Bayesian model averaging for the X-chromosome inactivation dilemma in genetic association study

X-chromosome is often excluded from the so called "whole-genome" association studies due to the differences it exhibits between males and females. One particular analytical challenge is the unknown status of X-inactivation, where one of the two X-chromosome variants in females may be randomly selected to be silenced. In the absence of biological evidence in favor of one specific model, we consider a Bayesian model averaging framework that offers a principled way to account for the inherent model uncertainty, providing model averaging-based posterior density intervals and Bayes factors. We examine the inferential properties of the proposed methods via extensive simulation studies, and we apply the methods to a genetic association study of an intestinal disease occurring in about 20% of cystic fibrosis patients. Compared with the results previously reported assuming the presence of inactivation, we show that the proposed Bayesian methods provide more feature-rich quantities that are useful in practice.

Decoy selection for protein structure prediction via extreme gradient boosting and ranking

Background

Identifying one or more biologically-active/native decoys from millions of non-native decoys is one of the major challenges in computational structural biology. The extreme lack of balance in positive and negative samples (native and non-native decoys) in a decoy set makes the problem even more complicated. Consensus methods show varied success in handling the challenge of decoy selection despite some issues associated with clustering large decoy sets and decoy sets that do not show much structural similarity. Recent investigations into energy landscape-based decoy selection approaches show promises. However, lack of generalization over varied test cases remains a bottleneck for these methods.

Results

We propose a novel decoy selection method, ML-Select, a machine learning framework that exploits the energy landscape associated with the structure space probed through a template-free decoy generation. The proposed method outperforms both clustering and energy ranking-based methods, all the while consistently offering better performance on varied test-cases. Moreover, ML-Select shows promising results even for the decoy sets consisting of mostly low-quality decoys.

Conclusions

ML-Select is a useful method for decoy selection. This work suggests further research in finding more effective ways to adopt machine learning frameworks in achieving robust performance for decoy selection in template-free protein structure prediction.

Repeatability and ranking of long-term enteric methane emissions measurement on dairy cows across diets and time using GreenFeed system in farm-conditions

The aims of this work were to study on dairy farm conditions: i) the repeatability of long-term enteric CH₄ emissions measurement from lactating dairy cows using GreenFeed (GF); ii) the ranking of dairy cows according to their CH₄ emissions across diets. Forty-five Holstein lactating dairy cows were randomly assigned to 3 equivalent groups at the beginning of their lactation. The experiment was composed of 3 successive periods: i) pre-experimental period (weeks 1 to 5) in which all cows received a common diet; ii) a dietary treatment transition period (weeks 6 to 10); and iii) an experimental period (weeks 11 to 26) in which each group was fed a different diet. Experimental diets were formulated to generate more or less CH₄ production: i) a diet based on ryegrass silage and concentrates, low in starch and lipid, designed to induce high CH₄ emissions (CH4+); ii) a diet based on maize silage and concentrates, rich in starch, designed to induce intermediate CH₄ emissions (CH4int); iii) a diet based on maize silage and concentrates, rich in starch and lipid, designed to induce low CH₄ emissions (CH4-). Gas emissions were individually measured using GF systems. Repeatability of gas emissions, dry matter intake (DMI) and dairy performances measurements was calculated from data averaged over 1, 2, 4, and 8 weeks for each animal. Hierarchical cluster analysis was performed to rank individual animals according to their CH₄ emissions. No significant differences were observed for daily CH₄ emissions (g/day) among diets, because of lower DMI of CH4+ cows. When CH₄ emissions were referred to units of DMI or milk, the differences among diets emerged as significant and persistent over the observed period of lactation. Repeatability values of gas emissions measurements were higher than 0.7 averaged over 8 weeks of measurement, but still higher than 0.6 for CH₄ g/day, CO₂ g/day, CH₄ g/kg milk, and CH₄/CO₂ even averaging only 2 weeks of measurement. The repeatability of CH₄ emissions measurement was systematically lower than those of DMI or dairy performance parameters, like milk and FPCM yield, irrespective of the averaged measurement period. The dairy cow ranking was not stable over time between all individuals or within any of the diets. In our experimental conditions, the GF performance in the long term can be considered reliable in differentiating dairy herds by their CH₄ emissions according to diets with different methanogenic potential, but did not allow the ranking of individual dairy cows within a same diet. Our data highlight the importance of phenotyping animals across environment in which they will be expected to perform.

