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The Eating Disorders In weight-related Therapy (EDIT) Collaboration brings together data from randomised controlled trials of behavioural weight management interventions to identify individual participant risk factors and intervention strategies that contribute to eating disorder risk. We present a protocol for a systematic review and individual participant data (IPD) meta-analysis which aims to identify participants at risk of developing eating disorders, or related symptoms, during or after weight management interventions conducted in adolescents or adults with overweight or obesity. We systematically searched four databases up to March 2022 and clinical trials registries to May 2022 to identify randomised controlled trials of weight management interventions conducted in adolescents or adults with overweight or obesity that measured eating disorder risk at pre- and post-intervention or follow-up. Authors from eligible trials have been invited to share their deidentified IPD. Two IPD meta-analyses will be conducted. The first IPD meta-analysis aims to examine participant level factors associated with a change in eating disorder scores during and following a weight management intervention. To do this we will examine baseline variables that predict change in eating disorder risk within intervention arms. The second IPD meta-analysis aims to assess whether there are participant level factors that predict whether participation in an intervention is more or less likely than no intervention to lead to a change in eating disorder risk. To do this, we will examine if there are differences in predictors of eating disorder risk between intervention and no-treatment control arms. The primary outcome will be a standardised mean difference in global eating disorder score from baseline to immediately post-intervention and at 6- and 12- months follow-up. Identifying participant level risk factors predicting eating disorder risk will inform screening and monitoring protocols to allow early identification and intervention for those at risk.

Preeclampsia is a leading cause of maternal and fetal morbidity and mortality. Currently, the only definitive treatment of preeclampsia is delivery of the placenta, which is central to the pathogenesis of the disease. Transcriptional profiling of human placenta from pregnancies complicated by preeclampsia has been extensively performed to identify differentially expressed genes (DEGs). The decisions to investigate DEGs experimentally are biased by many factors, causing many DEGs to remain uninvestigated. A set of DEGs which are associated with a disease experimentally, but which have no known association to the disease in the literature are known as the ignorome. Preeclampsia has an extensive body of scientific literature, a large pool of DEG data, and only one definitive treatment. Tools facilitating knowledge-based analyses, which are capable of combining disparate data from many sources in order to suggest underlying mechanisms of action, may be a valuable resource to support discovery and improve our understanding of this disease. In this work we demonstrate how a biomedical knowledge graph (KG) can be used to identify novel preeclampsia molecular mechanisms. Existing open source biomedical resources and publicly available high-throughput transcriptional profiling data were used to identify and annotate the function of currently uninvestigated preeclampsia-associated DEGs. Experimentally investigated genes associated with preeclampsia were identified from PubMed abstracts using text-mining methodologies. The relative complement of the text-mined- and meta-analysis-derived lists were identified as the uninvestigated preeclampsia-associated DEGs (n=445), i.e., the preeclampsia ignorome. Using the KG to investigate relevant DEGs revealed 53 novel clinically relevant and biologically actionable mechanistic associations., Comment: Preprint of an article submitted for consideration in Pacific Symposium on Biocomputing \copyright 2022 copyright World Scientific Publishing Company https://psb.stanford.edu/

With increased usage of long-read sequencing technologies to perform transcriptome analyses, there becomes a greater need to evaluate different methodologies including library preparation, sequencing platform, and computational analysis tools. Here, we report the study design of a community effort called the Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium, whose goals are characterizing the strengths and remaining challenges in using long-read approaches to identify and quantify the transcriptomes of both model and non-model organisms. The LRGASP organizers have generated cDNA and direct RNA datasets in human, mouse, and manatee samples using different protocols followed by sequencing on Illumina, Pacific Biosciences, and Oxford Nanopore Technologies platforms. Participants will use the provided data to submit predictions for three challenges: transcript isoform detection with a high-quality genome, transcript isoform quantification, and de novo transcript isoform identification. Evaluators from different institutions will determine which pipelines have the highest accuracy for a variety of metrics using benchmarks that include spike-in synthetic transcripts, simulated data, and a set of undisclosed, manually curated transcripts by GENCODE. We also describe plans for experimental validation of predictions that are platform-specific and computational tool-specific. We believe that a community effort to evaluate long-read RNA-seq methods will help move the field toward a better consensus on the best approaches to use for transcriptome analyses. Items: The LRGASP Registered Report Supplementary File Supplementary Figures Supplementary Table 1

