**1. Introduction**

The eye is a special sensory organ, as the retina is an extension ofthe brain.Both brain andretina derive from the neural tube and consist of neurons and glial cells. As with the CNS, any insult to the retina and optic nerve cause anterograde and retrograde axon degeneration, myelin destruction, and scar formation. Chronic progressive retinal neurodegeneration is involved in the pathophysiology of ocular diseases [1] such as glaucoma, age‐related macular degenera‐ tion (ARMD) and diabetic retinopathy (DR).

In the brain, neurodegeneration is a key event in disorders such as Parkinson's disease (PD) and Alzheimer's disease (AD). PD is a neurodegenerative disease of middle and old age; the origin of defect lies in the basal ganglia and it is characterised by deficiency of dopamine in the mid‐brain area.

AD, the most common cause of dementia, afflicts 67 in 1000 people over the age of 65 and more than 26 million people worldwide, its prevalence and incidence increasing exponentially with age [2, 3]. In 2006, the worldwide prevalence of Alzheimer's was 26.6 million, and by 2050, the prevalence is expected to quadruple [3]. A chronic progressive degenerative neurological disorder affecting cognition and memory [4], AD is characterised by the formation of extrac‐ ellular amyloid beta (Aβ) plaques and intracellular neurofibrillary tangles (made of hyper‐ phosphorylated tau), primarily in the cerebral cortex [5, 6]. Currently, there is no definitive antemortem diagnosis for AD, and therefore new biomarkers for diagnosis are needed. It can be argued that improved methods of screening and early detection are essential to identify patients without cognitive impairment but with a high risk of developing AD. Thus, protocols for early treatment could be established to help slow the disease progression [7]. Over the last few decades, the importance of ophthalmic examination in neurodegenerative diseases of the CNS has reportedly increased. As mentioned above, the retina is an extension of the CNS and thus the impairment of ocular function in patients with CNS degeneration should not be surprising. In fact, both the test exploring visual processing/visual pathways and those examining the retina of such patients display abnormal results. Current in vivo imaging techniques are allowing ophthalmologists to detect and quantify data consistent with the histopathological findings described in the retinas of AD patients years ago [8] and may help to reveal unsuspected retinal and optic‐nerve repercussions of other CNS diseases. Specifically, over the last decades, accurate tools for analysing the eye fundus such as optical coherence tomography (OCT) and laser polarimetry have been developed, opening new ways of examining the retina in vivo. The retinal nerve‐fibre layer (RNFL) is composed of retinal ganglion‐cell axons, which form the optic nerve. Decreased thickness of the RNFL can reflect retinal neuronal ganglion‐cell death and axonal loss in the optic nerve [9, 10], and RNFL reportedly thins with ageing [11, 12]. Notably, some studies have shown that AD patients show greater RNFL thinning than is normal for their age [9, 10, 13–20]. In this context, Hinton et al. [8] were the first to show histopathological evidence of retinal ganglion‐cell loss and optic‐ nerve degeneration in AD patients. These findings were later confirmed in several follow‐up studies [21–24]. Indeed, axonal degeneration of the large M‐cells in AD has been documented [22, 25, 26]. Nevertheless, other histopathological studies [27–33] have failed to confirm these findings, suggesting that methodological differences were responsible for the different results. In addition to the anatomical findings in AD, this disease can exert an impact on most aspects of visual processing, such as visual‐field abnormalities [34–36], colour‐perception deficits [37– 40], pattern electroretinogram changes [26, 41, 42] and reduced contrast sensitivity (CS) [43– 46]. Psychophysical investigations of CS in AD patients have demonstrated results consistent with the neuropathological evidence [47]. However, studies of CS in patients with AD have reported no AD‐related deficits in spatial CS [48, 49], while others have found deficits at all spatial frequencies tested [40, 50].

**1. Introduction**

380 Update on Dementia

the mid‐brain area.

tion (ARMD) and diabetic retinopathy (DR).

