**Author details**

Xiaoming Jiang

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School of Communication Sciences and Disorders, Neuropragmatics and Emotion Lab (Pell Lab), Faculty of Medicine, McGill University, Montréal, Canada


*Edited by John Kiogora Mworia*

This book is devoted to botany and covers topical issues in this diverse area of study. The contributions are designed for researchers, graduate students and professionals. The book also presents reviews of current issues in plant-environment interactions making it useful to environmental scientists as well. The book is organized in three sections. The first section includes contributions on responses to flood stress, tolerance to drought and desiccation, phytotoxicity to Chromium and Lead; the second has aspects of economic botany including a review of Smut disease in sugarcane and properties of plant extract used Tassaboount date juice; the last covers topical issues on morphogenesis and genetics on cotton fiber special cell, secretory glands Asphodelus aestivus flower ,pollen tube growth in Leucojum aestivum , morphological studies of Ardisia crenata complex, and hybrid lethality in the Genus Nicotiana.

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**Acknowledgements**

The paper was supported by the research grant of "Accelerating Higher Education and Development" (AHEAD) under development-oriented research (DOR) of STEM grant number AHEAD/RA3/DOR/WUSL/LAS No.57 funded by the World Bank.


*Article* **A New Physical Design Flow for a Selective State Retention Based Approach**

**Joseph Rabinowicz 1 and Shlomo Greenberg 1,2,\***

**Abstract:** This research presents a novel approach for physical design implementation aimed for a System on Chip (SoC) based on Selective State Retention techniques. Leakage current has become a dominant factor in Very Large Scale Integration (VLSI) design. Power Gating (PG) techniques were first developed to mitigate these leakage currents, but they result in longer SoC wake-up periods due to loss of state. The common State Retention Power Gating (SRPG) approach was developed to overcome the PG technique's loss of state drawback. However, SRPG resulted in a costly expense of die area overhead due to the additional state retention logic required to keep the design state when power is gated. Moreover, the physical design implementation of SRPG presents additional wiring due to the extra power supply network and power-gating controls for the state retention logic. This results in increased implementation complexity for the physical design tools, and therefore increases runtime and limits the ability to handle large designs. Recently published works on Selective State Retention Power Gating (SSRPG) techniques allow reducing the total amount of retention logic and their leakage currents. Although the SSRPG approach mitigates the overhead area and power limitations of the conventional SRPG technique, still both SRPG and SSRPG approaches require a similar extra power grid network for the retention cells, and the effect of the selective approach on the complexity of the physical design has not been yet investigated. Therefore, this paper introduces further analysis of the physical design flow for the SSRPG design, which is required for optimal cell placement and power grid allocation. This significantly increases the potential routing area, which directly improves the convergence time of the Place and Route tools.

**Keywords:** physical design; power grid; power-gating; SRPG; selective SRPG; floorplanning; place and route

#### **1. Introduction**

Leakage currents during standby mode become more significant in mobile devices as semiconductor processes continue to shrink [1]. These static leakage currents impact the battery standby time of low-power mobile devices when they are in an idle state. Therefore, to mitigate the static leakage currents, some Power-Gating (PG) techniques were developed [2–6]. Power-gating eliminates the static leakage but with no intention to retain the system state. As mobile devices are required to support many features and functions, resulting in a wide range of multitasking, a minimum delay for the state restoration of all active tasks is critical for user satisfaction [7]. Besides the additional delay, saving and restoring the system state presents additional dynamic power overhead that may not be acceptable for certain common applications.

Scan-based techniques, which are used for serially saving and restoring internal retention cells, also suffer from latency and energy overhead [8]. The State Retention Power Gating (SPRG) technique addresses the above-mentioned PG technique's limitations [9–13]. This technique uses unique retention cells to retain the flip-flops (FFs) values during power down (standby state). These cells have been widely adopted in standard library cells

**Citation:** Rabinowicz, J.; Greenberg, S. A New Physical Design Flow for a Selective State Retention Based Approach. *J. Low Power Electron. Appl.* **2021**, *11*, 35. https://doi.org/ 10.3390/jlpea11030035

Academic Editor: Alex Serb

Received: 28 July 2021 Accepted: 9 September 2021 Published: 13 September 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/).

of major FAB vendors (such as TSMC). The SRPG approach aims to retain the systems state during standby, thus eliminating the disadvantages of the power-gating technique. However, common SRPG implementations require additional retention cells for all the FFs in the design resulting in significant area overhead. Moreover, these retention cells need to be connected to a dedicated power supply network and retention control signals. This additional wiring increases the area overhead and also complicates the physical design implementation in terms of tools runtime and the ability to handle large designs.

A more advanced approach, called Selective State Retention Power Gating (SSRPG), dramatically reduces the SRPG area overhead and further decreases the static power consumption. The main idea is to find a minimized set of FFs which are sufficient to retain the system state during standby. Chiang et al. [14] propose an empirical nonformal method for the selection of registers whose retention is unnecessary. Darbari et al. [15] present a formal approach based on symbolic simulation for implementing selective state retention. However, this method requires a formal representation of the entire design, which is not always available, and also no automated techniques are proposed. The two recently published SSRPG approaches introduced by [16,17] provide pure formal methods for automatic selecting of all the FF's, which require retention and are essential for a proper system recovery upon power-up. Experimental results show a significant reduction of about 80% of the retention cells area overhead. Recent SSRPG techniques can be efficiently applied to new modern SoC designs for automatic selection and formal validation of essential FFs requires retention. The current work is based on our previous formal SSRPG approach presented in [17], which utilizes formal verification methods and therefore can be easily implemented using the new proposed physical design flow.

Although the SSRPG approach mitigates the area and power overhead limitations of the conventional SRPG technique, still both SRPG and SSRPG approaches require a similar extra power supply network for the retention cells. The impact of the extra power supply when applying the selective approach has not yet been investigated. Therefore, further analysis of the physical design flow for SSRPG design is needed for optimal cell placement and power grid allocation. This may significantly increase the routing area, which in turn directly improves the convergence time of the place and route tools [18].

Furthermore, minimizing the number of retention FFs not only results in reducing the area overhead but also reduces the additional wiring required in SRPG. Although it is shown in [16] that a significant potential area reduction of about 9% of the chip area can be achieved, the added wiring required in SRPG is ignored. In SSRPG, the retention cells footprint can be simply deducted from the total cell area, but the wire-length deduction is not straightforward since it can only be obtained after completing the physical design flow. The wire-length overhead in the SRPG approach is derived from: (1) The connectivity of the retention cells to a new non-gated power supply network [19], and (2) the addition of retention control signals, which need to be connected to all FFs that are being preserved during standby by using retention cells [20]. This wiring overhead complicates the place and route physical design stages in SRPG. This work demonstrates the benefit of applying the SSRPG approach in a real physical design implementation concerning area, power saving, back-end runtime, and wire length.

Although some previous research works [16,17] try to estimate the area and powersaving factor results from applying the SRPG selective approaches, none of them validate it on real physical design implementation. Hence, one of the main objectives of this work is to quantify the real area and power saving factors while using SSRPG comparing to SRPG.

This work also demonstrates the benefit of applying a new, improved localized physical design flow using unique placement rules. The proposed localized improved flow yields significant power supply network area reduction in cases where selective state retention is used. It is shown that by applying these placement rules, metal layers that were originally used for power-supply distribution are freed up to be used for signal routing applied when connecting the different logic gates in the physical design during the routing stage, and

therefore improving the routeability. This simplifies the implementation of selective state retention in the physical design flow and significantly reduces the tools' runtime.

Although the SSRPG approach [16,17] is not a new technique, the effect of the selective approach on the complexity of the physical design has not been yet investigated. Therefore, further analysis of the physical design flow for SSRPG design is needed for optimal cell placement and power grid allocation. This may significantly increase the potential routing area, which in turn directly improves the convergence time of the Place and Route tools. This paper aims at the physical implementation aspect to facilitate the complexity of the physical design suggesting a unique flow to efficiently address SoC design based on SSRPG. Moreover, this is the first work related to SSRPG implementation, which accurately quantifies the area, power, and tool runtime saving factors.

In this work, we provide a case study showing the accurate area, power, and tool runtime savings when comparing the physical design implementation of SSRPG to SRPG. Previous works provide area reduction estimations based on the percentage of FFs that does not require retention [16,17]. These area estimations suffer from inaccuracies since they do not take into account the additional wiring overhead required for connecting the retention cells to the non-gated power supply and power-gating controls. To quantify the selective state retention physical design flow benefits, a complete CMOS 28 nm physical design flow was carried out on a typical Double Data Rate (DDR) memory interface controller design.

This paper is organized as follows: Section 2 provides an improved physical design flow for an Application-Specific Integrated Circuit (ASIC) supporting state-retention. Section 3 describes the experiment and shows the comparison results for the four different physical design flows: no retention, full retention using SRPG, SSRPG without special placement rules, and an improved physical design flow for SSRPG. Finally, Section 4 summarizes the paper and states the conclusions.

#### **2. An Improved SSRPG Physical Design Flow**

We propose a new approach to the common SRPG technique, based on automatic classification of each of the design's FFs into one of two types: essential or non-essential. The flow begins with gathering the libraries and floor planning, followed by place and routing, and ends with verification of the physical design. Figure 1 depicts the five main stages of a typical physical design flow. Each stage is described in detail in the following section considering the specific additional requirements for state-retention. Two different physical SSRPG design flows are considered concerning the placement stage: distributed flow and improved localized SSRPG flow. Some unique placement rules are proposed for the implementation of the new localized SSRPG physical design approach.

