Search

Abstract The efficacy of the four-component 4CMenB vaccine is measured through the serum bactericidal antibody (SBA) assay on four meningococcal B (MenB) indicator strains. However, they are not epidemiologically relevant for disease, thus the real-world persistence of 4CMenB protection remains uncertain. Several mathematical models of waning immunity were fitted on longitudinal SBA data from persistence studies in adolescents, with up to eight years follow-up after 4CMenB priming vaccination. The best model was used to predict protection from indicator strains. MenB typing data from the United States were used to integrate antigen-level curves and predict the persistence of protection from real-world MenB strains, considering synergies between antigens. Models show that protection and its evolution varied by antigen and that 4CMenB likely elicits antibody-producing long-lived plasma cells. 4CMenB protection from real-world MenB disease persisted at 61.5% four years post-priming and 70.5% four years post-booster. This evidence could support decision-making on adolescent immunization programs.

A field campaign in the Vallunden lagoon in the Van Mijenfjorden on Spitsbergen was conducted to gather data on sea ice restoration by artificial flooding. Sea ice thickening was initiated by pumping sea water from below the first-year sea ice onto the surface without removing the covering snow layer. Part of the data was collected by four thermistor strings, two radiation sensors, and one anemometer. All measurement systems were left in the field until recovery of the floating systems in summer. Data provided by the measurement devices were received remotely to gather data before, during, and after the flooding phase (including the melting for as long as the sensors were sending data). Furthermore, coring systems were used to extract 88 ice cores for analysis of temperature, density and bulk salinity profiles along the full length of the ice cores before, during and within four days after flooding. The data set can be used to investigate physical processes involved in the ice growth before, during and after flooding. The data can be used to understand the development, growth and melting of snow ice. The radiation data can be used to analyze the (reflected) radiation of the initial, flooded and melting ice. Data gathered during the melting can be used to investigate the melting of thickened sea ice with different initial conditions prior to the onset of melting. Data on bulk salinity can be used to investigate short-term salt migration. Combing the different insights, growth- and melting models of sea ice including snow and snow ice can be validated. The understanding of melt-water drainage events could be improved and flow models for simulation of artificial flooding of snow-covered first-year sea ice could be further developed using the data.

The Galapagos Marine Reserve was established in 1986 to ensure protection of the islands' unique biodiversity. Unfortunately, the islands are polluted by marine plastic debris and the island authorities face the challenge to effectively remove plastic from its shorelines owing to limited resources. To optimize efforts, we have developed a methodology to identify the most effective cleanup locations on the Galapagos Islands using network theory. A network is constructed from a Lagrangian simulation describing the flow of macroplastic between the various islands within the Galapagos Marine Reserve, where the nodes represent locations along the coastline and the edges the likelihood of plastic leaving one location and beaching at another. We have found four network centralities that provide the best coastline ranking to optimize the cleanup effort based on various impact metrics. Locations with a high retention rate are particularly favorable for cleanup. The results indicate that using the most effective centrality for finding cleanup locations is a good strategy for heavily polluted regions if the distribution of marine plastic debris on the coastlines is unknown and limited cleanup resources are available.

Coastlines potentially harbor a large part of litter entering the oceans, such as plastic waste. The relative importance of the physical processes that influence the beaching of litter is still relatively unknown. Here, we investigate the beaching of litter by analyzing a data set of litter gathered along the Dutch North Sea coast during extensive beach cleanup efforts between the years 2014 and 2019. This data set is unique in the sense that data are gathered consistently over various years by many volunteers (a total of 14 000) on beaches that are quite similar in substrate (sandy). This makes the data set valuable to identify which environmental variables play an important role in the beaching process and to explore the variability of beach litter concentrations. We investigate this by fitting a random forest machine learning regression model to the observed litter concentrations. We find that tides play an especially important role, where an increasing tidal variability and tidal height leads to less litter found on beaches. Relatively straight and exposed coastlines appear to accumulate more litter. The regression model indicates that transport of litter through the marine environment is also important in explaining beach litter variability. By understanding which processes cause the accumulation of litter on the coast, recommendations can be given for more effective removal of litter from the marine environment, such as organizing beach cleanups during low tides at exposed coastlines. We estimate that 16 500–31 200 kg (95 % confidence interval) of litter is located along the 365 km of Dutch North Sea coastline.

