Node-personalized multi-graph convolutional networks for recommendation

Graph neural networks have revealed powerful potential in ranking recommendation. Existing methods based on bipartite graphs for ranking recommendation mainly focus on homogeneous graphs and usually treat user and item nodes as the same kind of nodes, however, the user-item bipartite graph is always heterogeneous. Additionally, various types of nodes have varying effects on recommendations, and a good node representation can be learned by successfully differentiating the same type of nodes. In this paper, we develop a node-personalized multi-graph convolutional network (NP-MGCN) for ranking recommendation. It consists of a node importance awareness block, a graph construction module, and a node information propagation and aggregation framework. Specifically, a node importance awareness block is proposed to encode nodes using node degree information to highlight the differences between nodes. Subsequently, the Jaccard similarity and co-occurrence matrix fusion graph construction module is devised to acquire user-user and item-item graphs, enriching correlation information between users and between items. Finally, a composite hop node information propagation and aggregation framework, including single-hop and double-hop branches, is designed. The high-order connectivity is used to aggregate heterogeneous information for the single-hop branch, while the multi-hop dependency is utilized to aggregate homogeneous information for the double-hop branch. It makes user and item node embedding more discriminative and integrates the different nodes' heterogeneity into the model. Experiments on several datasets manifest that NP-MGCN achieves outstanding recommendation performance than existing methods.

Simplified Spectrum Score (S3) app for pathogen-agnostic antimicrobial drug spectrum ranking to assess for antimicrobial de-escalation events

Antimicrobial/antibiotic de-escalation (ADE) is a key feature of antimicrobial stewardship programs (ASP) that relies mainly on individual panels for determining ADE events based on subjective ranking of antibiotics' spectrum activity. The lack of consensus among ASP experts leads to reproducibility issues in the measure of this clinical outcome, making difficult to assess its real impact on patient care. The S3 score (Simplified Spectrum Score) app was developed to allow an objective ranking of antibiotics. Ranking was achieved by developing a database harboring pairs of bacteria-antibiotics for which each molecule was assigned a score based on published and clinically validated data from a recognized international committee. S3 score shows a strong correlation relationship and substantial agreement to a clinically validated spectrum score, and its framework enables any person to use it for ADE detection without assuming prior knowledge or training. In addition, its design enables regular updates and sustainability.

A new distance between rankings

This paper analyzes the behavior of the well-known Spearman's footrule distance (F-distance) to measure the distance between two rankings over the same set of objects. We show that F-distance is not invariant to labeling, and therefore, it suffers from a serious drawback for its use in applications. To circumvent this problem, we propose a new distance between rankings which is invariant under indexing (i.e., labeling) and appears as a good alternative to the direct use of F-distance between rankings, and also the invariant-under-indexing Kemeny's distance as well. We also show how our new distance can work with importance weights. Some simple examples are given to show the interest of our method with respect to the classical one based on F-distance and Kemeny's distance.

Associations Between Program Doximity Reputation Rank and Well-Being in General Surgery Residents

INTRODUCTION

Burnout and mistreatment are prevalent among surgical residents with considerable program-level variation. Applicants consider "program reputation," among other factors, when ranking programs. Although highly subjective, the only available measure of program reputation is from a physician survey by Doximity. It is unknown how program reputation is associated with resident well-being and mistreatment.

METHODS

Resident burnout and personal accomplishment were assessed via the 2019 post-American Board of Surgery In-Training Examination survey. Additional outcomes included mistreatment, thoughts of attrition, and suicidality. Residents were stratified into quartiles based on their program's Doximity reputation rank. Multivariable logistic regression models examined the relationship between each outcome with Doximity rank quartile.

RESULTS

6956 residents (85.6% response rate) completed the survey. Higher-ranked programs had significantly higher burnout rates (top-quartile 41.3% versus bottom-quartile 33.2%; odds ratio [OR] 1.35, 95% confidence interval [CI] 1.04-1.76). There was no significant difference in personal accomplishment by program rank (OR 1.26, 95% CI 0.86-1.85). There also was no significant association between program rank and sexual harassment (OR 0.90, 95% CI 0.70-1.17), gender discrimination (OR 1.14, 95% CI 0.86-1.52), racial discrimination (OR 1.18, 95% CI 0.91-1.54), or bullying (OR 1.03, 95% CI 0.76-1.40). Suicidality (P = 0.97) and thoughts of attrition (P = 0.80) were also not associated with program rank.

CONCLUSIONS

Surgical residents at higher-ranked programs report higher rates of burnout but have similar rates of mistreatment and personal accomplishment. Higher-ranked programs should be particularly vigilant to trainee burnout, and all programs should employ targeted interventions to improve resident well-being. This study highlights the need for greater transparency in reporting objective program-level quality measures pertaining to resident well-being.

Preventing postpartum hemorrhage: A network meta-analysis on routes of administration of uterotonics

1.

OBJECTIVE

To perform a network meta-analysis to specify the route of administration that maximises the effectiveness of each of the available prophylactic uterotonics without increasing the risk for side effects. 2.

DATA SOURCES

Literature searches on 12th September 2022 included: CENTRAL, MEDLINE, Embase, CINAHL, ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform. The reference lists of the retrieved study records were also searched. 3.

STUDY ELIGIBILITY CRITERIA

Population: Randomized controlled trials involving women in the third stage of labour after a vaginal or caesarean delivery in hospital or community settings.

INTERVENTIONS

Systemically administered prophylactic uterotonics of any route and dose for primary postpartum hemorrhage prevention. Comparison: Any other prophylactic uterotonic, or a different route or dose of a given uterotonic, or placebo, or no treatment. Outcomes (primary): postpartum hemorrhage ≥ 500 mL and ≥ 1000 mL. 4.

STUDY APPRAISAL AND SYNTHESIS METHODS

Risk of bias and trustworthiness assessments were performed, according to Cochrane's guidance. Direct, indirect and network meta-analyses were conducted, and results were summarized either as risk ratio or mean difference with 95% confidence intervals for dichotomous and continuous outcomes, respectively. The certainty of generated evidence was assessed according to the GRADE approach. Cumulative probabilities were calculated and the surface under the cumulative ranking curve was used to create a ranking of the available drugs. 5.

RESULTS

One hundred eighty-one studies involving 122,867 randomised women were included. Most studies were conducted in hospital settings in lower-middle income countries and involved women delivering vaginally. When compared with intramuscular oxytocin, carbetocin (RR 0.58, 95 % CI 0.40-0.84) and oxytocin (RR 0.75, 95 % CI 0.59-0.97) by an intravenous bolus, and intramuscular ergometrine plus oxytocin combination (RR 0.71, 95 % CI 0.56-0.91) are probably more effective in preventing primary postpartum hemorrhage. Intramuscularly administered oxytocin and carbetocin by an intravenous bolus have a favourable side effects profile. 6.

CONCLUSIONS

Generated evidence was generally moderate and global inconsistency was low. Carbetocin and oxytocin by an intravenous bolus, and intramuscular ergometrine plus oxytocin combination are probably the top uterotonics for primary postpartum hemorrhage prevention. Large scale studies exploring different routes of administration for available prophylactic uterotonics, and women's views should be conducted.

Barriers to adoption of electric vehicles in Texas

Sustainable mobility options such as electric vehicles (EVs) have the potential to improve the quality of life for Americans as well as those in other countries, as they can enhance the quality of the air we breathe, while reducing greenhouse gas emissions, fossil fuel consumption, and the adverse impacts of global warming. Despite their many benefits, however, the demand for EVs remains low. Therefore, this study aims to identify the barriers that affect the widespread EV adoption in the United States. Seventeen barriers were identified from the literature, and a questionnaire survey was designed and distributed to potential consumers of EVs. The survey yielded 733 responses, and various statistical tests like cluster analysis and chi-squared tests were performed. The results revealed that the high purchase price of the vehicle, high battery replacement cost, and the lack of public infrastructures for charging them were the primary concerns. The results also revealed that middle-aged men with high education and income are more enthusiastic about adopting EVs. The results presented in this study indicate a range of developments that different stakeholders could implement. To surmount the economic barriers to EV adoption, policymakers should strengthen incentives countrywide, and automakers should introduce more affordable EVs to the market. To overcome the challenges associated with charging, it is necessary to make investments in rapid charging infrastructure along the primary toll routes.

