*2.3. Number of Daily Steps*

A fitness tracker, which includes a pedometer, is a useful device to monitor the number of daily steps and provide feedback to promote walking [33]. Although 10,000 daily steps are widely recommended, evidence from previous prospective studies is still incomplete. According to the literature, a greater number of steps was associated with lower CVD, cancer, and all-cause mortality. Furthermore, a higher gait speed could also be associated with a reduced risk of death [34].


**9. Simulation results Of RGSGCS and MSA**

The simulation results of algorithms RGSGCS, MSA and Min-Min of RM are shown in table 3 for eight different experiments. The reason of testing experimenting is to explore the dynamic behavior of grid environment. For the experiment 1, workloads of tasks are (6, 12, 16, 20, 24, 28, 30, 36, 40, 42, 48, 52, 60) cycles, and the computing capacities of resources are (4, 3, 2) Cycle Per Unit Time(CPUT). The computing capacities of resources, experiments 2 to 8 in the table 3, diverse randomly from 2 to 8 CPUT for each resource. Furthrmore, the workload of tasks ranges randomly from 10 to 150 cycles. The parameters of MSA and RGSGCS are listed in table 2. MSA, Min-Min and RGSGCS are simulated using MATLAB. In table 3, the values of makespan, average time and *LBF* for both algorithms RGSGCS and MSA are compiled together for purpose of comparison. Table 3 and figures( 10 (a), (b) and (c) ) lay out that:

The average gains in time of MSA expressed as percentages are 97.66%, 91.03%, 96.83%, 89.6%, 93.35%, 90.95%, 92% and 92.29% for experiments 1 upto 8 respectively. Hence the total average gain in time for all the experiments is 92.964%.

1 2 3 4 5 6 7 8 0

Expt.No.

0 100 200 300 400 500 600 700 800 900 1000 0

Min−Min MSA RGSGCS

TasksNo

(b) *LBF* values of MSA, Min-Min and RGSGCS

MSA RGSGCS Min−Min

1 2 3 4 5 6 7 8 0

Expt.No.

(c) Utilization of Resource of MSA, Min-Min

probability of crossover, probability of mutation, ppopulation size and maximum generations number to the values 1 , 1, 50 and 1000, respectively in the experiments in table 3. Table 4 dispays new values of solution for both algorithms MSA and RGSGCS. This table provides the results of MSA algorithm for two different termination criterions, namely *T < e*−300 for MSA(1) and *T < e*−50 for MSA(2). Note that, in tables 3, 4, *MR* denotes reduction in makespan, which is difference between makespan values for both algorithms RGSGCS and MSA. Two experiments 9 and 10 are added to ensure scalability of both algorithms MSA and

The Expected Time to Compute (ETC) model is another model can also test performance of MSA algorithm. Interestingly, ETC matrix model allows to capture important characteristics of task scheduling. For example, ETC model introduces possible inconsistencies among tasks and resources in grid system by assigning a large value to *ETC*(*t*, *m*) to indicate that task *t* is

Moreover, ETC matrix considers three factors: task heterogeneity, resource heterogeneity and consistency. The task heterogeneity depends upon the various execution times of the

Load Balancing Factor (LBF)

Task Scheduling in Grid Environment Using Simulated Annealing and Genetic Algorithm 103

(a) Makespan values of MSA, Min-Min and

and RGSGCS

Fig. 10. Simulation Results of Random model

Average Utilization of Resource

RGSGCS

RGSGCS.

**11. ETC model**

incompatible with resource *m*.

Makespan

MSA RGSGCS Min−Min

Table 2. Parameters used in RGSGCS/MSA

MSA algorithm has reduction in makespan value equals to eighteen (18) when it is compared with algorithm Min-Min and equals to three (3) when it is compared with algorithm RGSGCS. Moreover, *LBF* values of algorithms MSA, Min-Min and RGSGCS are in ranges [0–1.53], [6.4–29.56] and [0–8.12] respectively.

From results discussed above, it can be concluded that MSA algorithm dynamically optimizes output schedule closer to global optimal solution.

Note that the MSA algorithm outperforms RGSGCS algorithm within very less time to run the algorithm. Depending on SA algorithm and random-MCT heuristic, MSA algorithm is powerful when it is compared with RGSGCS algorithm, while RGSGCS algorithm has less convergence to the optimal solution.

The results of the comparison among algorithms RGSGCS , Min-Min and MSA in each experiment in the table 3, prove that MSA algorithm provides an effective way to enhance the search performance, because it obtains an optimal schedule within a short time along with high resource utilization.

Notably, the solutions of MSA algorithm are high quality and can be used for realistic scheduling in grid environment. The simulation results are consistent with the performance analysis in section 8, which clarifies that the improvement to the evolutionary process is reasonable and effective.


**4. Conclusion**

This current work has studied the effect of vehicle interior padding thickness on the response of three head form FEM models subjected to an impact loading. The

*Advancement and New Understanding in Brain Injury*

aspect involved in the causes of long-term TBI [22].

significant decrease of the peak strain values. However, when analyzing the middle region of the brain, the peak stresses resulted in a much lower value, overall. The stress wave fluctuations in this region, shown in **Figure 6(a)**, also resulted in a decrease of peak strain values with the presence of the CSF. However, for the most simplified model, SB, the drastic change in strain value due to the stiffness of the system, as well as the stress fluctuations between the coup and contrecoup could potentially cause a significant shear tear-out behavior of the brain tissue. Such behavior could lead to a diffuse injury, or shear injury, which is an important

**Figure 7** illustrates the pressure being transmitted through the brain due to the impact with a padding thickness of 25 mm at various time histories. The pressure

**148**

**Figure 7.**

**Figure 6.**

*Strain-time graph: (a) middle, (b) front.*

*Pressure response: (a) SB, (b) SCB, (c) SCCB.*

numeral results revealed that the responses of the head and the brain under impact load were highly influenced by the padding thickness, the head skull material modeling and assumptions, and neck compliance. The results from this study are summarized as follows:

Overall, the numerical simulations have provided qualitative and quantitative information about the response of the head against impact loading. The current work could be considered an alternative insight to understand the correlation between the vehicle interior padding, various types of head form models, materials modeling, and output parameters such as acceleration, strain, and pressure that can be correlated to TBI resulting from a vehicle crash.


**2. Etiology**

#### **2.1 Normal micturition**

The normal micturition cycle includes inhibition and contraction of detrusor smooth muscle, afferent signaling from the urothelium, contraction and relaxation of the smooth and striated sphincter muscles, and the central, peripheral, and autonomic nervous systems.

Urine storage occurs secondary to afferent signals stimulated by bladder filling. These afferent signals activate sympathetic pathways in the hypogastric and pudendal nerves, which lead to contraction of the smooth and striated sphincters, and at the same time inhibit detrusor contraction. In addition, high cortical centers are activated, (Griffiths et al, 2007), and stimulate the storage center in the pons. (Fowler et al, 2008) When further bladder filling leads to increased afferent signaling from the bladder, spinobulbospinal reflex pathways are carried via the pelvic nerve and spinal cord to the pontine micturition center, which activates parasympathetic pathways that cause bladder contraction and inhibit sympathetic and pudendal contraction of the sphincter (Fowler et al, 2008)

Diagnosis and Treatment of Overactive Bladder 165

is a reduction of blood flow due to increased intravesical pressure during voiding or the increased tissue pressure of hypertrophied bladder wall during filling. (Azadzoi et al, 1996)

The prevalence of OAB in both men and women increases with age. In addition, storage symptom scores increase with age while bladder compliance decreases. (Koelbl et al, 2009) This implies that bladder function in both sexes has age related alterations. (Araki et al, 2003) There can be difficulty however, in determining in the elderly, the difference between

Menopause and estrogen deficiency have been implicated in the etiology of OAB symptoms. (Koelbl et al, 2009) Estrogen receptors (ERs) have been identified in the bladder and urethra. (Blakeman et al, 2000) The effect estrogen has on bladder contractility has yet to be elucidated. However, it has been shown that estrogen replacement therapy can significantly improve the symptoms of frequency, urgency and urge incontinence. (Eriksen & Rasmussen,1992) In addition, a metaanalysis of the effects of estrogen therapy on symptoms of OAB in postmenopausal women showed that estrogen therapy was associated with significant improvements in all symptoms of OAB. (Cardoza et al, 2004b) Thus it appears that menopause plays role in the development of bladder overactivity and OAB symptoms in

Idiopathic detrusor overactivity is a diagnosis of exclusion of all other known causes. Mechanisms that have been proposed for idiopathic detrusor overactivity include myogenic,

Mills et al, noted that denervation is consistently found in detrusor biopsies from patients with non-neurogenic detrusor overactivity. (Mills et al, 2000) They hypothesize that partial denervation of the detrusor alters the properties of smooth muscle, which leads to increased excitability and increased coupling between cells. Thus, myogenic changes in the bladder

Another mechanism that has garnered interest in idiopathic detrusor overactivity is the roles of the urothelium and suburothelial myofibroblasts in afferent activation. The C-fiber afferents have endings in the suburothelial layer of the bladder wall, and may reach the urothelium. (Koelbl et al, 2009) Upon bladder distention ATP has been shown to be released from the urothelium. (Ferguson et al, 1997) ATP receptors on afferent nerve terminals are stimulated by ATP release to evoke a neural discharge. It has been proposed that there is up-regulation of the afferent activation mechanisms(eg. an increased generation/release of ATP increased sensitivity of afferent nerves to mediators, increased number of afferent

nerves) can cause the symptoms of OAB. (Koelbl et al, 2009)

(Greenland & Brading, 2001)

**<IP_ADDRESS> Estrogen deficiency**

**2.2.3 Idiopathic etiologies**

urothelial and muscarinic.

increase contractility locally.

**<IP_ADDRESS> Myogenic**

**<IP_ADDRESS> Urothelial**

neurogenic and non-neurogenic causes.

**<IP_ADDRESS> Aging**

women.

