Common Approaches Research Articles

A Statistical Framework for Analysis of Trial-Level Temporal Dynamics in Fiber Photometry Experiments.

Fiber photometry has become a popular technique to measure neural activity in vivo, but common analysis strategies can reduce detection of effects because they condense within-trial signals into summary measures, and discard trial-level information by averaging across-trials. We propose a novel photometry statistical framework based on functional linear mixed modeling, which enables hypothesis testing of variable effects at every trial time-point, and uses trial-level signals without averaging. This makes it possible to compare the timing and magnitude of signals across conditions while accounting for between-animal differences. Our framework produces a series of plots that illustrate covariate effect estimates and statistical significance at each trial time-point. By exploiting signal autocorrelation, our methodology yields joint 95% confidence intervals that account for inspecting effects across the entire trial and improve the detection of event-related signal changes over common multiple comparisons correction strategies. We reanalyze data from a recent study proposing a theory for the role of mesolimbic dopamine in reward learning, and show the capability of our framework to reveal significant effects obscured by standard analysis approaches. For example, our method identifies two dopamine components with distinct temporal dynamics in response to reward delivery. In simulation experiments, our methodology yields improved statistical power over common analysis approaches. Finally, we provide an open-source package and analysis guide for applying our framework.

Investigating the Effect of Carbon Nanomaterials Reinforcing Poly(ε-Caprolactone) Printed Scaffolds for Bone Repair Applications.

Scaffolds, three-dimensional (3D) substrates providing appropriate mechanical support and biological environments for new tissue formation, are the most common approaches in tissue engineering. To improve scaffold properties such as mechanical properties, surface characteristics, biocompatibility and biodegradability, different types of fillers have been used reinforcing biocompatible and biodegradable polymers. This paper investigates and compares the mechanical and biological behaviors of 3D printed poly(ε-caprolactone) scaffolds reinforced with graphene (G) and graphene oxide (GO) at different concentrations. Results show that contrary to G which improves mechanical properties and enhances cell attachment and proliferation, GO seems to show some cytotoxicity, particular at high contents.

To Stick or Not to Stick? Studying the Impact of Offset Recovery Techniques During Mid-Air Interactions.

During mid-air interactions, common approaches (such as the god-object method) typically rely on visually constraining the user's avatar to avoid visual interpenetrations with the virtual environment in the absence of kinesthetic feedback. This paper explores two methods which influence how the position mismatch (positional offset) between users' real and virtual hands is recovered when releasing the contact with virtual objects. The first method (sticky) constrains the user's virtual hand until the mismatch is recovered, while the second method (unsticky) employs an adaptive offset recovery method. In the first study, we explored the effect of positional offset and of motion alteration on users' behavioral adjustments and users' perception. In a second study, we evaluated variations in the sense of embodiment and the preference between the two control laws. Overall, both methods presented similar results in terms of performance and accuracy, yet, positional offsets strongly impacted motion profiles and users' performance. Both methods also resulted in comparable levels of embodiment. Finally, participants usually expressed strong preferences toward one of the two methods, but these choices were individual-specific and did not appear to be correlated solely with characteristics external to the individuals. Taken together, these results highlight the relevance of exploring the customization of motion control algorithms for avatars.

Dark Energy Survey Deep Field photometric redshift performance and training incompleteness assessment

Context. The determination of accurate photometric redshifts (photo-zs) in large imaging galaxy surveys is key for cosmological studies. One of the most common approaches is machine learning techniques. These methods require a spectroscopic or reference sample to train the algorithms. Attention has to be paid to the quality and properties of these samples since they are key factors in the estimation of reliable photo-zs. Aims. The goal of this work is to calculate the photo-zs for the Year 3 (Y3) Dark Energy Survey (DES) Deep Fields catalogue using the Directional Neighborhood Fitting (DNF) machine learning algorithm. Moreover, we want to develop techniques to assess the incompleteness of the training sample and metrics to study how incompleteness affects the quality of photometric redshifts. Finally, we are interested in comparing the performance obtained by DNF on the Y3 DES Deep Fields catalogue with that of the EAzY template fitting approach. Methods. We emulated – at a brighter magnitude – the training incompleteness with a spectroscopic sample whose redshifts are known to have a measurable view of the problem. We used a principal component analysis to graphically assess the incompleteness and relate it with the performance parameters provided by DNF. Finally, we applied the results on the incompleteness to the photo-z computation on the Y3 DES Deep Fields with DNF and estimated its performance. Results. The photo-zs of the galaxies in the DES deep fields were computed with the DNF algorithm and added to the Y3 DES Deep Fields catalogue. We have developed some techniques to evaluate the performance in the absence of “true” redshift and to assess the completeness. We have studied the tradeoff in the training sample between the highest spectroscopic redshift quality versus completeness. We found some advantages in relaxing the highest-quality spectroscopic redshift requirements at fainter magnitudes in favour of completeness. The results achieved by DNF on the Y3 Deep Fields are competitive with the ones provided by EAzY, showing notable stability at high redshifts. It should be noted that the good results obtained by DNF in the estimation of photo-zs in deep field catalogues make DNF suitable for the future Legacy Survey of Space and Time (LSST) and Euclid data, which will have similar depths to the Y3 DES Deep Fields.

