**3. The pharmacological influences on the blood cell metabolism – Antioxidant drugs in cardiovascular risk status and roll of red blood cell antioxidant defense capacity**

There are growing evidences on the role of adaptive mechanisms of erythrocyte in pathological processes: atherosclerosis, ischemic attack, bacterial infections, etc. All of this processes involve as main mechanism oxidative stress. Erythrocytes have an intracellular enzyme and non-enzyme defense system. In order to remove reactive species of oxygen, superoxide dismutase (SOD), glutathione peroxidase (GPx) and catalase act together. Glutathione (GSH) participates as a co-substrate for GPx in order to detoxify H2O2 generated by SOD enzyme. GSH is a critical tripeptide that oxidizes to glutathione disulphide (GS-SG) inactive form after reacting with oxygen radicals. GSH proves to be essential for reactive species detoxification as a consequence it is permanently restore in its reduced active form by glutathione reductase based on nicotinamide adenine dinucleotide phosphate-oxidase (NADPH) from Glucose-6-phosphate dehydrogenase (G6P-DH) catalysed reaction in pentose phosphate pathway. When reactive species of oxygen are quickly and intensely generated under external or internal stimulus the activity of SOD, GPx and GSH concentration are severely changed.

When erythrocytes are undergo shear stress in constricted vessels, they release ATP which causes the vessel walls to relax and dilate so as to promote normal blood flow [78].

52 Blood Cell – An Overview of Studies in Hematology

thrombotic process.

[76].

metabolism in [77].

**antioxidant defense capacity**

concentration are severely changed.

within these domains, resulting in impaired function in [74]. The result is the promotion of

Hcy acts on both endothelial cells and smooth muscle where it generates contrasting effect. On endothelium it promotes injury and impairs DNA repair, in smooth muscle Hcy stimulates proliferation in [69]. Md S. Jamaluddin considers that Hcys promotes vascular injury through hypomethylation. When Hcys accumulates it uses adenosine, a normal constituent of all cells, to form S-adenosyl-homocysteine (SAH) a potent inhibitor of cellular methylation. By impairing methylation Hcy arrests cell growth, increases cellular SAH concentration in endothelial cells (EC) and decreased DNA synthesis thus decreasing cellular repair. This chain of events was not found in vascular smooth muscle cells in [75].

Erythrocytes are also affected by homocysteine-induced hypometilation. High intracellular SAH impairs the posttranslational methylation of membrane proteins. Reduction in membrane protein methylation was particularly observed for erythrocyte cytoskeletal component ankyrin, which is known to be involved in membrane stability and integrity. Because of hypomethylation, structural damages accumulate in erythrocyte membrane proteins, and are not adequately repaired thus affecting membrane physical properties. Erythrocyte deformability is a crucial properties for circulatory function in

As a conclusion the effect of elevated homocysteine appears multifactorial affecting both the vascular wall structure and the blood coagulation system as well as erythrocytes

**Antioxidant drugs in cardiovascular risk status and roll of red blood cell**

There are growing evidences on the role of adaptive mechanisms of erythrocyte in pathological processes: atherosclerosis, ischemic attack, bacterial infections, etc. All of this processes involve as main mechanism oxidative stress. Erythrocytes have an intracellular enzyme and non-enzyme defense system. In order to remove reactive species of oxygen, superoxide dismutase (SOD), glutathione peroxidase (GPx) and catalase act together. Glutathione (GSH) participates as a co-substrate for GPx in order to detoxify H2O2 generated by SOD enzyme. GSH is a critical tripeptide that oxidizes to glutathione disulphide (GS-SG) inactive form after reacting with oxygen radicals. GSH proves to be essential for reactive species detoxification as a consequence it is permanently restore in its reduced active form by glutathione reductase based on nicotinamide adenine dinucleotide phosphate-oxidase (NADPH) from Glucose-6-phosphate dehydrogenase (G6P-DH) catalysed reaction in pentose phosphate pathway. When reactive species of oxygen are quickly and intensely generated under external or internal stimulus the activity of SOD, GPx and GSH

**3. The pharmacological influences on the blood cell metabolism –**

Also, when their hemoglobin molecules are deoxygenated, erythrocytes release **S**nitrosothiols which acts to dilate vessels, thus directing more blood to areas of the body depleted of oxygen in [35].

Using L-arginine as substrate, erythrocytes can also synthesize nitric oxide enzymatically, just like endothelial cells. The nitric oxide synthase is activated when the erythrocytes are exposure to physiological levels of shear stress, thus, nitric oxide is synthesized, exported and it may contribute to the regulation of vascular tonus [79].

Another mechanism that involves the erythrocytes in relaxing vessel walls is the production of hydrogen sulfide. It works as a signaling gas. It is believed that the cardioprotective effects of garlic are due to erythrocytes converting its sulfur compounds into hydrogen sulfide. [80]

The free radicals released by erythrocytes when they are lysed by pathogens break down the pathogen's cell wall and cell membrane, and so, they are killing them. This represents the involving of erythrocytes in the body's immune response in [81].

On the other hand, as response of injury after several stressors, including oxidative stress, energy depletion, as well as a wide variety of endogenous mediators and xenobiotics, the erythrocytes can initiate the self suicidal death (eryptosis). Eryptosis is characterized by cell shrinkage, membrane blebbing, activation of proteases, and phosphatidylserine exposure at the outer membrane leaflet. This can make the macrophages to recognized and engulf erythrocyte to be degraded. Eryptosis can be considered a mechanism of defective erythrocytes to escape hemolysis. Conversely, excessive eryptosis favors the development of anemia. Conditions with excessive eryptosis include iron deficiency, lead or mercury intoxication, sickle cell anemia, thalassemia, glucose 6- phosphate dehydrogenase deficiency, malaria, and infection with hemolysis-forming pathogens. Inhibitors of eryptosis include erythropoietin, nitric oxide, catecholamine and high concentrations of urea in [82, 83]

The red blood cell SOD activity has been found to be useful in evaluating the biochemical index of copper, zinc and manganese nutrition. The largest amount of SOD enzyme is found in liver and erythrocytes. There are two forms of SOD in human tissue. One form is present in cytosol and it is a protein containing two atoms each of copper and zinc. The other form is a much larger molecule containing four atoms of manganese and it is found in mitochondria and cytosol. Significant changes in cellular concentration of copper, manganese and zinc have the potential of altering the antioxidant activity of SOD. On the other hands, the correlation between of copper and zinc plasma level, the oxidase activity of ceruloplasmin in serum, and Cu,Zn-SOD activity in erythrocytes can be a way to investigate involvement of oxidative stress in pathological conditions, as atherosclerosis obliterans [84]

Another element involved in the function of necessary enzyme for cellular protection is selenium. Selenium functions primarily as an activator of enzymes necessary for cellular protection from oxidative damage and maintenance of normal redox potentials. A primary role of selenium in erythrocytes appears to be the activation of the enzyme glutathione peroxidase whereby glutathione (the critical tripeptide antioxidant/antitoxin for all cells) reacts with oxygen radicals. Importantly, selenium catalyzes glutathione reductase, an enzyme that maintains the glutathione in its reduced or active form [85].

Homocysteine in Red Blood Cells Metabolism – Pharmacological Approaches 55

In recent studies was shown that probucol protect against diabetes-associated and adriamycin-induced cardiomyopathy by enhancing the endogenous antioxidant system

Specific for hypercholesterolemia status is the high production of free oxygen radicals. These can impair the endothelial function because destroying of nitric oxide (NO) and secondary affecting its beneficial and protective effects on the vessel wall. Most of the other cholesterol-lowering therapies present, also, antioxidant effects. There are two way improving antioxidant defence system in hypercolesterolemiant patients: either increasing the activities of CuZn-SOD and GSH-Px or preventing the production of the superoxide

Malone dialdehyde (MDA), more than cholesterol plasma level, is considered a marker of patients with increased risk of coronary heart disease, because MDA is a marker of lipid peroxidation. In individuals who smoke or who have diabetes are particularly prone to oxidative stress that can lead to the formation of oxidized LDL (oxLDL). Oxidatively modified LDL is considered to be highly atherogenic and can be considered a biochemical risk marker for coronary heart disease. Oxidative modification of LDL increases their ability to bind to the extracellular matrix, increasing its retention within the intima and accumulation of oxLDL in macrophages, so, it contributes to the formation of an

The oxLDL accumulation within macrophages promotes the chemotaxis of monocytes into the vessel wall and initiates the various pro-inflammatory effects by different scavenger receptor pathways: CD36 class B scavenger receptors from human macrophages (activates nuclear factor kB that regulates the expression of many pro-inflammatory genes), class A scavenger receptors (modify macrophage activation), lectin-like oxidized LDL receptor - LOX-1 (the expression of endothelial cell adhesion molecule). On the other hands, the accumulation of inflammatory cells can further increase the levels of oxidative stress. Oxidative stress inactivates nitric oxide (NO) and inhibits its synthesis by endothelial nitric oxide synthase (eNOS). On this way, the vasoprotectant effect of NO (anti-inflammatory,

Statins inhibit 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA) reductase the rate-limiting enzyme in the mevalonate pathway through which cells synthesizes cholesterol. On this way, the "statins" increase the resistance of LDL to oxidation. Statins may also exert effects beyond cholesterol lowering. These "pleiotropic" vascular effects of statins are involved in restoring or improving endothelial function: by increasing the bioavailability of nitric oxide, promoting reendothelialization, reducing oxidative stress, and inhibiting inflammatory

Other effects of statins that explain their involving in preserving normal vascular function and blood flow are: inhibition of the uptake and generation of Ox-LDL*,* decreasing the

including glutathione peroxidase, catalase and superoxide dismutase [90].

*3.1.2. The HMG Co-A reductase inhibitors, or "statins"*

anti-platelets, antioxidant and vasodilator) is affected [92].

radicals*.*

atherosclerotic lesion.

responses.

