**2.3 Network Aware Differentiated Bandwidth Allocation Mechanism (nDBAM)**

Though eDBAM improves the throughput, the algorithm is indifferent to the current network conditions. Especially, if eDBAM method-1 is implemented, it could result in delays for regular and low-priority RTPS. Users might face jitter when they are viewing videos. This might not be desirable. nDBAM takes care of current network conditions before allocating bandwidth to the different service flows. The steps for nDBAM algorithm as given below.

**Step 1.** Users shall be allotted bandwidth as per one of the selected Seven methods of eDBAM.

BS keeps monitoring the network condition. BS could poll the SS to know their current queue length and the average queuing delays faced for each service flow. BS and SS can use the ranging mechanism to pass the information between them.

#### **2.3.1 Implementation**

BS does ranging at periodically with the SS. Ranging process is generally done to adjust the power levels and the clock skews. During the ranging process, BS can also request for the current queue state for the different service flows. As a part of ranging response (RNG-RSP) The SS can send the queue state to BS. The information is generally sent as a TLV (Type-Length-Value) header. A new header will be required to send the queue state information. Table 9 lists an example for the TLV.

Table 9. TLV header used to send Queue state.

BS receives the RNG-RSP from all the SS for each of their service class. BS then checks if the queuing delay is within the QoS limits for the service class. If not then it means that the eDBAM algorithm is introducing delay for regular and low-priority users. So, BS shifts from eDBAM to DBAM.


**Author details**

Michelle Jovanne Rivera-Rivera\* and Elvira Cuevas Department of Biology and Center for Applied Tropical Ecology and Conservation, University of Puerto Rico, Rio Piedras, Puerto Rico

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**7. MPC Based HEMS—Experimental Results**

A laboratory system has been constructed to evaluate the performance of the MPC using a real HBSS in a typical operating environment. The MPC-based HEMS was tested experimentally for one day at the FlexElec Laboratory in the University of Nottingham, using the "Smart Home Rig" (SHR) shown in Figures 3 and 4.

**Figure 3.** The smart home rig at the University of Nottingham Laboratory.

**Figure 4.** The connection diagram of the smart home rig at the University of Nottingham Laboratory.

This SHR comprises:

The HEMS-based MPC was implemented experimentally. At each sample time (every two minutes): (1) the Raspberry pi measures the SOC of the HBSS (from SMA converter in the HBSS) and sends it to the HEMS; (2) a MATLAB script is used to execute the MILP optimization and calculate the optimal power setting for the HBSS; (3) the Raspberry Pi receives the HBSS optimal power setting for just the next sample and passes it to the HBSS's SMA inverter; (4) these steps are repeated every two minutes.

Figure 5 shows the performance of the MPC-based HEMS for one day. The TOU tariff scheme and a fixed export electricity tariff were used in this experiment. The methods used for forecasting demand and generation are the previous week same day load profile (L-PWSD) and the previous day generation profile (PV-PD), respectively. The mean absolute percentage error (MAPE) for the load and generation forecasts were 29.3% and 22.66%, respectively. A two-minute sample time has been used—the MPC updates the HBSS references every two minutes and it can therefore respond to relatively fast disturbances in the system.

**Figure 5.** (**a**) Actual daily, load demand, PV generation, utility power (negative value—house is exporting power to the utility, positive value—importing), and the optimal power settings sent to the HBSS (negative value—charging, positive value—discharging); (**b**) Daily actual SOC of the HBSS.

A sample time of two minutes is the shortest sample time that can be used in this experiment. When a one-minute sample time was attempted for MPC operation, it was found that the MPC takes 5.62 min to perform just the optimization process, making a sample time of less than two minutes unfeasible for this experiment.

