Strong Influence Research Articles

Looking into how nickel doping affects the structure, morphology, and optical properties of TiO2 nanofibers

In this paper, we have systematically studied the structural, morphological, and optical properties of Ni-doped TiO2, synthesized via a simple, cost-effective electrospinning method followed by calcination at 500 °C. The nanofibers with a core-shell structure were relatively homogeneous, smooth and randomly oriented, and there were no significant differences in fiber diameters due to Ni2+ content. Core loss mapping using electron energy loss spectroscopy confirmed an even distribution of titanium and relatively uniform nickel in the fibers. It was found that doping with 0.5 mol.% Ni2+ decreased the rutile content, while doping with 1 mol.% Ni2+resulted in a pure anatase phase with a significantly increased specific surface area (36.6 m2/g). Further increase in Ni2+ content (3–10 mol.%) not only prolonged the response of TiO2 nanofibers to visible light, but also increased the specific surface area (49.5 m2/g), decreased crystallite size (7 nm), and increased rutile content in TiO2 (33 wt.%). Photoluminescence analysis revealed that doping TiO2 with different amounts of Ni2+ leads to a gradual decrease of emission spectra intensity and red shift in the maxima positions. The XPS results confirmed that as the Ni2+ content enlarged, the Ti2+ and Ti3+ content increased significantly, effectively promoting the formation of oxygen vacancies. Raman analysis showed that an increase in nickel content (3–5 mol.%) led to a decrease and shift in peak intensity due to Ti3+ formation and also the possible presence of NiTiO3 phases. HRTEM analysis showed that Ni was doped into the substitution sites of both the anatase and rutile TiO2 lattice but had a stronger influence on the distortion of the anatase phase. The photocatalytic activity of Ni-doped TiO2 nanofibers was explored by analyzing the degradation of an antibiotic, oxytetracycline, monitored in laboratory conditions under visible light irradiation. After 60 min of irradiation, the degradation of OTC with 1Ni-TiO2 reached 76.4 % and with 10Ni-TiO2 70.5 %.

Relating laser powder bed fusion process parameters to (micro)structure and to soft magnetic behaviour in a Fe-based bulk metallic glass

This work aims to establish fundamental processing-(micro)structure-property links in a commercial Fe-based Kuamet6B2 bulk metallic glass (BMG) processed by laser powder bed fusion (LPBF). With that purpose, amorphous powders were processed using a pulsed-wave system and a simple meander strategy. The laser power, the scan speed, and the hatch distance were varied over wide intervals within the conduction regime. The processability window leading to samples with good dimensional accuracy and mechanical stability was determined. Within this window, the manufactured samples were crystalline/amorphous composites and the crystalline regions were formed by equiaxed ultrafine and nanograins with random orientations. Processing parameters yielding the densest prints caused severe crystallization while, conversely, parameter sets allowing the material to retain a high amorphous fraction led to significant lack-of-fusion defects and residual cracking along directions perpendicular to crystalline/amorphous interfaces. Comparatively, for a fixed hatch distance, the scanning speed had a stronger effect than the laser power in the resulting amorphous fraction due to its stronger influence on the melt pool size and, in turn, on the corresponding HAZ volume. The saturation magnetization and the coercive field were inversely related to the amorphous fraction. This work allows to derive fundamental guidelines for the successful additive manufacturing of soft magnetic Fe-based bulk metallic glasses (BMGs) by laser powder bed fusion (LPBF).