Specify participation of erythrocyte enzymatic system as adaptive mechanism to different pathological processes and specify how nutritional deficiencies and oxidative drugs can interfere these systems introduces the chapter on pharmacology of erythrocyte antioxidant system.


**Suriyanarayanan Sarvajayakesavalu**

Professor and Deputy Director Research Vinayaka Missions Kirupanandavariyar Arts and Science College, Vinayaka Missions Research Foundation Deemed to be University, Salem, India

SCOPE – India (*Scientific Committee* of *Problems of the Environment*), JSS Academy of Higher Education and Research (JSSAHER), Mysuru, Karnataka, India

#### **Dr. Kannan Karthikeyan**

Senior Scientist and Head, Environmental Laboratory Division, Gujarat Institute of Desert Ecology, Bhuj, Dist: Kachchh, Gujarat, India

Section 1

Carbon Capture, Storage

and Utlization

**1**

Section 1


**5. Conclusions**

This research compares the sensitivity of a single FPI and the Vernier effect performed with two FPIs for the same number of fringes. The results reveal that the Vernier effect envelope, where 60 ± 1 nm/μm is achieved, has twice the sensitivity of the single FPI, 29.2 ± 0.3 nm/ μm. Moreover, with the optimization of the system, an envelope sensitivity of 254 ± 6 nm/μm with *M*-factor of 1071 and a *MVernier* of 1.9 was obtained and corresponded to a resolution of 79 pm (considering the spectral resolution of 0.02 nm). In addition, the sensitivity of the Vernier effect envelope depends only on the relationship between interferometers, which allows problems in the production of ultra-sensitive sensors to be overcome as well as the same sensitivity for any distance of the fibers to the reflecting surface to be observed. This new displacement sensor architecture allows the implementation of giant-sensitive non evasive measurements with high relevance in areas such as micro and nano manufacturing and biology.

**Author Contributions:** P.R. performed the experimental work, analyzed the data, and wrote the article; O.F. supervised and reviewed. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is financed by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020.

**Data Availability Statement:** Data available on request due to restrictions privacy. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to it is from the Barcelona supercomputer.

**Acknowledgments:** Paulo Robalinho acknowledges the support of the Foundation for Science and Technology (FCT), Portugal through the Grant 2020.04562.BD.

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


*3.1. Overview*

In this study, the construction process of the apartment housing was divided into transportation, construction, and disposal stage for the purpose of analyzing CO2 emission characteristics of the apartment housing construction process in Figure 4.

**Figure 4.** CO2 emission assessment method proposed in construction process.

In order to estimate the amount of energy used in vehicles transporting materials to the apartment housing construction site, the amount of CO2 emissions in the transportation stage was limited to the amount of fuel used by transportation vehicles when transporting materials from the supply location to the construction site. The distance for fuel consumption calculation was estimated based on scenario. Based on previous research, a CO2 emissions-assessment method considering the distance, average fuel efficiency, number of transportation vehicles, and load (depending on the amount of construction materials used) was presented. The amount of CO2 emissions during the construction stage was the total fuel and electricity used by construction machines and equipment, transportation equipment, the field office, and other facilities used for and during construction on site. The amount of fuel and electricity used during the construction stage for machine equipment and electricity usage was calculated by analyzing data from Korean standard construction estimates and from Korea breakdown cost tables. The amount of CO2 emissions during the disposal stage was estimated by first calculating the amount of construction waste that occurred (by work type) based on surcharges on the construction materials, then quantitatively calculating the amount of CO2 emissions according to the method of handling construction waste, based on a statistical research report on the recycling of construction waste. With regard to the CO2 emission factor regarding the amount of construction materials used and the amount of energy consumed in each stage, the Korean National LCI DB and the Korean National DB of the construction-material environmental information was applied [23,24]. In the transportation stage, distance and fuel for transportation equipment for CO2 evaluation were analyzed, and Korean standard product and national LCI DB were applied. In the construction phase, the construction specification, the national LCI DB, and the Korean Standard Specification were applied to analyze the amount of fuel and the amount of power applied to evaluate 13 construction types. In the disposal stage, the national LCI DB and the Korean Standard Package were applied to analyze the distance fuel for the waste transportation equipment.

#### *3.2. Estimation Method in the Transportation Stage*

The main factors that must be considered in order to estimate the amount of CO2 emissions in the transportation stage are mainly the amount of construction materials used, and the equipment used for transportation [25]. Also, it considered the transportation distance and the vehicles used to

load at the supply and construction sites. This data was used to estimate the amount of energy used, which was then converted to an estimate of the amount of CO2 emissions. This calculation method includes use of the amount of materials at the construction site to deduce the number of transportation vehicles used, and fuel consumption was calculated by using the transportation distance and vehicle fuel efficiency. To do this, the distance to the construction site (from the supply sites), type of vehicle and standard fuel efficiency, and the number of vehicles used must be considered. Because the length of time that transportation vehicles used at a site is flexible, it is impossible to quantify work capacity that depends on the type of vehicle [26,27].

In this study, the loads of vehicles listed in standard construction estimates were investigated, along with the main transportation vehicles observed at the actual site, in order to calculate the number of transportation vehicles according to the supply output value of each construction material. The amount of fuel that was consumed during the transportation stage was calculated by multiplying the distance to the site by the average fuel efficiency of each vehicle. The assessment range of the transportation stage was limited to the fuel consumption for one-way transportation of construction materials by the types of vehicles used for each work type.

In this study, the transportation vehicles and transportation distance regarding the materials used were set as shown in Table 2, and the number of transportation vehicles was calculated according to the supply output value for each construction material. Equations (1) and (2) show the calculation formulas for fuel consumption and CO2 emissions, respectively, during the transportation stage [28,29]. The transportation distance was set to 40 km as referenced from the guideline of Korea Ministry of Land, Infrastructure and Transport standard specification for construction [29].

$$DO\_t = \sum \left( \mathcal{U}\_t \times \frac{k}{M\_{\rm f}} \right) \tag{1}$$

*DOt*: Amount of Fuel Consumed in the Transportation Stage (-)

*Ut*: Number of Vehicles Used (n)

*k*: Distance (km)

*Mt*: Transportation Vehicle Fuel Efficiency (km/L)

$$C\_t = \sum (DO\_t \times EF\_t) \tag{2}$$

*Ct*: Amount of CO2 Emissions in the Transportation Stage (kg-CO2) *DOt*: Amount of Fuel Consumed in the Transportation Stage (L) *EFt*: Fuel Emission Factor (kg-CO2/L)

#### *3.3. Estimation Method in the Construction Stage*

In order to estimate the amount of CO2 emission in the construction stage, this study created categories of fuel consumption and electricity consumption amounts, and calculated the total amount of fuel and electricity used for construction machines and equipment, transportation equipment, field office, and other facilities at the construction site, used for constructing buildings. For the amount of fuel consumed, the number of days and time that equipment was used, according to each work type, was calculated by analyzing the equipment usage conditions from the construction report. The fuel efficiency for each kind of equipment was based on the operating expenses estimated

in the standard construction estimate. Electricity consumption was calculated by obtaining the monthly electricity usage statement for the entire construction period, and calculating the electricity consumption according to the duration of each work type based on the progress schedule. If multiple work types proceeded simultaneously, according to the characteristics of the construction process, the relevant electricity consumption was divided up between the number of construction days for the overlapping work types. In this study, for construction processes in which the construction method (including construction conditions by site) and equipment usage differed, data regarding construction machines and equipment, and transportation equipment used at the actual construction site were obtained from the supply output lists from the actual construction site, and from construction reports. Data regarding the amount of fuel used were analyzed. Data were also analyzed regarding electricity consumption from the actual field office. The calculation formula for the amount of CO2 emissions according to fuel and electricity consumption during the construction stage is shown in Equations (3) and (4), respectively [30].

$$C\_{\mathcal{O}^0} = \sum (M\_{\mathcal{E}} \times T\_{\mathcal{E}} \times EF\_{\mathcal{E}} \times \mathcal{U}\_{\mathcal{E}}) \tag{3}$$

*Cco*: Amount of Fuel CO2 Emissions in the Construction Stage

*Mc*: Fuel Efficiency (L/h)

*Tc*: Time (h)

*EFc*: Fuel Emissions Factor (kg-CO2/L)

*Uc*: Work Equipment Qty. (n)

$$\mathbb{C}\_{\mathfrak{C}^{\mathfrak{t}}} = \sum (I\_{\mathfrak{c}} \times EF\_{\mathfrak{c}}) \tag{4}$$

*Cce*: Amount of Electricity CO2 Emissions in the Construction Stage

*Ic*: Electricity Usage Amount (kwh)

*EFc*: Electricity Emissions Factor (kg-CO2/kwh)

#### *3.4. Estimation Method in the Disposal Stage*

In order to estimate the amount of CO2 emissions in the disposal stage, the amount of construction waste that occurs for each work type was estimated, and the amount of CO2 emissions, according to the processing method of each construction waste, was calculated. In order to estimate the amount of construction waste that occurs by work type, the waste generation estimation method based on surcharge was used. In this study, the premium rate was applied as a means to predict waste at the construction site, and the surcharge value that was presented in the standard construction estimate issued in 2012 (by the Korea Institution of Construction Technology and the Ministry of Land). Construction waste was divided into construction demolition waste, combustible waste, and nonflammable waste according to the method of handling construction waste, which are handled through intermediate processing agencies, incinerators, and landfills, respectively. When calculating data regarding intermediate processing agencies, because data differs from different companies, the incineration and landfill rate referred to the present processing conditions of waste for each construction material according to the Korea Waste Statistical Yearbook in 2012. The surcharges, weight conversion factor, landfill, and incineration emissions factor were considered for each construction material according to the amount used, in order to estimate the amount of CO2 emissions that occur in the incineration and landfill process Equation (5). The amount of energy consumed during the construction waste transportation process was calculated using Equations (6) and (7), based on the amount of fuel consumed in the vehicles that transport the waste to landfills and incinerators.

