Forekomst av akutt nyreskade ved Finnmarkssykehuset

Ifølge litteraturen øker forekomsten av akutt nyreskade (AKI). Vi ønsket å undersøke forekomsten ved et norsk sykehus, i hvilken grad tilstanden ble kodet, om det var ulik forekomst hos menn og kvinner, samt hos Hammerfest og Kirkenes sykehus. Oppgaven var en retrospektiv kasus-kontrollstudie, basert på uttrekksdata fra Finnmarkssykehuset, med opplysninger om innlagte pasienter i 2008 og 2018. Uttrekket ga bl.a. informasjon om diagnosekoder, kjønn, liggetid og dødsdato. Tilfeller av AKI ble definert av kreatininstigning på ≥27 µmol/L på ≤48 timer. Kontrollgruppa besto av innlagte pasienter uten AKI. Ved hjelp av statistiske analyser undersøkte vi datamaterialet for ulikheter mellom menn og kvinner, mellom Hammerfest og Kirkenes sykehus, og mellom kasus- og kontrollgruppa. Resultat: Insidensen av AKI blant innlagte pasienter var på 0,92 % både i 2008 og 2018. Insidensraten i den generelle befolkningen var i 2008 på 157 per 100 000 innbyggere per år, og i 2018 på 269 per 100 000 innbyggere per år. Like mange kvinner og menn utviklet AKI i 2008 mens flere menn enn kvinner utviklet AKI i 2018. Hammerfest hadde høyere insidens enn Kirkenes sykehus. Diagnosen AKI ble kodet hos 18,6 % av tilfellene i 2008 og 27,9 % av tilfellene i 2018. Liggetiden for innleggelser med AKI var signifikant lavere i 2018 enn i 2008. Pasienter med AKI hadde gjennomsnittlig flere liggedøgn enn pasienter uten AKI i 2018. Ettårsmortaliteten var signifikant lavere i 2018 enn i 2008. Konklusjon: Finnmarkssykehuset hadde en stabil insidens av AKI på 0,92% i 2008 og 2018, men insidensraten i befolkningen har økt. Flere tilfeller ble kodet med AKI i 2018. Liggetiden gikk signifikant ned fra 2008 til 2018 og ettårsmortaliteten var betydelig lavere i 2018 enn i 2008. Funnene kan tyde på økt oppmerksomhet rundt AKI, og bedre behandling. Ytterligere studier anbefales for å undersøke dette

Enhancing writing analytics in science education research with machine learning and natural language processing—Formative assessment of science and non-science preservice teachers’ written reflections

IntroductionScience educators use writing assignments to assess competencies and facilitate learning processes such as conceptual understanding or reflective thinking. Writing assignments are typically scored with holistic, summative coding rubrics. This, however, is not very responsive to the more fine-grained features of text composition and represented knowledge in texts, which might be more relevant for adaptive guidance and writing-to-learn interventions. In this study we examine potentials of machine learning (ML) in combination with natural language processing (NLP) to provide means for analytic, formative assessment of written reflections in science teacher education.MethodsML and NLP are used to filter higher-level reasoning sentences in physics and non-physics teachers’ written reflections on a standardized teaching vignette. We particularly probe to what extent a previously trained ML model can facilitate the filtering, and to what extent further fine-tuning of the previously trained ML model can enhance performance. The filtered sentences are then clustered with ML and NLP to identify themes and represented knowledge in the teachers’ written reflections.ResultsResults indicate that ML and NLP can be used to filter higher-level reasoning elements in physics and non-physics preservice teachers’ written reflections. Furthermore, the applied clustering approach yields specific topics in the written reflections that indicate quality differences in physics and non-physics preservice teachers’ texts.DiscussionOverall, we argue that ML and NLP can enhance writing analytics in science education. For example, previously trained ML models can be utilized in further research to filter higher-level reasoning sentences, and thus provide science education researchers efficient mean to answer derived research questions

Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network

The treatment of refractory epilepsy via closed-loop implantable devices that act on seizures either by drug release or electrostimulation is a highly attractive option. For such implantable medical devices, efficient and low energy consumption, small size, and efficient processing architectures are essential. To meet these requirements, epileptic seizure detection by analysis and classification of brain signals with a convolutional neural network (CNN) is an attractive approach. This work presents a CNN for epileptic seizure detection capable of running on an ultra-low-power microprocessor. The CNN is implemented and optimized in MATLAB. In addition, the CNN is also implemented on a GAP8 microprocessor with RISC-V architecture. The training, optimization, and evaluation of the proposed CNN are based on the CHB-MIT dataset. The CNN reaches a median sensitivity of 90% and a very high specificity over 99% corresponding to a median false positive rate of 6.8 s per hour. After implementation of the CNN on the microcontroller, a sensitivity of 85% is reached. The classification of 1 s of EEG data takes t=35 ms and consumes an average power of P≈140 μW. The proposed detector outperforms related approaches in terms of power consumption by a factor of 6. The universal applicability of the proposed CNN based detector is verified with recording of epileptic rats. This results enable the design of future medical devices for epilepsy treatment

