Miracle-dz/UniFAR-FTUs huggingface dataset:
Phase Contrast to Probe Tissue Composition and Structure

Gradient Recalled Echo (GRE) based MR imaging techniques have wide applications in MRI. Due to the absence of refocusing RF pulses GRE sequences are fast and since low flip angles are usually applied, GRE sequences have low specific absorption rate, hence are particularly well suited for high field MRI. While MR signal generated from GRE techniques is complex, most traditional applications rely only on magnitude information whereas the phase information from the data is usually discarded. However, certain newer applications also rely on phase data. Among these, Susceptibility Weighted Imaging (SWI) (Haacke et al. 2009; Reichenbach et al. 1997) uses a specific algorithm based on GRE T2* weighted magnitude and phase data to create images sensitive to variations in tissue magnetic susceptibility resulting from sources such as venous blood, calcification, hemorrhage, iron stores, etc. Other techniques, some of which rely exclusively on the phase data of the GRE signal have also been intensely explored for uniquely characterizing anatomical structures that are especially evident at high field MRI (Duyn et al. 2007; Marques et al. 2009; Rauscher et al. 2005; Zhong et al. 2008), and even for creating maps of tissue magnetic susceptibility – the so called Quantitative Susceptibility Mapping (QSM) (de Rochefort et al. 2008; de Rochefort et al. 2010; Li et al. 2011; Liu et al. 2010; Schweser et al. 2010; Shmueli et al. 2009). The information obtained from these techniques reveals image features complimentary to that obtained from traditional magnitudebased imaging and could improve our understanding of both normal tissue anatomy as well as changes in tissue in various pathological conditions.
Fault Detection in Managed Pressure Drilling Using Slow Feature Analysis

Correct detection of drilling abnormal incidents while minimizing false alarms is a crucial measure to decrease the non-productive time and, thus, decrease the total drilling cost. With the recent development of drilling technology and innovation of down-hole signal transmitting method, abundant drilling data are collected and stored in the electronic driller’s database. The availability of such data provides new opportunities for rapid and accurate fault detection; however, data-driven fault detection has seen limited practical application in well drilling processes. One particular concern is how to distinguish “controllable” process changes, e.g., due to set-point changes, from truly abnormal events that should be considered as faults. This is highly relevant for the managed pressure drilling technology, where the operating pressure window is often narrow resulting in necessary set-point changes at different depths. However, the classical data-driven fault detection methods, such as principal component analysis and independent component analysis, are unable to distinguish normal set-point changes from abnormal faults. To address this challenge, a slow feature analysis (SFA)-based fault detection method is applied. The SFA-based method furnishes four monitoring charts containing more information that could be synthetically utilized to correctly differentiate set-point changes from faults. Furthermore, the evaluation about controller performance is provided for drilling operator. Simulation studies with a commercial high-fidelity simulator, Drillbench, demonstrate the effectiveness of the introduced approach.
Synthesis of Silicone‐Methacrylate Copolymers by ATRP Using a Nickel‐Based Supported Catalyst

A nickel (II) bromide catalyst immobilized onto crosslinked diphenylpbosphinopolystyrene (PS-PPh 3 /NiBr 2 ) was used for the synthesis of silicone-methacrylate copolymers by atom transfer radical polymerization (ATRP) of various methacrylate monomers using ω-bromide silicone chains as macroinitiator. The polymerization proved to be very well-controlled when a sufficient amount of soluble ligand, i.e., triphenylphosphine (PPh 3 ), was added to the polymerization medium. Under these conditions, this technique efficiently led to the production of different copolymers with controlled corn-positions and molecular weights as well as narrow polydispersity indices (M w /M n < 1.5). The recovered copolymers proved to be almost free of catalyst residues. Indeed, inductively coupled plasma (ICP) analysis revealed a metal content lower than 100 ppm, representing only a few percent of the initial metal content in the polymerization medium.
Nutritional Value and Consumption Habit of Wild Mushroom in Tigray , Northern Ethiopia