The estimation method for the amount of CO2 emissions in the construction waste transportation stage was identical to that of the construction process above, and it was set as the amount of energy consumed by transportation equipment according to waste volume. In order to do this, the amount of construction equipment, standard fuel efficiency, and the distance to landfills and incinerators were applied to calculate the amount of fuel used during transportation. The assessment scope of the waste

transportation process was limited to the amount of fuel consumed by vehicles providing one-way transportation of construction waste [31,32].

$$\mathbb{C}\_{dl} = \sum (DM\_d \times EF\_{dl} \times L\_d) \tag{5}$$

*Cdl*: Amount of CO2 Emissions in Landfill Process of the Demolition Stage (kg-CO2)

*DMd*: Construction Waste Volume (kg)

*Ld*: Landfill Rate (%)

*EFdl*: Landfill Emissions Factor (kg-CO2/kg)

$$C\_{di} = \sum (DM\_d \times EF\_{di} \times I\_d) \tag{6}$$

*Cdi*: Amount of CO2 Emissions in Incineration Process of the Demolition Stage (kg-CO2) *DMd*: Construction Waste Volume (kg)

*Id*: Incineration Rate (%)

*EFdi*: Incineration Emissions Factor (kg-CO2/kg)

$$DO\_d = \sum \left( \mathcal{U}\_d \times \frac{k}{\mathcal{M}\_d} \right) \tag{7}$$

*DOd*: Amount of Fuel Consumed in the Construction Waste Transportation Stage (L)

*Ud*: Number of Vehicles Used (n)

*k*: Distance (km)

*Md*: Transportation Vehicle Fuel Efficiency (km/L)

$$\mathcal{C}\_d = \sum (TO\_d \times EF\_d) \tag{8}$$

*Cd*: Amount of CO2 Emissions in the Construction Waste Transportation Stage (kg-CO2)

*TOd*: Amount of Fuel Consumed by Transportation Equipment (L)

*EFd*: Fuel Emissions Factor (kg-CO2/L)


**4. Literature relevant to the visual and visual-attentional sides of 'The Tales of Jud the Rat' reading fluency programme**

### **4.1. Rapid visual and auditory processing as predictors of reading difficulty**

A number of studies have indicated that developmental dyslexics do poorly in tests requiring rapid visual and auditory processing. Witton et al. [111] reported that neuronal mechanisms that were specialised for detecting stimulus timing and change were dysfunctional in many dyslexic individuals. The dissociation observed in the performance of dyslexic individuals on different auditory tasks also suggested a sub-modality division in the auditory system similar to that already described in the visual system.

Both Farmer and Klein [112] and Stein and Walsh [113] reported that dyslexia was associated with difficulties with moving visual stimuli. Hari and Renvall [114] also reported that dyslexic subjects often suffered from impaired processing of rapid stimulus sequences and suggest that sluggish attentional shifting can account for the impaired processing of rapid stimulus sequences in dyslexia. Amitay et al. [115] concluded that disabled readers suffered from both visual and auditory impairments, showing impaired performance in both visual and auditory tasks requiring fine frequency discriminations.

Talcott et al. [116] reported that both visual motion sensitivity and auditory sensitivity to frequency differences were robust predictors of children's literacy skills and their orthographic and phonological skills. Cohen-Mimran and Sapir [117] reported auditory temporal processing deficits in children with reading disabilities, and that children with reading difficulties had difficulty in discriminating between pure tones with short, but not long, interstimulus intervals, whereas controls performed well with both short and long interstimulus intervals.

Rapid auditory processing deficits have also been found to be consistent predictors of later reading achievement [118]. Lervåg and Hulme [119] reported that rapid automatised naming (RAN) measured with non-alphabetic stimuli before reading instruction had begun was a predictor of later growth in reading fluency, and continued to exert an influence on the development of reading fluency over the next 2 years after reading instruction had begun. Equally important were indications that there was no evidence of reciprocal influence of reading fluency on the growth of RAN skill. This would suggest that RAN is a function which taps the integrity of the left-hemisphere object-recognition and naming circuits which form critical components of the child's developing visual word-recognition system.

#### **4.2. The influence of instruction on rapid processing ability**

The literature, however, also indicates that rapid processing dysfunctions are responsive to training. Temple et al. [120] reported disruption of the neural response to rapid acoustic stimuli in dyslexia, with normal readers showing left prefrontal activity in response to rapidly changing, relative to slowly changing, non-linguistic acoustic stimuli. Dyslexic readers, in contrast, showed no differential left frontal response. Temple et al. also reported that dyslexic readers who participated in a remediation program showed increased activity in left prefrontal cortex after training.

Temple et al.'s results would suggest that the left prefrontal regions are normally sensitive to rapid relative to slow acoustic stimulation, but are insensitive in the case of dyslexic readers. Equally important are the indications that the left prefrontal cortex would appear to be plastic enough in adulthood to develop such differential sensitivity after intensive training.

Gaab et al. [121] reported that children with dyslexia had a fundamental deficit in processing rapid acoustic stimuli, but that this was responsive to training. While typical-reading children showed activation for rapid compared to slow transitions in left prefrontal cortex, children with developmental dyslexia did not show differential response in these regions to rapid and slow transitions in acoustic stimuli. After 8 weeks of remediation which provided training in rapid auditory processing, phonological processing and language skills, Gaab et al. reported that the children with developmental dyslexia showed significant improvements in both language and reading skills. They also showed activation for rapid relative to slow transitions in the left prefrontal cortex after training. Gaab et al. thus concluded that neural correlates of rapid auditory processing were disrupted in children with developmental dyslexia, but could be ameliorated with training.

These findings suggested that many children with reading difficulties have difficulties with rapid visual processing, difficulties with rapid auditory processing, as well as impairments of perceptual processing of rapidly changing acoustic stimuli. These findings also suggest that reading disabilities are often accompanied by impaired perceptual skills as well as specific perceptual deficits and perceptual difficulties which have neurological correlates. As with other areas of functioning, the relationship between behaviour and underlying neural connectivity would appear to be a two -way association, in which improved perceptual processing leads to improved neural connectivity, and vice versa.

#### **4.3. The magnocellular theory of dyslexia**

Talcott et al. [116] reported that both visual motion sensitivity and auditory sensitivity to frequency differences were robust predictors of children's literacy skills and their orthographic and phonological skills. Cohen-Mimran and Sapir [117] reported auditory temporal processing deficits in children with reading disabilities, and that children with reading difficulties had difficulty in discriminating between pure tones with short, but not long, interstimulus intervals, whereas controls performed well with both short and long interstimulus intervals.

Rapid auditory processing deficits have also been found to be consistent predictors of later reading achievement [118]. Lervåg and Hulme [119] reported that rapid automatised naming (RAN) measured with non-alphabetic stimuli before reading instruction had begun was a predictor of later growth in reading fluency, and continued to exert an influence on the development of reading fluency over the next 2 years after reading instruction had begun. Equally important were indications that there was no evidence of reciprocal influence of reading fluency on the growth of RAN skill. This would suggest that RAN is a function which taps the integrity of the left-hemisphere object-recognition and naming circuits which form

The literature, however, also indicates that rapid processing dysfunctions are responsive to training. Temple et al. [120] reported disruption of the neural response to rapid acoustic stimuli in dyslexia, with normal readers showing left prefrontal activity in response to rapidly changing, relative to slowly changing, non-linguistic acoustic stimuli. Dyslexic readers, in contrast, showed no differential left frontal response. Temple et al. also reported that dyslexic readers who participated in a remediation program showed increased activity in left prefrontal

Temple et al.'s results would suggest that the left prefrontal regions are normally sensitive to rapid relative to slow acoustic stimulation, but are insensitive in the case of dyslexic readers. Equally important are the indications that the left prefrontal cortex would appear to be plastic

Gaab et al. [121] reported that children with dyslexia had a fundamental deficit in processing rapid acoustic stimuli, but that this was responsive to training. While typical-reading children showed activation for rapid compared to slow transitions in left prefrontal cortex, children with developmental dyslexia did not show differential response in these regions to rapid and slow transitions in acoustic stimuli. After 8 weeks of remediation which provided training in rapid auditory processing, phonological processing and language skills, Gaab et al. reported that the children with developmental dyslexia showed significant improvements in both language and reading skills. They also showed activation for rapid relative to slow transitions in the left prefrontal cortex after training. Gaab et al. thus concluded that neural correlates of rapid auditory processing were disrupted in children with developmental dyslexia, but could

These findings suggested that many children with reading difficulties have difficulties with rapid visual processing, difficulties with rapid auditory processing, as well as impairments of

enough in adulthood to develop such differential sensitivity after intensive training.

critical components of the child's developing visual word-recognition system.

**4.2. The influence of instruction on rapid processing ability**

278 E-Learning - Instructional Design, Organizational Strategy and Management

cortex after training.

be ameliorated with training.

Overall, the research reviewed in the previous section would suggest that many children with dyslexia or poor reading ability have difficulties in processing rapidly changing signals, both auditorally as well as visually [122]. Visual processing difficulties, as well as auditory proc‐ essing difficulties, have neurological correlates, suggesting the possibility of a general underlying attentional or processing difficulty affecting the development of reading ability.

The magnocellular theory of dyslexia [123, 124] suggests that underlying difficulties in auditory and visual processing can be traced to difficulties in the magnocellular component of the visual system. As Stein and Walsh comment:

'Developmental dyslexics often complain that small letters appear to blur and move around when they are trying to read. Anatomical, electrophysiological, psychophysical and brainimaging studies have all contributed to elucidating the functional organization of these and other visual confusions. They emerge not from damage to a single visual relay but from abnormalities of the magnocellular component of the visual system, which is specialized for processing fast temporal information. The m-stream culminates in the posterior parietal cortex, which plays an important role in guiding visual attention. The evidence is consistent with an increasingly sophisticated account of dyslexia that does not single out either phonological, or visual or motor deficits. Rather, temporal processing in all three systems seems to be impaired. Dyslexics may be unable to process fast incoming sensory information adequately in any domain.' [125]

Stein and Walsh's conclusions have been supported by a number of studies. Salmelin et al. [126] used magnetoencephalography to identify impaired word processing in the occipitotemporal areas of dyslexics, while Livingstone et al. [127] reported that dyslexic subjects exhibited diminished visually evoked potentials to rapid, low contrast stimuli, but normal responses to slow or high contrast stimuli. Livingstone et al. suggested that the abnormalities in the dyslexic subjects' responses to evoked potentials were associated with a defect in the magnocellular pathway at the level of visual area 1 or earlier.

