Contents

#### **Preface XI**

## Chapter 8 **How to Create Suitable Augmented Reality Application to Teach Social Skills for Children with ASD 119**

I-Jui Lee, Ling-Yi Lin, Chien-Hsu Chen and Chi-Hsuan Chung

Preface

development life cycle of VR/AR systems.

mental validations and high scientific value.

our in disseminating knowledge globally.

and content developers.

Virtual reality (VR) and augmented reality (AR) have long been attributed to science fiction movies and gaming industry. Although VR was popular for decades, AR has been democra‐ tised through famous Pokemon Go. Combination of VR/AR and the physical environment commonly called mixed reality on smartphones has prompted developers towards new heights of creativity and innovation. No need for high-tech wearables to experience VR/AR environments the cloud is quite helpful in processing large multimedia and code processing. One may think that VR/AR is more about multimedia development and visualisation techni‐ ques; nevertheless, there is a huge algorithmic component behind VR/AR systems. Major ICT players have come up with libraries for digital objects that have further accelerated the

Regarding challenges, there are technical limitations but also reluctance from users to adopt VR/AR technology. Nonetheless, few mobile manufacturers are not willing to implement such technologies. The main challenge remains dynamic content that would keep the inter‐ est of the users and with exciting user experience so that applications do not fall obsolete within a short time span. Like Multimedia Message Service (MMS), VR/AR has had poor adoption due to lack of content and appropriate business models between service providers

On the other hand, VR/AR has proven to be very much useful in healthcare and engineering fields, without mentioning educational technologies where it is already a legacy. Medical scientists, educators and engineers require deeper understanding of how VR/AR can assist them in their daily activities as well as pertinent issues. So far from user perspective, this book showcases research and development to enlighten readers with background in VR/AR. Moreover, it compiles latest advancement in the fields of VR/AR from a broad perspective. Chapters cover core areas where presently VR/AR technologies are applied, namely, health‐ care, education, building industry and visualisation. The chapters' content provides experi‐

We would like to express our whole-hearted appreciations to all the authors and their re‐ spective institutions who have contributed for the accomplishment of this book. Everything has been possible thanks to the InTech publishing process managers Mr. Julian Virag and Mrs. Danijela Vladika, the technical board, and the commissioning editor for their endeav‐

**Associate Professor (Dr) Nawaz Mohamudally**

University of Technology, Mauritius


**2. Historical background**

Reports of battle-associated stress reactions appeared as early as the 6th century BC. One of the first descriptions of PTSD was made by the Greek historian Herodotus. In 490 BC

Post Traumatic Stress Disorder – An Overview 5

Posttraumatic Stress Disorder Checklist(PCL), Acute Stress Disorder Scale (ASDS), Acute Stress Checklist for Children (ASC-Kids), Child PTSD Symptom Scale (CPSS) and Reactions to Research Participation Questionnaires for Children and Parents (RRPQ-C and RRPQ-P)[4,5,6]. All these might be used prior to or as a complement to the clinical interview. Such measures are used most frequently in research settings, some might be used clinically to provide additional sources of documentation, and others might be given to veterans at a health facility prior to their first interview with health professional. Screening tools can be useful in initiating a conversation about exposure to traumatic events or possible PTSD symptoms. However, as noted by Briere (2004) "no psychological test can replace the focused attention, visible empathy, and extensive clinical experience of a well-trained and seasoned trauma clinician [7]." Working in Vietnam war-zone, veterans have developed both psychometric and psycho physiologic assessment techniques that have proven to be both reliable and valid. Other investigators have modified such assessment instruments and used them with natural disaster victims, rape/incest survivors, and other traumatized

PTSD has been criticized from the perspective of cross-cultural psychology and medical anthropology, because it has usually been diagnosed by clinicians from Western industrialized nations working with patients from a similar background. . Despite these criticisms, PTSD is a real time mental disorder with devastating clinical, physical, social and economic consequences for sufferers. Though clinicians from developing countries continue to diagnose PTSD using diagnostic systems developed in industrialized countries, the major clinical features appear to be uniform across cultures. Major gaps remain in our understanding of the effects of ethnicity and culture on the clinical phenomenology of posttraumatic syndromes. We have only just begun to apply vigorous ethno cultural research strategies to delineate possible differences between Western and non-Western societies regarding the psychological impact of traumatic exposure and the clinical manifestations of

The United Nations' World Health Organization publishes estimates of PTSD impact for each of its member states; the latest data available are for 2004. The age-standardiseddisability adjusted life-year (DALY) rates for PTSD, per 100,000 inhabitants, in 10 most

The National Comorbidity Survey has estimated that the lifetime prevalence of PTSD among adult Americans is 7.8%, with women (10.4%) twice as likely as men (5%) to have PTSD at some point in their lives. In the United States, 60% of men and 50% of women experience a traumatic event during their lifetimes. The rate is highest for soldiers. The United States Department of Veterans Affairs estimates that 830,000 Vietnam War veterans suffered symptoms of PTSD. The National Vietnam Veterans' Readjustment Study (NVVRS) found 15.2% of male and 8.5% of female Vietnam Vets to suffer from PTSD. Life-Time prevalence of PTSD was 30.9% for males and 26.9% for females. For soldiers who fought in the Iraq war in 2008, the prevalence of PTSD was 13.8%. The National Survey of Adolescents, which included a household probability sample of 4,023 adolescents between the ages of 12 and 17, found that using accepted diagnostic criteria for PTSD, the six-month

prevalence was estimated to be 3.7% for boys and 6.3% for girls [8].

cohorts.

such exposure.

**3. Epidemiology and prevalence**

ranking countries is as table 1.

Herodotus described, during the Battle of Marathon, an Athenian soldier who suffered no injury from war but became permanently blind after witnessing the death of a fellow soldier. In the early 19th century military medical doctors started diagnosing soldiers with "exhaustion" after the stress of battle. This "exhaustion" was characterized by mental shutdown due to individual or group trauma. Soldiers during the 19th century were not supposed to be scared or show any fear in the midst of battle. The only treatment for this "exhaustion" was to bring the afflicted back for a bit for a short term therapy and then send them back into battle. During the intense and frequently repeated stress, the soldiers became fatigued as a part of their body's natural shock reaction. According to Stéphane Audoin-Rouzeau and Annette Becker, "One-tenth of mobilized American men were hospitalized for mental disturbances between 1942 and 1945, and after thirty-five days of uninterrupted combat, 98% of them manifested psychiatric disturbances in varying degrees."

Previous diagnoses now considered historical equivalents of PTSD include railway spine, stress syndrome, shell shock, battle fatigue, or traumatic war neurosis. Although PTSD-like symptoms have also been recognized in combat veterans of many military conflicts, the modern understanding of PTSD dates from the 1970s, largely as a result of the problems that were still being experienced by US military veterans of the war in Vietnam. In its initial DSM-III (formulation 1980), a traumatic event was conceptualized as a catastrophic stressor that was outside the range of usual human experience. The framers of the original PTSD diagnosis had in mind events such as war, torture, rape, the Nazi Holocaust, the atomic bombings of Hiroshima and Nagasaki, natural disasters (such as earthquakes, hurricanes, and volcano eruptions) and human-made disasters (such as factory explosions, airplane crashes, and automobile accidents). They considered traumatic events as clearly different from the very painful stressors that constitute the normal vicissitudes of life such as divorce, failure, rejection, serious illness and financial reverses. (By this logic adverse psychological responses to such "ordinary stressors" would, in DSM-III terms, be characterized as Adjustment Disorders rather than PTSD.) This dichotomization between traumatic and other stressors was based on the assumption that although most individuals have the ability to cope with ordinary stress, their adaptive capacities are likely to be overwhelmed when confronted by a traumatic stressor.

The DSM-III diagnostic criteria for PTSD were revised in DSM-III-R (1987) and DSM-IV (1994) [2]. A very similar syndrome is classified in ICD-10 [3]. Since 1980 there has been a great deal of attention devoted to the development of instruments for assessing PTSD. Although an optimal evaluation of a patient for PTSD consists of a face-to-face interview by a mental health professional trained in diagnosing psychiatric disorders, several instruments are available to facilitate the diagnosis and assessment of posttraumatic stress disorder (PTSD). These include screening tools, diagnostic instruments, and trauma and symptom severity scales. For example, there are brief screening tools, such as the 4-item Primary Care PTSD Screen, developed by the Department of Veterans Affairs National Center for Posttraumatic Stress Disorder; self-report screening instruments, such as the Posttraumatic Diagnostic Scale; and structured or semi-structured interviews, such as the Clinician-Administered PTSD Scale (CAPS), the Structured Clinical Interview for DSM-IV (SCID), the Diagnostic Interview Schedule for DSM-IV (DIS-IV), and the Composite International Diagnostic Interview (CIDI), Acute Stress Disorder Interview (ASDI),

Herodotus described, during the Battle of Marathon, an Athenian soldier who suffered no injury from war but became permanently blind after witnessing the death of a fellow soldier. In the early 19th century military medical doctors started diagnosing soldiers with "exhaustion" after the stress of battle. This "exhaustion" was characterized by mental shutdown due to individual or group trauma. Soldiers during the 19th century were not supposed to be scared or show any fear in the midst of battle. The only treatment for this "exhaustion" was to bring the afflicted back for a bit for a short term therapy and then send them back into battle. During the intense and frequently repeated stress, the soldiers became fatigued as a part of their body's natural shock reaction. According to Stéphane Audoin-Rouzeau and Annette Becker, "One-tenth of mobilized American men were hospitalized for mental disturbances between 1942 and 1945, and after thirty-five days of uninterrupted combat, 98% of them manifested psychiatric disturbances in varying