Quantitative comparison of three thyroidectomy approaches on neck muscles, voice, and swallowing functions

ObjectiveThis study compares endoscopic thyroidectomy by gasless unilateral axillary approach (ETGUA) and sternocleidomastoid leading-edge approach (SLEA) with conventional open thyroidectomy (COT) in hemithyroidectomy. The main focus is on the protection of neck muscles (sternocleidomastoid, omohyoid, sternothyroid) and the postoperative function of voice and swallowing yielded through these common approaches. MethodsA total of 302 patients who underwent hemithyroidectomy were enrolled and divided into three groups: ETGUA (n = 101), SLEA (n = 100), and COT (n = 101). Ultrasound was used to measure the thickness of bilateral neck muscles, including the sternocleidomastoid, omohyoid, and sternothyroid. The changes in thickness on the surgical side compared to the non-surgical side. Analyzed factors included muscle thickness changes, Swallowing Impairment Score (SIS), Voice Handicap Index (VHI), Scar Cosmesis Assessment and Rating (SCAR), Neck Incision Index (NII), surgery duration, drainage volume, hospitalization, and number of lymph nodes. ResultsThe clinical characteristics among the three groups were consistent except for differences in sex, age, and BMI. Metrics such as sternocleidomastoid muscle, NII, hypocalcemia, postoperative PTH, transient hoarseness, and number of lymph nodes showed no significant differences among the three groups. However, significant differences were found in the duration of surgery, drainage volume, hospitalization period omohyoid muscle, Sternohyoid muscle, VHI, SIS, and SCAR (all p < 0.001). ConclusionIn comparison to COT, ETGUA and SLEA demonstrate superiority in protecting neck muscles and preserving voice and swallowing function without compromising surgical safety or radicality.

Covariate adjusted nonparametric methods under propensity analysis

Propensity score is one of the most commonly used score functions in adjusting for covariates effect in statistical inference. It is important to understand the impact with propensity score in case some of the prespecified covariates are severely imbalanced. In this article, we performed simulation evaluation the empirical type 1 error and empirical power under scenario of imbalanced covariates in several nonparametric two sample tests with propensity score or with other covariate adjustments. Our results suggest common propensity score approaches might have type 1 error inflation at scenarios with severe imbalanced covariates or model is mis-specified.

Bioactive Moldable Click Chemistry Polymer Cement with Nano-Hydroxyapatite and Growth Factor-Enhanced Posterolateral Spinal Fusion in a Rabbit Model.

Spinal injuries or diseases necessitate effective fusion solutions, and common clinical approaches involve autografts, allografts, and various bone matrix products, each with limitations. To address these challenges, we developed an innovative moldable click chemistry polymer cement that can be shaped by hand and self-cross-linked in situ for spinal fusion. This self-cross-linking cement, enabled by the bioorthogonal click reaction, excludes the need for toxic initiators or external energy sources. The bioactivity of the cement was promoted by incorporating nanohydroxyapatite and microspheres loaded with recombinant human bone morphogenetic protein-2 and vascular endothelial growth factor, fostering vascular induction and osteointegration. The release kinetics of growth factors, mechanical properties of the cement, and the ability of the scaffold to support in vitro cell proliferation and differentiation were evaluated. In a rabbit posterolateral spinal fusion model, the moldable cement exhibited remarkable induction of bone regeneration and effective bridging of spine vertebral bodies. This bioactive moldable click polymer cement therefore presents a promising biomaterial for spinal fusion augmentation, offering advantages in safety, ease of application, and enhanced bone regrowth.

