Human ROBO1 regulates white matter structure in corpus callosum

The axon guidance receptor, Robo1, controls the pathfinding of callosal axons in mice. To determine whether the orthologous ROBO1 gene is involved in callosal development also in humans, we studied polymorphisms in the ROBO1 gene and variation in the white matter structure in the corpus callosum using both structural magnetic resonance imaging and diffusion tensor magnetic resonance imaging. We found that five polymorphisms in the regulatory region of ROBO1 were associated with white matter density in the posterior part of the corpus callosum pathways. One of the polymorphisms, rs7631357, was also significantly associated with the probability of connections to the parietal cortical regions. Our results demonstrate that human ROBO1 may be involved in the regulation of the structure and connectivity of posterior part of corpus callosum.Peer reviewe

Optimal physical and chemical environment for vegetative gametophyte culture of Saccharina latissima: - with emphasis on nutrient composition and light quality

The production of sporelings is a bottle-neck when cultivating Saccharina latissima. Establishing a vegetative gametophyte stocks gives a constant supply of healthy sporelings, enabling a year-round cultivation independent of the availability of natural spores. Finding optimal conditions for vegetative growth of gametophytes are of importance to produce mass quantities of sporelings. In the present study, it was desirable to establish optimal fysical and chemical environments for vegetative gametophyte culture of S. latissima which can be used in large scale cultivation systems. This were examined by five different experiments evaluating the effect of nutrient composition and light quality on growth and development on S. latissima gametophytes. The two most commonly used media when cultivating laminaria; Provasoli s Enriched seawater (PES) and Guillard s f/2 medium (f/2), were examined. The nutrient compisiton were examined through the addition of nitrogen (N) and phosphorous (P) in different concentrations and ratios to steralized seawater (SSW). The growth hormones kinetin (KIN) and Indole-3-acetic acid (IAA) was added in different concentrations to PES in a concentration gradient and the seaweed extract AlgeaFert was added in different concentrations to half strength PES (PES/2) due to its assumed presence of several plant growth substances. The growth of gametophytes was compared under red LED lights preventing fertility and under white florescence lights. White light contains blue wavelengths, thereby inducing fertility. Altering the nutrient ratio can be used to manipulate gametophyte cultures to grow vegetatively. The experiments conducted in white light therefore had a gradient of N:P ratios to evaluate the effect on fertility. The experiments revealed the growth of S. latissima gametophytes to be highly dependent on the chemical composition of the medium it was grown in and it had a significantly higher growth in PES compared to f/2. An increased growth was strongly affected by the presence of chelating agents, an increased concentration of N and P and a low ratio between them, and the addition of a low concentration of seaweed extract. The present study demonstrated that an alternation of light quality from red LED light to warm-white fluorescence light gave a significantly increased growth of S. latissima gametophytes. Since none of the treatments in this study resulted in fertility it cannot be concluded which treatments that inhibit fertilization and promote vegetative growth

Directed polymer in a random medium of dimension 1+1 and 1+3: weights statistics in the low-temperature phase

We consider the low-temperature $T<T_c$ disorder-dominated phase of the directed polymer in a random potentiel in dimension 1+1 (where $T_c=\infty$) and 1+3 (where $T_c<\infty$). To characterize the localization properties of the polymer of length $L$, we analyse the statistics of the weights $w_L(\vec r)$ of the last monomer as follows. We numerically compute the probability distributions $P_1(w)$ of the maximal weight $w_L^{max}= max_{\vec r} [w_L(\vec r)]$, the probability distribution $\Pi(Y_2)$ of the parameter $Y_2(L)= \sum_{\vec r} w_L^2(\vec r)$ as well as the average values of the higher order moments $Y_k(L)= \sum_{\vec r} w_L^k(\vec r)$. We find that there exists a temperature $T_{gap}<T_c$ such that (i) for $T<T_{gap}$, the distributions $P_1(w)$ and $\Pi(Y_2)$ present the characteristic Derrida-Flyvbjerg singularities at $w=1/n$ and $Y_2=1/n$ for $n=1,2..$. In particular, there exists a temperature-dependent exponent $\mu(T)$ that governs the main singularities $P_1(w) \sim (1-w)^{\mu(T)-1}$ and $\Pi(Y_2) \sim (1-Y_2)^{\mu(T)-1}$ as well as the power-law decay of the moments $\bar{Y_k(i)} \sim 1/k^{\mu(T)}$. The exponent $\mu(T)$ grows from the value $\mu(T=0)=0$ up to $\mu(T_{gap}) \sim 2$. (ii) for $T_{gap}<T<T_c$, the distribution $P_1(w)$ vanishes at some value $w_0(T)<1$, and accordingly the moments $\bar{Y_k(i)}$ decay exponentially as $(w_0(T))^k$ in $k$. The histograms of spatial correlations also display Derrida-Flyvbjerg singularities for $T<T_{gap}$. Both below and above $T_{gap}$, the study of typical and averaged correlations is in full agreement with the droplet scaling theory.Comment: 13 pages, 29 figure