Wild mushroom has been abundant and the consumption habit of mushrooms by local people has long history in northern Ethiopia though decreased with the prevailing degradation. The paper assessed the phenotypic species diversity, nutritional value and farmer’s perception on consumption habit of wild mushrooms. Fifteen 1000 m plots were established to analyze the phenotypic species diversity and nutritional value from four different micro habitats. Managed grassland encompassed greater mushroom species diversity. A total of 1.189 individual mushrooms belonging to two genera and eleven species were identified. Most mushroom species preferred open to slightly open canopy cover. The moisture, protein, carbohydrate, fat, ash, fiber and phosphorus content of mushrooms ranged from 81.8-87.9%, 23.4-37.2%, 32.4-43.9%, 1.94. 4%, 14.9-19.6%, 12.3-18.1%, 0.6-0.9% respectively. There were significant difference in moisture, protein and carbohydrate contents between mushroom species (P<0.05). About 96% of the respondents had awareness about mushroom consumption. 95.3% of the respondents have attested that the current status of edible wild mushroom population decrease in distribution. 40% of the respondents whom were previously consuming mushroom have currently stopped to use it. There is sharp decline in distribution and consumption of mushroom. In order to protect the decreasing status of mushroom, grassland management and mushroom conservation should be promoted and encouraged.
Quantifying the degree of nanofiller dispersion by advanced thermal analysis: application to polyester nanocomposites prepared by various elaboration methods

An innovative thermal analysis methodology is applied for the characterization of poly(e-caprolactone) (PCL) nanocomposites containing layered silicates, needle-like sepiolite or polyhedral oligomeric silsesquioxane (POSS) nano-cages, aiming at assessing the key factors affecting nanofiller dispersion and nanocomposite properties. This methodology takes benefit of the fact that—for a given nanofiller aspect ratio—the magnitude of the excess heat capacity recorded during quasi-isothermal crystallization is directly related to the occurrence of pronounced changes to the PCL crystalline morphology. The extent of these changes, in turn, directly depends on the amount of matrix/filler interface and can therefore be considered a reliable measure for the degree of nanofiller dispersion, as supported by complementary morphological characterization. The importance of processing parameters is demonstrated in a comparative study using various melt processing conditions, evidencing the need for high shear to effectively exfoliate and disperse individual nanoparticles throughout the polymer matrix. Furthermore, the choice of the nanocomposite elaboration method is shown to profoundly affect the final morphology, as illustrated in a comparison between nanocomposites prepared by melt mixing, by in situ polymerization and by a masterbatch approach. Grafting PCL onto the filler strongly enhances its dispersion quality as compared to conventional melt mixing; subsequently further dispersing such grafted nanohybrids into the polymer matrix through a masterbatch approach provides a highly efficient method for the elaboration of well-dispersed nanocomposites. Finally, the crucial issue of interfacial compatibility is addressed in a comparison between various surface-treated layered silicates, showing that high degrees of filler dispersion in a PCL matrix can only be achieved upon polar modification of the silicate.
Quantitative functional MRI

In the last twenty years, MRI has made much progress in providing non-invasive alternative methodologies for the quantification of physiological parameters in tissue. It is now possible to measure cerebral blood flow (CBF) and cerebral blood volume (CBV) and blood oxygenation. When combining CBF, CBV and BOLD experiments, it is in principle possible to quantify and map the cerebral metabolic rate of oxygen metabolism (
Neuroimaging 1 : Post-Processing & Quantification of Spinal Cord Images

Spinal cord is affected by a number of diseases, such as multiple sclerosis (MS) and traumatic injury. MRI-based quantitative information could greatly help in noninvasive and objective evaluation of spinal cord pathology and therapeutic efficacy. Extraction of quantitative information from spinal MRI requires significant image processing. The most commonly extracted quantitative measures from spinal cord include atrophy, diffusion measures, lesion segmentation, and perfusion. While robust processing methods are developed and applied to brain MRI, quantitative analysis of spinal cord MRI is rather limited for a number of reasons. Here we review some of the processing methods that are relevant for extracting quantitative information about spinal cord pathology. Image Preprocessing: The image preprocessing steps that are described below are necessary to condition the images for quantitative analysis. The preprocessing steps include: 1) image denoising, 2) intensity inhomogeneity correction, 3) spinal cord extraction, and 4) image registration. Denoising: Because of the small structure, MRI of spinal cord tends to be noisy. The noise is generally minimized by the application of appropriate filters. Filters such as median and Gaussian can reduce noise, but they also tend to blur the images. Adaptive filters such as anisotropic diffusion filters (1) Perona and Malik) are commonly used to reduce the noise without concomitant image blurring. Recently, nonlocal means (NLM) filter was proposed,
eltoo : A Simple Layer 2 Protocol for Bitcoin

Bitcoin, and other blockchain based systems, are inherently limited in their scalability. On-chain payments must be verified and stored by every node in the network, meaning that the node with the least resources limits the overall throughput of the system as a whole. Layer 2, also called off-chain protocols, are often seen as the solution to these scalability issues: by renegotiating a shared state among a limited set of participants, and not broadcasting the vast majority of state updates to the blockchain, the load on the network is reduced. Central to all Layer 2 protocols is the issue of guaranteeing that an old state may not be committed once it has been replaced. In this work we present eltoo, a simple, yet powerful replacement mechanism for Layer 2 protocols. It introduces the idea of state numbers, an on-chain enforceable variant of sequence numbers that were already present in the original implementation, but that were not enforceable.
Fast Language Adaptation Using Phonological Information