Historically, agencies have been reliant on physical infrastructure, crash data, manual data collection, and modeling to evaluate their road networks. Over the past several years, enhanced probe data has become commercially available and has shown itself to be a relatively inexpensive and scalable way to evaluate the performance of road networks. In January 2022 alone, 11.3 billion passenger vehicle trajectory waypoints and 279 million passenger vehicle event records were logged in the state of Indiana. This data, typically segmented into vehicle trajectory waypoints and vehicle event records, contains a variety of information including, but not limited to, location, speed, heading, and timestamp. One use for this enhanced probe data is the evaluation of traffic signals for safety improvements. Typically, agencies require 3 – 5 years of crash data to be able to statistically identify intersections in need of safety improvements. This study compared crash data over a 4.5-year period at 8 signalized intersections to one month of weekday hard-braking and hard-acceleration data from July 2019. A Spearman’s rank-order correlation test was used, and a strong to very strong correlation between event data and crashes could be found indicating that just one month of event data could be an adequate substitute for 3 – 5 years of crash data. The representativeness of this data is often a major concern for many agencies as the usefulness of the data is only as good as the data itself. This paper describes and demonstrates a methodology for measuring connected vehicle penetration using data provided by state highway performance monitoring stations. This study looked at 1.7 billion count station vehicle counts and 70 million connected vehicle records across 381 count stations in 11 different states (California, Connecticut, Georgia, Indiana, Minnesota, North Carolina, Ohio, Pennsylvania, Texas, Utah, and Wisconsin). Across the 11 states and 381 stations, the average percent penetration was 3.8% in August 2020 and 3.9% in August 2021. Drilling down to August 2021, the percent penetration observed among the 187 interstate stations varied from 1.6% in Indiana to 10.0% in Wisconsin. A similar comparison of 162 non-interstate count stations showed a variation of 2.1% in MN and 18.0% in WI on non-interstates.

Additional file 1: Supplementary Table 1. Comparison of the median sample size between Indigenous-Australian and General Australian trials based on registration year from 2008-2018. Supplementary Table 2. Types of funding displayed in absolute number and (percentage) for Indigenous-Australian trials registered from 2008-2018, for top 10 priority (and other) conditions as per Australian Institute of Health and Welfare (AIHW) % Total Burden for Indigenous Australians, by disease group 2011. (Note that one trial can study multiple conditions hence numbers do not reflect the number of trials). Supplementary Table 3. Participant Size in each respective year for All-Australian, General Australian and Indigenous Australian trials and Participant Size for Indigenous Australian Trials demonstrated as a proportion to All Australian Trials, 2008-2018. Supplementary Table 4. Types of intervention assigned for each health condition enlisted in Indigenous-Australian trials registered from 2008-2018, where included health conditions are from top 10 priority areas as per Australian Institute of Health and Welfare (AIHW) % Total Burden for Indigenous Australians, by disease group 2011. Supplementary Figure 1. Absolute sample size of Indigenous Australian Trials and as a proportion of All Australian Trials, 2008-2018.

Additional file 1: Table S1. Adjustment for GERD status in LABW bile acid and ALAD logistic regression. Table S2. Association of BLAD with LABW bile acids in univariable logistic regression. Figure S1. A: Spearman correlation of LABW TCA levels with reflux episodes. B: Spearman correlation of LABW GCA levels with reflux episodes. C: Spearman correlation of LABW CA levels with reflux episodes.

