*2.12.1 Polycystic ovarian syndrome*

Polycystic ovarian syndrome (PCOS) is a common endocrine disorder associated with obesity. Women with PCOS have hyperandrogenism and hyperinsulinemia with subsequent insulin resistance and infertility [43]. LSG is effective in treating PCOS, resulting not only in WL, but also significant improvement in the hormonal profile [43, 44]. Significant decrease is observed as early as 3 months post-surgery in luteinizing hormone (LH) levels (7.2 vs. 4.5 mIU/mL), with inversion of LH/ FSH ratio (P = 0.008), as well as significant decrease in fasting insulin levels (24.4 mIU/mL vs. 9.0 mIU/mL) [44]. LSG also positively augments fertility rates [43, 45]. A cohort of 53 women had a progressive increase of serum anti-Mullerian hormone (marker of ovarian reserve) levels 6 months after LSG [45]. These hormonal changes were also associated with the regulation of the menstrual cycle and resolution of dysmenorrhea [45]. Moreover, 22% percent of PCOS patients became pregnant within 12 months, 69% of which were previously nulliparous [43].


**8.2. Stationary target detection in an unknown indoor environment**

Indoor stationary object detection in an unknown environment is important for many civilian and military applications, such as indoor surveillance, search and rescue operations, logistics, security and so on. Compared with target detection in a known environment, it presents some challenges:

### *8.2.1. Signal enhancement*

Due to the challenges mentioned above, a possible detection scheme is given in Fig. 53. In the scheme, the objective of the first step, "signal enhancement" which is realized by a "*time-shift* & *accumulation*" operation, is to raise the SINR of system, and to transform the unknown environment into a Gaussian clutter and noise environment so that the detector could be

**Figure 52.** Measurement configuration. *T*1...*Tn*...*TN* are different transmission positions on the track **L***l*. "*R*1...*RM*" are sparsely spaced receivers with different reception angles. The dashed curve is an ellipse segment with the foci *Rm* and *Tn*.

**Figure 53.** Flow chart of the algorithm .

48 Will-be-set-by-IN-TECH

is situated and even traces of these ellipses make the interpretation of the image difficult. Figure 51 (b) illustrates the image of the same scenario obtained by the cross-correlated algorithm. The color scaling of both images in Fig. 51 (a) and (b) is the same. The reduction of image artifacts by means of the cross-correlated algorithm is evident. Elliptical traces were significantly reduced. This helps to identify the inspected environment in this figure more clearly. A more detailed discussion on the cross-correlated imaging with measurement

Y−direction [m]

X−direction [m]

(b) Cross-correlated imaging

0 1 2 3 4 5 6 7 8

examples is given in [74] and [75].

Y−direction [m]

**Figure 51.** Imaging in sensor networks.

challenges:

*8.2.1. Signal enhancement*

X−direction [m]

(a) Range migration

**8.2. Stationary target detection in an unknown indoor environment**

unknown, leading to further complications in the detector design.

cancellation) between sequence snapshots, etc [58].

Indoor stationary object detection in an unknown environment is important for many civilian and military applications, such as indoor surveillance, search and rescue operations, logistics, security and so on. Compared with target detection in a known environment, it presents some

• *The detection takes place in an unknown environment (e.g. an environment after disaster).* Thus, it is not possible to probe the environment before the presence of target. A range of techniques based on "*a priori*" information of the background cannot be utilized (e.g. [41, 64]). Additionally, the statistical distributions of clutter and noise are sometimes

• *The targets concerned are stationary with respect to the background.* There is no distinct speed difference between the targets and the background. Hence, it prevents the application of the motion-based detection techniques, such as Doppler based approaches, subtraction (or

• *It is a highly cluttered environment.* The targets (objects of interest) are typically surrounded by clutter (objects, which are not of interest, as shown in Fig. 52). In this case, the response of targets is not always stronger than clutter. In other words, the Signal to Interference and Noise Ratio (SINR) of the system is not always high enough to ensure a reliable detection.

