli bay seabed, 13 thermal springs were identified [18] (Figure 4).

**Figure 4.** Location of natural sources inside the Bagnoli bay [12,18].

In addition to the presence of sewage discharges and thermal springs, concentration patterns can be influenced by marine dynamics that remobilize sediments and accumulate them in places where there are steady-state conditions. In order to include the analysis, the main effects of the wave-induced hydrodynamics inside the GoP area in the analysis, we incorporated wave properties generated by a numerical model of the gulf.

#### *2.3. Numerical Wave Modelling*

The wave climate analysis was carried out using the hindcast data from the European Centre for Medium-Range Forecasts (ECMWF). Significant wave height (*Hs*), mean period (*Tm*), and mean direction (Θ*m*) for the time period ranging from January 1979 to December 2018 were extracted from the wave model (WAM) of the ERA-interim archive (ERA: ECMWF Atmospheric Reanalysis), available for download online [44]. Both the offshore energetic patterns and the nearshore water conditions have been studied by means of the MIKE 21 SW coastal propagation model [45]. The model has been previously calibrated and validated applying a multi-collocation-based estimation approach as described in [46]. Such studies perform an optimization procedure for:

It is worth noting that spreading and dispersion studies and studies of water quality or ecological systems in marine areas are generally carried out by means of coastal hydrodynamic models coupled with sediment transport and particle tracking models. Generally, such modelling is computationally demanding. Therefore, in the perspective to undertake multi-year wave hindcast studies more quickly, a spectral modeling of wave propagation has been used. In order to assess the hypothesis that the bay's hydrodynamics is a factor of influence on pollution patterns, a single wave scenario able to represent the yearly average condition in the bay in terms of wave energy flux, had to be selected. Such a sea state, here defined as "energy equivalent", was obtained considering the wave power content of each wave from the whole dataset of 40-year wave records. Operatively, from the 6-h data of *Hs*, *Tm*, θ*m* provided by the fictious WAM model, a 6-h wave power and a wave energy dataset were obtained. For a specific sea state, the average wave energy per unit area is proportional to the square of the significant wave height, *Hs*, according to the known relationship [48]:

$$E\_{\rm new\nu} = \frac{1}{16} \rho g H\_s^2 \left[\frac{f}{s}\right] \tag{1}$$

where ρ is the sea water density, assumed equal to 1025 kg/m3, *g* is the gravity acceleration (equal to 9.81 m/s2). For a given spectrum, the significant wave height computation is based on zero-order moment of the spectral function and readily estimated as follows:

$$H\_s = H\_{m0} = 4\sqrt{m\_0} \tag{2}$$

and the wave characteristic/statistic periods can be defined as:

$$T\_{\varepsilon} = 2\pi \frac{m\_{-1}}{m\_0} \tag{3}$$

$$T\_m = T\_{01} = 2\pi \frac{m\_0}{m\_1} \tag{4}$$

where *Te*, *T*01 are the energy period and the spectral mean period, respectively, with the spectral moment being defined as:

$$m\_n = \int\_0^\infty S(f) f^n df \tag{5}$$

where *n* = −2, −1, 0, 1, 2, ...

The wave power in irregular waves can be computed as:

$$P = \frac{\rho \times g^2 \times H\_s^2 \times T\_c}{64 \times \pi} \tag{6}$$

Following a conservative approach, according to [49,50], the energy period in the present study has been assumed as 1.14 *Tm*. Equation (6) can be written in the approximate deep water expression:

$$P = 0.459 \times H\_s^2 \times T\_\varepsilon \tag{7}$$

For each *j*-th year, an average wave power (*PJ*) has been computed. Considering that the energy period can be computed directly using the approximate formula:

$$T\_c = 4.5 \times \sqrt{H\_s} \tag{8}$$

then, an "energetic" yearly significant wave height, *He*, can be calculated as:

$$H\_{\mathfrak{e},j} = \sqrt[2.5]{\frac{P\_j}{0.459 \times 4.5}}\tag{9}$$

for *j* = 1, ... , 40, and a correspondent energy period can be estimated as follows:

$$T\_{\rm av,j} = 4.5 \times \sqrt{H\_{\rm c,j}} \tag{10}$$

The 40-year average for these parameters gives *He* = 0.93 m and *Tee* = 4.3 s. Regarding the representative direction, *De*, a vectoral analysis about the energy flows (carried out comparing records of ADCP installed in the bay and correspondent offshore waves) indicate as the offshore wave direction can be considered 217◦ N. Finally, the "energy equivalent scenario" with *He*, *Tee* and *De* has been propagated applying the MIKE 21 SW model. The seabed was performed by interpolating at the grid nodes the information provided by the General Bathymetric Chart of the Oceans (GEBCO) database [51]. A gross verification of the 10-m isobath, as representative of the nearshore region in a wave propagation model, has been applied by comparison with a recent bathymetric campaign [19].

#### *2.4. Statistical Analysis: Multivariate Analysis and Bivariate Correlation*

All the statistical computations were implemented using the statistical software IBM SPSS Statistics® (version 26.0), while spatial queries, distance calculation and data mapping were managed through the open source environmental software QGIS (version 3.8.3, https://qgis.org/en/site/).

PCA [52] is a powerful technique to investigate patterns of correlations among a set of variables. PCA in fact allows to extract a set of new variables, uncorrelated, called principal components (herein after PCs), each aggregating the variables more strongly correlated with the PC and among themselves. PCA seeks in fact a linear combination of input variables, which extracts the maximum variance of the bivariate correlation matrix. Following this, with a second linear combination, orthogonal from the first one, PCA extracts the maximum variance of the remaining variance, and so on. PCs represent all the linear combinations of the original variables weighted by their contribution in explaining the variance, in a specific orthogonal dimension. PCA was turned into a factor analysis (FA) to reduce the contribution of the less significant parameters within each component, by extracting a new set of vari-factors through rotating the axes defined by the PCA extraction. The Varimax rotation criterion was used to rotate the PCA axes allowing us to maintain the axes, orthogonality. The number of factors to be retained was chosen based on the "eigenvalue higher than 1" criterion (i.e., all the factors that explained less than the variance of one of the original variables were discarded).

