8. Conclusion

According to Figure 15; the highest Von Mises tensions were occurred the coating layer and the lowest values were observed in the base layer. It is fixed that The Von Mises tensions increased in the coating layer within 1 year period. It has been determined that the highest Von Mises tension were in SK type road coating whereas, the lowest Von Mises tensions were in N type coating. The Von Mises tensions of base layer indicated similarity properties and no

Vertical deformation (y)-time changes of the super-structure of the road is given in Figure 16.

When Figure 16 is evaluated, it is seen that the highest vertical deformations are found at the base and lower base layers and the lowest deformations are found at the coating layer. Within a year, there have been small deviations in the amount of deformation. The amounts of deformation were confirmed as 0.014492 mm in SK-type, and 0.0145 mm in NK-type in the 12th month [34]. Von Misses stress and vertical deformation function and R2 values are submitted in Table 10.

Von Mises stress function R2 Vertical deformation function R2

3E-05x + 0,0059 0,9683 y = 0,0001x + 0,0199 0,9686

S y = 3E-05x2 + 0,003x + 0,0088 0,9583 y = 0,0001x + 0,0199 0,9329

N y = 0,0001x2 + 0,0019x + 0,0092 0,9095 y = 0,0001x + 0,0199 0,8744

3E-05x + 0,0059 0,929 y = 3E-06x2

–0,0047x + 0,0096 0,9282 y = 3E-06x2

–0,0001x + 0,0199 0,9678

–0,0002x + 0,02 0,8994

1E-04x + 0,0199 0,9301

9E-05x + 0,0199 0,8785

–0,0001x + 0,0199 0,9059

Figure 16. Vertical deformation (y)-time changes of the super-structure of the road [34].

Coating SK y = 0,0001x2 + 0,0021x + 0,0101 0,977 y = 1E-06x2

NK y = 0,0099e0,1654x 0,9291 y = 8E-06x2

NK y = 7E-05x2 + 0,0003x + 0,0057 0,4428 y = 6E-06x2

significant changes were observed [34].

36 Modified Asphalt

Layer types Regression models Function type

Base SK y = 6E-07x2

S y = 4E-07x2

N y = 0,0012x2

It came out that the performance characteristics of the SBS-added the HMA are improved due to the use of the SBS polymer material as compared to the unmodified ones. This is because when the SBS polymer is used, the adhesion between the aggregates increases. It has been seen that the flexible structure of asphalt is stiffened due to environmental conditions due to oxidation, which is caused by temperature changes and precipitation during transportation, storage, mixing and production processes. In addition to, as the air void ratio increases, the hardening time decreases due to air contact. The hardening of the asphalt road coating was found to be higher in the coating without additive material and it was appeared that the polymer increased the deformation resistance at high temperatures and increased the tendency to fracture at low temperatures.

Analytical calculations from different material types and different strata in the flexible road coating are complex. The traffic loads and the distribution of pressure on the superstructures of the roads are varied, and different materials are used on each layer, and therefore the mechanical properties and the load distribution ability vary. Climate and various environmental conditions affect the road. Because of these reasons, empirical studies must be promoted with numerical analyzes to establish performance characteristics of the road.

### Acknowledgements

The author would like to thank Transportation Laboratory, Department of Civil Engineering, Firat University; Geotechnical &Transportation Laboratory, Department of Civil Engineering, Dicle University, Istanbul University and Bimtas Research Laboratories, Istanbul.


**3.2 Vasomotor symptoms**

Vasomotor symptoms are the main bothering and common symptoms in the early menopause. They include hot flushes, night sweats, and palpitation and are termed vasomotor symptoms because of vascular reactivity with initial prominent vasodilatation.

**49**

*Symptoms of Menopause*

**3.3 Mood disorders**

*DOI: http://dx.doi.org/10.5772/intechopen.88572*

osteoporosis, fragility fractures, and diabetes [4].

other menopausal symptoms as hot flushes and night sweats.

tive life events, impairment of activity, or physical illnesses.

flushes, employment status, ethnicity, and cultural attitudes.

obese women are at a higher risk.