Due to the challenges mentioned above, a possible detection scheme is given in Fig. 53. In the scheme, the objective of the first step, "signal enhancement" which is realized by a "*time-shift* & *accumulation*" operation, is to raise the SINR of system, and to transform the unknown environment into a Gaussian clutter and noise environment so that the detector could be

0 1 2 3 4 5 6 7 8

designed based on Gaussian clutter and noise. Mathematically, the "*time-shift* & *accumulation*" operation is described as

$$\sum\_{m=1}^{N} s\_{Rm,Tn}(t + t\_{Tn,T1}^{diff})\tag{19}$$

where *sRm*,*Tn*(*t*) is the signal received by the receiver *Rm* with respect to the transmission position *Tn*, and *t di f f Tn*,*T*1 = |**T***n* − **T**1| /*c* is the time-delay to be compensated for. **T***n* and **T**1 are the position vectors of *Tn* and reference transmission position *T*1 , respectively. In the operation,

50 Will-be-set-by-IN-TECH 228 Ultra-Wideband Radio Technologies for Communications, Localization and Sensor Applications Cooperative Localization and Object Recognition in Autonomous UWB Sensor Networks 51

where, *θRi* and *θRj* are the reflection angles of the receiver *Ri* and *Rj*,respectively. **V***bi* is the bisector of the angle *RiXT*1. *θbi* is the reflection angle of **V***bi*. According to this principle,

Cooperative Localization and Object Recognition in Autonomous UWB Sensor Networks 229

In practice, (20) is demonstrated in Fig. 55, where the spectra of receivers *R*1-*R*4 are

According to the principle discussed above, if we take the observation of *R*1, *s*¯*R*1,*Tn*(*τ*), as a reference signal, the observation of receiver *Rj* can be given by *s*¯*Rj*,*Tn*(*τ*) ≈ *s*¯*R*1,*Tn*(*τ*) exp(−*jγj*,1*τ*), where *γj*,1 is determined by the illuminating geometry according to (20). Based on this relationship, if we consider the effects of unwanted contributions due to

where **y** is an *NMN* × 1 vector. *NMN* = *M* × *N*, where *M* and *N* are the numbers of receivers

According to the Central Limit Theorem (CLT) of the probability theory, if a large number of clutter from different sources (scattered from different objects) is accumulated, the statistical distribution of the sum will approach a Gaussian distribution. As the scenario concerned takes place in a cluttered indoor environment which has many scatterers, we assume that the clutter **c** and the noise **n** can approach a Gaussian distribution due to the "*time-delay* & *accumulation*" operations given by (19). Hence, our detection problem simplifies to searching targets in Gaussian clutter and noise. We assume that the noise **n** and the clutter **c** are *NMN* × 1 independent zero-mean complex Gaussians with *NMN* × *NMN* known covariance matrices **M***c*+*n* = *E*[(**c** + **n**)(**c** + **n**)*H*]. **M***c*+*n* is a positive semidefinite and Hermitian symmetric matrix [63]. The superscript *H* indicates conjugate transpose of a matrix. Based on the signal model

*c*+*n***x** = **M**−1

In terms of the Neyman-Pearson criterion, the threshold *kth* could be set as *kth* = *D*Φ(1 − *Pf*),

*c*+*n*(**¯s**

, *fRj* ∈ [ *fR*1 , *fR*2 , *fR*3 , *fR*4 ] are the frequencies at which the maximum amplitudes are located. The differences between *fR*1-*fR*4 indicate the spectrum shifts between *R*1, *R*2, *R*3

*fRj*−*fR*1 ≈ *γi*,1−1

*ref T*1 , *s*¯ *ref Tn* , ...,*s*¯

*ref* ⊗ **K**) (22)

*D* ). Here, *kth* is the threshold for a likelihood

2/2*dt*. The parameter *D* is defined as *D*2 = **x***H***M**−1

*av* is the average signal power of **x**. Generally, *x*2

*D* ), and the probability of

*c*+*n***x** =

*av E*[(*y*−*x*)*H* (*y*−*x*)]

*T*. The symbol ⊗ denotes the

*ref* ⊗ **K** + **c** + **n** = **x** + **c** + **n** (21)

