Hypothesis-driven Research Research Articles

Enhanced Sample Multiplexing-Based Targeted Proteomics with Intelligent Data Acquisition.

Targeted proteomics has been playing an increasingly important role in hypothesis-driven protein research and clinical biomarker discovery. We previously created a workflow, Tomahto, to enable real-time targeted pathway proteomics assays using two-dimensional multiplexing technology. Coupled with the TMT 11-plex reagent, hundreds of proteins of interest from up to 11 samples can be targeted and accurately quantified in a single-shot experiment with remarkable sensitivity. However, room remains to further improve the sensitivity, accuracy, and throughput, especially for targeted studies demanding a high peptide-level success rate. Here, bearing in mind the goal to improve peptide-level targeting, we introduce several new functionalities in Tomahto, featuring the integration of gas-phase fractionation using the FAIMS device, an accompanying software program (TomahtoPrimer) to customize fragmentation for each peptide target, and support for higher multiplexing capacity with the latest TMTpro reagent. We demonstrate that adding these features to the Tomahto platform significantly improves overall success rate from 89% to 98% in a single 60 min targeted assay of 290 peptides across human cell lines, while boosting quantitative accuracy via reducing TMT reporter ion interference.

Experiences of Black Women With Pelvic Floor Disorders-A Qualitative Analysis Study.

Disparities research in Black women with pelvic floor disorders (PFDs) has primarily focused on epidemiology or surgical outcomes, but little is known about the patient perspective on seeking PFD care. To provide quality and equitable care to Black women with PFDs, we conducted a qualitative study to hear their perspectives and lived health care experiences. Black women seeking care for PFDs at a tertiary care institution were invited to participate in qualitative interviews. Open-ended questions explored participants' knowledge, attitudes, and health care experiences. Interviews were transcribed verbatim and coded line-by-line. Inductive content analysis was performed to identify key themes, and consensus was achieved among the research team. Eight Black women aged 21-83 years consented to participate. Patients noted a stigma surrounding PFDs, both among health care professionals and their community. They noted several barriers to care: financial, logistical, and racial bias. They cited difficulties in patient-health care professional communication, including not feeling heard or treated as an individual. They preferred gender and racial concordance with their physicians. These women expressed both a desire for more knowledge and to share this knowledge and advocate for other women. Black women expressed distinct obstacles in their PFD health care. They reported not feeling heard or treated as a unique individual. The themes derived from this study identify complex patient-centered needs that can serve as the basis for future quality improvement work and/or hypothesis-driven research. By grounding health disparities research in patient perspectives, we can improve the health care experiences of Black women.

Differential posttraumatic stress disorder symptom cluster response to stellate ganglion block: secondary analysis of a randomized controlled trial

Empirically supported treatments for posttraumatic stress disorder (PTSD) exist, but research suggests these therapies are less effective, acceptable, and feasible to deliver to active duty service members (SMs) compared to civilians. Stellate ganglion block (SGB) procedure, in which a local anesthetic is injected around the cervical sympathetic chain or stellate ganglion to temporarily inhibit sympathetic nervous activity, is gaining popularity as an alternative PTSD treatment in military settings. However, it is unknown whether certain PTSD symptoms are more responsive to SGB than others. The current study involved a secondary analysis of data collected from a previous randomized controlled trial of SGB compared to sham (normal saline) injection (N = 113 SMs). PTSD symptoms were assessed via clinical interview and self-report at baseline and 8 weeks post-SGB or sham. Logistic regression analyses showed that the marked alterations in arousal and reactivity PTSD symptom cluster demonstrated the greatest symptom severity reductions after SGB, relative to sham. The reexperiencing cluster also showed pronounced response to SGB in clinician-rated but not self-reported outcomes. Post-hoc item-level analyses suggested that arousal and reactivity cluster findings were driven by reductions in hypervigilance, concentration difficulties, and sleep disturbance, whereas clinician-rated reexperiencing cluster findings were driven by reductions in physiological reactions to trauma cues, emotional reactions to trauma cues, and intrusions. Our findings align with a burgeoning literature positioning SGB as a potential novel or adjunctive PTSD treatment. Results could guide future hypothesis-driven research on mediators of therapeutic change during SGB for PTSD symptoms in SMs.

