Brain and Buying: Consumer Behavior Explained by Psychology and Consumer Neuroscience

Imagine walking into a three-storied shopping mall with hundreds of shops and big screens displaying advertisements. What would you look for the most and what would you buy? Well, psychology has been playing an imminent role in designing advertisements and influencing our choice of products for a very long time now. Human attentional process is largely involved in determining which product or advertisement we are going to attend to the most. There is a reason why shops put up huge neon signs with flickering lights and bright colours. Or, advertisements of deodorant, cars and bikes project how beautiful women find such men using that particular product to be extremely attractive. In 1957, James Vicary, a market researcher, inserted the words “Eat Popcorn” and “Drink Coca-Cola” on the screen during an ongoing movie show.

The words were displayed for an extremely short period of time such that the audience could not consciously process the information, but long enough for their brain processes to perceive the information. It was found that during the intermission, sales of coca-cola increased by 18.1% and that of popcorn increased by 57.8%. What happened was that although the advertisement was not consciously attended by the audience, it was perceived subliminally and therefore influenced their choice. Subliminal messages have been later on used in hundreds of advertisements, targeted towards sex stereotypes and power perceptions. Take for example, Fig. 1 shows the advertisement of McDonald’s burger where a dollar sign appears at a particular point of time.

Fig. 1: McDonald’s advertisement showing a dollar bill

Fig 2. Intel advertisement

Or consider the advertisement of Intel (Fig. 2) where power is represented by the white man having authority over a number of black men. Both these messages in the advertisements are meant to trigger the need for power as in, the power that white men exerted on black men for centuries. Every day, we see hundreds of advertisements on television, social media and YouTube. So, how does such advertisement influence our choice of products? Which are the properties of a particular advertisement that contributes the most to influence our decision? It took psychologists, neuroscientists and marketing researchers several decades to realise how our brain processes advertisements and influence our choices. It was only during the last decade that Consumer Neuroscience as a field of academics and Neuromarketing as a commercial field of research emerged and gained lots of attention. Consumer Neuroscience lies at the centre of the overlapping boundaries of Psychology, Neuroscience and Marketing and intends to explain the science behind our decision to buy a product.

How does Consumer Neuroscience work?

Human brain has separate areas for decision making and experiencing emotions. It is primarily the function of the dorsal prefrontal cortex and orbito-frontal cortex of the frontal lobe to execute visual search, reason, judge and decide. While the amygdala, hypothalamus and nucleus accumbens located deep inside the brain gives the emotional colouring to an incoming stimulus. Deep inside the nucleus accumbens lies a “the pleasure centre” which determines “what” we like. Neuroscience facilitates recognizing the “why” behind such a choice. In 2011, Volkswagen produced an advertisement where a child was shown to attempt “The Force” on his parents’ washing machine, bikes and even on the family dog but nothing worked (Fig. 3).

As his father arrived in Volkswagen Pissat, the boy went outside and tried to use “The Force” on the car for the last time. As the father realised what the boy was trying to do, he started the car’s ignition using the remote control and the boy was astonished, thinking that “The Force” actually worked. This advertisement scored the highest “neuro-engagement score” and won all the awards for that year including two Gold Lions at Cannes. When further investigated, research indicated that the advertisement engaged our attention and emotion to a large extent and therefore predicted end-market performance powerfully. Neuroscientists and psychologists working in this area, specifically work to predict which underlying attributes leads to higher attentional and emotional engagement. For instance, in a popular Indian advertisement, Katrina Kaif was seen biting and licking an Alphanso mango with the juice running down her chin. Sexual hints undoubtedly excite the “pleasure centre” of our brain and produce an overall positive impression of the product. Thus the Maaza advertisement sold the product after successfully manipulating our mindset.

Scientific evidence suggesting the role of brain areas in consumer decision-making is huge. For instance, brand preference over other brands is predicted by the activities of the ventromedial prefrontal cortex (vmPFC), dorsolateral prefrontal cortex (dlPFC), visual

cortex, anterior cingulated cortex (ACC) and hippocampus (Deppe, Schwindt, Kugel, Plassmann, and Kenning, 2005; Deppe et al., 2005; McClure et al., 2004). As a matter of fact, all these brain areas are highly involved in recalling memories, emotional processing and decision making (Fig 4). For instance, in the infamous experiment on brand preference of Pepsi and Coca Cola, subjects were given a two tasting sessions one of which was blind. Researchers found that when they tasted Pepsi without knowing its’ brand, the “reward centre” of brain was activated more, indicating their preference. But when they were exposed to the brands, tasting Coca Cola stimulated hippocampus, mid brain and dorsolateral prefrontal cortex (areas for memory and emotion), an event that can be attributed to the nostalgia regarding its’ brand value. Undoubtedly, people like the brand of Coca Cola more than how it tastes.