**3.4 Genitourinary symptoms**

Hot flushes are transient periods of intense heat in the upper part of the body usually accompanied by sweating. Its exact mechanism is not known, but is believed to be due to narrowing of thermoregulatory zone of the hypothalamus with decline of estrogen level. Hot flushes and night sweats are experienced by about 40% in early transition, increasing to 60–80% in late menopausal women [3]. About 25% can be severe to cause significant distress. Sleep disturbances caused by hot flushes and sweating can lead to lethargy, poor physical functioning, and depressed mood. In about 25% of women, hot flushes may continue for over 5 years; in a small percentage of women, it may continue up to 10 years or more. In surgical menopause, vasomotor symptoms are abrupt and severe. Low body weight, lack of exercise, and smoking are risk factors for hot flushes. Severity, frequency, and duration of hot flushes vary with menopausal status, ethnicity, age, cultural factors, and woman's perception of menopause. In many western countries and Australia, 60–80% of women experience hot flushes, while the prevalence is 20–30% in Chinese and Japanese women. In Asia 40–60% of women have hot flushes. Hot flushes have a negative impact on quality of life as well as an association with adverse morbid conditions like cardiovascular disease,

Mood swings, anxiety, and depression are some of the psychological symptoms during menopausal change. Nearly half of women on menopause transition can get easily irritated. They become less patient with the members of the family, friends, and colleagues and often feel tired and sad. With emotional changes, they can appear nervous, stressful, and sometimes aggressive. Mood swings are due to changes in serotonin activity following estrogen decline but may also be caused by

Anxiety in the form of nervousness, worry, or panic attacks may occur during perimenopause. Hormonal changes, vasomotor symptoms, and midlife stresses contribute to anxiety during this period. Panic disorders are associated with nega-

Depression is more common in the menopause transition and early postmenopause than premenopausal. A number of reports indicate that there is a significant increased risk of new-onset depression in women during menopause [5]. Those who had previous episodes of depression are at a higher risk. Depression is associated with hormonal changes during this period, stressful life events, poor sleep, hot

Insomnia may be seen in some women, and they are more likely to have anxiety, stress, and depressive disorders. Sleep disorders are associated with menopause transition and also related to hot flushes, other physical health problems, psychosocial problems, and medication. Sleep apnea too may occur during this period, and

Estrogen receptors are present abundantly in the vagina, vestibule, and trigone area of the bladder. With estrogen deficiency after menopause, many anatomical and physiological changes occur in this area, which results in GSM. Genitourinary syndrome of menopause (GSM) is the new term for vulvovaginal atrophy (VVA). In 2012, the International Society for the study of Women's Sexual Health and the North American Menopause Society introduced genitourinary syndrome of menopause as a more accurate, comprehensive, and publicly acceptable term to replace atrophy or atrophic vaginitis. The Society considered "atrophy" to be a negative

#### *Symptoms of Menopause DOI: http://dx.doi.org/10.5772/intechopen.88572*

*Hormone Therapy and Replacement in Cancer and Aging-Related Diseases*

ing menopause commence) and the first year after menopause.

This term can be used synonymously with "premenopausal."

fied from the WHO 96238) is shown in the table below.

biological marker for the event does not exist.

Terminology Reproductive Menopausal

Natural menopause is recognized to have occurred after 12 consecutive months of amenorrhea, for which there is no other obvious pathological or physiological cause. Menopause occurs with the final menstrual period, which is known with certainty only in retrospect a year or more after the event. An adequate independent

The term "perimenopause" should include the time immediately prior to the menopause (when the endocrinological, biological, and clinical features approach-

The term "menopausal transition" should be reserved for the time before the final menstrual period when variability in the menstrual cycle is usually increased.

Stages −5 to –4 −3 −2 −T″ +1 +2

transition

Early peak Late Early Late Early late

Duration of stage variable Variable l year 4 years

length > 7 days different from normal

Regular Variable cycle

Endocrine Normal FSH ↑ FSH ↑ FSH FSH

*Stages of reproductive aging workshop (STRAW) system.*

Relationship between different time periods surrounding the menopause (modi-

Post Menopause

12 months amenorrhea

>2 skipped cycles and an interval of amenorrhea

demise

None

Until

Menstrual irregularities are a common complaint during menopause transition. Infrequent **ovulation** and anovulation lead to changes in the length of proliferative phase, and in the absence of progesterone, decline of estrogen leads to heavier and irregular menstruation. In some women scanty periods with long cycles may be the main symptom. The changes of bleeding pattern due to hormonal fluctuation usually occurs during perimenopause. The analysuisreported median age at inception of perimenopause is 47.5 years and smokers have not only early but a shorter perimenopause [2]. However, postcoital bleeding, prolonged bleeding, and post-

Vasomotor symptoms are the main bothering and common symptoms in the early menopause. They include hot flushes, night sweats, and palpitation and are termed vasomotor symptoms because of vascular reactivity with initial prominent vasodilatation.

