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**19**

*Brazil*

**Two-Step Sintering Applied to Ceramics**

Rubens Maribondo do Nascimento, Uílame Umbelino Gomes

During the process of sintering of ceramics, it is necessary to apply high temperature owing the high melting point of the raw materials. In general, a ceramist, wishing to produce a material with particular properties, must identify the required microstructure and then design processing conditions that will produce this required microstructure (Lutgard et al., 2003). One of the options to adapt the microstructure is a technique called two step sintering (TSS), this technique has been applied to the sintering of ceramic oxides to achieve full density without grain growth in final stage of sintering without loss densification (Chen & Wand, 2000). The two-step sintering process consists in to heat a ceramic body to a peak temperature (T1) to achieve an intermediate density and then the temperature is reduced to a dwell temperature (T2), which is held till full density is achieved. To succeed in two-step sintering, a sufficiently high relative density (70% or greater) needs to be achieved at T1 (Chen & Wang, 2000 & Chen, 2000). Once this critical density is reached, a lower temperature, T2, used for the isothermal hold will be sufficient to achieve full density. Difference between kinetics of grain boundary diffusion and grain boundary migration is used to obtain almost full dense, nanostructured ceramics. During the last stage of the sintering occur the grain growth in materials, this implicate in final properties, like mechanical resistance, density, ionic and electrical conductivity and others (Robert et al., 2003). The two-step sintering has been applied in many mateirals with the main goal of avoiding the grain growth in final stage of sintering, the results show the TSS is a technique efficient for it. Some application for two-step sintering are materials which need high density and small grain size, for example electrolytes of solid oxide fuel cell, as ceramics based in Y2O3 and CeO2, both with and without doppant (Wang et al., 2006; Wright, 2008 & Lapa, 2009). Others examples in which TSS are used also as nanostructural fosterite (Fathi, et al., 2009), alumina-zirconia ceramics (Wang et al., 2008), TiBaO3 and Ni-Cu-Zn Ferrite (Wang et al., 2006), ZnO (Shahraki et al., 2010). In this cases, the researchs are getting the

relative density higher than 97% and the size grains in level sub-micrometer.

During the process of the TSS the first step needs high temperature enough to achieve the critical diameter spherical (*dc*) of the core to become the crystallization, in this step the

**2. Mechanisms of two-step sintering**

**1. Introduction**

Gislâine Bezerra Pinto Ferreira, José Ferreira da Silva Jr,

and Antonio Eduardo Martinelli *Federal University of Rio Grande do Norte*


*3.2. Catalyst Characterization*

X-ray powder diffraction (XRD) patterns of these catalysts were measured by a Rigaku D/max2550VB3+/PC diffractometer (Rigaku International Corporation, Tokyo, Japan) using Cu K radiation at 40 KV and 40 mA. BET surface areas and average pore size of the catalysts were carried out by N2 adsorption at 77 K with a Micrometric ASAP 2020 apparatus (Micromeritics GmbH, Aachen, Germany). Before measurements, the samples were degassed at 150 for 12 h. Temperature programmed reduction (TPR) of the samples was done in a Micromeritics AutoChem II 2920 apparatus equipped with TCD (Micromeritics GmbH, Aachen, Germany). A 50 mg sample was heated from room temperature to 800 ◦C in 10% H2/90%N2. Scanning electron microscope (SEM) images were recorded using an FEI Quanta 200 instrument (FEI, Eindhoven, Netherlands). High-resolution transmission electron microscopy (HRTEM) images were recorded using a JEM-2100 TEM Field Emission Electron Microscope (JEOL GmbH, Freising, Germany).


**6.2 RFLP (Restriction Fragment Length Polymorphism)**

This analysis exploits variation in the occurrence of restriction sites in genomic sequences hybridizing to a cloned probe. Originally, RFLP analysis required Southern blotting and hybridization, making the method fairly slow and laborious. This technique is still used to generate ''anchor'' markers, used by many scholars to make consensus recombinational maps, though it is often implemented with the polymerase chain reaction (PCR) to generate the polymorphic fragments (Schulman, 2007).

Chung *et al*. (2006) evaluated levels of genetic diversity in USDA soybean germplasm (107 accessions), originated from six provinces in central China, using RFLP analysis. They detected significant genetic differentiation among the six provinces (mean *G*ST = 0.133). These results suggest that Chinese germplasm accessions from various regions or provinces in the USDA germplasm collection could be used to enhance the genetic diversity of US Cultivars.


*3.3. Slope Length*

Slope length indicates the physical characteristic of the slope in terms of its extension and magnitude. It helps determine the flow and highlight possible regions of groundwater retention [100]. Being a primary factor for groundwater contribution, slope length determines runoff strength and the groundwater flow direction. Slope length also indicates the amount of rainfall that would reach the groundwater table through infiltration [100,108,109]. Gentle slopes have greater infiltration capacity, displaying greater groundwater recharge potential and vice versa [100]. Slope lengths help understand the flow of precipitation as the water runs off from higher elevation towards the lower elevation. Considering that our study area is predominantly sloped in the northern parts due to mountain ranges, this is an important factor in this research.


**3. Metrology in protein quantification**

### **3.1 Backgrounds**

Protein is a complex molecule composed of one or more amino acid chains and constitutes a large part of the body's cells, tissues, and organs. This building block of the body has been shown to be a potential biomarker of human health risk assessment. Over the years, therefore, substantial efforts have been made for the development of bioanalytical approaches for accurate quantification of proteins. Monitoring protein levels is important to understand the biological mechanism and regulation of targeted proteins. In clinical practice, such approaches for protein quantification are used to compare biological samples from healthy and diseased patients, providing more accurate diagnosis of human disease. Thus, technologies for accurate quantification of protein biomarkers has been a high priority for many public health related institutions.

Nevertheless, measurement traceability, the essential element of metrology, is hardly established at all in protein quantification. There are several bioassay-based or instrumental methods for protein quantification that are frequently used. All these methods have to be calibrated to produce accurate results, but are not supported with reference calibration materials of metrological grades. Therefore, those methods work at best for comparison of relative amounts. Certified reference materials of proteins to provide measurement traceability to the SI unit of mole is yet to be developed. Our analytical capability is not as advanced as to quantify a whole protein accurately. However, quantification of appropriately reduced forms such as amino acids or peptides using isotopic dilution mass spectrometry (IDMS) should be a feasible approach to quantify proteins in a metrological manner. Of course, such approaches should also be supported by certified reference materials of the reduced forms for accurate calibration. The technical issues in the reduction approaches combined with the use of IDMS are described below.


**Abstract**

With a growing proportion of online activities on social networking sites on different mediums like Facebook, Instagram, Twitter, LinkedIn the requirement for personality prediction associated with this online mediated behavior has also increased significantly. The user generated content on social media can be effectively leveraged to record, analyze and predict personality through different psychological approaches like MBTI, Big Five, and DISC. Predicting personality has displayed an intrinsic influence in multifarious domains like career choice, political influence, brand inclination, customized advertising, improvising learning outcomes, recommender system algorithms and so on. The objective of this paper is to stipulate an overview of different strategies used by researchers to predict personality based on the social media usage and user generated content across prominent social media platforms. It was observed that the personality traits can be accurately inferred from user behavior reflected on social media through attributes like status posted, pictures uploaded, number of friends, groups joined, network density, liked content. As of now, Facebook followed by Twitter are the most prominent social media platforms for conducting the study however, the use other social media platforms like Instagram, LinkedIn are expected to increase exponentially for carrying out personality prediction study.

