\r\n\t(1) Sustainable Waste Management;
\r\n\t(2) Micro(nano)plastics in the Environments;
\r\n\t(3) Electronic Waste and Circular Economy;
\r\n\t(4) Reducing, Recycling and Recovery of Agricultural and Food Waste;
\r\n\t(5) Biomass Valorization: Waste to Resources;
\r\n\t(6) Governmental Policy on Waste Management and Valorization.
\r\n\tThis book will offer a timely opportunity for knowledge exchange of sustainable management agenda for biological waste and remediation of soil, water and air in the local context, which satisfies the environmental compatibility, financial feasibility and social needs. It will deliberate on state-of-the-art treatment technologies, advanced management strategies, and political issues pertaining to recycling and recovery of organic waste.
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Hijazi",authors:[{id:"126661",title:"Dr.",name:"Ismael",middleName:null,surname:"Gonzalez",fullName:"Ismael Gonzalez",slug:"ismael-gonzalez"}]},{id:"35798",title:"Why, When and How Should Atrial Septal Defects Be Closed in Adults",slug:"why-when-and-how-should-atrial-septal-defects-be-closed-in-adults",signatures:"P. Syamasundar Rao",authors:[{id:"68531",title:"Dr.",name:"P. Syamasundar",middleName:null,surname:"Rao",fullName:"P. Syamasundar Rao",slug:"p.-syamasundar-rao"}]},{id:"35799",title:"Atrial Septal Defect Closure in Geriatric Patients",slug:"asd-closure-in-geriatric-population",signatures:"Teiji Akagi",authors:[{id:"113156",title:"Prof.",name:"Teiji",middleName:null,surname:"Akagi",fullName:"Teiji Akagi",slug:"teiji-akagi"}]},{id:"35800",title:"Atrial Septal Defect/Patent Foramen Ovale and Migraine Headache",slug:"atrial-septal-defect-patent-foramen-ovale-and-migraine-headache",signatures:"Mohammed Tawfiq Numan",authors:[{id:"126485",title:"Dr.",name:"Mohammed",middleName:null,surname:"Numan",fullName:"Mohammed Numan",slug:"mohammed-numan"}]},{id:"35801",title:"Transcatheter Occlusion of Atrial Septal Defects for Prevention of Recurrence of Paradoxical Embolism",slug:"transcatheter-occlusion-of-atrial-defects-for-prevention-of-recurrence-of-paradoxical-embolism",signatures:"Nicoleta Daraban, Manuel Reyes and Richard W. Smalling",authors:[{id:"132011",title:"Prof.",name:"Richard",middleName:null,surname:"Smalling",fullName:"Richard Smalling",slug:"richard-smalling"},{id:"138233",title:"Dr.",name:"Nicoleta",middleName:null,surname:"Daraban",fullName:"Nicoleta Daraban",slug:"nicoleta-daraban"},{id:"138237",title:"Dr.",name:"Manuel",middleName:null,surname:"Reyes",fullName:"Manuel Reyes",slug:"manuel-reyes"}]}]}],publishedBooks:[{type:"book",id:"2038",title:"Coronary Artery Disease",subtitle:"Current Concepts in Epidemiology, Pathophysiology, Diagnostics and Treatment",isOpenForSubmission:!1,hash:"55361f89e408a849c1153253684afe45",slug:"coronary-artery-disease-current-concepts-in-epidemiology-pathophysiology-diagnostics-and-treatment",bookSignature:"David Gaze",coverURL:"https://cdn.intechopen.com/books/images_new/2038.jpg",editedByType:"Edited by",editors:[{id:"71983",title:"Dr.",name:"David C.",surname:"Gaze",slug:"david-c.-gaze",fullName:"David C. 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Syamasundar Rao",coverURL:"https://cdn.intechopen.com/books/images_new/955.jpg",editedByType:"Edited by",editors:[{id:"68531",title:"Dr.",name:"P. Syamasundar",surname:"Rao",slug:"p.-syamasundar-rao",fullName:"P. Syamasundar Rao"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},onlineFirst:{chapter:{type:"chapter",id:"82476",title:"Joint Action of Herbicides on Weeds and Their Risk Assessment on Earthworm (Eisenia fetida L.)",doi:"10.5772/intechopen.105462",slug:"joint-action-of-herbicides-on-weeds-and-their-risk-assessment-on-earthworm-eisenia-fetida-l",body:'Heavy reliance on herbicides has increasingly raised environmental concerns [1, 2, 3]. The selection pressure of herbicides resulted in nature adapting and eventually developing herbicide-resistant and tolerant weeds biotype [4, 5, 6, 7]. The most effective tool to inhibit, delay, or control herbicide-resistant weeds is to substitute herbicides with different modes of action [8, 9]. But numerous studies have been conducted that simple switches do not delay the evolution of resistant weeds [10, 11]. Previous studies have shown that combining multiple herbicide modes of action in tank mixtures is more efficient in managing weeds [10, 12]. Mixing various modes of action in the mixture can control resistant weeds via broadening the selection pressure by targeting multiple metabolic pathways and delaying the evolution of herbicide-resistant weeds [13]. Ideal herbicide mixtures have proven beneficial over using a single herbicide in improving control and broadening the weed control spectrum [14, 15]. It contains active components with the same persistence and spectrum of controlled weeds but through a different mode of action [16]. Tank mixing increases in a spectrum of controlled weeds or an extension of weed control over a more extended period, which reduces production cost by saving time and labor, reduces the number of machine entrances into the production area, fuel consumption, water use to prepare the solution, and hours spent. This leads to lower soil compaction by eliminating multiple field operations. Crop safety is improved by adopting a combination of selected herbicides with minimum doses rather than a single high amount of one herbicide. The soil residues of persistent herbicides were decreased following the application of the minimum levels of such herbicides [17]. It is presupposed that herbicide tank mixtures with two or more herbicide partners behave and act independently so that the presence of each one does not affect the activity of another or may significantly modify the biological behavior of every herbicide in the mixture. Regarding the herbicide tank mixtures, the activity of the applied combination can be easily predicted as the sum of the activities related to each herbicide when applied separately.
