**GERIATRICS HEALTH**

#### **Geriatrics Health**

http://dx.doi.org/10.5772/intechopen.72140 Edited by Hülya Çakmur

#### **Contributors**

Elfriede Stangler-Zuschrott, Irudaya Rajan S, Sunitha S, A. James Alegbeleye, Dorina Cadar, Hülya Çakmur

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**6. Bioaccumulation of cyanotoxins in agricultural plants and consequences on human and animal health**

Humans were exposed to cyanobacteria toxins through many routes, including drinking water, recreational contact and health food products made from cyanobacteria, and food chain. While some of these routes are well enough informed the others are them less, notably that corresponding to the consumption of crop plants. Although, no case of poisoning by these products has been reported worldwide, this eventuality must not be ignored. Indeed, a recent epidemiological study showed that the excessive incidence of amyotrophic lateral sclerosis in the population of the islands of Guam in the Pacific was linked to a consumption of the seeds of cycas contaminated by a neurotoxin, β-methylamino-L-alanine (BMAA), produced by a species of cyanobacteria of the genus *Nostoc* living in symbiosis in the roots of this plant [134]. This last cited fact is gaining importance since plants could in a direct or indirect manner contribute to food chain cyanotoxin's transfer, and by the way constitute a potent health risk source. Therefore, the accumulation of cyanotoxins in cultivated plants could transform them into vectors of exposure as much for the herbivorous animals that for humans. However, it's important to notify that most of the published results on cyanotoxin's transfer on plants have been performed in hydroponic conditions, which can overestimated the availability of toxins to the root system. In addition, and as indicated previously the soil particles can adsorb microcystins, reducing therefore, their bioavailability for the plants' uptake. For example, recently, Kanzo et al. [135] reported that in hydroponic conditions, microcystins were able to accumulate in the roots, stems and leaves of *Brassica rapa* after exposure to 100 and 1000 µg MC-LR L−1. However, in the same plant when cultivated in a soil system no accumulation was detected after exposure to the same concentrations of MC-LR.

Nevertheless, the ability of microcystins and cylindrospermopsin to accumulate in the tissues of different agricultural plants has been reported in the literature, and it was recently reviewed by Corbel et al. [10]. Microcystins have been detected in tissues of terrestrial plants [92, 93, 104, 122, 136, 137], indicating that they can be absorbed and transported in plants although their transport mechanism is unclear yet. However, the ability of absorbing microcystins and their accumulation in different tissues was variable among different plant species and depends on toxins' concentrations [99, 107, 127]. For example, Järvenpää et al. [138] reported that microcystins were detected on roots (a non-edible plant tissue for human but can be for animal) but not detected in leaves of mustard and broccoli. Furthermore, numerous studies concerning accumulation of cyanotoxins in agronomic plants growing in the soil were reported in radish roots, leaves of arugula and dill [61], in rice grains [65], in leaves of lettuce and cabbage [61, 139], in leaves and stems of water spinach [139] and in fruits and seeds of tomato and pepper [64]. However, a recent study based on the use of 14C-labelled MC-LR showed that tomato fruits did not accumulate the toxin [92].


**Maria Principia Scavo, Annalisa Cutrignelli, Nicoletta Depalo, Elisabetta Fanizza, Valentino Laquintana, Giampietro Gasparini, Gianluigi Giannelli and Nunzio Denora**

Received: 11 June 2020; Accepted: 6 July 2020; Published: 10 July 2020

**Abstract:** The use of controlled delivery therapy in colorectal cancer (CRC) reduces toxicity and side effects. Recently, we have suggested that the Frizzled 10 (FZD10) protein, a cell surface receptor belonging to the FZD protein family that is overexpressed in CRC cells, is a novel candidate for targeting and treatment of CRC. Here, the anticancer effect of novel immuno-liposomes loaded with 5-Fluorouracil (5-FU), decorated with an antibody against FZD10 (anti-FZD10/5-FU/LPs), was evaluated in vitro on two different CRC cell lines, namely metastatic CoLo-205 and nonmetastatic CaCo-2 cells, that were found to overexpress FZD10. The anti-FZD10/5-FU/LPs obtained were extensively characterized and their preclinical therapeutic efficacy was evaluated with the MTS cell proliferation assay based on reduction of tetrazolium compound, scratch test, Field Emission Scanning Electron Microscopes (FE-SEM) investigation and immunofluorescence analysis. The results highlighted that the cytotoxic activity of 5-FU was enhanced when encapsulated in the anti-FZD10 /5-FU/LPs at the lowest tested concentrations, as compared to the free 5-FU counterparts. The immuno-liposomes proposed herein possess a great potential for selective treatment of CRC because, in future clinical applications, they can be encapsulated in gastro-resistant capsules or suppositories for oral or rectal delivery, thereby successfully reaching the intestinal tract in a minimally invasive manner.

**Keywords:** liposomes; target delivery nanosystem; FZD10 protein; colon cancer therapy


**4. Irrigation scheduling**

Irrigation scheduling is the process of deciding the period and quantity of irrigation water during the crop growth under different irrigation methods [24]. Its main objective is to apply irrigation at the right period and in right amount. Irrigation amount is determined in terms of gross irrigation requirement and pumping time per application, while, irrigation time is based on depletion of soil moisture content of the crop root zone reached at critical point [25]. That is basically dependent on the consumptive use rate of crop and method of water application to the plant root zone [26]. The quantity of irrigation water for each treatment was calculated based on the soil moisture content before irrigation and root zone depth of the plant using the Eq. 1.4:

$$\mathbf{SMD} = (\theta\_{\text{FC}} - \theta\_{\text{l}}) \times \mathbf{D} \times \mathbf{Bd} \times \mathbf{MAD} \tag{4}$$

Where,

SMD = Soil moisture deficit (mm). θFC = Soil moisture content at field capacity (%). θI = Soil water content before irrigation (%).

*Climatic Variation and Its Impacts on Yield and Water Requirement of Crops in Indian Central… DOI: http://dx.doi.org/10.5772/intechopen.94076*

D = Depth of root development (mm). Bd = Bulk density of the particular soil layer (g cm�3 ). MAD = Management allowable Depletion (%).

#### **4.1 Management allowable depletion (MAD)**

Producing optimal yield requires that the soil water content be maintained between an upper limit at which leaching becomes excessive and a lower point at which crops are stressed [27]. As water is removed from the soil through ET, there is a point below which the plant experiences increasing water stress. This point is known as the management allowable depletion (*MAD*). The typical MAD values considered are 33% for shallow-rooted, high value crops; 50% for medium-rooted, moderate value crops and 67% for deep-rooted, low value crops [28]. Selection of MAD value for different crops with respect to soil type, initial field capacity (FC), permanent wilting point (PWP), and threshold soil moisture content (TSMC) must be determined. Threshold soil moisture content ascertains what fraction of soil is allowed to dry before the next irrigation event. Threshold soil moisture content can be determined in the following form:

$$
\Theta\_{\rm TSMC} = \Theta\_{\rm FC} \text{-MAD} \left( \Theta\_{\rm FC} \text{-} \Theta\_{\rm PWP} \right) \tag{5}
$$

Where,

**3.3 Irrigation efficiency**

**3.4 Irrigation water requirement**

IR = irrigation requirement (mm).

Pe = effective rainfall (mm).

**3.5 Effective rainfall (Pe)**

**4. Irrigation scheduling**

of the plant using the Eq. 1.4:

SMD = Soil moisture deficit (mm).

θFC = Soil moisture content at field capacity (%). θI = Soil water content before irrigation (%).

Where,

**16**

Etc = total crop evapotranspiration (mm).

tive rainfall on a daily basis or monthly basis.

Ge = groundwater contribution from water table (mm).

Wb = water stored in the soil at the beginning of each period (mm).

It is only a part of the rainfall that can be effectively used by the crop, depending on its root zone depth and the soil storage capacity. Total rainfall amount is not considered as effective rainfall; some part of rainfall may be lost through surface runoff, deep percolation or evaporation. FAO CROPWAT ver. 8.0 model could be used rainfall data and employs the USDA S.C. method approach to estimate effec-

Irrigation scheduling is the process of deciding the period and quantity of irrigation water during the crop growth under different irrigation methods [24]. Its main objective is to apply irrigation at the right period and in right amount. Irrigation amount is determined in terms of gross irrigation requirement and pumping time per application, while, irrigation time is based on depletion of soil moisture content of the crop root zone reached at critical point [25]. That is basically dependent on the consumptive use rate of crop and method of water application to the plant root zone [26]. The quantity of irrigation water for each treatment was calculated based on the soil moisture content before irrigation and root zone depth

SMD ¼ ð Þ� θFC–θI D � Bd � MAD (4)

respectively [22].

*Agrometeorology*

below:

Where,

Irrigation efficiency is defined as the ratio of amount of water beneficially used by plant as evapotranspiration to the amount of water applied to the plant area. The irrigation efficiencies under different methods *i.e.* 40%, 50%, 55%, 75%, and 90% are taken for border, check basin, furrow, sprinkler, and drip irrigation system,

The irrigation water requirement represents the difference between the crop water requirement and effective rainfall. Other factors or losses have minimal effect on irrigation water requirement and can be neglected [23] as shown in the equation

IR ¼ ETc–ð Þ Pe þ Ge þ Wb (3)

θTSMC: Soil moisture content at threshold level (%).

θFC: Soil moisture content at field capacity (%).

θPWP: Soil moisture content at permanent wilting point (%).

The determination of soil moisture content at threshold level is most important factor for irrigation scheduling on real time basis. This value varies with crop, soil, climate and crop growth stages. Whenever the soil moisture content at field capacity is depleted through ET, percolation losses, etc. to equal or below the θTSMC value, irrigation scheduling must be given otherwise crop yield and plant growth will be affect harmful way.

### **5. Estimation of water requirement of major crops using CROPWAT model**

CROPWAT is a decision support tool developed by the land and water development division of FAO. CROPWAT model is extensively tested, widely accepted for calculation of crop water requirements based on soil, climate and crop data. In addition, the program allows the development of irrigation schedules for different management and the calculation of scheme water supply for varying crop patterns. CROPWAT ver. 8.0 model used weather data and employs the modified penmanmonteith approach used to estimate reference evapotranspiration on a daily basis. The meteorological data was taken from Agromet Observatory, ICAR-VPKAS, Experimental farm Hawalbagh, Almora. The mean annual rainfall at experimental site was 1000.13 mm. The general soil properties of the experimental field were used in CROPWAT model. Based on the details of soil characteristics, total available water was taken 135 mm m�1 depth of soil. Infiltration rate was measured using double ring infiltrometer and the basic rate was 6.8 mm hr.�1 and the unsaturated hydraulic conductivity was 0.77 cm h�1 . The estimation of irrigation water requirement (346–376 mm, 131–189 mm, 1.4 mm, 1.3 mm, 78.6 mm, 93.5 mm 104.1 mm, 176 mm, 96.9 mm, 16.2 mm, 18.3 mm, 15.5 mm, and 7.4 mm of Rice, Wheat, Maize, Soybean, Vegetable Pea, Rajma, Barley, Tomato, French Bean, chili, okra, mustard

#### **Table 2.**

*Estimation of irrigation water requirement of major crops grown on experimental site using CROPWAT model.*

and cowpea crop, respectively) and irrigation schedule plan of major crop was calculated using CROPWAT Model as presented in **Table 2**.