Managed Aquifer Recharge in the Gulf Countries: A Review and Selection Criteria

The Gulf Cooperation Council (GCC) countries are arid with very limited availability of water resources. In recent years, these countries have started an intensive program to increase the storage of groundwater through various techniques of managed aquifer recharge (MAR). Water consisting of varying quantity and quality (derived from various sources) are used via MAR techniques to increase the groundwater storage and, if possible to enhance its quality, respectively. This paper presents a review of the MAR techniques practiced in GCC countries including the implementation strategies of the different structures. Generally, seven MAR techniques are utilized in GCC countries including dams, aquifer storage and recovery (ASR) technique, aquifer storage transfer and recovery (ASTR) technique, ponds, soil aquifer treatment (SAT) technique, rooftop rainwater harvesting, and Karez/Ain system. Results indicated that ASR using excess desalinated water or treated sewage effluent (TSE) is the most used MAR technique in GCC countries, followed by the use of ASTR, dams, and ponds. Based on this review, twelve different selection criteria have been developed for GCC countries for better MAR practice in the future.

Classification and differential effectiveness of goal-directed hemodynamic therapies in surgical patients: A network meta-analysis of randomized controlled trials

PURPOSE

To investigate the most effective goal-directed hemodynamic therapy (GDHT) in surgical patients.

METHODS

GDHTs were classified as methods for intravascular volume, preload, stroke volume, cardiac output, oxygen delivery, systemic oxygenation, or tissue oxygenation optimization, alone or in combination. Their relative effectiveness and ranking were assessed using network meta-analysis and the surface under the cumulative ranking curve (SUCRA), respectively.

RESULTS

101 randomized controlled trials investigating GDHT effectiveness in surgical patients were eligible. The most commonly reported outcomes were 30-day mortality, acute kidney injury (AKI), and arrhythmia. Mortality was significantly reduced by GDHTs aimed at intravascular volume and cardiac output optimization (OR 0.40; 95% CrI 0.14-0.997; low quality). AKI was significantly reduced by GDHT aimed at intravascular volume optimization (OR 0.26; 95% CrI 0.08-0.71; moderate quality). No GDHT significantly reduced arrhythmia. GDHT aimed at intravascular volume and stroke volume optimization was likely most effective for mortality reduction (SUCRA = 78.8%) while that aimed at intravascular volume, stroke volume, and cardiac output optimization was likely most effective for AKI reduction (SUCRA = 85.4%).

CONCLUSIONS

Different GDHTs likely have different and outcome-dependent effectiveness in surgical patients. GDHTs aimed at intravascular volume, stroke volume, and cardiac output optimization are likely most effective as per the overall evidence.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration number: CRD42020159978.

Spie charts for quantifying treatment effectiveness and safety in multiple outcome network meta-analysis: a proof-of-concept study

BACKGROUND

Network meta-analysis (NMA) simultaneously synthesises direct and indirect evidence on the relative efficacy and safety of at least three treatments. A decision maker may use the coherent results of an NMA to determine which treatment is best for a given outcome. However, this evidence must be balanced across multiple outcomes. This study aims to provide a framework that permits the objective integration of the comparative effectiveness and safety of treatments across multiple outcomes.

METHODS

In the proposed framework, measures of each treatment's performance are plotted on its own pie chart, superimposed on another pie chart representing the performance of a hypothetical treatment that is the best across all outcomes. This creates a spie chart for each treatment, where the coverage area represents the probability a treatment ranks best overall. The angles of each sector may be adjusted to reflect the importance of each outcome to a decision maker. The framework is illustrated using two published NMA datasets comparing dietary oils and fats and psoriasis treatments. Outcome measures are plotted in terms of the surface under the cumulative ranking curve. The use of the spie chart was contrasted with that of the radar plot.

RESULTS

In the NMA comparing the effects of dietary oils and fats on four lipid biomarkers, the ease of incorporating the lipids' relative importance on spie charts was demonstrated using coefficients from a published risk prediction model on coronary heart disease. Radar plots produced two sets of areas based on the ordering of the lipids on the axes, while the spie chart only produced one set. In the NMA comparing psoriasis treatments, the areas inside spie charts containing both efficacy and safety outcomes masked critical information on the treatments' comparative safety. Plotting the areas inside spie charts of the efficacy outcomes against measures of the safety outcome facilitated simultaneous comparisons of the treatments' benefits and harms.

CONCLUSIONS

The spie chart is more optimal than a radar plot for integrating the comparative effectiveness or safety of a treatment across multiple outcomes. Formal validation in the decision-making context, along with statistical comparisons with other recent approaches are required.