In some cases, the interactions often result in declining or enhancing the activity of the combined herbicides compared with the sum. Practically, the herbicide combinations exhibit more activity on target weed species and less on crops (higher selectivity). However, the prediction of this issue is difficult since the behavior of each herbicide in the mixture is mainly influenced by the presence of the other(s), and the mixture activity may significantly vary depending on plant species, growth stage, and environmental conditions. Multiple herbicides applied in the mixture have three types of herbicide interaction: additive/neutral, synergistic, or antagonistic [18, 19, 20] (Figure 1). Synergism is favorable when two or more herbicide mixtures perform rather than the herbicides applied alone. It allows a lower application rate or frequency of herbicide treatment [22], but finding a new synergy remains challenging. In contrast, an antagonistic response is an interaction of two or more herbicides such that the effect, when combined, is less than the predicted effect based on the activity of each chemical applied separately. Antagonism is 2–3 times more common than synergy, especially when herbicides from different chemical families are combined [21]. Sometimes, synergism can be hypothesized based on mechanistic assumptions, as was done by [23], who predicted the synergism between glufosinate and protoporphyrinogen oxidase inhibitors and confirmed it experimentally; but generally, synergies are not predictable. A synergistic herbicide mixture for one species can also be antagonistic or additive for another species [24]. Thus, herbicide synergies appear to be rare and unpredictable. An additive/neutral response occurs when the observed response of two jointly applied herbicides is statistically similar to the expected value of the mixture. The interactions in herbicide mixtures can occur before, during, or after utilizing the mixture, the mechanisms of which can be broadly grouped into biochemical, competitive, physiological, and chemical categories [25]. This chapter aims to explain the importance of herbicide mixtures for weed control and to clarify the models to estimate combined herbicides’ effects. Meanwhile, discusses the risk assessment of herbicide mixtures on the earthworm population.
Schematic isobologram for additive, synergism, and antagonism response of herbicide interaction (ED50 = herbicides doses, applied singly or in the mixture for 50% weed control) (modified from [
The use of isobologram could determine the synergism and antagonism response of the mixtures [26]. Isobologram is a two-dimensional graph. There are two dose axes, x and y, in the mixtures. Herbicide A is the dose on the x-axis, and herbicide B is the dose on the y-axis. The mixtures follow the additive response when mixtures do not interact and present straight lines, and the analysis of this mixture is based on the additive dose model (ADM) [27]. The mixtures may interact, and the performance of combined herbicides is greater than that of herbicides applied alone. So, herbicides are more effective than expected and followed synergism. It means using a lower dose of combined herbicides to provide the same effect as herbicides applied alone. In contrast, if the efficacy of the herbicide mixture is less than that applied alone, then they show antagonism [26].
The reference model uses to determine synergism, antagonism, and additive response in the mixtures. Any consistent model must relate biological response to the doses of two or more herbicides. Choice of the reference model is crucial as the different models may produce different conclusions. The two most frequently referenced models in the study of joint action will be referred to as the additive dose model (ADM) and the multiplicative survival model (MSM) [28]. ADM assumes additivity of doses, i.e., that one herbicide can be replaced, wholly or partly, by another herbicide at equivalent doses. In contrast, MSM assumes that the expected efficacy of herbicide mixtures can be calculated by multiplying the percent survivals of the individual herbicides. Hence, a fundamental difference between the two models is that ADM considers dose rates, whereas MSM considers effects. Both dose addition and independent action should be helpful to approximations for defining the predicted response in the absence of herbicide interactions. A widely known characteristic of the ADM is that, for mixtures of two components, when the response surface predicted by the model is plotted against arithmetic scales of the component doses, the contours of equal response (i.e., isobols) are straight lines. At any particular level of response, the relative potency of the components when acting alone establishes scales of equivalent doses. In terms of this effective-dose (ED) scale, if one component of the mixture is replaced, wholly or in part, by the other, the predicted response is unchanged. By contrast, the MSM does not generally give straight-line isobols. The distinction between the ADM and MSM has not consistently been recognized, and different analysis methods have been confused with other models.
A third reference effect, effect addition, has been proposed, although it predicts implausible effects under certain realistic conditions [29, 30]. Therefore, it is unlikely to be helpful in practice. Likewise, the evaluation of adjuvants does not elicit any antagonistic or synergistic effects since there is no comparison with a reference effect, and it is the only so-called enhancement or potentiation effect [30]. There are various types of herbicide mixtures, experimental designs, and used models. A single-dose factorial design and multiple-dose factorial design are two main groups.
Two factors are involved in fixed-dose or single-dose experimental design. The first factor is several herbicides (two herbicides), and the second factor is dose with two levels (dose 0 and a nonzero dose). Overall, four treatments result in this design: control (dose 0 of both A and B) (E0), a nonzero dose of A and dose 0 of B (EA), dose 0 of A and a nonzero dose of B (EB), and a single mixture dose corresponding to nonzero doses of both A and B (EAB) [31].
Two nonzero doses justify certain model assumptions despite playing no role in the subsequent derivation. Thus, the doses should be carefully selected since any claim about an antagonistic or synergistic effect is only valid for the chosen doses. Synergism or antagonism can influence dose selection so that the use of a full recommended dose of each pesticide may mask potential synergism when trying to detect synergism for two highly effective herbicides. In this case, pesticide dose reduction (e.g., by 50%) is a common solution. The statistical analysis of 2 × 2 factorial design is based on the ordinary or linear mixed two-way Analysis of Variance (ANOVA) model depending on the experimental design [32]. It is assumed that fitting the two-way ANOVA model leads to the four estimates of E0, EA, EB, and EAB. In this regard, the subscript 0 refers to the control, A and B are considered as the separate effects of A and B, respectively, and AB indicates their combined effect. Regarding the ordinary two-way ANOVA, the estimates are simple treatment means for each group, while the weighted mean for the linear mixed one. Comparing E0, EA, EB, and EAB through pairwise comparisons does not demonstrate any antagonistic or synergistic effects after fitting a two-way ANOVA model. An antagonistic or synergistic effect may be reported where there is none. Further, the estimates can be used to derive the predicted effect under the assumptions of dose addition and independent action.