Livingstone et al. also compared the lateral geniculate nuclei from five dyslexic brains to five control brains, reporting abnormalities in the magnocellular, but not the parvocellular, layers in the dyslexic brains studied. As previous studies using auditory and somatosensory tests had shown that dyslexics perform poorly on tasks requiring rapid discriminations, Livingstone et al. hypothesised that many cortical systems can be divided into a fast and a slow subdivision; and further that that dyslexia is associated with difficulties in rapid processing within these fast subdivisions.

Similarly, Vidyasagar and Pammer [128] reported that impaired visual search in dyslexia relates to the role of the magnocellular pathway in attention, leading Vidyasagar [129] to suggest that attentional gating in primary visual cortex provides a physiological basis for dyslexia. Sireteanu et al. [130] also investigated the performance of children with develop‐ mental dyslexia on a number of visual tasks requiring selective visual attention and found that dyslexic children did not show the overestimation of the left visual field (pseudoneglect) characteristic of normal adult vision. Dyslexic children also showed shorter reaction times and a dramatically increased number of errors on these tasks, suggesting that children with developmental dyslexia have selective deficits in visual attention.

Misra et al. [131], for example, have identified a number of neurological correlates of rapid processing deficits, reporting that the majority of children and adults with reading disabilities also exhibit pronounced difficulties on naming-speed measures such as tests of rapid auto‐ matised naming, which required speeded naming of serially presented stimuli. In their study, functional magnetic resonance imaging was used to evaluate the neural substrates that were associated with performance on rapid naming tasks. Activation was found in neural areas associated with eye movement control and attention as well as in a network of cortical structures implicated in reading tasks, including the inferior frontal cortex, temporo-parietal areas and the ventral visual stream. Whereas the inferior frontal areas of the network were similarly activated for both letters and objects, activation in the posterior areas varied by task. These results suggested that rapid naming tasks recruited a network of neural structures which were also involved in more complex reading tasks, and suggested that rapid naming of letters pinpointed key components of this reading network.

#### **4.4. Prevalence of magnocellular deficits: Evidence from multiple case studies**

Vidyasagar and Pammer [132] have proposed that dyslexia is a deficit in visuo-spatial attention, not in phonological processing. However, the evidence from multiple case studies of disabled readers suggests that dyslexics may suffer from visual and auditory impairments but only a few suffer from a specific magnocellular deficit.

Amitay et al. [133], for example, reported that only six out of the thirty reading disabled subjects in their study had impaired magnocellular function, and that the performance of the other twenty four reading disabled subjects on magnocellular tasks did not differ from that of controls. Amitay et al. also reported that many of the reading disabled children showed impaired performance in both visual and auditory non‐magnocellular tasks which required fine frequency discriminations. Overall, Amitay et al. concluded that some reading disabled subjects have generally impaired perceptual skills, while many reading disabled subjects have more specific perceptual deficits. The 'magnocellular' level of description, however, did not capture the nature of the perceptual difficulties in any of the reading disabled individuals in the sample, as the six subjects with impaired magnocellular function were also consistently impaired on a broad range of other perceptual tasks.

Similarly, Ramus et al. [134] analysed sixteen dyslexic subjects and reported that all sixteen dyslexics suffered from a phonological deficit. Ten of the subjects could be characterised as suffering from an auditory deficit, four from a motor deficit and two from a visual magnocel‐ lular deficit. The results thus indicated that a phonological deficit can appear in the absence of any other sensory or motor disorder. A phonological deficit is also sufficient to cause a literacy impairment, as demonstrated by five of the dyslexics. Auditory disorders, when present, aggravated a phonological deficit, contributing to the literacy impairment.

Similarly, Vidyasagar and Pammer [128] reported that impaired visual search in dyslexia relates to the role of the magnocellular pathway in attention, leading Vidyasagar [129] to suggest that attentional gating in primary visual cortex provides a physiological basis for dyslexia. Sireteanu et al. [130] also investigated the performance of children with develop‐ mental dyslexia on a number of visual tasks requiring selective visual attention and found that dyslexic children did not show the overestimation of the left visual field (pseudoneglect) characteristic of normal adult vision. Dyslexic children also showed shorter reaction times and a dramatically increased number of errors on these tasks, suggesting that children with

Misra et al. [131], for example, have identified a number of neurological correlates of rapid processing deficits, reporting that the majority of children and adults with reading disabilities also exhibit pronounced difficulties on naming-speed measures such as tests of rapid auto‐ matised naming, which required speeded naming of serially presented stimuli. In their study, functional magnetic resonance imaging was used to evaluate the neural substrates that were associated with performance on rapid naming tasks. Activation was found in neural areas associated with eye movement control and attention as well as in a network of cortical structures implicated in reading tasks, including the inferior frontal cortex, temporo-parietal areas and the ventral visual stream. Whereas the inferior frontal areas of the network were similarly activated for both letters and objects, activation in the posterior areas varied by task. These results suggested that rapid naming tasks recruited a network of neural structures which were also involved in more complex reading tasks, and suggested that rapid naming of letters

developmental dyslexia have selective deficits in visual attention.

280 E-Learning - Instructional Design, Organizational Strategy and Management

pinpointed key components of this reading network.

but only a few suffer from a specific magnocellular deficit.

impaired on a broad range of other perceptual tasks.

**4.4. Prevalence of magnocellular deficits: Evidence from multiple case studies**

Vidyasagar and Pammer [132] have proposed that dyslexia is a deficit in visuo-spatial attention, not in phonological processing. However, the evidence from multiple case studies of disabled readers suggests that dyslexics may suffer from visual and auditory impairments

Amitay et al. [133], for example, reported that only six out of the thirty reading disabled subjects in their study had impaired magnocellular function, and that the performance of the other twenty four reading disabled subjects on magnocellular tasks did not differ from that of controls. Amitay et al. also reported that many of the reading disabled children showed impaired performance in both visual and auditory non‐magnocellular tasks which required fine frequency discriminations. Overall, Amitay et al. concluded that some reading disabled subjects have generally impaired perceptual skills, while many reading disabled subjects have more specific perceptual deficits. The 'magnocellular' level of description, however, did not capture the nature of the perceptual difficulties in any of the reading disabled individuals in the sample, as the six subjects with impaired magnocellular function were also consistently

Similarly, Ramus et al. [134] analysed sixteen dyslexic subjects and reported that all sixteen dyslexics suffered from a phonological deficit. Ten of the subjects could be characterised as suffering from an auditory deficit, four from a motor deficit and two from a visual magnocel‐ These data thus indicated that auditory deficits could not be characterised simply as rapid auditory processing problems, as would be predicted by the magnocellular theory. Nor were they restricted to speech. Contrary to the cerebellar theory, Ramus et al. also found little support for the notion that motor impairments had a cerebellar origin or reflected an automa‐ ticity deficit. Overall, Ramus et al. concluded that the phonological theory of dyslexia could account for all sixteen of the subjects in their sample. There were also additional sensory and motor disorders in certain individuals.

Ziegler et al. [135] reported that children with dyslexia had significant deficits for letter and digit strings, but not for symbol strings. Visual-attentional theories of dyslexia could not explain these findings, as visual attentional theories postulated identical deficits for letters, digits and symbols in dyslexics. Ziegler et al. also reported that dyslexics showed normal Wshaped serial position functions for letter and digit strings. This finding suggested that their deficit could not be attributed to an abnormally small attentional window. In addition, the data indicated that the size of the deficit was identical for letters and digits, suggesting that poor letter perception in dyslexic children was not just a consequence of lack of reading.

What could account for Ziegler et al.'s data was that the process of mapping symbols onto phonological codes was impaired, as this was the case for both letters and digits. In contrast, symbols that did not map onto phonological codes were not impaired. This dissociation suggested that impaired symbol-sound mapping rather than impaired visual-attentional processing was the key to understanding dyslexia.

#### **4.5. Both visual and visual attentional factors need to be taken into account in teaching reading**

Despite convergent evidence that dyslexia is a language disability which has its foundations in difficulties in phonological and phonemic processing, both Schulte-Körne and Bruder's [136] review and Stein's more recent [137] review of current literature suggest that rapid processing, attentional and magnocellular factors are important influences on reading ability which should not be overlooked. Research from both Australia [138] and from Italy [139] also indicate that it is important to take account of visual attentional factors in remediating language-based learning difficulties.

In addition, visual features stemming from layout of reading material have been found to influence reading as well as comprehension outcomes [140]. Spinelli et al. [141] have suggested that dyslexic readers are affected by crowding of multiple characters and large numbers of words onto printed pages. Visual features of text such as print size [142], visual span [143], spacing of letters [144], spacing between letters [145, 146], as well as font size and spacing between words relative to print size and visual acuity limits [147] are also important to consider when publishing materials for poor readers.

The above research has implications for the development of reading materials for dyslexic children. As I had found in working with Q, research post 2000 indicated that dyslexic children would be likely to respond best to reading material which took account of factors such as length of words [148, 149], amount of text in paragraphs [150] and amount of text on pages [151].

How I have taken account of phonological and phonemic factors, as well as crowding, visual and visual-attentional factors, in writing the 'Tales of Jud the Rat' series as well as in developing the procedures used in the implementation of the materials is covered in the rest of this chapter.


**5. Smoking and age-related macular degeneration**

It has been postulated that environmental oxidants are frequently implicated in RPE injury and may contribute to deposit formation. Cigarette smoking is the strongest environmental risk factor for all forms of AMD, even in people exposed to passive smoking [63-66]. People who have smoked at least 100 cigarettes (lifetime) have approximately triple risk of developing AMD compared to individuals who have never smoked. Current smokers and heavy smokers have even higher AMD risk.

