A non-exhaustive list of allergens identified in soya.
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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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This topic is a critical variable in industry to reach and maintain competitiveness. Therefore, proper management of the corrective, predictive, and preventive politics in any industry is required. This book complements other subdisciplines such as economics, finance, marketing, decision and risk analysis, engineering, etc. The book presents real case studies in multiple disciplines. It considers the main topics using prognostic and subdiscipline techniques. It is essential to link these topics with the areas of finance, scheduling, resources, downtime, etc. to increase productivity, profitability, maintainability, reliability, safety, and availability, and reduce costs and downtime. Advances in mathematics, modeling, computational techniques, dynamic analysis, etc. are employed analytically. Computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques are expertly blended to support the analysis of prognostic problems with defined constraints and requirements. The book is intended for graduate students and professionals in industrial engineering, business administration, industrial organization, operations management, applied microeconomics, and the decisions sciences, either studying maintenance or needing to solve large, specific, and complex maintenance management problems as part of their jobs. The work will also be of interest to researches from academia.",isbn:"978-1-78984-214-2",printIsbn:"978-1-78984-213-5",pdfIsbn:"978-1-78985-661-3",doi:"10.5772/intechopen.77564",price:119,priceEur:129,priceUsd:155,slug:"fault-detection-diagnosis-and-prognosis",numberOfPages:176,isOpenForSubmission:!1,hash:"d54796f7da58f58fa679b94a2b83af00",bookSignature:"Fausto Pedro García Márquez",publishedDate:"February 5th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/7751.jpg",keywords:null,numberOfDownloads:4128,numberOfWosCitations:0,numberOfCrossrefCitations:5,numberOfDimensionsCitations:6,numberOfTotalCitations:11,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 20th 2018",dateEndSecondStepPublish:"November 19th 2018",dateEndThirdStepPublish:"January 18th 2019",dateEndFourthStepPublish:"April 8th 2019",dateEndFifthStepPublish:"June 7th 2019",remainingDaysToSecondStep:"2 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:"Edited by",kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"22844",title:"Prof.",name:"Fausto Pedro",middleName:null,surname:"García Márquez",slug:"fausto-pedro-garcia-marquez",fullName:"Fausto Pedro García Márquez",profilePictureURL:"https://mts.intechopen.com/storage/users/22844/images/system/22844.jpeg",biography:"Fausto Pedro García Márquez has been accredited as Full Professor at UCLM, Spain since 2013. He also works as a Honorary Senior Research Fellow at Birmingham University, UK, Lecturer at the Postgraduate European Institute, and has worked as Senior Manager in Accenture (2013-2014). He obtained his European PhD with a maximum distinction. He is a holder of the Runner Prize for Management Science and Engineering Management Nominated Prize (2020), Advancement Prize (2018), First International Business Ideas Competition 2017 Award (2017), Runner (2015), Advancement (2013) and Silver (2012) by the International Society of Management Science and Engineering Management (ICMSEM), and Best Paper Award in the international journal of Renewable Energy (Impact Factor 3.5) (2015). He has published more than 150 papers (65 % ISI, 30% JCR, and 92% internationals), some recognized as follows: “Applied Energy” (Q1, as “Best Paper 2020”), “Renewable Energy” (Q1, as “Best Paper 2014”), “ICMSEM” (as “excellent”), “International Journal of Automation and Computing” and “IMechE Part F: Journal of Rail and Rapid Transit” (most downloaded), etc. He is an author and editor of 25 books (Elsevier, Springer, Pearson, Mc-GrawHill, IntechOpen, IGI, Marcombo, AlfaOmega, etc.), and 5 patents. He is also an Editor of 5 International Journals and Committee Member of more than 40 International Conferences. He has been a Principal Investigator in 4 European Projects, 6 National Projects, and more than 150 projects for universities, companies, etc. He is an European Union expert in AI4People (EISMD) and ESF. He is Director of www.ingeniumgroup.eu. His main interest are: artificial intelligence, maintenance, management, renewable energy, transport, advanced analytics, and data science.",institutionString:"University of Castile-La Mancha",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"10",totalChapterViews:"0",totalEditedBooks:"10",institution:{name:"University of Castile-La Mancha",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"968",title:"Mathematical Modeling",slug:"applied-mathematics-mathematical-modeling"}],chapters:[{id:"67579",title:"Introductory Chapter: Prognostics - An Overview",slug:"introductory-chapter-prognostics-an-overview",totalDownloads:236,totalCrossrefCites:1,authors:[{id:"22844",title:"Prof.",name:"Fausto Pedro",surname:"García Márquez",slug:"fausto-pedro-garcia-marquez",fullName:"Fausto Pedro García Márquez"}]},{id:"65747",title:"Prognostics 102: Efficient Bayesian-Based Prognostics Algorithm in MATLAB",slug:"prognostics-102-efficient-bayesian-based-prognostics-algorithm-in-matlab",totalDownloads:619,totalCrossrefCites:0,authors:[{id:"278745",title:"Prof.",name:"Nam-Ho",surname:"Kim",slug:"nam-ho-kim",fullName:"Nam-Ho Kim"},{id:"285438",title:"Ms.",name:"Ting",surname:"Dong",slug:"ting-dong",fullName:"Ting Dong"},{id:"285439",title:"Dr.",name:"Dawn",surname:"An",slug:"dawn-an",fullName:"Dawn An"}]},{id:"65362",title:"Predicting Sets of Automata: Architecture, Evolution, Examples of Prognosis, and Applications",slug:"predicting-sets-of-automata-architecture-evolution-examples-of-prognosis-and-applications",totalDownloads:312,totalCrossrefCites:0,authors:[{id:"4148",title:"Prof.",name:"Michael",surname:"Pecht",slug:"michael-pecht",fullName:"Michael Pecht"},{id:"234788",title:"Dr.",name:"Sergey",surname:"Kirillov",slug:"sergey-kirillov",fullName:"Sergey Kirillov"},{id:"235391",title:"Dr.",name:"Aleksandr",surname:"Kirillov",slug:"aleksandr-kirillov",fullName:"Aleksandr Kirillov"},{id:"247701",title:"Dr.",name:"Vitalii",surname:"Yakimkin",slug:"vitalii-yakimkin",fullName:"Vitalii Yakimkin"},{id:"279379",title:"Dr.",name:"Yuri",surname:"Kaganovich",slug:"yuri-kaganovich",fullName:"Yuri Kaganovich"}]},{id:"65244",title:"Fault Diagnosis Techniques for a Wind Turbine System",slug:"fault-diagnosis-techniques-for-a-wind-turbine-system",totalDownloads:590,totalCrossrefCites:1,authors:[{id:"209626",title:"Prof.",name:"Silvio",surname:"Simani",slug:"silvio-simani",fullName:"Silvio Simani"},{id:"209627",title:"Dr.",name:"Paolo",surname:"Castaldi",slug:"paolo-castaldi",fullName:"Paolo Castaldi"}]},{id:"70067",title:"Analytic Prognostic in the Linear Damage Case Applied to Buried Petrochemical Pipelines and the Complex Probability Paradigm",slug:"analytic-prognostic-in-the-linear-damage-case-applied-to-buried-petrochemical-pipelines-and-the-comp",totalDownloads:1413,totalCrossrefCites:1,authors:[{id:"248271",title:"Dr.",name:"Abdo",surname:"Abou Jaoude",slug:"abdo-abou-jaoude",fullName:"Abdo Abou Jaoude"}]},{id:"68233",title:"Fault Detection of Single and Interval Valued Data Using Statistical Process Monitoring Techniques",slug:"fault-detection-of-single-and-interval-valued-data-using-statistical-process-monitoring-techniques",totalDownloads:300,totalCrossrefCites:0,authors:[{id:"21281",title:"Prof.",name:"Hazem",surname:"Nounou",slug:"hazem-nounou",fullName:"Hazem Nounou"},{id:"21282",title:"Prof.",name:"Mohamed N.",surname:"Nounou",slug:"mohamed-n.-nounou",fullName:"Mohamed N. 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Kostogryzov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"18208",title:"Electromyography Assessment of Muscle Recruitment Strategies During High-Intensity Exercise",doi:"10.5772/24358",slug:"electromyography-assessment-of-muscle-recruitment-strategies-during-high-intensity-exercise",body:'\n\t\t
Electromyography (EMG) is an experimental technique concerned with the recording and analysis of myoelectric signals. Since the EMG signal detected on the surface of the skin directly reflects the recruitment and firing characteristics of the detected motor units within an area, EMG activity can be used to study the neuromuscular activation of muscles within postural tasks, functional movements, work conditions and treatment/training regimes (Basmajian and De Luca 1985). Furthermore, EMG activity has been correlated on multiple occasions with fatigue-related events occurring within the muscle (Bigland-Ritchie et al. 1986a; Bigland-Ritchie and Woods 1984; Moritani et al. 1986; Nordlund et al. 2004). Other chapters in this book describe the use of EMG for various applications. In the exercise sciences, EMG activity is typically used to explore muscle recruitment strategies (i.e., time-dependent, internal, physiological modifications) and, thereby, understand the complex relationship between the development of locomotor muscle fatigue and the cortical regulation of exercise intensity (Billaut et al. 2010; Marino 2004). Neuromuscular fatigue can be induced by sustained muscular contractions. It is essentially accompanied by external manifestations such as the inability to maintain a desired force output, muscular tremor, and localized pain. The effects of this fatigue are localized to the muscle or group of synergistic muscles performing the contraction. According to several authors (Bigland-Ritchie 1984; Fitts 1994; Gandevia 2001; Merton 1954; Szubski et al. 2007), this fatigue may have its source peripherally (within the muscle tissue or neuromuscular junction) and/or centrally (within the brain and spinal cord). During fatigue the EMG activity may display two typical characteristics. The first is a change in amplitude (Figure 1), whereby additional motor units are recruited or already-active motor units are de-recruited. The second characteristic is a shift of the EMG power frequency spectrum (Figure 2), which shows the relative electrical activity contributed by slow (on the left) or fast (on the right) motor units. A leftward shift suggests increased stimulation of smaller, slow, fatigue-resistant motor units.
