*3.2.8 Meeting 8: proliferation and selection*

The stories developed in the first two meetings were resumed and rewritten, according to the criterion of proliferation and selection of settings, situations, and characters in the light of the techniques learned.

3 https://www.youtube.com/watch?v=4mdEsouIXGM&t=8s [Accessed 2021-10-25].

4 https://www.fadedpage.com/showbook.php?pid=20210556 [Accessed 2021-10-25].

#### *3.2.9 Meeting 9: crossover*

The plot of the initial story was resumed, developing, setting, and characters also trying to include what was initially discarded.


2.1.2. Steel Columns

Two steel piles (Figures 3a and 4a) constituting the supporting structure for the cable car were examined. Visual inspection of the piles revealed surface corrosion of most steel members, deformations of some elements, as well as pitting corrosion and fatigue cracks on sections of those elements (Figures 3b and 4b). Corrosion intensified on the central surfaces of the flanges in the column structure, in particular in the area of gusset plates at welded and bolted joints. Corrosion was also found at the interface between the profiles and flanges of posts, crossbars, and chords, as well as at the place where the angles were connected to the webs and flanges. Corrosion processes and the loosening of bolts securing the column elements caused friction between some of the steel elements.

**Figure 3.** (**a**) View of column 1 with a safety platform. (**b**) View of anchor block and flanges at the connection between column 1 and foundation; signs of surface corrosion and pitting.

**Figure 4.** (**a**) Side view of column 2. (**b**) External view of the upper chord; signs of surface corrosion on truss members.

The damage was caused by rainwater and insufficient use of protective and anti-corrosive agents (no paint coating; sites of strongly developed corrosion). The photographs show changes in the structure of the material of the columns. Observed delamination of the steelwork and intergranular corrosion require additional maintenance and improved supervision.

Twelve sensors were installed with a spacing of 2 m.

## 2.1.3. Gas Pipeline

A section of steel high-pressure gas pipeline 400 mm in diameter and 200 m in length was examined (Figure 5). The age of the pipes is estimated at approximately 60 years. The pipeline operates under environmentally difficult conditions with wetlands underneath, electrical power lines, and a lumber transport route running nearby. Two 30–80 kHz sensors were installed in a linear configuration on the structure.

**Figure 5.** (**a**) Arrangement of measuring points on the gas pipeline. (**b**) Measuring scheme.

#### 2.1.4. My Thuan Bridge

The My Thuan cable-stayed bridge connects the banks of the Tien Giang River flowing between two large provinces in the Mekong basin and and is part of Vietnam highway network. Completed in 2000, the bridge was designed to Australian and Vietnamese standards.

The total length of the bridge is 1535.2 m. The main bridge has three continuous spans with dimensions 150 + 350 + 150 m supported by two main pylons. The main spans are made of prestressed concrete class 50 (fc = 50 MPa) and have two main girders internally attached by a set of floor beams. The bridge was constructed with the use of the cantilever method. The height of the main spans is 1760 mm with the width ranging from 1200 to 1400 mm. The deck slab is 250 mm thick. The end spans are suspended on 4 × 32 cable stays and each stay is composed of 22–29 seven-wire strands 15.2 mm in diameter—a system of dampers was installed on each side of the cable stays to control vibration. The two pylons made of reinforced concrete grade 50 have a splayed H-frame and the height of 123.5 m (from the foundation base) and 84.43 m (from the deck). Each pylon is supported on 16 bored piles 2.5 m diameter driven into the riverbed to the depth of −90 m (northern pylon) and −100 m (southern pylon).

Except for the weather stations (measurement of humidity level, wind speed and direction—installed on the main deck and on the northern pylon, plus two cameras monitoring the water level and traffic), no condition assessment systems for determining the integrity of the bridge were implemented during the design and construction stages. The current condition of these devices is not known.

#### *2.2. Methods*

Basic acoustic emission monitoring is carried out during the routine operation of the facility under load (in exceptional cases under the test load).

The tests performed during the routine operation of the facility aim to:

The μSamos system (PAC MISTRAS Corp., 195 Clarksville Rd, Princeton Jct, NJ 085, USA) with three PCI-8 cards was used to develop the reference signal databases (24 measurement channels) used in subsequent stages to assess the condition of steel and concrete structures. Flat type sensors

operating in the frequency range of 30–80 kHz was used for all structures. A 55 kHz sensor was also used in the concrete structures. Signal amplification was 40 dB. AEWin software was used for the measurements. To analyze the measurement data, Noesis 5.8—ADVANCED DATA ANALYSIS PATTERN RECOGNITION SOFTWARE (PAC MISTRAS Corp., Princeton, NJ, USA) was applied. The reference signal database was created using 13 parameters of acoustic emission: rise time (μs), counts to peak amplitude, counts, energy (EC), amplitude (dB), average frequency (kHz), root-mean square (RMS) (V), reverberation frequency (kHz), initiation frequency (kHz), absolute energy (aJ), signal strength (pVs), duration (μs), and average signal level (ASL) (dB).

The Identification of Active Damage Processes (IADP) method is based on the analysis of acoustic waves generated by active destructive processes developing under operational loads in buildings and engineering structures. The signals received by acoustic sensors mounted on the object are compared to the database of reference signals created earlier for defined destructive processes. The identified destructive processes are then localized by analyzing the difference in time of signal arrival to individual sensors. The identification and the location of active damage processes make it possible to assess the technical condition of structures, and thus can be the basis for the diagnosis and management strategies.

The big advantage of the IADP method is that the AE sensors can be arranged to cover the entire structure being measured and that the measurements can be performed under actual operational loads.

In its original version, the IADP method was developed to analyze destructive processes in prestressed concrete elements. It was then expanded and applied in the analysis of steel elements for which a new database was created with pattern signals of destructive processes resulting from cyclic and monotonic loads.

Patterns of AE signals were obtained as a result of tests carried out at temperatures ranging from −60 ◦C to +60 ◦C on monotonically loaded V-notch specimens made with St3S and 18G2A grade steels and with the steel recovered from an old bridge, on bent models of V-notch elements under monotonic and cyclic loading at +20 ◦C, and models of welded, riveted, and bolted nodes made of the same grade steel.

The following destructive processes being the AE signal sources can be distinguished in prestressed concrete structures:

The values of the AE signal parameters allow the classification of the signals into classes, each of which is characterized by dominant destructive processes and different risk levels. The signals characteristic of each class create the reference databases capable of identifying a destructive process, e.g., "cracking at the concrete-reinforcement interface" corresponds to the database that groups selected signal parameters assigned to the specific process.