Composite environmental indices-a case of rickety rankings

Composite indices have been widely used to rank the environmental performance of nations. Such environmental indices can be useful in communicating complex information as a single value and have the potential to generate political and media awareness of environmental issues. However, poorly constructed, or poorly communicated indices, can hinder efforts to identify environmental failings, and there are considerable differences in rank among existing environmental indices. Here, we provide a review of the conceptual frameworks and methodological choices used for existing environmental indices to enhance our understanding of their accuracy and applicability. In the present study, we review existing global indices according to their conceptual framework (objectives of the index and set of indicators included) and methodological choices made in their construction (e.g., weighting and aggregation). We examine how differences in conceptual frameworks and methodology may yield a more, or less, optimistic view of a country's environment. Our results indicate that (1) multidimensional environmental indices with indicators related to human health and welfare or policy are positively correlated; (2) environment-only indices are positively correlated with one another or are not correlated at all; (3) multidimensional indices and environment-only indices are negatively correlated with each other or are not correlated at all. This indicates that the conceptual frameworks and indicators included may influence a country's rank among different environmental indices. Our results highlight that, when choosing an existing environmental index-or developing a new one-it is important to assess whether the conceptual framework (and associated indicators) and methodological choices are appropriate for the phenomenon being measured and reported on. This is important because the inclusion of confounding indicators in environmental indices may provide a misleading view of the quality of a country's environment.

Probiotic potential of gluten degrading Bacillus tequilensis AJG23 isolated from Indian traditional cereal-fermented foods as determined by Multiple Attribute Decision-Making analysis

The present study reported the characterization of gluten hydrolyzing strains of Bacillus sp. from fermented cereal dough. The strains were characterized for probiotic as well as technological attributes. A total of 45 presumptive gluten degrading isolates were obtained on gliadin agar plate assay. Based on hemolytic and antibiotic susceptibility pattern, only six isolates were considered safe which also indicated gliadinase activity on zymography. All the six strains were able to resist the pH 2.0, 0.25% bile and also possessed ability to adhere to the organic solvents and mucin. The cell free supernatant of five strains exhibited antimicrobial activities against Gram-positive and Gram-negative pathogens. A more than 50% survival of the isolated strains was obtained at a salt concentration of 2%, phenol concentration of 0.1% and temperature upto 45 °C. All the strains exhibited antioxidant activities and biofilm forming ability. Furthermore, the ranking of strains based on probiotic as well as other functional attributes was determined using multidimensional Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). A matrix of multidimensional indicators was prepared using alternatives and criteria, the analysis indicated the strain Bacillus tequilensis AJG23 as the potential probiotic candidate based on all screening criteria. Further work still needs to be done about the protective role of the potential strain against gluten sensitivity using in vitro models.

Bayesian belief network modelling approach for predicting and ranking risk factors for malaria infections among children under 5 years in refugee settlements in Uganda

BACKGROUND

Malaria risk factors at household level are known to be complex, uncertain, stochastic, nonlinear, and multidimensional. The interplay among these factors, makes targeted interventions, and resource allocation for malaria control challenging. However, few studies have demonstrated malaria's transmission complexity, control, and integrated modelling, with no available evidence on Uganda's refugee settlements. Using the 2018-2019 Uganda's Malaria Indicator Survey (UMIS) data, an alternative Bayesian belief network (BBN) modelling approach was used to analyse, predict, rank and illustrate the conceptual reasoning, and complex causal relationships among the risk factors for malaria infections among children under-five in refugee settlements of Uganda.

METHODS

In the UMIS, household level information was obtained using standardized questionnaires, and a total of 675 children under 5 years were tested for malaria. From the dataset, a casefile containing malaria test results, demographic, social-economic and environmental information was created. The casefile was divided into a training (80%, n = 540) and testing (20%, n = 135) datasets. The training dataset was used to develop the BBN model following well established guidelines. The testing dataset was used to evaluate model performance.

RESULTS

Model accuracy was 91.11% with an area under the receiver-operating characteristic curve of 0.95. The model's spherical payoff was 0.91, with the logarithmic, and quadratic losses of 0.36, and 0.16 respectively, indicating a strong predictive, and classification ability of the model. The probability of refugee children testing positive, and negative for malaria was 48.1% and 51.9% respectively. The top ranked malaria risk factors based on the sensitivity analysis included: (1) age of child; (2) roof materials (i.e., thatch roofs); (3) wall materials (i.e., poles with mud and thatch walls); (4) whether children sleep under insecticide-treated nets; 5) type of toilet facility used (i.e., no toilet facility, and pit latrines with slabs); (6) walk time distance to water sources (between 0 and 10 min); (7) drinking water sources (i.e., open water sources, and piped water on premises).

CONCLUSION

Ranking, rather than the statistical significance of the malaria risk factors, is crucial as an approach to applied research, as it helps stakeholders determine how to allocate resources for targeted malaria interventions within the constraints of limited funding in the refugee settlements.

Evaluation of the accuracy of intraoral scanners for complete-arch scanning: A systematic review and network meta-analysis

OBJECTIVES

This network meta-analysis (NMA) aimed to compare the complete-arch scanning accuracy of different intraoral scanners (IOSs) to that of reference standard tessellation language (STL) files.

DATA

Studies comparing the trueness and precision of IOS STL files with those of reference STL scans for different arch types (dentate, edentulous, completely edentulous with implants, and partially edentulous with implants) were included in this study.

SOURCES

An electronic search of five databases restricted to the English Language was conducted in October 2021.

STUDY SELECTION

A total of 3,815 studies were identified, of which 114 were eligible for inclusion. After study selection and data extraction, pair-wise comparison and NMA were performed to define the accuracy of scanning for four arch subgroups using four outcomes (trueness and precision expressed as mean absolute deviation and root mean square values). Cochrane guidelines and the QUADAS-2 tool were used to assess the risk of bias. GRADE was used for certainty assessment.

RESULTS

Fifty-three articles were included in this NMA. Altogether, 26 IOSs were compared directly and indirectly in 10 network systems. The accuracy of IOSs scans were not significantly different from the reference scans for dentate arches (three IOSs), edentulous arches (three IOSs), and completely edentulous arches with implants (one IOS). The accuracy of the IOSs was significantly different from the reference scans for partially edentulous arches with implants. Significant accuracy differences were found between the IOSs, regardless of clinical scenarios.

CONCLUSIONS

The accuracy of complete-arch scanning by IOSs differs based on clinical scenarios.

CLINICAL SIGNIFICANCE

Different IOSs should be used according to the complete arch type.

Rating versus ranking in a Delphi survey: a randomized controlled trial

BACKGROUND

The Delphi technique has steeply grown in popularity in health research as a structured approach to group communication process. Rating and ranking are two different procedures commonly used to quantify participants' opinions in Delphi surveys. We explored the influence of using a rating or ranking approach on item prioritization (main outcome), questionnaire completion time, and evaluation of task difficulty in a Delphi survey aimed at identifying priorities for the organization of primary cardiovascular care.

METHODS

A randomized controlled parallel group trial was embedded in a three-round online Delphi survey. After an "open" first round, primary care patients, trained patient partners, and primary care clinicians from seven primary care practices were allocated 1:1 to a rating or ranking assessment group for the remainder of the study by stratified permuted block randomization, with strata based on participants' gender and status. Agreement on item prioritization between the experimental groups was measured by calculating Krippendorff's alpha reliability coefficient on the aggregate rank order of items in each group after the final round. Self-reported ease or difficulty with the assessment task was measured with the Single Ease Question.

RESULTS

Thirty-six panelists (13 clinic patients, 7 patient partners, 16 clinicians; 60% females) were randomized to the rating (n = 18) or ranking (n = 18) group, with 30 (83%) completing all rounds. Both groups identified the same highest priorities from a set of 41 items, but significant discrepancies were found as early as the seventh top item. There was moderately strong agreement between the priority ordering of top items common to both groups (Krippendorff's alpha = 0.811, 95% CI = 0.669-0.920). A 9-min mean difference to complete the third-round questionnaire in favor of the rating group failed to achieve statistical significance (p = 0.053). Ranking was perceived as more difficult (p < 0.001).

CONCLUSIONS

A rating or ranking procedure led to modestly similar item prioritization in a Delphi survey, but ranking was more difficult. This study should be replicated with a larger number of participants and with variations in the ranking and rating procedures.

TRIAL REGISTRATION

Not applicable.

