**Cognitive Aging Cognitive Aging**

[176] Kirkland JL, Tchkonia T, Zhu Y, Niedernhofer LJ, Robbins PD. The clinical potential of senolytic drugs. Journal of the American Geriatrics Society. 2017;**65**(10):2297-2301 [177] Lehmann M, Korfei M, Mutze K, Klee S, Skronska-Wasek W, Alsafadi HN, et al. Senolytic drugs target alveolar epithelial cell function and attenuate experimental lung

[178] Baar MP, Brandt RMC, Putavet DA, Klein JDD, Derks KWJ, Bourgeois BRM, et al. Targeted apoptosis of senescent cells restores tissue homeostasis in response to chemo-

[179] Schimke MM, Marozin S, Lepperdinger G. Patient-specific age: The other side of the coin in advanced mesenchymal stem cell therapy. Frontiers in Physiology. 2015;**6**:362

[180] Takahashi K, Yamanaka S. A decade of transcription factor-mediated reprogramming to pluripotency. Nature Reviews Molecular Cell Biology. 2016;**17**(3):183-193

[181] Soria-Valles C, Lopez-Otin C. iPSCs: On the road to reprogramming aging. Trends in

[182] Trounson A, DeWitt ND. Pluripotent stem cells progressing to the clinic. Nature

[183] Neves J, Sousa-Victor P, Jasper H. Rejuvenating strategies for stem cell-based therapies

[184] Rebo J, Mehdipour M, Gathwala R, Causey K, Liu Y, Conboy MJ, et al. A single heterochronic blood exchange reveals rapid inhibition of multiple tissues by old blood.

[185] Oh J, Sinha I, Tan KY, Rosner B, Dreyfuss JM, Gjata O, et al. Age-associated NF-KB signaling in myofibers alters the satellite cell niche and re-strains muscle stem cell func-

[186] Rinnerthaler M, Bischof J, Streubel MK, Trost A, Richter K. Oxidative stress in aging

[187] Rinnerthaler M, Duschl J, Steinbacher P, Salzmann M, Bischof J, Schuller M, et al. Age-related changes in the composition of the cornified envelope in human skin.

fibrosis ex vivo. The European Respiratory Journal. 2017;**50**(2):1602367

toxicity and aging. Cell. 2017;**169**(1):132

142 Gerontology

Molecular Medicine. 2016;**22**(8):713-724

in aging. Cell Stem Cell. 2017;**20**(2):161-175

Nature Communications. 2016;**7**

tion. Aging-Us. 2016;**8**(11):2871-2896

human skin. Biomolecules. 2015;**5**(2):545-589

Experimental Dermatology. 2013;**22**(5):329-335

Reviews Molecular Cell Biology. 2016;**17**(3):194-200

Neyda Ma Mendoza-Ruvalcaba, Elva Dolores Arias-Merino, María Elena Flores-Villavicencio, Melina Rodríguez-Díaz and Irma Fabiola Díaz-García Neyda Ma Mendoza-Ruvalcaba, Elva Dolores Arias-Merino, María Elena Flores-Villavicencio, Melina Rodríguez-Díaz and Irma Fabiola Díaz-García Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.71551

#### **Abstract**

The study of cognitive function in gerontology is considered relevant because it is an important risk factor for other pathologies in the old age, such as physical disability and dependence, depression, and frailty, mainly because of early pathological changes in cognitive function which are considered a preclinical state that may progress to dementia. In this chapter, cognitive functioning and the dimensions that are included in it (attention, memory, meta-memory, processing speed, executive functions, visuospatial skills, and language) are conceptualized. Additionally, the current evidence is analyzed regarding age-associated changes that are experienced during cognitive aging. These changes, or cognitive decline, are distinguished from those that are part of cognitive pathologies, the most common mild cognitive impairment and dementia. Such pathologies are conceptualized based on the current diagnostic criteria, and controversies and challenges are discussed. Additionally, we analyze the risk factors for cognitive functioning in aging, both modifiable and nonmodifiable ones. A review of the main nonpharmacological intervention techniques used from the gerontology approach is made. It includes the cognitive training in the case of age-related decline or techniques of stimulation and cognitive rehabilitation in the case of mild cognitive impairment or dementia. Finally, we conclude with an analysis of the current state of this topic in the field of gerontology and its relevance in professional practice.