 

Tricks of Consumer Neuroscience:

Consumer neuroscientists make use of different neuro-imaging techniques like Electroencephalograph (EEG), Functional Magnetic Resonance Imaging (fMRI) and Eye Tracking devices. fMRI techniques are more popular among these since it provides real-time information based on the oxygen level scanned at a particular brain area. Needless to say, research in Consumer Neuroscience is hugely expensive owing to the highly sophisticated techniques involved. Nonetheless, provided the benefits of predicting consumer choice based on neuroscience data, advertisement giants rarely hesitate to hire neuroscientists and psychologists to do the job. But that’s a commercial application of these techniques which are popular by the name of Neuromarketing.

Fig 5. Shopper fitted with EEG equipment and Eye-tracker glasses (adapted from Ramsǿy, 2014)

For the sake of understanding and predicting consumer choice, neuroscientists rely more on EEG data and Eye-Tracking devices. In a typical experiment of Consumer Decision making, the volunteering customer is fitted with the EEG equipment and an eye-tracking device that is capable to track eye movements (Fig. 5). According to Steve Sands, a researcher at the Max Planc Institute of Lepzig, Germany, a single eye movement takes only 200 milliseconds and it is this 200 milliseconds within which a product can persuade us to buy it. In their study, Sands and others analysed 80,000 movements and found that 76% shoppers make in-store decisions for a purchase. Additionally, shoppers using cards have greater probability to make an impulsive decision (Ramsǿy, 2014).

Another paradigm of studying consumer behavior is by analysing images and videos in NeuroVision which is a Artificial Intelligence (AI) based computing software that predicts what our brain are most likely to see and miss. Instead of conclusions based on tracking eye movements, NeuroVision enables the researcher to predict which aspect of an image or product will be attended to automatically by the brain. Such prediction model is mostly based on image analysis and heat maps generated after analysis (Fig 6). Thus, visual attention these days can also be predicted using AI before even the shopper takes a look at the product!

Fig 6. Heat map generated in NeuroVision

Miles to go:

Thanks to all the sophisticated brain imaging techniques, we now know that our brain makes a decision of making a purchase several seconds before we are making a conscious choice. However, even with the utilization of highly sophisticated techniques, predicting consumer behavior based on brain mapping, is still at its’ nascent phase. Most recent research in this area is focusing on predicting election polls and success of blockbusters based on brain mapping. However, the Neuromarketing industry is growing really fast and those days are not too far away when every aspect of advertising will be incorporating prediction models based on brain mapping and AI. Irrespective of the sales and profits facilitated by such prediction, it is indeed a fascinating feat for research in Psychology and Consumer Neuroscience that is contributing immensely to understand why we do what we do.

References:

Mathematical Modelling – a fresh opening for research due to COVID-19 ?

However, COVID-19 had spread out a long ago, but until now, the whole world is not getting rid of the disease. Every day the number is increasing throughout the world and of course, India is not an exception. At present India is at no 3 position based on the covid-19 cases. If we unable to control or predict the proper lockdown criteria, we may end up by top of the list soon. However, biological and medical research are going on, but Mathematical research is also not very lagging behind in this case. There is a powerful tool in Mathematics, namely, Mathematical Modelling that is working as a bridge between two extreme opposite subjects, Biology and Mathematics and give the opportunity for the researchers of both field to work together.

  • What is the need of Mathematical Modelling in this scenario?

Biology and medical research is going on to invent medicine and vaccine for COVID-19. Then the common question may arise, “what is the need of Mathematics then?” Sometimes, it is not sufficient to get the idea of the biological system without understanding thoroughly between various parts of the system and Mathematical modelling exactly does that. Therefore, this works as a block diagram to the overall scenario.

