Position of measurement points in bronchoscopy unit.
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IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\\n\\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
\\n\\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\\n\\nNote: Edited in October 2021
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\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\n\nLaunching 2021
\n\nArtificial Intelligence, ISSN 2633-1403
\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
\n\nInfectious Diseases, ISSN 2631-6188
\n\nPhysiology (Coming Soon)
\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
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In the developed world, people spend about 90% of their time indoors [3], where they are exposed to chemical and biological contaminants and possibly also to carcinogens [4]. Until recently, the health effects of indoor air pollution have received relatively little attention [5].
In particular, the air quality at hospitals carries with it a risk factor for serious health consequences not only for the medical staff but also for patients and visitors. Infection is a common event in hospitals, and many studies have investigated the levels, sources, and characteristics of bioaerosols in these settings [6]. Due to multiple sources of pollution and the presence of vulnerable people, the health sector is particularly at risk of low IAQ [7]. Additionally, outdoor air pollution can affect indoor air quality [8]. Since hospitals are primarily and traditionally a place for people to recover from illness or disease, improving IAQ can help reduce recovery times and thus boost overall productivity [9].
In Europe, more than two million people annually become infected due to healthcare-associated infection (HAI) [10]. Although direct contact is believed to be the main route for transfer of HAI, there is evidence that airborne bacteria may also cause infection due to inhalation [11]. Therefore, it is essential to understand the dynamics of infectious particles that are present in respiratory diseases such as severe acute respiratory syndrome (SARS) and tuberculosis (TB).
This paper examines infection transfer in hospitals caused by poor air quality. To date, only a few researchers have investigated this topic. Cheong and Phua [1] conducted an experimental study on ventilation strategies for improving indoor air quality inside a hospital isolation room, and Qian and Li [12] analyzed ventilation strategies in a hospital isolation chamber using numerical digital tools.
The purpose of the present study is to develop a model for air velocity, temperature profile, and some concentrations of gaseous contaminants in a hospital bronchoscopy unit. Modeling is done using large-eddy simulation (LES). The outcomes of this study will be applied to a hospital in Morocco (North Africa). To validate the work, a hospital in Kenitra, Morocco, has been chosen to measure air quality in the bronchoscopy unit.
To investigate the indoor air quality in the bronchoscopy unit, a Fire Dynamics Simulator was used to simulate the movement of air and the temperature profiles.
An FDS is a computational fluid dynamics (CFD) model of fire-driven fluid flow. The software described in this document numerically solves a form of the Navier–Stokes equations appropriate for low-air velocity thermally driven flow, with an emphasis on smoke and heat transport from fires.
In this study, the FDS software (version 5) was used. FDS is developed by the National Institute of Standards and Technology (NIST). The first version of FDS was publicly released in February 2000. To date, about half of the applications of the model have been for the design of smoke handling systems and sprinkler/detector activation studies. The other half consists of residential and industrial fire reconstructions [13].
The Hydrodynamic Model FDS numerically solves a form of the Navier–Stokes equations, which are appropriate for low-air velocity thermally driven flow, with an emphasis on smoke and heat coming from fires. The core algorithm is an explicit predictor–corrector scheme with second-order accuracy in space and time. Turbulence is treated by means of the Joseph Smagorinsky form of Large-Eddy Simulation. The LES is the default mode of operation [14].
The equations of conservation of mass and momentum written in a system of Cartesian coordinates are as follows [15]:
Equation of conservation of mass:
where ρ = air density (kg/m3);
Equation of conservation of momentum:
where
Such that:
Smokeview is a software developed as FDS by NIST. It is used to view the geometry, mesh size, and results obtained by FDS and includes several visualization techniques.
The studied room, as shown in Figure 1, consists of a chamber and a corridor. The chamber measures 4.80 m in length, 3.35 m in width, and 2.5 m in height. It contains a bed in the middle. The corridor is 2.65 m long and 1.2 m wide. The corridor is the same height as the chamber.
Geometry of bronchoscopy.
The room is classified as a high-risk setting (Class 1). According to international standards and the American Society of Heating, Refrigerating and Air Conditioning Engineers (ASHRAE) standards ([16], p. 62), the room must be maintained under negative pressure. This is to prevent any exfiltration of contagious microorganisms or antibiotic-resistant bacteria that may be emitted by a sick patient to other parts of the hospital, as these organisms and bacteria might infect other patients, medical staff, or even visitors.
We use here six points of measure: SP1, SP2, SP3, SP4, SP5, and SP6. These points fall in the plane z = 1.4 m (Table 1).