The reference effect (Eadd) under the assumption of dose addition is defined as follows [33]:
The definition in Eq. (1) may be justified as reflecting dose addition (even though effects and not doses are added up) by supposing linear dose-response relationships for the two pesticides [32]. Given the availability of only a single nonzero dose for the two pesticides, it is not meant to assume any nonlinear dose-response relationships. However, a linear dose-response relationship may often be assumed as a local approximation to the true nonlinear relationship. This assumption can be justifiable if amounts were chosen as the effective doses, which are not too extreme since the dose-response relationship within a restricted dose range may be supposed to be approximately linear. Particularly, let yA = a0 + bAxA and yB = a0 + bBxB denote the simple linear regression equations for the two pesticides with the response values of yA and yB, as well as the doses of xA and xB, respectively. Then, the reference effect Eadd is as follows:
representing that the sum of effects is equal to that of doses after appropriate scaling [34]. Each antagonistic or synergistic effect can be defined as the difference (DDA) between the observed response (expressed as the difference from the control) and predicted effect (Eq. (1)). Especially, the difference is considered as follows:
Based on the definition of difference DDA in Eq. (3), the values significantly larger and smaller than zero exhibit a synergistic and an antagonistic effect, respectively. Testing the null hypothesis of no antagonistic or synergistic effect corresponds to testing for no interaction in a standard two-way ANOVA model. Regarding reporting, the difference must be accompanied by the corresponding standard error or 95% confidence interval to allow for the uncertainty attached to the estimate.
The reference effect (Eind) under the assumption of independent action is defined as follows:
as rephrasing in terms of the parameters in the two-way ANOVA model [35]. Similar to the dose addition, the reference effect only involves the three estimates corresponding to the control group (E0) and the two separate effects of pesticides A and B (EA and EB, respectively). In contrast to the definition of dose addition in Eq. (4), which only includes contrasts (i.e., the differences relative to the control), the definition in Eq. (3) relies heavily on the absolute level of the control group (E0). Furthermore, any antagonistic or synergistic effect may be expressed as the discrepancy between the observed and reference effect under the assumption of independent action in the same way as for dose addition. The difference (DIA) is defined as follows:
The difference DIA significantly below or above zero demonstrates an antagonistic or synergistic effect, respectively. The difference should be reported with the corresponding standard error or 95% confidence interval, which can be obtained by using the delta method. The delta approach is a statistical technique for estimating the standard errors of derived parameter estimates (i.e., the parameters that do not explicitly feature the model parameterization) [18].
The multidose design is similar to the single-dose one except that a dose range is selected for one or both pesticides, and mixture doses are obtained based on a complete or incomplete two-way factorial design (Figure 2). The statistical modeling approach outlined for single-dose designs can be simply applied in multidose designs by analyzing one mixture dose at a time in the separate statistical analyses corresponding to fitting two-way ANOVA models. A multidose design can be considered as a collection of single-dose designs, and a design involving multiple mixtures in single doses can be analyzed in the same way. However, this method may or may not imply the suboptimal use of data depending on the type of response and experimental design. Fitting a simultaneous model and borrowing strength across mixture doses may improve the analysis in some cases [36].
Factorial and fixed-ratio designs for binary mixture experiments (black and light-gray points illustrate fixed-/single-dose and multidose factorial designs, respectively. The dark-gray lines reflect the rays in a fixed-ratio design with five rays. In addition, three mixtures (virtual proportions of 25:75, 50:50, and 75:25) and two degenerate mixture rays are observed for the individual pure pesticides (virtual proportions of 100:0 and 0:100). The dark-gray points represent the amounts selected along the rays. The doses for the factorial and fixed-ratio designs hardly overlap [
The single-ray mixture fixed-ratio design consists of several mixture doses so that the two individual herbicides contribute to doses in a constant ratio (in a single ray), which may be specified in terms of so-called actual or virtual proportions. Further, the design involves the two rays corresponding to the individual, pure pesticides, utilized in several doses. Determining total mixture doses is an important preliminary step in planning a fixed-ratio mixture experiment. These doses can be used for subsequent dose-response modeling. Ideally, this step requires prior knowledge about effective doses. Therefore, it is assumed that ED50A and ED50B are available from the previous experiment. The resulting relative potency of pesticide B relative to A is denoted ρ (=ED50B/ED50A). For a given mixture fraction f ∈ [0, 1], which is respectively related to virtual (mixture) proportions
Three scenarios are distinguished depending on how similar or dissimilar the dose-response curves are assumed. The assumptions have profound implications on how to evaluate antagonistic and synergistic effects.
That imposing shared lower and upper limits and slopes for all three dose-response curves often referred to as parallelism have been used for a long time. These models involve only a single parameter for the common lower and upper limits, slope, and three parameters for the ED50 (one for each curve). Accordingly, there are a total of six model parameters. Under the assumption of dose addition, the estimated mixture ED50 (ED50add) can be calculated by the linear combination of the ED50 values estimated for individual pesticides as [33]:
by using the virtual proportions
where xA =
Fitting the dose-response model(s) results in estimating ED50A, ED50B, and ED50mix (expressed as total doses). Furthermore, both a difference and a ratio may be used to examine departures from the assumption of dose addition. In any case, the corresponding standard error or 95% confidence interval should be reported, the first of which can be computed by employing the delta method. Particularly, the definition of the difference is as follows [33]:
An estimated difference significantly more or less than zero reflects an antagonistic or synergistic effect. It is worth noting that ED50add and ED50mix, which do not incorporate the uncertainty of both estimates, should not be compared [40]. The ratio, combination, or interaction index is defined as follows [32]:
where a value significantly larger than 1 illustrates an antagonistic effect, while a synergistic effect is detected when a value is significantly lower than 1. The use of arbitrary cutoffs such as RDA < 0.8 and > 1.2 is not enough for declaring synergism or antagonism, respectively, since the variation in RDA is ignored entirely. The utilization of a difference in terms of logarithm-transformed estimated ED50 values corresponds to the application of ratio RDA. These difference and ratio respectively expressed by Eqs. (9) and (10) need not lead to the same results because of using various approximations while calculating the corresponding standard errors based on the delta approach.