\n\t\t\tHowever, studies have mainly explored constant work-rate tests, along with incremental tests to maximum effort. In these examples, ecological validity has been limited due to work rate being either fully or partly dictated by the protocol; this excludes the individual subjective assessment of the task. Because performance in competitive events depends largely upon pacing strategies (Figure 3) (Billaut et al. 2011; Hettinga et al. 2006; Palmer et al. 1997; Paterson and Marino 2004; St Clair Gibson et al. 2006), it is necessary to investigate the neuromuscular responses to self-paced exercise to further understand the role of the central nervous system (CNS) in the regulation of exercise performance. Thus, a more realistic paradigm for future research in the exercise sciences is one that would permit the individual to use sensory cues to adjust the effort along with the fatigue process. In fact, Marino and colleagues (2011) recently re-emphasised that bringing the brain (and subsequent muscle recruitment strategies) into modern fatigue research represents the next phase in the unravelling of the fatigue process.
\n\t\t\tTypical increase in EMG amplitude (represented on the y axis as integrated EMG, µV.s) with time (s) at constant intensities (275, 300, 350 and 400 watts). The increase in iEMG indicates the recruitment of additional motor units by the central nervous system to maintain the required power output as time (and fatigue) goes on. It can be seen that the slope of the linear regression (rate of rise in iEMG as a function of time) is higher at higher intensities because fatigue occurs more quickly. It was therefore suggested that the non-invasive analysis of iEMG slope coefficient could provide a sensitive measure of motor unit fatigability. Modified from Moritani et al. (1993).
Traditionally, endurance exercise has been researched extensively but our understanding of the factors that regulate muscle recruitment during very high-intensity exercise is much poorer. This is surprising since
\n\t\t\tmany sports (e.g., team and court sports, athletics) involved burst of “all-out” activity (Billaut and Bishop 2009; Spencer et al. 2005), and
newly-developed training regimes targeted at improving health include repetitions of sprint exercise (Gibala 2007).
Certainly, a greater understanding of neural recruitment strategies during high-intensity tasks would lead to better training programs to enhance fitness in athletes and patient populations.
\n\t\t\tTypical changes in EMG power frequency spectrum (Hz) of a muscle during a fatiguing contraction. The solid line indicates the pre-fatigue state, and the dash line indicates the fatigued state. The leftward shift in the EMG power frequency spectrum indicates the recruitment of more fatigue-resistant motor units (with lower firing frequency) to cope with the task constraint. The non-invasive analysis of the power spectra could provide a sensitive measure of motor unit fatigability that may reflect the activities of different types of muscle fibers.
Performing a sprint at “all-out” intensity requires very high levels of neural drive (typically +/– 5000 µV in elite sprinters) (Ross et al. 2001); therefore failure to activate fully the contracting musculature can theoretically decrease force and power production and, thereby, impair the ability to sprint. Whilst not a well-studied mode of exercise, several studies have demonstrated that the fatigue that develops during single and repeated sprints is associated with changes in muscle recruitment strategies which ultimately originate within the CNS. The aims of this chapter are
\n\t\t\tto use most recent data to describe the behaviour of the EMG signal (serving as a surrogate for muscle recruitment) during sprint exercise using traditional and innovative analysis techniques, and
to give some insights into the main mechanisms thought to contribute to the regulation of muscle recruitment and the fatigue process during sprint exercise.
For over a century, neuromuscular fatigue has been viewed and researched as a finite quantity of essential (metabolic and/or cardiovascular) resources causing exhaustion, independent of any regulation by the CNS (Allen et al. 1995; Bassett and Howley 2000; Fitts 1994; Hill 1924; Shephard 2009). This view has encouraged the interpretation that exercise results in linear changes in metabolism, in energy provision, and in the cardiovascular, respiratory, thermoregulatory, and hormonal responses, among many others. Ultimately, demand exceeds capacity in one or more systems, which causes them to fail. As a result, this failure to maintain homeostasis in the active muscles causes the termination of exercise. Overall, this has produced a “brainless” physiology (Marino 2004; Marino et al. 2011; Noakes 2011; Noakes et al. 2001) that is still currently taught in most exercise science classes throughout the world. In contrast, increasing evidence has accumulated in the last few years suggesting an anticipatory regulation of exercise intensity (Billaut et al. 2011; Kayser 2003; Marino 2004; Noakes 2011; Noakes et al. 2001; Noakes and St Clair Gibson 2004; St Clair Gibson et al. 2006; St Clair Gibson and Noakes 2004; Tucker et al. 2004). This mechanism allows feedback from varied sources to influence the magnitude of the feed-forward neural drive that determines the quantity of muscle mass recruited (i.e., the number of motor units recruited in the exercising limbs). In this model, therefore, an athlete’s pace is continuously regulated via the action of varied physiological and psychological inputs before and during exercise (Noakes 2011; Noakes and Tucker 2008; St Clair Gibson and Noakes 2004). In other words, the brain is able to anticipate a future failure and modify accordingly and in “real time” muscle recruitment and, hence, exercise intensity to ensure homeostasis is protected.
\n\t\t\tAverage running speed (meter.s-1) for each interval during world-record performances in 800-m, 1-mile, 5000-m and 10000-m events. * Significantly slower than the first lap (P < 0.05). $ Significantly faster than the preceding interval (P < 0.05). This figure clearly shows different paces selected by athletes at the beginning of every race that depends upon the distance to run. The presence of an end-spurt is also shown in “endurance-type” events. Modified from Tucker et al. (2006a).
In this perspective, the results from several recent studies conducted on well-trained athletes and patient populations reveal that during high-intensity, constant-load and self-paced exercises participants terminate the task with a given level of severe locomotor muscle fatigue (assessed via quadriceps twitch force) that appears to be never exceeded under ‘normal’ exercise conditions, despite manipulations of exercise performance (Amann 2011; Amann et al. 2006a; Duffield et al. 2010; Romer et al. 2007; Saey et al. 2003). This end-exercise level of peripheral muscle fatigue has been purported to be task specific and to vary across participants (for review see (Amann 2011)). So, overall, in the early years of the 21st century, several scientists worldwide agree that the CNS constantly monitors and deliberately regulates and limits the development of peripheral fatigue in the exercising limbs (via changes in the extent of muscle recruitment). This may presumably be to avoid overtraining / overexertion and potentially long-lasting harmful consequences to critical organs (Amann 2011; Calbet 2006; Kayser 2003; Marino et al. 2011; Noakes 2011; Nybo and Secher 2004). Several physiological variables (e.g., afferent feedback from exercising, fatiguing muscles (Amann et al. 2008; Amann et al. 2009), cerebral oxygenation (Billaut et al. 2010; Nielsen et al. 1999), rate of heat storage (Marino 2004; Marino et al. 2004; Tucker et al. 2006b)) would potentially be closely monitored depending upon the exercise conditions, and would influence more or less the CNS to attenuate muscle recruitment.
\n\t\t\tTo the author’s best knowledge, the presence of pacing during short and “all-out” sprints has been examined in one study. The authors (Billaut et al. 2011) deceived the participants to evaluate the degree of pacing depending upon the number of sprints to be performed. Astonishingly, the anticipation of performing fewer sprints (i.e., 5 instead of 10) resulted in enhanced muscle recruitment (as evidenced by higher EMG activity in the lower limb), which was associated, in turn, with a higher mechanical output during the first 5 sprints. On the other hand, when participants were not informed of the actual sprint number they recruited less muscle and, hence, exhibited a lower mechanical output profile, despite similar levels of encouragement and similar perceived exertion. These results demonstrated that pacing occurs during short repeated-sprint efforts in anticipation of the number of sprints that are included in the trial (Billaut et al. 2011). Furthermore, it may be speculated from these findings that the pacing strategy adopted by these athletes has been mostly subconscious, because it is not clear how athletes can consciously regulate their muscle recruitment and power output precisely enough to account for such variations at this very high intensity and in this short time frame. Importantly, a key measure in all these experiments has been the central motor drive, namely muscle recruitment, which is directly assessed via EMG activity. Therefore, the investigation of EMG events holds a critical role in understanding humans’ ability to cope with physical challenges.
\n\t\tAlthough not extensively studied, changes in skeletal muscle recruitment may contribute to performance decrement during maximal sprint exercise. Vandewalle and colleagues (1991) observed a parallel decline in power output and integrated EMG of the vastuslateralis (average values not reported by the authors) during a 45-second cycle sprint, and suggested a progressive attenuation of spatial and/or temporal recruitment of motor units during the exercise. These findings are supported by another study that reported a ~15% (P < 0.05) decline in the integrated EMG of the plantar flexors and a ~15–19% (P < 0.05) reduction in the median frequency of the EMG power spectrum of the plantar flexors and the knee extensors during a 30-s Wingate anaerobic cycle sprint (Greer et al. 2006). While Hunter et al. (2003) did not observe any change in the EMG amplitude of the vastuslateralis muscle during a 30-second cycle sprint (whereas power output declined), the authors reported a shift of the mean power frequency towards lower values (14.7%; P < 0.05). These acute frequency changes may be caused by an accumulation of metabolites and a consequent decrease in muscle pH, and/or some form of neural control through reflex regulation of muscle force to prevent excessive metabolic perturbations (De Luca 1997; Juel 1988; Noakes 2011; Noakes et al. 2004; St Clair Gibson et al. 2001). Unfortunately, skeletal muscle function was not examined in these studies (via maximal voluntary contraction and twitch technique), which precludes from drawing firm conclusions about the (maximal allowed) level of peripheral muscle fatigue at the end of these tasks. Clearly, more studies using electrically- or magnetically-evoked stimulation need to be conducted to clarify whether central motor drive parallels the power output decline observed during a single maximal effort and whether it is regulated in an anticipatory manner to limit development of excessive limb muscle fatigue.