**Figure 1.** High-level stages of the physical design flow.

Although some physical implementation steps can be controlled by the common UPF and CPF industrial tools for power-aware content, those tools do not provide any specific placing rules except for limiting the logic cells placement to the appropriate power-domain (PDN).

#### *2.1. Gathering Libraries*

The libraries' physical design flow contains the list of basic cells and their attributes, such as physical layout abstractions, timing delay models, functional models, and transistorlevel circuit descriptions [21].

To implement state retention, the libraries should contain special retention FFs. Such FFs are divided into different types that can be categorized by the two following criteria: (1) the transistors threshold voltages (low, high, or multi-threshold) (2) Using an additional latch (referred to as balloon latch) or rather than using the FF slave latch (in a common

master-slave FF) for retention. Table 1 depicts the different types of retention FFs that are used in state retention approaches and their impact on low power, propagation delay, and physical design flow [13,22,23].

**Table 1.** Retention FFs types and tradeoffs.

Retention FF's implemented with low threshold voltage transistors have less impact on the propagation delay since the low voltage threshold allows fast switching between off and on states. However, since the leakage increases exponentially when decreasing the threshold voltage, the efficiency of reducing the static leakage is limited for this type of FF. The static leakage is given by the following equation:

$$P\_{\text{leakage}} = V\_{dd} \cdot I\_{\text{leakage}} = V\_{dd} \cdot I\_0 \cdot \exp\left\{ \left[ \left( V\_{GS} - V\_{TH} \right) / V\_T \right] / \left[ 1 - \exp\left( \frac{-V\_{DS}}{V\_T} \right) \right] \right\} \tag{1}$$

where *VTH* is the threshold voltage of the transistor, *VT* is the thermal voltage, *VGS* is the voltage between gate and source, and *VDS* is the voltage between drain and source of a MOSFET transistor. Some improvement in static leakage reduction can be achieved by adding a specific balloon latch, as shown in Figure 2. This additional latch is designed to consume less power during standby since it does not affect the master-slave functional path and therefore supports higher frequencies compared to FFs that use the slave latch for retention.

**Figure 2.** Retention FF implementation using a balloon latch.

Retention FFs that are implemented with high threshold voltage transistors, perform better with respect to static leakage reduction. A high voltage threshold leads to a better closure of the source/drain channels and thus preventing leakage currents when the transistor is in its off state. However, a high voltage threshold also impacts the propagation delay and therefore limits the clock frequency rates. Using both multi-voltage threshold transistors and an additional retention balloon latch allows better static leakage reduction and higher clock frequencies. However, this is at the expense of additional area overhead and extra external SoC power supply, which requires dedicated supply pads and balls, complicating the design [22]. Therefore, while choosing the physical design libraries in case of state retention, the SoC designer should consider the following factors and their tradeoffs: clock frequency, static leakage reduction, area overhead, and implementation complexity.

#### *2.2. Floorplanning*

A well-thought-out floor plan leads to a design with higher performance and optimum area [21]. In this stage, the physical designer determines the size of the macro instance, which includes the physical representation of the design. Additionally, the structure and placement of the power and ground strips referred to as power-supply networks are determined.

Some industrial SoCs may contain several power-gated domains and, therefore, many power switches to reduce IR drop [24]. This work aimed specifically at low power designs and referred to the hard macro level of implementation using only one or two power switches (as illustrated in Figure 3). To maintain minimum voltage drop and to prevent performance degradation, the power and ground strips should be as dense as possible. The following section refers to specific floorplanning adjustments required for state-retention-based designs. State-retention approaches require some modifications to the typical floorplan with respect to the power supply network. Specifically, two kinds of floorplan modifications are required: (1) adding an extra retention power supply network and (2) integration of dedicated sleep transistors for disconnecting the main power supply on standby. Figure 3 illustrates two power grids networks with a single power switch. The extra power grid uses a significant portion of the metal layers, which are actually needed for routing the logic gate connections (routeability) [13]. Although the strips of the extra power supply network are thinner compared to those of the main power supply, since there is no need to support full clock rate in standby, they should be spread over the entire macro instance.

**Figure 3.** Power grids networks for State Retention-based SoC.

Any power gating implementation, including SRPG, requires a dedicated sleep transistor per gated power supply. The sleep transistors are based on high voltage threshold transistors and are responsible for disconnecting both the power supply source and the ground in standby, as shown in Figure 4. Unique SLEEP signals are used to control the sleep transistors and define two control modes: active and standby modes (SLEEP is driven to 1 during standby and 0 during active modes). The active mode utilizes the low voltage threshold transistors to operate at higher frequencies. In Standby mode, the SLEEP signals are activated to turn off the sleep transistors. Since the sleep transistors are based on high voltage threshold transistors, their static leakage is very small during standby. The size of the sleep transistor is critical in terms of performance, area, and leakage current [19]. While

the sleep transistor should be large enough to drive sufficient current to meet frequency performance, it should not cause excessive leakage.

**Figure 4.** Sleep transistors.

#### *2.3. Place and Route*

The placement stage is responsible for placing the overall standard logic gates in a given macro instance and inserting buffer cells along with the clock and reset signal paths. Since the long wiring induces different propagation delays between different FFs, a clock balancing process is required. The buffer cells are used both for clock balancing and to support high fan-out and long wiring. This process of buffer insertion is commonly referred to as Clock Tree Synthesis (CTS) and has a significant impact on timing closure. In addition to the clock and reset signals, the CTS process is also applied to the retention FFs' control signals. This wiring and buffering overhead to support the additional retention control signals is significant in designs that include many sequential elements and might be similar to the overhead of the clock network [20]. Since the additional buffers should be connected to the retention power supply network, they have a significant impact on the routing to support the distributed retention controls signal paths. Power-supply network optimization is usually carried out after placement and before signal routing. The objective is to reserve more chip area for signal routing and, at the same time, maintain the performance of the power supply network. However, it is difficult to fully utilize the reserved chip-routing resource [25], especially in the case of a design that requires a dedicated power supply for the retention cells. Therefore, minimizing the area of the retention power supply network will lead a better routing utilization. The routeability in an SSRPG design can be further improved due to the small number of the required retention cells compared to SRPG. The routeability improvement can be achieved by making some appropriate adjustments both in the floorplan and the placement stages.

This work considers two different flows for SSRPG: the more straightforward distributed flow and a unique localized flow. In the distributed flow, the retention FFs are distributed all over the hard macro, while in the localized flow, the retention FFs are placed in a limited area using some placement constraints. Therefore, the region of the PDN of the always-on domain becomes smaller and requires less routing overhead. Furthermore, the proposed physical design flow is implemented within a hard macro level and applied to a specific functional design module. Therefore, since each hard macro commonly contains only one or two power domains, it is feasible to place all the retention FFs, connected to the always-on domain of the specific PDN, within a localized concentrated area.

We propose a unique physical design approach that is based on the assumption that the retention cells can be placed all together in a localized and relatively small area within the entire macro instance. This will lead to a reduced retention power supply network area. Figure 5 depicts placement results for two different physical design flows carried out on

the proposed DDR controller design using the Cadence Encounter tool. Figure 5a shows the placement results for the distributed SSRPG flow in which the retention power grid (i.e., power supply network) is distributed throughout the entire macro instance area without any placement constraints as in the common SRPG flow. The figure depicts the spreading of the retention FFs. Figure 5b shows the placement results for the new proposed localized flow. It can be noticed that the retention FFs are now located together in a relatively small localized area.

**Figure 5.** (**a**) Placement of the distributed retention FFs (mark in red and spread mostly on the mid-left-hand side). (**b**) Placement for the proposed flow where retention FFs are placed in a localized area (red square on the mid-left-hand side).

Two modifications were applied to the localized physical design flow based on the distributed flow placement results and using the common SRPG flow. First, the power grid was limited to a specific and localized area in the floorplan stage. Then, some specific placement constraints were provided to the Encounter tool, forcing all retention cells to be placed in a limited minimized localized area within the retention power grid region. The results show that the retention cells and the relevant retention power grid were successfully placed in a minimized area enabling better routeability compared to the common approach. Since the extra power grid utilizes only a small part (about 1/16) of the metal layer used for the retention power supply network (Figure 5b), more metal area is freed up for routing. To further reduce wire-length and additional buffers, the external retention control input ports are also placed in the same selected area close to the retention power grid. Applying such constraints to the placement tool may result in timing violations since the interconnect length between FFs may significantly increase. However, since the number of retention cells in SSRPG is relatively small, and most of the retention FFs are not part of the data path, the timing violations are not critical [26]. In the next stage, the routing process is carried out. Routing is becoming more difficult, especially for state retention-based designs, like SRPG, since the design is getting more complex due to the additional retention cells and the required extra wiring. Therefore, SSRPG facilitates the routing process by significantly reducing the amount of routing and hence decreasing the route runtime.

