**4. Obesity**

Obesity, by definition, is an excess of body fat that poses a risk to health. It is assessed clinically via the expression of body weight as a function of height – the body mass index (BMI). This is calculated by dividing an individual's weight (in kilograms) by the square of their height (in meters) (kg/m2 ) [3, 24]. BMI is used in adults to delineate overall body fatness where a BMI of 18.5–24.9 kg/m2 is considered to be normal. Abnormal BMI's are sub-categorized according to severity, where a BMI ≥ 25 kg/m2 is considered to be within the overweight range, BMIs ≥30 kg/m2 are considered as obesity and BMI ranges ≥40 kg/m2 are classified as severe/morbid obesity. Obesity is, however, regarded as a disease of opulence, easily remedied by the reduction of food intake and considered to be irrelevant elsewhere. Furthermore, obesity was not a major concern for the WHO as their priority was to deal with malnutrition and the problems of the third world. By the mid-1990's, obesity had become a prominent problem for obesity specialists but was still not being taken seriously by most national governments. The prevalence is generally higher in women than in men, although the highest incidence was recorded in males aged 25–29 years residing in low-middle income countries.

## **4.1 Causes of obesity**

Obesity is an unintended consequence of a 'Western lifestyle' [25] where economic, social and technological advances [26] have resulted in urbanization and reduced physical activity [27]. Diets have transitioned from natural, organic foods to refined, high fat and high sugar alternatives, leading to a nutrition transition parallel to the observed economic growth [28]. These changes have led to a rapid increase in the prevalence of obesity [25]. As a historically Western disease, obesity was largely localized to developed countries until recent decades where middle- and low income-countries began to experience rates comparable to those of highincome countries. The rapid economic expansion and modernization experienced

by middle- and low-income countries, as well as changing diets, are the greatest contributors to this epidemic [28] as more and more of these countries adopt Western diets and lifestyles. Close to 30% of the populations within middle income countries are classified as overweight or obese, with South Africa (SA) having the highest prevalence in sub-Saharan Africa, where the prevalence was reported to be 27% in adults over the age of 18 years in 2016 [29]. While the predominant causes of the energy imbalance seen in obesity are inadequate physical activity and unhealthy diets, not all those who are exposed to these unfavorable behaviors will develop the disease. Obesity arises from the interactions between an at-risk genetic profile and environmental risk factors, such as physical inactivity, excessive caloric intake, the intrauterine environment, medications, socioeconomic status, and possibly novel factors such as insufficient sleep, endocrine disruptors, and the gastrointestinal micro biome [30]. Obesity is a multifactorial disease, and many underlying factors disproportionately predispose subsets of the population to its development, and several of these are non-modifiable factors including age, gender, race, and genetics. Specific structural and functional changes are observed in obese visceral adipose tissue, together with local inflammation and adipokine production, promoting metabolic disturbances [31, 32]. Obesity leads to the presence of ectopic fat surrounding organs and to the accumulation of lipids in tissues themselves. Peripancreatic adipose tissue is implicated in glucose homeostasis regulation and can be impaired in obesity.


**6.2. Experimental demonstrator device**

## *6.2.1. Device concept and aim*

The aim of an experimental demonstrator device is to investigate the impact of individual sub-components on the performance of the whole device, as well as to have the opportunity to flexibly perform device modifications without the need of redesigning complex RF-PCBs. The device is organized in a modular concept as symbolically depicted in Fig. 51. Fig. 52 shows an example of a demonstrator implementation of such kind.

The individual sub-components as e.g. shift register for stimulus generation, T&H-circuits, RF-power distribution, RF-synchronization etc. are organized as plug-ins. Hence, one can simply replace a device component by a new one if improved circuits, better IC-housing or 422 Ultra-Wideband Radio Technologies for Communications, Localization and Sensor Applications

RF-PCBs are available. Furthermore, the various modules may be interconnected to different device structures as shown in Fig. 4 or Fig. 55.

HaLoS – Integrated RF-Hardware Components for Ultra-Wideband Localization and Sensing 423

The clock synchronization unit which precisely defines the receiver sampling points is a 9*th* order binary divider with a maximal toggle rate of 24 GHz. Random fluctuations of the sampling point (jitter) could be reduced down to some tens of femtoseconds [48] due to the balanced circuit topology and the optimized architecture of the timing system (see [2], [49] detailed discussions). Note, that the time position uncertainty of the measured impulse response (compare Fig. 5) is father decreased as consequence of the impulse compression

The clock distribution plug-in is an active device which recovers and distributes the sampling clock among the receivers and the analog-to-digital converters. The unit can handle clock pulses with 20 ps falling/rising edges and features wideband reverse signal

The receivers are ultra wideband sampling gates with an 18 GHz analog input bandwidth, better than -40 dB signal feed-through over the full bandwidth, -15 dBm input compression points and a decay rate of about 20 % per ms relative to full scale (i.e. 5…200 ppm per sampling cycle depending on the clock rate (0.5 – 20 GHz) of the system). Other potential

The transmitter-receiver and receiver-receiver cross-talk is better than 130 dB over the full operational band. In order to achieve this value, attention was paid to RF-housing, clock signal distribution and power supply decoupling (see also Fig. 57). The recent configuration of the demonstrator RF electronics is able to handle (internally) up to about 70 000 IRFs per second ( 9*th* order M-sequence at 18 GHz system clock). The data transfer to a host PC (based on commercial standard interfaces like USB and LAN) reduces, however, the actually achievable update rate to about 300 IRFs/s. The corresponding gap is filled by synchronous averaging in order to use the available data amount for noise suppression. The achievable receiver dynamic is about 114 dB @ 1 IRF/s. It has to be noted that device non-linearity is classically qualified by the intercept point which is based on a Taylor-series model of the device under test and sine wave stimulation. In order to keep this established philosophy,

This is illustrated by Fig. 53. In the example at the top, the Tx- and RX-port of an Msequence device were connected via a variable attenuator and the impulse response was recorded for attenuator values between 0 and 120 dB. In the case of very weak input signals (large attenuation), we can only observe noise and device internal cross-talk. If we reduce the attenuation, the wanted signal peak (it is called "main pulse" in Fig. 53) appears and increases linearly with the signal level while the cross-talk level remains constant. By reducing the attenuation further, other signal parts become to protrude from noise. They also increase linearity at the beginning. These signals are caused from device internal reflections, deviations from the ideal time shape of an M-sequence and misalignments of the ADC timing (refer to Fig. 54). We call them device internal clutter. For very high signal levels, the receiver will tend to saturate which leads to the compression of the main peak and the internal clutter signals. Furthermore, the appearing non-linear distortions create

components of the experimental demonstrator are discussed in sub-chapters 3 to 5.