*γj*,1−1 , where

*ref TN*] *T* ,

measured based on the scenario of Fig. 54. It can be shown that *fRi*−*fR*1

clutter **c** and noise **n**, the signal model for detection can be given as

**y** = **¯s**

*Tn* = *s*¯*R*1,*Tn*(*τ*), and **K** = [1, exp(−*jγ*2,1*τ*), ..., exp(−*jγM*,1*τ*]

above, a matched filter detector is given as

false alarms can be given as *Pf* = 1 − Φ( *kth*

, where *x*2

1 2*π x* −∞ *e*−*t*

and transmission positions, respectively. **x** = **s**¯*ref* ⊗ **K**, where **s**¯*ref* = [*s*¯

**h** = **M**−1

and the detection probability can be given as *Pd* = 1 − Φ(Φ−1(1 − *Pf*) − *D*).

The probability of detection can be given as *Pd* = 1 − Φ( *kth*−**h***H* **x**

can be regarded as the SINR at the output of the detector [43].

stationary plane-surface objects can be detected.

*fRi*

*s*¯ *ref*

and *R*4, correspondingly.

*8.2.3. Detection*

Kronecker product.

test and Φ(*x*) = √

*av E*[(**y**−**x**)*H* (**y**−**x**)]

*NMN x*2

**Figure 55.** Measured spectrum-shift

### *8.2.2. The detection principle*

For a detection problem, generally, detection algorithms are designed based on the differences (or deviations) between the targets and the background (clutter, noise). The ability to distinguish objects depends on how much their properties (e.g. electromagnetic properties, motion properties) deviate from the properties of the background. For the concerned stationary target which has no speed differences relative to the environment it can be detected based on the reflection characteristic deviations between the target and the background. Here, we take a planar-surface target as an example to illustrate the principle of indoor stationary target detection.

Consider a detection scenario as shown in Fig. 52 and Fig. 54. The transmitter moves along track **L***l* to probe the environment, and to enhance the signal. The target is a concrete brick-like object with a square plane-surface (50*cm* × 50*cm*). It is located at *x* = 24*cm* on the track **L***l*, with an orientation angle of 60◦. The receivers *R*1, *R*2, *R*3 and *R*4 receive the scattered signals from the surroundings. They are located at different directions with respect to the target. The transmitter-target-receiver angles are 13.9◦, 20.3◦, 26.3◦ and 31.7◦, respectively. The antennas of the transmitter and receivers are omnidirectional and horn antennas, respectively. The receivers *R*1 and *R*2 are in the same room with the target and the transmitter, while the receivers *R*3 and *R*4 are placed behind a 20cm-thick concrete wall.

Theoretically, it can be proved that if the target is a planar-surface object with diffuse reflections, after passing through the down-converter and certain mathematical transforms, the received signals from different directions would have a spectrum-shift, which is given by Ω*j*,*i* = *ωc*(*γj*,*i* − 1) under the illuminating geometry in Fig. 52. Here, the subscripts *i* and *j* are the indices of the receivers located at the different directions, *ωc* is the angular central frequency, and *γj*,*i* is a parameter associated with the reflection angles,

$$\gamma\_{j,i} \approx 1 + \left(\sin\theta\_{Rj} - \sin\theta\_{Ri}\right) / \left(|\mathbf{V}\_{bi}|\sin\theta\_{bi}\right) \tag{20}$$

where, *θRi* and *θRj* are the reflection angles of the receiver *Ri* and *Rj*,respectively. **V***bi* is the bisector of the angle *RiXT*1. *θbi* is the reflection angle of **V***bi*. According to this principle, stationary plane-surface objects can be detected.

In practice, (20) is demonstrated in Fig. 55, where the spectra of receivers *R*1-*R*4 are measured based on the scenario of Fig. 54. It can be shown that *fRi*−*fR*1 *fRj*−*fR*1 ≈ *γi*,1−1 *γj*,1−1 , where *fRi* , *fRj* ∈ [ *fR*1 , *fR*2 , *fR*3 , *fR*4 ] are the frequencies at which the maximum amplitudes are located. The differences between *fR*1-*fR*4 indicate the spectrum shifts between *R*1, *R*2, *R*3 and *R*4, correspondingly.