Cost-Effectiveness Analysis of Using Digital Platforms to Alleviate the Gap in Access to Healthcare Services for the Elderly in China

The issue of healthcare access has transcended individual concerns to become a major societal challenge, especially in the context of an aging population. The problem of access to health care has led to a growing healthcare divide. The approach presented in this paper aims to delve into the intricate interplay of social, economic, political, and cultural determinants, the reasons why these factors contribute to the widening disparities in healthcare access experienced by older adults, and the proposed feasible solutions. The implementation of this solution requires a multi-faceted strategy, including an important part - hypothesis-driven experimental research driven by university student volunteers. By proposing hypothetical principles and analyzing data to make a comprehensive economic assessment, this paper aims to propose a pragmatic solution to this pressing problem. Ultimately, we aim to significantly mitigate disparities in healthcare access for older adults, aiming to minimize the impact of the problems of older adults themselves due to the uneven distribution of physician resources, and insufficiently comprehensive government policies.

Understanding Physician's Perspectives on AI in Health Care: Protocol for a Sequential Multiple Assignment Randomized Vignette Study.

As the availability and performance of artificial intelligence (AI)-based clinical decision support (CDS) systems improve, physicians and other care providers poised to be on the front lines will be increasingly tasked with using these tools in patient care and incorporating their outputs into clinical decision-making processes. Vignette studies provide a means to explore emerging hypotheses regarding how context-specific factors, such as clinical risk, the amount of information provided about the AI, and the AI result, may impact physician acceptance and use of AI-based CDS tools. To best anticipate how such factors influence the decision-making of frontline physicians in clinical scenarios involving AI decision-support tools, hypothesis-driven research is needed that enables scenario testing before the implementation and deployment of these tools. This study's objectives are to (1) design an original, web-based vignette-based survey that features hypothetical scenarios based on emerging or real-world applications of AI-based CDS systems that will vary systematically by features related to clinical risk, the amount of information provided about the AI, and the AI result; and (2) test and determine causal effects of specific factors on the judgments and perceptions salient to physicians' clinical decision-making. US-based physicians with specialties in family or internal medicine will be recruited through email and mail (target n=420). Through a web-based survey, participants will be randomized to a 3-part "sequential multiple assignment randomization trial (SMART) vignette" detailing a hypothetical clinical scenario involving an AI decision support tool. The SMART vignette design is similar to the SMART design but adapted to a survey design. Each respondent will be randomly assigned to 1 of the possible vignette variations of the factors we are testing at each stage, which include the level of clinical risk, the amount of information provided about the AI, and the certainty of the AI output. Respondents will be given questions regarding their hypothetical decision-making in response to the hypothetical scenarios. The study is currently in progress and data collection is anticipated to be completed in 2024. The web-based vignette study will provide information on how contextual factors such as clinical risk, the amount of information provided about an AI tool, and the AI result influence physicians' reactions to hypothetical scenarios that are based on emerging applications of AI in frontline health care settings. Our newly proposed "SMART vignette" design offers several benefits not afforded by the extensively used traditional vignette design, due to the 2 aforementioned features. These advantages are (1) increased validity of analyses targeted at understanding the impact of a factor on the decision outcome, given previous outcomes and other contextual factors; and (2) balanced sample sizes across groups. This study will generate a better understanding of physician decision-making within this context. DERR1-10.2196/54787.

A preliminary investigation of epigenome-wide DNA methylation and temperament during infancy.

This study provides preliminary evidence for an epigenetic architecture of infant temperament. At 12 months of age, blood was collected and assayed for DNA methylation and maternally reported infant temperament was assessed using the Infant Behavior Questionnaire in 67 mother-infant dyads. Epigenome-wide analyses showed that the higher order temperament dimensions Surgency and Negative Affect were associated with DNA methylation. The epigenetic signatures of Surgency and Negative Affect were situated at genes involved in synaptic signaling and plasticity. Although replication is required, these results are consistent with a biologically based model of temperament, create new avenues for hypothesis-driven research into epigenetic pathways that underlie individual differences in temperament, and demonstrate that infant temperament has a widespread epigenetic signature in the methylome.

