Ensembles of knowledge graph embedding models improve predictions for drug discovery.

Recent advances in Knowledge Graphs (KGs) and Knowledge Graph Embedding Models (KGEMs) have led to their adoption in a broad range of fields and applications. The current publishing system in machine learning requires newly introduced KGEMs to achieve state-of-the-art performance, surpassing at least one benchmark in order to be published. Despite this, dozens of novel architectures are published every year, making it challenging for users, even within the field, to deduce the most suitable configuration for a given application. A typical biomedical application of KGEMs is drug-disease prediction in the context of drug discovery, in which a KGEM is trained to predict triples linking drugs and diseases. These predictions can be later tested in clinical trials following extensive experimental validation. However, given the infeasibility of evaluating each of these predictions and that only a minimal number of candidates can be experimentally tested, models that yield higher precision on the top prioritized triples are preferred. In this paper, we apply the concept of ensemble learning on KGEMs for drug discovery to assess whether combining the predictions of several models can lead to an overall improvement in predictive performance. First, we trained and benchmarked 10 KGEMs to predict drug-disease triples on two independent biomedical KGs designed for drug discovery. Following, we applied different ensemble methods that aggregate the predictions of these models by leveraging the distribution or the position of the predicted triple scores. We then demonstrate how the ensemble models can achieve better results than the original KGEMs by benchmarking the precision (i.e., number of true positives prioritized) of their top predictions. Lastly, we released the source code presented in this work at https://github.com/enveda/kgem-ensembles-in-drug-discovery.

Natural Products Have Increased Rates of Clinical Trial Success throughout the Drug Development Process.

Natural products (NPs) or their derivatives represent a large proportion of drugs that successfully progress through clinical trials to approval. This study explores the presence of NPs in both early- and late-stage drug discovery to determine their success rate, and the factors or features of natural products that contribute to such success. As a proxy for early drug development stages, we analyzed patent applications over several decades, finding a consistent proportion of NP, NP-derived, and synthetic-compound-based patent documents, with the latter group outnumbering NP and NP-derived ones (approximately 77% vs 23%). We next assessed clinical trial data, where we observed a steady increase in NP and NP-derived compounds from clinical trial phases I to III (from approximately 35% in phase I to 45% in phase III), with an inverse trend observed in synthetics (from approximately 65% in phase I to 55% in phase III). Finally, in vitro and in silico toxicity studies revealed that NPs and their derivatives were less toxic alternatives to their synthetic counterparts. These discoveries offer valuable insights for successful NP-based drug development, highlighting the potential benefits of prioritizing NPs and their derivatives as starting points.

On the correspondence between the transcriptomic response of a compound and its effects on its targets.

Better understanding the transcriptomic response produced by a compound perturbing its targets can shed light on the underlying biological processes regulated by the compound. However, establishing the relationship between the induced transcriptomic response and the target of a compound is non-trivial, partly because targets are rarely differentially expressed. Therefore, connecting both modalities requires orthogonal information (e.g., pathway or functional information). Here, we present a comprehensive study aimed at exploring this relationship by leveraging thousands of transcriptomic experiments and target data for over 2000 compounds. Firstly, we confirm that compound-target information does not correlate as expected with the transcriptomic signatures induced by a compound. However, we reveal how the concordance between both modalities increases by connecting pathway and target information. Additionally, we investigate whether compounds that target the same proteins induce a similar transcriptomic response and conversely, whether compounds with similar transcriptomic responses share the same target proteins. While our findings suggest that this is generally not the case, we did observe that compounds with similar transcriptomic profiles are more likely to share at least one protein target and common therapeutic applications. Finally, we demonstrate how to exploit the relationship between both modalities for mechanism of action deconvolution by presenting a case scenario involving a few compound pairs with high similarity.

Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery.

Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two case studies showing how one can deconvolute the predictions made by RPath as well as predict novel targets.

A comparison of 2- and 3-dimensional mandibular superimposition techniques against Björk's structural superimposition method.