Objectives We sought to further determine whether cognitive test results changed for advanced cardiac life support (ACLS) taught in the team-based learning/flipped classroom format (TBL/FC) versus a lecture-based (LB) control. Methods We delivered 2010 ACLS to two classes of fourth-year medical students in the TBL/FC format (2015-2016), compared to three classes in the LB format (2012-2014). There were 27.5 hours of instruction for the TBL/FC model (TBL - 10.5 hours, podcasts - nine hours, small-group simulation - eight hours), and 20 hours (lectures - 12 hours, simulation - eight hours) in LB. We taught TBL for 13 cardiac cases while LB had none. Didactic content and seven simulated cases were the same in lecture (2012-2014) or in podcast formats (2015-2016). Testing was the same using 50 multiple-choice (MC) format questions, 20 rhythm-matching questions, and seven fill-in management of simulated cases. Results Some 468 students enrolled in the course 259 (55.4%) in the LB format in 2012-2014, and 209 (44.6%) in the TBL/FC format in 2015-2016. The scores for two out of three tests (MC and fill-in) increased with TBL/FC. Combined, median scores increased from 93.5% (IQR 90.6, 95.4) to 95.1% (92.5, 96.8, p = 0.0001). More students did not pass one of three tests with LB versus TBL/FC (24.7% versus 18.2%), and two or three parts of the test (8.1% versus 4.3%, p = 0.01). On the contrary, 77.5% passed all three with TBL/FC versus 67.2% with LB (change 10.3%, 95% CI 2.2%-18.2%). Conclusion TBL/FC teaching for ACLS improved written test results compared with the LB format.

Abstract Identification of risk factors for early mortality (EM) in multiple myeloma (MM) patients may contribute to different therapeutic approaches in patients at risk for EM. This population-based study aimed to assess trends in EM and risk factors for EM among MM patients diagnosed in the Netherlands. All MM patients, newly diagnosed between 1989 and 2018, were identified in the Netherlands Cancer Registry. Patients were categorized into three calendar periods (1989–1998, 1999–2008, 2009–2018) and into five age groups (≤65, 66–70, 71–75, 76–80, >80 years). EM was defined as death by any cause ≤180 days post-diagnosis. We included 28,328 MM patients (median age 70 years; 55% males). EM decreased from 22% for patients diagnosed in 1989–1998 to 13% for patients diagnosed in 2009–2018 (P 70 years. Therefore, novel strategies should be explored to improve the outcome of patients at risk for EM.

This phase 2 trial investigated reinduction with carfilzomib, pomalidomide, and dexamethasone (KPd) and continuous pomalidomide/dexamethasone in patients at first progression during lenalidomide maintenance. The second objective was to evaluate high-dose melphalan with autologous stem cell transplantation (HDM/ASCT) at first progression. Patients were eligible who had progressive disease according to International Myeloma Working Group (IMWG) criteria. Treatment consisted of 8 cycles carfilzomib (20/36 mg/m2), pomalidomide (4 mg) and dexamethasone. Patients without prior transplant received HDM/ASCT. Pomalidomide 4 mg w/o dexamethasone was given until progression. One hundred twelve patients were registered of whom 86 (77%) completed 8 cycles of KPd. Thirty-five (85%) eligible patients received HDM/ASCT. The median time to discontinuation of pomalidomide w/o dexamethasone was 17 months. Best response was 37% ≥ complete response, 75% ≥ very good partial response, 92% ≥ partial response, respectively. At a follow-up of 40 months median PFS was 26 and 32 months for patients who received KPd plus HDM/ASCT and 17 months for patients on KPd (hazard ratio [HR] 0.61, 95% confidence interval [CI] 0.37-1.00, P = 0.051). PFS was better after longer duration of prior lenalidomide (HR 3.56, 95% CI 1.42-8.96, P = 0.035). Median overall survival (OS) was 67 months. KPd-emerging grade 3 and 4 adverse events included hematologic (41%), cardiovascular (6%), respiratory (3%), infections (17%), and neuropathy (2%). KPd followed by continuous pomalidomide is an effective and safe triple drug regimen in second-line for patients previously exposed to bortezomib and/or refractory to lenalidomide.