Toxicity of Heavy Metals that Affect Germination, Development and Cell Cycle of Allium cepa L

This study aimed to investigate the impact of heavy metals copper, cadmium, lead, aluminum and nickel, on the growth, physiology, metabolism, and cell cycle of Allium cepa L. Five treatments with increasing concentrations (0, 50, 100, 250, and 500 µM) were applied to the seeds. The results showed that the highest concentrations of copper and cadmium had phytotoxic and biochemical effects on the onion. Additionally, copper concentrations caused an increase in mitodepressive effect and chromosomal abnormalities. Aluminum also induced several chromosomal abnormalities. The study found that Cd > Cu > Pb > Ni > Al and Cu > Al > Ni > Pb > Cd had the highest phytotoxic and cytotoxic potentials, respectively. Furthermore, the UPGMA method revealed three divergent groups. These results suggest that heavy metals, especially copper, have a significant pollution potential when present in high concentrations.

Blockchain Technology Application Challenges in Renewable Energy Supply Chain Management

With the advent of new technologies and globalization of business, supply chains have turned into indispensable tools for gaining competitive advantage. The application of new technologies like blockchain can benefit sustainable energy supply chains by improving chain and logistics operations in the areas of trust, transparency and accountability, cooperation, information sharing, financial exchanges, and supply chain integration. However, the efforts to adopt such technologies in supply chains tend to face many challenges and challenges, which can seriously threaten their success. Therefore, it is crucial to carefully examine the challenges to blockchain technology application. This research focuses on identifying the criteria and challenges to the application of blockchain in renewable energy supply chains and also ranks the identified challenges in terms of their capacity to disrupt the process. The applicability of the suggested structure is examined in a case study of the renewable energy supply chain of Iran. In this study, the challenges are evaluated and ranked by the hybrid developed methods by the integration of the concept of gray numbers into the gray stepwise weight assessment ratio analysis (SWARA-Gray) and the gray evaluation based on distance from average solution (EDAS-Gray). Another group of hybrid methods including the gray weighted sum method (WSM-Gray), the gray complex proportional assessment (COPRAS-Gray), and the gray technique for order of preference by similarity to ideal solution (TOPSIS-Gray) is used to validate the results. The rankings obtained from all of these techniques show high degree of correlation. Among the identified challenges, "high investment cost" is found to be the most important challenge to the application of blockchain in sustainable energy supply chains.

TiO2 nanoparticles' library toxicity (UV and non-UV exposure) - High-throughput in vivo transcriptomics reveals mechanisms

The hazards of nanomaterials/nanoparticles (NMs/NPs) are mostly assessed using individual NMs, and a more systematic approach, using many NMs, is needed to evaluate its risks in the environment. Libraries of NMs, with a range of identified different but related characters/descriptors allow the comparison of effects across many NMs. The effects of a custom designed Fe-doped TiO₂ NMs library containing 11 NMs was assessed on the soil model Enchytraeus crypticus (Oligochaeta), both with and without UV (standard fluorescent) radiation. Effects were analyzed at organism (phenotypic, survival and reproduction) and gene expression level (transcriptomics, high-throughput 4x44K microarray) to understand the underlying mechanisms. A total of 48 microarrays (20 test conditions) were done plus controls (UV and non-UV). Unique mechanisms induced by TiO₂ NPs exposure included the impairment in RNA processing for TiO₂_10nm, or deregulated apoptosis for 2%FeTiO₂_10nm. Strikingly apparent was the size dependent effects such as induction of reproductive effects via smaller TiO₂ NPs (≤12 nm) - embryo interaction, while larger particles (27 nm) caused reproductive effects through different mechanisms. Also, phagocytosis was affected by 12 and 27 nm NPs, but not by ≤11 nm. The organism level study shows the integrated response, i.e. the result after a cascade of events. While uni-cell models offer key mechanistic information, we here deliver a combined biological system level (phenotype and genotype), seldom available, especially for environmental models.

Origami plot: a novel multivariate data visualization tool that improves radar chart

OBJECTIVES

We propose the origami plot, which maintains the original functionality of a radar chart and avoids potential misuse of its connected regions, with newly added features to better assist multicriteria decision-making.

STUDY DESIGN AND SETTING

Built upon a radar chart, the origami plot adds additional auxiliary axes and points such that the area of the connected region of all dots is invariant to the ordering of axes. As such, it enables ranking different individuals by the overall performance for multicriteria decision-making while maintaining the intuitive visual appeal of the radar chart. We develop extensions of the origami plot, including the weighted origami plot, which allows reweighting of each attribute to define the overall performance, and the pairwise origami plot, which highlights comparisons between two individuals.

RESULTS

We illustrate the different versions of origami plots using the hospital compare database developed by the Centers for Medicare & Medicaid Services (CMS). The plot shows individual hospital's performance on mortality, readmission, complication, and infection, as well as patient experience and timely and effective care, as well as their overall performance across these metrics. The weighted origami plot allows weighing the attributes differently when some are more important than others. We illustrate the potential use of the pairwise origami plot in electronic health records (EHR) system to monitor five clinical measures (body mass index [BMI]), fasting glucose level, blood pressure, triglycerides, and low-density lipoprotein ([LDL] cholesterol) of a patient across multiple hospital visits.

CONCLUSION

The origami plot is a useful visualization tool to assist multicriteria decision making. It improves radar charts by avoiding potential misuse of the connected regions. It has several new features and allows flexible customization.

DNAsmart: Multiple attribute ranking tool for DNA data storage systems

In an ever-growing need for data storage capacity, the Deoxyribonucleic Acid (DNA) molecule gains traction as a new storage medium with a larger capacity, higher density, and a longer lifespan over conventional storage media. To effectively use DNA for data storage, it is important to understand the different methods of encoding information in DNA and compare their effectiveness. This requires evaluating which decoded DNA sequences carry the most encoded information based on various attributes. However, navigating the field of coding theory requires years of experience and domain expertise. For instance, domain experts rely on various mathematical functions and attributes to score and evaluate their encodings. To enable such analytical tasks, we provide an interactive and visual analytical framework for multi-attribute ranking in DNA storage systems. Our framework follows a three-step view with user-settable parameters. It enables users to find the optimal en-/de-coding approaches by setting different weights and combining multiple attributes. We assess the validity of our work through a task-specific user study on domain experts by relying on three tasks. Results indicate that all participants completed their tasks successfully under two minutes, then rated the framework for design choices, perceived usefulness, and intuitiveness. In addition, two real-world use cases are shared and analyzed as direct applications of the proposed tool. DNAsmart enables the ranking of decoded sequences based on multiple attributes. In sum, this work unveils the evaluation of en-/de-coding approaches accessible and tractable through visualization and interactivity to solve comparison and ranking tasks.

Analysis of Current Neurological Surgery Residents and Prior Medical Education: Do Medical School Attributes Matter?

BACKGROUND

With the recent changes to the U.S. Medical Licensing Examination grading system, an understanding of the factors that influence the neurological surgery residency match process is crucial for residency directors. The aim of the present retrospective study was to explore the associations of medical school location, ranking, private school status, size, and presence of an American Association of Neurological Surgeons (AANS) chapter or neurological surgery interest group (NSIG) with the neurosurgery match outcomes.

METHODS

An enrollment list of all accredited U.S. neurosurgery residency programs was compiled on June 28, 2021. For the included residents, the residency program, degree, and previously attended medical school were retrieved. The geographic location, ranking, private school status, and size were collected for the residency programs and medical schools attended by the residents at each program.

RESULTS

A total of 1437 residents from 101 neurosurgery residency programs (89%) were included. Graduates from the top 25 medical schools were more likely to match into their home residency programs (P < 0.001) and highly ranked residency programs (P < 0.001). Students from larger medical schools were also more likely to match into larger (P < 0.001) and highly ranked (P < 0.001) programs than were applicants from smaller schools. Students from medical schools with an AANS chapter or NSIG were also more likely to match into top ranked programs (P < 0.001 for both).

CONCLUSIONS

Medical students from the top 25 medical schools, private medical schools, medical schools with an AANS chapter, and medical schools with an NSIG were more likely to match into a prestigious residency program. These findings suggest that underlying biases might be present for program directors to consider in the resident selection process.

Considering multiple outcomes with different weights informed the hierarchy of interventions in network meta-analysis

OBJECTIVES

Ranking metrics in network meta-analysis (NMA) are computed separately for each outcome. Our aim is to 1) present graphical ways to group competing interventions considering multiple outcomes and 2) use conjoint analysis for placing weights on the various outcomes based on the stakeholders' preferences.