Survey data to identify the selection criteria used by breeders of four strains of Pakistani beetal goats

This article presents raw data from a survey conducted to identify the selection criteria of breeders raising either of four strains of Beetal goats, namely Beetal Faisalabadi, Beetal Makhi-Cheeni, Beetal Nuqri, and Beetal Rahim Yar Khan. After a pre-survey, a questionnaire was developed and a survey was conducted at four sites of the Punjab province of Pakistan: Faisalabad/Sahiwal, Bahawalpur/Bahawalnagar, Rajanpur, and Rahim Yar Khan. Each of these sites was the home tract of one strain. During the survey breeders (n = 162) were asked to rank the traits of their selection criteria based on the relative importance of those traits. Furthermore, the prevailing production system was also characterized by the breeders. For the interpretation of the results of this survey the readers are referred to Ref. [1]. The raw data set provided in this article can be extended in the future to include more strains of Beetal goats as well as other goat breeds. The selection criteria of breeders can change over time. This data set can also be used in future studies to investigate the temporal changes in the relative importance of different traits for the breeders. The factors potentially influencing those changes can also be investigated. This data set can further be utilized to design community based breeding plans tailored to the needs of the goat farming community.

Ranking provincial power generation sources of China: a decision-maker preferences based integrated multi-criteria framework

The ranking of power generation sources is a very important prerequisite for power generation installation planning and power supply security. This study proposed a new multi-criteria system for ranking regional power generation sources in one country, including resources, economy, technology, environment, and society, using 11 sub-criteria. Based on the system, a novel decision-maker (DMs) preference-based integrated MCDM framework involving four methods (Visekriterijumsko Kompromisno Rangiranje (VIKOR), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), and Weighted Sum Method (WSM)) was developed for ranking six power generation sources (thermal, nuclear, wind, hydro, solar PV, and biomass) at the level of China's 30 provinces. Six different preferences of DMs are considered in the ranking according to five criteria. The results show that wind should be the power generation source given the top priority in most provinces in China whereas nuclear power and thermal power are the last choice for 26 provinces. Biomass is the most preferable power source for 17 provinces based on technological preference in which DMs regard the technology criteria is prior to all other criteria. Thermal power would still the preferred or secondary power source for provinces rich in coal resources such as Shanxi, Inner Mongolia, Henan, and Shaanxi.

Applying sorting algorithms to sensory ranking tests - A proof of concept study

In a sensory or consumer setting, panelists are commonly asked to rank a set of stimuli, either by the panelist's liking of the samples, or by the samples' perceived intensity of a particular sensory note. Ranking is seen as a “simple” task for panelists, and thus is usually performed with minimal (or no) specific instructions given to panelists. Despite its common usage, seemingly little is known about the specific cognitive task that panelists are performing when ranking samples. It becomes quickly unruly to suggest a series of paired comparisons between samples, with 45 individual paired comparisons needed to rank 10 samples. Comparing a number of elements with regards to a scaled value is common in computer science, with a number of differing sorting algorithms used to sort arrays of numerical elements. We compared the efficacy of the most basic sorting algorithm, Bubble Sort (based on comparing each element to its neighbor, moving the higher to the right, and repeating), vs a more advanced algorithm, Merge Sort (based on dividing the array into sub arrays, sorting these sub arrays, and then combining), in a sensory ranking task of 6 ascending concentrations of sucrose (n = 73 panelists). Results confirm that as seen in computer science, a Merge Sort procedure performs better than Bubble Sort in sensory ranking tasks, although the perceived difficulty of the approach suggests panelists would benefit from a longer period of training. Lastly, through a series of video recorded one-on-one interviews, and an additional sensory ranking test (n = 78), it seems that most panelists natively follow a similar procedure to Bubble Sorting when asked to rank without instructions, with correspondingly inferior results to those that may be obtained if a Merge Sorting procedure was applied. Results suggests that ranking may be improved if panelists were given a simple set of instructions on the Merge Sorting procedure.

•

Ranking is common in both sensory and consumer testing.

•

Despite its wide adoption, no standard set of procedural instructions exists on how to perform a ranking exercise.

•

This report details 2 approaches, borrowed from computer science, to ensure that all panelists are ranking using the same strategy.

•

In addition, a separate panel were asked to rank the same samples without instruction, with in-depth follow up interviews on a smaller cohort to delineate the natural approach.

•

The strategies are contrasted in terms of ease, speed and accuracy.