Cigarette smoke is comprised of a gas and tar phase. Each phase contains both inorganic and organic free radicals, including ROS, epoxides, peroxides, NO, nitrogen dioxide, peroxynitrite, peroxynitrates and various other free radicals [67]. Moreover, cigarette smoke contains a high concentration of potent oxidants such as acrolein [68-70], dioxin [71], Benzo(a)Pyrene [72], cadmium, [73-75] quinones, nicotine, and NO [76-79]. Many of them demonstrated to be toxic to ocular tissue affecting the eye through oxidative damage and abnormal vascularization. Although the pathogenesis of AMD and the mechanism of action of smoking on the eye are not fully understood [80], the risk of developing AMD is likely to involve more than one mechanism.

of the alternative pathway of complement activation [121]. Smoking seems to alter the C3 component of the complement and to reduce the efficacy by which it binds to complement

Cigarette Smoking and Hypertension Two Risk Factors for Age-Related Macular Degeneration

http://dx.doi.org/10.5772/53958

47

The association between smoke and ApoE has also been investigated. Smokers are known to have higher serum cholesterol and LDL levels, a possible risk factor for AMD development [102]. Impaired cholesterol metabolism in particular ApoE isoforms carriers could be further enhanced among smokers. Moreover, smoking is associated with decreased endothelial NO production, while ApoE-4 genotype has been reported to increase NO synthesis, relative to ApoE2 and ApoE3 [103]. Thus, the presence of the ApoE2 protein in smokers may reduce endothelial NO levels in cell tissues. Given that the normal function of NO in the retina is to neutralize circulating oxidized lipids, its decreased availability may promote oxidative

As mentioned above, quinones, acrolein, BenzoPyrenes, and cadmium can damage the eye through oxidative processes. It has been demonstrated that hydroquinone the most abundant quinone in cigarette smoke causes oxidative damage, apoptosis, increases lipid peroxidation and mitochondrial superoxide production, and decreases intracellular glutathione in the RPE cells [104]. Acrolein, a product of lipid peroxidation in vivo, is a mitochondrial toxicant in RPE cells, which induces oxidative mitochondrial dysfunction and oxidative damage to proteins and DNA causing loss of cell viability [70-72]. Whereas Benzo(a)Pyrene causes mtDNA damage, alteration in the lysosomal activity, complement activation in the aged RPE, and upregulates expression of complement pathway components such as C3a, C5, C5b-9, and CFH in the RPE/choroid contributing to drusen formation [105,106]. Cadmium (Cd), is another toxic which is concentrated in the body through cigarette smoke. Cd has been demonstrated to be a potent inflammatory, an oxidant agent, and accumulates with aging, effects which are linked to AMD [77, 107]. In vitro studies have revealed Cd as an important potential factor in RPE

Cigarette smoke also induces angiogenesis promoting CNV and progression to neovascular AMD. Nicotine is one of the major components of the cigarette and has been demonstrated to enhance the generation of radical oxygen species producing oxidative stress [108-110] and promote the generation of pro-inflammatory responses leading to chronic inflammation in smokers [111]. Futhermore, nicotine is responsible for the mitogenesis of endothelial and smooth muscle cells [112] regulating abnormal responses to vascular injury [113] and endo‐ thelial functions [114]. We and other previously showed that nicotine by its direct action on nicotinic receptors promoted angiogenesis and increased size and severity of CNV in a mouse model [112, 114]. Moreover, it has been shown that nicotine targets the retinal microvasculature by reducing the apoptotic rate of vascular endothelial cells and inducing the formation of new capillarity, mediated at least in part by release of vascular endothelial growth factor (VEGF) [113], which is one of the key growth factors involved in neovascularization and implicated in the most severe form of AMD [115-118]. Nicotine has also the ability to stimulate the growth of smooth muscle cells after oxidative injury, leading to the development of CNV in the retina. Moreover other cigarette smoke components such as dioxin induce expression and release of

factor H (CFH).

damage to the RPE cells.

cell death associated retinal disease [76].

VEGF by RPE cells promoting CNV [104, 119].

Smoking habit appears to be related to the long-term incidence and progression of AMD [81]. Plausible biological mechanisms such as direct oxidation, depletion of antioxidant protection (e.g. decreases plasma vitamin C and carotenoids), immune system activation, atherosclerotic vascular changes, induction of hypoxia and alteration of choroidal blood flow [82,83], support the involvement of smoking in the etiology of AMD [84]. Interestingly, the Age-related Eye Disease Study (AREDS) [85] found that reduction in plasma glutathione and cysteine oxidation correlated with benefit from anti-oxidant treatment for intermediate AMD [86]. Collectively, these studies, along with the studies on cigarette smoking, implicate oxidative stress as a mechanism of AMD. Genetic variations such as a susceptibility locus in or near the hypothetical LOC387715 gene were associated with AMD [87,88]. It has been reported that this locus encodes a mitochondrial protein, raising suspicion for a role of the oxidative defense response in this disease. This locus is associated with smoking, and the combination of the LOC387715 polymorphism and smoking confers a higher risk for AMD than either factor alone [89,90]. Moreover, chemical in cigarette smoke modifies oxidative‐ ly docosohexanoic acid (DHA), the most abundant fatty acid in photoreceptor tips, to carboxyethylpyrrole (CEP) [91] and other lipids which have been identified which "tag" oxidatively oxidized damaged photoreceptors in AMD [92]. In the RPE, multiple proteins isolated from lipofuscin are also oxidatively damaged by cigarette smoke compounds including malondialdehyde, 4-hydroxynonenal and advanced glycation end products (AGE) [93,94]. Interestingly, AGEs accumulate in BrM including basal deposits and drusen, and CEP adducts appear in drusen isolated from AMD samples [95,96].The ability to defend against oxidative stress by upregulating the anti-oxidant defense response is likely to be a pivotal event that mediates the initiation and progression of AMD. The molecular damage from oxidative modification illustrated here, suggests that the anti-oxidant response in the macula at some point, becomes unable to neutralize oxidative stress.

In addition to direct oxidative damage to tissue, oxidative free radicals from cigarette smoke can modulate the immune-inflammatory system in part, through enhanced expression of proinflammatory genes, as reviewed in Biswas and Rahman (2009) [97]. The discovery of poly‐ morphisms in several complement factors with AMD susceptibility points toward a specific role for complement mediated inflammation in the pathophysiology of AMD [98,99]. AMD has been associated with local inflammatory responses in the RPE/choroid [100]. In previous works, it was demonstrated that the aged RPE/choroid becomes immunologically active [101] due to increased expression of complement components. Smoking also influences the function of the alternative pathway of complement activation [121]. Smoking seems to alter the C3 component of the complement and to reduce the efficacy by which it binds to complement factor H (CFH).

Cigarette smoke is comprised of a gas and tar phase. Each phase contains both inorganic and organic free radicals, including ROS, epoxides, peroxides, NO, nitrogen dioxide, peroxynitrite, peroxynitrates and various other free radicals [67]. Moreover, cigarette smoke contains a high concentration of potent oxidants such as acrolein [68-70], dioxin [71], Benzo(a)Pyrene [72], cadmium, [73-75] quinones, nicotine, and NO [76-79]. Many of them demonstrated to be toxic to ocular tissue affecting the eye through oxidative damage and abnormal vascularization. Although the pathogenesis of AMD and the mechanism of action of smoking on the eye are not fully understood [80], the risk of developing AMD is likely to involve more than one

46 Age-Related Macular Degeneration - Etiology, Diagnosis and Management - A Glance at the Future

Smoking habit appears to be related to the long-term incidence and progression of AMD [81]. Plausible biological mechanisms such as direct oxidation, depletion of antioxidant protection (e.g. decreases plasma vitamin C and carotenoids), immune system activation, atherosclerotic vascular changes, induction of hypoxia and alteration of choroidal blood flow [82,83], support the involvement of smoking in the etiology of AMD [84]. Interestingly, the Age-related Eye Disease Study (AREDS) [85] found that reduction in plasma glutathione and cysteine oxidation correlated with benefit from anti-oxidant treatment for intermediate AMD [86]. Collectively, these studies, along with the studies on cigarette smoking, implicate oxidative stress as a mechanism of AMD. Genetic variations such as a susceptibility locus in or near the hypothetical LOC387715 gene were associated with AMD [87,88]. It has been reported that this locus encodes a mitochondrial protein, raising suspicion for a role of the oxidative defense response in this disease. This locus is associated with smoking, and the combination of the LOC387715 polymorphism and smoking confers a higher risk for AMD than either factor alone [89,90]. Moreover, chemical in cigarette smoke modifies oxidative‐ ly docosohexanoic acid (DHA), the most abundant fatty acid in photoreceptor tips, to carboxyethylpyrrole (CEP) [91] and other lipids which have been identified which "tag" oxidatively oxidized damaged photoreceptors in AMD [92]. In the RPE, multiple proteins isolated from lipofuscin are also oxidatively damaged by cigarette smoke compounds including malondialdehyde, 4-hydroxynonenal and advanced glycation end products (AGE) [93,94]. Interestingly, AGEs accumulate in BrM including basal deposits and drusen, and CEP adducts appear in drusen isolated from AMD samples [95,96].The ability to defend against oxidative stress by upregulating the anti-oxidant defense response is likely to be a pivotal event that mediates the initiation and progression of AMD. The molecular damage from oxidative modification illustrated here, suggests that the anti-oxidant response in the

macula at some point, becomes unable to neutralize oxidative stress.

In addition to direct oxidative damage to tissue, oxidative free radicals from cigarette smoke can modulate the immune-inflammatory system in part, through enhanced expression of proinflammatory genes, as reviewed in Biswas and Rahman (2009) [97]. The discovery of poly‐ morphisms in several complement factors with AMD susceptibility points toward a specific role for complement mediated inflammation in the pathophysiology of AMD [98,99]. AMD has been associated with local inflammatory responses in the RPE/choroid [100]. In previous works, it was demonstrated that the aged RPE/choroid becomes immunologically active [101] due to increased expression of complement components. Smoking also influences the function

mechanism.