DOI: 10.5772/intechopen.71551

**Keywords:** cognitive aging, age-related decline, mild cognitive impairment, dementia, cognitive intervention

#### **1. Introduction**

Population aging is a global reality that is happening in a gradual and unavoidable manner, as a result of the low birth rate and mortality in the population, and at the same time, due to

© 2016 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, and reproduction in any medium, provided the original work is properly cited. © 2018 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.

the increase in life expectancy. However, aging is not only a population phenomenon but also an individual reality [1], which involves a series of changes in people at biological, psychological and social levels. In the psychological field, changes in the domains related to personality, affectivity, emotions, emotional control, and interpersonal relationships have been reported [2].

Cognitive functioning has been defined as an evolutionary process in which individuals are immersed, which begins in fertilization and ends in death. In this process, both the organism in general and the nervous system in particular experience a series of changes that, in interaction with the environment, enable the development and maturation of both the nervous

Cognitive Aging

145

http://dx.doi.org/10.5772/intechopen.71551

A more integrative view of mind-brain relationships defines the cognitive functions as functional interactions within and among cortical networks, which in turn are distributed throughout the cerebral cortex as memory, attention, perception, language, and intelligence;

From another perspective, cognitive functions come from the information processing activity in neural networks distributed along the cortex and represent past and future schemes of action. This perspective suggests that temporal organization affects perceptual processes,

From a psychopedagogical framework, complex cognitive functions consist of the organizing and sequencing of plans, the ability to respond to various stimuli at the same time, cognitive flexibility, the ability to respond according to the context, resistance to distraction, and inhibi-

From Piaget's theoretical position, cognitive functions are considered as the mechanisms of information processing, which main function is to transform the internal and external stimuli into inputs for development and, in addition, to provide the individual with tools to face the positive entropy, and also the trend to exhibit states of thermodynamic equilibrium [12].

From the point of view of the structural cognitive modifiability theory, the cognitive functions are classified as perceptual thinking (basic functions), strategic (executive functions), analogical (educational functions), and reflexive (meta-cognitive functions) according to the

Finally, from the neuropsychology perspective, the different components of cognitive functions are defined as the abilities developed by brain structures that allow them to work with the information that is acquired from the environment. These cognitive abilities are divided into two groups: those known as basic cognitive functions such as sensation, perception, memory, attention and concentration; and higher cognitive functions such as thought, language, and intelligence, which are considered complex systems and also group different functions [14].

During the last decades, several scientific efforts have been focused on the study of normal cognitive aging. This has resulted in agreements, as well as numerous discrepancies around the topic, mostly regarding the use of different research methodologies, as well as the little control of other variables that are considered to be closely related to cognitive functioning.

In addition, finding differences between normal cognitive aging and a cognitive impairment involving pathology is clinically difficult, since the limits of diagnosis are not precise.

action, and cognition within a sequence designed to achieve a goal [10].

system itself and the behavior [9].

all sharing the same structure [4].

tion of inappropriate behaviors [11].

**2. Age-associated changes**

last generation of the constructivism paradigm [13].

Regarding cognitive functioning, the changes that occur during aging are of increasing interest for gerontology because of the implications they could have in case they finally appear in their most pathological form: dementia.

Historically, the research of cognitive functions has its epistemological origin in the studies carried out by the philosopher Galenus, who argued that in the ventricles of the brain, the consciousness of the human being was found as a set of different capacities: perception, intellect, and memory. From the philosophy of Rene Descartes (1596–1650) arises the neurophysiological theory, which defined the relationship between body and spirit and tried to find the explanation of mental function in the ventricles as the basis of psychic functions, later setting the pineal gland as related to mental disorders.