### *8.2.3. Detection*

50 Will-be-set-by-IN-TECH

3900 4000 4100 4200 4300

*fR4 fR3 fR2 fR1*

R1 R2 R3 R4

Frequency Sampling Bins

For a detection problem, generally, detection algorithms are designed based on the differences (or deviations) between the targets and the background (clutter, noise). The ability to distinguish objects depends on how much their properties (e.g. electromagnetic properties, motion properties) deviate from the properties of the background. For the concerned stationary target which has no speed differences relative to the environment it can be detected based on the reflection characteristic deviations between the target and the background. Here, we take a planar-surface target as an example to illustrate the principle of indoor stationary

Consider a detection scenario as shown in Fig. 52 and Fig. 54. The transmitter moves along track **L***l* to probe the environment, and to enhance the signal. The target is a concrete brick-like object with a square plane-surface (50*cm* × 50*cm*). It is located at *x* = 24*cm* on the track **L***l*, with an orientation angle of 60◦. The receivers *R*1, *R*2, *R*3 and *R*4 receive the scattered signals from the surroundings. They are located at different directions with respect to the target. The transmitter-target-receiver angles are 13.9◦, 20.3◦, 26.3◦ and 31.7◦, respectively. The antennas of the transmitter and receivers are omnidirectional and horn antennas, respectively. The receivers *R*1 and *R*2 are in the same room with the target and the transmitter, while the

Theoretically, it can be proved that if the target is a planar-surface object with diffuse reflections, after passing through the down-converter and certain mathematical transforms, the received signals from different directions would have a spectrum-shift, which is given by Ω*j*,*i* = *ωc*(*γj*,*i* − 1) under the illuminating geometry in Fig. 52. Here, the subscripts *i* and *j* are the indices of the receivers located at the different directions, *ωc* is the angular central

sin*θRj* − sin*θRi*

/ (|**V***bi*| sin*θbi*) (20)

**Figure 54.** Measurement environment. *R*3 and *R*4 are placed behind the wall.

0.4 0.6 0.8 1

receivers *R*3 and *R*4 are placed behind a 20cm-thick concrete wall.

frequency, and *γj*,*i* is a parameter associated with the reflection angles,

*γj*,*i* ≈ 1 +

Normalized Amplititude

**Figure 55.** Measured spectrum-shift

*8.2.2. The detection principle*

target detection.

According to the principle discussed above, if we take the observation of *R*1, *s*¯*R*1,*Tn*(*τ*), as a reference signal, the observation of receiver *Rj* can be given by *s*¯*Rj*,*Tn*(*τ*) ≈ *s*¯*R*1,*Tn*(*τ*) exp(−*jγj*,1*τ*), where *γj*,1 is determined by the illuminating geometry according to (20). Based on this relationship, if we consider the effects of unwanted contributions due to clutter **c** and noise **n**, the signal model for detection can be given as

$$\mathbf{y} = \mathbf{\bar{s}}^{ref} \otimes \mathbf{K} + \mathbf{c} + \mathbf{n} = \mathbf{x} + \mathbf{c} + \mathbf{n} \tag{21}$$

where **y** is an *NMN* × 1 vector. *NMN* = *M* × *N*, where *M* and *N* are the numbers of receivers and transmission positions, respectively. **x** = **s**¯*ref* ⊗ **K**, where **s**¯*ref* = [*s*¯ *ref T*1 , *s*¯ *ref Tn* , ...,*s*¯ *ref TN*] *T* , *s*¯ *ref Tn* = *s*¯*R*1,*Tn*(*τ*), and **K** = [1, exp(−*jγ*2,1*τ*), ..., exp(−*jγM*,1*τ*] *T*. The symbol ⊗ denotes the Kronecker product.