**Keywords:** digital twin, AI, personality traits, social networking, personality prediction


**8. Results and Discussion**

In this section the results from the ORC simulations are presented, analyzing how efficiently the integrated system operated and where room for improvement could be identified. Lastly, the exact figures of the vehicle's fuel consumption are shown, respectively, for each of the three different driving cycles and constitute the deciding factor for the selection of the optimum hybrid profile for the integrated powertrain. Several aspects that control the efficiency of the ORC system and how this system is combined with the IC engine to provide more power to the wheels are analyzed in this section. It is worth stating at this stage that the global assumptions considered for the ORC configuration of the HEV powertrain, namely, that even though the backpressure caused by the ORC's evaporator placed in the exhaust pipe, and also all of the extra weight imposed by ORC components to the vehicle system, deteriorate the overall performance and the fuel consumption of the vehicle to some extent. In this early evaluation study of ORC WHR systems for HEVs, it has been assumed that the effects of these are negligible, but should be borne in mind by the reader when considering the results.

The main target set for the results of the ORC analysis is that the maximum efficiency of the turbine and the pump does not have a percentage difference of higher than 15%. Moreover, the power output of the turbine is critical, as it is added to the power output of the IC engine to provide the total power of the powertrain, so it is required to be as high as possible. Another parameter that is tested in this analysis is the pressure rise in the pump which indicates the degree to which the pump ensures the successful provision of the required pressure level throughout the entire cycle. Finally, this section concludes with the presentation of the new BSFC map of the upgraded powertrain (with ORC). The distribution of the turbine and pump efficiency is illustrated in Figure 5a,b, respectively, over the tested engine load and speed range. In the figures it can be observed how the efficiencies of these two components fluctuate with engine speed and the BMEP, as well as how the contours of constant efficiency are formed inside the plot. The maximum value of turbine efficiency is 61% and it is obtained at part-load, while the maximum efficiency of the pump is 72% at higher BMEP values and engine speeds. Great effort was expended to reach the highest efficiencies of these two components and resulted in an efficiency improvement of almost 10% from the initial model.

**Figure 5.** Schematic view of the engine system model in GT-Power code: (**a**) turbine efficiency distribution; (**b**) pump efficiency distribution; (**c**) turbine power distribution; (**d**) EVAPORATOR energy rate; (**e**) Pump pressure rise; (**f**) BSFC percentage reduction distribution; (**g**) ORC efficiency distribution; and (**h**) fuel consumption per driving cycle.

Figure 5c represents the allocation of the turbine power output over the tested range of engine loads and speeds. It can be observed that the maximum power is obtained at full load and high engine speeds because the exhaust gases have greater temperature and mass flow at these speeds. The plot shows that the maximum power output that the turbine produces is almost 5 kW at high engine speed and load. At part-load (around the median of 6 bar) and lower engine speeds the turbine can produce typical values of around 2.5 kW. These values may seem small at first sight, but they proved to be high enough to cause a decrease in BSFC of almost 7% under the same operating conditions.

Figure 5d,e provides information about the evaporator energy rate and the pump pressure rise, respectively. Figure 5f illustrates the BSFC reduction contour plot superimposed on the engine map. It can be interpreted from the plot that the BSFC percentage reduction differs for different engine loads and speeds with the maximum values of the reduction being obtained at the lower BMEP values, reaching a maximum 12% decrease. In addition, as the BMEP values increase at the mid-load range (6 bar), the average percentage reduction is approximately 7%, which is higher than the set target. This region is the one of highest interest as the vehicle spends most of the driving time in this region, so the 7% decrease in fuel consumption can highly benefit the hybrid vehicle. At higher engine loads and BMEP values of 9 bar, the BSFC reduction percentage is lower, but always above 5%.

One final parameter that can be examined regarding the ORC system is the overall efficiency of the system that produced the above reduction in BSFC. The efficiency of the ORC system is defined as the fraction of the turbine power output over the evaporator energy rate, showing how efficiently the available energy is used inside the ORC WHR system. However, as for this investigation a four-cylinder 2000 cc engine was selected, the available energy rate is limited and the constraints inside the system are greater when compared to a larger (commercial-type) diesel engine (which can exploit the full potential of the system and reach higher efficiencies). In order to mitigate this drawback, the ORC system has been modified to give the higher possible efficiency.

Figure 5g shows the distribution of the ORC efficiency superimposed on the engine map. It can be seen that, overall, the ORC efficiency fluctuates at different engine speeds and loads, as well as the efficiency values are not very high. At lower engine speeds and BMEP values the ORC efficiency reaches a maximum of 27%, which is expected because the energy rate is fairly low, while at least the simulated turbine power has already reached near-maximum efficiencies even from lower loads and speeds. As the engine speeds and loads rise, the evaporator energy rate increases, resulting in the decrease of the efficiency because the turbine power is not increasing accordingly due to restrictions in the ORC system design and in the exhaust mass flow rate and temperature. The ORC efficiency reaches an average value of 10.5% at the engine speed of 3000 rpm and BMEP of 6.5 bar, which is an important region of the powertrain operating points. A further increase in engine speed and BMEP values show an efficiency of 9%, which is the minimum percentage obtained in this study and for the entire engine operating range. In general, the efficiency of the ORC is significant, but it is not reaching its full potential due to several inherent limitation of the smaller capacity powertrain. However, with this efficiency distribution, the ORC system is able to produce an average decrease in BSFC values of 7%, which is significant compared to a conventional HEV configuration.

Hereinafter, the overall results of the project are further interpreted in order to obtain a clear ORC WHR system characterization of its performance and benefit that it brings to the conventional HEV architecture. In addition, the discussion indicates how the different hybrid modes of the vehicle should be chosen in order to further decrease the fuel consumption of the vehicle; specifically, the discussion identifies which hybrid mode should be chosen in the different driving scenarios, such that the vehicle can operate more efficiently, while consuming the minimum amount of fuel.

During the various driving cycle tests that were performed in the engine simulation software, the vehicle model was run with and without the ORC configuration, so that the absolute difference between these two setups could be distinguished. Moreover, the three different driving cycle tests can validate the efficiency of the whole powertrain in different driving conditions, providing useful information about the hybrid mode which emerges in each case. Finally, these tests revealed possible areas where the ORC system could be further optimized for greater HEV vehicle benefit.

For the FTP-75 driving cycle test the obtained data from the analysis showed that the fuel consumption of the vehicle without the ORC system was 9.74 L/100 km. When the ORC system was included in the powertrain, the new fuel consumption was calculated to be 8.76 L/100 km, which is almost 1 L less per 100 km.

On the other hand, on the NEDC, the results showed that the fuel consumption without the ORC system was 8.79 L/100 km, while when the ORC system was included in the analysis the new fuel consumption was 8.07 L/100 km. The difference between these two values is almost 0.8 L/100 km.

The final driving cycle test was the US06, giving a fuel consumption of 10.57 L/100 km without the ORC system. When the ORC system was added in the analysis, the improved fuel consumption was decreased to 9.84 L/100 km, which is almost 0.7 L/100 km less. This driving cycle is more aggressive than the previous two, which is why the powertrain reached higher fuel consumption values in comparative terms to NEDC and FTP-75.

Moreover, the results from the driving cycle analysis can assist the understanding of how the ORC system cooperates with the engine under different loads, accelerations, and decelerations. The three selected driving cycles constitute a representative example of driving regulations in use today and which were required to validate HEV performance with the novel component (ORC WHR system).