The association between smoke and ApoE has also been investigated. Smokers are known to have higher serum cholesterol and LDL levels, a possible risk factor for AMD development [102]. Impaired cholesterol metabolism in particular ApoE isoforms carriers could be further enhanced among smokers. Moreover, smoking is associated with decreased endothelial NO production, while ApoE-4 genotype has been reported to increase NO synthesis, relative to ApoE2 and ApoE3 [103]. Thus, the presence of the ApoE2 protein in smokers may reduce endothelial NO levels in cell tissues. Given that the normal function of NO in the retina is to neutralize circulating oxidized lipids, its decreased availability may promote oxidative damage to the RPE cells.

As mentioned above, quinones, acrolein, BenzoPyrenes, and cadmium can damage the eye through oxidative processes. It has been demonstrated that hydroquinone the most abundant quinone in cigarette smoke causes oxidative damage, apoptosis, increases lipid peroxidation and mitochondrial superoxide production, and decreases intracellular glutathione in the RPE cells [104]. Acrolein, a product of lipid peroxidation in vivo, is a mitochondrial toxicant in RPE cells, which induces oxidative mitochondrial dysfunction and oxidative damage to proteins and DNA causing loss of cell viability [70-72]. Whereas Benzo(a)Pyrene causes mtDNA damage, alteration in the lysosomal activity, complement activation in the aged RPE, and upregulates expression of complement pathway components such as C3a, C5, C5b-9, and CFH in the RPE/choroid contributing to drusen formation [105,106]. Cadmium (Cd), is another toxic which is concentrated in the body through cigarette smoke. Cd has been demonstrated to be a potent inflammatory, an oxidant agent, and accumulates with aging, effects which are linked to AMD [77, 107]. In vitro studies have revealed Cd as an important potential factor in RPE cell death associated retinal disease [76].

Cigarette smoke also induces angiogenesis promoting CNV and progression to neovascular AMD. Nicotine is one of the major components of the cigarette and has been demonstrated to enhance the generation of radical oxygen species producing oxidative stress [108-110] and promote the generation of pro-inflammatory responses leading to chronic inflammation in smokers [111]. Futhermore, nicotine is responsible for the mitogenesis of endothelial and smooth muscle cells [112] regulating abnormal responses to vascular injury [113] and endo‐ thelial functions [114]. We and other previously showed that nicotine by its direct action on nicotinic receptors promoted angiogenesis and increased size and severity of CNV in a mouse model [112, 114]. Moreover, it has been shown that nicotine targets the retinal microvasculature by reducing the apoptotic rate of vascular endothelial cells and inducing the formation of new capillarity, mediated at least in part by release of vascular endothelial growth factor (VEGF) [113], which is one of the key growth factors involved in neovascularization and implicated in the most severe form of AMD [115-118]. Nicotine has also the ability to stimulate the growth of smooth muscle cells after oxidative injury, leading to the development of CNV in the retina. Moreover other cigarette smoke components such as dioxin induce expression and release of VEGF by RPE cells promoting CNV [104, 119].


**Abstract**

Multiple sclerosis (MS) is an autoimmune disease in which discrete central nervous system lesions result from perivascular immune cell infiltration associated with damage to myelin (demyelination), oligodendrocytes and neurons. This culminates in debilitating neurological symptoms, primarily affecting women in their child-bearing years. Both the innate and adaptive branches of the immune system have been implicated in disease initiation and progression, and although the underlying cause remains elusive, there is compelling evidence for a complex interaction between genetic and environmental factors, leading to inflammation and neurodegeneration. Both direct cellular toxicity and antibody-dependent cellular cytotoxicity (ADCC) involving several cell types have been identified in playing major roles. These cells and their interactions in the pathogenesis of MS will be discussed.

**Keywords:** multiple sclerosis, cellular cytotoxicity, cell subsets, human, MS mouse models


**Using 3D Convolutional Neural Networks for Tactile Object Recognition with Robotic Palpation**

### **Francisco Pastor \*, Juan M. Gandarias, Alfonso J. García-Cerezo and Jesús M. Gómez-de-Gabriel**

Robotics and Mechatronics Group, University of Málaga, 29071 Málaga, Spain<EMAIL_ADDRESS>(J.M.G<EMAIL_ADDRESS>(A.J.G.-C<EMAIL_ADDRESS>(J.M.G.-d.-G.)

**\*** Correspondence<EMAIL_ADDRESS>
Received: 31 October 2019; Accepted: 2 December 2019; Published: 5 December 2019

**Abstract:** In this paper, a novel method of active tactile perception based on 3D neural networks and a high-resolution tactile sensor installed on a robot gripper is presented. A haptic exploratory procedure based on robotic palpation is performed to get pressure images at different grasping forces that provide information not only about the external shape of the object, but also about its internal features. The gripper consists of two underactuated fingers with a tactile sensor array in the thumb. A new representation of tactile information as 3D tactile tensors is described. During a squeeze-and-release process, the pressure images read from the tactile sensor are concatenated forming a tensor that contains information about the variation of pressure matrices along with the grasping forces. These tensors are used to feed a 3D Convolutional Neural Network (3D CNN) called 3D TactNet, which is able to classify the grasped object through active interaction. Results show that 3D CNN performs better, and provide better recognition rates with a lower number of training data.

**Keywords:** tactile perception; robotic palpation; underactuated grippers; deep learning

### **1. Introduction**

Recent advances in Artificial Intelligence (AI) have brought the possibility of improving robotic perception capabilities. Although most of them are focused on visual perception [1], existing solutions can also be applied to tactile data [2–4]. Tactile sensors measure contact pressure from other physical magnitudes, depending on the nature of the transducer. Different types of tactile sensors [5–9] have been used in robotic manipulation [10,11] for multiple applications such as slippage detection [12,13], tactile object recognition [14,15], or surface classification [16,17], among others.

Robotic tactile perception consists of the integration of mechanisms that allow a robot to sense tactile properties from physical contact with the environment along with intelligent capacities to extract high-level information from the contact. The sense of touch is essential for robots the same way as for human beings for performing both simple and complex tasks such as object recognition or dexterous manipulation [18–20]. Recent studies focused on the development of robotic systems that behaves similar to humans, including the implementation of tactile perception capabilities [21,22]. However, tactile perception is still a fundamental problem in robotics that has not been solved so far [23]. In addition, there are multiple applications, not limited to classic robotic manipulation problems that can benefit from tactile perception such as medicine [24], food industry [3], or search-and-rescue [4], among others.

Many works related to tactile perception use pressure images after the interaction [25], which means that the interaction is considered static or passive. However, tactile perception in the real world is intrinsically active [26]. A natural or bio-inspired haptic Exploratory Procedure (EP) for perceiving pressure or stiffness of an object must consider dynamic information [27]. According to [28], the haptic attributes that can be perceived depends on the EP.

A survey on the concept of active tactile perception considering biological and psychological terms is presented in [29]. In this chapter, and according to [30], two approaches for tactile perception in robots are possible: perception for action, which means that the perceived information is used to guide the robot (i.e., dexterous manipulation and grasp control), and action for perception, which means that the robot explores the environment to collect data (i.e., active perception and haptic exploration). Hence, an active tactile perception approach can be defined as one in which the data are collected during an active EP using an active sensing approach (e.g., tactile sensing). This means that action and perception are not separated, and the robot collects dynamic data depending on the action, while this action is occurring. Therefore, although both static and dynamic tactile data are useful for many robotic applications, it can be considered that active perception is more faithful to the real sense of touch, and the information acquired using active tactile sensing reflects the attributes of the grasped objects better. Static pressure images only contain information about stiffness and shape of the object when a certain force is applied [14], while the changes of the pressure distribution over force contain information about the variation of shape and stiffness during the whole EP [31]. This dynamic information allows us to distinguish both rigid and deformable objects [32].

This paper addresses the shortcomings mentioned above and is focused on the active tactile perception problem in robotics. A robotic palpation process with active tactile sensing, based on a squeeze-and-release motion for distinguishing grasped objects, both rigid and deformable, is presented (see Figure 1). The robotic EP conceives a novel representation of dynamic tactile information based on sequences of pressure images and an AI method based on 3D Convolutional Neural Networks (3D CNNs) for active tactile perception. A tactile sensor array is integrated into the thumb of a gripper with two underactuated fingers to get sequences of tactile images. These sequences are represented as 3D tensors similar to Magnetic Resonance Imaging (MRI). However, in this case, 3D tactile tensors represent the variation of pressure distribution over applied force, whereas MRI contains information about cross-sectional images of internal structures and organs over distance. Although the type of information contained in MRIs and 3D tactile tensors is different, methods such as 3D CNNs used to process MRI information [33,34] might be used for tactile data with good results in this application as we explored in our previous work [35]. In this work, our preliminary study is expanded: a high-resolution tactile sensor has been integrated into a new gripper where the palpation process (e.g., the EP) is fully autonomous, so the robot controls the grasping force. As a result, not only objects with different elasticity are compared and classified, but also objects that contain internal inclusions and bags of objects which provide different pressure images each time, have been tested. In particular, 24 objects have been used: rigid, deformable, and in-bag; and the results are compared against 2D CNN-based methods. Altogether, the main contribution of this paper relates to the entire process of active tactile perception, considering the use of an underactuated, sensorized gripper to carry out the EP, and a 3D CNN for tactile perception.

The relevance of this contribution relies on different factors. First, the presented method achieves better performance in the discrimination problem for all kinds of objects, and, in case the number of classes increases, a lower number of training data are needed to obtain higher accuracy rates than classic 2D networks. Second, it is also shown that, in case of misclassification, the resulting object class has almost indistinguishable physical features (e.g., soda cans of different capacities), where 2D CNNs, in the event of failure, give disparate output classes unrelated to the class of the grasped object.