Later, Flourens (1794–1867) argued that all neural tissues are involved in the different cognitive functions. But it was until Gall's studies (1758–1828) with his Frenology theory, that on one side, cognitive functions were associated with structures by examining the skull, and on the other side, the role of the cerebral cortex in relation to cognitive functions was presented. It was until the nineteenth century, with the establishment of the neuropsychology, when the correlation of anatomo-clinical structures with the alterations in cognitive functions was clearly set up [3, 4].

During the nineteenth century, the first stage of neuropsychology was established. Its study object is the relationship between the cerebral organization and the behavior in its broadest sense: actions, emotions, motivations, and social relations. The unit of analysis of neuropsychology is the individual, including his personal history, and his social and cultural environment. The founders of this approach are Luria, Vygotsky, and Leontiev, with the concern of locating psychological functions within circumscribed parts of the brain, defined higher mental human functions as complex reflex-like processes of social origin whose functioning is both conscious and voluntary and are possible due to their structure and functioning [5]. Later, in 1981, Luria proposed that cognitive functioning analysis should be done by looking for what is located outside the individual, the place where the origins of conscious activity are found. He also developed the idea that several macroanatomical areas and brain regions help each other to ensure control of the so-called human cognitive functions [6]. The cognitive psychology perspective studies the cognitive functioning as the way to know the world, through the construction of reality guided by experience. From there, the cognitive structure is formed and the concept of a cognitive scheme arises [7].

Piaget's theory can be found under this perspective, where the study of structures is left aside to focus on the development of cognitive functioning and its schemes, from a constructive approach of knowledge that at the same time disproves empiricists and innatists theories, based on a psychogenetic perspective [8].

Neuropsychology is a discipline with an integrative view, which today contributes decisively to our knowledge about how the brain and the alterations of its functioning work, focused on the cognitive development in relation to sociocultural factors.

The conceptualization of cognitive functioning functions had several meanings.

Cognitive functioning has been defined as an evolutionary process in which individuals are immersed, which begins in fertilization and ends in death. In this process, both the organism in general and the nervous system in particular experience a series of changes that, in interaction with the environment, enable the development and maturation of both the nervous system itself and the behavior [9].

A more integrative view of mind-brain relationships defines the cognitive functions as functional interactions within and among cortical networks, which in turn are distributed throughout the cerebral cortex as memory, attention, perception, language, and intelligence; all sharing the same structure [4].

From another perspective, cognitive functions come from the information processing activity in neural networks distributed along the cortex and represent past and future schemes of action. This perspective suggests that temporal organization affects perceptual processes, action, and cognition within a sequence designed to achieve a goal [10].

From a psychopedagogical framework, complex cognitive functions consist of the organizing and sequencing of plans, the ability to respond to various stimuli at the same time, cognitive flexibility, the ability to respond according to the context, resistance to distraction, and inhibition of inappropriate behaviors [11].

From Piaget's theoretical position, cognitive functions are considered as the mechanisms of information processing, which main function is to transform the internal and external stimuli into inputs for development and, in addition, to provide the individual with tools to face the positive entropy, and also the trend to exhibit states of thermodynamic equilibrium [12].

From the point of view of the structural cognitive modifiability theory, the cognitive functions are classified as perceptual thinking (basic functions), strategic (executive functions), analogical (educational functions), and reflexive (meta-cognitive functions) according to the last generation of the constructivism paradigm [13].

Finally, from the neuropsychology perspective, the different components of cognitive functions are defined as the abilities developed by brain structures that allow them to work with the information that is acquired from the environment. These cognitive abilities are divided into two groups: those known as basic cognitive functions such as sensation, perception, memory, attention and concentration; and higher cognitive functions such as thought, language, and intelligence, which are considered complex systems and also group different functions [14].