\n\t\tThe first investigations of the changes of the EMG signal (serving as surrogate for muscle recruitment) provoked by “all-out” repeated sprints have only been conducted recently (Billaut and Basset 2007; Billaut et al. 2006; Giacomoni et al. 2006). These studies have examined the EMG activity of the quadriceps muscles during brief, maximal, isometric voluntary contractions of the knee extensors performed before and immediately after the ten 6-s sprints separated by 30 s of passive rest. In every case, the maximal knee extensors force was reduced after the sprints (average: ~12%; P < 0.05) and this was accompanied by a higher EMG activity (average: ~15%; P < 0.05). The authors also observed a concomitant decrease in frequency components, which suggests a modification in the pattern of muscle fibre recruitment and a decrease in conduction velocity of active fibres (Basmajian and De Luca 1985; De Luca 1997; Gerdle and Fugl-Meyer 1992; Linnamo et al. 2000; Moritani et al. 1986). In fact, during the final sprints, the relative contribution of less-powerful type I muscle fibres involved in the production of power may have increased as a result of the greater fatigability of type II muscle fibres, highly solicited during this type of exercise (Casey et al. 1996; Gerdle and Fugl-Meyer 1992; Komi and Tesch 1979; Ross et al. 2001). However, the conclusions from these studies may have been confounded by methodological factors. Indeed, while investigating EMG activity during isometric contractions may greatly reduce muscle movements underneath EMG electrodes and artefacts due to wire movements, and thus ease signal processing (Farina et al. 2004; Merletti and Lo Conte 1997), it is now well accepted that physiological responses and neuromuscular fatigue are highly task specific (Hunter 2009; Maluf and Enoka 2005). Accordingly, results and conclusions would be highly specific to the task performed, hence, the isometric contraction, with less relevance to the actual fatigue that develops during the repeated sprints.
\n\t\t\t\tMore recently, muscle recruitment strategies have been investigated during sprints, and authors have reported a concurrent decline in mechanical performances and the amplitude of the EMG signal in primer-mover muscles (Billaut and Smith 2009; Billaut and Smith 2010; Mendez-Villanueva et al. 2007, 2008; Racinais et al. 2007; Smith and Billaut 2010). For example, Billaut and Smith (2010) demonstrated a ~15% decline (P < 0.05) in the quadriceps muscles EMG activity from the first to the twentieth cycle sprint of 5 s separated by 25 s of rest. Importantly, such a reduction in agonist muscle recruitment during repeated cycle sprints has been strongly correlated (r = 0.91 to 0.98, P < 0.05, Figure 4) with the decline in mechanical output (Billaut and Smith 2009; Billaut and Smith 2010; Mendez-Villanueva et al. 2008). That being said, it is interesting to note that when fatigue is moderate (e.g., fatigue index or sprint decrement score is ~10% or lower), studies have typically reported a steady level of muscle recruitment during repeated-sprint exercise (Billaut and Basset 2007; Billaut et al. 2005; Girard et al. 2008; Hautier et al. 2000; Matsuura et al. 2007). However, when the neuromuscular fatigue level is more substantial (> 20%), a concurrent decline in mechanical performance and the amplitude of EMG signals has consistently been reported across sprint repetitions (Billaut et al. 2011; Billaut and Smith 2010; Mendez-Villanueva et al. 2007, 2008; Racinais et al. 2007; Smith and Billaut 2010). Furthermore, a reduction in the knee flexor EMG amplitude (i.e., antagonist muscle) has been observed during fifteen cycle sprints of 5 s separated by 25 s of rest (Hautier et al. 2000). Overall, these data suggest that under conditions of considerable neuromuscular fatigue development the failure to fully activate the contracting musculature may become an important factor limiting performance during “all-out” sprint repetitions.
\n\t\t\t\tRelationship of EMG amplitude (integrated EMG) of the vastuslateralis muscle (expressed as a percentage of initial-sprint value) to mechanical work (expressed as a percentage of initial-sprint value) performed over twenty 5-s cycle sprints interspersed by 25 s of rest. The correlation shows that the greater the drop in EMG amplitude the greater the drop in mechanical work [r = 0.91 (95% Confidence Interval, 0.75;0.97)]. The short-dashed lines represent 95% confident intervals. Reproduced from Billaut and Smith (2010) with permission.
Thus far, the above sections have particularly highlighted the importance of the “quantity” of EMG activity or muscle recruitment in the production of maximal power. During “all-out” actions, the timing of muscle activity also comes into play in determining neuromuscular performance. In fact, during sprint exercises maximal power production requires muscles to be fully activated during shortening and fully relaxed during lengthening phases (Neptune and Kautz 2001). Thus, when studying sprint exercises, the inter-muscle coordination patterns (i.e., the kinematics of muscle activation and deactivation) should also be taken into account when analysing the fatigue process (Neptune and Kautz 2001; Prilutsky and Gregor 2000; Van Ingen Schenau et al. 1992). Muscle coordination is defined as a “distribution of muscle activation or force among individual muscles to produce a given combination of joint moments” (Prilutsky and Gregor 2000), and, therefore, can be studied from both EMG and force patterns of individual muscles. The appropriateness of using EMG recordings for studying muscle coordination has been discussed on several occasions and the reader is referred to the following detailed reviews (Hug 2011; Hug and Dorel 2009). Studies that focus on muscle coordination usually report muscle activity profiles (i.e., EMG patterns; Figure 5). Typically, a linear envelope of the EMG signal is first computed by rectification (Hug and Dorel 2009). Then, the EMG is related to mechanical events (e.g., foot switches, force signal), and is time normalized. Finally, a representative EMG profile is obtained for every muscle by averaging the linear envelopes for a number of consecutive cycles or trials. EMG profiles are characterised by bursts of activity. Muscle coordination is presumably modulated by the neural command in order to generate an appropriate neural control strategy or more simply reflects task constraints (Hug 2011; Tresch and Jarc 2009). Thus, it provides an attractive simplifying strategy to study the control of complex movements in sports and daily activities. Because this approach allows describing multiple patterns of muscle activity in an integrative fashion, it is believed to be a useful way of examining muscle coordination.
\n\t\t\t\tAs this is a rather innovative approach of using EMG signals to study the brain regulation of exercise capacity, very few data are available during sprint exercise. To the author’s best knowledge, the only study on coordination patterns in sprints has demonstrated that the agonist–antagonist activation strategy is altered during ten 6-s cycle sprints. The biceps femoris muscle was activated earlier within a pedaling cycle presumably to account for the loss of force of the prime movers in order to maintain the transfer of force to the pedal. Importantly, this locomotor alteration occurred before any changes in the “quantity” of muscle recruitment (i.e., EMG amplitude), which suggests that muscle activation sequence is an important parameter in the regulation of exercise intensity (Billaut et al. 2005). Admittedly, much work lies ahead to substantiate this hypothesis and fully comprehend the neural processes behind the complex regulation of muscle actions. Future studies devoted at challenging muscle coordination and investigating how the brain copes with task constraints are warranted.
\n\t\t\tWhile it is suspected that multiple feedback originating from various locations within the body alter the output of the spinal motor neuron pool, and therefore muscle recruitment, research into neuromuscular fatigue during “all-out” sprints has defined at least two potential influences. The mechanisms that lead to a decreased output of the spinal motor neuron pool are still not well understood, especially during repeated sprints. Nevertheless, it is now clear that the CNS receives sensory input from muscle afferents (e.g., muscle spindles, Golgi tendon organs, group III and IV fibres) which are integrated into the determination of central motor drive. Evidence is accumulating that the purpose of this reflex mechanism may be to limit the development of excessive peripheral muscle fatigue (Amann 2011; Kayser 2003; Marino et al. 2011; Noakes 2011). This consensus is supported by experimental studies showing that pre-existing locomotor muscle fatigue has a dose-
\n\t\t\t\tExample of surface electromyographic signal processing to examine muscle coordination strategies. Panel A displays a raw EMG signal recorded from the vastuslateralis muscle during pedalling. The linear envelope is first computed by rectification and low-pass filtering (9 Hz in this example). All cycles are then extracted using a trigger pulse that is delivered at the top dead centre (crank angle = 0°; Panel B) and time normalised. Finally, a representative EMG profile is obtained for every muscle by averaging the linear envelopes for a number of consecutive cycles (Panel C). Onset and offset values are then determined from this averaged pattern using an EMG threshold (fixed at 20% of the peak EMG recorded during the cycle in this example; horizontal dashed line). Reproduced from Hug (2011) with permission.
dependent, inverse effect on muscle recruitment and power output during a 5-km time trial (Amann and Dempsey 2008). Specifically, the higher the level of pre-existing fatigue, the lower the muscle recruitment and power output during the trial. Furthermore, in a nicely-designed series of studies Amann and colleagues (2008; 2009) presented convincing results that sensory feedback from fatiguing muscles restrict muscle recruitment and, therefore, the exercise-induced development of peripheral fatigue during whole-body endurance exercise. However, whether the central projection of afferent feedbacks from fatigued muscles changes according to the continuous vs. intermittent nature of the exercise task remains unknown. It is intuitive that the dramatic metabolic disturbances that occur within the exercising muscle during “all-out” and repeated sprints (for review see (Billaut and Bishop 2009)) influence the central projection of these feedbacks and thus affect the central neural drive during such intense tasks (Billaut et al. 2005; Billaut and Smith 2010; Mendez-Villanueva et al. 2008; Racinais et al. 2007). That being said, the manipulation of intramuscular pH, for example, by oral administration of sodium bicarbonate does not influence EMG activity and mechanical performance during repeated cycling sprints (Matsuura et al. 2007). Further studies manipulating the metabolic “milieu interieur” during sprint repetitions to examine the effects on centrally-regulated muscle recruitment are needed.