To assess the variability within the whole set of contaminants, PCA/FA was performed considering all 11 metals, and all 18 PAHs (Table 1). For each of the *N* sampling points, PCA enabled to calculate principal component scores aggregating the information of the different parameters' concentration values. Moreover, in order to assess correlations between the sampled contaminants concentrations and the position of sewage discharges and thermal springs, a distance matrix was created through the QGIS vector toolbox, containing the pairwise distance between each sampling point and each discharge point. The measurements-discharges distance matrix was then associated to the component loadings and a bivariate Pearson's linear correlation analysis was performed. Finally, in order to assess the possible effects of the waves' dynamics on the concentration patterns found with PCA, a Kruskal Wallis test was performed to test the significance of hydrodynamics' effect on the contaminant concentrations, likewise similar studies [53–56]. Five wave characteristics were considered, such as the direction, the height, the mean peak period, the energy and the resulting power of the wave motion inside the bay. These characteristics were subdivided into classes, identified based on a hierarchical cluster analysis (HCA) [52] and tested as factors on the vari-factors assumed as dependent variable in the Kruskal Wallis test. Therefore, the KW null hypothesis was the equality of the vari-factor medians across wave characteristics classes. The level of significance assumed was *P*-value lower than 0.05.

## **3. Results**


*4.2. Transformation*

The transit journey data may be illustrated as a triangle where each point of triangle represents the coordinate of the trip origin, destination and transfer. The size of the journey triangles varies by the actual trip distance and therefore the journey data needs to be converted into a homogeneous coordinate system to analyse the spatial distribution pattern. The conversion is done by applying a series of transformation techniques in this study. The first step of the transformation is to transform the journey triangle *OTD* (origin–transfer–destination) on a spherical Earth's surface to a 2D plan, given the latitudes and longitudes of each point of interest, as shown in Figure 2.

**Figure 2.** Transformation from a spherical Earth's surface to a 2D plan.

The great-circle distance between two points, which is the shortest distance over the Earth's surface, is calculated based on the spherical law of cosines. The spherical law of cosines states that, for a spherical triangle,

$$
\cos OD = \cos OT \cos TD + \sin OT \sin TD \cos T \tag{1}
$$

where,

*O*, *T*, *D* = Interest points of the journey triangle, *OTD OD* = Distance between origin point and destination point *OT* = Distance between origin point and transfer point *TD* = Distance between transfer point and destination point

The location of any point on the earth can be defined by its latitude and longitude. In reference to Equation (1), the *OD* distance can be calculated as the arccosine of cos *OD*, as shown in Equation (2).

$$\begin{aligned} \cos OD &= \cos OT \cos TD + \sin OT \sin TD \cos T\\ OD \text{ (in rad.)} &= \cos^{-1} \left[ \cos OT \cos TD + \sin OT \sin TD \cos T \right] \end{aligned} \tag{2}$$

The unit used for angles is in radians, which gives the distance between origin and destination in radians. Given the convenient mean radius of the earth to be equivalent to 6371 km, the distance between origin and destination, in km, can be calculated by multiplying the *OD* distance (in radians) with 6371 km, as shown in Equation (3).

$$OD\ (\text{in km}) = \cos^{-1}\left[\cos OT \cos TD + \sin OT \sin TD \cos T\right] \* 6371 \text{ km} \tag{3}$$

The same technique was applied to calculate the great-circle distance of *OT* and *TD*. With the great-circle distance of *OT*, *TD* and *OD*, the respective angles of any triangle on a 2D plane could be calculated using the law of cosines, as shown in Equation (4).

$$\begin{array}{l} \cos O = (OT^2 + OD^2 - TD^2)/2(OT\*OD) \\ O = \cos^{-1} \left[ (OT^2 + OD^2 - TD^2)/2(OT\*OD) \right] \end{array} \tag{4}$$

After the journey triangle *OTD* was obtained, it needs to undergo a series of Euclidean transformations to display all the origin, destination and transfer points in a standardised Euclidean space, as illustrated in Figure 3.

**Figure 3.** Euclidean transformations.

The first step of Euclidean transformation is translation. Translation relocates the journey triangle *OTD* to set the triangle's origin point, *O*, at (0, 0). This transformation preserves the congruence and distance of the journey triangle *OTD*. Applying the translation process to the single-transfer journeys results in all the journey triangles originating from the same point at (0, 0). The notation for translation (*Th*,*k*) is shown in Equation (5). The origin and destination points will undergo the same transformation.

$$T\_{h,k}\left(T'\_{x\*}, T'\_{y}\right) = \left(T\_x + h\_{\prime}, T\_y + k\right) \tag{5}$$

Preserving the congruence and distance, the journey triangle *OTD* is rotated at *O* (0, 0) until the triangle plane, *OD*, rests on the *x*-axis. This transformation rotates all the journey triangles to lie along the *x*-axis for the destination point, *D*, to have the coordinate of (*x*, 0). The notation for rotation is shown in Equation (6).

$$
\begin{bmatrix} T'\_{\times} \\ T'\_{\times} \end{bmatrix} = \begin{bmatrix} \cos \theta & -\sin \theta \\ \sin \theta & \cos \theta \end{bmatrix} \begin{bmatrix} T'\_{\times} \\ T'\_{\times} \end{bmatrix} \tag{6}
$$