A ranking method of chemical substances in foods for prioritisation of monitoring, based on health risk and knowledge gaps

Chemical contaminants are present in all foods. Data on the occurrence of contaminants in foods that are often consumed or contain high contaminant concentrations are critical for the estimation of exposure and evaluation of potential negative health effects. Due to limited resources for the monitoring of contaminants and other chemical substances in foods, methods for prioritisation are needed. We have developed a straightforward semi-quantitative method to rank chemical substances in foods for monitoring as part of a risk-based food control. The method is based on considerations of toxicity, level of exposure including both occurrence in food and dietary intake, vulnerability of one or more population groups due to high exposure because of special food habits or resulting from specific genetic variants, diseases, drug use or age/life stages, and the adequacy of both toxicity and exposure data. The chemical substances ranked for monitoring were contaminants occurring naturally, unintentionally or incidentally in foods or formed during food processing, and the inclusion criteria were high toxicity, high exposure and/or lack of toxicity or exposure data. In principle, this method can be used for all classes of chemical substances that occur in foods, both unintended contaminants and deliberately added chemical substances. Foods considered relevant for monitoring of the different chemical substances were also identified. The outcomes of ranking exercises using the new method including considerations of vulnerable groups and adequacy of data and a shortened version based on risk considerations only were compared. The results showed that the resolution between the contaminants was notably increased with the extended method, which we considered as advantageous for the ranking of chemical substances for monitoring in foods.

A proof-of-concept framework for the preference elicitation and evaluation of health informatics technologies: the online PRESENT patient experience dashboard as a case example

BACKGROUND

Constrained budgets within healthcare systems and the need to efficiently allocate resources often necessitate the valuation of healthcare interventions and services. However, when a technological product is developed for which no market exists it is a challenge to understand how to place the product and which specifications are the most sought after and important for end users. This was the case for a dashboard we developed, displaying analyses of patient experience survey free-text comments.

METHOD

We describe a customisation and evaluation process for our online dashboard that addresses this challenge, using a Discrete Choice Experiment (DCE). We were not interested in the exact content of the dashboard, which was determined in previous stages of our larger study, but on the availability of features and customization options and how they affect individuals' purchasing behaviours.

RESULTS

Our DCE completion rate was 33/152 (22%). Certain features were highly desirable - the search function, filtering, and upload own data - and would contribute significant added value to the dashboard. Purchasing behaviour was dependent on the dashboard features, going from a 10 to 90% probability to purchase when we moved from a baseline to a fully-featured dashboard. The purchasing behaviour elicited in this study assumes individuals already have buy-in to the online dashboard, so we assessed only how the various features of our dashboard influence the probability of purchasing the product. Results were used to inform development of a generic checklist of desirable healthcare dashboard features as well as to refine the dashboard itself. Our study suggests the development of the online dashboard and its roll-out in the market would result in a positive net benefit in terms of utilities. The cost-benefit analysis offers a lower bound estimate of the net benefit as it does not acknowledge or incorporate non-monetary benefits that would result from the use of the online dashboard, such as from improved healthcare management.

CONCLUSION

DCEs can be successfully used to inform development of an online dashboard by determining preferences for particular features and customisation options and how this affects individuals' purchasing behaviours. The process should be transferable to the development of other technologies.

Challenging the selection for consistency in the rank of endurance competitions

Background

Control of the environmental variability by genetic selection offers possibilities for new selection objectives for productive traits. This methodology aims at reducing heterogeneity in productive traits and has been applied to several traits and species for which animal homogeneity is profitable. In horse breeding programmes, rank in competitions is a common selection objective but has been challenging to model. In this study, the parameters of environmental variability for the rank of a horse were computed to analyse the capability of a horse to maintain the best ranking across competitions that consist of long-distance races in which the adapted physical condition of the horse is essential. The genetic component of the environmental variance for the rank in endurance competitions was evaluated, which resulted in proposing a new transformation of horse scores in competitions.

Results

Homogeneous and heterogeneous variance models were compared by assaying three random effects that affect both the rank and its variability, using endurance ride data consisting of 2863 records. The pedigree relationship matrix contained 5931 animals. The rank trait was transformed into a normalized variable to prevent false estimates of the genetic correlation by inappropriate artificial skewness. The models included the number of participants in the race, sex, and age as systematic effects. The rider, the rider-horse interaction, or an environmental permanent effect were tested as random effects, in addition to additive genetic and residual effects. The models were analysed using the GSEVM program. Estimates of heritability for rank ranged from 0.12 to 0.15. The heterogeneous variance model that fitted the rider was assessed as the best model based on the deviance information criterion. Estimates of genetic variance for rank variability ranged from 0.12 to 0.13. The genetic correlation between the rank and its environmental variability was low and did not differ from 0.

Conclusions

These results offer an opportunity to select animals for canalization by reducing the variability of race results and achieving the best positions, which could be a new selection objective by weighting estimated breeding values for rank and its variability in a selection index.

Global and local diet popularity rankings, their secular trends, and seasonal variation in Google Trends data

OBJECTIVES

The Internet has become the main source of health-related information including nutrition. The aim of this study was to rank the most popular diets among Google users globally and regionally in addition to secular and seasonal trends in the years 2004 to 2019.