3.4.3. Viscosity Measurement

The ASC and PSC viscosities at different temperatures were measured according to Kimura and Ohno [84] procedure with some modification. The Ubbelohde viscometer (AVS 470, SI Analytics, Weilheim, Germany) was first filled with one of the collagen solution (1 mg/mL 0.1 M acetic acid), immersed in the water bath at 15 ◦C for 20 min; then temperature was increased stepwise up to 50 ◦C. The collagen solution viscosities were measured in each step of a 5 ◦C-temperature interval maintained for 20 min. The fractional viscosity of ASC and PSC at a designated temperature was calculated according to the formula below:

$$\text{Fractional viscosity} = \frac{(\text{maximum viscosity} - \text{measured viscosity})}{(\text{maximum viscosity} - \text{minimum viscosity})}$$

#### 3.4.4. Ultraviolet Spectrophotometric Analysis

The UV absorption spectrum of collagen samples was measured with an UV-spectrophotometer (LLG Labware, model unispec, Meckenheim, Germany). The spectrum of ASC and PSC samples (5 mg/mL) in acetic acid (0.5 M) were identified by scanning the wavelength between 190 and 500 nm. The baseline was set with 0.5 M acetic acid.


*2.1. Study Area*

PLB is located in southern China, between 113◦ E–118◦ E and 24◦ N–30◦ N, as shown in Figure 1a. The drainage area of PLB is 162,271 km2 , of which 156,743 km2 is located in Jiangxi Province (the total land area of Jiangxi Province is 166,946 km2 ), accounting for 97% of the drainage area of PLB and 94% of the total land area of Jiangxi Province [46].  The average annual temperature is about 16.3–19.5 ◦C, generally increasing from north to south. The vegetation type is subtropical, evergreen, broad-leaved forest. PLB is composed of five main tributary rivers, namely, Ganjiang River, Fuhe River, Xinjiang River, Raohe River, and Xiushui River, as well as several smaller rivers. These rivers finally flow into Poyang Lake, and the outflow of Poyang Lake inject into Yangtze River through the Hukou station. The span of PLB covers a length of 620 km from north to south and a width of 490 km from east to west. The basin is surrounded by mountains in the east, west, and south; has hills and valley plains crisscrossing the central part; and there is the Poyang Lake plain in the north. The terrain of PLB is high in the south and low in the north, which is conducive to water convergence. water convergence.

and valley plains crisscrossing the central part; and there is the Poyang Lake plain in the north. The terrain of PLB is high in the south and low in the north, which is conducive to

**Figure 1.** Location and Digital Elevation Model (DEM) of (**a**) PLB and (**b**) HRB. **Figure 1.** Location and Digital Elevation Model (DEM) of (**a**) PLB and (**b**) HRB.

The annual average total amount of water resources in PLB was 163.06 billion m3, while the annual average total amount of water uses is 17.67 billion m3 and the annual average quantity of wastewater is 4.39 billion m3. The total water resources of Ganjiang River, Fuhe River, Xinjiang River, Raohe River, and Xiushui River are 89.13, 20.19, 21.11, 15.77, and 16.86 billion m3, respectively. The average annual runoff in PLB into Poyang Lake is 142.73 billion m3. The annual average runoff totals of Ganjiang River, Fuhe River, Xinjiang River, Raohe River, and Xiushui River into Poyang Lake are 81.26, 16.20, 18.53, 14.04, and12.70 billion m3, respectively. The annual average total amount of water resources in PLB was 163.06 billion m3 , while the annual average total amount of water uses is 17.67 billion m3 and the annual average quantity of wastewater is 4.39 billion m3 . The total water resources of Ganjiang River, Fuhe River, Xinjiang River, Raohe River, and Xiushui River are 89.13, 20.19, 21.11, 15.77, and 16.86 billion m3 , respectively. The average annual runoff in PLB into Poyang Lake is 142.73 billion m3 . The annual average runoff totals of Ganjiang River, Fuhe River, Xinjiang River, Raohe River, and Xiushui River into Poyang Lake are 81.26, 16.20, 18.53, 14.04, and 12.70 billion m3 , respectively.