At this stage, all journey triangles *OTD* lie on the same plane (*x*-axis). The next step of the transformation is to loosen up the restriction to consider bijection, which preserves the shape and angles of the triangle, but not distance. The aim of this step is to transform all journey triangles *OTD* to have the same *OD* unit distance, as shown in Figure 4. The notation for compression and dilation (*CDk*) is shown in Equation (7).

$$\text{CD}\_k\left(T^{\prime\prime}{}\_{\text{x}\prime}T^{\prime\prime}{}\_{\text{y}}\right) = \left(kT^{\prime\prime}{}\_{\text{x}\prime}kT^{\prime}{}\_{\text{y}}\right) \tag{7}$$

**Figure 4.** Transfer locations on a homogeneous coordinate.

### *4.3. Transfer Location Map*

Figure 4 illustrates the transfer points of the single-transfer bus journeys, transformed to the scale of *OD* unit distance for both the *x* and *y* axis. In the figure, the scale is not the actual distance, but is adjusted through either compression or dilation. This study assumes that the plot represents the "acceptable" or "viable" transfer locations in relation to the straight path to destination. Consequent analysis quantifies and ranks the viability of transfer points and validates its impact on the travel mode choice.

The distribution of transfer points in Figure 4 may have been influenced by the availability and quality of transit services in the area. They are chosen transfer locations possibly among multiple alternatives, available to the travellers. The transit network structure must be an important determinant of the distribution pattern. The transit network of Brisbane takes the typical radial structure, with no trunk or feeder service. It is common that a transfer requires a significant deviation from the direct path to the destination or even in the opposite direction from the destination. The distribution pattern in the figure may reflect the inconvenience factor of the service transfer under the existing network structure.

### *4.4. Grid-Based Hierarchical Clustering*

To analyse the spatial distribution pattern of the transfer points, this study used the grid-based hierarchical clustering method, which combines the grid-based clustering and hierarchical clustering methods. Cluster analysis is a data reduction tool that partitions a sample dataset into clusters, where objects within a specific cluster share many characteristics, but are very dissimilar to objects not belonging to that cluster [40]. The grid-based clustering (also known as density-based clustering) is one of the most e fficient approaches for mining large data sets. Unsupervised clustering such as K-means was inappropriate for this study because it clusters the data in similar sizes (i.e., point densities). The underlying assumption of the choice modelling in the next step is that the cells with higher point densities are considered as more viable transfer locations by travellers. This method adopts algorithms that partition the data space into a finite number of cells to form a grid structure [41] as shown in Figure 5.

**Figure 5.** Grid structure on the transformed transfer location map.

For grid-clustering, each grid is defined as 0.2 × *OD* (origin to destination) unit distance increment. These transfer points are plotted in reference to 1.0 × *OD* unit distance. Figure 5 shows the clear concentration of transfer points in the cells, along with the direct path between the origin and destination. For cell clustering, the cell density is calculated for each cell as follows:

$$\text{Cell density} = \frac{\text{Total number of transfer points in grid } x}{\text{Total number of transfer points}} \tag{8}$$

The hierarchical clustering method was applied to sort the cells into clusters. Hierarchical clustering is useful for finding relatively homogenous clusters of cases based on measured characteristics. It starts with each case as a separate cluster. Next, these clusters are combined sequentially until only one cluster is left. The algorithm for this clustering method uses the dissimilarities or distances between objects when forming the clusters [40]. Figure 6 shows the cell-density for each cell, and to which cluster each cell is assigned by the hierarchical clustering method.

Figure 6 presents the preferred transfer locations with relatively high cell densities. Figure 7 shows the result of hierarchical clustering. Some interesting results were observed in the travellers' transfer selection. The majority of bus journeys conducted had made a transfer located in the cells F1 and J1. These two cells were identified to have the highest transfer point density at 13.78% and 15.47%, respectively (Cluster A). These two cells may be regarded as the most preferred transfer location and

by having a transfer service in those cell locations will increase the likelihood of making a transfer and eventually taking a transit, compared to other cells.

**Figure 6.** Cell-density in respective clusters.

**Figure 7.** Cell density dendrogram.

All the other cells are categorised into five different clusters by the cell's grid density. The hierarchical clustering uses the Ward's method to measure the dissimilarity among clusters. Ward's method uses an analysis of variance approach, instead of distance metrics to evaluate the distances between clusters, where cluster membership is assessed by calculating the total sum of squared deviations from the mean of a cluster [42]. The dendrogram allows the tracing backward and forward to any cluster at any level. It gives an idea of how great the distance is between clusters in a particular step, using the 0–25 scale along the top of the chart.

Cluster B includes G1, H1 and I1. Transfer points in those five cells of Cluster A and Cluster B account for 54.1% out of the total 150 cells in the map. This implies that most travellers prefer the transfer point to be closely located along the direction to their trip destination. Cluster C consists of seven cells, E1, F2 to J2 and K1. The average cell density significantly declined to 3.67%. Some bus users travelled to transfer points in the opposite direction from their destination, but not too far from their origination location. Similarly, some travellers made a transfer farther from their destination location. The cell density further decreased for the cells in Cluster D with the average density value at 1.37%. The transfer points located in the Cluster A to D groups accounted for 89.35% of the total transfers. The average density value of the cells in Cluster E and Cluster F was negligible at 0.27% and

0.02%, respectively although they accounted for more than 87.33% of the total map area (131 out of 150 cells).

In general, based on the transfer point density in each cell, it was observed that the majority of transfers are conducted at the locations along the direct path to the destination. Transit users occasionally travel to the opposite direction from the destination, or slightly farther away from the destination to make a transfer. When a transit journey is required to make a transfer that is deviated from the direct path between origin and destination, the realisation of such trips is unlikely. This demonstrates the impedance of transfer location, and the interpretation must take into account the transit network structure.

