**2. The maintenance of a weight loss**

Several criteria for "successful weight loss maintain" have been developed during the last 10 years. Successful weight loss managers, according to Avnell, are "persons who have consciously dropped at least 10% of their body weight and maintained it off for at least one year." A persistent weight reduction of roughly 5–10% of baseline body weight, according to Barners, indicates a high level of effectiveness. The 2013 American Heart Association (AHA), American College of Cardiology (ACC), and Term of Service (TOS) Guideline for the Management of Overweight and Obesity in Adults also recommends this aim as shown in **Figure 2** [15].

The preceding criteria all agree that good weight management does not need a huge weight decrease, but rather a modest 5–10% reduction. This level of weight reduction, from a clinical standpoint, dramatically lowers the risk of type 2 diabetes in susceptible people and removes the majority of the additional hazards linked to obesity [16]. Furthermore, even minor weight reduction has been shown to enhance mental wellbeing, including happiness, self-image, and bingeing [17].

Teixeria et al. [18] definition adds two more weight-maintenance markers. First and foremost, fat loss should be planned. Such a parameter is critical since various studies have found that inadvertent weight loss is widespread and might have unique causes and impacts than deliberate weight reduction. Weight loss must be sustained for at least one year. This criterion was established as an acceptable goal for study into the elements that enable people to sustain their weight loss. Nevertheless, the term "success" might imply a considerably extra duration of weight management, ideally throughout the rest of one's life [19].

### **Figure 2.**

*Weight loss and weight control scope of the study Weight loss is attributed to a reduction of at minimum 10% of benchmark body mass, while weight stabilization is defined as maintaining a body mass of at minimum 10% less than initial body mass for at minimum one year.*

### **2.1 Physical activity**

The quantity of energy exerted vs the quantity of energy eaten influences weight fluctuations. As a result, weight gain will occur if the metabolic rate stays low but food levels of consumption are excessive. Some researchers suggest that decreases in regular exercise, both at work and in leisure, may have played a significant part in the rising obesity prevalence over the previous 30 years [20].

In addition, several epidemiological data imply that physical activity plays a key role in weight growth. Low levels of self-reported recreational physical activity were related to three-fold increased risk of substantial weight gain in males and even a four-fold larger hazard in women, according to [21] who used data from the National Health and Nutrition Examination Survey (NHANES) and he found that in a retrospective study of 34,079 middle-aged women (mean: 52.2 years), the chance of increased weight over three years was 11 percent higher in women who engaged in fewer than 7.5 metabolic equivalents (MET).

For overweight people who are healthful, enhanced physical activity and exercise are part of a complete weight-loss plan. The capacity to create and maintain an exercise regime is one of the strongest indicators of results of this case in the therapy of overweight [22]. Exercise and fitness regimens that are required to satisfy the forces' physical preparedness demands overall, and for weight management, in particular, can be boosted by the presence of engaging in physical activity in army facilities. The intensity, length, frequency, and kind of physical exercise for a specific individual will be determined by pre-existing medical issues, past activity levels, physical constraints, and personal preferences. Individuals who have more than one of the aforementioned mitigating situations may need to be referred for extra expert examination [23]. Physical activity has several advantages that can be experienced

*Inability to Understand the Complexity of Maintaining Weight Loss and the Complications DOI: http://dx.doi.org/10.5772/intechopen.105362*

even if weight loss is not achieved. One of the advantages, an increase in high-density low-density lipoprotein, has been demonstrated to be achievable with a minimum of 10–11 h of cardiovascular exercise each month as shown below in **Figure 3** [24].

### **2.2 Alterations of habits and attitudes**

By use of habit and varying levels of intensity in weight control is based on a weight of information indicating people develop or stay obese as a consequence of adjustable routines or activities, and that weight reduction and maintenance may be achieved by modifying those tendencies [25]. The main aim of psychological weight-control techniques is to promote a healthy lifestyle and reduce calorie intake via changing dietary patterns. Cognitive treatment can be given to a single person or a group of people. Individuals typically participate in 15–30 weekly sessions lasting 2–3 h each, with a weight-loss objective of 2–3 pounds each week. Behavioral techniques were formerly used as hold therapies to just change eating patterns and lower calorie intake. Nevertheless, these methods have lately been applied to induce weight reduction and as an element of routine maintenance in addition to low diets, nutritional support therapy, proper nutrition, fitness programs, supervision, pharmaceutical medications, and social protection as shown in **Figure 4** [26].

### **2.3 Input efforts and self-control**

One of the pillars of behavioral interventions is the identity of nutritional intake and physical exercise, which allows the client to establish a sense of social responsibility. Participants are advised to keep a usual dietary diary in which participants note what they did eat, how very much patients did eat, where and when they did

**Figure 3.** *Physical activity guideline.*

### **Figure 4.** *Engagement in a new way of life.*

eat it, and the environment in which they started eating it. Individuals may also be required to keep a log sheet of their physical activity. Self-monitoring of food intake is frequently linked to a rapid decrease in energy consumption and, as a result, losing weight. This decrease in food intake is thought to be the consequence of greater food consciousness and/or fear of what the nutritionist or nutritional therapist may say about the participant's eating habits. Food diaries are also used to discover internal and social variables that lead to excess eating, as well as to choose and implement appropriate weight-loss techniques for the person [27].

The same might be said for regular exercise tracking, even though the little study has been done in this area. Self-control also allows therapists and clients to assess which approaches are effective and how changes in sleep and eating habits or exercise affect weight reduction [28].


**2.1 Introduction**

Jordan is an arid to semi-arid country with land area of 92,000 sq km, located to the east of the Jordan River. Jordan's topographic features are variable. A mountainous range runs from the north to the south of the country. To the east of the mountain ranges, ground slopes gently to form the eastern deserts, to the west ground slopes steeply towards the Jordan Rift valley. The Jordan Rift valley extends from lake Tiberias in the north, at ground elevation of –220 m, to the Red Sea at Aqaba. At 120 km south of lake Tiberias lies the Dead Sea with water level at approximately –405 m. The southern Ghors and Wadi Araba, south of the Dead Sea, form the southern part of the Rift Valley. To the south of Wadi Araba region lies a 25 km coastline which stretches along the northern shores of the Red Sea. Due to the variable topographic features of Jordan, the distribution of rainfall varies considerably with location.


**3. Conclusion(s)**

Dengue is still a leading public health problem. The diagnostic tests must improve and be standardized in all research and diagnostic laboratories with the use of technology. This will allow having comparable results in different studies and reference centers that contribute to the knowledge development needed to understand transmission mechanisms and DENV propagation and setting preventive and appropriate control measures as well as developing new vaccines and antivirals helping to control this disease.


**4.2. Nonlinear resonance prevention**

16 Nuclear Power – Practical Aspects

Subsequent Restrike Voltages.

**design**

**Figure 12.** Oscillation Voltage (VOSC) Initiated by a Strike or Restrike Can Be Superimposed to

lessons learned from this event and their recommended preventive measure.

**4.1. Bus configuration and fault area isolation**

**4. Lesson learned and important aspects of NPP power system protection**

Events like the "318 Event" were seldom caused by one single reason. It can be seen from the above discussion that Taipower 3rd NPP was under multiple stresses before the event and there were mutiple mechanisms for the generation, amplification, and transfering of overvoltages which, combined with the operation practices and equipment history, eventually led to the explosion of CB#17 and total blackout of the NPP. Below are the key

The "318 Event" was essentially triggered by a single equipment failure but leading to a complete blackout of the power plant. There are 2 key lessons learned from this event: (1) Among all the scenarios considered in this Chapter, nonlinear resonance is the most difficult one to be detected. In view of the potential hazard it could cause, precautionary measure must be taken to prevent it from initiating.

The first step is to prevent motor-generating effect from ever occuring (thus removing the key source of initiation.) As explained above, the essential conditions of motor-generating effect are (1) rotating motor with large inertia, (2) large capacitor bank in an isolation system to support the terminal voltage. Since a rotating motor with large inertia can not be stop immediately, the focus is to remove the capacitive support. In the case of Taipower 3rd NPP, the capacitive support came from the long transmission line who were tripped only on the remote end. It is recommended that Direct Transfer Trip (DTT) function be implemented for transmission line protection to greatly reduce the risk of motor generating effect.