#### **5.1 Water budgeting equation**

This equation could be used to measure evaporation, seepage from pond and volume of water available in pond. The water budget method of determining long term available water present in pond can be used as a standard for comparing other methods. This method is not most accurate, but could be used satisfactory for practical purpose. The volume of water available in pond can be calculated using this equation in the following form:

and fertilizer. It is the biggest practical alternative, low-cost as well as associated ecological advantages, compatibility to reduce poverty, role of the social dimension/ expansion are the key to global climate change mitigation and adaptation [7, 9, 12]. Carbon storage in agroforestry remains in aboveground biomass (wood biomass), leaf group (foliage), shrub, vine, herb, dead biomass (dead wood, litter) and below ground biomass (roots), soil organic carbon etc. According to Nair et al. [29], the world's vegetation carbon sequestration ability by major agroforestry systems is

*Carbon sequestration potential in vegetation of world's major agroforestry systems.*

**Agroforestry/land use systems Age Average vegetation C (Mg ha**�**1 y**�**1**

*Climatic Variation and Its Impacts on Yield and Water Requirement of Crops in Indian Central…*

1. Fodder bank, Segu, Mali,South Africa, Sahel 7.5 0.29 2. Live fense, Segu, Mali, South Africa, Sahel 8.0 0.59 3. Tree based intercropping, Canada 13.0 0.83 4. Park lands, Segu, Mali,South Africa, Sahel 35.0 1.09 5. Agrisilviculture, Chhatisgarh, Central India 5.0 1.26 6. Silvopasture, South Oreogaon, USA 11.0 1.11 7. Silvopastoralism, Kurukshetra, India 6.0 1.37 8. Silvopastoralism, Kerala, India 5.0 6.55 9. Cocoa agroforestry, Makoe, Cameroon 26.0 5.85 10. Cocoa agroforestry, Durialban, Costarica 10.0 11.08 11. Shaded coffee, South-West Congo 13.0 6.31 12. Agroforestry woodlots, Partorico 4.0 12.04 13. Agroforestry woodlots, Kerala, India 8.8 6.53 14. Home and farm garden 23.2 4.29 15. Indonesian homegarden, Sumatra 13.4 8.00 16. Mixed species stand, Puertorico 4.0 15.21

*DOI: http://dx.doi.org/10.5772/intechopen.94076*

**)**

In Himalayan region of India agroforestry is promising and distributed in large area in different forms. The area ranged from 4.95 in a watershed to 137 ha in of North-west Himalaya (**Table 4**). Carbon storage ranged from 3.31 to 31.71 t/ha in

**System Area (ha) Region Author** Cardamom agroforestry 27.59 North-East Himalaya [30] Agroforestry 4.95 North-West Himalayan Watershed [31] Willow based agroforestry 137 North-West Himalaya [20]

listed in **Table 3**.

**Table 4.**

**19**

*Source: [29].*

**Table 3.**

Indian Himalaya (**Table 5**).

**7. Status of agroforestry in Himalaya**

*Reported area under agroforestry systems of Himalaya.*

$$\sum\_{i=1}^{12} P\_p + \sum\_{i=1}^{12} R\_{cbt} - \left[ \sum\_{i=1}^{12} S\_p + \sum\_{i=1}^{12} E v\_p + a \sum\_{i=1}^{12} \sum\_{j=1}^{n=crop} \text{WR}\_{crop} \right] = \sum\_{i=1}^{12} W S\_p \tag{6}$$

Where,

Pp = precipitation in surface area of pond (m3 ), Rcbt = runoff from conservation bench terraces areas or plain surface (m3 ). SP = seepage losses from Pond (m3 ), Evp = evaporation losses from Pond (m3 ). WRcrop = water requirement of crop (m3 ). WSp = water storage in pond (m3 ).

#### **6. Ways to increase carbon storage in tree based land use systems**

Carbon sequestration can be enhanced by adopting plantations or agroforestry. Loss of carbon storage can be prevented by reducing felling of forests, blocking or reducing emissions from agricultural activities and by reduced use of energy, oil

*Climatic Variation and Its Impacts on Yield and Water Requirement of Crops in Indian Central… DOI: http://dx.doi.org/10.5772/intechopen.94076*

#### **Table 3.**

and cowpea crop, respectively) and irrigation schedule plan of major crop was

*Estimation of irrigation water requirement of major crops grown on experimental site using CROPWAT model.*

**Effective rainfall (mm)**

Vegetable pea 209.8 131.7 78.6 Barley 250 147.9 104.1 Rajma 123.3 29.7 93.5 Tomato 262.4 84.3 176 French Bean 166.9 70.1 96.9 Chili 310.6 370.7 16.2 Rice 434–505.9 464–491 346–376 Wheat 269.2–375.1 163–194.6 131–189 Maize 247.9 395 1.4 Soybean 350.6 494.4 1.3 Okra 234.9 470.1 18.3 Mustard 155 145.5 15.5 Cowpea 171.9 280.2 7.4

This equation could be used to measure evaporation, seepage from pond and volume of water available in pond. The water budget method of determining long term available water present in pond can be used as a standard for comparing other methods. This method is not most accurate, but could be used satisfactory for practical purpose. The volume of water available in pond can be calculated using

*Evp* þ *α*

Rcbt = runoff from conservation bench terraces areas or plain surface (m3

),

).

**6. Ways to increase carbon storage in tree based land use systems**

" #

X 12

*n*X¼*crop j*¼1

),

*WRcrop*

¼ X 12

*i*¼1

**Irrigation water requirement (mm)**

*WSp* (6)

).

*i*¼1

).

).

Carbon sequestration can be enhanced by adopting plantations or agroforestry. Loss of carbon storage can be prevented by reducing felling of forests, blocking or reducing emissions from agricultural activities and by reduced use of energy, oil

calculated using CROPWAT Model as presented in **Table 2**.

**5.1 Water budgeting equation**

**Crop name Crop water requirement**

*Agrometeorology*

**(mm)**

this equation in the following form:

*Rcbt* � X 12

SP = seepage losses from Pond (m3

WSp = water storage in pond (m3

Evp = evaporation losses from Pond (m3

WRcrop = water requirement of crop (m3

*i*¼1

Pp = precipitation in surface area of pond (m3

*Sp* þX 12

*i*¼1

X 12

**Table 2.**

*Pp* þX 12

*i*¼1

*i*¼1

**18**

Where,

*Carbon sequestration potential in vegetation of world's major agroforestry systems.*

and fertilizer. It is the biggest practical alternative, low-cost as well as associated ecological advantages, compatibility to reduce poverty, role of the social dimension/ expansion are the key to global climate change mitigation and adaptation [7, 9, 12]. Carbon storage in agroforestry remains in aboveground biomass (wood biomass), leaf group (foliage), shrub, vine, herb, dead biomass (dead wood, litter) and below ground biomass (roots), soil organic carbon etc. According to Nair et al. [29], the world's vegetation carbon sequestration ability by major agroforestry systems is listed in **Table 3**.


*Technical Note* **On-Orbit Calibration of the KBR Antenna Phase Center of GRACE-Type Gravity Satellites**

**Zhiyong Huang 1,2,\*, Shanshan Li 1, Lingyong Huang 2 and Diao Fan 1**

**Abstract:** The coordinates of the KBR (K-band ranging system) antenna phase center of GRACEtype gravity satellites in the satellite Science Reference Frame should be precisely known, and the determination accuracy should reach 0.3 mrad in the Y (pitch) and Z (yaw) directions. Due to the precision limitation of ground measurement and the change of space environment during orbit, the KBR antenna phase center changes. In order to obtain more accurate KBR antenna phase center coordinates, it is necessary to maneuver the satellite to achieve the on-orbit calibration of the KBR antenna phase center. Based on the in-orbit calibration data of KBR of GRACE-FO satellites, a new method is proposed to estimate the antenna phase center of KBR using the inter-satellite range acceleration as the observation value. The antenna phase center of KBR is solved by the robust estimation method, and the obtained calibration results are better than 72 μm in the Y and Z directions and better than 1.3 mm in the X direction, which is 50% better than the least squares estimation algorithm. The accuracy of KBR calibration results obtained by using the data of positive maneuvers or mirror (negative) maneuvers, respectively, does not meet 0.3 mrad. It is shown that mirror maneuvers are required for KBR calibration of a GRACE-type gravity satellite to obtain antenna phase center estimation results that meet the accuracy requirements. The calibration algorithm proposed in this paper can provide reference for KBR antenna phase center calibration of Chinese GRACE-type gravity satellites.

**Keywords:** GRACE; GRACE follow-on; gravity satellite; KBR antenna phase center; calibration; M-estimation

### **1. Introduction**

GRACE consists of two near-circular polar orbit satellites orbiting at an altitude of approximately 500 km in coplanar orbits, carrying a KBR (K-band ranging) instrument that measures inter-satellite range on the micron scale. However, in the recovery of the gravitational field, the required observation values are the inter-satellite range and range rate between the center of mass (CoM) of the satellite, so it is necessary to convert the range measurements between the KBR antenna phase center (APC) into the range observation values between the CoM of the satellite. Accurate satellite attitude and KBR APC coordinates are needed to ensure that the accuracy of range measurements is not lost during the conversion process.

The KBR APC vector of satellites relative to the CoM of satellites (CoM-to-APC [1]) is generally accurately measured before it enters orbit. However, the accuracy of ground measurement is limited and affected by the measurement accuracy of the whole satellite structure. After the satellite enters orbit, both the satellite body and the APC are affected by stress release, platform vibration, and space temperature change, leading to changes in the CoM-to-APC vector [2]. It is necessary to calibrate the CoM and KBR APC of the satellite periodically after the satellite is in orbit to obtain an accurate CoM-to-APC vector. JPL (Jet

**Citation:** Huang, Z.; Li, S.; Huang, L.; Fan, D. On-Orbit Calibration of the KBR Antenna Phase Center of GRACE-Type Gravity Satellites. *Remote Sens.* **2022**, *14*, 3395. https:// doi.org/10.3390/rs14143395

Academic Editor: Xiaogong Hu

Received: 12 June 2022 Accepted: 11 July 2022 Published: 14 July 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Propulsion Laboratory), the official agency of GRACE and GRACE-FO satellites, typically writes initial and calibrated CoM-to-APC vectors into Level-1B VKB1B products.