Figure 5a shows that the HEMS/BESS matches the household demand from 16:00 to 20:00 (during peak-tariff hours) so the home did not have to import energy from the main utility during this period. The PV generation was used in the home (including charging the HBSS) instead of being exported to the utility. From 00:00 am to 07:00 am (off-peak tariff time), a greater amount of energy was drawn from the supply utility at the low tariff rate (5 pence/kWh) to cover the home energy demands and charge the HBSS. It is clear from Figure 5b that the HBSS was charged from both the surplus PV generation during daytime and the imported energy from the supply utility during the off-peak tariff time.

Unwanted export power can be seen in Figure 5a (negative values of the utility power (black) profiles). The reason for this unwanted export was the errors associated with the load and generation forecasts at certain points in the day (i.e., when there is a sudden increase or decrease of the load or generation/export at power levels higher than the BESS can manage). The unwanted export power was one of the reasons for the lost energy when using the MPC for HEMS. The HECIR and ELR were 27% and 14%, respectively.

It is clear from Figure 5b that the HBSS charged to 67% overnight (i.e., not to its maximum limit of 90%) because this overnight charging level (a) enables the HBSS to provide the expected load demand during the morning period (i.e., no energy is purchased from the supply utility from 7:00 to 10:00), and (b) leaves space for the surplus PV generation during the following day to be stored in the HBSS (i.e., no energy is exported to the main utility from 9:00 to 15:00). The battery is fully charged at 16:00hrs, ready for the peak tariff period.

### **8. Performance Analysis for the MPC-Based HEMS**

This section will analyse the performance of the MPC-based HEMS over a one-year period.

### *8.1. Sample Time Resolution*

With a sample time of 60 min for the MPC operation, the HBSS power settings received from the MPC optimization will stay fixed for 60 min. As a result, any change in generation and/or load in this period will be compensated by the supply utility to balance the total active power in the home (4) and this may affect the total energy costs. If a sample time of two-minutes is selected for the MPC, it will update the HBSS references every two minutes and therefore will respond to fast changes in load and generation to minimize the home's energy costs and reduce lost energy.

Figure 6 compares the use of a 60 min sample time and a two minute sample time for the MPC. Figure 6a shows the HBSS power settings obtained using 60 min (red settings) and two minutes (blue settings), respectively. Figure 6b shows the power drawn from the supply utility when using 60 min (red) and two minutes (blue), respectively. Figure 6c shows the load and the generation profiles for a two-minute sample time.

It can be seen from Figure 6a that when a 60 min sample time was used for the MPC, the HBSS references remained constant for 60 min and changes in load and generation were compensated by the supply utility, as can be seen in Figure 6b. Energy is purchased from the supply utility during the peak-tariff period, and there is also unwanted export to the supply utility during the late afternoon. This export could be captured in the HBSS. Figure 6a shows that when the MPC updates the HBSS power settings (blue line) every two minutes, it can respond appropriately to fast changes in load and generation (seen in Figure 6c).

**Figure 6.** Comparison of a two-minute sample time and a 60 min sample time for the MPC. (**a**) The HBSS optimal power settings when using a two-minute sample time (blue settings) and when using a 60 min sample time(red), (**b**) the power drawn from the supply utility when using a two-minute sample time (blue) and a 60 min sample time (red), (**c**) PV generation and load demand profiles for a two-minute sampling time.

Table 3 shows the effect on the operation of the MPC using different sample times. An ideal forecast of load and generation was used for these tests so that the effect of sample time only was studied. The best case is where a two-minute sample time was used.

**Table 3.** Effect of sample time on the MPC computational time, the annual HECIR, and the annual energy lost ratio (ELR).

\* This case cannot be applied in a real system. The optimization process was only performed to show the required computation time.

It can be seen that the computational time of the optimization process can pose a problem if too short a sample time is used. For example, it is seen that if a one min sample time is used, the MPC takes 5.62 min to perform just the optimization process (a much larger time for computation than the rolling step size itself). This makes the use of this sampling time resolution unfeasible. In addition, if a very short sample time is used, this will force the controller to respond to each and every change in the load or generation. The controller action then has a high frequency content which can affect the lifetime of the HBSS: the battery will be exposed to high operational stresses if it changes between charging and discharging too quickly.