### **5. Mode Choice Analysis**

Two binomial logistic regression models (base and expanded model) are drawn on two travel modes: private vehicles and the bus. For a mode choice analysis, the information of mode specific variables of the alternative (unchosen) mode is necessary. Information of the alternative mode only can be inferred. This study used the GTFS (General Transit Feed Specification) data to infer the bus journey information for those who have chosen a private vehicle as their travel mode; and Google Maps to infer the private vehicle travel time for those who have chosen the bus as their travel mode. This analysis considers only the home-based work journeys. If a traveller has used the bus as the mode of transport, the journey must include one service transfer using the same travel mode (bus). Due to the strict criteria, only 330 private vehicle journeys and 63 bus journeys were used for the analysis. The 2009-10 SEQHTS is the most recent and detailed dataset available to demonstrate travellers' travel patterns and mode choice.

The dependent variables of the model are dichotomous, representing travel mode choice (transit or private vehicle). The independent variables tested in this analysis include individual characteristics (gender, age, individual weekly income, household size and number of cars in the household), journey attributes (travel time, initial wait time, first mile walk time and last mile walk time) and transfer attributes (proportion of in-vehicle bus travel time, proportion of transfer walk time, proportion of transfer wait time, type of transfer and transfer location). Table 1 presents the list of independent variables with brief descriptions.

**Table 1.** List of independent variables.

Two di fferent models were developed to test the e ffectiveness of the transfer location variable. The base model (Model I) takes the conventional approach to account for the e ffect of transfer by incorporating the proportion of the in-vehicle bus travel time, proportion of transfer walk time and

proportion of transfer wait time variables. The expanded model (Model II) used the same set of independent variables and an additional the "transfer location" variable. The test results of those two models are presented in Table 2.

**Table 2.** Binomial logit model results: transfer location as an ordinal variable.

Notes: \*\*\*: *p* < 0.01; \*\*: *p* < 0.05; \*: *p* < 0.1. Coefficients that are statistically insignificant (*p* ≥ 0.1) are not shown in this table.

Table 2 shows only the variables that provided the best fitting model fit. For instance, gender, age, network distance, transfer walking time, transfer wait time and transfer type were found not to be significant. The best-fitting basic model (Model I) incorporated six independent variables including: individual weekly income, household size, number of cars, car travel time, bus travel time, initial wait time for the first bus service, first mile walk time, last mile walk time and proportion of in-vehicle bus travel time. In Model II, the transfer location variable was found significant at the 0.05 level. This is notable as the new variable was found to make substantial influence on the mode choice, significant at the 0.05 level. As for socioeconomic variables, the household size had a positive effect on the utility of transit, whereas the individual weekly income and number of cars in the household had a negative effect on the transit utility. The car travel time factor was found significant (at the 0.01 level) among other journey attributes. Other journey attributes such as the first mile and last mile walk time were found significant at the 0.1 level for both the base and expanded models, which are consistent with the literature. As the access and egress increases, the use of transit decreases [38,43]. The initial wait time for the first bus service was found significant at the 0.1 level, only for the expanded models. If transit is not available at the time when individuals needed to travel, it decreases the attractiveness of transit.

As for the transfer-related variables, only the proportion of the in-vehicle bus travel time factor was found to be significant in both the base and expanded models (significant at the 0.05 level). Exp. β shows the effect of the independent variable on the odds ratio. The Exp. β coefficient relating the proportion of in-vehicle transit travel to the likelihood of using transit was 49.45 and 69.27 in the base and expanded model, respectively. These results implied that travellers are more likely to use transit as the proportion of in-vehicle bus travel time increases. This finding is consistent with the literature that shorter in-vehicle transit travel times could lead travellers to perceive the walking and wait times during transfer more onerous and eventually increases the relative attractiveness of a private vehicle [1,20,44,45].

The transfer location variable in the expanded model was found to be significant at the 95% confidence level. The negative coefficient suggests that a transfer location farther from the OD path will decrease the utility of bus and the probability to choose the bus mode. In fact, it turns out that the transfer location factor is one of the most important determinants of travel mode choice. This variable has the Exp. β (the odds ratio) value of 0.75, which shows that a change in the transfer location from a more preferred cluster to a less preferred cluster (e.g., from Cluster A to Cluster B) would decrease the probability of choosing the bus to 0.43, and increase the probability of choosing a private vehicle to 0.57.

The prediction capability of Model I and Model II was compared using McFadden rho squared to demonstrate the effectiveness of the new transfer location variable and its impact on the travel mode choice. Model I resulted in the pseudo R-squared, ρ2 at 0.29, whereas Model II increased it to 0.31. McFadden suggested ρ2 values of between 0.2 and 0.4 should represent a very good fit of the model [46]. The increase in ρ2 by Model II demonstrates that with the inclusion of the new variable, Model II has a better explanatory power on mode choice as compared to Model I.

The chi-squared (χ2) test was conducted to investigate the statistical improvement between Model I and Model II, by gauging the change in the log-likelihood function relative to the change in degrees of freedom. The chi-squared, χ2 value of 5.04 exceeds the critical chi-squared of 1 degree of freedom of 3.84, at the 0.05 significant level. This gives a sufficient evidence to reject the null hypothesis that Model II is no better than Model I. With the inclusion of the transfer location variable into Model II, it outperforms Model I (base model).

The transfer location variable in Table 2 is ordered from the most preferred cluster to the least preferred cluster, in an ordinal-scale. This approach is effective to study the impact of transfer location as a variable, based on the assumption that the distance between clusters is equal. To study the relationship between the clusters, an additional binomial logistic regression model is conducted to include the transfer location variable as nominal variables. The result is shown in Table 3.

**Table 3.** *Cont.*

Notes: \*\*\*: *p* < 0.01; \*\*: *p* < 0.05; \*: *p* < 0.10. Coe fficients that are statistically insignificant (*p* ≥ 0.10) are not shown in this table.

