Input estimation for drug discovery using optimal control and Markov chain Monte Carlo approaches

Input estimation is employed in cases where it is desirable to recover the form of an input function which cannot be directly observed and for which there is no model for the generating process. In pharmacokinetic and pharmacodynamic modelling, input estimation in linear systems (deconvolution) is well established, while the nonlinear case is largely unexplored. In this paper, a rigorous definition of the input-estimation problem is given, and the choices involved in terms of modelling assumptions and estimation algorithms are discussed. In particular, the paper covers Maximum a Posteriori estimates using techniques from optimal control theory, and full Bayesian estimation using Markov Chain Monte Carlo (MCMC) approaches. These techniques are implemented using the optimisation software CasADi, and applied to two example problems: one where the oral absorption rate and bioavailability of the drug eflornithine are estimated using pharmacokinetic data from rats, and one where energy intake is estimated from body-mass measurements of mice exposed to monoclonal antibodies targeting the fibroblast growth factor receptor (FGFR) 1c. The results from the analysis are used to highlight the strengths and weaknesses of the methods used when applied to sparsely sampled data. The presented methods for optimal control are fast and robust, and can be recommended for use in drug discovery. The MCMC-based methods can have long running times and require more expertise from the user. The rigorous definition together with the illustrative examples and suggestions for software serve as a highly promising starting point for application of input-estimation methods to problems in drug discovery

Lärplattformar i skolan - En studie av lärares användaracceptans

Idag använder sig en majoritet av alla svenska grund- och gymnasieskolor av en lärplattform. En lärplattform är mjukvara som stödjer både det administrativa och pedagogiska arbetet och används bland annat till kommunikation, bedömning och till att tillhandahålla material i undervisningen. Uppsatsen kopplar samman lärares upplevelser av dessa lärplattformar till teorier gällande användaracceptans. UTAUT, Status quo bias theory och Bingimlas hinder för IKT-införande i skolan sammanställdes till ett ramverk med fem kategorier som påverkar användaracceptans: nytta, användarvänlighet, organisation, sociala normer samt personliga faktorer. Baserat på dessa kategorier skapades sedan en intervjuguide som användes vid intervjuer av fem lärare, en IKT-pedagog och en IKT-samordnare vid två kommunala gymnasieskolor i Lund. Studien fann att användarna i dagsläget var övervägande positivt inställda till lärplattformen. Det största motståndet existerade när lärplattformar först infördes tio år tidigare och har senare avtagit efter hand. Av studien framgår att Lagen om offentlig upphandling verkar ha en stor inverkan på lärares intentioner till användning; idag är motstånd främst relaterat till systembyte då många lärare känner att de förlorar arbetet de lagt ner i det tidigare systemet

Multiomics analysis of naturally efficacious lipid nanoparticle coronas reveals high-density lipoprotein is necessary for their function

In terms of lipid nanoparticle (LNP) engineering, the relationship between particle composition, delivery efficacy, and the composition of the biocoronas that form around LNPs, is poorly understood. To explore this we analyze naturally efficacious biocorona compositions using an unbiased screening workflow. First, LNPs are complexed with plasma samples, from individual lean or obese male rats, and then functionally evaluated in vitro. Then, a fast, automated, and miniaturized method retrieves the LNPs with intact biocoronas, and multiomics analysis of the LNP-corona complexes reveals the particle corona content arising from each individual plasma sample. We find that the most efficacious LNP-corona complexes were enriched with high-density lipoprotein (HDL) and, compared to the commonly used corona-biomarker Apolipoprotein E, corona HDL content was a superior predictor of in-vivo activity. Using technically challenging and clinically relevant lipid nanoparticles, these methods reveal a previously unreported role for HDL as a source of ApoE and, form a framework for improving LNP therapeutic efficacy by controlling corona composition.</p

IDOL regulates systemic energy balance through control of neuronal VLDLR expression.