For coordinated micturition to occur, parasympathetic stimulation of the detrusor occurs via cholinergic muscarinic receptors. Urethral smooth muscle contraction occurs chiefly by stimulation of alpha-adrenerigic receptors. (Fowler et al, 2008) In addition, a variety of neurotransmitter systems in the urothelial lining of the bladder and in bladder interstitial cells likely play a role in mediating bladder contraction and relaxation via afferent signaling. (Andersson, 2002) This complex interplay results in socially appropriate and effective voiding. Any disruption in this pathway can lead to storage and/or emptying disorders. Bladder overactivity may be related to neurogenic, myogenic, or idiopathic origins.

### **2.2 OAB etiology**

The presumed etiology of OAB is uninhibited bladder contractions, but overactivity is not sufficient to cause incontinence (e.g. OABdry). In addition, leakage symptoms may be due to factors outside of the lower urinary tract such as failure of compensatory mechanisms in the lower urinary tract (e.g. fascial and muscular urethral support "hammock" that compresses the urethra when there is increased abdominal pressure or when the pelvic muscles are contracted) , and functional impairments in some patients.


**3.2. Ketamine-induced apoptosis**

TUNEL staining was used to assess cell death in hESC-derived neurons following ketamine exposure by labeling breaks in the DNA. The cells were exposed to 100 μM ketamine or control conditions for 24 h. The number of TUNEL-positive cells was significantly increased when compared to control treated cells following ketamine exposure (**Figure 2**). These findings confirm many of the previously published animal studies which have shown increased neuronal cell death following exposure of the developing brain to ketamine [39]. We focused on the investigation of the effect of ketamine on the intracellular calcium level, mitochondrial signaling, and microRNA expression in order to understand the mechanisms governing ketamine-induced cell death.

#### **3.3. Alterations in intracellular calcium levels**

exhibited a characteristic neuronal morphology with small cell bodies and long projections. The neurons also formed extensive, interconnected networks over time. The cells displayed a very distinct morphology at each stage of the differentiation protocol. The hESCs formed tight colonies when cultured on a feeder layer and the EBs formed three-dimensional aggregates when suspended in culture media. At the neural rosette stage, the NSCs formed tightly packed arrangements with a characteristic design and were bordered by several additional cell types. Once mechanically separated from the surrounding non-NSCs and digested, the NSCs separated from one another and spread out on the matrigel-coated dishes. At this point,

**Figure 1.** Differentiation protocol and confocal images of immunolabeled human embryonic stem cell (hESC)-derived neurons. (A) Neurons were derived from hESCs through a four step differentiation protocol. (B) Neurons were stained with antibodies against the neuron-specific proteins, microtubule-associated protein 2 (MAP2) and β-tubulin III to assess

the differentiation efficiency, and doublecortin to confirm the immaturity of the neurons [37].

The neurons were immunostained after 2 weeks in differentiation media and expressed the neuron-specific markers MAP2 and β-tubulin III and formed synapses as assessed by the positive staining of Synapsin I. Based upon the immunostaining, the differentiation protocol was 90–95% efficient in the generation of neurons. In an attempt to better gauge the maturity level of the hESC-derived neurons, the cells were also immunostained for doublecortin, a marker of immature/migrating neurons. In assessing the staining results, most of the neurons in culture (90–95%) were positive for this marker of immature neurons (**Figure 1B**) which suggests that this model system is a valuable representation of developing human

the cells proliferated extensively.

neurons.

428 Mitochondrial Diseases

Following exposure of 2-week-old hESC-derived neurons to 100 μM ketamine or control conditions for 24 h, the intracellular calcium levels in the cells were assessed using Fluo-4AM fluorescence. The intracellular calcium levels were significantly elevated in the ketaminetreated cells when compared to control treatment (**Figure 3**). Calcium is a critical ion in the body and is fundamental in proper neuronal functioning. In neurons, calcium is crucial in synaptic activity and plasticity, cell signaling, neurotransmitter release and is involved in nearly every aspect of the cell cycle [24]. The careful balance of intracellular calcium levels is crucial to cell survival. Calcium homeostasis dysregulation, in particular calcium overload in the cell, has been linked to many different neurodegenerative diseases [25]. The findings from this study suggest that disrupted intracellular calcium homeostasis may also be linked to ketamine-induced cell death in developing neurons which could prove to be a novel therapeutic target.

#### **3.4. Ketamine induces neuronal apoptosis via mitochondrial pathway**

The mitochondria are extremely important organelles involved in many cellular processes including energy production, cell signaling, and apoptosis [40–46]. Given that ketamine may

**Figure 2.** Exposure to 100 μM ketamine for 24 h induced significant cell death in the hESC-derived neurons. Ketamine induced an increase in the number of TUNEL-positive cells indicating significant cell death when compared to controltreated cells. \*P < 0.05 vs. control. n = 3 for each group.

**Figure 3.** Ketamine exposure for 24 h induced elevated cytosolic Ca2+ [(Ca2+) c ] of neurons. (A) Ketamine increased (Ca2+) c . Neurons were loaded with the Ca2+ indicator Fluo-4AM. The fluorescence images of free cytosolic Ca2+ in the 2-week-old differentiated neurons are shown. (B) Ketamine (100 μM, 24 h) significantly increased (Ca2+)c (\*\*P < 0.01, n = 3).

cause mitochondrial damage, we measured ΔΨm and distribution of cytochrome c in the cells. Treatment of hESC-derived neurons with 100 μM ketamine for 24 h significantly decreased ΔΨm (**Figure 4A**). In order to investigate the distribution of cytochrome c within the cells, the neurons were transduced with the virus CellLight™ mitochondria-GFP (green). GFP and TMRE signals were colocalized in the cells (**Figure 4B**), confirming that successfully labeling of mitochondria with GFP. GFP-positive cells reached 40% (**Figure 4C**). The distribution of cytochrome c in mitochondria and cytosol was examined using immunofluorescence staining. The results showed that cytochrome c was located within mitochondria in the control culture (**Figure 4D**). However, in the ketamine-treated cells, cytochrome c was released from the mitochondria into cytosol.

Neuroapoptosis is a commonly recognized harmful effect by anesthetics with mechanisms that are not fully understood. Apoptosis is a programmed cell death. Cytochrome c release from mitochondria precededing the loss of ∆Ψm is a key event in initiating mitochondria-involved apoptosis [47], eventually leading to the typical alterations related to apoptosis such as DNA fragmentation in cell nuclei [48, 49]. In this study, following ketamine exposure, there was a significantly increase in the TUNEL-positive apoptotic cells (**Figure 2**). Ketamine-induced cell death was accompanied by the significant decrease in ∆Ψm and the increased cytochrome c release from mitochondria into cytosol (**Figure 4**). These data were in agreement with those previously reported by others [48, 50–52], suggesting that ketamine induces human neuron to undergo mitochondria-mediated apoptosis pathway.

To maintain proper cellular function, the mitochondria continuously undergo cycles of fusion and fission. Unbalanced fusion/fission, particularly excessive fission/fragmentation can lead to various pathological conditions including neurodegeneration [53]. To assess mitochondrial fission shape in the hESC-derived neurons following exposure to ketamine, electron microscopy was used. The neurons were exposed to either 20 μM or 100 μM ketamine or control conditions for 24 h. The cells were then prepared and imaged on an electron microscope. The mitochondria appeared considerably fragmented in the ketamine- treated neurons when compared to control cells (**Figure 5**). Increased mitochondrial fission has been linked to the propofol-induced cell death in hESC-derived neurons as previously published by our group [54]. Our findings suggest that ketamine exposure results in an increase of mitochondrial fission within the developing neurons, which may contribute to the increased cell death observed in the ketamine-treated group.

**Figure4.** Ketamine decreases mitochondrial membrane potential (ΔΨm) and increases cytochrome c release from mitochondria into cytosol. (A) ΔΨm assay. Ketamine (100 Μm) treatment for 24 h decreased ΔΨm (\*P < 0.05 vs. control, n = 3). (B) Labeling mitochondria of neurons with CellLight™ mitochondria-green fluorescence protein (GFP) reagent. The GFP-positive cells were loaded with TMRE. GFP expression in mitochondria was confirmed by the colocalization of tetramethylrhodamine ethyl ester (TMRE/mitochondrial probe) and GFP signals within the cells. (C) Representative fluorescent images of the neurons transduced with CellLight™ mitochondria-GFP reagent. Blue are cell nuclei. 40% cells were GFP positive. (D) the effect of ketamine on the distribution of cytochrome c in neurons. Cells were labeled with CellLight™ mitochondria-GFP reagent expressed GFP in mitochondria and then treated with ketamine (100 μM, 24 h). The distribution of cytochrome c in cells was analyzed by immunofluorescence staining. Column 1 is the image of mitochondria; column 2 is the image of cytochrome c; and column 3 is the merged image. The inset in the top corner of each image is the magnified box indicated by white arrows. The orange signals in the merged images indicate the existence of cytochrome c inside the mitochondria, and the signals in the merged images indicate the existence of cytochrome c outside the mitochondria. Ketamine treatment (100 μM, 24 h) increased cytochrome c release from mitochondria into cytosol. *Note: The cytochrome c signals (indicated by blue*

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431

*arrows) that do not overlap with GFP florescence were from non-transduced cells*. Scale bar = 10 μm [20].

Ketamine Induces Neuroapoptosis in Stem Cell–Derived Developing Human Neurons Possibly… http://dx.doi.org/10.5772/intechopen.72939 431

cause mitochondrial damage, we measured ΔΨm and distribution of cytochrome c in the cells. Treatment of hESC-derived neurons with 100 μM ketamine for 24 h significantly decreased ΔΨm (**Figure 4A**). In order to investigate the distribution of cytochrome c within the cells, the neurons were transduced with the virus CellLight™ mitochondria-GFP (green). GFP and TMRE signals were colocalized in the cells (**Figure 4B**), confirming that successfully labeling of mitochondria with GFP. GFP-positive cells reached 40% (**Figure 4C**). The distribution of cytochrome c in mitochondria and cytosol was examined using immunofluorescence staining. The results showed that cytochrome c was located within mitochondria in the control culture (**Figure 4D**). However, in the ketamine-treated cells, cytochrome c was released from

Neurons were loaded with the Ca2+ indicator Fluo-4AM. The fluorescence images of free cytosolic Ca2+ in the 2-week-old

c

] of neurons. (A) Ketamine increased (Ca2+)

(\*\*P < 0.01, n = 3).

c .