Positions | SP1 | SP2 | SP3 | SP4 | SP5 | SP6 | |
---|---|---|---|---|---|---|---|
Coordinates (m) | x | 2.30 | 1.05 | 2.30 | 1.50 | 1.5 | 0.60 |
y | 3.90 | 2.40 | 1.03 | 3.90 | 1.3 | −1.32 | |
z | 1.40 | 1.40 | 1.40 | 2.50 | 2.5 | 1.40 |
Position of measurement points in bronchoscopy unit.
For a typical building design simulation using FDS, a large volume of space is simulated. In describing this computation volume, one or more subsections of the overall volume are referred to as a “mesh” and entered as “&MESH” in the FDS input file. In many cases, multiple meshes of different resolutions are required to accurately define the simulated domain. Most modern computers have multiple “cores” or processors per CPU chip, and FDS through the Message Passing Interface (MPI) feature allows for each mesh to be assigned to a specific processor using the MPI_PROCESS keyword. This feature enables any number of meshes to be assigned to the same processor to improve simulation efficiency. In the present study, three different meshes have been tested:
Dense mesh (DM)
Less dense mesh (LDM)
Coarse mesh (CM)
Note that DM has 2.4 times more mesh nodes than LDM and 7.3 times more nodes than CM.
Figure 2 shows LDM according to the x-y, x-z, and y-z planes. The mesh is subdivided in two parts: mesh for the closed room (zone 1) and mesh for the corridor (zone 2).
LDM mesh.
Three ventilation scenarios are used. In the first scenario, the isolated room is supplied with fresh air by two square diffusers measuring 0.6 m. The diffusers are at the sides of the room and are located in the ceiling. The air is extracted from the room by two square grilles also measuring 0.6 m and also positioned at the sides of the room and located in the ceiling. The objective of this strategy is to dilute the contaminant as effectively as possible in order to obtain a uniform concentration of sulfur hexafluoride (SF6) across the entire volume of the room. In the first scenario, the contaminant is released in the room at a flow rate of 0.31 L/min. Numerically, we impose the release of the contaminant evenly on the six sides of a cube of 27 cm3 as a condition. We obtain the flow volume by a surface unit of 0.97 × 10−3 m3. Cheong and Phua [1] present some numerical and experimental results for the first scenario, which we demarcate as “scenario 1.”
In “scenario 2” the diffusers’ positions are similar to those in scenario 1. However, the air is extracted by two mural grilles located 30 cm above the floor near the patient’s bed. In this scenario, SF6 is released at a rate of 0.63 l/min (1.94 × 10−3 m3/s/m2). This second ventilation strategy aims at creating a flow from the top of the room to the floor. The aim is to quickly direct the gaseous contaminant “rejected” by the patient towards the extraction grilles. Numerically, it is very difficult to simulate the structure to the output of a diffuser of flow.
“Scenario 3” is similar to scenario 2, except that the supplying diffusers are replaced by blow grilles located on the ceiling directly above the patient. Unlike the diffusers, the air in scenario 3 is blown directly to the floor.
The component positions of the modeled room are presented in the table below.
In the first ventilation scenario, two different software (FDS and Smokeview) allow the exploitation of a quantity of information at the end of every simulation. In this study, our attention is focused on the average concentrations of SF6 in the closed room and the spatial and temporal distribution of the concentration of SF6. Air velocity flow and temperature are also part of the reserved results (Tables 2 and 3).
Type of grid | Number of nodes |
---|---|
Dense mesh (DM) | 3,218,400 |
Less dense mesh (LDM) | 1,327,500 |
Coarse mesh (CM) | 442,500 |
Number of nodes for each mesh.