Usually, it is desirable to use a short sample time for the MPC. It can be seen from Table 3 that when a small sample time is used, this results in a lower energy cost increment ratio and a lower lost energy ratio. For a 60 min sample time for the MPC, the HECIR increases by 35.19% and the lost energy ratio increases by 29.86% compared with the smaller sample time. If a short scanning and response time is used, the MPC controller can respond to rapid changes in load and generation, and this therefore guarantees better performance and a greater reduction in costs for the householders. The compromise is that a longer computation time is required for the MPC optimization process.

### *8.2. The E*ff*ect of Forecasting Errors*

To measure the accuracy of the forecasting methods for load and generation for the following day, the mean absolute percentage error (MAPE) is calculated (19).

$$\text{M.A.P.E} = \frac{1}{\text{N}} \sum\_{\text{to}}^{\text{T}} \left| \frac{\text{A}\_{\text{t}} - \text{F}\_{\text{t}}}{\text{A}\_{\text{t}}} \right| \times 100 \tag{19}$$

where At is the actual point, Ft is the forecast and N is the number of forecasts considered.

Table 4 shows the MAPE values for the forecasted load and generation for the next day using the forecasting methods listed in Section 3.2. The forecasted load and generation profiles using the methods (i.e., L-PD, L-PWSD, L-AV, PV-PD, and PV-AV) were found using the historical dataset available.

**Table 4.** The mean absolute percentage error (MAPE) values for the load demand and the PV generation forecasting methods listed in this research.

When using the PV-FP forecasting method, as historical data has been used, the forecasted PV generation profile was created by adding Gaussian noise to the actual PV generation profile of the current day. The Gaussian noise represents the MAPE for the forecasted profile. The value of the MAPE (14% in this case) is obtained from the results available from the Sheffield solar website for PV generation forecasting for the following day [46].

For the next day household demand forecasting using the L-FP case, an adaptive neuro-fuzzy inference system (ANFIS) forecasting method, developed in [51], was used. Other load demand forecasting techniques could be used to potentially obtain better results.

Figure 7 shows the effect of forecasting uncertainty for both the load and generation on the annual HECIR and the annual lost energy ratio using the TOU purchasing tariff scheme. The sample time used in these calculations is two minutes and is fixed in all the cases to investigate the effect of the forecasting uncertainty only. It can be seen from the results that the forecasting uncertainty for the load demand and PV generation for the following day greatly affect the household energy costs and the lost energy ratio. The HECIR approaches 67.98% when using the L-AV and PV-AV forecasting methods (i.e., more than half of the cost that would be achieved when using ideal forecasting). Ideal forecasting is the perfect forecasting (100% accurate) of generation and load profiles for the next 24-h period, which can be created as we are using historical data profiles. The ELR also approaches 32.33% for the same case. This lost energy should be saved in the HBSS and used at the appropriate time period rather than being lost to the utility with little reward. From Figure 7 it can be seen that using a forecasting method such as L-FP and PV-FP achieves lower HECIR and ELR. It is worth noting that the actual HECIR and ELR will be higher than the values shown in Figure 7 if a longer sample time is used for the MPC.

**Figure 7.** The effect of forecasting uncertainty for both the load demand and PV generation on (**a**) the annual HECIR and (**b**) the annual ELR, using the time of use (TOU) purchasing tariff scheme and two-minute sample time.

### *8.3. The E*ff*ect of Changing Tari*ff

Table 5 shows the annual household energy costs calculated using the three purchasing tariff schemes shown in Figure 1. The sample time used in this section is two minutes and perfect forecasting was used for both the load demand and PV generation as we are using historical data.