The second step is to ensure effective grounding of transformer neutrals as designed. Due to the objective of minimizing short circuit current, the neutral groundings in NPP are usually multi-configured: arrestor grounded under normal condition and direct grounding when in islanding operation. The switching from one grounding scheme to another often requires manual operation and this increases the risks of leaving the islanded system ungrounded as well as nonlinear resonance of power. Proper interlock or checking mechanism should be implemented to ensure proper grounding as designed at all times.


*Supporting open minds since 2005*

Insights Into Drug Resistance in Staphylococcus aureus http://dx.doi.org/10.5772/intechopen.87320 Edited by Amjad Aqib

Part of IntechOpen Book Series: Infectious Diseases, Volume 10 Book Series Editor: Shailendra K. Saxena

#### Contributors

Nada Khazal Kadhim Hindi, Mohammed Malih Radhi, Fatima Malik AL-Rubea, Rusull Hamza Kh. AL-Jubori, Muhammad Farhab, Amjad Islam Aqib, Muhammad Shahid Khan, Muhammad Tahir Aleem, Shakseema Shaukat, Ayesha Qadry, Muhammad Zeeshan Ul Haq, Muhammad Jawad, Muhammad Farooq, Ifra Siddique, Zia Ullah, Atamjit Singh, Kirandeep Kaur, Komalpreet Kaur, Shilpa Heer, Preet Mohinder Singh Bedi, Neena Bedi, Saroj Arora, Pallvi Mohana, Avneet Kaur, Antresh Kumar, Manisha Kaushal, Muhammad Ijaz, Iqra Muzamil, Aamir Naseer, Iftikhar Hussain, Rais Ahmad, Yasir Razzaq Khan, Tean Zaheer, Iqra Sarwar, Muhammad Shoaib, Simone Ulrich Ulrich Picoli, Nicole Mariele Santos Röhnelt, Tiago Sfredo Schenkel, Ramendra Pati Pandey, Vethakkani Samuel Raj, Anjali Priyadarshini, Riya Mukherjee, Vasco Azevedo, Brenda Silva Rosa da Luz, Yves Le-Loir, Eric Guédon, Rudra Mishra, Bhama Mishra, Kodiveri Muthukaliannan Gothandam, Nalini Easwaran, Arun Kumar, Roma A. Chougale, Furqan Awan, Muhammad Muddassir Ali, Muhammad Hassan Mushtaq

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**Selected Papers from the 19th International Conference on Microand Nano-Technology for Power Generation and Energy Conversion Applications (Power MEMS 2019)**

> Printed Edition of the Special Issue Published in *Energies*  Paweł Knapkiewicz and Rafał Walczak Edited by

www.mdpi.com/journal/energies

**Selected Papers from the 19th International Conference on Micro- and Nano-Technology for Power Generation and Energy Conversion Applications (Power MEMS 2019)**

## **Selected Papers from the 19th International Conference on Micro- and Nano-Technology for Power Generation and Energy Conversion Applications (Power MEMS 2019)**

Editors

**Paweł Knapkiewicz Rafał Walczak**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Paweł Knapkiewicz Faculty of Microsystem Electronics and Photonics, Wroclaw University of Science and Technology Poland

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*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

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2.3.1. RUSLE Model

The RUSLE model is a modified version of the Universal Loss Equation (USLE). Due to its simple structure and ease of operation, scholars worldwide widely use it to estimate soil erosion. Its equation is as follows:

$$A = R \times K \times L \times S \times \mathbb{C} \times P \tag{1}$$

where *A* is the average annual soil erosion (t·ha−1·a−1), which means the average annual soil loss from fine gully or inter-gully erosion on slopes caused by rainfall and runoff. *R* is the rainfall erosivity factor (MJ·mm·ha−1·h−1·a−1), *K* is the soil erodibility factor (t·ha·h·MJ−1·mm−1·ha−1), *L* is the slope length factor (dimensionless), *S* is the slope gradient factor (dimensionless), *C* is the vegetation cover and management factor (dimensionless), and *P* is the soil and water conservation measures factor (dimensionless).

Gao modified the RUSLE model based on the correlation coefficients between exposed bedrock and surface sediments [23], with the following expressions:

$$A = \left(1 - 0.076^2 \times \alpha\right) R \times K \times L \times S \times \mathbb{C} \times P \tag{2}$$

where α is a correction factor whose value is determined by the average of the bedrock exposure rates for the different rocky desertification classes (Table 1). Different equations were chosen to simulate soil erosion depending on the lithology of the study area. Equation (1) was selected for non-karst regions, and Equation (2) was selected to simulate soil erosion in karst regions.

**Table 1.** α values for different karst rocky desertification classes.

The K factor is a measure of soil erosion resistance and reflects the sensitivity of the soil to erosion. The *K* factor is calculated using the erosion-productivity impact calculator (EPIC) model (Equation (3)) developed by Williams [36].

$$K = \begin{cases} 0.2 + 0.3 \exp\left[0.0256 \text{SAN} \left(1 - \frac{SIL}{100}\right)\right] \Big| \left(\frac{SIL}{\text{CLA} + \text{SIL}}\right)^{0.5} \\ 1.0 - \frac{0.25 \text{C}}{\text{C} + \exp(3.72 - 2.95)} \Big[ 1.0 - \frac{0.7 \text{SN}\_1}{\text{SN}\_1 + \exp\left(-5.51 + 22.9 \text{SN}\_1\right)} \Big] \end{cases} \tag{3}$$

where *K* is the soil erodibility factor in t·hm2·h·MJ−1·mm−1·hm−2. *SAN* is the sand content; *SIL* is the silt content; *CLA* is the clay content; *C* is the organic carbon content, *SN*1 = 1 − *SAN*/100.

The *R* factor is the driving force behind soil erosion and reflects the potential for soil loss through precipitation. As there are no weather stations in the study area, the nearest weather station to the study area was selected and the *R* factor for the study area was obtained by interpolation using the rainfall erosion equation (Equation (4)) created by Arnoldus [37]. The equation is as follows:

$$R = \sum\_{i=1}^{12} 1.735 \ast 10^{\left[1.5 \ast \log\left(\frac{P\_i}{P}\right) - 0.8188\right]}\tag{4}$$

where *R* is the rainfall erosion factor, *Pi* is the monthly rainfall (mm), and *P* is the annual rainfall (mm).

The *L* factor and *S* factor are closely related to topography and accumulated flow, and *L* factor and *S* factor together reflect the influence of topographic features on soil erosion. LS represents the ratio of soil loss on a given slope length and gradient to soil loss on a typical slope in a standard runoff plot, all other things being equal, and the LS value is proportional to soil loss, playing an accelerating role in soil erosion [38]. We chose to calculate the *L* factor [39] and *S* factor [40] by applying the formulae to the soil and rocky mountainous areas of southwest China as follows:

$$L = \left(\frac{\lambda}{22.13}\right)^m \tag{5}$$

$$m = \frac{\beta}{\beta + 1} \tag{6}$$

$$\beta = (\sin \theta / 0.0896) / [3 \* (\sin \theta)^{0.8} + 0.56] \tag{7}$$

$$S = \begin{cases} 10.80 \ast \sin \theta + 0.03 \ (\theta < 5^{\circ}) \\ 16.80 \ast \sin \theta - 0.50 \ (5^{\circ} \le \theta \le 10^{\circ}) \\ 20.204 \ast \sin \theta - 1.2404 \ (10^{\circ} \le \theta > 25^{\circ}) \\ 29.585 \ast \sin \theta - 5.6079 \ (\theta \ge 25^{\circ}) \end{cases} \tag{8}$$

where *λ* is the sum of the slope lengths in the horizontal direction, *m* is the slope length factor, *β* is a factor related to the slope value, and *θ* is the slope angle extracted on the basis of the digital elevation.

*C* factor refers to the ratio of soil loss under a particular crop or vegetation cover to that of continuous recreational land after cultivation, all other factors being equal, and this factor measures the inhibitory effect of vegetation cover and management on soil erosion [41,42]. Currently, there are two forms for obtaining *C* factor values, equation calculation and assigning values on the basis of land use type. In this paper, the formula created by Durigon [43] was chosen to calculate the *C* value as follows:

$$\mathcal{C} = \frac{-NDVI + 1}{2} \tag{9}$$

where *NDV I* is the normalized vegetation index, calculated from the near-infrared band and visible red band in remote sensing imagery.