Until now, we have no medicine or vaccine other that Lockdown. Now by analyzing the data of patients from various countries, a more suitable suggestion can be given for imposing the lockdown. Modelling comprises of Differential Equations, Statistics that help us to know about the stability of the situation. Therefore, for non-mathematics person, a brief idea is given about Mathematical Modelling.

  • What is Mathematical Modelling

Using mathematical symbol and logic a real world phenomena can be represented mathematically, which is called the Mathematical Model for that. The process of constructing a mathematical model is called Mathematical Modelling.

After creating a mathematical Model, the model can be analyzed and the derived results can be justified with real world data. If the result match with real world biological data, then we may end up with a successful model. A schematic diagram can be more appropriate in this regard,

There are some limitations also of Mathematical Modelling. In earlier time, there were manual mode of data taking, which leads to data loss or unavailability of data. Due to that, justification of Models with real world data was sometimes failed. Now days each patient data is computerized in most cases and so availability of data is more and we can consider the data we want. Mathematical Models are more realistic for that reason.

Again, biological interactions within a body is very much nonlinear in nature. For representing these terms we got huge nonlinear terms which is not suitable for Mathematics. Some of the terms may be simplified using various mathematical software but most of the cases are very tough for calculations. In this regard, sometimes we use a simplification of nonlinearity by ignoring higher order terms or by linearizing those equations. By doing that, the nature of the original equations were changing which is not desirable.

Modelling is then also very much important to predict to the nature of a problem apart from its difficulties. For any branch of science formulation of situations are very important mathematically and it will give rise to a good research article. So basically, modelling is important thought branches of sciences and even in some other disciplines.

  • Opportunities of research in Modelling on COVID 19

Many people often asked, “What is the direct applications of Mathematics in the real world?” The answer is COVID-19. Before COVID-19, many people were not aware of mathematical modelling and prediction. However, after this disease happens many researches are going on about the lifting of lockdown, namely unlock-1. Again, in many states including West Bengal, fresh lockdown is given to control the spreading of this disease. For this kind of decision, proper mathematical calculations will be there, which is coming from Mathematical Model. Now for various country, the nature of the same disease is different, so different models need to be implemented. Those models can be validated with data of that country only. For researchers in bio mathematics, this kind of disease is a golden opportunity for doing a fresh research. This unknown disease gives rise to many different aspects of symptoms every day which will enrich the research aspects of a researcher in bio mathematics. Till now no drug or vaccine is present.  Once it will be discovered, the drug aspects can be incorporated in the mathematical model, which will make the model more realistic and help to predict the future upto some level.

If anybody want to do research in this field, he/she need to know the idea of differential equations and mathematical software like MATLAB, MATHEMATICA etc. This kind of research is very realistic now a days and interesting.

Readers are requested to understand that Mathematical modelling is not capable of invent any drug or vaccine and it will not be able to remove any disease from society. It is basically a block representation of a real life phenomena which will help researcher from other discipline to do their job easily.

When Biotechnology adopts Artificial Intelligence

Artificial Intelligence (AI) in Biotechnology adding great values as it explores more applications, broadening its field in a more transformative way. Famous stories regarding the use of Artificial Intelligence goes like this that after the creation of one of the first autonomous robots it was asked a question “Do you know God” and it promptly replied “I am God” and this goes on to show the world how powerful and revolutionary it’s role will be in reshaping the upcoming future of this entire planet.

Introduction of AI and Machine Learning – these two rather synonymous technologies have could change our view towards the use of modern technologies. Even the greatest minds, like Stephen Hawking and Elon Musk, used to acknowledge its unlimited power beyond anyone’s imagination and feared that it could have proven dangerous if misused. There is a possibility that it may come into the picture in the upcoming decades, but today we are not anywhere close to that yet. The AI which is making headlines these days is a “Narrow Artificial Intelligence”, a rather limited functioning machine “intelligence” which can solve only a few specific assignments or a group of tasks. Already AI showed its efficiency in providing meaningful real-world solutions on those narrow tasks, like language processing, image recognition of various images, developing self-driving cars, and in drug developments more specifically in the field of biotechnology.

The usefulness of AI and machine learning proved its ability to find and analyses hidden and unintuitive patterns from big data sets in quick time, which proves impossible in ways that human achieve. AI represents a significant potential to have a transformative impact on many industries, especially the pharma and biotech companies.