Effect of physical exercise on immune, inflammatory, cardiometabolic biomarkers, and fatty acids of breast cancer survivors: results from the MAMA_MOVE Gaia After Treatment trial.

Physical exercise has positive effects on clinical outcomes of breast cancer survivors such as quality of life, fatigue, anxiety, depression, body mass index, and physical fitness. We aimed to study its impact on immune, inflammatory, cardiometabolic, and fatty acids (FA) biomarkers. An exploratory sub-analysis of the MAMA_MOVE Gaia After Treatment trial (NCT04024280, registered July 18, 2019) was performed. Blood sample collections occurred during the control phase and at eight weeks of the intervention phase. Samples were subjected to complete leukocyte counts, cytokine, and cardiometabolic marker evaluation using flow cytometry, enzyme-linked immunoassays, and gas chromatography. Ninety-three percent of the 15 participants had body mass index ≥ 25kg/m2. We observed a decrease of the plasmatic saturated FA C20:0 [median difference - 0.08% (p = 0.048); mean difference - 0.1 (95%CI - 0.1, - 0.0)], positively associated with younger ages. A tendency to increase the saturated FA C18:0 and the ratio of unsaturated/saturated FA and a tendency to decrease neutrophils (within the normal range) and interferon-gamma were observed. Positive trends of physical exercise on circulating immune cells, inflammatory cytokines, and plasmatic FA were observed. Larger studies will further elucidate the implications of physical exercise on metabolism. These exploratory findings may contribute to future hypothesis-driven research and contribute to meta-analyses.

Large-scale citizen science reveals predictors of sensorimotor adaptation.

Sensorimotor adaptation is essential for keeping our movements well calibrated in response to changes in the body and environment. For over a century, researchers have studied sensorimotor adaptation in laboratory settings that typically involve small sample sizes. While this approach has proved useful for characterizing different learning processes, laboratory studies are not well suited for exploring the myriad of factors that may modulate human performance. Here, using a citizen science website, we collected over 2,000 sessions of data on a visuomotor rotation task. This unique dataset has allowed us to replicate, reconcile and challenge classic findings in the learning and memory literature, as well as discover unappreciated demographic constraints associated with implicit and explicit processes that support sensorimotor adaptation. More generally, this study exemplifies how a large-scale exploratory approach can complement traditional hypothesis-driven laboratory research in advancing sensorimotor neuroscience.

Metagenomic probing toward an atlas of the taxonomic and metabolic foundations of the global ocean genome

The global ocean genome (the pool of genes in marine organisms and the functional information they encode) is a major, untapped resource for science and society with a growing range of biotechnology applications in sectors such as biomedicine, energy, and food. Shotgun sequencing and metagenomics can now be used to catalog the diversity of ocean microbial life and to explore its functional potential, but has been limited by sample coverage, access to suitable sequencing platforms, and computational capacity. Here we provide a novel synthesis of the global ocean genome based on analysis of 2,102 sampled ocean metagenomes, with gene assembly and annotation via the KAUST Metagenome Analysis Platform (KMAP) Global Ocean Gene Catalog 1.0 containing 308.6 million gene clusters. Taxonomically, we report the distribution of marine genes across the tree of life and different ocean basins and depth zone biomes. Functionally, we map its relationship to protein families and biogeochemical processes, including the major microbial metabolic pathways that process three elements that play fundamental roles in biogeochemical cycles and are relevant to climate change. These data extend our understanding of the complex, dynamic nature of the ocean microbiome and its metabolic capabilities. Further research is of critical global importance both to unlock the potential of the ocean genome and to understand and predict the effects of human-induced changes, including pollution and climate change. Further hypothesis-driven research should target under-sampled deep sea and benthic microbial communities using enhanced metagenomic methods, to better understand marine ecosystem functioning. Investment in the necessary computational capacity is essential, as are suitable intellectual property frameworks.