*P* factor refers to the ratio of soil erosion with soil conservation measures to soil erosion with down-slope planting, and its value is distributed between 0 and 1. When *p* = 1, it means that no water conservation measures are taken in the unit area; when *p* = 0, it means that no soil erosion will theoretically occur in the unit area, such as water bodies, construction sites, etc. The *p*-values were obtained by summarizing the results of previous authors in the karst region of southern China [23,44,45]. The results are presented in the Table 2.

**Table 2.** Land use types and *p*-value.

#### 2.3.2. Geographical Detector

The geographical detector is a statistical method that allows for the exploration of spatial dissimilarity and its drivers [46]. The principle of the geographical detector is based on the idea that if the independent variable has a significant effect on the dependent variable, then there should be some similarity in the spatial distribution of the two [47]. It consists of four models, namely, factor detector, interaction detector, risk detector, and ecological detector. In this paper, we analyzed the spatial heterogeneity with the factor detector, interaction detector, and risk detector.

The factor detector detects the extent to which the independent variable can explain the spatial divergence of the dependent variable, and the *q* value can measure the extent of explanation with the following expression:

$$q = 1 - \frac{SSW}{SST} \tag{10}$$

$$SSW = \sum\_{h=1}^{L} N\_h \sigma\_{h'}^2 \text{ SST} = N\sigma^2 \tag{11}$$

where *h* = 1,2,..., *L* is the partition of the independent and dependent variables; *Nh* and *N* are the number of cells in stratum *h* and all partitions; *σ*2 *h* and *σ*2 are the variances of the dependent variables in stratum *h* and all partitions. *SSW* and *SST* are the sum of the within-stratum variances and the total variance of all partitions, respectively. *q* has a value of [0,1], with larger values indicating that the independent variable explains more of the dependent variable.

The interaction detector can be used to detect the interaction between factors *XS*, i.e., the change in the degree of explanation of the dependent variable Y when *X*1 and *X*2 act together. The principle is to calculate the *q* values of *X*1 and *X*2 separately, then to calculate the new layer *q*(*X*1 ∩ *X*2) values by superimposing the *X*1 and *X*2 layers and comparing them among the three. The comparison between the two factors can be divided into the following results: non-linearly diminished, one-factor non-linearly diminished, two-factor enhanced, independent, and non-linearly enhanced.

The risk detector can be used to count the mean values of attributes between subregions of a single factor and to determine whether the heterogeneity between sub-regions is significant. The mean attribute results are expressed as numerical data, while whether the means are significantly different is expressed as 'N/Y' binary data.

#### 2.3.3. Slope Units

The slope cell delineation method based on the principle of curvature-based watershed segmentation [48] proceeds as follows (Figure 2).

**Figure 2.** Flow chart of slope unit division.


**1. Introduction**

The study of the fetal face may be performed during the early stages of gestation. Depending on the gestational age, we can identify various elements of anatomy, such as the orbits or the

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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forehead, from the 12th week. Yet, after that time, we can easily identify and study the forehead, the nose, the lips, the ears, and the orbits of the fetus [1]. Prenatal recognition of facial abnormalities during pregnancy has many benefits. It can lead to the diagnosis of multiple genotypic syndromes and chromosomal anomalies. Also, it allows more adequate counseling and preparation of the parents. Considering that the sonographic assessment of the fetal face is a major part of the anatomic survey of the fetus, sagittal, axial, and coronal planes are used when examining the fetus.

The facial anomalies are divided into nose, orbit, lip, mandible, and palate anomalies. The US method may reveal also benign and less frequent anomalies, for example, lacrimal duct cysts, hemangiomas, and so on.


**3.1 Risk factors in association to flights**

Travel participation can expose passengers to a variety of risk factors that may have impact on their health. Health professionals can obtain information from the International Maritime Health Association and Aerospace Medical Association website (www.imha.net and www.asma.org), respectively.


**4. CS‐AHP model for optimal selection problem**

In this section, we formally construct the quality model under the presen**c**e of variability and uncertainty among demands in decision‐making process. The CS‐AHP algorithm is adopted for specification of stakeholders' preferences and for further measurements during the optimal selection. Furthermore, we analyze different kinds of preferences and define final configura‐ tion goals induced by the constructed quality model.

#### **4.1. The two‐layered selection criteria structure**

In a given model, let each demand will be available with a finite set of options characterized with respect to selection criteria. Having in mind that stakeholder defines his/her own preferences about overall values (i.e., person A specified overall budget and expectations regarding the level of personal satisfaction and conformity), we will create the structure of concerns and qualifier tags with respect to aggregated values of each selection‐making criterion:

**Step 1.** Generate *lower and upper bound values of each selection criteria dimension* (*Qagg LB*, *Qagg UB*) =(*Q*1 *LB*, ..., *Qk LB*, *Q*1 *UB*, …, *Qk UB*)∈*R k xR k*

**Step 2.** Stakeholders should define own preferences about aggregated values presenting own attitudes and personal fillings about each selection criteria dimension, in the form of covering subintervals *QT* =*QT*1 1 , .., *QTk*1 1 , …, *QT*1 *k* , .., *QTkn k* . *Each subinterval QTi j is open, semi‐open or*

combination of options. The formal foundations of measurements are presented later in Section

Additionally, as usual for the optimal selection tasks, *hard* constraints might be defined for special demands for limiting the corresponding selection‐making criteria. That is, those preferences are not only defined as appropriate relative importance in the selection criteria model. For example, person A defined that total costs of stay should not be over 5000. It means that any combination of options which has the best characteristics with respect to the stake‐ holders' other preferences and which violates this specified value of price should be eliminated

{ 1 , .. ,*l*}, is a constant limitation value. In our illustrative example, both total budget limitations

The *optimal selection problem* can be defined as a problem of unique options derivation, such that stakeholders' preferences are satisfied. In our approach, once the preferences are obtained, genetic algorithm (GA) is used for the selection of the most desirable demands and the relevant options that collectively maximize the stakeholders' satisfaction. Furthermore, constraints defined in GA will guarantee that every combination of options will be valid with respect to the interdependencies and other relations between demands, as proved in reference [17].

In this section, we formally construct the quality model under the presen**c**e of variability and uncertainty among demands in decision‐making process. The CS‐AHP algorithm is adopted for specification of stakeholders' preferences and for further measurements during the optimal selection. Furthermore, we analyze different kinds of preferences and define final configura‐

In a given model, let each demand will be available with a finite set of options characterized with respect to selection criteria. Having in mind that stakeholder defines his/her own preferences about overall values (i.e., person A specified overall budget and expectations regarding the level of personal satisfaction and conformity), we will create the structure of concerns and qualifier tags with respect to aggregated values of each selection‐making

**Step 1.** Generate *lower and upper bound values of each selection criteria dimension*

*UB*)∈*R k xR k*

( *o*1, … , *o*n, ≤ *u*i

, *i* ∈

Formally, *hard constraints* can be defined as a set of constraints: *cl*i

200 Applications and Theory of Analytic Hierarchy Process - Decision Making for Strategic Decisions

(defined by person A) should be considered as hard constraints.

**4. CS‐AHP model for optimal selection problem**

tion goals induced by the constructed quality model.

**4.1. The two‐layered selection criteria structure**

criterion:

(*Qagg LB*, *Qagg*

*UB*) =(*Q*1

*LB*, ..., *Qk*

*LB*, *Q*1

*UB*, …, *Qk*

4.

and should not be evaluated any further.

**3.3. Problem of optimal selection**

*close interval* (*ai j* , *bi j* ), (*ai j* , *bi j* , *ai j* , *bi j* ), *ai j* , *bi j which satisfies the conditionsU i*=1 *kj QTi j* = *Qj LB*, *Qj UB* : *and QTi j* ∩ *QTl j* = ∅, 1≤*i* <*l* ≤*kj* , *for each fixed j* ∈{1, …, *n*}.

Furthermore, each combination of options *o*1, .., *on* is characterized by aggregated values of selection‐making criteria, *Qagg* =*q*1 *agg*, …, *qk agg* ∈*R k* [18], which belongs to exactly one combina‐ tion of covering subintervals *QTi* 1 1 *x* … *xQTi k k* .