Previous diagnoses now considered historical equivalents of PTSD include railway spine, stress syndrome, shell shock, battle fatigue, or traumatic war neurosis. Although PTSD-like symptoms have also been recognized in combat veterans of many military conflicts, the modern understanding of PTSD dates from the 1970s, largely as a result of the problems that were still being experienced by US military veterans of the war in Vietnam. In its initial DSM-III (formulation 1980), a traumatic event was conceptualized as a catastrophic stressor that was outside the range of usual human experience. The framers of the original PTSD diagnosis had in mind events such as war, torture, rape, the Nazi Holocaust, the atomic bombings of Hiroshima and Nagasaki, natural disasters (such as earthquakes, hurricanes, and volcano eruptions) and human-made disasters (such as factory explosions, airplane crashes, and automobile accidents). They considered traumatic events as clearly different from the very painful stressors that constitute the normal vicissitudes of life such as divorce, failure, rejection, serious illness and financial reverses. (By this logic adverse psychological responses to such "ordinary stressors" would, in DSM-III terms, be characterized as Adjustment Disorders rather than PTSD.) This dichotomization between traumatic and other stressors was based on the assumption that although most individuals have the ability to cope with ordinary stress, their adaptive capacities are likely to be overwhelmed when

The DSM-III diagnostic criteria for PTSD were revised in DSM-III-R (1987) and DSM-IV (1994) [2]. A very similar syndrome is classified in ICD-10 [3]. Since 1980 there has been a great deal of attention devoted to the development of instruments for assessing PTSD. Although an optimal evaluation of a patient for PTSD consists of a face-to-face interview by a mental health professional trained in diagnosing psychiatric disorders, several instruments are available to facilitate the diagnosis and assessment of posttraumatic stress disorder (PTSD). These include screening tools, diagnostic instruments, and trauma and symptom severity scales. For example, there are brief screening tools, such as the 4-item Primary Care PTSD Screen, developed by the Department of Veterans Affairs National Center for Posttraumatic Stress Disorder; self-report screening instruments, such as the Posttraumatic Diagnostic Scale; and structured or semi-structured interviews, such as the Clinician-Administered PTSD Scale (CAPS), the Structured Clinical Interview for DSM-IV (SCID), the Diagnostic Interview Schedule for DSM-IV (DIS-IV), and the Composite International Diagnostic Interview (CIDI), Acute Stress Disorder Interview (ASDI),

degrees."

confronted by a traumatic stressor.

Posttraumatic Stress Disorder Checklist(PCL), Acute Stress Disorder Scale (ASDS), Acute Stress Checklist for Children (ASC-Kids), Child PTSD Symptom Scale (CPSS) and Reactions to Research Participation Questionnaires for Children and Parents (RRPQ-C and RRPQ-P)[4,5,6]. All these might be used prior to or as a complement to the clinical interview. Such measures are used most frequently in research settings, some might be used clinically to provide additional sources of documentation, and others might be given to veterans at a health facility prior to their first interview with health professional. Screening tools can be useful in initiating a conversation about exposure to traumatic events or possible PTSD symptoms. However, as noted by Briere (2004) "no psychological test can replace the focused attention, visible empathy, and extensive clinical experience of a well-trained and seasoned trauma clinician [7]." Working in Vietnam war-zone, veterans have developed both psychometric and psycho physiologic assessment techniques that have proven to be both reliable and valid. Other investigators have modified such assessment instruments and used them with natural disaster victims, rape/incest survivors, and other traumatized cohorts.

PTSD has been criticized from the perspective of cross-cultural psychology and medical anthropology, because it has usually been diagnosed by clinicians from Western industrialized nations working with patients from a similar background. . Despite these criticisms, PTSD is a real time mental disorder with devastating clinical, physical, social and economic consequences for sufferers. Though clinicians from developing countries continue to diagnose PTSD using diagnostic systems developed in industrialized countries, the major clinical features appear to be uniform across cultures. Major gaps remain in our understanding of the effects of ethnicity and culture on the clinical phenomenology of posttraumatic syndromes. We have only just begun to apply vigorous ethno cultural research strategies to delineate possible differences between Western and non-Western societies regarding the psychological impact of traumatic exposure and the clinical manifestations of such exposure.

#### **3. Epidemiology and prevalence**

The United Nations' World Health Organization publishes estimates of PTSD impact for each of its member states; the latest data available are for 2004. The age-standardiseddisability adjusted life-year (DALY) rates for PTSD, per 100,000 inhabitants, in 10 most ranking countries is as table 1.

The National Comorbidity Survey has estimated that the lifetime prevalence of PTSD among adult Americans is 7.8%, with women (10.4%) twice as likely as men (5%) to have PTSD at some point in their lives. In the United States, 60% of men and 50% of women experience a traumatic event during their lifetimes. The rate is highest for soldiers. The United States Department of Veterans Affairs estimates that 830,000 Vietnam War veterans suffered symptoms of PTSD. The National Vietnam Veterans' Readjustment Study (NVVRS) found 15.2% of male and 8.5% of female Vietnam Vets to suffer from PTSD. Life-Time prevalence of PTSD was 30.9% for males and 26.9% for females. For soldiers who fought in the Iraq war in 2008, the prevalence of PTSD was 13.8%. The National Survey of Adolescents, which included a household probability sample of 4,023 adolescents between the ages of 12 and 17, found that using accepted diagnostic criteria for PTSD, the six-month prevalence was estimated to be 3.7% for boys and 6.3% for girls [8].

Post Traumatic Stress Disorder – An Overview 7

of PTSD include experiencing or witnessing an event perceived as life-threatening such as accidents, terminal illnesses, or employment in occupations exposed to war (such as soldiers) or disaster (such as emergency service workers).  Children or adults may develop PTSD symptoms by experiencing bullying or mob violence. Preliminary research suggests that child abuse may interact with mutations in a stress-related gene to increase the risk of PTSD in adults[9]. Multiple studies show that parental PTSD and other posttraumatic disturbances in parental psychological functioning can, despite a traumatized parent's best efforts, interfere with their response to their child as well as their child's response to trauma[10,11]. Parents with violence-related PTSD may, for example, inadvertently expose their children to developmentally inappropriate violent media due to

Military experience as risk factors for the development of PTSD include coming from an unstable family, being punished severely during childhood, childhood anti-social behavior and depression as pre-military factors, war-zone exposure, peri-traumatic dissociation, depression as military factors and recent stressful life events and depression as post-military factors[14]. Certain protective factors against PTSD in war-conditions include high school degree or college education, older age at entry to war, higher socioeconomic status, and positive paternal relationship as pre-military protective factors and social support at homecoming and current social support as post-military factors[15]. Research also indicates the protective effects of social support in averting and recovery from PTSD[16]. There may also be an attitudinal component; for example, a soldier who believes that they will not sustain injuries may be more likely to develop symptoms of PTSD than one who anticipates the possibility, should either be wounded[15]. Likewise, the later incidence of suicide among those injured in home fires above those injured in fires in the workplace suggests this

Posttraumatic stress responses have been documented in children who have suffered traumatic loss of their parents, siblings, and peers[17,18,19,20]. Results from a study indicated that knowing someone who was injured or killed, female gender, and bombrelated television viewing or other media exposure were associated with the most severe psychological reactions. Bereaved youths who suffered severe loss (e.g. a parent, sibling, close relative, or friend) as a result of the bombing were more likely to report posttraumatic

Although most people (50-90%) encounter trauma over a lifetime, only about 8% develop full PTSD[21]. Vulnerability to PTSD presumably stems from an interaction of biological diathesis, early childhood developmental experiences, and trauma severity. Predictor models have consistently found that childhood trauma, chronic adversity, and familial stressors increase risk for PTSD as well as risk for biological markers of risk for PTSD after a traumatic event in adulthood[22]. This effect of childhood trauma, which is not well understood, may be a marker for both traumatic experiences and attachment problems. Proximity to, duration of, and severity of the trauma also make an impact; and interpersonal traumas cause more problems than impersonal ones[21]. People vary in susceptibility to PTSD. Genetic factors may play a significant role in susceptibility. Women develop PTSD at about twice the rate as men, even for the same crimes[21]. Individuals with a prior trauma history or multiple traumas are at increased risk[21]. A premorbid psychiatric history also

stress symptoms than did children who did not experience this degree of loss.

their need to manage their own emotional dysregulation[12,13].

possibility.

Table 1.


In the phylogenetic analyses of all the genes, it can be observed that Uruguayan strains clustered mainly with South American strains. The only gene that did not show any South American-specific lineage was NSP3, in which the Uruguayan strains clustered mainly with Argentinian strains, but also with strains from other continents. These data, together with the identification of the E12 genotype in all the Uruguayan sequences, suggest a South American origin of RVA lineages [44]. Furthermore, the phylogenetic analyses showed an intricate pattern of diversity, with evidence of gene reassortments, interspecies transmission, local dispersion of some strains, and circulation of strains that are most prevalent in cattle worldwide.