The CoM of the satellite can always coincide with the proof mass of the accelerometer by periodic CoM calibration and CoM-Trim (the Mass Trim Assembly Mechanism (MTM) on all three axes of the GRACE-type satellite is adjusted accordingly in order to realize the compensation of CoM offset; this is called a CoM-Trim event). Therefore, it is more advantageous to set the origin of the Science Reference Frame (SRF) at the proof mass of the accelerometer. VKB1B products in the SRF can be accurately determined by periodic KBR phase center calibration, which can be used for KBR Level-1A to Level-1B data processing. In order to ensure that the KBR APC error does not affect the accuracy of inter-satellite range and range rate, the CoM-to-APC vector determination accuracy should reach 0.3 mrad in the Y (pitch) and Z (yaw) directions [3].

Although the determination accuracy of the mounting matrix of the star camera (QSA1B, rotation from star camera frames into the SRF), the estimation accuracy of the CoM (VCM1B, vector offset file for CoM solution from calibration maneuvers or tracking model in the SRF) of the satellite, and the realization accuracy of the inter-satellite pointing (QCP1B, rotation from the combined SCA "pilot" frame to the KBR pointing frame) will all have an impact on the KBR range measurement, this paper does not discuss the influence of the above three factors, and assumes that they all meet the accuracy requirements.

For the problem of KBR calibration, two maneuvering modes are proposed [3], linear drift maneuvering and periodic oscillation maneuvering, for on-orbit calibration of the KBR APC. Inter-satellite range and inter-satellite range rate are used in the calculation, and polynomial fitting is adopted to eliminate the low-frequency residual errors of precision orbit determination (POD). However, phase deviation parameters need to be introduced when the inter-satellite range is used to estimate the KBR APC. In [4], a nonlinear Kalman filter is used to estimate the KBR APC, and the simulated observation value does not consider the influence of orbit low-frequency error. The authors of [5,6] proposed the use of a predictive Kalman algorithm to estimate the star camera quaternion of the satellite, and the use of an extended Kalman filter algorithm to achieve the on-orbit calibration of the KBR APC. However, when the frequency of star camera observation data is high, the improvement of attitude estimation accuracy by the Kalman algorithm is not obvious, and the influence of orbit low-frequency error is not considered in this method.

To counteract the effects of residual low frequency errors and time-varying gravity fields, this paper proposes using the inter-satellite range acceleration as the observation value to obtain a high precision KBR APC estimation result. At the same time, the intersatellite range and the inter-satellite range rate are taken as the observation values, and polynomial fitting is carried out. The KBR APC calibration is calculated with three observation values at the same time, and the result with higher estimation accuracy is selected as the final KBR APC calibration result. In view of the parameter estimation algorithm, this paper proposes using the M-estimation (robust estimation) method [7,8]. Through an IGGG-3 weighting scheme [9,10], the influence brought by the gross error of observation value and the gross error of design matrix can be overcome.

#### **2. KBR Calibration Maneuver Scheme of a GRACE-Type Gravity Satellite**

Due to its complexity, an analytical solution for the determination of the KBR APC can only be given under quite a number of restrictions. Therefore, experimental methods are generally used to determine the APC. In the ground stage, the calibration of the KBR APC is achieved by the method of one phase center swinging with certain regularity relative to another phase center [3].

This concept is based on rotating the satellite back and forth along a certain axis: the KBR antenna generates maneuvering signals with the satellite and the KBR APC reference point is found at the location of the observed minimum change in generating phase response [3]. However, only maneuvers in the direction perpendicular to the intersatellite pointing (pitch axis (Y), yaw axis (Z)) can generate the sensitive inter-satellite

ranging signal, and the maneuvers in the rotation axis (X) will not generate the ranging signal. In order to generate strong signals, the offset angle is generally set in advance, and then the satellite is shaken with a certain amplitude and period, because the ranging signal variation caused by the attitude maneuver of the satellite increases with the increase of the offset angle [3].

The KBR calibration of GRACE and GRACE-FO satellites adopted the calibration maneuver scheme proposed by Wang [3]. Using the satellite sequence of events (SOE) files recorded by JPL, the dates of KBR calibration and calibration events of GRACE-FO and GRACE are listed in Tables 1 and 2.

**Table 1.** KBR calibration events of GRACE-FO.

**Table 2.** KBR calibration events of GRACE.

Taking the KBR calibration maneuver of GRACE-C on 17 September 2020 and GRACE-D on 28 September 2020 as an example, four sub-maneuvers were carried out for each satellite, respectively: + pitch, −pitch, +yaw, and −yaw, where "+" means that the satellite offset angle is +2 degrees and "−" means that the satellite offset angle is −2 degrees. After setting the offset angle of 2 degrees, a sinusoidal maneuver signal with a period of 250 s and an amplitude of 1 degree is applied to the satellite, which generally lasts for 15 cycles with a total of 3750 s. The maneuver execution time required to achieve offset angle and return to normal inter-satellite pointing is about 30 s, and the total maneuver time is 3780 s. Figure 1 shows the inter-satellite pointing angle of GRACE-C during a single +pitch maneuver.

**Figure 1.** Inter-satellite pointing during single +pitch maneuver.

On-orbit calibration maneuvers of KBR are performed by a magnetorquer (MTQ) and ACTs (attitude control thrusters) of an attitude and orbit control system (AOCS). The subsystem of attitude and orbit control automatically calculates the required rotation torque, that is, the combination of the rotation torque of the attitude control thrusters and MTQ is used to drive the satellite to complete the designed attitude maneuver.

The inter-satellite pointing angle of GRACE and GRACE-FO during KBR calibration was calculated [11]. The intersatellite pointing angle comprises *αA* and *αB* in Figure 6, which represent the satellite KBR Frame (KF, K-Band Frame) and the line between the centers of mass of the satellites (LOSF, Line-of-Sight Frame). Figure 2 shows the inter-satellite pointing angles of GRACE-C and GRACE-D.

**Figure 2.** GRACE-FO KBR calibration maneuvers (September 2020).

In this paper, the inter-satellite pointing angles of the GRACE satellites during the KBR calibration period (except the time when Level-1B observation data were missing) were calculated, respectively. As can be seen from Figure 3, the maneuver strategy determined by GRACE in 2002 was not clear, and there is about 1 degree deviation in the pitch and roll directions; the author speculated that the maneuver strategy was explored and improved (implemented by AOCS) after this unqualified sub-maneuver.

**Figure 3.** GRACE KBR calibration maneuvers (April 2002).

As can be seen from Figures 4 and 5, it adopted a maneuver strategy consistent with that of the in-orbit GRACE-FO satellite in Figure 2. However, its maneuver amplitude in the pitch direction reached about 1.5 degrees and its maneuver amplitude in the yaw direction reached about 1.7 degrees, while the amplitude of GRACE-FO in the KBR calibration maneuver was 1 degree.

**Figure 4.** GRACE KBR calibration maneuvers in February 2003 and March 2003.

**Figure 5.** GRACE KBR calibration maneuvers of GRACE-B (April 2004).

The potential multipath effect of the KBR ranging signal can be reduced by using a positive and negative maneuver as mirror maneuvers of each other. When the mirror maneuver is not included, the KBR calibration of GRACE-FO contains the following four sub-maneuver signals.

Sub-maneuver 1: The following signal *θ*1 *pitch* around the pitch axis (Y) is applied to GRACE-C, where superscript 1 represents GRACE-C, superscript 2 represents GRACE-D, *θ*0 represents constant deviation angle, *A* represents the angular amplitude of the periodic signal, and *f* represents the frequency of the periodic signal. The maneuver signal is expressed as

$$
\theta\_{pitch}^1 = \theta\_0 + A \sin(2\pi f t) \tag{1}
$$

During the pitch maneuver of GRACE-C, the GRACE-C deviation from the nominal roll and yaw angle should be kept as small as possible, and GRACE-D must maintain its nominal attitude. In this case, the KBR ranging measurements are highly sensitive to the CoM-to-APC vector of the Z and X direction of GRACE-C. Therefore, a single GRACE-C pitch maneuver can calculate the CoM-to-APC vector in the Z and X directions of GRACE-C. However, due to the higher signal-to-noise ratio in the Z direction than in the X direction, the accuracy of the estimated Z direction APC vector is better than that in the X direction. When the KBR range value is converted to the range of the CoM of the satellite, the KBR APC estimation error in the X direction of the two satellites is absorbed by the constant phase deviation of the KBR ranging measurements, which does not affect the gravity field reversion accuracy.

Sub-maneuver 2: The following signal *θ*1 *yaw* about yaw axis (Z) is applied to GRACE-C, and other parameters have the same meaning as sub-maneuver 1.

$$
\theta\_{grav}^1 = \theta\_0 + A \sin(2\pi f t) \tag{2}
$$

Sub-maneuver 3: The following signal *θ*2 *pitch* about pitch axis (Y) is applied to GRACE-D, and other parameters have the same meaning as sub-maneuver 1.

$$
\theta\_{pitch}^2 = \theta\_0 + A \sin(2\pi ft) \tag{3}
$$

Sub-maneuver 4: The following signal *θ*2 *yaw* about yaw axis (Z) is applied to GRACE-D, and other parameters have the same meaning as sub-maneuver 1.

$$
\theta\_{grav}^2 = \theta\_0 + A \sin(2\pi ft) \tag{4}
$$

#### **3. Estimation Algorithm of the KBR APC**

The position vectors of the satellite in the inertial system are, respectively, *rA* and *rB*, the rotation matrix transformed from the SRF to the inertial system is *RA* and *RB*, and the vector between the CoM of the satellite [12] is

$$
\mu = r\_B - r\_A \tag{5}
$$

The distance between the CoM of the satellites is

$$
\rho\_{\text{com}} = \|\|\|\|\tag{6}
$$

However, KBR observes the range between the KBR APC, as shown in Figure 6. Through vector conversion, the KBR observation range can be expressed as

$$\left| \rho\_{KBR} = \right| \left( r\_B + R\_B c\_B \right) - \left( r\_A + R\_A c\_A \right) \parallel \tag{7}$$

where *ci*(*i* = *A*, *B*) is the corrected KBR APC vector (CoM-to-APC) in the SRF. More succinctly, it can be expressed as

$$\rho\_{KBR} = \left| \begin{array}{c} \boldsymbol{\mu} + \boldsymbol{\nu} \end{array} \right| \tag{8}$$

**Figure 6.** Observation geometry of the GRACE KBR measurement.

Among them,

$$
\upsilon = \mathcal{R}\_B \mathfrak{c}\_B - \mathcal{R}\_A \mathfrak{c}\_A \tag{9}
$$