Abstract PS10-09: Development of an absolute risk prediction model for premenopausal breast cancer in an international consortium

Abstract Risk prediction models that have been developed for overall breast cancer risk are based on a limited number of premenopausal cases in individual cohorts. As some risk factors differ in their associations with pre- versus postmenopausal breast cancer, a distinct risk prediction model is needed for premenopausal breast cancer. We developed a risk prediction model for premenopausal breast cancer using 779,601 participants and 9,665 incident cases from 18 prospective studies within the Premenopausal Breast Cancer Collaborative Group (PBCCG), across North America (N=9), Europe (N=6), Australia (N=1), and Asia (N=2). Data were split, within each cohort, into training (2/3) and testing (1/3) datasets. Individual risk was assessed in five-year intervals, using variables reported at the start of the time interval. Cox proportional hazards regression was used to model risk factors in a backwards-selection method, stratified by cohort: age at menarche, age at first birth, parity, breastfeeding (months), height (cm), BMI (kg/m2), BMI at age 18, recent weight change (kg), alcohol consumption (drinks/week), first-degree family history of breast cancer, and personal history of benign breast disease. To enable the use of information from all cohorts despite differences in missing variables by design, cohorts were grouped by available variables and risk models were fit for each group. In cohorts with incomplete data, model coefficients were adjusted based on the correlation of covariates with the missing variables in the complete case dataset. Coefficients were meta-analyzed with inverse variance weighting to obtain final coefficients. Discrimination was evaluated in the testing dataset by calculation of the c-index. Work is ongoing to calibrate the model based on five-year absolute risk using GLOBOCAN continent- and age-specific incidence rates to represent baseline risk. The final model included age at menarche, parity, height, BMI, BMI at age 18, first-degree family history of breast cancer, and history of benign breast disease. Young adulthood BMI and BMI (at start of the 5-year risk interval) were associated with a decreased risk (Hazard Ratio (HR) (95% confidence interval (CI)) per 5 kg/m2 = 0.87 (0.81-0.93) and 0.90 (0.86-0.95), respectively) as was parity (HR (95% CI) = 0.92 (0.90-0.94)). Height was associated with increased risk (HR (95% CI) per 10 cm = 1.14 (1.07-1.21)), while history of benign breast disease and family history were associated with larger increases in risk (HR (95% CI) = 1.64 (1.30-2.06) and 1.76 (1.63-1.90), respectively). Model discrimination was higher than that reported for women under 50 years in existing breast cancer risk prediction models considering clinical factors (AUC (95% CI) = 0.61 (0.59-0.62)). Calibration of absolute 5-year risk is ongoing. Several factors driving risk prediction of postmenopausal breast cancer have similar influence on risk of premenopausal breast cancer, while family history has a stronger influence on premenopausal breast cancer risk. Our model demonstrates acceptable discrimination and will enable individual 5-year absolute risk prediction for premenopausal breast cancer. Citation Format: Kristen Brantley, Michael Jones, Minouk Schoemaker, Hazel Nichols, Anthony Swerdlow, Robert MacInnis, Roger Milne, Tess Clenenden, Yu Chen, Xiao-Ou Shu, Wei Zheng, Woon-Puay Koh, Jian-Min Yuan, Cari Kitahara, Martha Linet, Dale Sandler, Bernard A Rosner, Peter Kraft, A. Heather Eliassen. Development of an absolute risk prediction model for premenopausal breast cancer in an international consortium [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS10-09.

Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data

BackgroundMachine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios.MethodsWe compared the use of six ML classifiers in predicting CVD risk factors using blood-derived metabolomics, epigenetics and transcriptomics data. Upstream omic dimension reduction was performed using either unsupervised or semi-supervised autoencoders, whose downstream ML classifier performance we compared. CVD risk factors included systolic and diastolic blood pressure measurements and ultrasound-based biomarkers of left ventricular diastolic dysfunction (LVDD; E/e' ratio, E/A ratio, LAVI) collected from 1,249 Finnish participants, of which 80% were used for model fitting. We predicted individuals with low, high or average levels of CVD risk factors, the latter class being the most common. We constructed multi-omic predictions using a meta-learner that weighted single-omic predictions. Model performance comparisons were based on the F1 score. Finally, we investigated whether learned omic representations from pre-trained semi-supervised autoencoders could improve outcome prediction in an external cohort using transfer learning.ResultsDepending on the ML classifier or omic used, the quality of single-omic predictions varied. Multi-omics predictions outperformed single-omics predictions in most cases, particularly in the prediction of individuals with high or low CVD risk factor levels. Semi-supervised autoencoders improved downstream predictions compared to the use of unsupervised autoencoders. In addition, median gains in Area Under the Curve by transfer learning compared to modelling from scratch ranged from 0.09 to 0.14 and 0.07 to 0.11 units for transcriptomic and metabolomic data, respectively.ConclusionsBy illustrating the use of different machine learning strategies in different scenarios, our study provides a platform for researchers to evaluate how the choice of omics, ML classifiers, and dimension reduction can influence the quality of CVD risk factor predictions.