In log-logistic and Weibull dose-response models, the approximations of estimates for the slope parameter b and parameter e (ED50 in the log-logistic one) have recently been established by supposing dose addition [41]. The approximations can be compared with the parameters estimated for the fitted dose-response curve of the mixture. Regarding the log-logistic model, this approach provides a framework for comparing the observed ED50 for the mixture with the predicted ED50 under this assumption. The approximation of ED50 coincides with Eq. (7) for the identical slope scenario. In addition, a slight difference is observed in the approximations for the identical and varying slope scenarios [42]. Thus, varying slopes may not warrant a different analysis than for the earlier case of identical slopes and lower limits when interest lies in ED50. In other words, Eqs. (7), (9), and (10) may still be applied for assessing synergistic and antagonistic effects. However, a different definition of reference effect under the assumption of dose addition may be required for varying slope scenario if interest is in other effective doses [42].
The varying lower limits may be caused by the lack of absorption or solubility, complicating the evaluation of synergistic and antagonistic effects. For example, the assumption of dose addition needs to no longer correspond to the linear relationships between effective doses (Eq. (7)) [43]. A crude approximation is obtained by supposing identical limits, which should be flagged during use. The literature has proposed several approaches for handling varying lower limits or relevant varying upper limit scenario. Further, many generalizations of existing dose-response models have been suggested [44], often involving highly nonlinear regression models or additional assumptions to present suitable predictions. However, the generalizations are not yet readily available to practitioners. The estimation and quantification of departure from the reference effect remain difficult. The utilization of an absolute effect level, which is separately reached for both pesticides, can be addressed as an alternative. The corresponding (relative) effective doses need not correspond to (relative) ED50, although they are defined independently of the lower limit (as if the lower limit is zero for both pesticides). This approach can provide a viable solution in pesticide science since the control (dose 0) mostly corresponds to the highest response level. Differing lower limits often occur for relatively high doses. The procedure previously described for the case with identical slopes and lower limits can be employed in the case of selecting the appropriate absolute effect level. However, the definition of the effective dose under the assumption of dose addition may not be straightforward for the varying slope scenario.
In analogy with Eq. (4), the dose-response function for the mixture
for any dose x. The denominator can be the mean response level at dose zero for each of the two individual pesticides, which should have the same upper limit by the assumption. In many applications, in which the response values are pre-standardized against the control [45], Eq. (11) reduces to simply being the product (e.g., standardization means
With respect to mathematical form, the function find expressed by Eqs. (11) or Eqs. (12) is not the same as the model functions
In the case of an experimental design with multiple mixture rays (Figure 2), the earlier methods for the identical and varying slope scenarios for ED50 may still be implemented, repeating the analysis for each mixture ray. Since these separate analyses share the same control group, some overlaps are detected in the used data, although they may be acceptable [47].
We note in this section several research results that concluded additivity, antagonism, and synergism effects on weeds.
One of the most common herbicide mixtures is different graminicides with broadleaf herbicides mixture to broaden the weed control spectrum. The postemergence application of various graminicides in a mixture with one or more broadleaf herbicides often results in reduced efficacy of graminicides [48]. Antagonistic interactions are probably due to morphological and physiological differences between grasses and broadleaf weeds. Broadleaf weeds have meristems at the top of the plant, whereas grasses have them at the base. On the other hand, this difference affects absorption and mainly translocation of the foliar-applied herbicides, particularly the systemic ones that are translocated and accumulated at the meristematic tissues of the plant where they act. The herbicide amount translocated to its site of action can be declined by the presence or concomitant translocation of another herbicide into the plant [48]. Increasing the ratio of graminicide to broadleaf herbicide in a mixture can alleviate the antagonism of the graminicide [49]. Historically, ACCase inhibiting herbicide antagonism has been observed when applied in a mixture with broadleaf or sedge herbicides, such as ALS inhibiting herbicides and photosystem II inhibiting herbicides [19, 50]. Research by [19] showed that quizalofop (120 g ha−1) mixed with the full labeled rate of halosulfuron at 53 g ha−1 could result in an antagonistic interaction for weedy rice and barnyardgrass control. The interaction of herbicides in-tank mixing depended on weed species. Noticeably, the highest dose of halosulfuron (53 g ha−1) mixed with quizalofop followed an additive response on red rice (
An antagonistic effect of metribuzin with halosulfuron and metribuzin with flumioxazin at the different dose and mixture ratios was observed on common lambsquarters (
Historically, adjuvants are essential components for herbicide-resistant weeds control. To improve herbicides’ performance or application objective, adjuvants are used in the spray tank. These adjuvants are commonly added to the spray tank to improve herbicidal activity or application characteristics [66]. According to the [67] “adjuvants are the substances used with a herbicide to improve its performance.” In the last definition, “adjuvants are already included in the formulations of some herbicides available for sale. They may be purchased separately and added into a tank mix before use” [68]. Generally, adjuvants have been developed to assist herbicides. They allow mix and handle with herbicide active ingredients better, contact to target weed, increase droplet coverage, and spray retention and droplet drying [66]. Adjuvants diminish or even eliminate spray application problems [69] (e.g., drift reduction) [70], enhance herbicide cuticle penetration and cellular accumulation [71], and decline herbicide amount and total weed control costs. Furthermore, they lead to a significantly greater herbicide efficacy [72] and consequently a lower total herbicide concentration to achieve a given effect [73], as well as promoting the formulation’s ability to kill the targeted species without harming other plants [74]. In terms of environmental aspects, they can decrease herbicide leaching through soil profile [75]. However, adjuvant addition does not significantly improve control in some circumstances. Adjuvants can sometimes exhibit adverse effects such as declined herbicide activity (antagonistic effects) [76], enhanced formulation ability to spread or persist in the unwanted environment [77], and increased harmful effects on nontarget plants and aquatic species [78]. Adjuvants are divided into activators, spray modifiers, and utility modifiers [79]. Activators are components that change characteristic herbicides such as viscosity and particle size, evaporation, etc. They improved herbicide activity, spread, absorption into a tissue, rainfastness, and reduced herbicide photodegradation. There are three categories of activators: surfactants, wetting agents, and oils [79].