STUDY DESIGN AND SETTING

We used multidimensional scaling (MDS) and hierarchical tree clustering to visualize the extent of similarity of interventions in terms of the relative effects they produce through a random effect NMA. We reanalyzed a published network of 212 psychosis trials taking three outcomes into account as follows: reduction in symptoms of schizophrenia, all-cause treatment discontinuation, and weight gain.

RESULTS

Conjoint analysis provides a mathematical method to transform judgements into weights that can be subsequently used to visually represent interventions on a two-dimensional plane or through a dendrogram. These plots provide insightful information about the clustering of interventions.

CONCLUSION

Grouping interventions can help decision makers not only to identify the optimal ones in terms of benefit-risk balance but also choose one from the best cluster based on other grounds, such as cost, implementation etc. Placing weights on outcomes allows considering patient profile or preferences.

Using filled prescription sequences to rank antidepressants: A nationwide replication study

Ranking antidepressants according to their acceptability (i.e., a combination of both efficacy and tolerability) in the general population may help choosing the best first-line medication. This study aimed to replicate the results of a proof-of-concept study ranking anti-depressants according to the proportion of filled prescription sequences consistent with a continuation of the first treatment versus those consistent with a change. We used a nationwide cohort from the French national health data system (SNDS) to support the use of this method as a widely available tool to rank antidepressant treatments in real life settings. About 1.2 million people were identified as new antidepressant users in the SNDS in 2011. The outcome was clinical acceptability as measured by the continuation/failure ratio over the six-month period following the introduction of the first-line treatment. Continuation was defined as at least two refills of the same treatment. Failure was defined as a psychiatric hospitalization, death or at least one filled prescription of another antidepressant, an antipsychotic medication, or a mood-stabilizer. Adjusted Odds Ratios (aOR) and 95% Confidence Interval (CI) were computed through multivariable binary logistic regressions. We ranked antidepressant medications according to clinical acceptability. Escitalopram again was the most acceptable option, and the five following antidepressants were the same as in the replication sample of the proof-of-concept study, in order Fluoxetine, Paroxetine, Sertraline, Citalopram and Venlafaxine with aOR (95% CI) for continuation ranging from 0.79 (0.77-0.81) to 0.66 (0.64-0.67). The present study provides evidence that filled prescription sequences is a widely available, robust and reproductible tool to rank antidepressant treatments in real life settings.

The Probabilistic Final Standing Calculator: a fair stochastic tool to handle abruptly stopped football seasons

The COVID-19 pandemic has left its marks in the sports world, forcing the full stop of all sports-related activities in the first half of 2020. Football leagues were suddenly stopped, and each country was hesitating between a relaunch of the competition and a premature ending. Some opted for the latter option and took as the final standing of the season the ranking from the moment the competition got interrupted. This decision has been perceived as unfair, especially by those teams who had remaining matches against easier opponents. In this paper, we introduce a tool to calculate in a fairer way the final standings of domestic leagues that have to stop prematurely: our Probabilistic Final Standing Calculator (PFSC). It is based on a stochastic model taking into account the results of the matches played and simulating the remaining matches, yielding the probabilities for the various possible final rankings. We have compared our PFSC with state-of-the-art prediction models, using previous seasons which we pretend to stop at different points in time. We illustrate our PFSC by showing how a probabilistic ranking of the French Ligue 1 in the stopped 2019-2020 season could have led to alternative, potentially fairer, decisions on the final standing.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10182-021-00416-6.

More than just a number of publications: Unity Index, an alternative "ranking" for bibliometric analyses

This paper presents the unity index- an index designed in response to the call to find a more holistic way to assess scientific production. In this case, the scientific production is understood from a multivariate perspective using existing measurements, such as the number of publications, the number of, and the H-Index. It can be used especially when generating or assessing production in bibliometric studies. The index consists of a 3-part point system where points are assigned to the authors based on their position in the rank of the 3 traditional indicators for production, acceptance and impact. The index is calculated by attributing 1 point to the author with most published articles and increasing the number of points attributed by 1 to all the subsequent authors. The same point attribution is done to the remaining 2 indicators, acceptance -citations- and impact -h-index-. In the presented method, 3 different means of calculation are shared. Finally, the method is validated and different ways to analyze the results and generate insights are suggested.•The Unity Index offers a new way to assess scientific production from a multivariate perspective.•The index can be calculated easily using MS Excel formulas, but automatic ways of calculation are also easily available.•Bibliometric studies can profit from a less linear way to analyze the information and generate more insightful information and guide the research.

Is the changing landscape of fellowship recruitment during COVID-19 here to stay?

BACKGROUND

The 2020 Pediatric Surgery (PS) fellowship selection process was heavily impacted by the COVID-19 pandemic. A review of lessons learned can help determine best practices for the future. The purpose of the study was to analyze the virtual interview experience and assess opportunities to improve the post-pandemic fellowship recruitment process.

STUDY DESIGN

Using a 28-question survey of Program Directors (PDs) of PS fellowships as well as a 44-question survey of applicants to PS fellowships in the US and Canada, we gathered information on the recruitment process during the COVID-19 pandemic (2020). Dichotomous, multiple choice and open-ended questions about the changes in process, platforms used, format, comparison to on-site interviews and overall satisfaction were used for objective and subjective feedback.

RESULTS

A 95% participation rate was recorded for the PD survey. 24 out of 55 programs (44%) changed their on-site interviews to virtual format due to the pandemic. Most PDs described their overall impression of virtual interviews as satisfactory (66%, 16/24) and did not have an impact on the applicant's success in the match (35/54; 65%). About 50% of PDs preferred to have on-site interviews with virtual screening in the future. While the participation rate from applicants was much less (26 of 70), responses confirmed our survey results. Majority preferred on-site interviews (17/26), 6 of which preferred virtual screening followed by on-site interviews.

CONCLUSION

Components of virtual screening and interviews were found to have benefits financially and from both time and stress perspectives, and thus might survive past the pandemic. LEVELS OF EVIDENCE LEVEL IV: .

Success in pediatric surgery: An updated survey of Program Directors 2020

BACKGROUND

One of the most competitive surgical sub-specialty fellowships remains Pediatric Surgery (PS), which requires candidates to develop a strong and research-oriented curriculum vitae. Although some objective factors of matriculation are known, factors for the interview selection and ranking per the program directors (PDs) have not been reviewed in over a decade.

METHODS

A web-based survey of US and Canadian PS program directors (PDs) (n = 58) was used to evaluate a comprehensive list of factors in the selection criteria for PS fellowships. A mix of dichotomous, ranking, five-point Likert scale, and open-ended questions evaluated applicant characteristics, ABSITE scores, research productivity, interview day, and rank order criteria.

RESULTS

Fifty-five programs responded to the survey for a 95% participation rate. PDs desired an average of two years in dedicated research and weighted first authorship and total number of publications heavily. Only 38% of programs used an ABSITE score cutoff for offering interviews; however, the majority agreed that an overall upward trend was important. Quality letters of recommendation, especially from known colleagues, carried weight when deciding to offer interviews. Interview performance, being a team player, observed interpersonal interactions, perceived operative skills and patient care, and leadership were some of the notable factors when finalizing rank lists.

CONCLUSIONS

A multitude of factors define a successful matriculant, including quality of letters of recommendation, quality and quantity of publications, supportive phone calls, observed interactions, interview performance, perceptions of being team player with leadership skills as well as perceptions of good operative skills and patient care.

LEVEL OF EVIDENCE

Type II.

TYPE OF STUDY

Prognostic (retrospective).

Interventions for Glaucoma Medication Adherence Improvement: A Network Meta-analysis of Randomized Controlled Trials

TOPIC

Comparative effectiveness of interventions to improve glaucoma medication adherence.

CLINICAL RELEVANCE

High adherence to ocular hypotensive therapy is essential for prevention of visual impairment in glaucoma patients. Various types of intervention for adherence enhancement have been proposed, although there is still no firm evidence of their relative efficacies.

METHODS

We searched PubMed, EMBASE, Scopus, the Cochrane Central Register of Controlled Trials, the World Health Organization International Clinical Trials Registry Platform, and ClinicalTrials.gov on November 30, 2021. Randomized controlled trials (RCTs) entailing interventions for improved adherence to ocular hypotensive therapy were identified. A network meta-analysis (NMA) was performed, and the following 11 interventions (single category or combinations of categories) were compared: (1) standard of care ([SOC] control), (2) short message service, (3) telephone call, (4) device reminder, (5) motivational interview, (6) multimedia education, (7) physician education, (8) provision of own medical records, (9) incentives, (10) tailored care, and (11) enhanced SOC. The primary outcome was the postintervention mean adherence score. The standardized mean differences (SMDs) were analyzed, and the effectiveness was ranked by P-score (probability of being best treatment). We appraised trials using the Cochrane risk-of-bias tool for RCTs. Confidence of results was assessed by Confidence in Network Meta-analysis.

