Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of Anti-Osteopontin Antibodies in Multiple Sclerosis and Experimental Autoimmune Encephalomyelitis

Osteopontin (OPN) is highly expressed in demyelinating lesions in multiple sclerosis (MS) and experimental autoimmune encephalomyelitis (EAE). OPN is cleaved by thrombin into N- (OPN-N) and C-terminal (OPN-C) fragments with different ligands and functions. In EAE, administering recombinant OPN induces relapses, whereas treatment with anti-OPN antibodies ameliorates the disease. Anti-OPN autoantibodies (autoAbs) are spontaneously produced during EAE but have never been detected in MS. The aim of the study was to evaluate anti-OPN autoAbs in the serum of MS patients, correlate them with disease course, and recapitulate the human findings in EAE. We performed ELISA in the serum of 122 patients collected cross-sectionally, and 50 patients with relapsing–remitting (RR) disease collected at diagnosis and followed longitudinally for 10 years. In the cross-sectional patients, the autoAb levels were higher in the RR patients than in the primary- and secondary-progressive MS and healthy control groups, and they were highest in the initial stages of the disease. In the longitudinal group, the levels at diagnosis directly correlated with the number of relapses during the following 10 years. Moreover, in patients with active disease, who underwent disease-modifying treatments, autoAbs were higher than in untreated patients and were associated with low MS severity score. The autoAb displayed neutralizing activity and mainly recognized OPN-C rather than OPN-N. To confirm the clinical effect of these autoAbs in vivo, EAE was induced using myelin oligodendrocyte glycoprotein MOG35–55 in C57BL/6 mice pre-vaccinated with ovalbumin (OVA)-linked OPN or OVA alone. We then evaluated the titer of antibodies to OPN, the clinical scores and in vitro cytokine secretion by spleen lymphocytes. Vaccination significantly induced antibodies against OPN during EAE, decreased disease severity, and the protective effect was correlated with decreased T cell secretion of interleukin 17 and interferon-γ ex vivo. The best effect was obtained with OPN-C, which induced significantly faster and more complete remission than other OPN vaccines. In conclusion, these data suggest that production of anti-OPN autoAbs may favor remission in both MS and EAE. Novel strategies boosting their levels, such as vaccination or passive immunization, may be proposed as a future strategy in personalized MS therapy.
Intelligence in DSM‐IV combined type attention‐deficit/hyperactivity disorder is not predicted by either dopamine receptor/transporter genes or other previously identified risk alleles for attention‐deficit/hyperactivity disorder

A major goal of genetic studies of attention deficit hyperactivity disorder (ADHD) is to identify individual characteristics that might help segregate the disorder's inherent heterogeneity. [Mill et al. (2006); Arch Ger Psychiatry 63:462–469] recently reported a potentially important association between two dopamine‐related risk polymorphisms (DRD4 variable number tandem repeat (VNTR) in exon 3 and DAT1 VNTR in the 3′ UTR) and lowered IQ in ADHD. The objective of the current study was to replicate the [Mill et al. (2006); Arch Ger Psychiatry 63:462–469] findings in a clinical sample and to extend the analysis to a large range of alternative SNP markers of putative ADHD risk alleles identified in a recent study [Brookes et al. (2006); Mol Genet 11:934–953]. Participants were 1081 children and adolescents with a research‐confirmed combined type ADHD diagnosis and 1300 unaffected siblings who took part in the International Multi‐centre ADHD Genetics (IMAGE) project. They were recruited from multiple settings from across Europe: Belgium, Britain, Germany, Ireland, Israel, Netherlands, Spain and Switzerland. The results were that ADHD was associated with reduced IQ. However, there was no association between the two dopamine‐related risk polymorphisms and IQ in either the probands or their siblings. Furthermore, other selected genetic markers previously demonstrated to be associated with ADHD in this sample were not associated with IQ. This large scale study with a clinically ascertained and regorously diagnosed sample failed to replicate the association between genetic polymorphisms in the dopamine system and IQ in ADHD. We also observed no association of other SNPs with IQ in ADHD. © 2007 Wiley‐Liss, Inc.
SOI slot photonic crystal heterostructure cavities in the 1.3 µm – 1.6 µm range for hybrid photonic integration

In this paper, we report a series of slot photonic crystal heterostructure cavities with resonant wavelengths in the range of 1.3 μm - 1.6 μm. These cavities, which have a wide range operation as well as high Q/V factors (above 800,000), can be used for various applications based on the integration of active materials in the silicon photonics platform.
Stress and health-related quality of life in mothers seeking a diagnosis of high-functioning autism for their child : a mixed methods investigation