(i.e. correlation; see [2] for discussion).

rejection better than 40 dB per branch.

the approach was extended to wideband signals [48].

**Figure 51.** Modular concept of the experimental system.

**Figure 52.** Photograph of the demonstrator implementation example.


6.1.1. Scenarios

Simulations are conducted over synthetic scenarios (See Figure 10) where long distances and obstacle presence may offer similar challenging conditions that would be expected in the real world. The *Loop* and *Cross* scenarios (see Figure 10a,b) were mainly used to confirm the correctness of the implemented solutions and to show the advantages of using a multi-robot approach over a single one. Unfortunately, and caused by the shape and size of the free zones, on those scenarios there are nearly no possibilities to demonstrate any advantage of the proposed approaches over the others. Finally, the *Maze* scenario (Figure 10c), that represents the most challenging environment, was used to establish comparative results among the approaches. These results are further analysed below in Sections 6.3 and 6.4, respectively. Due to the big amount of collected data, only the values related to *Maze* runnings are summarised and discussed here. Even so, all charts and screen-shots generated from data concerning all of the three environments are available online: www.fing.edu.uy/~fbenavid/ projects/MuRE/mure.html.

**Figure 10.** Benchmark scenarios. All terrains cover an 80 × 80 m2 flat surface with static obstacles (walls including the outer perimeter). Proposed as benchmarks in [45]. (**a**) Loop-like scenario; (**b**) Cross-like scenario; (**c**) Maze-like scenario.


*4.1. General Experimental Procedures*

Genomic DNA samples were prepared using the CTAB isolation buffer at pH 8.0 (20 g/L cetyltrimethylammonium bromide, 1.4 M sodium chloride, and 20 mM EDTA). Polymerase chain reaction (PCR) was performed using Phusion® High-Fidelity DNA Polymerase (New England Biolabs, NEB, Beijing, China). PCR analyses were conducted using a 2×Hieff® PCR Master Mix (With Dye, Yeasen, Shanghai, China). DNA restriction enzymes were used as recommended by the manufacturer (New England Biolabs, NEB, Beijing, China). RT-PCR analysis was performed using Direct-zol™ RNA MiniPrep (Zymo Research, Irvine, CA, USA) and PrimeScript RT-PCR Kit (TaKaRa, Gunma, Japan). The genespecific primers are listed in Table S1. Custom oligonucleotides synthesis and fragments sequencing were served by Shanghai Sangon DNA Technologies. LC-MS was performed using an Acquity UPLC H-Class coupled to a SQ Detector 2 mass spectrometer using a BEH C18 column (1.7 μm, 2.1 × 50 mm, 1 mL/min) (Waters Corporation, Milford, MA, USA). Semi-preparative HPLC (YMC Co., Ltd., Kyoto, Japan) was performed on an ODS column (YMC-Pack ODS-A, 10 × 250 mm, 5 μm, 3 mL/min). 1H NMR and 13C NMR spectra were recorded on an Agilent 500 MHz DD2 spectrometer (Agilent Technologies Inc., Santa Clara, CA, USA). The spectra were processed by the software MestReNova 6.1.0 (Metrelab, Coruña, Spain). NMR data are provided in Tables S3 and S4, and spectra in Figures S8–S11.


**1. Introduction**

In several papers evidence has been presented that is able to refute the widely recognized electromagnetic theory of today [1-5]. One such fundamental law is Lorentz' force law. Already 1997 a paper presented mathematical proofs showing that this law is unable to explain the repulsive force between collinear currents, demonstrated in the case of Ampère's bridge [1]. Even Graneau's exploding wires and Hering's pump cause difficulties, when trying to use Lorentz' force law in order to explain the effects that have been registered [6-11]. Therefore it is most exciting to explain one of the most frequent applications of Lorentz force law, the attractive force exerted between two parallel conductors carrying a DC current. Confessedly,

© 2015 The Author(s). Licensee InTech. 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.

others have made efforts in this respect already near 200 years ago, most famous of them Ampère [12]. Successors, like Grassmann, have made serious efforts to make the Lorentz force (in his early pre-Lorentz formulation) appear to be in accordance with Ampère's results [13]. In a more recent paper, this claim has been discarded through a mathematical analysis of Grassmann's derivation [14]. Additionally, in order to introduce a new theory, it must be able not only to explain experiments that a recognized theory cannot, but also to explain the experiments that it apparently is successful in explaining. One crucial phenomenon is that of light, or electromagnetic radiation. In fact, it has been possible to explain this, too, using basically Coulomb's law [15-18]. One may mention also electromagnetic induction [3-5].

The traditional methods have the benefit of being able to predict certain experiments, but not all. A new method must therefore in order to be better both done the first thing, but also be able to explain more evidence. By going back to the most basic well-corroborated law, Coulomb's law, one would expect a possible solution, provided one is very careful and applies mathematical method in a very strict fashion.


**4. Many-Valued Truth Institution-Independent Model Theory**

In standard institution theory, the satisfaction relation between models and sentences is considered to be binary, *M* |= *ρ* either holds true or it does not. Many-valued institution theory considers a generalisation of ordinary institution theory where *M* |= *ρ* is not necessarily binary. Such a generalisation can be achieved, and basic concepts such as semantic consequence, the Galois connection between syntax and semantics, internal logic, but also more advanced concepts such as filtered products, preservation, interpolation, definability, logic translation, etc. do "survive" it but in a subtler form. From a pure theoretical standpoint (there are also more practical motivations), this generalisation brings further clarifications to the complex network of causal relationships underlying model

theory. This has to do with binary truth being a collapsed form of truth where many things happen somehow "by accident". Much institution-independent model theory may be developed in the many-valued truth fashion.