Databases for individual processes (or their groups) are determined on the samples of materials, on models used in special laboratory tests (where a given destructive process or a group of processes predominates) and on full-size structural elements during strength tests, attenuation tests, and during normal operation of the facility.

The database of AE reference signals makes it possible to identify active destructive processes over the entire element under test. Long-term measurements can be used to determine the damage under true loading conditions, with the external factors such as rain, frost, or wind taken into account. Appropriate installation of AE sensors allows the measurement of the entire volume of the examined element and the location of the emission source (the point of damage).

To build the reference signal database with the Recognition of Destructive Processes (RPD) method, the NOESIS 5.8 program was used with hierarchical and non-hierarchical statistical grouping methods and neural networks. For the RPD database, two versions of the pattern recognition method were used: with arbitrary division into classes—unsupervised pattern recognition (USPR) and self-learning, in which the division into classes was carried out using the reference signals—supervised pattern recognition (SPR).

In the first case, analysis of arbitrary patterns is mainly used for creating a reference signal database if the number of classes is unknown. The second method is applied when model signals characterizing the destructive processes are available. The reference signals are the signals previously collected in databases generated during independent experiments.

While the USPR method classifies AE sources based on the similarity of signals without assigning appropriate mechanisms to the groups, the SPR method assigns specific processes to the groups, provided they have a base of reference signals.

Creating a reference signal database is organized in several stages. These are:

2.2.1. Acoustic Emission Evaluation of Steel Structures

Assessment involves the analysis of changes in the intensity of acoustic emission signals generated in particular zones of structural elements under routine use. Recorded AE signals are grouped into classes to which various destructive mechanisms are assigned. The number of parameters used to build the database of reference signals must be consistent with the number of previously registered parameters of AE signals [11,12].

The risk posed by generating processes within one class is determined by the so-called intensity code of destructive processes. These processes are best illustrated by graphs where each AE signal is assigned to one point. The color and shape of the point indicate the class to which the given AE signal belongs. The classes, symbols, and codes are summarized in Table 1.

**Table 1.** Classes, symbols, and risk codes for steel bridges.

Training patterns are used for the grouping and classification of AE signals.

Class No.5 signals are generated by the constant AE noise. Classes No. 4, No. 3, and No. 2 of signals are generated by single destructive mechanisms such as:

No.4—yielding of steel at the crack tip;

No.3—crack initiation;

No.2—crack propagation.

Class No.1 includes the signals resulting from the superposition of waves generated by more than one destructive process and by crack surface friction.

Occurrence of signals of all classes during the monitoring period is regarded as another item among the codes defining the influence of defects on the structural condition and indicates the presence of destructive processes in the structure.

The extent of damage is assessed using the zone location results and AE signal classification in the zones.

The individual signal classes mean (Table 2):

No.1—rupture;

No.2—friction;

No.3—crack propagation;

No.4—crack initiation;

No.5—perforation/deformation;

No.6—material losses;

No.7—surface corrosion;

No.8—work in the elastic range.

**Table 2.** Classes, symbols, and risk codes for steel engineering constructions.

2.2.2. Acoustic Emission Evaluation of Gas Pipelines

Classes, symbols and codes of AE signals together with the risk level for gas pipeline are compiled in Table 3.

**Table 3.** Classes, symbols, and risk levels for steel gas pipelines.

2.2.3. Acoustic Emission Evaluation of Reinforced Concrete Structures; Classes, Symbols, and Risk Codes for Reinforced Concrete Structures

The database of reference signals was created by conducting a series of tests on various types of reinforced concrete beams and specimens under various loads, including cyclic loading for the modeling of the car's passage [18–24]. The tests aimed at finding predominant destructive processes that might occur during testing of reinforced concrete structures under operation. The database was verified on real facilities [25–32].

The reference databases were classified on the basis of 12 AE parameters (rise time (μs), counts to peak amplitude, counts, energy (EC), amplitude (dB), average frequency (kHz), RMS (V), reverberation frequency (kHz), initiation frequency (kHz), absolute energy (aJ), signal strength (pVs), duration (μs), ASL (dB)) and marked for reinforced concrete (RC) structures as Class (Table 4):

The results of the cracking tests under monotonic, cyclic, and variable loading were used to develop the following criteria of structural damage:

**Table 4.** Classes, symbols, and risk levels for reinforced concrete structures.

#### **3. Results and Discussion**

#### *3.1. Steel Bridge*

The following is the AE measurement data processed for selected groups of components.

The bottom chord (span 1): Analysis of the recorded AE signals against the reference signal database reveals the presence of signals representing all classes. Signal duration is variable and reaches 350,000 μs. The energy emitted when signals are generated is low and reaches 20,000 ec (Figure 6). Individual signals attain slightly higher values. The generation of AE signals is not continuous; the signals are initiated by passing vehicles with specific characteristics (tractors with semi-trailers moving at a speed of more than 60 km/h or heavy trucks moving in the column at speeds exceeding 60 km/h). This is due to the condition of expansion joints and dynamic loads initiating AE signals at the seats of diagonals and vertical members in the gusset plates. The resulting signals suggest that stresses increase in riveted and welded joints due to significant surface corrosion and in some locations due to pitting, slight loosening of rivets, and their movement. The number of signals describing the yielding and crack development is small and concentrated in three zones of the chord. These signals are generated when trucks traveling at excessively high speeds pass through.

Nodes 1–3 (span 1): Analysis of the recorded AE signals against the reference signal database reveals the presence of signals representing all classes. Signal duration does not exceed 50,000 μs. Energy is low, 25,000 ec (Figure 7). Individual signals reach slightly higher values. The generation of AE signals is not continuous; the signals are initiated by the movement of vehicles with specific characteristics or originate from the work of the element working both in tension/compression. This should be interpreted to mean that the change in stress level will result in slight movement at riveted connections and at the connections of the gusset plate with the upper chord. The recorded signals inform about the behavior of elements in the elastic range, and individual signals inform about local yielding and corrosion at the nodes.

**Figure 6.** Energy–time relationship for the bottom chord of the truss in span 1 with the numbers of signal classes marked.