• We supplement acoustic analysis with frequent-word analysis for speaker comparison. • We see improved Cllr across datasets, speaker genders and recording conditions. • We observe that gains are the highest when recording conditions are noisy. Verifying the speaker of a speech fragment can be crucial in attributing a crime to a suspect. The question can be addressed given disputed and reference speech material, adopting the recommended and scientifically accepted likelihood ratio framework for reporting evidential strength in court. In forensic practice, usually, auditory and acoustic analyses are performed to carry out such a verification task considering a diversity of features, such as language competence, pronunciation, or other linguistic features. Automated speaker comparison systems can also be used alongside those manual analyses. State-of-the-art automatic speaker comparison systems are based on deep neural networks that take acoustic features as input. Additional information, though, may be obtained from linguistic analysis. In this paper, we aim to answer if, when and how modern acoustic-based systems can be complemented by an authorship technique based on frequent words, within the likelihood ratio framework. We consider three different approaches to derive a combined likelihood ratio: using a support vector machine algorithm, fitting bivariate normal distributions, and passing the score of the acoustic system as additional input to the frequent-word analysis. We apply our method to the forensically relevant dataset FRIDA and the FISHER corpus, and we explore under which conditions fusion is valuable. We evaluate our results in terms of log likelihood ratio cost ( C llr ) and equal error rate (EER). We show that fusion can be beneficial, especially in the case of intercepted phone calls with noise in the background. [ABSTRACT FROM AUTHOR]

Abstract Background In premenopausal women, treatment with direct oral factor Xa inhibitors is associated with an increased risk of heavy menstrual bleeding (HMB) compared with vitamin K antagonists (VKA). Treatment with the direct oral thrombin inhibitor dabigatran appears to be associated with a reduced risk of HMB compared with VKA. These findings come from small observational studies or post hoc analyses of trials in which HMB was not a primary outcome. Use of tranexamic acid during the menstrual period may be effective in patients with HMB, but prospective data regarding efficacy and safety in patients on anticoagulant treatment are lacking. Rationale and Design A direct comparison of a factor Xa inhibitor and a thrombin inhibitor with HMB as primary outcome, as well as an evaluation of the effects of adding tranexamic acid in women with anticoagulant‐associated HMB is highly relevant for clinical practice. The MEDEA study is a randomized, open‐label, pragmatic clinical trial to evaluate management strategies in premenopausal women with HMB associated with factor Xa inhibitor therapy. Outcomes Women using factor Xa inhibitors with proven HMB, as assessed by a pictorial blood loss assessment chart (PBAC) score of >150, will be randomized to one of three study arms: (i) switch to dabigatran; (ii) continue factor Xa inhibitor with addition of tranexamic acid during the menstrual period; or (iii) continue factor Xa inhibitor without intervention. The primary outcome is the difference in PBAC score before and after randomization. Here, we present the rationale and highlight several unique features in the design of the study.

PurposeIt aimed to find if written test results improved for advanced cardiac life support (ACLS) taught in flipped classroom/team-based Learning (FC/TBL) vs. lecture-based (LB) control in University of California-Irvine School of Medicine, USA.MethodsMedical students took 2010 ACLS with FC/TBL (2015), compared to 3 classes in LB (2012-14) format. There were 27.5 hours of instruction for FC/TBL model (TBL 10.5, podcasts 9, small-group simulation 8 hours), and 20 (12 lecture, simulation 8 hours) in LB. TBL covered 13 cardiac cases; LB had none. Seven simulation cases and didactic content were the same by lecture (2012-14) or podcast (2015) as was testing: 50 multiple-choice questions (MCQ), 20 rhythm matchings, and 7 fill-in clinical cases.Results354 students took the course (259 [73.1%] in LB in 2012-14, and 95 [26.9%] in FC/TBL in 2015). Two of 3 tests (MCQ and fill-in) improved for FC/TBL. Overall, median scores increased from 93.5% (IQR 90.6, 95.4) to 95.1% (92.8, 96.7, P=0.0001). For the fill-in test: 94.1% for LB (89.6, 97.2) to 96.6% for FC/TBL (92.4, 99.20 P=0.0001). For MC: 88% for LB (84, 92) to 90% for FC/TBL (86, 94, P=0.0002). For the rhythm test: median 100% for both formats. More students failed 1 of 3 tests with LB vs. FC/TBL (24.7% vs. 14.7%), and 2 or 3 components (8.1% vs. 3.2%, P=0.006). Conversely, 82.1% passed all 3 with FC/TBL vs. 67.2% with LB (difference 14.9%, 95% CI 4.8-24.0%).ConclusionA FC/TBL format for ACLS marginally improved written test results.