*2.3. Methods*

2.3.1. Absorption Wavelength Mapping

To map absorption features, namely absorption wavelengths and depths, new tools, which are described here for the very first time, were programmed to allow automatic detection of multiple absorption feature parameters. The tools—e.g., called QUANTools—have been created using IDL programming language (ENVI/IDL: version 5.0 and higher, [62]).

The tools can process both spectral libraries and hyperspectral image data. They consist of basic modules (GUIs) (Figure 2) allowing users to:

The only decisions made by an operator/expert are to define the spectral region (Figure 2, step 1) and the number of desired absorption features mapped in each spectral region (Figure 2, step 4). All the other steps are done automatically when processing the datasets. A detailed explanation of the processing steps available in the toolbox is given in Section 2.3.2, below.

#### 2.3.2. The Toolbox (QuanTools) Description

QuanTools can be used for spectral absorption band mapping using spectral libraries or hyperspectral image data (Figure 2). A user first defines the spectral range to be analysed within the visible (VIS), near-infrared (NIR), shortwave infrared (SWIR) or longwave infrared (LWIR) regions (Figure 2, step 1). Different spectral ranges can be defined and analysed after one another. Next comes continuum removal (CR, [63]), by which the continuum—that is, a convex hull of straight-line segments, is fitted over a reflectance spectrum and subsequently removed by division or rationing (Figure 2, step 2). The next step is noise detection and removal using local minima of the spectral curve defined as:

$$R = \left\{ \begin{array}{l} 1 \ if \rho\_{\mathcal{CR}}(\lambda - 1) \ge \rho\_{\mathcal{CR}}(\lambda) \\ 0 \text{ if } \rho\_{\mathcal{CR}}(\lambda - 1) < \rho\_{\mathcal{CR}}(\lambda) \end{array} \right\} \tag{1}$$

$$\inf\left[R(\lambda - 1) = 1\right] \land \left[R(\lambda) = 0\right] \Rightarrow \text{local minimum},\tag{2}$$

where *ρCR*(*λ*) and *ρCR*(*λ* − 1) are the values of the spectral curve after Continuum Removal and *R* is an auxiliary variable. When the local minima are found, it has to be decided whether the value represents noise or not. The decision is based on two sets of values (a defined number of surrounding bands, e.g., called a spectral window)—derived from bands placed before and after the local minimum:

$$A(\lambda) = \begin{Bmatrix} \rho\_{\mathcal{CR}}(\lambda - nn); (\lambda - nn) \\ \vdots \\ \rho\_{\mathcal{CR}}(\lambda - 2); (\lambda - 2) \\ \rho\_{\mathcal{CR}}(\lambda - 1); (\lambda - 1) \end{Bmatrix} \tag{3}$$

$$\rho\_c(\lambda) = \left\{ \begin{array}{c} \rho\_{\mathcal{CR}}(\lambda+1); (\lambda+1) \\ \rho\_{\mathcal{CR}}(\lambda+2); (\lambda+2) \\ \vdots \\ \rho\_{\mathcal{CR}}(\lambda+nn); (\lambda+nn) \end{array} \right\},\tag{4}$$

where *A*(*λ*) represents the set of surrounding bands before the local minimum, *B*(*λ*) represents the set after the local minimum, and *nn* is the number of surrounding bands. Then the minimum values of these sets are detected as:

$$A\_{\min}(\lambda) = \min \rho\_{\mathbb{C}\mathcal{R}} \in A(\lambda) \tag{5}$$

$$B\_{\min}(\lambda) = \min \rho\_{\text{CR}} \in B(\lambda). \tag{6}$$

The minimum of the spectral window (*Amin*(*λ*), *Bmin*(*λ*)) is compared to the local minimum *ρCR*(*λ*). Using this comparison, the noise is detected as follows:

$$\left[B\_{\min}(\lambda) < \rho\_{\mathcal{CR}}(\lambda)\right] \land \left[A\_{\min}(\lambda) > \rho\_{\mathcal{CR}}(\lambda)\right] :\tag{7}$$

$$if\rho\_{CR} \in A(\lambda) < \rho\_{CR\max} \in B(\lambda) \Rightarrow \text{print}\left\{\begin{array}{l} \text{Err}(\lambda - 1) \\ \text{Err}(\lambda - 2) \\ \vdots \\ \text{Err}(\lambda - n) \end{array} \right\}; n \leq mn \\\\ if\left\{ \begin{array}{l} (\lambda + 1) \\ (\lambda + 2) \\ \vdots \\ (\lambda + m) \end{array} \right\} \in B(\lambda) < \rho\_{CR\max}(\lambda) \in B(\lambda) \Rightarrow \text{print}\left\{ \begin{array}{l} \text{Err}(\lambda + 1) \\ \text{Err}(\lambda + 2) \\ \vdots \\ \text{Err}(\lambda + n) \end{array} \right\}; n \leq mn \\\\ \left[A\_{\text{min}}(\lambda) < \rho\_{CR\max} \in A(\lambda) \Rightarrow \rho\_{CR\max}(\lambda)\right] : \\\\ if\rho\_{CR} \in B(\lambda) < \rho\_{CR\max} \in A(\lambda) \Rightarrow \text{print}\left\{ \begin{array}{l} \text{Err}(\lambda + 1) \\ \text{Err}(\lambda + 2) \\ \vdots \\ \text{Err}(\lambda + n) \end{array} \right\}; n \leq mn \\\\ if\left\{ \begin{array}{l} (\lambda - 1) \\ (\lambda - 2) \\ \vdots \\ (\lambda - m) \end{array} \right\} \in A(\lambda) < \rho\_{CR\max}(\lambda) \in A(\lambda) \Rightarrow \text{print}\left\{ \begin{array}{l} \text{Err}(\lambda - 1) \\ \text{Err}(\lambda - 2) \\ \vdots \\ \text{Err}(\lambda - n) \end{array} \right\}; n \leq mn,\end{aligned}$$

where *Err*(*λ*) represents noise ("bad" bands).

These error values are recalculated using the values of the surrounding bands (their number is defined by a spectral window size). For sample spectral data, a graphical interphase can be used to set up a spectral window size and check the corrected spectral curves (Figure 3, step 3). This allows the user to compare the results of different settings and finally employ a correction that is optimally tuned for the data under analysis.