This paper is structured as follows: In Section 2, the current state-of-the-art related to this topic is introduced. In Section 3, the underactuated gripper and the 3D CNN-based method used for tactile perception are described. The experimental protocol and results are explained in Section 4, and a thorough and detailed discussion of our results in comparison with related works is presented in Section 5. Finally, the conclusion and future research lines are exposed in Section 6.

**Figure 1.**  In (**b**), the tensor is sectioned to show the intrinsic attributes and pressure variations of the grasped object.

(c)

### **2. Related Work**

Related works within the scope of tactile perception in robotics focus on tactile object-recognition from pressure-images, deep-learning methods based on CNNs, and active tactile perception.

### *2.1. Tactile Object Recognition*

Two main approaches for tactile object recognition may be considered depending on the nature of the EP: On one hand, perceiving attributes from the material composition, which are typically related to superficial properties like roughness, texture, or thermal conductivity [36–38]. On the other hand, other properties related to stiffness and shape may also be considered for object discrimination [39–41]. Most of these works are based on the use of novel machine learning-based techniques. That way, different approaches can be followed, such as Gaussian Processes [42], k-Nearest Neighbour (kNN) [25], Bayesian approaches [43], k-mean and Support Vector Machines (SVM) [44], or Convolutional Neural Networks (CNNs) [45], among others. Multi-modal techniques have also been considered in [46], where they demonstrated that considering both haptic and visual information generally gives better results.

### *2.2. Tactile Perception Based on Pressure Images*

Concerning the latter approach, most of the existing solutions in literature acquire data from tactile sensors, in the form of matrices of pressure values, analog to common video images [47]. In this respect, multiple strategies and methodologies can be followed. In [25], a method, based on *Scale Invariant Feature Transform* (SIFT) descriptors, is used as a feature extractor, and the kNN algorithm is used to classify objects by their shape. In [15], Luo et al. proposed a novel multi-modal algorithm that mixes kinesthetic and tactile data to classify objects from a 4D point cloud where each point is represented by the 3D position of the point and the pressure acquired by a tactile sensor.

### *2.3. CNNs-Based Tactile Perception*

One recent approach for tactile object discrimination consists of the incorporation of modern deep learning-based techniques [48,49]. In this respect, the advantages of Convolutional Neural Networks (CNNs) such as translational and rotational invariant property enable the recognition in any pose [50]. A CNN-based method to recognize human hands in contact with an artificial skin has been presented in [44]. The proposed method benefits from the CNN's translation-invariant properties and is able to identify whether the contact is made with the right or the left hand. Apart from that, the integration of the dropout technique in deep learning-based tactile perception has been considered in [49], where the benefits of fusing kinesthetic and tactile information for object classification are also described, as well as the differences of using planar and curved tactile sensors.

### *2.4. Active Tactile Perception*

In spite of the good results obtained by existing solutions in tactile object recognition, one of the main weaknesses is that most of these solutions only consider static or passive tactile data [25]. As explained, static tactile perception is not a natural EP to perceive attributes like pressure or stiffness [27]. Pressure images only have information about the shape and pressure distribution when a certain force is applied [14]. On the other hand, sequences of tactile images also contain information about the variation of shape (in the case of deformable objects [32]), stiffness, and pressure distribution over time [31].

Time-series or sequential data are important to identify some properties. This approach has been followed in some works for material discrimination [51,52]. In [53], an EP is carried out by a robotic manipulator to get dynamic data using a 2D force sensor. The control strategy of the actuator is critical to apply a constant pressure level and perceive trustworthy data. For this purpose, a multi-channel neural network was used achieving high accuracy levels.

Pressure images obtained from tactile sensors have also been used to form sequences of images. In [3], a flexible sensor was used to classify food textures. A CNN was trained with sequences of tactile images obtained during a food-biting experiment in which a sensorized press is used to crush food, simulating the behavior of a mouth biting. The authors found that the results when using the whole biting sequence or only the first and last tactile images were very similar because the food was crushed when a certain level of pressure was applied. Therefore, the images before and after the break point were significantly different. For other applications, as it was demonstrated in [54], Three-Dimensional Convolutional Neural Networks (3D CNNs) present better performance when dealing with sequences of images than common 2D CNNs.

### **3. Materials and Methods**

The experimental setup is composed of a gripper with a tactile sensor. The gripper, the representation of 3D tactile information, and the 3D CNN are described next.

### *3.1. Underactuated Gripper*

The active perception method has been implemented using a gripper with two parallel underactuated fingers and a fixed tactile-sensing surface (see Figure 2). The reason for using an underactuated gripper is that this kind of gripper allows us to apply evenly spread pressure to the grasped objects, and the fingers could adapt to their shape, which is especially useful when grasping deformable or in-bag objects. In our gripper, each underactuated finger has two phalanxes with two (DOFs) *θ*1 and *θ*2, and a single actuator *θa* capable of providing different torque values *τa*. The values of the parameters of the kinematics are included in Table 1. A spring provides stiffness to the finger to recover the initial position when no contact is detected.

**Figure 2.** Gripper design (**left**) with two independent underactuated fingers and one fixed thumb with a tactile sensor covered with a silicone pad. The kinematic structure of the underactuated fingers (**right**) shows the five-bar structure with associated parameters and degrees of freedom (DOFs).

Two smart servos (Dynamixel XM-430 from ROBOTIS (Seoul, Korea)) have been used to provide different torques trough rigid-links, using a five-bar mechanical structure to place the servos away from the first joint. Thus, the relationship between *τa* and the joint torques (*τ*1, *τ*2) can be expressed as a transfer matrix T, and the computation of the Cartesian grasping forces (*f*1, *f*2) from the joint torques is defined by the Jacobian matrix **F** = **J**(*θ*)*τ*.

The computation of those matrices requires knowledge of the actual values of the underactuated joints. For this reason, a joint sensor has been added to the second joint of each finger. The remaining joint can be computed as the actual value of the servo joint, which is obtained from the smart servos. Two miniature potentiometers (*muRata* SV01) have been used to create a special gripper with both passive adaptation and proprioceptive feedback.

The dynamic effects can be neglected when considering slow motions and lightweight fingers. This way, a kinetostatic model of the forces can be derived in Equation (1) as described in [55]:

$$\mathbf{F} = \mathbf{J}(\boldsymbol{\theta})^{-T} \mathbf{T}(\boldsymbol{\theta})^{-T} \boldsymbol{\pi}. \tag{1}$$

Although the actual Cartesian forces could be computed, each object with a different shape should require feedback control to apply the desired grasping forces. In order to simplify the experimental setup, an open-loop force control has been used for the grasping operations, where the actuation (pulse-width modulation - PWM) of the direct current (DC) motors of the smart servos follows a slow triangular trajectory from a minimum value (5%) to a maximum (90%) of the maximum torque of 1.4 N.m of each actuator. The resulting position of each finger depends on the actual PWM and the shape and impedance of each contact area.

**Table 1.** Parameter values for the kinematic model of the gripper with underactuated fingers.

Finally, a microcontroller (Arduino Mega2560) has been used to acquire angles form the analog potentiometers and communicating with the smart servos in real time, with a 50 ms period.

### *3.2. Tactile Sensor*

A Teskcan (South Boston, MA, USA) sensor model 6077 has been used. This high-resolution tactile-array has 1400 tactels (also called taxels o sensels), with 1.3 × 1.3 mm size each. The sensor presents a density of 27.6 tactels/cm2 distributed in a 28 × 50 matrix. The main features of the sensor are presented in Table 2. The setup includes the data acquisition system (DAQ) (see Figure 1a), and the Tekscan real-time software development kit (SDK) (South Boston, MA, USA).

**Table 2.** Main features of the *Tekscan* 6077 tactile sensor.

A silicone pad of 3 mm has been added to the tactile sensor to enhance the grip and the image quality, especially when grabbing rigid objects. In particular, the Ecoflex*TM*00 − 30 rubber silicone has been chosen due to its mechanical properties.

### *3.3. Representation of Active Tactile Information*

As introduced in Section 1, a natural palpation EP to get information about the stiffness of an in-hand object is dynamic. In this respect, it seems evident that a robotic EP should also be dynamic so that the information acquired during the whole squeeze-and-release process describes the external and internal tactile attributes of an object.

The pressure information can be represented in multiple ways, commonly as sequences of tactile images. However, in this case, a more appropriate structure is in the form of 3D tactile tensors. An example of this type of representation is presented in Figure 1b, which is similar to MRI, except that, in this case, the cross-sectional images contain information about the pressure distribution at the contact surface for different grasping forces.

To show the advantages of 3D tactile tensors, sectioned tensors of the same sponge, with and without hard inclusions, are shown in Figure 3. The inclusions become perfectly visible as the grasping force increases.

**Figure 3.** 3D tactile tensors (**bottom**) of the same sponge with and without hard inclusions (**top**). The inclusions become visible as grasping force increases but cannot be seen in the picture of the sponge.

### *3.4. 3D TactNet*

When using 3D tactile information, it is necessary to control the applied forces to obtain a representative pressure-images from a certain object. For 3D CNNs, each tensor has information about the whole palpation process. On the other hand, when dealing with soft or shape-changing objects, this operation is more challenging using 2D CNNs, as a high amount of training data would be necessary, or selected data captured at optimal pressure levels, which also depends on the stiffness of each object.

In previous works, we trained and validated multiple 3D CNNs with different structures and hyperparameters to discriminate deformable objects in a fully-supervised collection and classification process [35]. Here, although the classification is still supervised, the grasping and data collection processes have been carried out autonomously by the robotic manipulator. According to the results of our previous work, the 3D CNN with the highest recognition rate, and compatible with the size of the 3D tensors read from our tactile sensor, was a neural network with four layers, where the first two were 3D convolutional, and the last two were fully connected layers. The network's parameters have been slightly modified to fit a higher number of classes and to adjust the new 3D tensor, which has a dimension of [28 × 50 × 51].