In resource-rich countries, almost all severe hemophilia patients receive prophylactic replacement therapy with factor concentrates to prevent spontaneous bleeding in joints and muscles to decrease the development of arthropathy and risk of long-term disability. Pharmacokinetic (PK)-guided dosing can be applied to individualize factor replacement therapy, as interindividual differences in PK parameters influence factor VIII (FVIII) and FIX activity levels. PK-guided dosing may therefore lead to more optimal safeguarding of FVIII/FIX levels during prophylaxis and on demand treatment. The OPTI-CLOT TARGET study is a multicenter, nonrandomized, prospective cohort study that aims to investigate the reliability and feasibility of PK-guided prophylactic dosing of factor concentrates in hemophilia-A and -B patients in daily clinical practice. At least 50 patients of all ages on prophylactic treatment using standard half-life (SHL) and extended half-life (EHL) factor concentrates will be included during 9 months and will receive PK-guided treatment. As primary endpoint, a minimum of four FVIII/FIX levels will be compared with FVIII/FIX levels as predicted by Bayesian forecasting. Secondary endpoints are the association of FVIII and FIX levels with bleeding episodes and physical activity, expectations and experiences, economic analyses, and optimization of population PK models. This study will lead to more insight in the reliability and feasibility of PK-guided dosing in hemophilia patients. Moreover, it will contribute to personalization of treatment by greater knowledge of dosing regimens needed to prevent and treat bleeding in the individual patient and provide evidence to more clearly associate factor activity levels with bleeding risk.

We agree wholeheartedly with Biedermann (2022) FSI Synergy article 100222 in its criticism of research publications that treat forensic inference in source attribution as an “identification” or “individualization” task. We disagree, however, with its criticism of the use of machine learning for forensic inference. The argument it makes is a strawman argument. There is a growing body of literature on the calculation of well-calibrated likelihood ratios using machine-learning methods and relevant data, and on the validation under casework conditions of such machine-learning-based systems.

Endophenotypes are heritable and quantifiable markers that may assist in the identification of the complex genetic underpinnings of psychiatric conditions. Here we examined global hypoconnectivity as an endophenotype of autism spectrum conditions (ASCs). We studied well-matched groups of adolescent males with autism, genetically-related siblings of individuals with autism, and typically-developing control participants. We parcellated the brain into 258 regions and used complex-network analysis to detect a robust hypoconnectivity endophenotype in our participant group. We observed that whole-brain functional connectivity was highest in controls, intermediate in siblings, and lowest in ASC, in task and rest conditions. We identified additional, local endophenotype effects in specific networks including the visual processing and default mode networks. Our analyses are the first to show that whole-brain functional hypoconnectivity is an endophenotype of autism in adolescence, and may thus underlie the heritable similarities seen in adolescents with ASC and their relatives.

Anatomical tract tracing methods are the gold standard for estimating the weight of axonal connectivity between a pair of pre-defined brain regions. Large studies, comprising hundreds of experiments, have become feasible by automated methods. However, this comes at the cost of positive-mean noise making it difficult to detect weak connections, which are of particular interest as recent high resolution tract-tracing studies of the macaque have identified many more weak connections, adding up to greater connection density of cortical networks, than previously recognized. We propose a statistical framework that estimates connectivity weights and credibility intervals from multiple tract-tracing experiments. We model the observed signal as a log-normal distribution generated by a combination of tracer fluorescence and positive-mean noise, also accounting for injections into multiple regions. Using anterograde viral tract-tracing data provided by the Allen Institute for Brain Sciences, we estimate the connection density of the mouse intra-hemispheric cortical network to be 73% (95% credibility interval (CI): 71%, 75%); higher than previous estimates (40%). Inter-hemispheric density was estimated to be 59% (95% CI: 54%, 62%). The weakest estimable connections (about 6 orders of magnitude weaker than the strongest connections) are likely to represent only one or a few axons. These extremely weak connections are topologically more random and longer distance than the strongest connections, which are topologically more clustered and shorter distance (spatially clustered). Weak links do not substantially contribute to the global topology of a weighted brain graph, but incrementally increased topological integration of a binary graph. The topology of weak anatomical connections in the mouse brain, rigorously estimable down to the biological limit of a single axon between cortical areas in these data, suggests that they might confer functional advantages for integrative information processing and/or they might represent a stochastic factor in the development of the mouse connectome.