Accordingly, aforementioned combination of options in our running example, *o*13 and *o*22 (with total comfort of 10 and travel costs of 850) belongs to combination of covering subintervals [8, 10] *x* (700, ‐) defined by stakeholder A. Since stakeholder A mostly prefers values from combination of subintervals [8, 10] *x* (‐, 500) (see Section 3.2), other combinations of available options should be analyzed in order to check if any belongs to the most preferable combination of subintervals. Furthermore, combination of options belonging to the most reachable combination of subintervals should be considered as the most appropriate combination of options. Finally, if we have several combinations of options belonging to the same combinations of covering subintervals, the difference of their selection criteria values should be quantitatively measured and compared.

Thus, based on the output ranks of the CS‐AHP method, we define measures of selection‐ making criteria fitting degrees, as follows:

**Definition 1 (CS‐AHP selection making degree measurements).***For a given model (C, Qagg LB*, *Qagg UB*, *QT, P), where C is a set of concerns, QT is a set of qualifier tags over aggregated intervals Qagg LB*, *Qagg UB for k selection‐making criteria, and P represents the set of specified preferences, the standard CS‐AHP algorithm defines the measurements obtained on the bases of output ranks over the set of selection‐making criteria (written asr*1 *C*, …, *rk C*) *and a collection of covering subintervals (written asr*1 1 , …, *ri* 1 1 , .., *r*1 *k* , …, *ri k k* ), *as follows:*

*(1') 1‐dimension selection criteria fitting degree of covering intervalQTj i :r QT* (*QTij i* ) =*ri C∙rj i*

*(1) Selection criteria fitting degree of combination of covering subintervalsQTi* 1 1 *x* … *xQTi k k :*

$$r^{QT}\left(\mathbf{Q}T^1\_{i\_1}\mathbf{x}\dots\mathbf{x}\mathbf{Q}T^k\_{i\_k}\right) = \frac{1}{k}\sum\_{j=1}^k r^{QT}\left(\mathbf{Q}T^j\_{i\_j}\right), i\_j \in \{1, \dots, k\_1\}, l \in \{1, \dots, k\}, i$$

*(2') Fitting degree of combination of options for the ith selection criteria dimension:*

$$\boldsymbol{r}\_{i}^{S}\left(\boldsymbol{o}\_{1},\ldots,\boldsymbol{o}\_{n}\right) = \begin{cases} \boldsymbol{r}\_{i}^{c} \cdot \left[\boldsymbol{r}\_{j}^{i} + \frac{\boldsymbol{m}\_{i}^{j} - \boldsymbol{q}\_{i}^{\text{agg}}}{\boldsymbol{m}\_{i}^{j} - \boldsymbol{m}\_{i}^{f^{i-1}}} \left(\boldsymbol{r}\_{j}^{i} - \boldsymbol{r}\_{j-1}^{i}\right)\right], & \boldsymbol{q}\_{i}^{\text{agg}} \le \boldsymbol{m}\_{i}^{f}, \\\ \boldsymbol{r}\_{i}^{c} \cdot \left[\boldsymbol{r}\_{j}^{i} + \frac{\boldsymbol{q}\_{i}^{\text{agg}} - \boldsymbol{m}\_{i}^{f}}{\boldsymbol{m}\_{i}^{f^{i+1}} - \boldsymbol{m}\_{i}^{f}} \left(\boldsymbol{r}\_{j+1}^{i} - \boldsymbol{r}\_{j}^{i}\right)\right], & \boldsymbol{q}\_{i}^{\text{agg}} \ge \boldsymbol{m}\_{i}^{f}, \end{cases}$$

*whereqi agg* ∈*QTj i is the aggregated value of the ith selection criteria dimension,mi j* = *ai j* 2 + *bi j* 2 *‐middle of the jth covering subintervalQTj i* , *r*0 *i* =*r*1 *i* + *r*2 *i* − *r*1 *i m*2 *i* − *m*1 *i* (*a*1 *i* −*m*1 *i* )*and rki* +1 *i* =*rk i* + *rk* −1 *i* − *rl i mk* −1 *i* − *mk i* (*bk i* −*mk i* ).

*(2) Fitting degree of combination of options: if the overall selection criteria values Qagg* =*q*1 *agg*, …, *qk agg* ∈*R k of the combination of optionso*1, …, *onbelongs to the combination of covering subintervalsQTi* 1 1 *x* … *xQTi k k , then its selection criteria fitting degree is measured by: r S* (*o*1, …, *on*) = 1 *k* ∑ *i*=1 *k ri S* (*o*1, …, *on*).

For better clarity, measurements are at first defined for one selection criteria dimension (*1'* and *2'*), and then generalized for *k* selection criteria dimensions (*1* and *2*). It is succeeded by considering all interested selection criteria dimensions in the weighted sum in the func‐ tions *rQT()* and *rS()* under the hypothesis that the aggregated values for quality dimensions can be evaluated as the average of the corresponding quality dimensions of component options [18, 19].

The main aim of these two measurements are to (i) measure **stakeholders' interests over selection criteria** (as quantified with measure *rQT* measure), and (ii) measure **how the selected combination of options fulfill defined preferences** (as quantified with measure *rS* measure).

#### **4.2. Characteristics of the two‐layered model for selection‐making criteria**

Created two‐layered structure with both measurements represents an integral **selection‐ making model,** with the following characteristics induced by the basic characteristics of its integrated components:

not required to be monotonically increasing or decreasing, and, thus, observed ranks do not correspond to any monotonic function;

**iv.** The ranks obtained by CS‐AHP algorithm are also assigned the middle values of covering subintervals with uniform distributions to ranks of the first neighbors. Thus, higher values of selection criteria fitting degree measurement for combination of options *rS()* correspond to the **combination of options that are better suited for the stakeholders' preferences with respect to selection criteria** [20];

( )

*S i i i agg j*

202 Applications and Theory of Analytic Hierarchy Process - Decision Making for Strategic Decisions

*m*2 *i* − *m*1 *i* (*a*1 *i* −*m*1 *i* )*and rki* +1 *i* =*rk*

*n o o*

, ,

*i* , *r*0 *i* =*r*1 *i* + *r*2 *i* − *r*1 *i*

*S* (*o*1, …, *on*).

1

*whereqi*

*Qagg* =*q*1

*subintervalsQTi*

*r S* (*o*1, …, *on*) = 1

options [18, 19].

integrated components:

*agg* ∈*QTj i*

*jth covering subintervalQTj*

*agg*, …, *qk*

1 1

*k* ∑ *i*=1

*k ri*

*agg* ∈*R k*

*x* … *xQTi k k* ( )

,

,

*i* + *rk* −1 *i* − *rl i*

*mk* −1 *i* − *mk i* (*bk i* −*mk i* ).

*j* = *ai j* 2 + *bi j*

2 *‐middle of the*

1 1

*C i i i i i agg j i j jj i i j j*

*C i i i i i agg j i j jj i i j j*

*q m rr r r q m m m*

( )

*of the combination of optionso*1, …, *onbelongs to the combination of covering*

*, then its selection criteria fitting degree is measured by:*

1 1

+ +

ì é ù - ï ×+ - £ ê ú ï ë - û ¼ = í

*m q r r rr q m m m r*

*is the aggregated value of the ith selection criteria dimension,mi*

*(2) Fitting degree of combination of options: if the overall selection criteria values*

For better clarity, measurements are at first defined for one selection criteria dimension (*1'* and *2'*), and then generalized for *k* selection criteria dimensions (*1* and *2*). It is succeeded by considering all interested selection criteria dimensions in the weighted sum in the func‐ tions *rQT()* and *rS()* under the hypothesis that the aggregated values for quality dimensions can be evaluated as the average of the corresponding quality dimensions of component

The main aim of these two measurements are to (i) measure **stakeholders' interests over selection criteria** (as quantified with measure *rQT* measure), and (ii) measure **how the selected combination of options fulfill defined preferences** (as quantified with measure *rS* measure).