After a noise-cleaned image is retained, the operator decides how many absorption features are to be derived (Figure 2, step 4) and the analysis is done automatically (Figure 2, step 5) using the following processing:

$$
\mathbb{C}Rdepth(\lambda) = 1 - \rho\_{\mathbb{C}R}(\lambda). \tag{9}
$$

Then the trend of the spectrum is analysed in a similar way to noise detection. First the saddle points (the local absorption maxima) are detected using these conditions:

$$R\_{i\bar{j}} = \left\{ \begin{array}{l} 1 \text{ if } \mathsf{CRdepth}(\lambda)\_{i\bar{j}} \ge \mathsf{CRdepth}(\lambda - 1)\_{i\bar{j}} \\ 0 \text{ if } \mathsf{CRdepth}(\lambda)\_{i\bar{j}} < \mathsf{CRdepth}(\lambda - 1)\_{i\bar{j}} \end{array} \right\} \tag{10}$$

*Remote Sens.* **2017**, *9*, 1006

$$\operatorname{if}\left[\mathcal{R}(\lambda - 1)\_{i\bar{j}} = 1\right] \land \left[\mathcal{R}(\lambda)\_{i\bar{j}} = 0\right] \Rightarrow \operatorname{print}\left[\operatorname{Loc\\_max}\_{i\bar{j}}\right]$$

where *R* is an auxiliary variable that records increase (1) or decrease (0). During this step, local absorption maximum depths and wavelengths are registered:

$$\operatorname{Loc}\_{\operatorname{max}ij} = \left\{ \begin{array}{c} \operatorname{Loc}\_{\operatorname{max}1} \operatorname{depth}(\lambda)\_{ij} \operatorname{Loc}\_{\operatorname{max}1}(\lambda)\_{ij} \\ \operatorname{Loc}\_{\operatorname{max}2} \operatorname{depth}(\lambda)\_{ij} \operatorname{Loc}\_{\operatorname{max}2}(\lambda)\_{ij} \\ \vdots \\ \operatorname{Loc}\_{\operatorname{max}max} \operatorname{depth}(\lambda)\_{ij} \operatorname{Loc}\_{\operatorname{max}nn}(\lambda)\_{ij} \end{array} \right\}. \tag{11}$$

A set number of desired absorption features is detected as the most pronounced local absorption maxima (a set number of the most pronounced absorptions according to the absorption depths). If the desired number is larger than the number of local maxima detected, the final array is completed by zero values:

$$\operatorname{Loc}\_{\operatorname{max}ij} = \left\{ \begin{array}{c} \operatorname{Loc}\_{\operatorname{max}1} \operatorname{depth}(\lambda)\_{ij} \operatorname{Loc}\_{\operatorname{max}1}(\lambda)\_{ij} \\ \operatorname{Loc}\_{\operatorname{max}2} \operatorname{depth}(\lambda)\_{ij} \operatorname{Loc}\_{\operatorname{max}2}(\lambda)\_{ij} \\ \vdots \\ \operatorname{Loc}\_{\operatorname{max}Tol\_{\operatorname{am}}} \operatorname{depth}(\lambda)\_{ij} \operatorname{Loc}\_{\operatorname{max}n}(\lambda)\_{ij} \\ \operatorname{0,0} \\ \vdots \\ \vdots \\ \operatorname{0}\_{\operatorname{min}} \operatorname{0}\_{\operatorname{min}} \end{array} \right\}. \tag{12}$$

Finally, the local absorption maximum features are assigned to image matrices. For *z* absorption features, two matrices with *z* bands are created; one has the absorption wavelengths assigned, while the second one has corresponding absorption depths assigned, respectively.

**Figure 2.** QUANTools: a simplified processing scheme (CR: continuum removal, λ : wavelength loc\_mac: local absorption feature maximum). The detected absorption feature parameters are sorted in ascending order by wavelength (λ1, λ2 − λ*z*).

**Figure 3.** Bad band detection (spectral range 0.450–1.200 μm) in QuanTools where the bad bands detected are shown by the green square points and the corrected curve as a dashed line (set spectral window: three neighbouring bands).

#### 2.3.3. Specific Setting of the QUANTools

The main aim was to test if the major absorption features present though the VIS/NIR/SWIR and LWIR ranges can be (i) derived and integrated into one raster dataset; or (ii) successfully used for a final mineral mapping. The spectral ranges from which the absorption feature parameters were derived were defined as follows: VIS/NIR (HyMap): 0.450–1.200 μm, SWIR (HyMap): 2.100–2.400 μm and LWIR (AHS): 8.500–12.500 μm. Noise detection and correction was employed to the VIS/NIR reflectance (0.450–1.200 μm) and the spectral window was set to 3 (three spectral bands before and after the local minimum were analysed). The number of absorption features to be mapped in each spectral region was set to 2. As a result, two new raster datasets were derived for each spectral range, one having assigned the two major absorption wavelengths and the second raster dataset with corresponding absorption depths.

2.3.4. Integration of Absorption Feature Information Detected In VIS/NIR/SWIR and LWIR Data and Further Classification

After the wavelength mapping was employed to different spectral ranges, it was necessary to find a way to further integrate the absorption feature mapping results (Figure 4), more specifically the absorption wavelength and depth matrices. The MNF transformation [64] was employed to compress the data variability of these image matrices (six raster absorption wavelength matrices and six raster absorption depth matrices).

The intention was to test what the advantage will be of adding LWIR data (AHS) to the further mineral classification compared to using only the VIS/NIR/SWIR data (HyMap). Therefore, the MNF transformation was employed for two different scenarios:

For each scenario, the first three MNF bands were visually analysed further to identify training areas (ROIs) representing different material/surfaces. The ROIs were defined as representative pixels of different colour clusters/regions. These were easy to identify when MNF1/MNF2/MNF3 images were displayed as RGB (Figure 5). The ROIs were then used for further supervised classification. In this case the simple non-parametric supervised (parallelepiped) classification was employed [65–67], which uses a simple decision rule to classify multispectral data. The parallelepiped classifier uses the threshold of each class signature to determine if a given pixel falls within the class or not. The thresholds specify the dimensions (in standard deviation units) of each side of a parallelepiped surrounding the mean of the class in the feature space. The best results (e.g., class separability, lowest number of unclassified pixels) were achieved when the upper and lower limits of each parallelepiped were set to ±1.5 standard deviations. In the last step, the classifications were overlaid over the original HyMap and AHS image data and the average spectrum from the original HyMap and AHS image data was computed for each class to (i) ensure that each class represents different surface material (is represented by a unique spectral signature) and (ii) be able to describe the mineral composition of each class when interpreting the spectral property.