INTRODUCTION: The purpose of this research was to compare mandibular growth rotation relative to the cranial base in different vertical facial patterns on the basis of multiple 2-dimensional (2D) and 3-dimensional (3D) superimposition methods. METHODS: Cone-beam computed tomography (CBCT) images taken at a mean interval of 54.8 ± 16.8 months were assessed from a sample of 70 growing patients. Three mandibular superimposition methods were compared against Björk's structural method: (1) a 2D landmark method (2D-M1), (2) a voxel-based 3D method based on a previously reported method (3D-M1), and (3) a voxel-based 3D method incorporating symphyseal structures as references (3D-M2). After superimposition, the relative change in cranial base lines as depicted in sagittal views were measured for true mandibular rotation. Agreement between methods was assessed with Lin's concordance correlation coefficient, Bland-Altman's limits of agreement, and the Bradley-Blackwood test. RESULTS: Lin's concordance correlation coefficients ranged between 0.924 for the 2D-M1 method, 0.695 for the 3D-M1 method, and 0.965 for the 3D-M2 method. Bland-Altman limits of agreement were wide for all but the 3D-M2 method. Finally, the Bradley-Blackwood test of equality of means and variances was significant in all except the 3D-M2 method. CONCLUSIONS: For time intervals between CBCT volume acquisitions >3 years, the use of the 2D-M1 and 3D-M1 methods is not recommended. There was a high concordance between the 3D-M2 method and Björk's structural method when assessing mandibular growth rotation using relative changes in cranial base lines. The high concordance was displayed across all vertical facial types and for all time differences between first and second CBCT data acquisitions.

Dental arch relationship outcomes for children with complete unilateral and complete bilateral cleft lip and palate in New Zealand.

OBJECTIVES: To evaluate dental arch relationships of patients with complete unilateral and complete bilateral cleft lip and palate (CUCLP/CBCLP) in New Zealand. SETTING AND SAMPLE POPULATION: Retrospective nationwide observational outcomes study involving 100 CUCLP and 32 CBCLP non-syndromic patients. MATERIAL AND METHODS: Four calibrated assessors, blinded to the origin of the randomized digital models, used the GOSLON (UCLP) and the Bauru-BCLP (BCLP) Yardsticks and a 100 mm visual analogue scale (VAS) (UCLP&BCLP) to assess dental arch relationships. Weighted Kappa statistics were used to determine the intra- and inter-rater reliability for the GOSLON/Bauru-BCLP Yardsticks and correlations for the VAS. RESULTS: Intra-rater reliability ranged from 0.57 to 0.88 (GOSLON), 0.62-0.84 (Bauru-BCLP) and 0.45-0.93 (VAS). Inter-rater reliability ranged from 0.62 to 0.86, (GOSLON), 0.48-0.75 (Bauru-BCLP) and 0.64-0.93 (VAS). Of the 100 CUCLP models, 46% had poor/very poor, 28% fair and 26% had good/very good dental arch relationships. Of the 32 CBCLP models, 37.5% were poor/very poor, 40.6% fair and 21.9% had good/very good dental arch relationships. The mean CUCLP VAS score was 50.5 mm (SD 19.9 mm) whilst the mean CBCLP VAS score was 40.0 mm (SD 22.0 mm) and both showed a strong relationship with their respective Yardstick scorings. CONCLUSION: The dental arch relationships of children in New Zealand with CUCLP are similar to those centres in the Eurocleft and Americleft studies which had less favourable outcomes. Those with CBCLP are inferior to those reported elsewhere. Continued monitoring will allow for tracking of improvement in outcomes.

Negative frequency-dependent interactions can underlie phenotypic heterogeneity in a clonal microbial population.