In Figure 5h the bar charts illustrate the differences in fuel consumption between the original HEV engine and the ORC-equipped equivalent in order to minimize fuel consumption. Furthermore, the percentage difference is calculated, so that the absolute difference of the improved powertrain can be differentiated. This bar chart includes information for all three of the driving cycle tests and presents the exact amount of burned fuel per 100 km of driving. It can be noticed that in all of the driving cycle scenarios, the fuel consumption was decreased after the implementation of the ORC system. The largest reduction was found to be in the FTP-75 driving cycle and the calculation of the absolute percentage difference showed a 10% lower fuel requirement for 100 km. The NEDC required the least amount of fuel, as it is not as aggressive as the other two cycles. Moreover, in this driving cycle the ORC system improved the fuel consumption by 8%, which is a significant reduction if the strict European regulations are taken into consideration. Finally, the US06 driving cycle required the most fuel overall, but the ORC system managed to lower the fuel consumption of the vehicle by 7%, which resulted in a drop of fuel consumption below 10 L/100 km.

A closer look into the three driving cycles was required to reveal the driving strategy needed in order to maximize the powertrain performance in the electric hybrid vehicle. Taking into account the three major hybrid operation modes, which are the traction mode, braking mode, and coasting mode, the driver can be guided in how to handle the accelerator pedal, the braking pedal, and the clutch pedal to maximize the efficiency of the overall vehicle.

As far as the NEDC is concerned, it can be identified that it consists of numerous accelerations and decelerations, and the average cruising speed is 60 km/h. This driving cycle profile can exploit the braking and coasting operation modes of the hybrid configuration to gain advantages regarding the fuel consumption of the vehicle. During the decelerations the brake should be lightly applied and this will lead to the battery being charged by dissipating the braking energy and, hence, more power can be delivered to the wheels during the acceleration zones. Moreover, the driver can use the coasting mode, while no brakes or throttle are applied, before reaching the deceleration zones and prevent, in that way, the engine from burning fuel in a non-beneficial manner.

The FTP-75 driving cycle has a complicated profile of accelerations and decelerations, which makes it difficult for the driver to adopt the optimum driving strategy. However, as there are multiple fluctuations in the speed profile of this cycle, the driver can exploit the braking mode to charge the battery of the hybrid setup. This gained power can be used during steep acceleration gradients to assist the work of the IC engine, leading to less fuel being burned during the numerous accelerations. As long as there are no flat speed regions in this driving cycle, the coasting mode can hardly be applied and cannot influence a further reduction of the fuel consumption.

Finally, the US06 driving cycle results exhibit lower fluctuations in the speed profile compared to the FTP-75, which means that the coasting mode can be applied, offering significant benefits in fuel consumption reduction. This driving cycle pushes the vehicle to reach high speeds of more than 120 km/h, which undoubtedly increases the consumption of fuel of the vehicle. In order to counteract this, judicious use of the accelerator pedal would be recommended, along with use of the power of the electric motor to assist with significant accelerations. On the other hand, the braking mode can be used to gather the essential electric power in the battery and the coasting mode can be used when the car needs to decrease its speed without the strict application of the brakes.


**Lithium Niobiosilicate Glasses Thermally Treated**

Manuel Pedro Fernandes Graça and Manuel Almeida Valente

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50243


On each trial, participants chose via a single button press to view pictures hidden under flipped-over cards, arranged in four decks. Immediately after choosing from a particular "deck," an image was revealed that covered the entire screen for 2 s of passive viewing. The images were arranged probabilistically: each deck contained 26 (out of a total of 30) pictures from a particular category (e.g., pleasant), allowing pictures from other categories to be interspersed within each deck (two pictures from a secondary category, e.g., food; and one picture from each of the two remaining categories, e.g., unpleasant and neutral). After participants selected from a particular deck eight total times (corresponding to one task run), deck location of the four picture categories shifted. Thus, this task contained a "seeking" component, rather than a "working" component: throughout, participants needed to seek (and re-seek, once task contingencies changed) their preferred deck(s). In contrast to the explicit task, choice on the probabilistic task was meant to model more standard notions of choice; here, from a behavioral economic perspective, probabilistic choice can be thought to reflect intensity (i.e., preference for one category of image over another when there is no cost associated with the choice) (e.g., Mackillop et al., 2010; Moeller et al., 2013). We summed the total number of cards selected per picture category across four task runs.

### Food Craving Questionnaires State Food Craving

Current food craving was measured with the Food Craving Questionnaire-State [FCQ-S (Cepeda-Benito et al., 2000; Nijs et al., 2007)]. The FCQ-S consists of 15 items assessing: desire to eat, anticipation of positive reinforcement from eating, anticipation of negative reinforcement (reduction of negative affect) from eating, lack of control over eating, and

(physiological) hunger; higher scores reflect stronger food craving. Participants respond, using a Likert-type scale, how much each item is true of them right now. Demonstrating applicability of this questionnaire to the current study, FCQ-S scores were increased after exposure to food images in "high cravers" of chocolate (Meule et al., 2012), and were correlated with increased attention allocation (that interfered with response inhibition) to food vs. neutral pictures (Meule et al., 2014a).

### Trait Food Craving

Trait food craving was assessed with the Food Craving Questionnaire-Trait (FCQ-T) (Cepeda-Benito et al., 2000; Nijs et al., 2007). The FCQ-T consists of 39 items assessing: intentions/plans to eat, anticipation of positive reinforcement from eating, anticipation of negative reinforcement from eating, lack of control over eating, preoccupation with food, (physiological) hunger, emotions preceding or following food cravings or eating, environmental cues that may elicit food cravings, negative emotions including guilt experienced as a consequence of food cravings, and/or indulging such cravings; higher scores again indicate higher trait craving. Individuals are asked to respond, using a Likert-type scale, how much each item is true of them in general. Unlike the FCQ-S, and appropriately for a trait measure, the FCQ-T was unaffected by food deprivation (Meule et al., 2014b). In the current study, active CS had higher trait (but not state) food craving than OHC (**Table 1**).

### Urine Cortisol and Cortisone Measures

Twenty-four hour UFC and cortisone were quantified by gas chromatography-mass spectroscopy (GC-MS) as previously described (Yehuda et al., 2009a,b). The limit of sensitivity is 2 ng, the inter-assay coefficients of variations are <10%, and the normal range for UFC is 10–100µg/24 h. As to be expected, these measures were highest in active CS (**Table 1**).

## Statistical Analyses

### Effects of Disease Status on Food Picture Choice

We analyzed both choice tasks using a 4 (Picture Type: pleasant, unpleasant, neutral, food) × 3 (Disease Status: active CS, CS in remission, OHC) mixed analysis of covariance (ANCOVA), with the total number of presses (explicit task) or selections (probabilistic task) entered as covariates in the appropriate model to control for individual differences in response frequency. Significant interactions were followed by paired (within-group) and independent (between-group) comparisons that similarly controlled for response frequency. For the latter, a core interest was in testing for between-group linear contrasts (i.e., stepwise patterns in the effects as a function of Disease Status). In all ANCOVAs and follow-up comparisons, p < 0.05 was considered significant.