**Figure 7.** Energy–time relationship for node 1 in span 1 with the numbers of signal classes marked.

Stringers 1–6 (span 1): Analysis of the recorded AE signals against the reference signal database reveals the presence of signals representing all classes. Signal duration is long, up to 360,000 μs, and the energy emitted during signal generation reaches a value of up to 40,000 ec (Figure 8). Individual signals have slightly higher energy values of up to 60,000 ec. High parameter signals are not emitted continuously. The signals are initiated by passing vehicles with specific characteristics (tractors with semi-trailers moving at a speed of more than 60 km/h or heavy vehicles moving in the column at speeds above 60 km/h). Signals located and recorded originate mainly in the central zone of the stringers. The high rise time of 25,000 μs suggests that the signals originate from corrosion processes and friction occurring on the surface of the upper flange of the stringer and on the orthotropic plate. The signals indicate that corrosion processes are advanced and that dynamic loads cause delamination cracks on the corroded surfaces and friction between them. The number of signals from higher classes 1, 2, 3 is significant, which suggests that these processes do not pose a risk to the bridge although strong corrosion processes and the formation of fatigue microcracks have begun, which left without repair, can lead to fatigue cracks, thus weakening the entire structure.

Stringers 7–8 (span 2): Analysis of the recorded AE signals against the reference signal database reveals the presence of signals representing only class 5. Signal duration is short, 15,000 μs and energy emitted when signals are generated reaches 2500 ec (Figure 9). The rise time of 4500 μs confirms good performance of the members in the elastic range.

**Figure 8.** Energy-time relationship for stringer 2 in the span 1 with the numbers of signal classes marked.

**Figure 9.** Energy–time relationship for stringer 8 in span 2 with the numbers of signal classes marked.

#### *3.2. Steel Columns*

Twelve sensors were installed on column 1. Spatial location demonstrated that the column of the supporting structure was working in the range that might, sometime in the future, pose a threat to the safety of the cableway. The tests were performed when the cable cars were stationary and during their regular operation. When stationary, no processes generating dangerous phenomena were recorded, but when the cable cars started moving (within the normal operating envelope), destructive processes Class 2–8 were revealed (Table 2), which indicated the growth of microcracks in the members. This demonstrates that some of the members were exposed to fatigue-induced destructive processes. Surface corrosion attack were found at the bolted connections and at half height of the vertical elements of the column.

Figures 10–13 show damage classes occurring in the tested elements. Data from 12 sensors were combined using the supervised pattern recognition method.

Analysis of the recorded AE signals against the reference signal database (Figure 10) reveals the presence of signals representing classes from 2 to 8 (Table 2). The signal strength is high, up to 2.6 × 109 pVs. Continuous emission of the signals is initiated by passing trolleys with weight of 2500 kg, which translates to the presence of locations with fatigue and overload cracks and corrosion centers. The welded and bolted connections in the nodes may be affected due to substantial surface corrosion and pitting. The condition of the members revealed during the examination was an indication for further observation of the column and for placing spacer blocks between the column and the safety platform. A portion of damage may result from additional load caused by the movement of the platform (fatigue).

Fatigue cracks were detected in the indicated places on the basis of magnetic particle tests (Figure 11). It allowed to verify the visual assessment and AE measurements with the actual condition of the element.

**Figure 10.** Signal strength versus time for sensors 1–12 on column 1 with the numbers of signal classes marked.

**Figure 11.** (**a**) Photograph of the tested node. on column 1 (**b**) A photograph of the crack detected on column 1.

Twelve sensors were installed on column 2. Spatial location indicated that the column of the supporting structure worked in the range that may in the future cause a cableway safety risk. The tests were performed during routine operation of the facility. Two processes that generated events within the elastic range were revealed, as were the locations where the steel yielded, described as Class 4 destructive processes. In the case of column 2, yielding was probably caused by the additional load due to vibrations of the steel platform securing the road. The lack of spacers at the connection of these two structural elements may cause steel yielding and, as a consequence, fatigue microcracks and cracks. This indicates that some elements are exposed to destructive processes caused by fatigue. Locations of surface corrosion were also detected. The most strained locations were discovered at the bolted connections and at the mid-height of the column vertical elements.

The recorded AE signals analyzed using the reference signal database (Figures 12 and 13) indicate that in the node under analysis only Class 3 and 8 signals appear. The duration of these signals is long, up to 350,000 μs, while the signal energy is low, 30,000 ec on average and up to 65,000 ec locally. The acoustic emission signals are generated dis-continuously and are not initiated by passing trolleys weighing approximately 2500 kg, which indicates the existence of sites with plastic deformations, which may degenerate into fatigue microcracks and corrosion sites. Welded and bolted connections within the nodes may be affected due to surface corrosion, with some bolts (plates) loosened. Those destructive processes were the reason for the observation of this column and for the use of spacer blocks between the column and the safety platform. Part of the damage may result from the additional load caused by the movement of the platform contributing to material fatigue.

**Figure 12.** Energy versus time for sensors 1–12 on column 2 with the numbers of signal classes marked.

**Figure 13.** Acoustic emission (AE) signal duration versus time for sensors 1–2 on column 2 with the numbers of signal classes marked.

## *3.3. Gas Pipeline*

The results of the acoustic emission evaluation of the high-pressure gas pipeline were analyzed by grouping using non-hierarchical methods (k-means) and by comparison to the reference signals (Table 3). The results showed reduced thickness of the pipe wall due to pitting and deformations and perforations of the structure due to changing working and corrosion conditions.

Analysis of absolute energy parameter as a function of time, correlated with parameters such as duration, amplitude, rise time, and AE counts, shows that destructive processes exhibit low intensity across a small area of the gas pipeline (Figure 14).

It should be emphasized that the destructive processes do not proceed continuously, which indicates the local character of these phenomena. The next step in the analysis is to locate places generating destructive processes.

Linear location of destructive process sources shows the area of increased intensity of AE signals extending between 55.79 m and 59.66 m, which corresponds to the pipes laid under the woodland path (Figure 15). Further analysis of destructive process location on the examined section of the pipeline indicated two points, at 57 m and 59.5 m.

**Figure 14.** AE signal energy as a function of time for the gas pipeline with the numbers of signal classes marked.

**Figure 15.** Exact location of destructive process sources.

Analysis of the amplitude versus time graph for AE signals recorded in the area beyond the indicated points shows that no signals indicating destructive processes were recorded in that area.


