Consensus Approaches Research Articles

Exploring real-world prescribing patterns for maintenance treatment in bipolar disorders: a focus on antidepressants and benzodiazepines

Objective Bipolar disorders (BD) are characterized by highly recurrent nature, necessitating adequate maintenance treatment for long-term disorder control. This study aimed to investigate real-world prescribing patterns among outpatients with BD, focusing on the utilisation of antidepressants (AD) and benzodiazepines (BDZ). Methods We analysed prescription patterns of the five main groups of psychotropic medications (antipsychotics, mood stabilizers, AD, BDZ, and anticholinergic medications) and their relationships with basic socio-demographic and clinical data in a sample of 107 clinically stable BD outpatients (75.7% female, age 44.8 ± 11.7). Results Maintenance therapy predominantly involved polypharmacy (92.5%), with mood stabilizers (87.9%) and antipsychotics (80.4%, predominantly second-generation) being the most commonly prescribed. Our findings highlight a high percentage of patients prescribed AD (50.5%) and BDZ (54.2%). BDZ patients, compared to the non-BDZ group in maintenance treatment, were significantly older with longer psychiatric history and a decreased likelihood of comorbid personality disorder diagnoses. Conclusions This study offers insights into prescribing practices within a university psychiatric clinic in the Western Balkans. The prevalent use of polypharmacy in real-world clinical settings, along with high percentage of patients prescribed AD and BDZ, suggests a gap between guideline recommendations and clinical practice, indicating a lack of consensus or standardized approaches in clinical practice.

Uncertainty in consensus predictions of plant species' vulnerability to climate change

AbstractAimVariation in spatial predictions of species' ranges made by various models has been recognized as a significant source of uncertainty for modelling species distributions. Consensus approaches that combine the results of multiple models have been employed to reduce the uncertainty introduced by different algorithms. We evaluate how estimates of habitat suitability, projected using species distribution models (SDMs), varied among different consensus methods relative to the variation introduced by different global climate models (GCMs) and representative concentration pathways (RCPs) used for projection.LocationCalifornia Floristic Province (California, US portion).MethodsWe modelled the current and future potential distributions of 82 terrestrial plant species, developing model predictions under different combinations of GCMs, RCPs, time periods, dispersal assumptions and SDM consensus methods commonly used to combine different species distribution modelling algorithms. We assessed how each of these factors contributed to the variability in future predictions of species habitat suitability change and aggregate measures of proportional change in species richness. We also related variability in species‐level habitat change to species' attributes.ResultsAssuming full dispersal capacity, the variability between habitat predictions made by different consensus methods was higher than the variability introduced by different RCPs and GCMs. The relationships between species' attributes and variability in future habitat predictions depended on the source of uncertainty and dispersal assumptions. However, species with small ranges or low prevalence tended to be associated with high variability in range change forecasts.Main ConclusionsOur results support exploring multiple consensus approaches when considering changes in habitat suitability outside of species' current distributions, especially when projecting species with low prevalence and small range sizes, as these species tend to be of the greatest conservation concern yet produce highly variable model outputs. Differences in vulnerability between diverging greenhouse gas concentration scenarios are most readily observed for end‐of‐century time periods and within species' currently occupied habitats (no dispersal).

Scientific, transparent and applicable rankings of Chinese pathological guidelines and consensus published in the medical journals in 2022

The STAR tool was used to evaluate and analyze the science, transparency, and applicability of Chinese pathology guidelines and consensus published in medical journals in 2022. There were a total of 18 pathology guidelines and consensuses published in 2022, including 1 guideline and 17 consensuses. The results showed that the guideline score was 21.83 points, lower than the overall guideline average (43.4 points). Consensus ratings scored an average of 27.87 points, on par with the overall consensus level (28.3 points). Areas that scored above the overall level were "conflict of interest" and "working groups", while areas that scored below the overall level were "proposals", "funding", "evidence", "consensus approaches" and "accessibility". To sum up, the formulation of pathology guidelines and consensuses in 2022 is not standardized, and the evidence retrieval process, evidence evaluation methods and grading criteria for recommendations on clinical issues are not provided in the formulation process; the process and method for reaching consensus are not provided, the plan is lacking, and registration is not carried out. It is therefore suggested that guidelines/consensus makers in the field of pathology should attach importance to evidence-based medical evidence, strictly follow guideline formulation methods and processes, further improve the scientific, applicable and transparent guidelines/consensuses in the field, and better provide support for clinicians and patients.

Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches

Genome-scale metabolic models (GEMs) of microbial communities offer valuable insights into the functional capabilities of their members and facilitate the exploration of microbial interactions. These models are generated using different automated reconstruction tools, each relying on different biochemical databases that may affect the conclusions drawn from the in silico analysis. One way to address this problem is to employ a consensus reconstruction method that combines the outcomes of different reconstruction tools. Here, we conducted a comparative analysis of community models reconstructed from three automated tools, i.e. CarveMe, gapseq, and KBase, alongside a consensus approach, utilizing metagenomics data from two marine bacterial communities. Our analysis revealed that these reconstruction approaches, while based on the same genomes, resulted in GEMs with varying numbers of genes and reactions as well as metabolic functionalities, attributed to the different databases employed. Further, our results indicated that the set of exchanged metabolites was more influenced by the reconstruction approach rather than the specific bacterial community investigated. This observation suggests a potential bias in predicting metabolite interactions using community GEMs. We also showed that consensus models encompassed a larger number of reactions and metabolites while concurrently reducing the presence of dead-end metabolites. Therefore, the usage of consensus models allows making full and unbiased use from aggregating genes from the different reconstructions in assessing the functional potential of microbial communities.

Exploring Fault Tolerance Consensus for Wireless Sensor Networks: A Comprehensive Detailed Study

The integration of wireless networks into modern communication systems has revolutionized information exchange across various applications. However, achieving reliable agreement in these networks is significantly impeded by their unique properties, such as blurring, interruption, and transparency. A fundamental component of distributed systems, fault-tolerant consensus ensures that nodes in the network can agree on a consistent value even in the presence of malfunctioning or corrupted elements. This study explores both non-Byzantine and Byzantine fault-tolerant consensus approaches, with a focus on achieving agreement in the presence of benign faults. The importance of fault-tolerant consensus in wireless connections is underscored, particularly in applications like wireless blockchain, IoT, and vehicular networks. The research delves into wireless network-specific fault-tolerant consensus algorithms to address the specific challenges posed by networking environments. Furthermore, a comparative analysis of Byzantine Fault Tolerance mechanisms in distributed systems is provided to shed light on their features and benefits. It highlights the importance of ensuring system reliability and consistency in wireless networks to maintain seamless communication and data integrity. Moreover, the paper emphasizes the critical role of fault-tolerant consensus in enhancing system resilience and performance. It underscores the need for robust algorithms and protocols to detect and mitigate errors, ensuring reliable communication and coordination despite potential node failures. By providing insights into fault-tolerant consensus mechanisms tailored to dynamic conditions, the study aims to optimize system adaptation and effectively mitigate both Byzantine and non-Byzantine failures in wireless network environments.

Standardized reporting for systematic global evaluation of axial spondyloarthritis: An evidence-based and consensus-driven initiative

IntroductionNational and international scientific societies advocate for a regular, systematic, and standardized global evaluation of axial spondyloarthritis (axSpA) patients. However, there are no recommendations specifying the content of this global evaluation. This initiative aimed to propose a standardized reporting framework, using evidence-based and consensus approaches, to collect data on all domains of axSpA. MethodsA literature review and consensus process involved a steering committee and an expert panel of 37 rheumatologists and health professionals. The first steering committee took place in March 2022 and identified the main domains for inclusion in the standardized report. A hierarchical literature review was conducted to identify items within these domains and tools for assessment. The items and tools for assessment were discussed and consensus was reached through a vote session during an expert meeting that took place in March 2023. ResultsThe steering committee identified four main domains to include in the standardized reporting framework: disease assessment, comorbidities, lifestyle, and quality of life. Items and tools for assessment were adopted after the expert meeting. Additionally, recommendations regarding digital tools (websites, apps, social media) were provided. ConclusionThis initiative led to a consensus, based on evidence and expertise, on a reporting framework for use during periodic systematic global evaluations of axSpa in daily practice.