### Correlation Analyses

For both tasks, we examined associations between the food choice variables and the food vs. pleasant choice difference scores (food > pleasant) with the variables of interest specified below. The focus on this specific food > pleasant difference score follows from our prior results in individuals with cocaine use disorder, in which cocaine vs. pleasant (cocaine > pleasant) choice was particularly useful in predicting drug-relevant outcomes (Moeller et al., 2012a, 2013). Correlations were conducted across all participants and split by the three study groups. Due to the presence of some outliers across various measures, correlations were conducted using non-parametric (Spearman) analyses. In all correlational analyses, p < 0.01 was considered significant to protect against Type I error. After satisfying this initial criterion, however, we retained significant correlations if they achieved a significance level of p < 0.05 when accounting for covariates.

### **Between-task reliability and initial validity**

Because these tasks were new to a CS population, and because they included a new image category (food), we tested for intercorrelations between the explicit and probabilistic task scores. To provide evidence of construct validity of these tasks in CS, we also examined correlations between the task variables with BMI and self-reported food craving (state and trait).

### **Associations with cortisol levels**

To provide further attribution of our effects to GCs, we performed correlations between the two food-choice variables and the two food > pleasant choice scores with urine markers of cortisol inclusive of free cortisol and cortisone in the subsample of participants for whom these data were available.

### Effects of Covariates

Because medication use, BMI, and dysphoric symptoms (i.e., state and trait anxiety, perceived stress, and depression symptoms) differed between the groups (**Table 1**), these variables were covaried in subsequent ANCOVAs or multiple regressions as appropriate. Note that food craving and urine cortisol/cortisone were considered dependent variables, not covariates.

### RESULTS

### Effects of Disease Status on Food Picture Choice

Results of the mixed 4 (Picture Type) × 3 (Disease Status) ANCOVAs revealed main effects of Picture Type [F(3, 32) > 36.50, p < 0.001]: on both tasks, unpleasant choice was lowest, followed by neutral choice, and these were both lower than pleasant and food choice (all pairwise comparisons, p < 0.01). There were no main effects of Disease Status (both p > 0.096). Of greater interest, on the explicit task, the Picture Type × Disease Status interaction reached significance [F(6, 64) = 3.12, p = 0.010] (**Figure 2A**). Planned linear contrast analyses showed that active CS participants made fewer food choices than CS participants in remission, who in turn made fewer food choices than OHC (p = 0.021); linear contrasts for the other picture categories did not reach significance (all p > 0.074), indicating specificity to food choice. Follow-up within-group comparisons showed that both active CS and CS in remission pressed for fewer food pictures than pleasant pictures (both p < 0.012) but not neutral pictures (both p > 0.063). OHC showed an opposite pattern of results, pressing for food pictures more than neutral pictures (p = 0.003) but not pleasant pictures (p = 0.099). These collective analyses suggest that CS patients displayed reduced vigor for responding to food reward, particularly in those with active disease. Because the Picture Type × Disease Status interaction did not reach significance on the probabilistic task [F(6, 64) = 1.18, p = 0.34], no follow-up comparisons were performed.

### Correlation Analyses

### Between-Task Reliability and Initial Validity

Correlations did not reach significance at p < 0.01 across the whole sample. Nevertheless, after splitting the three groups, a positive correlation emerged between the food > pleasant choice variables on the two tasks in OHC (rs = 0.74, p = 0.001) but not in either of the CS groups (rs < 0.39, p > 0.19). In addition, higher food > pleasant choice, this time on the probabilistic task, positively correlated with higher state (rs = 0.82, p = 0.001) and trait (rs = 0.79, p = 0.001) food craving in active CS but not in CS in remission or OHC (rs < |0.38|, p > 0.28) (**Figures 2B,C**). This correlation suggests that food-related choice on this task is a valid metric of the desire to eat, both currently and in general, in a CS patient population with active disease.

### Associations with Cortisol Levels

Across all participants, food > pleasant choice on the explicit task negatively correlated with urine free cortisone (the higher the cortisone, the lower the food-related choice) (rs = −0.56, p = 0.004) (**Figure 2D**). A similar trend was observed for UFC (rs = −0.42, p = 0.041), which, although not reaching nominal significance, increases confidence in the cortisone effect.

### Effects of Covariates

The Picture Type × Disease Status interaction remained significant after controlling for medication use (p = 0.048), BMI (p = 0.018), and dysphoric symptoms (state and trait anxiety, perceived stress, and depression symptoms: p < 0.022). In addition, results of multiple regression analyses controlling for these same covariates showed that correlations were still detected between the food > pleasant scores on both tasks in OHC (p < 0.004), between food > pleasant probabilistic choice with state (p < 0.007) and trait (p < 0.032) food craving in active CS, and between food > pleasant explicit choice and urine cortisone in all participants (p < 0.025). Thus, it is unlikely that these potential confounds drove the current results.

### DISCUSSION

In this study, CS with active disease, CS with disease in remission, and OHC participants completed two behavioral tasks that tested the choice to view food pictures vs. non-food-related pleasant, unpleasant, and neutral pictures. We hypothesized that, as a model of chronic exposure to excessive GCs, individuals with CS (and especially those individuals with active disease) would make fewer food-related choices (and fewer pleasantrelated choices) than OHC. Results supported the hypothesis that individuals with higher endogenous GC levels, independent of BMI, exhibited decreased food (but not pleasant) choice on the explicit (but not probabilistic) task.

The primary result of this study was that, when task contingencies were explicit (i.e., fully certain) and choice required working (continuously pressing buttons) to view the respective stimuli, individuals with active CS chose to view fewer food images than individuals with CS in remission, who in turn chose to view fewer food images than OHC; thus, CS patients, especially those with active disease, were less apt to exert effort to view

food cues. Although at first blush this decreased food choice may be unexpected given that individuals exposed to chronic stress or GCs often consume more calories and/or eat less healthfully (Tomiyama et al., 2011; Groesz et al., 2012; Hoffman et al., 2012; Talbot et al., 2013; Aschbacher et al., 2014), it is critical to note that the "control" group was also overweight (i.e., not differing from the overall CS group on BMI, which was a goal in our recruitment; see Methods). Moreover, our results are consistent with prior work showing that chronic excessive exposure to GCs, here attributable to active CS but potentially also generalizable to chronic stress or chronic GC administration, is associated with blunted reward sensitivity in human- (Dillon et al., 2009; Goff et al., 2013) and animal models (Mateus-Pinheiro et al., 2014; Kvarta et al., 2015). Because in the current study results were specific to the food-choice task that required "working," it is possible that active CS patients have a higher threshold for perceiving food as reinforcing, potentially needing to consume more to achieve the same hedonic effect. An important parallel can be made to addiction literature, whereby reduced drug sensitivity is associated with increased or uncontrolled drug use [e.g., whether because of putatively drug-mediated adaptations to dopamine neurotransmission in drug addiction (Volkow et al., 1997, 2010; Martinez et al., 2009; Peechatka et al., 2015) or because of genetics that elevate susceptibility to developing

addiction later in life (Edenberg, 2007; de Wit and Phillips, 2012; Gubner et al., 2013)].

In further analyses, we correlated our behavioral choice results with self-reported food craving and in vivo GC levels; these analyses, respectively, were meant to validate these food-choice tasks as a model of food reward in a CS population and to provide a putative neurobiological correlate of the food-choice results. For the latter (neurobiological correlate), and corroborating our main between-group analyses above, correlations emerged between food-related choice on the explicit task and urine GCs: across all participants, 24-h urine cortisone correlated with explicit food-related choice, with similar albeit nonsignificant effects emerging for UFC. The underlying mechanism of these effects, which requires testing in further studies, could involve GC modulation of the dopamine system that alters the incentive salience of food cues (Borges et al., 2013; Yau and Potenza, 2013; Soares-Cunha et al., 2014). For the former (task validity), probabilistic (though not explicit) food-related choice positively correlated with state and trait food craving within active CS. Thus, although between-group effects in the current study were not observed on this probabilistic task, correlations with food craving help to validate these food-choice tasks as a model of food reward in active CS.