The analyses of VP7 and VP4 showed a conserved pattern with all the Uruguayan strains clustering, with strains detected only in cattle and mainly from Argentina, indicating a probable host species and geographic linkage. Due to the shortage of G24 and P[33] sequences in the database (2 and 1, respectively), no phylogenetic analyses were performed for these genotypes. In the VP7 and VP4 phylogenetic analysis, the majority of strains characterized in this study clustered closely with strains detected in Argentinian cattle. The exceptions were one G6 lineage that clustered with European strains isolated from cattle, and one in P[11] sublineage that clustered with Brazilian strains isolated from cattle. There is a clear phylogenetic relationship between the strains detected in the cattle in Uruguay and Argentina, whereas Brazilian strains were, in general, phylogenetically distant from the Uruguayan strains. In addition, Uruguayan strains clustered together among themselves, suggesting that limited introductions of RVA into the country have occurred, but the strains were widely dispersed in the cattle. A possible explanation for the genetic similarity between the Uruguayan and Argentinian strains and their divergence to the Brazilian strains could be explained, in part, by the breed of cattle. In Uruguay and Argentina, most of the cattle breeds are *Bos taurus*, while in Brazil, there are mostly *Bos indicus* or *Bos indicus* x *Bos taurus* crosses. Although it has not been studied in cattle, different human subpopulations appeared to have different susceptibility infection and clinical disease, and this susceptibility is dependent on the rotavirus genotype, and in some cases, it also depends on different rotavirus strains of the same genotype [49].

Based on the phylogenetic analyses, we observed evidence of gene reassortment and interspecies transmission events. Regarding the former event, in addition to the previously mentioned gene reassortment of the G24P[33] strain, strong evidence was observed in the strains RVA/Cow-wt/URY/ LVMS1812/2016/G6P[5] and RVA/Cow-wt/URY/LVMS3206/2016/GxP[11] because both strains clustered together in all the genes, except in VP4 (which showed different genotypes, (P[5] and P[11], respectively), indicating that a possible gene reassortment event may have occurred. Another piece of evidence was observed in the RVA/Cow-wt/URY/LVMS1788/2016/GxP[11] strain because it clustered together with other Uruguayan strains in most of the genes, except in NSP1 and NSP3 genes, which clustered alone in different genetic lineages, also suggesting a gene reassortment event. Furthermore, the strain RVA/Cow-wt/URY/LVMS1837/2016/G10P[11] clustered together with RVA/Cow-wt/URY/LVMS2625/ 2016/G10P[11] and RVA/Cow-wt/URY/LVMS3053/2016/G10P[x] in most of the genes, but clustered

separately in distant genetic lineages in NSP2 and NSP5; this was probably due to gene reassortment. On the other hand, an interesting observation was that, in general, G6 strains tended to cluster together in most of the genes, and the same was observed for the G10 strains, with the exceptions aforementioned.

Regarding interspecies transmission, we observed that in the analyses of VP7 and VP4, all the Uruguayan strains clustered with other bovine strains, so these gene segments seem to be more host-specific than the other genes. On the other hand, and based on the phylogenetic analyses, we observed evidence suggesting interspecies transmission because the bovine strains detected in Uruguay closely clustered with strains detected in other host species. We observed that bovine Uruguayan strains A13 (NSP1 gene) clustered together with strains isolated from humans and a goat, possibly indicating events of interspecies transmission. Two lineages showed a close relationship between Uruguayan bovine strains and human strains (from South America and Europe); these human strains were reported to be Artiodactyl-like and a product of interspecies transmission [10,47,50], as well as the goat strain of a third lineage [45], which is in accordance with our results. In the NSP2-5 and VP6 genes, we observed that the Uruguayan bovine strains clustered in some lineages with strains isolated from other host species (human, goat, guanaco, vicuna, roe deer, llama, and sheep), mainly from South America, that were proposed to be originated by interspecies transmission [12,45,47,51], again in accordance with our results. Another piece of evidence supporting this event was observed in the NSP4; all the RVA strains detected in South America were E12, independent of the host species where they were isolated (horse, cow, guanaco, human, goat), suggesting interspecies transmission and fixation of this genotype in South America [44]. The interspecies transmission of RVA is widely documented [10–13], and our results support this event. In South America, it is common to raise different livestock species on the same farm in close contact with humans [45], which increases the possibility of interspecies transmission. Our results support that interspecies transmission is a common event in South America, including the possibility of zoonotic transmission [45,51,52].

Lastly, our study had some limitations. In Uruguay, dairy farming is concentrated in the southwest region and calves are raised under intensive production systems that facilitated the collection of the samples, while beef calves are mostly bred in extensive production systems and dispersed throughout the country, which hindered the access to samples. This resulted in an overrepresentation of dairy (92%) versus beef (8%) samples in our study. Another limitation was that we had no spiked control to determine if there was inhibition of the qPCR, which may lead to false-negatives. Regarding coinfections, the methodology used has the limitation that sequences obtained from a single animal would have only represented the predominant strain and/or sequences with multiple traces that were not included in the study. It is important to mention that, from our analyses, we could not determine the route nor the time in which the gene reassortment and the interspecies transmission events took place.

#### **4. Materials and Methods**

#### *4.1. Samples*

Fecal samples of 766 live calves and intestinal contents from 67 naturally-deceased calves were collected from 833 different calves from dairy and beef herds in Uruguay between 2015 and 2018. Sampled herds were distributed in 10 of the 19 regions of the country (Figure 6), and throughout the year, including samples collected in the four climate seasons. In addition, 766 samples were from dairy calves, and 67 from beef calves. We compared the frequency of the RVA infection between groups only for dairy calves. A total of 240 dairy calves had diarrhea at the time of sampling, while 272 were nondiarrheic dairy calves (this information was unavailable for 321 calves). The distribution by age in the first, second, third, and fourth weeks of life was 148, 201, 110, and 34 dairy calves, respectively (the age was unavailable for 340 calves). A total of 225 calves were from dairy herds vaccinated against NCD and 246 calves were from nonvaccinated dairy herds (herd vaccination history was unavailable for 362 calves).

**Figure 6.** Map of Uruguay, the regions from which samples were collected shown in grey.

#### *4.2. Sample Suspension, RNA Extraction, Reverse Transcription, Detection and Quantification of RVA*

Samples were diluted 1:10 (*v*:*v*) in phosphate-buffered saline solution, centrifuged at 3000× *g* for 20 min at 4 ◦C, and supernatants were collected and stored at −80 ◦C. Viral RNA was extracted using a QIAamp® cador® Pathogen Mini Kit (Qiagen®, Hilden, Germany), following the manufacturer's instructions. Reverse transcription (RT) was carried out with RevertAid® Reverse Transcriptase (Thermo Fisher Scientific®, Waltham, MA, USA) and random hexamers primers (Qiagen®), following the manufacturer´s instructions. All RNAs and cDNAs were stored at −80 ◦C until further viral analyses. Screening and quantification of the samples for RVA identification were carried out through a quantitative polymerase chain reaction (qPCR) targeted to the NSP3 gene, as described elsewhere [9]. Briefly, 12.5 µL of SensiFAST™ Probe No-ROX Kit (Bioline®, London, UK), 5.0 µL of nuclease-free water, 1.0 µL of 10 µM forward primer, 1.0 µL of 10 µM reverse primer, 0.5 µL of 10 µM probe, and 5 µL of cDNA were mixed in 0.2-mL PCR tubes. All samples were analyzed in duplicate. In order to validate the complete process, an RVA-positive (G6P[5] strain) and an RVA-negative fecal sample were used as positive and negative controls, respectively.

#### *4.3. Rotavirus A Genotyping*

Quantitative-PCR positive samples were subsequently subjected to amplification of VP7 and VP4 (VP8\*). Briefly, 12.5 µL of MangoMix™ (Bioline®), 5 µL of cDNA, 5.5 µL of nuclease-free water, 1 µL of dimethyl sulfoxide, 0.5 µL of 20 µM forward primer and 0.5 µL of 20 µM reverse primer were mixed in 0.2-mL PCR tubes. Forward and reverse primers for VP7 and VP4 (VP8\*) amplification are described elsewhere [20,53]. In addition, 10 samples, including representative VP7 and VP4 genotype combinations observed in this study, were selected for VP6 and NSP1-5 gene characterization. Primers and cycling conditions were used, as described elsewhere [10], and PCR reagents were used, as described above. Genotyping was performed using the web-based genotyping tool RotaC v2.0 [54].

#### *4.4. PCR Product Purification, Sequencing, and GenBank Accession Numbers*

PCR products were visualized in 1–2% agarose gels and positive samples were purified using PureLink™ Quick Gel Extraction and PCR Purification Combo Kit (Invitrogen®, Carlsbad, CA, USA), according to the manufacturer's instructions. Both cDNA strands were sequenced by Macrogen Inc. (Seoul, Korea). Sequences were deposited in GenBank with accession numbers: MN649559—MN649674 (VP4), MN649675—MN649732 (VP7), MN649733—MN649742 (VP6), MN649743—MN649751 (NSP1), MN649752—MN649761 (NSP2), MN649762—MN649770 (NSP3), MN649771—MN649780 (NSP4), and MN649781—MN649789 (NSP5).

#### *4.5. Phylogenetic Analysis*

All the available sequences corresponding to the genotypes observed in the RVA strains detected in this study, previously determined with RotaC, were downloaded from the Virus Variation Resource (http://www.ncbi.nlm.nih.gov/genome/viruses/variation/) [55]. A dataset was created for each genotype, and multiple sequence alignments were obtained using ClustalW implemented in MEGA 7 software [56]. The final alignment of each gene comprised all the worldwide sequences that covered the length of the sequences obtained in this study. The length of the sequences and the nucleotide position, involved in the phylogenetic analysis of each gene, are detailed in Table 3. The nucleotide substitution models that best fit each dataset (Table 3) and the maximum likelihood trees were obtained using W-IQ-TREE (available at http://iqtree.cibiv.univie.ac.at) [57]. The branches support was estimated with the Shimodaira–Hasegawa-approximate likelihood-ratio test (SH-aLRT) [58]. Trees were visualized in FigTree (http://tree.bio.ed.ac.uk/software/figtree/).

**Table 3.** Information about the final alignments obtained for the phylogenetic analyses.

\* Reference strain: WC3.