Funding for Refugee Health Research From the National Institutes of Health Between 2000 and 2020

The US has historically resettled more refugees than any other country, with over 3.5 million refugees since 1980. The National Institutes of Health (NIH) is the largest public funder of biomedical research and development, but its role in mitigating many health disparities refugees experience through its funded research remains unknown. To examine the NIH's research funding patterns on refugee health research over the last 2 decades. Secondary analysis of NIH-funded grants between 2000 and 2020 using a cross-sectional study design. The NIH Research Portfolio Online Reporting Tools database was used to find relevant grants. Data were analyzed from November 2021 to September 2022. NIH grants awarded by year, state, grant type, research area, funding institute, grant duration, and amount funded. Of 1.7 million NIH grants funded over the 20-year study period, only 78 addressed refugee health. Funded grants were mostly training grants (23 grants [29%]), followed by hypothesis-driven research (R01 grants; 22 grants [28%]), pilot or preliminary investigation proposals (13 grants [17%]), and other types of grants (20 grants [26%]). The most studied research domain was mental health (36 grants [46%]), followed by refugee family dynamics and women's and children's health (14 grants [18%]). A total of 26 grants (33%) were funded by the National Institute of Mental Health and 15 (19%) were funded by the National Institute of Child Health and Human Development. Most grants were US-based (60 grants [76%]) and the state of Massachusetts received the greatest amount of funding ($14 825 852 [18%]). In 2020, the NIH allocated about $2.3 million to refugee health research, or less than 0.01% of its $42 billion budget that year. The number of grants funded in each time period did not always reflect changes in the number of refugees resettled in the US over the years. This cross-sectional study found that there remain significant gaps in the understanding of and interventions in the health research needs of refugees locally and along the migratory route. To close these gaps, the NIH should increase its investments in comprehensive studies assessing the physical, mental, and social well-being of this expanding population. This can be achieved by ensuring that all NIH institutes allocate budgets specifically for refugee health research and extend support for the training of refugee researchers.

Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol.

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.

Terms of debate: Consensus definitions to guide the scientific discourse on visual distraction.

Hypothesis-driven research rests on clearly articulated scientific theories. The building blocks for communicating these theories are scientific terms. Obviously, communication - and thus, scientific progress - is hampered if the meaning of these terms varies idiosyncratically across (sub)fields and even across individual researchers within the same subfield. We have formed an international group of experts representing various theoretical stances with the goal to homogenize the use of the terms that are most relevant to fundamental research on visual distraction in visual search. Our discussions revealed striking heterogeneity and we had to invest much time and effort to increase our mutual understanding of each other's use of central terms, which turned out to be strongly related to our respective theoretical positions. We present the outcomes of these discussions in a glossary and provide some context in several essays. Specifically, we explicate how central terms are used in the distraction literature and consensually sharpen their definitions in order to enable communication across theoretical standpoints. Where applicable, we also explain how the respective constructs can be measured. We believe that this novel type of adversarial collaboration can serve as a model for other fields of psychological research that strive to build a solid groundwork for theorizing and communicating by establishing a common language. For the field of visual distraction, the present paper should facilitate communication across theoretical standpoints and may serve as an introduction and reference text for newcomers.

Data-Driven Hypothesis Generation in Clinical Research: What We Learned from a Human Subject Study?

Hypothesis generation is an early and critical step in any hypothesis-driven clinical research project. Because it is not yet a well-understood cognitive process, the need to improve the process goes unrecognized. Without an impactful hypothesis, the significance of any research project can be questionable, regardless of the rigor or diligence applied in other steps of the study, e.g., study design, data collection, and result analysis. In this perspective article, the authors provide a literature review on the following topics first: scientific thinking, reasoning, medical reasoning, literature-based discovery, and a field study to explore scientific thinking and discovery. Over the years, scientific thinking has shown excellent progress in cognitive science and its applied areas: education, medicine, and biomedical research. However, a review of the literature reveals the lack of original studies on hypothesis generation in clinical research. The authors then summarize their first human participant study exploring data-driven hypothesis generation by clinical researchers in a simulated setting. The results indicate that a secondary data analytical tool, VIADS-a visual interactive analytic tool for filtering, summarizing, and visualizing large health data sets coded with hierarchical terminologies, can shorten the time participants need, on average, to generate a hypothesis and also requires fewer cognitive events to generate each hypothesis. As a counterpoint, this exploration also indicates that the quality ratings of the hypotheses thus generated carry significantly lower ratings for feasibility when applying VIADS. Despite its small scale, the study confirmed the feasibility of conducting a human participant study directly to explore the hypothesis generation process in clinical research. This study provides supporting evidence to conduct a larger-scale study with a specifically designed tool to facilitate the hypothesis-generation process among inexperienced clinical researchers. A larger study could provide generalizable evidence, which in turn can potentially improve clinical research productivity and overall clinical research enterprise.