#### *2.4. Verification*

The final stage of any physical design flow is verification. This stage focuses on functional testing and design manufacturability. A comprehensive design verification process consists of three categories: functional, timing, and physical. The functional verification includes logic simulations, formality checks, simulation randomization, incircuit emulation, and hardware/software co-verification [27]. The timing closure is carried out using Static Timing Analysis (STA) to verify the timing of a digital design [28]. The physical verification checks the design layout against the specific process rules and includes Layout Versus Schematic (LVS) and Design Rule Check (DRC) [21]. In the case of state

retention, some additional logic simulations scenarios should be considered. For example, entering standby and then restoring the design state upon power resumption and verifying the selection of the appropriate FF's which required retention.

#### **3. Experiment and Results**

In this section, we compare four different approaches in respect to the physical design flow: no retention, full retention using SRPG, SSRPG with no specific placement rules, and an improved SSRPG flow. All the flows were applied to a typical DDR controller design as a test case. The synthesis was carried out using the Cadence RTL compiler, and then a common full PD flow was applied using Cadence Encounter to each of the four approaches. One of the main purposes of this work was to quantify the efficiency of the selective approaches with respect to area and power saving. Additionally, this research compares the four different PD flows in respect to the ability of the tools to converge, tools runtime, total wiring length, static leakage, and area-saving factors. Figure 6 depicts the block diagram of the selected DDR controller design. The DDR controller contains about 62,000 FFs. The design contains a DDR control unit, a DDR PHY adaptor, and two ARM AXI bus interfaces. The control unit is used to configure the DDR controller and monitor the status registers. The DDR PHY interface is connected directly to the DDR PHY, while the AXI bus interfaces between the DDR PHY adaptor and the internal memories. The AXI bus is used to store and retrieve data to/from the internal memory using a First-in-First-out (FIFO) memory within the AXI interface. A clock generator is used to provide an accurate clock signal to the external DDR memory. The DDR controller has two different operating modes: consecutive and interleaving memory addressing. The DDR interleave mux selects the desired operating mode and supports data interleaving from two channels to one memory device, reducing the external memory access time. The chosen DDR controller is used in many common VLSI applications and is large enough to represent a typical macro instance. Moreover, the design has a significant amount of non-essential FFs and, therefore, can be efficiently implemented using the SSRPG flow. In addition, the working frequency of the DDR controller is relatively high (533 MHz) and makes the comparison qualify for high-frequency designs as well.

**Figure 6.** DDR Controller—block diagram.

#### *3.1. Basic Synthesis*

Physical Design Flow Implementation

The design was first synthesized using the Cadence RTL compiler (RC). The synthesis results provide the physical designer with the following data: (1) a standard library cell design representation referred to as netlist, (2) the total cell area estimation needed for floorplanning, and (3) critical timing paths that should be addressed in the synthesis stage. For timing closure, the clock frequencies and some specific timing constraints should be defined in the synthesis stage. In our test case, two frequencies were applied: 533 MHz for the AXI bus and DDR PHY interfaces and a lower frequency of 133 MHz for the control logic.

The delay constraints take into consideration 30% of the clock period for output ports and 70% for input ports. Some more delay adjustments were needed for certain ports according to specific timing issues. In order to extract the essential FFs for the DDR controller test case, we have used the SSRPG approach described in [16]. This approach is based on a gate-level analysis and suggests a fully automatic algorithm to classify the FFs in a typical design into two categories essential and non-essential FFs. Results show that only 2522 FFs (out of the total 61,944 FFs) were classified as essential FFs, and therefore only 4.1% of the FFs require retention cells. The netlist was updated accordingly with the additional retention cells.

#### *3.2. Floorplanning*

An important step in floor planning is to specify the appropriate area to place macros and standard cells. In general, the floorplan can be determined according to the dimensions of the total macro area, Utilization Factor (UF), and die area. The utilization factor is defined as follows [29].

$$\text{Utilization Factor} = \frac{\text{Area of Standard cells}}{\text{Total Physical Design Area}} \tag{2}$$

This means that a larger area of 1/UF multiplied by the standard cell area is allocated for the Encounter tool to place the standard cells and to permit enough routing resources for the cells' interconnections. Selection of the UF should both provide the Encounter tool with enough space to place the cells and route between them and still meet timing. As the UF decreases, the area to place cells increases, and therefore the Encounter tool has a better ability to successfully route the cells. The effects of choosing a Utilization Factor on total wire length, congestion, and DRC (Design Rule Constraints) violations have been explored (studied) in [21]. It was observed that a Utilization Factor of 0.5 to 0.7 is appropriate depending on the metal layers in which the Power and Ground planning is done.

The Cadence Encounter tool was used to determine the size of the macro instance for the chosen DDR Controller design. The total cell area (including FFs and logic gates) was extracted from the synthesis results for the four different physical designs. The utilization factor's selection should be considered a tradeoff between the motivation to minimize the macro instance area and the need to reduce the place and route complexity.

An initial recommended utilization factor of 0.7 was examined in the floor planning stage. Then a unique utilization factor was chosen for each of the four different proposed physical design flows according to congestion and DRC violations which directly affect the Encounter tool runtime.

For the no-retention physical design flow, the initial recommended utilization factor of 0.7 was found to be appropriate and did not have much effect on congestion, placement run time, and tool convergence compared to lower utilization factors. However, while applying this initial utilization factor for the SRPG and SSRPG physical design flows, the runtime was significantly higher (a factor of 5) compared to lower utilization factors.

Figure 7 shows the empiric place and route tool's runtime versus the utilization factor for various examined flows. The utilization factor (UF) is given in Equation (2). The available area for placing the cells increases as the UF factor decreases, and therefore the Encounter tool has a better ability to successfully route the cells.

**Figure 7.** Place and Route runtime versus UF.

The effects of choosing a utilization factor on total wire length, congestion, and DRC (Design Rule Constraints) violations have been explored in [21]. The authors show that by using fewer number of metals to route between the standard cells spread across the core area (which is equivalent to the scenario of less available routing area), the tool has to do complex de-tour routing to avoid DRC violations. It was also observed that with fewer metals (a higher UF), the tool has fewer routing tracks to route between all the cells, introducing more congestion. Therefore, the number of available routing tracks available also decreases.

From Figure 7, we observe that the optimal UF factors are: 0.7, 0.65, and 0.67 for the noretention, SRPG, and both SSRPG flows accordingly. Any attempt to increase those chosen utilization factors resulted in the divergence of the Encounter tool. In all our experiments, the convergence time limit was defined to be 72 h. The relatively lower UF factor achieved for the SRPG and SSRPG can be explained due to the additional extra power grid and its connections to the retention cells buffers required for the CTS process and the additional route connectivity. We observed that the UF for the SSRPG flow is higher than the UF obtained in the case of SRPG. This means that the SSRPG physical implementation required less area compared to SRPG.

As a part of the floor planning, certain physical elements, such as antenna and latch-up cells, were added to maintain the integrity of the macro instance [30]. Then, pin placement was done according to the SoC constraints. Finally, the appropriate power grid was defined according to the specific physical design flow. While in the case of no-retention flow, only one power grid is required and is spread out uniformly across the macro instance area, the SRPG and SSRPG flow require an extra power grid which should be connected to the additional retention cells.

Figure 8 shows a snapshot, taken from the floorplanning tool, of the two power grids required in SRPG and SSRPG. The common VDD grid is represented by the thick purple line wrapped by two thin red lines. The extra VDDG power grid is represented by two closely placed thin red lines. Since the VDDG supplies power only to the retention cells, it can be composed of fewer gridlines compared to VDD. It can be observed that the VDDG strips are less dense and are placed in a 1.8 μm interval once every second VDD strip. The distance between the VDD and VDDG grid lines was set to 0.125μm. These power grid configurations were validated using the Cadence encounter power analysis tool.

As discussed in Section 2.3, the power grid distribution in the localized SSRPG flow can be limited to a localized area in the floorplan. The exact flow used to determine the localized area in which the retention cells are located is described as follows. First, the floorplan with a uniform distributed power grid is used as an input to the placement stage. Then the results of this placement (location of the retention cells) are used to create a new floorplan in which the power grid is limited to a specific area. Finally, the retention control

signals (RETN) which should be connected to all the retention cells, are placed close to this specific region to reduce routing.

**Figure 8.** VDD and VDDG power grid floorplanning.

#### *3.3. Placement and Routing*

The placement stage was carried out the same way for the four physical design flows. The Cadence Encounter was used as the placement tool in order to meet timing and area constraints as derived from the floorplanning stage. The same clock tree methodology was used for the four examined flows using the CTS Cadence tool with the same timing constraints. In the case of SRPG and SSRPG flows, the additional RETN control signals used for retention purposes were also balanced in the clock tree process. The routing for the three-state retention flows also included the additional connections of the state-retention cells to the extra VDDG power grid.

#### *3.4. Results*

During the implementation of the four physical design flows DRC checks were carried out according to the 28 nm library requirements. The timing analysis implemented by the STA tool also included exhaustive signal integrity checks [28]. The difference in timing closure between all four physical design flows was less than 11 ps, which is less than 0.6% of the clock period. All flows were executed on a 64 bit Linux server (64 bit, 2.8 GHz with 64 GB RAM).