\n\t\t\t\tA second potential line of inquiry for fatigue research during “all-out” activity is the influence of oxygenation on motor neuron activity. Recently, studies have been performed to further unravel changes in muscle activation. In this perspective, it is worth noting that the drop in EMG amplitude observed during repeated sprints has been shown to be strongly correlated (r = 0.80 to 0.95, P < 0.05) with a decline in arterial blood oxygen (O2) saturation (Billaut and Smith 2009; Billaut and Smith 2010). This relationship is also very similar in men and women, suggesting that there is no sex dimorphism in this phenomenon (Billaut and Smith 2009) and demonstrating the strong recurrence of this phenomenon. So, is it simply coincidence that muscle recruitment falls concomitantly with arterial saturation? One may interpret existing correlative evidence (Billaut et al. 2010; Billaut and Smith 2009; Billaut and Smith 2010) to mean that the CNS deliberately down-regulates muscle activity to keep peripheral fatigue within “tolerable” limits. We further tested this hypothesis and used a hypoxia paradigm to alter O2 delivery to tissues and evaluate its impact on muscle recruitment. The reduction in prefrontal cortex oxygenation induced by acute hypoxia was correlated (r = 0.89 to 0.92, P < 0.05) with the changes in EMG amplitude of active muscles during ten 10-s cycle sprints (Smith and Billaut 2010). This shows that the larger the brain deoxygenation, the lower the muscle recruitment. At this point however, it is important to acknowledge that the use of surface EMG as a sole determinant of the neural response is questionable and requires caution, even though its rate of change throughout the exercise may be used as an index of the rate of motor unit recruitment (Amann et al. 2008; Amann et al. 2006b; Amann et al. 2007; Romer et al. 2007). Taken as it is (i.e., without neuromuscular stimulation technique), our EMG data are compatible with the general finding that hypoxia has an effect on cortical and subcortical functions of the CNS, and thereby, on motor neuron excitability and synaptic neurotransmission. This hypothesis is consistent with the findings that systemic hypoxaemia and insufficient brain oxygenation depress central neural drive (Amann et al. 2006a; Amann and Kayser 2009; Bigland-Ritchie et al. 1986b; Dillon and Waldrop 1992; Dousset et al. 2003). The latest research has strengthened this consensus by showing that scalp-recorded brain cortical activity (as assessed via electroencephalography combined with localized electromagnetic tomography) is affected during whole-body exercise at high intensity (Brummer et al. 2011; Schneider et al. 2010) and in a hypoxic environment (Schneider and Struder 2009). The use of electromagnetic tomography during supra-maximal exercise is restricted by head movement artifacts, but remains a powerful tool to shed light on muscle recruitment strategies in future years.
\n\t\t\tIt is becoming clear that the CNS modulates muscle recruitment (both quantity of muscle mass and coordination strategies) to cope with task constraints during high-intensity exercise. It may do so presumably to counteract the increasing fatigue process and limit the development of peripheral muscle fatigue to a non-harmful level. Exercise physiologists and sport scientists can no longer pretend that conscious or subconscious decision making plays no role in the fatigue process and, ultimately, in the regulation of exercise intensity. Perhaps, as Marino and colleagues wrote, “this means that studying fatigue as a closed feedback loop will no longer suffice. At this stage in our understanding of the brain, bringing feed-forward mechanisms of the brain into fatigue process” is necessary to promote greater understanding of human physiology (Marino et al. 2011). Now is an appropriate time to move the field of exercise physiology forward and acknowledge the controlling role of the brain. Understanding these complex neural strategies during sprint exercise constitutes a necessary step if we want to implement such tasks in interventions designed to enhance performance and health. And in this quest, the recording of surface EMG activity holds potential.
\n\t\tSoybean (Glycine max (L.) Merr.) has become one of the most important, versatile globally traded commodities, being a widely used source of protein, oil, and biofuel. Its uses include as a source of protein and fibre for livestock and an alternative to meat and dairy products in humans. Soya products are also increasingly used widely in the food industry, in particular as texturisers, emulsifiers, and protein fillers; soya flour is often added to bakery products, such as bread, biscuits, pastry, etc. Soybean oil is the second largest source of vegetable oil globally and is also used in products such as biodiesel and detergents.
Soybeans are crushed to form meal, typically used in animal feed, and oil. The hull or husk of the soybean is a by-product of soybean oil and meal production where the beans are de-hulled prior to crushing. Soya hull is also internationally traded as an animal feedstuff, providing a good source of digestible fibre, albeit of lower protein content of soya meal.
The EU imported about 18 million metric tons of soya in 2018 [1]. Approximately 90% of these imports are used to feed livestock and reflect about 28% of global soya imports. China imports approximately 88 million metric tons. Although the USA remains the largest exporter of soya, projected export growth is concentrated in South America, particularly Brazil, Argentina, Paraguay, and Bolivia. The UK imports some 3 million tons annually with more than 70% directly from Argentina and Brazil. There is also an inter-trade within Europe, with the Netherlands being an important hub. The UK imports approximately two thirds as soya meal/hulls and one third as soybeans [2]. The UK only imports a relatively small quantity of soya oil, approximately 200,000 tons. The UK does not produce biofuels to any extent from imported soya.
Therefore, there is considerable bulk transportation by sea, involving handling at ports equipped to handle bulk grains and foodstuffs. Thereafter there is onward transportation for use in the animal feed industry, further processing, and the human food sector.
However, soya is not without associated risks to health. Soya products are recognised as one of the EU’s 14 major food allergens and listed in Annex II of the EU Regulation 1169/2011 on labelling of foods and UK equivalent domestic legislation [3]. It is also listed as a major food allergen by the FDA (USA) labelling regulations. As soybean and its products are used in many processed foods, it is difficult for the allergic consumer to avoid and is often classified as a “hidden allergen”. Additionally, evidence from a number of sources identify proteins found in soybean and its products as respiratory allergens capable of producing a range of ocular and upper and lower respiratory symptoms, including asthma.
This chapter focuses on both published evidence and our own studies related to the respiratory risk from airborne dusts related to soya.
Soybean (Glycine max (L.) Merr.) or soya bean is the edible seed of an annual legume of the pea family (Fabaceae). The hull or husk of the mature bean is hard and water-resistant and protects the cotyledon of the seed from damage.
The major forms of soya usually encountered in end-user countries in the EU are:
Soybean, after removal of hull covering the bean, containing about 40% protein and 20% fat/lipid.
Soya meal (see Figure 1). This may be of two forms: pure meal produced after de-hulling and possibly extraction of oil or with subsequent added hull to extend the product. Soybean meal made from de-hulled beans has a total protein content of approximately 40–49% and 3% fibre.
Soybean hull, these are often pelletised as a commercial product to make a more handleable, less dusty product (see Figure 1). The protein content of hull is around 9–19%, with a fibre content of 53–74%. The proteins in hull tend to be of lower molecular weight than those in pure soya meal (Figure 2).
Soya oil is produced by crushing and/or chemical extraction. Soya oil, particularly the more highly purified, is considered less allergenic due to the low concentration of soya proteins within it [4, 5]. It is used widely in food processing.
Soya flour—milled in a similar way to cereal flour (e.g. wheat, rye). Flours from various cereals have the propensity to be “dusty”, and the control of their handling is necessary to prevent airborne exposure to flour dust and consequent health effects [6, 7]. Soya flour has become increasingly used in food processing. Allergens in soya flour have been identified and characterised [8].
The left hand image shows an image of a soya meal imported in the UK. The right-hand image shows a sample of soya hull imported into the UK. The pelletised hull material shows some evidence of breakdown, probably due to compaction in the hold of the ship.
Left-hand gel shows a Coomassie blue-stained reducing gel of an extracted soya hull material, lane labelled 1. Right-hand gel shows a similarly stained gel of an extract of soya meal, lane labelled 2. Molecular weight marker bands for lanes marked as M have molecular weights from the top of 200, 150, 100, 75, 50, 37, 25, 20, 15, and 10 kDa.
Figure 2 shows electrophoresis gels of extracts of a soya hull and soya meal, respectively, after extraction at 10% w/v using 0.1% Tween 20 in phosphate buffered saline. These gels separate proteins on the basis of their molecular weights. The patterns of proteins in soya hull show considerable differences to soya meal. There is a predominance of high molecular weight proteins in meal in comparison with hull where the majority of proteins appear to be less than 23 kDa. For some soya meal products, hull is reintroduced to adjust the overall protein content, so differing soya meal imports may contain differing levels of hull proteins.
However not all proteins are allergenic, in terms of sensitising an individual’s immune system to provoke an exaggerated IgE-mediated immune response on subsequent exposure to the same protein, i.e. a type 1 allergic response. Allergenic proteins appear to be restricted to classes or families of proteins based on their structural and functional properties [9, 10, 11, 12].
However, besides intrinsic, specific allergenic proteins, covered in the next section, there are other “contaminants” or extrinsic material that may be associated with soya products and possibly lead to respiratory illnesses or symptoms, if inhaled. These include:
Endotoxin is a pyrogenic lipopolysaccharide and a component of the exterior cell wall of gram-negative bacteria, like E. coli. High concentrations of airborne endotoxin can cause respiratory inflammation, symptoms, and lung function decline [13, 14, 15, 16]. The Netherlands has set a suggested health-based exposure limit for airborne endotoxin [17]. Endotoxin has been found to be extractable from soya meal and husk and becomes airborne when handling bulk [18, 19, 20, 21].
β-Glucans are naturally occurring polysaccharides, being constituents of the cell wall of certain pathogenic bacteria and almost all fungi. Their measurement in airborne samples has been used as an indicator of total fungal exposure. β-Glucans have been linked to activating macrophages, neutrophils, monocytes, and NK cells, thus involving the innate and adaptive immune systems. Biological activity seems related to their degree of branching and molecular weight; greater branching gives rise to greater biological activity, with the (1 → 3) chain essential in the induction of immune responses [22].