**2. Definition**

**FMP**

**Perimenopause**

Menstrual cycles

**3. Symptoms of menopause**

Variable to regular

menopausal bleeding require further investigation.

**3.1 Menstrual irregularities**

**3.2 Vasomotor symptoms**

**48**

Hot flushes are transient periods of intense heat in the upper part of the body usually accompanied by sweating. Its exact mechanism is not known, but is believed to be due to narrowing of thermoregulatory zone of the hypothalamus with decline of estrogen level. Hot flushes and night sweats are experienced by about 40% in early transition, increasing to 60–80% in late menopausal women [3]. About 25% can be severe to cause significant distress. Sleep disturbances caused by hot flushes and sweating can lead to lethargy, poor physical functioning, and depressed mood. In about 25% of women, hot flushes may continue for over 5 years; in a small percentage of women, it may continue up to 10 years or more. In surgical menopause, vasomotor symptoms are abrupt and severe. Low body weight, lack of exercise, and smoking are risk factors for hot flushes.

Severity, frequency, and duration of hot flushes vary with menopausal status, ethnicity, age, cultural factors, and woman's perception of menopause. In many western countries and Australia, 60–80% of women experience hot flushes, while the prevalence is 20–30% in Chinese and Japanese women. In Asia 40–60% of women have hot flushes. Hot flushes have a negative impact on quality of life as well as an association with adverse morbid conditions like cardiovascular disease, osteoporosis, fragility fractures, and diabetes [4].


3.3.2. Execution Times

We estimated the time spent on classifying a sample, that is, the execution time for one ANN classification. This value is important to determine the power consumption for the process 3 in the scenario 2 (see Figure 6-right). We also analysed the results for each compressed model version. The results, which can be seen in Table 4, show that there is a slight variation in power consumption as the number of nodes decreases. The compressed version of each model does not seem to disturb the execution times, except again in the case of the models with the highest number of nodes in the hidden layer, which take longer to perform a classification. Considering the previous results, the architecture with the greatest effectiveness for this problem and with a good classification time is the one with three nodes in its hidden layer, which in turn can be compressed by a factor ×8 without altering performance.

Regarding the power consumption analysis the ANN with three nodes in the hidden layer and a ×8 compression is used.

## 3.3.3. Power Consumption Analysis

As detailed in previous sections, the main differences of both analysed scenarios are the communications frequency and the amount of data transmitted (see Figure 5).

**Table 4.** Estimated execution times per classification (milliseconds), of each model integrated and running in STM32L476RG board.

In the first scenario (see Figure 5-left), every 20 ms (50 Hz reading frequency) the embedded system takes: 0.07 ms reading the sensors' values (that consumes 6.1 mA), 3.9 ms transmitting via Bluetooth (that consumes 43.16 mA) and the rest of the time (16.03 ms) in sleep mode (that consumes only 18 μA). So, using an average button battery of 125 mAh capacity, the system has a battery life of 14 h.

In the second scenario, the calculation is not that easy because the system only transmits information once per step. So, two possibilities are evaluated: first, sensors are read but the step is not finished; and, second, sensors are read and the step is finished. In the first possibility (step is not end yet): sensors are read and values are accumulated (in this case, the embedded system does not classify and does not transmit). In the second possibility: sensors are read, values are accumulated, the final amount of data for the full step is normalized and classified using the ANN; and, finally, the classification result is transmitted.

Both possibilities are very different in the power-consumption analysis—the second one spends much more power that the first one because of the ANN classification and the transmission.