Limitations of this study include the following. First, the current study did not include a sample of normal-weight controls, and without this reference group we cannot definitively conclude whether results are primarily driven by decreased foodchoice in CS, increased food-choice in OHC, or both. Future studies will need to include a normal-weight control group. Nevertheless, our paramount concern in this initial study was to equate the groups on BMI, which indeed was achieved for the full CS sample. Although a BMI group difference emerged when splitting the groups by disease status (**Table 1**), it is important to note that our results were robust to statistical correction for BMI, indicating that this variable did not drive our results. Second, urine cortisol markers were unavailable in half the study sample. However, effects with cortisone were indeed observed using a relatively strict statistical threshold, reducing the potential for spurious effects; and, more importantly, in our main analyses we found stepwise effects as a function of remission status on behavioral choice in the entire sample. Third, we did not obtain self-report ratings of preference for the food stimuli prior to their use in the current tasks. However, main effects of Picture Type indicated that these food images were indeed palatable, such that all participants chose them for viewing more often than neutral images or unpleasant images. Fourth, it is possible that demand characteristics or participants' task-related motivation could have contributed to the lower food-related choice in CS participants. However, if results were driven by demand characteristics associated with the desire to reduce food intake and lose weight, we strongly suspect that OHC participants would have been similarly motivated to avoid choosing food images. If results were driven by reduced task-related motivation in CS (e.g., due to anhedonia), one could anticipate that choice for non-food-related rewarding stimuli (here, pleasant images)

### REFERENCES

would be reduced as well (Pizzagalli et al., 2009; Domschke et al., 2015; Fletcher et al., 2015), but this pattern of effects did not occur; moreover, dysphoric symptoms did not explain our findings.

In conclusion, chronic GC exposure from active CS was associated with reduced simulated food choice when compared with such choice in overweight individuals with intact GC functioning; CS patients in remission with remediated GC functioning showed an intermediate pattern of food choice, suggesting that that prior GC exposure may exert lasting effects on brain reward systems. Future studies can use neuroimaging and/or neurochemical approaches in CS, OHC, and (importantly) normal-weight controls to test for commonalities and/or differences in food choice and its neurobiological underpinnings among these study groups. Longitudinal designs can also test whether GC-mediated foodchoices have downstream consequences, such as increasing risk for weight gain and/or cardiovascular events. More broadly, beyond illuminating neurocognitive sequelae of CS (and possibly chronic stress), our findings can also inform the neurobiology of elevated and/or dysregulated cortisol as seen in aging, major depressive disorder, and/or Alzheimer's disease (Pereira et al., 2012; Notarianni, 2013; Gupta and Morley, 2014; Du and Pang, 2015; Furtado and Katzman, 2015).

### AUTHOR CONTRIBUTIONS

SM, RG, and EG designed research. LC, VC, and YL performed research. SM, IM, NW, RY, and EG analyzed data. SM and EG wrote the paper. All authors provided critical revisions of the paper.

### ACKNOWLEDGMENTS

This study was supported by grants from the National Institute on Drug Abuse (to SM: K01DA037452), the National Institute of Diabetes and Digestive and Kidney Diseases (to EG: K23DK082617), and the Mount Sinai Clinical and Translational Science Awards (CTSA) Program (UL1TR000067). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

to ethanol-induced locomotor stimulation. Drug Alcohol Depend. 127, 108–114. doi: 10.1016/j.drugalcdep.2012.06.015

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Moeller, Couto, Cohen, Lalazar, Makotkine, Williams, Yehuda, Goldstein and Geer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


*1.2. Pathology*

Obesity-Related Glomerulopathy

Physical compression of the kidneys from accumulation of adipose tissue around the organs emphasizes the possible role of visceral obesity in the development of renal disease. Deposition of extracellular matrix throughout the renal medulla is expanded and the tissue surrounding the ducts of Bellini at the vascular pole tends to prolapse. Increased numbers of interstitial cells and material rich in lipids and proteoglycans press the renal parenchyma towards the pole of the kidney resulting in the formation of round-shaped, enlarged kidney in obese subjects. Renal compression affects both vascular (the vasa recta) and tubular (the Henle's loops) elements causing activation of the RAS and increased sodium reabsorption [32–34].

The primary histologic features are few lesions of focal-segmental glomerulosclerosis, profound glomerulomegaly due to glomerular hyalinosis and fibrosis, as well as lipid accumulation in the glomeruli and adhesion to Bowman's capsule [35,36].  Ectopic lipid accumulation in the kidneys induces structural and functional changes of the mesangial cells, podocytes, and proximal tubular cells. Perivascular fat in the renal sinus appears to participate in vascular function, modifying the blood flow in the underlying arteries. Obesity increases renal mass and glomerular diameter. Podocytes need to enlarge their processes to cover an expanded area that cause podocyte detachment, loss in protein selectivity, formation of denuded areas that trigger matrix deposition, and podocyte damage.

Glomerular changes in obesity-induced renal injury are unmatched to those of diabetic nephropathy, due to the lower severity in the first in the mesangial space changes. Patients with diabetic nephropathy more frequently have albuminuria, proteinuria, and ESKD compared to obesity induced nephropathy where the results are slower and the progression to end renal disease is less frequent. Other causes of renal injury, apart from high fat intake, could include overexpression of Ang II with a consequent increase in proliferative factors such as transforming growth factor (TGF-β) and plasminogen activator inhibitor and insulinemia giving genesis to cell growth. Hyperfiltration because of sodium reabsorption, increase the blood flow to the kidney causing gradual glomerular wall sclerosis due to physical shear stress and a dangerous circle starts in which nephrons are injured leading to their apoptosis, sodium retention attenuates, while blood pressure increases to maintain sodium balance. Proteinuria of nephrotic range is rare among the obese, but albuminuria may exist [35,37]. These findings suggest that obesity-related renal damage should be defined as a special form of focal-segmental glomerulosclerosis slowly progressing to end stage renal disease. Patients with the metabolic syndrome have high prevalence of microvascular disease manifested as tubular atrophy, interstitial fibrosis, and arterial sclerosis [38].