**Figure 4.** Scheme of the processing allowing integration of the absorption feature information detected in VIS/NIR/SWIR and LWIR data and further classification.

**Figure 5.** Lítov: MNF1/MNF2/MNF3 images displayed as RGB: ( **A**) Scenario 1 (absorption feature parameters derived only from the HyMap data were used); (**B**) Scenario 2 (absorption feature parameters derived from both datasets, HyMap and AHS, were used), the MNF bands were further visually analysed to identify training areas (ROIs) representing different material/surfaces.


**3. New drugs for type 2 diabetes**

### **3.1. Currently available**

The Incretin hormones released by the gut, gastric inhibitory peptide (GIP) and Glucagon like peptide1 (GLP-1) (liraglutide) stimulate insulin secretion upon nutrient entry into the gut, suppression of glucagon release,slow gastric emptying and decrease food intake [128, 129]. Therefore, they have an antidiabetogenic potential. Incretin mimetics e.g. Exenatide LAR from exendin 4 is currently in use and most resistant to DPP4 degredation. GIP has also been shown enhancing β cell proliferation and inhibiting apoptosis in islet cell lines [130, 131]. Additionally functional GIP receptors have been identified on adipocytes and shown to stimulate glucose transport, accelerating fatty acid synthesis and stimulating lipoprotein lipase activity in animal models [131, 133, 134]. Several novel GIP analogues have been developed which act as stronger GIP agonists, showing resistance to degradation by Dipeptidyl Peptidase-4 (DIPP-4) [135] and demonstrating increased insulinotropic and blood glucose lowering activity [135]. Dipeptidyl peptidase Inhibitors (vildagliptin & sitagliptin) suppress breakdown of Glucagon like peptide1 (GLP-1) show great potential and are undergoing clinical testing. Antihyperglycemic synthetic analogs of amylin a hormone which are produced by the pancreas to lower blood sugar levels are available in injectable form and require close monitoring. Dapagliflozin a renal glucose reabsorption inhibitor reduces glycemic reabsorption independent of insulin, promises to be a new drug for type 2 diabetes treatment [136]. Testosterone replacement therapy in diabetic hypogonadal men decreases insulin resistance [137] probably by protective effect on pancreatic beta cells through its action on inflammatory cytokines [138].

#### **3.2. Experimental new drugs**

A vanadium and allixin based drug [139] and macrophage migration inhibitory factor MIF blocking inhibitory synthetic oral drug reducing blood sugar levels was tried in the mouse model and found to be effective in both the type 1&2 diabetc model [140]. Lisofylline, a fat metabolism inhibitor which prevents buildup of ceramide a by product of fat metabolism in mouse skeletal muscle decreased the insulin resistance and thus appears to be a novel new approach for type 2 diabetes [141]. It also has the ability to protect insulin producing cells by inhibiting cytokines produced by immune cells leading to apoptosis and cellular dysfunction and is thus effective in type1 diabetes [142]. LXR agonists have shown potential and require further testing in human and model systems [143]. Growth factors and protein kinase C inhibitors may act as innovative therapies for diabetic retinopathy [144].

#### **3.3. Surgical interventions**

Recently a type of gastric bypass surgery has been successful in normalizing blood sugar in a small number of normal to moderately obese type 2 diabetics [145, 146]. This surgery may possibly reduce death rate by 40% from all causes in morbidly obese people [147].

#### **3.4. Micronutrient approaches to treatment of diabetic complications**

People with diabetes have reduced antioxidant capacity which lays the basis for usage of antioxidant vitamins such as β carotene or vitamin C or E. A reduced level of ascorbic acid (Vitamin C) leaves the body more at mercy of the detrimental effects of aldose reductase, an enzyme responsible for many diabetic complications, such as cataracts and peripheral neuropathy [148]. Quercetin is another powerful aldose reductase inhibitor. It has been shown to inhibit aldose reductase by upto 50% [149]. Vitamin E is a free radical scavenger. It may play a preventive role in diabetic retinopathy by decreasing DAG levels, normalizing protein kinase C activation, normalizing blood flow in retinal and renal microvasculature and restoring NO mediated endothelium dependent relaxation [150, 151]. Renal and retinal vascular flows and responses were normalized in individuals who had diabetes of less than 10 years duration with high dose oral vitamin E therapy given for short periods while unchanged glycaemic control was observed [152].

Magnesium and chromium deficiency have been associated with poor diabetic control, insulin resistance, macro vascular disease and hypertension [153] and decreased glucose tolerance respectively [154]. Reduction of neuronal damage in diabetics by inhibiting glutamate dehydrogenase via vitamin B6 therapy has also been observed [155].N-Reduced glutathione precursor NAcetyl Cysteine is a gene expression and cellular metabolism modulating antiox‐

idant and its role in prevention of β cell oxidatory damage by acting as NFKB (a genetic regulator) inhibitor and subsequent deintensification of inflammatory responses is well documented [156]. Trace element vanadyl sulfate that behaves like insulin normalized hyperglycemic levels in diabetic animals and decreased the insulin need by upto 75% [157]. In human with Type 2 diabetes, low doses of vanadyl sulfate enhanced insulin responsive glucose uptake, glycogen production and decreased endogenous glucose formation. This resulted in reduced lipid oxidation and plasma free fatty acids levels [158]. Alpha lipoic Acid has powerful antioxidant activity, insulinomimetic action and provides protection from insulin resistance linked diabetic stress while improving glucose utilization [159]. Hyperglycemia reduction in diabetic rats was observed along with improvement in GSH levels with selenium therapy [160].Calcium AEP has benefited both type 1 and type 2 diabetics as it is alpha cell membrane integrity factor required for cellular membrane function. The hormone dehydroepiandroster‐ one (DHEA) undergoes a decrease in levels with aging that many researchers have linked to impair glucose metabolism. It was found to be as effective in reducing body fats and main‐ taining insulin responsiveness as exercise [161]. Thiamine is also now showing potential as therapy for type 2 diabetes.