AI’s adoption of machine learning (ML) helps it to solve complex problems through its systematic testing. Instead of coding what it needs to know, developers create AI to use the ability to learn and analyze data – resulting in innovative solutions that are virtually impossible to reach through human ability alone.

With the amount of data available to the biotechnology scientists worldwide, we may still find ourselves while asking the big questions, but AI programs are finding the solutions already. Nowadays most of the biotechnology companies are comprehending the value that AI can bring to their entire field in the form of – · Expanding accessibility · Crucial predictions · Effective & efficient decision-making · Cost-effectiveness Biotechnology can be categorized into a few types like · Medical biotechnology, · Agricultural biotechnology · Animal biotechnology, · Industrial biotechnology · Bioinformatics.

Now we see the functions of AI in different domains of Biotechnology: Medical biotechnology Medical biotechnology uses living cells for the improvement of human health by producing various drugs and antibiotics. It also engages in the study of DNA and genetical manipulation of the cells to increase the production of desirable characteristics.

AI & Machine Learning are extensively applied in drug discovery. Machine Learning is widely used in diagnosing diseases because it uses the actual results to improve the diagnostic tests, that is to say, the more diagnostic test runs, the more accurate results can be obtained. Apart from the above-mentioned applications, these technologies are widely used in radiology gene editing, etc.

Machine learning and AI are significantly used in the diagnosis of cancer. With powerful companies like Quest Diagnostics, machine learning makes the identification of cancer more accurate. Agricultural biotechnology It helps to develop genetically modified plants to increase crop yields or introduce new characteristics to the existing plants.

It involves conventional plant breeding, molecular breeding, genetic engineering of plants, micro-propagation, and tissue culture, which forces the biotechnology companies to accept AI & Machine Learning methods to develop autonomous robots that can improve agricultural tasks like crops harvesting at a much faster rate.

Machine Learning algorithms help in tracking and predicting various environmental changes like the weather changes that impact the crop yield. Animal biotechnology applies various techniques of molecular biology to produce genetically modified animal species with improved sustainability for the sake of pharmaceutical, agricultural, and industrial use. Artificial Breeding of animals is another domain where AI models offer valuable insights. Artificial breeding of animals gives the advantage of inserting the selective genes in them, which provides the leverage of developing animals with the most desirable characteristics and becomes a very prevalent practice in food biotech industries such as milk industries, meat industries, etc. This practice is applied to the molecular level where desired genetic characteristics of the animals were selected & inserted in breeding such animals by using AI.

Machine Learning and AI aids in understanding the genomics and helps the experts in predicting the genetic expression. Industrial biotechnology hovers around making biopolymer substitutes, inventions in areas like vehicle accessories, fibers, fuels, chemicals, and their production process.

Machine Learning and AI analyzes the machines, enhance the efficiency of equipment, etc. to improve production and produce a better quality product. Bioinformatics helps the acquisition, storage, processing, distribution, analysis, and interpretation of and biological and biochemical information with the help of mathematical, computer science tools to understand the biological significance of a variety of data.

Artificial Intelligence and Biotechnology are by themselves two of the most promising and profitable fields in the future economy. This makes financial analysts believe that the economic impact will increase exponentially if these two sectors are combined. Artificial intelligence (AI) and biotechnology have the potential to improve and extend our lifestyles and life expectancy in a cost-effectiveness way.

Biotechnology has improved 10 times more every year in terms of cost-benefit. And one cannot deny the efficient involvement of AI in this. The cost of decoding the human genome has reduced from $3 billion in 2002 to around $1,000 recently; a painstaking process that took more than a month, little more than a decade ago and now it can be finished in less than sixty minutes.

 

Based on current developments, it is estimated that the contribution of Artificial Intelligence globally will reach $15.8 trillion by 2030 – more than the shared yield of China and India in today’s world. So, it is the absolute need to this time that more and more importance is given to this domain where the culmination of AI & Biotechnology is given utmost priority.

In Adamas University, students of BSc and B.Tech Biotech courses at the school of Life Science and Biotechnology, get to know both the subjects: Artificial Intelligence and modern technological developments in the field of Biology in a perfect ratio, which further broadens their knowledge base and increase their chances of getting jobs in various biotech industries. With a huge panel of experts both from Engineering fraternity and Biotechnology, Microbiology and Biochemistry fields, and modern and sophisticated Engineering and Biology laboratories, students from this course will surely start a leap ahead in their future career opportunities. Several professional clubs like Adamas Robotics club, Adamas Biotechnology club provide them to test their theoretical knowledge on hand and also to showcase their talents at the global stages through various competitions under the banner of Adamas University. Biotechnology courses at the school of Life Science and Biotechnology at Adamas University help the young and inquisitive minds from all around the world to groom, induce job-preparedness, and motivates them to become leaders for the Biotech industries in the future post-COVID era.

