Full Replication Research Articles

Ploidy-specific transcriptomes shed light on the heterogeneous identity and metabolism of developing tomato pericarp cells.

Endoreduplication, during which cells increase their DNA content through successive rounds of full genome replication without cell division, is the major source of endopolyploidy in higher plants. Endoreduplication plays pivotal roles in plant growth and development and is associated with the activation of specific transcriptional programmes that are characteristic of each cell type, thereby defining their identity. In plants, endoreduplication is found in numerous organs and cell types, especially in agronomically valuable ones, such as the fleshy fruit (pericarp) of tomato presenting high ploidy levels. We used the tomato pericarp tissue as a model system to explore the transcriptomes associated with endoreduplication progression during fruit growth. We confirmed that expression globally scales with ploidy level and identified sets of differentially expressed genes presenting only developmental-specific, only ploidy-specific expression patterns or profiles resulting from an additive effect of ploidy and development. When comparing ploidy levels at a specific developmental stage, we found that non-endoreduplicated cells are defined by cell division state and cuticle synthesis while endoreduplicated cells are mainly defined by their metabolic activity changing rapidly over time. By combining this dataset with publicly available spatiotemporal pericarp expression data, we proposed a map describing the distribution of ploidy levels within the pericarp. These transcriptome-based predictions were validated by quantifying ploidy levels within the pericarp tissue. This in situ ploidy quantification revealed the dynamic progression of endoreduplication and its cell layer specificity during early fruit development. In summary, the study sheds light on the complex relationship between endoreduplication, cell differentiation and gene expression patterns in the tomato pericarp.

Replication of the bSTAR sequence and open-source implementation.

The reproducibility of scientific reports is crucial to advancing human knowledge. This paper is a summary of our experience in replicating a balanced SSFP half-radial dual-echo imaging technique (bSTAR) using open-source frameworks as a response to the 2023 ISMRM "repeat it with me" Challenge. We replicated the bSTAR technique for thoracic imaging at 0.55T. The bSTAR pulse sequence is implemented in Pulseq, a vendor neutral open-source rapid sequence prototyping environment. Image reconstruction is performed with the open-source Berkeley Advanced Reconstruction Toolbox (BART). The replication of bSTAR, termed open-source bSTAR, is tested by replicating several figures from the published literature. Original bSTAR, using the pulse sequence and image reconstruction developed by the original authors, and open-source bSTAR, with pulse sequence and image reconstruction developed in this work, were performed in healthy volunteers. Both echo images obtained from open-source bSTAR contain no visible artifacts and show identical spatial resolution and image quality to those in the published literature. A direct head-to-head comparison between open-source bSTAR and original bSTAR on a healthy volunteer indicates that open-source bSTAR provides adequate SNR, spatial resolution, level of artifacts, and conspicuity of pulmonary vessels comparable to original bSTAR. We have successfully replicated bSTAR lung imaging at 0.55T using two open-source frameworks. Full replication of a research method solely relying on information on a research paper is unfortunately rare in research, but our success gives greater confidence that a research methodology can be indeed replicated as described.

Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks - RCR Report

This is the Replicated Computational Results (RCR) Report for our TOSEM paper “Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks”, where we propose a novel client-server architecture allowing to leverage the high accuracy of huge neural networks running on remote servers while reducing the economical and latency costs typically coming from using such models. As part of this RCR, we provide a replication package, which allows the full replication of all our results and is specifically designed to facilitate reuse.