Surfactants are the most widely used and probably the most essential adjuvants [80]. Surfactants can be classified into nonionic, cationic, anionic, and ampholytic based on their ability to ionize the aqueous solution. Organosilicone and silicone surfactants are two types of nonionic surfactants. Cationic surfactants, which have a positive charge, often are not applied with herbicides, and anionic ones are rarely utilized with herbicides. Ampholytic (amphoteric) have both positive and negative charges, that is, in aqueous solution are capable of forming cations or anions. Wetting agents increase solution spread on the leaves [79]. Oils increase herbicide uptake by increasing the time of retention. They mixed with water via emulsifiers. Oils have uniform droplet size (reduction of drift), decreasing spray evaporation and rainfastness time, and increasing penetration into waxy leaves. They can be classified as: crop oils, dormant oils, crop oil concentrates, vegetable oils, vegetable oil concentrate, modified vegetable oil, and modified vegetable oil concentrate. In addition, spray modifiers are among the most important adjuvants, which influence the delivery and placement of spray solution [81]. They limit or alter the physicochemical characteristics of spray solution, make herbicide spray easier to aim, reduce herbicide drift in the air, and cause the spray to adhere to plants more readily. Spray modifiers include thickening agents (i.e., invert emulsions and polymers), stickers, spreaders, spreader stickers, foaming agents, humectants, and UV absorbents. Utility modifiers are the third group of adjuvants, which help minimize handling and application problems. They do not directly improve efficacy, although they widen the conditions in which an herbicide can be used or maintain the integrity of the spray solution. For instance, utility modifiers diminish foaming, promote solubility, modify pH, or decrease spray drift. Emulsifiers, dispersants, cosolvents, ammonium fertilizers, and stabilizing, coupling, compatibility, buffering, and antifoam agents can be addressed as the types of modifiers.
Adjuvants can be especially effective in increasing the biological activity of many herbicides [82]. Previous studies reported that density, viscosity, surface tension, contact angle, droplet size, and droplet evaporation of the spray solution could change with the addition of adjuvants to the spray solution [83]. The activity of tribenuron-methyl significantly enhances following the use of NIS (20% isodecyl alcohol ethoxylate + 0.7% silicone surfactants), an anionic surfactant (25.5% alkyl ether sulfate sodium salt), and vegetable oil (95% natural rapeseed oil with 5% compound emulsifiers) on
Generally, environmental agents affect the efficacy of the mixture of herbicides with adjuvants. In the mixture, rain shortly after utilizing herbicides is among the most detrimental issues for performance. Given that the rainfastness of herbicides increases by applying adjuvants, the effect should be considered when selecting an adjuvant [92]. A study [93] represented a shorter critical rain-free period following the addition of an OSL adjuvant to glyphosate. This decline can be attributed to the lower liquid surface tension of glyphosate caused by the OSL (Organosilicone) adjuvant and the subsequent promotion of the stomatal infiltration of glyphosate into the plant. The conventional adjuvants produced slower absorption of the 14C-glyphosate, as the maximum absorption was not achieved until at least 24 h in redroot pigweed, remaining similar until 72 h [88]. The effect of the vegetable oil on tribenuron-methyl’s rainfastness was significantly lower than that of the surfactants with rain at 1 h, while no significant differences among the three adjuvants were observed when rain occurred at 2 and 4 h [84].
Adjuvants can significantly enhance the effect of an herbicide, while they fail to increase control and cause harmful effects on nontarget plants in some circumstances (antagonistic effect). Several studies have revealed that
Continuous application of herbicides may lead to soil pollution and affect soil fauna [97]. Generally, herbicides applied alone and in mixture negatively influenced nottargeted animals [98]. As soil inhabitant animals, earthworms might be affected, although the site of action herbicides is not targeted toward animals. They are bioindicators for determining herbicide and heavy metals pollution in soil due to their high sensitivity to soil pollution [99, 100]. The
As mentioned before, additive, synergism, and antagonism are three types of herbicide interactions. Concentration addition (CA) and independent action (IA) are two common reference models for determining mixture toxicity.
The toxicity of herbicide mixtures with a similar mode of action is estimated by concentration addition (CA) [103], which has extensively been used for herbicides, and is most straightforward [104]. Generally, CA assumes additivity of toxicity that components will not interact with each other in the mixtures, and the relative potency is equal to the sum of singly potencies [105].
The independent action model (IA) is used for components with the dissimilar mode of action on the organism. They act independently. The toxicity of the total mixture is calculated by the expected effects of each component [106].
Physical, chemical, and biological interactions of herbicides do not account for by CA and IA models. MIXTOX is an empirical model that determines how much mixture toxicity results deviate from CA and IA model predictions [107]. MIXTOX considered a difference between synergism and antagonism based on concentration and mixture ratios along with deviations [108]. Therefore, experimental design for MIXTOX is considerable due to covering all concentration and mixture ratios [109]; to date, MIXTOX has been used with binary mixture toxicity [110]. The median-effect/combination index (CI) is a method used by [111] to expound chemical interactions. It quantitatively determines the mixtures interactions at various concentrations and mixtures ratios. Pollution interaction is developed by [112].
