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Background and purpose — Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have become common research fields in orthopedics and medicine in general. Engineers perform much of the work. While they gear the results towards healthcare professionals, the difference in competencies and goals creates challenges for collaboration and knowledge exchange. We aim to provide clinicians with a context and understanding of AI research by facilitating communication between creators, researchers, clinicians, and readers of medical AI and ML research. Methods and results — We present the common tasks, considerations, and pitfalls (both methodological and ethical) that clinicians will encounter in AI research. We discuss the following topics: labeling, missing data, training, testing, and overfitting. Common performance and outcome measures for various AI and ML tasks are presented, including accuracy, precision, recall, F1 score, Dice score, the area under the curve, and ROC curves. We also discuss ethical considerations in terms of privacy, fairness, autonomy, safety, responsibility, and liability regarding data collecting or sharing. Interpretation — We have developed guidelines for reporting medical AI research to clinicians in the run-up to a broader consensus process. The proposed guidelines consist of a Clinical Artificial Intelligence Research (CAIR) checklist and specific performance metrics guidelines to present and evaluate research using AI components. Researchers, engineers, clinicians, and other stakeholders can use these proposal guidelines and the CAIR checklist to read, present, and evaluate AI research geared towards a healthcare setting.

The aim of this study was to investigate the oxidation kinetics of copper at low temperatures (60 °C to 100 °C) in air by isothermal thermogravimetric analysis (TGA) and quartz crystal microbalance (QCM). The weight change in thermogravimetric tests showed periodic weight increase and decrease. In thermogravimetric tests the mass of the copper sample increased until the oxidation gradually slowed down and finally started to decrease due to cracking and spalling of the oxide formed on the surface. In QCM tests using electrodeposited copper film, the weight change was rapid at the beginning but slowed to a linear relationship after few minutes. Temperature and exposure time appeared to have a large effect on oxide film thickness and composition. With QCM, oxidation at 60–80 °C produced less than 40 nm films in 10 days. Oxidation at 90–100 °C produced 40 nm thick films in a day and over 100 nm films in a week. Although SEM-EDS analyses in TGA tests indicated that oxygen was adsorbed on the copper surface, neither XRD patterns nor Raman spectroscopy measurements showed any trace of Cu2O or CuO formation on the copper surface. Electrochemical reduction analysis of oxidized massive copper samples indicated that the oxide film is mostly Cu2O, and CuO develops only after several days at 90–100 °C.

Abstract Removing function from a developed and functional sensory system is known to alter both cerebral morphology and functional connections. To date, a majority of studies assessing sensory-dependent plasticity have focused on effects from either early onset or long-term sensory loss and little is known how the recent sensory loss affects the human brain. With the aim of determining how recent sensory loss affects cerebral morphology and functional connectivity, we assessed differences between individuals with acquired olfactory loss (duration 7–36 months) and matched healthy controls in their grey matter volume, using multivariate pattern analyses, and functional connectivity, using dynamic connectivity analyses, within and from the olfactory cortex. Our results demonstrate that acquired olfactory loss is associated with altered grey matter volume in, among others, posterior piriform cortex, a core olfactory processing area, as well as the inferior frontal gyrus and angular gyrus. In addition, compared to controls, individuals with acquired anosmia displayed significantly stronger dynamic functional connectivity from the posterior piriform cortex to, among others, the angular gyrus, a known multisensory integration area. When assessing differences in dynamic functional connectivity from the angular gyrus, individuals with acquired anosmia had stronger connectivity from the angular gyrus to areas primary responsible for basic visual processing. These results demonstrate that recently acquired sensory loss is associated with both changed cerebral morphology within core olfactory areas and increase dynamic functional connectivity from olfactory cortex to cerebral areas processing multisensory integration.

This paper elaborates on how design judgement can be practiced in design education, as explored in several iterations of an advanced course in interaction design. The students were probed to address four separate design tasks based on distinct high-level intentions, i.e. to 1) take societal responsibility, 2) to generate profit, 3) to explore a new concept, and 4) to trigger reflection and debate. This structure, we found, served as a valuable tool in our context for bringing important topics to discussion in class and for actively practicing design judgement. We discuss what we see as the main qualities of this approach in relation to more conventional course structures in this area, with a focus directed more towards aspects of methodology, specific interaction techniques, and design principles more generally.

This study explores the experiences of Swedish upper secondary school chemistry students working with a socioscientific issue about per- and polyfluoroalkyl substances (PFAS) in everyday products, and how they perceive that this teaching has influenced them in their role as citizens in a democratic society. Data collection has been carried out on several occasions over a period of one and a half year after the teaching event, and data includes answers from questionnaires and focus-group interviews. The results show that the students are able to view the issue from different perspectives and to understand the inherent complexity. The results also suggest that the students developed their critical thinking skills and awareness as consumers. Furthermore, students report to have developed an interest in environmental issues and a feeling that they have opportunities to make a difference. These aspects constitute important conditions for active citizenship and informed decision-making.