**Figure 3.** Ketamine exposure for 24 h induced elevated cytosolic Ca2+ [(Ca2+)

differentiated neurons are shown. (B) Ketamine (100 μM, 24 h) significantly increased (Ca2+)c

Neuroapoptosis is a commonly recognized harmful effect by anesthetics with mechanisms that are not fully understood. Apoptosis is a programmed cell death. Cytochrome c release from mitochondria precededing the loss of ∆Ψm is a key event in initiating mitochondria-involved apoptosis [47], eventually leading to the typical alterations related to apoptosis such as DNA fragmentation in cell nuclei [48, 49]. In this study, following ketamine exposure, there was a significantly increase in the TUNEL-positive apoptotic cells (**Figure 2**). Ketamine-induced cell death was accompanied by the significant decrease in ∆Ψm and the increased cytochrome c release from mitochondria into cytosol (**Figure 4**). These data were in agreement with those previously reported by others [48, 50–52], suggesting that ketamine induces human neuron to undergo mitochondria-mediated apop-

To maintain proper cellular function, the mitochondria continuously undergo cycles of fusion and fission. Unbalanced fusion/fission, particularly excessive fission/fragmentation can lead to various pathological conditions including neurodegeneration [53]. To assess mitochondrial fission shape in the hESC-derived neurons following exposure to ketamine, electron microscopy was used. The neurons were exposed to either 20 μM or 100 μM ketamine or control conditions for 24 h. The cells were then prepared and imaged on an electron microscope. The mitochondria appeared considerably fragmented in the ketamine- treated neurons when compared to control cells (**Figure 5**). Increased mitochondrial fission has been linked to the propofol-induced cell death in hESC-derived neurons as previously published by our group [54]. Our findings suggest that ketamine exposure results in an increase of mitochondrial fission within the developing neurons, which may contribute to the increased cell death

the mitochondria into cytosol.

observed in the ketamine-treated group.

tosis pathway.

430 Mitochondrial Diseases

**Figure4.** Ketamine decreases mitochondrial membrane potential (ΔΨm) and increases cytochrome c release from mitochondria into cytosol. (A) ΔΨm assay. Ketamine (100 Μm) treatment for 24 h decreased ΔΨm (\*P < 0.05 vs. control, n = 3). (B) Labeling mitochondria of neurons with CellLight™ mitochondria-green fluorescence protein (GFP) reagent. The GFP-positive cells were loaded with TMRE. GFP expression in mitochondria was confirmed by the colocalization of tetramethylrhodamine ethyl ester (TMRE/mitochondrial probe) and GFP signals within the cells. (C) Representative fluorescent images of the neurons transduced with CellLight™ mitochondria-GFP reagent. Blue are cell nuclei. 40% cells were GFP positive. (D) the effect of ketamine on the distribution of cytochrome c in neurons. Cells were labeled with CellLight™ mitochondria-GFP reagent expressed GFP in mitochondria and then treated with ketamine (100 μM, 24 h). The distribution of cytochrome c in cells was analyzed by immunofluorescence staining. Column 1 is the image of mitochondria; column 2 is the image of cytochrome c; and column 3 is the merged image. The inset in the top corner of each image is the magnified box indicated by white arrows. The orange signals in the merged images indicate the existence of cytochrome c inside the mitochondria, and the signals in the merged images indicate the existence of cytochrome c outside the mitochondria. Ketamine treatment (100 μM, 24 h) increased cytochrome c release from mitochondria into cytosol. *Note: The cytochrome c signals (indicated by blue arrows) that do not overlap with GFP florescence were from non-transduced cells*. Scale bar = 10 μm [20].

**Figure 5.** Ketamine increases mitochondrial fission as evidenced by the electron microscope images of differentiated neurons treated with the indicated concentrations (20 and 100 μM) of ketamine for 24 h. Scale bars = 500 nm.

expression changes following exposure to 24 h of ketamine along with additional cell death and mitochondrial signaling assays following 6 h of exposure to ketamine which will allow

**Figure 6.** The expression of several microRNAs was significantly altered following exposure to ketamine. Among 88 microRNAs investigated, exposure to ketamine decreased the expression of six microRNAs and increased the expression

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433

The mechanisms governing anesthetic-induced neurotoxicity in the developing brain are currently not well understood and this research has been limited by the lack of an appropriate human model. Collectively, the findings from this study indicate that (1) hESC-derived neurons represent a promising model to investigate the effects of anesthetic exposure in the developing human brain, (2) ketamine induces neuroapoptosis via mitochondrial pathway, and (3) dysregulation of intracellular calcium, increased mitochondrial fission, and altered microRNA expression might play important roles in ketamine-induced neuroapoptosis. The role of microRNAs in anesthetic-induced neurotoxicity is just beginning to be understood. Functional studies including knockdown and overexpression of microR-NAs of interest will further establish their role in the ketamine-induced neurotoxicity. Additionally, much work remains to establish a functional link between dysregulation of intracellular calcium, increased mitochondrial fission, decreased mitochondrial membrane potential, altered microRNA expression, and the observed neuroapoptosis. These studies are very promising and may reveal novel mechanisms of ketamine-induced developmental

The following grants supported this work: R01GM112696 from the NIH (to Dr. Xiaowen Bai)

and P01GM066730 from the NIH (to Dr. Zeljko J. Bosnjak).

for proper interpretation of the results.

of one microRNA. n = 3–4 for each group.

**3.6. Summary**

neurotoxicity.

**Acknowledgements**

#### **3.5. qRT-PCR analysis of microRNA expression**

Our lab was the first to show the role of microRNAs, specifically miR-21 in propofol-induced neurotoxicity in hESC-derived neurons [37]. Using a similar approach, we have identified several microRNAs with altered expression profiles in ketamine-treated hESC-derived neurons when compared to control-treated cells suggesting a role of microRNAs in ketamineinduced neurotoxicity. A total of 84 of the most abundantly expressed microRNAs were analyzed using the miFinder miRNA PCR arrays. A fold change of 1.3 between the control and ketamine-treated cells was considered significant. The data are expressed as percent of control with the control group set to 100%. The expression of seven microRNAs was found to be significantly altered following exposure to 6 h of ketamine when compared to controltreated cells. Of these seven, only miR-96 was found to be significantly upregulated with ketamine treatment. The remaining microRNAs (miRs-Let 7A, Let 7E, 21, 23b, 28-5p, 423-5p) were significantly downregulated with ketamine exposure compared to control-treated cells (**Figure 6**). Of these microRNAs, the downregulation of miR-21 was of particular interest since miR-21 is protective against ischemic injuries [55]. Downregulation of a protective microRNA may provide a mechanism by which ketamine is inducing neuronal toxicity in the hESCderived neurons. Interestingly, we also reported a significant downregulation of miR-21 in hESC-derived neurons following exposure to the anesthetic propofol and went on to confirm a functional role of this expression change in the propofol-induced neurotoxicity [37]. This research suggests that the mechanism of anesthetic-induced neurotoxicity among multiple anesthetic agents might converge on altered expression of microRNAs such as miR-21. While the other 6 microRNAs identified through these array studies have not been implicated previously in neuronal diseases, this approach has the potential to uncover novel roles of these microRNAs in ketamine-induced neurotoxicity. An aim of future studies will include functional studies to further elucidate the role of these microRNAs and the signaling components connecting these altered microRNAs and attenuated mitochondrial function in ketamineinduced neurotoxicity. Additionally, the current microRNA studies were done using a different time point (6 h) than the cell death and mitochondrial studies previously mentioned which used a time point of 24 h. This was done to ensure any potentially transient changes in microRNA expression were observed. Future studies will include the addition of microRNA

Ketamine Induces Neuroapoptosis in Stem Cell–Derived Developing Human Neurons Possibly… http://dx.doi.org/10.5772/intechopen.72939 433

**Figure 6.** The expression of several microRNAs was significantly altered following exposure to ketamine. Among 88 microRNAs investigated, exposure to ketamine decreased the expression of six microRNAs and increased the expression of one microRNA. n = 3–4 for each group.

expression changes following exposure to 24 h of ketamine along with additional cell death and mitochondrial signaling assays following 6 h of exposure to ketamine which will allow for proper interpretation of the results.