HRB is located in northern China, between 112° E–120° E and 35° N–43° N, as shown in Figure 1b. The scope of HRB covers eight provinces (or cities). The total area of Beijing and Tianjin, 91% of the area of Hebei province, 38% of the area of Shanxi Province, 20% of the area of Shandong Province, 9.2% of the area of Henan province, 13,600 km2 of Inner Mongolia Autonomous Region, and 1700 km2 of Liaoning Province belong to Haihe River Basin—318,200 km2 in total. The climate of HRB is semi-humid and semi-arid. It is located in the temperate East Asian monsoon climate area. The annual average temperature is 1.5–14.0 °C [47]. The vegetation types in HRB are various due to the monsoon climate. HRB is constituted by seven major river watersheds, Luan River, Beisanhe River, Yongding River, Daqing River, Ziya River, Zhangweinan Canal, and Tuhai-Majia River. These rivers mostly flow from west to east, and finally discharge into Bohai Bay. HRB is bordered by the Shanxi Plateau and Yellow River Basin in the west, the Mongolia Plateau and the inland river basin in the north, the Yellow River in the south, and the Bohai Sea in the HRB is located in northern China, between 112◦ E–120◦ E and 35◦ N–43◦ N, as shown in Figure 1b. The scope of HRB covers eight provinces (or cities). The total area of Beijing and Tianjin, 91% of the area of Hebei province, 38% of the area of Shanxi Province, 20% of the area of Shandong Province, 9.2% of the area of Henan province, 13,600 km2 of Inner Mongolia Autonomous Region, and 1700 km2 of Liaoning Province belong to Haihe River Basin—318,200 km2 in total. The climate of HRB is semi-humid and semi-arid. It is located in the temperate East Asian monsoon climate area. The annual average temperature is 1.5–14.0 ◦C [47]. The vegetation types in HRB are various due to the monsoon climate. HRB is constituted by seven major river watersheds, Luan River, Beisanhe River, Yongding River, Daqing River, Ziya River, Zhangweinan Canal, and Tuhai-Majia River. These rivers mostly flow from west to east, and finally discharge into Bohai Bay. HRB is bordered by the Shanxi Plateau and Yellow River Basin in the west, the Mongolia Plateau and the inland river basin in the north, the Yellow River in the south, and the Bohai Sea in the east. The total terrain of HRB is high in the northwest and low in the southeast, which is composed of three landforms: plateau, mountain, and plain. Plateaus and mountains are located in the north and west of HRB, covering an area of 189,400 km2 , accounting for 60% of the basin area. The east and southeast of HRB are covered by a plain, which covers

128,400 km2 , accounting for 40% of the basin's area. The annual average total amount of water resources in HRB was 30.30 billion m3 , while the annual average total amount of water uses is 37.00 billion m3 and the annual average quantity of wastewater is 5.98 billion m3 . HRB belongs to an area with an extreme water shortage. The total amount of inter basin water transfer in HRB was 3.51 billion m3 (including the amount of water diverted from Yangtze River and Yellow River). The total water resources of Luanhe River, Beisanhe River, Yongding River, Daqing River, Ziya River, Zhangweinan Canal, and Tuhai-Majia River are 4.67, 4.38, 2.49, 4.65, 6.36, 4.41, and 3.32 billion m3 , respectively. The average annual runoff in HRB into Bohai Bay was 3.51 billion m3 . The annual average runoff totals of Luanhe River, Beisanhe River, Daqing River, Ziya River, and Tuhai-Majia River into Bohai Bay are 0.92, 1.05, 0.64, 0.34, and 0.54 billion m3 , respectively. It is obvious that PLB has far more water resources than HRB, especially the amounts of water delivered to the outlets. Such difference might be explained by the lesser amounts of precipitation, the larger area of plain terrain, and the larger demand for water supply in HRB, leading to the lesser NH4 + -N loads delivered to the outlets compared to PLB. The summary of water resources of the main rivers in PLB and HRB is in Tables S1 and S2.