Evolving Models of Pavlovian Conditioning: Cerebellar Cortical Dynamics in Awake Behaving Mice

Three decades of electrophysiological research on cerebellar cortical activity underlying Pavlovian conditioning have expanded our understanding of motor learning in the brain. Purkinje cell simple spike suppression is considered to be crucial in the expression of conditional blink responses (CRs). However, trial-by-trial quantification of this link in awake behaving animals is lacking, and current hypotheses regarding the underlying plasticity mechanisms have diverged from the classical parallel fiber one to the Purkinje cell synapse LTD hypothesis. Here, we establish that acquired simple spike suppression, acquired conditioned stimulus (CS)-related complex spike responses, and molecular layer interneuron (MLI) activity predict the expression of CRs on a trial-by-trial basis using awake behaving mice. Additionally, we show that two independent transgenic mouse mutants with impaired MLI function exhibit motor learning deficits. Our findings suggest multiple cerebellar cortical plasticity mechanisms underlying simple spike suppression, and they implicate the broader involvement of the olivocerebellar module within the interstimulus interval. Purkinje cell simple spike suppression is a central driving mechanism in cerebellar conditioning. Here, ten Brinke etal. show how simple spike suppression, conditioned stimulus-related complex spikes, and molecular layer interneuron (MLI) activity correlate to conditioned eyelid behavior. Moreover, transgenic impairment of MLI input results in deficits in conditioned behavior

Doubling the mobility of InAs/InGaAs selective area grown nanowires

Selective area growth (SAG) of nanowires and networks promise a route toward scalable electronics, photonics, and quantum devices based on III-V semiconductor materials. The potential of high-mobility SAG nanowires however is not yet fully realised, since interfacial roughness, misfit dislocations at the nanowire/substrate interface and nonuniform composition due to material intermixing all scatter electrons. Here, we explore SAG of highly lattice-mismatched InAs nanowires on insulating GaAs(001) substrates and address these key challenges. Atomically smooth nanowire/substrate interfaces are achieved with the use of atomic hydrogen (a-H) as an alternative to conventional thermal annealing for the native oxide removal. The problem of high lattice mismatch is addressed through an InxGa1-xAs buffer layer introduced between the InAs transport channel and the GaAs substrate. The Ga-In material intermixing observed in both the buffer layer and the channel is inhibited via careful tuning of the growth temperature. Performing scanning transmission electron microscopy and x-ray diffraction analysis along with low-temperature transport measurements we show that optimized In-rich buffer layers promote high-quality InAs transport channels with the field-effect electron mobility over 10 000 cm2 V-1 s-1. This is twice as high as for nonoptimized samples and among the highest reported for InAs selective area grown nanostructures.The project was supported by Microsoft Quantum, the European Research Council (ERC) under Grant No. 716655 (HEMs-DAM), and the European Union Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant No. 722176. The authors acknowledge Dr. Keita Ohtani for technical support and fruitful discussions. D.V.B. is grateful to Dr. Juan-Carlos Estrada Saldaña for careful reading of the manuscript. The authors thank Francesco Montalenti, Marco Albani and Leo Miglio for scientific discussions. ICN2 acknowledges funding from Generalitat de Catalunya 2017 SGR 327. ICN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and is funded by the CERCA Programme/Generalitat de Catalunya. Part of the present work has been performed in the framework of Universitat Autònoma de Barcelona Materials Science Ph.D. program. The HAADF-STEM microscopy was conducted in the Laboratorio de Microscopias Avanzadas at Instituto de Nanociencia de Aragon-Universidad de Zaragoza. M.C.S. has received funding from the European Unionâs Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 754510 (PROBIST). The funding agency is Consejo Superior de Investigaciones Científicas (CSIC) and the project reference is “Research Platform on Quantum Technologies PTI-001”

Levels of SARS-CoV-2 antibodies among fully vaccinated individuals with Delta or Omicron variant breakthrough infections

SARS-CoV-2 variants of concern have continuously evolved and may erode vaccine induced immunity. In this observational cohort study, we determine the risk of breakthrough infection in a fully vaccinated cohort. SARS-CoV-2 anti-spike IgG levels were measured before first SARS-CoV-2 vaccination and at day 21–28, 90 and 180, as well as after booster vaccination. Breakthrough infections were captured through the Danish National Microbiology database. incidence rate ratio (IRR) for breakthrough infection at time-updated anti-spike IgG levels was determined using Poisson regression. Among 6076 participants, 127 and 364 breakthrough infections due to Delta and Omicron variants were observed. IRR was 0.29 (95% CI 0.15–0.56) for breakthrough infection with the Delta variant, comparing the highest and lowest quintiles of anti-spike IgG. For Omicron, no significant differences in IRR were observed. These results suggest that quantitative level of anti-spike IgG have limited impact on the risk of breakthrough infection with Omicron