Equipment | Positions (m) | |||||
---|---|---|---|---|---|---|
Xmin | Xmax | Ymin | Ymax | Zmin | Zmax | |
Bed | 1.40 | 3.20 | 2.05 | 2.75 | 0.0 | 0.40 |
Patient | 1.50 | 3.15 | 2.25 | 2.55 | 0.40 | 0.65 |
Door | 0.15 | 1.05 | −0.02 | 0.00 | 0.05 | 2.00 |
Lamp 1 | 0.40 | 1.00 | 0.40 | 1.00 | 2.50 | 2.50 |
Lamp 2 | 2.20 | 2.80 | 0.40 | 1.00 | 2.50 | 2.50 |
Lamp 3 | 2.20 | 2.80 | 2.10 | 2.70 | 2.50 | 2.50 |
Lamp 4 | 0.40 | 1.00 | 2.10 | 2.70 | 2.50 | 2.50 |
Lamp 5 | 2.20 | 2.80 | 4.20 | 4.80 | 2.50 | 2.50 |
Lamp 6 | 0.40 | 1.00 | 4.20 | 4.80 | 2.50 | 2.50 |
Plaque 1 | 2.40 | 3.00 | 1.05 | 1.65 | 2.40 | 2.43 |
Plaque 2 | 2.40 | 3.00 | 3.65 | 4.25 | 2.40 | 2.43 |
Supply diffuser 1 (scenario 1) and (scenario 2) | 2.40 | 3.00 | 1.05 | 1.65 | 2.50 | 2.50 |
Supply diffuser 2 (scenario 1) and (scenario 2) | 2.40 | 3.00 | 3.65 | 4.25 | 2.50 | 2.50 |
Exhaust grille 1 (scenario 1) and supply diffuser 1 (scenario 3) | 1.00 | 1.60 | 3.65 | 4.24 | 2.50 | 2.50 |
Exhaust grille 1 (scenario 1) and supply diffuser 2 (scenario 3) | 1.00 | 1.00 | 1.00 | 1.60 | 2.50 | 2.50 |
Exhaust grille 1 (scenario 2) and (scenario 3) | 2.40 | 2.90 | 4.80 | 4.80 | 0.40 | 1.00 |
Exhaust grille 2 (scenario 2) and (scenario 3) | 2.40 | 2.90 | 2.40 | 2.40 | 0.40 | 1.00 |
Component positions of modeled room, supply diffuser, and exhaust grille in each scenario.
The results of scenario 1 are presented in Tables 4 and 5. As can be seen, the tables show a comparison between concentrations simulated by FDS and those simulated by Cheong and Phua [1]. The concentration simulated by FDS [CFDS] and the experimental concentrations obtained by Cheong and Phua [1] [Cexp] are presented, respectively, in Table 4.
SP1 | SP2 | SP3 | SP4 | SP5 | |
---|---|---|---|---|---|
[CFDS] | 20.00 | 22.50 | 20.40 | 34.20 | 28.60 |
Cheong and Phua [Cexp] | 28.9 ± 0.7 | 28.0 ± 0.5 | 28.2 ± 0.6 | 33.4 ± 1.7 | 32.9 ± 0.9 |
Error (%) | 31 | 20 | 28 | 2 | 10 |
SF6 concentration (ppm) simulated by FDS and Cheong and Phua [1].
SP1 | SP2 | SP3 | |
---|---|---|---|
VFDS (m/s) | 0.15 | 0.15 | 0.12 |
Vex (m/s) | 0.14 ± 0.1 | 0.18 ± 0.2 | 0.16 ± 0.2 |
Error (%) | 7 | 17 | 25 |
Air velocity simulated by FDS and Cheong and Phua [1].
Error expressed as a percentage is given by | ([CFDS] − [Cexp])/[Cexp] |for the concentration and by | ([VFDS]−[Vex])/[Vex] |for the air velocity.
The concentrations obtained at points SP1, SP2, SP3, and SP5 by the LES code are inferior to the experimental results of Cheong and Phua [1]. These low concentrations can partly be explained by the infiltration of air under the door. Although Cheong and Phua [1] do not specify this infiltration rate or the pressure difference between the corridor and the closed room, AIA recommends that, for an operating room maintained at negative differential pressure, air flow to the extraction outlets must be 10% higher than the permitted air flow rate. In this case, the flow rate is 14% higher than the blowing rate. Since the infiltration rate is slightly higher than recommended, the gaseous contaminant dilution will tend to be more efficient. This will lead to an average concentration of SF6 in the room, which is inferior to that obtained at a lower infiltration rate.
Table 5 shows both the simulated and experimental flow velocity modules, expressed in m/s. The VFDS represents the average air velocity by the FDS over a range of 200 s (800–1000 s). In contrast, Vex is the experimental air velocity in Cheong and Phua’s [1] study. As can be seen, there is excellent correlation between what is simulated by both methods and what is simulated by Cheong and Phua [1]. The deviations, shown as percentages, appear to be high and are expressed in cm/s. Specifically, they are less than 5 cm/s, which is not significant.
The concentrations obtained for scenarios 2 and 3 are presented in the following table.
Although FDS predicts a concentration that is slightly lower than the numerical code used by Cheong and Phua [1], the results obtained by FDS in scenario 2 are in agreement with those estimated by the researchers. On the other hand, considerable error is observed for scenario 3, in which the room is supplied with fresh air by two grids located on the ceiling. These grids tend to force the air to the floor. Points SP1 and SP3 are directly under the grids, and the concentration of SF6 is very low (<1 ppm). In this context, it is surprising that Cheong and Phua [1] obtained a concentration of 29.0 ppm at these points, as the only possible explanation would be related to the position of the supply grids. Although a plan of the room in Cheong and Phua’s [1] article seems to indicate that the supply grids are located directly under points SP1 and SP3, there is no information on the exact positioning of the grids (Tables 6 and 7).