Dependence matters: Statistical models to identify the drivers of tie formation in economic networks

Networks are ubiquitous in economic research on organizations, trade, and many other areas. However, while economic theory extensively considers networks, no general framework for their empirical modeling has yet emerged. We thus introduce two different statistical models for this purpose – the Exponential Random Graph Model (ERGM) and the Additive and Multiplicative Effects network model (AME). Both model classes can account for network interdependencies between observations, but differ in how they do so. The ERGM allows one to explicitly specify and test the influence of particular network structures, making it a natural choice if one is substantively interested in estimating endogenous network effects. In contrast, AME captures these effects by introducing actor-specific latent variables affecting their propensity to form ties. This makes the latter a good choice if the researcher is interested in capturing the effect of exogenous covariates on tie formation without having a specific theory on the endogenous dependence structures at play. After introducing the two model classes, we showcase them through real-world applications to networks stemming from international arms trade and foreign exchange activity. We further provide full replication materials to facilitate the adoption of these methods in empirical economic research.

Fundamental theorem of asset pricing with acceptable risk in markets with frictions

We study the range of prices at which a rational agent should contemplate transacting a financial contract outside a given market. Trading is subject to nonproportional transaction costs and portfolio constraints, and full replication by way of market instruments is not always possible. Rationality is defined in terms of consistency with market prices and acceptable risk thresholds. We obtain a direct and a dual description of market-consistent prices with acceptable risk. The dual characterisation requires an appropriate extension of the classical fundamental theorem of asset pricing where the role of arbitrage opportunities is played by good deals, i.e., costless investment opportunities with acceptable risk–reward tradeoff. In particular, we highlight the importance of scalable good deals, i.e., investment opportunities that are good deals regardless of their volume.

Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach

The design and management of smart cities and the IoT is a multidimensional problem. One of those dimensions is cloud and edge computing management. Due to the complexity of the problem, resource sharing is one of the vital and major components that when enhanced, the performance of the whole system is enhanced. Research in data access and storage in multi-clouds and edge servers can broadly be classified to data centers and computational centers. The main aim of data centers is to provide services for accessing, sharing and modifying large databases. On the other hand, the aim of computational centers is to provide services for sharing resources. Present and future distributed applications need to deal with very large multi-petabyte datasets and increasing numbers of associated users and resources. The emergence of IoT-based, multi-cloud systems as a potential solution for large computational and data management problems has initiated significant research activity in the area. Due to the considerable increase in data production and data sharing within scientific communities, the need for improvements in data access and data availability cannot be overlooked. It can be argued that the current approaches of large dataset management do not solve all problems associated with big data and large datasets. The heterogeneity and veracity of big data require careful management. One of the issues for managing big data in a multi-cloud system is the scalability and expendability of the system under consideration. Data replication ensures server load balancing, data availability and improved data access time. The proposed model minimises the cost of data services through minimising a cost function that takes storage cost, host access cost and communication cost into consideration. The relative weights between different components is learned through history and it is different from a cloud to another. The model ensures that data are replicated in a way that increases availability while at the same time decreasing the overall cost of data storage and access time. Using the proposed model avoids the overheads of the traditional full replication techniques. The proposed model is mathematically proven to be sound and valid.

A conserved Y-shaped RNA structure in the 3’UTR of chikungunya virus genome as a host-specialized element that modulates viral replication and evolution

RNA viral genomes compact information into functional RNA structures. Here, using chikungunya virus as a model, we investigated the structural requirements of conserved RNA elements in the 3' untranslated region (3'UTR) for viral replication in mosquito and mammalian cells. Using structural predictions and co-variation analysis, we identified a highly stable and conserved Y-shaped structure (SLY) at the end of the 3'UTR that is duplicated in the Asian lineage. Functional studies with mutant viruses showed that the SLY has host-specific functions during viral replication and evolution. The SLY positively modulates viral replication in mosquito cells but has the opposite effect in mammalian cells. Additional structural/functional analyses showed that maintaining the Y-shaped fold and specific nucleotides in the loop are critical for full SLY functionality and optimal viral replication in mosquito cells. Experimental adaptation of viruses with duplicated SLYs to mammalian cells resulted in the generation of heterogeneous viral populations comprising variants with diverse 3'UTRs, contrasting with the homogeneous populations from viruses without SLY copies. Altogether, our findings constitute the first evidence of an RNA secondary structure in the 3'UTR of chikungunya virus genome that plays host-dependent functions.