A Review of Blockchain-Rooted Energy Administration in Networking

Energy Administration (EA) in networking involves improving energy efficiency by managing energy. The blockchain framework involves a chain of associated blocks that obviously protects the genuineness, upholds accountability, and upholds disguised-anonymity of its transactions/entries with the help of peer-to-peer consensus techniques and cryptographic mechanisms. Driven by the fact that existing surveys do not focus on the EA in the broad scope of networking, we review diverse blockchain-rooted EA solutions, where we recognize 7 roles of blockchain in EA and explore them in detail with regard to EA techniques, EA approaches, blockchain-linked factors, network-linked factors, and such. We assembled a first-stage sample of 80 document citations by appraising the articles for qualification criteria hunted from E-libraries operating a detailed and prolonged process. Considering the review, in blockchain-rooted EA, blockchain can facilitate storing and exchanging data in a trustworthy manner, operate energy-efficient consensus approaches, act as an energy manager, provide authentication and access control for EA, facilitate secure offloading for EA, and provide automated EA tasks operating smart contracts. Detailed exploration shows that from blockchain-rooted EA, 32.5% operate blockchain to store and exchange data for an EA task, 95% operate uniform blockchain, 30% operate PoW consensus, 82.5% operate fully decentralized EA, 57.5% operate cross-layer EA, and 10% operate in IoT networks. Finally, we debate the possibilities and barriers to the conception of blockchain-rooted EA and then present guidance to vanquish them.

Improving somatic exome sequencing performance by biological replicates

BackgroundNext-generation sequencing (NGS) technologies offer fast and inexpensive identification of DNA sequences. Somatic sequencing is among the primary applications of NGS, where acquired (non-inherited) variants are based on comparing diseased and healthy tissues from the same individual. Somatic mutations in genetic diseases such as cancer are tightly associated with genomic instability. Genomic instability increases heterogenity, complicating sequencing efforts further, a task already challenged by the presence of short reads and repetitions in human DNA. This leads to low concordance among studies and limits reproducibility. This limitation is a significant problem since identified mutations in somatic sequencing are major biomarkers for diagnosis and the primary input of targeted therapies. Benchmarking studies were conducted to assess the error rates and increase reproducibility. Unfortunately, the number of somatic benchmarking sets is very limited due to difficulties in validating true somatic variants. Moreover, most NGS benchmarking studies are based on relatively simpler germline (inherited) sequencing. Recently, a comprehensive somatic sequencing benchmarking set was published by Sequencing Quality Control Phase 2 (SEQC2). We chose this dataset for our experiments because it is a well-validated, cancer-focused dataset that includes many tumor/normal biological replicates. Our study has two primary goals. First goal is to determine how replicate-based consensus approaches can improve the accuracy of somatic variant detection systems. Second goal is to develop highly predictive machine learning (ML) models by employing replicate-based consensus variants as labels during the training phase.ResultsEnsemble approaches that combine alternative algorithms are relatively common; here, as an alternative, we study the performance enhancement potential of biological replicates. We first developed replicate-based consensus approaches that utilize the biological replicates available in this study to improve variant calling performance. Subsequently, we trained ML models using these biological replicates and achieved performance comparable to optimal ML models, those trained using high-confidence variants identified in advance.ConclusionsOur replicate-based consensus approach can be used to improve variant calling performance and develop efficient ML models. Given the relative ease of obtaining biological replicates, this strategy allows for the development of efficient ML models tailored to specific datasets or scenarios.

HESI workshop summary: Interpretation of developmental and reproductive toxicity endpoints and the impact on data interpretation of adverse events.