RESULTS

We obtained data for 19 RCTs (4981 participants). Tailored care, as inclusive of face-to-face needs assessment and a personalized care plan, was superior to SOC in improving adherence (SMD, 1.28; 95% confidence interval [CI], 0.08-2.48; P-score, 0.810). Multifaceted interventions that included tailored care showed further adherence improvement: tailored care + multimedia education (SMD, 1.44; 95% CI, 0.20-2.67; 0.850) and tailored care + multimedia education + device reminder (SMD, 1.61; 95% CI, 0.75-2.47; 0.914). The ranking of the remaining interventions by P-scores was as follows: incentives (0.606), short message service (0.535), enhanced SOC (0.458), multimedia education (0.430), device reminder (0.429), telephone call (0.401), provision of own medical records (0.391), physician education (0.281), SOC (0.230), and motivational interview (0.165).

CONCLUSIONS

The NMA indicated that tailored care can improve adherence to glaucoma medication compared with SOC. A multifaceted approach might yield additional improvements.

Bio-SODA UX: enabling natural language question answering over knowledge graphs with user disambiguation

The problem of natural language processing over structured data has become a growing research field, both within the relational database and the Semantic Web community, with significant efforts involved in question answering over knowledge graphs (KGQA). However, many of these approaches are either specifically targeted at open-domain question answering using DBpedia, or require large training datasets to translate a natural language question to SPARQL in order to query the knowledge graph. Hence, these approaches often cannot be applied directly to complex scientific datasets where no prior training data is available. In this paper, we focus on the challenges of natural language processing over knowledge graphs of scientific datasets. In particular, we introduce Bio-SODA, a natural language processing engine that does not require training data in the form of question-answer pairs for generating SPARQL queries. Bio-SODA uses a generic graph-based approach for translating user questions to a ranked list of SPARQL candidate queries. Furthermore, Bio-SODA uses a novel ranking algorithm that includes node centrality as a measure of relevance for selecting the best SPARQL candidate query. Our experiments with real-world datasets across several scientific domains, including the official bioinformatics Question Answering over Linked Data (QALD) challenge, as well as the CORDIS dataset of European projects, show that Bio-SODA outperforms publicly available KGQA systems by an F1-score of least 20% and by an even higher factor on more complex bioinformatics datasets. Finally, we introduce Bio-SODA UX, a graphical user interface designed to assist users in the exploration of large knowledge graphs and in dynamically disambiguating natural language questions that target the data available in these graphs.

Plastic Surgery Program Leadership Perspectives on Doximity Residency Navigator Rankings: Do We Need a Better Guide for Prospective Applicants?

INTRODUCTION

Doximity has become integrated into the residency application process without any clear merit, comparing programs based on reputation and research. Our study aims to gather program directors' and Chiefs/Chairs' perspectives on the Doximity ranking system and to assess what a better system might entail.

METHODS

A 16-question survey was sent to 177 program directors and Chief/Chairs of plastic surgery residency programs. The questions covered three categories: (1) demographic information; (2) Doximity ranking perceptions; (3) input on characteristics of a better tool. The responses were statistically analyzed.

RESULTS

Ninety-three questionnaires were received (53%). Twenty-nine (31%) respondents represented programs in the Northeast, 23 (25%) South, 20 (21%) Midwest, and 21 (23%) West. Seventy-three (79%) respondents were male and 16 (17%) female. 90% of respondents (n = 84) believe Doximity rankings are not accurate, all indicating their institution should be ranked higher. No significant association between program geography and ranking satisfaction was observed (p = 0.75). Only 33% (n = 31) of respondents were aware of Doximity methodology. Most respondents (95%; n = 88) do not recommend the use of Doximity to medical students. Most participants (87%; n = 81) are willing to share resident case logs to inform a future tool. "Strength of technical training/preparedness" was ranked most highly as important training program qualities.

CONCLUSIONS

The results of this program leadership survey show dissatisfaction with and a lack of understanding of the Doximity system. When considering future steps, program leadership support a strength-based categorization system and sharing case logs to guide student decision-making.

Save lives or save livelihoods? A cross-country analysis of COVID-19 pandemic and economic growth

This paper studies whether containing COVID-19 pandemic by stringent strategies deteriorates or saves economic growth. Since there are country-specific factors that could affect both economic growth and deaths due to COVID-19, we first start with a cross-country analysis on identifying risk and protective factors on the COVID-19 deaths using large across-country variation. Using data on 100 countries from 3 January to 27 November 2020 and taking into account the possibility of underreporting, we find that for deaths per million population, GDP per capita, population density, and income inequality are the three most important risk factors; government effectiveness, temperature, and hospital beds are the three most important protective factors. Second, inspired by the stochastic frontier literature, we construct a measure of pandemic containment effectiveness (PCE) after controlling for country-specific factors and rank countries by their PCE scores for deaths. Finally, by linking the PCE score with GDP growth data in Quarters 2 and 3 of 2020, we find that PCE is positively associated with economic growth in major economies. Countries with average PCE scores, such as Malaysia, would gain more GDP growth by 3.47 percentage points if they could improve their PCE scores for deaths to South Korea's level in Q2 of 2020. Therefore, there is not a trade-off between lives and livelihood facing by governments. Instead, to save economy, it is important to contain the pandemic first. Our conclusion is also mainly valid for infections due to COVID-19.

How Meaningful is the Elite Quality Index Ranking?

The Elite Quality Index (EQx) attempts to measure the propensity of elites-on aggregate-to create value, rather than to rent seek. The index has attracted worldwide media and press attention. In their articles, journalists have based their analyses primarily on their own countries' position in the EQx ranking. But how meaningful is the EQx ranking? How do the uncertainties underlying some of the assumptions made in the index propagate to the country rankings? We conduct a global uncertainty and sensitivity analysis (UA and SA) of the EQx and compute Sobol' first and total order sensitivity indices using state of the art estimators, in order to scrutinise the implications of index assumptions and assess the reliability of the EQx ranking. The UA suggests that the EQx ranking of 2021 (EQx2021) is largely stable for the top 50 countries, but exhibits considerable uncertainties especially for middle and lower performing countries. The SA highlights the handling of missing data, the normalisation process and the weighting scheme as most important methodological choices, while the largest potential for improvement is observed in how raw missing indicator data is handled.

All types of Research and Publication (including in Diabetes) have shown a healthy rising trend from India in the last decade

India has moved up its publications ranking globally to fourth, and to second amongst Asiatic countries in 2020, in all subject areas. In Diabetes journals the Diabetes & Metabolic Syndrome: Clinical Research and Reviews is the fastest growing journal with the highest Cite Scoree, currently.

Assessment of the Ecotoxicity of Pollution by Potentially Toxic Elements by Biological Indicators of Haplic Chernozem of Southern Russia (Rostov region)

The content of various chemical elements such as metals, metalloids, and nonmetals in the environment is associated with natural and anthropogenic sources. It is necessary to normalize the content of metals, metalloids, and nonmetals as potentially toxic elements (PTE) in the Haplic Chernozem. The soils of the Southern Russia are of high quality and fertility. However, this type of soil, like Haplic Chernozem, is subject to contamination with a wide range of PTE. The aim of the work was to rank metals, metalloids, and nonmetals by ecotoxicity in Haplic Chernozem. To assess the ecotoxicity of chernozem, data for 15 years (2005-2020) were used. Biological indicators used to assess the ecotoxicity of Haplic Chernozem: catalase activity, cellulolytic activity, number of bacteria, Azotobacter spp. abundance, to change of length of radish's roots. Based on these biological indicators, an integral indicator of the state of Haplic Chernozem was calculated. The ecotoxicity of 23 metals (Cd, Hg, Pb, Cr, Cu, Zn, Ni, Co, Mo, Mn, Ba, Sr, Sn, V, W, Ag, Bi, Ga, Nb, Sc, Tl, Y, Yb), 5 metalloids (B, As, Ge, Sb, Te) and 2 nonmetals (F, Se) as priority pollutants. It is proposed to distinguish three hazard classes of metals, metalloids, and nonmetals to Haplic Chernozem: I class - Te, Ag, Se, Cr, Bi, Ge, Sn, Tl, Hg, Yb, W, Cd; II class - As, Co, Sc, Sb, Cu, Ni, B, Nb, Pb, Ga; III class - Sr, Y, Mo, Zn, V, Ba, Mn, F. It is advisable to use the results of the study for predictive assessment of the impact of metals, metalloids, and nonmetals on the ecological state of the soil during pollution.