Created two‐layered structure with both measurements represents an integral **selection‐ making model,** with the following characteristics induced by the basic characteristics of its

**i.** Fitting degree measurements *rQT()* and *rS()* are unit measurements, i.e.,

**ii.** The ranks obtained by CS‐AHP algorithm are at first assigned to the covering

**iii.** Quality measure *rQT()*, in general case, does not correspond to monotonic tendency

0≤*r QT* (), *r S* ()≤1, according to the basic characteristics of the CS‐AHP algorithm [2];

subintervals for creation of selection criteria fitting degree measurement *rQT()*, and thus its higher values correspond to the **more preferable combination of covering**

of selection criteria dimensions (i.e., increasing and decreasing) introduced in reference [18]. The CS‐AHP framework takes as input the set of stakeholders' preferences about the relative importance between covering subintervals which are

**4.2. Characteristics of the two‐layered model for selection‐making criteria**

**subintervals according to stakeholders' preferences**;

*j agg*

ï é ù ï ×+ - ³ ê ú ë û - î

*i i*

The characteristics of introduced measurements reflect uncertainty and conditionality in user preferences (which, to the best of our knowledge, mostly correspond to realistic scenarios [2]), and thus the *OptSelectionAHP* approach is developed in a manner which uses identified char‐ acteristics for faster convergence in the heuristic approach for optimal selection problem.

**Figure 3.** Schematic representation of *rQT* and *rs* measures for one selection criteria dimension.

#### **4.3. Optimal selection goals**

Finally, in the proposed selection criteria model, the final optimal selection goal is defined as the determination of the most preferable combination of available options based on both measurements representing stakeholders' requirements and preferences. Thus:

**Definition 2** (**optimal selection goal**). *Given a set of user demands*{*di* }*i*=1,…,*n with interdependencies defined with q logical statements, and associated with available set of options* {*oij* }*i*=1…*n*, *j*=1…*mi , where mi is the number of available options for ith demand, and CS‐AHP selection criteria model (C, Qagg LB*, *Qagg UB*, *QT, P), where C is a set of concerns, QT is a set of qualifier tags over aggregated intervals Qagg LB*, *Qagg UB for k selection‐making criteria, and P represents the set of specified preferences; the* **optimal selection goal** *is to find a valid combination of options which maximizes the overall selection criteria fitting degree r S* (*o*1, …, *on*), *subject to its affiliation to the most preferable combination of selection criteria and the hard constraints satisfaction.*

It is necessary to notice that in comparison to standard optimization goals widely used in the literature [21–23], defined as *maximization of the overall aggregated values*, definition 2 addition‐ ally requires affiliation to the most preferable combination of selection criteria.

In the following, we propose a meta‐heuristic search approach that overcomes the aforemen‐ tioned complexities.


**INTRACEREBRAL HEMORRHAGE**

Edited by **Vikas Chaudhary**

#### **Intracerebral Hemorrhage**

http://dx.doi.org/10.5772/56977 Edited by Vikas Chaudhary

#### **Contributors**

Cathie Sudlow, Kristiina Rannikmae, Gaiqing Wang, Da-Zhi Liu, Stella Tsirka, Shahina Bano, Vikas Chaudhary, Sankalp Gokhale, Michael L. James

#### **© The Editor(s) and the Author(s) 2014**

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**4. Discussion**

#### *4.1. Predominance of Resource-Oriented Understanding of Bioeconomy*

The definitions of bioeconomy, expressed in the responding scientists' own words, with or without specifications, were predominantly resource-oriented. In line with this, the resource-oriented definition presented in the questionnaire achieved the highest agreement of all official definitions. According to this definition, bioeconomy is related to the production, conversion, and use of biological resources. Thus, the scientists' understanding of bioeconomy was strongly in line with the political understanding in Germany and Europe. This was confirmed by recent findings from Austria [12] and Norway [27], which also indicated the dominance of a bioresource-based understanding of bioeconomy among stakeholders. In the same way, the resource-oriented definition by the European Commission was generally supported by respondents (mainly from science and industry) in a public consultation in preparation for the EU Bioeconomy Strategy [28].

The great consistency in the bioeconomy understanding can be explained by the high engagement of scientific communities in the development of official bioeconomy strategies, both at the European and national level [7]. In Baden-Württemberg, for example, scientists were asked to participate in a steering committee and to develop proposals for strategic orientation [21].

Scientific analyses of bioeconomy strategies interpret different definitions as an indication of divergent understandings of bioeconomy (e.g., Reference [10]). In our survey, the technology-oriented definition was less supported than others, but all proposed definitions were agreed upon by the majority of respondents. Agreement with the technology-oriented definition was higher in the lignocellulose and microalgae networks. This preference can be explained by the stronger role of biotechnology in their research agenda. Despite the resource-oriented definition in official bioeconomy strategies, modern biotechnology is also regarded as a key technology in these strategies (e.g., Reference [24]). Overall, the high level of agreement with various definitions indicates that they are not perceived as contrary, but as complementary.

#### *4.2. High Expectance of Contribution of Research Activities to Objectives of the Regional Bioeconomy Strategy*

The goals of the German Policy Strategy Bioeconomy were widely supported by the respondents. Only the goal of taking on a leading role in solving global challenges such as climate protection and food security was seen rather critically. Key objectives in all clusters were "the production of raw materials in line with the objectives of environmental and climate protection and nature conservation" and "the long-term conversion of the economic production base from fossil to biogenic sources". Technology visions and objectives of official strategies can fulfill a guiding function for researchers and actors in the respective research area [29]. Our survey results indicated that the official objectives are well integrated into the thinking of the researchers. It can therefore be assumed that the official bioeconomy strategies achieve the objective of providing guidance.

In a similar way, an evaluation of the German Research Strategy Bioeconomy has shown that there is strong support for the overall approach oriented toward societal goals, though there is a lack of agreement on specific objectives among the heterogeneous research communities involved in bioeconomy research [20].

The majority of respondents expected their research results to make a high or medium contribution to achieving the objectives of the Baden-Württemberg Research Strategy Bioeconomy. There were differences between the research clusters regarding the extent of their contributions to the different objectives. This resulted from different research agendas. Of course, an individual research project can only contribute to a few objectives of the research strategy.

An evaluation of the German Research Strategy Bioeconomy [20] and the European Bioeconomy Strategy [30] showed that funded research has significantly contributed to achieving overall goals and that the majority of funded projects have aligned their objectives to the strategies. The EU review outlined that "there is early indication that the projects funded are generating relevant excellent multidisciplinary research (i.e., the quality of publications in terms of citations)" [30] (p. 12).

#### *4.3. Simultaneous Support for the Elements of Di*ff*erent Pathways in the Implementation of the Bioeconomy*

Di fferent implementation pathways are discussed for the development of the bioeconomy (e.g., References [11,14]. They are usually presented as alternative trajectories. In our survey, not all elements of the o fficial technology-based approach were supported by the majority of respondents. For example, "adapting nature to industrial processes" and "extending technological leadership" as key elements of the technology-based approach were approved only by a minority. At the same time, some key elements of the socio-ecological implementation pathway met with much approval: for example, "promoting sustainable consumption patterns" and "strengthening multifunctional, agroecological agriculture" were supported by nearly 90% of the respondents. Similarly, stakeholder surveys in Denmark and the United Kingdom, based on interviews, pointed to a potential overlap of the two approaches, which could give rise to a new concept of quality industrial biomass production, understood as using superior biomass quality attributes in place-specific biorefineries to produce di fferent products depending on the region [19].

Obviously, the survey participants considered the discussed implementation pathways of the bioeconomy as compatible or, more precisely, as combinable. This perception might have been due to the fact that projects on agroecology, local food chains, citizens' initiatives, and so on are incorporated in EU and national research programs, even if only marginally in comparison to the dominating technology-centered funding. However, in the background of this allegedly equal coexistence, rival stakeholder networks compete for influence on research priorities, policy changes, and the preferable bioeconomy trajectory [31].

With regard to sustainable development, the current EU bioeconomy policy is seen to lean strongly toward weak sustainability and to favor economic dimensions and concerns over environmental and social dimensions [32]. In addition, current developments in bioeconomy sectors adhere largely to the principles of weak sustainability [33]. The respondents' strong support for some elements of the socio-ecological approach in the survey can be interpreted as the scientists' recognition of the need for strong sustainability in the bioeconomy.

The assumed combinability of elements from di fferent implementation pathways contrasts with the irreconcilability of pathways formulated in scientific and societal discourses on bioeconomy. While the technology-based approach is understood as an enhancement of current practices in the production, processing, and consumption of biomass with new products and more environmentally friendly technologies, the socio-ecological pathway is conceptualized as a structural remodeling of today's production and consumption practices and as fundamental changes in the ways of thinking and living [8]. The divergence between survey statements and discourse viewpoints can be interpreted in two ways: the compatibility between the two perspectives has not been considered su fficiently in the discourse, or the interviewed scientists underestimate the tensions or potential conflicts between the two approaches. This question cannot be answered within the present study, but some indications can be given:

• Competing interests, power relations, and politics are inherent in bioeconomy development and make it difficult for stakeholders to negotiate compromises and reach consensus on the most sustainable pathways for the future [34]. Biomass produced from agriculture is subject to ongoing debates about the future of agriculture. Agricultural paradigms carry their own set of ideological and value assumptions and are fiercely contested by critics [19]. Scientists engaged in bioeconomy research are probably not familiar with these debates.