A Critical Discussion of Alert Evaluations in the Context of Continuous Glucose Monitoring System Performance.

Many continuous glucose monitoring (CGM) systems provide functionality which alerts users of potentially unwanted glycemic conditions. These alerts can include glucose threshold alerts to call the user's attention to hypoglycemia or hyperglycemia, predictive alerts warning about impeding hypoglycemia or hyperglycemia, and rate-of-change alerts. A recent review identified 129 articles about CGM performance studies, of which approximately 25% contained alert evaluations. In some studies, real alerts were assessed; however, most of these studies retrospectively determined the timing of CGM alerts because not all CGM systems record alerts which necessitates manual documentation. In contrast to assessment of real alerts, retrospective determination allows assessment of a variety of alert settings for all three types of glycemic condition alerts. Based on the literature and the Clinical and Laboratory Standards Institute's POCT05 guideline, two common approaches to threshold alert evaluation were identified, one value-based and one episode-based approach. In this review, a critical discussion of the two approaches, including a post hoc analysis of clinical study data, indicates that the episode-based approach should be preferred over the value-based approach. For predictive alerts, fewer results were found in the literature, and retrospective determination of CGM alert timing is complicated by the prediction algorithms being proprietary information. Rate-of-change alert evaluations were not reported in the identified literature, and POCT05 does not contain recommendations for assessment. A possible approach is discussed including post hoc analysis of clinical study data. To conclude, CGM systems should record alerts, and the episode-based approach to alert evaluation should be preferred.

Do machine learning methods lead to similar individualized treatment rules? A comparison study on real data.

Identifying patients who benefit from a treatment is a key aspect of personalized medicine, which allows the development of individualized treatment rules (ITRs). Many machine learning methods have been proposed to create such rules. However, to what extent the methods lead to similar ITRs, that is, recommending the same treatment for the same individuals is unclear. In this work, we compared 22 of the most common approaches in two randomized control trials. Two classes of methods can be distinguished. The first class of methods relies on predicting individualized treatment effects from which an ITR is derived by recommending the treatment evaluated to the individuals with a predicted benefit. In the second class, methods directly estimate the ITR without estimating individualized treatment effects. For each trial, the performance of ITRs was assessed by various metrics, and the pairwise agreement between all ITRs was also calculated. Results showed that the ITRs obtained via the different methods generally had considerable disagreements regarding the patients to be treated. A better concordance was found among akin methods. Overall, when evaluating the performance of ITRs in a validation sample, all methods produced ITRs with limited performance, suggesting a high potential for optimism. For non-parametric methods, this optimism was likely due to overfitting. The different methods do not lead to similar ITRs and are therefore not interchangeable. The choice of the method strongly influences for which patients a certain treatment is recommended, drawing some concerns about their practical use.

Directional ΔG Neural Network (DrΔG-Net): A Modular Neural Network Approach to Binding Free Energy Prediction.

The protein-ligand binding free energy is a central quantity in structure-based computational drug discovery efforts. Although popular alchemical methods provide sound statistical means of computing the binding free energy of a large breadth of systems, they are generally too costly to be applied at the same frequency as end point or ligand-based methods. By contrast, these data-driven approaches are typically fast enough to address thousands of systems but with reduced transferability to unseen systems. We introduce DrΔG-Net (or simply Dragnet), an equivariant graph neural network that can blend ligand-based and protein-ligand data-driven approaches. It is based on a 3D fingerprint representation of the ligand alone and in complex with the protein target. Dragnet is a global scoring function to predict the binding affinity of arbitrary protein-ligand complexes, but can be easily tuned via transfer learning to specific systems or end points, performing similarly to common 2D ligand-based approaches in these tasks. Dragnet is evaluated on a total of 28 validation proteins with a set of congeneric ligands derived from the Binding DB and one custom set extracted from the ChEMBL Database. In general, a handful of experimental binding affinities are sufficient to optimize the scoring function for a particular protein and ligand scaffold. When not available, predictions from physics-based methods such as absolute free energy perturbation can be used for the transfer learning tuning of Dragnet. Furthermore, we use our data to illustrate the present limitations of data-driven modeling of binding free energy predictions.

Towards identification of postharvest fruit quality transcriptomic markers in Malus domestica.