This section shows the comparison results for the four examined flows in terms of area, wire-length, static leakage, and runtime. First, we demonstrate the benefit of using the proposed improved SSRPG flow in terms of runtime. Then, we compare the proposed flow with the common SRPG and the no-retention flows. Table 2 depicts the comparison between the improved localized SSRPG flow, which uses the unique placement constraint rules, the common SRPG, and the distributed SSRPG physical design flows. It is shown that applying the extra placement rules, with regards to the selected retention FF's, improves the place and route Encounter tools' runtime by 11% compared to the distributed SSRPG and by 23% compared to the conventional SRPG flow. This is a considerable improvement compared to the runtime of the distributed flow, which does not apply any specific placement rules regarding the retention cells. The major improvement is achieved in the placement stage, in which the runtime is decreased by 29% compared to the distributed SSRPG flow. This is a significant result since the placement stage is an iterative stage due to the floorplan area estimation process. Moreover, the improved localized proposed flow outperforms the conventional SRPG by 63% in terms of placement runtime. The runtime for the routing stage is improved by 8% and 9% compared to the

distributed SSRPG and SRPG, respectively. The runtime for the CTS stage is improved by 13% compared to the SRPG flow. Table 3 depicts the comparison between the four examined flows in terms of area, design density, number of library cells, wire-length, static leakage, and back-end tools runtime. As expected, the required area for SRPG implementation is 20% larger compared to the no-retention case. The implementation of the SSRPG approach results in a 16% area saving factor compared to SRPG. Moreover, almost no extra area is required for implementing the SSRPG flow compared to the no-retention case. While the wire length for SRPG is significantly larger compared to the no-retention flow, with about a 12% wiring increase, both SSRPG flows require only about 4% extra wiring compared to the no-retention case. This additional wiring overhead is required for connecting the retention cells to the non-gated power supply and power-gating controls. The increased wire-length induced by gathering all retention flip-FFs in a localized region is less than 1% compared to the distributed SSRPG.

**Table 2.** Place and Runtime Routing Comparison.

**Table 3.** Physical design flow Comparison.

The increasing wiring can explain this since the retention FFs are associated along with other non-retention FFs. However, this wire-length is compensated due to the reduced distance between the retention cells to the always-on PDN and to the retention controls in the improved SSRPG flow. Table 3 shows that although the macro area is the same for both SSRPG flows, the design density (as measured by the Encounter Cadence tool) is reduced by 2.3% for the improved localized SSRPG compared to the distributed SSRPG. The lower density hints towards a lower crosstalk, though this still needs to be proved using bespoke benchmarks. Therefore, a better immune to crosstalk effects might be achieved using the localized PD approach. Spice simulations show that for both PD flows, the used gridlines meet the IR drop worst-case conditions (according to TSMC 28 nm library).

This can be explained due to the better routeability achieved by limiting the retention power grid to a specific localized region and therefore reducing the area occupied by both the always-on PDN and the retention control wiring. A significant improvement is also demonstrated for the static power leakage. Although SRPG reduces the static power leakage by 94% compared to the no-retention flow (whereas the supplies are always on), both SSRPG flows reduce the static power leakage by 99.7%. It is also important to notice that SSRPG outperforms the SRPG flow by 96% in terms of static leakage.

The efficiency of the improved SSRPG approach is expressed by the significant improvement in terms of back-end runtime. The required runtime for implementing the place and route stages is compared. While SRPG increases the runtime by a significant factor of 33%, the improved SSRPG flow can be implemented with a negligible overhead of only 3% compared to the non-retention flow. Moreover, the speed up comparing to the distributed SSRPG flow is about 11%. It should be noted that the improved SSRPG outperforms the distributed SSRPG in terms of back-end runtime in spite of the slightly increased wire length. This can be explained by the lower design density in the case of improved SSRPG due to the reduced buffers (as indicated by the total library cells) required to support the specific clock-tree for the retention controls compared to the distributed SSRPG flow.

#### **4. Summary and Conclusions**

This work presents a novel approach for SoC physical design implementation based on Selective State Retention techniques. The additional wiring required for the extra power grid network for the retention cells and power-gating controls for the state retention logic increases the complexity of the physical design and directly affects the tools' runtime and the ability to converge for large designs. Therefore, this work investigates the effect of the selective approach on the complexity of the physical design implementation and proposes a unique flow to efficiently address SoC design based on selective state retention techniques. We demonstrate a significant reduction of the metal area required for the extra power supply network using the proposed approach. This is done by applying some unique placement rules to the physical design implementation flow utilizing the selectivity feature. This results in optimal cell placement and power grid allocation, which significantly increase the potential routing area, directly improving the convergence time of the Place and Route tools. Furthermore, it is shown that reducing the extra power supply network area also leads to a significant reduction of the runtime required for the placement tools.

We also compare the SRPG and SSRPG physical design implementations in terms of power, area, wire-length, and physical design tools runtime and quantify the area and runtime saving factors result from selectivity. Experimental results show that implementing the SSRPG approach using the proposed physical design flow yields an area-saving factor of 16% compared to SRPG, which is in accordance with the previously estimated factor reported in recent publications. Furthermore, the static leakage is decreased by 96% compared to SRPG and is negligible compared to no retention. Tool complexity overhead was also reduced as such that the runtime overhead was negligible compared to the no retention physical design flow. Finally, by applying certain placement rules for the retention cells, the tool runtime for the improved SSRPG was further reduced by 11% compared to the common SSRPG and by 23% compared to SRPG.

The proposed improved localized SSRPG flow facilitates the complexity of the physical design implementation for retention-based design. This approach leads to both reducing the number of metal layers used for the always-on power distribution and therefore facilitates the signals routing, and reducing the wiring used for retention control signals as well as simplifying the isolation of the always-on domain from the power-gated domain. As a result, the runtime of the place and route tools is significantly reduced due to the wiring complexity reduction.

Moreover, to the best of our knowledge, this is the first work that demonstrates and quantifies the benefit of applying the SSRPG approach in real physical design implementation and demonstrating actual area, power, and tools runtime saving factor.

**Author Contributions:** Both authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

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

#### **References**


**Author details**

Jorge Reyes-Rivera Facultad de Estudios Superiores Zaragoza, Universidad Nacional Autónoma de México, Mexico City, Mexico

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Staphylococcal Infections - Recent Advances and Perspectives

*Edited by Jaime Bustos-Martínez and Juan José Valdez-Alarcón*

Published in London, United Kingdom

Staphylococcal Infections - Recent Advances and Perspectives http://dx.doi.org/10.5772/intechopen.100798 Edited by Jaime Bustos-Martínez and Juan José Valdez-Alarcón

#### Contributors

Liang Wang, Xue-Di Zhang, Bin Gu, Muhammad Usman, Jia-Wei Tang, Zheng-Kang Li, Xin-Qiang Zhang, Jia-Wei Yan, Songul Cetik Yildiz, Miguel Ángel Ortíz Gil, Monica Irasu Cardona Alvarado, Jaime Bustos-Martínez, Samuel González-García, Juan José Valdez-Alarcón, Aida Hamdan-Partida, Anaíd Bustos-Hamdan, František Zigo, Zuzana Farkašová, Ibrahim Farag Mohammed Rehan, Ahmed Sayed-Ahmed, Asdren Zajmi, Mohammed A. M. Alhoot, Fathimath Shiranee, Sairah Abdul Karim, Shirley Gee Hoon Tang, Abdalmohaimen Suood, Iman Mahdi, Mahmood Saleh, Enitan Omobolanle Adesanya, Akingbolabo Daniel Ogunlakin

#### © The Editor(s) and the Author(s) 2023

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*2.3. CT*/*MRI Comparison*

In order to validate the use of CT imaging compared to the standard MRI imaging, we compared the e-field calculated by the system in both modalities in four sets of imaging data. For each head model, 26 stimulation targets were put on the cortical surface, distributed around the central sulcus (Figure 1C). The virtual model was co-registered to a dummy head in 3 cases and to the actual patient in 1 case. Each target was subsequently stimulated with an intensity of 30% of maximum stimulator output and the calculated local maximum e-field was recorded. This specific stimulation intensity was selected since 30% of maximum stimulator output is a common benchmark for motor thresholds in neurosurgical motor mappings.


*Article* **Characterization of Protein Hydrolysates from Eel (***Anguilla marmorata***) and Their Application in Herbal Eel Extracts**

**I-Chun Cheng 1, Jin-Xian Liao 1, Jhih-Ying Ciou 2, Li-Tung Huang 3,4, Yu-Wei Chen 5 and Chih-Yao Hou 1,\***

Received: 17 December 2019; Accepted: 7 February 2020; Published: 8 February 2020

**Abstract:** The enzymatic hydrolysis of fish proteins is the principle method for converting under-utilized fish into valuable products for the pharmaceutical and health food industries. In this study, three commercial enzymes (alcalase, bromelain, and papain) were tested for their ability to create eel protein hydrolysates (EPHs) from whole eel (*Anguilla marmorata*). Freeze-dried EPHs had almost more than 80% solubility (*p* < 0.05) in solutions ranging from pH 2–10. The amino acid profiles of the EPHs showed a high percentage of essential amino acids, including histidine, threonine, valine, isoleucine, and leucine. The emulsion activity index (EAI) of EPH resulted as follows: alcalase group (36.8 ± 2.00) > bromelain group (21.3 ± 1.30) > papain group (16.2 ± 1.22), and the emulsion stability index (ESI) of EPH was: alcalase group (4.00 ± 0.34) > bromelain group (2.62 ± 0.44) > papain group (1.44 ± 0.09). As such, EPH has a high nutritional value and could be used as a supplement to diets lacking protein. EPH showed excellent solubility and processed interfacial properties, which are governed by its concentration. Among of them the alcalase group had the best antioxidant effect at 1,1-diphenyl-2-pyridinohydrazinyl (DPPH) radical method, determination of reducing power and ABTS test compared with other groups. EPH may be useful in developing commercial products like herbal eel extracts that are beneficial to human health.