On hidden factors and design-associated errors that may lead to data misinterpretation: An example from preclinical research on the potential seasonality of neonatal seizures.

Unintentional misinterpretation of research in published biomedical reports that is not based on statistical flaws is often underrecognized, despite its possible impact on science, clinical practice, and public health. Important causes of such misinterpretation of scientific data, resulting in either false positive or false negative conclusions, include design-associated errors and hidden (or latent) variables that are not easily recognized during data analysis. Furthermore, cognitive biases, such as the inclination to seek patterns in data whether they exist or not, may lead to misinterpretation of data. Here, we give an example of these problems from hypothesis-driven research on the potential seasonality of neonatal seizures in a rat model of birth asphyxia. This commentary aims to raise awareness among the general scientific audience about the issues related to the presence of unintentional misinterpretation in published reports.

A Deep Learning-Based Pathomics Methodology for Quantifying and Characterizing Nucleated Cells in the Bone Marrow Microenvironment

Introduction: The overall cellularity of the bone marrow (BM) microenvironment is a WHO criterion for differentiating BCRABL- myeloproliferative neoplasms (MPNs) like polycythemia vera (PV) and myelofibrosis (MF) [1]. However, manual assessment by hematopathologists is subjective, not scalable, and only records a single global measure, disregarding BM regional differences. Leveraging pathology slide scanners and deep learning (DL) can address these limitations and reveal valuable insights into MPN subtypes and treatment effects. Despite its potential, computational pathology (Pathomics) in this area remains understudied [2-3]. Methods: After institutional review from Stony Brook University and Weill Cornell Medicine (WCM), we identified 54 patients (pts) with a manually confirmed PV diagnosis (dx) at WCM (17% newly dx'ed, 61% treated, 17% disease progression). The dataset includes hematoxylin & eosin (H&E) stained whole slide images (WSIs), labs, molecular studies, and pathology report elements (reference standard for biopsy core size and cellularity). Numeric cellularity measures the ratio of hematopoietic cells to fat cells and excludes inevaluable trabecular bone. Normal cellularity ranges from (100-pt age [y])±20% and PV is characterized by hypercellularity. We evaluated three DL algorithms for nuclear segmentation: 1) Our U-Net model trained on 5B nuclei from 10 cancer types [4], 2) HistoCartography (HC): a graph neural network trained on 235K nuclei (PanNuke/MoNuSac datasets) [5], and 3) CellPose (CP): a gradient-driven U-Net model trained on 1K nuclei of varied modalities [6]. A color/morphology-based segmenter identified fat cells and evaluable BM regions, excluding vessels/tissue tears. Cellular characteristics (shape, texture, color) were computed and clustered by k-nearest neighbors (kNN) and k-means to explore differences between PV stages. A tissue-based approach for cellularity estimation derived the proportion of non-fat tissue to evaluable regions. A cell-based approach derived the proportions of nucleated cell and fat cell areas. The study calculated mean differences between biopsy core size estimates, assessed correlation (r) and intra-class correlation (ICC) for numeric cellularity estimates, and Cohen's kappa (κ) for categorical cellularity estimates. Results: Measured and estimated biopsy core sizes differed by an acceptable margin (3±4 mm). Tissue-based cellularity estimator results: r=0.2, ICC=0.0, κ=0.2. Cell-based cellularity estimator result: our approach (r=0.7, ICC=0.6, κ=0.7), HC (r=0.7, ICC=0.5, κ=0.6), and CP (r=0.2, ICC=0.2, κ=0.7). Cell size clusters aligned with known distributions in PV, with the prominent Int1 class (5.1-9μm) likely containing erythrocytes and myeloid progenitors. Overall and cell-sized cellularity heatmaps visualize regional differences (Figure 1). K-means clustering separated new and progressing PV (Figure 2). Discussion: DL and classical image analysis were employed to segment nucleated cells, fat cells, and other evaluable regions of hematopoietic tissue. Extracting shape, texture, and color features from segmented nuclei enhances BM microenvironment characterization. This pilot study resulted in a practical and interpretable Pathomics pipeline and downstream analytics as a proof of concept for hypothesis-driven research. Future directions involve applying these insights to a large cohort of diagnosis WSIs from other MPN subtypes and correlating findings with other tools, like immune cell detection models.