Visual resemblance and interaction history jointly constrain pictorial meaning

How do drawings—ranging from detailed illustrations to schematic diagrams—reliably convey meaning? Do viewers understand drawings based on how strongly they resemble an entity (i.e., as images) or based on socially mediated conventions (i.e., as symbols)? Here we evaluate a cognitive account of pictorial meaning in which visual and social information jointly support visual communication. Pairs of participants used drawings to repeatedly communicate the identity of a target object among multiple distractor objects. We manipulated social cues across three experiments and a full replication, finding that participants developed object-specific and interaction-specific strategies for communicating more efficiently over time, beyond what task practice or a resemblance-based account alone could explain. Leveraging model-based image analyses and crowdsourced annotations, we further determined that drawings did not drift toward “arbitrariness,” as predicted by a pure convention-based account, but preserved visually diagnostic features. Taken together, these findings advance psychological theories of how successful graphical conventions emerge.

Predicting reliability through structured expert elicitation with the repliCATS (Collaborative Assessments for Trustworthy Science) process.

As replications of individual studies are resource intensive, techniques for predicting the replicability are required. We introduce the repliCATS (Collaborative Assessments for Trustworthy Science) process, a new method for eliciting expert predictions about the replicability of research. This process is a structured expert elicitation approach based on a modified Delphi technique applied to the evaluation of research claims in social and behavioural sciences. The utility of processes to predict replicability is their capacity to test scientific claims without the costs of full replication. Experimental data supports the validity of this process, with a validation study producing a classification accuracy of 84% and an Area Under the Curve of 0.94, meeting or exceeding the accuracy of other techniques used to predict replicability. The repliCATS process provides other benefits. It is highly scalable, able to be deployed for both rapid assessment of small numbers of claims, and assessment of high volumes of claims over an extended period through an online elicitation platform, having been used to assess 3000 research claims over an 18 month period. It is available to be implemented in a range of ways and we describe one such implementation. An important advantage of the repliCATS process is that it collects qualitative data that has the potential to provide insight in understanding the limits of generalizability of scientific claims. The primary limitation of the repliCATS process is its reliance on human-derived predictions with consequent costs in terms of participant fatigue although careful design can minimise these costs. The repliCATS process has potential applications in alternative peer review and in the allocation of effort for replication studies.

The Geography of Mathematical (Dis)Advantage: An Application of Multilevel Simultaneous Autoregressive (MSAR) Models to Public Data in Education Research

Research has shown that mathematical proficiency gaps are related to students’ and schools’ indicators of poverty, with fewer studies on neighborhood effects on achievement gaps. Although this literature has accounted for students’ nesting within schools, so far, methodological constraints have not allowed researchers to formally account for multilevel and spatial effects. I contribute to this discussion by simultaneously considering test-takers’ own socioeconomic standing and the impact of their nesting schools and neighborhood structures. Multilevel simultaneous autoregressive (MSAR) models and population-level data of 2.09 million test-takers, whose standardized performances were measured at Grades 3–8 in New York State, revealed the presence of geography of mathematical (dis)advantage. Because mathematical performance is spatially dependent across schools and neighborhoods, moving forward, applied researchers should rely on MSAR to account for sources of spatially driven bias that cannot be handled with multilevel models alone. Full replication code and data are provided at https://cutt.ly/N4zRstL .