The result from Tables 2 and 3 did not di ffer much. The age of the travellers became significant at the 0.05 confidence level, with a negative e ffect on transit utility. Having the transfer location as nominal variables, Cluster F was assigned to be the reference category. The exponential β coe fficient shows that if a transfer location is in Cluster A, it will have 5.99 times more chance to use the bus over Cluster F. Transfer locations located in Cluster B, C and D were found to be significant at the 0.05 level, but not Cluster E. This implies that as the transfer location changes from a less preferred cluster to a more preferred cluster (e.g., from Cluster F to Cluster A), it will increase the probability of choosing the bus over an automobile.

### *Transfer Location and Transit Travel Time*

The analysis results indicate that the chance to make a transit trip was likely to decrease if the trip involves a transfer at the location that deviates from the direct path to the destination. In the conventional mode choice analysis, the level of deviation is quantified in terms of travel time incurred during the transfer. The new variable was created to capture the impact of deviation in the travel direction. We take two approaches to examine the potential collinearity between transfer location and transit travel time. Firstly, the Spearman's correlation coe fficient (ρ) was calculated between the bus travel time (continuous variable) and the transfer location (categorical variable) of the Household Travel Survey bus trip data. A weak correlation (ρ = −0.211) was found between two variables, which implies that the location of transfer may play as an independent factor for the travel mode choice.

The second approach presents three plots of the transfer point distribution by the length of the bus journey time (less than 30 min, between 30 and 45 min and more than 45 min), as shown in Figure 8.

The level of deviation of transfer points was derived using Equation (9). An arbitrary cell length of 4 was used—for example, the deviation of a transfer point (*x*, *y*) was calculated as the sum of the distance from the origination point (0, 0) and the distance from the destination point (20, 0).

$$\text{Level of deviation} = \sqrt{\mathbf{x}^2 + y^2} + \sqrt{(\mathbf{x} - 20)^2 + y^2} \tag{9}$$

The average level of deviation of a short (less than 30 min), medium (between 30 and 45 min) and long journey (longer than 45 min) was found at 24.7, 24.3 and 25.7, respectively. Although more deviation was found among the longer bus trips, the distribution pattern is largely unchanged regardless of the length of travel time. This suggests that the preference for transfer location was not a ffected by the travel time (or distance) and travellers might disfavour adjacent transfer services depending on their relative location with respect to destination. The conventional approach of using door-to-door travel time to capture the transfer cost is not su fficient. Transit travel time may be able

to capture the effect of deviation in the travel distance, but it is not capable to capture the effect of deviation in the travel direction towards transfer services.

**Figure 8.** Distribution of transfer points.


**12. Laparoscopic findings**

Laparoscopy is the gold standard for the diagnosis and staging of endometriosis and allows for curative surgical resection at the same time. Patients found to have endometriosis at the time of laparoscopy should either be treated through surgical ablation, resection, or laser treatment. In addition, biopsy is recommended, as lesions of endometriosis in adolescents often take on a different appearance as compared with the typical powder-burn lesions seen in adults. Interestingly, clear or red endometriotic lesions are much more commonly seen in the adolescent population (76).

Endometriosis 15

advantages for control of pelvic pain associated with endometriosis, including effective contraception, minimal systemic effects, and up to 5 years of benefit, as compared with 6

Dienogest (DNG), a progestin of 19‑nortestosterone derivative, has good oral bioavailability and is highly selective for progesterone receptors. Owing to its antiovulatory, antiproliferative activities in endometrial cells, and its inhibitory effects on the secretion of cytokines, DNG is expected to be an effective treatment for endometriosis. Progesterone

• DNG has moderate affinity to the progesterone receptor. DNG shows low binding to the androgen receptor and almost negligible binding to the estrogen receptor,

• An oral DNG dose of 1 mg/day is required for inhibition of ovulation in cyclic women. • DNG has an inhibitory effect on growth and cytokine production of endometriotic cells. • DNG is as effective as triptorelin (gonadotropin-releasing hormone agonist) for

• DNG is as effective as intranasal buserelin acetate for the relief of pain symptoms

Treatment with a GnRH-a followed by long-term dienogest therapy maintains the relief of endometriosis-associated pelvic pain achieved with GnRH-a therapy for at least 12months. This regimen reduces the amount of irregular uterine bleeding that often occurs during the

Complex mechanisms involving promoter regulation may be responsible for the observed aberrations in (Estradiol Receptor a, b) ERa, ERb, and PR (progesterone Receptor) expression in endometriosis. The stromal cell component of endometriotic tissue may be the primary site of these abnormalities. In endometriotic stromal cells, ERb promoter is pathologically hypomethylated and therefore hyperactive, leading to very high ERb levels. ERb suppresses ERa expression and results in strikingly high ERb-to-ERa ratios in

It is possible that this consequence of PR-B deficiency is only the tip of the iceberg with regard to pathogenesis of endometriosis, and that numerous other molecular aberrations may also contribute to the development of resistance to hormone treatments in women with endometriosis. Selective ERb ligands that target high levels of ERb in endometriotic tissue may be clinically beneficial via disrupting this mechanism.(84) PR deficiency is likely responsible for increased levels of E2 in endometriosis because progesterone fails to induce the E2-metabolizing enzyme HSD17B2 in endometriotic tissue. Some conclusion resonates with gene expression microarray studies performed on eutopic endometrium of women with endometriosis compared with that from disease-free women.(85, 86) These studies on eutopic endometrium identified distinct molecular defects that are consistent with the

ER b agonists act as immunomodulators, enhancing the immunologic response to the explants; a second possible explanation lies an antiangiogenic effect because ER b are

receptor-binding affinity is higher for DNG than for progesterone.

glucocorticoid receptor and mineralcorticoid receptor.