**Table 5.** The annual household energy costs for the three purchasing tariff schemes.

It can be seen from the results in Table 5 that lower home energy costs can be achieved using the TOU tariff compared to using the Economy 7 or the real-time tariff schemes. The TOU tariff offers lower energy prices during off-peak periods (i.e., 4.99 pence/kWh as shown in Figure 1), compared to the Economy 7 tariff (8.4 pence/kWh for the same off-peak period). Lower energy prices during off-peak periods give the HBSS a chance to store as much energy as needed at low cost to cover the home demands through the day. The TOU tariff also offers lower prices during the off-peak periods compared to the real-time (half-hourly) pricing scheme which can offer high prices at night (as can be seen in Figure 1). This is an area of ongoing research.

### *8.4. Variation of HBSS Capacity*

Table 6 shows the impact of changing the capacity of the HBSS on the annual household energy cost and the PV self-consumption ratio using the TOU tariff scheme and 1.4 kW peak PV system. It can be observed that as the battery capacity increases, the household energy costs decreases and the PVSCR increases. The PVSCR also increases at a high rate when the battery capacity changes from 0 kWh to 4.8 kWh. However, the increment rate in the PVSCR is low when the battery capacity increases from 6.4 to 13.5 kWh. This is related to the rated size of the PV system (1.4 kWp); when the battery capacity increases beyond a certain size, this additional storage capability cannot increase the capture of PV generation (the remaining excess PV generation is at a power level above the power rating of the HBSS), and it therefore cannot improve the PVSCR.

**Table 6.** Effect of changing battery storage's capacity on the annual household energy cost and the PV self-consumption ratio using TOU purchasing tariff scheme and 1.4 kW (peak) PV system.

It is worth noting that as the battery capacity increases, the initial investment cost of the battery system increases as well. An optimization technique is required to select the best battery size which minimizes both battery investment cost and the annual household energy costs, as discussed in Section 4 and [42].

### *8.5. Varying PV System Size*

Table 7 shows the effect of changing the size of the PV system on the annual household energy cost and PV self-consumption ratio using the TOU purchasing tariff scheme and a 6.4 kWh battery. Different PV system sizes, from 1 kW to 5 kW, were used by scaling the PV data accordingly. It is clear from Table 7 that as the PV system size increases, the household energy costs decreases. Furthermore, it is observed that the PVSCR decreases instead of moving to 100% as the PV system size increases. The reason for this is due to the battery power limit (2.5 kW in this case); the additional PV generation is at a power level higher than the battery system's converter and therefore much of the surplus PV energy is exported to the main electricity grid.

The appropriate PV system size for the house should be selected according to the household needs and in coordination with the power limits of the HBSS to improve the PVSCR and minimize the overall household energy costs.

**Table 7.** Effect of changing PV system size on the annual household energy cost and the PV self-consumption ratio using TOU purchasing tariff scheme and 6.4 kWh battery.

### **9. Conclusions**

This paper has assessed the performance of two home energy management systems based on (a) a real-time controller and (b) a model predictive controller over a one-year period. Using the real-time controller, the effect of adjusting the overnight charging level on the overall performance has been studied. The results showed that the lowest value for household energy cost increment ratio and the highest value for PV self-consumption ratio (i.e., 8.1% and 89.70%, respectively) could be achieved using a weather prediction for the next day to adjust the overnight charging level, but this would incur additional operational costs.

Load demand and PV generation forecasts can be made relatively easily using methods such as L-PWSD, L-PD, L-AV, PV-PD, and PV-AV, i.e., methods which use historical data only and do not require any complex forecasting model or meteorological data (i.e., temperature, irradiation, humidity, etc.), compared to using accurate prediction methods such as L-FP and PV-FP which require up-to-date weather prediction and complex modelling. L-FP and PV-FP forecasting packages achieve greater reductions in household energy costs and lower lost energy compared to simple prediction packages. However, these forecasting packages require a good communication infrastructure and also additional costs for complex modelling.