Position | Scenario 2 | Scenario 3 | ||||
---|---|---|---|---|---|---|
CFDS | Cheong and Phua.[C.num] | Error (%) | CFDS | Cheong and Phua.[C.num] | Error (%) | |
SP1 | 32.0 | 29.0 | 10 | ∼0 | 29.0 | 10.0 |
SP2 | 28.4 | 34.0 | 18 | 21.2 | 28.0 | 24.0 |
SP3 | 27.3 | 30.5 | 11 | ∼0 | 29.0 | 100.0 |
Numerical and simulated concentration (ppm) results.
Position | Scenario 2 | Scenario 3 | ||
---|---|---|---|---|
PREFDS | PRECheong and Phua | PREFDS | PRECheong and Phua | |
SP1 | 1.00 | 1.08 | ∞ | 1.08 |
SP2 | 1.13 | 0.91 | 1.22 | 1.12 |
SP3 | 1.17 | 1.03 | ∞ | 1.08 |
Pollutant removal efficiency.
Moreover, Cheong and Phua [1] do not give the recommended air temperature for the room. If the supply air temperature is high, then the Archimedes thrust will tend to significantly decrease the range of the jet. The influence of the blowing temperature was checked through the increase by 2°C in the air temperature admitted into the room. The concentrations obtained at SP1 and SP3 remained negligible, which indicates that much higher temperatures would be required to reduce the range of the jets to a few tens of cm.
The flow velocity achieved a value of 0.05 m/s in nearly the entire room, except for the floor, where the velocity reached a value of 0.8. At the level of the diffusers, the air velocity was 0.6 m/ s. Further, it was observed that the jet of air coming from the corridor faded before reaching the other end of the room. This jet of air diffused in the vertical direction, which helped to dilute the gaseous contaminant in the room.
The influence of the blowing grids on the concentrations is clearly visible. As mentioned earlier, very significant changes in concentration are observed in the area above the patient’s bed. A slight change in the position of the blown grids is likely to have a significant impact on the simulated concentrations at SP1 and SP3. Point SP2, which is situated at the foot of the patient’s bed, is in an area where variations in concentration are less important.
In order to compare the effectiveness of the three ventilation strategies, the pollutant removal efficiency (PRE) index is used.
PRE is calculated as follows:
where
The PRE can be calculated for a room with more exhaust grilles by averaging the concentrations obtained at the various extraction grids. The PRE index is used to quantify the effectiveness of ventilation to remove pollutants from a room. It depends on a number of factors, such as the location of the source of the pollutant, the supply flow, the ventilation strategy, etc. The strategy is effective for removing pollutants and represents a good ventilation solution if PRE > 1. On the other hand, if PRE < 1, there is an accumulation of contaminants in the room. This could be related to, for example, the existence of recirculation zones where the contaminants accumulate.
For scenario 2 in the three points of SP1, SP2, and SP3, we obtain PRE > 1. This result is the same as that in Cheong and Phua’s [1] work, except for SP2. In scenario 3, the FDS simulation gives completely different results than Cheong and Phua’s [1] for the two points of SP1 and SP3.
This paper investigated the air quality in a bronchoscopy unit. The numerical model used in the present study was based on the large-eddy simulation (LES) method. The numerical results obtained in this work have been generally validated by the experimental results found in the literature. For this investigation, we used Cheong and Phua’s [1] study for validation and comparison purposes. Three numerical scenarios (scenario 1, scenario 2, and scenario 3) were developed according to ASHRAE norms and standards. Fire Dynamics Simulator software was used to estimate the concentration of contaminants and the air velocity in the bronchoscopy unit. According to the results obtained, both scenario 1 and scenario 2 are effective for removing SF6 (pollutants). However, according to our results, scenario 3 should not be retained, as in this scenario, the concentration of pollutants was very high compared to the other two scenarios. Moreover, the concentration of SF6 accumulated around the patient’s bed.
In light of these findings, the authors of the present work suggest that more in-depth investigation into the air quality of hospitals is warranted. This could be combined with field experiments using scenarios 1 and 2.
Soybean (
As a leguminous plant, soybean roots bear nodules formed as a result of infection by nodulating rhizobia, which perform symbiotic nitrogen fixation, and the plant acquires atmospheric nitrogen in the form of ammonia through these root nodules. Major soybean-nodulating rhizobia include
The compatibility and preference for nodulation by bradyrhizobial strains of soybean cultivars and varieties exhibiting the
In this chapter, we describe breeding and selection processes, shoot growth, yield components, and infection tendency of useful bradyrhizobia of
To select
Theoretically, all F1 lines obtained by crossing “Bonminori” and “Fukuyutaka” should present the
Appearance of nodulation following the inoculation of
Phenotypes of F3 seeds | Number of soybean lines |
---|---|
non-nodulation | 8 |
non-nodulation or nodulation | 63 |
nodulation | 82 |
not tested | 4 |
Total | 157 |
Nodulation phenotypes of F3 seeds.