Fungi such as Aspergillus spp., particularly the A. glaucus group, and Penicillium spp. are known as storage moulds. Contamination of batches of soya with uncontrolled fungal growth, particularly Aspergillus spp. and Penicillium spp., leads to spoilage. Certain toxicogenic Aspergillus species under the right conditions of moisture and temperature can lead to the production of mycotoxins and carcinogenic aflatoxins [23, 24]. In addition, Aspergillus and Penicillium species are allergenic and can also cause hypersensitivity pneumonitis (HP), also known as extrinsic allergic alveolitis (EAA). Aspergillus fumigatus can produce significant numbers of conidia (spores) containing allergenic proteins, e.g. Asp f 1, and in immune-compromised humans is the most common life-threatening, opportunistic fungal pathogen. Nonetheless, several strains of Aspergillus are used in the controlled fermentation of soya to produce soy sauce, including A. oryzae. Alpha amylase from this fungal source is used as an additive improver in cereal flour and associated with significant sensitisation in bakers [25, 26].
Organic dust with no identifiable toxic properties can cause irritation and inflammatory responses in the lungs if the particles are small enough. Larger dust particles will lodge in the nasal passage or the throat and be cleared from the body. Particles of less than 10 μm aerodynamic diameter can enter the lungs past the bronchus, and particles less than 4 μm can reach the alveoli deep in the lungs, producing significant lower respiratory tract symptoms. Limited evidence suggests that this mechanism may be relevant for soya dust [27]. Organic dust toxic syndrome (ODTS) and EAA are distinct pathological entities associated with smaller particles below 5 μm [28]. Asthmatic reactions are generally provoked by particle sizes of 5–10 μm [29].
Of these intrinsic and extrinsic factors potentially associated with soya, it is the health effects from exposure to intrinsic soya allergens that are underpinned by significant scientific evidence. This will be the major thrust of the remainder of this chapter.
A number of allergens have been identified and characterised in soya and its products. A 2012 OECD document on soybean allergens lists 15 proteins designated as allergens, largely derived from one literature review [12]. However there has been a criticism about the lack of evidence for some of these “putative” allergens [30]. A non-exhaustive list of allergens is shown in the Table 1 below. Many of the allergens were identified from a food perspective, with subsequent work to produce “hypoallergenic” cultivars [12]. While there has been considerable research on genetically modified soya with lower levels of endogenous major food allergens, a large natural variation (9–15-fold) in the levels of Gly m 4, Gly m 5, Gly m 6, Gly m Bd 28 k, and Gly m Bd 30 k has also been identified [31].
Allergen | Description | Comments |
---|---|---|
Gly m 1 | Hydrophobic soybean protein. MW 7–8 kDa with two isoforms | Abundant in soybean dust. Husk and pods are a rich source. Implicated in epidemic asthma outbreaks in harbour cities caused by soy dust [32, 33] |
Gly m 2 | MW 8 kDa protein with a pI 6. A member of the defensin family | Gly m 2 is abundant in soya husk and implicated in epidemic asthma outbreaks in Spanish dock cities [34]. Shows some homology with a storage protein in the cotyledon of cowpea and green pea |
Gly m 3 | MW 12–15 kDa protein | A profilin type of allergen. Shows some cross-reactivity with birch profilin [11, 35] |
Gly m 4 | MW 17 kDa. Homolog of Bet v 1, a birch allergen | Implicated as the major allergen where patients are allergic to birch pollen and have soy allergy [36] |
Gly m 8 | MW 28 kDa. 2S albumin | 2S albumins [37]. Some homology with Ara h 2, a peanut allergen. Identified as a food allergen |
Gly m 39kD | MW 39 kDa | P39 protein was detectable only in the fully mature dry seed distributed in the matrix of the protein storage vacuoles [38] |
Gly m Bd28K | MW 28 kDa. A vicilin-like glycoprotein | A major food allergen [39] |
Gly m Bd30K | MW 30–34 kDa protein, a thiol protease of the papain superfamily | A soybean oil body-associated glycoprotein, shows 30% sequence homology with Der p 1, a major allergen of house dust mite. An important dietary allergen, widely known as P34 [40] |
Gly m Bd 60 K | MW 63–67 kDa protein | An alpha subunit of beta-conglycinin well-known as a major soybean storage protein. Major food allergen |
Gly m TI | MW 20 kDa, a trypsin inhibitor | Has been implicated as a workplace inhalant allergen in bakers [41]. Found in the seed and soya flour |
Gly m 5 | β-Conglycinin, three isoallergens | Seed storage protein. Sensitisation to Gly m 5 is potentially indicative for severe allergic reactions to soy [42] |
Gly m 6 | Glycinin, five isoallergens | Sensitisation to Gly m 6 is potentially indicative for severe allergic reactions to soy [42] |
Gly m 7 | MW 76 kDa Seed biotinylated protein (SBP) | SBP may represent a class of biologically active legume allergens with structural resilience to many food-manufacturing processes [43] |
A non-exhaustive list of allergens identified in soya.
MW refers to molecular weight.
However, a much smaller number of the allergens in Table 1 have been implicated in terms of airborne exposure during occupational practices and associated health effects.
Further airborne exposure to an allergen in an individual already sensitised can cause a range of symptoms affecting the eyes, nose, and upper and lower respiratory systems, including the development of occupational asthma (OA). OA is a disease characterised by variable airflow limitation and airway hyperresponsiveness due to a particular occupational environment. Two main types of OA are identified [44]. Immunological OA develops after a latent period of exposure during which the worker acquires sensitisation to the causal agent, typically involving IgE-mediated immunological sensitisation to allergenic proteins. Non-immunologic OA is usually due to irritant mechanisms associated with the cumulative effects of exposure to a workplace dust or chemicals. Both forms of OA can be serious enough to prevent an individual’s continued employment in that workplace and even cause permanent disability.
The first study describing soya allergy related to dust from a soybean mill was published in 1934 [45]. In 1977 a study was published of immediate and late-onset OA in a previously non-allergic subject exposed to soya flour in the manufacture of food supplements [46]. Exposure to soya dust and soya flour has been implicated in causing OA or other respiratory health symptoms in persons working in a variety of occupations, such as farmers, millers, soybean processors, and bakers [8, 26, 27, 41, 45, 47, 48].
In the 1990s, a number of scientific papers were published that investigated “asthma epidemics” in harbour cities, the cases of asthma being found in the general population. Investigation discovered these asthma cases were related to the loading or unloading of soya products. Reports were related to New Orleans, the USA [49], Cartagena, Spain [50, 51], Tarragon, Spain [52], Saint-Nazaire, France [53], Naples, Italy [54], Valencia, Coruna, Spain [55], and Barcelona, Spain [56].
The original outbreaks of asthma epidemics occurred in New Orleans, starting in 1953 and continuing for almost 20 years. Sometimes more than 200 people sought treatment in a single day at a hospital serving a largely black, poor population [57]. Initial investigations associated the outbreaks with low wind speeds but from a specific direction and together with particular climatic conditions. However, it was only in 1997, and after the investigations concerning Barcelona, that these community asthma outbreaks were specifically linked to the loading of soya (but not wheat or corn) into ships using an elevator system [49], suggesting that soy dust may be particularly asthmagenic compared with some grain dusts.
The asthma epidemics that occurred in Barcelona have been the best documented, and a considerable amount of research was expended in linking soya unloading at the docks with the asthma epidemics in the city, rather than other possible precipitating factors, such as traffic pollution, moulds, etc. [24, 34, 58, 59, 60]. From 1981 to 1987, 26 outbreaks of asthma occurred in the city of Barcelona, affecting a total of 687 subjects and causing 958 emergency room admissions and 20 deaths. Further outbreaks occurred in 1994 and 1996. The initial asthma events coincided with the unloading of soya into silos without a filter, climatic conditions of high-pressure areas, and the wind direction from the harbour to the city [61].
While it might be that very specific geo-climatic conditions were the drivers for the Barcelona and other asthma epidemics, a number of important factors emerged of wider significance. Some of which were confirmed from other studies of asthma epidemics, and some of which suggested the need for further work as follows:
The latency period from initial unloading of soya in Barcelona to asthma outbreaks appears consistent with that of occupational asthma. Children were rarely affected in these asthma epidemics, and age appeared a risk factor [62].
The primacy of implementing exposure control measures on the occupational processes to control dust emissions and prevent further asthma outbreaks [49, 63].
While climatic conditions may have been important, these phenomena suggest that some soya dusts generated are of a small aerodynamic diameter with high buoyancy to travel relatively large distances and penetrate deep in the lungs.
The allergenic material identified in Barcelona implicated glycoproteins with molecular weights lower than 14 kDa, with the major allergen identified as Gly m 1 [33, 64], localised in soybean hulls/husks (see Table 1). Gly m 2 was also implicated [34]. Ninety-two percent of patients in the Tarragona epidemics were sensitised to soybean hull extracts [52].
In response to the Barcelona episodes, significant effort was put into developing immunoassays capable of quantifying the putative allergen(s) with the necessary sensitivity to measure airborne levels. As with many other aeroallergen immunoassays, they progressed from initial competitive immunoassays utilising pools of serum from sensitised individuals [65] to non-competitive, sandwich assays based on polyclonal [66] or monoclonal antibodies [67]. As found for other aeroallergens, the inhibition assays are less sensitive and give considerably higher results when compared with non-competitive sandwich immunoassays [66].
Airborne Gly m 1 levels were measured by monoclonal sandwich immunoassay, at progressive distances from Ancona’s (Italy) port, where soya is unloaded [68]. Allergen concentrations were less than 171 ng m−3, whereas HSP levels (highly homologous with Gly m 1 [66]) measured by sandwich immunoassay during dockside activities in Barcelona and the UK were considerably higher [19]. Decreases in allergen away from the unloading area in Ancona were detected. Airborne Gly m 1 was not coupled with the presence of soya-carrying ships in the port, but significant relationships between allergen and meteorological parameters were found, suggesting that Gly m 1 appeared part of Ancona’s atmospheric dust. The authors suggest these allergen levels seem consistent with the absence of asthma epidemic outbreaks in Ancona.