The performance of the MPC has been studied considering the effect of forecasting errors (this technique requires forecasting for its fundamental operation), the sample time, and different purchasing tariffs. The results show that with appropriate selection of the forecasting method for load demand and PV generation, a significant reduction in household peak energy demand from the supply utility and also the cost of home utility bills can be achieved. Using a 60 min sample time for MPC operation increases the household energy cost increment ratio by 35.2% and the lost energy ratio by 29.8% compared to using a two-minute sample time. Using a short scanning and response time of two minutes, the MPC controller can respond to changes in load and generation that occur over a short time, and therefore guarantees better performance and a higher reduction in costs for the householders. Using the time of use tariff scheme with a PV-battery system reduces the household energy costs even further compared to the other tariff schemes considered.

**Author Contributions:** Writing—original draft preparation, investigation and analysis, M.E.; methodology, M.S.; conceptualization, M.S. and D.T.; simulation, M.E.; experimental validation, M.E.; writing—review and editing, M.S. and D.T.; Visualization and Investigation, S.P.; validation and Software, R.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is supported by the University of Nottingham, the Egyptian Government—ministry of higher education (cultural affairs and missions sector) and the British Council through Newton-Mosharafa fund.

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

### **Appendix A**

**Figure A1.** The annual household energy costs and the annual PV self-consumption ratio using different overnight charging levels for the yearly optimized case (case 2).

### **References**

© 2020 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 (http://creativecommons.org/licenses/by/4.0/).

*Article*


**5.4 Potential role of plant miRNAs in breast cancer**

According to the potential function of plant miRNAs, the authors sought to clarify the influence of exogenous molecules on tissues outside the GI tract. By sequencing serum and tumor tissue samples from stage II and III breast cancer patients, plant miR159 was detected up to six times more frequently than plant miRNAs. The results showed a higher abundance of miRNA in healthy patients than in breast cancer (BC) patients and patients with metastasis. Therefore, it was proposed that miR159 might influence breast cancer progression. By transfecting cells containing miR159 isolated from patient serum, they had the ability to reduce proliferation in BC cells. To identify miR159 target genes they used three independent methods: computational prediction, RNA-induced silencing, and sequencing. All three methods identified three potential target genes: transcription factor 7 (TCF7), nuclear receptor coactivator 6 (NCOA6), and engrailed homeobox 2 (EN2). Of which the TCF7 gene belongs to a family of transcription factors of the Wnt signaling pathway, which is overexpressed in breast cancer. Finally, the authors report that mice fed with MIR159 showed a significant reduction of TCF7 and MYC expression in their tumors with decreased tumor cell proliferation, reduced tumor growth, and increased apoptosis, providing important information on kingdom interactions for the prevention and treatment of various human diseases [39].


**3. Quantification of stochastic and deterministic effects**

The interaction of radiation with matter leads to the deposition of some or full of its energy in the absorption medium, the temperature of which may increase. Since the deposition energy is very low, this is not the main cause of consequent effects in the living tissues where the type of particles, the density of the energy lost per unit of the tracking sensitivity of different tissues exposed, and other factors play a more significant role. This is why the response of the body cannot be expressed by pure physical quantities, and other factors related to the tissue reactions to formed radicals are of primary importance.

The risk created by radiation to the human body cannot be expressed by means of only physical quantities and some specific quantities—we may call them *biophysical* rather than *physical quantities.* The biophysical quantities are based on the physical quantities weighted by specific factors taking into account the biological harm of various types of radiation as well as the sensitivity of particular organs and tissues to the exposure.

## **3.1 Physical quantities and units**

One of the first attempts to quantify radiation exposure to a person was based on ionizing abilities of radiation (at that time, only X-ray photos were assumed) where a unit *roentgen* was introduced as a measure of the ability of photons to ionize the air. Later on, the roentgen (R) became a unit of a quantity *exposure*, introduced by the equation

*Basic Radiation Protection for the Safe Use of Radiation and Nuclear Technologies DOI: http://dx.doi.org/10.5772/intechopen.108379*

$$X = \frac{dQ}{dm} \tag{1}$$

where *dQ* is the total charge of the ions of one sign generated by the electrons (negatrons and positrons) produced by photons in the mass of air *dm*.