#### *4.6. Statistical Analyses*

Data were organized and graphics were generated using Microsoft® Office Excel. Categorical data were evaluated with RStudio v1.0.136 software through Pearson's chi-squared tests. Odds ratios (OR) and 95% confidence intervals (CI) were calculated with jamovi software (available at https://www. jamovi.org/). Viral load values (genome copies/milliliter of feces) were log10 transformed. For the viral load and mean age analyses, the Shapiro–Wilk test was performed, rejecting the normality of the data, so the Mann–Whitney U test was performed with the same software. For all tests, differences were considered statistically significant if the obtained *p*-value was < 0.05.

#### **5. Conclusions**

Rotavirus A is widespread in cattle in Uruguay and is associated with diarrhea in calves, with a peak of viral shedding at 2–3 weeks of age, and higher viral shedding in diarrheic versus non-diarrheic calves. Even though the main genotypes observed in this country are the most prevalent worldwide, a rare strain was detected with a G24-P[33]-I2-A13-N2-T9-E12-H3 genotype constellation. The E12 genotype detected in all strains, regardless of the VP7 and VP4 genotypes, appears to be a South American geographic marker. An intricate genetic scenario was evidenced, with gene reassortment and interspecies transmission events, including transmission between animals and humans.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2076-0817/9/7/570/s1, Figure S1: Maximum likelihood tree of the NSP1 gene. The best nucleotide substitution model (TIM + I + G) and the maximum likelihood tree were obtained with W-IQ-TREE. Uruguayan strains are shown in different colors. SH-aLRT values ≥ 80 are shown. Figure S2: Maximum likelihood tree of the NSP2 gene. The best nucleotide substitution model (TIM + G) and the maximum likelihood tree were obtained with W-IQ-TREE. Uruguayan strains are shown in different colors. SH-aLRT values ≥ 80 are shown. Figure S3: Maximum likelihood tree of the NSP3 gene. The best nucleotide substitution model (TIM3 + G) and the maximum likelihood tree were obtained with W-IQ-TREE. Uruguayan strains are shown in different colors. SH-aLRT values ≥ 80 are shown. Figure S4: Maximum likelihood tree of the NSP4 gene. The best nucleotide substitution model (HKY + G) and the maximum likelihood tree were obtained with W-IQ-TREE. Uruguayan strains are shown in different colors. SH-aLRT values ≥ 80 are shown. Figure S5: Maximum likelihood tree of the NSP5 gene. The best nucleotide substitution model (TN + I + G) and the maximum likelihood tree were obtained with W-IQ-TREE. Uruguayan strains are shown in different colors. SH-aLRT values ≥ 80 are shown. Figure S6: Maximum likelihood tree of the VP6 gene. The best nucleotide substitution model (TIM + I + G) and the maximum likelihood tree were obtained with W-IQ-TREE. Uruguayan strains are shown in different colors. SH-aLRT values ≥ 80 are shown.

**Author Contributions:** Conceptualization, M.C. and R.C.; methodology, M.C., R.D.C., M.L.C., C.S., S.M., F.C. and D.C.; resources, M.C., F.R.-C., F.G., V.P. and R.C.; writing—original draft preparation, M.C.; writing—review and editing, M.C., R.D.C., M.L.C., S.M., F.C., D.C., M.V., F.R.-C., F.G., V.P. and R.C.; funding acquisition, M.C., F.R.-C. and R.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by "Instituto Nacional de Investigación Agropecuaria" (INIA), grant number PL\_015 N-15156 and N-23398, and by the "Universidad de la República" program "Polo de Desarrollo Universitario". The APC was funded by "Universidad de la República".

**Acknowledgments:** M.C. acknowledges support from the "Agencia Nacional de Investigación e Innovación" (ANII) through a PhD scholarship, and "Comisión Sectorial de Investigación Científica" (CSIC) and ANII for mobility fellowships.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Group A Rotavirus Detection and Genotype Distribution before and after Introduction of a National Immunisation Programme in Ireland: 2015–2019**

#### **Zoe Yandle \* , Suzie Coughlan, Jonathan Dean, Gráinne Tuite, Anne Conroy and Cillian F. De Gascun**

UCD National Virus Reference Laboratory, University College Dublin, Dublin 4, Ireland<EMAIL_ADDRESS>(S.C<EMAIL_ADDRESS>(J.D<EMAIL_ADDRESS>(G.T<EMAIL_ADDRESS>(A.C<EMAIL_ADDRESS>(C.F.D.G.) **\*** Correspondence<EMAIL_ADDRESS>Tel.: +353-1-716-4401

Received: 19 May 2020; Accepted: 5 June 2020; Published: 7 June 2020

**Abstract:** Immunisation against rotavirus infection was introduced into Ireland in December 2016. We report on the viruses causing gastroenteritis before (2015–2016) and after (2017–2019) implementation of the Rotarix vaccine, as well as changes in the diversity of circulating rotavirus genotypes. Samples from patients aged ≤ 5 years (n = 11,800) were received at the National Virus Reference Laboratory, Dublin, and tested by real-time RT-PCR for rotavirus, Rotarix, norovirus, sapovirus, astrovirus, and enteric adenovirus. Rotavirus genotyping was performed either by multiplex or hemi-nested RT-PCR, and a subset was characterised by sequence analysis. Rotavirus detection decreased by 91% in children aged 0–12 months between 2015/16 and 2018/19. Rotarix was detected in 10% of those eligible for the vaccine and was not found in those aged >7 months. Rotavirus typically peaks in March–May, but following vaccination, the seasonality became less defined. In 2015–16, G1P[8] was the most common genotype circulating; however, in 2019 G2P[4] was detected more often. Following the introduction of Rotarix, a reduction in numbers of rotavirus infections occurred, coinciding with an increase in genotype diversity, along with the first recorded detection of an equine-like G3 strain in Ireland.

**Keywords:** gastroenteritis; rotavirus; Rotarix; pediatric; diagnostics; molecular epidemiology; G3P[8]; equine-like

#### **1. Introduction**

Rotavirus is a leading cause of pediatric acute gastroenteritis, causing fever, vomiting, and diarrhoea. Mortality rates are highest in low income developing countries, where it causes approximately 128,000 fatal cases per year in those under five years old [1,2]. With the availability of rotavirus vaccines, the rate of global hospitalisations due to rotavirus or acute gastroenteritis, as well as deaths due to acute gastroenteritis, has decreased [3]. In Europe, pediatric rotavirus infection results in approximately 75,000−150,000 hospitalisations annually, with 2−4 times more children seeking out-patient medical care [4]. In Ireland, average crude incidence rates were 55 per 100,000 population in the 2007–2015 period [5], with hospitalisation rates of approximately 1190 per 100,000 [6], compared to the majority of EU member states, which report rates of 300–600 per 100,000 [4].

In 2009, the World Health Organisation (WHO) recommended global rotavirus vaccination [7] and, in Europe, 13 countries include it in their universal immunisation programmes, with a further five offering the vaccine for certain risk groups, specific regions, or requiring partial payment [8]. Two licensed live-attenuated vaccines are available in Europe; the pentavalent bovine-human reassortment rotavirus vaccine, RotaTeq (Merck & Co., West Point, PA, USA), and the human monovalent vaccine, Rotarix (GlaxoSmithKline, Rixensart, Belgium). In December 2016, Rotarix was introduced into the Irish national immunisation programme, with the vaccine administered in two doses at 2 and 4 months of age. Most recent figures (Q3, 2019) show the national uptake of the vaccine is 89% [9]. Rotavirus is a notifiable disease in Ireland, and laboratory confirmed cases are reported to the Health Protection Surveillance Centre. Effectiveness of both RotaTeq and Rotarix has been well documented, with the UK, Germany, and Belgium reporting an approximate 85% reduction in the presentation of severe rotavirus disease following vaccination [10–12].

Rotaviruses are double stranded RNA viruses containing 11 genome segments. There are 10 groups, A–J, defined by the middle VP6 capsid antigen, [13] two of which (I and J) were recently discovered in dogs and bats, respectively [14,15]. However, in humans, the majority of infections are caused by Group A rotavirus. Classification is a binary system depending on the expression of two outer proteins; the G and P-type, encoded by VP7 and VP4, respectively. Full genome analysis (where the VP7-VP4-VP6-VP1-VP2-VP3-NSP1-NSP2-NSP3-NSP4-NSP5/6 genes of rotavirus (RV) strains are described using the abbreviations Gx-P[x]-Ix-Rx-Cx-Mx-Ax-Nx-Tx-Ex-Hx), is required to monitor the evolution of the virus and detect reassortment [16].

Despite the theoretical possibility for numerous rotavirus G/P constellations, six account for 80–90% of circulating genotypes, namely G1P[8], G2P[4], G3P[8], G4P[8], G9P[8] and G12P[8]. Distribution of these commonly detected genotypes can vary by year, country, and age [17]. Despite the natural fluctuation of genotype diversity, increasing data suggest that the changes may be due to the impact of strain-specific vaccines [18]. Both in Belgium and the UK, before immunisation, G1P[8] was the most common circulating genotype; however, following vaccination, G2P[4] has been more frequently detected [19,20]. In Finland, following the introduction of RotaTeq, G9P[8] and G12P[8] have now become the main genotypes, where, previously, G1P[8] dominated [21]. However, changes in genotype distribution also occurs in countries with no immunisation [22,23], so whether the vaccine directly leads to a change in genotype diversity remains unclear [24,25].

Surveillance of rotavirus genotypes has been recommended by the WHO in countries with immunisation programmes to detect and monitor strain variation and ensure vaccine effectiveness is maintained [26]. The surveillance network, EuroRotaNet, has been monitoring rotavirus diversity in 12 European countries and has reported an increase in diversity since vaccination [17,27]. As Ireland is not currently part of any European or global surveillance network, we aim to fill that current gap of knowledge.

The purpose of this study is two-fold; firstly, to report on the viruses causing gastroenteritis, including rotavirus, 2 years prior and 3 years post implementation of the Rotarix immunisation programme, and, secondly, to describe the diversity of rotavirus genotypes in Ireland.

#### **2. Results**

#### *2.1. Sample Demographics*

Ireland has a population of 4.8 million with 36% of people living in the eastern health region, which includes Dublin, the surrounding areas, and the country's largest children's hospitals [28]. The National Virus Reference Laboratory (NVRL), Dublin, provides a diagnostic and reference service for all health care regions, though testing is also provided in regional hospitals.

This study analyzed the results from pediatric (≤5 years) patient samples received at the NVRL between 1 January 2015 and 31 December 2019 for the investigation of viral gastroenteritis. In total, 11,800 faecal samples were included in the analysis, 5267 (45%) from females, 6511 (55%) from males, and 22 (0.2%) for which details were not provided. Samples tested were predominantly from the eastern health region 10,644/11,800 (90%), and of these 10,180/10,644 (96%) were from a children's hospital. Other samples were from the northern 672/11,800 (6%), western 204/11,800 (2%), midlands 139/11,800

(1%), and southern health regions 141/11,800 (1%). As vaccine history was not available for each patient, cohorts are described as vaccine-eligible, using age as a proxy for vaccination status.

During 2015 to 2019, there were 312,013 births recorded in Ireland; 159,821 males (51.2%) and 152,192 females (48.8%). To establish how representative the samples tested were, the percentage of the annual birth cohort investigated for the detection of viruses causing gastroenteritis was calculated for those aged 0–12 months in each year. In 2015, 2280/65,536 (3.5%) were tested, in 2016 2065/63,841 (3.3%) were tested, in 2017 760/61,824 (1.2%) were tested, in 2018 587/61,016 (1%) were tested, and in 2019 608/59,796 (1%) were tested.

#### *2.2. Detection of Viral Pathogens*

The most frequently detected viral pathogen in 2015 and 2016 was rotavirus, followed by norovirus. Norovirus has been detected in approximately 12% of samples each year, whereas enteric adenovirus (adenovirus subgenus F), sapovirus, and astrovirus were detected in 2.8–6.3% of samples from 2015–2019 (Table 1). The number of samples with no virus detected ranged from 51.4% to 65.0%, depending on the year.

<sup>a</sup> 185 dual infections, 10 triple infections <sup>b</sup> 154 dual infections, 11 triple infections, 1 quadruple infection, <sup>c</sup> 93 dual infections, 8 triple infections, 2 quadruple infections <sup>d</sup> 41 dual infections <sup>e</sup> 25 dual infections. Additional viruses detected in Rotarix samples, 2017: norovirus n = 3, adenovirus F n = 1, astrovirus n = 2; 2018: norovirus n = 8, adenovirus F n = 1; 2019: norovirus n = 3, sapovirus n = 1, astrovirus n = 1.

There were 1753 samples tested from vaccine-eligible children in 2017–2019, and of these 43 (2.5%) had wild-type rotavirus, 179 (10.2%) had Rotarix, 257 (14.7%) had norovirus, 113 (6.4%) had adenovirus F, 97 (5.5%) had sapovirus, and 95 (5.4%) had astrovirus detected. In this group, there were 70 dual infections and 1039 (59.3%) samples had no detectable virus.

#### *2.3. Detection of Wild-Type Rotavirus*

The median age of those testing positive for rotavirus in the pre-vaccine era, 2015–2016 (n = 1181), was significantly lower at 1.19 years (interquartile range (IQR) 0.64–1.85), compared to the median in the entire 3 years post-vaccine, 2017–2019 (n = 373) at 1.85 years (IQR 1.12–2.84) *p* < 0.0001 (Table 2).

In the 2015/16 pre-vaccine era, a total of 485/4345 (11.2%) children aged 0–1 year had detectable wild-type rotavirus. This compares with 12/1195 (1.0%) in the post-vaccine 2018 and 2019 era, representing a 91.1% relative decrease in the number of wild-type rotavirus detected in this age range. The 1–2-year age group showed a relative reduction of 79.1% when 2015/16 was compared with 2018/19; 444/1691 (26.3%) compared to 28/505 (5.5%), respectively. This contrasts with the 5–6-year age group, which showed an increase in the detection of rotavirus from 20/324 (6.2%) to 12/96 (12.5%).

**Table 2.** Number of wild-type rotavirus positive cases by age group in the pre-vaccine years (2015–2016) compared to the post-vaccine years (2017–2019).

Interquartile range (IQR).

#### *2.4. Seasonal Variation of Wild-Type Rotavirus*

Prior to vaccination, rotavirus was a seasonal infection. In 2015, the season ran from weeks 1–29, peaking in week 11; in 2016 from weeks 11–27, peaking in week 19; and in 2017 from weeks 2–30, peaking in week 11. However, in 2018 and 2019, there was no clear seasonal onset and end, and rotavirus was most frequently detected in weeks 14 and 22, respectively (Figure 1).

**Figure 1.** Number of wild-type rotavirus (Wt-RV) cases detected by month, 2015–2019.

#### *2.5. Detection of Vaccine-Derived Rotavirus (Rotarix)*

Of all 3814 samples tested in 2017–2019, 1753 (46.0%) were eligible for the vaccine, and Rotarix was detected in 179/1753 (10.2%) of these (Table 1). In addition, one sample from a vaccine eligible patient was received and tested in December 2016 and found to be positive for Rotarix. In 20/180 (11.1%) of Rotarix-positive samples, another virus was detected, most commonly norovirus.

The age at which Rotarix was most frequently detected was 2 months (Table 3). Rotarix was not detected in any samples from patients older than 7 months of age.

#### *2.6. Distribution of Genotypes in Ireland*

In total, 786/1554 (51%) samples with detectable wild-type rotavirus were genotyped. Of these, 728 (93%) were from the eastern health board, 33 (4%) northern, 14 (2%) western, 8 (1%) southern, and 3 (0.4%) from the midlands. No significant correlation was observed between genotype and region or age (data not shown).

As the total numbers of rotavirus cases decreased following the introduction of immunization in December 2016, the proportion of samples genotyped was increased to reliably detect significant changes. In 2015, 293/662 (44%), and in 2016, 242/519 (47%) positive samples were genotyped, while in 2017, 135/250 (54%), in 2018, 48/53 (91%), and in 2019, 68/70 (97%) were genotyped.

#### *2.7. Comparison of the Genotype Diversity Pre- and Post-Vaccine*

G1P[8] was the most common genotype detected in 2015, 2016, and 2017 (Table 4). Conversely, G2P[4] was the most frequently detected genotype in 2019 (Figure 2). G3P[4], G8P[8], G9P[4], G12P[6], and G2P[8] remain uncommon genotypes in Ireland, detected in five, two, four, two, and one samples, respectively, over the 5-year period.

**Table 4.** Comparison of genotype diversity between the pre-vaccine (2015–2016) and post-vaccine (2017–2019) eras. Confidence interval (CI) significance 0.95. Comparison of the genotype proportion between pre- and post-vaccine year groups by Chi-square *p* < 0.05.

**Figure 2.** Genotype diversity in Ireland 2015–2019. Data are presented as the proportion (%) of a specific genotype compared to the total genotype results. Uncommon genotypes: <1% of total results, 2015: G9P[4] n = 2; 2016: G12P[6] n = 2; 2017: G8P[8] n = 1. 2018; G3P[4] n = 1; 2019 G2P[8] n = 1, G3P[4] n = 4, G8P[8] n = 1, G9P[4] n = 2. Mixed genotypes: those with >1 G or P-type, 2015: G1/4P[8] n = 7; 2016: G8/12P[8] n = 1, G2/3P[8] n = 1, G2/9 P[4/8] n = 1; 2017: G1/3P[8] n = 1; 2018: G1/3P[8] n = 1; 2019: G8/12P[8] n = 2, G3/12P[8] n = 1, G8P[8] n = 1, G9/12P[8] n = 1. Untypable results are those where either the G or P type was untypable.

#### *2.8. Detection of Human and Equine-Like Rotavirus G3*

G3P[8] was detected in 19/535 (4%) of genotyped samples in 2015–2016, compared to 40/251 (16%) in 2017–2019, a significant increase (p < 0.0001), whilst the uncommon G3P[4] was only detected in the post-vaccine era. Four G3 strains were detected as a mixed infection. Of the 68 G3 types (63 P[8] and 5 P[4]), 17 were selected for sequencing of the VP7 gene, which identified two G3P[8] samples from 2018, containing viruses that clustered within the equine-like G3 lineage and the remaining 15 G3 samples clustered within the human lineage (Figure 3).

**Figure 3.** Phylogenetic tree of VP7 G3 rotavirus gene sequences. The tree was constructed by using the maximum likelihood method and the Tamura-Nei model [29]. Bootstrap values (1000 replicates) above 75% are shown. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. This analysis shows 46 nucleotide sequences; 17 from Irish strains identified in this study (colour coded with green circle) and 29 from reference strains in GenBank. Phylogenetic analyses were conducted in MEGA X [30].