METHODS

We used Google Trends (GT) to identify and analyze course over time and regional interest of 47 topics related to diets. We analyzed secular trends using the Seasonal Mann-Kendall test and seasonal variation using time-series decomposition. The topic "Mediterranean diet" (MedD) was used as a benchmark. We calculated the interest of all topics in proportion to the relative search volume (RSV) of MedD.

RESULTS

Globally, Google users were particularly interested in veganism (19.54 times higher than MedD), vegetarianism (15.09 times higher than MedD), and gluten-free diet (11.11 times higher than MedD). Veganism was the most frequently searched diet type in 23 countries followed by vegetarianism (14), ketogenic diet (7), and low-carbohydrate diet (7). Whereas an increase of RSV over time was observed for 23 diets, a decrease was noted for 20. The most dynamic increase was found for FODMAP (6.12 RSV/year), gluten-free diet (5.95 RSV/year), and raw veganism (5.72 RSV/year). Sharp declines concerned negative-calorie food (-4.34 RSV/year), macrobiotic diet (-3.89 RSV/year), and cabbage soup diet (-3.50 RSV/year). The interest in most diets falls in December but peaks in January.

CONCLUSION

Veganism, vegetarianism, and gluten-free diet attract the largest public interest globally. Both secular trends and seasonal variation shape the ever-changing landscape of diet popularity. GT holds promise as a valuable tool in local and international nutrition research.

Heterogeneous information network based clustering for precision traditional Chinese medicine

Background

Traditional Chinese medicine (TCM) is a highly important complement to modern medicine and is widely practiced in China and in many other countries. The work of Chinese medicine is subject to the two factors of the inheritance and development of clinical experience of famous Chinese medicine practitioners and the difficulty in improving the service capacity of basic Chinese medicine practitioners. Heterogeneous information networks (HINs) are a kind of graphical model for integrating and modeling real-world information. Through HINs, we can integrate and model the large-scale heterogeneous TCM data into structured graph data and use this as a basis for analysis.

Methods

Mining categorizations from TCM data is an important task for precision medicine. In this paper, we propose a novel structured learning model to solve the problem of formula regularity, a pivotal task in prescription optimization. We integrate clustering with ranking in a heterogeneous information network.

Results

The results from experiments on the Pharmacopoeia of the People’s Republic of China (ChP) demonstrate the effectiveness and accuracy of the proposed model for discovering useful categorizations of formulas.

Conclusions

We use heterogeneous information networks to model TCM data and propose a TCM-HIN. Combining the heterogeneous graph with the probability graph, we proposed the TCM-Clus algorithm, which combines clustering with ranking and classifies traditional Chinese medicine prescriptions. The results of the categorizations can help Chinese medicine practitioners to make clinical decision.

Risk-management tool for environmental prioritization of pharmaceuticals based on emissions from hospitals

Over the past decade, the health care sector has become increasingly aware of the impact of pharmaceutical emissions to the environment. Yet, it remains unclear which compounds are the most relevant to address and at what point emission control is most effective. This study presents a modelling framework to prioritize pharmaceuticals based on their relative risks for aquatic organisms, using purchase and prescription data from hospitals. The framework consists of an emission prediction module and a risk prioritization module. The emission prediction module accounts for three different routes of intake (oral, intravenous, rectal), for non-patient consumption, and for delayed athome excretion due to relatively long half-lives or prescription durations of selected pharmaceuticals. We showcase the modelling framework with 16 pharmaceuticals administered at two Dutch academic hospitals. Predictions were validated with experimental data from passive sampling in the sewer system. With the exception of metformin, all predictions were within a factor of 10 from measurements. The risk prioritization module ranks each pharmaceutical based on its predicted relative risk for aquatic organisms. The resulting prioritization suggests that emission mitigation strategies should mainly focus on antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs).

The Influence of Gender and Underrepresented Minority Status on Medical Student Ranking of Residency Programs

BACKGROUND

Physician diversity is linked to improved quality of care of diverse patient populations. The transition from medical school to residency is an opportunity to improve and increase workforce diversity in all specialties. However, there is limited published literature on the factors contributing to the ranking of residency programs on women and underrepresented minorities (URMs).

OBJECTIVE

To characterize factors medical students used to rank residency programs and describe any differences based on race/ethnicity or gender.

METHODS

A mixed-methods study consisting of a web-based survey and semi-structured interviews with National Resident Matching Program (NRMP) participating graduates over a two-year period. The survey assessed demographics and a 6-point Likert scale rating of various factors used to rank residency programs. Unpaired student t-tests were used to compare means. A subset of students was interviewed and a modified grounded theory approach identified decision-making themes as well as the role of gender and URM status.