Among the five seeds sown for selection, lines that did not form root nodule on all plants were classified as “non-nodulation,” lines that formed root nodule more than one of the five plants were classified as “non-nodulation or nodulation,” and lines that formed root nodule all plants classified as “nodulation.”
A 2-year field trial was conducted in 2016 and 2017 in the experimental field of the Agricultural Science Section, Education and Research Center for Biological Resources, Faculty of Life and Environmental Science, Shimane University, Japan (35°30′55″N, 133°06′36″E). The experimental sites were located at 35°30′60″N, 133°06′35″E in 2016 and 35°31′02″N, 133°06′40″E in 2017. Both experimental fields had gray lowland soil (paddy conversion fields). Soil pH (H2O) and electrical conductivity (mS cm−1) were respectively 6.42 and 0.10 in 2016 and respectively 6.72 and 0.06 in 2017. Before sowing, nitrogen, potassium, and phosphorus were applied at doses of 40, 100, and 100 kg ha−1, respectively. To correct soil pH, magnesium lime was applied at the dose of 1000 kg ha−1. The experiment followed the split-plot design with three replicates. Three soybean cultivars, namely “Enrei,” “Sachiyutaka,” and “Fukuyutaka,” as well as F10 or F11 plants of three
Soybean growth was evaluated during the flowering and harvest periods. Samples were collected by from 10 consecutive plots per replicate. During the flowering period, plant height, node number, branch number, stem and leaf dry weight, and main culm dry weight (2017 only) were measured. During the harvest period, plant height, node number, shoot dry weight, pod number, seed number, 100-seed weight, and yield were measured. Plant dry weight was measured after drying at 70°C for over 72 h in a drying apparatus. All statistical analyses were performed using R version 4.0.3 [49]. Soybean growth parameters during the flowering period were analyzed using Tukey’s honestly significant difference (HSD) test for multiple comparisons using the R package “multcomp.” Soybean yield components were subjected to two-way analysis of variance using anovakun version 4.8.5 [50]. Meteorological data during soybean cultivation were collected from past information provided by the Japan Weather Association (Table 2).
Year | month | Temperature (°C) | Precipitation Sunshine | |||
---|---|---|---|---|---|---|
minimum | maximum | mean | (mm) | duration (h) | ||
2016 | Jun. | 19.0 | 26.8 | 22.3 | 166.0 | 4.9 |
Jul. | 23.5 | 30.8 | 26.6 | 77.0 | 5.7 | |
Aug. | 23.6 | 32.2 | 27.2 | 140.5 | 7.7 | |
Sep. | 20.7 | 26.7 | 23.3 | 293.0 | 2.8 | |
Oct. | 15.3 | 22.3 | 18.5 | 103.5 | 3.5 | |
Nov. | 8.8 | 16.2 | 12.2 | 120.0 | 3.2 | |
2017 | Jun. | 16.5 | 25.9 | 20.9 | 86.5 | 7.3 |
Jul. | 24.8 | 31.5 | 27.6 | 168.5 | 5.6 | |
Aug. | 24.4 | 31.5 | 27.4 | 141.5 | 6.8 | |
Sep. | 18.5 | 26.3 | 22.1 | 214.5 | 4.9 | |
Oct. | 14.3 | 21.0 | 17.5 | 358.0 | 3.6 | |
Nov. | 6.7 | 15.7 | 11.1 | 93.0 | 4.6 |
Meteorological data during soybean cultivation in 2016 and 2017.
Values indicate monthly averages, and each value was calculated based on meteorological data provided by the Japan Weather Association.
The results of soybean growth during the flowering period in 2017 are presented in Table 3. The measurements during the flowering period were conducted on August 10, 2017, for B × F − E and “Enrei”; August 17, 2017, for B × F − M and “Sachiyutaka”; and August 26, 2017, for B × F − L and “Fukuyutaka.” The plant height of
Cultivar | Plant height (cm plant−1) | Node number (No. plant−1) | Branching number (No. plant−1) | Main culm dry weight (g m−2) | Stem and leaf dry weight (g m−2) | Shoot dry weight (g m−2) |
---|---|---|---|---|---|---|
BxF–E | 66.2 ab | 13.5 | 6.1 ab | 13.0 | 23.1 | 36.1 |
BxF–M | 67.9 ab | 13.1 | 6.5 ab | 15.3 | 25.1 | 40.4 |
BxF–L | 74.9 b | 13.6 | 7.2 bc | 16.7 | 22.4 | 39.0 |
Enrei | 53.7 a | 11.5 | 4.5 a | 12.2 | 27.0 | 39.2 |
Sachiyutaka | 57.3 a | 12.3 | 6.4 ab | 12.7 | 22.6 | 35.4 |
Fukuyutaka | 64.5 ab | 12.2 | 9.2 c | 17.4 | 23.8 | 41.2 |
ANOVA | * | ns | *** | ns | ns | ns |
Growth of soybean cultivars during the flowering period in 2017.