#### **3.6. Summary**

**3.5. qRT-PCR analysis of microRNA expression**

432 Mitochondrial Diseases

Our lab was the first to show the role of microRNAs, specifically miR-21 in propofol-induced neurotoxicity in hESC-derived neurons [37]. Using a similar approach, we have identified several microRNAs with altered expression profiles in ketamine-treated hESC-derived neurons when compared to control-treated cells suggesting a role of microRNAs in ketamineinduced neurotoxicity. A total of 84 of the most abundantly expressed microRNAs were analyzed using the miFinder miRNA PCR arrays. A fold change of 1.3 between the control and ketamine-treated cells was considered significant. The data are expressed as percent of control with the control group set to 100%. The expression of seven microRNAs was found to be significantly altered following exposure to 6 h of ketamine when compared to controltreated cells. Of these seven, only miR-96 was found to be significantly upregulated with ketamine treatment. The remaining microRNAs (miRs-Let 7A, Let 7E, 21, 23b, 28-5p, 423-5p) were significantly downregulated with ketamine exposure compared to control-treated cells (**Figure 6**). Of these microRNAs, the downregulation of miR-21 was of particular interest since miR-21 is protective against ischemic injuries [55]. Downregulation of a protective microRNA may provide a mechanism by which ketamine is inducing neuronal toxicity in the hESCderived neurons. Interestingly, we also reported a significant downregulation of miR-21 in hESC-derived neurons following exposure to the anesthetic propofol and went on to confirm a functional role of this expression change in the propofol-induced neurotoxicity [37]. This research suggests that the mechanism of anesthetic-induced neurotoxicity among multiple anesthetic agents might converge on altered expression of microRNAs such as miR-21. While the other 6 microRNAs identified through these array studies have not been implicated previously in neuronal diseases, this approach has the potential to uncover novel roles of these microRNAs in ketamine-induced neurotoxicity. An aim of future studies will include functional studies to further elucidate the role of these microRNAs and the signaling components connecting these altered microRNAs and attenuated mitochondrial function in ketamineinduced neurotoxicity. Additionally, the current microRNA studies were done using a different time point (6 h) than the cell death and mitochondrial studies previously mentioned which used a time point of 24 h. This was done to ensure any potentially transient changes in microRNA expression were observed. Future studies will include the addition of microRNA

**Figure 5.** Ketamine increases mitochondrial fission as evidenced by the electron microscope images of differentiated

neurons treated with the indicated concentrations (20 and 100 μM) of ketamine for 24 h. Scale bars = 500 nm.

The mechanisms governing anesthetic-induced neurotoxicity in the developing brain are currently not well understood and this research has been limited by the lack of an appropriate human model. Collectively, the findings from this study indicate that (1) hESC-derived neurons represent a promising model to investigate the effects of anesthetic exposure in the developing human brain, (2) ketamine induces neuroapoptosis via mitochondrial pathway, and (3) dysregulation of intracellular calcium, increased mitochondrial fission, and altered microRNA expression might play important roles in ketamine-induced neuroapoptosis. The role of microRNAs in anesthetic-induced neurotoxicity is just beginning to be understood. Functional studies including knockdown and overexpression of microR-NAs of interest will further establish their role in the ketamine-induced neurotoxicity. Additionally, much work remains to establish a functional link between dysregulation of intracellular calcium, increased mitochondrial fission, decreased mitochondrial membrane potential, altered microRNA expression, and the observed neuroapoptosis. These studies are very promising and may reveal novel mechanisms of ketamine-induced developmental neurotoxicity.

#### **Acknowledgements**

The following grants supported this work: R01GM112696 from the NIH (to Dr. Xiaowen Bai) and P01GM066730 from the NIH (to Dr. Zeljko J. Bosnjak).


**2.1 Experimental conditions for DSC**

The observed or apparent thermodynamics of solution systems generally include linked contributions from other solutes in addition to the species of interest. They include buffers,

Probing Solution Thermodynamics by Microcalorimetry 873

**Buffer pKa** *H***°, kJ mol-1** *C***p°, J K-1 mol-1** *V***°, mL mol-1 a**

*H C dT* . (4)

. (5)

Acetate 4.756 -0.41 -142 -10.6 Bicine 8.334 26.34 0 -2.0 Bis-tris 6.484 28.4 27 3.1 Cacodylate 6.28 -3 -86 -13.3

HEPES 7.564 20.4 47 4.8 Imidazole 6.993 36.64 -9 1.8 MES 6.27 14.8 5 3.9 MOPS 7.184 21.1 25 4.7

Tris 8.072 47.45 -142 4.3

A complete DSC experiment consists of matched scans of a sample and a sample-free reference solution. Blank-subtracted data can be empirically analyzed to obtain modelindependent thermodynamic parameters. The difference between pre- and post-transition baselines gives the change in heat capacity, *C*p. After subtracting a suitable baseline across the transition range, the arithmetic integration of the *C*p vs. *T* trace yields the so-called

> cal p *T T f i*

*H*cal is the value of the transition enthalpy at the transition temperature, *T*m. The entropy at

cal ( ) *m m*

*T*

*H S T*

Thus, a single DSC experiment yields the complete thermodynamics of a transition. Modelfree determination of thermodynamics, including the direct measurement of *C*p, is a unique feature of DSC not possible with optical techniques (such as absorption and fluorescence spectroscopy) which access *H* via the van't Hoff equation. However, model

4.07 2.23 -3.38

4 44.2

> -8 3.6 16

3.0 -0.5

Table 1. Thermodynamic properties of ionization by common aqueous buffers

Citrate

Phosphate

Glycine 2.351

Succinate 4.207

**2.2 Analysis of DSC data**

calorimetric enthalpy:

*T*m is

3.128 4.761 6.396

9.780

2.148 7.198 12.35

5.636

a Ionization volume at atmospheric pressure at infinite dilution.

salts, neutral cosolutes, and cosolvents. Of these, the choice of buffer, or any ionizable species in general, must take into account the change in p*K*a with respect to temperature *i.e.*, the enthalpy of ionization (*H*ion). Unless a buffer's *H*ion is negligibly small, its p*K*a will exhibit a temperature dependence, leading to a change in pH of the solution upon heating or cooling. Failure to take this fact into account may introduce significant artifacts into the observed melting behavior. Such changes in pH represent a different issue from any coupled ionization enthalpy arising from the release or uptake of protons associated with the transition of interest.

The direction and extent of the temperature of pH for a buffered solution depends on the sign and magnitude of *H*ion as well as the concentration of the buffering species. Consider the ionization of a buffer A*Z* in the direction of deprotonation to produce its conjugate base B*Z-1*:

$$\mathbf{A}^{\circ} \rightleftharpoons \mathbf{B}^{\circ - \mathbf{t}} + \mathbf{H}^{\circ} \tag{1}$$

According to the van't Hoff equation,

$$\frac{dK\_{\rm ion}}{d(1/T)} = \frac{d10^{-\rm p\%}}{d(1/T)} = -\frac{dH\_{\rm ion}}{R} \,, \tag{2}$$

where *R* is the gas constant, *T* is absolute temperature, and

$$K\_{\rm ion} = \frac{[\rm H'][\rm B^{z+}]}{[\rm A']}.\tag{3}$$

Thus, for a buffer with a positive (endothermic) *H*ion, its ionization equilibrium shifts towards deprotonation as temperature increases, leading to a drop in pH. Conversely, the pH of a solution buffered by an exothermic buffer rises with increasing pH.

Table 1 lists several common buffers for aqueous solutions (King 1969; Disteche 1972; Lo Surdo et al. 1979; Kitamura and Itoh 1987; Goldberg et al. 2002). As a group, substituted ammonium compounds exhibit substantial positive values of *H*ion, making them poor choices for DSC experiments. These compounds include the so-called "Good buffers" (Good et al. 1966) that are prevalent in biochemistry. Among these, Tris, is a particular offender: the pH of a 25 mM solution initially buffered at pH 9.0 drops by more than one pH unit from 0 to 37°C (Poon et al. 2002). In contrast, the ionization of carboxylic acids and their analogues is far less sensitive to temperature. Generally, buffers based on acetate, cacodylate, and phosphate, for example, are preferred choices for DSC experiments.

Another important note relates to polyprotic species such as phosphates, citrates, and borates, whose p*K*a also depends markedly on ionic strength. This relationship is quantitatively given by the volume changes of ionization (*V*°) and interpreted in terms of electrostriction of solvent water molecules. Thus, the addition of salts such as NaCl or guanidinium salts (the latter commonly used to denature proteins) will systematically reduce the pH of a solution buffered by polyprotic acids. The pH of a 0.1 M phosphate buffer at pH 7.2, for example, can fall by 0.5 pH unit upon addition of 0.5 M of NaCl. Molar concentrations of guanidinium hydrochloride will produce an even greater drop. On the other hand, inorganic cosolvents have the opposite effect by affecting the solution dielectric. Of course, once the pH of these buffers is adjusted to a value that is compatible with the apparent p*K*a, it will be stable with respect to temperature. As seen in Table 1, it is generally the case that a buffer is either sensitive to temperature or ionic strength in aqueous solution.

salts, neutral cosolutes, and cosolvents. Of these, the choice of buffer, or any ionizable species in general, must take into account the change in p*K*a with respect to temperature *i.e.*, the enthalpy of ionization (*H*ion). Unless a buffer's *H*ion is negligibly small, its p*K*a will exhibit a temperature dependence, leading to a change in pH of the solution upon heating or cooling. Failure to take this fact into account may introduce significant artifacts into the observed melting behavior. Such changes in pH represent a different issue from any coupled ionization enthalpy arising from the release or uptake of protons associated with

The direction and extent of the temperature of pH for a buffered solution depends on the sign and magnitude of *H*ion as well as the concentration of the buffering species. Consider the ionization of a buffer A*Z* in the direction of deprotonation to produce its conjugate base

ion ion 10

+ -1

[H ][B ] [A ] *z*

Thus, for a buffer with a positive (endothermic) *H*ion, its ionization equilibrium shifts towards deprotonation as temperature increases, leading to a drop in pH. Conversely, the

Table 1 lists several common buffers for aqueous solutions (King 1969; Disteche 1972; Lo Surdo et al. 1979; Kitamura and Itoh 1987; Goldberg et al. 2002). As a group, substituted ammonium compounds exhibit substantial positive values of *H*ion, making them poor choices for DSC experiments. These compounds include the so-called "Good buffers" (Good et al. 1966) that are prevalent in biochemistry. Among these, Tris, is a particular offender: the pH of a 25 mM solution initially buffered at pH 9.0 drops by more than one pH unit from 0 to 37°C (Poon et al. 2002). In contrast, the ionization of carboxylic acids and their analogues is far less sensitive to temperature. Generally, buffers based on acetate,

Another important note relates to polyprotic species such as phosphates, citrates, and borates, whose p*K*a also depends markedly on ionic strength. This relationship is quantitatively given by the volume changes of ionization (*V*°) and interpreted in terms of electrostriction of solvent water molecules. Thus, the addition of salts such as NaCl or guanidinium salts (the latter commonly used to denature proteins) will systematically reduce the pH of a solution buffered by polyprotic acids. The pH of a 0.1 M phosphate buffer at pH 7.2, for example, can fall by 0.5 pH unit upon addition of 0.5 M of NaCl. Molar concentrations of guanidinium hydrochloride will produce an even greater drop. On the other hand, inorganic cosolvents have the opposite effect by affecting the solution dielectric. Of course, once the pH of these buffers is adjusted to a value that is compatible with the apparent p*K*a, it will be stable with respect to temperature. As seen in Table 1, it is generally the case that a buffer is either sensitive to temperature or ionic strength in aqueous solution.