There is evidence of genetic factors, atopy, and smoking status modifying the response to exposure to soybean dust [62, 69]. Atopy and smoking have been identified as risk factors for sensitisation and work-related respiratory symptoms with a number of other occupational allergens, e.g. bakers [70], laboratory animals workers [71], and seafood processors [72, 73].
There is evidence of co-exposure and sensitisation to some fungi and moulds, but it does not appear to have been causative of the symptoms/illnesses. Specific IgEs in a small group of asthma epidemic (AE) patients were compared with asthmatic non-epidemic patients and non-allergic controls [24]. The AE group showed low levels of specific IgE to A. flavus, A. fumigatus, A. glaucus, Penicillium notatum, and P. chrysogenum but significantly lower than IgE levels against soybean hull. All the AE group were sensitised to soya hull but between 8 and 92% against the moulds (A. flavus, A. nidulans, A. glaucus, and P. notatum being predominant).
Alvarez [74] showed in a small-scale study that 25% of bakers were sensitised to soybean. A review of cross-sectional studies employing skin prick tests in bakers showed that 5–77% were sensitised to soybean flour [75]. A relatively recent UK study suggested a prevalence of 21% sensitisation using similar methodology [76]. Baur [77] found 21% serological sensitisation to soybean flour in 140 bakers who had a history of greater than 6 months of employment and work-related asthma, rhinitis, and/or conjunctivitis. Two workers were shown to be sensitised to soybean lecithin, although the lecithin was possibly contaminated with low levels of soya proteins [78]. Baur [41] studied a relatively small group of bakers both sensitised to soybean and suffering workplace symptoms. Twelve were also sensitised to wheat, ten to rye, and five to alpha amylase from A. oryzae (FAA). The latter being an enzyme often added to flour in small quantities, but it is now regarded as a potent allergen [25]. Baur identified soya trypsin inhibitor (STI) or Gly m T1 as a major allergen, being recognised by IgE antibodies in the sera of 86% of the examined sensitised bakers. This research was one of the drivers for the Health and Safety Executive (HSE) to develop an immunoassay sensitive enough to detect airborne levels of STI from the use of soya flour and possibly other soya products [18, 19].
In a laboratory study of components of flour improvers, a representative soya flour was neither more inherently “dusty” nor showed a shift to smaller particle sizes than three different wheat flours [79]. However, although the improvers contained a higher percentage of wheat flour than soya flour, there was roughly 10-fold more extractable STI in comparison to wheat alpha amylase inhibitor (WAAI) per unit weight of improver. WAAI is a major allergen and sensitiser in bakers, with a subunit size of around 14–16 kDa and is restricted to the seed storage tissue (endosperm) [80, 81].
Quirce [82] examined four bakers or confectioners who were sensitised to both soya and wheat using skin prick tests. A positive response to STI and FAA was noted in 2/4 cases. IgE-binding bands against soya flour showed bands at molecular weights between 25 and 55 kDa and also high molecular weight IgE-binding bands against hull extract. A case study [8] of a sensitised individual presenting with asthma after 6 years of using soya flour in food processing (not a bakery) showed immunoreactivity against nine soya proteins in the molecular weight range of 15–55 kDa. Interestingly, cross-reactivity studies with other legumes demonstrated apparent immunologic identity between a component in green pea extract and a soybean protein with a molecular weight of 17 kDa [8].
Overall these data confirm that the allergens caused by soya flour are predominantly higher molecular weight proteins, whereas the asthma epidemics in harbour cities were caused by low molecular weight proteins, specifically the allergenic proteins, Gly m1 and Gly m 2.
Early investigations in Yugoslavian soya processors by Zuskin [27, 83] studied dust inhalation and respiratory symptoms after the oil had been extracted. Exposed workers showed a considerable increase in respiratory symptoms over controls, e.g. cough, nasal symptoms, and wheezing being reported by 56, 41, and 30% of workers, respectively. Most workers were smokers, and inhalable dust levels were considerable, with a mean (range) of 29.5 (7.7–59.9) mg m−3. Decreases in lung function were noted over the working week and pre-shift Monday testing suggesting evidence of chronic impairment [83]. Sixteen percent showed serological evidence of specific anti soya IgE, although 68% were positive against house dust mite. Zuskin appeared to be suggesting an irritant rather than immunologic mechanism for the airways disease.
Two related studies [21, 26] investigated sensitisation, symptoms, and exposure measurements in three South African soya processing plants. These plants were producing soya flour, based on de-hulling, cooking, and finally milling. Median (range) of inhalable dust levels were 2.58 (0.24–35.02) mg m−3; STI allergen levels gave a median (range) of 70 (50–2580) ng m−3 and were higher in the later parts of the process. There was no significant correlation between dust levels and allergen levels. Thirty-one percent of workers were current smokers, much lower than found in Zuskin’s study. There were significant associations between worked-related chest tightness, nasal symptoms, and cough/chest tightness after handling soya and sensitisation to soybean. Thirty-three percent of the workers were atopic, and 14% were sensitised to soybean not containing hull allergens. Atopy but not smoking was associated with sensitisation to soybeans, confirming the association between atopy and sensitisation to occupational allergens (Section 2.4.1).
Interestingly, Harris-Roberts reported that those transferring soybeans from farms into the processing plants’ silos, where soya hull would be present, had an excess of “flu-like “symptoms of fever, aching, and tiredness [26]. Such work-related, flu-like symptoms unrelated to soya sIgE levels were also noted by Cummings, but in processors not exposed to hull [84]. The biological reason for the “flu-like” symptoms is unclear. Harris-Roberts [26] hypothesised that these symptoms may suggest organic dust toxic syndrome (ODTS) in which inhaled endotoxin has been implicated [85]. Higher levels of endotoxin are found in hull rather than soybean or soya meal [18, 19, 26], but unfortunately airborne endotoxin levels were not measured in the Harris-Roberts study. Hypersensitivity pneumonitis (HP) also called extrinsic allergic alveolitis (EAA) has been reported in a single case while handling soybean as an animal feed [86]. Both ODTS and HP can give rise to similar “flu-like” symptoms some 4–12 hours after exposure. Whatever the cause or pathology, it raises the possibility of other health problems in soya-exposed workers besides those caused by IgE-mediated sensitisation.
A study was carried out in 2007 at a US soya processing plant receiving de-oiled, de-hulled, and crushed soya flakes for further processing. Concerns had been raised about asthma and other respiratory symptoms [48, 87]. Serum IgE immunoblotting studies showed multiple soya antigens, with 48, 54, and 62 kDa being most prominent, including storage proteins Gly m 5 and Gly m 6. As possibly expected, no sIgE to Gly m 1 or Gly m 2 was detected in this de-hulled material. The prevalence of soya specific IgE was 21% (versus 4% in controls), albeit only 7% gave a positive skin prick test for soya. Ten percent showed specific IgE towards storage mites. Those participants with soya-specific IgE had a threefold risk of current asthma or asthma-like symptoms and a six fold risk of work-related asthma symptoms. Thus asthmas and symptoms of asthma were associated with immunogenic nature of this de-hulled soya material. Work-related sinusitis, nasal allergies, and rash were also associated with reported mould exposure.
A single case study was reported from an animal feed factory, where for 5 years a man had been separating the soybean from hull before grinding [88]. He was atopic, although negative to storage mite. He showed a strong bronchial response to a challenge by soya hull but negative to soya flour. Unfortunately this short report is not clearer on the specific tasks being undertaken.
Heederik [89] studied sensitisation and respiratory effects in atopics and asthmatics (cases) living close to a Dutch soya oil producing factory. Soybeans and the oil product were transported by ship. Soya waste, aſter oil extraction, was removed by truck and noted to be “very dusty”. Soybean unloading was carried out without any emission controls and caused visible dust clouds. Loading trucks with waste also caused dust clouds around the factory area, with spillages in transit. Only 11% of the cases were sensitised to soya by skin prick test, the same as in matched controls. Soya-sensitised individuals living in proximity to the factory reported more respiratory symptoms, used bronchodilators more often, and had poorer lung function after having been downwind of the factory. Airborne soya allergen, measured by competitive immunoassay, was found more frequently surrounding the factory with levels higher than in the control area but much lower than found on the factory premises. Periodic, high endotoxin concentrations close to the factory exceeded the suggested Dutch threshold level of 90 EU m−3 [17]. Interestingly only 14% of workers, although more highly exposed than the cases, were sensitised to soya, with 31% being atopic.
A study in Argentina, which is an important producer of soybeans and its products, looked at 365 cases of asthma or allergic rhinitis and 50 healthy controls. Both groups were classified as to whether they had occupational exposure to soya, were in proximity to soybean fields or grain elevators, or lived in an urban environment without obvious exposure to soybean dust [90] . The overall prevalences of sensitisation by skin prick test to soya hull in cases and controls were 15 and 0%, respectively. In the cases subdivided by exposure classification, these sensitisation prevalences were 39% (occupationally), 20% (proximity), and 8% (urban). Positive skin prick tests were higher for mites (mainly storage mites), pollen, and moulds in those positive to soya hull extract. Serological sensitisation (sIgE) to soya hull was 39 and 10% in cases and controls, respectively. The data suggest that atopic status and inhalation of soybean dust are necessary for sensitisation to soya hull. The authors opine that sensitisation to moulds could be related to contaminated soya and noted that no near-fatal or fatal asthma had occurred, unlike the situation in epidemic asthma outbreaks involving sudden exposures to soya dust. The authors suggested that their data indicates that an immunologic mechanism rather than irritancy is responsible for soybean-induced asthma in those repeatedly exposed.