The evaluation of the German Research Strategy Bioeconomy has also shown that the heterogeneous scientific communities interviewed do not share a common understanding of implementation pathways for the bioeconomy up to now [20] (p. 296). Some of them call for more technical and laboratory research in the field of biotechnology; others want to foster more non-technical research in the fields of sustainability and social innovation.

#### *4.4. Prevailing of Scientific Collaborations*

The research clusters had in common that most of their collaborations take place within the scientific system, and only a few collaborations exist with societal stakeholders and users. There are certain peculiarities in the biogas network, where cooperation with the latter group of players and industrial companies is more frequent than in the lignocellulose and microalgae networks. With its long-established biogas technologies, this cluster has been able to build extensive networks with industry and other stakeholders, whereas in the fields of lignocellulose and microalgae, partnerships and cooperation are still evolving due to the early stage of development of the technologies. Overall, the forms of cooperation are dominated by conventional scientific formats, such as meetings for exchanges of information, discussions of results, and the development of research questions, while there are only a few cooperation formats for discussing joint marketing strategies and developing new business models. One reason could be that, in the lignocellulose and algae networks, basic research is in the foreground, and therefore the marketing of products lies in the future.

Similarly, the involvement of societal stakeholders has been lagging behind the European level. As highlighted in a review of the European Bioeconomy Strategy from 2012, the Bioeconomy Panel is the central body for implementing "Area of Action 2: Reinforced Policy Interaction and Stakeholder Engagement" [30] (p. 13). The panel was created in 2013 to support interactions between different policy areas, sectors, and stakeholders in the bioeconomy. So far, the main outputs of the panel have been four bioeconomy stakeholder conferences, held between 2012 and 2016, and the 2017 "Stakeholders Manifesto" [35]. The latter represents a marked shift to the European Commission's own strategy paper from 2012 [12]. It should be taken into account that the panel was established on political initiative. The review provided no information on the level of involvement of societal stakeholders in European research projects. A group of experts evaluating the European strategy concluded that initiatives such as the Stakeholder Panel have increased awareness of the bioeconomy concept among a broad range of stakeholders, but that the involvement of public and private stakeholders and civil society organizations should be reinforced with an updated "Bioeconomy Strategy and Action Plan" [36].

The evaluation of the German Research Strategy Bioeconomy showed that the experts surveyed considered it necessary to incorporate a wide range of stakeholders in research, from business and industry to NGOs in the fields of environment and consumption to citizens and the broader public [20] (p. 285). However, until now, funding aimed at fostering dialogue with civil society and the public has been rather rare, and these groups have been only marginally addressed in the existing funding scheme [20] (p. 293, 296). An evaluation of the individual projects revealed that the goal of intensifying networking with partners and establishing new networks was reached less frequently than that of scientific knowledge exchange and interdisciplinary learning [20] (p. 294).


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myelin/oligodendrocyte glycoprotein membrane topology. *J Neurochem*, 67, 5,

McClanahan, T, Kastelein, RA, Cua, DJ. (2005). IL-23 drives a pathogenic T cell population that induces autoimmune inflammation. *J Exp Med*, 201, 2, (Jan 17), 233-

(2003). Multiple sclerosis and Epstein-Barr virus. *JAMA*, 289, 12, (Mar 26), 1533-6,

(2005). Temporal relationship between elevation of epstein-barr virus antibody titers and initial onset of neurological symptoms in multiple sclerosis. *JAMA*, 293,

demyelination in rat acute allergic encephalomyelitis by circulating mouse monoclonal antibodies directed against a myelin/oligodendrocyte glycoprotein.

the relationship between th17 cells and unexplained recurrent spontaneous

Increased frequency and broadened specificity of latent EBV nuclear antigen-1 specific T cells in multiple sclerosis. *Brain*, 129, Pt 6, (Jun), 1493-506, 1460-2156

(2007). Cerebrospinal fluid-infiltrating CD4+ T cells recognize Borrelia burgdorferi lysine-enriched protein domains and central nervous system autoantigens in early

EBNA1-specific T cells from patients with multiple sclerosis cross react with myelin antigens and co-produce IFN-gamma and IL-2. *J Exp Med*, 205, 8, (Aug 4), 1763-73,

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't Hart, BA, Hintzen, RQ, Laman, JD. (2009). Multiple sclerosis - a response-to-damage

't Hart, BA, van Kooyk, Y. (2004). Yin-Yang regulation of autoimmunity by DCs. *Trends*

Tzartos, JS, Friese, MA, Craner, MJ, Palace, J, Newcombe, J, Esiri, MM, Fugger, L. (2008).

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Van der Aa, A, Hellings, N, Bernard, CC, Raus, J, Stinissen, P. (2003). Functional properties

Vergelli, M, Le, H, van Noort, JM, Dhib-Jalbut, S, McFarland, H, Martin, R. (1996). A novel

Villoslada, P, Abel, K, Heald, N, Goertsches, R, Hauser, SL, Genain, CP. (2001). Frequency,

von Büdingen, HC, Hauser, SL, Ouallet, JC, Tanuma, N, Menge, T, Genain, CP. (2004).

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of myelin oligodendrocyte glycoprotein-reactive T cells in multiple sclerosis patients and controls. *J Neuroimmunol*, 137, 1-2, (Apr), 164-76, 0165-5728 (Print) Van der Goes, A, Kortekaas, M, Hoekstra, K, Dijkstra, CD, Amor, S. (1999). The role of anti-

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**1. Introduction**

simplex and influenza viruses, respectively.

services such as the American Type Culture Collection.

**22**

*Japan*

**Alternative Medicines**

Kenji Sorimachi and Takaaki Nakamoto

Encephalitis can be caused by several viruses, including western equine encephalitis (WEE) virus, Japanese encephalitis virus, herpes simplex virus (HSV), human immunodeficiency virus (HIV), influenza viruses and the measles virus. Bacteria such as *Neisseria meningitidis* and *Treponema pallidum* have also been shown to be causative agents of encephalitis. There are no medications currently available that selectively target virus-infected cells, although some nucleotide derivatives, and oseltamivir, are exceptionally effective against herpes

Vaccination is one way to prevent infection; however, vaccines have not been developed for every viral disease. Western medicines based on herbal extracts, and alternative Eastern medicines have been used for treatment and as preventative measures in instances of viral infection. In particular, the latter has been developed based on daily life from ancient times when molecular biological knowledge was lacking. An example of this is the bark of *Cinchona succirubra*, which contains quinine and has been used as a remedy for malaria. Following infection, certain viruses cause cytopathic effects (CPE) in cells that grow as monolayers *in vitro*. This phenomenon is a useful tool in virology to determine whether a material is applicable for treatment of viral diseases. Viral infection is usually controlled by species specificity, and inhibited by interferons produced by infected cells. Not every cell line exhibits a CPE following infection. African Green Monkey (Vero) cells (Yasumura & Kawakita, 1963) are defective in the production of interferons, and are highly susceptible to arboviruses and many other pathogenic viruses (Simizu & Terasima,). The Vero cell line is used for basic virus research in laboratories, diagnostics in hospitals, epidemiological surveys and bacterial toxin assays. Additionally, the potential of Vero cells for vaccine production has been examined. The cell line has been distributed to scientists globally by

It has been shown that *Agaricus blazei* Murille water extracts, prepared as an alternative medicine, inhibit the formation of CPE in Vero cell cultures caused by the WEE virus. Vero cells were continuously cultured in a modified Eagle's minimal essential medium without serum, protein and lipids (Yasumura et al., 1978). The cells were grown on 24-well plastic culture plates in a chemically defined medium for viral assay experiments, cells infected after they reached confluency. Before virus infection, the cells were washed with the phosphate-buffered saline (PBS, pH 7.4) without Mg2+ and Ca2+. A virus-containing solution (0.2 ml) with a 50% tissue culture infective dose (TCID50) of 100 was added to each well and