Understanding and Evaluating Policies for Sequential Decision-Making

Sequential-decision making is a critical component of many complex systems, such as finance, healthcare, and robotics. The long-term goal of a sequential decision-making process is to optimize the policy under which decisions are made. In safety-critical domains, the search for an optimal policy must be based on observational data, as new decision-making strategies need to be carefully evaluated before they can be tested in practice. In this thesis, we highlight the importance of understanding sequential decision-making at different stages of this procedure. For example, to assess which policies can be evaluated with the available data, we need to understand the policy that actually generated the data. And once we are given a policy to evaluate, we need to understand how it differs from current practice.First, we focus on the evaluation process, where a target policy is evaluated using off-policy data collected under a different so-called behavior policy. This problem, commonly referred to as off-policy evaluation, is often solved with importance sampling (IS) techniques. Despite their popularity, IS-based methods suffer from high variance and are hard to diagnose. To address these issues, we propose estimating the behavior policy using prototype learning. Using the learned prototypes, we describe differences between target and behavior policies, allowing for better assessment of the IS estimates.Next, we take a clinical direction and study the sequential treatment of patients with rheumatoid arthritis (RA). The armamentarium of disease-modifying anti-rheumatic drugs (DMARDs) for RA patients has greatly expanded over the past decades. However, it is still unclear which treatment work best for individual patients. To examine how observational data can be used to evaluate new policies, we describe the most common patterns of DMARDs in a large patient registry from the US. We find that the number of unique patterns is large, indicating a significant variation in clinical practice which can be exploited for evaluation purposes. However, additional assumptions may be required to arrive at statistically sound results

Mutation in CEP63 co-segregating with developmental dyslexia in a Swedish family

Developmental dyslexia is the most common learning disorder in children. Problems in reading and writing are likely due to a complex interaction of genetic and environmental factors, resulting in reduced power of studies of the genetic factors underlying developmental dyslexia. Our approach in the current study was to perform exome sequencing of affected and unaffected individuals within an extended pedigree with a familial form of developmental dyslexia. We identified a two-base mutation, causing a p.R229L amino acid substitution in the centrosomal protein 63 kDa (CEP63), co-segregating with developmental dyslexia in this pedigree. This mutation is novel, and predicted to be highly damaging for the function of the protein. 3D modelling suggested a distinct conformational change caused by the mutation. CEP63 is localised to the centrosome in eukaryotic cells and is required for maintaining normal centriole duplication and control of cell cycle progression. We found that a common polymorphism in the CEP63 gene had a significant association with brain white matter volume. The brain regions were partly overlapping with the previously reported region influenced by polymorphisms in the dyslexia susceptibility genes DYX1C1 and KIAA0319. We hypothesise that CEP63 is particularly important for brain development and might control the proliferation and migration of cells when those two events need to be highly coordinated.Peer reviewe

Intracellular Drug Concentrations and Transporters: Measurement, Modeling, and Implications for the Liver