Regional characteristics of extreme precipitation events over Aotearoa New Zealand

We document 1394 extreme precipitation events (EPEs) over Aotearoa New Zealand’s (ANZ) Regional Councils between March 1996 and December 2021. The characteristics of EPEs are documented using a novel spatio-temporal framework that diagnoses the peak intensity, duration, and accumulation of the EPE using the ERA-5 and MERRA-2 reanalysis products. Properties of EPEs were evaluated according to region across ANZ, and clear regional differences are highlighted. In particular, it is found that the duration of an EPE has a stronger influence than the peak intensity on the total accumulated precipitation across all regions and precipitation event types (large-scale or convective). Since larger precipitation accumulations have greater potential to cause extensive flooding over larger areas, an important implication is the need for numerical weather prediction in ANZ to forecast the duration of an intense precipitation event adequately in order to improve emergency preparedness.

Spatiotemporal variations in eco-environmental quality and responses to drought and human activities in the middle reaches of the Yellow River basin, China from 1990 to 2022

The middle reaches of the Yellow River basin (MYRB) are among the regions most severely affected by soil erosion globally. It has always held a pivotal role in soil and water conservation and ecological restoration efforts in China. Nonetheless, in the face of recurrent drought occurrences and growing human intervention, there have been notable alterations in the eco-environmental quality (EEQ) within the MYRB. However, the influences of drought and human intervention on the EEQ of MYRB remain unclear. In this study, the remote sensing ecological index (RSEI) was applied to quantify the spatiotemporal changes in EEQ and the contributions of drought and land use type transitions to EEQ in the MYRB from 1990 to 2022. The results showed that the EEQ in the MYRB fluctuated significantly and exhibited a weak overall improvement trend over the past 33 years. The proportion of good and excellent grades for EEQ significantly improved, while the proportion of poor and fair grades significantly decreased, especially in the northern regions. The trend of changes in EEQ follows a phased pattern. During the periods of 1990–2002 and 2011–2022, an improving trend is observed, while the period of 2003–2010 shows no significant change in EEQ. Drought had the strongest influence on the EEQ from 2003 to 2010, followed by that from 1990 to 2002, and had a lesser impact in the period from 2011 to 2022. The EEQ was primarily positively influenced by spring, autumn and winter droughts and negatively affected by summer droughts, especially in arid grassland and unused land areas. The EEQ in the MYRB improved primarily during the initial and final phases of the ecological projects, with a lesser impact from drought. The increase in EEQ during the initial phases of ecological project implementation was less noticeable, and drought significantly affected EEQ during this period.

Hierarchical Design of Tissue-Mimetic Fibrillar Hydrogel Scaffolds.