The architecture of this network, called TactNet3D, is presented in Figure 4. This network has two 3D convolutional layers (C = [3D conv1 , 3D conv2]) with kernels 16 × [3 × 5 × 8] and 32 × [3 × 5 × 8], respectively, and two fully connected layers (F = [fc3, fc4]) with 64 and 24 neurons, respectively. Each convolutional layer also includes a Rectified Linear Unit (ReLU), batch normalization with  = 10−5, and max-pooling with filters and stride equal to 1. In addition, fc3 incorporated a dropout factor of 0.7 to prevent overfitting. Finally, a softmax layer is used to extract the probability distribution of belonging to each class. The implementation, training, and testing of this network has been done using the Deep Learning Toolbox in Matlab (R2019b, MathWorks,Natick, MA, USA).

**Figure 4.** Architecture of TactNet3D, which is formed by four layers, the first two are 3D convolutional layers with kernel sizes 16 × [5 × 3 × 8] and 32 × [5 × 3 × 8], respectively, and two fully connected layers with 64 and 24 neurons, respectively.

### **4. Experimental Protocol and Results**

This section presents the procedure for the dataset collection and the experiments. The dataset is conformed by three subsets of data: Rigid, deformable, and in-bag objects, which are described in more detailed below. Similarly, four experiments have been carried out to show the performance of the method and compare the results of dynamic and static methods: Experiment 1 for rigid objects, experiment 2 for deformable objects, experiment 3 for in-bag objects, and experiment 4 for the whole dataset.

### *4.1. Dataset*

### 4.1.1. Collection Process

The dataset collection process consists of capturing sequences of tactile images and creating a 3D tactile tensor. For this purpose, the underactuated gripper holds an object and applies incremental forces while recording images over the whole palpation process. Each object, depending on its internal physical attributes, has a unique tactile frame for each amount of applied force. The dataset collection has been carried out by the gripper, recording 51 tactile frames per squeeze. This process is made by the two active fingers of the gripper, which are moved by the two smart servos in torque control mode with incremental torque references. Finally, 1440 3D tactile tensors have been obtained, for a total of 24 objects with 60 tactile tensors each. In Figure 1c, a grasping sequence is shown. The sequence at the top, from the left to the right, shows the grasping sequence due to the progressive forces applied by the underactuated gripper to the ball 2, and the sequence at the bottom, from the left to the right, shows the tactile images captured by the pressure sensor.

For machine learning methods, it is important to have the greatest possible variety in the dataset. In order to achieve this goal, the incremental torque is increased in random steps, so that the applied forces between two consecutive frames are different in each case. This randomness is also applied due to the intention to take a dataset that imitates the palpation procedure that could be carried out by a human, in which the exact forces are not known. Another fact that has been considered for the dataset collection process is that the force is applied to the object through the fingers of the gripper; therefore, non-homogeneous pressure is exerted on the whole surface of the object. Therefore, in order to obtain all of the internal features of the objects, multiple grasps with random positions and orientations of the objects have been obtained.

### 4.1.2. Rigid Objects

Eight objects of the dataset are considered as rigid because they barely change their shape when the gripper tightens them. The rigid dataset is composed of subsets of objects with similar features (e.g., the subset of bottles and the subset of cans) which are very different from each other. The subset of rigid objects is shown in Figure 5a.

**Figure 5.** Pictures of the 24 objects used in experiments. Rigid objects (**a**), from left to right: bottle of coke, energy drink can, mouse 1, mouse 2, bottle of ice tea, skate wheel, soda can, and bottle of water. Deformable objects (**b**), from left to right: ball 1, ball 2, sponge rough, sponge rough with inclusions, sponge scrunchy, sponge soft, sponge soft with inclusions, and sponge pipe. In-bag objects (**c**), from left to right: gears, mixed nuts, mixed washer, M6 nuts, M8 nuts, M10 nuts, rivets, and rubber pipes.

### 4.1.3. Deformable Objects

Another subset of the dataset is the deformable objects. This subset consists of eight objects that change his initial shape substantially when a pressure is applied over it but recover its initial shape when the pressure ends. This subgroup also has objects with similar elasticity (e.g., balls and sponges). The set of deformable objects is shown in Figure 5b.

### 4.1.4. In-Bag Objects

The last subset of objects included in the dataset is composed of plastic bags with a number of small objects. Bags are shuffled before every grasp, so that the objects in the bag are placed in different positions and orientations. Hence, the tactile images are different depending on the position of the objects. Another characteristic of this group is that in-bag objects may change their position randomly during the grasping process. As in the other subgroups, bags with similar objects have been chosen (e.g., M6, M8, or M10 nuts). In-bag objects are shown in Figure 5c.

### *4.2. Experiments and Results*

According to [45], three approaches can be followed to classify tactile data with 2D CNNs: training the network from scratch (method 1), using a pre-trained network with standard images and re-training the last classification layers (method 2), or changing the last layers by another estimator (method 3). The best results for each approach were obtained by TactNet6, ResNet50\_NN, and VGG16\_SVM, respectively. In this work, four experiments have been carried out to validate and compare the performance of TactNet3D against these 2D CNN structures considering only the subset of rigid objects, the subset of deformable objects, the subset of in-bag objects, and the whole dataset. The training, validation, and test sets to train the 2D CNN-based methods are formed using the individual images extracted from the 3D tactile tensors.

The performance of each method has been measured in terms of recognition accuracy. Each network has been trained 20 times with each subset, and the mean recognition rate and standard deviation for each set of 20 samples have been compared in Figure 6, where, for each experiment, the results of each method have been obtained using data from 1, 2, 5, 10, and 20 grasps of each object.

Moreover, representative confusion matrices for each method trained in subsets of rigid, deformable, and in-bag objects are presented in Figure 7. In contrast, the confusion matrices related to the whole dataset are presented in Figure 8. These confusion matrices have been obtained for the case in which each method is trained using data from two grasps to show the differences in classification performance.

**Figure 6.** Experimental results of the experiment with rigid objects (**a**), deformable objects (**b**), in-bag objects (**c**), and all objects (**d**). Error bars represent the standard deviation *σ* of each recognition rate distribution over a 20-sample testing process.

**Figure 7.** Confusion matrices of the methods, from left to right, TactNet3D, TactNet6, VGG16\_SVM and ResNet50\_NN, in experiments with rigid objects (**a**), deformable objects (**b**), and in-bag objects (**c**). All of the methods are trained using data from two grasps.

**Figure 8.** Confusion matrices of the methods, from left to right, TactNet3D, TactNet6, VGG16\_SVM and ResNet50\_NN, in the experiments with the whole dataset. All the methods are trained using data from two grasps.

### **5. Discussion**

Regarding the performance of TactNet3D in comparison with 2D CNN-based methods, the results shown in Figure 6 prove that the recognition rate of the first one is better than the latter in all the studied cases. For all kinds of objects, rigid, deformable, or in-bag, and all the amount of grasps used as training data, TactNet3D outperforms 2D CNNs.

In addition, the differences in classification accuracy are higher when the number of training data are lower, getting better results when training TactNet3D with one or two grasps than 2D CNNs with five or ten grips in some cases. Therefore, it is not only shown that the performance is better, but also the adaptability of TactNet3D as the amount of data needed to train the network is lower, which is especially interesting for online-learning.

In addition, in the misclassification cases, the resulting object class given by TactNet3D has almost indistinguishable physical features to those of the grasped object, unlike 2D CNNs, which may provide disparate results, as can be seen in the confusion matrices presented in Figures 7 and 8. Looking at some object subsets with similar physical features such as the sponges, the different bag of nuts or the cans, it can be observed that the output given by TactNet3D corresponds to objects form the same subset, whereas 2D CNN output classes of objects with different features in some cases (e.g., bottle of coke and M10 nuts in Figure 8 bottom left). This phenomenon is interesting from the neurological point of view of an artificial touch sense as 3DTactNet behaves more similarly to human beings' sense of touch. However, a broad study of this aspect is out of the scope of this paper and will be considered in future works.

### **6. Conclusions**

A novel method for the active tactile perception based on 3D CNN has been presented and used for an object recognition problem in a new robot gripper design. This gripper includes two underactuated fingers that accommodate to the shape of different objects, and have additional proprioceptive sensors to get its actual position. A tactile sensor has been integrated into the gripper, and a novel representation of sequences of tactile images as 3D tactile tensors has been described.

A new 3D CNN has been designed and tested with a set of 24 objects classified in three main categories that include rigid, deformable, and in-bag objects. There are very similar objects in the set, and objects that have changing and complex shapes such as sponges or bags of nuts, in order to assess the recognition capabilities. 3D CNN and classical CNN with 2D tensors have been tested for comparison. Both perform well with high recognition rates when the amount of training data are high. Nevertheless, 3D CNN gets higher performance even with a lower number of training samples, and misclassification is obtained just in very similar classes.

As future works, we propose the use of additional proprioceptive information to train multi-channel neural networks using the kinesthetic information about the shape of the grasped object, along with the tactile images for multi-modal tactile perception. In addition, the use of other dynamic approaches, such as temporal methods (e.g., LSTMs), for both tactile-based and multi-modal-based perception strategies, need to be addressed in more detail. Moreover, a comparison of new active tactile perception methods will be studied in depth.

**Author Contributions:** Conceptualization, F.P. and J.M.G.; software, F.P.; validation, F.P., J.M.G.-d.-G. and J.M.G.-d.-G.; investigation, F.P., J.M.G.-d.-G., and J.M.G.; data curation, F.P.; writing—original draft preparation, F.P., J.M.G.-d.-G., and J.M.G.; writing—review and editing, J.M.G.-d.-G. and J.M.G.; visualization, F.P. and J.M.G.; supervision, J.M.G.-d.-G.; project administration, A.J.G.-C. and J.M.G.-d.-G.; funding acquisition, A.J.G.-C.

**Funding:** This research was funded by the University of Málaga, the Ministerio de Ciencia, Innovación y Universidades, Gobierno de España, Grant No. DPI2015-65186-R and RTI2018-093421-B-I00, and the European Commission, Grant No. BES-2016-078237.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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