Genetically identical cells in microbial populations often exhibit a remarkable degree of phenotypic heterogeneity even in homogenous environments. Such heterogeneity is commonly thought to represent a bet-hedging strategy against environmental uncertainty. However, evolutionary game theory predicts that phenotypic heterogeneity may also be a response to negative frequency-dependent interactions that favor rare phenotypes over common ones. Here we provide experimental evidence for this alternative explanation in the context of the well-studied yeast GAL network. In an environment containing the two sugars glucose and galactose, the yeast GAL network displays stochastic bimodal activation. We show that in this mixed sugar environment, GAL-ON and GAL-OFF phenotypes can each invade the opposite phenotype when rare and that there exists a resulting stable mix of phenotypes. Consistent with theoretical predictions, the resulting stable mix of phenotypes is not necessarily optimal for population growth. We find that the wild-type mixed strategist GAL network can invade populations of both pure strategists while remaining uninvasible by either. Lastly, using laboratory evolution we show that this mixed resource environment can directly drive the de novo evolution of clonal phenotypic heterogeneity from a pure strategist population. Taken together, our results provide experimental evidence that negative frequency-dependent interactions can underlie the phenotypic heterogeneity found in clonal microbial populations.

Nascent peptides that block protein synthesis in bacteria.

Although the ribosome is a very general catalyst, it cannot synthesize all protein sequences equally well. For example, ribosomes stall on the secretion monitor (SecM) leader peptide to regulate expression of a downstream gene. Using a genetic selection in Escherichia coli, we identified additional nascent peptide motifs that stall ribosomes. Kinetic studies show that some nascent peptides dramatically inhibit rates of peptide release by release factors. We find that residues upstream of the minimal stalling motif can either enhance or suppress this effect. In other stalling motifs, peptidyl transfer to certain aminoacyl-tRNAs is inhibited. In particular, three consecutive Pro codons pose a challenge for elongating ribosomes. The translation factor elongation factor P, which alleviates pausing at polyproline sequences, has little or no effect on other stalling peptides. The motifs that we identified are underrepresented in bacterial proteomes and show evidence of stalling on endogenous E. coli proteins.

Treatment-associated changes in malocclusion and oral health-related quality of life: A 4-year cohort study.

INTRODUCTION: The impact of orthodontic treatment on oral health-related quality of life (OHRQoL) in adolescents being treated in orthodontic practices has not yet been explored longitudinally. The aim of this cohort study was to describe the changes in both malocclusion and OHRQoL with orthodontic treatment. METHODS: One hundred seventy-four patients (ages, 10-17 years; 64.4% girls; 81.6% European) underwent 2-arch, fixed-appliance treatment in a 4-year prospective study conducted across 19 specialist orthodontic practices throughout New Zealand. They were assessed before treatment, at debond (when 87.4% of the baseline sample were reassessed), and at a mean 21 months postdebond (when 59.4% of the baseline sample were reassessed). OHRQoL was measured using the Child Perceptions Questionnaire, and the Dental Aesthetic Index was used to measure occlusion. RESULTS: Among the 104 patients who took part in all 3 assessments, little change in OHRQoL overall was seen at the end of treatment, despite considerable improvement in malocclusion (with the mean Dental Aesthetic Index score falling from 35.9 at baseline to 21.3 at debond). The mean Child Perceptions Questionnaire 11-14 was slightly greater at debond, and this was most notable in the functional limitations subscale. By the end of the study (21 months postdebond, on average), the decreases in Child Perceptions Questionnaire 11-14 scores were all substantial, especially in the emotional well-being and social well-being subscales. CONCLUSIONS: Malocclusion affects orthodontic patients' OHRQoL before treatment. A temporary increase in symptomatic impacts seen by the debond stage appears to ameliorate with time, with the benefits of orthodontic treatment for OHRQoL manifesting themselves some months later.

The socio-demographic and malocclusion characteristics of adolescents presenting for specialist orthodontic treatment in New Zealand practices.

BACKGROUND: There are few reports of the socio-demographic and malocclusion characteristics of those undergoing clinical orthodontic treatment in private specialist practice. AIM: To describe the pretreatment characteristics of individuals presenting for orthodontic treatment. METHODS: Individuals (N = 174) presenting for orthodontic treatment in 19 private specialist orthodontic practices in New Zealand were randomly selected and examined (at the beginning of a three-year prospective study) and their malocclusions compared using the Dental Aesthetic Index (DAI). RESULTS: The mean DAI score was 35.8 (SD 8.4). There were no statistically significant socio-demographic differences in DAI score other than by household-based socio-economic status (SES), whereby mean scores were considerably higher in those of low SES. The majority of patients attending for treatment had severe or very severe/handicapping malocclusions. Females had less severe malocclusions than males, on average, although the difference was not statistically significant. CONCLUSIONS: The malocclusion severity threshold for seeking orthodontic treatment appears to be higher in those of lower SES. The study findings highlight the need to improve access to orthodontic treatment for this group.