**Pathological Characteristics of Periodontal Disease in Patients with Chronic Kidney Disease and Kidney Transplantation**

**Mineaki Kitamura 1,2, Yasushi Mochizuki 2,3, Yasuyoshi Miyata 3,\*, Yoko Obata 1, Kensuke Mitsunari 3, Tomohiro Matsuo 3, Kojiro Ohba, Hiroshi Mukae 4, Atsutoshi Yoshimura 5, Tomoya Nishino 1 and Hideki Sakai 2,3**

Received: 9 June 2019; Accepted: 10 July 2019; Published: 11 July 2019

**Abstract:** Chronic kidney disease (CKD) is recognized as an irreversible reduction of functional nephrons and leads to an increased risk of various pathological conditions, including cardiovascular disease and neurological disorders, such as coronary artery calcification, hypertension, and stroke. In addition, CKD patients have impaired immunity against bacteria and viruses. Conversely, kidney transplantation (KT) is performed for patients with end-stage renal disease as a renal replacement therapy. Although kidney function is almost normalized by KT, immunosuppressive therapy is essential to maintain kidney allograft function and to prevent rejection. However, these patients are more susceptible to infection due to the immunosuppressive therapy required to maintain kidney allograft function. Thus, both CKD and KT present disadvantages in terms of suppression of immune function. Periodontal disease is defined as a chronic infection and inflammation of oral and periodontal tissues. Periodontal disease is characterized by the destruction of connective tissues of the periodontium and alveolar bone, which may lead to not only local symptoms but also systemic diseases, such as cardiovascular diseases, diabetes, liver disease, chronic obstructive pulmonary disease, and several types of cancer. In addition, the prevalence and severity of periodontal disease are significantly associated with mortality. Many researchers pay special attention to the pathological roles and clinical impact of periodontal disease in patients with CKD or KT. In this review, we provide information regarding important modulators of periodontal disease to better understand the relationship between periodontal disease and CKD and/or KT. Furthermore; we evaluate the impact of periodontal disease on various pathological conditions in patients with CKD and KT. Moreover, pathogens of periodontal disease common to CKD and KT are also discussed. Finally, we examine the importance of periodontal care in these patients. Thus, this review provides a comprehensive overview of the pathological roles and clinical significance of periodontal disease in patients with CKD and KT.

**Keywords:** periodontal disease; chronic kidney disease; kidney transplantation; immunosuppressive therapy


**Agnieszka Kempinska-Podhorodecka 1,\*, Monika Adamowicz 1, Mateusz Chmielarz 1, Maciej K. Janik 2, Piotr Milkiewicz 2,3 and Malgorzata Milkiewicz 1**

Received: 20 June 2020; Accepted: 23 July 2020; Published: 27 July 2020

**Abstract:** Vitamin D deficiency has been associated with depressive symptoms and reduced physical functioning. The aim of the study was to characterize the relationship between polymorphisms of the vitamin D receptor (VDR) gene and the quality of life in patients with autoimmune hepatitis (AIH) and primary biliary cholangitis (PBC). Three polymorphisms of the *VDR* gene (*TaqI-rs731236*, *BsmI-rs1544410*, and *ApaI-rs7975232*) were analyzed in patients with AIH (*n* = 142) and PBC (*n* = 230) and in healthy individuals (*n* = 376). Patient quality of life was assessed by validated questionnaires such as Medical Outcomes Study Short-Form 36 (SF-36), State Trait Anxiety Inventory (STAI), Modified Fatigue-Impact Scale (MFIS), Patient-Health Questionnaire 9 (PHQ-9), and PBC-40. The *TaqI* C and *ApaI* A alleles are risk alleles in both AIH and PBC, and a significant dominance of the A allele in *BsmI* was observed in AIH patients. In terms of quality of life, the presence of the CC or CT *TaqI* genotype was associated with emotional reactions, including the fatigue and the cognitive skills of patients with PBC, whereas in the group of AIH patients, homozygotes CC of *TaqI*, AA of *BsmI*, and AA of *ApaI* had worse physical, social, emotional, and mental function. The genetic variations of *VDR* gene can influence individual susceptibility to develop chronic autoimmune liver diseases such as AIH and PBC and affect quality of life.

**Keywords:** autoimmune hepatitis; primary biliary cholangitis; vitamin D; health-related quality of life; mental well-being


*4.1. A General Overview*

Many studies have investigated the influence of the use of fly ash and CDW on the physical–mechanical properties of concretes, showing that the results improve the mechanical properties and durability in ages longer than 28 days, as presented in Table 5.

**Table 5.** Some recent studies on the mechanical and durability properties of concrete with fly ash.

1 RCA = recycled concrete aggregate; 2 MRA = mixed recycled aggregate; 3 RMA = recycled masonry aggregate 4 RA = recycled aggregate; 5 FA = fly ash.

In general, the addition of fly ash in partial replacement of Portland cement presents a positive influence with respect to mechanical strength and durability in concretes with recycled aggregate from construction and demolition when compared to concretes with recycled aggregate and without fly ash at higher ages of healing. Kamal et al. [105] analyzed sample data where more than a thousand pieces of information was extracted from the literature, and through the nonlinear model, the effect of fly ash on the resistance properties of concrete was investigated. The study involved high cement replacement content by fly ash (up to 70%), different water/binder ratios, and a 90-day curing period. The authors observed, through mathematical models, that there is a good correlation between compressive strength and the water/cement ratio in cured concrete up to 90 days without fly ash, but no correlation was verified between compressive strength and water/binder ratio in concrete with cured fly ash up to 90 days. A good correlation was also verified between compressive strength and tensile and bending strength. According to the authors, compressive strength can be calculated through mathematical models constructed through the suggested methodology.

According to Limbachiya, Meddah, and Ouchgour [20], whenever recycled aggregate (RA) is added to concrete with 30% fly ash in partial replacement to natural aggregate, regardless of the content to be replaced, the tendency of durability and mechanical strength is to decrease, and shrink drying is increased.

According to Limbachiya, Meddah, and Ouchagour [20], as the content of substitution of natural coarse aggregate by recycled concrete aggregate increases (ARC), the strengths (compression and traction), and the modulus of elasticity decrease. On the other hand, shrinkage by drying increases. It is a consensus among all authors that the use of recycled aggregate in concrete decreases the mechanical properties and durability, but with replacement levels below 30% of natural aggregate by the recycled aggregate, the adverse effects are not so significant. The addition of fly ash in small proportions in concretes with recycled

aggregate tends to minimize the adverse effects, but the impact of fly ash is observed in concretes with higher ages, where, according Lorca et al. (2014), the fly ash reacts with calcium hydroxide released by the cement hydration product in older ages.