Gene expression is highly impacted by the environment and can be reflective of past events that affected developmental processes. It is therefore expected that gene expression can serve as a signal of a current or future phenotypic traits. In this paper we identify sets of genes, which we call Prognostic Transcriptomic Biomarkers (PTBs), that can predict firmness in Malus domestica (apple) fruits. In apples, all individuals of a cultivar are clones, and differences in fruit quality are due to the environment. The apples transcriptome responds to these differences in environment, which makes PTBs an attractive predictor of future fruit quality. PTBs have the potential to enhance supply chain efficiency, reduce crop loss, and provide higher and more consistent quality for consumers. However, several questions must be addressed. In this paper we answer the question of which of two common modeling approaches, Random Forest or ElasticNet, outperforms the other. We answer if PTBs with few genes are efficient at predicting traits. This is important because we need few genes to perform qPCR, and we answer the question if qPCR is a cost-effective assay as input for PTBs modeled using high-throughput RNA-seq. To do this, we conducted a pilot study using fruit texture in the 'Gala' variety of apples across several postharvest storage regiments. Fruit texture in 'Gala' apples is highly controllable by post-harvest treatments and is therefore a good candidate to explore the use of PTBs. We find that the RandomForest model is more consistent than an ElasticNet model and is predictive of firmness (r2 = 0.78) with as few as 15 genes. We also show that qPCR is reasonably consistent with RNA-seq in a follow up experiment. Results are promising for PTBs, yet more work is needed to ensure that PTBs are robust across various environmental conditions and storage treatments.

Mechanical differences between three block jump approaches in NCAA DII college volleyball players

The purpose of the study was to examine the kinematics and kinetics differences between three common block approaches used in volleyball games: (1) shuffle block, (2) chicken wing block, and (3) swing block, from a fixed distance of 1.8 m. Ten female collegiate volleyball players from NCAA DII participated in the study. They performed, in a randomized order, a total of 18 blocks equally distributed among the three block types. Noraxon MyoResearch 3 software was used to analyze the block approaches. The statistical analysis was performed by running a Repeated Measurement ANOVA on Jamovi statistical software 2.3.24. The results showed that there was a significant main effect for time to take off, jump height, max knee flexion angles, peak power, relative peak power, net impulse, reactive strength index, max rate of force development, peak force, and relative peak force between the three types of blocks (p &lt; .05). While max valgus knee angles and max flexion hip angles did not show any effect (p &gt; .5). To cover a distance of 1.8m, it was clear that shuffle block was the weakest option for good block performances, while chicken wing and swing blocks were similar in many aspects. Both chicken wing and swing blocks can be used to elevate the block effectiveness of volleyball players compared to shuffle block.

Emergent teaching movements in leadership development: Group relations, case-in-point, and intentional emergence.

In a post-pandemic context, the need for leadership students to navigate ambiguous conditions and examine their automatic responses to authority has increased. Yet, common approaches to teaching leadership, such as group discussions and simulations, overlook the potential for using development spaces as living laboratories. This article explores three emergent-based pedagogies (group relations, case-in-point, and intentional emergence) that de-center the instructor, prioritize co-creation and emergence, and provide living laboratories for students to examine their assumptions and default behaviors related to leadership.

Evaluation of Surrogate Aerosol Experiments to Predict Spreading and Removal of Virus-Laden Aerosols

Estimating the infection risks of indoor environments comprises the assessment of the behavior of virus-laden aerosols, i.e., their spreading, mixing, removal by air purifiers, etc. A promising experimental approach is based on using non-hazardous surrogate aerosols of a similar size, e.g., salt particles, to mimic virus aerosol behavior. This manuscript addresses the issue of how a successful transfer of such experiments can be accomplished. Corresponding experiments in two very different environments, a large community hall and a seminar room, with the optional use of air purifiers in various constellations, are conducted. While high particle concentrations are advantageous in terms of avoiding the influence of background aerosol concentrations, it is shown that the appropriate consideration of aggregation and settling are vital to theoretically describe the experimentally determined course of particle concentrations. A corresponding model equation for a well-mixed situation is derived, and the required parameters are thoroughly determined in separate experiments independently. It is demonstrated that the clean air delivery rates (CADRs) of air purifiers determined with this approach may differ substantially from common approaches which do not explicitly take aggregation into account.

