Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of Intrathecal Immunoglobulin G Synthesis With Disability Worsening in Multiple Sclerosis.

Importance Reliable biomarkers associated with disability worsening in multiple sclerosis (MS) are still needed. Objective To determine a possible association of intrathecal IgG synthesis and early disability worsening as measured by Expanded Disability Status Scale (EDSS) scoring in patients with relapsing-remitting MS or clinically isolated syndrome. Design, Setting, and Participants Cerebrospinal fluid measurements and clinical data from the observational longitudinal German national multiple sclerosis cohort were analyzed. Patients were recruited between August 2010 and November 2015 from 18 centers. Data analysis was completed from August 2018 to December 2018. Exposure Patients were offered standard immunotherapies per national treatment guidelines. Main Outcomes and Measures A possible association between intrathecal IgG synthesis and risk of EDSS worsening 4 years after study inclusion was tested as the primary end point by multivariable binomial regression analysis. Kaplan-Meier analysis with a log-rank test was used to assess the association of intrathecal IgG synthesis with the time to EDSS worsening. Associations between intrathecal IgM or IgA synthesis and other cerebrospinal fluid parameters and EDSS worsening were analyzed as exploratory end points. Data collection began before the hypotheses were formulated. Results Of all 1376 patients in the German Competence Network of Multiple Sclerosis cohort, 703 patients were excluded owing to missing cerebrospinal fluid or EDSS data. Of the 673 included patients, 459 (68.2%) were women. The mean (SD) age at baseline was 34 (10) years. Intrathecal IgG synthesis was associated with a higher risk of EDSS worsening after 4 years (odds ratio, 2.02 [95% CI, 1.15-3.58]; P = .01), independent of the occurrence of relapses and disease-modifying therapy. Additionally, intrathecal IgG synthesis was associated with earlier EDSS worsening; 4 years after study entry, worsening occurred in 28.4% (95% CI, 22.7%-34.1%) and 18.1% (95% CI, 12.4%-23.9%) of patients with and without intrathecal IgG synthesis, respectively. No association of other routine cerebrospinal fluid parameters with EDSS worsening was found. Conclusions and Relevance Patients with new diagnoses of relapsing-remitting multiple sclerosis or clinically isolated syndrome with intrathecal IgG synthesis had a higher risk of and shorter time to EDSS worsening across a 4-year period of follow-up. Intrathecal IgG synthesis is a potentially useful marker for disability worsening in patients with multiple sclerosis and may be useful for early treatment decisions.
MicroRNA-10b controls the metastasis and proliferation of colorectal cancer cells by regulating Krüppel-like factor 4

Abstract Mir-10b has been reported as a key regulator of metastasis in many human tumours. Moreover, it has also been regarded as a prognostic marker and therapeutic target of colorectal cancer (CRC). Whether miR-10b could affect the metastasis and proliferation of CRC is unclear. MiR-10b expression was detected by qPCR in human CRC tissues and cell line, Luciferase activity was employed for miR-10b binding to the 3`UTR of KLF4, Genes expression were examined by western blot, and mRNA by qPCR. PI and Annexin V staining were used to evaluate the cell cycle and apoptosis. Cell proliferation was detected with MTT, and cell migration and invasion were performed with Transwell assay. We found that miR-10b expression was up-regulated in metastatic CRC tissues and cell lines. Inhibition of miR-10b prevented cancer cell metastasis and growth by inducing cell-cycle arrest and apoptosis in vitro. Moreover, we found that KLF4 was a direct target of miR-10b. MiR-10b inhibitor led to the up-regulation of E-cadherin expression and the down-regulation of cyclin D1, which were partly abrogated after silencing KLF4.
MedTag: A Collection of Biomedical Annotations

We present a database of annotated biomedical text corpora merged into a portable data structure with uniform conventions. MedTag combines three corpora, MedPost, ABGene and GENETAG, within a common relational database data model. The GENETAG corpus has been modified to reflect new definitions of genes and proteins. The MedPost corpus has been updated to include 1,000 additional sentences from the clinical medicine domain. All data have been updated with original MEDLINE text excerpts, PubMed identifiers, and tokenization independence to facilitate data accuracy, consistency and usability. The data are available in flat files along with software to facilitate loading the data into a relational SQL database from ftp://ftp.ncbi. nlm.nih. gov/pub/lsmith/MedTag/medtag.tar. gz.
Diagnostic values of serum tumor markers Cyfra21-1, SCCAg, ferritin, CEA, CA19-9, and AFP in oral/oropharyngeal squamous cell carcinoma