Some studies have developed mathematical models to estimate the mechanical strength of concrete with pozzolanic materials as well as with recycled aggregate [105–107]. Shakr Piro et al. [105] used five different models to estimate the compressive strength of concrete with carbon nanotubes. Therefore, the artificial neural network model, M5P tree model, nonlinear regression model, and multilinear model have been used. The variables used in the model were curing time in days, coarse aggregate content, water/binder ratio, cement intake in kg/m3, and carbon nanotube. As a methodology, the authors used information such as the reference concrete compressive strength and concrete with different contents of carbon nanotube. Based on sample data with 282 records analyzed statistically, the authors developed a multiscale model to estimate the compressive strength of the concretes. Another very relevant study developed by the authors [107] was the analysis of the correlation between compressive strength and electrical resistivity of concrete slag residue. The models used for this study were the multi logistic regression model, complete quadratic model, M5P tree model, and neural network. Barkhordari et al.'s [106] mathematical models have been used to estimate the compressive strength of concrete with fly ash. The models used were super apprentice algorithm, simple average, weighted average, and stacking employed. The database contained information from 270 samples that were collected and preprocessed. Next, some recent research on the use of fly ash in concretes with recycled aggregate will be presented.


*2.2. The Quantification of Aggregate Protein at the Wound Site*

An adequate supply with proteins is necessary for consistent wound healing. Therefore, protein content can be used to evaluate the conditions of wound healing [27]. Total protein expression at the wound site of all groups in the excision wound model is shown in Table 1. On day 3 and 7, protein content of the experimental groups was higher than the control and the woundplast groups (*p* < 0.05), and the protein content was high in BCE I and BCE II treated groups compared to other groups on day 7 (*p* < 0.05). On day 14, protein content of the experimental groups was significantly higher than control group (*p* < 0.05). Protein content of ACS treated group was significantly higher than BCS, BCE I, and BCE II treated groups (*p* < 0.05).

**Table 1.** The effect of each group on total protein content in wound area tissues (*x* ± *s*, *n* = 6).

Note: Different superscript alphabets in each column represent statistical significance (*p* < 0.05).

#### *2.3. Hydroxyproline (Hyp) Content at the Wound Site*

Hyp is a specific component of the protein collagen. Therefore, Hyp content might be used as an indicator to determine collagen deposition to measure the speed of wound healing [28]. The significant increase of Hyp in collagen treated group implied faster rate of wound healing process than control group (*p* < 0.05). Indeed, collagen is a major protein of the extracellular matrix and it ultimately contributes to wound healing [29]. Hyp content at the wound site of all groups in the excision wound model is shown in Table 2. Hyp content was higher in ACS and BCE II treated groups (5.87 ± 0.42 mg/g, 5.66 ± 0.12 mg/g tissue) on day 3 and in PCS, ACS, BCE II treated groups on day 7 (*p* < 0.05). On day 14, PCS and ACS treated groups had high Hyp content (7.32 ± 0.43 mg/g, 7.41± 0.42 mg/g tissue) than control and woundplast treated groups.

**Table 2.** The effect of each group on the Hydroxyproline content in wound area tissues (*x* ± *s*, *n* = 6).

Note: Different superscript alphabets in each column represent statistical significance (*p* < 0.05).

### *2.4. Histopathological Examination*

In control group, a large number of inflammatory cells appeared in the wound than collagen-treated groups on day 3 (Figure 2). Granulation tissue and fibroblasts activity were more pronounced in collagen-treated groups compared to control group. A large number of inflammatory cells, blood vessels, small amount of collagen fibers and fibroblasts were seen in woundplast and BCS groups. Fibroblast cells were important in the wound site and predominant collagen expression could be seen in PCS, ACS, BCE I, and BCE II treated groups. On day 7, healed regions of the wounds were covered by epithelial tissue with significant fibroblast proliferation, collagen deposition and granulation tissue formation. However, the granulation tissue organization and vascularization in unhealed regions of collagen-treated groups were notably different from the control group. Collagen-treated groups developed collagen deposition and vascularization than the control group. The H&E staining revealed that in collagen-treated wound on day 7, cutaneous appendages like hair, hair follicles and sebaceous glands began to appear, which signify the formation of epidermal layer. A large number of inflammatory cells and collagen fibers and fibroblasts were observed in control group. A large number of fibroblasts and collagen were seen in woundplast and BCS treated groups and skin appendages and skin tissues were gradually formed in collagen treated groups. More fibroblasts and collagen and less inflammatory cells were seen in PCS, ACS, BCE I, and BCE II treated groups than the control group. On day 14, a sufficient number of fully formed skin adnexal and epithelial tissues were present in collagen-treated groups. PCS, ACS, BCE I, and BCE II treated groups showed well-formed stratified epithelial layer, granulation tissue formation and collagen deposition in healed regions than the control, woundplast and BCS groups. As shown in Table 3, a scoring system was used to quantify the pathological results of the H&E stained samples. In this scoring system five criteria were scored. An increasing score denotes the better wound healing. The histological scoring result showed that the PCS, ACS, BCE I, and BCE II treated groups had better wound healing than the control group.

**Table 3.** Histological evaluation of wound tissue in all groups by H&E staining.

**Figure 2.** Histological analysis of H&E stained wounded tissues (Magnification, ×100) with histological scoring. The alphabetic letters e, d, gt and hf represent epidermis, dermis, granulation tissue, hair follicle, respectively. (Each bar represents the mean ± SD. \* *p* < 0.05: significantly different from the control group.).

#### *2.5. Collagen Promotes the Expression of EGF, FGF, and CD31 in the Wounds*

Immuno-histochemical analysis of the reconstituted tissue was shown in Figures 3–5. EGF stands for epidermal growth factor, which can promote proliferation and differentiation of keratinocyte. EGF can induce fibroblasts proliferation and collagen synthesis, resulting in epithelization. Fibroblast growth factor (FGF) can accelerate migration and proliferation of fibroblast, and vascularization. EGF and FGF play an important role in the wound healing process. Prolonging the treated time, the expression of EGF and FGF in control group showed an increasing trend, the expression of EGF and FGF in woundplast and BCS groups increased in the beginning and then decreased, and the expression of EGF and FGF in PCS, ACS, BCE I, and BCE II treated groups showed a decreasing trend. On day 3 and 7, the expression of EGF and FGF in PCS, ACS, BCE I, and BCE II treated groups was higher than control, woundplast and BCS groups. CD31 is a platelet endothelial cells adhesion molecule-1, stands for vessel proliferation. The relative quantity and distribution of CD31 in the construct-treated wound bed is important in wound healing. Brown granules were positive signal in Figure 3. The expression of CD31 showed similar trends same as EGF and FGF of collagen treated groups.