The response to toxic exposure of
where
The
These parameters were then used to calculate concentrations of the pesticides and their combinations required to produce various effect levels according to Eq. (14); combination index (CI) values were then calculated according to the general combination index equation for n chemical combination at 10%, 50%, and 90% mortality rate:
where n(CI)x is the combination index for n chemicals at x% effect level; (Dx)1_n is the sum of the concentration of n pesticides causing x% mortality rate of the earthworms in the mixture,
CIx comp is the computed combination index value for the mixture at the x level of effect (x%) from the experimental toxicity curve of the mixture [113].
The study of herbicide mixtures on
Several herbicides (acetochlor, anilofos, flutamone, pretilachlor, S-metolachlor, and terbutryn) were very toxic in contact toxicity but were low in soil toxicity testing [120]. The mixture of tribenuron methyl (TBM) plus tebuconazole (TEB) showed an antagonistic effect on the earthworms in filter paper and artificial soil tests. In the chronic toxicity experiment, both high concentrations of TBM and TEB, single or combined, induced oxidant stress in the earthworms, and the cellulase activity was inhibited in the earthworm exposed to high concentrations of TBM at the early 35 exposure period. However, both pesticides did not damage the DNA of earthworms in all treatments [99]. Both acute and chronic toxicity tests play an essential role in the risk evaluation of pesticides to earthworms. They are considered valuable for predicting the responses of soil organisms to pesticides [121]. An antagonistic effect was observed the binary mixture of butachlor plus λ-cyhalothrin at all effect levels in artificial soil test, while it shows synergism effect in filter paper test [122]. In the research of Chen et al., [122], the binary mixture of butachlor plus atrazin showed moderate synergism at the highest effect levels. An additive and slightly synergism were observed at <0.2
Herbicide resistance is a pervasive challenge in intensive agriculture. Applying multiple modes of action can help to manage herbicide-resistant weeds. Herbicide mixture is a powerful tool to prevent, delay, and control herbicide-resistant weeds. The choice of the most appropriate mixture is crucial and is based on herbicide components, formulation, and weed species. The reference models used to determine the interaction of herbicide and the use of isobologram can illustrate the synergism, additive, and antagonism responses by the ED scale. Another method to manage herbicide-resistant weeds is utilizing adjuvant. Adjuvants are the best tool for improving herbicide performance and optimizing herbicide application. In addition, the adjuvant can overcome antagonist response in the tank mixture. Despite the positive effect, the synergism response in high doses can influence the soil animals such as earthworms. Therefore, growers need knowledge of the management strategies to maximize the long-term benefits of herbicide mixture and reduce weed shifts to difficult-to-control and herbicide-resistant weeds.
This paper was supported by the Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Iran, and Mississippi State University, USA, for financial support. This work is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, Hatch project under accession number 230100.
The authors declare no conflict of interest.
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He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. Prof. Emeje’s several international fellowships include the prestigious Raman fellowship. He has published more than 150 articles and patents. He is also the head of R&D at NIPRD and holds a visiting professor position at Nnamdi Azikiwe University, Nigeria. He has a postgraduate certificate in Project Management from Walden University, Minnesota, as well as a professional teaching certificate and a World Bank certification in Public Procurement. Prof. Emeje was a national chairman of academic pharmacists in Nigeria and the 2021 winner of the May & Baker Nigeria Plc–sponsored prize for professional service in research and innovation.",institutionString:"National Institute for Pharmaceutical Research and Development",institution:{name:"National Institute for Pharmaceutical Research and Development",country:{name:"Nigeria"}}},{id:"436430",title:"Associate Prof.",name:"Mesut",middleName:null,surname:"Işık",slug:"mesut-isik",fullName:"Mesut Işık",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/436430/images/19686_n.jpg",biography:null,institutionString:null,institution:{name:"Bilecik University",country:{name:"Turkey"}}},{id:"268659",title:"Ms.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/268659/images/8143_n.jpg",biography:"Dr. Zhan received his undergraduate and graduate training in the fields of preventive medicine and epidemiology and statistics at the West China University of Medical Sciences in China during 1989 to 1999. He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. His current main research interest focuses on the studies of cancer proteomics and biomarkers, and the use of modern omics techniques and systems biology for PPPM in cancer, and on the development and use of 2DE-LC/MS for the large-scale study of human proteoforms.",institutionString:null,institution:{name:"Xiangya Hospital Central South University",country:{name:"China"}}},{id:"40482",title:null,name:"Rizwan",middleName:null,surname:"Ahmad",slug:"rizwan-ahmad",fullName:"Rizwan Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/40482/images/system/40482.jpeg",biography:"Dr. Rizwan Ahmad is a University Professor and Coordinator, Quality and Development, College of Medicine, Imam Abdulrahman bin Faisal University, Saudi Arabia. Previously, he was Associate Professor of Human Function, Oman Medical College, Oman, and SBS University, Dehradun. Dr. Ahmad completed his education at Aligarh Muslim University, Aligarh. He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. He has more than sixteen years of teaching experience and has supervised numerous postgraduate and Ph.D. students. He has to his credit more than seventy papers in SCI- and SCOPUS-indexed journals, fifty-five conference proceedings, four books, six Best Paper Awards, and five projects from different government agencies. He is currently an editorial board member of eight international journals and a reviewer for more than fifty scientific journals. He received Top Reviewer and Excellent Peer Reviewer Awards from Publons in 2016 and 2017, respectively. He is also on the panel of The International Reviewer for reviewing research proposals for grants from the Royal Society. He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"354817",title:"Dr.",name:"Anubhab",middleName:null,surname:"Mukherjee",slug:"anubhab-mukherjee",fullName:"Anubhab Mukherjee",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y0000365PbRQAU/ProfilePicture%202022-04-15%2005%3A11%3A18.480",biography:"A former member of Laboratory of Nanomedicine, Brigham and Women’s Hospital, Harvard University, Boston, USA, Dr. Anubhab Mukherjee is an ardent votary of science who strives to make an impact in the lives of those afflicted with cancer and other chronic/acute ailments. He completed his Ph.D. from CSIR-Indian Institute of Chemical Technology, Hyderabad, India, having been skilled with RNAi, liposomal drug delivery, preclinical cell and animal studies. He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. Ltd. and heads the Hyderabad R&D center of the organization.",institutionString:"Esperer Onco Nutrition Pvt Ltd.",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a Principal Investigator and Scientist at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via machine-learning-based analyses of exosomal signatures. Dr. Paul has published in more than fifty peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award, a senior member of the Institute of Electrical and Electronics Engineers (IEEE), and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. He is currently working on the protective activity of phenolic compounds in disorders associated with oxidative stress and inflammation.",institutionString:null,institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. In 2016, he joined Kafkas University, Faculty of Veterinary Medicine, Department of Physiology as an assistant professor. Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"418963",title:"Dr.",name:"Augustine Ododo",middleName:"Augustine",surname:"Osagie",slug:"augustine-ododo-osagie",fullName:"Augustine Ododo Osagie",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/418963/images/16900_n.jpg",biography:"Born into the family of Osagie, a prince of the Benin Kingdom. I am currently an academic in the Department of Medical Biochemistry, University of Benin. Part of the duties are to teach undergraduate students and conduct academic research.",institutionString:null,institution:{name:"University of Benin",country:{name:"Nigeria"}}},{id:"192992",title:"Prof.",name:"Shagufta",middleName:null,surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192992/images/system/192992.png",biography:"Prof. Shagufta Perveen is a Distinguish Professor in the Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. Dr. Perveen has acted as the principal investigator of major research projects funded by the research unit of King Saud University. She has more than ninety original research papers in peer-reviewed journals of international repute to her credit. She is a fellow member of the Royal Society of Chemistry UK and the American Chemical Society of the United States.",institutionString:"King Saud University",institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"49848",title:"Dr.",name:"Wen-Long",middleName:null,surname:"Hu",slug:"wen-long-hu",fullName:"Wen-Long Hu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49848/images/system/49848.jpg",biography:"Wen-Long Hu is Chief of the Division of Acupuncture, Department of Chinese Medicine at Kaohsiung Chang Gung Memorial Hospital, as well as an adjunct associate professor at Fooyin University and Kaohsiung Medical University. Wen-Long is President of Taiwan Traditional Chinese Medicine Medical Association. He has 28 years of experience in clinical practice in laser acupuncture therapy and 34 years in acupuncture. He is an invited speaker for lectures and workshops in laser acupuncture at many symposiums held by medical associations. He owns the patent for herbal preparation and producing, and for the supercritical fluid-treated needle. Dr. Hu has published three books, 12 book chapters, and more than 30 papers in reputed journals, besides serving as an editorial board member of repute.",institutionString:"Kaohsiung Chang Gung Memorial Hospital",institution:{name:"Kaohsiung Chang Gung Memorial Hospital",country:{name:"Taiwan"}}},{id:"298472",title:"Prof.",name:"Andrey V.",middleName:null,surname:"Grechko",slug:"andrey-v.-grechko",fullName:"Andrey V. Grechko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/298472/images/system/298472.png",biography:"Andrey Vyacheslavovich Grechko, Ph.D., Professor, is a Corresponding Member of the Russian Academy of Sciences. He graduated from the Semashko Moscow Medical Institute (Semashko National Research Institute of Public Health) with a degree in Medicine (1998), the Clinical Department of Dermatovenerology (2000), and received a second higher education in Psychology (2009). Professor A.V. Grechko held the position of Сhief Physician of the Central Clinical Hospital in Moscow. He worked as a professor at the faculty and was engaged in scientific research at the Medical University. Starting in 2013, he has been the initiator of the creation of the Federal Scientific and Clinical Center for Intensive Care and Rehabilitology, Moscow, Russian Federation, where he also serves as Director since 2015. He has many years of experience in research and teaching in various fields of medicine, is an author/co-author of more than 200 scientific publications, 13 patents, 15 medical books/chapters, including Chapter in Book «Metabolomics», IntechOpen, 2020 «Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology».",institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"199461",title:"Prof.",name:"Natalia V.",middleName:null,surname:"Beloborodova",slug:"natalia-v.-beloborodova",fullName:"Natalia V. Beloborodova",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/199461/images/system/199461.jpg",biography:'Natalia Vladimirovna Beloborodova was educated at the Pirogov Russian National Research Medical University, with a degree in pediatrics in 1980, a Ph.D. in 1987, and a specialization in Clinical Microbiology from First Moscow State Medical University in 2004. She has been a Professor since 1996. Currently, she is the Head of the Laboratory of Metabolism, a division of the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russian Federation. N.V. Beloborodova has many years of clinical experience in the field of intensive care and surgery. She studies infectious complications and sepsis. She initiated a series of interdisciplinary clinical and experimental studies based on the concept of integrating human metabolism and its microbiota. Her scientific achievements are widely known: she is the recipient of the Marie E. Coates Award \\"Best lecturer-scientist\\" Gustafsson Fund, Karolinska Institutes, Stockholm, Sweden, and the International Sepsis Forum Award, Pasteur Institute, Paris, France (2014), etc. Professor N.V. Beloborodova wrote 210 papers, five books, 10 chapters and has edited four books.',institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"354260",title:"Ph.D.",name:"Tércio Elyan",middleName:"Azevedo",surname:"Azevedo Martins",slug:"tercio-elyan-azevedo-martins",fullName:"Tércio Elyan Azevedo Martins",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/354260/images/16241_n.jpg",biography:"Graduated in Pharmacy from the Federal University of Ceará with the modality in Industrial