Approximately, the KBR range measurements can be expressed as the distance between the CoM of GRACE-A and GRACE-B minus the APC correction (that is, the projected distance of the APC vectors of the two satellites in the LOSF (the line between *rA* and *rB*)):

$$
\rho\_{\text{KBR}} = \rho\_{\text{COM}} - \rho\_{\text{AOC}} \tag{10}
$$

and *ρAOC* can be expressed as

$$
\rho\_{\rm AOC} = \langle R\_A \mathbf{c}\_A \mathbf{c} \rangle + \langle R\_B \mathbf{c}\_B \mathbf{c} \rangle = -e^T R\_A \cdot \mathbf{c}\_A + e^T R\_B \cdot \mathbf{c}\_B \tag{11}
$$

where *e* is the unit vector of the satellite CoM LOSF (Line-of-Sight Frame) obtained by

$$\mathcal{E} = \frac{u}{||\!\!\!\!\!\/]} \tag{12}$$

Then, the observation equation of KBR calibration is obtained by

$$
\rho\_{KBR} - \rho\_{COM} = A \cdot \mathbf{x} = \begin{bmatrix} e^T \mathcal{R}\_A & -e^T \mathcal{R}\_B & 1 \end{bmatrix} \cdot \begin{bmatrix} \mathbf{x}\_{pc\_A} \\ \mathbf{x}\_{pc\_B} \\ b \end{bmatrix} \tag{13}
$$

where *A* is the design matrix, *xpcA xpcB b T* is the estimated value of the KBR APC and the phase deviation term to be obtained, and *ρKBR* − *ρCOM* contains maneuver signals applied during KBR calibration, but it contains a large low-frequency error. Firstly, the error of orbit 1 cycle per revolution (cpr) contained in *ρKBR* and *ρCOM* cannot be completely offset; secondly, the inter-satellite ranging signal in *ρKBR* measures the current gravity field signal, while the gravity signal contained in *ρCOM* (the difference of the reduced dynamic orbit) is greatly affected by the prior gravity field. Therefore, the residual effect of the low frequency error will still exist after the minus: *ρKBR* − *ρCOM*. Polynomial fitting is considered to remove its influence first, and then KBR APC parameters are estimated to avoid excessive parameters caused by estimating polynomial parameters and KBR APC parameters at the same time.

When the difference of the satellite velocity is . *u* = . *rB* − . *rA*, the velocity between the CoM of the satellite can be expressed as

$$
\dot{\rho}\_{COM} = \langle \mathbf{e}\_\prime \dot{\mathbf{u}} \rangle \tag{14}
$$

When the difference of the satellite accelerations is .. *u* = .. *rB* − .. *rA*, then the acceleration between the CoM of the satellite can be expressed as

$$
\ddot{\rho}\_{COM} = \langle e, \ddot{u} \rangle + \langle \dot{e}, \dot{u} \rangle \tag{15}
$$

Then, the inter-satellite range rate measured by KBR can be expressed as

$$
\dot{\rho}\_{\text{KBR}} = \dot{\rho}\_{\text{COM}} - \dot{\rho}\_{\text{AOC}} \tag{16}
$$

The observation equation of KBR calibration based on inter-satellite range rate is

$$
\dot{\rho}\_{KBR} - \dot{\rho}\_{COM} = A \cdot \mathbf{x} = D \begin{bmatrix} -e^T R\_A & e^T R\_B \end{bmatrix} \cdot \begin{bmatrix} \mathbf{x}\_{pc\_A} \\ \mathbf{x}\_{pc\_B} \end{bmatrix} \tag{17}
$$

where *D* represents the partial derivative matrix of the polynomial; the third-order polynomial is used in this paper.

The inter-satellite range acceleration measured by KBR can be expressed as

$$
\ddot{\rho}\_{\rm KBR} = \ddot{\rho}\_{\rm COM} - \ddot{\rho}\_{\rm AOC} \tag{18}
$$

The KBR calibration observation equation based on inter-satellite range acceleration is

$$
\ddot{\rho}\_{KBR} - \ddot{\rho}\_{COM} = A \cdot \mathbf{x} = DD \begin{bmatrix} -e^T R\_A & e^T R\_B \end{bmatrix} \cdot \begin{bmatrix} \mathcal{X}\_{pc\_A} \\ \mathcal{X}\_{pc\_B} \end{bmatrix} \tag{19}
$$

Observation Equations (13), (17), and (19) are constructed according to the above equation, and the estimated KBR APC values of the two satellites can be calculated according to the three observation values based on the LS estimation and M-estimation.

In the LS estimation and the initial weighting of the M-estimation, the weight matrix of the observed value is taken as the identity matrix in this paper. When constructing the observed values of inter-satellite range, range rate, and range acceleration, only light time correction term is carried out.

#### **4. KBR Calibration Results of GRACE-Type Gravity Satellites**

The RL04 version Level-1B GNI1B, SCA1B, and KBR1B data during the KBR calibration period of the GRACE-FO satellite were downloaded from ftp://isdcftp.gfz-potsdam.de on 5 April 2022 [13], and their meanings are shown in Table 3. At the same time, the RL02 version GNV1B, SCA1B, and KBR1B data of GRACE satellites during the KBR calibration stage were downloaded [14]. The data sampling rate was 5 s, and the data meaning is similar to that in Table 3.

**Table 3.** GRACE-FO data product for KBR calibration.

#### *4.1. Low Frequency Error Processing*

In a single maneuver, 700 epochs were selected in total for calculation. In order to avoid the influence of large maneuvers, 125 s before and after maneuvers were removed. Firstly, the inter-satellite pointing angle, angular velocity, and angular acceleration of +pitch maneuver and +yaw maneuver of GRACE-C were calculated. The corresponding KBR ranging value (KBR), range rate (KBRR), and range acceleration (KBRA) were calculated and taken as the observed value (O). Then, the inter-satellite baseline, baseline rate, and baseline acceleration determined by POD (precision orbit determination) were taken as the calculated values (C), and the differences between the KBR measurements (O) and the inter-satellite baseline (C) was obtained (O−C): represented as "KBR-POD", "KBRR-POD velocity", and "KBRA-POD acceleration", respectively.

As can be seen from Figure 7, O−C has a good consistency with the applied pitch and yaw maneuver signals. However, the "KBRA-POD acceleration" contains significant high-frequency noise, which may affect the estimation accuracy of KBR APC. "KBR-POD" and "KBRR-POD velocity" still contain low-frequency periodic signals, which need to be fitted. In this paper, the 15-order and 13-order polynomial fitting is carried out, respectively, and the filtered results (after removing the best fitting polynomials) are obtained in Figure 8, where Figure 8a–d are the results of polynomial fitting on the single +pitch, +yaw maneuver data of GRACE-C and Figure 8e,f are the filtering results of observation values of "KBR-POD" and "KBRR-POD velocity" obtained after polynomial fitting on all the KBR calibration data of eight sub-maneuvers. As can be seen from Figure 8a–f, the low frequency errors were effectively removed.

**Figure 7.** Match of attitude variation and three types of observations of KBR-POD.

**Figure 8.** Polynomial fitting to remove low frequency errors: (**a**) polynomial fitting of KBR-POD (+pitch); (**b**) polynomial fitting of KBRR-POD velocity (+pitch); (**c**) polynomial fitting of KBR-POD (+yaw); (**d**) polynomial fitting of KBRR-POD velocity (+yaw); (**e**) polynomial fitting of KBR-POD; and (**f**) polynomial fitting of KBRR-POD velocity.

For the three kinds of observation values, the first and last 50 s observation values were removed, and then Fourier transform was carried out. The polynomial fitting effect was analyzed in terms of frequency, as shown in Figure 9a–d, corresponding to Figure 8a–d. The maneuvering frequency signal of 40 mHz and twice the maneuvering frequency signal of 80 mHz were retained, and the low-frequency noise was effectively suppressed. While, as shown in Figure 8d, higher amplitudes can be seen for the filtered results for nearly all frequencies above 1 mHz, this may be due to errors from the polynomial fitting. As shown in Figure 9e,f, analyzing the "KBRR-POD acceleration" observation value from the frequency domain, the polynomial fitting is not required and the low-frequency noise interference is small.

**Figure 9.** Polynomial fitting to remove low frequency errors (FFT): (**a**) polynomial fitting of KBR-POD (+pitch); (**b**) polynomial fitting of KBRR-POD velocity (+pitch); (**c**) polynomial fitting of KBR-POD (+yaw); (**d**) polynomial fitting of KBRR-POD velocity (+yaw); (**e**) KBRA-POD acceleration (+pitch); and (**f**) KBRA-POD acceleration (+yaw).

#### *4.2. KBR APC Estimation Based on LS Estimation and M-Estimation*

The filtered observation values of "KBR-POD" and "KBRR-POD velocity" and the unfiltered observation values of "KBRA-POD acceleration" were used to estimate the vector (CoM-to-APC) of KBR APC in the SRF, corresponding to the angle *βA* and *βB* in Figure 6. The least squares fitting curves and fitting residual results by the three estimation methods were obtained. It can be seen from Figure 10 that all the three methods can achieve good fitting effects. In this calculation, we stripped out the observations of 50 epochs at the beginning and the end, and combined the data of four sub-maneuvers and four mirror sub-maneuvers.

**Figure 10.** The fitting curves and the fitting residuals obtained by the three estimation methods.

The GRACE-C and GRACE-D APC vectors estimated by the three observation values obtained from the eight sub-maneuvers were denoted as XAPC, YAPC, and ZAPC, respectively, and the formal errors were calculated according to the least squares accuracy estimation theory. As can be seen from Table 4, YAPC and ZAPC estimated by "KBR-POD" had high accuracy. However, due to the need to solve the phase deviation term (Bias) of the ranging value, the accuracy of the estimated XAPC and phase deviation term was low. The reason is that the collinearity of XAPC and the phase deviation term leads to the ill-conditioned normal equation, which indirectly leads to the unreliable estimated YAPC and ZAPC, and the results are deviated by the other two estimation methods. The XAPC estimated by "KBRA-POD acceleration" had a high accuracy, and the calibration result obtained by "KBRA-POD acceleration" was selected as the final KBR calibration result in this paper. Among them, the LS estimation accuracy of YAPC and ZAPC was about 138 μm, about 0.096 mrad, which meets the index of 0.3 mrad. The LS estimation accuracy of XAPC was 2.4 mm.

**Table 4.** KBR calibration results and formal errors based on LS estimation.

In order to verify the effectiveness of the mirror maneuver, the paper only uses the positive maneuver and mirror maneuver for KBR APC calibration data, respectively, to obtain the calibration results. The results are shown in Table 5. The YAPC and ZAPC values are obviously large, and the estimation accuracy (formal error) does not meet the accuracy requirements of 0.3 mrad. The results show that the KBR calibration results are easily affected by multipath error when the mirror maneuver is not adopted, which leads to the inaccurate KBR APC estimation results. According to the positive and negative sign characteristics of YAPC and ZAPC calibrated values in the calibration results, they are just opposite, which is the reason why correct results can be obtained by combining positive and negative maneuvers.