Moreover, if we compare the power-consumption between the first scenario (always transmitting) and the second possibility of the second scenario (transmission only once per step), there is a big difference too—in the second scenario, only one data transmission per step is done and the time spent in the transmission is less than in the first scenario because the amount of data transmitted is much lower (3 bytes versus 56 bytes), taking only 0.2 ms in the transmission process.

So, evaluating the second scenario, 0.07 ms are spent for sensors' reading (6.1 mA), 0.061 ms are spent for the ANN classification (255.1 μA), 0.2 ms are spent for the transmission (43.16 mA) and the rest of the time (19.66 ms) the system is in sleep mode (18 μA). However, if the step is not ended, the system does not transmit and there is no classification process (only periodic sensors' reading); so, during the time spent in these two phases, the system is in sleep mode too.

The first scenario is relatively easy to evaluate in the power-consumption study, but the second scenario is much more difficult because it depends on the user's gait cadence. So, in order to obtain a more accurate power consumption study for it, the gait cadence of the user must be evaluated. Using the information obtained after the study done in [31], we can observe than a gait cadence less than 100 steps/min corresponds to a low intensity (walking), a cadence between 100 and 130 steps/min corresponds to medium intensity (jogging) and a cadence higher than 130 steps/min corresponds to high intensity (running).

Thus, in our case, we have analysed the power consumption with a sensors' reading frequency fixed at 50 Hz and cadence values between 30 steps/min and 160 steps/min, obtaining the results presented in Table 5. The first column indicates the gait cadence in number of steps per minute and varies from 30 to 160; in the second one, the time spent for each step (in seconds) for each gait cadence is detailed; the third one calculates the total number of samples collected for each step using the data from the previous columns and using only one foot (as one instrumented insole collects information from one foot).

Instead of evaluating the power consumption of both possibilities in the second scenario (step ends or not), for this power-consumption estimation we assume that all the sensors' readings imply a classification

step and a data transmission and, depending on the number of samples for each step, the consumption of those processes are multiplied by a factor (between 0 and 1) that indicates the proportion of transmissions depending on the gait cadence. For example, if we always transmit, this factor will be 1; or, if we have 10 samples per step, we transmit only 10% times, so this factor is 0.1. So, the fourth column of Table 5 represents the result of the power consumption of the classification and transmission processes (43.16 mA approx.) multiplied by this factor. Finally, the fifth and sixth columns indicate the average consumption of the full system (in μA) and the final battery life (in hours), respectively.

**Table 5.** Number of steps, transmissions and power consumption varying the gait cadence of the user.

Hence, as can be seen in Table 5, in the worst case (highest gait cadence evaluated) the battery life exceeds 25 days. So, it improves the battery life of the first scenario by more than 43 times (an improvement of 4321%). This comparison can be observed in Figure 7.

**Figure 7.** Battery life (in hours) with the power consumption estimation for each scenario.

The results presented in Figure 7 show that the higher the gait cadence, the lower the battery life is. Despite this, the life of the used battery (125 mAh) even for cases in which the user is running high enough to allow a biomechanical gait study without recharges.


3.6.1. Clinical Signs and Mortality

After being challenged with virulent HP-PRRSV JXA1, all piglets in the PRRSV group (piglets challenged with JXA1) displayed high rectal temperature (Figure 6A) and exhibited typical severe clinical signs, including in-appetence, lethargy, dyspnea, periocular and eyelid edema and hyperspasmia. Piglets treated with Rg1 after JXA1 challenge (PRRSV+Rg1 group) showed moderate anorexia and depression. Animals in Rg1 group (piglets only treated with Rg1) and mock group (piglets only treated with DMEM) behaved normally during the course of the experiment. The scores of the evaluation of clinical signs in the PRRSV+Rg1 group were significantly lower than PRRSV group at 7 d.p.i. (*p* < 0.05) (Table 3), a time-point of peak period of morbidity and mortality. All of the piglets displayed increased rectal temperature higher than 40 ◦C after challenge with HP-PRRSV JXA1, while piglets treated with Rg1 showed gradual descending since 8 dpi (Figure 6A). The survival rate of piglets in PRRSV group is 0% to 11 dpi, however, 40% animals in the PRRSV+Rg1 group (piglets treated with Rg1 after JXA1 challenge) survived to 14 dpi (Figure 6B). Since piglets showed different loss of appetite during infection course, the body weight of PRRSV and PRRSV+Rg1 displayed obviously drop after challenge, while the body weight of piglets in PRRSV+Rg1 displayed slower drop trend and begin to rise at 10 dpi (Figure 6C).