The SI unit of this quantity is C kg�1 , the relation with the old unit—roentgen (R)—is 1 R = 2.58 � 10�4 C.kg�1 (exactly). Because of the definitions, the quantity of exposure could be applied in practice only to photons of energy up to about 300 keV [4].

Later on, when radiation protection had to address the results of interactions of other types of radiations, including beta, alpha, neutrons, and others, a universal quantity of (absorbed) dose was introduced. This is a universal physical quantity reflecting the deposition of radiation in any substance. The *dose* was introduced as follows:

$$D = \frac{dE\_i}{dm} \tag{2}$$

where *dEi* is the mean energy imparted to the matter of mass dm. The unit of the dose and the dose rate are Gy (gray) and Gy.h�1 (gray per hour). Commonly, units mGy, μGy, and mGy h�1 or μGy.h�1 are frequently used. Before, for the old unit, the rad unit was in use, where 1 Gy = 100 rad [5].

The dose is considered to be a universal quantity in dosimetry, and it is a basis for most quantities used in radiation protection. It can be used for any type of radiation and for any medium or absorber.

The last physical quantity to be mentioned here is the *kerma (K)*, which is the acronym for Kinetic Energy Released per unit Mass. This quantity can only be used for photons and neutrons in any media. It is still widely used especially in computational dosimetry. The kerma is defined by the equation

$$K = \frac{dE\_{tr}}{dm} \tag{3}$$

where *dEtr* is the sum of the initial kinetic energies of all the charged particles liberated by uncharged particles in a mass *dm* of material. The medium should always be specified.

The special name for the unit of kerma is gray (Gy); the unit for the kerma and dose is thus the same. In addition, here, one can specify this quantity related to the unit of time as the *kerma rate*, defined as the kerma per second. The main unit for this quantity is analogical to the dose rate, i.e., Gy.s�1 .

The illustration of the dose and kerma is shown in **Figure 5**, documenting their relationship. It is obvious that the kerma reflects the energy of secondary particles released by indirectly ionizing radiation at the point of interest, while the dose represents the energy absorbed by these particles. This absorption takes place at a certain distance from the origin of their production.

**Figure 5** shows the attenuation of photons in their penetration through the absorber where at the surface, the kerma has a maximum value and then shows a continuous decrease, while the dose is first increasing its value and after reaching the maximum; it decreases with the same rate as the kerma (equilibrium). This behavior is due to the fact that at a certain depth, the particles from the layer above contribute to the dose where the kerma is lower because of the attenuation.

**Figure 5.** *The relationship between the kerma and the dose depends on the depth.*


**1. Introduction**

The major histocompatibility complex (MHC) genomic region consists of a large group of evolutionary-related genes involved functionally with the innate and adaptive immune systems in jawed vertebrates [1]. In humans, the MHC is located on the short arm of chromo‐ some 6, band p21.3, and the MHC class I and class II genomic regions encode the highly polymorphic gene complex classified as the human leukocyte antigen (HLA) complex [2, 3]. The HLA class I and class II molecules expressed by the MHC play important roles in restricted

© 2015 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

cellular interactions and tissue histocompatibility due to cellular discrimination of "self" and "nonself" that require an essential knowledge of the effects of HLA allele matched and mismatched donors in transplantation medicine [4] and transfusion therapy [5]. While the HLA class I molecules are expressed by all nucleated cells to present processed peptides of intracellular origin to CD8+ cytotoxic T cells and serve as ligands for natural killer cells, the class II molecules are expressed by antigen-presenting cells such as B cells, dendritic cells, or macrophages to present exogenous peptides to CD4+ helper T cells of the immune system [6]. In addition, the classical HLA class I genes, HLA-A, HLA-B, and HLA-C, and the classical HLA class II genes, HLA-DR, HLA-DQ, and HLA-DP are distinguished by their extraordinary polymorphisms, whereas the nonclassical HLA class I genes, HLA-E, HLA-F, and HLA-G, are distinguished by their tissue-specific expression and limited polymorphism [2, 3, 7].