### 2.1.1. Nutrient Sources

Point source data in 2017 of NH4 + -N in PLB and HRB were provided by Chinese Research Academy of Environmental Sciences and Ecological Environment Monitoring and Scientific Research Center of Haihe River Basin and Beihai Sea Area, respectively. The load from point sources involved municipal and industrial wastewater treatment plants. The median annual load in PLB from point sources was 125,744 kg/year of NH4 + -N, with a range of 4479–1,568,808 kg/year. The median annual load in HRB from point sources was 42,084 kg/year of NH4 + -N, with a range of 0–2,952,298 kg/year.

Farmland, woodland, grassland, and residential land were considered as non-point sources of nutrients [48]. Land use data in 2015 were acquired from the Data Center for Resources and Environmental Sciences of Chinese Academy of Sciences (https:// www.resdc.cn/data.aspx?DATAID=184, accessed on 22 December 2020) and used to label farmland, woodland, grassland, water body, residential land, and barren land. In PLB, there were 24.60% farmland, 67.15% woodland, 4.20% grassland, 1.98% water body, 2.04% residential land, and 0.01% barren land, as shown in Figure 2a. In HRB, there were 49.03% farmland, 19.16% woodland, 18.45% grassland, 2.44% water body, 9.74% residential land, and 1.18% barren land, as shown in Figure 2b. It is clear that the areas of woodland and grassland in PLB were almost equal to those in HRB; and that the areas of farmland and residential land in PLB were far smaller than those in HRB.

#### 2.1.2. River Network and Nutrient Load Estimates

The river network was obtained from the Digital Elevation Model (DEM) data (90 m × 90 m) of PLB and HRB by using ArcHydro tools; 85 and 310 stream reaches were delineated, respectively. The DEM data were downloaded from the Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences (http://www.gscloud. cn/sources/accessdata/306?pid=302, accessed on 28 December 2020).

Streamflow data in 2017 were available from only Qiujing, Meigang, Wanjiabu, Hushan, Waizhou, Dufengkeng, and Lijiadu stations in PLB, which were provided by the Jiangxi Academy of Environmental Sciences. Streamflow at ungauged watersheds in PLB was estimated by using a GWLF model [49]. The version of the GWLF model used in this study was ReNuMa version 2.2.2 [50]. Streamflow data were available from 203 stations in HRB, which were provided by Ecological Environment Monitoring and Scientific Research Center of Haihe River Basin and Beihai Sea Area. Streamflow at ungauged watersheds in HRB was simulated by interpolation analysis using the GIS platform. Water quantity station distributions are shown in Figure 3a,c.

**Figure 2.** Land use distributions in (**a**) PLB and (**b**) HRB. **Figure 2.** Land use distributions in (**a**) PLB and (**b**) HRB.

**Figure 3.** Water quantity station distribution (**a**) and Water quality station distribution (**b**) in PLB, Water quantity station distribution (**c**) and Water quality station distribution (**d**) in HRB. (http://data.cma.cn/data/cdcdetail/dataCode/SURF\_CLI\_CHN\_MUL\_DAY\_V3.0.html, **Figure 3.** Water quantity station distribution (**a**) and Water quality station distribution (**b**) in PLB, Water quantity station distribution (**c**) and Water quality station distribution (**d**) in HRB.

Water quality data in 2017 were provided monthly by the Jiangxi Academy of Environmental Sciences from 58 sites for PLB, and by Ecological Environment Monitoring and

where is the Annual nutrient load of reach *i*, is simulated by the GWLF model (or interpolation analysis) as above, and represents the mean annual in-

The model's land-to-water delivery variables were mainly determined by the spatial attribute data. Annual average precipitation, annual average temperature, and slope were used in model calibration. The data of precipitation and temperature were downloaded from China Meteorological Data Service Centre

= × (1)

stream NH4+-N concentration.