Human Activity Recognition System with Smartphones Using Machine Learning

Abstract: Human activity recognition (HAR) is a crucial task in the field of physical activity monitoring. It involves the identification of a person's activities and movements using sensors. The accuracy of HAR systems plays a significant role in enhancing physical health, preventing accidents and injuries, and improving security systems. One of the most common approaches to HAR is using smartphones as sensors. However, smartphones have limited processing power and battery life, which can impact the performance of HAR systems. To overcome these challenges, we can apply different machine learning models and compare their performances. We begin by selecting a standard dataset, the HAR dataset from the Machine Learning Repository. We then apply several Machine learning models, including Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN).To compare the performance of these models, we train and test each model on the HAR dataset. We also select the best set of parameters for each model using grid search. Our results show that the Support Vector Machine (SVM) performed the best (average accuracy 96.33%), significantly outperforming the other models. We can confirm the statistical significance of these results by employing statistical significance test methods.The SVM model demonstrated superior performance in recognizing human activities. This highlights the potential of machine learning models in revolutionizing the field of HAR. However, further research is needed to address the challenges of limited processing power and battery life in smartphones and to explore other potential applications of HAR systems.

Multi-omics Integrative Analysis for Incomplete Data Using Weighted p-Value Adjustment Approaches

AbstractThe advancements in high-throughput technologies provide exciting opportunities to obtain multi-omics data from the same individuals in a biomedical study, and joint analyses of data from multiple sources offer many benefits. However, the occurrence of missing values is an inevitable issue in multi-omics data because measurements such as mRNA gene expression levels often require invasive tissue sampling from patients. Common approaches for addressing missing measurements include analyses based on observations with complete data or multiple imputation methods. In this paper, we propose a novel integrative multi-omics analytical framework based on p-value weight adjustment in order to incorporate observations with incomplete data into the analysis. By splitting the data into a complete set with full information and an incomplete set with missing measurements, we introduce mechanisms to derive weights and weight-adjusted p-values from the two sets. Through simulation analyses, we demonstrate that the proposed framework achieves considerable statistical power gains compared to a complete case analysis or multiple imputation approaches. We illustrate the implementation of our proposed framework in a study of preterm infant birth weights by a joint analysis of DNA methylation, mRNA, and the phenotypic outcome. Supplementary materials accompanying this paper appear online.

Hospital doctor turnover and retention: a systematic review and new research pathway

PurposeSociety is critically dependent on an adequate supply of hospital doctors to ensure optimal health care. Voluntary turnover amongst hospital doctors is, however, an increasing problem for hospitals. The aim of this study was to systematically review the extant academic literature to obtain a comprehensive understanding of the current knowledge base on hospital doctor turnover and retention. In addition to this, we synthesise the most common methodological approaches used before then offering an agenda to guide future research.Design/methodology/approachAdopting the PRISMA methodology, we conducted a systematic literature search of four databases, namely CINAHL, MEDLINE, PsycINFO and Web of Science.FindingsWe identified 51 papers that empirically examined hospital doctor turnover and retention. Most of these papers were quantitative, cross-sectional studies focussed on meso-level predictors of doctor turnover.Research limitations/implicationsSelection criteria concentrated on doctors who worked in hospitals, which limited knowledge of one area of the healthcare environment. The review could disregard relevant articles, such as those that discuss the turnover and retention of doctors in other specialities, including general practitioners. Additionally, being limited to peer-reviewed published journals eliminates grey literature such as dissertations, reports and case studies, which may bring impactful results.Practical implicationsGlobally, hospital doctor turnover is a prevalent issue that is influenced by a variety of factors. However, a lack of focus on doctors who remain in their job hinders a comprehensive understanding of the issue. Conducting “stay interviews” with doctors could provide valuable insight into what motivates them to remain and what could be done to enhance their work conditions. In addition, hospital management and recruiters should consider aspects of job embeddedness that occur outside of the workplace, such as facilitating connections outside of work. By resolving these concerns, hospitals can retain physicians more effectively and enhance their overall retention efforts.Social implicationsFocussing on the reasons why employees remain with an organisation can have significant social repercussions. When organisations invest in gaining an understanding of what motivates their employees to stay in the job, they are better able to establish a positive work environment that likely to promote employee well-being and job satisfaction. This can result in enhanced job performance, increased productivity and higher employee retention rates, all of which are advantageous to the organisation and its employees.Originality/valueThe review concludes that there has been little consideration of the retention, as opposed to the turnover, of hospital doctors. We argue that more expansive methodological approaches would be useful, with more qualitative approaches likely to be particularly useful. We also call on future researchers to consider focussing further on why doctors remain in posts when so many are leaving.