 

Persister-like cells in lower eukaryotic pathogens: a new challenge for drug research and elimination programmes

(Student contributor: Swarnav Bhakta, PG II-Biotechnology)

One of the biggest challenges of the current health care system is to detect and treat pathogens with developing resistances against the drugs, especially which manifest high antibiotic tolerance. In addition to the more well-known host-pathogen or drug-pathogen interaction mechanisms, development of persister cell populations in chronic infections is getting progressive importance due to its widespread association with intervention failures(Fischer et al., 2017), including Escherichia coli, Pseudomonas aeruginosa, Mycobacterium tuberculosis, Salmonella enterica and Staphylococcus aureus (Helaine& Kugelberg 2014, Harms et al., 2016, Michiels et al., 2017). Persistence of ‘dormancy’ is a phenomenon that describes the ability of a pathogenic subpopulation to survive against the treatment with the potential drug for an extended period. This evolutionarily conserved adaptive mechanism for drug tolerance is associated with non-heritable phenotypic variations (e.g. phenotypic switching in the bacterial populations, macrophage-induced mechanisms and dramatic change in metabolism, etc.) which make this adaptation different from other mechanisms that generate genetic resistance (Balaban et al., 2004, Adams et al., 2011, Amato et al., 2014). Interestingly, little after the discovery of antibiotics, Joseph W. Bigger, an Irish physician who was working in England back in 1944, recognized the presence of persister cells in the form of slow-growing, penicillin-tolerant populations of Staphylococcus aureus which survived high lethal doses of penicillin (Kim et al., 2016). He termed these cocci, with the phenotypic change of lack of growth, as persisters so to separate them from the resisters, found in bacteria due to heritable genetic mutations.   Such adaptation is prominently exemplified latent infection of Mycobacterium tuberculosis which can persist even for lifelong in a metabolically dormant state (Mandal et al., 2019).

Persister cell development is associated with developing a subset of the population that is metabolically quiescent and hence cannot be intervened by drug treatment (Fischer et al., 2017). Persisters though represent a subpopulation of the total cells, but their survival shows some kin selection and altruistic effect by allowing the whole population to survive at the time of high drug exposure and immunological stress. After the disappearance of stress-factors, persisters revert to normal proliferative mode, reinitiate growth and repopulate the local environment. This post-treatment sensitization phenomenon emulates ecological succession, where immunological stress and drug exposure represent bottlenecks events, with persisters acting as the founders of new environmental niches (Bhattacharya et al., 2020). Eukaryotic pathogens, including fungal and parasitic protozoans, are also akin to metabolic switching from proliferative to dormant state (Barrett et al., 2019). For a range of fungal pathogens including Candida albicans and C.auris nutrient depletion and stress renders metabolic drop off to circumvent fungicides like amphotericin B (Wuyts et al., 2018).The hypnozoite liver stages of Plasmodium that is often associated with the relapse of infection even years after successful therapeutic clearance is one such persister-like stage for Plasmodium vivax and is a marked threat for the eradication of malaria from the human populations (Markus 2017). In the case of lower eukaryotic pathogens like trypanosomatids, semi-quiescence to quiescence intracellular forms are detected for several species of Leishmania and in Trypanosomacruzi (Barrett et al., 2019). Persistence adaptations are particularly relevant clinically for Leishmania as a relapsing condition like Post kala-azar dermal leishmaniasis (PKDL). Post-treatment sensitization occurs several years after the treatment for visceral leishmaniasis(VL) and leishmaniasis recidivans, occurring after the treatment of cutaneous leishmaniasis, emerge from possible metabolically distinct parasites that circumvent drug treatment due to dormancy without acquiring resistance by signature genetic alterations (Rutte et al., 2019).