**Table 3.** The scores of clinical signs and lung lesions of the different groups at 7 d.p.i.a.

a Values followed by letters 1 represents significant difference (*p* < 0.05) between PRRSV and PRRSV+Rg1; letter 2 represents significant difference (*p* < 0.05) between Mock/Rg1 and PRRSV/PRRSV+Rg1. b The clinical sign score was calculated by sum of behavior performance, appetite, respiration according to the extent of severity. c Analyzing the percentage of the macroscopic lesion features of pneumonia in entire lung.

### 3.6.2. Pathological Examination

As previously described, piglets infected with JXA1 began to die within 6–8 d.p.i. [9], and animals in the PRRSV group start to die at 6 d.p.i. in our study. Therefore, three living pigs of each group were euthanized for pathological examination at 7 d.p.i. to evaluate whether Rg1 treatment relieved lung injury.

The lungs of pigs in PRRSV+Rg1 group showed fewer pathological lesions and got significantly lower scores of macroscopic injury of lungs than those in the PRRSV group as shown in Table 3 (*p* < 0.05). Further histological examination of lung tissue showed that PRRSV+Rg1 group exhibited moderate interstitial pneumonia, while the PRRSV group were characterized by thickened alveolar walls, interstitial fibro-plastic proliferation and intensive mononuclear cell infiltration, which revealed the severe viral pneumonia (Figure 6D). At 15 dpi, all of the living pigs were euthanized for pathological examination. Due to no piglet survived at 14 dpi in the PRRSV group, the deceased pigs at 10 dpi of it were collected to perform pathological examination. The results displayed the same trend as 7 dpi. In the PRRSV+Rg1 group, the survived piglets at 15 dpi exhibited mild pneumonia compared with that of the mock group and Rg1 control group (Figure 6D).

#### 3.6.3. Viremia and Tissue Viral Load

Blood samples of the piglets were collected at 2, 4, 7, 10, 14 dpi to determine viral load and N protein antibody of PRRSV. And, the viral load in tissues, include lung, lymph node and thymus were determined by qRT-PCR as described previously [31]. Since all of the animals in the PRRSV group died at 11 d.p.i., there was no viremia and tissue viral load analysis on it at 14 or 15 h.p.i. The data revealed that piglets treated with Rg1 showed significantly lower viremia, indicated by antibody against PRRSV and viral load in serum, in the blood than those in PRRSV group at 7 (*p* < 0.05) and 10 dpi (*p* < 0.01) (Figure 6E,F). Meanwhile, PRRSV+Rg1 group displayed significantly reduced viral load in the lung, lymph node and thymus at 7 dpi (*p* < 0.01) (Figure 6G).

**Figure 6.** Rg1 exhibits anti-PRRSV activity in 4-week-piglet. (**A**) Daily rectal temperature of the pigs in the PRRSV, PRRSV+Rg1, Rg1 and mock groups. Rectal temperature reach or beyond 40 ◦C was defined as fever. (**B**) The mortality of each group was recorded daily and calculated as survival rate until 14 dpi. (**C**) The body weight gain of different groups during the experiment. (**D**) Severe lung lesions in the PRRSV group characterized by swelling, congestion, fibrosis, and inflammatory cell aggregates; however, in the PRRSV+Rg1 group, these index were moderate. Due to no piglet survived at 14 dpi in the challenge control group (PRRSV), the lung of the deceased pigs at 10 dpi was used to perform pathological analysis. (**E**) The anti-PRRSV antibody levels in serum at different time-points. The value of S/*p* ratio ≥ 0.4 was considered antibody positive. (**F**) The level of PRRSV mRNA in the serum was measured by real-time PCR. (**G**) The expression level of PRRSV mRNA level in lungs, lymph node and thymus was measured by qRT-PCR. Each tissue sample was measured three times, and the error bars represent the standard deviations of samples.