Objective: Understanding the molecular drivers of disease is a vital component of personalized medicine. Unfortunately, molecular data are not currently available in most electronic health records (EHRs). To solve this problem we created Med2Mech, a joint learning framework for inferring molecular characterizations of patients from clinical data and publicly available biomedical data. Methods: Med2Mech was evaluated using pediatric EHR data from a subset of rare disease and other similarly medically complex patients. First, patient-level clinical embeddings were generated. Then, a PheKnowLator knowledge graph (KG) was used to generate mechanism embeddings. Finally, patient-level mechanism embeddings were derived by summing or averaging each patient���s unique set of mechanism embeddings. A one-vs-the-rest multiclass classification strategy, with five cross-fold validation, was used to evaluate the discriminatory ability of the mechanism and clinical embeddings. Rare disease subphenotype differences, using both clinical and mechanism embeddings, were further investigated using K-Means, which were verified by PhD- and MD-level domain experts. As external validation, the ability to infer the genotype of the rare disease patients using an independent sample of publicly available transcriptomic data was examined. Results: Clinical embeddings were built for four rare disease groups (n = 2,646) and 10,000 similarly complex patients using 6,382 conditions, 2,334 medications, and 272 measurements. Mechanism embeddings were generated from a PheKnowlator KG with 129,875 nodes and 3,838,935 edges. On classification, the mechanism embeddings out-performed 82.2% of the clinical embedding parameterizations. Domain expert review confirmed the mechanism embeddings produced more clinically-relevant clusters of comorbidities for each rare disease subphenotype than the clinical embeddings. External validation further demonstrated the utility of this framework by accurately inferring the genotype and phenotype of EHR-derived rare disease patients from publicly available molecular data. Conclusion: These results illustrate the translational utility of PheKnowLator KGs and demonstrate that it is possible to derive clinically meaningful and biologically relevant patient representations from disparate sources of EHR data and expert-curated publicly available transcriptomic data., PILOT STUDY: These results are preliminary and will be updated prior to publication

Patients with childhood, adolescent, and young adult cancer who will be treated with gonadotoxic therapies are at increased risk for infertility. Many patients and their families desire biological children but effective communication about treatment-related infertility risk and procedures for fertility preservation does not always happen. The PanCareLIFE Consortium and the International Late Effects of Childhood Cancer Guideline Harmonization Group reviewed the literature and developed a clinical practice guideline that provides recommendations for ongoing communication methods for fertility preservation for patients who were diagnosed with childhood, adolescent, and young adult cancer at age 25 years or younger and their families. Moreover, the guideline panel formulated considerations of the ethical implications that are associated with these procedures. Grading of Recommendations Assessment, Development and Evaluation methodology was used to grade the evidence and recommendations. In this clinical practice guideline, existing evidence and international expertise are combined to develop transparent recommendations that are easy to use to facilitate ongoing communication between health-care providers and patients with childhood, adolescent, and young adult cancer who might be at high risk for fertility impairment and their families.

MotivationAlthough knowledge graphs (KGs) are used extensively in biomedical research to model complex phenomena, many KG construction methods remain largely unable to account for the use of different standardized terminologies or vocabularies, are often difficult to use, and perform poorly as the size of the KG increases in scale. We introduce PheKnowLator (Phenotype Knowledge Translator), a novel KG framework and fully automated Python 3 library explicitly designed for optimized construction of semantically-rich, large-scale biomedical KGs. To demonstrate the functionality of the framework, we built and evaluated eight different parameterizations of a large semantic KG of human disease mechanisms. PheKnowLator is available at: https://github.com/callahantiff/PheKnowLator.

Although knowledge graphs (KGs) are used extensively in biomedical research to model complex phenomena, many KG construction methods remain largely unable to account for the use of different standardized terminologies or vocabularies, are often difficult to use, and perform poorly as the size of the KG increases in scale. We introduce PheKnowLator (Phenotype Knowledge Translator), a novel KG framework and fully automated Python 3 library explicitly designed for optimized construction of semantically-rich, large-scale biomedical KGs. To demonstrate the functionality of the framework, we built and evaluated eight different parameterizations of a large semantic KG of human disease mechanisms. PheKnowLator is available at: https://github.com/callahantiff/PheKnowLator.