**Keywords:** *Anguilla marmorata*; eel protein hydrolysates; functional properties; herbal eel extracts

#### **1. Introduction**

As the world's population continues its expansion towards nearly 10 billion people by 2050, increasing wealth in developing countries adds to the demand for protein [1], an essential component of the human diet. This is due to changing food preferences and a growing recognition of the importance of protein as a key dietary ingredient. Dietary protein supplements are becoming popular, especially for people on restricted diets, like athletes and the elderly [2]. Dairy and soy are the main sources of protein in nutritional beverages and herbal extract products, with a whey protein concentration of 80% the most widely used. However, fish is an excellent source of protein, with proven satiating effects and higher protein content than most terrestrial animals. The proper utilization of limited aquatic resources

has been a topic of great interest for many decades. The use of proteases in fish processing leads to the hydrolysis of proteins in the source material, which can then be separated from the muscle [3,4] and used in beverage or herbal products.

*Anguillid* eels are one of the main high value species of fish used in aquaculture, with the Japanese eel (*Anguilla japonica*) and European eel (*Anguilla anguilla*) the most popular [5,6]. World production and consumption of *Anguilla* sp. eels in 1987 was ~100,000–110,000 tons and ~70–80% of it was produced and consumed in Japan and Taiwan [7]. Eel has been an important aquatic export in these countries for nearly 30 years. In recent years, there has been a severe shortage of *Anguilla japonica* glass eels, as artificial propagation of freshwater eels had not yet been commercially successful. Most eel seedlings come from natural fishing, which has prompted a search for other *Anguilla* species, such as *Anguilla marmorata*. These adult eels are greyish-yellow with a white belly and brownish-black marbling on their backs that can fade over time. Mass production of *Anguilla marmorata* has been cultivated gradually and its breeding habits are still being explored, but little research exists on its functionality [8]. *Anguilla marmorata* are not only economically valuable, but are also rich in nutritional value due to their high levels of proteins, Carnosine, vitamins, and minerals [9,10]. However, research into *Anguilla marmorata* is relatively rare. Recent research into fish protein hydrolysates (FPHs) and their antioxidant activity, for instance skin gelatin hydrolysates from brownstripe red snapper or meat protein hydrolysates from yellowstripe trevally (*Selaroides leptolepis*) [11,12], show that hydrolysis can release functional peptides which may be used in numerous food products. R. Hartmann and H. Meisel (2007) pointed out that many peptides released in vitro or in vivo from animal or plant proteins are bioactive and have regulatory functions in humans beyond normal adequate nutrition. Different health effects have been attributed to food-derived peptides [13]. Water-holding and fat-binding capacities are functional properties that are closely related to texture based on the interactions between water, oil, and other components [14]. Protein hydrolysates rich in bioactive compounds represent promising ingredients for food and industrial applications. Some recent studies have gone beyond producing and characterizing EPH and have tested their ability to fortify foods [15,16]. Because the protein hydrolysates characterization and process application properties research of *Anguilla marmorata* is relatively few, and protein hydrolysates are often used in nutritional supplements. Therefore, the objectives of this study are to compare the use of three commercial enzymes (alcalase, bromelain and papain) in the production of eel protein hydrolysates (EPHs) and to characterize these EPHs, compare their chemical, functional properties, and sensory properties, and application to herbal eel extracts.

#### **2. Results and Discussion**

#### *2.1. Enzyme Activity and Degree of Hydrolysis*

Before hydrolysis, an enzyme activity assay was performed to estimate the quality of the respective enzyme. Proteolytic activity for each enzyme was: alcalase 2.4 L at 2500 U/mg, papain at 10,000 U/mg, and bromelain at 1250 U/mg. Alcalase is produced from microbes while the other two enzymes are produced from plants. Of the three, bromelain had the lowest activity level. Degree of hydrolysis (DH) is defined as the percentage of peptide bonds cleaved and it is the standard parameter commonly used to monitor and compare the level of protein proteolysis, which also affects protein solubility, emulsification, and foaming. Protein hydrolysates with high solubility can be easily mixed into liquid and have excellent wettability. Different DH values affect the structure and amino acid composition. Figure 1a shows the DH for alcalase, bromelain, and papain at optimal pH and temperature (pH 7.5 and 55 ◦C, pH 4.5 and 45 ◦C, and pH 6.0 and at 50 ◦C, respectively) for different reaction times (from 1 h to 16 h). Figure 1b shows DH depending on the amount of enzyme added (from 0.1 to 2.0 g/g). In Figure 1a, there are significant differences in DH between the enzymes, as alcalase shows greater hydrolytic ability than bromelain or papain. Similar results are seen in Figure 1b, with alcalase still showing the greatest degree of hydrolysis. The functionality of protein hydrolysates is a major factor in their success as functional supplements in food. The physicochemical properties of protein

hydrolysates depend on the protein substrate, the specificity of the enzyme used for proteolysis, and the hydrolysis conditions [14].

**Figure 1.** (**a**) Degree of hydrolysis of eel (*Anguilla marmorata*) at different times with three different commercial proteases (alcalase, bromelain, and papain) at optimal pH and temperature; (**b**) Degree of hydrolysis of eel (*Anguilla marmorata*) with different amounts of commercial proteases (alcalase, bromelain, and papain) at optimal pH and temperature. Means in the same form with various characters have significant differences (*p* < 0.05). Data are expressed as mean ± SD, from experiments performed in triplicate.

#### *2.2. Molecular Mass Distribution Profile*

Prior to enzymatic hydrolysis, the peptide molecular mass distribution of eel (*Anguilla marmorata*) was analyzed via high performance liquid chromatography (HPLC). All EPHs were reduced to smaller peptides with molecular mass between 1056.55 Da and 1554.53 Da (Table 1). Small molecule proteins and peptides are easier for the human body to digest and absorb which mainly include dipeptide or tripeptide [17]**.**

**Table 1.** Average molecular mass of peptides in eel (*Anguilla marmorata*) following hydrolysis with alcalase, bromelain, and papain.

\* Prior to enzymatic hydrolysis, eel (*Anguilla marmorata*) were analyzed by HPLC. a–c Different letters in the same column denote significant differences between hydrolysates (*p* < 0.05).

#### *2.3. Amino Acid Composition*

Amino acid levels in the hydrolyzed EPHs were higher than in unhydrolyzed eels. Among the 20 types of standard amino acids, there are 9 essential amino acids (EAAs) that adults cannot produce and must obtain from their diet; arginine (Arg), in particular, is essential for babies. Among the EPHs, the levels of histidine (His), threonine (Thr), valine (Val), isoleucine (Ile), and leucine (Leu) were higher with alcalase than for bromelain or papain (Table 2). In fact, the alcalase EPH provided the equivalent of the World Health Organization's recommended daily intake of EAAs needed to meet the protein requirement for adults. Protein hydrolysates are mainly used in health products and energy drinks, but hydrolysis results in protein structural changes, causing bitterness. Since bitterness was produced by proline (Pro), Leu, Val, and phenylalanine (Phe), an herbal eel extract recipe was used to remove the unpleasant taste. The amino acid composition of the hydrolyzed eel protein solution could help supply adults with EAAs and be used to develop nutritional supplements in the future.

**Table 2.** Total amino acids of the three EPHs (A = alcalase, B = bromelain, P = papain) and recommended amino acid requirements of adults (WHO, 2002). Branched chain amino acids are highlighted in bold font. WHO = World Health Organization.

\* required for adults. Results are expressed as the mean ± SD from triplicate determinations. Values in the same column with different superscript letters are significantly different (*p* < 0.05).

#### *2.4. Functional Properties of EPHs*

The functional properties of the hydrolyzed protein solution are affected by amino acid composition and the molecular weight of the peptides. The solubility, emulsifying properties, stability, turbidity, color, oil binding capacity, and flavor are important in food processing. Functional properties can also affect the sensory evaluation, especially for chewy and smooth textures.

#### 2.4.1. Emulsifying Properties

EAI is a function of oil volume fraction, protein concentration, and the type of equipment used to produce the emulsion [14] EAI (m2g−1) and ESI (min) of EPHs at different concentrations (0.5%, 1%, and 2% *w*/*v*) are shown in Table 3. All EPHs had reduced EAI as concentration increased. The most significant difference among the EPHs was at 0.5% (*w*/*v*) concentration, with Alcalsae having the highest EAI (36.8 ± 2.00 m2g−1) and papain having the lowest (16.2 ± 1.22 m2g−1). ESI was also significantly impacted by concentration. Similar to EAI, differences in ESI were largest at a concentration of 0.5% (*w*/*v*). Alcalase had the highest ESI (4.00 ± 0.34 min) at a concentration of 2% (*w*/*v*). Emulsification occurs during homogenization when proteins are absorbed at the surface of the oil droplets as they form, creating a membrane which prevents them from consolidating [18].

**Table 3.** Emulsifying activity index (EAI, m2 g−1) and emulsion stability index (ESI, min) at different EPH concentrations (0.5%, 1%, and 2% *w*/*v*) and oil binding capacities (OBC, g/g).

Values are given as mean ± SD from triplicate determinations (*n* = 3). a–c Different letters in the same column denote significant differences between hydrolysates (*p* < 0.05).