Phoneme-based multilingual connectionist temporal classiﬁcation (CTC) model is easily extensible to a new language by concatenating parameters of the new phonemes to the output layer. In the present paper, we improve cross-lingual adaptation in the context of phoneme-based CTC models by using phonological information. A universal (IPA) phoneme classiﬁer is ﬁrst trained on phonological features generated from a phonological attribute detector. When adapting the multilingual CTC to a new, never seen, language, phonological attributes of the un-seen phonemes are derived based on phonology and fed into the phoneme classiﬁer. Posteriors given by the classiﬁer are used to initialize the parameters of the unseen phonemes when extending the multilingual CTC output layer to the target language. Adaptation experiments show that the proposed initialization approaches further improve the cross-lingual adaptation on CTC models and yield signiﬁcant improvements over Deep Neural Network / Hidden Markov Model (DNN/HMM)-based adaptation using limited data.
Freeze-thaw cycles induce content exchange between cell-sized lipid vesicles

Early protocells are commonly assumed to consist of an amphiphilic membrane enclosing an RNA-based self-replicating genetic system and a primitive metabolism without protein enzymes. Thus, protocell evolution must have relied on simple physicochemical self-organization processes within and across such vesicular structures. We investigate freeze-thaw (FT) cycling as a potential environmental driver for the necessary content exchange between vesicles. To this end, we developed a conceptually simple yet statistically powerful high-throughput procedure based on nucleic acid-containing giant unilamellar vesicles (GUVs) as model protocells. GUVs are formed by emulsion transfer in glass bottom microtiter plates and hence can be manipulated and monitored by fluorescence microscopy without additional pipetting and sample handling steps. This new protocol greatly minimizes artefacts, such as unintended GUV rupture or fusion by shear forces. Using DNA-encapsulating phospholipid GUVs fabricated by this method, we quantified the extent of content mixing between GUVs under different FT conditions. We found evidence of nucleic acid exchange in all detected vesicles if fast freezing of GUVs at −80 °C is followed by slow thawing at room temperature. In contrast, slow freezing and fast thawing both adversely affected content mixing. Surprisingly, and in contrast to previous reports for FT-induced content mixing, we found that the content is not exchanged through vesicle fusion and fission, but that vesicles largely maintain their membrane identity and even large molecules are exchanged via diffusion across the membranes. Our approach supports efficient screening of prebiotically plausible molecules and environmental conditions, to yield universal mechanistic insights into how cellular life may have emerged.
Universitet Improvement in Activities of Daily Living among Danish Centenarians ? A Comparative Study of Two Centenarian Cohorts Born Twenty Years Apart

Citation for pulished version (APA): Rasmussen, S. H. , Thinggaard, M. , Højgaard, M. B. , Jeune, B. , Christensen, K. , & Andersen-Ranberg, K. (2018). Improvement in Activities of Daily Living among Danish Centenarians? A Comparative Study of Two Centenarian Cohorts Born Twenty Years Apart. The journals of gerontology. Series A, Biological sciences and medical sciences, 73(8), 1125–1131. https://doi.org/10.1093/gerona/glx113
Visual tracking via ensemble autoencoder

The authors present a novel online visual tracking algorithm via ensemble autoencoder (AE). In contrast to other existing deep model based trackers, the proposed algorithm is based on the theory that the image resolution has an influence on vision procedures. When the authors employ a deep neural network to represent the object, the resolution is corresponding to the network size. The authors apply a small network to represent the pattern in a relatively lower resolution and search the object in a relatively larger area of the neighbourhood. After roughly estimating the location of the object, the authors apply a large network, which can provide more detailed information, to estimate the state of the object more accurately. Thus, the authors employ a small AE mainly for position searching and a larger one mainly for scale estimating. When tracking an object, the two networks interact to operate under the framework of particle filtering. Extensive experiments on the benchmark dataset show that the proposed algorithm performs favourably compared with some state-of-the-art methods.
Diffusion Acquisition and Diffusion Weighting

Water self diffusion or random thermal motion (“Brownian motion”) is a microscopic process that within certain limits can be measured in the body by diffusion-weighted MRI (DWI). The random thermal motion of water molecules in different tissue structures is however very complicated. In DWI, we typically have voxel volumes around 10 mm3, wherein multiple tissue compartments having different self-diffusion are averaged together hence the commonly used name “apparent diffusion coefficient” or ADC.
Title Quantifying and Accounting for Quality Differences in Services in International Price Comparisons : A Bilateral Price Comparison between United States and Japan