**3. Structure of the** *H. pylori* **PZ**

*H. pylori* is considered to have a high genetic diversity due to the presence or absence of genes, which indicates the adaptation and coevolution of the pathogen within different populations worldwide [5, 6]. Recently, advances in DNA sequencing technology have enabled the comparison of hundreds of sequences from the genomes of related bacteria. These comparative genome analyses between species have led to a concept that encompasses all of the genetic content within a bacterial species. This concept is referred to as a "pan-genome", which is defined as the complete genetic repertoire of a specific species, composed of both the central

Comparative genomic studies of *H. pylori* began with the sequencing of strains J99 and 26,695. The *H. pylori* genome is approximately 1.6 million base pairs (1.6 Mb) in size, containing 1500 open reading frames (ORFs) and approximately 1500 protein-coding genes, with a G + C content of approximately 39%. Sequencing of these two strains showed that approximately 6–7% of the genes present in one strain are absent in the other. These genes are referred to as strain-specific genes, almost half of which are located in hypervariable regions within the *H. pylori* genome. Such regions are called plasticity zones (PZs), based on the variability of gene content between different isolates of *H. pylori*. These regions have a lower G + C content than the rest of the genome (34–35%), which suggests horizontal transfer [8]. The *cag* pathogenicity

Several studies have shown that *H. pylori* strains gain and lose genes during an infection, which suggests that a continuous genetic flow occurs, primarily within the PZ [4, 6, 10, 11]. The frequency of the PZ genes, notably *jhp0940*, *jhp0945*, *jhp0947* and *jhp0949*, has been studied among *H. pylori* isolates from different geographical regions, which has demonstrated that some of these genes are associated with disease. Currently, some of these PZs have roles in *H. pylori* pathogenesis and are included in one of the three categories of *H. pylori* virulence

After strains J99 and 26695 were sequenced, the first DNA microarray could be designed [13], allowing the first genotyping studies of diverse strains of *H. pylori* to be conducted*.* The first study compared 15 *H. pylori* isolates and observed that 362 genes (22%) were variable among the strains, which were called strain-specific genes. Subsequently, another study explored the genomic diversity of *H. pylori* isolates from children and adults who presented a single or a mixed infection (the presence of more than one strain). The number of variable genes in individuals with a mixed infection was found to be higher than that in individuals infected with a single strain. No difference was observed in the number of strain-specific genes between strains from children and adults [14]. However, a study of 56 strains from different geographical regions observed that 25% of the genomic content between these strains corresponded to strain-specific genes [6]. The genetic content of the strains has also been correlated with their pathogenesis in an animal model [4, 15]. Moreover, Romo-González et al. [16] identified and compared the differences in the genetic content of strains isolated from Mexican patients with non-atrophic gastritis, duodenal ulcers and gastric cancer. The authors observed that the core genome shared by these

genome and the accessory genome, also known as the dispensable genome [7].

island (PAI) is a region exhibiting this reduced G + C content [9].

78 Helicobacter Pylori - New Approaches of an Old Human Microorganism

classified by Yamaoka [12].

**2. Genotyping of** *H. pylori* **strains**

The PZ of strain J99 is the most studied and best characterised hypervariable region within the *H. pylori* genome. In the J99 *H. pylori* strain, the PZ is a continuous region from *jhp0916* to *jhp0961* [8]. Of the 48 ORFs that compose the PZ in strain J99, only six are present in strain 26695, and 10 are present in strain HPAG1. The PZs in strains J99 and 26695 are 45 and 68 kb in length, respectively. The PZ contains genes of unknown function as well as those encoding restriction-modification systems, topoisomerases, integrons, secretion systems, outer membrane proteins and transposons, the latter of which can be inserted into a genome without recombination. This insertion facilitates the propagation of transposons among bacterial species. These transposons are important because they can generate genomic deletions and rearrangements and even alter the expression of genes contiguous to their insertion site [17–19].

Kersulyte et al. [20] studied the nature of the PZ in the *H. pylori* genome by sequencing this locus in other strains and locating this area in strains that had been recently deposited in GenBank. The authors observed that the PZ is composed of inserted transposable elements

**Figure 1.** Plasticity zone of reference strain *Helicobacter pylori* J99 (*jhp0917-jhp0951*), figure adapted from [20]. This PZ is located between the *ftsZ* (*jhp0913*) gene and the 5S, 23S rRNA gene pair and is composed of TnPZ fragments: type 1 (*jhp0917-jhp0924*), type 1b (*jhp0944-jhp0951*; *jhp0925*, *jhp0926,* potentially part of *jhp0927-jhp0929* (*orfQ*) as well), and type 2 (*jhp0943-jhp0930,* potentially part of *orfQ* as well). Blue and green boxes indicate ORFs exclusive to J99 but not 26695 (another reference strain); black boxes indicate ORFs found in both strains; grey boxes indicate ORFs located between the *ftsZ* gene and the 5S, 23S rRNA gene pair that do not belong to the TnPZ.

that are flanked by discrete sequences of 5′AAGAATG and are each referred to as a TnPZ or "transposon, plasticity zone". Each TnPZ generally contains genes encoding type IV secretion proteins (tfs3), *xerT*, an ORF coding for a protein with helicase and DNA methylase domains and additional ORFs. Among the studied strains, several types of TnPZs with different gene arrangements or DNA sequence variations were observed and classified as type 1, type 1b and type 2 TnPZs. The genes *jhp0945*, *jhp0947* and *jhp0949* are located on a type 1b TnPZ, and *jhp0940* is located on a type 2 TnPZ in strain J99 (see **Figure 1**).

the gene [23]. In addition, no association was found between this gene and disease in isolates from Chinese patients with chronic active gastritis, duodenal ulcers or gastric cancer [24].

*Helicobacter pylori* Genes *jhp0940*, *jhp0945*, *jhp0947* and *jhp0949* are Associated with Gastroduodenal Disease

http://dx.doi.org/10.5772/intechopen.81290

A study in India observed less than a 20% prevalence of the *jhp0940* gene among *H. pylori* isolates [22]. However, in other isolates from India, the reported *jhp0940* prevalence was higher than 80% [25]. In Argentina, the frequency of the *jhp0940* gene was reported as 74.6%, and the frequency in isolates from individuals with chronic gastritis and peptic ulcer disease was similar, indicating no association with a specific disease [26]. However, Rizwan et al. [25] found no relationship between clinical results and the prevalence of *jhp0940* or the cag PAI in isolates from seven countries (India, South Africa, Japan, Costa Rica, Peru, France and Spain). The highest prevalence of this gene was observed in isolates from India, South Africa and

However, Yakoob et al. [27] and Gholizade Tobnagh et al. [28] reported an association between *jhp0940* and gastric ulcers and gastric cancer, respectively. In general, the prevalence of this gene is lower than that of *jhp0945*, *jhp0947* and *jhp0949* in isolates from the different geographical regions that have been studied. There appears to be a difference between the presence of PZ genes between isolates from children and adults. Romo-González et al. [29] observed that the prevalence of *jhp0940* in *H. pylori* isolates from children is higher than that in adults and that the presence of the specific gene patterns (including *jhp0940-jhp0945-jhp0947*-*jhp0949* and *jhp0940*-*jhp0947*-*jhp0949*) is more common in isolates from children than in adults. Therefore, these authors suggest that this locus is more integrated in the early stages of infection, which could contribute to the bacterial virulence and evolution of the infection. These authors found no association between the presence of these four PZ-associated genes and the presence of

Romo-González et al. evaluated the *in vitro* expression of four PZ-associated genes in isolates from children and adults and observed the expression of only *jhp0945*, *jhp0947* and *jhp0949*, without significant differences among the expression of these three genes between the isolates obtained from children and adults. However, a correlation was observed among the expres-

A possible explanation for the discordant results regarding the prevalence of *jhp0940* in different geographical regions is that geographical diversity exists in the sequence of this gene that does not allow for its identification with a single pair of primers. Another potential reason is that the absence of this gene in adult isolates is due to the loss of this gene during infection.

Studies on the prevalence of *jhp0945*, *jhp0947* and *jhp0949* among *H. pylori* isolates of different geographical origins have proposed that these genes are disease markers, suggesting that they could play a role in the pathogenesis of *H. pylori* [12]. This relationship was discovered in Costa Rica, where the prevalence of 21 ORFs present in the PZ of the J99 strain (*jhp0914-jhp0961*)

**5. The PZ-associated genes** *jhp0945***,** *jhp0947* **and** *jhp0949* **and their**

France, in contrast to isolates from Spain, where less than 10% contained this gene.

*cagA*, cag PAI or *dupA*.

sion of these three genes (unpublished data).

**relationship with gastroduodenal disease**


**5. Synthesis by slow evaporation at room temperature**

It is the simplest method, but it is suitable only for certain materials. The preparation protocol consists in weighing the reagents in the desired proportion and adding water or an organic base in a ratio that should be determined experimentally. Then the solution must be heated to reach the saturation.

This method takes a few hours to obtain crystals as in the synthesis of NH4H2AsO4 or NH4H2PO4, and for other materials, it needs a few days. The condensation of ions

**17**

**Figure 11.**

*location of the alkali metal cations [34].*

*Synthesis Methods in Solid-State Chemistry DOI: http://dx.doi.org/10.5772/intechopen.93337*

the laboratory.

by different types of oxygenated bridges, HO−

in aqueous solution forms entities in which cations, identical or different, are linked

The following phosphate KCdHP2O7.2H2O (**Figure 11**) [34] has been obtained by slowly evaporation at room temperature. The method consists in the preparation of a saturated solution containing the reagents in the desired proportion. The reagents are dissolved in water then heated at 100°C for a few minutes to have a saturated solution. The solution obtained is transferred into a petri dish and left in a corner in

The hybrid materials (organic base+ salt) may be synthesized by slow evaporation at room temperature. The best solvent can be used is the water. It is possible to add other solvent with water such as alcohol like in the preparation of (C7H7N2)2[CuCl4].2H2O [35]. In this type of materials, the organic part and the inorganic part are linked via hydrogen bond and π▬π bond as shown in **Figure 12**. Another example may be sited in this section, the synthesis and the crystal structure of (C3H6N3)4Bi2Cl10 [36] which was grown by a slow evaporation of an aqueous solution of bismuth chloride (BiCl3) and 3-aminopyrazole (C3H5N3) in molar amount (1: 2) with a small excess of hydrochloric acid (HCl). The single

Many organic compounds also have been crystallize by slow evaporation at room

temperature such as alkyl-2-(2-imino-4-oxothiazolidin-5-ylidene) acetate [37], 3,4-*cis*-disubstituted pyrrolidin-2-ones [38] ethyl 2-(4-chlorophenyl)-3-cyclopentyl-4-oxo-1-propylimidazolidine-5-carboxylate (C20H27ClN2O) [39]. The obtaining

*Perspective view of the structure of KCdHP2O7.2H2O showing the hydrogen bonds (dashed lines) and the*

crystals were obtained after 7 months of slow evaporation (**Figure 13**).

important factor in this method. In fact, the choice of pH must be chosen to coprecipitate the various reagents simultaneously. For example, in the ternary system H2O-NH3-As2O5, the adjusting of the pH at 4.3 allows the formation of NH4H2AsO4, and for a basic value of pH, the preparation gives a second phase: (NH4)2HAsO4.

or O2−. Thus, the value of pH is a very

#### **Figure 10.** *Structures of olivine-, Na2CrO4-, and spinel-like LiCoXO4 (X = P, As) [29].*

#### *Synthesis Methods in Solid-State Chemistry DOI: http://dx.doi.org/10.5772/intechopen.93337*

*Synthesis Methods and Crystallization*

dral 8a sites [29] (**Figure 10**).

chemical properties.

from the hexagonal close packing into the cubic close packing of the spinel structure [29]. Upon high pressure conditions (6 GPa, 1173 K) olivine-like LiMAsO4

randomly occupy 16d octahedral positions and the As+5 cations occupy the tetrahe-

Since 2006, the prediction of the structure at high pressures became an area of intense activity thanks to the development of the new computer program USPEX [30] by Oganov et al. The code was used with success to predict many new crystal structures, and the results were confirmed by the synthesis of same predicted materials such as Na-Cl system: Na3Cl, Na2Cl, Na3Cl2, Na4Cl3, NaCl3, and NaCl7 [31] and H-Cl system: H2Cl, H3Cl, H5Cl, and H4Cl7 [32, 33]. This result allows the discovery of new generation of materials where the core electrons can participate in the formation of chemical bonds. Thus, obviously, we will have very interesting physical and

It is the simplest method, but it is suitable only for certain materials. The preparation protocol consists in weighing the reagents in the desired proportion and adding water or an organic base in a ratio that should be determined experimentally.

This method takes a few hours to obtain crystals as in the synthesis of NH4H2AsO4 or NH4H2PO4, and for other materials, it needs a few days. The condensation of ions

and M+2 ions

(M = Fe, Co, Ni) transforms to spinel-like compounds where Li+

**5. Synthesis by slow evaporation at room temperature**

Then the solution must be heated to reach the saturation.

*Structures of olivine-, Na2CrO4-, and spinel-like LiCoXO4 (X = P, As) [29].*

**16**

**Figure 10.**

in aqueous solution forms entities in which cations, identical or different, are linked by different types of oxygenated bridges, HO− or O2−. Thus, the value of pH is a very important factor in this method. In fact, the choice of pH must be chosen to coprecipitate the various reagents simultaneously. For example, in the ternary system H2O-NH3-As2O5, the adjusting of the pH at 4.3 allows the formation of NH4H2AsO4, and for a basic value of pH, the preparation gives a second phase: (NH4)2HAsO4.

The following phosphate KCdHP2O7.2H2O (**Figure 11**) [34] has been obtained by slowly evaporation at room temperature. The method consists in the preparation of a saturated solution containing the reagents in the desired proportion. The reagents are dissolved in water then heated at 100°C for a few minutes to have a saturated solution. The solution obtained is transferred into a petri dish and left in a corner in the laboratory.

The hybrid materials (organic base+ salt) may be synthesized by slow evaporation at room temperature. The best solvent can be used is the water. It is possible to add other solvent with water such as alcohol like in the preparation of (C7H7N2)2[CuCl4].2H2O [35]. In this type of materials, the organic part and the inorganic part are linked via hydrogen bond and π▬π bond as shown in **Figure 12**.

Another example may be sited in this section, the synthesis and the crystal structure of (C3H6N3)4Bi2Cl10 [36] which was grown by a slow evaporation of an aqueous solution of bismuth chloride (BiCl3) and 3-aminopyrazole (C3H5N3) in molar amount (1: 2) with a small excess of hydrochloric acid (HCl). The single crystals were obtained after 7 months of slow evaporation (**Figure 13**).

Many organic compounds also have been crystallize by slow evaporation at room temperature such as alkyl-2-(2-imino-4-oxothiazolidin-5-ylidene) acetate [37], 3,4-*cis*-disubstituted pyrrolidin-2-ones [38] ethyl 2-(4-chlorophenyl)-3-cyclopentyl-4-oxo-1-propylimidazolidine-5-carboxylate (C20H27ClN2O) [39]. The obtaining

#### **Figure 11.**

*Perspective view of the structure of KCdHP2O7.2H2O showing the hydrogen bonds (dashed lines) and the location of the alkali metal cations [34].*

#### **Figure 12.**

*Projection of the structure of (C7H7N2)2[CuCl4].2H2O showing the alternating stacking of the organic and inorganic layers connected through hydrogen bonds. The face-to-face π▬π stacking between parallel organic molecules is noteworthy with a centroid-centroid distance of 3.968 (3) [35].*

**Figure 13.** *Bi2Cl10 octahedron surrounded by aminopyrazolium entities, showing the H-Cl contacts [36].*

**19**

*Synthesis Methods in Solid-State Chemistry DOI: http://dx.doi.org/10.5772/intechopen.93337*

the hottest region to the coldest region.