RESULTS

Out of a total of 316 invitations sent, 148 completed the survey (46.8% response rate), of which 21% of respondents self-identified as URMs. The majority of respondents graduated in 2014 (53%), and were male (51%). Participants ranked program atmosphere, reputation, location, and proximity to family the highest. URM students ranked patient population (p < 0.01), revisit opportunities (p = 0.04), gender diversity (p < 0.01), and ethnic diversity (p < 0.01) significantly higher than non-URM students. Female students ranked patient population (p < 0.01) and gender diversity (p < 0.01) significantly higher than males. Qualitative findings revealed differences in perceptions by URMs and non-URMs of patient population, revisit opportunities, gender diversity, and ethnic diversity.

CONCLUSIONS

While all students prioritized pragmatic factors, women and URM students assess and weigh additional factors related to culture, inclusion, and diversity more than others. By tailoring recruitment strategies to meet the expectations of women and URMs, residency programs can better meet goals in becoming more diverse and inclusive.

Impact of different types of olive oil on cardiovascular risk factors: A systematic review and network meta-analysis

BACKGROUND AND AIM

This network meta-analysis (NMA) compares the effects of different types of olive oil (OO) on cardiovascular risk factors.

METHODS AND RESULTS

Literature search was conducted on three electronic databases (Medline, Web of Science, and Cochrane Central).

INCLUSION CRITERIA

Randomized controlled trials (RCTs) (≥3 weeks duration of intervention) comparing at least two of the following types of OO: refined OO (ROO), mixed OO (MOO), low phenolic (extra) virgin OO (LP(E)VOO), and high phenolic (extra) virgin OO (HP(E)VOO). Random-effects NMA was performed for seven outcomes; and surface under the cumulative ranking curve (SUCRA) was estimated, using an analytical approach (P-score). Thirteen RCTs (16 reports) with 611 mainly healthy participants (mean age: 26-70 years) were identified. No differences for total cholesterol, HDL-cholesterol, triacylglycerols, and diastolic blood pressure were observed comparing ROO, MOO, LP(E)VOO and HP(E)VOO. HP(E)VOO slightly reduce LDL-cholesterol (LDL-C) compared to LP(E)VOO (mean difference [MD]: -0.14 mmol/L, 95%-CI: -0.28, -0.01). Both, HP(E)VOO and LP(E)VOO reduces SBP compared to ROO (range of MD: -2.99 to -2.87 mmHg), and HP(E)VOO may improve oxidized LDL-cholesterol (oxLDL-C) compared to ROO (standardized MD: -0.68, 95%-CI: -1.31, -0.04). In secondary analyses, EVOO may reduce oxLDL-C compared to ROO, and a dose-response relationship between higher intakes of phenolic compounds from OO and lower SBP and oxLDL-C values was detected. HP(E)VOO was ranked as best treatment for LDL-C (P-score: 0.83), oxLDL-C (0.88), and SBP (0.75).

CONCLUSIONS

HP(E)VOO may improve some cardiovascular risk factors, however, public health implications are limited by overall low or moderate certainty of evidence.

Drought risk evaluation model with interval number ranking and its application

In the context of more and more extreme weather events around the world, it is of great practical significance to accurately monitor drought and evaluate its drought risk for the sustainable development of regional agriculture. This study aims at establishing a regional drought risk evaluation method based on remote sensing drought monitoring and uncertainty method. In this paper, multi-model optimization method is adopted. 5 models were used to invert soil moisture content. After analysis and verification, the most suitable drought monitoring model of Temperature and vegetation polynomial model (TVPM) was obtained. The uncertainty method is introduced in this paper using Statistical-based interval weight determination of evaluation index method and Interval number sorting method based on two-dimensional information to establish drought risk evaluation model. On this basis, it was applied in Heilongjiang province of China to evaluate and rank the risk of drought in 8 regions in April 2018. The ranking result was: Nenjiang > Boli > Linkou > Wudalianchi > Ning'an > Suifenhe > Hailin > Yanshou. The results show that the evaluation method based on interval number can better deal with the uncertainty in reality.

Measuring rank robustness in scored protein interaction networks

BACKGROUND

Protein interaction databases often provide confidence scores for each recorded interaction based on the available experimental evidence. Protein interaction networks (PINs) are then built by thresholding on these scores, so that only interactions of sufficiently high quality are included. These networks are used to identify biologically relevant motifs or nodes using metrics such as degree or betweenness centrality. This type of analysis can be sensitive to the choice of threshold. If a node metric is to be useful for extracting biological signal, it should induce similar node rankings across PINs obtained at different reasonable confidence score thresholds.