Purchasing power parities (PPPs) from the International Comparison Program (ICP) are used for cross-country comparisons of price levels and real gross domestic product (GDP), household consumption and investment. PPPs from the ICP are also used in compiling internationally comparable output aggregates and making productivity comparisons in the KLEMS initiative. PPP compilation is anchored on the principle of comparing the like with like and price data are collected for goods and services with detailed specifications in the form of structural product descriptions. While this approach works well for goods, it is not effective in the case of services. If differences in service quality exist, these get reflected in the PPPs from the ICP. In this paper, we focus on the USA-Japan bilateral price comparison in the 2014 ICP in the OECD region and estimate bias induced by differences in quality of services in. Service quality is driven by various unobservable factors. In this paper we make use of data on quality differences and consumers’ willingness to pay collected through a specialised survey conducted by the Japan Productivity Center early in 2017. Data are collected from a large sample of 517 respondents from USA and 519 respondents from Japan, covering 28 service items including transport, restaurants, retail services, health and education. Estimates of consumers’ willingness to pay for quality differences in services by the US and Japanese consumers are obtained using standard econometric methodology, these are in turn used in estimating quality adjustment factors that can be applied to price data used in PPP computation. Using the Sato-Vartia index, which has useful analytical and decomposition properties, we find PPP for household consumption (including real estate services) of 113 JPY per US dollar reduces to 104 JPY per dollar after adjusting for quality differences. When real estate services are not included, PPP reduces from 95 JPY to 87 JPY after quality adjustment. The paper also presents labour productivity estimates before and after quality adjustment for a number of service sectors including transport and storage; retail trade; hotels and restaurants; and other subsectors. Our exploratory study demonstrates that adjustment for quality differences in services is feasible and such adjustments are important for making meaningful international price comparisons.
Sansa: A Modified Sansula for Extended Compositional Techniques Using Machine Learning

Sansa is an extended sansula, a hyper-instrument that is similar in design and functionality to a kalimba or thumb piano. At the heart of this interface is a series of sensors that are used to augment the tone and expand the performance capabilities of the instrument. The sensor data is further exploited using the machine learning program Wekinator, which gives users the ability to interact and perform with the instrument using several di↵erent modes of operation. In this way, Sansa is capable of both solo acoustic performances as well as complex productions that require interactions between multiple technological mediums. Sansa expands the current community of hyper-instruments by demonstrating the ways that hardware and software can extend an acoustic instrument’s functionality and playability in a live performance or studio setting. Author
The norm of a discretized gradient in H ( div ) ∗ for a posteriori ﬁnite element error analysis

This paper characterizes the norm of the residual of mixed schemes in their naturalfunctionalframeworkwithﬂuxesorstressesin H ( div ) anddisplacementsin L 2 . Under some natural conditions on an associated Fortin interpolation operator, reliable andefﬁcienterrorestimatesareintroducedthatcircumventthedualitytechniqueandsodonotsufferfromreducedellipticregularityfornon-convexdomains.FortheLaplace,Stokes,andLaméequations,thisgeneralizesknownestimatorstonon-convexdomainsandintroducesnewaposteriorierrorestimators. the 1748 “Reliable simulation techniques in solid mechanics. Development of non-standard discretization methods, mechanical and mathematical analysis” under the projects “Foundation and application of generalized mixed FEM towards nonlinear problems in solid mechanics”, “Adaptive isogeometric modeling of propagating strong discontinuities in heterogeneous materials” and “High-order immersed-boundary methods in solid mechanics for structures generated by additive processes”.
Quantification of MT effects & Quantitative MT Imaging

What is magnetization transfer? Magnetization transfer is an MRI technique that generates contrast based on the exchange of magnetization between macromolecules (proteins, lipids, cell-membranes) and water. First applied to MRI in 1989 by Wolf & Balaban [26], this technique has been widely used to in research studies to monitor changes in tissue structure that occur with development and disease. In the context of MRI, magnetization transfer is narrowly defined as exchange of magnetization between the nuclei of hydrogen atoms bound to macromolecules and those bound to water. This is distinguished from chemical exchange saturation transfer (CEST) where the exchange is with hydroden nuclei from mobile compounds [25] or the nuclear overhauser effect which deals more generally with exchange between dissimilar spin populations. Magnetization transfer can occur by physical (chemical) exchange of hydrogen atoms or by transfer of spin states, the latter mediated by dipolar coupling.