Purpose: It aimed to find if written test results improved for advanced cardiac life support (ACLS) taught in flipped classroom/team-based Learning (FC/TBL) vs. lecture-based (LB) control in University of California-Irvine School of Medicine, USA. Methods: Medical students took 2010 ACLS with FC/TBL (2015), compared to 3 classes in LB (2012-14) format. There were 27.5 hours of instruction for FC/TBL model (TBL 10.5, podcasts 9, small-group simulation 8 hours), and 20 (12 lecture, simulation 8 hours) in LB. TBL covered 13 cardiac cases; LB had none. Seven simulation cases and didactic content were the same by lecture (2012-14) or podcast (2015) as was testing: 50 multiple-choice questions (MCQ), 20 rhythm matchings, and 7 fill-in clinical cases. Results: 354 students took the course (259 [73.1%] in LB in 2012-14, and 95 [26.9%] in FC/TBL in 2015). Two of 3 tests (MCQ and fill-in) improved for FC/TBL. Overall, median scores increased from 93.5% (IQR 90.6, 95.4) to 95.1% (92.8, 96.7, P=0.0001). For the fill-in test: 94.1% for LB (89.6, 97.2) to 96.6% for FC/TBL (92.4, 99.20 P=0.0001). For MC: 88% for LB (84, 92) to 90% for FC/TBL (86, 94, P=0.0002). For the rhythm test: median 100% for both formats. More students failed 1 of 3 tests with LB vs. FC/TBL (24.7% vs. 14.7%), and 2 or 3 components (8.1% vs. 3.2%, P=0.006). Conversely, 82.1% passed all 3 with FC/TBL vs. 67.2% with LB (difference 14.9%, 95% CI 4.8-24.0%). Conclusion: A FC/TBL format for ACLS marginally improved written test results.

Many applications give rise to separable parameterized equations of the form $A(y, mu)z+b(y, mu)=0$, where $y in mathbb{R}^n$, $z in mathbb{R}^N$ and the parameter $mu in mathbb{R}$; here $A(y, mu)$ is an $(N+n) imes N$ matrix and $b(y, mu) in mathbb{R}^{N+n}$. Under the assumption that $A(y,mu)$ has full rank we showed in [21] that bifurcation points can be located by solving a reduced equation of the form $f(y, mu)=0$. In this paper we extend that method to the case that $A(y,mu)$ has rank deficiency one at the bifurcation point. At such a point the solution curve $(y,mu,z)$ branches into infinitely many additional solutions, which form a straight line. A numerical method for reducing the problem to a smaller space and locating such a bifurcation point is given. Applications to equilibrium solutions of nonlinear ordinary equations and solutions of discretized partial differential equations are provided.

The validity of a probabilistic genotyping (PG) system is typically demonstrated by following international guidelines for the developmental and internal validation of PG software. These guidelines mainly focus on discriminatory power. Very few studies have reported with metrics that depend on calibration of likelihood ratio (LR) systems. In this study, discriminatory power as well as various calibration metrics, such as Empirical Cross-Entropy (ECE) plots, pool adjacent violator (PAV) plots, log likelihood ratio cost (Cllr and Cllr cal ), fiducial calibration discrepancy plots, and Turing' expectation were examined using the publicly-available PROVEDIt dataset. The aim was to gain deeper insight into the performance of a variety of PG software in the 'lower' LR ranges (∼LR 1–10,000), with focus on DNAStatistX and EuroForMix which use maximum likelihood estimation (MLE). This may be a driving force for the end users to reconsider current LR thresholds for reporting. In previous studies, overstated 'low' LRs were observed for these PG software. However, applying (arbitrarily) high LR thresholds for reporting wastes relevant evidential value. This study demonstrates, based on calibration performance, that previously reported LR thresholds can be lowered or even discarded. Considering LRs >1, there was no evidence for miscalibration performance above LR ∼1000 when using Fst 0.01. Below this LR value, miscalibration was observed. Calibration performance generally improved with the use of Fst 0.03, but the extent of this was dependent on the dataset: results ranged from miscalibration up to LR ∼100 to no evidence of miscalibration alike PG software using different methods to model peak height, HMC and STRmix. This study demonstrates that practitioners using MLE-based models should be careful when low LR ranges are reported, though applying arbitrarily high LR thresholds is discouraged. This study also highlights various calibration metrics that are useful in understanding the performance of a PG system. • Discriminatory power and calibration performance of PG software are evaluated. • The utility of various calibration metrics are explored in 'low' LR ranges. • Focus was on DNAStatistX and EuroForMix software using the MLE method. • Calibration performance was dependent on Fst value and dataset size. • Results suggest reconsideration of lower LR thresholds and cautious reporting of 'low' LRs. [ABSTRACT FROM AUTHOR]

Practical analysis of the role played by the staff in charge of the university's academic management. Three aspects are covered. First, a strategic view to discuss the Knowledge Society, the profile required of alumni in the work market and the European policy concerning Higher Education. Second, an outlook on quality, based on the management model from the European Foundation of Quality Management and the Deming cycle: planning, doing, measuring and improving. And finally, the stimulating role of academic management in developing an institutional pedagogical model to be applied in study programs, in the different teaching and learning methods, and developing an atmosphere of academic quality. The model is the result of exchanges carried out among coworkers of Universidad Nacional in the workshops organized by the MHO project Developing Institutional Quality , and also through other Dutch experiences.