Objective: To investigate mothers’ experiences in raising a child with suspected high functioning autism or Asperger’s syndrome while trying to get a diagnosis for their child, specifically focusing on stress and health-related quality of life. Mothers who already have a diagnosis for their child acted as a comparison group. Method: A cross-sectional mixed methods approach was applied. The Parenting Stress Index Short Form (PSI/SF), the 12 Item Short-Form Health Survey (SF12) and a demographic questionnaire was administered to a convenience sample of 40 mothers in the experimental group and 44 in the comparison group, all of whom resided in the UK. Qualitative semi-structured interviews with a sample of 4 mothers in each group followed to expand on those findings; data was analysed using thematic analysis. Results: Although both groups displayed clinically high mean scores for all the PSI/SF subscales and overall stress, the experimental group displayed significantly higher scores in the Parenting Stress and Difficult Child subscales and overall stress scores. The experimental group scored significantly worse in the Mental Component Summary scores of the Health Survey than the comparison group, indicating poorer mental-health related quality of life. The interviews, which discussed mother’s experiences of raising their child, the diagnostic process and its implications, also supported the quantitative findings. Implications: This study highlights the health and mental health implications on the large population of mothers seeking diagnoses of autism for their children. This population group is largely neglected in the literature. This brings into focus areas where further in-depth research and development of support systems for this population would potentially benefit these parents, specifically in terms of health related quality of life and it supports the drive by the National Autistic Society for earlier diagnoses and more efficient diagnostic processes.
Multiway analysis of epilepsy tensors

MOTIVATION The success or failure of an epilepsy surgery depends greatly on the localization of epileptic focus (origin of a seizure). We address the problem of identification of a seizure origin through an analysis of ictal electroencephalogram (EEG), which is proven to be an effective standard in epileptic focus localization. SUMMARY With a goal of developing an automated and robust way of visual analysis of large amounts of EEG data, we propose a novel approach based on multiway models to study epilepsy seizure structure. Our contributions are 3-fold. First, we construct an Epilepsy Tensor with three modes, i.e. time samples, scales and electrodes, through wavelet analysis of multi-channel ictal EEG. Second, we demonstrate that multiway analysis techniques, in particular parallel factor analysis (PARAFAC), provide promising results in modeling the complex structure of an epilepsy seizure, localizing a seizure origin and extracting artifacts. Third, we introduce an approach for removing artifacts using multilinear subspace analysis and discuss its merits and drawbacks. RESULTS Ictal EEG analysis of 10 seizures from 7 patients are included in this study. Our results for 8 seizures match with clinical observations in terms of seizure origin and extracted artifacts. On the other hand, for 2 of the seizures, seizure localization is not achieved using an initial trial of PARAFAC modeling. In these cases, first, we apply an artifact removal method and subsequently apply the PARAFAC model on the epilepsy tensor from which potential artifacts have been removed. This method successfully identifies the seizure origin in both cases.
The influence of galaxy formation physics on weak lensing tests of general relativity

Forthcoming projects such as the Dark Energy Survey, Joint Dark Energy Mission, and the Large Synoptic Survey Telescope, aim to measure weak lensing shear correlations with unprecedented accuracy. Weak lensing observables are sensitive to both the distance-redshift relation and the growth of structure in the Universe. If the cause of accelerated cosmic expansion is dark energy within general relativity, both cosmic distances and structure growth are governed by the properties of dark energy. Consequently, one may use lensing to check for this consistency and test general relativity. After reviewing the phenomenology of such tests, we address a major challenge to such a program. The evolution of the baryonic component of the Universe is highly uncertain and can influence lensing observables, manifesting as modified structure growth for a fixed cosmic distance scale. Using two proposed methods, we show that one could be led to reject the null hypothesis of general relativity when it is the true theory if this uncertainty in baryonic processes is neglected. Recent simulations suggest that we can correct for baryonic effects using a parameterized model in which the halo mass-concentration relation is modified. The correction suffices to render biases small compared to statistical uncertainties. We study the ability of future weak lensing surveys to constrain the internal structures of halos and test the null hypothesis of general relativity simultaneously. Compared to alternative methods which null information from small-scales to mitigate sensitivity to baryonic physics, this internal calibration program should provide limits on deviations from general relativity that are several times more constraining. Specifically, we find that limits on general relativity in the case of internal calibration are degraded by only ~ 30% or less compared to the case of perfect knowledge of nonlinear structure.
Ieee Transactions on Information Theory 1 Regret Minimization for Reserve Prices in Second-price Auctions

—We show a regret minimization algorithm for setting the reserve price in a sequence of second-price auctions, under the assumption that all bids are independently drawn from the same unknown and arbitrary distribution. Our algorithm is computationally efficient, and achieves a regret of O(√ T) in a sequence of T auctions. This holds even when the number of bidders is stochastic with a known distribution.
Blocking Methods Applied to Casualty Records from the Syrian Conflict