Background: Novel Research enabled cloud-based computing environments methods and data analytics equip researchers with the ability unprecedented opportunities to explore large data sets and obtain novel insights into disease patterns (e.g. ) and patient outcomes (e.g.). Furthermore, contemporary programming methods allow extensive data sets to be evaluated in a time-efficient manner. Machine learning, or algorithms that perform better when trained on large amounts of diverse data improve with experience, can be utilized to identify prognostic patterns or make predictions based on multiple types of patterns using a combination of data from different data sets. Ensemble learning is one such method that pools the output of several models in order to enhance predictive performance compared to a single model. Here we apply these methods to a cloud-based health database in order to demonstrate the value of these tools in the field of Physical Medicine & Rehabilitation. Design: An IRB-approved retrospective cohort study was initiated to evaluate outpatient data. Patients seen at an academic spine center between the ages of 18-99 were included in the study. Data was provided by Health Data Compass and stored/processed using Google Cloud Platform Tools. For this abstract, data was collected from a pool of 2,212,431 (2.2 million) patient charts. Results: Of 2,212,431 unique patient charts, 72,555 patients were eligible. A query was run on the full sample of patients for all diagnoses, procedures, medications, laboratory studies, and chart data. Our sample contained 40,047 female patients (55.2%) and 32,491 male patients (44.7%). Seventeenpatients did not have gender data reported. The mean age at first encounter was 52.502 years old, with a standard deviation of 16.29. Conclusions:Cloud-based health data tools allow for evaluation of extensive data sets. Our study included a query of 2.2 million unique patient charts, with an emphasis on 72,555 who met specified criteria for further analysis. These samples are clinically meaningful and can be used for future cohort analyses.Limitations include the observational nature of the study and computational resources available. In summary, cloud computing has significant potential for analyzing big data, but must be harnessed within the limitations of the health data systems and computational capacity.Overall, cloud-based health data tools represent a promising avenue to efficiently explore big data sets in the field of Physical Medicine & Rehabilitation. &nbsp

The Institute for Defense Analyses (IDA) was asked to conduct a comprehensive study of financial and non-financial incentives for civilian and military program managers (PMs) for major defense acquisition programs in response to the requirement in Section 841(b)(1) of the National Defense Authorization Act for Fiscal Year 2018. In this study, the IDA team reviewed relevant previous research, interviewed government and industry personnel, analyzed data, and identified and assessed incentives to recruit, retain, and reward Department of Defense PMs. Naval Postgraduate School Acquisition Research Program

The need for increased food and feed supply to support future global demand with the added challenges of resistance pressure and an evolving regulatory environment necessitates the discovery of new crop protection agents for growers of today and tomorrow. Lead generation is the critical 'engine' for maintaining a robust pipeline of new high-value products. A wide variety of approaches exist for the generation of new leads, many of which have demonstrated success. Each approach features some degree of merit or benefit while also having some inherent drawback or level of risk. While risk for any single approach can be mitigated in a variety of different ways depending on the approach, long-term viability of a successful lead generation program merits utilization of a portfolio of different approaches and methodologies for the generation of new leads. © 2016 Society of Chemical Industry.

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity., Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 tables

The Northern Range (a.k.a., Northern Yellowstone Range) is 380,000 acres of rangeland and forest in northwestern Wyoming and south-central Montana within and adjacent to Yellowstone National Park (YNP). Sixty percent of the Northern Range is within YNP and 40% is north of YNP on federal, state, and private lands in Montana (Fig. 1). Inside YNP, about 60% of the Northern Range is rangeland and 40% is forest. Outside YNP, the Northern Range in Montana is mostly foothill grassland and sagebrush steppe, while the bottomlands are dominated by irrigated pastures and hayfields. The Northern Range outside YNP is a working landscape comprised of multiple use federal and state lands, ranches, rural residences and vacation homes, small unincorporated towns, and tourist facilities. Federal and state lands and ranches provide open spaces that buffer YNP from more developed lands. Wildlife migrating north from YNP depends on these working lands for winter and summer habitat.

Current crop protection chemicals span an array of chemistry classes and modes of action. Typically, within each chemistry class, there are multiple chemically distinct active ingredients competing with each other for market position. In this competition, the first product to market in a new class or mode of action may or may not have an advantage depending upon a number of parameters, including relative efficacy against the target pests, pest resistance, regulatory pressures, synthetic complexity, and marketing effectiveness. The number of companies involved in the discovery of new crop protection compounds has been declining, and patenting strategies have become more sophisticated, making it more challenging to break into an existing area of chemistry. One result is new classes of chemistry tend to be smaller, making first to market more beneficial than in the past. Additionally, the first into a market with a new class of chemistry has the opportunity to set positioning and expectations.