**Figure 3.** Immunohistochemical analysis of EGF expression in wounded tissues (Magnification, ×100); the histogram shows the total of positively stained cells of EGF in the dermal tissue per group. (Each bar represents the mean ± SD. \* *p* < 0.05: significantly different from the control group.).

**Figure 4.** Immunohistochemical analysis of fibroblast growth factor (FGF) expression in wounded tissues (Magnification, ×100); the histogram shows the total of positively stained cells of FGF in the dermal tissue per group. (Each bar represents the mean ± SD \* *p* < 0.05: significantly different from the control group.

**Figure 5.** Immunohistochemical analysis of CD31 expression in wounded tissues (Magnification, ×200). The histogram summarizes the microvessel density (MVD), which was determined by immunohistochemical staining for CD31. (Each bar represents the mean ± SD. \* *p* < 0.05: significantly different from the control group.).

#### **3. Discussion**

As the structural and functional component of dermal extracellular matrix, collagen plays a vital role in wound healing process [18,30–32]. In the present study, PCS, ACS, and electrospun bovine skin collagen nanofibers were successfully fabricated. Due to its unique perforated structure, controllable fiber diameter (50 nm–5 μm), large surface area, high porosity and unique biological property [33], PCS, ACS and BCE I, BCE II were examined by using the full-thickness wound model in SD rats and the total protein, hydroxyproline content, H&E, and immunohistochemical examinations were assessed with control group. In full-thickness wounds in SD rats, PCS, ACS, BCE I and BCE II treated groups revealed significantly higher wound healing ability, total protein and hydroxyproline content, fibroblasts proliferation and collagen synthesis when compared to control, BCS and woundplast groups.

Based on the earlier findings, the possible reasons for accelerating wound healing of PCS, ACS, BCE I, and BCE II were due to its unique structures and the high porosity of the matrix, which could induce fibroblasts proliferation and collagen synthesis, and play crucial roles in re-epithelialization and vascularization in the wound healing process [34,35]. In addition, PCS, ACS, BCE I, and BCE II exhibited good biocompatibility to support the adhesion and proliferation of fibroblasts that lead the deposition and maturation of collagen [9]. Collagen could be completely degraded and absorbed by the wound that helps to formation of fibroblasts and collagen fibers over time in epidermal tissue and therefore, the wounds were replaced with regenerated dermis.

Histopathological and immunohistochemical examinations indicate that PCS, ACS, BCE I, BCE II had a positive effect on neovascularization, inducing fibroblasts proliferation, collagen synthesis, re-epithelialization and regeneration of skin appendages, and consequently led to an increased wound healing ability compared with control, BCS and woundplast groups. Besides, PCS, ACS, BCE I and BCE II were similar to normal skin because of smooth surface along with loose collagen fiber. PCS, ACS, BCE I, and BCE II could provide a proper microenvironment for fibroblasts attachment in skin due to its three-dimensional features and high porosity.

Vascular endothelial marker CD31, growth factors EGF and FGF are involved in wound healing process [36]. It has been confirmed that PCS, ACS, BCE I, and BCE II significantly induce EGF and FGF expression, which can promote proliferation and differentiation of fibroblasts and keratinocytes. EGF can induce keratinocytes proliferation to impel re-epithelialization of the wound. As chemotactic agent and mitogenic agent of fibroblasts, EGF can promote the proliferation and differentiation of fibroblasts to synthesize collagen. The increased expression of CD31 and FGF revealed the vascularization and wound healing properties of collagens.

In this work, PCS and ACS groups revealed significantly better wound healing ability than woundplast and control groups, slightly higher than BCS group, which might be due to the three-dimensional features and high porosity of PCS and ACS than BCS. However, ACS had a more positive effect than PCS, which was similar to previous report [22]. There was no much difference between BCE I and BCE II treated groups. However, there was less inflammatory cells in BCE II treated group than BCE I, which may be related to the bactericidal effect of chitosan. These effects were probably owed to the biomimetic structure, unique biological property and high porosity of the collagen nanofibers.


**Abstract**

One of the most important problems in arid and semi-arid zones in the Mexican Mesa Central is the one related to the exploration and exploitation of groundwater. It is found at depths over 200 m, and movement is primarily through fractures. This work presents a geophysical methodology, which shows the potential of combining natural and induced methods to locate confined aquifers in fault zones. The study begins by interpreting the aeromagnetic survey, mainly by searching alignments associated with low magnetic anomalies, which are correlated with faults zones, and/or fractures and/or geologic contacts where ferromagnetic minerals have undergone oxidation due to their association with recharged zones. These aeromagnetic alignments are confirmed on land by a ground magnetic survey. Based on these interpretations, electrical methods include sections and vertical electrical sounding are used to verify if the zones are correlated to the underground moisture. If both permeability and moisture are met together, then they considered as zones with a high probability of locating ground water in the Mexican Mesa Central.

**Keywords:** fractured-media aquifers, geophysical methods, aeromagnetics, semi-arid áreas, Mexican Mesa Central, San Luis Potosi


Meet the editors

Robert L. Woolfolk, PhD, is Professor of Psychology and Philosophy at Rutgers University. He has served on the faculties of Princeton University and the University of Texas at Austin. He has published numerous papers and several books on psychotherapy, psychopathology, and the philosophical foundations of psychology. A practicing clinician for nearly five decades, Dr. Woolfolk has sought in both his work with patients and his schol-

arly endeavors to integrate the scientific and humanistic traditions of psychotherapy. He is the author of *The Cure of Souls* and *The Value of Psychotherapy: The Talking Cure in an Age of Clinical Science*, and the coauthor of *Stress, Sanity, and Survival*  and *Treating Somatization: A Cognitive Behavioral Approach*.

Dr. Allen is Adjunct Associate Professor of Psychiatry at Rutgers Robert Wood Johnson Medical School and a licensed clinical psychologist. She completed her BA at Dartmouth College and graduate degrees (MS and PhD in Clinical Psychology) at Rutgers University. She spent the majority of her academic career developing and studying the efficacy of various treatments for depression, anxiety, and somatic symptom disorders. She

received grant funding from the National Institutes of Health and various pharmaceutical companies to study treatments for depression, somatization, and fibromyalgia. Her findings and interpretations of research outcomes have been published in numerous articles, book chapters, and books.