Estimation of death counts and associated standard errors is of great importance in armed conflict such as the ongoing violence in Syria, as well as historical conflicts in Guatemala, Per\'u, Colombia, Timor Leste, and Kosovo. For example, statistical estimates of death counts were cited as important evidence in the trial of General Efra\'in R\'ios Montt for acts of genocide in Guatemala. Estimation relies on both record linkage and multiple systems estimation. A key first step in this process is identifying ways to partition the records such that they are computationally manageable. This step is referred to as blocking and is a major challenge for the Syrian database since it is sparse in the number of duplicate records and feature poor in its attributes. As a consequence, we propose locality sensitive hashing (LSH) methods to overcome these challenges. We demonstrate the computational superiority and error rates of these methods by comparing our proposed approach with others in the literature. We conclude with a discussion of many challenges of merging LSH with record linkage to achieve an estimate of the number of uniquely documented deaths in the Syrian conflict.
A computationally efficient multipitch analysis model

A computationally efficient model for multipitch and periodicity analysis of complex audio signals is presented. The model essentially divides the signal into two channels, below and above 1000 Hz, computes a "generalized" autocorrelation of the low-channel signal and of the envelope of the high-channel signal, and sums the autocorrelation functions. The summary autocorrelation function (SACF) is further processed to obtain an enhanced SACF (ESACF). The SACF and ESACP representations are used in observing the periodicities of the signal. The model performance is demonstrated to be comparable to those of recent time-domain models that apply a multichannel analysis. In contrast to the multichannel models, the proposed pitch analysis model can be run in real time using typical personal computers. The parameters of the model are experimentally tuned for best multipitch discrimination with typical mixtures of complex tones. The proposed pitch analysis model may be used in complex audio signal processing applications, such as sound source separation, computational auditory scene analysis, and structural representation of audio signals. The performance of the model is demonstrated by pitch analysis examples using sound mixtures which are available for download at http://www.acoustics. hut.fi/-ttolonen/pitchAnalysis/.
Towards a novel safety norm for domestic robotics

Safety is a critical success factor for consumer acceptance of domestic robotic products. Some researchers have adopted the head injury criterion (HIC) as absolute safety norm. However, this norm covers only part of the safety risk. In many cases skin damage (e.g. cuts, wounds, etc) can be a more serious risk. This article shows how to work towards a novel absolute safety measure for evaluating the shape and material choices of a robotic design w.r. t. skin damage. The proposed safety norm evaluates the situation of an unintended uncontrolled collision of a robotic part against a human. Maximum curvatures of the exterior robotic shape are approximated as a sphere in contact with the human skin (locally approximated as a flat surface). This local spheric approximation of the impact contact is used to predict maximum tensile stress during impact of the robotic part on the human. Robotic designs that include points for which the tensile strength of the skin is exceeded will cause at least skin fracture and are therefore considered intrinsically unsafe. While in general applicable, this paper specifically addresses how to apply the proposed norm in the case of safety evaluation of robotic manipulators.
Value-based process improvement

For years, “improving business processes” has been and is the primary business priority of IT. In business process management (BPM), common criteria to evaluate the improvement of a process are time, costs, customer satisfaction and output quality. In contrast, the management of companies focuses on increasing the company’s value, using a value-based management approach, which is hard to be linked to these criteria. A value-based process improvement can alleviate this drawback by incorporating value-based management into the area of BPM. In this paper we introduce, based on the design science paradigm, an approach that is suitable for the value-based improvement of processes. Demonstrating the feasibility and the advantage of our approach, we show its applicability within a real world scenario and evaluate it by comparing it to a competing work in the field of valuebased process management.
Advanced processing for ontological queries

Ontology-based data access is a powerful form of extending database technology, where a classical extensional database (EDB) is enhanced by an ontology that generates new intensional knowledge which may contribute to answer a query. The ontological integrity constraints for generating this intensional knowledge can be specified in description logics such as DL-Lite. It was recently shown that these formalisms allow for very efficient query-answering. They are, however, too weak to express simple and useful integrity constraints that involve joins. In this paper we introduce a more expressive formalism that takes joins into account, while still enjoying the same low query-answering complexity. In our framework, ontological constraints are expressed by sets of rules that are so-called tuple-generating dependencies (TGDs). We propose the language of sticky sets of TGDs, which are sets of TGDs with a restriction on multiple occurrences of variables (including joins) in the rule bodies. We establish complexity results for answering conjunctive queries under sticky sets of TGDs, showing, in particular, that ontological conjunctive queries can be compiled into first-order and thus SQL queries over the given EDB instance. We also show how sticky sets of TGDs can be combined with functional dependencies. In summary, we obtain a highly expressive and effective ontological modeling language that unifies and generalizes both classical database constraints and important features of the most widespread tractable description logics.
Developing language technology tools and resources for a resource-poor language: Sindhi