Scholars in the field of conflict resolution in schools theoretically argued that minor distractions and disturbances are conflicts. In the present study, we refer to them as emerging conflicts. The study has been carried out within the phenomenographic research tradition and used semi-structured interviews. We addressed the professionals -- the teachers -- who deal with emerging conflicts every day, investigating their different ways of understanding an emerging conflict. The 9 different ways we found make the collective and shared understandings of emerging conflicts visible and form a professional language with which to discuss these kinds of conflicts. These nine could be divided into three groups, "the social practice of the classroom," something that stems from "outside" the classroom, and something that characterises "all human interaction." The awareness of the existing understandings could further be discussed in relation to what is actually taught in teacher education in Sweden.

Abstract This study evaluated the MRI-derived fat fraction (FF), from a Cooling-reheating protocol, for estimating the cold-induced brown adipose tissue (BAT) metabolic rate of glucose (MRglu) and changes in lipid content, perfusion and arterial blood volume (VA) within cervical-supraclavicular fat (sBAT). Twelve volunteers underwent PET/MRI at baseline, during cold exposure and reheating. For each temperature condition, perfusion and VA were quantified with dynamic [15O]water-PET, and FF, with water-fat MRI. MRglu was assessed with dynamic [18F]fluorodeoxyglucose-PET during cold exposure. sBAT was defined using anatomical criteria, and its subregion sBATHI, by MRglu > 11 μmol/100 cm3/min. For all temperature conditions, sBAT-FF correlated negatively with sBAT-MRglu (ρ ≤ − 0.87). After 3 h of cold, sBAT-FF decreased (− 2.13 percentage points) but tended to normalize during reheating although sBATHI-FF remained low. sBAT-perfusion and sBAT-VA increased during cold exposure (perfusion: + 5.2 ml/100 cm3/min, VA: + 4.0 ml/100 cm3). sBAT-perfusion remained elevated and sBAT-VA normalized during reheating. Regardless of temperature condition during the Cooling-reheating protocol, sBAT-FF could predict the cold-induced sBAT-MRglu. The FF decreases observed after reheating were mainly due to lipid consumption, but could potentially be underestimated due to intracellular lipid replenishment. The influence of perfusion and VA, on the changes in FF observed during cold exposure, could not be ruled out.

Abstract Childhood aggressive behavior (AGG) has a substantial heritability of around 50%. Here we present a genome-wide association meta-analysis (GWAMA) of childhood AGG, in which all phenotype measures across childhood ages from multiple assessors were included. We analyzed phenotype assessments for a total of 328 935 observations from 87 485 children aged between 1.5 and 18 years, while accounting for sample overlap. We also meta-analyzed within subsets of the data, i.e., within rater, instrument and age. SNP-heritability for the overall meta-analysis (AGGoverall) was 3.31% (SE = 0.0038). We found no genome-wide significant SNPs for AGGoverall. The gene-based analysis returned three significant genes: ST3GAL3 (P = 1.6E–06), PCDH7 (P = 2.0E–06), and IPO13 (P = 2.5E–06). All three genes have previously been associated with educational traits. Polygenic scores based on our GWAMA significantly predicted aggression in a holdout sample of children (variance explained = 0.44%) and in retrospectively assessed childhood aggression (variance explained = 0.20%). Genetic correlations (r g ) among rater-specific assessment of AGG ranged from r g = 0.46 between self- and teacher-assessment to r g = 0.81 between mother- and teacher-assessment. We obtained moderate-to-strong r g s with selected phenotypes from multiple domains, but hardly with any of the classical biomarkers thought to be associated with AGG. Significant genetic correlations were observed with most psychiatric and psychological traits (range $$\left| {r_g} \right|$$ r g : 0.19–1.00), except for obsessive-compulsive disorder. Aggression had a negative genetic correlation (r g = ~−0.5) with cognitive traits and age at first birth. Aggression was strongly genetically correlated with smoking phenotypes (range $$\left| {r_g} \right|$$ r g : 0.46–0.60). The genetic correlations between aggression and psychiatric disorders were weaker for teacher-reported AGG than for mother- and self-reported AGG. The current GWAMA of childhood aggression provides a powerful tool to interrogate the rater-specific genetic etiology of AGG.