Facilitating or compromising inclusion? Language policies at Swedish higher education institutions as workplaces

Abstract Research has suggested that Swedish higher education institutions’ (HEIs’) language policies may exclude some academic staff from work-related activities due to (dual) monolingual ideologies requiring one language at a time. This study, based on the analysis of twenty-one language policy texts, investigates HEIs’ policies using a lens of inclusion at workplaces with linguistic diversity, drawing on concepts from diversity management and language policy for democracy of inclusion. All documents examined began with statements of HEIs’ values relevant to the policies. Inclusion was seldom explicitly emphasized, although policies suggested ways to facilitate it. We argue that some of the approaches – namely, taking a top-down monolinguistic approach to language choice, requiring staff to be highly proficient in both Swedish and English, and offering unspecified language support – reinforce language-based in-groups and out-groups, likely compromising rather than facilitating inclusion. Another approach, emphasizing individuals’ rights to choose what language they use, facilitates inclusion only if support is provided for everyone’s understanding. Providing immediate language support and encouraging bottom-up, flexible language choice were less common approaches but seem particularly likely to facilitate inclusion. Our analysis suggests that policies prioritizing successful communication, not specific languages, facilitate inclusion and help employees develop job-related language and intercultural communicative competence.

XPORT ENTRAP: A droplet microfluidic platform for enhanced DNA transfer between microbial species

A significant hurdle for the widespread implementation and use of synthetic biology is the challenge of highly efficient introduction of DNA into microorganisms. This is especially a barrier for the utilization of non-model organisms and/or novel chassis species for a variety of applications, ranging from molecular biology to biotechnology and biomanufacturing applications. Common approaches to episomal and chromosomal gene editing, which employ techniques such as chemical competence and electroporation, are typically only amenable to a small subset of microbial species while leaving the vast majority of microorganisms in nature genetically inaccessible. To address this challenge, we have employed the previously described B. subtilis broad-host conjugation strain, XPORT, which was modularly designed for loading DNA cargo and conjugating such DNA into recalcitrant microbes. In this current work, we have leveraged and adapted the XPORT strain for use in a droplet microfluidic platform to enable increased efficiency of conjugation-based DNA transfer. The system named DNA ENTRAP (DNA ENhanced TRAnsfer Platform) utilizes cell-encapsulated water-in-oil emulsion droplets as pico-liter-volume bioreactors that allows controlled contacts between the donor and receiver cells within the emulsion bioreactor. This allowed enhanced XPORT-mediated genetic transfer over the current benchtop XPORT process, demonstrated using two different Bacillus subtilis strains (donor and receiver), as well as increased throughput (e.g., number of successfully conjugated cells) due to the automated assay steps inherent to microfluidic lab-on-a-chip systems. DNA ENTRAP paves the way for a streamlined automation of culturing and XPORT-mediated genetic transfer processes as well as future high-throughput cell engineering and screening applications.

Optimal ensemble construction for multistudy prediction with applications to mortality estimation.

It is increasingly common to encounter prediction tasks in the biomedical sciences for which multiple datasets are available for model training. Common approaches such as pooling datasets before model fitting can produce poor out-of-study prediction performance when datasets are heterogeneous. Theoretical and applied work has shown multistudy ensembling to be a viable alternative that leverages the variability across datasets in a manner that promotes model generalizability. Multistudy ensembling uses a two-stage stacking strategy which fits study-specific models and estimates ensemble weights separately. This approach ignores, however, the ensemble properties at the model-fitting stage, potentially resulting in performance losses. Motivated by challenges in the estimation of COVID-attributable mortality, we propose optimal ensemble construction, an approach to multistudy stacking whereby we jointly estimate ensemble weights and parameters associated with study-specific models. We prove that limiting cases of our approach yield existing methods such as multistudy stacking and pooling datasets before model fitting. We propose an efficient block coordinate descent algorithm to optimize the loss function. We use our method to perform multicountry COVID-19 baseline mortality prediction. We show that when little data is available for a country before the onset of the pandemic, leveraging data from other countries can substantially improve prediction accuracy. We further compare and characterize the method's performance in data-driven simulations and other numerical experiments. Our method remains competitive with or outperforms multistudy stacking and other earlier methods in the COVID-19 data application and in a range of simulation settings.