The Health and Environmental Sciences Institute Developmental and Reproductive Toxicology (HESI-DART) group held a hybrid in-person and virtual workshop in Washington, DC, in 2022. The workshop was entitled, "Interpretation of DART in Regulatory Contexts and Frameworks." There were 154 participants (37 in person and 117 virtual) across 9 countries. The purpose of the workshop was to capture key consensus approaches used to assess DART risks associated with chemical product exposure when a nonclinical finding is identified. The decision-making process for determining whether a DART endpoint is considered adverse is critical because the outcome may have downstream implications (e.g., increased animal usage, modifications to reproductive classification and pregnancy labeling, impact on enrollment in clinical trials and value chains). The workshop included a series of webinar modules to train and engage in discussions with federal and international regulators, clinicians, academic investigators, nongovernmental organizations, contract research organization scientists, and private sector scientists on the best practices and principles of interpreting DART and new approach methodologies in the context of regulatory requirements and processes. Despite the differences in regulatory frameworks between the chemical and pharmaceutical sectors, the same foundational principles for data interpretation should be applied. The discussions led to the categorization of principles, which offer guidance for the systematic interpretation of data. Step 1 entails identifying any hazard by closely analyzing the data at the study endpoint level, while Step 2 involves assessing risk using weight of evidence. These guiding principles were derived from the collective outcomes of the workshop deliberations.

High-throughput micro-CT analysis identifies sex-dependent biomarkers of erosive arthritis in TNF-Tg mice and differential response to anti-TNF therapy.

Development of reliable disease activity biomarkers is critical for diagnostics, prognostics, and novel drug development. Although computed tomography (CT) is the gold-standard for quantification of bone erosions, there are no consensus approaches or rationales for utilization of specific outcome measures of erosive arthritis in complex joints. In the case of preclinical models, such as sexually dimorphic tumor necrosis factor transgenic (TNF-Tg) mice, disease severity is routinely quantified in the ankle through manual segmentation of the talus or small regions of adjacent bones primarily due to the ease in measurement. Herein, we sought to determine the particular hindpaw bones that represent reliable biomarkers of sex-dependent disease progression to guide future investigation and analysis. Hindpaw micro-CT was performed on wild-type (n = 4 male, n = 4 female) and TNF-Tg (n = 4 male, n = 7 female) mice at monthly intervals from 2-5 (females) and 2-8-months (males) of age, since female TNF-Tg mice exhibit early mortality from cardiopulmonary disease at approximately 5-6-months. Further, 8-month-old WT (n = 4) and TNF-Tg males treated with anti-TNF monoclonal antibodies (n = 5) or IgG placebo isotype controls (n = 6) for 6-weeks were imaged with micro-CT every 3-weeks. For image analysis, we utilized our recently developed high-throughput and semi-automated segmentation strategy in Amira software. Synovial and osteoclast histology of ankle joints was quantified using Visiopharm. First, we demonstrated that the accuracy of automated segmentation, determined through analysis of ~9000 individual bones by a single user, was comparable in wild-type and TNF-Tg hindpaws before correction (79.2±8.9% vs 80.1±5.1%, p = 0.52). Compared to other bone compartments, the tarsal region demonstrated a sudden, specific, and significant bone volume reduction in female TNF-Tg mice, but not in males, by 5-months (4-months 4.3± 0.22 vs 5-months 3.4± 0.62 mm3, p<0.05). Specifically, the cuboid showed significantly reduced bone volumes at early timepoints compared to other tarsals (i.e., 4-months: Cuboid -24.1±7.2% vs Talus -9.0±5.9% of 2-month baseline). Additional bones localized to the anterolateral region of the ankle also exhibited dramatic erosions in the tarsal region of females, coinciding with increased synovitis and osteoclasts. In TNF-Tg male mice with severe arthritis, the talus and calcaneus exhibited the most sensitive response to anti-TNF therapy measured by effect size of bone volume change over treatment period. We demonstrated that sexually dimorphic changes in arthritic hindpaws of TNF-Tg mice are bone-specific, where the cuboid serves as a reliable early biomarker of erosive arthritis in female mice. Adoption of automated segmentation approaches in pre-clinical or clinical models has potential to translate quantitative biomarkers to monitor bone erosions in disease and evaluate therapeutic efficacy.

Secure forensic image analysis by optimised iterative model with random consensus approaches

Secure forensic image analysis by optimised iterative model with random consensus approaches

Reflecting on Students' Experiences of an Innovative Teaching Model During Aged Care Visit in Clinical Placements: A Qualitative Pilot Study.

