Miracle-dz/UniFAR-FTUs huggingface dataset:
Editorial: Role of Stem Cells in Skeletal Muscle Development, Regeneration, Repair, Aging, and Disease

Skeletal muscle is a highly dynamic and plastic tissue, able to modify its intrinsic size or strength following electric impulse, mechanical loading, or diet. Several muscular dystrophic disorders have been characterized but the development of therapies, although promising, is still at an early phase. Muscle dysfunction is not restricted to dystrophic patients; during aging, there is a gradual loss of muscle function that results in a significant negative impact on the individual's health, increasing fall and lesion risks, loss of mobility and independence, and associated elevation of morbidity and mortality. This loss of muscle has an estimated prevalence between 5 and 13% among 60–70 year old individuals, increasing to 11–50% in those over the age of 80 (Morley, 2008). According to the WHO, the expected number of individuals over 65 years old by the year 2050 will be around 1.5 billion (WHO, 2015); by extrapolation, this suggests that over 150 million patients will suffer from muscle wasting disorders associated with aging. The vertiginous development of the stem cell therapy and cellular reprogramming fields will eventually result in the emergence of cell replacement therapies to treat a wide range of pathologies. In skeletal muscle, the satellite cell—long regarded as a heterogeneous cell population—is intimately linked to muscle physiology and regeneration processes (Chang and Rudnicki, 2014; Dayanidhi and Lieber, 2014; Snijders et al.). The activation of these muscle-specific stem cells is essential for injury healing, maintenance of muscle strength and tone, and delayed onset of age-related sarcopenia. In this issue, eight review and eight original research articles on this Research Topic summarize recent progress and current challenges of the muscle biology field in aging and disease.
Data fusion for overtaking vehicle detection based on radar and optical flow

Trustworthiness is a key point when dealing with vehicle safety applications. In this paper an approach to a real application is presented, able to fulfill the requirements of such demanding applications. Most of commercial sensors available nowadays are usually designed to detect front vehicles but lack the ability to detect overtaking vehicles. The work presented here combines the information provided by two sensors, a Stop&Go radar and a camera. Fusion is done by using the unprocessed information from the radar, and computer vision based on optical flow. The basic capabilities of the commercial systems are upgraded giving the possibility to improve the front vehicles detection system, by detecting overtaking vehicles with a high positive rate.
Anxiety Predicts Mortality in ICD Patients: Results from the Cross‐Sectional National CopenHeartICD Survey with Register Follow‐Up

Although highly effective in preventing arrhythmic death, patients receiving an implantable cardioverter defibrillator (ICD) may still experience psychological difficulties such as anxiety, depression, and reduced quality of life. The objectives of this study were to describe patient‐reported outcomes among ICD patients: (1) compared to a matched healthy population, (2) compared by ICD indication, (3) factors predicting patient‐reported outcomes, and (4) if patient‐reported outcomes predicted mortality.
An Algorithm to Extract Rules from Artificial Neural Networks for Medical Diagnosis Problems

Artificial neural networks (ANNs) have been successfully applied to solve a variety of classification and function approximation problems. Although ANNs can generally predict better than decision trees for pattern classification problems, ANNs are often regarded as black boxes since their predictions cannot be explained clearly like those of decision trees. This paper presents a new algorithm, called rule extraction from ANNs (REANN), to extract rules from trained ANNs for medical diagnosis problems. A standard three-layer feedforward ANN with four-phase training is the basis of the proposed algorithm. In the first phase, the number of hidden nodes in ANNs is determined automatically by a constructive algorithm. In the second phase, irrelevant connections and input nodes are removed from trained ANNs without sacrificing the predictive accuracy of ANNs. The continuous activation values of the hidden nodes are discretized by using an efficient heuristic clustering algorithm in the third phase. Finally, rules are extracted from compact ANNs by examining the discretized activation values of the hidden nodes. Extensive experimental studies on three benchmark classification problems, i.e. breast cancer, diabetes and lenses, demonstrate that REANN can generate high quality rules from ANNs, which are comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy.
Src Phosphorylation of Alix/AIP1 Modulates Its Interaction with Binding Partners and Antagonizes Its Activities*

Alix/AIP1 is an adaptor protein involved in regulating the function of receptor and cytoskeleton-associated tyrosine kinases. Here, we investigated its interaction with and regulation by Src. Tyr319 of Alix bound the isolated Src homology-2 (SH2) domain and was necessary for interaction with intact Src. A proline-rich region in the C terminus of Alix bound the Src SH3 domain, but this interaction was dependent on the release of the Src SH2 domain from its Src internal ligand either by interaction with Alix Tyr319 or by mutation of Src Tyr527. Src phosphorylated Alix at a C-terminal region rich in tyrosines, an activity that was stimulated by the presence of the Alix binding partner SETA/CIN85. Phosphorylation of Alix by Src caused it to translocate from the membrane and cytoskeleton to the cytoplasm and reduced its interaction with binding partners SETA/CIN85, epidermal growth factor receptor, and Pyk2. As a consequence of this, Src antagonized the negative regulation of receptor tyrosine kinase internalization and cell adhesion by Alix. We propose a model whereby Src antagonizes the effects of Alix by phosphorylation of its C terminus, leading to the disruption of interactions with target proteins.
A System for Experiments with Dependency Parsers

In this paper we present a system for experimenting with combinations of dependency parsers. The system supports initial training of different parsing models, creation of parsebank(s) with these models, and different strategies for the construction of ensemble models aimed at improving the output of the individual models by voting. The system employs two algorithms for construction of dependency trees from several parses of the same sentence and several ways for ranking of the arcs in the resulting trees. We have performed experiments with state-of-the-art dependency parsers including MaltParser, MSTParser, TurboParser, and MATEParser, on the data from the Bulgarian treebank -- BulTreeBank. Our best result from these experiments is slightly better then the best result reported in the literature for this language.
A Hybrid Sentiment Lexicon for Social Media Mining

Sentiment lexicon is a crucial resource for opinion mining from social media content. However, standard off-the-shelve lexicons are static and typically do not adapt, in content and context, to a target domain. This limitation, adversely affects the effectiveness of sentiment analysis algorithms. In this paper, we introduce the idea of distant-supervision to learn a domain-focused lexicon to improve coverage and sentiment context of terms. We present a weighted strategy to integrate scores from the domain-focused with the static lexicon to generate a hybrid lexicon. Evaluations of this hybrid lexicon on social media text show superior sentiment classification over either of the individual lexicons. A further comparative study with typical machine learning approaches to sentiment analysis also confirms this position. We also present promising results from our investigations into the transferability of this distant-supervised hybrid lexicon on three different social media.
Epidural Haematoma Causing Paraplegia in a Patient with Ankylosing Spondylitis: A Case Report

Introduction We present a case of paraplegia due to cord compression from epidural hematoma following an uneventful epidural catheter insertion in a patient with ankylosing spondylitis. Case Presentation A 65-year-old gentleman was scheduled for a major laparotomy for abdominal wall reconstruction. He has a past medical history of mild asthma, ankylosing spondylitis, duodenal ulcer and a superior mesenteric artery thrombosis in the past which led to bowel ischemia and intestinal failure. His drug allergies included Oxycodone. The anaesthetic plan was to do an awake epidural with catheter insertion followed by a general anaesthetic. The insertion of the epidural and the catheter was uneventful with the space identified in first attempt and no bloody tap. Intra-operative analgesia was maintained by a continuous epidural infusion of low dose local anaesthetic and opioid. The total operative time was eight hours and the patient was extubated at the end of the surgery. Following extubation, the motor block was checked in recovery using the modified Bromage scale. A dense block was noted and the epidural infusion was stopped. An MR scan was performed immediately, which showed an epidural hematoma in T5 - T11 segments. An urgent decompressive laminectomy was performed to evacuate the haematoma. However, neurological recovery was minimal with persistent paraplegia. Conclusions The increased incidence of epidural haematoma in patients with ankylosing spondylitis is well documented . Earlier detection and decompression can help in preserving neurological function. We recommend being more cautious when the decision for epidural analgesia is made in patients with higher grades of ankylosing spondylitis. If an epidural is considered necessary, use of x-ray guidance and some form of intra-operative neurological monitoring should be considered, particularly in prolonged surgeries which last over several hours.
Inferring cancer subnetwork markers using density-constrained biclustering

Motivation: Recent genomic studies have confirmed that cancer is of utmost phenotypical complexity, varying greatly in terms of subtypes and evolutionary stages. When classifying cancer tissue samples, subnetwork marker approaches have proven to be superior over single gene marker approaches, most importantly in cross-platform evaluation schemes. However, prior subnetwork-based approaches do not explicitly address the great phenotypical complexity of cancer. Results: We explicitly address this and employ density-constrained biclustering to compute subnetwork markers, which reflect pathways being dysregulated in many, but not necessarily all samples under consideration. In breast cancer we achieve substantial improvements over all cross-platform applicable approaches when predicting TP53 mutation status in a well-established non-cross-platform setting. In colon cancer, we raise prediction accuracy in the most difficult instances from 87% to 93% for cancer versus non−cancer and from 83% to (astonishing) 92%, for with versus without liver metastasis, in well-established cross-platform evaluation schemes. Availability: Software is available on request. Contact: alexsch@math.berkeley. edu; ester@cs.sfu. ca Supplementary information: Supplementary data are available at Bioinformatics online.
Lazy Learning Meets the Recursive Least Squares Algorithm

Lazy learning is a memory-based technique that, once a query is received, extracts a prediction interpolating locally the neighboring examples of the query which are considered relevant according to a distance measure. In this paper we propose a data-driven method to select on a query-by-query basis the optimal number of neighbors to be considered for each prediction. As an efficient way to identify and validate local models, the recursive least squares algorithm is introduced in the context of local approximation and lazy learning. Furthermore, beside the winner-takes-all strategy for model selection, a local combination of the most promising models is explored. The method proposed is tested on six different datasets and compared with a state-of-the-art approach.
Gabapentin and pregabalin for the acute post-operative pain management. A systematic-narrative review of the recent clinical evidences.

BACKGROUND Gabapentin and pregabalin inhibit Ca(2+) currents via high-voltage-activated channels containing the alpha2delta-1 subunit, reducing neurotransmitter release and attenuating the postsynaptic excitability. They are antiepileptic drugs successfully used also for the chronic pain treatment. A large number of clinical trials indicate that gabapentin and pregabalin could be effective as postoperative analgesics. This systematic-narrative review aims to analyse the most recent evidences regarding the effect of gabapentinoids on postoperative pain treatment. METHODS Medline, The Cochrane Library, EMBASE and CINHAL were searched for recent (2006-2009) randomized clinical trials (RCTs) of gabapentin-pregabalin for postoperative pain relief in adults. Quality of RCTs was evaluated according to Jadad method. Visual analogue scale (VAS), opioid consumption and side-effects (nausea, vomiting, dizziness and sedation) were considered the most important outcomes. RESULTS An overall of 22 gabapentin (1640 patients), 8 pregabalin (707 patients) RCTs and seven meta-analysis were involved in this review. Gabapentin provided better post-operative analgesia and rescue analgesics sparing than placebo in 6 of the 10 RCTs that administered only pre-emptive analgesia. Fourteen RCTs suggested that gabapentin did not reduce PONV when compared with placebo, clonidine or lornoxicam. Pregabalin provided better post-operative analgesia and rescue analgesics sparing than placebo in two of the three RCTs that evaluated the effects of pregabalin alone vs placebo. Four studies reported no pregabalin effects on preventing the PONV. CONCLUSION Gabapentin and pregabalin reduce pain and opioid consumption after surgery in confront with placebo, but comparisons with other standard post-operative regimens are not sufficient. Gabapentin and pregabalin seem not to have any influence on the prevention of PONV.
Explicit Runge-Kutta Schemes and Finite Elements with Symmetric Stabilization for First-Order Linear PDE Systems

We analyze explicit Runge-Kutta schemes in time combined with stabilized finite elements in space to approximate evolution problems with a first-order linear differential operator in space of Friedrichs type. For the time discretization, we consider explicit second- and third-order Runge-Kutta schemes. We identify a general set of properties on the space stabilization, encompassing continuous and discontinuous finite elements, under which we prove stability estimates using energy arguments. Then we establish $L^2$-norm error estimates with quasi-optimal convergence rates for smooth solutions in space and time. These results hold under the usual CFL condition for third-order Runge-Kutta schemes and any polynomial degree in space and for second-order Runge-Kutta schemes and first-order polynomials in space. For second-order Runge-Kutta schemes and higher polynomial degrees in space, a tightened 4/3-CFL condition is required. Numerical results are presented for smooth and rough solutions. The case of finite volumes is briefly discussed.
Some new non-Schurian association schemes on 2p2 points, p an odd prime, and related combinatorial structures

Let p be an odd prime. We provide a construction of four non-Schurian association schemes for every prime p ≥ 5 and two for p = 3. For p > 3 the construction is new, while for p = 3 it coincides with the nonSchurian schemes, obtained with the aid of a computer by A. Hanaki and I. Miyamoto. The discovered non-Schurian objects appear as algebraic mergings of the Schurian coherent configuration on 2p points and rank 6p− 2, which corresponds to the action of the Heisenberg group of order p on the set of points and lines of the classical biaffine plane. The results obtained are considered in a wider framework.
RNAi screening: new approaches, understandings, and organisms

RNA interference (RNAi) leads to sequence‐specific knockdown of gene function. The approach can be used in large‐scale screens to interrogate function in various model organisms and an increasing number of other species. Genome‐scale RNAi screens are routinely performed in cultured or primary cells or in vivo in organisms such as C. elegans. High‐throughput RNAi screening is benefitting from the development of sophisticated new instrumentation and software tools for collecting and analyzing data, including high‐content image data. The results of large‐scale RNAi screens have already proved useful, leading to new understandings of gene function relevant to topics such as infection, cancer, obesity, and aging. Nevertheless, important caveats apply and should be taken into consideration when developing or interpreting RNAi screens. Some level of false discovery is inherent to high‐throughput approaches and specific to RNAi screens, false discovery due to off‐target effects (OTEs) of RNAi reagents remains a problem. The need to improve our ability to use RNAi to elucidate gene function at large scale and in additional systems continues to be addressed through improved RNAi library design, development of innovative computational and analysis tools and other approaches. WIREs RNA 2012, 3:145–158. doi: 10.1002/wrna.110
Analysis and control of active neutral-point-clamping three-level inverters under fault tolerant operation modes

This paper studies the fault tolerant control schemes for the active neutral-point-clamping three-level (ANPC-3L) inverters under different fault conditions. The fault conditions include the device-level faults in one inverter leg, the inverter-leg faults, and the open-circuit faults in load. The impacts of faults in devices, inverter leg and load on output voltage levels and mid-point voltages of DC link are analyzed in detail. Based on those, the syntheses of remaining voltage vectors and reconfiguration of power circuits are developed to implement the fault tolerant control. The experiments on a laboratory prototype are given to verify the validity of the theoretical analysis and control schemes.
Ras GTPase activating (RasGAP) activity of the dual specificity GAP protein Rasal requires colocalization and C2 domain binding to lipid membranes

Rasal, belonging to the GAP1 subfamily of Ras GTPase-activating proteins (RasGAPs) with dual RasGAP/RapGAP specificity, is epigenetically silenced in several tumor types. Surprisingly, the isolated protein has GAP activity on Rap but not on Ras. Its membrane recruitment is regulated by interaction with calcium and lipids, which simultaneously induces its RasGAP activity through a yet unknown mechanism. Here we show that the interaction of Rasal with membranes induces Rasal RasGAP activity by spatial and conformational regulation, although it does not have any effect on its RapGAP activity. Not only is colocalization of Rasal and Ras in the membrane essential for RasGAP activation, but direct and Ca-dependent interaction between the tandem C2 domains of Rasal and lipids of the membrane is also required. Whereas the C2A domain binds specifically phosphatidylserine, the C2B domain interacts with several phosphoinositol lipids. Finally we show, that similar to the C2 domains of synaptotagmins, the Rasal tandem C2 domains are able to sense and induce membrane curvature by the insertion of hydrophobic loops into the membrane.
MRI in evaluation of perianal fistulae