Three studies were undertaken by the HSE during 2012–2017. Two studies involved occupational hygiene monitoring at different UK ports handling soya. The third study was laboratory-based, investigating inherent “dustiness” in seven imported bulk soya products. Two established allergen assays were employed: a polyclonal sandwich assay for hydrophobic seed protein (HSP) established by our collaborators in Barcelona—HSP is highly homologous with the two Gly m 1 isoallergens [66, 91]—and soya trypsin inhibitor (STI) that has been implicated as a major allergen in bakers handling soya flour [41]. Endotoxin measurements were employed to establish the extent of endotoxin contamination of soya products and the levels of airborne endotoxin that workers may inhale.
One of these studies was in response to a complaint of respiratory symptoms in a workplace situated some 300 m from a dock in the South of England. This dock is used for the unloading of soya from bulk cargo ships, its storage, and onward transport to end-users [19, 92]. Essentially, this was an occupational hygiene study but also measured the levels of soya allergen at the perimeter of the dockside operation and slightly beyond. The dock is situated to the west of a city centre of some 250,000 individuals. Containers ships are emptied by dockside grab cranes into hoppers for loading of either heavy goods vehicles for onward transportation or a conveyor belt whereby the soya was transferred to storage warehouses on-site. Concerns had been raised by the stevedores and harbour managers about the unloading of a particularly dusty batch of finely ground soya meal. But generally soya dust was visually noticeable during any unloading activity of soya meal or hull.
Samples of four different soya bulks unloaded during the study were collected. One bulk had evidence of areas of gross fungal contamination, which was identified as Aspergillus glaucus with moderate amounts of Aspergillus fumigatus. Both these fungi are common on vegetation and stored agricultural material and with sufficient available water can allow for potential heavy growth. Inhalation of these fungi is also implicated as causing hypersensitivity pneumonitis. Hull was unloaded on day 1 and meal on days 2 and 3. The hull product was a pelletised material, showing evidence of breakdown (see Figure 1).
The hull sample had considerably more endotoxin than meal samples (Table 2). While the hull sample had 15-fold more HSP than the meal, the difference in STI levels between the hull and meal was much lower. Table 3 shows the results from static air monitoring at or outside the perimeter of the dock operation. Amounts of allergen were measurable at these peripheral sampling sites. On the day of hull unloading, significant levels of the allergen associated with asthma epidemics were measured some 150–200 m in the prevailing wind direction from where soya dust clouds were being generated.
Bulk material | Endotoxin (EU g−1) | HSP (μg g−1) | STI (μg g−1) | Asp f 1 (μg g−1) | Der p 2 (μg g−1) |
---|---|---|---|---|---|
Pelletised hull | 80,364 | 2824 | 798 | Trace | Trace |
Meal | 4630 | 196 | 270 | ND | ND |
Meal, GM-free | 1309 | 178 | 233 | ND | ND |
Amounts of allergens and endotoxin extracted from the three bulk samples not showing evidence of gross fungal contamination.
ND is non-detected.
Site | Position | STI (ng m−3) | HSP (ng m−3) |
---|---|---|---|
Cruise ship customers’ car park | 170 m from nearest source (conveyor or hopper). In prevailing wind direction | 13 (ND-40) | 26 (11–125) |
East end of building off-site | 150–200 m from open sources. In prevailing wind direction | ND (ND-4) | 87 (30–1300)a |
Road entrance | 100–150 m from sources of conveyor or hopper | 19 (2–24) | 54 (26–85) |
Steps at boundary wall | 150 m from hopper. In prevailing wind direction | 56 (7–80) | 339 (27–898)a |
Allergen levels at the perimeter of an UK dock operation and, beyond, unloading soya.
High value associated with day of hull unloading.
Visual dust clouds outside were noted during various activities: (a) loading of hoppers; (b) loading of lorries from the hopper, see Figure 3; (c) the moving conveyor, which was subject to spillages; and (d) from craneage of soya out of the ship’s holds, see Figure 4. Visual clouds of dust were also produced within the storage facility as the unloaded soya was formed into piles by a pusher loader or loaded into vehicles for onward transport. Respiratory protective equipment (FFP2 respirators) was worn by workers in the ship’s hold, but was not uniformly worn elsewhere.
The median (range) of personal atmospheric monitoring sampling in workers over the 3 days of study were 130 (33–3071) ng m−3 and 583 (170–12,629) ng m−3 for STI and HSP, respectively. High allergen values were found when moving soya within the enclosed storage warehouses and within the ship’s holds. Inhalable dust exposures (personal samples), expressed as 8 hour TWA, ranged from 1.2 to 4.5 mg m−3; the current UK workplace exposure limit (WEL) for flour dust and grain dust is 10 mg m−3. Interestingly on the day that hull was unloaded, high levels of HSP (2925 ng m−3) were sampled within the crane’s cab although some 50 m above the dockside.
The visual dust cloud from a lorry loading soya from the hopper and the crane grab depositing soya into the hopper.
The crane is being used to move soya from the ship’s hold to the dockside hoppers. Spillages and dust clouds from the crane’s grab are noticeable.
The second HSE study addressed issues concerning the likely differences in dustiness of various bulk soya products and the categorisation of the particle size of dusts generated [18]. A rotating drum testing method has been established that can investigate the generated levels of a dust under standardised conditions that are associated with the defined inhalable, thoracic, and respirable particle size fractions [93, 94, 95]. Inhalable particles of an aerodynamic diameter (AD) ≤ 100 μm can enter the respiratory tract via the nose and mouth. Thoracic sized particles (AD < 30 μm) are defined as those small enough to penetrate past the larynx as far as the trachea and bronchial areas of the lung. Respirable particles (AD < 10 μm) can enter the deeper part of the lungs.
Essentially, a fixed amount of bulk material is rotated at a set speed and time-period in a drum with vanes that lift and drop the bulk material during rotation. A constant airflow through the drum entrains any airborne dust that is collected on an in-line series of two metal foams with different pore densities and finally a glass microfibre filter. Three replicate runs with gravimetric analysis and extraction of allergens of the foams and filter are used to calculate an average dustiness in the inhalable, thoracic, and respirable sized fractions. This technique was used to compare the intrinsic dustiness in seven different bulk soya consignments recently imported into the UK and Ireland and extended to include the two major soya respiratory allergens (HSP and STI) in the generated dust fractions during dustiness testing. However, care has to be taken about not over-interpreting such results as defining actual worker exposure [96] but rather an indication of the relevant propensity of different bulks to generate dust and allergen aerosols of certain defined sizes.
The seven bulks tests included two pelletised hull and five meal bulks. None of them showed any visual fungal contamination. The mean concentration of allergens and endotoxin for meal and hull samples is shown in Table 4. Whereas the amount of extractable low molecular weight HSP in hull is 23-fold that in meal, there is also on average 4-fold more of 20 kDa STI in the hull product than the meal. Very low levels of the Aspergillus fumigatus allergen were found in all of the bulks. As reported previously [26], higher endotoxin levels tend to be found in hull than meal samples and may represent a potential additional respiratory risk [28, 85, 97].
Type | STI (μg g−1) | HSP (μg g−1) | Asp f 1 (μg g−1) | Endotoxin (EU g−1) | Moisture (%) |
---|---|---|---|---|---|
Meal | 127 (28–270) | 122 (54–196) | 17 × 10−3 (5–33) × 10−3 | 12,922a (1309–51,455) | 8.3 (5.2–13.9) |
Hull | 528 (258–798) | 2862 (2824–2900) | 13 × 10−3 (8–19) × 10−3 | 66,577 (52,769–80,364) | 7.4 (6.1–8.7) |
Mean (range) of allergens and endotoxin extractable from the bulks and their moisture content.
Mean inflated by one high bulk value.
Of the seven bulk samples, one sample showed “high” dustiness (gravimetric results) in both the thoracic and respiratory fractions compared with the other samples. This may suggest that this particular material may be the sort of bulk that produces small, buoyant dust particles that could travel further with prevailing winds and penetrate deep into the lungs to cause symptoms of irritation. Interestingly allergen concentrations in the smaller particles of this specific material did not parallel the gravimetric results. The levels of allergen in the three fractions sizes largely depend upon both the dust levels in those fractions and the amount of allergen that was readily extractable from the bulk material. So the highest concentration of HSP in small respirable particles was one of the two hull samples but generally of lower “dustiness”. What is clear is that all the small respirable fractions of the seven generated dusts contained measureable but highly variable levels of allergen, and in 5/7 samples the HSP content was significantly greater than STI, even in meal samples. These data are consistent with asthma epidemics where there was distance between the point source and causation of asthma, the putative allergens being Gly m1 and Gly m 2, measured by the HSP immunoassay.
The data from the drum dustiness testing are compatible with a health risk for lung irritancy or allergic responses depending on the nature of the specific bulk material. The pelletised hull material (both of the tested hull products, Figure 1) seems to be largely assumed by harbour managers and importers to be a “low dust” product. However, it does show some evidence of breakdown after transportation and unloading and has higher content of low molecular weight allergens and endotoxins.
The third HSE study was an occupational hygiene survey in a different dock unloading soya, but the focus on this study was investigating the levels of airborne endotoxin, as well as the allergens STI and HSP (paper in preparation). The levels of these analytes in the unloaded bulks (hull and meal) were also measured. During both days a meal bulk was unloaded, while on the second day, a pelletised hull product was also handled that included manual cleaning or “trimming” of one of the ship’s hold.
The dockside operation was very similar to the previous UK study. Bulk cargo ships were emptied using a dockside crane into a hopper, which was then used to load trailers and transported to the storage facility via a weighbridge. Inside the storage warehouse, the soya was tipped from the trailers and formed into piles, using a pusher vehicle loaded into lorries as required for onward transportation. Some lorries were also directly loaded from the hopper. In emptying the ship’s hold, an excavator and Bobcat shovel loader were lowered into the hold allowing the grab crane to access material efficiently. Final “trimming” of the ship’s hold was done by workers manually scraping and shovelling from the hold’s sides. Spillages on the dockside were cleaned up manually and by the use of a shovel loader. Respiratory protective equipment was available to all staff and invariably worn by those working in the hold or as the hatch man, but not necessarily at other times. The excavator, crane, tractors, loading shovel, and pusher were all fitted with cab filtration.