**Table 5.** KBR calibration results and formal errors based on LS-estimation and different maneuver data.

Since the observed values may still contain gross errors, in order to further improve the estimation accuracy, this paper uses the method of M-estimation to estimate the KBR APC, combining the data of four positive maneuvers and four mirror maneuvers. The estimation results are shown in Table 6. The KBR APC accuracy estimated by the three observation values was greatly improved. Among them, the estimation accuracy of the proposed intersatellite range acceleration estimation in the Y and Z directions was improved from 138 μm to 72 μm, and the X direction was improved from 2.5 mm to 1.3 mm, with an increase of nearly 50%.

**Table 6.** KBR calibration results and formal errors based on M-estimation.

VKB1B results of GRACE-FO recommended by this paper obtained from M-estimates are shown in Table 7, while the SOE file gives GRACE-FO VKB1B in May 2019, indicating a significant change in the ZAPC of GRACE-D. The authors recommend that this change be taken into account during KBR data preprocessing.

**Table 7.** Results of GRACE-FO KBR APC.

At the same time, the VKB1B of GRACE recommended by this paper (in 2004, GRACE-A did not perform the KBR calibration maneuver, so the APC of GRACE-A could not be estimated) was calculated by using the method of M-estimation, and VKB1B results calculated by JPL recorded in SOE files are shown in Table 8. The results of the Y and Z directions estimated in this paper are close to those of JPL's solution, which shows that the KBR calibration algorithm proposed in this paper is feasible.

**Table 8.** Results of GRACE KBR APC.

#### **5. Discussion**

The above maneuver strategy can be used to estimate the KBR APC of GRACE-type satellites. Although any other maneuvers can also be used for KBR calibration (as shown in Figure 3 or the linear drift maneuver method), the sinusoidal maneuver strategy is a more reliable KBR calibration maneuver scheme. The positive maneuver and negative maneuver (mirror maneuver) mode is very important for KBR calibration and can effectively eliminate the multipath influence of the KBR ranging signal. The results of KBR calibration with combined positive and negative maneuvers are reasonable and accurate.

In estimating the KBR APC, we can choose the strategy of using CoM-to-APC in the fixed X direction as the known value and only solving the Y and Z direction APC. In fact, when the APC vectors of three directions are solved simultaneously, the accuracy of the X direction is the worst. Therefore, the APC of the X direction is generally not updated, but the measured value of the ground is used. In addition, the APC vectors of the Y and Z direction obtained by the two strategies (fixed X direction or not) are consistent. Therefore, the APC vectors of the three directions were solved at the same time in this paper, but the APC vector of the X direction was not recommended.

Although the application of "KBRA-POD acceleration" as the observation value can effectively eliminate the influence of low-frequency error, it amplifies the influence of high-frequency noise, and there is no effect method to eliminate the high-frequency noise contained in the observation value and the design matrix at the same time. In fact, as shown in Figure 7, better accuracy can be obtained by using "KBRA-POD acceleration" as an observation value to estimate the CoM-to-APC if the maneuvering strategy implemented by

the attitude and orbit control system results in smoother angular acceleration. By calculating the CoM-to-APC of the three observation values at the same time, the results can be better verified and the estimated results with better integrity can be obtained. In the estimation of GRACE satellite APC, due to the ill-posed problem of the normal equation, the solution results deviated from the results of the other two algorithms. Finally, the solution results obtained by "KBRR-POD velocity" observation values with higher accuracy were adopted. "KBR-POD" is not recommended for subsequent calculation.

Compared with the least squares estimation method, the improved accuracy of the M-estimation method is obvious. The reason is that there may still be gross error or high frequency error in the observed value and design matrix, and the weight of these observed value can be reduced by using M-estimation. Because the weight matrix of the observed value is taken as the identity matrix in this paper, other weighted least squares strategies can also resist gross errors and improve accuracy, which is worth further study.

#### **6. Conclusions**

This paper summarized the KBR calibration algorithm of GRACE-type gravity satellites, proposed estimating the APC of KBR using inter-satellite range, range rate, and range acceleration simultaneously, and verified the KBR calibration results based on three kinds of Level-1B observation data of GRACE and GRACE-FO. The results show that the APC of KBR obtained by LS estimation using "KBRA-POD acceleration" was 138 μm in the Y and Z directions, and 2.5 mm in the X direction. When M-estimation was used, the accuracy was 72 μm in the Y and Z directions and 1.3 mm in the X direction. In this paper, KBR calibration calculation was performed on the data of positive and mirror (negative) maneuvers. It was shown that the mirror maneuver is required for the KBR calibration of GRACE-type gravity satellites in order to obtain the APC estimation results that meet the accuracy requirements.

In this paper, positive and mirror (negative) maneuvers are recommended for KBR calibration. During KBR calibration, range rate and range acceleration can be used as observation values to estimate the KBR APC, and the optimal solution results are selected as the final VKB1B recommended value. M-estimation is recommended for parameter estimation.

**Author Contributions:** Conceptualization, Z.H.; data curation, Z.H.; formal analysis, Z.H.; funding acquisition, S.L.; investigation, S.L. and D.F.; methodology, Z.H.; project administration, S.L. and L.H.; resources, S.L.; software, Z.H.; supervision, S.L.; validation, Z.H.; visualization, D.F.; writing original draft, Z.H.; writing—review and editing, S.L., D.F.; and L.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was funded by the National Natural Science Foundation of China (No. 42174007).

**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.

**Acknowledgments:** We are very grateful to the GFZ and JPL for providing the GRACE and GRACE-FO Level-1B data.

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

#### **References**


**4. IUDs mechanism of action**

Both copper and hormonal IUDs involve multiple mechanisms that provide contraceptive protection. The major described effect is represented by the induction of a *local inflammatory reaction* in the endometrium. Therefore, the endometrial and myometrial function is disturbed by cellular and humoral immune-inflammatory reaction. Intrauterine foreign bodies are known to interfere with reproduction in all species [21]. Other mechanisms of action for *copper IUDs* are related to copper ions' effect on the *sperm motility and viability* and also to *changes of the cervical mucus* [7]. However, recent studies demonstrated no significant increase in inflammatory reaction, so the effect of copper on the sperm or oocyte remains as the main mechanism of action [22]. The mechanism of inhibiting fertilization or sperm viability impairment may be responsible for the high efficacy of copper IUDs as emergency contraception [23]. The reported failure rate for copper IUD placement for emergency contraception within 5 days of intercourse or ovulation is less than 1‰, significantly more effective than any hormonal emergency contraceptive [24, 25]. Even if copper IUDs do not usually influence the menstrual frequency or quantity, 10–13% of women have their IUD removed in the first year due to increase of menstrual flow and cramping-type abdominal pain [26].

Besides the foreign body reaction, another mechanism of the *hormonal IUD* is represented by the release of small amount of progestin hormones, respectively, levonorgestrel. The hormonal effects induce an unfriendly and even *fatal environment to sperm* inside the uterus, with inhibition of capacitation, penetration, and survival [27]. Hormonal IUDs interfere with sperm motility by *thickening cervical mucus*, thus preventing the sperm to travel up into the uterus [28], while copper IUDs rather impair the sperm viability, with various effects as head-tail disruption [29]. A *thinner endometrium* develops under the progesterone influence and egg implantation is affected. Also, menstrual blood flow is reduced, thus hormonal IUDs are frequently used to treat menorrhagia [30].

We underline that IUDs, whether hormonal or non-hormonal, do not provide protection against sexually transmitted diseases. Still, the historical association with pelvic inflammatory disease (PID) and tubal infertility is considered false [31, 32].

The indomethacin-releasing copper IUD contains a nonsteroidal anti-inflammatory agent, indomethacin, that do not interfere with copper ions [33, 34], aimed to reduce the menstrual blood flow and cramping that are frequently related by IUDs users [33] (**Table 1**).

**Table 1.** Mechanisms of action of the main types of IUDs. Modified, Schulman et al. [35].

**4. IUDs mechanism of action**

260 Family Planning

the inferior part of the vertical body stem just above the isthmus.

Both copper and hormonal IUDs involve multiple mechanisms that provide contraceptive protection. The major described effect is represented by the induction of a *local inflammatory reaction* in the

**Figure 2.** General structure with stems, arms, and strings of the IUD. Normal location after proper IUD insertion in the middle of the endometrial cavity with no extension in the myometrium, both arms spread toward the uterine horns and

**Figure 1.** Various types of IUD. Copper devices presented in the upper row, from left to right: T Safe Cu380A®, Nova T380®, Flexi-T300®, Multiload Cu375®, GyneFix®, Ocon® Cooper intrauterine ball. In the middle row—hormonal IUDs: Mirena®, Skyla®, FibroPlant® frameless levonorgestrel-releasing device. In the lower row, other IUD types are presented,

as the indomethacin-releasing cooper device AiMu® and the flexible, low copper dose VeraCept®.


**1. Introduction**

Current trends in the analysis of climate-driven events on urban societies, infrastructures, and services have guided study towards the direct and indirect impacts of these events, resulting in disruptions within the interdependent infrastructure systems. These studies draw our awareness to the importance of analyzing the impacts generated by extreme events, such as heavy rainfall, flooding, storm surge, and other extreme natural phenomena which severely compromise city services. This introduction presents the importance of assessing Metro systems' flooding impacts, pointing to the research gap on this topic and supporting the motivations leading this study.

Climate change represents a severe threat to cities and their resilience, which are complex systems characterized by constant flow and are the result of a lot of dynamic variables that change in space and time [1,2]. As discussed by Wan et al. [3], transportation resilience can be described as the capacity of a transportation system to "absorb disturbances," sustaining its fundamental structure and purpose, plus recovering to an expected service level following occurring disruptions. As an initial action for a risk/resilience appraisal [4], a potential hazard for urban resilience is the occurrence of flooding events which might impact the continuity of Metro services. Metro systems, as one strategic transport service on urban areas, represent one key variable for assessing resilience in the context of physical infrastructure used by citizens. Decreasing the risks that may affect the continuity of the Metro service, therefore, will increase resilience at the municipal level [5]. Although there are a variety of words for underground transport railway systems (e.g., subway, underground, or tube), this paper will use the term "Metro systems" when referring to them.

Metro systems' importance worldwide for cities' operability is evident. According to 2018 figures [6], 178 cities in 56 countries have a Metro transportation system, with an average ridership of 168 million per day, and increasing annual use of 19.5%. Figure 1 presents the currently in-service number of Metro systems in Europe. Between China, India, and Iran, Asia is building 16 new systems, shaping the decarbonizing urban transport effort as a response to climate change [6].