Adaptive Storage Optimization Scheme for Blockchain-IIoT Applications Using Deep Reinforcement Learning

Blockchain-IIoT integration into industrial processes promises greater security, transparency, and traceability. However, this advancement faces significant storage and scalability issues with existing blockchain technologies. Each peer in the blockchain network maintains a full copy of the ledger which is updated through consensus. This full replication approach places a burden on the storage space of the peers and would quickly outstrip the storage capacity of resource-constrained IIoT devices. Various solutions utilizing compression, summarization or different storage schemes have been proposed in literature. The use of cloud resources for blockchain storage has been extensively studied in recent years. Nonetheless, block selection remains a substantial challenge associated with cloud resources and blockchain integration. This paper proposes a deep reinforcement learning (DRL) approach as an alternative to solving the block selection problem, which involves identifying the blocks to be transferred to the cloud. We propose a DRL approach to solve our problem by converting the multi-objective optimization of block selection into a Markov decision process (MDP). We design a simulated blockchain environment for training and testing our proposed DRL approach. We utilize two DRL algorithms, Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO) to solve the block selection problem and analyze their performance gains. PPO and A2C achieve 47.8% and 42.9% storage reduction on the blockchain peer compared to the full replication approach of conventional blockchain systems. The slowest DRL algorithm, A2C, achieves a run-time 7.2 times shorter than the benchmark evolutionary algorithms used in earlier works, which validates the gains introduced by the DRL algorithms. The simulation results further show that our DRL algorithms provide an adaptive and dynamic solution to the time-sensitive blockchain-IIoT environment.

Cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience.

Expectations shape our experience of music. However, the internal model upon which listeners form melodic expectations is still debated. Do expectations stem from Gestalt-like principles or statistical learning? If the latter, does long-term experience play an important role, or are short-term regularities sufficient? And finally, what length of context informs contextual expectations? To answer these questions, we presented human listeners with diverse naturalistic compositions from Western classical music, while recording neural activity using MEG. We quantified note-level melodic surprise and uncertainty using various computational models of music, including a state-of-the-art transformer neural network. A time-resolved regression analysis revealed that neural activity over fronto-temporal sensors tracked melodic surprise particularly around 200ms and 300-500ms after note onset. This neural surprise response was dissociated from sensory-acoustic and adaptation effects. Neural surprise was best predicted by computational models that incorporated long-term statistical learning-rather than by simple, Gestalt-like principles. Yet, intriguingly, the surprise reflected primarily short-range musical contexts of less than ten notes. We present a full replication of our novel MEG results in an openly available EEG dataset. Together, these results elucidate the internal model that shapes melodic predictions during naturalistic music listening.

A relative answer to the growth–saving puzzle

AbstractProlonged rapid growth, that is, the ‘catching‐up’ process through which countries close the gap to the development frontier, is known to be accompanied by high rates of household saving. This phenomenon is central in explaining the direction of international capital flows and trade imbalances in the past several decades, yet it is very much in contradiction to prevailing macroeconomic theory. This paper finds that a standard life‐cycle model, even when integrated with uncertainty about future growth and with credit constraints, is completely unable to replicate the relations between growth and saving, represented by three stylized facts gleaned from the empirical literature. However, adding utility from relative consumption to the model allows for the full replication of these relations.

XS004 Dasatinib (XS004) Improves Variability and Bioavailability in Humans Using Amorphous Solid Dispersion Formulation of Dasatinib with Potential Implications for Its Clinical Use