**6. Flux method**

**7. Crystal growth**

of single crystals depends in the case on the nature of the organic compounds and solvent. The presence of heterocyclic group in the organic molecule promotes the

Usually, the flux method is used to grow materials as single crystals [40]. The main objective of this method is to decrease the crystallization temperature. This technique has been used to grow high melting phosphate crystals, arsenates, oxides, minerals, and ceramic crystals which cannot be obtained by the conventional solid-state method (Section 1). In this growth technique, the basic materials (solute precursors to crystallize) are reduced to a liquid form in an appropriate flow and the growth process starts when the solution reaches critical supersaturation. The resulting supersaturation and crystal growth are achieved by flow evaporation, solution cooling, or a transport process in which the solute is caused to flow from

The understanding of the phase information about the materials is indispensable to optimize the crystal growth of multi-component system. Practically, binary or other above compositional systems can be easily described using the phase diagram. The phase diagram shows the crystallization or solidification within a material as a function of the material composition (% elements) and material temperature. The solvent can be a single element, compound, or combination of chemical compounds. The solute is an element or a compound with a melting point generally higher than that of the solvent but in principle it is quite possible to grow crystals

from eutectic systems in which the "solvent" has a higher melting point.

lites connect to each other, and the size increases [41–43].

may proceed in the light of several different ways:

1.Vapor-solid (condensation)

2.Solution-solid (precipitation)

4.Solid A-Solid B (transformation)

3.Melt-solid (freezing)

For more clarification, taking the example of LiPr(PO3)4 [40]. The material has been synthesized as a single crystal by the means of flux method. A mixture of Li2CO3 and Pr6O11 with stoichiometric ratio was dissolved in an excess of phosphoric acid H3PO4 (85%). The mixture was heated to 200°C for 12 h, then to 325°C for 5 days. Finally, the mixture was cooled slowly to room temperature. The single crystal was separated from the excess phosphoric acid by washing in boiling water [40].

When a solid is heated, some physical and chemical changes sintering, melting, and thermal decomposition can be observed. Sintering process results from crystal growth at the contact area between adjacent crystallites. As to conclude, the crystal-

In high temperatures, as a result of ion movements, melting occurs. The ordered lattice array is replaced by the short-range order of the liquid state. Crystallization

crystallization by the formation of hydrogen and π▬π bond.

of single crystals depends in the case on the nature of the organic compounds and solvent. The presence of heterocyclic group in the organic molecule promotes the crystallization by the formation of hydrogen and π▬π bond.


*3.3. Phosphorylation of IRS1 at Age-Specific Ser Residues with the Activation of Downstream Kinases Is Linked to Age-Related Memory Deficits*

Although memory decline associated with physiological aging can occur naturally, in contrast to pathogenic memory deficits, whether the onset processes of age-related memory impairment are related to the mechanisms underlying pathogenic memory deficits in T2DM and AD remains unclear. We examined the modification of IRS1 at Ser residues and the activity of downstream factors in the hippocampi of 21-month-old mice (aged mice). Aged mice displayed memory impairments without an increase in blood glucose and plasma insulin levels, despite an increase in body weight (Figure 3A,B, Figure S2C). Meanwhile, the Aβ40 and Aβ42 levels in the T-PER fractions of the hippocampus were comparable between young and aged WT mice (Figure 3C).

In common with DIO mice, the activation of mSer307, but not of mSer1097, on IRS1 accompanied by p70S6K activation and unchanged AMPK was observed with increased phosphorylation of IRS1 at mSer612 and mSer632/635 in the hippocampi of aged mice (Figure 3D, Figure S4E). Nonetheless, unlike in DIO mice, the basal phosphorylation levels of Akt and GSK3β prominently increased in the hippocampus of aged mice (Figure 3E, Figure S4F); however, the basal activities of aPKC ζ/λ, and JNKs remained unchanged, similar to those in DIO mice (Figure S3C and S5C). These results suggest that aging causes the concomitant phosphorylation of hippocampal IRS1 at mSer307, mSer612, and mSer632/635 with the activation of canonical downstream kinases, which may be associated with Aβ-unrelated physiological decline in memory function.

**Figure 3.** Age-related memory deficits accompanied by increased phosphorylation of IRS1 Ser sites in the hippocampus. (**A**) The graphs of body weight and blood glucose levels in young (12 weeks of age, n = 7 mice per group) and aged (84 weeks of age, n = 7 mice per group) wild-type (WT) mice. (**B**) Evaluation of learning memory functions in young WT mice (n = 16 mice per group) and aged WT mice (n = 18 mice per group) using the water T-maze test. (**C**) Quantitative analysis of T-PER-extractable Aβ40 and Aβ42 levels in the hippocampi of young (12 weeks of age, n = 6 biologically independent samples) and aged (84 weeks of age, n = 6 biologically independent samples) WT mice using the human/rat/mouse β amyloid (1–40 and 1–42) ELISA. (**D**) Western blot analysis of phosphorylated insulin receptor substrates 1 mouse Ser307 [p-IRS1 (mSer307)], p-IRS1 (mSer612), p-IRS1 (mSer632/635), p-IRS1 (mSer1097), IRS1, and ß-tubulin in the hippocampi of young WT mice (12 weeks of age, n = 6 biologically independent samples) and aged WT mice (84 weeks of age, n = 6 biologically independent samples). Quantitative analysis of the phosphorylation of IRS1 at mSer307, mSer612, mSer632/635, and mSer1097 normalized to total protein. (**E**) Western blot analysis of phosphorylation levels of Akt Ser473, p70S6K Thr389, AMPK Thr172, and GSK3β Ser9 as well as total protein levels of Akt, p70S6K, AMPK, GSK3β, and ß-tubulin in the hippocampi of young WT mice (12 weeks of age, n = 6 biologically independent samples) and aged WT mice (84 weeks of age, n = 6 biologically independent samples). Arrows indicate the p-IRS1 mSer612-corresponding band (lower band) and the p-IRS1 mSer632/635-corresponding band (lower band) in (**D**). Quantitative analysis of the phosphorylation of Akt Ser473, p70S6K Thr389, AMPK Thr172, and GSK3β Ser9 normalized to the respective total protein contents. Results are presented as mean ± SEM, \* *p* < 0.05; \*\* *p* < 0.01.


**6.1 Quantitative data analysis**

Quantitative data were obtained from primary and secondary data discussed above in this chapter. This data analysis was based on their data type using Excel, SPSS 20.0, Office Word format, and other tools. This data analysis focuses on numerical/quantitative data analysis.

Before analysis, data coding of responses and analysis were made. In order to analyze the data obtained easily, the data were coded to SPSS 20.0 software as the data obtained from questionnaires. This task involved identifying, classifying, and assigning a numeric or character symbol to data, which was done in only one way pre-coded [9, 10]. In this study, all of the responses were pre-coded. They were taken from the list of responses, a number of corresponding to a particular selection was given. This process was applied to every earlier question that needed this treatment. Upon completion, the data were then entered to a statistical analysis software package, SPSS version 20.0 on Windows 10 for the next steps.

Under the data analysis, exploration of data has been made with descriptive statistics and graphical analysis. The analysis included exploring the relationship between variables and comparing groups how they affect each other. This has been done using cross tabulation/chi square, correlation, and factor analysis and using nonparametric statistic.