The effect of disease misclassification on the ability to detect a gene-environment interaction: implications of the specificity of case definitions for research on Gulf War illness

BackgroundSince 1997, research on Gulf War illness (GWI) has predominantly used 3 case definitions—the original Research definition, the CDC definition, and modifications of the Kansas definition—but they have not been compared against an objective standard.MethodsAll 3 case definitions were measured in the U.S. Military Health Survey by a computer-assisted telephone interview in a random sample (n = 6,497) of the 1991 deployed U.S. military force. The interview asked whether participants had heard nerve agent alarms during the conflict. A random subsample (n = 1,698) provided DNA for genotyping the PON1 Q192R polymorphism.ResultsThe CDC and the Modified Kansas definition without exclusions were satisfied by 41.7% and 39.0% of the deployed force, respectively, and were highly overlapping. The Research definition, a subset of the others, was satisfied by 13.6%. The majority of veterans meeting CDC and Modified Kansas endorsed fewer and milder symptoms; whereas, those meeting Research endorsed more symptoms of greater severity. The group meeting Research was more highly enriched with the PON1 192R risk allele than those meeting CDC and Modified Kansas, and Research had twice the power to detect the previously described gene-environment interaction between hearing alarms and RR homozygosity (adjusted relative excess risk due to interaction [aRERI] = 7.69; 95% CI 2.71–19.13) than CDC (aRERI = 2.92; 95% CI 0.96–6.38) or Modified Kansas without exclusions (aRERI = 3.84; 95% CI 1.30–8.52) or with exclusions (aRERI = 3.42; 95% CI 1.20–7.56). The lower power of CDC and Modified Kansas relative to Research was due to greater false-positive disease misclassification from lower diagnostic specificity.ConclusionsThe original Research case definition had greater statistical power to detect a genetic predisposition to GWI. Its greater specificity favors its use in hypothesis-driven research; whereas, the greater sensitivity of the others favor their use in clinical screening for application of future diagnostic biomarkers and clinical care.

A challenge to the evidence behind noise guidelines for UK hospitals.

Teams assessing hospital noise against international guidelines regularly find that noise exceeds perceived safe levels in clinical settings. The care of sick people may be inherently noisy but recent efforts to tackle the problem propose a wider scope to identify sources and qualities of noise as well as more precision with noise recording. We sought to challenge the scientific evidence cited in the four major documents pertaining to hospital noise in the UK to clarify if evidence of harm from noise included in guidelines is available, contemporary and of high quality. Our team of hearing-health clinicians, acoustic scientists and acoustic engineers have conducted a narrative scoping review focused on critically appraising four of the most cited guidelines against which noise is measured in healthcare settings in the UK. There is a lack of high-quality evidence for commonly accepted consequences of noise cited in current guidelines. The current evidence base for noise guidelines in a healthcare setting is largely based on subjective heterogeneous and inconclusive research. Whilst reduced noise is not disputed as potentially beneficial for patient care, further hypothesis-driven research and interventions assessing the benefits or outcomes of any such intervention should be sought to produce high-quality evidence of relevance on the clinical coalface.