Most tissues of the human body present hierarchical fibrillar extracellular matrices (ECMs) that have a strong influence over their physicochemical properties and biological behavior. Of great interest is the introduction of this fibrillar structure to hydrogels, particularly due to the water-rich composition, cytocompatibility, and tunable properties of this class of biomaterials. Here, the main bottom-up fabrication strategies for the design and production of hierarchical biomimetic fibrillar hydrogels and their most representative applications in the fields of tissue engineering and regenerative medicine are reviewed. For example, the controlled assembly/arrangement of peptides, polymeric micelles, cellulose nanoparticles (NPs), and magnetically responsive nanostructures, among others, into fibrillar hydrogels is discussed, as well as their potential use as fibrillar-like hydrogels (e.g., those from cellulose NPs) with key biofunctionalities such as electrical conductivity or remote stimulation. Finally, the major remaining barriers to the clinical translation of fibrillar hydrogels and potential future directions of research in this field are discussed.

Bison and cattle grazing increase soil nitrogen cycling in a tallgrass prairie ecosystem

Nitrogen (N) is a necessary element of soil fertility and a limiting nutrient in tallgrass prairie but grazers like bison and cattle can also recycle N. Bison and cattle impact the nitrogen (N) cycle by digesting forage that is consumed, and recycled back to the soil in a more available forms stimulating soil microbial N cycling activities. Yet we do not know how both grazers comparatively affect N cycling in tallgrass prairie. Thus, we investigated if bison and cattle had similar impacts on N cycling in annually burned tallgrass prairie relative to ungrazed conditions over a 3-year period (2020–2022) at the Konza Prairie Biological Station. We examined: soil pH, soil water content, mineralized N, nitrification potential, denitrification potential and extracellular enzyme assays. Interannual variability in precipitation controlled soil water and N cycling microbial activities but grazing effects had a stronger influence on N cycling. We found significant differences and increased soil pH, nitrification and denitrification potential and less N limitation in bison vs cattle grazed soils where bison grazed soils exhibited faster N cycling. Differences between the grazers may be attributed to the different management of bison and cattle as both can impact N cycling. Overall, these data provide some evidence that bison and cattle affect N cycling differently at this study site, and improve the ecological understanding of grazer impacts on N cycling dynamics within the tallgrass prairie ecosystem.

Health Motivation in the Influencer Era: Analyzing Entertainment, Personal, and Social Media Role Models

ABSTRACT This research explored how personal and entertainment role models motivate an individual’s health goals, and how following a role model on social media affects this process. A survey of 404 adults revealed that identifying a personal role model, such as a family member, friend, or healthcare professional, had a stronger influence on health motivation in comparison to identifying an entertainment role model. Additionally, our findings indicated that following any role model on social media, whether a personal acquaintance or a celebrity, enhanced motivation. Role model attributes that mediated these relationships included perceived similarity, and in some instances, positive and negative norm deviance. This study highlights the importance of leveraging role model relationships in health communication campaigns and developing authentic influencer-driven health initiatives that feature relatable entertainment figures. Additionally, this research supports the need for further examination of how a role model’s perceived positive or negative norm deviance influences motivational capability. We discuss implications for the motivational theory of role modeling, health promotion, and entertainment education.

The Impact of Ambivalence on Persuasion: A Preliminary Meta-Analysis

This study is the first meta-analysis on the relationship between ambivalence and persuasive outcomes in communication. The meta-analysis included 22 studies with 51 effect sizes based on 14,173 (68–1,604) participants. When all the persuasive outcomes were considered together, ambivalence had a significant but small negative overall impact, r = -0.134, 95% CI [-0.192, -0.076], k = 33, z = -4.471, p < 0.001. Specifically, ambivalence had a small influence on attitude and intention. Its impact on perception and behavior were less pronounced. There was significant heterogeneity across effect sizes in overall persuasion, as well as specific persuasive outcomes. The ambivalence targeting behavior had a stronger influence on attitude than did ambivalence targeting other objects. Methodology significantly moderated the magnitude connecting ambivalence and behavioral intention, studies using experiments yielded greater sizes compared to those using surveys. With respect to context, the influence of ambivalence on attitude was stronger in advertising and health communication, compared to political communication. Studies in advertising yielded significantly greater effect sizes on behavioral intention compared to those in health communication. This study suggests that the implication of ambivalence in communication is likely a complicated and nuanced matter. Limitations and future research have also been outlined.