#### *2.9. Genotypes Detected in Rotavirus Positive Samples from Those of Vaccine-Eligible Age*

There are six common genotypes circulating in Europe, namely, G1P[8], G2P[4], G3P[8], G4P[8], G9P[8], and G12P[8] [17], and all other genotypes are considered uncommon. Of the 43 samples with detectable wild-type rotavirus and of vaccine-eligible age, 37 were genotyped by RT-PCR (Table 5). Of these, 30/37 (81.1%) had a common genotype, 4/37 (10.8%) had an uncommon genotype, 2/37 (5.4%) had a mixed infection, and one sample (2.7%) could not be fully genotyped. Ten of the 37 samples (27.0%) had G3 genotype detected (seven P[8] and three P[4]).

**Table 5.** Wild-type rotavirus genotypes detected in the age group eligible for the vaccine. Six additional samples had detectable wild-type rotavirus but were unavailable for genotyping.

#### **3. Discussion**

This study describes the reduction in rotavirus detection following implementation of a national immunization program for all children in Ireland, as well as previously unknown data regarding the extent of genotype diversity during 2015–2019. Our study shows that the largest reduction in the detection of rotavirus occurred in those aged 0–12 months, where a relative decrease of 91% was achieved between 2015/16 and 2018/19. Although the vaccine status was unknown in detail, the effectiveness of the vaccination program has been clearly shown. Our results support the national data collated by the Health Protection Surveillance Centre (HPSC), where a crude incidence rate (CIR) of rotavirus for all age groups was 13.3 per 100,000 population in 2018, representing a decrease of 76%, compared to the mean CIR during 2008–2017 of 55.5 per 100,000 [5]. In addition, since the introduction of the vaccine, there was a reduction in visits to three large pediatric emergency departments with acute gastroenteritis, where median weekly presentations in 2017–2018 (126; interquartile range (IQR), 103–165) were lower than in 2012–2016 (160; IQR 128–214) (*p* < 0.001) [31]. Furthermore, an 86% (95% CI 79.3–90.2%) decrease in hospitalizations due to rotavirus has been reported nationally in those aged <1 year [32]. In our study, we found that the median age of wild-type rotavirus infection significantly increased in the years following vaccination, from 1.2 years in 2015 to 2.9 years in 2019 (*p* < 0.0001). This is consistent with the findings of other researchers, who also noted the later age of infection in the post-vaccine era [11,33]. In Ireland, vaccination uptake is recorded by individual General Practitioners and health care professionals which are submitted to the HPSC on a quarterly basis. Vaccine uptake data from Q1 2017 to Q3 2017 was unavailable, but the evidence suggests that this must have been suboptimal as there was little change in rotavirus detection in those aged 0–1 year in 2016 compared to 2017 (9.7% versus 7.5%, respectively). The substantial increase in rotavirus infection in the post-vaccine years in the 5–6-year age group who would not be eligible for the vaccine was also somewhat surprising. Although the number of children tested in this age group were lower in the post-vaccine compared to pre-vaccine years, the proportion of positives was almost double (6.2% versus 12.5%). The short timeframe is a limitation of this study; however, collection of data is ongoing, and it will be of interest to follow up on the impact of vaccination on rotavirus detection in all age groups in the post-vaccine era. A further finding in our data is a diminution of the characteristic rotavirus seasonal pattern, a phenomenon that has been noted by others following introduction of the rotavirus vaccine [17,34].

The live-attenuated vaccine Rotarix replicates in the gut of the recipient and is excreted, albeit at lower amounts compared to a wild-type infection [35]. We detected Rotarix in 10% of patients who were of vaccine-eligible age and, as rotavirus is notifiable in Ireland, this highlights the importance of differentiating between wild-type and vaccine-derived viruses, particularly when screening with a sensitive method, such as RT-PCR. By not excluding vaccine-derived rotavirus from diagnostic tests, there may be an over-estimation of rotavirus disease burden and unnecessary clinical intervention [36–38]. We identified 180 samples with detectable Rotarix, 20 (11%) of which had another virus detected, the most common being norovirus. We found norovirus to be the second most common pathogen detected after rotavirus in 2015/16, which then became the most common cause of viral gastroenteritis in our study group in the post-vaccine era. Our results are consistent with that observed in earlier studies, where norovirus is now the leading cause of viral gastroenteritis in those vaccinated for rotavirus [39–41]. Of note, sapovirus, astrovirus, and enteric adenovirus were detected in similar proportions over the 5-year time period and demonstrated no increase or decrease in detection rates following Rotarix introduction. Depending on the year, we report that 51–65% had no detectable viral pathogen. This apparent diagnostic gap highlights a further limitation of this study, in that it is quite possible that parallel samples were sent for the investigation of bacterial or parasitic pathogens, which are common causes of gastroenteritis [42,43]. Unfortunately, we did not have access to these results. In addition, other viruses, such as bocavirus, enterovirus, and parechoviruses, which may cause gastroenteritis, would not have been detected by our routine screening test.

Prior to the introduction of Rotarix, we found the circulating genotypes in Ireland were comparable to other European countries, with G1P[8] being the most commonly detected. The findings of the current study are consistent with those observed in several earlier reports from samples tested in Ireland from 1995 to 2009, where it was reported that the most commonly detected genotype was G1P[8], with fluctuating levels of G2P[4], G3P[8], G4P[8], and G9P[8] [44–49]. The current study matches those findings. However, we can report that the diversity of genotypes increased in the years following the introduction of a vaccine and that, in 2018/19, G1P[8] was no longer the most common genotype. Furthermore, genotypes detected in children eligible for the vaccine was more varied than those detected in the vaccine-ineligible cohort. With regards to wild-type and vaccine rotavirus strains, they can be described in terms of being homotypic, partly heterotypic, and fully heterotypic based on the G and P proteins. For instance, the monovalent G1P[8] Rotarix vaccine is homotypic to other circulating G1P[8] strains (both proteins are the same), G12P[8] is partly heterotypic (one protein different), and G2P[4] is fully heterotypic (both G and P proteins are different) [50]. Rotarix provides exposure to G1P[8] rotavirus among infants, with protection that is likely to be higher against homotypic strains than heterotypic strains, such as G2P[4]. This suggests that natural infection leading to disease is more likely to be caused by such heterotypic strains [19,51] and that a vaccinated population could possibly drive selective pressure, increasing the likelihood of these genotypes to circulate in the community [52,53]. That being said, the monovalent vaccine Rotarix provides significant protection from G1, G2, G3, G4, and G9, and efficacy against severe G2 rotavirus gastroenteritis was as high as for other rotavirus types [54]. Clearly, the immune response to rotavirus infection is a complex issue, with a previous report suggesting that type-specific neutralizing antibodies induced by the vaccine against VP7/VP4 epitopes are not solely responsible for a protective effect [55]. The report proposes that, as there are a limited number of diverse circulating strains worldwide, these antibodies are not driving long-term selective pressure, which itself would favor antigenic drift or the emergence of novel genotypes.

Interestingly, the genotype G12P[8] was not circulating widely at the time of Rotarix and RotaTeq vaccine development; however, it has now become established as an increasingly common genotype [17]. A large study in the USA found G12P[8] more frequently than any other rotavirus genotype in fully vaccinated children [56]. Another example of an uncommon genotype becoming more prevalent is the recently emerged equine-like G3 strain, first identified in Japan in 2013 [57] but now detected world-wide [58–60]. We identified, for the first time in Ireland, two samples from 2018 that clustered within the equine-like G3 lineage. A further 15 G3 samples were sequenced and all clustered in the human lineage, suggesting that the equine-like lineage has not yet become established in Ireland compared to other countries [59,61]. Of note, five of the uncommon human G3P[4] strains were

also identified in our study in 2018–2019, and this strain has been detected before in Ireland in 2006/07 [48]. The detection of uncommon genotypes, along with the additional potential for zoonotic reassortment [62,63], reinforces the WHO recommendation for surveillance, emphasising the need for continued monitoring of rotavirus vaccine efficacy against emerging rotavirus.

Several important limitations need to be considered for our study group. Firstly, the results are somewhat biased due to the observational nature of the study and samples tested would have been from those with moderate to severe gastroenteritis that warranted clinical investigation. In addition, there was no denominator for the population not suffering from symptoms of viral gastroenteritis, so we are unable to calculate incidence and prevalence of rotavirus infection. Furthermore, with no access to vaccination data, it was not possible to determine vaccine effectiveness rates or describe definitive vaccine failures. However, due to the large data set (n = 11,800), we can show relative reductions in the detection of rotavirus and the changes in the diversity of circulating genotypes. The geographical distribution of samples is nationwide, although the data are skewed to some extent due to the density of the population in Dublin and the location of the main children's hospitals, and therefore samples were predominantly from the eastern health region. It should also be noted that the number of samples tested has decreased year on year. The reason for this may be the decrease in symptomatic children or the increase in localized testing and possible availability of point of care assays. Finally, it was not possible to categorize samples as the community or hospital acquired and we could not identify samples belonging to outbreaks.

We have shown that rotavirus continues to circulate in the pediatric population, albeit in low numbers, and this is expected to decrease further with the increasing cohort of vaccinated children. Binary genotypic classification is useful to establish circulating genotypes and can be used for reassortment studies of the VP7 and VP4 encoding genes; however, whole genome genotyping is required for a more detailed analysis of the virus. Indeed, a future aim from this ongoing study is to perform whole genome sequencing from samples in this dataset to allow identification of possible reassortment of non VP7/VP4 genes or mutation events. In addition, all samples identified as G3 will be categorized as either equine-like or of human lineage. By collaborating with clinicians at the children's hospitals, it is hoped that any sample from a child with rotavirus with a full or partial vaccine history will be referred to the NVRL for whole genome sequencing to establish definitive strains circulating in this group of children.