The nonlinear least squares problem m i n y , z ∥ A ( y ) z + b ( y ) ∥ , where A ( y ) is a full-rank ( N + ℓ ) × N matrix, y ∈ R n , z ∈ R N and b ( y ) ∈ R N + ℓ with ℓ ≥ n , can be solved by first solving a reduced problem m i n y ∥ f ( y ) ∥ to find the optimal value y * of y, and then solving the resulting linear least squares problem m i n z ∥ A ( y * ) z + b ( y * ) ∥ to find the optimal value z * of z. We have previously justified the use of the reduced function f ( y ) = C T ( y ) b ( y ) , where C ( y ) is a matrix whose columns form an orthonormal basis for the nullspace of A T ( y ) , and presented a quadratically convergent Gauss–Newton type method for solving m i n y ∥ C T ( y ) b ( y ) ∥ based on the use of QR factorization. In this note, we show how LU factorization can replace the QR factorization in those computations, halving the associated computational cost while also providing opportunities to exploit sparsity and thus further enhance computational efficiency.

Surveillance systems of contagious diseases record information on cases to monitor incidence of disease and to evaluate effectiveness of interventions. These systems focus on a well-defined population; a key question is whether observed cases are infected through local transmission within the population or whether cases are the result of importation of infection into the population. Local spread of infection calls for different intervention measures than importation of infection. Besides standardized information on time of symptom onset and location of cases, pathogen genotyping or sequencing offers essential information to address this question. Here we introduce a method that takes full advantage of both the genetic and epidemiological data to distinguish local transmission from importation of infection, by comparing inter-case distances in temporal, spatial and genetic data. Cases that are part of a local transmission chain will have shorter distances between their geographical locations, shorter durations between their times of symptom onset and shorter genetic distances between their pathogen sequences as compared to cases that are due to importation. In contrast to generic clustering algorithms, the proposed method explicitly accounts for the fact that during local transmission of a contagious disease the cases are caused by other cases. No pathogen-specific assumptions are needed due to the use of ordinal distances, which allow for direct comparison between the disparate data types. Using simulations, we test the performance of the method in identifying local transmission of disease in large datasets, and assess how sensitivity and specificity change with varying size of local transmission chains and varying overall disease incidence.

Abstract This resource is a stand-alone video presentation that can be used in the classroom, small-group sessions, or continuing education courses, or by individuals as an interprofessional means of attaining a better understanding of the psychosocial, physiologic, emotional, and dental clinical management aspects of eating disorders. The video chronicles the experience of a dental student who overcame an eating disorder. The featured speaker (author MY) describes how her eating disorder first “came out” following a biochemistry lecture during her dental education. The faculty member involved (author KRE) was instrumental in helping her cope with the rigors of dental school and was intimately involved in her recovery. This presentation was based on a symposium put on by the authors at a national dental meeting. The audience response was so overwhelming that it was decided to share this story worldwide. The video is approximately 1 hour long. This length makes it an ideal substitute for a classroom lecture. If utilized in a small-group learning environment, the video can be followed by facilitator-led discussion; for such a use, a time period of 90 minutes to 2 hours would be needed. This presentation is the true life story of one of the authors. Its effectiveness is evident in the emotion it creates in the audience.

Online personalization of hearing instruments refers to learning preferred tuning parameter values from user feedback through a control wheel (or remote control), during normal operation of the hearing aid. We perform hearing aid parameter steering by applying a linear map from acoustic features to tuning parameters. We formulate personalization of the steering parameters as the maximization of an expected utility function. A sparse Bayesian approach is then investigated for its suitability to find efficient feature representations. The feasibility of our approach is demonstrated in an application to online personalization of a noise reduction algorithm. A patient trial indicates that the acoustic features chosen for learning noise control are meaningful, that environmental steering of noise reduction makes sense, and that our personalization algorithm learns proper values for tuning parameters.

In this study, a DNA decision support tool was developed. With this tool we aim to gain insight in what actions are performed with which (types of) DNA profiles in casework, to improve decision making by DNA experts in future cases. [ABSTRACT FROM AUTHOR]