Tyrosine kinase inhibitors (TKIs) have significantly improved the prognosis of patients with chronic myeloid leukemia (CML). Dasatinib, one of the most utilized TKIs, is formulated as a crystalline dasatinib monohydrate. This results in a low and pH-dependent solubility with highly variable intestinal absorption and plasma pharmacokinetics (PK). Poor dissolution of an anti-cancer drug is generally expected to result in low and highly variable bioavailability and plasma exposure, consequently jeopardizing an optimal and anticipated therapeutic response. Applying bioavailability enhancement techniques, such as amorphous solid dispersion (ASD) formulations, will reduce variability in plasma PK as solubility and intestinal absorption improves, and most importantly increase patient compliance, safety, and efficacy. XS004 dasatinib (XS004) (Xspray Pharma, Solna, Sweden) is a novel immediate release and ASD formulation of dasatinib produced with the HyNap™ technique. XS004 is designed to provide an increased solubility of dasatinib in acidic to neutral pH conditions, mitigating gastric pH dependency and leading to reduced variability and increased absorption and bioavailability of dasatinib. Here we report the results of a clinical study comparing the PK of XS004 and the original dasatinib product (reference) (Sprycel®, Bristol-Myers Squibb Company, NJ, USA), and results of a comparative in vitro solubility study. Plasma PK parameters were compared following single doses of XS004 (100 mg dasatinib ASD) and the reference product (140 mg dasatinib non-ASD) in 107 healthy volunteers at fasting conditions in a randomized, single dose, laboratory-blinded, 2 treatments, 4 period, 2 sequence, full replicate crossover, bioavailability study. In the in vitro study, dasatinib ASD and dasatinib monohydrate solubility was determined in the gastrointestinal (GI) relevance pH-interval of 1.2 to 8.0 at 37°C. The plasma exposure of XS004 (100 mg dasatinib ASD) was equivalent for all PK parameters with the reference product (140 mg dasatinib non-ASD) with least square geometric mean Cmax, AUC0-24h and AUC0-inf ratios of 100.8 (CI 90%, 90.8-112.0%), 94.2 (CI 90%, 87.5-101.4) and 92.2 (CI 90%, 86.2-98.6), respectively (Table 1). The intra-subject variability (coefficient of variation (CV%)) for AUC0-24 and AUC0-inf for the reference was approximately 3-fold and 2.5-fold higher than for XS004 (Table 2). For Cmax, the intra-subject variability was approximately 2-fold greater with the reference compared to XS004 (Table 2). The inter-individual variability was generally higher in all plasma PK parameters for the reference formulation (range: 38-128%) when compared to XS004 (range: 20-48%). There was considerably greater inter-subject variability (CV%) in Cmax, AUC0-24 and AUC0-inf after single dose administration of the reference product, when compared with XS004 (Table 2). The inter-individual variability was 4.5-fold, 4.3-fold and 4.8-fold greater for the reference product when compared to the test formulation for the three PK parameters. The in vitro solubility study provided higher apparent solubility of dasatinib ASD compared to the original crystalline dasatinib, explaining the improved in vivo GI absorption for the new preparation. Dasatinib ASD was unaffected and kept in solution over at least 2 hours in the pH range 1.2 to 5.0 and the solubility was at least 10-fold higher for dasatinib ASD compared to the dasatinib monohydrate at pH-values in the range 5.0 to 8.0. In conclusion, XS004 displayed a more robust and consistent GI-absorption and bioavailability, with significantly reduced intra- and inter-individual variability in plasma dasatinib concentrations, compared to the original non-ASD formulated dasatinib. The in vitro data showed that the solubility of dasatinib ASD was substantially less pH-dependent at GI-relevant pH values of 2.0 to 5.0. The results suggest that XS004, an ASD formulation of dasatinib, with improved biopharmaceutical features, reduces the risk of both low exposure and high exposure outliers, with lower risk of exposure outside the therapeutic window. This would likely diminish the risk of adverse events and optimize the therapeutic efficacy of dasatinib in patients with CML. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Long non-coding RNA PCAT19 safeguards DNA in quiescent endothelial cells by preventing uncontrolled phosphorylation of RPA2.

In healthy vessels, endothelial cells maintain a stable, differentiated, and growth-arrested phenotype for years. Upon injury, a rapid phenotypic switch facilitates proliferation to restore tissue perfusion. Here we report the identification of the endothelial cell-enriched long non-coding RNA (lncRNA) PCAT19, which contributes to the proliferative switch and acts as a safeguard for the endothelial genome. PCAT19 is enriched in confluent, quiescent endothelial cells and binds to the full replication protein A (RPA) complex in a DNA damage- and cell-cycle-related manner. Our results suggest that PCAT19 limits the phosphorylation of RPA2, primarily on the serine 33 (S33) residue, and thereby facilitates an appropriate DNA damage response while slowing cell cycle progression. Reduction in PCAT19 levels in response to either loss of cell contacts or knockdown promotes endothelial proliferation and angiogenesis. Collectively, PCAT19 acts as a dynamic guardian of the endothelial genome and facilitates rapid switching from quiescence to proliferation.