In conclusion, we describe the detection and characterization of rotavirus in pediatric samples circulating in Ireland over a 5-year time period. We show that, following the introduction of Rotarix, there is a relative reduction in the number of rotavirus infections diagnosed, coinciding with an increase in genotype diversity, along with the first recorded detection of an equine-like G3 strain in Ireland.

#### **4. Materials and Methods**

#### *4.1. Study Design*

This opportunistic study presents the results of faecal samples from pediatric samples (≤5 years) investigated for viral gastroenteritis at the National Virus Reference Laboratory (NVRL), Dublin, Ireland. Test results for wild-type rotavirus, vaccine-derived rotavirus (Rotarix), norovirus, sapovirus, astrovirus, and enteric adenovirus subgenus F were obtained with genotype and sequence results, if available. Samples dated 1 January 2015 to 31 December 2019 were included in the study. Samples dated 1 January 2015 to 31 December 2016 were designated as "pre-vaccine". The first doses of Rotarix were given for those aged 2 months from the 1 December 2016. Only one sample was received from a 2-month old in December 2016, and this patient had detectable Rotarix. Samples dated 1 January 2017 to 31 December 2019 were designated as "post-vaccine". Routine testing for Rotarix was introduced into the NVRL from 11 December 2017. Rotavirus-positive samples received from 1 December 2016 to 10 December 2017 were tested for Rotarix, retrospectively.

#### *4.2. Annual Birth Cohort in Ireland*

The Central Office of Statistics provides the annual number of births in Ireland [64]. The number of annual births by year are: 2015: 65,536 (33,480 males, 32,056 females); 2016: 63,841 (32,709 males, 31,132 females); 2017: 61,824 (31,779 males, 30,045 females); 2018: 61,016 (31,298 males, 29,718 females); 2019: 59,796 (30,555 males, 29,241 females,). Data for 2018 and 2019 are provisional. The overall male: female ratio for 2015–2019 was 1.1: 1 (51.2% versus 48.8%).

#### *4.3. Data Analysis*

Data were extracted from the NVRL Laboratory Information Management System and analyzed in Excel. All samples were assumed to be from symptomatic patients. Samples with no date of birth recorded or duplicate samples were excluded from the study. Patients were de-identified, and the variables recorded in the database were patients' age at sample collection, sex, sample date, geographical region, and test result(s). Geographical regions were categorized as eastern (which includes Dublin), western, southern (south and south-east), northern (north-west and north-east), and midlands (midlands and mid-west), as defined by the Health Service Executive areas used by the Health Protection Surveillance Centre [5].

#### *4.4. Sampling Strategy for Genotyping and Sequencing of Samples*

To determine the sample size required to reliably detect a change in genotype frequency, a sample size calculator was used (CL95%, www.openepi.com), and then a random selection of wild-type rotavirus samples was selected for genotyping. In addition, all uncommon genotypes and a random subset of genotyped samples was selected for sequencing of VP7 and VP4 genes. A subset of those identified as G3 and were sequenced and analyzed to determine a human or equine-like lineage.

#### *4.5. Seasonality*

Seasonal onset, peak, and end were calculated: Onset: First of 2 consecutive weeks, where the median percentage of positive results was >10%. Peak: Week with the highest proportion of positive samples. End: Last of two consecutive weeks, where the median percentage was <10%. Denominator: total samples tested; numerator: the number of positive samples.

#### *4.6. Vaccine Eligibility*

The vaccine status was unknown, and patients were categorised by vaccine eligibility. Vaccine-eligible samples were those born after 1 October 2016 and were ≥2 months of age. Vaccine-ineligible samples were those born after 1 October 2016 and were <2 months of age or were born prior to 1 October 2016 and were aged 0–5 years of age.

#### *4.7. Laboratory Methods*

Upon receipt into the laboratory, approximately 20% *w*/*v* suspension of the fecal sample was prepared in 400 µL Stool Transport and Recovery Buffer (Roche) and 400 µL external lysis buffer (Roche). A total of 450 µL of the suspension was extracted by Roche MagNAPure 96 and eluted into 100 µL. During extraction, Brome Mosaic Virus RNA (University of Indiana) was added as an internal control (IC) at 1 pg/µL to the sample prior to extraction. The eluates were tested in five one-step RT-PCR assays, as previously described [65–70]. Briefly, eluates were tested in a 25 µL or 10 µL reaction mixture (depending on the 96- or 384-well format, respectively), containing 2× Superscript™ III Platinum One-Step qRT-PCR mix (Invitrogen), as per product insert. Final concentrations of primers and probes ranged from 80 nM to 400 nM, depending on the target. Each sample eluate was tested by five (RT-)PCR reactions, namely: (i) norovirus G1/G2/IC; (ii) adenovirus F/pan-rotavirus/IC; (iii) Rotarix; iv) astrovirus/IC; v) sapovirus/IC. Amplification was performed on the ABI 7500 Fast (96-well format) or the ABI Viia7 (384-well format) instrument under the following conditions: 15 mins 50 ◦C, 2 mins 95 ◦C, 38 cycles of 15 secs 95 and 30 secs 60 ◦C (56 ◦C for norovirus). Amplification data was collected and analyzed with Sequence Detection Software version 2.3 or the Viia7 software version 1.2.1 (both from Applied Biosystems).

Genotyping was either by a multiplex RT-PCR [71,72] or by hemi-nested RT-PCR, as described previously [73], with fragment visualization and size determination performed on the TapeStation (Agilent software, version 2200). Samples with an indeterminate G or P type were tested by both methods before being categorized as untypable. A selection of genotypes were confirmed by Sanger sequencing of the VP7 and VP4 genes, using previously described methods [73] on the ABI 3500Dx genetic analyzer (Applied Biosystems) and typed using the RotaC typing tool [74] or by the Basic Local Alignment and Search Tool, BLAST (http://www.ncbi.nlm.nih.gov/blast/Blast.cgi). G3 VP7 sequences (450 nucleotides) were aligned with appropriate reference sequences using ClustalW. Phylogenetic analyses were conducted in MEGA X [30] using the maximum likelihood method, with 1000 bootstrap replicates, based on the Tamura-Nei model [29]. This model was selected as it generated the lowest Bayesian information criterion (BIC) score in MEGA X.

#### *4.8. GenBank Accession Numbers*

Partial VP7 fragments of the equine-like G3 strains identified in this study were deposited in GenBank under the following accession numbers: strains; MT475885 and MT4758866, whereas the human-lineage G3 VP7 fragments were MT537569-537583.

#### *4.9. Statistical Analysis*

The study was observational and therefore most data presented was descriptive. The median age of rotavirus infection in the pre- and post-vaccination groups was compared using Mann–Whitney *U* test. The Chi-square test for proportions was used to compare genotypes in the pre- and post-vaccination groups. *P* values for both tests of ≤0.05 were considered statistically significant. Confidence intervals (95%) were calculated using the Wilson method for a proportion of the genotypes detected. Statistical analysis was performed using SPSS 26 (IBM Corp; Armonk, NY, USA) software or www.openepi.com.

#### *4.10. Ethical Statement*

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or research committee and with the 1964 Helsinki declaration and its later amendments or comparable standards. This study was approved for ethical exemption by University College, Dublin LS-E-17-09.

**Author Contributions:** Conceptualization, Z.Y., S.C., and C.F.D.G.; data curation, S.C.; formal analysis, Z.Y.; investigation, Z.Y. and S.C.; methodology, Z.Y., S.C., and C.F.D.G.; project administration, Z.Y. and S.C.; resources, Z.Y., G.T., A.C., and C.F.D.G.; supervision, S.C. and C.F.D.G.; validation, Z.Y., S.C., and C.F.D.G.; visualization, Z.Y., S.C., and C.F.D.G.; writing—original draft, Z.Y.; Writing—review and editing, Z.Y., S.C., J.D., G.T., and C.F.D.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding

**Acknowledgments:** The authors would like to thank all clinicians and general practitioners for referring samples to the National Virus Reference Laboratory, Dublin, and to acknowledge the hard work of all the laboratory staff for processing and testing samples for the gastroenteritis screen.

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

#### **References**

1. Hungerford, D.; Smith, K.; Tucker, A.; Iturriza-Gomara, M.; Vivancos, R.; McLeonard, C.; N, A.C.; French, N. Population effectiveness of the pentavalent and monovalent rotavirus vaccines: A systematic review and meta-analysis of observational studies. *BMC Infect. Dis.* **2017**, *17*, 569. [CrossRef] [PubMed]

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**Production of Biofuels and Numerical Modeling of Chemical Combustion Systems**

Editors

**Miguel Torres Garc´ıa Juan Francisco Garc´ıa Mart´ın**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Miguel Torres Garc´ıa Department of Energy Engineering University of Seville Seville Spain

Juan Francisco Garc´ıa Mart´ın Department of Chemical Engineering University of Seville Seville Spain

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Processes* (ISSN 2227-9717) (available at: www.mdpi.com/journal/processes/special issues/Biofuel combustion).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-1332-4 (Hbk) ISBN 978-3-0365-1331-7 (PDF)**

© 2021 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

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**Author details**

Winardi Dwi Nugraha, Syafrudin and Lathifah Laksmi Pradita\* Diponegoro University, Semarang, Indonesia

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**1. Introduction**

Higher education institutions (HEI) are trusted with multiple tasks, which are equally important to improve the lives of citizens and develop and direct new knowledge and innovative solutions to the challenges bedevilling society [1, 2]. The threepronged university responsibilities include teaching and learning, and research and community engagement. Through community engagement, HEIs are to ensure that

community needs are met to ensure social transformation [3, 4]. This commitment demands quality, efficient and effective leadership.

