EP.TU.590Making the cut – Tailoring an undergraduate surgical teaching programme a QI project

Aims This project set out to improve the quality of surgical teaching for final year students on a DGH placement. A four-week programme was introduced and regular modification was undertaken through systematic gathering of feedback. The sessions developed were interactive question-based tutorials, suturing, and on-call simulation with common F1 scenarios. Methods Material was developed and piloted based on topics the facilitators felt were poorly taught as students. Feedback was gathered by questionnaire after each session comparing the student’s confidence before and after, along with suggestions for improvement. This process was repeated through all six rotations. Results A total of 95 questionnaires were completed. Average confidence increased from 52% to 77%. The programme was expanded based on suggestions to include orthopaedic examinations and plastic surgery. The tutor pool was increased to include more foundation doctors to ensure sustainable weekly delivery. Examples of how suggestions were implemented include: Conclusions The success of the teaching programme was evident through positive feedback and good student attendance. By playing to the strength of foundation trainees as recent graduates, the programme complemented content-based teaching from more senior colleagues with sessions targeting practical F1 skills and exam specific needs of students. Early piloting also meant ability to constantly adapt the programme to enhance its benefit to the students.

Genomic analysis of ionome-related QTLs in Arabidopsis thaliana

Ionome contributes to maintain cell integrity and acts as cofactors for catalyzing regulatory pathways. Identifying ionome contributing genomic regions provides a practical framework to dissect the genetic architecture of ionomic traits for use in biofortification. Meta-QTL (MQTL) analysis is a robust method to discover stable genomic regions for traits regardless of the genetic background. This study used information of 483 QTLs for ionomic traits identified from 12 populations for MQTL analysis in Arabidopsis thaliana. The selected QTLs were projected onto the newly constructed genetic consensus map and 33 MQTLs distributed on A. thaliana chromosomes were identified. The average confidence interval (CI) of the drafted MQTLs was 1.30 cM, reduced eight folds from a mean CI of 10.88 cM for the original QTLs. Four MQTLs were considered as stable MQTLs over different genetic backgrounds and environments. In parallel to the gene density over the A. thaliana genome, the genomic distribution of MQTLs over the genetic and physical maps indicated the highest density at non- and sub-telomeric chromosomal regions, respectively. Several candidate genes identified in the MQTLs intervals were associated with ion transportation, tolerance, and homeostasis. The genomic context of the identified MQTLs suggested nine chromosomal regions for Zn, Mn, and Fe control. The QTLs for potassium (K) and phosphorus (P) were the most frequently co-located with Zn (78.3%), Mn (76.2%), and Fe (88.2% and 70.6%) QTLs. The current MQTL analysis demonstrates that meta-QTL analysis is cheaper than, and as informative as genome-wide association study (GWAS) in refining the known QTLs.

A Remote Surveillance System for the Detection of Farmland Invasion by Cattle in Sub-Saharan African Country

Farmer-herder conflict in Nigeria mainly involves disputes over land between agrarian communities and nomadic Fulani herdsmen resulting to loss of farmlands and crops, which consequently affects the nation's economy. Engagement of local security operatives in stopping this menace of the herders have proved abortive. Hence, this work proposes a farmland surveillance-alert system using unmanned aerial vehicles for the detection of cattle presence on farmlands as a solution to curbing the problem of farm invasion and destruction. The technique modifies CNN-YOLOV2 architecture, the outcome was accessed with DJI phantom 4 captured 656 images for the detection of cattle invasion. The system on detecting cattle presence above a threshold level sends SMS to farmer’s designated number. The system achieved an average confidence score of 0.92 for the test dataset and 0.72 on real-life data. Hence, it can be employed to mitigate incessant farm invasion and destruction problem and in other surveillance systems. Keywords: convolutional neural network, unmanned aerial vehicles, surveillance, mean average confidence score, yolov2, short messaging service.