Dr. Federico Durbano received a degree in Medicine and specialized in Psychiatry in Milan. He has had different work experiences in a number of hospitals (Milan "Ospedale Maggiore Policlinico," Treviglio, Melegnano, and Fatebenefratelli), where he achieved significant career milestones. He is now the Director of the Psychiatric Unit in Melzo Hospital—ASST Melegnano e della Martesana—and Vice Director of the Mental Health and Sub-

stance Dependence Department at the same ASST. He has had teaching assignments from the University of Milan (Nursing School) and the University of Castellanza (Master in Criminology). He has attended more than 70 local and national congresses and courses as an invited speaker, and has published more than 160 papers in national and international journals and books. He also works as a technical advisor to the court in the field of forensic psychiatry.

Floriana Irtelli, psychoanalyst, psychotherapist, member of the Italian Society of Psychoanalysis of Relationships, has been lecturing for several years at the Catholic University of the Sacred Heart, Milan, Italy. She has worked at the Fatebenefratelli Hospital, Milan, performing scientific research and clinical activities. She is the coauthor of *A Fresh Look at Anxiety Disorders and Psychopathy: New Updates on an Old Phenomenon*, and has

published articles for the *Journal of Affective Disorders, Journal of Research in Psycho-*

*therapy*, and *Journal of Psychiatric and Mental Health Nursing*. She has participated in numerous conferences, seminars, and congresses, such as the 2015 World Congress of Psycho-Oncology. She is the author of *Illuminarsi di Ben-essere, Familiar-mente, Contemporary Perspectives on Relational Wellness, Psychoanalysis and the Modern Family*, and the editor of *Psychosis, Biopsychosocial and Relational Perspectives.*

Contents

**Section 1**

Concepts

**Section 2**

*by Martin L. Vargas*

The Mental Health of Combatants

*by Robert L. Woolfolk*

Hormones: A Long and Crucial History

**Preface III**

Psychopathology: An International, Interdisciplinary Perspective **1**

**Chapter 1 3**

**Chapter 2 7** Evidence for Link Between Mental Disorders and in Utero Exposure to Synthetic

**Chapter 3 23**

**Chapter 4 43**

**Chapter 5 61**

**Chapter 6 75**

Anxiety as Modulator of Life **93**

**Chapter 7 95**

Prevalence and Treatment of Anxiety Disorders Comorbidities in a Clinical Romanian Sample of Children and Adolescents with Psychiatric Disorders

*Renata Barreto Fernandes de Almeida, Rossana Carla Rameh-de-Albuquerque,*

Neuroscience-Based Anthropological Psychiatry (NBAP): Ten Introductory

Introductory Chapter: New Directions in the Study of Psychopathology

*by Marie-Odile Soyer-Gobillard, Laura Gaspari and Charles Sultan*

Cocaine and Its Variations in Forms of Presentation and Addiction *by Antonio Gomes de Castro Neto, Magda da Silva Figueiroa,*

*Iandê dos Santos Gomes de Moura and Solange Aparecida Nappo*

*by Andrey Soloviev, Elena Ichitovkina and Marina Zlokazova*

*by Elena Predescu, Anna Boglarka Asztalos and Roxana Șipoș*

A Meta-Analysis of Sleep Disturbances in Panic Disorder

*by Geneviève Belleville and Alenka Potočnik*


**3. Computational examples**

#### **3.1. Tohoku-Oki models**

Now, numerical results [9] for a Tohoku-Oki model are again shown for self-containing. When not specifically defined, the same physical values as for Hakata Bay were used. Regarding the mesh data, the total number of nodes is 56,562, the total number of elements is 113,013, and the ocean floor ordinate is set to become deep gradually from *h* = –10 to *h* = –1,000 [m]. We set the initial conditions at ζ0 (*x*) = 0 and *Ui*0 (*x*) = 0, and the boundary conditions o (,) = 50 [m] (0 < *t* < 60 [s]) at the tsunami-generating area and o (,)=0 [m] at other areas (see **Figure 2**). It is noted that 50 [m] at *Γ*o may be too high. In our computation, the tsunami arrived at Oshika Peninsula in **Figure 2** after 20 min and the highest wave height reached 15 [m]. These numerical results should more carefully be checked with boundary data on *Γ*o.

In the above,

and Ushijima [13, 14].

the open boundary.

**3.1. Tohoku-Oki models**

o

**3. Computational examples**

+ 1 is determined by Eq. (13). Here,

the following symbols are also used:

,

66 Tsunami

+ 1 is determined from Eq. (12).  (12) holds for all nodes except for the nodes on *Γ*o and Eq. (13) holds for all the nodes except for the nodes on *Γ*c. However, on the boundary *Γ*o, an approximation method is adopted in which the advective term of Eq. (13) uses Tabata's upwind approximation [9, 12]. A mathematical justification for the approximation scheme for linearized equations related to the above scheme was given by Kanayama

In general, tsunami is excited in the following two ways. The first one is to consider the tsunami excitation as the initial condition of the water surface, for which we do not have sufficient input information in such an artificial tsunami of Hakata Bay [9]. The second one is to consider it as the boundary condition of the water surface as in the next subsection. In the setting of Hakata Bay, a computational domain is not so wide that the above approach may be the only way. It is also noted that 50 [m] at the open boundary for the later Tohoku-Oki case may be too high. In the computation, the tsunami arrived at Oshika Peninsula after 20 min, and the highest wave height reached 15 [m]. These numerical results should be checked more carefully with data on

Now, numerical results [9] for a Tohoku-Oki model are again shown for self-containing. When not specifically defined, the same physical values as for Hakata Bay were used. Regarding the mesh data, the total number of nodes is 56,562, the total number of elements is 113,013, and the ocean floor ordinate is set to become deep gradually from *h* = –10 to *h* = –1,000 [m]. We set the initial conditions at ζ0 (*x*) = 0 and *Ui*0 (*x*) = 0, and the boundary conditions

(,) = 50 [m] (0 < *t* < 60 [s]) at the tsunami-generating area and o

 f

æ ö ¶ ¶

It is well known that integration by parts is used for the viscosity term in (13).

respectively, at the node *k* after *n* time steps, and Δ*t* represents the size of time steps. In addition,

, the value

are approximate values of (,) and (,)

(15)

(,)=0 [m] at other

ç ÷ = ç ÷ ¶ ¶ è ø ò (14)

**Figure 2.** A computational model of Tohoku-Oki [9]. (a) Contour map of *ζ* [9]. (b) Time histories of *ζ* [9].

**Figure 2a** shows the ordinates of the water surface (water level) after 18 [min]. Since the computational domain is not wide, it looks that there is a reflection from the northern boundary in **Figure 2a**. This artificial reflection can be removed by suitable boundary conditions on *Γ*o. Details are later mentioned. **Figure 2b** shows the water level change at the two points A and B in **Figure 2**. The wave height at the point B is higher than the point A after about 1500 [s]. When the tsunami reaches coastal areas, the water depth becomes shallow and the wave height becomes high.