A comparison between phone-based psychotherapy with and without text messaging support in between sessions for crisis patients.

BACKGROUND: Few studies have tested whether individually tailored text messaging interventions have an effect on clinical outcomes when used to supplement traditional psychotherapy. This is despite the potential to improve outcomes through symptom monitoring, prompts for between-session activities, and psychoeducation. OBJECTIVE: The intent of the study was to explore the use of individually tailored between-session text messaging, or short message service (SMS), as an adjunct to telephone-based psychotherapy for consumers who present to the Emergency Department (ED) in situational and/or emotional crises. METHODS: Over a 4-month period, two therapists offered 68 prospective consumers of a telephone-based psychotherapy service individually tailored between-session text messaging alongside their telephone-based psychotherapy. Attendance and clinical outcomes (depression, anxiety, functional impairment) of those receiving messages were compared against a historical control group (n=157) who received telephone psychotherapy only. RESULTS: A total of 66% (45/68) of the consumers offered SMS accepted the intervention. A total of 432 messages were sent over the course of the trial, the majority involving some kind of psychoeducation or reminders to engage in therapy goals. There were no significant differences in clinical outcomes between consumers who received the SMS and those in the control group. There was a trend for participants in the intervention group to attend fewer sessions than those in the control group (mean 3.7, SD 1.9 vs mean 4.4, SD 2.3). CONCLUSIONS: Both groups showed significant improvement over time. Individually tailored SMS were not found to improve clinical outcomes in consumers receiving telephone-based psychotherapy, but the study was underpowered, given the effect sizes noted and the significance level chosen. Given the ease of implementation and positive feedback from therapists and clients, individually tailored text messages should be explored further in future trials with a focus on enhancing the clinical impact of the tailored text messages, and utilizing designs with additional power to test for between-group effects.

The IAPT@Flinders Service: adapting the Improving Access to Psychological Therapies model to the emergency department setting in Australia.

OBJECTIVE: To describe the implementation of an Improving Access to Psychological Therapies (IAPT) service at Flinders Medical Centre emergency department (IAPT@Flinders). IAPT, a population-based model of guided self-help for anxiety and depression delivered mainly by phone, was rolled-out nationally in the UK in 2010. There is a growing body of evidence demonstrating its clinical effectiveness and efficiency that can improve treatment adherence, reduce stigma, remove appointment attendance barriers and improve access for hard-to-reach populations. CONCLUSIONS: IAPT@Flinders was the test site for the first IAPT in Australia and also the first IAPT service that was integrated with an emergency department (ED). IAPT@Flinders offers rapid access, low-intensity cognitive behavioural therapy, social prescribing and signposting to clients with adjustment disorders, anxiety and/or depressive symptoms. Successful implementation within an Australian crisis setting has demonstrated that many IAPT structures and protocols are applicable to ED settings and the model can be implemented with fidelity. Adaption required consideration of positioning of the service within the Australian mental health framework; staff qualifications; the referral pathways; and exclusion criteria. It is recommended additional test sites and larger scale trials are conducted to provide further evidence of the applicability of large-scale adoption of the UK IAPT model into Australian ED settings.

Weighting Low-Intensity MS/MS Ions and m/z Frequency for Spectral Library Annotation.

Calculating spectral similarity is a fundamental step in MS/MS data analysis in untargeted metabolomics experiments, as it facilitates the identification of related spectra and the annotation of compounds. To improve matching accuracy when querying an experimental mass spectrum against a spectral library, previous approaches have proposed increasing peak intensities for high m/z ranges. These high m/z values tend to be smaller in magnitude, yet they offer more crucial information for identifying the chemical structure. Here, we evaluate the impact of using these weights for identifying structurally related compounds and mass spectral library searches. Additionally, we propose a weighting approach that (i) takes into account the frequency of the m/z values within a spectral library in order to assign higher importance to the most common peaks and (ii) increases the intensity of lower peaks, similar to previous approaches. To demonstrate our approach, we applied weighting preprocessing to modified cosine, entropy, and fidelity distance metrics and benchmarked it against previously reported weights. Our results demonstrate how weighting-based preprocessing can assist in annotating the structure of unknown spectra as well as identifying structurally similar compounds. Finally, we examined scenarios in which the utilization of weights resulted in diminished performance, pinpointing spectral features where the application of weights might be detrimental.