**Figure 7.** Structure of thiamine diphosphate molecule.

are available in injectable form and require close monitoring. Dapagliflozin a renal glucose reabsorption inhibitor reduces glycemic reabsorption independent of insulin, promises to be a new drug for type 2 diabetes treatment [136]. Testosterone replacement therapy in diabetic hypogonadal men decreases insulin resistance [137] probably by protective effect on pancreatic

A vanadium and allixin based drug [139] and macrophage migration inhibitory factor MIF blocking inhibitory synthetic oral drug reducing blood sugar levels was tried in the mouse model and found to be effective in both the type 1&2 diabetc model [140]. Lisofylline, a fat metabolism inhibitor which prevents buildup of ceramide a by product of fat metabolism in mouse skeletal muscle decreased the insulin resistance and thus appears to be a novel new approach for type 2 diabetes [141]. It also has the ability to protect insulin producing cells by inhibiting cytokines produced by immune cells leading to apoptosis and cellular dysfunction and is thus effective in type1 diabetes [142]. LXR agonists have shown potential and require further testing in human and model systems [143]. Growth factors and protein kinase C

Recently a type of gastric bypass surgery has been successful in normalizing blood sugar in a small number of normal to moderately obese type 2 diabetics [145, 146]. This surgery may

People with diabetes have reduced antioxidant capacity which lays the basis for usage of antioxidant vitamins such as β carotene or vitamin C or E. A reduced level of ascorbic acid (Vitamin C) leaves the body more at mercy of the detrimental effects of aldose reductase, an enzyme responsible for many diabetic complications, such as cataracts and peripheral neuropathy [148]. Quercetin is another powerful aldose reductase inhibitor. It has been shown to inhibit aldose reductase by upto 50% [149]. Vitamin E is a free radical scavenger. It may play a preventive role in diabetic retinopathy by decreasing DAG levels, normalizing protein kinase C activation, normalizing blood flow in retinal and renal microvasculature and restoring NO mediated endothelium dependent relaxation [150, 151]. Renal and retinal vascular flows and responses were normalized in individuals who had diabetes of less than 10 years duration with high dose oral vitamin E therapy given for short periods while unchanged glycaemic

Magnesium and chromium deficiency have been associated with poor diabetic control, insulin resistance, macro vascular disease and hypertension [153] and decreased glucose tolerance respectively [154]. Reduction of neuronal damage in diabetics by inhibiting glutamate dehydrogenase via vitamin B6 therapy has also been observed [155].N-Reduced glutathione precursor NAcetyl Cysteine is a gene expression and cellular metabolism modulating antiox‐

possibly reduce death rate by 40% from all causes in morbidly obese people [147].

beta cells through its action on inflammatory cytokines [138].

inhibitors may act as innovative therapies for diabetic retinopathy [144].

**3.4. Micronutrient approaches to treatment of diabetic complications**

**3.2. Experimental new drugs**

36 Treatment of Type 2 Diabetes

**3.3. Surgical interventions**

control was observed [152].

Thiamine (termed aneurin or antineuritic vitamin initially) was the premier discovery of the B vitamins and thus ranked vitamin B1(Fig 7). It has relative temperature, acid stability and water solubility containing a pyrimidine ring and a thiazole nucleus linked with a methylene bridge. Thiamine is an essential micronutrient with a dietary reference intake (DRI) for normal healthy subjects of 1.1 mg/day for females and 1.3 mg/day for males [162]. Found in range of foodstuffs such as cereal grains. Its rich sources are brown rice, bran, oat meal, flax, poultry, egg yolks, beef, pork, liver, nuts, fruits and vegetables such as oranges, asparagus, kale, cauliflower, potatoes [163].UK law demands compulsary fortification of flour with thiamin of not less than 0.24mg/100g flour to replace losses during milling. In Pakistan no compulsory fortification is done and the general public consumes milled white flour which is easily available and probably thiamine deficient. Thiamine is naturally found in 4 forms in varying degrees of phosphorylation in TMP thiamine monophosphate, TPP thiamine pyrophosphate or diphosphate and TTP=thiamine triphosphate. It is commercially available as salt in its mononitrate HCl (also natural byproduct) and relatively inaccessible semi lipid soluble form S-acyl derivative benfotiamine and truly lipid soluble thiamine disulphide derivatives sulbutiamine and fursultiamine. Out of these, Thiamine HCl is the water soluble, easily accessible and commonly used vitamin supplement available with the trade name Benerva.

#### **3.5. Pharmacokinetics**

#### *3.5.1. Thiamine absorption in normal conditions*

Thiamine is released from its administered form by phosphatase and pyrophosphatase in the proximal part of the small intestine, following which absorption occurs mainly from this site with some from the stomach and the colon; thiamine absorbed in the colon may originate from intestinal microflora. Its absorption is hindered by alcohol consumption and folic acid deficiency [164].High affinity organic anion transporters THTR1 [165], THR2 for thiamine and reduced folate transporter RFC-1 transports both folic acid thiamine monophosphate (TMP) intracellularly [166, 168] at normal physiological concentrations. At high expression levels RFC1 also transports TPP out of the cells [167]. At higher concentrations thiamine crosses cell membranes in its open unionized form of the thiazolium ring even by passive diffusion.THTR2 is placed on the luminal surface of the gastrointestinal epithelial cells and THTR1 is on the basolateral surface mainly but not exclusively [169]. THTRI is expressed widely in human tisseues with particular high expression in skeletal muscles, placenta, heart liver and kidney [168, 170].Mutations in the SLC19A2 (D93H, S143F and G172D) cause malfunctioning of the thiamine transporter THTR1, thiamine deficiency and thiamine responsive megaloblastic anaemia (TRMA) [171, 172]. THTR2 is widely expressed most abundantly in placenta, kidney and liver [173]. Also highly expressed RFC-1, is in human tissues including mitochondrial membranes [168, 174]. It has affinities for TMP and TPP of 26µM and 32µM respectively [167, 168]. Cellular efflux is the probable reason for the presence of thiamine in plasma and cere‐ brospinal fluid [175-177].Thiamine in the glomerular filtrate is reabsorbed by the renal brush border membrane high affinity transporters where influx is increased by an outward directed H+gradient [178] RFC-1 is expressed on the apical and basolateral surface of the proximal tubular epithelial cells [179]; it may mediate the reuptake of TMP and provide a solution to the normal absence of TMP in the urine. Proton antiport membrane transport may operate in both intestinal and renal proximal tubular thiamine uptake [180]