INFLUENCE OF STIFFNESS RELATED TO THE C40 STRENGTH CLASS OF THE HARDWOOD GROUP ESTABLISHED BY THE BRAZILIAN STANDARD IN THE DESIGN OF TIMBER STRUCTURES

The Brazilian standard ABNT 7190 (1997) establishes the strength classes C20, C30, C40 and C60 for the proper framework of the different wood types in the group of hardwoods. Associated with the strength class, which is based on the compressive strength characteristic value parallel to the fibers (fc0,k), the standard stipulates the respective values representing the stiffness (Ec0), with 19500 MPa being the reference value for the class C40, essential variables in structural design. For being the C40 class is the one with the greatest amplitude (20 MPa), it is possible that the value 19500 MPa is not the best representation of stiffness. This work aimed to verify the representativeness the stiffness value established by the Brazilian standard for C40 wood. The result obtained from the average confidence interval indicates the value of 14110 MPa as being the most representative, which may imply structures that are supposedly more rigid than they really are.

343 Knowledge and Confidence of Nursing Staff in Burns Management

Background Nursing staff play a pivotal role in the management of burns patients. It is essential that nurses are confident in following the various protocols for these patients. Due to Covid-19, many nursing staff have been redeployed to other specialties, with reduced availability of Burns teaching days also impacting on Burns care. The aim of this audit was to determine the level of education and confidence of burns management amongst nursing staff. Method Data collection proformas were handed out to all nursing staff working on a ward that receives burns patients. The data was subsequently collated, analysed and presented at a local meeting. Results A total of 44 proformas were distributed. The average confidence ratings of nursing staff (on a scale of 1-10) when managing 5%, 10% and 15% burns patients were 6.4, 5.2 and 3.8 respectively. 89% had received no formal burns education within the past 2 years. Conclusions Optimal burns care is dependent on a multidisciplinary approach, with burns nurses playing a central role. The complex requirements of these patients require nursing staff to have a broad range of knowledge and skills so as to coordinate the patient’s management. This study shows that confidence increases with experience and decreases with an increasing severity of burn. Through this study we were able to identify the main areas of knowledge discrepancy amongst the nurses, as well as determine their preferred means of learning. We are currently organising a Burns education programme for the nursing staff tailored to the feedback received in this audit.

Moderators of relative confidence calibration: A meta-analysis of the across-subject relationship between confidence and accuracy

If one friend confidently tells us to buy product A while another friend thinks that product B is better but is not confident, we may go with the advice of our confident friend. Should we? The relationship between people’s confidence and accuracy has been of great interest in many fields, especially in the context of high-stakes situations like eye-witness testimony, but there is still little consensus about how much we should trust someone’s overall level of confidence. Here we examine the across-subject relationship between average accuracy and average confidence in 214 unique datasets from the Confidence Database. This approach allows us to empirically address this issue with unprecedented statistical power and check for the presence of various moderators. We find that the across-subject correlation between average accuracy and average confidence in this sample is R = .22. Importantly, this relationship is much stronger for memory than for perception tasks, as well as for confidence scales with fewer points. These results show that we should take one’s confidence seriously (and perhaps buy product A) and suggest several factors that moderate the relative consistency of how people make confidence judgments.

Pandemic Metric with Confidence (PMC) Model to Predict Trustworthy Probability of Utilized COVID-19 Pandemic Trajectory across the Global

Lots of works aim to reveal the driving factors of COVID-19 pandemic trajectory yet ignore the confidence of utilized trajectory data, making consequent results suspicious. Hereby, we proposed a pandemic metric with confidence (PMC) model in the hypothesis of Bernoulli Distribution of nine trajectories reported from 113 countries. Results exhibit the average confidence of trajectories across the global not in excess of 12.1% with the error threshold configuration of 1E-5. In contrast, the 95% high confidence setting also failed to predict the trajectory containing the acceptable error not beyond 1E-3. Thus, a proposed trade-off strategy between two contradictory expections (>50% confidence, <1E-3 error) supports 61% of investigated countries to predict the varying trajectory with confidence beyond 50%. Moreover, PMC model recommend the remanent 39% countries to extend the proportion of populaces in COVID-19 detecting-pool to a suggested-value (>1% of populations), ensuing the average confidence up to 70%.