Artificial Evolutionary Optimization Process to Improve the Functionality of Cell Penetrating Peptides

Crossing cellular membranes is a versatile molecular property that allows for a wide variety of peptides with cell penetrating capabilities. This broadness complicates identification of candidates suited best for a specific application. To facilitate the screening of this enormous molecular space in a supervised manner we here present a method to "breed" the desired molecules by applying the rules of Darwinian evolution. With this mate-and-check protocol, which combines an in silico evolution step with an in vitro performance test, cell penetrating peptides that are optimized for a specific task can be achieved in a few rounds of breeding. The procedure is simple and straightforward on the synthetic site but requires robust, highly reproducible and close-to-reality biological assays to yield realistic functional output. With this technology even top-performing peptides can be further improved and functionally adjusted.

Love at First Click: Surgery Residency Websites in the Virtual Era

INTRODUCTION

Frequently, a residency program's website is the first interaction students interested in surgery have with the program. In the setting of virtual interviews for residency in 2020, the online availability of program information is of heightened importance. We sought to assess how academic versus community-based general surgery residency programs compared with respect to certain details on their websites.

METHODS

A total of n = 268 surgery residency programs were investigated. Our database of website characteristics included: direct link to residency website from the American Council on Graduate Medical Education (ACGME) program page, resident research requirement, listing of residents' publications, availability of residents' demographic information, program alumni information, board pass rates, attrition rate, detail of educational/academic activities, residents' evaluation methods, diversity, and mentorship. Inter-group analyses between academic and community-based programs were performed using Pearson's Chi-squared test.

RESULTS

Academic and community-based general surgery residency program websites were compared based on twelve (12) different parameters. Statistically significant differences were observed for eight of these comparisons: direct website access from ACGME (p = 0.007), research highlighted (p < 0.001), resident research requirement (p = 0.002), resident demographic information available (p=0.004), alumni information (p = 0.005), resident evaluation methods (p = 0.016), diversity (p < 0.001), and mentorship (p = 0.012). Across these domains, academic programs had more information available on their websites than the community programs did. The program websites did not differ significantly based on the frequency of mentioning resident publications, board pass rate, attrition rate, or resident education.

CONCLUSION

Many general surgery programs are lacking detailed information on their websites. The amount of website information available on general surgery residency programs differs when comparing academic and community-based programs.

Efficiency analysis in the management of COVID-19 pandemic in India based on data envelopment analysis

Purpose: This article measured the performance of 32 states and union territories (UTs) of India against COVID-19 disease using efficiency score which was calculated by data envelopment analysis (DEA) and compared the efficiency score with the different models which are used in many articles to evaluate the efficiency of healthcare system. Here the input parameters are taken as public health expenditure in a million, number of hospitals, number of hospital beds, percentage of health workers, population density, and number of infected, and output parameters divided into good and bad categories such as the number of recovered are taken as good output. The number of death is taken as bad outputs. The modified undesirable output model is used to calculate efficiency score and compared the efficiency score with Charnes, Cooper, and Rhodes (CCR) and Banker, Charnes, and Cooper (BCC) models. Finally, the states & UTs are completely ranked with the help of efficiency score and Maximal Balance Index, and evaluated benchmarking for each states & UTs. Data Source: Secondary data were collected from Census 2011 and the Ministry of health & family welfare, Government of India on 32 stats & UTs (NHAC, 2018; NHP, 2019; COVID19India, 2021). Results: According to Undesirable model results, 16 (50%) of 32 Indian states & UTs s were found to be efficient. Among the efficient DMUs, Chandigarh is the most efficient unit and Meghalaya is the most inefficient unit. Rajasthan was the most referenced state for inefficient states. Limitation: The efficiency score is affected by changing the number of inputs and outputs. The lack of more effective parameters are used to evaluate performance and enable qualitative variable comparison.

Ranking NIH Funding of Surgical Departments Based Upon a Modified Index

BACKGROUND

The Blue Ridge Institute for Medical Research (BRIMR) reports a ranking of surgical department NIH funding each fiscal year based on more than 41,000 individual investigators. This report is used to measure the research productivity of the faculty or department. However, this method includes institutional grants awarded to Cancer Centers or Centers for Research, which do not reflect individual or departmental research. To measure the research productivity of a surgical department more directly, we created a modified BRIMR index excluding grants to cancer or research centers. We evaluated how our modified index of surgical departments compared to the rankings by BRIMR.

METHODS

Publicly available BRIMR data was filtered for all grants awarded to principal investigators in a surgical department within a medical school. All funding for Cancer Centers or Centers for Research was excluded. The remaining grants were totaled, producing a new ranking of surgical departments.

RESULTS

After excluding $42,761,752 in grants to Cancer Centers and Centers for Research, there was individual movement of 33 surgical departments on the ranking list. However, only four departments moved either up or down one quartile. No surgical department moved 2 or more quartiles.

CONCLUSIONS

NIH funding for Cancer Centers and Centers for Research comprised 10% of all NIH funding for medical school-associated surgical departments. Exclusion of this funding resulted in no significant change within surgical department quartile rankings. This suggests the BRIMR measure of research productivity does not need modification.

Soft tissue dimensional changes after alveolar ridge preservation using different sealing materials: a systematic review and network meta-analysis

Background

Alveolar ridge preservation (ARP) is a proactive treatment option aiming at attenuating post-extraction hard and soft tissue dimensional changes. A high number of different types of biomaterials have been utilized during ARP to seal the socket, but their effectiveness in terms of soft tissue outcomes has rarely been investigated and compared in the literature.

Objective

To evaluate the efficacy of different types of membranes and graft materials in terms of soft tissue outcomes (keratinized tissue width changes, vertical buccal height, and horizontal changes) after ARP, and to assign relative rankings based on their performance.

Materials and methods

The manuscript represents the proceedings of a consensus conference of the Italian Society of Osseointegration (IAO).

PUBMED (Medline), SCOPUS, Embase, and Cochrane Oral Health’s Information Specialist were utilized to conduct the search up to 06 April 2021. English language restrictions were placed and no limitations were set on publication date. Randomized controlled trials that report ARP procedures using different sealing materials, assessing soft tissue as a primary or secondary outcome, with at least 6-week follow‐up were included.

Network meta-analysis (NMA) was performed using mean, standard deviation, sample size, bias, and follow-up duration for all included studies. Network geometry, contribution plots, inconsistency plots, predictive and confidence interval plots, SUCRA (surface under the cumulative ranking curve) rankings, and multidimensional (MDS) ranking plots were constructed.

Results

A total of 11 studies were included for NMA. Overall, the level of bias for included studies was moderate. Crosslinked collagen membranes (SUCRA rank 81.8%) performed best in vertical buccal height (VBH), autogenous soft tissue grafts (SUCRA rank 89.1%) in horizontal width change (HWch), and control (SUCRA rank 85.8%) in keratinized mucosa thickness (KMT).

Conclusions

NMA confirmed that the use of crosslinked collagen membranes and autogenous soft tissue grafts represented the best choices for sealing sockets during ARP in terms of minimizing post-extraction soft tissue dimensional shrinkage.

Clinical relevance

Grafting materials demonstrated statistically significantly better performances in terms of soft tissue thickness and vertical buccal height changes, when covered with crosslinked collagen membranes. Instead, soft tissue grafts performed better in horizontal width changes. Non-crosslinked membranes and other materials or combinations presented slightly inferior outcomes.

Sustainable metabolic engineering for sustainability optimisation of industrial biotechnology

Industrial biotechnology represents one of the most innovating and labour-productive industries with an estimated stable economic growth, thus giving space for improvement of the existing and setting up new value chains. In addition, biotechnology has clear environmental advantages over the chemical industry. Still, biotechnology’s environmental contribution is sometimes valued with controversy and societal aspects are frequently ignored. Environmental, economic and societal sustainability of various bioprocesses becomes increasingly important due to the growing understanding about complex and interlinked consequences of different human activities. Neglecting the sustainability issues in the development process of novel solutions may lead to sub-optimal biotechnological production, causing adverse environmental and societal problems proportional to the production volumes.