#### *3.5.2. Assessment of thiamine status*

Erythrocytes contain approximately 90% of total thiamine in the blood and therefore conven‐ tionally their transketolase levels have generally been considered to be the measure of thiamine status in the body [181].Thiamine deficiency is assessed conventionally by measuring the percentage below complete saturation of the thiamine dependant enzyme transketolase (TK) in RBCs-"thiamine effect". The normal value of the thiamine effect in human subjects is in the range 0-15%, mild deficiency is 15-25% and severe thiamine deficiency >25% [182]. Latest research has however questioned its reliability as thiamine transporters THTR1 and RFC1 in erythrocytes are upregulated in thiamine deficieny and RBC TK levels are not decreased in tandem [183]. Furthermore it doesn't account for changes in TK expression in RBC and other precursor cells. The expression of TK is decreased in thiamine deficiency [184]. Currently assessment of mononuclear TK activity and plasma thiamine concentration determination using HPLC flourimetric determination with respect to normal healthy controls gives greater insight into thiamine status [185-187]. More recently in capillary enzyme reaction and capillary electrophoresis methods are emerging as potential alternative monitoring and determining techniques for thiamine in samples [188].

### *3.5.3. Thiamine metabolism within the cells*

sulbutiamine and fursultiamine. Out of these, Thiamine HCl is the water soluble, easily accessible and commonly used vitamin supplement available with the trade name Benerva.

Thiamine is released from its administered form by phosphatase and pyrophosphatase in the proximal part of the small intestine, following which absorption occurs mainly from this site with some from the stomach and the colon; thiamine absorbed in the colon may originate from intestinal microflora. Its absorption is hindered by alcohol consumption and folic acid deficiency [164].High affinity organic anion transporters THTR1 [165], THR2 for thiamine and reduced folate transporter RFC-1 transports both folic acid thiamine monophosphate (TMP) intracellularly [166, 168] at normal physiological concentrations. At high expression levels RFC1 also transports TPP out of the cells [167]. At higher concentrations thiamine crosses cell membranes in its open unionized form of the thiazolium ring even by passive diffusion.THTR2 is placed on the luminal surface of the gastrointestinal epithelial cells and THTR1 is on the basolateral surface mainly but not exclusively [169]. THTRI is expressed widely in human tisseues with particular high expression in skeletal muscles, placenta, heart liver and kidney [168, 170].Mutations in the SLC19A2 (D93H, S143F and G172D) cause malfunctioning of the thiamine transporter THTR1, thiamine deficiency and thiamine responsive megaloblastic anaemia (TRMA) [171, 172]. THTR2 is widely expressed most abundantly in placenta, kidney and liver [173]. Also highly expressed RFC-1, is in human tissues including mitochondrial membranes [168, 174]. It has affinities for TMP and TPP of 26µM and 32µM respectively [167, 168]. Cellular efflux is the probable reason for the presence of thiamine in plasma and cere‐ brospinal fluid [175-177].Thiamine in the glomerular filtrate is reabsorbed by the renal brush border membrane high affinity transporters where influx is increased by an outward directed H+gradient [178] RFC-1 is expressed on the apical and basolateral surface of the proximal tubular epithelial cells [179]; it may mediate the reuptake of TMP and provide a solution to the normal absence of TMP in the urine. Proton antiport membrane transport may operate in

**3.5. Pharmacokinetics**

38 Treatment of Type 2 Diabetes

*3.5.1. Thiamine absorption in normal conditions*

both intestinal and renal proximal tubular thiamine uptake [180]

Erythrocytes contain approximately 90% of total thiamine in the blood and therefore conven‐ tionally their transketolase levels have generally been considered to be the measure of thiamine status in the body [181].Thiamine deficiency is assessed conventionally by measuring the percentage below complete saturation of the thiamine dependant enzyme transketolase (TK) in RBCs-"thiamine effect". The normal value of the thiamine effect in human subjects is in the range 0-15%, mild deficiency is 15-25% and severe thiamine deficiency >25% [182]. Latest research has however questioned its reliability as thiamine transporters THTR1 and RFC1 in erythrocytes are upregulated in thiamine deficieny and RBC TK levels are not decreased in tandem [183]. Furthermore it doesn't account for changes in TK expression in RBC and other precursor cells. The expression of TK is decreased in thiamine deficiency [184]. Currently

*3.5.2. Assessment of thiamine status*

When TMP enters cells by RFC-1 it is hydrolyzed to thiamine by phosphatases [168]. Thiamine deficiency decreased the activity of TPPK [189] and was implicated in decreased hepatic levels of TPP with normal levels of thiamine in STZ diabetic rats [190].Within mitochondria TPP is slowly hydrolyzed to TMP by phosphatases which may leave the mitochondria via the same transporter. High concentrations of thiamine monophosphate inhibit thiamine pyrophspho‐ kinase activity noncompetitively [191] and inhibit the entry of TPP into the mitochondria competitively [189]. A small amount of TPP is further phosphorylated to thiamine triphosphate (TTP) by thiamine pyrophosphate kinase and hydrolyzed to TPP by TPPphosphatase [192] [168]. Plasma half life is relatively short (2days) [193] but its tissue half life is approximately 9-18 days [194]. Thiamine is stored largely in skeletal muscle and the highly perfused organs such as heart, brain, liver and kidneys [163]. Subcellularly only 10% of total TPP is available for binding to transketolase most of it is associated with the mitochondria [185].Thiamine and its acid metabolites are are excreted primarily in the urine [195].


**3. System Description**

The structure of the CCHP multi-microgrid system combined with ESS is shown in Figure 1. In this study, the number of the CCHP microgrid is three. Considering the relevant policy restrictions and technical demands, the CCHP system is stipulated not to sell electricity to the grid. The system can be divided into power supply equipment, heating equipment, and cooling equipment. Among them, the power supply equipment includes photovoltaic generator unit (PV), micro turbine (MT), and energy storage station (ESS). Heating equipment includes gas boiler (GB), heat recovery (HR), heat exchanger (HE), and thermal storage tank (TST). Cooling equipment includes electric chiller (EC) and absorption chiller (AC). The mathematical model of the main equipment of the system is described below.

