Publikationer från Uppsala Universitet
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Ultrafast charge transfer dynamics in lead sulfide quantum dots probed with resonant Auger spectroscopy at the lead M-edge
No full textPbS quantum dots (QDs) hold significant potential for next-generation photovoltaic and photodetector applications due to their size-dependent electronic properties and strong absorption in the near-infrared region. In this study, we investigate charge transfer dynamics in PbS quantum dots of varying sizes, bulk PbS, and PbI2 reference samples using Resonant Auger- (RAS) and Core-Hole Clock Spectroscopy (CHCS). Mapping the Pb M-edge, we capture attosecond-scale electron transfer, using the Pb 3d core-hole lifetime as an internal clock. Our results reveal that PbS bulk samples and larger quantum dots exhibit faster charge transfer rates compared with smaller quantum dots and PbI2, which display slower rates. Additionally, by comparing charge transfer times in the Pb MNN and S KLL Auger regions, we demonstrate consistent behavior across different resonant excitation edges, reinforcing our understanding of how quantum dot size and ligand environment influence charge transport. These insights highlight the importance of optimizing QD size and surface chemistry to improve charge transfer efficiency, a critical factor for high-performance energy materials
Machine Learning for More Efficient Traffic Flows : End-Time Predictions for Accidents
No full textRoad traffic accidents pose significant problems on Swedish roads, not only in their own devastating nature for the people involved but also because they tend to disrupt traffic flow. The Swedish Transport Administration, Trafikverket, actively works towards decreasing the negative consequences of accidents through coordinating traffic and communicating information to SOS, road assistance and other personnel in order to efficiently handle the incidents. Road traffic operators in Stockholm and Skåne publish announcements of accidents, in which they describe the type and location of accidents as well as give an end-time estimate, for when they think the state of the road and traffic flow will return to normal operation. The accident data is registered into a database called Nationellt Trafikledningssystem, NTS, where an array of information is stored for each accident, together with mentioned end-time estimate. This thesis explores the possibility of using predictive analysis, machine learning, in the task of predicting traffic accident end-times, based on historical road traffic accident data. The goal is to make estimates that outperform the manual ones set by the road traffic operators and hopefully prove that such techniques can be used in the future. Through preprocessing, NTS and meterological data were merged and transformed for a wide range of machine learning models with the top performers being Extreme Gradient Boosting, XGBoost and Support Vector Regressor, SVR. The thesis concludes that it is possible to successfully use machine learning models to predict end-times for accidents on the roads of Stockholm and Skåne, while also outperforming the ones set by Trafikverket. It is however important to consider the complex system of road traffic. Some accidents’ end-times are more important to predict than others depending on time, place and severity. Suggested improvements include the use of more detailed attributes, more descriptive of the actual accidents, as well as better quality control for NTS data registration. The results of this thesis can be seen as a proof of concept and an assistive tool rather than an applicable method
Gene expression-based identification of prognostic markers in lung adenocarcinoma
No full textIntroduction: Many studies have aimed at identifying additional prognostic tools to guide treatment choices and patient surveillance in lung cancer by assessing the expression of individual proteins through immunohistochemistry (IHC) or, more recently, through gene expression-based signatures. As a proof-of-concept, we used a multi-cohort, gene expression-based discovery and validation strategy to identify genes with prognostic potential in lung adenocarcinoma. The clinical applicability of this strategy was further assessed by evaluating a selection of the markers by IHC. Materials and methods: Publicly available gene expression data sets from six microarray-based studies were divided into four discovery and two validation data sets. First, genes associated with overall survival (OS) in all four discovery data sets were identified. The prognostic potential of each identified gene was then assessed in the two validation data sets, and genes associated with OS in both data sets were considered as potential prognostic markers. Finally, IHC for selected potential prognostic markers was performed in two independent and clinically well-characterized lung cancer cohorts. Results and conclusions: The gene expression-based strategy identified 19 genes with correlation to OS in all six data sets. Out of these genes, we selected Ki67, MCM4 and TYMS for further assessment with IHC. Although an independent prognostic ability of the selected markers could not be confirmed by IHC, this proof-of-concept study demonstrates that by employing a gene expression-based discovery and validation strategy, potential prognostic markers can be identified and further assessed by a technique universally applicable in the clinical practice. The concept of studying potential prognostic markers through gene expression-based strategies, with a subsequent evaluation of the clinical utility, warrants further exploration
Development of a CRISPR activation system for targeted gene upregulation in Synechocystis sp. PCC 6803