In late 2017, the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) proposed a project to develop in silico models of toxicity titled "Predictive Models for Acute Oral Systemic Toxicity". The goal was to predict five specific endpoints needed by regulatory agencies: “Very toxic” chemicals (LD50 less than 50 mg/kg) “Nontoxic” chemicals (LD50 greater than or equal to 2000 mg/kg) Point estimates for rodent LD50s Categorization of toxicity hazard using the U.S. Environmental Protection Agency (EPA) codes Categorization of toxicity hazard using the United Nations Globally Harmonized System of Classification and Labelling (GHS) codes In collaboration with EPA's National Center for Computational Toxicology (NCCT), they provided training and validation datasets for a sizeable body of oral acute toxicity data from rat models. We did not participate directly in this shared task, but used the published datasets for a proof of concept to determine the accuracy of a semantic knowledge-based classifier. We focused our efforts on the categorical classification tasks (all endpoints but the LD50 point estimations).

We present a re-imaged swept volume display enabling mid-air interaction with 3D floating objects without requiring a head-mounted apparatus. The presence of the volume is so strong that everyone reaches out to touch it, at which point the illusion is broken. To resolve this, we implemented interaction techniques to mitigate occlusion conflicts between the hand and virtual volume during direct manipulation. Our long term goal is to prototype direct spatial 3D manipulation techniques that highlight direct manipulation scenarios such as viewing 3D scans, previewing 3d prints, and product design visualization.

Augmented content from three-dimensional virtual objects, real-world light field captures, and live RGBD camera captures are retargeted onto multi-panel displays. The displayed views are synthesized and aligned dynamically, achieving enhanced perception of aerial three-dimensional volume.

The temporal relationship between meltwater pulse 1a (mwp-1a) and the climate history of the last deglaciation remains a subject of debate. By combining the Greenland Ice Core Project δ18O ice core record on the new Greenland ice core chronology 2005 timescale with the U/Th-dated Barbados coral record, we conclusively derive that mwp-1a did not coincide with the sharp Bølling warming but instead with the abrupt cooling of the Older Dryas. To evaluate whether there is a relationship between meltwater injections, North Atlantic Deep Water (NADW) formation, and climate change, we present a high-resolution record of NADW flow intensity from Eirik Drift through the last deglaciation. It indicates only a relatively minor 200-year weakening of NADW flow, coincident with mwp-1a. Our compilation of records also indicates that during Heinrich event 1 and the Younger Dryas there were no discernible sea level rises, and yet these periods were characterized by intense NADW slowdowns/shutdowns. Clearly, deepwater formation and climate are not simply controlled by the magnitude or rate of meltwater addition. Instead, our results emphasize that the location of meltwater pulses may be more important, with NADW formation being particularly sensitive to surface freshening in the Arctic/Nordic Seas.

In this report we briefly review recent evidence which shows that a substantial proportion of intracellular K+ is "bound" or perturbed from the physicochemical properties expected in dilute aqueous solutions. In addition, we present evidence from electron probe x-ray microanalysis of thin cryosections of cells which indicates that the binding of K+ to anionic groups either carboxyl groups (HCO2) on proteins or to phosphate groups in creatine phosphate (CrP) , in adenosine triphosphate, (ATP), in protein and in nucleic acids, are the main determinants of the maintenance of (as differentiated from the generated of) the well known intra- to extracellular K+ concentration difference. The collective evidence suggests that much of cellular K+ is reduced in its mobility and in its chemical activity due to association with negative charge groups (e.g. carboxyl and phosphates). This fact forces abandonment of the misleading assumption that the majority of intracellular K+ and other inorganic ions are as free as would be expected under ideal solution conditions. This realization should have far reaching consequences toward understanding transmembrane movement of water and solutes in cells.

Quench cooling (rate > 4000°C/sec) of biological specimens limits growth of ice crystals by processes different from slow cooling methods. Quench cooling in liquid propane cooled in liquid nitrogen induced ice crystal segregation compartments, as imaged by scanning transmission electron micrographs of freeze-dried cryosections of tissues and protein solutions. The observed imprints of ice crystals were relatively small and roughly spherical. The size of these ice crystal imprints increased with distance from the specimen/quenchant surface. Beyond a depth of 150 microns the size of the imprints was constant but differed among subcellular compartments. The size of the imprints was found to be dependent on: water content, extent of hydration water and the degree of protein aggregation. Determination of extent of hydration water and degree of protein aggregation in protein solutions by measurements on the size of ice crystal imprints yields data in agreement with macroscopic methods. Thus ice crystal imprints give information about the interactions of macromolecules and water at a subcellular level of resolution.