#### **4. Discussion**

PRRSV was identified in Europe in 1991 and emerged in United States in 1992 [32,33]. This virus is classified into European genotype (type 1) and North American genotype (type 2) [3], and the widespread outbreaks of PPRS in China are associated with constant evolution of viruses through high frequency of recombination and immune suppression events in recent decades. The protective immunity to PRRS elicited by current vaccines is effective only against homologous infections and exhibits partial protection from heterologous PRRSV. New antiviral therapeutic strategies became an urgent need. In the present study, we demonstrate that Rg1 suppressed broad lineages of type 2 PRRSV infection, including HP-PRRSV, classical strain and NADC30-like strains, both in Marc-145 cells and PAMs. It suggests that Rg1, as a natural herbal molecular, could be applied in broad-spectrum anti-PRRSV medicament.

PRRSV infection triggers the up-regulated release of IL-1β, IL-6, IL-8 and TNF-α [6,34,35]. These pro-inflammatory factors contribute to the stimulation of protective immune responses, while it also leads to the development of excessive systemic inflammatory reactions and causing inflammatory lesions [36]. Extensive studies have illustrated the potent anti-inflammatory effect of Rg1 treatment in various diseases by regulating inflammatory cytokine expression [37–39]. However, the anti-viral activity of Rg1 associated with modulating inflammatory response was poorly described. In our study, the results reveal that the expression level of several pro-inflammatory factors, including IL-1β, IL-6, IL-8 and TNF-α, is significantly reduced by Rg1 treatment in Marc-145 cell and PAMs upon PRRSV infection. It suggested that Rg1 moderate the PRRSV-induced inflammatory responses by decreasing mRNA expression of pro-inflammatory cytokines in vitro.

PRRSV infects host via membrane receptor mediated endocytosis process including virus attachment and binding, membrane fusion, and followed internalization [40,41]. Once the viral genome, single strand positive-sense RNA, is released into the cytoplasm, it substantially process translation to generate replication and transcription complex. Here, we performed analysis to determine whether Rg1 affect PRRSV lifecycle. The results indicated that pre-treatment of Rg1 could not affect virus host-cell tropism. Marc-145 cells treated with Rg1 during different period of infection process were analyzed respectively, the results showed virus attachment, internalization, and release were impaired (Figure 2). Meanwhile, the inhibitory effect or Rg1 on PRRSV replication stage was more obvious.

NF-κB, key transcription factor that regulates the activation of inflammatory cytokines, can be activated by virus infection, viral gene expression or by LPS stimulation [29], and it could be exploited by influenza viruses or type 1 HIV to sustain a high viral replication [42,43]. In view of the role of Rg1 played in suppressing pro-inflammatory factor expression triggered by PRRSV (Figure 4) and PRRSV-induced NF-κB activation facilitated its replication was demonstrated in previous report [30]. Therefore, we wondered Rg1 inhibited virus replication were associated with cellular process indirectly. In the present study, we identified that Rg1 alleviated PRRSV infection induced IκB degradation, phosphorylation level of p65 and p65 nuclear aggregation, important factors contribute to NF-κB activation, in Marc-145 cells (Figure 5). Moreover, our data indicated that Rg1 treatment suppressed NF-κB activity in LPS-treated Marc-145 cells (Figure 5), which was consistent with a previous study in mouse RAW 264.7 cells and macrophages [44]. This result suggested that the decreased NF-κB activation in Marc-145 cells, which treated with Rg1 upon PRRSV infection, was not due to a reduction in virus infection, it involved in the interaction between Rg1 and cellular process. Overall, the inhibitory effect on both IκB degradation and NF-κB nuclear translocation signaling contributes to the anti-PRRSV replication activity of Rg1. Possibly, it can be speculated that Rg1 mediated cellular process contributes to its anti-viral activity and it might be widely applied in other viral infection.