The eye is a special sensory organ, as the retina is an extension ofthe brain.Both brain andretina derive from the neural tube and consist of neurons and glial cells. As with the CNS, any insult to the retina and optic nerve cause anterograde and retrograde axon degeneration, myelin destruction, and scar formation. Chronic progressive retinal neurodegeneration is involved in the pathophysiology of ocular diseases [1] such as glaucoma, age‐related macular degenera‐

In the brain, neurodegeneration is a key event in disorders such as Parkinson's disease (PD) and Alzheimer's disease (AD). PD is a neurodegenerative disease of middle and old age; the origin of defect lies in the basal ganglia and it is characterised by deficiency of dopamine in

AD, the most common cause of dementia, afflicts 67 in 1000 people over the age of 65 and more than 26 million people worldwide, its prevalence and incidence increasing exponentially with age [2, 3]. In 2006, the worldwide prevalence of Alzheimer's was 26.6 million, and by 2050, the prevalence is expected to quadruple [3]. A chronic progressive degenerative neurological disorder affecting cognition and memory [4], AD is characterised by the formation of extrac‐ ellular amyloid beta (Aβ) plaques and intracellular neurofibrillary tangles (made of hyper‐ phosphorylated tau), primarily in the cerebral cortex [5, 6]. Currently, there is no definitive antemortem diagnosis for AD, and therefore new biomarkers for diagnosis are needed. It can be argued that improved methods of screening and early detection are essential to identify patients without cognitive impairment but with a high risk of developing AD. Thus, protocols for early treatment could be established to help slow the disease progression [7]. Over the last few decades, the importance of ophthalmic examination in neurodegenerative diseases of the CNS has reportedly increased. As mentioned above, the retina is an extension of the CNS and thus the impairment of ocular function in patients with CNS degeneration should not be surprising. In fact, both the test exploring visual processing/visual pathways and those examining the retina of such patients display abnormal results. Current in vivo imaging techniques are allowing ophthalmologists to detect and quantify data consistent with the histopathological findings described in the retinas of AD patients years ago [8] and may help to reveal unsuspected retinal and optic‐nerve repercussions of other CNS diseases. Specifically, over the last decades, accurate tools for analysing the eye fundus such as optical coherence tomography (OCT) and laser polarimetry have been developed, opening new ways of examining the retina in vivo. The retinal nerve‐fibre layer (RNFL) is composed of retinal ganglion‐cell axons, which form the optic nerve. Decreased thickness of the RNFL can reflect retinal neuronal ganglion‐cell death and axonal loss in the optic nerve [9, 10], and RNFL reportedly thins with ageing [11, 12]. Notably, some studies have shown that AD patients show greater RNFL thinning than is normal for their age [9, 10, 13–20]. In this context, Hinton et al. [8] were the first to show histopathological evidence of retinal ganglion‐cell loss and optic‐ nerve degeneration in AD patients. These findings were later confirmed in several follow‐up studies [21–24]. Indeed, axonal degeneration of the large M‐cells in AD has been documented [22, 25, 26]. Nevertheless, other histopathological studies [27–33] have failed to confirm these

**Figure 1.** Retinal nerve fibre layer (RNFL) thickness analysis. Optical coherence tomography (OCT) study. (**A**) Peripa‐ pillary OCT. Upper left: peripapillary retinography with a green circle marking the retinal tissue considered for analy‐ sis. Upper right: diagram of the peripapillary quadrants analysed: temporal quadrant (316–45), superior quadrant (46– 135), nasal quadrant (136–225), inferior quadrant (226–315). Bottom: retinal b‐scan and diagram of thickness normality. (**B**) Macular OCT. Upper left: central retinography with a green square marking the retinal tissue considered for analy‐ sis. Upper right: diagram showing the concentric rings and quadrants considered for analysis of the macular RNFL thickness and measurements automatically provided by the analyser. Bottom: retinal b‐scan of the macula. ETDRS: Early Treatment Diabetic Retinopathy Study (from Figure 1 of [19] with permission).

Diagnosis and follow‐up of AD, especially the early‐onset cases, become difficult, due to imprecise neuropsychological testing, sophisticated but expensive neuroimaging techni‐ ques, and invasive sampling of cerebrospinal fluid [31, 32]. OCT is a reliable noninvasive technique, routinely used in ophthalmology to visualise and quantify the layers of the retina. This technique enables quantitative cross‐sectional imaging of the RNFL and macular volume. As a measure of neuronal degeneration, changes in longitudinal OCT measurements of the RNFL can act as a surrogate marker of axonal health. Thus, OCT could become an invalua‐ ble tool for measuring axonal loss, as a biomarker, in different neurological conditions [33, 51– 61] (**Figure 1**).