Values are presented as the means of three replicates. *
The results of yield components of soybean cultivars during the harvest period in 2016 and 2017 are presented in Table 4. In ANOVA, all yield components, except 100-seed weight, significantly differed between years and among cultivars. Specifically, pod and seed number and yield were significantly higher in 2016 than in 2017. Conversely, plant height, node number, and shoot dry weight were significantly higher in 2017 than in 2016. Based on the average values of the 2 years, pod and seed number in B × F − M was significantly higher than that in the other cultivars. Moreover, the yield of B × F − M and “Sachiyutaka” was significantly higher than that of B × F − E, B × F − L, and “Enrei.” Furthermore, 100-seed weight of “Sachiyutaka” was significantly higher than that of the other cultivars, except “Fukuyutaka.” Plant height and shoot dry weight of B × F − L tended to be higher than those of the other cultivars. The interaction between year and cultivar was detected for all test parameters, except seed number and yield. Therefore, multiple comparison analysis was performed among 12 cohorts for each test item, and the results are shown in Figure 2. Briefly, pod and seed number and yield were lower in all soybean cultivars in 2017 than in 2016. Furthermore, pod and seed number of B × F − E, B × F − M, “Enrei,” and “Sachiyutaka” decreased significantly. While the yield of “Sachiyutaka” decreased significantly, that of B × F − E, B × F − M, B × F − L, “Enrei,” and “Fukuyutaka” tended to decrease, albeit without significant differences. “Enrei,” “Sachiyutaka,” and “Fukuyutaka” are soybean cultivars that are suitable or possible to cultivate in the Chugoku region of Japan, including Shimane prefecture, where the cultivation test was conducted in the present study [51, 52]. Additionally, pod and seed numbers are the most important soybean yield components, which are primarily determined during the period from before and after flowering to pod set, including the beginning of the seed filling period [53]. However, increasing temperature during the growing season can negatively affect soybean leaf photosynthesis, growth, flowering, pod and seed number, and yield [54, 55]. In the present study, the monthly mean maximum temperature in August during the flowering period of soybean was 32.2°C in 2016 and 31.5°C in 2017 (Table 2). Specifically, in early August of 2017, when B × F − E and “Enrei” were flowering, the temperature remained above 35°C for 3 consecutive days. Furthermore, in late August of 2017, the temperature remained above 32°C for 4 consecutive days. Additionally, in October 2017, nearly 3.5 times the amount of precipitation in 2016 was recorded (Table 2). Soybean pod and seed number and yield in 2017 were significantly lower than the values in 2016 due to the effects of these meteorological factors (Table 4). Moreover, the 100-seed weight of B × F − M was lower than that of “Sachiyutaka” and “Fukuyutaka” (Table 4). Therefore, backcrossing with these cultivars is expected to produce soybean cultivars with larger seeds and higher yield.