No full textThe photosynthetic cyanobacterium Synechocystis sp. PCC 6803 offers a promising sustainable solution for simultaneous CO2 fixation and compound bioproduction. While various heterologous products have now been synthesised in Synechocystis, limited genetic tools hinder further strain engineering for efficient production. Here, we present a versatile CRISPR activation (CRISPRa) system for Synechocystis, enabling robust multiplexed activation of both heterologous and endogenous targets. Following tool characterisation, we applied CRISPRa to explore targets influencing biofuel production, specifically isobutanol (IB) and 3-methyl-1-butanol (3M1B), demonstrating a proof-of-concept approach to identify key reactions constraining compound biosynthesis. Notably, individual upregulation of target genes, such as pyk1, resulted in up to 4-fold increase in IB/3M1B formation while synergetic effects from multiplexed targeting further enhanced compound production, highlighting the value of this tool for rapid metabolic mapping. Interestingly, activation efficacy did not consistently predict increases in compound formation, suggesting complex regulatory interactions influencing bioproduction. This work establishes a CRISPRa system for targeted upregulation in cyanobacteria, providing an adaptable platform for high-throughput screening, metabolic pathway optimisation and functional genomics. Our CRISPRa system provides a crucial advance in the genetic toolbox available for Synechocystis and will facilitate innovative applications in both fundamental research and metabolic engineering in cyanobacteria
Abundance of dopamine and its receptors in the brain and adipose tissue following diet-induced obesity or caloric restriction
No full textWhile obesity and type 2 diabetes (T2D) are associated with altered dopaminergic activity in the central nervous system and in adipose tissue (AT), the directions and underlying mechanisms remain inconclusive. Therefore, we characterized changes in the abundance of dopamine, its metabolites, and receptors DRD1 and DRD2 in the brain and AT upon dietary intervention or obesity. Male Wistar rats were fed either a standard pellet diet, a cafeteria diet inducing obesity and insulin resistance, or a calorie-restricted diet for 12 weeks. Abundance of dopamine and its receptors DRD1 and DRD2 were examined in brain regions relevant for feeding behavior and energy homeostasis. Furthermore, DRD1 and DRD2 protein levels were analyzed in rat inguinal and epidydimal AT and in human subcutaneous and omental AT from individuals with or without obesity. Rats with diet-induced obesity displayed higher dopamine levels, as well as DRD1 or DRD2 receptor levels in the caudate putamen and the nucleus accumbens core. Surprisingly, caloric restriction induced similar changes in DRD1 and DRD2, but not in dopamine levels, in the brain. Both diets reduced DRD1 abundance in inguinal and epidydimal AT, but upregulated DRD2 levels in inguinal AT. Furthermore, in human obesity, DRD1 protein levels were elevated only in omental AT, while DRD2 was upregulated in both omental and subcutaneous AT. These findings highlight dopaminergic responses to changes in energy balance, occurring both in the brain and AT. We propose that dopaminergic pathways are involved in tissue crosstalk during the development of obesity and T2D
MATLAB software for recursive identification of Wiener Systems : Revision 3
No full textThis report is intended as a users manual for a package of MATLAB scripts and functions, developed for recursive identification of discrete time nonlinear Wiener systems and nonlinear static systems. Wiener systems consist of linear dynamics in cascade with a static nonlinearity. The core of the package is an implementation of 9 recursive SISO output error identification algorithms. Three main cases are treated. The first set of 5 algorithms identify the FIR or IIR linear dynamics in cases where the static nonlinearity is known. It is stressed that the nonlinearity is allowed to be non-invertible. The second set of 2 algorithms simultaneously identifies the linear dynamics and the static non-linearity. The nonlinearity is parameterized as a piecewise linear or a piecewise quadratic nonlinear function. The last set of two algorithms exploits the above parameterization of the static nonlinearity for estimation of static nonlinear systems. Colored, additive measurement noise is handled by all algorithms. The software can only be run off-line, i.e. no true real time operation is possible. The algorithms are however implemented so that true on-line operation can be obtained by extraction of the main algorithmic loops. The user must then provide the real time environment. The software package contains scripts and functions that allow the user to either input live measurements or to generate test data by simulation. The functionality for display of results include scripts for plotting of data, parameters and prediction errors. Model validation is supported by several methods apart from the display functionality. First, calculation of the RPEM loss function can be performed, using parameters obtained at the end of an identification run. Pole-zero plots can be used to investigate possible overparameterization in the linear dynamic part of the Wiener model. Finally, the static accuracy as a function of the output signal amplitude can be assessed by mean residual analysis
Designing edge currents using mesoscopic patterning in chiral d-wave superconductors