**Figure 1.** Metro systems in operation in Europe by 2019 [6].

Following the accelerated growth of cities and their Metro systems, and the increasing growth and complexity of Metro networks, the ability to study and improve the vulnerability of systems are more complex, according to the heterogeneity of the different components of a large scale system, such as a transport network [7]. The probability of disruptions should consider many factors, not

only the number of affected Metro stations, but also the distance between them [8], along with several other factors.

Considering hazards that Metro systems face worldwide, there is a notable lack of research on water-related hazards [5], including those related to flooding events and their impact on Metro systems' resilience [9]. In a context of climate change, flooding events can produce increasing significant socioeconomic risks in urban areas. Worse still, urbanization heightens the likelihood of water disasters such as floods because development decreases the amounts of permeable surfaces where water can soak into the soil, thereby originating runoff that contributes to flooding. This situation poses a critical risk to Metro systems, which can suffer from diverse types of flooding, such as riverine (fluvial) and rain-related (pluvial) flooding, and tidal surges.

Climate change endangers the Barcelona Metro system when subjected to flooding events. Flood events have caused property damage along with service disruptions, by the inundation of underground infrastructures (e.g., Metro tunnels and facilities) [10,11]. Therefore, it is essential to evaluate flood risks in Metro systems to plan for flood disasters and set mitigation strategies efficiently. Figure 2 illustrates how Metro flooding events affect both Metro systems' infrastructure and users, exposing them to collateral effects such as hard falls due to the loss of stability of passengers crossing through water flow [12,13], material damage, and reduction of service provision due to damage caused to both the users and the companies that administer the systems. These events have gradually increased in recent years due to cities' growth and climate change [14–16].

**Figure 2.** Flood events in major global Metro systems: (**a**) New York [17]; (**b**) Tokyo [18]; (**c**) Madrid [19]; (**d**) Prague [20]; (**e**) Washington [21]; (**f**) Brussels [22].

The main objective of this research is to analyze Metro stations' vulnerability facing floods under extreme rainfall events, then, exposing the system to climate change flooding influenced events. Other research approaches focus on ensuring transport service continuity using other public transport modes, responding to Metro system flooding events [23–26]. In order to address the cited research gaps, this study proposes and analyzes interactions connecting pluvial flood events and the water level thresholds inside Metro station tunnels that might result in service disruption, through a hydrodynamic model including the Metro system. Barcelona Metro Line 3 is the line most vulnerable to flood events due to extreme rainfall, according to TMB (Transports Metropolitans de Barcelona, Barcelona Metropolitan Transport—the leading public transport operator in Barcelona) records. Hence, in this study, the Metro

Line 3 is integrated into the 1D/2D hydrodynamic model developed for the entire drainage system of the city of Barcelona within the European project RESCCUE (RESilience to cope with Climate Change in Urban arEas—a multi-sectorial approach focusing on water) [27].

This study proposes a new methodology for a flood risk assessment in an underground Metro system, based on a surface pluvial flooding 1D/2D hydrodynamic modelling and the Metro infrastructure real data. The new flood model, derived from the integration of the infrastructure of Line 3 of the Barcelona Metro with the city's current drainage model, was calibrated based on flood observed from photos and visual inspection by TMB operators. The Metro elements through which floodwater enters into the tunnels (i.e., staircases, ventilation grates, hallways) were simulated based on their simplification, depicting the Metro entrances as pipelines addressing the Metro tunnels and stations, as an approach that can be applied to other case studies.

As a vital component of the flood risk assessment, future scenarios using rainfall projections under the climate change impact were applied. The model calibration aimed at reproducing observed water levels in the studied Metro stations, water levels in various real events were adequately reflected by the model. Also, it was possible to identify the likely effects of climate change on underground flooding by analyzing the effects of future rainfall conditions. Finally, a risk assessment of the Metro station was performed, adaptation strategies were proposed to reduce floods' impact and to prevent disruptions in Metro service, increasing the resilience of city transportation services.


**1. Introduction**

388 Selected Topics in DNA Repair

Zhang, J. (2003). Evolution by gene duplication: an update. *Trends in Ecology & Evolution*,

(June 2003) Vol. 18, No. 6, pp.292-298, ISSN 0169-5347

520, ISSN 0829-8211

chromatin template. *Biochemistry and Cell Biology*, Vol.85, (August 2007), pp. 509–

A cells genetic information, its 'blueprint of life', is contained within its DNA. This biologically important molecule, however, can be attacked by high–energy ionizing radiation and oxidizing agents resulting in a range of possible damage. For instance, nucleobases can undergo chemical modifications or degradation such as oxidation, deamination, alkylation or be cleaved from the sugar-phosphate backbone. (De Bont & van Larebeke 2004; Friedberg et al. 2004; Hecht 1999; Kamiya et al. 1998; Labet et al. 2008a; Lindahl 1993; Lysetska et al. 2002; Neeley & Essigmann 2006; Rydberg & Lindahl 1982; Taylor 1994; Wang 2008) Similarly, the deoxyribose sugar moieties may also undergo various chemical modifications. These events can lead further to the formation of DNA– DNA or DNA–protein cross–links or DNA–strand breaks. (Kumar & Sevilla 2010; Lipfert et al. 2004) Importantly, damage to DNA can significantly affect its replication and transcription. This can ultimately result in cell apoptosis or protein mutations and pathological diseases such as cancer. (Pages & Fuchs 2002)

Experimentally, there have been numerous detailed *in vivo* and *in vitro* investigations into the processes and pathways involved in the damage of DNA. (See, for instance, Kumar & Sevilla 2010; Mishina et al. 2006, Wetmore et al. 2001) Radiolysis experiments with photometric, electrochemical and electron paramagnetic resonance detection, enzymatic inhibition and mutagenesis studies have identified a large number of reaction intermediates and rate constants for many damage and repair processes. For more in-depth reviews of experimental investigations on DNA damage processes the reader is also directed to relevant chapters in this present book. Unfortunately, however, many uncertainties and questions still remain about DNA damage and repair.

Computational chemistry provides an alternate and also complementary approach for obtaining a deeper understanding of chemical processes. This is in part because it can not only be applied to systems that are amenable to experimental investigation but also to those

Mechanisms of Mutagenic DNA Nucleobase Damages and

this chapter a dielectric constant (

Their Chemical and Enzymatic Repairs Investigated by Quantum Chemical Methods 391

Many biochemical processes occur within a polar environment, in particular an enzyme active site or aqueous medium. This can influence the properties and reactions of a biomolecule. Hence, it can be important to include such effects in the computational model. Each study described in this chapter has used a polarizable continuum (PCM)-based approach(Cammi & Tomasi 1995; Miertus et al. 1981) in the integral equation formulism (IEF).(Cances & Mennucci 1998; Cances et al. 1997; Mennucci et al. 1997) In this method the chemical system is effectively 'wrapped' in a density-fitting polar dielectric medium. Within

within a protein active site, while the standard value for water at 298 K has been used for reactions modeled in aqueous environments. The specific computational details of each

In order to determine relative free energies of reactions in which protons and electrons act as independent ions, it can be necessary to use 'fundamental values' such as the chemical potential of an electron or proton in a vacuum or aqueous solution under standard conditions. The values used in this chapter are given in **Table 1.** These have been obtained by means of a first principles quantum and statistical mechanics approach, the details of

**Quantity eV kcal mol–1**

investigation are outlined in their respective section and in the appropriate article.

which are provided in a recent paper by Llano and Eriksson.(Llano & Eriksson 2002)

Table 1. Chemical potentials of an electron in vacuum and an electron and proton in

Ionizing radiation can potentially be absorbed by any of the three nucleotide components of DNA (i.e., phosphate, sugar or nucleobase) or its surrounding waters; also an essential part of DNA structure.(Kumar & Sevilla 2010) Primary damage of, for example, nucleobases, is caused by their direct absorption of radiation. Secondary damage (such as that described later in this chapter) can also occur, for instance, when the radiation is absorbed by the solvent, thus generating radicals or solvated electrons which then attack the nucleobase.(von

Direct absorption (primary damage) can cause the formation of a radical-cationic base via the loss of an electron, i.e., generation of an electron hole. Due to the stacking of nucleobases within DNA charge transfer (transfer of the hole) can then occur along the strand. Consequently, this enables 'primary damage related' reactions to occur distant from the site of initial damage. The potential for charge transfer due to the *π*–orbital interactions between bases was proposed as early as 1962. (Eley & Spivey 1962) Alternatively, however, it may enable charge recombination to occur further along the chain. This is because radical-anionic bases can be formed by the capture of the free electrons, where the resulting damage to the nucleobase also constitutes primary damage. Guanine has the lowest ionization potential of

aqueous reference states.(Llano & Eriksson 2002).

Sonntag 1987; von Sonntag 1996)

**2. Nucleobase oxidation via ionizing radiation**

) value of 4.0 has been used when modeling reactions

0.0 0.0

–4.34 ± 0.02 –100.03 ± 0.5

–1.6638 ± 0.04 –38.37 ± 0.5

–11.6511 ± 0.02 –268.68 ± 0.5

ε

systems or reactions that may be too challenging or even impossible to study using such experimental techniques. Furthermore, it is nowadays possible to apply highly accurate and reliable computational methods to larger, more complete biochemical models. Thus, using such approaches one can not only reconcile theory with experiment but, for example, compare the feasibility of differing proposed reaction mechanisms or identify new pathways.