Abstract The impoundment of the Three Gorges Dam on the Yangtze River begins in 2003 and a full pool level is first attained in 2010. This process leads to reciprocal adjustments in flow discharge, sediment transport and morphology downstream of the dam. Based on 26-year recorded hydrologic data 1990–2015 and surveyed bathymetries 1998, 2010 and 2015, this study elucidates, before and after the commissioning of the dam, the alterations along the 500-km reach of the river. Two-dimensional numerical simulations are performed to predict future morphological changes by 2025. The analyses demonstrate that the impoundment modulates the seasonal flow discharges and traps an appreciable amount of sediment, resulting in enhanced erosion potential and coarsening of sediment. On a multi-year basis, the maximum discharge varies by a factor of 1.3 and the corresponding suspended load concentration and transport rate differ by a factor of 3.0 and 3.8, respectively. Combinations of surveyed and simulated bathymetries reveal its morphological responses to the changes. A general pattern of erosion is observed along the reach. In its upper 120 km, the process slows down towards 2025. In the middle 200 km, the erosion shifts, following the gradual impounding, to slight deposition, which then shifts back to erosion around September 2018. In the final 180 km, erosion continues without any sign of de-escalation, which is presumedly ascribed to tidal actions. The reach has not yet achieved a hydro-morphological equilibrium; the riverbed down-cutting is supposed to continue for a while. The combination of the field and numerical investigations provides, with the elapse of time, insight into the morpho-dynamics in the 500 km river reach.

Abstract Background At the time of the first wave of the COVID-19 pandemic in Sweden, little was known about how effective our regular end-of-life care strategies would be for patients dying from COVID-19 in hospitals. The aim of the study was to describe and evaluate end-of-life care for patients dying from COVID-19 in hospitals in Sweden up until up until 12 November 2020. Methods Data were collected from the Swedish Register of Palliative Care. Hospital deaths during 2020 for patients with COVID-19 were included and compared to a reference cohort of hospital patients who died during 2019. Logistic regression was used to compare the groups and to control for impact of sex, age and a diagnosis of dementia. Results The COVID-19 group (1476 individuals) had a lower proportion of women and was older compared to the reference cohort (13,158 individuals), 81.8 versus 80.6 years (p

Abstract Background Coronavirus disease 2019 (COVID-19), is a disease with diverse presentation. Several studies have shown different occurrence of symptoms for women and men, but no studies have been found examining sex differences in clinical presentation for nursing home residents dying from COVID-19. The objective of this study was to describe sex and age differences and the impact of a dementia diagnosis on symptom occurrence during the last week in life for persons dying from COVID-19 in nursing homes. Methods This is a population-based retrospective study based on data from the Swedish Register of Palliative Care. A total of 1994 residents aged 65 or older who died from COVID-19 in nursing homes were identified. The impact of sex, age and a dementia diagnosis on six different symptoms was analysed using chi2-test and multivariate logistic regression. Results Residents dying from COVID-19 were more often men (p

The accommodative response of the human eye is predominantly driven by foveal vision, but reacts also to off-foveal stimuli. Here, we report on monocular accommodation measurements using parafoveal and perifoveal annular stimuli centered around the fovea and extending up to 8° radial eccentricity for young emmetropic and myopic subjects. The stimuli were presented through a sequence of random defocus step changes induced by a pupil-conjugated tunable lens. A Hartmann–Shack wavefront sensor with an infrared beacon was used to measure real-time changes in ocular aberrations up to and including the fourth radial order across a 3 mm pupil at 20 Hz. Our findings show a significant reduction in accommodative response with increased radial eccentricity.

In this study, the leaching behaviour of aluminium from waste pharmaceutical blister packages (WPBs) is investigated in sulphuric acid media to build future strategies for aluminium recycling from this non-recycled waste fraction. The results suggest that in hydrometallurgical recycling, passivation of aluminium during leaching can be mitigated in dilute sulphuric acid solutions (0.25 M), at high temperatures (60–80 °C) and specifically with H2O2 addition. With this system, 100% extraction was achieved within five hours under optimized conditions (H2SO4 = 0.25 M, T = 80 °C, H2O2 = 1.25 vol.%). The leaching mechanism is suggested to be based on electrochemical dissolution of metallic aluminium oxidized by H+ or H2O2, followed by fast passivation by Al2O3 and consequent chemical dissolution of Al2O3 at slower kinetics. The calculated activation energy (~69 kJ/mol) suggests that the leaching reaction is controlled by the chemical or electrochemical reaction step rather than diffusion. By WPB leaching, an aluminium sulphate solution could be obtained, suitable for further aluminium sulphate crystallization. This may provide a potential route for the valorisation of aluminium from a currently overlooked waste fraction of pharmaceutical blister packages.