*pKa dK d dH dTdT R*

(1 / ) (1 / )

ion

cacodylate, and phosphate, for example, are preferred choices for DSC experiments.

pH of a solution buffered by an exothermic buffer rises with increasing pH.

1 A BH *z z* (1)

, (2)

*z K* . (3)

the transition of interest.

According to the van't Hoff equation,

where *R* is the gas constant, *T* is absolute temperature, and

B*Z-1*:

a Ionization volume at atmospheric pressure at infinite dilution.

Table 1. Thermodynamic properties of ionization by common aqueous buffers

#### **2.2 Analysis of DSC data**

A complete DSC experiment consists of matched scans of a sample and a sample-free reference solution. Blank-subtracted data can be empirically analyzed to obtain modelindependent thermodynamic parameters. The difference between pre- and post-transition baselines gives the change in heat capacity, *C*p. After subtracting a suitable baseline across the transition range, the arithmetic integration of the *C*p vs. *T* trace yields the so-called calorimetric enthalpy:

$$
\Delta H\_{\text{cal}} = \int\_{T\_l}^{T\_f} \mathbb{C}\_p dT \,\,. \tag{4}
$$

*H*cal is the value of the transition enthalpy at the transition temperature, *T*m. The entropy at *T*m is

$$
\Delta S(T\_{\text{w}}) = \frac{\Delta H\_{\text{cal}}}{T\_{\text{w}}} \,. \tag{5}
$$

Thus, a single DSC experiment yields the complete thermodynamics of a transition. Modelfree determination of thermodynamics, including the direct measurement of *C*p, is a unique feature of DSC not possible with optical techniques (such as absorption and fluorescence spectroscopy) which access *H* via the van't Hoff equation. However, model

Probing Solution Thermodynamics by Microcalorimetry 875

perform manual baseline subtraction before fitting a excess heat capacity function. This is

baseline subtraction requires either heuristic or semi-empirical criteria to connect the preand post-transitional states. This is both unnecessary and questionable practice, since manual editing may (and probably do) bias the data. The most appropriate approach is to fit both the excess and intrinsic heat capacities directly according to Eq (7). Since both terms are functions of *i*(*T*), the fitted baseline will objectively track the progress of each transition. Note that *C*p,0 and *C*p,*i* are taken to be constants in Eqs (6) and (7) since their temperature dependence is generally weak over the experimental range. They can be formulated, if desired, as polynomials to define nonlinear baselines. Care must be taken, however, to ensure that such curvature is not masking some low-enthalpy transition such as

The principal task in formulating DSC models is deriving expressions for *i*(*T*) from the relevant equilibrium expressions and equations of state. Implicit in this task is the computation of *K*i from its corresponding thermodynamic parameters. This in turn requires the choice of a reference temperature, the most convenient of which is the characteristic

p ( ) ( )1 ln *T T GT HT CTT T*

Again *C*p is taken to be independent of temperature in the experimental range. The simplest DSC model involves the isomeric conversion of a species in a strictly two-state manner (*i.e.*, no intermediate state is populated at equilibrium). The denaturation of many

> 2 p p 2 2 ( ) ( 1) 1 *KHK C T C K RT K*

> > and int

p, ( ) *C Ti*

p, ( ) *C Ti*

function.

*K X nX* , (10)

p, ( ) *C Ti*

traces in Figure 1 are simulated using Eq (9) with *H* = 50 kcal/mol, *C*p = 500 cal mol-1 K-1, and *T*° = 50°C (1 cal 4.184 J). In this model, *T*° is the midpoint of the transition (*i.e.*, *K* = 1

For transitions involving changes in molecularity, *i*(*T*) includes total sample concentration, *c*t in addition to equilibrium constants. While the mechanics of formulating such models is not different, a potential source of inconsistency arises from the choice of unit in thermodynamic parameters. Specifically, every intensive thermodynamic parameter can be defined either per unit of monomer or oligomer. Either choice is correct, of course, but the resultant differences may lead to some confusion. Take for example a two-state homo-

single-domain proteins exemplifies this model. This excess heat capacity function is

) and marks the maximum of the int

*n*

oligomeric transition (Privalov and Potekhin 1986; Freire 1989):

*T T* . (8)

. (9)

, respectively. The DSC

conformation changes of proteins in the native state (Privalov and Dragan 2007).

from the fitting function. In the transition region, manual

intended to eliminate int

**2.2.1 Formulation of DSC models**

temperature *T*° at which *G*(*T*°) = 0:

The two terms on the right side represent tr

and 0.5 1 *K K*

p, ( ) *C Ti*

fitting by least-square analysis can extract considerably more useful information and facilitate quantitative hypothesis testing.

In general, the reference-subtracted DSC data represent the heat capacity of the initial state 0 (*C*p,0) as well as the excess heat capacity function, <*C*p(*T*)>:

$$\mathcal{C}\_{\rm p}(T) = \mathcal{C}\_{\rm p,0} + \left\{ \Delta \mathcal{C}\_{\rm p}(T) \right\}. \tag{6}$$

Consider a general model in which the sample undergoes a transition from initial state 0 through intermediates 1, 2, ... , *i* to the final state *n*. (One can readily envisage extensions of this model in which a heterotypic complex dissociates into subunits which then go on to further, independent transitions.) The excess heat capacity function is (Privalov and Potekhin 1986)

$$\begin{aligned} \left\{\Delta \mathbf{C}\_{\mathbf{p}}(T)\right\} &= \frac{d\left\{\Delta H(T)\right\}}{dT} = \frac{d}{dT} \left[\sum\_{i=1}^{n} \Delta H\_{i}(T) \alpha\_{i}(T)\right] \\ &= \sum\_{i=1}^{n} \Delta H\_{i} \frac{d\alpha\_{i}(T)}{dT} + \sum\_{i=1}^{n} \alpha\_{i}(T) \Delta C\_{p,i} \\ &= \sum\_{i=1}^{n} \left\{\partial \mathbf{C}\_{p,i}^{\rm tr}(T)\right\} + \sum\_{i=1}^{n} \left\{\partial \mathbf{C}\_{p,i}^{\rm int}(T)\right\} \end{aligned} \tag{7}$$

where *Hi* and Cp,*i* are the enthalpy and heat capacity change, respectively, from state 0 to state *i*, and *i*(*T*) is the fractional conversion at state *i*. tr p, ( ) *C Ti* and int p, ( ) *C Ti* are called "transition" and "intrinsic" heat capacities, respectively. The intrinsic heat capacity, which represents the summed heat capacities of the various species present at *T*, is the baseline function of the observed DSC trace (Figure 1). Some analytical protocols invite the user to

Fig. 1. An excess heat capacity function and its constituent transition and intrinsic heat capacities. Integration of the transition heat capacity, tr p, ( ) *C Ti* , gives *H*cal at the transition temperature (50°C here).

fitting by least-square analysis can extract considerably more useful information and

In general, the reference-subtracted DSC data represent the heat capacity of the initial state 0

Consider a general model in which the sample undergoes a transition from initial state 0 through intermediates 1, 2, ... , *i* to the final state *n*. (One can readily envisage extensions of this model in which a heterotypic complex dissociates into subunits which then go on to further, independent transitions.) The excess heat capacity function is (Privalov and

1 1

*dT*

*i i n n*

*n n i*

1 1

*i i*

tr int p, p,

*i i*

*CT CT*

( ) ( ) () ()

*d HT d C T HT T dT dT*

where *Hi* and Cp,*i* are the enthalpy and heat capacity change, respectively, from state 0 to

"transition" and "intrinsic" heat capacities, respectively. The intrinsic heat capacity, which represents the summed heat capacities of the various species present at *T*, is the baseline function of the observed DSC trace (Figure 1). Some analytical protocols invite the user to

270 280 290 300 310 320 330 340 350 360 370 380

T, K

Fig. 1. An excess heat capacity function and its constituent transition and intrinsic heat

1

*n*

( ) ( )

*d T H T C*

*i i i*

*i*

() ()

p p,0 p *CT C CT* () () . (6)

p,

p, ( ) *C Ti*

Cp

, gives *H*cal at the transition

p, ( ) *C Ti*

and int

, (7)

p, ( ) *C Ti*

are called

*i i*

facilitate quantitative hypothesis testing.

Potekhin 1986)

(*C*p,0) as well as the excess heat capacity function, <*C*p(*T*)>:

p

state *i*, and *i*(*T*) is the fractional conversion at state *i*. tr

3500 <Cp

> <Ctr p > <Cint p >

0

capacities. Integration of the transition heat capacity, tr

500

1000

Cp, cal mol-1 K-1

temperature (50°C here).

perform manual baseline subtraction before fitting a excess heat capacity function. This is intended to eliminate int p, ( ) *C Ti* from the fitting function. In the transition region, manual baseline subtraction requires either heuristic or semi-empirical criteria to connect the preand post-transitional states. This is both unnecessary and questionable practice, since manual editing may (and probably do) bias the data. The most appropriate approach is to fit both the excess and intrinsic heat capacities directly according to Eq (7). Since both terms are functions of *i*(*T*), the fitted baseline will objectively track the progress of each transition. Note that *C*p,0 and *C*p,*i* are taken to be constants in Eqs (6) and (7) since their temperature dependence is generally weak over the experimental range. They can be formulated, if desired, as polynomials to define nonlinear baselines. Care must be taken, however, to ensure that such curvature is not masking some low-enthalpy transition such as conformation changes of proteins in the native state (Privalov and Dragan 2007).