Sindhi, an Indo-Aryan language with more than 75 million native speakers 1 is a resourcepoor language in terms of the availability of language technology tools and resources. In this thesis, we discuss the approaches taken to develop resources and tools for a resourcepoor language with special focus on Sindhi. The major contributions of this work include raw and annotated datasets, a POS Tagger, a Morphological Analyser, a Transliteration and a Machine Translation System.
An Empirical evaluation of CostBoost Extensions for Cost-Sensitive Classification

Data Mining Technique, namely Classification, is used to predict group membership for data samples. Ensemble learning, combining multiple classifiers using bagging, boosting or stacking, are proven data-mining methods, we have used boosting in this paper for combining multiple classifiers. Cost-sensitive classification is used for classification tasks under the Cost-Based Model (CBM), unlike the Error-Based Model (EBM). EBM does not incorporate the cost of misclassifying a sample in a model building phase, while CBM does. CBM techniques usually modify the weight update equation to incorporate the misclassification cost from cost-matrix. Cost-sensitive boosters are studied and three new extensions of CostBoost algorithm CBE1, CBE2 and CBE3 are proposed and compared with existing cost based boosting classifiers. CSE1, CSE2 and CSE3 outperformed the original CostBoost by 5%, 4% and 4% respectively, in terms of misclassification cost.
Including Group-By in Query Optimization

In existing relational database systems, processing of group-by and computation of aggregate functions are always postponed until all joins are performed. In this paper, we present transformations that make it possible to push group-by operation past one or more joins and can potentially reduce the cost of processing a query significantly. Therefore, the placement of group-by should be decided based on cost estimation. We explain how the traditional System-R style optimizers can be modified by incorporating the greedy conservative heuristic that we developed. We prove that applications of greedy conservative heuristic produce plans that are better (or no worse) than the plans generated by a traditional optimizer. Our experimental study shows that the extent of improvement in the quality of plans is significant with only a modest increase in optimization cost. Our technique also applies to optimization of Select Distinct queries by pushing down duplicate elimination in a cost-based fashion.
Annealed Importance Sampling for Structure Learning in Bayesian Networks

We present a new sampling approach to Bayesian learning of the Bayesian network structure. Like some earlier sampling methods, we sample linear orders on nodes rather than directed acyclic graphs (DAGs). The key difference is that we replace the usual Markov chain Monte Carlo (MCMC) method by the method of annealed importance sampling (AIS). We show that AIS is not only competitive to MCMC in exploring the posterior, but also superior to MCMC in two ways: it enables easy and efficient parallelization, due to the independence of the samples, and lower-bounding of the marginal likelihood of the model with good probabilistic guarantees. We also provide a principled way to correct the bias due to order-based sampling, by implementing a fast algorithm for counting the linear extensions of a given partial order.
A User Study: Technology to Increase Teachers’ Linguistic Awareness to Improve Instructional Language Support for English Language Learners

This paper discusses user study outcomes with teachers who used Language Muse SM a webbased teacher professional development (TPD) application designed to enhance teachers’ linguistic awareness, and support teachers in the development of language-based instructional scaffolding (support) for their English language learners (ELL). System development was grounded in literature that supports the notion that instruction incorporating language support for ELLs can improve their accessibility to content-area classroom texts –in terms of access to content, and improvement of language skills. Measurement outcomes of user piloting with teachers in a TPD setting indicated that application use increased teachers' linguistic knowledge and awareness, and their ability to develop appropriate language-based instruction for ELLs. Instruction developed during the pilot was informed by the application’s linguistic analysis feedback, provided by natural language processing capabilities in Language Muse.
Recognizing and Analyzing Ball Screen Defense in the NBA

As the NBA’s go-to offensive play, determining how to defend the ball screen is among the most critical decisions faced by NBA coaching staffs. In this paper, we present the construction and application of a tool for automatically recognizing common defensive counters to ball screens. Using SportVU player tracking data and supervised machine learning techniques, we learn a classi ier that labels ball screens according to how they were defended. Applied to data from four NBA seasons, our classi ier identi ied 270,823 screens in total. These labeled data enable novel analyses of defensive strategies. We present observations and trends at both the team and player levels. Our work is a step towards the construction of a coaching assistance tool for analyzing one of the game’s most important actions.
Evaluating the effect of information sharing and production control strategies on the performance of supply chains

In this paper, the effect of various system control policies on the performance of a multistage supply chain is studied under a variety of conditions (factors). Three models are considered: pull systems with and without information sharing, and a centralised push system with information sharing. Factors studied include demand distribution, shortage penalty to holding cost ratio at the retailers, ratio of capacity costs at the manufacturer, and demand pattern. Total system costs and variability in production are examined. Improvements due to the use of information and a coordinated push strategy are presented.