No full textChiral superconductors are topological as characterized by a finite Chern number and chiral edge modes. Direct fingerprints of chiral superconductivity are thus often taken to be spontaneous edge currents with associated magnetic signatures. However, a number of recent theoretical studies have shown that the total edge current along semi-infinite edges is greatly reduced or even vanishes in many scenarios for all pairing symmetries except chiral p wave, thus impeding experimental detection. We demonstrate how mesoscopic finite-sized samples can be designed to give rise to a shape-and size-dependent strong enhancement of the chiral edge currents and their generated orbital magnetic moment and magnetic fields. In particular, we find that low rotational symmetry systems, such as pentagons and hexagons, give rise to the largest currents, while circular disks also generate large currents but in the opposite direction. We estimate the resulting magnetic fields to be as large as 0.01-0.5 mT, with a magnetic moment approaching mu B/2 per Cooper pair, where mu B is the Bohr magneton. The current and magnetic signatures diverge with shrinking system sizes, eventually cut off by finite-size suppression of chiral superconductivity. We thus also extract the full phase diagram as a function of temperature and system size for different geometries, including competing superconducting orders. In geometries strongly suppressing only one of the d-wave components, we find an additional heat capacity jump, as large as 10% of the bulk normal-superconducting transition, marking the transition between a chiral and a nodal d-wave state. This further acts as an indirect signature of chiral superconductivity, measurable with nanocalorimetry. Our results are relevant for system sizes on the order of tens to hundreds of coherence lengths, and highlight mesoscopic patterning as a viable route to experimentally identify chiral d-wave superconductivity
Anticoagulant prescribing trends, bleeding events, and reversal agent use in pediatric patients : A retrospective, real-world study
No full textThis retrospective real-world study aimed to describe anticoagulant prescribing trends, particularly for factor Xa (FXa) inhibitors, bleeding events, and reversal agent use in pediatric patients to assess potential populations for clinical trials of the FXa inhibitor reversal agent andexanet alfa. Real-world health care data from the TriNetX Global Network and Optum's deidentified Clinformatics (R) Data Mart Database (CDM) were analyzed to identify patients aged <18 years old who were prescribed a direct oral FXa inhibitor, warfarin, or low-molecular-weight heparins from 2007 through 2024 (TriNetX, N = 59,780) or 2023 (CDM, N = 6470). The only anticoagulants prescribed to children were warfarin and/or low-molecular-weight heparins in 2007 and 2008 in TriNetX and from 2007 through 2010 in CDM. Prescriptions of the FXa inhibitor rivaroxaban increased from 0.4% (2009) to 18.0% (2023) in TriNetX and from 0.8% (2011) to 34.0% (2023) in CDM, with similar trends for apixaban. Relevant bleeding was reported in 9.4% of patients prescribed an FXa inhibitor in TriNetX; <= 0.1% of patients received andexanet alfa the day of a bleed. Among patients prescribed an FXa inhibitor, <= 0.1% in TriNetX and 0 in CDM received andexanet alfa the day of surgery. Direct oral FXa inhibitor use in children is growing, as is the potential for associated bleeds; however, reversal agent use is rare in this population. Given the possible unmet need and subsequent patient recruitment challenges, designing pediatric clinical trials of reversal agents requires innovative approaches
Pinching rules in the chiral-splitting description of one-loop string amplitudes
No full textLoop amplitudes in string theories reduce to those of gauge theories and (super)gravity in their worldline description as the inverse string tension alpha ' tends to zero. The appearance of reducible diagrams in these α′ → 0 limits is determined through so-called pinching rules in the worldline literature. In this work, we extend these pinching rules to the chiral-splitting description of one-loop superstring amplitudes where left- and right-moving degrees of freedom decouple at fixed loop momentum. Starting from six points, the Kronecker-Eisenstein integrands of chiral amplitudes introduce subtleties into the pinching rules and integration-by-parts simplifications. Resolutions of these subtleties are presented and applied to produce a new superspace representation of the six-point one-loop amplitude of type IIA/B supergravity. The worldline computations and their subtleties are compared with the ambitwistor-string approach to one-loop field-theory amplitudes where integration-by-parts manipulations are shown to be more flexible. Throughout this work, the homology invariance of loop-momentum dependent correlation functions on the torus is highlighted as a consistency condition of α′ → 0 limits and their comparison with ambitwistor methods
Modelling Population-Level Hes1 Dynamics: Insights from a Multi-framework Approach
No full textMathematical models of living cells have been successively refined with advancementsin experimental techniques. A main concern is striking a balance between modellingpower and the tractability of the associated mathematical analysis. In this work wemodel the dynamics for the transcription factor Hairy and enhancer of split-1 (Hes1),whose expression oscillates during neural development, and which critically enablesstable fate decision in the embryonic brain. We design, parametrise, and analyse adetailed spatial model using ordinary differential equations (ODEs) over a grid cap-turing both transient oscillatory behaviour and fate decision on a population-level. Wealso investigate the relationship between this ODE model and a more realistic grid-based model involving intrinsic noise using mostly directly biologically motivatedparameters. While we focus specifically on Hes1 in neural development, the approachof linking deterministic and stochastic grid-based models shows promise in modellingvarious biological processes taking place in a cell population. In this context, our workstresses the importance of the interpretability of complex computational models intoa framework which is amenable to mathematical analysis