In the paper, sustainable metabolic engineering (SME) concept is proposed to assess and optimize the sustainability of biotechnological production that can be derived from the features of metabolism of the exploited organism. The SME concept is optimization of metabolism where economic, environmental and societal sustainability parameters of all incoming and outgoing fluxes and produced biomass of the applied organisms are considered. The extension of characterising features of strains designed by metabolic engineering methods with sustainability estimation enables ab initio improvement of the biotechnological production design.

Network Meta-Analysis Techniques for Synthesizing Prevention Science Evidence

Network meta-analysis is a popular statistical technique for synthesizing evidence from studies comparing multiple interventions. Benefits of network meta-analysis, over more traditional pairwise meta-analysis approaches, include evaluating efficacy/safety of interventions within a single framework, increased precision, comparing pairs of interventions that have never been directly compared in a trial, and providing a hierarchy of interventions in terms of their effectiveness. Network meta-analysis is relatively underutilized in prevention science. This paper therefore presents a primer of network meta-analysis for prevention scientists who wish to apply this method or to critically appraise evidence from publications using the method. We introduce the key concepts and assumptions of network meta-analysis, namely, transitivity and consistency, and demonstrate their applicability to the field of prevention science. We then illustrate the method using a network meta-analysis examining the comparative effectiveness of brief alcohol interventions for preventing hazardous drinking among college students. We provide data and code for all examples. Finally, we discuss considerations that are particularly relevant in network meta-analyses in the field of prevention, such as including non-randomized evidence.

An evaluation of performance measures for arterial brain vessel segmentation

BACKGROUND

Arterial brain vessel segmentation allows utilising clinically relevant information contained within the cerebral vascular tree. Currently, however, no standardised performance measure is available to evaluate the quality of cerebral vessel segmentations. Thus, we developed a performance measure selection framework based on manual visual scoring of simulated segmentation variations to find the most suitable measure for cerebral vessel segmentation.

METHODS

To simulate segmentation variations, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation. In 10 patients, we generated a set of approximately 300 simulated segmentation variations for each ground truth image. Each segmentation was visually scored based on a predefined scoring system and segmentations were ranked based on 22 performance measures common in the literature. The correlation of visual scores with performance measure rankings was calculated using the Spearman correlation coefficient.

RESULTS

The distance-based performance measures balanced average Hausdorff distance (rank = 1) and average Hausdorff distance (rank = 2) provided the segmentation rankings with the highest average correlation with manual rankings. They were followed by overlap-based measures such as Dice coefficient (rank = 7), a standard performance measure in medical image segmentation.

CONCLUSIONS

Average Hausdorff distance-based measures should be used as a standard performance measure in evaluating cerebral vessel segmentation quality. They can identify more relevant segmentation errors, especially in high-quality segmentations. Our findings have the potential to accelerate the validation and development of novel vessel segmentation approaches.

Consistency of ranking was evaluated as new measure for prediction model stability: longitudinal cohort study

OBJECTIVE

Clinical risk prediction models are generally assessed on population level with a lack of measures that evaluate their stability at predicting risks of individual patients. This study evaluated the use of ranking as a measure to assess individual level stability between risk prediction models.

STUDY DESIGN AND SETTING

A large patient cohort (3.66 million patients with 0.11 million cardiovascular events) extracted from the Clinical Practice Research Datalink was used in the exemplar of cardiovascular disease risk prediction.

RESULTS

It was found that 15 models (including machine learning and statistical models) had similar population-level model performance (C statistics about 0.88). For patients with high absolute risks, the models were more consistent in ranking of risk predictions (interquartile range (IQR) of differences in rank percentiles -0.6 to 1.0), but inconsistent in absolute risk (IQR of differences in absolute risk -18.8 to 9.0). At low risk, the reverse was true with inconsistent ranking but more consistent absolute risk.

CONCLUSION

Consistency of ranking of individual risk predictions is a useful measure to assess risk prediction models providing complementary information to absolute risk stability. Model developing guidelines including "TRIPOD" and "PROBAST" should incorporate ranking to assess individual level stability between risk prediction models.

Community-based performance indicators for monitoring and evaluating livestock interventions

The research aimed at identifying livestock performance indicators used by farmers in Malipati community, Zimbabwe, and use them in developing a monitoring and evaluation framework for livestock interventions. Mixed methods research was used in the study. A questionnaire was administered to identify performance indicators of preference by farmers. Focus group discussions were done to rank performance indicators. Data analysis was done using SPSS version 25, and data were analysed using the ranking matrix. Scientific validity of performance indicators was determined through literature review. The study concluded that performance indicators of importance in poultry, cattle, goats/sheep, and donkeys were egg production, milk yield, kidding/lambing interval, and animal power, respectively. All performance indicators identified by farmers in Malipati are scientifically valid and were used in the development of the monitoring and evaluation framework.

Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization

Social media platforms like Twitter have become an easy portal for billions of people to connect and exchange their thoughts. Unfortunately, people commonly use these platforms to share misinformation which can influence other people adversely. The spread of misinformation is unavoidable in an extraordinary situation like Covid-19, and the consequences can be dreadful. This paper proposes a two-step ranking-based misinformation detection (RMiD) technique. Firstly, a novel ranking-based approach leveraging the scalable information retrieval infrastructure is applied to detect misinformation from a huge collection of unlabelled tweets based on a related but very small labelled misinformation data set. Secondly, the identified misinformation tweets are represented as a coupled matrix tensor model and Nonnegative Coupled Matrix Tensor Factorization is applied to learn their spatio-temporal topic dynamics. The experimental analysis shows that RMiD is capable of detecting misinformation with better coverage and less noise in comparison with existing techniques. Moreover, the coupled matrix tensor representation has improved the quality of topics discovered from unlabelled data up to 4% by leveraging the semantic similarity of terms in labelled data.

SUPPLEMENTARY INFORMATION

The online version supplementary material available at 10.1007/s13278-021-00767-7.

Research ethics: a profile of retractions from world class universities

This study aims to profile the scientific retractions published in journals indexed in the Web of Science database from 2010 to 2019, from researchers at the top 20 World Class Universities according to the Times Higher Education global ranking of 2020. Descriptive statistics, Pearson's correlation coefficient, and simple linear regression were used to analyze the data. Of the 330 analyzed retractions, Harvard University had the highest number of retractions and the main reason for retraction was data results. We conclude that the universities with a higher ranking tend to have a lower rate of retraction.

Methodological approaches for analysing data from therapeutic efficacy studies

Several anti-malarial drugs have been evaluated in randomized clinical trials to treat acute uncomplicated Plasmodium falciparum malaria. The outcome of anti-malarial drug efficacy studies is classified into one of four possible outcomes defined by the World Health Organization: adequate clinical and parasitological response, late parasitological failure, late clinical failure, early treatment failure. These four ordered categories are ordinal data, which are reduced to either a binary outcome (i.e., treatment success and treatment failure) to calculate the proportions of treatment failure or to time-to-event outcome for KaplanMeier survival analysis. The arbitrary transition from 4-level ordered categories to 2-level type categories results in a loss of statistical power. In the opinion of the authors, this outcome can be considered as ordinal at a fixed endpoint or at longitudinal endpoints. Alternative statistical methods can be applied to 4-level ordinal categories of therapeutic response to optimize data exploitation. Furthermore, network meta-analysis is useful not only for direct comparison of drugs which were evaluated together in a randomized design, but also for indirect comparison of different artemisinin-based combinations across different clinical studies using a common drug comparator, with the aim to determine the ranking order of drug efficacy. Previous works conducted in Cameroonian children served as data source to illustrate the feasibility of these novel statistical approaches. Data analysis based on ordinal end-point may be helpful to gain further insight into anti-malarial drug efficacy.

Factors influencing medical student choices in the integrated vascular surgery match: Implications for future post-pandemic residency matches

OBJECTIVE

Integrated vascular surgery residency is among the most competitive specialties, but little is known about the applicant perspective. The coronavirus disease 2019 outbreak impacted the 2021 integrated vascular surgery residency match because of travel restrictions. We sought to better understand pre-pandemic applicant recruitment strategies, logistics of away rotations, and the residency interview process to identify areas for improvement in the application process.

METHODS

An anonymous survey was sent to matched students in 2020, inquiring about motivations for pursuing vascular surgery (VS), logistic of away rotations and interviews, and factors influencing students' rank lists.