According to the Central Limit Theorem (CLT) of the probability theory, if a large number of clutter from different sources (scattered from different objects) is accumulated, the statistical distribution of the sum will approach a Gaussian distribution. As the scenario concerned takes place in a cluttered indoor environment which has many scatterers, we assume that the clutter **c** and the noise **n** can approach a Gaussian distribution due to the "*time-delay* & *accumulation*" operations given by (19). Hence, our detection problem simplifies to searching targets in Gaussian clutter and noise. We assume that the noise **n** and the clutter **c** are *NMN* × 1 independent zero-mean complex Gaussians with *NMN* × *NMN* known covariance matrices **M***c*+*n* = *E*[(**c** + **n**)(**c** + **n**)*H*]. **M***c*+*n* is a positive semidefinite and Hermitian symmetric matrix [63]. The superscript *H* indicates conjugate transpose of a matrix. Based on the signal model above, a matched filter detector is given as

$$\mathbf{h} = \mathbf{M}\_{\mathbf{c}+\mathbf{n}}^{-1}\mathbf{x} = \mathbf{M}\_{\mathbf{c}+\mathbf{n}}^{-1}(\mathbf{\bar{s}}^{ref}\otimes\mathbf{K})\tag{22}$$

The probability of detection can be given as *Pd* = 1 − Φ( *kth*−**h***H* **x** *D* ), and the probability of false alarms can be given as *Pf* = 1 − Φ( *kth D* ). Here, *kth* is the threshold for a likelihood test and Φ(*x*) = √ 1 2*π x* −∞ *e*−*t* 2/2*dt*. The parameter *D* is defined as *D*2 = **x***H***M**−1 *c*+*n***x** = *NMN x*2 *av E*[(**y**−**x**)*H* (**y**−**x**)] , where *x*2 *av* is the average signal power of **x**. Generally, *x*2 *av E*[(*y*−*x*)*H* (*y*−*x*)] can be regarded as the SINR at the output of the detector [43].

In terms of the Neyman-Pearson criterion, the threshold *kth* could be set as *kth* = *D*Φ(1 − *Pf*), and the detection probability can be given as *Pd* = 1 − Φ(Φ−1(1 − *Pf*) − *D*).


**5. Conclusions**

The fundamental many-faceted aspects of biopolymer architectures afford greater versatility for configuration and exploitation compared with the main ubiquitous recalcitrant synthetic petroleum-based plastics. This is a pivotal trait that is available for harnessing to enable bioplastics to meet both the high mechanical and barrier performances of their petroleum-based counterparts together with full sustainable biodegradability and circularity. Their very bio-nature means bioplastics are inherently more elaborate at a structural level. This increases accessibility to bio-interactions for enzymatic biodegradation, biodepolymerisation and biorepolymerisation, as well as supporting routes to improving mechanical and barrier performances. While several bioplastics have already achieved certain mechanical and barrier performance criteria, which are equivalent and even exceed those of corresponding petroleum-based plastics, courses of action for resolving the remaining limitations are becoming increasingly accessible.

Here, avenues for the advancement of the performance of bioplastics with respect to mechanical and barrier properties alongside biodegradability are discussed. The key architectural features properties are *Mw* and crystallinity, which typically exhibit an inversely dependent relationship with mechanical performance and biodegradability for both bioplastics and petroleum-based plastics. Increasing *Mw* and crystallinity are generally associated with higher mechanical performance and decreased biodegradability as lower crystallinity corresponds to looser chain packing, facilitating enzyme access.

Both macro and micro strategies have the potential for dual positive correlation on the mechanical and biodegradability performances of bioplastics. Regarding the macro approach, new possibilities are afforded, such as harnessing intricate bioplastic structures and formats, which include microfibrillar frameworks and combination with advanced methods, such as in situ polymerisation. Blending and compounding with additives as selected fillers, plastisisers and compatibilisers are also being demonstrated for improved mechanical features, without decreasing biodegradability.

Whereas, regarding the micro approach, expanding PHAs families and large numbers for possible monomer units, present the potential to engineer biodegradable plastics with equivalent target petroleum plastic performances without associated environmental pollution. Further routes include metabolic pathway alteration, design of high specificity substrates, intricate copolymer and block copolymer and genetic modification to produce strains to achieve next-generation multifunctional biopolymers.

In these respects, bioplastic polymers, in contrast to Petroleum-based plastics, have not yet been tailored or even adequately explored to establish their capacities for current and future applications. Given the range and diversity of options available for bioplastics development, there are excellent prospects to extend their application range on a comparable scale to fossil-based thermoplastics and beyond. Further innovations can be expected as the knowledge and new capacities for the manipulation of biopolymers advances, and spawns outputs in related and novice disciplines. The realisation of high performance plastics, without recalcitrance, pollution or resource depletion and switching to regenerative low carbon circularity, has the potential to both safeguard and promote future prosperity for the planet and its inhabitants.