Next, new numerical results for the second Tohoku-Oki model are shown. When not specifically defined, the same physical values as for Hakata Bay were used. Regarding the mesh data, the total number of nodes is 563,100, the total number of elements is 1,123,178, and **Figure 3** shows the ocean floor ordinate, which is constructed from 30-arc sec interval grid of JTOPO30, provided by the Marine Information Research Center. We set the initial conditions at ζ0 (*x*) like **Figure 4** based on the data of Fujii et al. [15] as a source of tsunami. **Figure 5a–f** shows the ordinates of the water surface (water level) every 8 [min] from after 8 [min] to after 48 [min]. In our previous paper [9], since the computational domain is not wide, it looks that there is a reflection from the boundary *Γ*o (see **Figure 2a**). This artificial reflection can be removed by changing boundary conditions from (10) to (16) as follows:

$$\begin{aligned} U\_n(\mathbf{x}, t) &= \mathcal{L} \frac{\sqrt{|\mathbf{g}|} \| \mathbf{f} \|}{H} \mathcal{L} \left( \mathbf{x}, t \right) \\ U\_t(\mathbf{x}, t) &= \mathbf{0} \end{aligned} \quad \text{on } \Gamma\_o, \tag{16}$$

where *Un* and *Ut* denote the normal component of velocity and the tangential component of velocity, respectively, and *c* is a constant. We used (*c* = 0.9) in this chapter.

**Figure 3.** The second computational model of Tohoku-Oki.

**Figure 4.** Contour map of initial water level.

ordinates of the water surface (water level) every 8 [min] from after 8 [min] to after 48 [min]. In our previous paper [9], since the computational domain is not wide, it looks that there is a reflection from the boundary *Γ*o (see **Figure 2a**). This artificial reflection can be removed by

( )

ï = þ

*H on*

ü

ý G

denote the normal component of velocity and the tangential component of

(,) , ,

z

= ï

(,) 0

*g h U xt c xt*

*t*

*U xt*

velocity, respectively, and *c* is a constant. We used (*c* = 0.9) in this chapter.

o

(16)

changing boundary conditions from (10) to (16) as follows:

*n*

**Figure 3.** The second computational model of Tohoku-Oki.

**Figure 4.** Contour map of initial water level.

where *Un* and *Ut*

68 Tsunami

**Figure 5.** (a) Contour map of after 8 min, (b) contour map of after 16 min, (c) contour map of after 24 min, (d) contour map of after 32 min, (e) contour map of after 40 min, and (f) contour map of after 48 min.

**Figure 6** shows the water level change at the three points Miyako, Soma and Choshi in **Figure 4**. Solid curves show observed data [15] and dashed curves show numerical results. Though the arrival time of the first wave is almost same, the wave height is small compared with observed data at the points of Miyako and Soma. Results may be improved by changing initial and boundary conditions.

**Figure 6.** Time histories of *ζ*.


*4.7. Validation of DEGs by Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR)*

Twelve differentially expressed candidate genes were assessed by qRT-PCR to measure the reliability of the RNA-seq data, as previously described [79]. The primer sequences are presented in Table S8. The SYBR qPCR Master Mix (Vazyme) was used with the CFX96 for qRT-PCR analysis (BIO-RAD). Each sample was subjected to three technical replications. The 2−∆∆CT method was utilized to determine the relative expression of target genes using *B. napus* ACTIN2 as an internal control. [80].

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/ijms23147958/s1.

**Author Contributions:** X.D. planned and supervised the research; N.A., B.S., S.I., L.K., Z.T. and X.W. performed root traits investigation and analyzed the data; N.A. wrote the manuscript; X.D. and H.W. contributed to modify the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** The study was supported by the Major Project of Hubei Hongshan Laboratory (2021HSZD004), Agricultural Science and Technology Innovation Project (CAAS-ZDRW202109), Central Public-interest Scientific Institution Basal Research Fund (No.1610172020004), Agricultural Science and Technology Innovation Project (CAAS-ASTIP-2013-OCRI), and China Agriculture Research System of MOF and MARA (CARS-12).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The datasets generated or analyzed during the present study are available from the corresponding authors on reasonable request.

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

#### **References**

**Huanhuan Qi , Feng Yu , Jiao Deng † and Pingfang Yang \***

State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Science, Hubei University, Wuhan 430062, China<EMAIL_ADDRESS>(H.Q<EMAIL_ADDRESS>(F.Y<EMAIL_ADDRESS>(J.D.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +86-27-88661237 † Current Address: Research Center of Buckwheat Industry Technology, School of Life Sciences,

Guizhou Normal University, Guiyang 550001, China.

**Abstract:** Lotus (*Nelumbo nucifera*), under the Nelumbonaceae family, is one of the relict plants possessing important scientific research and economic values. Because of this, much attention has been paid to this species on both its biology and breeding among the scientific community. In the last decade, the genome of lotus has been sequenced, and several high-quality genome assemblies are available, which have significantly facilitated functional genomics studies in lotus. Meanwhile, re-sequencing of the natural and genetic populations along with different levels of omics studies have not only helped to classify the germplasm resources but also to identify the domestication of selected regions and genes controlling different horticultural traits. This review summarizes the latest progress of all these studies on lotus and discusses their potential application in lotus breeding.

**Keywords:** lotus; genome; variant; germplasm; breeding; omics

**Citation:** Qi, H.; Yu, F.; Deng, J.; Yang, P. Studies on Lotus Genomics and the Contribution to Its Breeding. *Int. J. Mol. Sci.* **2022**, *23*, 7270. https:// doi.org/10.3390/ijms23137270

Academic Editors: Zhiyong Li and Jian Zhang

Received: 26 May 2022 Accepted: 27 June 2022 Published: 30 June 2022

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

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


**Ion Exchange Applications**

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