In a review of a meta‐analysis which investigates the role of OCT in detecting RNFL thinning in AD patients, it was found that the OCT is a well‐suited paraclinical methodology to assess RNFL thickness in both AD and mild cognitive impairment (MCI) disorders [19]. Macular studies in AD using OCT have recently reported that mild AD patients with a high average score (23.3 ± 3.1) on the Mini‐Mental State Examination (MMSE) had significantly reduced macular nerve‐fibre‐layer thickness with or without significant peripapillary involvement [19, 40, 62, 63]. OCT thus offers the clinician a fast, reliable, reproducible, noninvasive method to evaluate and monitor several neurological diseases [64].


**1. Introduction**

290 Thermoplastic Elastomers

Tilley, K. A.; Benjamin, R. E.; Bagorogoza, K. E.; Okot-Kotber, B. M.; Prakash, O. & Kwen, H.

Vernaza, M. G.; Chang, Y. K. & Steel, C. J. (2009). Efeito do teor de farelo de maracujá e da

Vernaza, M. G.; Pedrosa, M. T.; Chang, Y. K. & Steel, C. J. (2010). Evaluation of the in vitro

Wang, W. M.; Klopfenstein, C. F. & Ponte, J. G. (1993). Effects of twin-screw extrusion on the

Wong, D. W. S. (1995). *Food enzymes: structure and mechanism*. Springer, ISBN<PHONE_NUMBER>-

Yao, N.; White, P. & Alavi, S. (2011). Impact of beta-glucan and other oat flour components

Zhu, S.; Riaz, M. & Lusas, E. (1996). Effect of different extrusion temperatures and moisture

(October/December 2010), pp.964-968, ISSN 0101-2061.

0023-6438.

0021-8561.

0950-5423.

0021-8561.

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91-8, New York, United States of America.

cooking. *Lebensmittel Wissenschaft und Technologie*, Vol.29, No.7, pp.526–535, ISSN

(2001). Tyrosine cross-links: molecular basis of gluten structure and function. *Journal of Agricultural Food Chemistry*, Vol.49, No.5, (May 2001), pp.2627-2632, ISSN

umidade e temperatura de extrusão no desenvolvimento de cereal matinal funcional orgânico. *Brazilian Journal of Food Technology*, Vol.12, No.2, (April/June

glycemic index of a fiber-rich extruded breakfast cereal produced with organic passion fruit fiber and corn flour. *Ciência e Tecnologia de Alimentos*, Vol.30, No.4,

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content on lipoxygenase inactivation and protein solubility in soybeans. *Journal of Agricultural and Food Chemistry,* Vol.44, No.10, (October 1996), pp.3315-3318, ISSN Lightweight constructions are increasingly used in automotive, aerospace and construction sectors, because using the low density materials allows reducing the structural weight of products. That may result in substantial fuel savings and a lower carbon footprint in transportation and facilitates manipulation of details in the house construction applications. Moreover, the low material density leads to conservation of natural resources, since less material is required for manufacturing consumer goods.

In polymer engineering the lightweight solutions include:

Cellular and hollow structure polymeric materials offer additional advantages resulting from their thermal insulating properties, thus allowing additionally energy savings.

Preference given to low density materials belong to the factors deciding on a success of polypropylene (PP) in the automotive sector. Being lighter than other plastics for 15-20% PP allows substantial fuel savings – it is assumed that a weight reduction in a car body of 100 kg brings about 0.3-0.5 litres of fuel savings per 100 km.


**Author details**

Saloni Jain1†, Ishita Gupta1†, Priyanshu Walia2† and Shalini Swami1 \*

1 Department of Microbiology, Ram Lal Anand College, University of Delhi, New Delhi, India

2 Department of Botany, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India

\*Address all correspondence to<EMAIL_ADDRESS>
† Contributed equally.

© 2022 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.

*Application of Actinobacteria in Agriculture, Nanotechnology, and Bioremediation DOI: http://dx.doi.org/10.5772/intechopen.104385*


**Author details**

Hani Hussain Sait King Abdulaziz University, Rabigh, Saudi Arabia

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 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.


*3.5. Aspen Plus Model*

Figure 8a,c show the trend of heat produced/required as computed by Aspen Plus under pyrolytic and oxidative atmosphere.