RESULTS

We propose three measures-rank continuity, identifiability, and instability-to evaluate how robust a node metric is to changes in the score threshold. We apply our measures to twenty-five metrics and identify four as the most robust: the number of edges in the step-1 ego network, as well as the leave-one-out differences in average redundancy, average number of edges in the step-1 ego network, and natural connectivity. Our measures show good agreement across PINs from different species and data sources. Analysis of synthetically generated scored networks shows that robustness results are context-specific, and depend both on network topology and on how scores are placed across network edges.

CONCLUSION

Due to the uncertainty associated with protein interaction detection, and therefore network structure, for PIN analysis to be reproducible, it should yield similar results across different confidence score thresholds. We demonstrate that while certain node metrics are robust with respect to threshold choice, this is not always the case. Promisingly, our results suggest that there are some metrics that are robust across networks constructed from different databases, and different scoring procedures.

Comparing the topological rank of journals in Web of Science and Mendeley

Recently, there has been a surge of interest in new data emerged due to the rapid development of the information technologies in scholarly communication. Since the 2010s, altmetrics has become a common trend in scientometric research. However, researchers have not treated in much detail the question of the probability distributions underlying these new data. The principal objective of this study was to investigate one of the classic problems of scientometrics-the problem of citation and readership distributions. The study is based on the data obtained from two information systems: Web of Science and Mendeley. Here we based on the concept of the cumulative empirical distribution function to explore the differences and similarities between citations and readership counts of biological journals indexed in Web of Science and Mendeley. The basic idea was to determine, for any journal, a "size" (it is said to be the topological rank) of citation and readership empirical cumulative distributions, and then to compare distributions of the topological ranks of Web of Science and Mendeley. In order to verify our model, we employ it to the bibliometric and altmetric research of 305 biological journals indexed in Journal Citation Reports 2015. The findings show that both distributions of the topological rank of biological journals are statistically close to the Wakeby distribution. The findings presented in this study add to our understanding of information processes of the scholarly communication in the new digital environment.

Some Effective Factors on the Webometric Status of Selected Universities of Medical Sciences: Lessons Learned from Iran

BACKGROUND

Web is considered an important tool for formal and informal communication and cooperation among members of the community including researchers. Universities' websites played a significant role in the development of social structure through the creation of cyberspace. We aimed to evaluate the growth and Webometrics rankings of the country's medical universities web sites base on interventional approach.

METHODS

This interventional study assessed Iranian universities of medical sciences' websites from the periods of Jan 2015, Jul 2015 and Jan 2016. Medical universities websites were ranked based on four Webometrics indicators; PRESENCE, VISIBILITY, OPENNESS, and EXCELLENCE. To enhance the ranking of websites, 4 strategies were introduced in 3 seminars conducted from Jan to Jul 2015. Information needed from these 30 medical universities were obtained from Webometrics Ranking of World Universities based on 4 indicators and were collected in 2 steps (before and after introduction of strategies).

RESULTS

About 21% to 70% of the country's medical universities have increased ranking after the interventions while 9% to 30% obtained a downward trend in their rankings, Tehran University of Medical Sciences obtained the highest rank followed by Shahid Beheshti University of Medical Sciences and the third rank was obtained by Shiraz University of Medical Sciences.

CONCLUSION

The presence of websites play a vital role in the academic community. Doubtlessly, the idea of designing and launching a website without any knowledge of the principles and standards would be problematic and impossible.

Accuracy of genomic-polygenic estimated breeding value for milk yield and fat yield in the Thai multibreed dairy population with five single nucleotide polymorphism sets

Objective

The objectives were to compare variance components, genetic parameters, prediction accuracies, and genomic-polygenic EBV rankings for milk yield (MY) and fat yield (FY) in the Thai multibreed dairy population computed using five SNP sets from GeneSeek GGP80K chip.

Methods

The dataset contained monthly MY and FY of 8,361 first-lactation cows from 810 farms. Variance components, genetic parameters, and EBV for five SNP sets from the GeneSeek GGP80K chip were obtained using a 2-trait single-step average-information REML procedure. The SNP sets were the complete SNP set (all available SNP; SNP100), top 75% set (SNP75), top 50% set (SNP50), top 25% set (SNP25) and top 5% set (SNP5). The 2-trait models included herd-year-season, heterozygosity and age at first calving as fixed effects, and animal additive genetic and residual as random effects.