Year | Cultivar | Plant height (cm plant−1) | Node number (No. plant−1) | Shoot dry weight (g m−2) | Pod number (No. m−2) | seed number (No. m−2) | 100-seed weight (g) | Yield (g m−2) |
---|---|---|---|---|---|---|---|---|
2016 | BxF–E | 50.3 | 11.1 | 251.6 | 1426.7 | 937.0 | 23.9 | 214.4 |
BxF–M | 51.1 | 11.3 | 298.5 | 1927.0 | 2276.8 | 22.9 | 514.5 | |
BxF–L | 71.1 | 12.1 | 336.6 | 1036.0 | 1372.9 | 23.5 | 325.8 | |
Enrei | 34.3 | 9.5 | 215.7 | 542.6 | 421.4 | 22.6 | 96.1 | |
Sachiyutaka | 46.1 | 12.1 | 300.3 | 1531.1 | 1742.9 | 33.8 | 584.6 | |
Fukuyutaka | 56.3 | 12.1 | 267.1 | 1057.3 | 1377.3 | 29.8 | 407.4 | |
2017 | BxF–E | 67.4 | 13.4 | 251.8 | 318.8 | 218.9 | 24.6 | 53.8 |
BxF–M | 69.4 | 13.8 | 432.8 | 1180.7 | 1477.4 | 23.6 | 348.2 | |
BxF–L | 71.0 | 13.3 | 424.1 | 784.0 | 1037.7 | 20.7 | 214.6 | |
Enrei | 56.2 | 12.8 | 340.0 | 101.0 | 56.2 | 29.7 | 16.7 | |
Sachiyutaka | 55.2 | 12.1 | 344.2 | 704.1 | 974.4 | 29.3 | 285.8 | |
Fukuyutaka | 70.5 | 16.1 | 467.9 | 934.1 | 1245.6 | 25.0 | 311.7 | |
2016 | 51.5 | 11.4 | 278.3 | 1253.4 | 1354.7 | 26.1 | 357.1 | |
2017 | 64.9 | 13.6 | 376.8 | 670.4 | 835.0 | 25.5 | 205.1 | |
BxF–E | 58.8 bc | 12.2 a | 251.7 a | 872.7 b | 578.0 a | 24.2 a | 134.1 a | |
BxF–M | 60.3 bc | 12.5 ab | 365.7 b | 1553.8 c | 1877.1 c | 23.2 a | 431.4 c | |
BxF–L | 71.0 d | 12.7 ab | 380.4 b | 910.0 b | 1205.3 b | 22.1 a | 270.2 b | |
Enrei | 45.2 a | 11.1 a | 277.8 a | 321.8 a | 238.8 a | 26.1 a | 56.4 a | |
Sachiyutaka | 50.6 ab | 12.1 a | 322.3 ab | 1117.6 b | 1358.6 b | 31.6 b | 435.2 c | |
Fukuyutaka | 63.4 cd | 14.1 b | 367.5 b | 995.7 b | 1311.5 b | 27.4 ab | 359.6 bc | |
ANOVA | Year (Y) | *** | *** | *** | *** | *** | ns | *** |
Cultivar (C) | *** | *** | *** | *** | *** | *** | *** | |
Y x C | * | ** | ** | ** | ns | ** | ns |
Yield components of soybean cultivars in 2016 and 2017.
Values are presented as the means of three replicates. *
Yield components for each soybean cultivar in 2016 and 2017 growing seasons. Values are presented as the mean ± SE of three replicates. Different letters indicate significant differences (Tukey’s HSD test) at
The nodules used in the present experiment were collected from soybean roots at the flowering stage in 2017, as described in Section 3.2. The nodules were surface-sterilized with 70% ethanol for 3 min and diluted sodium hypochlorite solution (0.25% available chlorine) for 30 min, followed by washing with sterile distilled water. After washing, 24 nodules were randomly collected and transferred to 1.5 mL microcentrifuge tubes. Each nodule was homogenized in sterile distilled water and streaked onto a yeast extract–mannitol agar (YMA) plate [56]; to isolate a single colony per nodule, the plates were incubated for 5–7 days in the dark at 28°C. A total of 144 isolates were obtained from six soybean plants and used for PCR-restriction fragment length polymorphism (RFLP) analysis of the 16S–23S rRNA gene internal transcribed spacer (ITS) region.
For DNA extraction, each isolate was cultured in 1.5 mL HM medium supplemented with 0.1% l-arabinose for 5–7 days in the dark at 28°C. Total DNA for use as the PCR template was extracted from the isolates in BL extraction buffer, as described previously [42] based on the method reported by Hiraishi
The 16S–23S rRNA gene ITS region was PCR-amplified using
Eighty-seven indigenous soybean-nodulating bradyrhizobial isolates belonging to the cluster
The occupancy rate of indigenous bradyrhizobia infecting each soybean cultivar is presented in Table 5. Based on the fragment patterns obtained from PCR–RFLP analysis, the indigenous isolates with the similar patterns as the five reference strains, namely
Cultivar | Bj6 | Bd110 | Be46 | Be76 | Be94 | ||
---|---|---|---|---|---|---|---|
BxF–E | 12.5 | 8.3 | 62.5 | 12.5 | 0.0 | 0.0 | 4.2 |
BxF–M | 70.8 | 0.0 | 16.7 | 12.5 | 0.0 | 0.0 | 0.0 |
BxF–L | 0.0 | 8.3 | 45.8 | 25.0 | 4.2 | 16.7 | 0.0 |
Enrei | 83.4 | 0.0 | 8.3 | 8.3 | 0.0 | 0.0 | 0.0 |
Sachiyutaka | 13.0 | 8.7 | 34.8 | 39.1 | 0.0 | 4.4 | 0.0 |
Fukuyutaka | 25.0 | 20.8 | 16.7 | 37.5 | 0.0 | 0.0 | 0.0 |
Occupancy rate (%) of indigenous soybean-nodulating bradyrhizobia in each soybean cultivar in 2017.