Persistersin Trypanosomatids – Cellular and Molecular Perspectives

Till 2015, there was a dearth of systemic study on persister development in lower eukaryotes, particularly in trypanosomatids, due to technical constraints, principally the absence of exclusive labeling methods for quiescent cells. However, this has now become one of the most emerging avenues of research after the detailed identification and characterization of semi-quiescent Leishmania parasites. The seminal work by Kloehn and colleagues(Kloehn et al., 2015), for the first time, has clearly demonstrated that intracellular amastigotes in the infected non-healing lesions of the BALB/c mice are in a metabolically quiescent stage which leads to ~3 fold increase in doubling time for the infected state. The partly tranquil metabolic state was also reflected by low de novo synthesis and turnovers of lipid and protein molecules which possibly are responses to complex growth restriction in the intracellular microenvironments of the granulomas. Interestingly, the study also found two distinct macrophage populations, representing two distinct metabolic varieties of amastigotes in the inflammatory lesions. Mandell et al., 2015, also identified a definite fraction of L. major amastigotes with sparse proliferation in C57BL/6J mice.  This population was observed to the harbor in less infected macrophages and constituted a third of amastigotes under the condition of persistent infection; a remaining subset of amastigotes retained the ability to replicate (Mandell and Beverley 2015).L. major, lacking the Golgi GDP-mannose transporter required for lipophosphoglycan synthesis (lpg2-/-) persists in the absence of pathology and in mouse infections, this knocked outline attained persister like feature immediately after infection (Mandell and Beverley 2015). A comparative analysis between the L. braziliensis promastigotes and amastigotes depicted the “semi-quiescent characteristic features” of the amastigotes as they replicate at a negligible rate with minimal metabolic activity. Metabolomic profiles demarcated the amastigotes as a metabolically-restricted life stage compared to their rapidly dividing promastigote counterparts(Jara et al., 2017). Persistence adaptations gain further support for trypanosomatid infection as regimens including short term therapy or even 60-day long treatment for T.cruzi infection are not related to resistance development but possibly the parasite alleviates drug tolerance by adopting quiescence. After drug withdrawal, one or two dormant parasites are detectable in each infected cell in case of trypanosomatid and these dormant parasites can reinitiate proliferation.T. cruzi amastigotes regularly and spontaneously cease replication and become non-responsive to effective trypanocidal drugs like benznidazole and nifurtimox. The amastigotes, even after a long term of drug exposure, retain the ability to get converted to the infectious trypomastigotes for re-establishing new infections (Sánchez-Valdéz et al., 2018).

Exploring the intricacies of the alteration of physiological status for intracellular amastigotes in infected tissues by proteomic or transcriptomic approaches is impaired by the paucity of enrichment protocols. However, one significant difference between a viable cell and quiescent cell is the translational activity of ribosomal action, which is subsided with a concomitant decrease in the number of active ribosomes in the quiescent cell. Reduced transcription of rDNA loci can be a consistent marker for quiescence which in T. brucei is tightly regulated by the transcription factor TDP1 that strongly binds to the rDNA locus(Narayanan and Rudenko 2013). To better understand the role of rDNA loci, Jara and colleagues have developed an assay in which they checked for the expression of a reporter GFP gene under the control of the 18S ribosomal DNA locus; the GFP expression acts as a biosensor for quiescence in the laboratory and clinical strains of L. braziliensis and L. mexicana (Jara et al., 2019). The results revealed reduced expression of rGFPcoupled with the transition from promastigotes to amastigotes and this change in expression level was amicable with BrdU uptake which indicates proliferation. These outcomes together with the applicability of this assay in animal-models of latent inhibition, clearly demonstrate the association between the transcription status of the ribosomal RNA genes and particular life stages in trypanosomatids.

Clinical Significance

The concept of metabolic diversity in amastigotes with the coexistence of shallow and deep quiescent stages (Jara et al., 2019), it is now significant that quiescence is crucial for subclinical infections and transmissions with its potential role in drug tolerance (Bhattacharya et al., 2020). This phenomenon serves as reservoirs for transmission and to elicit protective response against subsequent infections in trypanosomatids, which warrants additional exploration (Mandell and Beverley 2017). For instance, about 2.5% to 20% of patients recovered from VL develop PKDL, sufficient to menace the success of the VL elimination program in the sub-continent by re-escalating spur of endemic (Gedda et al., 2020). Therefore, the identification of strategies to combat dormancy or exploit it in developing immunization strategy with some novel assay methods might expedite the success of elimination programs against lower eukaryotic pathogens en masse.

References

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