Pharmacy, Specialist in Production and Control of Medicines from the University of São Paulo (USP), Master in Pharmaceuticals and Medicines from the University of São Paulo (USP) and Doctor of Science in the program of Pharmaceuticals and Medicines by the University of São Paulo. Professor at Universidade Paulista (UNIP) in the areas of chemistry, cosmetology and trichology. Assistant Coordinator of the Higher Course in Aesthetic and Cosmetic Technology at Universidade Paulista Campus Chácara Santo Antônio. Experience in the Pharmacy area, with emphasis on Pharmacotechnics, Pharmaceutical Technology, Research and Development of Cosmetics, acting mainly on topics such as cosmetology, antioxidant activity, aesthetics, photoprotection, cyclodextrin and thermal analysis.",institutionString:null,institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"334285",title:"Ph.D. Student",name:"Sameer",middleName:"Kumar",surname:"Jagirdar",slug:"sameer-jagirdar",fullName:"Sameer Jagirdar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334285/images/14691_n.jpg",biography:"I\\'m a graduate student at the center for biosystems science and engineering at the Indian Institute of Science, Bangalore, India. I am interested in studying host-pathogen interactions at the biomaterial interface.",institutionString:null,institution:{name:"Indian Institute of Science Bangalore",country:{name:"India"}}},{id:"329248",title:"Dr.",name:"Md. Faheem",middleName:null,surname:"Haider",slug:"md.-faheem-haider",fullName:"Md. Faheem Haider",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329248/images/system/329248.jpg",biography:"Dr. Md. Faheem Haider completed his BPharm in 2012 at Integral University, Lucknow, India. In 2014, he completed his MPharm with specialization in Pharmaceutics at Babasaheb Bhimrao Ambedkar University, Lucknow, India. He received his Ph.D. degree from Jamia Hamdard University, New Delhi, India, in 2018. He was selected for the GPAT six times and his best All India Rank was 34. Currently, he is an assistant professor at Integral University. Previously he was an assistant professor at IIMT University, Meerut, India. He has experience teaching DPharm, Pharm.D, BPharm, and MPharm students. He has more than five publications in reputed journals to his credit. Dr. Faheem’s research area is the development and characterization of nanoformulation for the delivery of drugs to various organs.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"329795",title:"Dr.",name:"Mohd Aftab",middleName:"Aftab",surname:"Siddiqui",slug:"mohd-aftab-siddiqui",fullName:"Mohd Aftab Siddiqui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329795/images/system/329795.png",biography:"Dr. Mohd Aftab Siddiqui is an assistant professor in the Faculty of Pharmacy, Integral University, Lucknow, India, where he obtained a Ph.D. in Pharmacology in 2020. He also obtained a BPharm and MPharm from the same university in 2013 and 2015, respectively. His area of research is the pharmacological screening of herbal drugs/natural products in liver cancer and cardiac diseases. He is a member of many professional bodies and has guided many MPharm and PharmD research projects. 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This includes, but is not limited to: single-neuron modeling, sensory processing, motor control, memory, and synaptic plasticity, attention, identification, categorization, discrimination, learning, development, axonal patterning, guidance, neural architecture, behaviors, and dynamics of networks, cognition and the neuroscientific basis of consciousness. 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It has become a massive part of our daily lives, making predictions based on experience, making this a fascinating area that solves problems that otherwise would not be possible or easy to solve. This topic aims to encompass algorithms that learn from experience (supervised and unsupervised), improve their performance over time and enable machines to make data-driven decisions. It is not limited to any particular applications, but contributions are encouraged from all disciplines.",annualVolume:11422,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"43680",title:"Prof.",name:"Ciza",middleName:null,surname:"Thomas",fullName:"Ciza Thomas",profilePictureURL:"https://mts.intechopen.com/storage/users/43680/images/system/43680.jpeg",institutionString:null,institution:{name:"Government of Kerala",institutionURL:null,country:{name:"India"}}},{id:"16614",title:"Prof.",name:"Juan Ignacio",middleName:null,surname:"Guerrero Alonso",fullName:"Juan Ignacio Guerrero Alonso",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6HB8QAM/Profile_Picture_1627901127555",institutionString:null,institution:{name:"University of Seville",institutionURL:null,country:{name:"Spain"}}},{id:"3095",title:"Prof.",name:"Kenji",middleName:null,surname:"Suzuki",fullName:"Kenji Suzuki",profilePictureURL:"https://mts.intechopen.com/storage/users/3095/images/1592_n.jpg",institutionString:null,institution:{name:"University of Chicago",institutionURL:null,country:{name:"United States of America"}}},{id:"214067",title:"Dr.",name:"W. 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The area covers many techniques that offer solutions to emerging problems in robotics and enterprise-level software systems. Collaborative intelligence is highly and effectively achieved with multi-agent systems. Areas of application include swarms of robots, flocks of UAVs, collaborative software management. Given the level of technological enhancements, the popularity of machine learning in use has opened a new chapter in multi-agent studies alongside the practical challenges and long-lasting collaboration issues in the field. It has increased the urgency and the need for further studies in this field. We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",annualVolume:11423,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"275140",title:"Dr.",name:"Dinh Hoa",middleName:null,surname:"Nguyen",fullName:"Dinh Hoa Nguyen",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRbnKQAS/Profile_Picture_1622204093453",institutionString:null,institution:{name:"Kyushu University",institutionURL:null,country:{name:"Japan"}}},{id:"20259",title:"Dr.",name:"Hongbin",middleName:null,surname:"Ma",fullName:"Hongbin Ma",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRhDJQA0/Profile_Picture_2022-05-02T08:25:21.jpg",institutionString:null,institution:{name:"Beijing Institute of Technology",institutionURL:null,country:{name:"China"}}},{id:"28640",title:"Prof.",name:"Yasushi",middleName:null,surname:"Kambayashi",fullName:"Yasushi Kambayashi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYOQxQAO/Profile_Picture_1625660525470",institutionString:null,institution:{name:"Nippon Institute of Technology",institutionURL:null,country:{name:"Japan"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/170589",hash:"",query:{},params:{id:"170589"},fullPath:"/profiles/170589",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()