Bj6, Bd110, Be46, Be76, and Be94 showed RFLP patterns similar to those of
Correlation analysis was used to evaluate the association between the occupancy rate of indigenous bradyrhizobial strains and yield components. Correlation coefficients were computed based on data obtained from the measurement of yield components and occupancy rate of indigenous soybean-nodulating bradyrhizobia. The R package “psych” was used to compute and plot the correlations. Additionally, the significance of the correlations was tested using the “cor.test” function in R.
The results of correlation analysis between the occupancy rate of indigenous bradyrhizobial strains and yield components of soybean are presented in Figure 3. The correlation coefficients of the occupancy rate of Bj6 isolates with plant height, node number, shoot dry weight, pod number, seed number, 100-seed weight, and yield were − 0.30, −0.04, 0.09, −0.13, −0.15, 0.41, and − 0.16, respectively. The correlation coefficients of the occupancy rate of Bd110 isolates exhibiting
Correlation coefficient between the occupancy rate of Bd110 isolates carrying the
Correlation between the occupancy rate of
In the present chapter, we described the breeding and selection processes, shoot growth, yield components, and infection tendency of useful bradyrhizobial strains carrying the
First, we selected eight lines exhibiting the characteristics of the
Finally, to evaluate the association between the occupancy rate of indigenous bradyrhizobial strains and yield components of soybean, correlation analysis was performed. Correlation coefficients of the occupancy rate of Bd110 isolates exhibiting
The authors thank the members of the laboratories of Shimane University and University of Miyazaki involved in the present study. Additionally, the authors thank the technical staff of the Agricultural Science Section, Education and Research Center for Biological Resources, Faculty of Life and Environmental Science, Shimane University for their support in managing soybean cultivation. The authors also thank the Faculty of Life and Environmental Sciences at Shimane University for financial support to publish this chapter.
The authors declare no conflict of interest.
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\n\nIf this sounds like a place that you would like to work, whether you are at the beginning of your career or are an experienced professional, we invite you to drop us a line and tell us why you could be the right person for IntechOpen.
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This chapter explains briefly the fire retardation of wood by using fire retardant coatings.",book:{id:"5827",slug:"new-technologies-in-protective-coatings",title:"New Technologies in Protective Coatings",fullTitle:"New Technologies in Protective Coatings"},signatures:"Thirumal Mariappan",authors:[{id:"198114",title:"Dr.",name:"Thirumal",middleName:null,surname:"Mariappan",slug:"thirumal-mariappan",fullName:"Thirumal Mariappan"}]},{id:"75967",title:"Recent Advances in Ceramic Materials for Dentistry",slug:"recent-advances-in-ceramic-materials-for-dentistry",totalDownloads:774,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Dental ceramics constitute a heterogeneous group of materials with desirable optical and mechanical proprieties combined with chemical stability. They are inorganic non-metallic materials used in several applications. These materials are biocompatible to tissue, highly esthetic, with satisfying resistance to tensile and shear stress. 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Several geological surveys have identified bauxite, iron, gold, diamond, and several metal ores. Because of the diversity and the magnitude of its resources, the country is referred to as a geological scandal. Nowadays the aluminum industry is still at the quarrying stage of bauxite, the main raw material that is converted into alumina and further to aluminum. Approximately 35–40% of the processed bauxite ore goes into the waste as alkaline red mud RM slurry which consists of 15–40% solids. RM and other industrial wastes material such as fly ash FA, rice husk ash RHA, that poses environmental hazards can be mixed to make them apt for usage in engineering applications. Geopolymers GP represent a new class of materials consisting of Al2O3▬SiO2-based material suitable for several engineering application. The present chapter presents the bauxitic potential of Guinea, the subsequent developing alumina industry. It reviews the application of RM for the production of geopolymer materials in the perspective of the valorization of the huge bauxite potential of Guinea.",book:{id:"8612",slug:"geopolymers-and-other-geosynthetics",title:"Geopolymers and Other Geosynthetics",fullTitle:"Geopolymers and Other Geosynthetics"},signatures:"Sékou Traoré, A. Diarra, O. Kourouma and D.L. Traoré",authors:[{id:"266484",title:"Prof.",name:"Sekou",middleName:null,surname:"Traore",slug:"sekou-traore",fullName:"Sekou Traore"},{id:"272379",title:"Dr.",name:"Doussou L.",middleName:null,surname:"Traoré",slug:"doussou-l.-traore",fullName:"Doussou L. 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. 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He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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