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Although Mycobacterium bovis bacillus Calmette-Guérin (BCG) has been accepted as the most effective agent in clinical use against superficial bladder cancer, its mechanism of action remains incompletely understood. A kinetic analysis in assessing the potential role of cytokines from BCG-stimulated murine splenocytes showed that IL-12 expression preceded that of other cytokines. Experiments subtracting endogenous BCG-driven IL-12 using neutralizing Ab or augmenting its activity with supplemental rIL-12 revealed not only that IL-12 plays a dominant role in IFN-γ induction but also that it is normally dose limiting. A striking increase in IFN-γ production could be generated in both mouse and human immunocompetent cell culture by the addition of even a small amount of rIL-12. Moreover, this same synergistic effect could be replicated during in vivo administration of BCG plus rIL-12 into the mouse bladder and was observed in a patient receiving intravesical combination therapy. In costimulation cultures, this synergy appeared to partially rely on IL-18 and IL-2 and could be down-regulated by IL-10. This suggests that a dynamic interplay between Th1 and Th2 cytokines is responsible for net IFN-γ production. The ability of supplemental exogenous IL-12 to strongly shift this balance toward Th1 provides an immunological basis for using it in conjunction with intravesical BCG for bladder cancer immunotherapy.

IntroductionBehavioural interventions in early life appear to show some effect in reducing childhood overweight and obesity. However, uncertainty remains regarding their overall effectiveness, and whether effectiveness differs among key subgroups. These evidence gaps have prompted an increase in very early childhood obesity prevention trials worldwide. Combining the individual participant data (IPD) from these trials will enhance statistical power to determine overall effectiveness and enable examination of intervention-covariate interactions. We present a protocol for a systematic review with IPD meta-analysis to evaluate the effectiveness of obesity prevention interventions commencing antenatally or in the first year after birth, and to explore whether there are differential effects among key subgroups.Methods and analysisSystematic searches of Medline, Embase, CENTRAL, CINAHL, PsycInfo, and trial registries for all ongoing and completed randomised controlled trials evaluating behavioural interventions for the prevention of early childhood obesity have been completed up to March 2020 and will be updated annually to include additional trials. Eligible trialists will be asked to share their IPD; if unavailable, aggregate data will be used where possible. An IPD meta-analysis and a nested prospective meta-analysis (PMA) will be performed using methodologies recommended by the Cochrane Collaboration. The primary outcome will be body mass index (BMI) z-score at age 24 +/- 6 months using World Health Organisation Growth Standards, and effect differences will be explored among pre-specified individual and trial-level subgroups. Secondary outcomes include other child weight-related measures, infant feeding, dietary intake, physical activity, sedentary behaviours, sleep, parenting measures and adverse events.Ethics and disseminationApproved by The University of Sydney Human Research Ethics Committee (2020/273) and Flinders University Social and Behavioural Research Ethics Committee (project no. HREC CIA2133-1). Results will be relevant to clinicians, child health services, researchers, policy-makers and families, and will be disseminated via publications, presentations, and media releases.RegistrationProspectively registered on PROSPERO: CRD42020177408STRENGTHS AND LIMITATIONS OF THIS STUDYThis will be the largest individual participant data (IPD) meta-analysis evaluating behavioural interventions for the prevention of early childhood obesity to date, and will provide the most reliable and precise estimates of early intervention effects to inform future decision-making.IPD meta-analysis methodology will enable unprecedented exploration of important individual and trial-level characteristics that may be associated with childhood obesity or that may be effect modifiers.The proposed innovative methodologies are feasible and have been successfully piloted by members of our group.It may not be possible to obtain IPD from all eligible trials; in this instance, aggregate data will be used where available, and sensitivity analyses will be conducted to assess inclusion bias.Outcome measures may be collected and reported differently across included trials, potentially increasing imprecision; however, we will harmonise available data where possible, and encourage those planning or conducting ongoing trials to collect common core outcomes following prospective meta-analysis methodology.