2.2.3. Land-To-Water Delivery Variables

Annual nutrient loads of these sites were assessed as follows:

Water quality data in 2017 were provided monthly by the Jiangxi Academy of Environmental Sciences from 58 sites for PLB, and by Ecological Environment Monitoring and Scientific Research Center of Haihe River Basin and Beihai Sea Area from 144 sites for HRB. Water quality station distributions are shown in Figure 3b,d.

Annual nutrient loads of these sites were assessed as follows:

$$Load\_i = Flow\_i \times Conv\_i \tag{1}$$

where *Loadi* is the Annual nutrient load of reach *i*, *Flowi* is simulated by the GWLF model (or interpolation analysis) as above, and *Conci* represents the mean annual instream NH4 + -N concentration.

#### 2.1.3. Land-to-Water Delivery Variables

The model's land-to-water delivery variables were mainly determined by the spatial attribute data. Annual average precipitation, annual average temperature, and slope were used in model calibration. The data of precipitation and temperature were downloaded from China Meteorological Data Service Centre (http://data.cma.cn/data/cdcdetail/ dataCode/SURF\_CLI\_CHN\_MUL\_DAY\_V3.0.html, accessed on 19 June 2020). The slope data were extracted from DEM. The data of delivery variables were transformed to the final type of SPARROW model by using GIS platform.

### *2.2. SPARROW Model*

The total load leaving a given reach in the SPARROW model is considered as the sum of the load produced by itself and the load delivered from its upper stream [51]. The total load leaving reach *i* can be expressed mathematically as follows:

$$F\_i^\* = \left(\sum\_{j \in I(i)} F\_j'\right) \delta\_i A\left(Z\_i^S; \theta\_\mathcal{S}\right) + \left(\sum\_{n=1}^{N\_\mathcal{S}} \mathbb{S}\_{n,i} \mathbb{1}\_{\mathbb{N}} D\_{\mathbb{N}}\left(Z\_i^D; \theta\_\mathcal{D}\right)\right) A'\left(Z\_i^{\mathcal{S}}; \theta\_\mathcal{S}\right) \tag{2}$$

where *Fi \** is the total load leaving reach *i* (kg/year), *F* 0 *j* is the load leaving upstream reaches from reach *j*, the set *J(i)* is all upstream reaches of reach *i*, *δi* is the proportion of load delivered to reach *i* contributed by adjacent upstream reaches, *A Z S i* ; *θS* is a function of first-order loss processes related to stream size, and *A* 0 *Z S i* ; *θS* is the square root of *A Z S i* ; *θS* .

*Sn,j* is the nutrient source n in reach *i*, *NS* is the total number of nutrient sources, *αn* is the coefficient of nutrient source *n*, and the land-to-water delivery term *Dn Z D i* ; *θD* is defined as follows:

$$D\_n\left(Z\_i^D; \theta\_D\right) = \exp\left(\sum\_{m=1}^{M\_D} \omega\_{nm} Z\_{m}^D \theta\_{Dm}\right) \tag{3}$$

where *Z D m i* is the land-to-water variable *m* within reach *i*, *MD* is the total number of delivery variables, *θDm* is the coefficient of delivery variable *m*, and *ωnm* is the delivery index for judging whether source *n* uses delivery variable *m* or not.