*2.7. LC-MS/MS Analysis*

Analysis of the peptide mixtures was performed as described previously [25]. Briefly, the peptides were fractionated on a reversed phase column (50 cm × 75 μm inner diameter) packed with 1.8 μm diameter C18 particles (100 Å pore size; Dr. Maisch, Ammerbuch-Entringen, Germany) using a 105 min acetonitrile gradient in 0.1% formic acid at a flow rate of 250 nL/min. Peptide masses were analyzed using a Q-Exactive HF mass spectrometer (Thermo-Fisher Scientific, Palo Alto, CA, USA) operated in data-dependent mode with survey scans acquired at a resolution of 50,000 at *m*/*z* 400 (transient time 256 ms). Fifteen of the most abundant isotope patterns with charge ≥ +2 from the survey scan (300–1650 *m/z*) were selected with an isolation window of 1.6 *m/z* and fragmented by HCD with normalized collision energies of 25. The maximum ion injection times for the survey scan and the MS/MS scans were 20 and 60 ms, respectively. The ion target values for MS1 and MS2 scan modes were set to 3 × 106 and 1 × 105, respectively. The dynamic exclusion was 25 s and 10 ppm.


**3.5 Malignant conditions**

Primary thromboprophylaxis of ambulatory cancer patients should be decided according to the individual risk of bleeding, the type of cancer, or the stage of the disease [37]. For hospitalized patients without acute venous thromboembolism or a history of venous thromboembolism, the American Society of Hematology recommends thromboprophylaxis with low molecular weight heparin, but only for the duration of the hospital stay. If during hospitalization a patient has undergone surgery or if a patient is receiving outpatient systemic chemotherapy and is at high risk of thrombosis, continued administration of LMWH has been shown to be beneficial. Oral anticoagulation, in the form of vitamin K antagonists or DOAC, is not included in current guidelines because there is insufficient data on its efficacy [38].

For a patient with active cancer who develops venous thromboembolism, initial treatment can be with either LMWH or DOAC, the latter being the medication of choice. It is recommended to continue treatment for at least 3–6 months, which may be extended as a secondary prophylactic measure in patients with active cancer and/or recurrence of venous thromboembolism. Direct oral anticoagulation remains the first choice of treatment in this case as well [38].

In a cancer patient with visceral or splanchnic venous thrombosis, according to the guidelines, treatment should consist of short-term anticoagulation (3–6 months) or clinical observation [38].

#### **3.6 Pregnancy**

Despite the prothrombotic status of physiological changes occurring during pregnancy, prophylactic anticoagulation of asymptomatic patients with no history of venous thromboembolism should be judged on a case-by-case basis [21, 39].

Anticoagulation for a venous thromboembolism event should be with LMWH if occurring before the 36th week of pregnancy and should be switched to unfractionated heparin afterward to minimize complications of epidural anesthesia. Vitamin K antagonists are not recommended after the first trimester as they are known to cause "warfarin embryopathy." Direct oral anticoagulation is also not approved for administration during pregnancy [21, 40].

Following an episode of venous thromboembolism, anticoagulation should be continued for 3–6 months, or 4–6 weeks postpartum, with either low molecular dose heparin or unfractionated heparin [21].

Patients with antiphospholipid syndrome and a history of thrombotic complications during previous pregnancies may benefit from prophylactic anticoagulation during pregnancy and for an additional 6 weeks postpartum [34].

#### **3.7 SARS-CoV-2**

Anticoagulation management of patients with Covid-19 depends on the severity of the disease. The administration of unfractionated heparin to patients hospitalized in an uncritical state has been observed to reduce the need for intensive care maneuvers, such as specific organ support or intubation, and also reduces the death rate. On the other hand, the condition of critically ill patients has not been improved by heparin treatment, and heparin treatment actually increases the rate of complications and is subsequently not recommended [41].

After discharge, patients with high thrombotic risk and a low bleeding risk could benefit from low-dose rivaroxaban treatment for an optimal duration to be determined [41].


**GNSS, Space Weather and TEC Special Features**

Editors

**Serdjo Kos Jos ´e Fern´andez Juan F. Prieto**

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

*Editors* Serdjo Kos University of Rijeka Croatia

Jose Fern ´ andez ´ Instituto de Geociencias IGEO (CSIC-UCM) Spain

Juan F. Prieto Universidad Politecnica de ´ Madrid Spain

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

This is a reprint of articles from the Special Issue published online in the open access journal *Remote Sensing* (ISSN 2072-4292) (available at: https://www.mdpi.com/journal/remotesensing/ special issues/GNSS Space Weather TEC).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

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**ISBN 978-3-0365-7594-0 (Hbk) ISBN 978-3-0365-7595-7 (PDF)**

© 2023 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.


**5. Measure of natural habitat provision**

Measures of habitat provision need to account for different types of habitat and their associated biodiversity. Dymond et al. (2008) showed how proportions of unique habitat remaining may be combined to give a national measure of habitat provision. The habitat measure is based on the contribution it makes to the New Zealand Government goal of maintaining and restoring a full range of remaining natural habitats to a healthy and functioning state. For measuring indigenous forest and grasslands, the historical unique habitats come from Land Environments New Zealand (LENZ) (Leathwick et al., 2003). Wetlands are at the interface of terrestrial and freshwater habitats, and therefore another habitat framework representing both aquatic and terrestrial biota (Leathwick et al., 2007) is used. As such, the measure of habitat provision for wetlands is applied separately from the indigenous forests and grasslands measure.

#### **5.1 Indigenous forest and grasslands**

We used LENZ at level II (suitable for national to regional scale) and the most recent land cover (2008) to characterise historic and present habitats. The measure of habitat provision for a land environment is defined as:

$$H\_i = P\_i \left(\frac{a\_i}{A\_i}\right)^{0.5} \tag{1}$$

where

210 Biodiversity Loss in a Changing Planet

Pakihi Bog Swamp Marsh Fen Gumland Seepage Inland

regional analyses suggest that wetland extent continues to decline, although at a slower rate, as land drainage and agricultural development continue (Grove, 2010; Newsome & Heke, 2010). Wetland mapping is a challenging task as wetlands are sometimes too small in area to be identified using common satellite resolution. Their extent can vary seasonally (e.g., dryness, wetness) and therefore can change markedly at the time of imagery acquisition. While satellite images are useful for providing information at national scale, automatic classification is not possible as vegetation types in wetlands are so variable, making them difficult to characterise through spectral signature. Thus wetlands have been mapped on a manual or semi-automated basis (Ausseil et al., 2007), and this requires a significant amount

Measures of habitat provision need to account for different types of habitat and their associated biodiversity. Dymond et al. (2008) showed how proportions of unique habitat remaining may be combined to give a national measure of habitat provision. The habitat measure is based on the contribution it makes to the New Zealand Government goal of maintaining and restoring a full range of remaining natural habitats to a healthy and functioning state. For measuring indigenous forest and grasslands, the historical unique habitats come from Land Environments New Zealand (LENZ) (Leathwick et al., 2003). Wetlands are at the interface of terrestrial and freshwater habitats, and therefore another habitat framework representing both aquatic and terrestrial biota (Leathwick et al., 2007) is used. As such, the measure of habitat provision for wetlands is applied separately from the

Saline

Historic extent

Current extent

0

of effort for all of New Zealand.

**5. Measure of natural habitat provision**

indigenous forests and grasslands measure.

Fig. 5. Current and historic extent of wetland per class.

200

400

600

800

1,000

Area (x 1,000 ha)

1,200

1,400

1,600

1,800

*ai* is the area of natural habitat remaining in land environment *i,*

*Ai* the area of land environment *i*, and

*Pi* is the biodiversity value of the *i*th land environment when fully natural.

The 0.5 power index is used to produce a function monotonically increasing from zero to one with a decreasing derivative in order to represent the higher biodiversity value of rare habitat. In the absence of comprehensive and detailed biodiversity information, Dymond et al. (2008) suggested using species-area relationships (Connor & McCoy, 2001) to estimate *Pi* as the land environment area to the power of 0.4. The varying condition, or degree of naturalness, of individual sites also needs to be taken into account in the habitat measure:

$$\mathbf{H}\_i = \mathbf{P}\_i \left( \frac{\sum\_{j=1}^n c\_{ij} b\_{ij}}{A\_i} \right)^{0.5} \tag{2}$$

where

*cij* is the condition,

*bij* is the area of of the *j*th habitat site in the *i*th land environment, and

*n* is the number of habitats in the *i*th land environment.

The condition of indigenous forest, subalpine shrublands, alpine habitats, and tussock grasslands above the treeline, are assumed to have a condition of 1.0. Tussock grasslands below the treeline have a condition of 0.8 and indigenous shrublands have a condition of 0.5. All other landcovers have a condition of 0.0.

Figure 6 shows the input layers (current land cover and land environments at level II) and the resulting habitat provision map. This map shows the contribution per hectare to the

habitat measure (i.e. each pixel represents 1 *ij ij n i ij ij j c b H c b* ).