Clinical placement teaching could be challenging due to time constraints, lack of effective teaching models and consensus approaches. Learner-centred approach facilitated deeper learning by demonstrating "seeing-patients-under-supervision" being ideal during Residential-Aged-Care-Facility (RACF)-visit in GP clinical placements. The study aimed to reflect on the students' experiences in aged-care visits by applying an innovative teaching model of "students-being-the-GP-clinician-in-charge-of-RACF-visit-ward-round-under-the-supervision-of-clinical-supervisor". Through students' reflections, this study identified 12 commonly managed RACF problems to be introduced into the curriculum to optimise clinical reasoning learning during RACF-visit. This qualitative study used online surveys and interviews. All participating students reported all the encountered cases during the RACF visit through an online survey. The participating students acted as GP in charge of all clinical interactions with patients, caregivers, and nurses during RACF visits and final management plan discussions with GP supervisors to ensure clinical-service safety and teaching-and-learning quality. The interview questionnaires applied standard-and-open-ended-questions to examine the impact of this innovative teaching model on clinical-reasoning-learning, clinical-competence-improvement, Objective Structured Clinical Exam (OSCE) preparation, limitations-from-students'-patients'-and-supervisors' perspectives, and intern readiness. An online survey summarising students' encountered cases was returned by 30 students. The 12 most commonly-managed problems were tabulated. Falls, urinary tract infections, and behavioural and psychological symptoms of dementia were the three most commonly-managed problems. All thirty students' reflections indicated the positive impact of the innovative-teaching-models on "Improving-Clinical-Reasoning-Learning", "Enhancing-Clinical-Competency", "Enriching-Salient-Learning-Points", "Facilitating-Feedback-Discussion-with-Supervisor", "Strengthening-OSCE-exam-preparation", "Understanding-the-Limitation-from-students'-patients'-and-supervisors'-perspectives", "Enabling-intern-readiness". Twelve students' individual reflections were demonstrated. This qualitative pilot study demonstrated through students' reflection that "Student-doctor-in-charge-of-nursing-home-round" is an innovative teaching model for clinical reasoning learning. This model extended the concepts of "cognitive-apprenticeship" in the context of modern medical education. Students' reflections and summary of commonly managed problems indicated the need for further study to verify the feasibility of implementing this teaching model in the formal curriculum and creating a RACF-visit-specific curriculum for students.

Upper Extremity Trauma in Costa Rica - Evaluating Epidemiology and Identifying Opportunities.

Traffic accidents and musculoskeletal injuries represent a major cause of morbidity and mortality in Costa Rica. To inform capacity building efforts, we conducted a survey study of hand and upper extremity (UE) fellowship-trained surgeons in Costa Rica to evaluate the epidemiology, complications, and challenges in care of UE trauma. Aiming to capture all hand and UE trained surgeons in Costa Rica, we compiled a list of nine surgeons and sent a survey in Spanish using Qualtrics. Assessment questions were developed to understand the burden, complications, practice patterns, challenges, and capacity associated with care of UE trauma. Questions were designed to focus on opportunities for future investigation. Questions were translated and adapted by two bilingual speakers. Data were reported descriptively and open-ended responses were analyzed using content analysis. Nine (100%) surgeons completed the survey. Distal radius fractures, hand and finger fractures, and tendon injuries are the most frequently noted conditions. Stiffness and infection are the most common complications. About 29% of patients are unable to get necessary therapy and 13% do not return for follow-up care with monetary, distance, and transportation limitations being the greatest challenges. The burden of UE trauma in Costa Rica is high. Identifying common conditions, complications, challenges, and capacity allows for a tailored approach to partnership and capacity building (e.g. directing capacity building and/or research infrastructure toward distal radius fractures). These insights represent opportunities to inform community-driven care improvement and research initiatives, such as Delphi consensus approaches to identify priorities or the development of outcome measurement systems.