Abstract P2058: LncRNA PCAT19 Limits Angiogenesis And Safeguards Quiescent Endothelial DNA By Preventing Uncontrolled Phosphorylation Of Replication Protein A2

Background: Long noncoding RNAs (lncRNA) provide an additional layer of molecular control in pathways crucial for vascular development and cardiovascular disease. We set out to identify novel endothelial lncRNAs that could be exploited to treat vascular disease. Methods and Results: We identified the endothelial cell-specific long noncoding RNA (lncRNA) PCAT19, which contributes to the endothelial proliferation-quiescence switch and acts as a safeguard for the endothelial genome during quiescence. PCAT19 was upregulated with endothelial cell confluence and quiescence. Knockdown of PCAT19 promoted endothelial proliferation and angiogenic sprouting. Conversely, PCAT19 overexpression reduced endothelial proliferative capacity and angiogenic sprouting. Using a cardiac organoid model system, we observed a markedly denser vascular network after the removal of PCAT19. RNA-sequencing after PCAT19 knockdown revealed multiple differentially regulated cell cycle genes. An antisense-oligonucleotide pulldown of PCAT19 followed by mass spectrometry identified multiple DNA damage response and cell cycle-related proteins as PCAT19 interaction partners; among them was the full DNA replication protein A complex. PCAT19 knockdown was found to sensitise DNA to damage, as measured by heightened p53 and γH2AX levels as well as a positive TUNEL signal and longer comet tail olive moments. Mechanistically, PCAT19 limited the phosphorylation of RPA2 on the serine 33 (S33) residue by inhibiting RPA2 interaction with the ATR kinase. Conclusions: In healthy vessels, endothelial cells maintain a stable, differentiated and growth-arrested phenotype. Upon injury, a rapid phenotypic switch facilitates proliferation to restore tissue perfusion. PCAT19 was identified as a highly enriched endothelial lncRNA that acts as a dynamic guardian of the endothelial genome, promotes cellular longevity and facilitates rapid switching to proliferation. Therapeutic targeting of PCAT19 could potentially alter the course of vascular disease development and progression by modulating endothelial growth and angiogenic sprouting.

Heuristic methods for stock selection and allocation in an index tracking problem

Index tracking is one of the most popular passive strategy in portfolio management. However, due to some practical constrains, a full replication is difficult to obtain. Many mathematical models have failed to generate good results for partial replicated portfolios, but in the last years a data driven approach began to take shape. This paper proposes three heuristic methods for both selection and allocation of the most informative stocks in an index tracking problem, respectively XGBoost, Random Forest and LASSO with stability selection. Among those, latest deep autoencoders have also been tested. All selected algorithms have outperformed the benchmarks in terms of tracking error. The empirical study has been conducted on one of the biggest financial indices in terms of number of components in three different countries, respectively Russell 1000 for the USA, FTSE 350 for the UK, and Nikkei 225 for Japan.

Sparse Index Tracking Portfolio with Sector Neutrality

As a popular passive investment strategy, a sparse index tracking strategy has advantages over a full index replication strategy because of higher liquidity and lower transaction costs. Sparsity and nonnegativity constraints are usually assumed in the construction of portfolios in sparse index tracking. However, none of the existing studies considered sector risk exposure of the portfolios that prices of stocks in one sector may fall at the same time due to sudden changes in policy or unexpected events that may affect the whole sector. Therefore, sector neutrality appeals to be critical when building a sparse index tracking portfolio if not using full replication. The statistical approach to sparse index tracking is a constrained variable selection problem. However, the constrained variable selection procedure using Lasso fails to produce a sparse portfolio under sector neutrality constraints. In this paper, we propose a high-dimensional constrained variable selection method using TLP for building index tracking portfolios under sparsity, sector neutrality and nonnegativity constraints. Selection consistency is established for the proposed method, and the asymptotic distribution is obtained for the sparse portfolio weights estimator. We also develop an efficient iteration algorithm for the weight estimation. We examine the performance of the proposed methodology through simulations and an application to the CSI 300 index of China. The results demonstrate the validity and advantages of our methodology.