As previously reported, higher levels of endotoxin and HSP were found in the hull bulk compared to the meal. Airborne sampling results showed geometric means (ranges) of airborne levels of dust, endotoxin, STI, and HSP during unloading of 1.6 (<1–62) mg m−3, 34 (5–2450) EU m−3, 146 (1–122,462) ng m−3, and 608 (2–243,654) ng m−3, respectively. Expressed as 8-hour TWAs, 29% of all personal samples and 100% of those involved in cleaning within the ship’s hold had endotoxin levels greater than 90 EU m−3, the limit for endotoxin proposed by the Netherlands [17]. All workers involved in trimming activity within the hold as part of their working day (both manually trimming and operating the excavator and Bobcat) had estimated endotoxin 8-hour TWAs of endotoxin, between 175 and 888 EU m−3. Personal samples and static samples within the two vehicles involved in trimming activities suggested hold atmospheric levels of endotoxin between 275 and 2450 EU m−3. Two other workers’ exposure to endotoxin, when expressed as 8-hour TWAs and unrelated to trimming, breached the Dutch endotoxin guidance value. This happened on the second day, when hull was being unloaded, and is related to moving the bulk material within the storage warehouse. On day 2 when hull was being handled, there was twice as much endotoxin associated with the airborne dust collected compared with day 1.
So soya hull has higher levels of endotoxin associated with it, and considerable levels of airborne endotoxin are produced when it is handled and moved. A review of dust and endotoxin exposure in livestock farming suggested full-shift, average levels of inhalable dust and endotoxin between 0.8–10.8 mg m−3 and 300–6600 EU m−3, respectively [16], and a review of grain dust exposure in the UK reported a geometric mean exposure levels for endotoxin and dust of 1150 EU m−3 and 4.4 mg m−3, respectively. Trimming activities in the ship’s hold appear consistent with this level of endotoxin exposure. Swan [98], in sampling cereal grain dust exposure on the ships during unloading at two UK docks, measured endotoxin levels between 59 and 190,000 EU m−3 for personal samples and 74,000–7.7 x 106 EU m−3 for static samples. Swan’s study found an even more highly significant association between inhalable dust and endotoxin levels than what we found. These data may suggest higher endotoxin exposure from handling cereal grain dust in these circumstances.
Table 5 compares the airborne levels of dust (gravimetric), STI and HSP, in the two occupational hygiene studies that HSE has undertaken. The static samples at the periphery of the dockside operation, and beyond, in the first study have been removed to allow better comparison. The obvious high values in the upper ranges of the second dock study likely reflect that monitoring of trimming in the hold was monitored, an obviously dusty activity.
Gravimetric dust (mg m−3) | STI (ng m−3) | HSP (ng m−3) | |
---|---|---|---|
First UK dock study [19] | |||
Personal samples | 2.0 (1.2–4.5) | 130 (33–3071) | 583 (170–12,629) |
Workplace static samples | 0.7 (0.1–5.2) | 216 (11–845) | 1970 (40–7438) |
Second UK dock study | |||
Personal samples | 1.8 (0.04–62.3) | 178 (5–122,463) | 763 (15–243,654) |
Workplace static samples | 1.1 (0.2–35.6) | 85 (1–69,956) | 318 (2–139,390) |
Geometric means (range) of airborne concentrations of gravimetric dust and soya allergens compared in the two UK dock studies. Samples are categorised as personal samples or static/background samples.
The evidence from the Barcelona clearly shows the value of controlling emissions of soya dust during bulk soya unloading. Such measures decreased both the measured levels of airborne soya substantially and finally eliminated outbreaks of asthma epidemic that has been serious enough to cause fatalities [63]. However, the initial implementation of control measures in 1987 still led to further outbreaks in 1994 and 1996, and in 1998 the storage silos were fitted with even greater particle retaining filters. The value of airborne monitoring of soya aeroallergens, which started in Barcelona in 1986, was also shown [56].
A number of international authoritative and regulatory bodies have recognised the health hazards from grain dust, and, while soya is not a cereal, some have explicitly encompassed soya within their definition of grain dust [99] or highlighted the similarities in the hazards (intrinsic and extrinsic) posed by dust from grain and soya [100]. A number of regulatory and authoritative bodies in the USA, Canada, and Europe have set occupational exposure limits for grain dust ranging between 1.5 and 10 mg m−3 8 hour. TWA. While Great Britain has an exposure limit for grain dust of 10 mg m−3 (gravimetric measurement), this is augmented by the need under the Control of Substances Hazardous to Health (COSHH) Regulations, given soya is a respiratory sensitiser, to undertake risk assessments, control soya exposures to as low as reasonable practicable, and implement appropriate health surveillance.
Monitoring by gravimetric dust is not necessarily a good surrogate of the extent of exposure to soya allergens or endotoxin. Our two dock studies [19] [paper in preparation] identified that gravimetric measurements only explained 50–70% of the variation in the airborne levels of the two allergens measured, even in a relatively constrained number of bulks. The lack of a good relationship found between gravimetric dust and allergen (HSP and STI) levels in the respiratory-sized fraction generated by drum dustiness tests confirms this [18]. HSE’s second dock study suggested that gravimetric dust measurements only explained about 29–57% of the variation in endotoxin levels over the 2 days of sampling.
Issues about health risks in bakeries from exposure to cereal flour dust have been extensively investigated [6, 70, 81, 101, 102, 103], with many solutions identified for reducing bakers’ exposure in terms of engineering control, local exhaust ventilation, work activity modifications, and training. Such measures to reduce airborne levels of flour should also reduce exposure to soya flour dust. Interestingly one suggestion for reducing the dustiness of certain flours, such as in improver mixes, has been the addition of soya oil [79].
The importance of soya in the global nutrition of animals and humans is well recognised. Largely cultivated in the USA or South American countries, it involves large-scale handling, processing, transportation, and finally use by a wide variety of end-users. Health problems from exposure to soya dust have been found in those directly occupationally exposed and those in the general population, indirectly exposed from occupational/agricultural activities. The major health problem seems to relate to type I, sIgE-mediated allergic reactions. There appears to be a genetic component to sensitisation; atopy status and exposure to soya dust are both significant risk factors, as well as smoking. Interestingly the reports of “flu-like” symptoms, similar to ODTS or HP, in two studies of soya processing suggest that an additional pathological mechanism can occur.
The UK largely imports soya meal and soya hull; some meal products also have an amount of hull deliberately added. The protein and allergen profiles of the two pure products are very different. Hull, which is used solely as an animal feedstuff, has a particular low molecular weight protein signature, including the two allergens Gly m 1 and Gly m 2 identified as causative in harbour city asthma epidemics. Generally, energetic handling of hull at ports can lead to high airborne concentrations of dust containing these allergens and that can travel distances up to at least 200 m in the direction of the prevailing wind. On a smaller scale, energetic handling of hull-based animal feed may produce considerable airborne levels of allergen and endotoxin. Limited data suggest that soya products can be very different in their propensity to be dusty, the particle sizes generated, and their allergen content.
Methods for monitoring airborne levels of relevant soya allergens are available and can be used to good effect in monitoring the efficacy of control measures.
Hull products appear to have a higher endotoxin load that can be become airborne: endotoxin posing its own respiratory risks. Also poor storage conditions can lead to significant growth of fungal contamination, some of the fungal species also being associated with respiratory ill health.
Those employed and living near large-scale operations of agriculture growing soya, storing, processing, and transportation may be exposed. Occupations such as stevedores, farmers, millers, bakers, and food processors may be exposed to soya dust. Bakers, which have had significant problems with occupational asthma and allergic symptoms from cereal flour, are likely to benefit from the measures enacted to control exposure to cereal flour dust, in reducing soya exposure to soya.
Regulatory regimes that tackle issues of respiratory problems from exposure to grain dust appear to either directly or indirectly encompass soya dust. Such measures may involve setting gravimetric workplace exposure limits, although the relationship between airborne dust levels and their allergen content is not necessarily simple. Great Britain has the further regulation of COSHH for asthmagens such as soya. This mandates employers to undertake risk assessments, keep exposure to as low as reasonable practicable, and utilise appropriate health surveillance. However, the efficacy of such a regulatory framework obviously depends on its implementation where soya is encountered.
This publication and some of the work it describes were funded by the Health and Safety Executive (HSE). Its contents, including any opinions and/or conclusion expressed, are those of the author alone and do not necessarily reflect HSE policy. My thanks to Andrew Simpson and Peter Baldwin for supplying photos of soya unloading in the UK.
The author declares no “conflict of interest”.
General requirements for Open Access to Horizon 2020 research project outputs are found within Guidelines on Open Access to Scientific Publication and Research Data in Horizon 2020. The guidelines, in their simplest form, state that if you are a Horizon 2020 recipient, you must ensure open access to your scientific publications by enabling them to be downloaded, printed and read online. Additionally, said publications must be peer reviewed.
',metaTitle:"Horizon 2020 Compliance",metaDescription:"General requirements for Open Access to Horizon 2020 research project outputs are found within Guidelines on Open Access to Scientific Publication and Research Data in Horizon 2020. The guidelines, in their simplest form, state that if you are a Horizon 2020 recipient, you must ensure open access to your scientific publications by enabling them to be downloaded, printed and read online. Additionally, said publications must be peer reviewed. ",metaKeywords:null,canonicalURL:null,contentRaw:'[{"type":"htmlEditorComponent","content":"Publishing with IntechOpen means that your scientific publications already meet these basic requirements. It also means that through our utilization of open licensing, our publications are also able to be copied, shared, searched, linked, crawled, and mined for text and data, optimizing our authors' compliance as suggested by the European Commission.
\\n\\nMetadata for all publications is also automatically deposited in IntechOpen's OAI repository, making them available through the Open Access Infrastructure for Research in Europe's (OpenAIRE) search interface further establishing our compliance.
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\\n\\nRead more about Open Access in Horizon 2020 here.
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