An overview of consensus models for group decision-making and group recommender systems

Group decision-making processes can be supported by group recommender systems that help groups of users obtain satisfying decision outcomes. These systems integrate a consensus-achieving process, allowing group members to discuss with each other on the potential items, adapt their opinions accordingly, and achieve an agreement on a selected item. Such a process, therefore, helps to generate group recommendations with a high satisfaction level of group members. Our article provides a rigorous review of the existing consensus approaches to group decision-making. These approaches are classified depending on the applied consensus models such as reference domain where a set of group members or items is selected for calculating consensus measures, coincidence method that calculates the consensus degree between group members depending on the coincidence concept, operators that aggregate user preferences, guidance measures where the consensus-achieving process is guided by different consensus measures, and recommendation generation and individual centrality that enhance the role of a moderator or a leader in the consensus-achieving process. Further consensus techniques for group decision-making in heterogeneous and large-scale groups are also discussed in this article. Besides, to provide an overall landscape of consensus approaches, we also discuss new consensus models in group recommender systems. These models attempt to improve basic aggregation strategies, further consider social relationship interactions, and provide group members with intuitive descriptions regarding the current consensus state of the group. Finally, we point out challenges and discuss open topics for future work.

Violent and Unethical Non-Violent Abuse of Faith and Ethnoreligious Sentiments in Southeast Europe: Religious Peace-Building?

The article presents a theoretical and conceptual examination of religious violent and unethical non-violent behaviors, ethnopolitical and clerical synergism, and religious peace-building capacity. I argue that the phenomenon of religious/ethnic violent and non-violent interchangeability adopted by national political unethical behavior has adverse consequences on the post-Yugoslav social behavior and reconciliation process; religions should be a moral peace-building agency. The multiethnic/multireligious socialist Yugoslav society has been violently transformed into influential ethical and clerical cultures, producing antagonistic ethnonational societies sustaining pastoralism as potent identity manifestations of the social capital. War-period visual violence and emotions influenced violent behavior and policy within the discourse "our vs. their sacred ethnic land," creating an unbearable ease of creating fear and motivating violent antagonism and war crimes. The post-war antagonistic media rhetoric, visual antagonism, and abuse of faith adversely impact peaceful coexistence. Ethnic, religious, ideological, and political contextual factors are challenging to generate in post-conflict, divided Balkan societies. Fear of others, religiously distinct, is a category that's difficult to determine and prevent. Western-Balkan societies possess victimological and political mythical conventions, honoring ethnoreligious war victories, defeats, and agonies, maintaining hostility and revenge discourse. Historically, religions were misused to justify violence and maintain non-violence, unethical sociopolitical order, and negative peace. The ideologies of religious superiority intertwine with intensely dominant national perceptions, so belonging to the Serb, Croat, or Bosniak people is equated with Orthodoxy, Catholicism, or Islam. This entanglement is the groundwork for despondency and a hostile peace climate. Current clerical and ethnopolitical policies lead further away from conflict transformation, directing toward the renewal of monotheistic spirituality, cognition, and violence. Political involvement affects "authentic" religion. We should engage in all-inclusive theological and consensus approaches to demonstrate that religions are peace-building agencies, retrieving and revitalizing authentic morality criteria. Religious sentiments mobilize people more rapidly than other identities.

Immune response to COVID-19 vaccination in patients with Waldenström macroglobulinaemia who pause their BTKi therapy.

Patients with Waldenström macroglobulinaemia (WM) are at increased risk of severe COVID-19 infection and have poor immune responses to COVID-19 vaccination. This study assessed whether a closely monitored pause in Bruton's Tyrosine Kinase inhibitor (BTKi) therapy might result in an improved humoral response to a 3rd COVID-19 vaccine dose. Improved response was observed in WM patients who paused their BTKi, compared to a group who did not pause their BTKi. However, the response was attenuated after BTKi recommencement. This data contributes to our understanding of vaccination strategies in this patient group and may help inform consensus approaches in the future.