Analysis of the Evolution of Water Quality in Tanks According to the Connections and Operation Mode: Application to the Most Suitable Design

The analysis and simulation of water quality in distribution networks is a complex issue of great concern today. The analysis of the evolution of water age as a simple indicator of water quality in the network is of great interest in both the design and operation phases. Understanding the factors that have the strongest influence on water quality is key to developing adequate strategies aimed at preserving it. This paper first analyses the factors with the biggest influence on the tank’s water age to support the selection of the most appropriate configuration from the point of view of water quality during design phases. Then, the main considerations when modelling tanks following the different mixing models considered in Epanet are presented. Also, real tank behaviour is characterised through field measurements and Epanet simulations in order to determine the best-fitting mixing model.

The influence of catalyst structure on acidic strength of –SO3H groups for alkyl levulinate synthesis from biomass–derived feedstocks using sulfonic acid functionalized flexible MOF catalyst

A Brönsted acidic BUT−8(Cr)–SO3H metal–organic framework was used for the synthesis of ethyl levulinate from two different biomass–derived platform chemicals viz. furfuryl alcohol and levulinic acid. The catalyst was well characterized using various methods such as PXRD, FTIR spectroscopy, acid–base titration, NH3–TPD, TGA–DTA, SEM, TEM as well as the computational density functional theory (DFT) method. The performance of BUT–8(Cr)–SO3H catalysts was found to be superior to other catalysts (HZSM–5, H–Beta, and amberlyst –15) taken in this study. The sulfonic acid moieties bound to the aromatic naphthalene structure of BUT–8(Cr)–SO3H MOF were found to have a much stronger influence on the catalytic performance than the sulfonic acid moieties bound to the styrene framework of amberlyst–15. The key discoveries regarding the Brönsted acidic characteristics were computed via DFT incorporating the hydrogen removal energy, molecular electrostatic potential maps, and stability of the conjugate base of –SO3H moieties. The trend in the DFT–derived values was in the following order: BUT–8(Cr)–SO3H > Linker–naph –SO3H > Amberlyst–15. The results also indicate the influence of Cr metal on MOF structure to increase the acidic strength of the –SO3H group. The reaction optimization using DOE, recyclability, leaching study, and substrate scope study for both pathways using BUT–8(Cr)–SO3H were obtained.

Net Radiation Drives Evapotranspiration Dynamics in a Bottomland Hardwood Forest in the Southeastern United States: Insights from Multi-Modeling Approaches

Evapotranspiration (ET) is a major component of the water budget in Bottomland Hardwood Forests (BHFs) and is driven by a complex intertwined suite of meteorological variables. The understanding of these interdependencies leading to seasonal variations in ET is crucial in better informing water resource management in the region. We used structural equation modeling and AIC modeling to analyze drivers of ET using Eddy covariance water flux data collected from a BHF located in the Russel Sage Wildlife Management Area (RSWMA). It consists of mature closed-canopy deciduous hardwood trees with an average canopy height of 27 m. A factor analysis was used to characterize the shared variance among drivers, and a path analysis was used to quantify the independent contributions of individual drivers. In our results, ET and net radiation (Rn) showed similar variability patterns with Vapor Pressure Deficit (VPD) and temperature in the spring, summer, and autumn seasons, while they differed in the winter season. The path analysis showed that Rn has the strongest influence on ET variations via direct and indirect pathways. In deciduous forests like BHFs, our results suggest that ET is more energy dependent during the growing season (spring and summer) and early non-growing season (autumn) and more temperature dependent during the winter season.

Моральная конформность при разных формах виртуального давления

<p>The article presents the results of a study aimed at investigating the influence of two forms of virtual group pressure (depersonalized and personalized) on the manifestation of moral conformity. The experimental material consisted of two types of moral scenarios, where participants were asked to assess the acceptability of certain behaviors. Depersonalized pressure was implemented by informing participants, filling out an online form, about the percentage of people responding in a certain way to the presented moral scenarios. As for the personalized form of pressure, a situation simulating interactions among individuals in instant messaging systems (messengers) was utilized. The study involved 112 participants, with 20 of them forming the pilot study sample and 92 — the main experimental sample. The results indicate that social pressure in the virtual environment leads to the emergence of a conforming response, with personalized pressure in real-time having a stronger influence than passive exposure to depersonalized data. The obtained results are explained within the framework of the biological theory of social pressure, which views conforming behavior as a strategy to optimize metabolic costs. The manifestation of a conforming response for both types of moral scenarios and the absence of gender differences in its expression suggest the potential universality of this strategy.</p>

Unravelling the relationship between English reading habits and individual, family and school factors: A Chinese perspective

AbstractIn English teaching, developing students' reading habits has been emphasized for its critical role in promoting overall core competencies. However, in practice, many teachers and parents are not informed of the effective approaches to developing good English reading habits, hindering the overall development of students' core competencies. In light of this, an empirical study was carried out to explore the developmental features of students' English reading habits and individual, family and school factors that affect its development. A questionnaire was adopted to collect quantitative data. A total of 2,130 primary and secondary school students from China participated in the study. The results indicated that their English reading habits varied with age and gender. In terms of age, secondary school students, especially middle school students, performed better than primary school students. In terms of gender, females performed better in reading frequency, reading time and ways of reading, while males had a larger reading volume. Reading habits were found to be impacted by individual, school and family factors. Among them, age had the strongest influence on the development of English reading habits. These findings shed light on effective ways to develop English reading habits, especially in foreign‐language‐learning contexts.

The Impact of Gamification on Slovenian Consumers’ Online Shopping

Gamification involves integrating game mechanics into non-game environments such as business intranets, online communities, websites, and learning management systems to boost participation. Its aim is to actively engage employees, customers, and other stakeholders, fostering collaboration, sharing, and interaction. Gamification is a relatively unfamiliar term in Slovenia. The objective of this study is to investigate the influence of gamification on Slovenian consumers, specifically how it affects the online shopping process and user engagement during purchases. To test the hypotheses, we used appropriate statistical tools: chi-square, Friedman, and Wilcoxon tests. The findings indicate that gamification’s strongest influence is not on the post-purchase evaluation phase but rather on the alternative evaluation phase. It is interesting that highly rated reviewers significantly influence product purchases in online stores, while consumers are unwilling to increase their spending on online purchases in exchange for gamification-related benefits.

Research landscape of abdominal adhesions from 2004 to 2023: A bibliometric analysis

Adhesions are the most common complication of abdominal or pelvic surgery and remain a challenging problem. To better understand the development tendency of abdominal adhesions, we performed a comprehensive bibliometric analysis of the field of abdominal adhesions. In total, 2219 articles regarding abdominal adhesions were screened and analyzed from 3410 manuscripts indexed in the Web of Science-indexed manuscripts regarding abdominal adhesion from 2004 to 2023. A bibliometric analysis was performed, and CiteSpace [version 6.2. R3 (64-bit)] and VOSviewer (version 1.6.19) were used to visualize the results. The number of annual publications showed slight growth before 2019, and the USA contributed the most publications. The most prolific author in this domain was Diamond, while the publications from Ten Broek had the strongest influence. The most popular journal in this field was the Journal of Surgical Research, and the most frequently co-cited journal was Fertility and Sterility. After analyzing the keywords, “prevention”, “surgery” and “peritoneal adhesion” were the 3 most co-cited keywords, while “adhesive small bowel obstruction” was the strongest keyword in the citation burst. Here, for the first time, we used bibliometric methods to study abdominal adhesions over the past ten years. By summarizing the characteristics of publications and predicting future research prospects, we established a framework for researchers and provided a basis for subsequent research.