#### 2.4.2. Oil Binding Capacity (OBC)

Oil binding capacity was determined by physical methods, the density of oil in the bulk, and use the ratio of the mass of bound liquid oil to the solid fat content. In this study, alcalase has the highest OBC value of the three EPHs (Table 3). The oil binding capacity of a protein affects its functional properties and the taste of the end product. Water-holding and fat-binding capacities are functional properties that are closely related to texture, due to the interactions between water, oil, and other components.

#### *2.5. Antioxidant Properties of EPHs*

Antioxidants are very important in food preservation, and also play an important role in improving immunity and delaying human aging. In Figure 2a, DPPH free radical scavenging activity was compared for the three enzymes (alcalase, bromelain, and papain) at different times. Bromelain had significantly higher scavenging activity than the other enzymes. Figure 2b shows the DPPH free radical scavenging activity of EPHs at different concentration. The results for alcalase are significantly higher. Alcalase addition ratios of 1.0 and 2.0 resulted in significantly higher scavenging ability. Figure 2c,d used the reducing power assay to compare antioxidant activity at different times and with different amounts of the enzymes. Of the three, the alcalase hydrolysate had the highest reducing power. Figure 2e,f compare the impact hydrolysis time and the amount of enzyme have on ABTS total antioxidant capacity. Of the three, bromelain has a significantly higher ABTS total antioxidant capacity. Antioxidant tests were performed for three different mechanisms. As such, the antioxidant capacities of the EPHs are clearly different, since they were hydrolyzed by different enzymes, with different hydrolysis times, and with differing amounts of enzymes. However, based on all three assays, the alcalase hydrolysate appears to have the greatest total antioxidant capacity.

**Figure 2.** *Cont.*

**Figure 2.** Antioxidant effect of the three EPHs (alcalase, bromelain, and papain) using three antioxidant assays: (**a**) Antioxidant effect at different times based on DPPH free radical scavenging assay; (**b**) Antioxidant effect at different enzyme concentrations based on DPPH free radical scavenging assay; (**c**) Antioxidant effect at different times based on reducing power assay; (**d**) Antioxidant effect at different enzyme concentrations based on reducing power assay; (**e**) Antioxidant effect at different time based on ABTS total antioxidant capacity; (**f**) Antioxidant effect at different enzyme concentrations based on ABTS total antioxidant capacity. Means in the same form with various characters denote significant differences (*p* < 0.05). Data are expressed as mean±SD from triplicate determinations.

#### *2.6. Nitrogen Solubility of EPH and Herbal Eel Extracts*

Solubility is often considered the most important physicochemical property in protein hydrolysates, especially when considered in terms of fortifying herbal extracts. Many other functional properties, such as emulsification and foaming, are affected by solubility. Figure 3a shows the nitrogen solubility profile as a function of pH for EPHs produced from different enzymes. All three EPHs had high solubility (more than 80%) in different pH conditions and 85% solubility in alkaline conditions. Among them, EPH produced from alcalase performed the best. At low pH, the charges on the weakly acidic and basic side-chains of the amino acids become less soluble, and resulted in precipitation [18]. The reduced solubility of the alcalase hydrolysate may be due to the greater proportion of small molecule amino acids due to their hydrophilic relationship to the water molecules prior to nitrogen solubility analysis. As shown in Figure 3b, the pH of the herbal eel extract was 7.0 ± 1, and it showed good solubility at higher pH. The beverage matrix will contain other compounds that promote or hinder solubility. Higher solubility gives food and beverage products an appealing appearance and a smooth mouthfeel.

**Figure 3.** Nitrogen solubility (%) of the three EPHs in (**a**) water at different pH levels (2, 4, 6, 8, and 10) and (**b**) herbal eel extracts. Means in the same form with various characters have significant differences (*p* < 0.05). Data are expressed as mean ± SD from triplicate determinations.

#### *2.7. Color of EPH and Herbal Eel Extracts*

In the food industry, food color is the deciding factor in consumers' overall acceptance of a product. The color of eel protein hydrolysates is based on the ingredients, enzymes, and hydrolysis conditions. At a concentration of 15% (*w*/*v*), there were significant differences between the EPHs. As summarized in Table 4, L\* represents brightness, while a\* and b\* represent the color space. Both the EPHs and the herbal eel extract were brownish-yellow in color. Specifically, all three EPHs were yellowish in color, although alcalase (3.96 ± 0.04) was darker than both bromelain (6.37 ± 0.43) and papain (8.10 ± 0.18). Based on a\* value, alcalase (0.42±0.03) was preferable to bromelain (0.78 ± 0.02) and papain (0.28 ± 0.01). The same trend was seen with the b\* value, with alcalase (0.74 ± 0.07) again preferable to both bromelain (2.39 ± 0.02) and papain (5.75 ± 0.10). The color differences were mainly due to the ingredient sources and enzymes. Alcalase had the lowest brightness and therefore showed the darkest color following hydrolysis.

**Table 4.** Color analysis of the three EPHs (alcalase, bromelain, and papain) at concentration of 15% (1.5 g/10 mL).

Values are given as mean ± SD from triplicate determinations (*n* = 3). a–c Different letters in the same column denote significant differences between hydrolysates (*p* < 0.05).

#### **3. Materials and Methods**

#### *3.1. Materials*

2,2-diphenyl-1-picrylhydrazyl, phthaldialdehyde, and trifluoroacetic acid were purchased from Alfa Aesar (Haverhill, MA, USA). Glycerol and ascorbic acid were purchased from R&D Systems (Minneapolis, MN, USA). Glacial acetic acid was purchased from ThermoFisher Scientific (Waltham, MA, USA). Anhydrous iron (III) chloride, citric acid monohydrate, and sodium tetraborate decahydrate were purchased from Showa Chemical Industry Company (Tokyo, Japan). Methanol, hydrochloric acid, sodium bicarbonate, and anhydrous sodium acetate were purchased from Aencore Chemical Co. (Melbourne, VIC, Australia). Disodium (hydrogen) phosphate was purchased from J.T. Baker (Phillipsburg, NJ, USA). Hydrogen peroxide was purchased from SHOWA (Japan). Alcalase®2.5 L was purchased from Strem Chemicals (Newburyport, MA, USA, CAS number 9014-01-1), and papain (CAS number 9001-73-4) and bromelain (CAS number 9001-00-7) were purchased from ChappionBio (Taiwan). Olive oil was purchased from Carrefour (Taiwan).

#### *3.2. Methods*

#### 3.2.1. Anguilla Marmorata Raw Material Preparation

*Anguilla marmorata* were provided by the Honya eel farm (Dongshi Township, Cjiayi County, Taiwan). The heads of the eels were excised, and the skin and bones were removed. The remaining fish was sterilized at 121 ◦C for 30 min before storage at −20 ◦C for subsequent experiments and analysis.

#### 3.2.2. Anguilla Marmorata Meat Hydrolysate Preparation

1 g eel meat was added to 0.1 M phosphate-citrate buffer solution (pH 7.5) at a 1:50 ratio then homogenized, before the addition of different concentrations of the three enzymes, as shown in Table 5. The enzyme was inactivated by heating the solution to 95 ◦C for 20 min before it was freeze-dried to obtain EPH. The sample was then stored at −20 ◦C until further analysis. The scheme of the hydrolysis process of *Anguilla marmorata* is shown in Figure 4.

**Table 5.** Parameters used to obtain the optimal hydrolysis conditions for eel, using three different enzymes.

#### 3.2.3. Herbal Eel Extracts

The recipe of herbal eel extracts is to add 23 g of wolfberry, 20 g of red dates, 8 g of *Astragalus propinquus*,8g *Angelica sinensis*,6g *Cinnamomum cassia*, 6 g Licorice to the medicine bag, and put into the 1000 mL water contains 49 g sugar and 6 g salt for boil 30 min together. After cooling down remove the medicine bag and mix with 500 mL of 15% EPH as herbal eel extract for assay.

#### 3.2.4. Determination of Degree of Hydrolysis (DH)

The degree of hydrolysis (DH), defined as the percentage ratio of the number of peptide bonds cleaved to the total number of peptide bonds available in the substrate, was monitored throughout the reaction. 100 ppm serine (Seine) standard and a sample containing 0.08% protein content were added to 150 μL OPA colorant (1 mL 4% o-phthalaldehyde in ethanol added to 40 mL 1250 ppm sodium dodecyl sulfate in 0.125 M sodium tetraborate buffer solution, further dissolved in 44 mg of dithiothreitol to 50 mL). The reaction was allowed to stand for 2 min, before absorbance was measured by an ELISA reader at 340 nm [19].

DH (%) = [(OD(Sample) − OD(Blank))/(OD(Standard) − OD(Blank)) × 0.9516 meqv/L × 0.1/(X × P) − β]/α/htot × 100%

where OD (Standard) = standard absorbance, OD (Sample) = sample absorbance, OD(Blank) = blank absorbance, X = g sample, and P = protein % in sample.

The values of the constants α, β, and htot (total number of peptide bonds per protein equivalent, which is dependent on the amino acid composition of the raw material) for fish protein are estimated to be 1.00, 0.40, and 8.6, respectively.