• DNG has strong oral progestational activity but antiandrogenic activity.

consolidation therapy after surgery for the treatment of endometriosis.

months, typical of GnRHa treatment.

associated with endometriosis.(82)

early phase of dienogest therapy.(83)

progesterone resistance hypothesis.

endometriotic cells.


Author details

Jacobo Enrique Cruz Ortega, Leopoldo Partida Ruvalcaba\*, Raymundo Medina López, Tomás Díaz Valdés, Teresa de Jesús Velázquez Alcaraz and Felipe Ayala Tafoya

\*Address all correspondence to<EMAIL_ADDRESS>
Faculty of Agronomy, Autonomous University of Sinaloa, Culiacan Rosales, Sinaloa, Mexico


*Edited by Jagannathan Thirumalai*

This book provides a detailed overview of recent advances in the captivating world of quantum dots and outlines some possible imminent new directions for this important field. A variety of advanced techniques is rapidly developing in the application of quantum dots to solar photovoltaics, LEDs, quantum computing (qubits) and different biological spheres. The book presents, discusses and compares devices based on stateof-the-art structures, incipient material and new physical effects. Improved efficiency and reliability in these fields are already pointing the way to next-generation devices, especially in the nano regime. This book is addressed to students and scientists working in the field of quantum dots and progressive technologies.

Published in London, UK © 2023 IntechOpen © dmelnikau / iStock

Quantum Dots - Recent Advances, New Perspectives and Contemporary Applications

Quantum Dots

Recent Advances, New Perspectives and

Contemporary Applications

*Edited by Jagannathan Thirumalai*


**3. Results and Analysis**

In order to ensure the reliability of a building being erected it is necessary not only to choose a reliable foundation technology, but also to be able to predict the performance of the structure. With the help of Frost 3D simulation software, it is possible to obtain scientifically based predictions of the thermal regimes of permafrost soils in conditions of thermal influence of piles as well as of erected buildings and structures. This problem of heat distribution in the foundation over time in conditions of construction on permafrost soils is of primary importance.

According to the results of modeling for the year 2050, the following values and figures were obtained. Under the positive scenario with the temperature increase by 2.2 ◦C for the period from 2031 to 2050. (0.1 ◦C per year) the active layer will be 0.595 m. The modeling results of active layer changes due to warming up to 2050 under a positive scenario are shown in Figure 7.

**Figure 7.** Temperature distribution for 2050 under a positive warming scenario.

Under the neutral scenario with a temperature increase by 3 ◦C for the period from 2031 to 2050. (0.16 ◦C per year) the active layer will be 0.673 m. The modeling results of active layer changes due to warming up to 2050 under a neutral scenario are shown in Figure 8.

**Figure 8.** Temperature distribution for 2050 under a neutral warming scenario.

Under the negative scenario with an increase in temperature by 4.8 ◦C for the period from 2031 to 2050. (0.24 ◦C per year) the active layer will be 0.840 m. The modeling results of active layer changes due to warming up to 2050 under a negative scenario are shown in Figure 9.

**Figure 9.** Temperature distribution for the year 2050 under a negative scenario of warming.

Under the local negative scenario with temperature increase by 9.6 ◦C for the period from 2031 to 2050. (0.5 ◦C per year) the active layer will be 1.868 m. The modeling results of active layer changes due to warming up to 2050 under a local negative scenario are shown in Figure 10.

**Figure 10.** Temperature distribution for 2050 under a local negative warming scenario.

The simulation results (Table 3) showed that under a positive warming scenario (with an increase of temperature by 0.11 degrees per year), the active layer will be 0.595 m, the bearing capacity of the pile will be 81.93 tons in 2050. Under a neutral warming scenario (with an increase of temperature by 0.16 degrees per year), the active layer will be 0.673 m, and the bearing capacity of the pile will be 75.84 tons. In negative and locally negative scenarios, the active layer will be 0.840 and 1.868 m, respectively. The bearing capacity of the pile will be 71.32 and 63.79 tons. Analyzing the results, it can be concluded the active layer by 2050 may increase threefold, while the bearing capacity of the piles will decrease by more than 20%. In order to avoid emergency situations and to ensure further safe operation of construction facilities, it is necessary to provide additional measures.

**Table 3.** Results of modeling the bearing capacity of the soil for various scenarios.

Besides, there was carried out the ground temperature state modeling when using seasonally acting cooling devices. The distribution of ground temperature when using thermosiphons for the winter period of 2050 under the local negative scenario is shown in Figure 11.

The distribution of ground temperature when using thermosiphons for the summer period of 2050 under the local negative scenario is shown in Figure 12.

**Figure 12.** Modeling results of ground temperature distribution in the summer period.


**2.1. Materials**

from which spare proteins in the dry wheat gluten (DWG) form were extracted industrially, intended as a protein ingredient to improve the baking properties of flour and meat substitution in sausage products. In time of processing of wheat grains on DWG forms bran, which is additionally a source of valuable food protein. Therefore, this chapter is devoted to the results of a study of the physicochemical properties of DWG proteins and protein concentrates from wheat bran for the purpose of applying the information obtained for practical purposes to improve and regulate the functional properties of protein ingredients in the development of