**Author Contributions:** O.A.A. Conceptualization, Data curation, Investigation, Writing—original draft; E.L.G. Investigation, Data curation, Formal analysis; Writing—original draft; M.A. Investigation, Data curation; S.A. Investigation, Data curation; Y.C. Data curation, Formal analysis; Writing—original draft; M.B.F. Supervision, Validation, Writing—original draft; M.M. Investigation, Visualization, Writing—original draft, Writing—review & editing. All authors have read and agreed to the published version of the manuscript.

**Funding:** This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 870292 (BioICEP).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 870292 (BioICEP) and is supported by the National Natural Science Foundation of China (grant numbers: Institute of Microbiology, Chinese Academy of Sciences:<PHONE_NUMBER>6, Beijing Institute of Technology:<PHONE_NUMBER>5, Shandong University:<PHONE_NUMBER>4).

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


*2.1. Animals*

Female Wistar rats weighing 220–250 g were purchased from BioLASCO Taiwan Co. Ltd. (Taipei, Taiwan) and housed in the Experimental Animal Center, National Taiwan University. All the surgical and experimental procedures were approved by the ethics committee "Institutional Animal Care and Use Committee of the National Taiwan University College of Science" (identification code of approval: 20100244 and date of approval: 02/11/2011) and were in accordance with the guidelines of the National Science Council of Republic of China (NSC 1997). To monitor fecal and urinary excretion, the rats were placed into the metabolic cage. The feces and urine samples were collected and recorded every 12 h before surgical experiments. During the experiment, the rats were given free access to food and water.

#### *2.2. Cell Preparations (MSCS Isolation, Characterization, and Culture)*

Femora from Wistar rats (BioLASCO Taiwan Co Ltd., Taipei, Taiwan), 8 to 10 weeks of age, were removed, and soft tissues were detached aseptically. Bone marrow was extruded by inserting a 23-gauge needle into the shaft of the bone and was flushed out with basal medium ( α-minimal essential medium [ α-MEM], Gibco-BRL, Gaithersburg, MD, USA). Isolation of MSCs was performed according to similar procedures as described previously [18]. Briefly, mononuclear cells were isolated from the bone marrow aspirates by a density gradient centrifugation method were suspended in complete culture medium ( α-MEM supplemented with 16.6% fetal bovine serum, 100 U/mL penicillin, 100 μg/mL streptomycin, and 2 mM L-glutamine) and seeded in plastic dishes. After 24 h of the initial culture, nonadherent cells were removed by a change of medium and irrigation of the culture. The culture typically reaches 65% to 70% confluency within 6 to 8 days and reached subconfluency at 9 days, when the cells (passage 0) were harvested for further subculturing. Starting from passage 1, the cells were seeded at 100 cells/cm2 and were grown in complete culture medium with a medium change twice per week. For hypoxic MSC cultures, cells were cultured in a gas mixture composed of 94% N2, 5% CO2, and 1% O2 [19], whereas in normoxic MSC cultures, cells were cultured in 95% air and 5% CO2. For maintenance of the hypoxic gas mixture, an incubator with two air sensors, one for CO2 and the other for O2, was used. O2 concentration was achieved and maintained using delivery of N2 gas from a tank containing pure N2. If the O2 concentration rose above the desired level, N2 gas was automatically injected into the system to displace the excess O2.

#### *2.3. HIF-1*α *Determination and Growth Factors Array Assay*

The hypoxic inducible factor-1 α (HIF-1 α) concentration and multiple growth factors assay from cultured condition medium and MSCs or HMSCs were determined with HIF-1 α ELISA kit (MBS2702491, MyBioSource, San Diego, CA, USA) and RayBio ® rat growth factor array (AAR-GF-1-2, RayBiotech, Peachtree Corners, GA, USA) according to the manufacturer's instructions.