Results

The estimates of additive genetic variances for MY and FY from SNP subsets were mostly higher than those of the complete set. The SNP25 MY and FY heritability estimates (0.276 and 0.183) were higher than those from SNP75 (0.265 and 0.168), SNP50 (0.275 and 0.179), SNP5 (0.231 and 0.169) and SNP100 (0.251and 0.159). The SNP25 EBV accuracies for MY and FY (39.76% and 33.82%) were higher than for SNP75 (35.01% and 32.60%), SNP50 (39.64% and 33.38%), SNP5 (38.61% and 29.70%) and SNP100 (34.43% and 31.61%). All rank correlations between SNP100 and SNP subsets were above 0.98 for both traits, except for SNP100 and SNP5 (0.93 for MY; 0.92 for FY).

Conclusion

The high SNP25 estimates of genetic variances, heritabilities, EBV accuracies, and rank correlations between SNP100 and SNP25 for MY and FY indicated that genotyping animals with SNP25 dedicated chip would be a suitable alternative to maintain genotyping costs low while speeding up genetic progress for MY and FY in the Thai dairy population.

Challenges in electrochemical remediation of chlorinated solvents in natural groundwater aquifer settings

Establishment of electrochemical zones for remediation of dissolved chlorinated solvents in natural settings was studied. An undivided 1D-experimental column set-up was designed for the assessment of the influence of site-extracted contaminated groundwater flowing through a sandy aquifer material, on the execution of laboratory testing. A three-electrode system composed of palladium coated pure iron cathodes and a cast iron anode was operated at 12 mA under varying flow rates. The natural settings added complexity through a diverse groundwater chemistry and resistance in the sand. In addition, significant precipitation of iron released through anode corrosion was observed. Nevertheless, the complex system was successfully modelled with a simple geochemical model using PHREEQC. A ranking of the significances of system parameters on the laboratory execution of electrochemical remediation in natural settings was proposed: Geological properties > anode corrosion > site-extracted contaminated groundwater > the carbonate system > sulphate > hydrology > less significant unidentified parameters. This study provides insight in actual challenges that need to be overcome for in situ electrochemical remediation.

Measuring positive health: for now, a bridge too far

OBJECTIVES

Huber et al. introduced a new concept 'positive health', defined as 'the ability to adapt and self-manage in the face of social, physical and emotional challenges' and suggested a conceptual model comprising six domains covering 32 aspects. Our aim was to generate items and pilot test an outcome measurement instrument for measuring 'positive health' in Dutch adult citizens.

STUDY DESIGN

A mixed-method study: a literature search, a qualitative study with interviews and a quantitative ranking study for the development phase, to be followed by a content validity study for the validation phase.

METHODS

We developed items based on the concept elicitation study of Huber et al. A ranking study with end users, Dutch citizens and members of an Expert Group was performed for item reduction. Content validity of the prefinal questionnaire was evaluated.

RESULTS

A prefinal 46-item questionnaire was developed. The results of the content validity study, however, showed major concerns with regard to relevance, comprehensiveness and comprehensibility of the questionnaire.

CONCLUSIONS

Because of major concerns regarding the conceptual model of 'positive health', it was not possible to develop a valid questionnaire to measure 'positive health'. Future research should focus on the refinement of the conceptualization of 'positive health' before an adequate measurement instrument could be developed that can be used for outcome measurement purposes.

Review and ranking of crash risk factors related to the road infrastructure

The objective of this paper is the review and comparative assessment of infrastructure related crash risk factors, with the explicit purpose of ranking them based on how detrimental they are towards road safety (i.e. crash risk, frequency and severity). This analysis was carried out within the SafetyCube project, which aimed to identify and quantify the effects of risk factors and measures related to behaviour, infrastructure or vehicles, and integrate the results in an innovative road safety Decision Support System (DSS). The evaluation was conducted by examining studies from the existing literature. These were selected and analysed using a specifically designed common methodology. Infrastructure risk factors were structured in a hierarchical taxonomy of 10 areas with several risk factors in each area (59 specific risk factors in total), examples include: alignment features (e.g. horizontal-vertical alignment deficiencies), cross-section characteristics (e.g. superelevation, lanes, median and shoulder deficiencies), road surface deficiencies, workzones, junction deficiencies (interchange and at-grade) etc. Consultation with infrastructure stakeholders (international organisations, road authorities, etc.) took place in dedicated workshops to identify user needs for the DSS, as well as "hot topics" of particular importance. The following analysis methodology was applied to each infrastructure risk factor: (i) A search for relevant international literature, (ii) Selection of studies on the basis of rigorous criteria, (iii) Analysis of studies in terms of design, methods and limitations, (iv) Synthesis of findings - and meta-analysis, when feasible. In total 243 recent and high quality studies were selected and analysed. Synthesis of results was made through 39 'Synopses' (including 4 original meta-analyses) on individual risk factors or groups of risk factors. This allowed the ranking of infrastructure risk factors into three groups: risky (11 risk factors), probably risky (18 risk factors), and unclear (7 risk factors).