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109769/1/cptclpt201378.pd

'Brand Availability': Enhancing a Brand's Competitive Advantage

The purpose of this study is to explore the effects of vertical brand extensions on consumer perceptions and brand equity. The aim of this study is to take a strategic brand management perspective in order to answer two research questions: How does a vertical brand extension affect the brand? How does a vertical brand extension affect consumer perceptions of the core brand? The main finding in this study is the need for a new concept in order to explain how the introduction of a vertical brand extension affects consumers and their perception. This paper introduces the concept of ‘Brand Availability’, which we define as ‘the degree to which the consumer market has access to the brand when a vertical brand extension is created’, and emphasizes the effects of performing a vertical brand extension through the modification of the brand’s availability. An abductive approach is utilized through existing literature regarding brand extensions together with interviews as the framework to develop and introduce the concept of ‘Brand Availability’, illustrated through the Swedish fashion market. Semi-structured interviews are used to guide the conducted interviews, by examining the consumer's perspective of a brand when a change in ‘Brand Availability’ occurs

Electron Paramagnetic Resonance Studies of Succinate:Ubiquinone Oxidoreductase from Paracoccus denitrificans

Electron paramagnetic resonance (EPR) studies of succinate:ubiquinone oxidoreductase (SQR) from Paracoccus denitrificans have been undertaken in the purified and membrane-bound states. Spectroscopic “signatures” accounting for the three iron-sulfur clusters (2Fe-2S, 3Fe-4S, and 4Fe-4S), cytochromeb, flavin, and protein-bound ubisemiquinone radicals have been obtained in air-oxidized, succinate-reduced, and dithionite-reduced preparations at 4–10 K. Spectra obtained at 170 K in the presence of excess succinate showed a signal typical of that of a flavin radical, but superimposed with another signal. The superimposed signal originated from two bound ubisemiquinones, as shown by spectral simulations. Power saturation measurements performed on the air-oxidized enzyme provided evidence for a weak magnetic dipolar interaction operating between the oxidized 3Fe-4S cluster and the oxidized cytochrome b. Power saturation experiments performed on the succinate- and dithionite-reduced forms of the enzyme demonstrated that the 4Fe-4S cluster is coupled weakly to both the 2Fe-2S and the 3Fe-4S clusters. Quantitative interpretation of these power saturation experiments has been achieved through redox calculations. They revealed that a spin-spin interaction between the reduced 3Fe-4S cluster and the cytochrome b (oxidized) may also exist. These findings form the first direct EPR evidence for a close proximity (≤2 nm) of the high potential 3Fe-4S cluster, situated in the succinate dehydrogenase part of the enzyme, and the low potential, low spin b-heme in the membrane anchor of the enzyme

Personalized Software in Heavy-Duty Vehicles - Exploring the Feasibility of Self-Adapting Smart Cruise Control Using Machine Learning

Abstract This study aims to explore possible and feasible ways to personalize driving functions for heavy-duty vehicles. The idea is to use machine learning algorithms, specifically ocusing on Long Short-Term Memory (LSTM) neural networks and traditional classification algorithms for current state velocity predictions, independent velocity predictions, and driver classification. The goal is to explore potential approaches for enhancing the existing software to improve the vehicle’s drivability while not compromising fuel consumption. The research methodology involved collecting relevant data from the heavy-duty vehicle, including various readings using the CAN us and map-based data. The data was preprocessed and used to train and evaluate the LSTM neural network and traditional classification algorithms. The results obtained were satisfactory for all of the models. The predictions from the LSTM models were adequate. The one-second velocity predictions were favorable when compared to the ten-second velocity predictions. From the training progress, it is possible to see that the model learns and identified trends. Furthermore, the classification accuracy using traditional and LSTM classifiers ranged from 93 % to 99 %. These findings highlight the challenges and limitations of employing LSTM neural networks and traditional classification algorithms for software adaptation. Further research is necessary to explore alternative approaches, such as using sufficient and more suitable data for transfer- and deep learning. The insights gained from this study help comprehend machine learning applications in heavy-duty vehicles and suggest future research efforts to enhance software adaptation and thus improve vehicle performance