## *2.4. Experimental Model and Design*

Anti-thy1.1 GN was induced by injection of 0.2 mL of phosphate-bu ffered saline containing 250 μg anti-thy1.1 monoclonal antibody (Cedarlane, Burlington, ON, Canada) into rats via a jugular vein under sodium pentobarbital anesthesia (50 mg/kg, i.p.) at day 0, and 0.2 mL of saline injection into the jugular vein as a control group. This method for induction of acute GN had been reported previously [20]. Under avertin anesthesia (400 mg/kg, Acros Organics, Morris Plains, NJ, USA), one PE10 tubing was introduced into the left renal artery from the left femoral artery via the aorta for direct MSCs or HMSCs delivery (Figure 1A). Varied numbers of MSCs or HMSCs including 1, 2, and 5 × 105 cells were administered via this catheter in the therapeutic groups, and saline was administration in control groups. The grouping and experimental design are shown in Figure 1B.

**Figure 1.** The technique for locally intra-renal arterial administration of mesenchymal stem cells (MSCs) was displayed in ( **A**). The experimental grouping and design are displayed in the eight groups (**B**). High levels of green fluorescence were visualized under UV light in the glomeruli of nephritic kidney sections (**C3–4**) but not in the normal kidney sections (**C1–2**). C1, C3 magnification 200×; C2, C4 magnification 400×.

#### *2.5. Tracking of Intrarenal Arterial Injected MSCs in Rat Kidneys*

To ascertain the MSC expression in the kidney, we infused MSCs containing a green fluorescent protein (GFP) into the left kidney and examined the GFP expression in rat kidneys one hour later. The sections were examined under UV light for the detection of fluorescence around the glomeruli, arterial lining cells and tubular cells. Immunochemical stains with primary antibodies against MSC CD44 (MCA643GA, Serotec, Kidlington, UK) were also performed for identification of MSCs in kidneys.

#### *2.6. Measurements of Proteinuria and Hydroxyproline Degree*

Twenty four hours urine samples were collected (on day 5 after anti-Thy1.1 infusion) from all experimental rats with free access to water. Urinary protein concentration was determined by a Bio-Rad protein assay (Bio-Rad Laboratories, München, Germany). Hydroxyproline content was measured with Hydroxyproline Assay Kit (STA-675, Cell Biolabs, Inc., San Diego, CA, USA).


**4. Aggregation of photosensitizers and its influence in PDT**

406 Advanced Aspects of Spectroscopy

applications [145,

oxygen in PDT applications [139,

146, 147,

monocations with hydrogen ions [153].

works done on its interaction with DNA [155,

tumors [136] and can retard tumor growth [137,

Nile blue (RNB) and Meldola's Blue. It has been found to be localized selectively in animal

stain for Escherichia coli in flow cytometry [143], as a DNA probe [144] and many other

solvatochromism, they have been used as stains and imaging agents. These dyes present relatively low solubility in aqueous medium as well as their fluorescence is reduced significantly in in the presence of polar medium, which opens up new possibilities to develop aqueous analogues of these benzophenoxazines [149]. Together with the increase of the solubility in water, it is believed that the self-assembly process to form aggregates can be

NB shows thermochromic and solvatochromic behavior in its ultraviolet/visible spectra [151]. The variation in the absorption spectrum is due to the equilibrium between the monocation and the neutral molecule, where the monocationic form is the more stable in most solvents. In strong basic conditions the neutral form is observed, where in strong acidic conditions the dicationic and tricationic forms can be observed [152]. The fast decay processes study can be used to get information on the effect of medium condition, basic and acidic, on determining the excited state lifetime on the picosecond scale. It was shown that the reason for the faster decay in acidic conditions results from the formation of dications by reaction of excited state

Despite the photophysics of NB in pure solvents is well characterized in literature, the NB interaction with microheterogeneous systems, such as micelles, reverse micelles (RMs) and DNA is still not well understood. Electrochemical studies have shown that NB-DNA duplexes modified microelectrode can be used as a rapid and sensitive method to detect TATA binding to DNA in the presence of other proteins [154]. However, there are no many

> 156, 157,

NB with biomimicking self-organized assemblies (SDS micelles and AOT reverse micelles) and a genomic DNA (extracted from salmon sperm) (SS DNA), it has been shown that there are two different binding modes of NB with genomic DNA, electrostatic and intercalative modes [144]. There was no explanation for the mechanism related to these interaction modes. The electrostatic mode is believed to be responsible for electron transfer between the probe and DNA, which may result in a quenching process of the NB fluorescence emission intensity when in the presence of low concentration of DNA. The intercalative mode is believed to be the subsequent release of quenching due to the intercalation of the dye in DNA base pairs. In another study, it was shown that binding affinity of the probe is higher with SDS micelles than with the DNAs within its structural integrity in presence of the micelles. The complex rigidity of NB with various DNAs and its fluorescence quenching

with DNAs has shown a strong recognition mechanism between NB and DNA [159].