IntroductionLittle is known about how early (e.g., commencing antenatally or in the first 12 months after birth) obesity prevention interventions seek to change behaviour and which components are or are not effective. This study aims to 1) characterise early obesity prevention interventions in terms of target behaviours, delivery features, and behaviour change techniques (BCTs), 2) explore similarities and differences in BCTs used to target behaviours, and 3) explore effectiveness of intervention components in preventing childhood obesity.Methods and analysisAnnual comprehensive systematic searches will be performed in Epub Ahead of Print/MEDLINE, Embase, Cochrane (CENTRAL), CINAHL, PsycINFO, as well as clinical trial registries. Eligible randomised controlled trials of behavioural interventions to prevent childhood obesity commencing antenatally or in the first year after birth will be invited to join the TOPCHILD Collaboration. Standard ontologies will be used to code target behaviours, delivery features and BCTs in both published and unpublished intervention materials provided by trialists. Narrative syntheses will be performed to summarise intervention components and compare applied BCTs by types of target behaviours. Exploratory analyses will be undertaken to assess effectiveness of intervention components.Ethics and disseminationThe study has been approved by The University of Sydney Human Research Ethics Committee (project no. 2020/273) and Flinders University Social and Behavioural Research Ethics Committee (project no. HREC CIA2133-1). The study’s findings will be disseminated through peer-reviewed publications, conference presentations, and targeted communication with key stakeholders.DiscussionOur study will provide an in depth understanding of behavioural components and delivery features used in obesity prevention interventions starting antenatally or in the first 12 months after birth. Understanding common intervention approaches in a systematic way will provide much needed insight to advance the design of early obesity prevention interventions and provide the opportunity to undertake future quantitative predictive modelling.RegistrationPROSPERO registration no. CRD42020177408STRENGTHS AND LIMITATIONS OF THIS STUDYThis study provides an understanding of behaviours targeted, behaviour change techniques and delivery features used in early childhood obesity prevention trials identified in a systematic review as being eligible for inclusion in the Transforming Obesity Prevention in CHILDren (TOPCHILD) Collaboration.Extends previous methods by coding behaviour change techniques in published and unpublished intervention materials and performing cross validation with trialists through the TOPCHILD Collaboration.Using standardised coding taxonomies will allow for comparisons across studies, and we will pilot test new ontologies from the Human Behaviour Change Project.Explores the complex area of targeting parent and caregivers’ behaviours to impact child outcomes across four key obesity prevention behavioural domains (relating to infant feeding practices, food provision and parent feeding practices, movement practices, sleep health practices).This study will provide preliminary results regarding the examination of intervention components’ effectiveness based on exploratory analysis. Yet, the internationally unique database this project creates will further our understanding of effective intervention components in future research.To date we already have 38 out of 65 eligible trials agreeing to share data, since not all trials may provide unpublished material we may perform sensitivity analyses comparing trials that have shared data to trials that have not shared materials.

Summary Background Limitations of the transverse upper gracilis (TUG) flap for autologous breast reconstruction include: short pedicle, modest volume, muscle sacrifice and a problematic donor site. The Profunda Artery Perforator (PAP) flap utilises large perforators posterior to the gracilis muscle. We describe our preliminary experience of its use and compare it to our large series of TUG flaps. Method Our technique has evolved from frog-leg to lithotomy position, and from an anterio-posterior to cranio-caudal raise. This allows either the descending branch of the inferior gluteal artery perforators (IGAP) or the TUG flap as alternatives should PAP perforators be unsuitable intra-operatively. A prospective database was utilised to compare TUG and PAP flaps undertaken 2010–2013. Results 54 TUG and 22 PAP flaps were performed. 4 PAP flaps were converted to IGAP flaps and 1 to TUG intra-operatively. 97% of all flaps were successful. Mean flap weight was 295 g (TUG) and 242 g (PAP). Donor site complications for both series included seroma (4 TUG, 1 PAP) sensory disturbance (2 TUG, 1 PAP) and scar revision (3 TUG, 1 PAP). Conclusion Our preliminary experience of the PAP flap has not been universally favourable compared to the TUG flap. It is a more challenging flap to raise, which carries with it a learning curve, especially if raised in the supine position; we present our learning points for safer flap harvest, allowing the TUG as a bail out option. The benefits of the PAP include a longer pedicle, without the need to sacrifice muscle; the perforators should have a more defined and larger perfusion zone. The scar is better hidden, but we have not yet proven significant improvements to the donor site compared to the TUG flap. Level of Evidence: III