#### 3.2.5. Molecular Mass Identification

Each EPH was filtered and its molecular mass determined by HPLC (Hitachi, Chromaster, Japan) and BioSep-SEC-S 2000 (600 × 7.8 mm). The mobile phase used was 45% (*v*/*v*) acetonitrile with 1% trifluoracetic acid buffer at a rate of 1 mL/min by gradient. 25 μL of the sample was injected and determined at 214 nm. The molecular mass was analyzed by the standard curve with standard proteins maltose (360 Da), apoinin (6500 Da), ribonuclease (13,700 Da), and carbonic anhydrase (29,000 Da).

#### 3.2.6. Analysis of Amino Acid Composition

The sample or standard was dissolved in 0.1 N HCl, before 50 μL was added to 250 μL of 50 mM NaHCO3 (pH 8.1) and 200 μL of DABS-Cl solution (1.3 mg/mL) was dissolved in acetonitrile. The reaction was shaken for 5 min, then placed in a water bath at 70 ◦C for 12 min. After cooling to room temperature (about 5 min), 300 μL of 70% ethanol was added to stop the reaction and the sample was filtered through a 0.45 μm PVDF filter, using methods previously described [20].

Mobile phase:

(1) Solution A: 25 mM sodium acetate solution (pH 6.5) containing 4% dimethylformamide

(2) Solution B: acetonitrile Flow rate setting: 1 mL/min Gradient setting: initial concentration (solution A: solution B = 85: 15) 0~5 min (solution A: solution B = 78.7: 21.3) 5~60 min (solution A: solution B = 70: 30) 60~70 min (solution A: solution B = 30: 70) 70~75 min (solution A: solution B = 30: 70) 75~76 min (solution A: solution B = 30: 70) 76~80 min (solution B: solution C = 15: 85) 80~86 min (solution A: solution B = 85: 15) 86~90 min (solution A: solution B = 85: 15) Column: Inspire 5 μm C18, 250 × 4.6 mm Detection wavelength: 436 nm

#### 3.2.7. Functional Properties

#### Nitrogen Solubility of EPH and Herbal Eel Extracts

Nitrogen solubility was initially determined in distilled water over a range of pH values (2, 4, 6, 8, and 10), as described elsewhere [14]. 200 mg of EPH was added to 30 mL distilled water and mixed, then centrifuged at room temperature (5000 rpm, 20 min) and filtered (ADVANTEC paper no. 1). The nitrogen content of the resulting supernatant was determined using the Kjeldahl method and the nitrogen solubility was calculated as follows:

$$\text{Nitrogen solubility} (\%) = \frac{N\_1}{N\_0} \times 100\%$$

where *N*1 = supernatant nitrogen concentration and *N*0 = sample nitrogen concentration. Solubility analysis was carried out in triplicate. Subsequently, nitrogen solubility of the herbal eel extracts was determined following the method above.

#### Emulsifying Properties

The emulsion activity index (EAI) and emulsion stability index (ESI) of each EPH was determined with some modifications [14]. EPHs were reconstituted in distilled water (15 mL) at concentrations of 0.5%, 1%, and 2% (*w*/*v*). Olive oil (5 mL) was homogenized with the EPH solution for 1 min at room temperature. 50 μL aliquots of the emulsion were taken from the bottom of the conical flask directly after homogenization and again 10 min later, then diluted 100-fold in 0.1% (*w*/*v*) SDS solution. The new solution was mixed for 10 s and the absorbance measured at 500 nm

$$\text{ESI } m^2 g^{-1} = \frac{2 \times 2.303 \times A\_0}{0.25 \times Protenin weight \, (g)}$$

$$\text{ESI} = \frac{\Delta A}{A\_0} \times t$$

where A = absorbance, ΔA = (A0 − A10) and t = 10 min (A0 = Absorbance at 0 min, A10 = absorbance at 10 min).

#### 3.2.8. Antioxidant Capacity Analysis

#### Analysis of Reducing Power

Ascorbic acid was used as a standard. 200 μL of the standard and sample were mixed with 200 μL of 200 mM phosphate buffer solution (pH 6.6) and 200 μL of 1% potassium ferricyanide, then allowed to react in a 50 ◦C water bath for 20 min. 200 μL of 10% trichloroacetic acid solution was then added, and 200 μL of the supernatant was combined with 200 μL of distilled water and 40 μL of a 0.1% ferric chloride solution dissolved in 10% HCl. After standing for 10 min, the absorbance of the solution was measured using an ELISA plate reader at a wavelength of 700 nm [21].

#### Analysis of DPPH Free Radical Scavenging Ability

Using ascorbic acid as a standard, 160 μL of the standard and sample were added to 40 μL of 1 mM DPPH dissolved in methanol, then protected from the light for 30 min. The absorbance was measured using a reader at a wavelength of 517 nm [22].

#### *ABTS* Radical-Scavenging Activity Assay

The stock solution of 2,2 -azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) radical was produced by reacting 7mM ABTS with 2.45mM K2S2O8 (final concentration) and allowing the mixture to stand in dark incubation for 12 h at room temperature condition. The ABTS radical working solution was diluted with phosphate buffered saline (PBS) (pH7.4) at 730 nm to absorbance of 0.70 (±0.02). 1 mL of diluted solution is added with 10 μL of different sample and reacted for a minute, then tested under 730 nm [23].

#### 3.2.9. Color of EPH and Herbal Eel Extracts

Aliquots of EPH were diluted to 15% (*w*/*v*) and the herbal eel extracts to measured three times by a color difference meter (Nippon Denshoku Industries Co., Ltd., SA2000, Japan). *L\** denoted brightness, *a\** indicated red green and *b\** indicated yellow-blue values [24].

#### 3.2.10. Statistical Analysis

Data are expressed as the mean±standard deviation of three independent replicates. Statistical analysis was performed using the software SPSS statistics v24. All data were analyzed using one-way analysis of variance (ANOVA). Post hoc analysis of group differences was performed using Duncan's new multiple range tests. The criterion for significance was set at *p* < 0.05.

#### **4. Conclusions**

This study has shown that eel (*Anguilla marmorata*) are a good source of highly nutritious protein. Through hydrolysis, three commercial enzymes (alcalase, bromelain, and papain) produced low molecular mass peptides that are easy to digest. Among the three, the EAA content in the alcalase-hydrolyzed EPH was the most similar to the daily dose recommended by WHO. EAI was highest with the alcalase EPH (36.8 ± 2.00), while the bromelain and papain EPHs had EAI values of 21.3 ± 1.30 and 16.2 ± 1.22, respectively. Alcalase EPH also had a higher solubility (>80%) in a pH range of 2 to 10, as well as demonstrating the greatest antioxidant capacity. Finally, the alcalase-hydrolyzed EPH had a brighter color, good emulsifying properties, and showed good solubility, making it useful in food processing. In future studies of our research will investigate the fractionation, purification, structure identification of EPH then evaluate human nutrition efficacy by animal model assessment potential application of EPH.

**Author Contributions:** Conceptualization, L.-T.H., Y.-W.C., and C.-Y.H.; Data curation, I-C.C., J.-X.L., Y.-W.C., and J.-Y.C.; Formal analysis, I-C.C., J.-X.L., and Y.-W.C.; Funding acquisition, Y.-W.C., J.-Y.C., and C.-Y.H.; Investigation, I-C.C. and J.-X.L.; Methodology, I-C.C., J.-X.L., and C.-Y.H.; Project administration, C.-Y.H.; Resources, J.-Y.C.; Software, I-C.C., Y.-W.C., and C.-Y.H.; Supervision, L.-T.H. and J.-Y.C.; Validation, J.-X.L. and L.-T.H.; Visualization, J.-X.L. and L.-T.H.; Writing—original draft, L.-T.H. and C.-Y.H.; Writing—review and editing, J.-Y.C. and C.-Y.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Kaohsiung University of Science and Technology, grant number NKUST-108D04 and The APC was funded by National Kaohsiung University of Science and Technology, grant number NKUST-108D04.

**Conflicts of Interest:** The authors declare no conflict of interest. The authors alone are responsible for the content and writing of the manuscript.

#### **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*


*Edited by Hlanganani Tutu*

As concerns increase over the scarcity of water resources and the role of anthropogenic activities, water quality is evermore important. Activities ranging from agriculture to mining have had a bearing on the quality of water that they impact. Several studies assessing such impacts have been conducted at local and global scales over the years. This book, consisting of contributions by authors in various water-related fields, delves into some approaches that are used to understand and/or to improve water quality, and these include assessment of water chemistry, biomonitoring, modelling and water treatment. This book will be useful to environmental scientists, water professionals, researchers, academics and students.

ISBN 978-953-51-2881-6

Water Quality

Photo by kurmyshov / iStock


*2.3. Statistical Analysis*

Data are presented as mean ± SD. The normality of data was confirmed using the Shapiro–Wilk test. A two-way repeated-measures ANOVA (group × intensity) was used to compare changes in the involvement of individual breathing sectors and respiratory rate in the intervention and control groups. Significant interactions were examined using Bonferroni adjusted simple main effect post hoc comparisons. An alpha-level of 0.05 was used to assess statistical significance for all comparisons. Subsequently, effect size was determined using Cohen s d. The Pearson correlation coefficient was used to examine relationships between changes in tidal volume and pressure values on the dynamometer. The alpha-level was set to 0.05. The data processing was done in Excel 2016 (Microsoft, Oregon, WA, USA) and Statistica 12 (StatSoft, Tulsa, OK, USA).