Functional properties of protein products are understood as physicochemical indicators that determine the behavior of proteins in the production of food products, providing the necessary structure, and consumer properties [1]. The indicators characterize the parameters of products, some of which are substituted or supplemented with protein in the technological processes of food production. The functional properties of protein products are evaluated both in numerical values and in profiles of dependencies on various technological factors (temperature, pH, processing time, etc.) [2–5]. This approach to the evaluation of properties is reflected in the term "techno-functional," which includes the features of the reactivity of proteins in the technological processes of production and storage of food systems. Functional properties for concrete food systems are usually evaluated on model recipes, and then compared with the properties of traditional or known protein products. The presence of hydrophilic and hydrophobic groups in one chain ensures the interaction of proteins with water, lipids, carbohydrates, other compounds and leads to the formation of stable emulsions, foams, gels, and so on. In solutions proteins can perform a dispersing and suspending roles, it's able to cling to solid particles and by that forming cementing structures. The presence of polar and nonpolar, charged and uncharged groups in one polymer chain allows proteins to interact with different types of compounds and, thereby, influence the quality of

The most important functional properties of protein products are hydration, fat-binding capacity, foam ability, stability of emulsions, foam stability (FS), gel-forming ability, adhesion, rheological properties (viscosity, elasticity), ability to spin and texturing [1, 6, 7]. The values of the functional properties of protein products always determine the directions of their use in the production of food products as technological or nutritional ingredients, but not always these properties satisfy the requirements of the consumer; therefore, in the chemistry of dietary protein, there is a direction devoted to regulating the quality indices of vegetable

It is known that the functional properties of protein products depend on the chemical nature of the raw materials (wheat, rye, soybean, etc.), methods of isolation, processing, and technological regimes of food production (pH, temperature, recipe, etc.) [13–14]. When analyzing the nature of vegetable proteins, food recipes' designers limit themselves, as a rule, to a statement of facts showing how a particular kind of raw material affects the functional properties but does not study the molecular basis that conditions these properties. In the practice of using protein products, at best, technological factors affecting their functional properties (temperature, pH, electrolytes, etc.) are taken into account, whereas the characteristics of the chemical,

protein products by various modification processes [8–12].

food formulas.

206 Global Wheat Production

food products.

As protein products, two samples of dry wheat gluten were used from LLC "BM" (Kazakhstan) and "Royal Ingredients Group BV" (the Netherlands), as well as concentrates from wheat bran and their fractions obtained according to the technology developed by us [27]. To study the amino acid composition of proteins, three samples of wheat gluten were used, which were manually washed from the flour of a typical "strong" sort of grain Saratov 29 (spring), typically "weak"—Akmolinka 1 (spring) and typically medium Gorkovskaya 52 (winter). The crude gluten was dried on a lyophilic plant, it was regenerated by washing in tap water for 15 min and the deformation index on the IDG-1 instrument was determined. Regenerated gluten in the first grade of grain was characterized as slightly elongated, "strong," with an indicator of the device of 58 units, in the second—tensile, "normal" with an indicator of 70 equipment units, the third—as very extensible and "weak" with an indicator of 100 equipment units.

**2.4. Determination of the fractional composition of proteins**

of a 0.5 mol/dm3

was drained, 20 cm3

The centrifugate was drained, 10 cm3

and globulins. To extract the gliadin proteins, 20 cm3

of 0.1 mol/dm3

and insoluble protein from the total amount of protein in the sample.

**2.5. Determination of the functional properties of protein products**

bran was analyzed by the Ellman method in Bogdanov's modification [31].

**2.7. Determination of the constant of the final stage of protein aggregation**

and soybean were determined by the methods described in [30].

of 0.2 mol/dm3

**2.6. The content of thiol exchange groups**

accuracy of ±0.001g was suspended in 10 cm3

diluted with 0.05 mol/dm3

protein solution, 1.3 cm3

1 g of the protein product, weighed to within 0.001 g, was placed in a centrifuge tube, 10 cm3

thoroughly mixed, and centrifuged again. The combined centrifuges were taken as albumins

tates, shaken at 180–200 rpm for 1 h and left overnight at room temperature. The next day the sample was shaken for 30 min and centrifuged at 8000 g for 15 min. The centrifugate (gliadin)

for 1 h. The suspension was centrifuged under the same conditions. The extraction procedure was repeated one more time. The combined solutions of proteins soluble in acetic acid were considered to be soluble glutenin. To isolate insoluble glutenin to the precipitates, 20 cm3 of AUC included 0.1 N acidic acid, 6M urea, and cetyl three methyl ammonium bromide solvent (pH 4.1) were added [24]; the tubes were shaken for 1 h and centrifuged. The extraction operation was repeated once more, after which the centrifuges were combined and the protein content of Kjeldahl was determined therein. The protein precipitate was designated as an insoluble protein. The amount of each fraction was expressed as the percentage of soluble

Functional properties of DWG samples, protein products from wheat bran, amaranth, rye,

The content of disulfide bonds and sulfhydryl groups in protein preparations from wheat

To determine the aggregating properties of proteins, a sample of the product 1.0 g with an

1 h on a mechanical shaker. The solution was then centrifuged for 15 min at 3000 g, the centrifugate was filtered and the Lowry protein was determined in the filtrate. The solution was

was added to the spectrophotometer cuvette. Then, after 10 min at a wavelength of 350 nm, the optical density (turbidity) of the solution was measured. The constant of the final stage of aggregation (τ10/C) was calculated as the ratio of turbidity (τ) to protein concentration (C) [32]. Analyses were carried out in 3–5 replicates, the results were represented as arithmetic means. To determine the confidence interval of the average arithmetic result, the Student's test was

of a 0.05 mole/ dm3

acetic acid to a concentration of 0.02% protein. To 1.3 cm3

phosphate buffer containing 2 mol/dm3

NaCl solution was added, shaken for 1 h and centrifuged for 15 min at 8000 g.

Interrelation of Functional Properties of Protein Products from Wheat with the Composition…

of cold distilled water was added to the precipitates,

acetic acid was added to the precipitates and again shaken

of 70% ethanol was added to the precipi-

http://dx.doi.org/10.5772/intechopen.75803

209

solution of CH3

COOH for

NaCl (pH 5.6)

of the

Protein concentrates from wheat bran were obtained from various systems of the technological process of JSC "Moscow Combine of Bread Products," the quality of grain and bran was in accordance with the requirements of standards. The bran was combined, sieved through a sieve of different diameters, and granulometric fractions with a particle size of more than 1000, 670, 195, and less than 195 μm were obtained.

To compare the results of the relationship between functional properties and physicochemical parameters for proteins from wheat and protein products from another type of raw material, soy concentrate, soy isolate Supro 760 from "Soloe" Supro (USA), soy isolate ArdexF ADM (USA), concentrates from amaranth and grain of rye, obtained by our methods [28, 29].


**2. Visual landscape perception**

In landscape planning and environmental impact assessment studies, evaluation of visual landscape character is often based on assessment of physical characteristics of landscapes (such as topography, land cover etc.) and is done by experts. On the other hand public or user preferences are generally neglected. This section aims to present and provide understanding of psychophysical and cognitive dimensions of visual landscape perception for landscape designers and planners.

Although we receive spatial information through many of our senses (seeing, hearing, smelling and feeling) sight is assumed to be the most valued sense. More than 80% of our sensory input is through sight (Porteous, 1996). Hence, most of the environmental perception, and likewise landscape assessment studies, focus on visual dimension of the perception process. Assessment of landscape character is fundamental to decision making process in landscape planning. Landscape assessment is a tool for determination of landscape quality and provides a systematic analysis and classification for sustainable management of landscapes. Within this context, the criteria for landscape perception studies are mostly scenic beauty or preference (Palmer, 2003).

Landscape Perception 255

beauty as a "*social quality*" and linked beauty with the feeling of affection, particularly toward the other sex. According to him, the feeling of the beautiful is grounded in our social nature (Vandenabeele, 2012). On the contrary, German philosopher Immanuel Kant's (1724- 2804) approach to aesthetic judgment was based on logic and deduction (Lothian, 1999). He believed aesthetic judgments were based on the feeling of pleasure and they were disinterested. Daniels (2008) explains disinterestedness as *"... a genuine aesthetic judgment does not include any extrinsic considerations toward the object of judgment itself, such as political or utilitarian concerns*". Therefore, Kant claimed that aesthetic judgments were both subjective and universal. However, German philosophers Friedrich Schiller and Wilhelm Hegel rejected Kant's subjective approach on aesthetics (Lothian, 1999). Schiller (1759- 1805) claimed that beauty was the property of the object, thus aesthetic experience was rather objective. On the other hand, Hegel (1770-1831) believed that aesthetics was concerned with the beauty of art and beauty of art is higher than the beauty of nature. Like Schiller, for Hegel beauty was the property of the object. According to Baumgarten (1714-1762), who coined the term aesthetics, beauty is not connected to the feeling of pleasure or delight, indeed beauty is an intellectual category and perfection of sensitive cognition is a precondition for beauty (Gross, 2002). In 19th century, romanticism focused on nature as an aesthetic resource. In this period, landscape was viewed in objectivist terms and considered as having intrinsic qualities (Lothian, 1999). However, nature lost its importance as an aesthetic object by the end of 19th century and during the 20th century art has become the

George Santayana, Benedetto Croce, John Dewey and Susanne Langer are amongst the modern era philosophers on aesthetics. Spanish-American philosopher George Santayana (1863-1952) believed that beauty was a subjective concept, rather than objective. He defined beauty as the pleasure derived from perception of an object (Lothian, 1999). Croce (1866- 1952) interprets aesthetics as an experience. For Croce, intuition is basis for the sense of beauty. Dewey's (1859-1952) aesthetics is based on experience as well. In contrast to Kant's disinterestedness principle, Dewey's aesthetics require involvement and engagement (Lothian, 1999). While Dewey suggested that aesthetic experience was a biological response, Langer (1895-1985) strongly rejected this idea (Bourassa, 1988). Langer's aesthetics is based on the concept of semblance. According to Langer, semblance of a thing is an aesthetic

Although philosophical theories of aesthetics may seem relatively relevant to landscape assessment, landscape planners and designers need to understand the fundamentals of aesthetic theories of art and nature in order to develop valid and efficient approaches towards evaluation of landscape aesthetics in context of landscape planning and design. According to Berleant (1992), the idea of environment possesses deep philosophical assumptions about our world and ourselves, thus the study of aesthetics and environment

The biophilia hypothesis was developed by Edward O. Wilson, biologist in Harvard University, in 1984. The biophilia hypothesis proclaims that human beings have an inherent

symbolic form which constitutes its direct aesthetic quality (Kruse, 2007).

can provide mutual benefit in this changing world.

**2.2 Theories on perception and preferences**

**2.2.1 The biophilia hypothesis**

main concern for aesthetic debates.

Landscapes are aesthetic objects. There isn't a universally accepted theory for landscape aesthetics. According to Maulan *et al*. (2006) neglect of scenic or preferred landscapes during development stage is one of the problems. Bourassa (1990) argues that landscape aesthetics is beyond the traditional theories of aesthetics. Based on Scruton's approach, he states that people experience and respond to the whole scene, therefore "*it is not relevant to speak of the aesthetics of individual objects in the landscape (e.g. buildings) without asking how those objects contribute to the wholes (landscapes) of which they are only parts*". For Bourassa (1988) there are two principles for landscape aesthetics, namely biological and cultural. The biological principle states that "*aesthetic pleasure in landscape derives from the dialectic of refuge and prospect*". On the other hand, "*aesthetic pleasure derives from a landscape that contributes to cultural identity and stability*". As a product of either natural processes or human intervention, natural and cultural landscapes involve intrinsic (objective) and artistic (subjective) aesthetic values. Thus, theories of aesthetics may provide a basis for landscape scenic beauty assessments. Brief history of aesthetics in philosophical context is given below.