**for Encephalitis**

*Dokkyo Medical University*

### **Alternative Medicines for Encephalitis**

Kenji Sorimachi and Takaaki Nakamoto *Dokkyo Medical University Japan*

#### **1. Introduction**

340 Pathogenesis of Encephalitis

Wu, MS, Tani, K, Sugiyama, H, Hibino, H, Izawa, K, Tanabe, T, Nakazaki, Y, Ishii, H,

Yuki, N, Susuki, K, Odaka, M, Hirata, K. (2001). Overlapping Guillain-Barre syndrome and

(Print)

(Print)

Ohashi, J, Hohjoh, H, Iseki, T, Tojo, A, Nakamura, Y, Tanioka, Y, Tokunaga, K, Asano, S. (2000). MHC (major histocompatibility complex)-DRB genes and polymorphisms in common marmoset. *J Mol Evol*, 51, 3, (Sep), 214-22, 0022-2844

Bickerstaff's brainstem encephalitis associated with anti-GQ1b IgG antibody after herpes simplex virus infection. *Acta Neurol Scand*, 104, 1, (Jul), 57-60, 0001-6314

> Encephalitis can be caused by several viruses, including western equine encephalitis (WEE) virus, Japanese encephalitis virus, herpes simplex virus (HSV), human immunodeficiency virus (HIV), influenza viruses and the measles virus. Bacteria such as *Neisseria meningitidis* and *Treponema pallidum* have also been shown to be causative agents of encephalitis. There are no medications currently available that selectively target virus-infected cells, although some nucleotide derivatives, and oseltamivir, are exceptionally effective against herpes simplex and influenza viruses, respectively.

> Vaccination is one way to prevent infection; however, vaccines have not been developed for every viral disease. Western medicines based on herbal extracts, and alternative Eastern medicines have been used for treatment and as preventative measures in instances of viral infection. In particular, the latter has been developed based on daily life from ancient times when molecular biological knowledge was lacking. An example of this is the bark of *Cinchona succirubra*, which contains quinine and has been used as a remedy for malaria.

> Following infection, certain viruses cause cytopathic effects (CPE) in cells that grow as monolayers *in vitro*. This phenomenon is a useful tool in virology to determine whether a material is applicable for treatment of viral diseases. Viral infection is usually controlled by species specificity, and inhibited by interferons produced by infected cells. Not every cell line exhibits a CPE following infection. African Green Monkey (Vero) cells (Yasumura & Kawakita, 1963) are defective in the production of interferons, and are highly susceptible to arboviruses and many other pathogenic viruses (Simizu & Terasima,). The Vero cell line is used for basic virus research in laboratories, diagnostics in hospitals, epidemiological surveys and bacterial toxin assays. Additionally, the potential of Vero cells for vaccine production has been examined. The cell line has been distributed to scientists globally by services such as the American Type Culture Collection.

> It has been shown that *Agaricus blazei* Murille water extracts, prepared as an alternative medicine, inhibit the formation of CPE in Vero cell cultures caused by the WEE virus. Vero cells were continuously cultured in a modified Eagle's minimal essential medium without serum, protein and lipids (Yasumura et al., 1978). The cells were grown on 24-well plastic culture plates in a chemically defined medium for viral assay experiments, cells infected after they reached confluency. Before virus infection, the cells were washed with the phosphate-buffered saline (PBS, pH 7.4) without Mg2+ and Ca2+. A virus-containing solution (0.2 ml) with a 50% tissue culture infective dose (TCID50) of 100 was added to each well and

Alternative Medicines for Encephalitis 343

Fraction Ethanol (%) WEE HSV A-0 0 (++) - A-1 17 (+) - A-2 29 (++) - A-3 38 (++) - A-4 44 +++ (++) A-5 50 +++ (+) A-6 50 Sup - - Table 1. Inhibitory effect of *Agaricus blazei* mycelia fractions on CPE induced by western

(+), small inhibitory effect observed after 1 week in culture, but absent after 2 weeks in culture. (++), significant inhibitory effect observed after 1 week in culture, but no longer observable after 2 weeks in culture. +++, complete inhibition was observed after 2 weeks in culture. -, no significant inhibition was observed. The 50% tissue culture infective dose was

Lignin derivatives, such as EP3, extracted from the culture medium of the edible Japanese mushroom, *Lentinus edodes*, inhibit the formation of CPE usually caused by HSV and WEE virus (Table 2) (Sorimachi et al., 1990). Other lignin derivatives, such as lignosulfonate (LS), obtained from the waste liquor of acid sulfite pulping processes, demonstrate similar antiviral activity as EP3. Both EP3 and LS partially inhibit the formation of CPE caused by other viruses including polio, mumps and measles viruses. It is well known that a small proportion of measles cases can cause subacute sclerosing panencephalitis many years after

Virus EP3 LS

(Yasumura, type 1) +++ +++ (UR-3, type 1) +++ +++ (UW, type 2) +++ +++ (MS, type 2) +++ +++

Vaccinia (+) (+)

WEE +++ +++ Polio + ++ Mumps + + Measles (Sugiyama) + +

+++, complete inhibition; ++, a few cytopathogenic effects were observed; +, a small effect

Encephalitis caused by HIV is another serious problem that needs to be considered. HIV has been well characterized at a molecular biological level and clinically, but with a complete

Table 2. Inhibitory effects of EP3 and LS on CPE induced by various viruses.

**2.3 Effect of alternative medicines on HIV**

was observed; and (+), a small drug effect was observed after 4 days in culture.

equine encephalitis (WEE) virus and herpes simplex virus (HSV). Cells were cultured for 2 weeks following virus inoculation.

100. The concentration of *Agaricus* fractions was 100 µg/ml.

the initial infection.

Herpes simplex

**2.2 Effect of lignin derivatives on virus-infected VERO cells**

allowed to adsorb for 90 min at room temperature. The virus solution was then removed from the wells, and fresh culture medium with and without *Agaricus* extracts added (Sorimachi et al., 2001).

#### **2. Effect of alternative medicine on viruses**

#### **2.1 Effect of Agaricus blazei water extracts on virus-infected VERO cells**

When Vero cells were infected with WEE virus, CPE was observed as shown in Figure 1-D and Table 1. The original water extract from *Agaricus blazei* mycelia (A-0) and other fractions (A-1, A-2 and A-3) showed a significant inhibitory effect on CPE formation by WEE virus after 1 week in culture, but this effect was no longer observable after 2 weeks in culture (Table 1) (Sorimachi et al., 2001). Fractions A-4 and A-5 showed a small inhibitory effect on the occurrence of CPE induced by HSV (Table 1). However, neither the original water extracts of *Agaricus blazei* fruiting bodies nor their fractions obtained by ethanol precipitation demonstrated significant inhibitory effects on CPE due to WEE virus or HSV.

Fig. 1. Inhibitory effect of *Agaricus blazei* fractions on the CPE of Vero cells induced with western equine encephalitis (WEE) virus. Cells were infected with WEE virus and cultured for 1 week. (A) Control, uninfected Vero cells; (B) cells infected with WEE virus; (C) cells treated with fraction A-4; and (D) cells infected with WEE virus and cultured with fraction A-4. The concentration of *Agaricus* fraction A-4 was 100 µg/ml. This figure is reproduced from *Bioscience, Biotechnology and Biochemistry*, 65 (7); 16451647, 2001, and has been used with permission from Japan Society for Bioscience, Biotechnology, and Agrochemistry.

allowed to adsorb for 90 min at room temperature. The virus solution was then removed from the wells, and fresh culture medium with and without *Agaricus* extracts added

When Vero cells were infected with WEE virus, CPE was observed as shown in Figure 1-D and Table 1. The original water extract from *Agaricus blazei* mycelia (A-0) and other fractions (A-1, A-2 and A-3) showed a significant inhibitory effect on CPE formation by WEE virus after 1 week in culture, but this effect was no longer observable after 2 weeks in culture (Table 1) (Sorimachi et al., 2001). Fractions A-4 and A-5 showed a small inhibitory effect on the occurrence of CPE induced by HSV (Table 1). However, neither the original water extracts of *Agaricus blazei* fruiting bodies nor their fractions obtained by ethanol precipitation demonstrated significant inhibitory effects on CPE due to WEE virus or HSV.