#### **3. Results**

Participants carried out the yoga-based breathing program for an average of 13.3 ± 2.8 min per day during the two-month period. In the experimental group, there was a significant increase in the involvement of the abdominal segment during deep breathing and at 2 and 3 W·kg−1 (*p* < 0.05; see Figure 2 and Table 1). The only significant change in thoracic involvement was seen at 3 W·kg−1 (*p* < 0.01). In subclavian respiration, there was no significant change in involvement at any of the intensities, even at rest or at rest during deep resting breathing. In the control group, there was no significant change in the involvement of individual breathing sectors at rest or at any load level (*p* > 0.05; Table 1).

**Table 1.** Average values measured by probes and standard deviations of pressure on individual breathing sectors at rest and at different load intensities in the experimental group (EG) and control group (CG).

Note: \* *p* < 0.05, \*\* *p* < 0.01, Cohen s d: s small effect size, m medium effect size, l large effect size.

> As a result of the breathing exercise intervention, the experimental group experienced a significant reduction (*p* < 0.05) of respiratory rate under load 3 and 4 W·kg−1, with medium (4 W·kg−1) or small (rest, deep rest, 2 and 3 W·kg−1) effect sizes. We noted a significant increase of tidal volume at 2 W·kg−1, there are changes with small effect size, during all intensities of load. Minute ventilation and oxygen consumption were not significantly altered (see Table 2). The overall effect of breathing exercise intervention in all phases on changes of respiratory rate and tidal volume was confirmed at level *p* < 0.01.

**Table 2.** Percent change in respiratory rate (RR), tidal volume (VT), minute ventilatory volume (VE) and oxygen consumption (VO2) after breathing exercises intervention versus exercise prior to breathing exercises intervention at different intensities in the experimental group (EG) and control group (CG).

**Figure 2.** Engagement of breathing sectors at rest (**A**), during deep breathing (**B**), under load 2 W·kg−1 (**C**), under load 3 W·kg−1 (**D**), under load 4 W·kg−1 (**E**) pre and post intervention.

Changes in tidal volume were significantly related to abdominal probe pressure at all intensities (see Table 3).

**Table 3.** Pearson correlation coefficient of change in tidal volume and abdominal probe pressure.

In all intensities, greater abdominal and less subclavian percentage contribution was noted (see Figure 2).

#### **4. Discussion**

The primary finding of the present study was an alternation in breathing patterns at rest, and during cycling exercise at various intensities, in young healthy individuals following an eight-week breathing intervention. This finding corroborates previous research by our group in showing greater and more efficient abdominal contribution to respiration following a breathing intervention [37]. Moreover, it is worthy to note that the respiratory musculature involvement following the intervention was close to what may be recommended [4].

Physical exertion often increases the perception of respiratory effort in healthy people and leads to a feeling of dyspnea. Sports activities, be it intensive, short duration (≥85% of the maximum oxygen uptake) or less intense, longer-lasting duration ("ultramarathon" etc.) can lead to fatigue of the inspiratory and/or expiratory muscles [24]. Moreover, tired respiratory muscles impair athletic performance. During physical activity and sport, work of the respiratory muscles is compounded by greater demand for postural stabilization and movement efficiency [40]. Body stability is impaired when the respiratory muscles are tired, which can increase the risk of tripping or falling [24].

Respiratory therapy is an integral part of treatment for many patients with various diseases. Respiratory contributions have been shown to limit exercise in patients with heart failure. The manner in which the respiratory system limits exercise is due to abnormalities in ventilation, perfusion, or both ventilation and perfusion inspiratory muscle weakness may induce several impairments in both healthy and athletic individuals [22]. Similarly, studies have demonstrated that inspiratory muscle strength also has an important role in the pathophysiology of exercise limitation in several clinical conditions. Indeed, IMT is becoming an effective complementary treatment with positive effects on muscle strength and exercise capacity. More recently, studies have found that maximal inspiratory pressure (MIP) is strongly correlated with VO2 peak in patients after acute myocardial infarction and heart failure, reinforcing the influence of the inspiratory muscles on functional capacity [41]. The exercises primarily reduced end-expiratory lung volume rather than end-inspiratory lung volume, which is constrained by the presence of a thoracic load. Consequently, the training stimuli may be targeting and strengthening the inspiratory muscles throughout an operational range, which may not be utilised during exercise with load carriage. Importantly, previous work has identified that fatigue of the expiratory muscles is not an influencing factor in determining operational lung volumes, despite reduced end-expiratory time and increased peak gastric and esophageal pressures, and it may be more appropriate to assess influences that inhibit flow [42]. In general, the IMT performed at an intensity of 30% MIP resulted in decreased cardiac sympathetic modulation (LF) and increased parasympathetic (HF) at rest in patients with hypertension, heart failure, and diabetes mellitus [43]. Nevertheless, this measure has been questioned as interventions can elicit either complex non-linear reciprocal or parallel changes in either division of ANS, and these complex interactions can influence the calculation and interpretation of LF/HF [44]. However, applying IMT to different diseases, associated with a variety of training protocols, as well as few studies found in the literature, makes the effects of IMT on cardiovascular autonomic control inconclusive. Inspiratory muscle training promotes

changes in cardiovascular autonomic responses in humans [43]. Though inspiratory muscle training seems to improve maximal inspiratory pressure, it remains unclear whether these benefits translate to weaning success and a shorter duration of mechanical ventilation [25].

It is important to note that all participants were encouraged to breathe spontaneously during the testing period to ensure that any observed changes were in fact attributable to the intervention. Interestingly, the observed significant increase in abdominal contribution to breathing was noted at rest and during light/moderate intensities (2 and 3 W·kg−1), but not at the greatest load (i.e., 4 W·kg−1). Greater involvement of the thoracic musculature was also observed at lower (i.e., 3 W·kg−1) but not the greatest workload. This may be ascribed to greater anaerobic energy contribution at the greatest workload and thus the need for excess ventilation to remove rapidly accumulating CO2 [13,45]. However, of relevance was the observation of a trend towards a reduction in subclavian involvement at the higher workload in the experimental group. At the same time, there was a decrease in respiratory rate, an increase in tidal volume while maintaining the minute ventilation volume and oxygen consumption. Reduced subclavian involvement together with decreased respiratory rate and increased tidal volume at the same minute ventilation volume and same VO2 denotes greater respiratory efficiency and thus greater oxygen availability for mechanical musculature. At the same time, a decrease in respiratory rate also signals a decrease in respiratory work as one of the possible effects of a targeted breathing exercise program. A link has been shown between an increase of pressure on the abdominal probes and an increase of tidal volume. Furthermore, the increased contribution of the abdominal sector to respiration, together with the decreased respiratory rate, and increased tidal volume with the similar minute ventilation, indicates an improved breathing economy [38,46]. This is important as respiratory muscle efficiency is one of the conditions for good performance in endurance.

At rest, and during deep breathing, greater recruitment of abdominal muscles helps to optimize respiratory efficiency and delay the onset of respiratory muscle fatigue. However, during submaximal exercise, a significant alteration in respiratory musculature characterized by a reduction in abdominal and increased subclavian contribution is observed. Our results suggest that while it is possible to manipulate spontaneous breathing patterns during exercise, these benefits may be limited to lighter loads that are likely below the ventilatory threshold. However, reduced respiratory rate following breath training at both low and high workloads may be of benefit across a range of exercise performance disciplines.

The present findings should be interpreted in the context of the population; a greater training effect may be anticipated in adolescents in whom respiratory patterns during exercise are not as well engrained [47]. Like previous research in the field, we strategically selected an eight-week training intervention [48–51]. Future studies to determine the possible benefits of shorter breath training interventions, as well as the persistence of these adaptations if breath training is stopped, are warranted. Other limitations of the present work include our relatively modest sample size as well as that much of the training was performed at-home without direct supervision. Verification of these findings in non-athletic populations and potentially less healthy individuals, such as those with breathing illnesses, would also be of interest.

#### **5. Conclusions**

These data highlight an increased reliance on more efficient abdominal and thoracic musculature, and less recruitment of subclavian musculature, in young endurance athletes following a two-month breathing intervention. More efficient ventilatory muscular recruit at both lower and higher intensities during exercise may benefit endurance performance by reducing oxygen demand of the ventilator musculature and thus increasing oxygen availability for mechanical work.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/jcm10163514/s1.

**Author Contributions:** Conceptualization, P.B., R.M., and D.M.; methodology, P.B., V.B., R.M., D.M., G.J.G., and J.S.; software, P.B. and D.M.; validation, P.B., V.B., R.M., and D.M.; formal analysis, P.B., D.M., and J.S.; investigation, P.B., R.M. and D.M.; resources, P.B., R.M., D.M., and J.S.; data curation, P.B., V.B., and G.J.G.; writing—original draft preparation, P.B., V.B. and G.J.G.; writing—review and editing, P.B., V.B., R.M., D.M., G.J.G., and J.S.; visualization, P.B.; supervision, P.B., V.B., and G.J.G.; project administration, P.B., R.M., D.M., and J.S.; funding acquisition, P.B. and R.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Grant Agency of University of South Bohemia within the framework of Team grant project No. 021/2019/S.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Faculty of Education, University of South Bohemia, Ref. No.: 001/2018, from 19 October 2018.

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

**Data Availability Statement:** Data sharing not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

### **References**


**2. Risk factors**

Children's risk of being born/identified with cerebral palsy may increase due to some medical condition that happens during pregnancy, delivery and post-delivery. These may be categorized as follows: