Miracle-dz/UniFAR-FTUs huggingface dataset:
Selective Nonunit Protection Technique for Multiterminal VSC-HVDC Grids

The development of multiterminal voltage-source converters high-voltage direct current (VSC-HVDC) grids poses a new challenge: A fast and selective protection algorithm for such grids must be implemented. Therefore, this paper addresses a novel protection algorithm, which proves to be accurate, fast, and selective for various combinations of fault distance, resistances, and power-flow scenarios in a multiterminal VSC-HVDC environment. It relies on the wavelet transform and principal component analysis to retrieve information from the local DC current measurement, generating a highly informative input for a genetic fuzzy system. Based on previously simulated fault scenarios, the genetic fuzzy system is able to decide if the input signal refers to an internal or external fault, generating the trip signal when needed. Furthermore, the method has the advantage of using local measurements to avoid communication delay. Finally, the algorithm was implemented in hardware and tested in a hardware-in-the-loop simulation, which emphasizes the practical use of the proposed protection scheme.
Forecasting Energy Commodities Prices with Bayesian Model Combination Schemes

The biggest advantage of Bayesian approach is that it allows to use data-rich models in a reasonable way. The conventional approach is not suitable when the number of variables exceeds the number of observations. Bayesian formulas remain reasonable in such a case. Researchers usually deal with numerous potentially important variables in forecasting. Newly methods like Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model nicely deal with model uncertainty. The study discusses energy commodity prices development. FRED-MD large macroeconomic database is applied. Forecasting commodities prices is a hard task. Therefore, it seems interesting to check the novel methodology. It allows both explanatory variables and regression coefficients to vary in time. Thus in different periods, different factors can be treated as the important ones.
New highly sensitive sandwich ELISA system for soluble endoglin quantification in different biological fluids

Abstract Soluble endoglin (sEng) is a fragment of a membrane-associated receptor (CD105) expressed on endothelial cells, mesenchymal stem cells and trophoblast cells. It is considered as a regulatory factor involved in the development of preeclampsia (PE) and cancer-associated neo-angiogenesis. The purpose of this study was to describe a new sandwich ELISA for sEng quantification. In contrast to three commercial kits, the new ELISA was able to quantify sEng not only in blood plasma and cell culture supernatants, but also in urine and cerebrospinal fluid. The assay detected up to two orders of magnitude higher antigen levels than did the commercial kits. Using Western blot assay followed by SDS-PAGE or Blue Native PAGE, we demonstrated a heterogeneous nature of sEng molecules. Antigen heterogeneity is considered as a factor contributing to the pronounced differences in its content estimations by different ELISAs. We obtained evidence indicating that the assay was capable of detecting heterogenious sEng molecules. The new ELISA was validated as a quick, specific and accurate method for sEng quantification. Despite the differences in antigen content estimates, the assay had similar diagnostic performance as widely used commercial kit for the detection of severe PE in pregnant women based on plasma sEng contents. Moreover, the new assay was able to delineate diseased patients based on antigen levels in urine. Therefore, the new ELISA is a potentially valuable tool for in vitro and clinical studies.
A Sub- $\mu\text{W}$ /Channel, 16-Channel Seizure Detection and Signal Acquisition SoC Based on Multichannel Compressive Sensing

An accurate 16-channel seizure detection system-on-chip is presented, which is based on extracting features from the compressed recorded data using multichannel compressive sensing (MCS). MCS is used to reduce the transmission data rate of sparse biological signals and to lower the power consumption of the resource-constrained recording and detection systems. Conventional MCS architecture introduces several issues such as requiring high-resolution analog-to-digital-converter (ADC) and offset-proportional random signal generation at the output of MCS block. We introduce a new multi-input single-output compressive sensing (MISOCS) block that uses a straightforward technique to embed the data of all channels in each sample of the compressed signal. Hence, the proposed MISOCS block provide more information of recorded data than the conventional MISOCS block, mathematically. This technique is shown to require less ADC resolution in comparison to the conventional MCS technique. Furthermore, the problem of offset-proportional random signal generation at the output of MCS block is solved in the proposed architecture. The system is implemented in a UMC 0.18- $ \mu \text{m}$ CMOS technology. The proposed seizure detection system is tested over 420 h of clinical iEEG data including 23 seizures and reaches a perfect sensitivity of 100% and an average false alarm rate of 0.09 $\text{h}^{-1}$ for artifact-free channels.
Weakly-Supervised Localization and Classification of Proximal Femur Fractures

In this paper, we target the problem of fracture classification from clinical X-Ray images towards an automated Computer Aided Diagnosis (CAD) system. Although primarily dealing with an image classification problem, we argue that localizing the fracture in the image is crucial to make good class predictions. Therefore, we propose and thoroughly analyze several schemes for simultaneous fracture localization and classification. We show that using an auxiliary localization task, in general, improves the classification performance. Moreover, it is possible to avoid the need for additional localization annotations thanks to recent advancements in weakly-supervised deep learning approaches. Among such approaches, we investigate and adapt Spatial Transformers (ST), Self-Transfer Learning (STL), and localization from global pooling layers. We provide a detailed quantitative and qualitative validation on a dataset of 1347 femur fractures images and report high accuracy with regard to inter-expert correlation values reported in the literature. Our investigations show that i) lesion localization improves the classification outcome, ii) weakly-supervised methods improve baseline classification without any additional cost, iii) STL guides feature activations and boost performance. We plan to make both the dataset and code available.
Access to care for people with alcohol use disorder in France: a mixed-method cross-sectional study protocol (ASIA)

Introduction Alcohol use disorder (AUD) is a major public health concern worldwide. In France, only 10% of people with AUD (PWAUD) receive medical care. General practitioners (GP) are one of the main entry points for AUD care. The present ongoing study, entitled ASIA (Access to Care and Indifference toward Alcohol, Accès aux Soins et Indifference à l’Alcool in French), aims to improve knowledge about factors associated with access to care for AUD by exploring related GP and PWAUD practices, experiences and perceptions. Methods and analysis The ASIA project is an ongoing cross-sectional multisite study based on a complementary mixed-method approach (quantitative and qualitative) using a convergent parallel design. The double-perspective design of the study will enable us to collect and compare data regarding both PWAUD and GP points of view. For the PWAUD quantitative study, 260 PWAUD will be interviewed using a telephone-based questionnaire. For the qualitative study, 36 PWAUD have already been interviewed. The GP quantitative study will include 100 GP in a 15 min survey. Fifteen GP have already participated in semistructured interviews for the qualitative study. Logistic regression will be used to identify predictors for access to care. With respect to data analyses, qualitative interviews will be analysed using semantic analysis while quantitative logistic regression will be used for quantitative interviews. Ethics and dissemination This study was approved by the CNIL (French National Commission on Informatics and Liberties) (approval reference number: C16-10, date of approval: 17 July 2017), the CCTIRS (Advisory Committee on Information Processing in Material Research in the Field of Health) and the CEEI (Evaluation and Ethics Committee) (approval reference number: 16–312, date of approval: 8 July 2016) of INSERM (French National Institute of Health and Medical Research). Results from ASIA will be disseminated in peer-reviewed publications, conference presentations, reports and in a PhD thesis.
Hispaglabridin B, a constituent of liquorice identified by a bioinformatics and machine learning approach, relieves protein‐energy wasting by inhibiting forkhead box O1

Liquorice is the root of Glycyrrhiza glabra, which is a popular food in Europe and China that has previously shown benefits for skeletal fatigue and nutrient metabolism. However, the mechanism and active ingredients remain largely unclear. The aim of this study was to investigate the active ingredients of liquorice for muscle wasting and elucidate the underlying mechanisms.
Isolated Wrist Drop Presenting as Acute Stroke: Rare Case Report with Review of Literature

ABSTRACT Isolated wrist drop is very unusual clinical presentation due to cerebral stroke. It is highly confused with peripheral neuropathy. However, timely detection of acute stroke as one of the causes of wrist drop is necessary as it changes the line of treatment and prognosis significantly. Here we are presenting a 62 year-old diabetic and hypertensive male patient who came with acute onset weakness of right hand. Initial Magnetic resonance imaging (MRI) of the brain showed hyperacute infarct in the splenium of corpus callosum. Magnetic resonance angiography (MRA) was normal. Repeat brain MRI on next day showed acute infarct at hand area of motor strip in posterior frontal region. The patient underwent intravenous thrombolysis and thereafter improved significantly. Isolated hand palsy is a rare presentation of stroke, often mistaken for peripheral lesion.
Multi-Market Price Forecasting in Hydro-Thermal Power Systems

This paper presents a framework for price forecasting in hydro-thermal power systems. The framework consists of a long-term strategic and a short-term operational model. The strategic model provides the end-of-horizon valuation of water in hydro storages as input to the operational model. We emphasize on the operational model, and discuss work in progress to facilitate more detailed fundamental market modeling to enable realistic multi-market price forecasting. A case study of the Nordic power system demonstrates the use of the framework, quantifying the impact of constraints on cable ramping and reserve capacity on prices.
Assessment of Ageing in the Insulation of HVDC Model Cables under Thermo-electric Stress

The assessment of insulating materials regarding space charge features is one important aspect of HVDC cable development. Whereas most of space charge characterization is achieved under relatively short stressing time, it is important to understand if there is an evolution of materials behavior during long time under stresses, including service stresses such as combination of electrical and thermal stresses along with polarity reversal. Our purpose in this communication is to present the implementation of long term ageing tests on crosslinked polyethylene materials in such a way that relevant and valuable information can be gained as a function of stressing time. The designed stress cycle on model cables encompasses maximum fields up to 90 kV/mm at the inner semiconductor and temperature up to 100°C with repetitive grounding or polarity reversal. The stressing time is up to 18 months. The electrical and physicochemical characterizations techniques as well as the first ageing results are presented.
Fully Synthesizable PUF Featuring Hysteresis and Temperature Compensation for 3.2% Native BER and 1.02 fJ/b in 40 nm

This paper presents a physically unclonable function (PUF) that can be designed with an automated digital design flow with a standard-cell approach. Compared to conventional PUFs designed in an analog or array style, the proposed PUF can be immersed in logic for inherent PUF obfuscation, and entails significantly lower design effort. The proposed PUF, belongs to the static monostable PUF class, is based on regulated cascode current mirrors (RCCM) and has hysteretic behavior to improve robustness against bit flips caused by noise, voltage, and temperature fluctuations. Hysteresis is achieved by inserting a Muller C-element in the PUF output stage and skewed inverters. The impact of temperature fluctuations on the stability is further reduced through a temperature compensation feedback loop. A 40-nm testchip was designed through a digital design flow based on the proposed PUF standard cell. Testchip measurements show native worst case bit error rate (BER) of 3.2% at 0.8–1.0-V voltage and 25 °C–85 °C temperature. A temperature sensitivity of 0.015%/°C in terms of PUF instability, an energy per bitcell of 1.02 fJ/b, and an autocorrelation function (ACF) of 0.00735 at 95% confidence are achieved, being the lowest reported to date. In view of the low design effort and low power consumption, the proposed PUF is amenable for low-cost and low-power systems on chip (SoC) (e. g., for Internet of Things applications).
Incorporation of a working memory strategy in GMT to facilitate serial-order behaviour in brain-injured patients

ABSTRACT Goal Management Training (GMT) is an effective method for improving disorganised behaviour in multistep real-life tasks after brain damage. In the present study we incorporated Working Memory Training (WMT) in GMT to explore their combined efficacy in facilitating the serial-order maintenance of the steps that had to be learned. GMT+WMT was compared to a control WMT designed for other purposes. For this purpose 18 brain-injured patients (aged 20–54), who were at least 4 months post-onset, were randomly assigned to either the GMT+WMT or the WMT treatment. Inclusion was based on a baseline score of less than six correct steps on each of two multistep everyday tasks. Alternative versions of these tasks were used as primary outcome tasks. Pre-treatment and post-treatment comparisons of scores on these primary tasks and on several secondary neuropsychological measures were collected. The results show that post-treatment the GMT+WMT group performed significantly better than the WMT group on the primary outcome measures and on several ecologically valid executive tests that demanded a step-by-step maintenance of multiple actions. Time effects were found for both groups on the secondary measures. Other measures showed no significant differences. We conclude that our results support the efficacy of the combined GMT+WMT in facilitating performance in everyday multistep tasks.
The Profitability of Energy Storage in European Electricity Markets

In this work, we study the profitability of energy storage operated in the Nordic,German, and UK electricity day-ahead markets during 2006-2016. During this time period, variable renewable energy sources (vRES) have been rapidly penetrating the markets and increasing the volatility of the residual load, which is often assumed to be associated with improving financial viability of energy storages. However, storage operator profits are not publicly available, in particular not at plant level. We therefore develop a linear optimisation model which maximises profits from arbitraging hourly prices and use the model output of profits and storage operating hours in further econometric analyses. This is a novel approach merging two strands of literature (optimisation and econometrics) in a single energy storage study. Specifically, we quantify and disentangle the effects of electricity demand, solar and wind generation, the spread between gas and coal prices, carbon emission prices and tructural breaks on profits and operation of 1-13MWh/MW energy storages. Among others we find that solar generation is associated with lower profits but higher operating frequency of energy storages in Germany. Wind power generation is associated with positive effects on profits in the UK and Germany. vRES does not affect profits or operation of new Nordic energy storages.
KChIP3 coupled to Ca2+ oscillations exerts a tonic brake on baseline mucin release in the colon

Regulated mucin secretion from specialized goblet cells by exogenous agonist-dependent (stimulated) and -independent (baseline) manner is essential for the function of the epithelial lining. Over extended periods, baseline release of mucin can exceed quantities released by stimulated secretion, yet its regulation remains poorly characterized. We have discovered that ryanodine receptor-dependent intracellular Ca2+ oscillations effect the dissociation of the Ca2+-binding protein, KChIP3, encoded by KCNIP3 gene, from mature mucin-filled secretory granules, allowing for their exocytosis. Increased Ca2+ oscillations, or depleting KChIP3, lead to mucin hypersecretion in a human differentiated colonic cell line, an effect reproduced in the colon of Kcnip3-/- mice. Conversely, overexpressing KChIP3 or abrogating its Ca2+-sensing ability, increases KChIP3 association with granules, and inhibits baseline secretion. KChIP3 therefore emerges as the high-affinity Ca2+ sensor that negatively regulates baseline mucin secretion. We suggest KChIP3 marks mature, primed mucin granules, and functions as a Ca2+ oscillation-dependent brake to control baseline secretion. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
ERBB2 signaling drives supporting cell proliferation in vitro and apparent supernumerary hair cell formation in vivo in the neonatal mouse cochlea

In mammals, cochlear hair cells are not regenerated once they are lost, leading to permanent hearing deficits. In other vertebrates, the adjacent supporting cells act as a stem cell compartment, in that they both proliferate and differentiate into de novo auditory hair cells. Although there is evidence that mammalian cochlear supporting cells can differentiate into new hair cells, the signals that regulate this process are poorly characterized. We hypothesize that signaling from the epidermal growth factor receptor (EGFR) family may play a role in cochlear regeneration. We focus on one such member, ERBB2, and report the effects of expressing a constitutively active ERBB2 receptor in neonatal mouse cochlear supporting cells, using viruses and transgenic expression. Lineage tracing with fluorescent reporter proteins was used to determine the relationships between cells with active ERBB2 signaling and cells that divided or differentiated into hair cells. In vitro, individual supporting cells harbouring a constitutively active ERBB2 receptor appeared to signal to their neighbouring supporting cells, inducing them to down‐regulate a supporting cell marker and to proliferate. In vivo, we found supernumerary hair cell‐like cells near supporting cells that expressed ERBB2 receptors. Both supporting cell proliferation and hair cell differentiation were largely reproduced in vitro using small molecules that we show also activate ERBB2. Our data suggest that signaling from the receptor tyrosine kinase ERBB2 can drive the activation of secondary signaling pathways to regulate regeneration, suggesting a new model where an interplay of cell signaling regulates regeneration by endogenous stem‐like cells.
Learning Salient Features for Speech Emotion Recognition Using CNN

In this work, a framework based on Convolution Neural Network (CNN) is proposed for speech emotion recognition (SER). We focus on extracting the most salient frames via the proposed CNN structure from the entire frame sequence to represent the utterance. A particular pooling method named global k-max pooling is utilized in our CNN structure (GCNN) to achieve the above objective. We implemented SER experiments on Interactive Emotional Dyadic Motion Capture (IEMOCAP), results are compared to those of some other CNN structures to validate the advancement of the presented framework. The experimental results turn out that GCNN outperforms others CNN models. Besides, experiments are also done to explore how many key frames should be output from GCNN to involve salient emotional information, results illuminate that limited length representation is properer while too long representation is likely containing redundant information decreasing the performance of the model.
What Evidence is There for Biochemical Testing?

Evidence based medicine has become accepted practice in the provision of modern healthcare. However, the approach has had limited impact on laboratory medicine compared with that in other clinical disciplines. The appropriate use of diagnostic tools in clinical decision-making is of crucial importance, as further management of patients depends on it. But many diagnostic investigations have never been subjected to systematic evaluation using modern standards of clinical epidemiology, especially when compared with the rigorous approval of therapeutic drugs. Furthermore, despite the remarkable achievements in the analytical performance of tests, less attention has been paid to outcomes research to evaluate the diagnostic impact and clinical utility of laboratory investigations (1). The lack of good quality research in the field contributes not only to inappropriate utilisation of laboratory services, which might harm patients, but also to wasting significant resources. Clinical laboratories may therefore be seen by some managers as cost centres, rather than – what they should be – resource centres. Patients and society expect physicians to base their approach to any type of clinical problem on informed diagnostic reasoning. Informed diagnostics means that clinicians understand and readily apply the principles of diagnostic decision making, which include an estimate of the pre-test probability/prevalence of diseases and information about the performance characteristics and discriminatory power of the applied investigations. Interpretation of results is also concerned with supporting informed clinical decisions by synthesizing and converting analytical and research information to knowledge that, together with clinical experience and patients’ preferences, can be transformed to the wisdom needed in making individual choices for patients (2). If laboratory medicine professionals wish to offer high quality, efficacious and effective services, it is important that the pre-analytical, analytical, and post-analytical phases of the diagnostic process are based, as far as possible, on the best available scientific evidence. In this paper, I would like to discuss the definition and aims of evidence-based laboratory medicine (EBLM), what kind of evidence we need, what kind of evidence we have in laboratory medicine, and what we can do about improving the current situation.