RESULTS

Seventy of the 73 matched students completed the survey (95.9% response rate). The median age was 27 (range, 25-41); 32.9% were female, 91.4% were U.S. medical students, and 77.1% were from institutions with a VS training program. Factors most strongly influencing the decision to choose VS as a career were interest in open vascular procedures, endovascular procedures, perceived job satisfaction, emerging technologies, and influence of a mentor. The prospect of the job market, future salary, and competitiveness of the application process had the least impact. Of the matched students, 82.9% completed an away rotation (median, 2; range, 1-4), with 51.7% of students paying a total cost of more than $2500. Fifty percent of students matched either at their home institution or where they had performed an away rotation. Students reported application submissions to a median of 50 programs (range, 1-70) and interviewed at 17 (range, 1-28), with 40% of students paying a total of more than $4000 for interview costs. The most significant factors affecting students' rank lists included program culture, open aortic surgical volume, geography, and complex endovascular procedure volume. Tours of facilities, resident salary, and male/female distribution had the least importance.

CONCLUSIONS

Successfully matched applicants in 2020 prioritized operative case volume and program collegiality when ranking programs. Despite their high cost, away rotations played an important role in the Match, suggesting that time spent at potential institutions allowed ideal assessment of factors for students. The high average number of away rotations and in-person interviews performed in 2019-2020 was limited for the 2021 Match due to coronavirus disease 2019 restrictions. Programs will have to continue developing creative alternatives or additions to away rotations and the application processes to assure continued success in future post-pandemic Match cycles.

miRcorrNet: machine learning-based integration of miRNA and mRNA expression profiles, combined with feature grouping and ranking

A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, which performs machine learning-based integration to analyze miRNA and mRNA gene expression profiles. miRcorrNet groups mRNAs based on their correlation to miRNA expression levels and hence it generates groups of target genes associated with each miRNA. Then, these groups are subject to a rank function for classification. We have evaluated our tool using miRNA and mRNA expression profiling data downloaded from The Cancer Genome Atlas (TCGA), and performed comparative evaluation with existing tools. In our experiments we show that miRcorrNet performs as good as other tools in terms of accuracy (reaching more than 95% AUC value). Additionally, miRcorrNet includes ranking steps to separate two classes, namely case and control, which is not available in other tools. We have also evaluated the performance of miRcorrNet using a completely independent dataset. Moreover, we conducted a comprehensive literature search to explore the biological functions of the identified miRNAs. We have validated our significantly identified miRNA groups against known databases, which yielded about 90% accuracy. Our results suggest that miRcorrNet is able to accurately prioritize pan-cancer regulating high-confidence miRNAs. miRcorrNet tool and all other supplementary files are available at https://github.com/malikyousef/miRcorrNet.

Improved cluster ranking in protein-protein docking using a regression approach

We develop a Regression-based Ranking by Pairwise Cluster Comparisons (RRPCC) method to rank clusters of similar protein complex conformations generated by an underlying docking program. The method leverages robust regression to predict the relative quality difference between any pair or clusters and combines these pairwise assessments to form a ranked list of clusters, from higher to lower quality. We apply RRPCC to clusters produced by the automated docking server ClusPro and, depending on the training/validation strategy, we show improvement by 24-100% in ranking acceptable or better quality clusters first, and by 15-100% in ranking medium or better quality clusters first. We compare the RRPCC-ClusPro combination to a number of alternatives, and show that very different machine learning approaches to scoring docked structures yield similar success rates. Finally, we discuss the current limitations on sampling and scoring, looking ahead to further improvements. Interestingly, some features important for improved scoring are internal energy terms that occur only due to the local energy minimization applied in the refinement stage following rigid body docking.

Predicting the 2016 US News & World Report rankings using a reputation and prestige model

INTRODUCTION

The objective was to determine institutional, faculty, and student attributes predictive of the 2016 US News & World Report (USNWR) pharmacy rankings and to explore if student attributes modify program rankings.

METHODS

Institutional attributes and student and faculty resources and outcomes were obtained from various sources. Regression analyses predicted rankings.

RESULTS

USNWR rankings were higher for older programs, those located at an academic health center or classified as a research-intensive institution, and members of a Power Five athletic conference. Number of fulltime equivalent faculty, Pharmacy College Admissions Test composite percentile, pharmacy practice h-index score, funding rank, and first time North American Pharmacist Licensure Examination (NAPLEX) pass rate predicted higher rankings. Nearly 45% of programs in the empiric models left or entered the top 25 rankings compared with the existing USNWR method (range 31 place increase to 22 place decrease). Among USNWR top 25 ranked programs, 16 remained in the top 25 in all four empiric models and three were not included in any of the models. Six USNWF unranked programs moved into the top 25 by one or more of the empiric models.

CONCLUSIONS

Faculty and student attributes significantly impact program rankings, while impact of institutional attributes is negligible if independent of student and faculty attributes. Faculty numbers and productivity influence USNWR rankings more than student academic preparation and success on NAPLEX. These findings will inform efforts to improve the validity of the USNWR rankings and identify programs that are both prestigious and of high quality.

Prioritisation for future surveillance, prevention and control of 98 communicable diseases in Belgium: a 2018 multi-criteria decision analysis study

Background

National public health agencies are required to prioritise infectious diseases for prevention and control. We applied the prioritisation method recommended by the European Centre for Disease Prevention and Control to rank infectious diseases, according to their relative importance for surveillance and public health, to inform future public health action in Belgium.

Methods

We applied the multi-criteria-decision-analysis approach. A working group of epidemiologists and statisticians from Belgium (n = 6) designed a balanced set of prioritisation criteria. A panel of Belgian experts (n = 80) allocated in an online survey each criteria a weight, according to perceived relative importance. Next, experts (n = 37) scored each disease against each criteria in an online survey, guided by disease-specific factsheets referring the period 2010–2016 in Belgium. The weighted sum of the criteria’s scores composed the final weighted score per disease, on which the ranking was based. Sensitivity analyses quantified the impact of eight alternative analysis scenarios on the top-20 ranked diseases. We identified criteria and diseases associated with data-gaps as those with the highest number of blank answers in the scoring survey. Principle components of the final weighted score were identified.

Results

Working groups selected 98 diseases and 18 criteria, structured in five criteria groups. The diseases ranked highest were (in order) pertussis, human immunodeficiency virus infection, hepatitis C and hepatitis B. Among the five criteria groups, overall the highest weights were assigned to ‘impact on the patient’, followed by ‘impact on public health’, while different perceptions were identified between clinicians, microbiologists and epidemiologists. Among the 18 individual criteria, ‘spreading potential’ and ‘events requiring public health action’ were assigned the highest weights. Principle components clustered with thematic disease groups. Notable data gaps were found among hospital-related diseases.

Conclusions

We ranked infectious diseases using a standardised reproducible approach. The diseases ranked highest are included in current public health programs, but additional reflection for example about needs among risk groups is recommended. Cross-reference of the obtained ranking with current programs is needed to verify whether resources and activities map priority areas. We recommend to implement this method in a recurrent evaluation cycle of national public health priorities.

Attention dynamics on the Chinese social media Sina Weibo during the COVID-19 pandemic

Understanding attention dynamics on social media during pandemics could help governments minimize the effects. We focus on how COVID-19 has influenced the attention dynamics on the biggest Chinese microblogging website Sina Weibo during the first four months of the pandemic. We study the real-time Hot Search List (HSL), which provides the ranking of the most popular 50 hashtags based on the amount of Sina Weibo searches. We show how the specific events, measures and developments during the epidemic affected the emergence of different kinds of hashtags and the ranking on the HSL. A significant increase of COVID-19 related hashtags started to occur on HSL around January 20, 2020, when the transmission of the disease between humans was announced. Then very rapidly a situation was reached where COVID-related hashtags occupied 30-70% of the HSL, however, with changing content. We give an analysis of how the hashtag topics changed during the investigated time span and conclude that there are three periods separated by February 12 and March 12. In period 1, we see strong topical correlations and clustering of hashtags; in period 2, the correlations are weakened, without clustering pattern; in period 3, we see a potential of clustering while not as strong as in period 1. We further explore the dynamics of HSL by measuring the ranking dynamics and the lifetimes of hashtags on the list. This way we can obtain information about the decay of attention, which is important for decisions about the temporal placement of governmental measures to achieve permanent awareness. Furthermore, our observations indicate abnormally higher rank diversity in the top 15 ranks on HSL due to the COVID-19 related hashtags, revealing the possibility of algorithmic intervention from the platform provider.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1140/epjds/s13688-021-00263-0.