Effects of current forest practices on organic matter dynamics in headwater streams at the Trask river watershed, Oregon

Forest management in riparian ecosystems can significantly alter biotic and abiotic processes in streams. Forest harvest without the retention of buffers along small streams can affect organic matter dynamics, and drive instream characteristics like trophic food webs. To investigate the extent to which differing levels of tree retention adjacent to the channel mitigated changes in organic matter dynamics, we examined coarse particulate organic matter delivery, transport, and retention, as well as canopy cover, along small streams with four harvest treatments, both before and after harvest. Our research was part of a larger effort of the Trask River Watershed Study (TRWS) in the northern Oregon Coast Range, which examined the long-term physical, chemical, and biological effects of forest management on aquatic ecosystems at multiple spatial scales. Canopy cover at reference and treatment sites prior to harvest was approximately 91%, but following harvest, mean canopy cover at treatment sites decreased with increasing harvest intensity: 3% decrease after clearcut with buffer, 14% after clearcut with leave trees, and 57% after complete clearcut. Organic matter delivery (i.e., the amount of leaf, needle, wood, reproductive, or miscellaneous litter material) to streams was dominated by leaves and varied seasonally but decreased overall after harvest with complete clearcut. Organic matter transport (i.e., the amount of material netted during a sampling period at the end of each stream reach) values fell within the observed range of reference values at all harvested sites. Organic matter retention (i.e., the amount of material sampled in plots systematically placed along the stream reach) post-harvest was dominated by woody material and was consistent with pre-harvest measurements and reference sites for all the harvested streams. The number of log jams, and the total weight of log jams, increased for all treatments after harvest, although the most dramatic increase in log jam weight (140% increase) was observed for the complete clearcut and clearcut with leave tree sites. On these five sites, our results indicated that while complete clearcutting reduced organic matter delivery, retention of even a small number of trees along these streams appeared to support litter delivery rates after harvesting comparable to before harvest. We acknowledge the limitations of results from single treatment sites; however, our results were generally consistent with studies that had full replication. As part of the Trask River Watershed study, our research adds to the comprehensive effort to understand the ecological significance of riparian buffers and forest harvest in headwater streams.

Methods of engagement and levels of involvement of stakeholders in the management of work-related musculoskeletal disorders: A systematic scoping review

AimStakeholders have an important role in both prevention and management of work related musculoskeletal disorders (WRMSDs). However, the scope of practice is unclear regarding the methods and levels of stakeholder engagement (SE) in management of WRMSDs. The aim of this review is to map the literature regarding various methods and levels of SE and how it is best implemented when managing WRMSDs.MethodsA systematic literature search was conducted in several major databases using key search phrases. Various methods and levels of SE in management of WRMSDs were extracted. Comprehensiveness and repeatability of the SE methods were evaluated. The final results on SE in WRMSDs were presented as per the Guidance for Reporting Involvement of Patients and Public (GRIPP-2) guidelines.ResultsA total of 32 studies reported various methods and levels of SE in management of WRMSDs. Two distinct areas, namely prevention (n=14) and primary management of WRMSDs (n=18) were reported. Overall, 17 unique methods of SE in management of WRMSDs were presented. A total of 30 studies gave sufficient information for either full (n=9) or partial (=21) replication of methods of SE. Only a few studies (n=6) involved higher levels of SE in management of WRMSDs.ConclusionA clear mapping of SE in management of WRMSDs was presented for effective involvement of stakeholders in WRMSDs polices and care plan. SE approaches in WRMSDs management should move forward from a tokenistic approach of engaging stakeholders and it should involve them in a more lead roles in the future.