Stream delivery function, considered as an attenuation process acting on flux, is formulated by a first-order reaction rate process. The proportion of the load remaining after the delivery to the outlet of reach *i* is expressed as an exponential function:

$$A\left(Z\_i^{\mathcal{S}}; \theta\_{\mathcal{S}}\right) = \exp\left(-\sum\_{c=1}^{\mathbb{C}\_{\mathcal{S}}} \theta\_{\mathcal{S}c} T\_{c\
i}^{\mathcal{S}}\right) \tag{4}$$

where *T S c i* is the average travelling time of a stream in reach *i*, which is classified as instream decay class *c*. *c* is the number of in-stream decay class *c* streams, *CS* is the total number of in-stream decay classes, and *θSc* is the coefficient corresponding to average travelling time of stream. Two in-stream decay classes were used in the PLB and HRB SPARROW models.

The estimation method of the SPARROW model is a nonlinear weighted least squares (NWLS) algorithm performed by the SAS procedure PROC MODEL, based on Equation (2). NWLS, which is a robust technique to solve nonlinear problems, can be considered as an iterative linear estimation process, since it is related to the ordinary least squares [51]. Bootstrap analysis is used to validate the model and perform the uncertainty analysis. The bootstrap procedure is executed by randomly selecting with replacement monitored loads from the observations in the original calibration data set and fitting separate regression models to the resampled data [32].


**6. Contribution of insects to food and feed security**

The contribution of edible insects in augmenting food supply is unpredictable. There is an extreme need to increase the traditional animal feed such as soy, grains, and fishmeal by using alternative sources. It is estimated that by 2030, over nine billion people and billions of animals will need to be fed. Furthermore, massive livestock production can cause land and water pollution and their overgrazing can also cause forest degradation that imparts a negative impact on our climate and damage our environment more rapidly. In order to reduce this rapidly destroying environment, alternative ways to feed livestock must be explored in which the consumption of edible insect provide innovation. For addressing food and feed security, insect farming is always the best option. Insects are ubiquitous, have high and rapid growth rates, and impart less environmental imprint throughout their lifecycle. They have high nutritional value, can be raised on a variety of different waste streams, and do not require any specific land for cultivation. In the coming decade, the usage of insects as poultry and aquaculture fodder is projected to be more common.

The usage of insects for feeding purpose is beneficial for us from health, environmental, and social perspective [71] which are described as follow:


**10. The future**

Developments in minimally invasive techniques could potentially lead to totally endoscopic aortic valve replacement (TEAVR) as standard of care for aortic valve replacement [36]. It aims to avoid any sternal fracture or costal spreading by decreasing iatrogenic chest wall trauma. It also aims to maintain advantages of a surgical approach over TAVI. Suture less bio prosthesis technologies are essential to do valve replacement procedures using trocars.

**65**

**Author details**

Victoria, Australia

Imran Khan1

provided the original work is properly cited.

, Julian A. Smith1

2 Medanta Heart Institute, Gurgaon, India

\*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,

\* and Naresh Trehan2

1 Department of Cardiothoracic Surgery, Monash Health and Department of Surgery, School of Clinical Sciences at Monash Health, Monash University, Clayton,

*Minimally Invasive Right Anterior Mini-Thoracotomy Aortic Valve Replacement*

real alternative to TAVI for the high risk operable patient.

MIAVR minimally invasive aortic valve replacement

TEAVR total endoscopic aortic valve replacement

TOE trans oesophageal echocardiography

The authors have no conflict of interest.

**Abbreviations and acronyms**

MS mini-sternotomy

SVC superior vena cava

ABG arterial blood gas

AVR aortic valve replacement CPB cardiopulmonary bypass

RAMT right anterior mini-thoracotomy

MIAVR using RAMT or MS is a safe and effective procedure with excellent post-operative outcomes. Although cross clamp and CPB times may be longer than conventional AVR, use of suture less valve may increase the permeation of MIAVR in the cardiac surgery community. MIAVR with suture less bio prosthesis may be a

*DOI: http://dx.doi.org/10.5772/intechopen.85242*

**11. Conclusion**

**Conflict of interest**

*Minimally Invasive Right Anterior Mini-Thoracotomy Aortic Valve Replacement DOI: http://dx.doi.org/10.5772/intechopen.85242*