Exploring the known chemical space of the plant kingdom: insights into taxonomic patterns, knowledge gaps, and bioactive regions.

Plants are one of the primary sources of natural products for drug development. However, despite centuries of research, only a limited region of the phytochemical space has been studied. To understand the scope of what is explored versus unexplored in the phytochemical space, we begin by reconstructing the known chemical space of the plant kingdom, mapping the distribution of secondary metabolites, chemical classes, and plants traditionally used for medicinal purposes (i.e., medicinal plants) across various levels of the taxonomy. We identify hotspot taxonomic clades occupied by a large proportion of medicinal plants and characterized secondary metabolites, as well as clades requiring further characterization with regard to their chemical composition. In a complementary analysis, we build a chemotaxonomy which has a high level of concordance with the taxonomy at the genus level, highlighting the close relationship between chemical profiles and evolutionary relationships within the plant kingdom. Next, we delve into regions of the phytochemical space with known bioactivity that have been used in modern drug discovery. While we find that the vast majority of approved drugs from phytochemicals are derived from known medicinal plants, we also show that medicinal and non-medicinal plants do not occupy distinct regions of the known phytochemical landscape and their phytochemicals exhibit properties similar to bioactive compounds. Moreover, we also reveal that only a few thousand phytochemicals have been screened for bioactivity and that there are hundreds of known bioactive compounds present in both medicinal and non-medicinal plants, suggesting that non-medicinal plants also have potential therapeutic applications. Overall, these results support the hypothesis that there are many plants with medicinal properties awaiting discovery.

Modern drug discovery using ethnobotany: A large-scale cross-cultural analysis of traditional medicine reveals common therapeutic uses.

For millennia, numerous cultures and civilizations have relied on traditional remedies derived from plants to treat a wide range of conditions and ailments. Here, we systematically analyzed ethnobotanical patterns across taxonomically related plants, demonstrating that congeneric medicinal plants are more likely to be used for treating similar indications. Next, we reconstructed the phytochemical space covered by medicinal plants to reveal that (i) taxonomically related medicinal plants cover a similar phytochemical space, and (ii) chemical similarity correlates with similar therapeutic usage. Lastly, we present several case scenarios illustrating how mining this information can be used for drug discovery applications, including: (i) investigating taxonomic hotspots around particular indications, (ii) exploring shared patterns of congeneric plants located in different geographic areas, but which have been used to treat the same indications, and (iii) showing the concordance between ethnobotanical patterns among non-taxonomically related plants and the presence of shared bioactive phytochemicals.

Human nuclear hormone receptor activity contributes to malaria parasite liver stage development.

Chemical genetic approaches have had a transformative impact on discovery of drug targets for malaria but have primarily been used for parasite targets. To identify human pathways required for intrahepatic development of parasite, we implemented multiplex cytological profiling of malaria infected hepatocytes treated with liver stage active compounds. Some compounds, including MMV1088447 and MMV1346624, exhibited profiles similar to cells treated with nuclear hormone receptor (NHR) agonist/antagonists. siRNAs targeting human NHRs, or their signaling partners identified eight genes that were critical for Plasmodium berghei infection. Knockdown of NR1D2, a host NHR, significantly impaired parasite growth by downregulation of host lipid metabolism. Importantly, treatment with MMV1088447 and MMV1346624 but not other antimalarials, phenocopied the lipid metabolism defect of NR1D2 knockdown. Our data underlines the use of high-content imaging for host-cellular pathway deconvolution, highlights host lipid metabolism as a drug-able human pathway and provides new chemical biology tools for studying host-parasite interactions.

Rapid behavior-based identification of neuroactive small molecules in the zebrafish.