The highly polymorphic HLA genomic region is critically involved in the rejection and graftversus-host disease (GVHD) of hematopoietic stem cell transplants [8, 9], the pathogenesis of numerous autoimmune diseases [10–13], and infectious diseases [14]. Apart from regulating immunity, the MHC genes may have a role in reproduction and social behavior, such as pregnancy maintenance, mate selection, and kin recognition [15]. The MHC genomic region also appears to influence drug adverse reactions [16, 17], CNS development and plasticity [18– 22], neurological cell interactions [23, 24], synaptic function and behavior [25, 26], cerebral hemispheric specialization [27], and neurological and psychiatric disorders [28–32]. Hence, the MHC is one of the most biomedically important genomic regions that warrant special attention for genetic investigation.

In general, the study of the diversity and polymorphic variation of the MHC genomic region has been focused more on humans than any other species and animal population [1] largely because of the high cost and limited throughput of the first generation Sanger sequencing method [33, 34]. However, this is now changing because the next-generation sequencing (NGS) technologies are becoming the method of choice for lower-cost, high-throughput genotyping of MHC genes that are composed of highly homologous multiple loci such as those found in the macaque primate species [35]. Thus, the NGS technologies are expected to perform precise MHC genotyping in human and model animals that already have a collection of MHC allele references, and to facilitate MHC genotyping of wild animals that as yet have no MHC allele references. In addition, the NGS technologies are likely to replace traditional genotyping methods such as subcloning, Sanger sequencing, and previously developed PCR-based MHC typing methods (PCR-RFLP, PCR-SSP, and so on) in the near future. Recently, many articles concerning the development of NGS technologies for precise MHC genotyping and genotyp‐ ing data of MHC genes using the new NGS technologies have been published on the investi‐ gations of human and nonhuman MHC polymorphisms in various fields of study such as medical science, evolutionary biology, ecology, and population genetics.

In this chapter, we provide a short review of the current HLA polymorphism information and the use of PCR-based NGS for MHC genotyping in human and nonhuman species, particularly for the Filipino crab-eating macaque MHC (*Mafa*) class I (Mafa-A, -B, -E, -F, and -I) and class II loci (Mafa-DPA1, -DPB1, -DQA1, -DQB1, and -DRB1).


**1. Introduction**

Growing global energy demand and the need to replace fossil fuels with renewable fuels are the major challenges of modern society [1]. The current production of liquid biofuels, such as bioethanol, is dominated by biochemical conversion routes mainly based on food-related feedstocks, such as corn starch and sugarcane sugar. Lignocellulosic materials can serve as additional resources for the biofuel sector [1,2]. Agricultural and agroindustrial residues, e.g., wheat straw, corn stover, and sugarcane bagasse, are lignocellulosic materials of interest in many countries due to their availability [3,4]. Co-utilization of lignocellulosic residues with starch or sugar can further boost bioethanol production through integration into 1.5 G processes [5].

Wheat (*Triticum aestivum* L.) is the world's most widely cultivated crop [6]. Based on the data on wheat production worldwide (FAOSTAT) and on a residue/crop ratio of 1.3 [7], around 980 million tons of wheat straw are estimated to be available on a yearly basis. Dry wheat straw consists mainly of cellulose (30–40%), hemicelluloses (20–30%), and lignin

**Citation:** Ilanidis, D.; Stagge, S.; Jönsson, L.J.; Martín, C. Hydrothermal Pretreatment of Wheat Straw: Effects of Temperature and Acidity on Byproduct Formation and Inhibition of Enzymatic Hydrolysis and Ethanolic Fermentation. *Agronomy* **2021**, *11*, 487. https:// doi.org/10.3390/agronomy11030487

Academic Editors: Tony Vancov and Peter Langridge

Received: 19 January 2021 Accepted: 1 March 2021 Published: 5 March 2021

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**Copyright:** © 2021 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/).