Metacognition and Crossmodal Correspondences Between Auditory Attributes and Saltiness in a Large Sample Study

Mounting evidence demonstrates that people make surprisingly consistent associations between auditory attributes and a number of the commonly-agreed basic tastes. However, the sonic representation of (association with) saltiness has remained rather elusive. In the present study, a crowd-sourced online study ( participants) was conducted to determine the acoustical/musical attributes that best match saltiness, as well as participants’ confidence levels in their choices. Based on previous literature on crossmodal correspondences involving saltiness, thirteen attributes were selected to cover a variety of temporal, tactile, and emotional associations. The results revealed that saltiness was associated most strongly with a long decay time, high auditory roughness, and a regular rhythm. In terms of emotional associations, saltiness was matched with negative valence, high arousal, and minor mode. Moreover, significantly higher average confidence ratings were observed for those saltiness-matching choices for which there was majority agreement, suggesting that individuals were more confident about their own judgments when it matched with the group response, therefore providing support for the so-called ‘consensuality principle’. Taken together, these results help to uncover the complex interplay of mechanisms behind seemingly surprising crossmodal correspondences between sound attributes and taste.

Meta-QTLs, Ortho-metaQTLs and Candidate Genes for Grain Yield and Associated Traits in Wheat (Triticum Aestivum L.)

Meta-QTL analysis was conducted using 8,998 known QTLs, which included 2,852 major QTLs for grain yield (GY) and its following ten component/related traits: (i) grain weight (GWei), (ii) grain morphology related traits (GMRTs), (iii) grain number (GN), (iv) spikes related traits (SRTs), (v) plant height (PH), (vi) tiller number (TN), (vii) harvest index (HI), (viii) biomass yield (BY), (ix) days to heading/flowering and maturity (DTH/F/M) and (x) grain filling duration (GFD). The QTLs used for this study were retrieved from 230 reports (including 19 studies conducted in tetraploid wheat) that were based on 190 mapping populations (1999–2020). The study resulted in the identification of 141 meta-QTLs (MQTLs), with an average confidence interval (CI) of 1.37 cM (reduced 8.87 fold), the average CI in the initial QTLs being > 12.15 cM. As many as 63 MQTLs, each based on at least 10 initial QTLs were stable and robust; with 13 MQTLs are described as breeder’s QTLs. MQTLs were also utilized for the identification of 1,202 candidate genes (CGs), which included 18 known genes. The MQTLs were also found to contain 50 wheat genes that were homologous to 35 known yield-related genes from rice, barley, and maize. Further, the use of synteny and collinearity allowed the identification of 24 ortho-MQTLs which were common among the wheat, barley, rice, and maize. The results of the present study should prove useful for wheat breeding and future basic research in cereals including wheat, barley, rice, and maize. In particular, the breeder’s QTLs can be used for marker-assisted selection for grain yield and fine mapping leading to cloning of QTLs/genes for yield and related traits.

Bayesian Inference for the Parameters of Kumaraswamy Distribution via Ranked Set Sampling

In this paper, we address the estimation of the parameters for a two-parameter Kumaraswamy distribution by using the maximum likelihood and Bayesian methods based on simple random sampling, ranked set sampling, and maximum ranked set sampling with unequal samples. The Bayes loss functions used are symmetric and asymmetric. The Metropolis-Hastings-within-Gibbs algorithm was employed to calculate the Bayes point estimates and credible intervals. We illustrate a simulation experiment to compare the implications of the proposed point estimators in sense of bias, estimated risk, and relative efficiency as well as evaluate the interval estimators in terms of average confidence interval length and coverage percentage. Finally, a real-life example and remarks are presented.