Assessing the Quality of Student-Generated Content at Scale: A Comparative Analysis of Peer-Review Models

Engaging students in creating learning resources has demonstrated pedagogical benefits. However, to effectively utilize a repository of student-generated content (SGC), a selection process is needed to separate high- from low-quality resources as some of the resources created by students can be ineffective, inappropriate, or incorrect. A common and scalable approach is to use a peer-review process where students are asked to assess the quality of resources authored by their peers. Given that judgments of students, as experts-in-training, cannot wholly be relied upon, a redundancy-based method is widely employed where the same assessment task is given to multiple students. However, this approach introduces a new challenge, referred to as the consensus problem: How can we assign a final quality to a resource given ratings by multiple students? To address this challenge, we investigate the predictive performance of 18 inference models across five well-established categories of consensus approaches for inferring the quality of SGC at scale. The analysis is based on the engagement of 2141 undergraduate students across five courses in creating 12 803 resources and 77 297 peer reviews. Results indicate that the quality of reviews is quite diverse, and students tend to overrate. Consequently, simple statistics such as mean and median fail to identify poor-quality resources. Findings further suggest that incorporating advanced probabilistic and text analysis methods to infer the reviewers' reliability and reviews' quality improves performance; however, there is still an evident need for instructor oversight and training of students to write compelling and reliable reviews.

Managing intractability: Wrestling with wicked problems and seeing beyond consensus in public relations

Managing perceived intractable problems—seemingly irrevocable, difficult-to-solve, often values-driven conflicts—is a regular occurrence for public relations practitioners. Yet, understanding and managing such problems is often outside of the bounds of public relations theories aimed primarily at building consensus. This paper builds on existing conflict-centered literature by carving out a place for dissensus-oriented or agonistic public relations theories and practices. Through interviews with 41 public relations practitioners facing perceived intractable scenarios and the integration of dissensual and agonistic perspectives of Lyotard (1984), Rancière (2010), and others, the paper provides evidence of these approaches in everyday practice. Finally, it offers a framework for social issue engagement that orients agonistic and dissensus-minded professional practices alongside consensus approaches.

GeoWaVe: geometric median clustering with weighted voting for ensemble clustering of cytometry data.

Clustering is an unsupervised method for identifying structure in unlabelled data. In the context of cytometry, it is typically used to categorize cells into subpopulations of similar phenotypes. However, clustering is greatly dependent on hyperparameters and the data to which it is applied as each algorithm makes different assumptions and generates a different 'view' of the dataset. As such, the choice of clustering algorithm can significantly influence results, and there is often not one preferred method but different insights to be obtained from different methods. To overcome these limitations, consensus approaches are needed that directly address the effect of competing algorithms. To the best of our knowledge, consensus clustering algorithms designed specifically for the analysis of cytometry data are lacking. We present a novel ensemble clustering methodology based on geometric median clustering with weighted voting (GeoWaVe). Compared to graph ensemble clustering methods that have gained popularity in single-cell RNA sequencing analysis, GeoWaVe performed favourably on different sets of high-dimensional mass and flow cytometry data. Our findings provide proof of concept for the power of consensus methods to make the analysis, visualization and interpretation of cytometry data more robust and reproducible. The wide availability of ensemble clustering methods is likely to have a profound impact on our understanding of cellular responses, clinical conditions and therapeutic and diagnostic options. GeoWaVe is available as part of the CytoCluster package https://github.com/burtonrj/CytoCluster and published on the Python Package Index https://pypi.org/project/cytocluster. Benchmarking data described are available from https://doi.org/10.5281/zenodo.7134723. Supplementary data are available at Bioinformatics online.

Swimming against the stream: A systems approach to rebuilding fishing stocks

Solutions occurring within complex systems such as recovery of species are urgently needed. One path forward involves action agendas that extend across the full range of stakeholder groups. Approaches that can foster cooperative behavior across a range of vested interests can create environments supporting species recovery. This paper reports the stakeholder identification process used to gather divergent opinions. A total of 923 priorities from 239 stakeholders were identified. Consensus approaches were utilized, resulting in 25 top priorities, selected by 61 stakeholders. This study demonstrates how consensus areas for action can be illuminated and supported by diverse stakeholder groups whose relationships have previously been indicated as antagonistic. Results indicate support for a range of actions that can be implemented to protect iconic fish species such as pearl perch and snapper in Southeast Queensland. Stakeholders supported actions that; change fishing practices for groups (recreational, commercial and charter); extended monitoring and reporting; improved compliance; changes to fishing management practices; extended fishing control measures and the construction of additional artificial reef habitats.