(10–20%) [8]. Theoretically, bioconversion processes can yield around 270 L of ethanol per ton of dry wheat straw [9]. Biochemical conversion of agricultural residues, such as corn stover and wheat straw, has been significantly improved [7,10], but further research is still required for designing highly competitive and sustainable processes for the production of biofuels and other bio-based commodities.

The production of biofuels from lignocellulosic biomass comprises several steps, such as pretreatment, enzymatic saccharification, and microbial fermentation [11,12]. For achieving effective enzymatic saccharification of cellulose and, consequently, a higher release of fermentable sugars, a pretreatment step is indispensable. By disrupting the lignocellulosic matrix, pretreatment exposes cellulose and makes it more reactive towards cellulases [13]. Among different existing pretreatment methods, hydrothermal processing is an attractive option for agricultural residues, and it has good potential for industrial implementation [14]. Hydrothermal pretreatment (HTP) can be catalyzed by either hydronium ions generated by water autoionization or externally added acid species [15]. In HTP, moist lignocellulosic biomass is heated to around 200 ◦C for a certain period of time. Under those conditions, degradation of hemicelluloses and relocation of lignin can occur, and that leads to better accessibility of cellulose, which facilitates enzymatic hydrolysis [15,16].

While aiming at producing pretreated biomass with highly digestible cellulose, most pretreatment methods unavoidably lead to the formation of byproducts, resulting mainly from partial degradation of polysaccharides and lignin [14]. The formed byproducts can negatively affect the efficiency of enzymatic saccharification and microbial fermentation [14,17]. Certain groups of substances that inhibit microorganisms, such as aliphatic carboxylic acids, furan aldehydes, and phenolic compounds, have been extensively studied [18], while the importance of other groups of inhibitors has only recently started to emerge. Recent studies [19,20] have shown the significance of small aliphatic aldehydes as inhibitors of microbes used for biochemical conversion of biomass. Furthermore, the presence of benzoquinones in pretreatment liquids of different materials and their inhibitory effects on *Saccharomyces cerevisiae* have recently been discovered [21]. With regard to the inhibition of enzymes, aromatic substances such as phenolics have been found to play a role [14]. Pseudolignin, another byproduct, consists of thermal degradation products of carbohydrates. Pseudolignin remains insoluble and is accounted for as Klason lignin in the analytical two-step treatment with sulfuric acid (TSSA). The formation of pseudolignin can affect the enzymatic saccharification process [22,23]. In previous studies on hydrothermal pretreatment of wheat straw, the inhibition problem has generally been limited to substances such as furfural, HMF (5-hydroxymethylfurfural), acetic acid, formic acid, and certain phenolic compounds [8,9,24], while newly discovered inhibitors, such as aliphatic aldehydes and benzoquinones, have not been considered.

There are still several issues about hydrothermal pretreatment of wheat straw that require further research and innovation efforts. Issues that have so far not received enough attention include (i) how autocatalyzed (A-HTP) and sulfuric acid-catalyzed (SA-HTP) hydrothermal pretreatments affect the formation of byproducts, including pseudolignin and newly discovered inhibitors, (ii) how the pretreatment liquids inhibit the enzymatic hydrolysis of cellulose, and (iii) how xylan and lignin affect the digestibility of pretreated solids. In the current study, hydrothermal pretreatment of wheat straw under different temperatures between 160 and 205 ◦C, using two catalytic approaches, auto-catalysis and catalysis with sulfuric acid, was investigated. The investigation covered the evaluation of the effects of pretreatment conditions on (i) release of sugars and bioconversion inhibitors, (ii) chemical composition and enzymatic digestibility of pretreated solids, and (iii) inhibitory effects of pretreatment liquids on enzymatic saccharification and yeast fermentation. The results were backed by advanced analytical techniques, such as pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS), high-performance anion-exchange chromatography (HPAEC), and ultra-high performance liquid chromatography-electrospray ionization-triple quadrupole-mass spectrometry (UHPLC-ESI-QqQ-MS).