Predicting breakdowns is becoming one of the main goals for vehicle manufacturers so as to better allocate resources, and to reduce costs and safety issues. At the core of the utilization of vehicle sensors is the fact that early detection of anomalies facilitates the prediction of potential breakdown issues, which, if otherwise undetected, could lead to breakdowns and warranty claims. However, the making of such predictions is too complex a challenge to solve using simple predictive models. The strength of heuristic optimization techniques in solving np-hard problems, and the recent success of ensemble approaches to various modeling problems, motivated us to investigate a hybrid optimization- and ensemble-based approach to tackle the complex task. In this study, we propose a snapshot-stacked ensemble deep neural network (SSED) approach to predict vehicle claims (in this study, we refer to a claim as being a breakdown or a fault) by considering vehicle operational life records. The approach includes three main modules: Data pre-processing, Dimensionality Reduction, and Ensemble Learning. The first module is developed to run a set of practices to integrate various sources of data, extract hidden information and segment the data into different time windows. In the second module, the most informative measurements to represent vehicle usage are selected through an adapted heuristic optimization approach. Finally, in the last module, the ensemble machine learning approach utilizes the selected measurements to map the vehicle usage to the breakdowns for the prediction. The proposed approach integrates, and uses, the following two sources of data, collected from thousands of heavy-duty trucks: Logged Vehicle Data (LVD) and Warranty Claim Data (WCD). The experimental results confirm the proposed system’s effectiveness in predicting vehicle breakdowns. By adapting the optimization and snapshot-stacked ensemble deep networks, we demonstrate how sensor data, in the form of vehicle usage history, contributes to claim predictions. The experimental evaluation of the system on other application domains also indicated the generality of the proposed approach.

The importance of preventing failures in bearings has led to a large amount of research being conducted to find methods for fault diagnostics and prognostics. Many of these solutions, such as deep learning methods, require a significant amount of data to perform well. This is a reason why publicly available data are important, and there currently exist several open datasets that contain different conditions and faults. However, one challenge is that almost all of these data come from a laboratory setting, where conditions might differ from those found in an industrial environment where the methods are intended to be used. This also means that there may be characteristics of the industrial data that are important to take into account. Therefore, this study describes a completely new dataset for bearing faults from a pulp mill. The analysis of the data shows that the faults vary significantly in terms of fault development, rotation speed, and the amplitude of the vibration signal. It also suggests that methods built for this environment need to consider that no historical examples of faults in the target domain exist and that external events can occur that are not related to any condition of the bearing.

The social risks of green energy transition are underexplored. One of the important questions is which materials used in green energy technologies offer the greatest social benefits, such as ensuring decent living conditions, and which pose the most social risks. To address this issue, we develop a dynamic material-energy flow model integrating system dynamics, social life cycle assessment, and geometallurgical approaches. The analysis focuses on critical materials: Rare Earth Elements, Nickel, Silicon, Graphite, Magnesium, Gallium, Germanium, Indium, Aluminum, Cobalt, Lithium, Zinc, and Tellurium used in wind turbines, electric vehicles, lithium-ion batteries and solar photovoltaic panels. We assess their social impact on work safety, gender equality, informal employment, labor income share, employment rate, and child labor—key issues addressed by Sustainable Development Goals 1, 5, and 8. Here we show that Aluminum production for electric vehicles, wind turbines and solar photovoltaic panels generates the most jobs and income opportunities, while extraction of Cobalt, Lithium, Silicon, and Zinc carry the highest social risks. This study identifies materials used in green energy technologies with the most social benefits and risks. Aluminum production creates the most jobs while cobalt, lithium, silicon and zinc pose the highest risks including child labor and unsafe work conditions. [ABSTRACT FROM AUTHOR]

Structural- and functional heterogeneity, as well as allosteric regulation, in homo-monomeric enzymes is a highly active area of research. One such enzyme is human nuclear-associated deoxyuridine 5'-triphosphate nucleotidohydrolase (dUTPase), which has emerged as an interesting drug target in combination therapy with traditional nucleotide analogue treatment of cancer. We report, for the first time, a full structural dynamics study of human dUTPase by NMR. dUTPase has been investigated in terms of structural dynamics in its apo form, in complex with the modified substrate resistant to hydrolysis, 2'-deoxyuridine 5'-α,β-imido-triphosphate (dUpNHpp), as well as the product, 2'-deoxy-uridine-monophosphate (dUMP). The apo form of the enzyme displayed slow dynamics in the milli- to microsecond regime in relaxation dispersion experiments, which was further slowed down to observable heterogeneity upon substrate-analogue binding. The results suggest that the non-hydrolysable substrate-analogue traps the enzyme in the conformational isomerization step that has been previously suggested to be part of the enzyme catalysis kinetics cycle. The observed heterogeneity fits well with the pattern expected to emerge from the suggested kinetic model, and no evidence for homotropic allosterism was found. The heatmaps of the slow dynamics, chemical shift perturbation upon substrate binding and conserved regions of the enzyme sequence all displayed a similar pattern, which suggests that the structural dynamics is finely tuned and important for the biological function of the enzyme for binding, conformational shift, catalysis and substrate release. [ABSTRACT FROM AUTHOR]