Neuroactive small molecules are indispensable tools for treating mental illnesses and dissecting nervous system function. However, it has been difficult to discover novel neuroactive drugs. Here, we describe a high-throughput, behavior-based approach to neuroactive small molecule discovery in the zebrafish. We used automated screening assays to evaluate thousands of chemical compounds and found that diverse classes of neuroactive molecules caused distinct patterns of behavior. These 'behavioral barcodes' can be used to rapidly identify new psychotropic chemicals and to predict their molecular targets. For example, we identified new acetylcholinesterase and monoamine oxidase inhibitors using phenotypic comparisons and computational techniques. By combining high-throughput screening technologies with behavioral phenotyping in vivo, behavior-based chemical screens can accelerate the pace of neuroactive drug discovery and provide small-molecule tools for understanding vertebrate behavior.

Development and preliminary clinical evaluation of a peptide immunotherapy vaccine for cat allergy.

BACKGROUND: Allergic sensitization to cat allergens is common and represents a major risk factor for asthma. Specific immunotherapy (SIT) is effective but cumbersome and associated with IgE-dependent adverse events. Immunotherapy targeting allergen-specific T cells, with synthetic peptides representing T-cell epitopes, might improve safety and reduce the duration of treatment. OBJECTIVE: We sought to define major T-cell epitopes of Fel d 1 for peptide immunotherapy, generate a peptide vaccine, and evaluate its safety and tolerability in subjects with cat allergy. METHODS: We determined the binding affinities of Fel d 1 peptides for 10 commonly expressed HLA-DR molecules. Functionally immunodominant peptides were identified by means of proliferation and cytokine secretion. Histamine-releasing activity was assessed, and a peptide vaccine was formulated. Safety and tolerability were evaluated in a dose-ranging phase IIa clinical trial. RESULTS: MHC-binding sequences were identified throughout Fel d 1. Some regions contained multiple overlapping T-cell epitopes that bound multiple MHC molecules. Immunodominant sequences were identified on the basis of proliferative and cytokine (IFN-γ, IL-10, and IL-13) responses. Cat allergen extract, but not peptides, induced histamine release in blood basophils. A single administration of peptide vaccine was safe and well tolerated. The dose of vaccine resulting in the greatest inhibition of the late-phase skin response to intradermal whole allergen challenge was 3 nmol. CONCLUSIONS: Fel d 1 contains multiple overlapping MHC-binding motifs. A peptide vaccine comprising the immunodominant regions of the allergen was safe and well tolerated when given to subjects with cat allergy as a single dose. The dose of vaccine resulting in the greatest reduction in late-phase skin response was defined for future clinical development.

Slipping rib syndrome.

Slipping rib syndrome (SRS) is an underdiagnosed condition that can lead to debilitating lower chest and upper abdominal pain (Am J Med Sci 2019; 357: 168). It is caused by hypermobility of the anterior ends of the costal cartilages of false ribs, allowing the eighth to tenth ribs to slip under the rib above, giving its name (Semin Pediatr Surg 2018; 27: 183). Failure to recognize this syndrome can expose patients to extensive and unnecessary investigations for unclear symptoms. Although more commonly reported in adults, SRS is a relatively uncommon but recognized cause of lower chest or upper abdomen pain in adolescence (Pediatr Anesth 2001; 11: 740). It is important for clinicians to familiarize themselves with and consider the diagnosis of SRS when assessing and managing adolescents with persistent thoracic pain. We present a case of a 14-year-old girl with unresolved thoracic pain for more than 4 years and was ultimately diagnosed with SRS.

Quality of life and orthodontics: a challenge to traditional concepts. Report of the 2009 NZAO Symposium.

The biennial symposium of the Education, Research and Development Group (ERDG) of the New Zealand Association of Orthodontists (NZAO) was held in Wellington on 2 and 3 October 2009. The challenging topic was the application of Quality of Life research to the practice of orthodontics. The well-established format of pre-symposium reading by participants, symposium lectures and discussion groups leading to consensus once again proved an excellent basis upon which to engage this subject. The findings reported here represent the consensus reached by delegates attending the symposium.