Spatiotemporal Dynamics and Driving Factors of Ecosystem Services in the Yellow River Delta, China

Exploring the dynamic variation in ecosystem services and clarifying the driving mechanism will help with the formulation of effective ecological environment protection policies. Accordingly, this study sought to reveal the complex variability in ecosystem services in the Yellow River Delta (YRD) at a higher temporal resolution and the transition between the main driving factors in different periods. To this end, we used the economic equivalent factor valuation method to quantify the ecosystem service value from 2000 to 2019 at 5-year intervals. Furthermore, the Geo-detector model was used to identify the main driving factors and interaction between the driving factors of ecosystem service value variations. Then, we analyzed the temporal and spatial dynamic variations in the ecosystem service value and the transitions between the main driving factors in different periods. The main results are as follows: (1) From 2000 to 2019, the ecosystem service value of the YRD showed an increasing trend followed by a decline, whereby water and construction land increased and the other classes of land decreased. Overall, the inland and coastal distribution patterns exhibited low and high values, respectively. (2) The main driving factors of ecosystem service value variations were the NDVI and topographical factors (aspect, slope, elevation), which had q values that were stable and greater than those of the other factors. Although human activity, tourist resource concentration and traffic convenience factors had a comparatively minor effect on ecosystem services, we noted a trend where their effects increased from 2000 to 2019. (3) The detection of interactions revealed complex mechanisms affecting the variation in the YRD. Interactions between variables had a stronger influence than individual effects. The interactions between the Normalized Difference Vegetation Index (NDVI) and other factors consistently had the most significant impact. These interactions primarily shaped the spatial and temporal distribution of ecosystem services. The NDVI and human activities exhibited nonlinear enhancement. These results contribute to improving our cognition of the factors and mechanisms influencing ecosystem services, offering theoretical support for the improvement of ecosystem services in the YRD.

Preparing tomorrow's doctors for the genomics era: A nationwide survey of UK medical students

IntroductionLow confidence in genomics knowledge among clinicians is a major barrier to the integration of genomics into mainstream medicine. Here, we assessed the genomics confidence of UK medical students approaching graduation. MethodsWe conducted a web-based nationwide survey of UK medical students in the final 2 years of study where participants rated their confidence in genomics concepts. ResultsIn total, 145 medical students across 19 medical schools participated. The amount of genomics teaching students reported receiving was positively associated with genomics confidence, with the amount of basic science teaching having the strongest influence. While confidence was high in core genomics principles, such as the difference between DNA, genes and chromosomes (95%), confidence dropped in clinical applications of genomics – only 50% reported a good understanding of the genetic contribution to disease and 28% reported good knowledge of clinically used genomic tests. Overall, 59% reported a poor understanding of variant interpretation; however, over half who reported receiving ‘lots’ of genomic medicine teaching reported a good understanding of this topic. ConclusionGaps in genomics knowledge and drivers in confidence have been identified herein, highlighting the need for improvements in undergraduate genomics education to prepare future doctors to confidently practise in the genomics era.

Impact of unilateral vs. bilateral evaluation on cochlear implant candidacy

Background The indication criteria for cochlear implantation differ considerably across regions. Objectives To estimate the effects of different candidacy criteria on the number of cochlear implant (CI) candidates. Methods We analysed a very large clinical audiological database comprising pure-tone thresholds and speech-audiometric data in order to identify CI candidates on the basis of different audiometric candidacy criteria. In particular, we simulated the effects of three different CI candidacy criteria. Results The bilateral evaluation of CI candidacy has the strongest influence on the number of potential CI candidates. Additionally, the cut-off criteria for middle-ear implants have a large effect on numbers of candidates when air conduction has deteriorated. Conclusions and Significance: Expanding the indication criteria opens up the possibility of improving the accurate identification of individual cases suitable for cochlear-implant provision.