Liver X receptors limit cellular lipid uptake by stimulating the transcription of Inducible Degrader of the LDL Receptor (IDOL), an E3 ubiquitin ligase that targets lipoprotein receptors for degradation. The function of IDOL in systemic metabolism is incompletely understood. Here we show that loss of IDOL in mice protects against the development of diet-induced obesity and metabolic dysfunction by altering food intake and thermogenesis. Unexpectedly, analysis of tissue-specific knockout mice revealed that IDOL affects energy balance, not through its actions in peripheral metabolic tissues (liver, adipose, endothelium, intestine, skeletal muscle), but by controlling lipoprotein receptor abundance in neurons. Single-cell RNA sequencing of the hypothalamus demonstrated that IDOL deletion altered gene expression linked to control of metabolism. Finally, we identify VLDLR rather than LDLR as the primary mediator of IDOL effects on energy balance. These studies identify a role for the neuronal IDOL-VLDLR pathway in metabolic homeostasis and diet-induced obesity

Monoclonal antibody targeting of fibroblast growth factor receptor 1c ameliorates obesity and glucose intolerance via central mechanisms.

We have generated a novel monoclonal antibody targeting human FGFR1c (R1c mAb) that caused profound body weight and body fat loss in diet-induced obese mice due to decreased food intake (with energy expenditure unaltered), in turn improving glucose control. R1c mAb also caused weight loss in leptin-deficient ob/ob mice, leptin receptor-mutant db/db mice, and in mice lacking either the melanocortin 4 receptor or the melanin-concentrating hormone receptor 1. In addition, R1c mAb did not change hypothalamic mRNA expression levels of Agrp, Cart, Pomc, Npy, Crh, Mch, or Orexin, suggesting that R1c mAb could cause food intake inhibition and body weight loss via other mechanisms in the brain. Interestingly, peripherally administered R1c mAb accumulated in the median eminence, adjacent arcuate nucleus and in the circumventricular organs where it activated the early response gene c-Fos. As a plausible mechanism and coinciding with the initiation of food intake suppression, R1c mAb induced hypothalamic expression levels of the cytokines Monocyte chemoattractant protein 1 and 3 and ERK1/2 and p70 S6 kinase 1 activation

Soft Chemical Control of Superconductivity in Lithium Iron Selenide Hydroxides Li1–x_{1–x}Fex_x(OH)Fe1–y_{1–y}Se

Hydrothermal synthesis is described of layered lithium iron selenide hydroxides Li$_{1–x}$Fex(OH)Fe$_{1–y}$Se (x$\sim$0.2; 0.02 < $y$ < 0.15) with a wide range of iron site vacancy concentrations in the iron selenide layers. This iron vacancy concentration is revealed as the only significant compositional variable and as the key parameter controlling the crystal structure and the electronic properties. Single crystal X-ray diffraction, neutron powder diffraction, and X-ray absorption spectroscopy measurements are used to demonstrate that superconductivity at temperatures as high as 40 K is observed in the hydrothermally synthesized samples when the iron vacancy concentration is low ($y$ < 0.05) and when the iron oxidation state is reduced slightly below +2, while samples with a higher vacancy concentration and a correspondingly higher iron oxidation state are not superconducting. The importance of combining a low iron oxidation state with a low vacancy concentration in the iron selenide layers is emphasized by the demonstration that reductive postsynthetic lithiation of the samples turns on superconductivity with critical temperatures exceeding 40 K by displacing iron atoms from the Li$_{1–x}$Fe$_x$(OH) reservoir layer to fill vacancies in the selenide layer

Inverse Current Source Density Method in Two Dimensions: Inferring Neural Activation from Multielectrode Recordings

The recent development of large multielectrode recording arrays has made it affordable for an increasing number of laboratories to record from multiple brain regions simultaneously. The development of analytical tools for array data, however, lags behind these technological advances in hardware. In this paper, we present a method based on forward modeling for estimating current source density from electrophysiological signals recorded on a two-dimensional grid using multi-electrode rectangular arrays. This new method, which we call two-dimensional inverse Current Source Density (iCSD 2D), is based upon and extends our previous one- and three-dimensional techniques. We test several variants of our method, both on surrogate data generated from a collection of Gaussian sources, and on model data from a population of layer 5 neocortical pyramidal neurons. We also apply the method to experimental data from the rat subiculum. The main advantages of the proposed method are the explicit specification of its assumptions, the possibility to include system-specific information as it becomes available, the ability to estimate CSD at the grid boundaries, and lower reconstruction errors when compared to the traditional approach. These features make iCSD 2D a substantial improvement over the approaches used so far and a powerful new tool for the analysis of multielectrode array data. We also provide a free GUI-based MATLAB toolbox to analyze and visualize our test data as well as user datasets

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis