Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Sclerosis Cerebrospinal Fluid Biomarkers

Cerebrospinal fluid (CSF) is the body fluid closest to the pathology of multiple sclerosis (MS). For many candidate biomarkers CSF is the only fluid that can be investigated. Several factors need to be standardized when sampling CSF for biomarker research: time/volume of CSF collection, sample processing/storage, and the temporal relationship of sampling to clinical or MRI markers of disease activity. Assays used for biomarker detection must be validated so as to optimize the power of the studies. A formal method for establishing whether or not a particular biomarker can be used as a surrogate end-point needs to be adopted. This process is similar to that used in clinical trials, where the reporting of studies has to be done in a standardized way with sufficient detail to permit a critical review of the study and to enable others to reproduce the study design. A commitment must be made to report negative studies so as to prevent publication bias. Pre-defined consensus criteria need to be developed for MS-related prognostic biomarkers. Currently no candidate biomarker is suitable as a surrogate end-point. Bulk biomarkers of the neurodegenerative process such as glial fibrillary acidic protein (GFAP) and neurofilaments (NF) have advantages over intermittent inflammatory markers.
Diagnosing phosphorus limitations in natural terrestrial ecosystems in carbon cycle models

Most of the Earth System Models (ESMs) project increases in net primary productivity (NPP) and terrestrial carbon (C) storage during the 21st century. Despite empirical evidence that limited availability of phosphorus (P) may limit the response of NPP to increasing atmospheric CO2, none of the ESMs used in the previous Intergovernmental Panel on Climate Change assessment accounted for P limitation. We diagnosed from ESM simulations the amount of P need to support increases in carbon uptake by natural ecosystems using two approaches: the demand derived from (1) changes in C stocks and (2) changes in NPP. The C stock‐based additional P demand was estimated to range between −31 and 193 Tg P and between −89 and 262 Tg P for Representative Concentration Pathway (RCP) 2.6 and RCP8.5, respectively, with negative values indicating a P surplus. The NPP‐based demand, which takes ecosystem P recycling into account, results in a significantly higher P demand of 648–1606 Tg P for RCP2.6 and 924–2110 Tg P for RCP8.5. We found that the P demand is sensitive to the turnover of P in decomposing plant material, explaining the large differences between the NPP‐based demand and C stock‐based demand. The discrepancy between diagnosed P demand and actual P availability (potential P deficit) depends mainly on the assumptions about availability of the different soil P forms. Overall, future P limitation strongly depends on both soil P availability and P recycling on ecosystem scale.
A preliminary study

Scoops are important parts in an aircraft engine design, as they provide airflow to different equipment and subsystems. The optimization of such a component is essential in order to find a design that can perform properly within a range of flight conditions, with a minimum impair of main flow aerodynamic performances. Scoop design methods are generally based on previous experimental results and are usually constrained by the limited space available. The studied configuration concerns the flush scoop located inside the secondary flow of turbofans which provides flow for a turbine cooling equipment. Depending on flight conditions and engine working point, this scoop will experience various flow regimes, from low mass flow rates to choke flows. Therefore, the study of several scooped mass flow rates is mandatory to extract the scoop behaviour. The thesis concerns the preliminary step before a 3D CFD optimization : a study of influence is run on the baseline geometry in order to investigate the robustness of the solution computed using different methods and to determine the parameters to be optimized. Firstly, the full post-processing methodology is defined to properly evaluate the performance of a design (scoop efficiency, induced pressure losses). A second step consists in analysing the ability of CFD solvers to capture the different flow behaviour. This point is addressed by comparing solvers (Fluent, elsA, PowerFLOW), meshes (structured, unstructured) and turbulencemodels. The third step deals with the optimization strategy definition to improve the scoop design and thus the engine fuel consumption.
Confidential Computation of Corresponding Elements of Set Intersection

Secure multi-party computing is one of the most concerned fields in international cryptography. Besides, the problem of the sum of corresponding elements in the intersection of confidential computing sets is relatively new and has wide application scenarios. By using the addition homomorphism of the Lifted ElGamal threshold cryptosystem, this paper solves two problems related to the corresponding elements of the set intersection, namely, the sum of the associated values of the set intersection elements and the secret judgment of the relationship with the threshold. In the case of known data range, the corresponding secret calculation scheme is designed, in the process of calculation, the correlation value of the intersection elements of the set will not be disclosed, and only the final result is obtained. At the same time, the Lifted ElGamal threshold cryptosystem is used in this paper to resist collusive attacks.
Comparison of Various Meteorological Parameters for Solar Radiation Prediction Using Artificial Neural Network

Renewable energy sources are currently considered an alternative source of energy because they swiftly regenerate themselves and may be utilized repeatedly. Weather, time of day, and geographic location all affect solar radiation. In the proposed work, solar radiation is predicted using an Artificial Neural Network (ANN) model. The prediction of diffuse insolation under a clear sky is done using the neural fitting tool (nftool). The suggested ANN model's mean square error and regression values are generated using the various meteorological data, such as humidity, air temperature, atmospheric pressure, and the number of days in the year, as input. The performance of the forecast solar radiation is then assessed using the various combinations of meteorological factors produced and applied to the neural network.
A requirement for calcium in the caspase‐independent killing of Burkitt lymphoma cell lines by Rituximab

The therapeutic monoclonal antibody rituximab has previously been shown to kill B cells in a caspase‐independent manner. The signalling pathways underpinning this novel death pathway are unknown. The present study showed that rituximab treatment of Burkitt lymphoma cell lines induced a slow rise in intracellular calcium ([Ca2+]i). This rise was only witnessed in cell lines that were killed by antibody, suggesting a critical role for Ca2+ in mediating rituximab‐driven caspase‐independent cell death. Inhibition of the two main intracellular store‐located Ca2+ channels, i.e. the ryanodine and inositol‐1,4,5‐triphosphate receptor channels by dantrolene and xestospongen‐c respectively did not prevent the rise in Ca2+ seen with rituximab or protect cells from subsequent death. In sharp contrast, inhibition of Ca2+ entry via plasma membrane channels with (2‐aminoethoxy) diphenylborate or SKF‐96365 or complete chelation of extracellular Ca2+ with ethyleneglycol bis (aminoethylether) tetra‐acetate inhibited the rise in [Ca2+]i and increased cell viability. Together, these data suggest that ligation of the CD20 receptor with rituximab allows a slow sustained influx of Ca2+ from the external environment that under certain conditions can lead to cell death.
The genetic and environmental contributions to attention deficit hyperactivity disorder as measured by the Conners' Rating Scales--Revised.

OBJECTIVE The majority of published reports on twin studies of attention deficit hyperactivity disorder (ADHD) have indicated robust additive genetic influences and unique environmental influences. These studies typically used DSM ADHD symptoms collected by telephone or interviews with mothers. The purpose of this study was to test the genetic architecture of ADHD by using the ADHD index from Conners' Rating Scales-Revised. METHOD From the Conners' scale forms, data for the ADHD index were collected from the mothers of 1,595 7-year-old twin pairs from the Netherlands Twin Registry. Rates of ADHD diagnoses were computed by using Conners' gender- and age-specific cutoff points. Contributions from additive, dominant, unique environmental, interaction, and gender effects were computed by using gender-genetic models. RESULTS The prevalence of ADHD across the sample of 7-year-old twin pairs was about 4% according to the mothers' reports, consistent with other reported rates of ADHD. However, using the gender norms provided with the ADHD index, the authors found slightly higher rates of ADHD in girls than previously reported. Genetic analyses yielded a model that includes genetic dominance (48%), additive genetic factors (30%), and unique environmental factors (22%). CONCLUSIONS The ADHD index from Conners' Rating Scales-Revised identified an appropriate percentage of children across this epidemiologic twin sample as being at risk for ADHD. The results of the genetic analyses are consistent with prior reports that ADHD is predominantly influenced by genetic factors that are both dominant and additive.
A hybrid approach for data analytics for internet of things

The vision of the Internet of Things is to allow currently unconnected physical objects to be connected to the internet. There will be an extremely large number of internet connected devices that will be much more than the number of human being in the world all producing data. These data will be collected and delivered to the cloud for processing, especially with a view of finding meaningful information to then take action. However, ideally the data needs to be analysed locally to increase privacy, give quick responses to people and to reduce use of network and storage resources. To tackle these problems, distributed data analytics can be proposed to collect and analyse the data either in the edge or fog devices. In this paper, we explore a hybrid approach which means that both in-network level and cloud level processing should work together to build effective IoT data analytics in order to overcome their respective weaknesses and use their specific strengths. Specifically, we collected raw data locally and extracted features by applying data fusion techniques on the data on resource constrained devices to reduce the data and then send the extracted features to the cloud for processing. We evaluated the accuracy and data consumption over network and thus show that it is feasible to increase privacy and maintain accuracy while reducing data communication demands.
Refining PTZ camera calibration

Due to the increased need for security and surveillance, PTZ cameras are now being widely used in many domains. Therefore, it is very important for the applications like video mosaic generation or automatic surveillance that these camera be accurately calibrated. In this paper, we address the problem of parameter refinement for such pan-tilt-zoom (PTZ) cameras. Use of bundle-adjustment for parameter refinement has widely been adopted in the computer vision field. However, as has been shown by researchers, in presence of noise, this Maximum Likelihood estimate looses its optimality. We propose a novel statistically optimal error function that is shown to experimentally outperform this ML estimate in presence of significant noise. We perform tests on synthetic as well as on real data to verify our method.
OWL-Q for Semantic QoS-based Web Service Description and Discovery

Semantic Web Services are emerging for their promise to produce a more accurate and precise Web Service discovery process. However, most of research approaches focus only on the functional part of semantic Web Service description. The above fact along with the proliferation of Web Services is highly probable to lead to a situation where Web Service registries will return many functionally-equivalent Web Service advertisements for each user request. This problem can be solved with the semantic description of QoS for Web Services. QoS is a set of non-functional properties encompassing performance and network-related characteristics of resources. So it can be used for distinguishing between functionally-equivalent Web Services. Current research approaches for QoS-based Web Service description are either syntactic or poor or non-extensible. To solve this problem, we have developed a rich and extensible ontological specification called OWL-Q for semantic QoS-based Web Service description. We analyze all OWL-Q parts and reason that rules should be added in order to support property inferencing and constraint enforcement. Finally, we line out our under-development semantic framework for QoS-based Web Service description and discovery.
Dysphoria and Self-Focused Attention: Effects of Feedback on Task Strategy and Goal Adjustment

This study investigated the effects of dysphoria and self-focused attention (SFA) on task performance and goal adjustment following negative performance feedback. Participants (70 dysphoric and 70 nondysphoric) set a performance goal and completed an attention task before and after receiving performance feedback. SFA was induced using mirror exposure in half the sample. Consistent with motivational theories of depression, during the first task the dysphoric participants' response pattern reflected a cautious, vigilant approach. When the task was repeated following negative feedback, all groups improved overall performance, but the dysphoric participants in the low versus high SFA condition showed differential changes in rates of omission errors. An interaction between gender and dysphoria on goal adjustment also was found. Implications for the role of SFA and dysphoria in self-regulation are discussed.
System/360 and Bayond

The evolution of modern large-scale computer architecture within IBM is described, starting with the announcement of System/360 in 1964 and covering the latest extensions to System/370. Emphasis is placed on key attributes and on the motivation for providing them, and an assessment is made of the experience gained in the implementation and use of the architecture. The main approaches are discussed for obtaining implementations at widely differing performance levels, and a number of significant implementation parameters for all processors are listed.
Validation of a Comprehensive Executive Learning and Development Measure

Evaluating the returns on intangible assets in general and executive human capital in particular is still a challenging endeavour. One possible means of addressing this challenge involves developing a broad measure of executive learning and development (L&D), encapsulating both the formal and informal activities that closely reflect the dynamic job profile of modern business executives. Yet an extensive review of the literature fails to confirm the existence of such a broad measure. The present study seeks to fill this gap. It puts forward an executive L&D measure defined by four main dimensions: strategy, self‐direction, experience and participation. This measure has been validated through exploratory and confirmatory factor analysis. All four factors within the construct exhibited good unidimensionality, validity and reliability characteristics and are underpinned by high Cronbach's alphas. We conclude that organizations can increase executive human capital capacity by focusing on strategically‐aligned informal learning interventions supported by carefully selected structured activities. In terms of originality, this research offers a broad and internationally validated executive L&D measure which we believe is suitable for evaluating and monitoring executive L&D interventions within organizations.
Service Description Based on CW for Pervasive Service Discovery

Current existing service discovery mechanisms are lack of representation about imprecise or vague information of service on semantic level. This paper proposes a novel approach that service advertisements or requests can be expressed in natural language involving linguistic variables, which are considered as one of the essential factors to achieve the vision of pervasive computing. This new service description method has three features by comparing with existing architectures: First, service description is on semantic level, especially allows users to express their requests involving imprecise or vague data in a natural way. Second, the approach is based on the theory of computing with words (CW), which is defined to be an extension of fuzzy logic. Third, the architecture has ontology support which makes the service descriptions be understandable by all the participants.
Tied Factor Analysis for Face Recognition across Large Pose Differences

Face recognition algorithms perform very unreliably when the pose of the probe face is different from the gallery face: typical feature vectors vary more with pose than with identity. We propose a generative model that creates a one-to-many mapping from an idealized "identity" space to the observed data space. In identity space, the representation for each individual does not vary with pose. We model the measured feature vector as being generated by a pose-contingent linear transformation of the identity variable in the presence of Gaussian noise. We term this model "tied" factor analysis. The choice of linear transformation (factors) depends on the pose, but the loadings are constant (tied) for a given individual. We use the EM algorithm to estimate the linear transformations and the noise parameters from training data. We propose a probabilistic distance metric that allows a full posterior over possible matches to be established. We introduce a novel feature extraction process and investigate recognition performance by using the FERET, XM2VTS, and PIE databases. Recognition performance compares favorably with contemporary approaches.
‘Seize That Chance!’ Leaving Home and Transitions to Higher Education

This paper interrogates assumptions surrounding the practices of leaving home and going to higher education in England and Wales. As more students from non-traditional backgrounds are encouraged to go to university, this is leading to greater diversity in students’ experiences of university life, and one of the key aspects of this is that more students are choosing to stay at home for the duration of their studies. This paper explores how and why students make the decision to stay at home. While recognising the financial advantages of living at home we argue that the decision cannot be reduced to economic expediency, but reflects young people's access to legitimate cultural capital and family and peer endorsement of leaving home as an expected process and outcome of going to university.
Seeing More Clearly with Glasses?: The Impact of Glasses and Technology on Unfamiliar Face Matching and Identification of Facial Composites

The development of facial identification technologies is of vital importance for security. We demonstrate that psychology can ensure innovation produces applications with effective human outcomes. Experiments 1a-c show spectacle removal imaging techniques that enhance automated face matching, can seriously impair accuracy if employed within a human interface. Experiment 2 shows how understanding and working with human perceptual strategies can optimize identification of facial composites.
Telmisartan Attenuates Colon Inflammation, Oxidative Perturbations and Apoptosis in a Rat Model of Experimental Inflammatory Bowel Disease

Accumulating evidence has indicated the implication of angiotensin II in the pathogenesis of inflammatory bowel diseases (IBD) via its proinflammatory features. Telmisartan (TLM) is an angiotensin II receptor antagonist with marked anti-inflammatory and antioxidant actions that mediated its cardio-, reno- and hepatoprotective actions. However, its impact on IBD has not been previously explored. Thus, we aimed to investigate the potential alleviating effects of TLM in tri-nitrobenezene sulphonic acid (TNBS)-induced colitis in rats. Pretreatment with TLM (10 mg/kg p.o.) attenuated the severity of colitis as evidenced by decrease of disease activity index (DAI), colon weight/length ratio, macroscopic damage, histopathological findings and leukocyte migration. TLM suppressed the inflammatory response via attenuation of tumor necrosis factor-α (TNF-α), prostaglandin E2 (PGE2) and myeloperoxidase (MPO) activity as a marker of neutrophil infiltration besides restoration of interleukin-10 (IL-10). TLM also suppressed mRNA and protein expression of nuclear factor kappa B (NF-κB) p65 and mRNA of cyclo-oxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) proinflammatory genes with concomitant upregulation of PPAR-γ. The alleviation of TLM to colon injury was also associated with inhibition of oxidative stress as evidenced by suppression of lipid peroxides and nitric oxide (NO) besides boosting glutathione (GSH), total anti-oxidant capacity (TAC) and the activities of superoxide dismutase (SOD) and glutathione peroxidase (GPx). With respect to apoptosis, TLM downregulated the increased mRNA, protein expression and activity of caspase-3. It also suppressed the elevation of cytochrome c and Bax mRNA besides the upregulation of Bcl-2. Together, these findings highlight evidences for the beneficial effects of TLM in IBD which are mediated through modulation of colonic inflammation, oxidative stress and apoptosis.
ECG biometrics: A robust short-time frequency analysis

In this paper, we present the results of an analysis of the electrocardiogram (ECG) as a biométrie using a novel short-time frequency method with robust feature selection. Our proposed method incorporates heartbeats from multiple days and fuses information. Single lead ECG signals from a comparatively large sample of 269 subjects that were sampled from the general population were collected on three separate occasions over a seven-month period. We studied the impact of long-term variability, health status, data fusion, the number of training and testing heartbeats, and database size on ECG biométrie performance. The proposed method achieves 5.58% equal error rate (EER) in verification, 76.9% accuracy in rank-1 recognition, and 93.5% accuracy in rank-15 recognition when training and testing heartbeats are from different days. If training and testing heartbeats are collected on the same day, we achieve 0.37% EER and 99% recognition accuracy for decisions based on a single heartbeat.
Tech Report: Compiling GreenMarl into GPS

The massive size of the data in large graph processing requires distributed processing. However, conventional frameworks for distributed graph processing, such as Pregel, use programming models that are well-suited for scalability but inconvenient for programming graph algorithms. In this paper, we use Green-Marl, a Domain-Specific Language for graph analysis, to describe graph algorithms intuitively and extend its compiler to generate equivalent Pregel programs. Using the semantic information exposed by Green-Marl, the compiler applies the same kinds of transformation rules that programmers apply when manually implementing graph algorithms with Pregel. Our experiments show that the Pregel programs generated by Green-Marl compiler perform similarly to native Pregel implementations of the same algorithms. The compiler is even able to generate a Pregel implementation of a complicated graph algorithm whose native Pregel implementation is very challening.