Regarding pyrolysis, all samples follow a similar trend, characterized by an exothermic behavior at low temperatures and an endothermic one at high temperatures. Figure 9 reports the trends of the products at different pyrolysis temperatures resulting from the 180 ◦C hydrochar (the other samples present the same qualitative trends) and can help to interpret the heat trends. At low temperatures, thermodynamics favors exothermic reactions like char oxidation to CO2 and CO, oxidation of hydrogen, and methanation. Conversely, higher temperatures enhance endothermic reactions, like the Boudouard reaction (and therefore the conversion of CO2 into CO), and methane steam reforming (thus the conversion of CH4 and H2O to CO and H2). At low temperatures, more carbonized substrates show a higher exothermic behavior due to a higher carbon partial oxidation and methane production (they present a higher C/H ratio than less carbonized substrates). Conversely, at higher temperatures, less carbonized samples show a higher endothermic behavior, probably due to the higher production of CO. As expected from its high C/O ratio and low volatile matter content, the profile of coal is flatter than hydrochar, and it is always endothermic. The different starting points can be explained by the higher ash content of coal than hydrochars (13.7% vs. 1.9–2.5%): ash, being not reactive, lowers the energy input/output per unit of mass involved. The slight difference in the coal profile with the previous work of our group [40] is due to the different inlet temperatures (isotherm to the reactors here vs. at ambient temperature in [31]).

**Figure 9.** Aspen Plus results: main products from pyrolysis of hydrochar 180 ◦C.

Besides, Figure 8b shows the progressive integration of pyrolytic DSC data; computing the energy integral is necessary because DSC measures the instantaneous heat flow absorbed or released (which depends on the "history" of the sample due to the thermal program), while thermodynamics considers the energy absorbed or released when converting all the reactants into products at that determined temperature (and therefore the values obtained from thermodynamics do not depend on the thermal history of the sample). Over-

all, DSC data show a trend towards a cumulative exothermic behavior, whose final value corresponds to the integral reported in Table 4. The different behavior concerning Aspen data can be due to the nature of the thermodynamic approach. Indeed, real systems do not behave as predicted by thermodynamics at low temperatures because of reaction kinetics constraints. Moreover, as previously mentioned, thermodynamics does not consider the "history" of what happened before a certain condition. Indeed, inside the DSC, the starting material progressively undergoes pyrolysis, and the material at every time is the result of what happened before (i.e., the thermal program). In addition, thermodynamics does not consider typical "out of equilibrium" products, like tarry compounds, always present in real systems. Therefore, the thermodynamic approach requires particular attention and criticism before being used.

Regarding oxidation, both thermodynamic and DSC curves are clearly exothermic, with values that progressively increase with temperature. Under both atmospheres, absolute values differ between the Aspen and DSC approaches. This is due to the mass involved: DSC curves measure the energy absorbed/released by an amount of material that progressively decreases with time (the process is occurring), while Aspen predicts the energy absorbed/released by the same mass at every temperature. Since the energy is normalized by the amount of starting feedstock (and not the mass measured at every temperature), the amount of heat computed through the DSC approach is smaller than the Aspen one. Finally, it is interesting to note that in an oxidative environment, and for the whole range of temperature investigated, the thermodynamic approach foresees the complete oxidation of the biomass with the production essentially of CO2, H2O, and residual ash (equal to about 2% of the biomass fed to the reactor, Table 1). Conversely, in an oxidative environment in DSC, at the maximum temperature reached of 600 ◦C, there is still a substantial mass of non-oxidized carbon: the residual mass at 600 ◦C is of the order of 30% (Table 4). Through this critical comparison, the two approaches, thermodynamic and DSC, turn out to actually be very distant from each other, which is an aspect worthy of interest.

#### **4. Conclusions**

This work investigated the kinetic behavior during pyrolysis and oxidation of hydrochars derived from an agro-industrial residue (grape seeds). The topic was approached through experimental techniques (TGA and DSC) that were used to develop two different DAEMs for biomass decomposition. Indeed, there is a certain lack in the literature regarding hydrochar decomposition, especially for the computation of the kinetic parameters. Therefore, this work covers both the experimental aspects and the comparison of two DAEMs using a critical approach. Thermogravimetric analysis and differential scanning calorimetry highlighted the importance of the HTC severity on the decomposition profiles. Indeed, hydrochars obtained at 180 and 220 ◦C still decompose with two/three stages (pyrolysis/oxidation) due to the presence of lignocellulosic constituents and oil that only partially degraded during HTC. Meanwhile, the 250 ◦C hydrochar shows a more stable profile, with one/two decomposition peaks, highlighting that HTC temperature hugely affected the structure of the biomass. Interestingly, all hydrochars show a certain exothermic behavior during pyrolysis.