There have been numerous computational investigations on or related to DNA damage and repair. In this chapter the application of Computational Chemistry to the study of DNA damage and repair is illustrated through a review of a number of relevant computational investigations that we have performed. More specifically, we have applied high accuracy density functional theory (DFT)-based methods to the study of several important primary and secondary nucleobase damage pathways and repair mechanisms. The chapter is divided into sections, each of which focuses on select results concerning a damage and/or repair process involving either the nucleobase or phosphate components of DNA:

#### **1.1 Methods**

For the detailed study of electronic properties of biochemical systems the current computational chemistry methods of choice for investigators are those of density functional theory (DFT). The principle reasons for this is that they inherently include electron correlation effects via their basis upon the electron density of the chemical system. Such effects are often important, for example, in accurately describing bond making and breaking processes, or weakly bound systems such as reaction transition structures. Furthermore, regardless of the number of electrons in the chemical system, the electron density is only ever a function of the three Cartesian coordinates. Thus, with such methods one can use larger and more complete chemical models consisting of 150 or more atoms. In addition, they are usually highly reliable and accurate.(See, for example, Llano 2010) For biochemical systems, the currently most widely used DFT functional is B3LYP,(Becke 1993a; Becke 1993b; Lee et al. 1988) and it has been used throughout this chapter.

systems or reactions that may be too challenging or even impossible to study using such experimental techniques. Furthermore, it is nowadays possible to apply highly accurate and reliable computational methods to larger, more complete biochemical models. Thus, using such approaches one can not only reconcile theory with experiment but, for example, compare the feasibility of differing proposed reaction mechanisms or identify new

There have been numerous computational investigations on or related to DNA damage and repair. In this chapter the application of Computational Chemistry to the study of DNA damage and repair is illustrated through a review of a number of relevant computational investigations that we have performed. More specifically, we have applied high accuracy density functional theory (DFT)-based methods to the study of several important primary and secondary nucleobase damage pathways and repair mechanisms. The chapter is divided into sections, each of which focuses on select results concerning a damage and/or

2. **Nucleobase oxidation via ionizing radiation**: describes primary redox damage in nucleobases. In particular, the Gibbs Free Energies of solution-phase electron (ET), proton (PT) and proton-coupled electron (PTET) transfers for all DNA bases are examined. In addition, the potential of alkylthiols to act as repair agents of such damage

3. **8-Oxopurine formation in purine, adenine and guanine**: focuses on secondary damage of purine nucleobases. Specifically, the mechanisms of •OH attack leading to formation of 8–oxopurine derivatives are discussed as well as the Gibbs Free Energy changes of

4. **Deamination of oxidized cytosine**: examines the thermochemistry of several possible mechanisms by which oxidized cytosine, a pyrimidine nucleobase, may undergo

5. **Oxidation of serine phosphate: Implications for DNA**: investigates radicals formed

6. **Oxidative repair of alkylated nucleobases: The catalytic mechanism of AlkB**: describes key results of our studies on the enzymatic mechanism of AlkB, a member of the family of α-ketoglutarate-Fe(II)-dependent dioxygenase enzymes that catalyse the

For the detailed study of electronic properties of biochemical systems the current computational chemistry methods of choice for investigators are those of density functional theory (DFT). The principle reasons for this is that they inherently include electron correlation effects via their basis upon the electron density of the chemical system. Such effects are often important, for example, in accurately describing bond making and breaking processes, or weakly bound systems such as reaction transition structures. Furthermore, regardless of the number of electrons in the chemical system, the electron density is only ever a function of the three Cartesian coordinates. Thus, with such methods one can use larger and more complete chemical models consisting of 150 or more atoms. In addition, they are usually highly reliable and accurate.(See, for example, Llano 2010) For biochemical systems, the currently most widely used DFT functional is B3LYP,(Becke 1993a; Becke

repair process involving either the nucleobase or phosphate components of DNA:

possible associated ET, PT and PTET processes.

oxidative repair of alkylated nucleobases.

from radiation–induced damage of serine phosphate.

1993b; Lee et al. 1988) and it has been used throughout this chapter.

pathways.

is considered.

deamination.

**1.1 Methods**

Many biochemical processes occur within a polar environment, in particular an enzyme active site or aqueous medium. This can influence the properties and reactions of a biomolecule. Hence, it can be important to include such effects in the computational model. Each study described in this chapter has used a polarizable continuum (PCM)-based approach(Cammi & Tomasi 1995; Miertus et al. 1981) in the integral equation formulism (IEF).(Cances & Mennucci 1998; Cances et al. 1997; Mennucci et al. 1997) In this method the chemical system is effectively 'wrapped' in a density-fitting polar dielectric medium. Within this chapter a dielectric constant (ε) value of 4.0 has been used when modeling reactions within a protein active site, while the standard value for water at 298 K has been used for reactions modeled in aqueous environments. The specific computational details of each investigation are outlined in their respective section and in the appropriate article.

In order to determine relative free energies of reactions in which protons and electrons act as independent ions, it can be necessary to use 'fundamental values' such as the chemical potential of an electron or proton in a vacuum or aqueous solution under standard conditions. The values used in this chapter are given in **Table 1.** These have been obtained by means of a first principles quantum and statistical mechanics approach, the details of which are provided in a recent paper by Llano and Eriksson.(Llano & Eriksson 2002)

Table 1. Chemical potentials of an electron in vacuum and an electron and proton in aqueous reference states.(Llano & Eriksson 2002).


**5. Research Methodology**

#### *5.1. Research Paradigm and Design*

This study is framed in the positivist paradigm [60] to achieve objective truths, facts, and laws by using a quantitative methodology. It makes use of a non-experimental, quantitative approach, comprising a range of different methods, which aim to describe the relationship between constructs by testing any causal relationships between them [60].


**14. Treatment, prevention and control**

shown good specificity in the diagnosis of JEV in both blood and CSF samples but it has poor sensitivity as the virus is often cleared from the peripheral circulation/CSF by the time the test is performed. With the advent of monoclonal antibodies as potential diag‐ nostic tool (Chávez *et al.,* 2010), the rapid detection of JE antigen in cerebrospinal fluid has become possible. The different diagnostic tests have been given in Table 2. However, the most rapid and potential diagnostic tool for JE diagnosis have been shown to be MAC-ELISA (Robinson *et al.,* 2010) and indirect fluorescent antibody. MRI of the brain can also be used in diagnosis. MRI changes can be co-related (Misra *et al.,* 2011) with the

Immunization against JE is cost effective strategy for control and prevention of JE. It has been reported globally that there is a decrease in incidence rates of JE in endemic areas which are administered with high immunization. The 3 most important types of JE vac‐ cines, administered in current era are: the mouse brain derived, purified, inactivated vac‐ cine based on either the Nakayama or Beijing strains of the JE virus; the cell culture derived inactivated JE vaccine based on the viral Beijing P3 strain and the cell culture derived live attenuated JE vaccine based on the SA 14‐14‐2 strain of the JE virus. In JEV infection, the immunity against prM, E and NS1 proteins is more effective than that of other viral proteins in host defense. (Gao et al., 2010). Currently available vaccines against JE include chemically inactivated vaccines (INV) and a live attenuated vaccine (LAV). Although a mouse brain derived INV produced by BIKEN had been the only in‐ ternationally approved vaccine and has been used worldwide since the 1960s. But it had a drawback as there were reports of severe adverse events including acute disseminated encephalomyelitis (ADEM) in people vaccinated with it. In early 2009, Vero cell derived INVs produced by Intercell (Austria) and BIKEN (Japan) were licensed. Although these INVs are useful in developed markets, INVs are not ideally suited for nationwide vacci‐ nation programs for many endemic countries, since INVs require multiple doses to in‐ duce long lasting immunity. LAVs are thus a useful alternative and have been used for decades in China, and other Asian countries, but their substrate and the production methods have still not been approved in other markets, which serve as a drawback to this vaccine. (Ishikawa et al., 2011). Live-attenuated virus vaccines (LAVs) and inactivat‐ ed virus vaccines (INVs) serve against flaviviral disease, they are potent and economical but do not suit immunocompromised patients. INVs are safer, but are more expensive to produce and less potent. Hence there is an immense need of devicing new and im‐

Type I IFNs are critical for controlling pathogenic virus infections and can enhance immune responses. Hence their impact on the effectiveness of live-attenuated vaccines involves a bal‐ ance between limiting viral antigen expression and enhancing the development of adaptive immune responses. The influence of type I IFNs on these parameters has been examined fol‐ lowing immunization with RepliVAX WN, a single-cycle flavivirus vaccine (SCFV). Repli‐

type of encephalitis and duration of illness.

172 Encephalitis

**13. Vaccines: Immunization against JE**

proved products.

There is no specific treatment or anti-viral agent for JEV infection, it is proving to be a per‐ sistent threat. Monoclonal antibodies (Yamanaka et al., 2010), corticosteroids, interferonα-2a or ribavirin were not that effective in clinical outcome. The effect of rosamarinic acid (RA) has been shown as an effective anti-viral agent that reduces JE viral load along with proin‐ flammatory cytokines in experimental animal. Neutrophils have been also shown to have degradative effect on JEV. Usage of anti-sense molecules (vivo-morpholino) directed against the viral genome, in combating the virus through inhibiting viral replication has been dem‐ onstrated (Nazmi et al., 2010). Mycophenolic acid (Sebastian et al., 2011) inhibits JE virus by inhibiting its replication.

**15. Conclusion and future implications**

sense of urgency is required to address this matter.

, Sneham Tiwari1

1 Centre for Cellular and Molecular Biology (CCMB−CSIR), Uppal, India

College of Medicine, Florida International University, Miami, USA

**Acknowledgements**

**Author details**

Lucknow, India

Shailendra K. Saxena1

Madhavan P. N. Nair3

Viral encephalitis has proved to be a huge disaster globally, which has engulfed several lives and has shattered various economies. It has been a hot topic amongst the researchers today globally and various ways necessary to combat against the virus are on the way. In‐ tense research for the knowabouts of the virus is carried in several countries, devising strat‐ egies to fight with the virus. As a result of severe efforts, JE has been virtually eliminated in most of the countries after the immunization with inactivated mouse brain-derived vaccine, during last four decades. Because of absence of treatment strategies personal protection is the only apt way to reduce disease incidence. Mosquito control is the sole available preven‐ tive measure for JEV transmission. Research on JEV needs to be initiated at much wider scale, which should include development of effective anti-viral agents and vaccine strat‐ egies. Immunization is needed in JE prone areas. Over use of the vaccines should be avoided otherwise the virus might develop resistance against drugs which are administered fre‐ quently. Quarantine checks should be done at international immigration and emigration points, to keep a check on the spread of virus via foreign travelers. Vector control program should be designed in a way that they can control the risk of vectors in an efficient way. General awareness camps should be organized in rural areas to spread alertness in the local population and confronting them with hygiene management and preventive measures. Sys‐ tematic and combinatorial approach with the joint efforts of scientists, molecular biologists, doctors, drug developers, policy makers and local population is the need of hour. A high

Japanese Encephalitis Virus: The Complex Biology of an Emerging Pathogen

http://dx.doi.org/10.5772/54111

175

Authors are grateful to Dr Ch. Mohan Rao, Director, Centre for Cellular and Molecular Biol‐ ogy (Council of Scientific and Industrial Research, India), for the encouragement and sup‐ port for this work. NIH Awards (R37DA025576; R01MH085259) support S.K.S. and M.P.N.