#### **5.2 Freshwater wetlands**

Wetlands are at the interface between water and terrestrial dry environments. They have been categorised with freshwater environments in the past, and as such require a different definition of biogeographic units than the terrestrial environments. We replaced land environments data with biogeographic units defined by climatic and river basin characteristics (Leathwick et al., 2007). This framework was used to define priority conservation for rivers (Chadderton et al. 2004) and wetlands (Ausseil et al., 2011). A condition index for wetlands, similar to *ci* in equation (1), was calculated for all current wetland sites in New Zealand (Ausseil et al., 2011). This condition index reflects the major

Provision of Natural Habitat for Biodiversity: Quantifying Recent Trends in New Zealand 213

anthropogenic pressures on wetlands, including nutrient leaching, introduced species,

The measure of habitat provision for wetlands in a biogeographic unit now needs to account

1

*c b*

*n m ijk ijk j ik k ik*

Wetland habitats are defined at the class level (*m*=8 classes) using the wetland classification of Johnson & Gerbeaux (2004). As with *Pi* in equation (2), *Pik* is defined as the historical area

total area of class *k* wetlands in biogeographic unit *i*, weighted by the condition index *cijk* for each wetland site. If all the wetlands were in pristine condition, the sum would equal the

Figure 7 shows wetland habitat provision for each biogeographic unit. The colours represent

Though there are still large areas of natural habitat remaining in New Zealand, there continues to be ongoing loss. Prior to the settlement of humans, there were 23 million hectares of indigenous forest. Today, only 6.5 million hectares of indigenous forest are remaining.  Despite continuing losses in lowland ecosystems, the net area of indigenous forest may well be increasing due to regeneration of indigenous shrublands in marginal hill country. Indigenous grasslands have a similar pattern of change. Over the last 170 years, 4.7 million hectares of indigenous grasslands have been lost. Though the total area of remaining grasslands is large, little of that is in lowland ecosystems, and over the last 20 years more lowland ecosystems have been lost. Wetlands are the most severely impacted ecosystems. Of the 2.4 million hectares of wetlands existing pre-Maori, only 250 thousand hectares are remaining – that is, only 10% of what was there originally. Again, lowland wetlands are mostly affected, with a higher proportion of swamps lost. Recent trend analyses shown in this chapter reveal that loss is still continuing, and is a precursor to negative impacts on

The habitat provision map for indigenous forest and grasslands show large spatial variability. High values are usually associated with rarer habitats in good condition, but also with habitats in very small land environments. For wetlands, the habitat provision map is

1

*P A W*

0.5

(3)

*n ijk ijk j c b*

reflects the

imperviousness, loss of naturalness, woody weeds, and drainage pressure.

*i*

*cijk* is the condition index of wetland site *j* in class *k* in biogeographic unit *i,*

per wetland class per biogeographic unit to the power of 0.4. The sum 1

provision of ecosystem services and subsequent human well-being.

*bijk* is the area of wetland site *j* in class *k* in biogeographic unit *i,*

*n* is the number of class *k* wetland sites in biogeographic unit *i*.

*Aik* is the historic area of class *k* in biogeographic unit *i,*

*m* is the number of wetland classes, and

total areal extent in that class.

the value *Wi* from equation (3).

**6. Discussion**

for different wetland classes, so is defined as

where

Fig. 6. Habitat provision per hectare from forests and grasslands.

anthropogenic pressures on wetlands, including nutrient leaching, introduced species, imperviousness, loss of naturalness, woody weeds, and drainage pressure.

The measure of habitat provision for wetlands in a biogeographic unit now needs to account for different wetland classes, so is defined as

$$\mathcal{W}\_i = \sum\_{k=1}^{m} P\_{ik} \left( \frac{\sum\_{j=1}^{n} c\_{ijk} b\_{ijk}}{A\_{ik}} \right)^{0.5} \tag{3}$$

where

212 Biodiversity Loss in a Changing Planet

Fig. 6. Habitat provision per hectare from forests and grasslands.

*cijk* is the condition index of wetland site *j* in class *k* in biogeographic unit *i,*

*bijk* is the area of wetland site *j* in class *k* in biogeographic unit *i,*

*Aik* is the historic area of class *k* in biogeographic unit *i,*

*m* is the number of wetland classes, and

*n* is the number of class *k* wetland sites in biogeographic unit *i*.

Wetland habitats are defined at the class level (*m*=8 classes) using the wetland classification of Johnson & Gerbeaux (2004). As with *Pi* in equation (2), *Pik* is defined as the historical area

per wetland class per biogeographic unit to the power of 0.4. The sum 1 *n ijk ijk j c b* reflects the

total area of class *k* wetlands in biogeographic unit *i*, weighted by the condition index *cijk* for each wetland site. If all the wetlands were in pristine condition, the sum would equal the total areal extent in that class.

Figure 7 shows wetland habitat provision for each biogeographic unit. The colours represent the value *Wi* from equation (3).

## **6. Discussion**

Though there are still large areas of natural habitat remaining in New Zealand, there continues to be ongoing loss. Prior to the settlement of humans, there were 23 million hectares of indigenous forest. Today, only 6.5 million hectares of indigenous forest are remaining.  Despite continuing losses in lowland ecosystems, the net area of indigenous forest may well be increasing due to regeneration of indigenous shrublands in marginal hill country. Indigenous grasslands have a similar pattern of change. Over the last 170 years, 4.7 million hectares of indigenous grasslands have been lost. Though the total area of remaining grasslands is large, little of that is in lowland ecosystems, and over the last 20 years more lowland ecosystems have been lost. Wetlands are the most severely impacted ecosystems. Of the 2.4 million hectares of wetlands existing pre-Maori, only 250 thousand hectares are remaining – that is, only 10% of what was there originally. Again, lowland wetlands are mostly affected, with a higher proportion of swamps lost. Recent trend analyses shown in this chapter reveal that loss is still continuing, and is a precursor to negative impacts on provision of ecosystem services and subsequent human well-being.

The habitat provision map for indigenous forest and grasslands show large spatial variability. High values are usually associated with rarer habitats in good condition, but also with habitats in very small land environments. For wetlands, the habitat provision map is

Provision of Natural Habitat for Biodiversity: Quantifying Recent Trends in New Zealand 215

covenant is registered against the title of the land in perpetuity and there are obligations to manage the land in accordance with the covenant document. Over 70,000 ha are now protected by QEII covenants (Ministry for the Environment, 2007). Nga Whenua Rahui is a contestable fund to negotiate the voluntary protection of native forest on Maori-owned land. Legal protection is offered through covenants, setting aside areas as Maori reservations or through management agreements. About 150,000 ha of native ecosystems are now protected under this fund. The Nature Heritage Fund (NHF) is a third contestable fund for voluntary protection of nature on private land. Its aim is to add to public conservation land those ecosystems important for indigenous biodiversity that are not represented within the existing protected area network. Since 1990, the fund has protected over 100,000 hectares of indigenous ecosystems through direct land purchases, covenants on private land or fencing. The information on habitat provision could feed into the Department of Conservation (DOC) management system. DOC is responsible for managing biodiversity on the conservation estate, and is developing the Natural Heritage Management System (NHMS). DOC's statement of intent is to legally protect the best possible examples of each native ecosystem type, by fencing, reinstating water levels, replanting, controlling pest animals and weeds, and reintroducing native species to restore and maintain natural ecosystems. The framework proposed in this chapter is envisaged to help achieve this goal through accurate information on habitat extent and ecosystem loss, and provides a measure for comparing habitats within and across land environments where species level assessments may not be

Continuing loss of natural habitat may be due to a lack of market prices for associated ecosystem services (TEEB, 2010). Monetisation of habitat provision could partly redress this. In New Zealand, Patterson & Cole (1999) estimated natural forests and wetlands to both have total economic values of approximately 6 billion dollars in 1994. From this, it is possible to convert the units of habitat measure to economic value in dollars per year – Dymond et al. (2007) estimated this as 60 units to one (NZ) dollar per year (assuming areas are in hectares). This monetisation would permit the comparison of changes in habitat alongside changes in other ecosystem services in the same units. This reduces the complexity of results when analysing impacts of different land-use decisions. Using dollars also provides context for stakeholders unfamiliar with biodiversity impacts associated with habitat loss. The negative side of monetisation is that some stakeholders may be encouraged to make trade-offs on the basis of the monetisation alone, not realising the assumptions and limitations involved, or being aware of environmental bottom-lines. Indeed, the risk of valuation is to get the figure very wrong. There are numerous valuation methods, often based on subjective, hypothetical, and questionable assumptions, which can all give vastly different values (Spangenberg & Settele, 2010). Altogether, monetisation, although easy to comprehend, can be misleading and should be used with caution. It should be used in close consultation with decision-makers, so that they are fully aware of the pitfalls and

The measure of habitat provision is a landscape approach which makes several assumptions. First, it uses particular GIS databases, each of which has a certain level of sensitivity and accuracy. Land environments has been tailored to forest ecosystems, and does not encompass the full breadth of other ecosystem types. Biogeographic units were used for wetlands, assuming that freshwater species would be concentrated within defined hydrological boundaries. Second, it assumes that landscape morphology and pattern can be used as a surrogate for species. Though this overcomes the issues surrounding availability

assumptions behind the valuation, to avoid misallocation of resources.

possible.

Fig. 7. Habitat provision for freshwater wetlands in each biogeographic unit.

shown at the biogeographic unit level, mainly because wetland boundaries are difficult to depict at the scale shown here. The contribution to the national habitat measure comes mostly from biogeographic units with minimal conversion to productive land. Low values represent units where wetland areas have depleted or where wetlands have been degraded. This information can be used by decision-makers to prioritise the allocation of conservation funds. For example, the maps can be intersected with legally protected areas, like those from Walker et al. (2008), which target areas under private ownership with high natural values. Several legislative tools can be used to protect remnant habitats, including the establishment of conservation covenants like the Queen Elizabeth the Second National Trust (QEII), Nga Whenua Rahui, and the National Heritage Fund.

QEII's goal is to help New Zealand farmers protect open space on private land for the benefit and enjoyment of the present and future generations of New Zealanders. The

Fig. 7. Habitat provision for freshwater wetlands in each biogeographic unit.

