In0.71Ga0.29As/InP (12/2 nm) quantum well MOSFETs using sacrificial amorphous silicon (a:Si) spacers to achieve low parasitic capacitance are fabricated. Radio frequency characterization of 73 devices is used to study the intrinsic and extrinsic capacitances. The total gate intrinsic parasitic capacitance of 0.55 fF/μm is achieved with an intrinsic gate capacitance of 0.39 μF/cm2. The various para

Objective: To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG). Methods: A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction–confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Cli

Objective: To study childbirth and the risk of miscarriage and infertility among women who have received a diagnosis of type 2 diabetes before the start of their reproductive journey. Design: Register-based cohort study using the Skåne Healthcare Register Setting: All healthcare visits for the whole population of the southernmost region in Sweden over the past 20 years Patient(s): All women who we

Aim: MicroRNAs play an important role in the maintenance of cellular functions by fine-tuning gene expression levels. The aim of the current study was to identify genetically caused changes in microRNA expression which associate with islet dysfunction in diabetic mice. Methods: To identify novel microRNAs involved in islet dysfunction, transcriptome and miRNome analyses were performed in islets of

Carbon monoxide (CO) is an odorless and colorless gas with multiple sources that include engine exhaust, faulty furnaces, and other sources of incomplete combustion of carbon compounds such as house fires. The most serious complications for survivors of consequential CO exposure are persistent neurological sequelae occurring in up to 50% of patients. CO inhibits mitochondrial respiration by specif

Since prenatal glucocorticoids (GC) excess increases the risk of metabolic dysfunctions in the offspring and its effect on β-cell recovery capacity remains unknown we investigated these aspects in offspring from mice treated with dexamethasone (DEX) in the late pregnancy. Half of the pups were treated with streptozotocin (STZ) on the sixth postnatal day (PN). Functional and molecular analyses were

I denna text är fokus på Öresundsbron, en förbindelse över ett specifikt vatten som gett den sitt namn. Från att tidigare ha varit en trafikled för båtar blev det med förbindelsen, som även inkluderar en tunnel, möjligt att färdas mellan Danmark och Sverige med bil eller tåg. Jag vill förstå den ambivalens inför bron som framträder när den av skilda anledningar inte är farbar. Centralt är de stark

OBJECTIVE: To develop an index assessing the risks of low energy fractures (LEF) in patients prescribed antiepileptic drugs (AED) by exploring five previously suggested risk factors; age, gender, AED-type, epilepsy diagnosis and BMI.METHODS: In a population-based retrospective open cohort study we used real world data from the Electronic Health Register (EHR) in Region Kalmar County, Sweden. 23 20

Even though microRNAs have been viewed as promising biomarkers for years, their clinical implementation is still lagging far behind. This is in part due to the lack of RT-qPCR technologies that can differentiate between microRNA isoforms. For example, A-to-I editing of microRNAs through adenosine deaminase acting on RNA (ADAR) enzymes can affect their expression levels and functional roles, but ed

We introduce the sequential neural posterior and likelihood approximation (SNPLA) algorithm. SNPLA is a normalizing flows-based algorithm for inference in implicit models, and therefore is a simulation-based inference method that only requires simulations from a generative model. SNPLA avoids Markov chain Monte Carlo sampling and correction-steps of the parameter proposal function that are introdu

Mathematical modelling is an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic and extrinsic noise. Here we present PEPSDI, a scalable and flexible framework for Bayesian

This doctoral thesis in computational statistics utilizes both Monte Carlo methods(approximate Bayesian computation and sequential Monte Carlo) and machine­-learning methods (deep learning and normalizing flows) to develop novel algorithms for infer­ence in implicit Bayesian models. Implicit models are those for which calculating the likelihood function is very challenging (and often impossible),