MRI in evaluation of perianal fistulae Background. Fistula is considered to be any abnormal passage which connects two epithelial surfaces. Parks' fistulae classification demonstrates the biggest practical significance and divides fistulae into: intersphincteric, transsphincteric, suprasphincteric and extrasphincteric. Etiology of perianal fistulae is most commonly linked with the inflammation of anal glands in Crohn's disease, tuberculosis, pelvic infections, pelvic malignant tumours, and with the radiotherapy. Diagnostic method options are: RTG fistulography, CT fistulography and magnetic resonance imaging (MRI) of pelvic organs. Patients and methods. We have included 24 patients with perirectal fistulae in the prospective study. X-rays fistulography, CT fistulography, and then MRI of the pelvic cavity have been performed on all patients. Accuracy of each procedure in regards to the patients and the etiologic cause have been statistically determined. Results. 29.16% of transphincteric fistulae have been found, followed by 25% of intersphincteric, 25% of recto-vaginal, 12.5% of extrasphincteric, and 8.33% of suprasphincteric. Abscess collections have been found in 16.6% patients. The most frequent etiologic cause of perianal fistulae was Crohn's disease in 37.5%, where the accuracy of classification of MRI was 100%, CT was 11% and X-rays 0%. Ulcerous colitis was the second cause, with 20.9% where the accuracy of MRI was 100%, while CT was 80% and X-rays was 0%. All other etiologic causes of fistulae were found in 41.6% patients. Conclusions. MRI is a reliable diagnostic modality in the classification of perirectal fistulae and can be an excellent diagnostic guide for successful surgical interventions with the aim to reduce the number of recurrences. Its advantage is that fistulae and abscess are visible without the need to apply any contrast medium.
Toxicology in the Fast Lane: Application of High-Throughput Bioassays to Detect Modulation of Key Enzymes and Receptors

Background Legislation at state, federal, and international levels is requiring rapid evaluation of the toxicity of numerous chemicals. Whole-animal toxicologic studies cannot yield the necessary throughput in a cost-effective fashion, leading to a critical need for a faster and more cost-effective toxicologic evaluation of xenobiotics. Objectives We tested whether mechanistically based screening assays can rapidly provide information on the potential for compounds to affect key enzymes and receptor targets, thus identifying those compounds requiring further in-depth analysis. Methods A library of 176 synthetic chemicals was prepared and examined in a high-throughput screening (HTS) manner using nine enzyme-based and five receptor-based bioassays. Results All the assays have high Z′ values, indicating good discrimination among compounds in a reliable fashion, and thus are suitable for HTS assays. On average, three positive hits were obtained per assay. Although we identified compounds that were previously shown to inhibit a particular enzyme class or receptor, we surprisingly discovered that triclosan, a microbiocide present in personal care products, inhibits carboxylesterases and that dichlone, a fungicide, strongly inhibits the ryanodine receptors. Conclusions Considering the need to rapidly screen tens of thousands of anthropogenic compounds, our study shows the feasibility of using combined HTS assays as a novel approach toward obtaining toxicologic data on numerous biological end points. The HTS assay approach is very useful to quickly identify potentially hazardous compounds and to prioritize them for further in-depth studies.
A New Model of Synchronous Machine Internal Faults Based on Winding Distribution

A synchronous machine internal faults model based on the actual winding arrangement is described in this paper. Based on the winding function approach, the machine inductances are calculated directly from the machine winding distribution, thereby the space harmonics produced by the machine windings are readily taken into account. Moreover, the calculation of the machine inductances is made easier by the use of the machine electrical parameters instead of the geometrical ones. Simulation results for internal faults on a laboratory generator are compared with experimental results to verify the accuracy of the proposed model