Whenua Rahui, and the National Heritage Fund.

shown at the biogeographic unit level, mainly because wetland boundaries are difficult to depict at the scale shown here. The contribution to the national habitat measure comes mostly from biogeographic units with minimal conversion to productive land. Low values represent units where wetland areas have depleted or where wetlands have been degraded. This information can be used by decision-makers to prioritise the allocation of conservation funds. For example, the maps can be intersected with legally protected areas, like those from Walker et al. (2008), which target areas under private ownership with high natural values. Several legislative tools can be used to protect remnant habitats, including the establishment of conservation covenants like the Queen Elizabeth the Second National Trust (QEII), Nga

QEII's goal is to help New Zealand farmers protect open space on private land for the benefit and enjoyment of the present and future generations of New Zealanders. The covenant is registered against the title of the land in perpetuity and there are obligations to manage the land in accordance with the covenant document. Over 70,000 ha are now protected by QEII covenants (Ministry for the Environment, 2007). Nga Whenua Rahui is a contestable fund to negotiate the voluntary protection of native forest on Maori-owned land. Legal protection is offered through covenants, setting aside areas as Maori reservations or through management agreements. About 150,000 ha of native ecosystems are now protected under this fund. The Nature Heritage Fund (NHF) is a third contestable fund for voluntary protection of nature on private land. Its aim is to add to public conservation land those ecosystems important for indigenous biodiversity that are not represented within the existing protected area network. Since 1990, the fund has protected over 100,000 hectares of indigenous ecosystems through direct land purchases, covenants on private land or fencing. The information on habitat provision could feed into the Department of Conservation (DOC) management system. DOC is responsible for managing biodiversity on the conservation estate, and is developing the Natural Heritage Management System (NHMS). DOC's statement of intent is to legally protect the best possible examples of each native ecosystem type, by fencing, reinstating water levels, replanting, controlling pest animals and weeds, and reintroducing native species to restore and maintain natural ecosystems. The framework proposed in this chapter is envisaged to help achieve this goal through accurate information on habitat extent and ecosystem loss, and provides a measure for comparing

habitats within and across land environments where species level assessments may not be

possible. Continuing loss of natural habitat may be due to a lack of market prices for associated ecosystem services (TEEB, 2010). Monetisation of habitat provision could partly redress this. In New Zealand, Patterson & Cole (1999) estimated natural forests and wetlands to both have total economic values of approximately 6 billion dollars in 1994. From this, it is possible to convert the units of habitat measure to economic value in dollars per year – Dymond et al. (2007) estimated this as 60 units to one (NZ) dollar per year (assuming areas are in hectares). This monetisation would permit the comparison of changes in habitat alongside changes in other ecosystem services in the same units. This reduces the complexity of results when analysing impacts of different land-use decisions. Using dollars also provides context for stakeholders unfamiliar with biodiversity impacts associated with habitat loss. The negative side of monetisation is that some stakeholders may be encouraged to make trade-offs on the basis of the monetisation alone, not realising the assumptions and limitations involved, or being aware of environmental bottom-lines. Indeed, the risk of valuation is to get the figure very wrong. There are numerous valuation methods, often based on subjective, hypothetical, and questionable assumptions, which can all give vastly different values (Spangenberg & Settele, 2010). Altogether, monetisation, although easy to comprehend, can be misleading and should be used with caution. It should be used in close consultation with decision-makers, so that they are fully aware of the pitfalls and assumptions behind the valuation, to avoid misallocation of resources.

The measure of habitat provision is a landscape approach which makes several assumptions. First, it uses particular GIS databases, each of which has a certain level of sensitivity and accuracy. Land environments has been tailored to forest ecosystems, and does not encompass the full breadth of other ecosystem types. Biogeographic units were used for wetlands, assuming that freshwater species would be concentrated within defined hydrological boundaries. Second, it assumes that landscape morphology and pattern can be used as a surrogate for species. Though this overcomes the issues surrounding availability

Provision of Natural Habitat for Biodiversity: Quantifying Recent Trends in New Zealand 217

to thank Fiona Carswell and Garth Harmsworth from Landcare Research for their valuable

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Conservation International. (2010). *Biodiversity hotspots*. Retrieved 21 December, 2010, available from http://www.conservation.org/Documents/cihotspotmap.pdf. de Lange, PJ, Norton, DA, Courtney, S, Heenan, PB, Courtney, S, Molloy, BPJ, Ogle, CC,

plants of New Zealand. *New Zealand Journal of Botany*, Vol 42, pp. 45-76. Diaz, S, Fargione, J, Chapin, FS, III & Tilman, D 2006. Biodiversity loss threatens human well-being. *PLoS Biol*, Vol 4, No 8, e277. doi:10.1371/journal.pbio.0040277. Dickinson, KJM, Mark, AF, Barratt, BIP & Patrick, BH (1998). Rapid ecological survey,

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*rivers*. Department of Conservation Discussion Document, Wellington. Cieraad, E (2008). *How much indigenous biodiversity remains in land under indigenous cover?* Unpublished contract report LC0708/145, Landcare Research Ltd, Lincoln. Connor, EF & McCoy, ED (2001). Species-area relationship. In: *Encyclopaedia of Biodiversity*.

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**8. References**

of data, application is limited at the various levels and components of biodiversity. In other words, provisions can not be assessed at multiple scales (i.e. habit, community and/or species). Third, the condition of indigenous forest and grassland assumes that all sites are characterised by one condition, though condition could vary within large sites. For wetlands, the condition does vary per site, but it is based on landscape indicators. It is appropriate for a rapid assessment of sites, and can help for prioritising field visits (Ausseil et al., 2007), but does not necessarily reflect the true condition in the field.

The loss of indigenous forest is well characterised by the habitat provision analysis, but the gain of indigenous forest from regenerating indigenous shrublands is not. This is because both the LCDB and the LUM datasets focus on mapping change primarily between woody and herbaceous vegetation, and the subtle changes in the spectral signature of regenerating indigenous forest and mature forest are not accurately characterised or determined, making it difficult to decide whether indigenous vegetation is mature enough to be classified as forest. This is important because there are large areas of indigenous shrublands in New Zealand, approximately 1.6 million hectares. Much of these shrublands are currently regenerating to forest and could make a significant contribution to the areal extent of indigenous forest if this trend continues. If we assume a conservative time of 100 years to reach forest maturity and a uniform distribution of shrubland age, then we would expect about 1% of the shrubland area to change to indigenous forest each year – this amounts to 16 thousand hectares per year. Over 18 years this would equate to approximately 300 thousand hectares, which is six times the estimated current loss of indigenous forest. This fills an important information gap in our understanding of the changing areal extent of indigenous forest and indicates the importance of using objective mapping techniques to monitor change.

Conservation management in New Zealand is becoming increasingly strategic, systematic, and reliant on accurate information on which to plan and prioritise goals and actions. A range of sophisticated tools and approaches have been developed to support these efforts in the past ten years. These include measuring Conservation Achievement (Stephens et al., 2002), the Land Environments of New Zealand (Leathwick et al., 2003), and measuring provision of natural habitat (Dymond et al., 2008). In addition, these efforts have spawned considerable activity for acquiring underlying data, such as biodiversity value (Cieraad, 2008), land cover (the LCDB3 project), and threats to biodiversity (Overton et al., 2003; Walker et al., 2006). However, a national coordinated approach to conservation management taking into account species distributions is required. Overton et al. (2010) are developing a tool called Vital Sites to assess ecological integrity. This incorporates current and natural distributions of native species based on a modeling approach, pressures (e.g., pests or habitat loss) on biodiversity, and the effects of management on relieving pressures. It operates at two levels (species and landscape) and assessments of significance and priorities can be made at each separate level or by combining the two levels. This research tool will provide another step to helping achieve goals towards identifying the most vulnerable ecosystems in New Zealand requiring urgent protection and management.

#### **7. Acknowledgment**

This work was supported by the New Zealand Foundation for Research, Science, and Technology through Contract C09X0912 "An ecosystem services approach to optimise natural resource use for multiple outcomes" to Landcare Research. The authors would like to thank Fiona Carswell and Garth Harmsworth from Landcare Research for their valuable comments on an earlier draft and Anne Austin for internal editing.


**Author details**

Vincenza Ferrara Faculty of Pharmacy and Medicine, Sapienza University, Rome, Italy

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**5. Conclusions**

Torque monitoring system was successfully implemented in the control of the ASPIRE robotic system. The development of the system started from the requests identified during the clinical trials performed in the fall of 2019 in Cluj-Napoca. A series of effects that may influence the effectiveness of the rehabilitation were presented and torque control proved to be a solution in identifying and overcoming some of these effects. After the development of the torque control system the performance was tested using healthy subjects chosen among the staff of the research center. The results have validated the functionality of the proposed improved control system and pushed the development of the experimental model towards the next level of maturity, preparing it for further clinical trials. At the same time the torque-based control adds to patient safety and the possibility to improve the HRI characteristics of the robotic system with high levels of safety and user-friendliness.

**Author Contributions:** Conceptualization, D.P., P.T., C.V., G.B. and G.C.; Data curation, D.T., N.T., I.D.G. and C.A.; Formal analysis, N.P.; Investigation, D.T., P.T., N.T., C.V., I.D.G., G.B., C.A. and G.C.; Methodology, I.D.G., G.B., G.C. and N.P.; Project administration, D.P.; Resources, D.T.; Software, G.B.; Validation, C.V.; Writing—original draft, D.P. and P.T.; Writing—review and editing, P.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by a grant from the Romanian Ministry of Research and Innovation, CCCDI—UEFISCDI, project number PN-III-P2-2.1-PED-2019-3022/546PED/2020 (NeuroAssist) within PNCDI III, and by the project POCU/380/6/13/123927—ANTREDOC, "Entrepreneurial competencies and excellence research in doctoral and postdoctoral studies programs", co-funded from the European Social Fund through the Human Capital Operational Program 2014–2020.

**Institutional Review Board Statement:** Ethical review and approval were waived for this study, due to the fact that the validation tests were intended only to assess the feasibility and range of motion of ASPIRE for the rehabilitation of the upper limb, being achieved in laboratory conditions, which required the participation of volunteers, healthy subjects. Furthermore, the volunteers have given their written consent after being informed about the test circumstances and the robotic system's operation, as well as any hazards or safety concerns that may arise during the tests.

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

**Data Availability Statement:** The data presented in this study are openly available in reference number [37,39–41].

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