**Figure 1.** The structure of the CCHP multi-microgrid system combined with ESS.


**1. Introduction**

454 Chronic Obstructive Pulmonary Disease – Current Concepts and Practice

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Small Interfering RNA Targeting Heme Oxygenase-1 Enhances Ischemia-Reperfusion-induced Lung Apoptosis *J. Biol. Chem.* Vol.279 No.(11): pp.10677-10684 The pulmonary diseases of obstructive character have high prevalence in the human population and has been subject of several clinical and experimental studies in order to seek a wider understanding of their pathogeny, physiopathology and, especially, the establishment of more rational ways for their treatment. Accordingly, this great effort has led to an extraordinary widening in the concepts of obstructive diseases in the last years, involving the integration of mechanical factors, inflammatory agents, autonomic regulation of airways and environmental aspects.

The COPD may be understood as a pathologic condition in which a non-reversible and limited gas exchange occurs. There are two clinical entities that constitute the COPD: chronic bronchitis and emphysema. Within the COPD spectrum, the main characteristic of pulmonary emphysema is air flow blockage and progressive dyspnea, arising out of the impairment of alveolar walls and increase of air spaces distal to terminal bronchiole, without significant pulmonary fibrosis (Barnes et al., 2003; GOLD, 2009; Oliveira et al., 2000).

The oxidative damage to which lungs are submitted to, as well as the inflammation occurring as a response to irritant agents, such as those coming from air pollution and cigarette smoke, contributes to the induction of the pulmonary degeneration (Lee et al., 2011). Therefore, it may be concluded that chief characteristic of COPD is the acceleration of functional and morphologic loss, with limitation of gas exchanges, resulting in progressive dyspnea, disability and premature death.

Therefore, the development and progression of the pathology are resulting from the interaction of genetic and environmental factors (Ribeiro-Paes et al., 2009). About 1-3% of cases of emphysema are generated by enzyme α1-antitrypsin deficiency, that characterizes a genetic abnormality as an inheritance of autossomal recessive pattern. The other risk factors include: age, infections, as well as social and economic factors (Mannino & Buist, 2007).

Smoking, however, has been established as the major cause related with COPD, resulting for active or passive exposure to the cigarette smoke, and corresponds to 15-20% of cases of

Cell Therapy in Chronic Obstructive Pulmonary Disease: State of the Art and Perspectives 457

physiopathological mechanisms involved in the formation of the emphysema. Up to the beginning of the 80's decade, the studies involving induced pulmonary emphysema in animals by exposure to cigarette smoke were scarce and their reliability questioned (March, 2000). In 1981, Huber and coworkers proposed a study based on the model of induced emphysema through exposure to cigarette smoke. Some achieved results in the study, with respect to morphometric and physiological aspects provided the basis for the ensuing research. According to the report from the First Siena International Conference on Animal Models of COPD held at the University of Siena in 2001, the induced lesions with the use of this model are similar to those observed in emphysematous humans, highlighting the importance of the stimulus through cigarette smoke in COPD experimental models. (Hele,

At our laboratory, a new apparatus (Figure 1) for induced emphysema through exposure to cigarette smoke is under test. The present device has a series of innovations when compared to the already existing inhaling models, such as the fact that the animals are contained inside acrylic containers making up the device, while in other cages the animals stay freed. Another important aspect worth highlighting relates to the smoke, which is pumped inside the box. In the device created by our team, the smoke pumped into the box interior comes from puffing on the cigarette; therefore, the situation of an active smoking human is mimicked. This apparatus is expected to lead to a model which mimics, as close as possible, the human pathology and, accordingly, which can be applied to research projects oriented to the analysis of physiopathological processes and to the development of new therapies in

Fig. 1. Apparatus created by the team of the Laboratory of Genetics and Cell Therapy –

Notwithstanding the challenges involved in some parameters related to the cigarette-smokeinduced emphysema models, mainly with respect to the age of the animals, exposure time and reproducibility difficulty due to the required resources and time, this is a promising approach to turning animal models closer to the human, chiefly in relation to the physiopathological processes featured in the human pulmonary emphysema (March, 2000).

2001).

chronic degenerative pulmonary diseases.

GenTe Cel to induce emphysema by cigarette smoke.

pulmonary emphysema (Ribeiro-Paes et al., 2009). The cigarette smoke in their gas and particle stages has a significant quantity of oxidant substances. A high number of particles and oxidant agents are contained in cigarette smoke. Oxidant agents are capable of reducing the effect of the anti-protease system through the oxidation of the active site of those enzymes and leading to a direct injury to the extracellular matrix (Barnes 2000; Barnes et al., 2003; Bast et al., 1991; Rufino & Lapa e Silva, 2006).

The Global Initiative for Chronic Obstructive Lung Disease (GOLD, 2009) has pointed out COPD as a serious public health issue. The pathology is considered the fifth largest cause of death worldwide, and has 210 million patients, with 80 million already in the moderate and/or serious stage of the disease. Estimates put it at the third ranking of cause of death in 2020 (GOLD, 2009; WHO, 2008). Moreover, faced with the ageing of world population, the economic burden of COPD should represent a significant parcel of the future global investments in health (Mannino & Buist, 2007).

Several clinical strategies, associated with the pulmonary rehabilitation techniques have contributed to the extension and improvement of the quality of life of emphysema patients. Notwithstanding the significant advances resulting from the introduction of new therapeutic approaches and rehabilitation, there has not been any efficient form of treatment up to now, other than the one in the palliative scope. The surgery treatment entails highly complex procedures and, in the specific case of lung transplant, a shortage of donors. By taking these aspects into account, experimental models have been proposed, in order to advance the knowledge about the physiopathological processes and new therapeutic approaches to the pulmonary emphysema (Gross et al., 1965; Hele, 2002; Mahadeva & Shapiro, 2002; Martorana et al., 1989; Nikula et al., 2000; Ribeiro-Paes et al., 2009).