NB was immobilized in two different surfaces, a nonreactive surface (SiO2), with its conduction band at much higher energies, and a reactive surface (SiO2), with a conduction band situated at lower energies. The former is used to directly probe the excited-state dynamics of the dye undisturbed by other competing processes. The latter is used to study

disrupted resulting in an enhancement of the fluorescence intensity [150].

138]. NB has been used as a photosensitizer for

158]. In a work done on the interaction of

142], as a

140], in processes that depend on solvent polarity [141,

148]. Due to their high fluorescence quantum yield together with their

Most of these dyes form aggregates in the ground state [161, 162, 163], even when the dye concentration is low (approximately 10-6 M) and in the presence of salts and aggregation inducing agents, such as anionic micelles, heparin, polyelectrolyte, liposome and vesicles. The planar structure of such dyes is a key factor that contributes to the approaching and dimerization of the dyes [164, 165].

The presence of hydrophobic ligands in the dye structure facilitates the aggregate formation in polar medium. The effects of the planar structure of the dye, hydrophobicity and the interaction with cell membranes were observed in photosynthetic systems II of plants [166] and other systems [167]. Some studies have shown that the interaction among phenothiazines and cyclodextrins results in the aggregate formation with different sizes depending on the cyclodextrins cavity size [162,168].

Studies that were conducted previously have shown that methylene blue molecules form aggregates and the photophysical behavior changes depending on the ground state aggregation. It results in a decreasing of the fluorescence intensity and on the singlet oxygen formation [49]. These studies have shown that the interaction with micelles is responsible for the dimerization process and not the interaction with monomer of the surfactant, as it has been postulated in some works [169]. In this stage of the work it is important to study the nature of the aggregates formed in different negatives interfaces and in biological systems, more specifically in micelles, vesicles and mitochondria.

It is well known that dimerization and medium composition effects changes the energy transfer process among triplets species and molecular oxygen and other triplet suppressors [170, 171, 172, 173, 174, 175, 176, 177, 178]. Some studies carried out using thionine and MB have shown some of these effects [179]. Azure A, azure B, thionine e MB are dimerized with different dimerization constants.

The aggregation of ionic dyes cannot be assigned to a specific type of chemical interaction. There is a significant contribution of several influences, such as van der Waals interactions, intermolecular hydrogen bounds and pi-electrons interactions, being that, frequently, it is not trivial to evaluate the specific contribution of each one of these interactions [180].

The quantum behavior of extended aggregates of atomic and molecular monomers, containing from a just a few up to thousands of subunits, is attracting increasing attention in chemistry and physics, being that proeminent examples are aggregates of large dye molecules, chromophore assemblies describing the photosynthetic unit of assemblies of ultra-cold atoms [181].

According to their structure, dyes, such as phenotiazinium, exhibit J- or H-aggregates, which present very typical J- or H-absorption bands [182]. The aggregate absorption band is red-shifted in relation to the monomer absorption. These are the J-aggregates showing a very narrow band whose position is well-predicted by a theory ignoring intramolecular vibrations. By contrast, other dyes showed a shift towards the blue (i.e. higher absorption energies) and were termed H-aggregates (hypsochromic shift). Unlike the J-band, the line shape of the H-band generally shows a rich vibrational structure and has a width of the order of the monomeric band [183]. The J-band is polarized parallel to the rods, while the Hband is polarized perpendicularly to the rod long-axis [184].

Self-organized J-aggregates of dye molecules, known for over 60 years, are emerging as remarkably versatile quantum systems with applications in photography, opto-electronics, solar cells, photobiology and as supramolecular fibres [185].