Fig. 1. Inhibitory effect of *Agaricus blazei* fractions on the CPE of Vero cells induced with western equine encephalitis (WEE) virus. Cells were infected with WEE virus and cultured for 1 week. (A) Control, uninfected Vero cells; (B) cells infected with WEE virus; (C) cells treated with fraction A-4; and (D) cells infected with WEE virus and cultured with fraction A-4. The concentration of *Agaricus* fraction A-4 was 100 µg/ml. This figure is reproduced from *Bioscience, Biotechnology and Biochemistry*, 65 (7); 16451647, 2001, and has been used with permission from Japan Society for Bioscience, Biotechnology, and Agrochemistry.

**2.1 Effect of Agaricus blazei water extracts on virus-infected VERO cells**

(Sorimachi et al., 2001).

**2. Effect of alternative medicine on viruses**

Table 1. Inhibitory effect of *Agaricus blazei* mycelia fractions on CPE induced by western equine encephalitis (WEE) virus and herpes simplex virus (HSV).

Cells were cultured for 2 weeks following virus inoculation.

(+), small inhibitory effect observed after 1 week in culture, but absent after 2 weeks in culture. (++), significant inhibitory effect observed after 1 week in culture, but no longer observable after 2 weeks in culture. +++, complete inhibition was observed after 2 weeks in culture. -, no significant inhibition was observed. The 50% tissue culture infective dose was 100. The concentration of *Agaricus* fractions was 100 µg/ml.

#### **2.2 Effect of lignin derivatives on virus-infected VERO cells**

Lignin derivatives, such as EP3, extracted from the culture medium of the edible Japanese mushroom, *Lentinus edodes*, inhibit the formation of CPE usually caused by HSV and WEE virus (Table 2) (Sorimachi et al., 1990). Other lignin derivatives, such as lignosulfonate (LS), obtained from the waste liquor of acid sulfite pulping processes, demonstrate similar antiviral activity as EP3. Both EP3 and LS partially inhibit the formation of CPE caused by other viruses including polio, mumps and measles viruses. It is well known that a small proportion of measles cases can cause subacute sclerosing panencephalitis many years after the initial infection.

Table 2. Inhibitory effects of EP3 and LS on CPE induced by various viruses.

+++, complete inhibition; ++, a few cytopathogenic effects were observed; +, a small effect was observed; and (+), a small drug effect was observed after 4 days in culture.

#### **2.3 Effect of alternative medicines on HIV**

Encephalitis caused by HIV is another serious problem that needs to be considered. HIV has been well characterized at a molecular biological level and clinically, but with a complete remedy yet to be established. Reasons for this include antigenic variation of HIV surface proteins and a lack of adequate medications. The compound agaritine, which is a carcinogen and is contained in *Agaricus bisuporus* and *Agaricus blazei* Murrile, inhibits HIV protease activity (Gao et al., 2007). Although agaritine is found within alternative medicines and is toxic to healthy people, it is useful in treating HIV-positive patients. Additionally, the method to specifically remove agaritine from *Agaricus blazei* water extracts has been recently established in our laboratory (Koge et al., 2011). Thus, we can now select *Agaricus* water extracts with or without agaritine products depending on their end purpose. EP3 (Suzuki et al., 1989a) and LS (Suzuki et al., 1989b) showed anti-viral activity against HIV *in vitro*, and the oral administration of LEM, which contains EP3, improved the hepatic functions of hepatitis B patients *in vivo* without serious side effects. Therefore, *Agaricus blazei* extracts and lignin derivatives, which are used as alternative medicines, may be promising antiviral compounds, even though we do not understand the inhibitory mechanisms of these drugs on certain viruses *in vitro* or *in vivo*.

#### **3. References**

remedy yet to be established. Reasons for this include antigenic variation of HIV surface proteins and a lack of adequate medications. The compound agaritine, which is a carcinogen and is contained in *Agaricus bisuporus* and *Agaricus blazei* Murrile, inhibits HIV protease activity (Gao et al., 2007). Although agaritine is found within alternative medicines and is toxic to healthy people, it is useful in treating HIV-positive patients. Additionally, the method to specifically remove agaritine from *Agaricus blazei* water extracts has been recently established in our laboratory (Koge et al., 2011). Thus, we can now select *Agaricus* water extracts with or without agaritine products depending on their end purpose. EP3 (Suzuki et al., 1989a) and LS (Suzuki et al., 1989b) showed anti-viral activity against HIV *in vitro*, and the oral administration of LEM, which contains EP3, improved the hepatic functions of hepatitis B patients *in vivo* without serious side effects. Therefore, *Agaricus blazei* extracts and lignin derivatives, which are used as alternative medicines, may be promising antiviral compounds, even though we do not understand the inhibitory mechanisms of these drugs

Gao, W.-N., Wei, D.-Q., Li, Y., Gao, H., Xu, W. –R., Li, A.-X., et al. (2007). Agaritine and its

Koge, T., Komatsu, W. & Sorimachi, K. (2011). Heat Stability of agartitine in water extracts

Simizu, B. & Terasima, T. (Eds.). (1987). *VERO CELLS* – *Origin, Properties and Biological*

Sorimachi, K., Niwa, A., Yamazaki, S., Toda, S. & Yasumura, Y. (1990). Anti-viral activity of

Sorimachi, K., Ikehara, Y., Maezato, G., Okubo, A., Yamazaki, S., Akimoto, K. & Niwa, A.

Suzuki, H., Okubo, A., Yamazaki, S., Suzuki, K., Mitsuya, H. & Toda, S. (1989a). Inhibition of

vitro. *Agricultural and Biological Chemistry*, Vol. 53, No. 12, pp. 1201-1215. Yasumura, Y. & Kawakita, Y. (1963). Studies on SV40 in tissue culture – Preliminary step for cancer research in vitro. *Nihon Rinsho* (in Japanese), Vol. 21, No. 6, pp. 1201-1215 Yasumura, Y., Niwa, A. & Yamamoto, K. (1978). Phenotypic requirement for glutamine of

fractionation. Food Chemistry, Vol. 126, No. 3, pp. 1172-1177.

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derivatives are potential inhibitors against HIV proteases. *Medicinal Chemistry*, Vol.

from Agaricus blazei and other edible fungi, and removal of agaritine by ethanol

*Applications.* Department of Microbiology, School of Medicine, Chiba University,

water-solubilized lignin derivatives in vitro. *Agricultural and Biological Chemistry*,

(2001). Inhibition by *Agaricus blazei* Murill fractions of cytophathic effect induced by western equine encephalitis (WEE) virus on VERO cells in vitro. *Bioscence,*

the infectivity and cytophathic effect of human immunodeficiency virus by watersoluble lignin in an extract of the culture medium of *Lentinus edodes* mycelia (LEM). *Biochemical and Biophysical Research Communications,* Vol. 160, No. 1, pp. 3369-3372. Suzuki, H., Tochikura, T.S., Iiyama, K., Yamazaki, S., Yamamoto, N. & Toda, S. (1989b).

Lignosulfonate, a water-solubilized lignin from the waste liquor of pulping process, inhibits the infectivity and cytophatic effect of human immunodeficiency virus in

kidney cells and for glutamine and arginine of liver cells in culture. In: *Nutritional Requirements of Culture Cells*, H. Katsuta (Ed.), Japan Sci. Soc. Press, Tokyo and

on certain viruses *in vitro* or *in vivo*.

3, pp. 221-226

**3. References**

### *Edited by Daisuke Hayasaka*

Many infectious agents, such as viruses, bacteria, and parasites, can cause inflammation of the central nervous system (CNS). Encephalitis is an inflammation of the brain parenchyma, which may result in a more advanced and serious disease meningoencephalitis. To establish accurate diagnosis and develop effective vaccines and drugs to overcome this disease, it is important to understand and elucidate the mechanism of its pathogenesis. This book, which is divided into four sections, provides comprehensive commentaries on encephalitis. The first section (6 chapters) covers diagnosis and clinical symptoms of encephalitis with some neurological disorders. The second section (5 chapters) reviews some virus infections with the outlines of inflammatory and chemokine responses. The third section (7 chapters) deals with the non-viral causative agents of encephalitis. The last section (4 chapters) discusses the experimental model of encephalitis. The different chapters of this book provide valuable and important information not only to the researchers, but also to the physician and health care workers.

Pathogenesis of Encephalitis

Pathogenesis of Encephalitis

*Edited by Daisuke Hayasaka*

Photo by Ralwel / iStock