While institutions of higher education prioritise leadership for teaching, learning and research, the same has not been the case with community engagement. This is despite the notion that community engagement is among the three components or functions of the HEI. Designated top leaders such as the Deputy Vice-Chancellor (DVC) in community engagement are thought leaders charged with responsibility and accountability to ensure the achievement of the strategic objective of community engagement [5]. On the other hand, the supporting structures or offices will ensure daily operations of community engagement activities across the university. The importance of community engagement is well noted through its endorsement in the policies governing higher education institutions [6]. A plethora of scholars [1, 3] has affirmed challenges for full implementation of the community engagement wing across various universities Such challenges include poor leadership direction, lack of insufficient funding and lack of community engagement outcome evaluation among others [7–9].

The conceptualisation of community engagement has been an issue for decades. For example, in some institutions it is called social responsibility; in others, it is referred to as the Third Mission, while others call it community service, engaged campus, civic or public engagement and so on [5, 8–10]. In most instances, community engagement has been institutionalised to fit the contextual mission of the respective facilities. The 2024 Carnegie First Time Documentation Guide recommended that whereas there are different conceptualisations of community engagement, the important thing is the core value that grounds the process. These are 'participatory practices, reciprocity, co-construction, democratic practices, shared authority, and shared resources' ([11], p. 2). In midst of these various conceptualisations, this chapter will adopt the concept of community engagement. It is acknowledged that the contestation along the conceptualisation of community engagement lends challenges in monitoring and evaluating and reporting outcomes and performance measurement among universities globally [5]. In the absence of clear measuring tools, activities performed by universities as services and skills shared or exchanged with communities or other institutions at the consultancy level may not be quantified or credited correctly to such institutions. For example, in view of Boyer's model (discussed below) in the scholarship of the application when university faculty or academics are invited to provide expert knowledge on national television or radio programme to educate the community on certain social issues. Contrasting views in the conceptualisation impact largely on the measuring of outcomes because one institution would devalue what others values. For example, when community engagement is measured based on benefits to communities or public good, the question is who determines whether the community has benefited or not? How much voice or power does the community have to determine their benefits?

The concept of leadership is elusive and fluid based on the nature of various dimensions played by those in the position [12, 13]. Leaders influence desired organisational culture and goals through various approaches and styles [12]. Community engagement is all about innovation and creativity within the respective disciplines to collaborate with communities to address identified needs. Effective leadership influences members to challenge, build capacity and inspire intellectual capacity to change the status quo and break boundaries to advance change [12]. In academia, senior leaders are responsible for driving the vision and monitoring performance towards the intended goal [14]. According to Arntzen [15] whether the leader is a

*Advancing Community Engagement in Higher Education Institutions in South Africa… DOI: http://dx.doi.org/10.5772/intechopen.108150*

Dean or not academic leadership entails comprehensive responsibility for setting goals for all the functions of the university. Leaders provide strategic direction by infusing energy through their management role to stimulate interest and innate ability to those lead [15].

Leadership inoculates energy among various structures in the university, 'establishing and strengthening culture' by demonstrating the ability to maintain external relations through 'networking, representing, conveying and convincing, transmitting and buffering' [Arntzen p. 53].

Those occupying top positions cannot be dissociated from both leadership and management responsibilities. This is because the leadership position is governed by university statutes, laws and policies, while at the same time the person must influence organisational goals by employing unprescribed innate abilities and experiences. Organisational leadership is a matter of collective efforts because no single person can make an organisation achieve its vision alone [16]. In the case of community engagement, the person assigned and charged with the position uses his situational and authoritative power to influence goal attainment in collaboration with academics, administrators, technical support staff and students to network with external stakeholders for a common goal.

This chapter aims to present leadership challenges and gaps to facilitate the macro-, meso- and micro-levels of community engagement among the four South African universities. The chapter will furthermore present the extent of strategic processes among some universities and related implications, funding and its implications. The methodology, findings and discussion are thereafter presented. The chapter closes by stating the recommendations and concluding remarks.


**UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments**

Editors

**Felipe Gonzalez Toro Antonios Tsourdos**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Felipe Gonzalez Toro Airborne Systems Lab/ QUT Centre for Robotics Queensland University of Technology Brisbane Australia

Antonios Tsourdos Autonomous and Cyber-Physical Systems Centre Centre for Autonomous and Cyberphysical Systems Cranfield UK

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Sensors* (ISSN 1424-8220) (available at: www.mdpi.com/journal/sensors/special issues/UAV Drones Remote Sensing Applications).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

© 2021 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.


*6.3. Category 3: Expanding Sustainability Practices and Policies*

The third research question sought to discover how sustainability is practiced and what policies drive these practices. The question allowed for discussion of PSE conceptions of sustainability as well as Indigenous conceptions of sustainability. Findings 4 and 5 indicate that social, economic, and environmental dimensions of sustainability are practiced through Indigenous knowledges and driven by the right of Indigenous self-determination supported by national and international standards. The findings also indicated that Indigenous PSE engagement in conservation and environmental decision-making could be increased if those programs participated in a network to build their capacity on sustainability issues. These findings are significant because non-Indigenous sustainability processes could become more congruent with Indigenous knowledges, and perhaps more successful, if they also supported policies appropriate to Indigenous worldviews. The category brought forward the concepts of sustainability and Indigenous knowledges, conservation, environmental decision-making, and networking.

#### 6.3.1. Sustainability and Indigenous Knowledges

Participants in the research were unanimous that since the goal of cultural communities is to support Indigenous learners' cultural identity, Indigenous communities need to be involved with PSE institutions working to integrate Indigenous knowledges. Reliance on local Indigenous knowledge holders was seen by participants to be essential in ensuring integrity in teaching local traditions. Although sustainability planning may not be recorded in writing, Indigenous communities are aware of environmental changes within their traditional territories and act when needed. Decisions made by Indigenous communities about sustainability are based on a confluence of factors that might include a variety of social, economic, and environmental considerations.

#### 6.3.2. Conservation and Environmental Decision-Making

Participants expressed strong views about the need to sustain a healthy natural environment since it is foundational to understanding Indigenous peoples' worldviews. Generally, projects that do not evolve from the local level are less successful, or fail, because they do not consider the spectrum of Indigenous knowledges that are based on intergenerational knowledge within a particular region [30]. Some participants in this study also said that top-down approaches imposed on people do not work. People have to be part of the decision-making processes.

#### 6.3.3. Sustainability Networking

Academics have pointed out the absence of literature on Indigenous worldviews and sustainability in environmental education and the need for its integration [31–33]. One way to remedy this problem is to create more links among Indigenous academics and institutions. Research participants maintained that the formation of a national network would be very useful for PSE programs, enabling institutions to collaborate and take up issues of sustainability as they relate to Indigenous knowledges in PSE.

My discussion of the findings revealed and affirmed that Indigenous knowledges are culture-specific, belonging to those cultures from which it emerged and whose primary use is for reinforcement of cultural identity. Examining the real-world benefits of adopting Indigenous traditional cultural and linguistic practices that "reflected sustainability and harmonious interactions with the natural world" [34] (p. 67) goes beyond the study of environmental crises to rediscover and integrate new ways of human behaviour necessary in this millennium.

Based on the research conclusions, there are implications for PSE institutions addressing Indigenous knowledges and sustainability. For example, PSE institutions need to have programs and financial supports that facilitate traditional knowledge holders' participation in appropriate locations on and off campuses. As well, PSE institutions should develop and offer Indigenous programs that combine traditional cultural knowledges, Indigenous languages, and environmental education for campus communities, professional development, and Indigenous communities. Additionally, PSE institutions should retain Indigenous personnel to collaborate on the development of holistic frameworks appropriate to their region. In addition, PSE institutions should develop a series of training modules for professional and Indigenous community development, with information on national and international standards supporting Indigenous self-determination. Finally, PSE institutions should provide resources that support the development of a national PSE Indigenous sustainability network [6].

There are some other areas that would benefit from additional research, such as exploring local, regional, and global relationships between Indigenous knowledges and sustainability. There is a need to look at how PSE sustainability researchers and program administrators currently include Indigenous knowledges in their work. Research is needed on how an increased Indigenous presence within formal sustainability organizations and events might support dialogue and collaboration. We need a greater understanding of the generational benefits of Indigenous PSE programming. Finally, there is room for research on the integration of existing and new indicators of Indigenous knowledges and sustainability [6].


*2.2. Clinical Evaluation*

A retrospective medical record review of the recruited individuals evaluated was performed. Probands and available family members studied were evaluated by history taking, physical examination, 12-lead electrocardiography, 24-h Holter monitoring, TTE and CMRI or APE. Left ventricular ejection fraction (LVEF) evolution will be categorized into normal, slightly depressed (LVEF <55%), moderately (LVEF 45–35%) depressed, or severely depressed (LVEF <35%).

Possible trigger factors for LVNC (overloading conditions like pregnancy, anaemia o fistula as well as high intensity sport activity) were specifically investigated. Available relatives were screened with the same protocol.


*2.4. Numerical Results*

Pricing swing contracts involves a large number of parameters and in this section we provide some results which illustrate the validity of our method across a variety of specifications. We assume that the underlying assets follow a risk neutral stochastic process, there are no transaction costs and other than penalties, there are no other constraints considered. We also assume a constant risk free rate of interest and the volatilities of all assets are known constant functions of time.

The option swing rights may be exercised at discrete times up to and including expiry and the volume choices given are in discrete amounts. That is, anytime the holder chooses to exercise a right, they must choose from a finite list of possible volume amounts. The rationale behind allowing all time steps to be exercise opportunities is the exponential growth in computational time caused by adding intermediate non-exercise times. However, the method can easily be modified to incorporate these extra time steps. As previously mentioned penalties can be implemented globally and are based on the net volume exercised during the contract.