, Rakhi Saxena1

2 Department of General Pathology & Microbiology, Saraswati Medical & Dental College,

3 Department of Immunology, Institute of NeuroImmune Pharmacology, Herbert Wertheim

, Asha Mathur2

and

Prevention methods are very important for minimizing JE infection (Saxena *et al.,* 2008). Childhood immunization is done by using inactivated mouse brain-derived vaccine which is based on either the Nakayama or Beijing strains of the JE virus, the cell culture derived, inactivated JE vaccine based on the Beijing P-3 strain ; and the cell cultures derived, live – attenuated vaccine based on the SA 14-14-2 strain (Halstead *et al.,* 2011) of the JE virus. Re‐ combinant poxvirus vectors expressing the E and NS1 proteins of the JEV boosting a good immune response in mice models can be used as a vaccine. The prevention of vector –man contact is very good preventive method this can be done by eliminating potential mosquito breeding areas, environmental sanitation, waste water management by treating the water with larvicide either by *Gambusa* (larva-eating fish), drying and wetting of rice fields, fre‐ quent vaccination should be implemented, and as well as personal protective measures. Re‐ ports have shown that induction of nitric oxide synthase plays a protective role against JEV (Saxena *et al*., 2001). Diethyldithiocarbamate has been also experimentally shown to inhibit JEV infection. Future predictions of the disease and drug designing can be enhanced by computer aided design databases, which can design *in silico* the most efficient drugs which can be tested experimentally and then can be clinically tried. For the development of appro‐ priate and effective therapy there is an immediate need to understand host factors role in JEV –induced neuropathogenesis (Gupta *et al.,* 2011). Effective anti-viral drugs have yet to be found. Medicines are given mainly to relieve symptoms. Vaccination for people at risk, eliminate mosquito breeding grounds, improve drainage, maintain clean piggeries, use in‐ sect repellent and mosquito nets are some of the preventive measures which should be com‐ monly undertaken.

The strategy for prevention and control of JE should include major components such as awareness among general public on the prevention and control of the disease, vector control and immunization. Environmental control is also one important factor as it has been studied that there is a positive impact of urbanization and economic development in the reduction of JE transmission, as clean and sanitized economies will not support environment necessa‐ ry for mosquito breeding. Land areas under cultivation with impact of agrochemicals may work for reduction of vector density. Along with good environmental strategies vector con‐ trol is also an important aspect. Maintenance of low vector densities is the need of hour.

Spraying, larviciding and aerial application are the method used for reduction of vector den‐ sities. However, alternatives to aerial application like spraying/fogging/Ultra Low Volume (ULV) application are also under consideration. Along with this long term i.e. non‐chemical vector control such as water management is also helpful. Use of agrochemicals to control pests may have had indirect effect on vector control. Also, use of larvivorous fish may also be applicable in permanent water bodies. Personal protection is very important as vectors can feed on humans in outdoors, hence over vegetation and shaded humid places should be bit ignored. Minimizing outdoor activity for reducing the exposure time to mosquitoes and wearing long sleeved clothes are some habits needed to be undertaken consideration along with public information and awareness.


**Meet the editor**

Maha BEN ZID is an associate Professor at the National School of Engineering of Le Mans (ENSIM), France. She received the Diploma in telecommunications engineering, in 2006 and the M.Sc. degree in communications systems, in 2007 from the National Engineering School of Tunis (ENIT), Tunisia. She joined the Grenoble Automatic Signal-Speech-Images (GIPSA-Lab) as a Ph.D. Stu-

dent in 2010. She received the Ph.D. degree in Signal, Image, Speech and Telecoms (SIPT) from the University of Grenoble, France, in 2012. Maha BEN ZID is the co-author of three book chapters and has also served as a reviewer for international conferences. Her research topics are multiple antenna systems and wireless sensor networks.

Contents

**Preface VII**

**Challenges 3**

Ben Zid Maha and Raoof Kosai

**Section 1 Fundamentals of Multi-User MIMO Communication 1**

**Section 2 Random Beamforming in Multi-User MIMO Systems 25**

Chapter 2 **Random Beamforming in Multi – User MIMO Systems 27** Hieu Duy Nguyen, Rui Zhang and Hon Tat Hui

Chapter 3 **Multi-user MIMO in LTE and LTE-Advanced - Receiver Structure**

**Section 4 Interference Cancellation in Multiuser MIMO Systems 91**

Chapter 4 **Multi-User Interference Suppression by Using Frequency**

**Domain Adaptive Antenna Array 93**

**Section 5 Multi-User MAC Schemes for High-Throughput**

Chapter 5 **Multiuser MAC Schemes for High-Throughput IEEE**

Wei Peng and Fumiyuki Adachi

**IEEE 802.11n 109**

**802.11n/ac WLANs 111**

Rizwan Ghaffar, Raymond Knopp and Florian Kaltenberger

Elli Kartsakli, Nizar Zorba, Luis Alonso and Christos Verikoukis

**Section 3 Multi-User MIMO in LTE Precoding Design 59**

**and Precoding Design 61**

Chapter 1 **Multi User MIMO Communication: Basic Aspects, Benefits and**


*2.4. Phylogeny*

To assess the phylogenetic position, 16S rRNA gene sequences were either acquired from previously published sources, or newly sequenced in this study. A list of accession numbers to partial 16S rRNA gene sequences attained from public repositories was provided in Tables A1 and A2. From unsequenced strains, DNA was extracted via the phenol chloroform method [32], and sequenced using Sanger technique [33]. Primer set 27F and 1492R, 5-AGAGTTTGATCCTGGCTCAG-3 and 5-GGTTACCTTGTTACGACTT-3 , respectively, was used to achieve a total contiguous 16S rRNA gene sequence length of >1400 bp per strain. New sequences of 16S rRNA genes from previously unidentified AAP strains were deposited to GenBank under accession numbers (MW970346–MW970408) as listed in Tables A1 and A2. Genetic relation of 16S rRNA gene sequences acquired from each AAP isolate were compared to the archived sequences of type species using the web-based software Basic Local Alignment Search Tool, BLAST [34]. Phylogenetic trees were constructed via MEGA X software [35] with 1000 bootstraps [36], using Maximum Likelihood method to align all AAP 16S rRNA gene sequences to one another based on the General Time Reversible model [37]. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with superior log likelihood value.

#### *2.5. Siderophore Isolation and Concentration from C. halotolerans*

Strain EG19 was grown at 28 ◦C on MA agarized (2%) plates in the dark for 5 days. Pink-purple colonies developed with brown hue dispersed in agarized medium (Figure 1A), which corresponded to zone of clearing on Fe-Chromeazurol S Assay (Figure 1B). To obtain liquid cultures, EG19 was inoculated at 5% in Fe-free MA and grown for 5 days at 28 ◦C shaking at 200 rpm in the dark. Fe-free MA was prepared as described in Chromeazurol S Assay, excluding Fe from the trace element solution. In addition, after all components were added, 5 g/L of Chelex resin enclosed in dializing tubing was placed in the medium for 1 h prior to autoclaving. This step allowed the Chelex resin to remove traces of Fe introduced through the complex nutrients such as yeast extract, bactopeptone and casamino acids [38].

After the cultivation of *C. halotolerans*, Fe-free medium became dark brown. The pinkpurple cells were pelleted in ~450 mL bottles at 10,000 rpm for 30 min using a Beckman J2HS centrifuge and a JA-10 rotor (Figure 1C). Siderophore containing supernatant was collected and 1.25 L was transferred into 2 L Erlenmeyer flasks (Figure 1D) to freeze overnight at −20 ◦C. Highly concentrated dark brown-pigmented high-salt slurry was formed predominantly on top of ice, while the lower part of ice was close to colourless. The flask with frozen material was removed from freezer and allowed to defrost at room temperature inserted upside down in a new collection beaker. Thawed supernatant was fractioned into 250 mL batches, with concentrated siderophore thawed and collected first, leaving frozen medium behind (Figure 1D), both observable visually (Figure 1E), and from the absorbance spectrum (Figure 1F).

A batch-type method of siderophore purification was used in this study [39]. Here, the combined freeze-concentrated samples were adjusted to pH 6.0 and XAD 7-HP resin was added (20 g resin per L of supernatant). This slurry was shaken at 200 rpm for 1 h on a rotary shaker and then filtered through a glass Millipore filter funnel that would collect resin, but allow supernatant to easily flow through. Concentrated siderophore required several extractions with resin to remove all pigment from supernatant. Siderophore-boundresin (now brown in colour) was thoroughly washed with ddH2O to remove all residual salts and other soluble contaminants. Vacuum assisted drying using Millipore system was performed, ddH2O discarded, and the cleaned siderophore-bound-resin was soaked

in methanol for 30 min to release the siderophore. Pigmented methanol was collected, and resin was soaked once more using the same batch of solvent for additional extraction. Methanol extracts were combined and then evaporated to dryness. The resulting dark brown powder was the presumed highly concentrated siderophore, and kept at −20 ◦C until further testing. Unbound-resin was washed with methanol, then soaked with ddH2O prior to reuse.

**Figure 1.** Extraction and concentration of brown pigment excreted by *C. halotolerans*. (**A**) Strain EG19 released a brown pigment into MA medium. (**B**) Fe-CAS plate with siderophore production by EG19. (**C**) Pelleted cells under supernatant. (**D**) Pigment concentrated with freezing-out technique. (**E**) Collected fractions during thawing with concentrated pigment released first. (**F**) Absorbance spectrum of each fraction.

#### *2.6. Purification and Fe-Chelation of Siderophore from C. halotolerans*

Concentrated siderophore powder from resin purification was resuspended in ddH2O (10 mg in 50 μL), and decimally diluted up to 10−3. In addition, a solution containing 5 mg of concentrate in 100 μL ddH2O was filtered through an Amicon® Ultra 0.5 mL 3000 Da spin column manufactured by Millipore Ireland, which removed any contaminating proteins over that size. This filtered concentrate was also decimally diluted up to 10−2. Once the dilutions were made, 10 μL of each sample, and an aliquot of freeze–thawed concentrate, were individually mixed with 10 μL of loading buffer, prior to filling into the wells of a Mini-Protean tris-tricine gel, 16.5% from Bio-Rad, Hercules, CA, USA. Loading buffer contained 200 mM tris-HCl, pH 6.8, 2% SDS, 40% glycerol and 0.04% Coomassie Brilliant Blue G-250 (CBB) from Bio-Rad, USA. Running buffer was made up of 1 M tris, 1 M tricine and 1% SDS at pH 8.3. Wells were 30 μL, filled with 20 μL of sample (10 μL of sample, 10 μL of loading buffer). Protein ladder was a C6210—color marker ultra-low range (M.W. 1060–26,600) from Sigma-Aldritch, St. Louis, MO, USA. Gel electrophoresis was run at 100 volts for 1 h prior to being stained in a CBB solution (1 g of CBB dissolved in 1 L of [50% MeOH, 10% glacial acetic acid, 40% H2O]). The gel was then destained with a mixture containing 5% acetic acid and 20% MeOH overnight.

Fe chelation was tested with the concentrated siderophore powder from the resin concentration step, the brown pigment filter-purified below 3 kDa, as well as with a sample from the remaining proteins that were >3 kDa from the spin filtration procedure. Each fraction was solubilized, where 10 mg of dried pigment was dissolved in 500 μL of 60% MeOH, prior to applying each solution into a blank diffusion disk, manufactured by Oxoid in the UK, allowing it to become dry and concentrated within the disk. These were then placed on a Fe-CAS plate and left to react with chromeazurol S overnight.