DAEMs predicted the kinetic parameters involved during the pyrolysis and oxidation processes, i.e., the activation energy and the pre-exponential factor. The single-stage Gaussian model does not highlight differences among the various hydrochars obtained at different HTC temperatures, with average values of activation energy in the range 203–206 kJ/mol for pyrolysis and 213–220 kJ/mol for oxidation. Actually, the Gaussian DAEM turned out to be unsatisfactory to model complex feedstock such as hydrochars characterized by multi-decomposition peaks. Meanwhile, the Miura–Maki model, even if not applicable over the entire decomposition region, enabled the determination of the distribution of activation energies: the more severe the HTC process, the lower the activation energy values.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/en15030950/s1, Figure S1: (1-α) at different temperatures of feedstock during: (a) pyrolysis; (b) oxidation. Heating rate: 10 ◦C/min.; Figure S2: Comparison between DTGA and DSC curves during pyrolysis at 10◦C/min of: (a) grape seeds and hydrochar 180 ◦C; (b) hydrochars 220 and 250 ◦C; Figure S3: Comparison between experimental and predicted data (1-α vs temperature) computed through the Gaussian model, during oxidation of different samples: (a) hydrochar 180 ◦C; (b) hydrochar 220 ◦C; (c) hydrochar 250 ◦C; Table S1: Details of Miura-Maki model, pyrolysis (on a dry basis); Table S2: Details of Miura-Maki model, oxidation (on a dry basis); Gaussian DAEM – MATLAB code.

**Author Contributions:** G.G.: methodology, software, data curation, writing—original draft preparation; G.I.: data curation, visualization, writing—original draft preparation; L.F. (Luca Fambri): experimental planning, writing—review and editing, supervision, project administration; L.F. (Luca Fiori): writing—review and editing, supervision, project administration. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

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

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** All the authors acknowledge Claudia Gavazza for thermal analysis. G.I. acknowledges the financial support provided by the ERICSOL project of the University of Trento, which partially covered her Ph.D. scholarship. All individuals included in this section have consented to the acknowledgement.

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


**2. Geologic background**

Volcanic activity commenced around 45 Ma in southern Ethiopia [7], resulting in volumetrically significant basaltic flows and associated rhyolites. Nevertheless, the peak of magmatism had occurred c. ~30 Ma ago, resulting from the impinging Afar mantle plume at the base of the Ethiopian lithosphere and leading to flooding basalt eruptions in Ethiopia and Yemen [8]. The Yemen plateau basalts were united to their Ethiopian counterparts prior to the opening of the Red Sea basin. At ~25 Ma continental rifting commenced in the southern Red Sea [9]. In Southern Ethiopia, extension began ~18 Ma ago [10] and was accompanied by basaltic magmatism, active for about seven to eight million years [7]. The southern Ethiopian rift propagated northward, reaching the present central MER ~14 Ma ago and ultimately joining the southern Red Sea rift ~11 Ma ago [11]. Contemporaneously to the connection between the main Ethiopian and Red Sea rifts, a flood basalt event occurred in this area. Beginning in the late Miocene and continuing throughout the Pliocene, silicic volcanic centres emerged from the rift floor [6]. Progressive weakening of the lithosphere in Afar, associated with heating and the thermomechanical erosion of the lower crust generated by the Afar mantle plume, resulted in the onset of oceanic rifting at 5.3 Ma [9]. Oceanic rifting is still active in Afar, whereas it has not commenced in the MER yet [11].

The Pliocene-Pleistocene boundary was marked by a change in the stress field, giving rise to oblique rifting [12]. Since then, an extension has been localized in narrow (50 km long, maximum 20 km wide) en-echelon arranged segments on the rift floor [6], with a system of bounding faults that are referred to as the Wonji Fault Belt [12]. Moreover, these segments have been the locus of volcanic activity throughout the Quaternary and are thus referred to as magmatic segments by [6]. Volcanic activity associated with the magmatic segment was initially characterized by large volumes of felsic lavas. When these faults reached the upper mantle in recent times, basaltic volcanism commenced [12]. At present, volcanic activity within the magmatic segment is dominated by fissural basalt eruptions [1, 6].