Insight into manta ray behaviour using animal-borne Crittercams

Abstract Animal-borne video cameras equipped with depth and temperature sensors were deployed on 16 reef manta rays (Mobula alfredi) in Raa Atoll, Maldives and 12 oceanic manta rays (Mobula birostris) in the Revillagigedo Archipelago, Mexico. These deployments provided descriptive behavioural data that give vital context to existing biotelemetry data and enabled a comparison of the social dynamics between the two manta ray species. Overall, cruising was the most dominant daytime behaviour recorded for both species. For M. alfredi, cleaning was the second most common behaviour, followed by courtship and feeding. No courtship behaviour was recorded for M. birostris. Across M. alfredi and M. birostris deployments, individuals spent an average of 43 and 8% of recorded time interacting with conspecifics, respectively. Sociability was higher in M. alfredi than M. birostris, however the findings should be interpreted with caution beyond the two deployment populations and times. Crittercams captured multiple courtship events of M. alfredi at depths greater than recreational scuba diving limits and captured previously undocumented interspecific interactions with M. mobular. Crittercam deployments also recorded M. alfredi travelling in groups and hugging the contours of the ocean floor, possibly as a tactic to reduce predation risk and/or improve swimming efficiency, enforcing the importance of this novel technology as a valuable tool to gain new insight into the ecological drivers of habitat use by these species. Lastly, these quantitative and descriptive results provide context for future hypothesis-driven research questions using animal-borne video cameras for mobulid rays.

Using "Real-World Data" to Study Cleft Lip/Palate Care: An Exploration of Speech Outcomes from a Multi-Center US Learning Health Network.

To assess the ability of a cleft-specific multi-site learning health network registry to describe variations in cleft outcomes by cleft phenotypes, ages, and treatment centers. Observed variations were assessed for coherence with prior study findings. Cross-sectional analysis of prospectively collected data from 2019-2022. Six cleft treatment centers collected data systematically during routine clinic appointments according to a standardized protocol. 714 English-speaking children and adolescents with non-syndromic cleft lip/palate. Routine multidisciplinary care and systematic outcomes measurement by cleft teams. Speech outcomes included articulatory accuracy measured by Percent Consonants Correct (PCC), velopharyngeal function measured by Velopharyngeal Competence (VPC) Rating Scale (VPC-R), intelligibility measured by caregiver-reported Intelligibility in Context Scale (ICS), and two CLEFT-Q™ surveys, in which patients rate their own speech function and level of speech distress. 12year-olds exhibited high median PCC scores (91-100%), high frequency of velopharyngeal competency (62.50-100%), and high median Speech Function (80-91) relative to younger peers parsed by phenotype. Patients with bilateral cleft lip, alveolus, and palate reported low PCC scores (51-91%) relative to peers at some ages and low frequency of velopharyngeal competency (26.67%) at 5 years. ICS scores ranged from 3.93-5.0 for all ages and phenotypes. Speech Function and Speech Distress were similar across phenotypes. This exploration of speech outcomes demonstrates the current ability of the cleft-specific registry to support cleft research efforts as a source of "real-world" data. Further work is focused on developing robust methodology for hypothesis-driven research and causal inference.

PLANTdataHUB: a collaborative platform for continuous FAIR data sharing in plant research.

In modern reproducible, hypothesis-driven plant research, scientists are increasingly relying on research data management (RDM) services and infrastructures to streamline the processes of collecting, processing, sharing, and archiving research data. FAIR (i.e., findable, accessible, interoperable, and reusable) research data play a pivotal role in enabling the integration of interdisciplinary knowledge and facilitating the comparison and synthesis of a wide range of analytical findings. The PLANTdataHUB offers a solution that realizes RDM of scientific (meta)data as evolving collections of files in a directory - yielding FAIR digital objects called ARCs - with tools that enable scientists to plan, communicate, collaborate, publish, and reuse data on the same platform while gaining continuous quality control insights. The centralized platform is scalable from personal use to global communities and provides advanced federation capabilities for institutions that prefer to host their own satellite instances. This approach borrows many concepts from software development and adapts them to fit the challenges of the field of modern plant science undergoing digital transformation. The PLANTdataHUB supports researchers in each stage of a scientific project with adaptable continuous quality control insights, from the early planning phase to data publication. The central live instance of PLANTdataHUB is accessible at (https://git.nfdi4plants.org), and it will continue to evolve as a community-driven and dynamic resource that serves the needs of contemporary plant science.