Recently, multiple Chinese traditional medicines were shown to possess anti-PRRSV ability in vitro [12–14]. However, the antiviral activities of these identified natural herbal extracts, active ingredients, compound or drugs have not been further evaluated in vivo. In present study, piglets challenged with HP-PRRSV JXA1 under Rg1 treatment showed increased survival rate, moderate pneumonia and lower serum and tissue viral loads compared to those in the PRRSV group (challenge

control) (Figure 6). It suggests Rg1 might be a natural anti-PRRSV agent that could be considered as an adjuvant therapy in the pig herd production. Furthermore, it has been confirmed that ginseng extract inhibited virus infection including influenza virus and hepatitis C virus, and Rg1, which purified from the roots or stems of Panax notoginseng (PN) and Panax ginseng (PG), could also be used as an immunoadjuvant to improve immune responses [45,46]. In the current status, there are several Modified live Virus (MLV), which induced immune response did confer protection to vaccinated animals against homologous PRRSV challenge, licensed in various countries and extensively applied in pig farms [47,48]. However, existing evidence suggests that these MLV vaccines of type 1 or type 2 both stimulates limited humoral and cellular immunity and fail to against heterogeneous strain [49,50]. Therefore, it would be possible that ginseng extract used as a natural supplement in feeding management for disease prevention. Besides, it might be used in a combination with some direct-acting antivirals to achieve widely effective disease prevention or as immune-adjuvant of vaccination to reach immune enhancement. For the proper application of it, the pharmacokinetic data of ginseng extract in swine model needs further systematic study. Meanwhile, the optimum dosage and administration mode should be defined reasonably to avoid drug residues in meat production. Considering the economic benefit and most of the nutrient component were similar between PN and PG, PN might possess more potential value of application in the scaled raising of pigs.

In summary, our study demonstrated that ginsenoside Rg1 with low cytotoxicity and possess anti-PRRSV activity both in vitro and in piglets. And, it suppressed different lineages of type 2 PRRSV infections. These findings not only provide new insights into the molecular mechanism of Rg1 against PRRSV infection but also suggest a potential immune-modulatory and anti-viral agent in the control of the PRRS.

**Author Contributions:** Conceptualization, G.-h.Z. and Z.-q.Y.; Methodology, Z.-q.Y. and Y.C.; Software, H.-y.Y.; Validation, Q.L. and R.-t.Z.; Formal Analysis, M.-k.C.; Investigation, Z.-q.Y., H.-y.Y., J.M., C.-x.Q., G.L., Q.D., Y.-j.C., and X.-l.H.; Resources, G.-h.Z., H.W. and K.T.; Data Curation, Y.-f.W.; Writing—Original Draft Preparation, Z.-q.Y.; Writing—Review & Editing, G.-h.Z.; Visualization, Z.-q.Y.; Supervision, H.W.; Project Administration, G.-h.Z. and Z.-q.Y.

**Funding:** This project was supported by the National Natural Science Foundation of China (Grant No. 31872489), the Modern Agro-industry Technology Research System (Grant number CARS-35) and the Natural Science Foundation of Guangdong province (Grant No. 2018A030313712).

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


**INTERDISCIPLINARY EXPANSIONS IN ENGINEERING AND DESIGN WITH THE POWER OF BIOMIMICRY**

Edited by **Gulden Kokturk** and **Tutku Didem Akyol Altun**

#### **Interdisciplinary Expansions in Engineering and Design With the Power of Biomimicry**

http://dx.doi.org/10.5772/65987 Edited by Gulden Kokturk and Tutku Didem Akyol Altun

#### **Contributors**

Ayça Tokuç, Feyzal Ozkaban, Özge Andiç Çakır, Julien R Serres, Thierry Darmanin, Sabri Taleb, Frédéric Guittard, Petra Gruber, Tim McGinley, Manuel Muehlbauer, Irem Deniz, Tugba Keskin, Maria Lorena Lehman, Hyunsoo Lee, Nayeon Kim

#### **© The Editor(s) and the Author(s) 2018**

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**Abstract**

This chapter aims to analyze works in the literature that show the therapeutic effects of using the Leap Motion Controller (LMC) sensor to assess hand fine motor dexterity, especially those involving the Box and Blocks Test. Besides the introduction, we will describe: (a) the LMC device and its forms of interaction in a Virtual Reality environment (immersive and non-immersive); (b) aspects of manual function assessment; (c) the functioning of the traditional Box and Blocks Test (BBT) and its virtual version (VBBT) developed with Virtual Reality technologies; (d) discussion about the VBBT integrated with the LMC, in physical therapy practice.

**Keywords:** moto rehabilitation, manual dexterity, leap motion, box and blocks test, virtual reality