Background At present, the research on serum tumor markers in the early diagnosis of malignant tumors has aroused widespread concern. The aim of this study was to investigate the diagnostic values of serum tumor markers cytokeratin 19 fragment (Cyfra21-1), squamous cell carcinoma antigen (SCCAg), ferritin, carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), and α-fetoprotein (AFP) for patients with oral/oropharyngeal squamous carcinoma (OSCC/OPSCC). Methods One hundred and sixty-nine cases of patients with OSCC/OPSCC as the experimental group, 86 cases of oral benign tumor patients as the control group, and 30 cases of healthy people as the normal control group were studied. The levels of serum Cyfra21-1, SCCAg, ferritin, CEA, CA19-9, and AFP were measured using electrochemiluminescence immunoassay. Results The levels of serum Cyfra21-1, SCCAg, ferritin, and CEA in patients with OSCC/OPSCC were significantly higher than those of benign tumor and healthy control group (P<0.05). The levels of CA19-9 and AFP showed no significant difference between patients with OSCC/OPSCC, benign tumor, and healthy group (P>0.05). The level of serum Cyfra21-1 in patients with early OSCC/OPSCC (stage I + II) was significantly higher than that of benign tumor and healthy control group (P<0.05). However, the levels of serum SCCAg, ferritin, CEA, CA19-9, and AFP showed no significant difference between patients with early OSCC/OPSCC, benign tumor, and healthy control group (P>0.05). The levels of serum Cyfra21-1, SCCAg, ferritin, and CEA in the middle-late stage of patients with OSCC/OPSCC (stage III + IV) were significantly higher than those of patients with the early OSCC/OPSCC, benign tumor, and healthy control group (P<0.05). The diagnostic cutoff levels of Cyfra21-1, SCCAg, ferritin, and CEA were 2.17, 0.72, 109.95, and 1.99 ng/mL, respectively. The sensitivities were 60.36%, 73.37%, 81.66%, and 66.27%, respectively. The specificities were 81.03%, 68.10%, 40.52%, and 61.21%, respectively. Conclusion Cyfra21-1, SCCAg, ferritin, and CEA had diagnostic values for patients with OSCC/OPSCC. Meanwhile, Cyfra21-1 had better early diagnostic value for patients with OSCC/OPSCC.
Translate-to-Recognize Networks for RGB-D Scene Recognition

Cross-modal transfer is helpful to enhance modality-specific discriminative power for scene recognition. To this end, this paper presents a unified framework to integrate the tasks of cross-modal translation and modality-specific recognition, termed as Translate-to-Recognize Network TRecgNet. Specifically, both translation and recognition tasks share the same encoder network, which allows to explicitly regularize the training of recognition task with the help of translation, and thus improve its final generalization ability. For translation task, we place a decoder module on top of the encoder network and it is optimized with a new layer-wise semantic loss, while for recognition task, we use a linear classifier based on the feature embedding from encoder and its training is guided by the standard cross-entropy loss. In addition, our TRecgNet allows to exploit large numbers of unlabeled RGB-D data to train the translation task and thus improve the representation power of encoder network. Empirically, we verify that this new semi-supervised setting is able to further enhance the performance of recognition network. We perform experiments on two RGB-D scene recognition benchmarks: NYU Depth v2 and SUN RGB-D, demonstrating that TRecgNet achieves superior performance to the existing state-of-the-art methods, especially for recognition solely based on a single modality.
Probabilistic Energy Management Strategy for EV Charging Stations Using Randomized Algorithms

Electric vehicle charging stations (EVCSs) come along with great challenges for the power grid due to their highly uncertain load characteristic. This is particularly the case for charging stations located in nonresidential areas, such as commercial centers, company sites, or car-rental stations. For a safe and sustainable operation of the power grid, distribution system operators require reliable load forecasts of such charging stations. In this brief, a robust EVCS management strategy is proposed, which provides a day-ahead upper limit profile of the EVCS’s power consumption. In real time, this upper limit profile is strictly respected while guaranteeing—at a configurable probability—the Quality of Service. The strategy is based on randomized algorithms and relies on a statistic occupancy model of the EVCS while not requiring any online forecasts of each EVs’ arrival and departure schedules. In a case study based on statistic data, which has been provided by the Euref Campus in Berlin, the feasibility and relevance of the proposed approach are demonstrated.
Rejoinder to “Statistical inference problems in sequential parallel comparison designs”

ABSTRACT In this rejoinder the authors stipulate further for two challenging issues. First, if placebo non-responders are selected simply by their response meeting a threshold, this selection may have misclassification error and consequently the treatment effect estimate may be biased, regardless of whether the estimand at the second stage is the treatment effect in the entire population or placebo non-responders. Secondly, the weak null hypothesis considered in our article Statistical Inference Problems in Sequential Parallel Comparison Design (2019) is that the expected treatment effects in placebo non-responders and in the entire set of patients entering the trial are both zero, in contrast to the strong null hypothesis that the statistical distribution of the response variable is equal in the compared treatments. The impact of violating the assumption of equal moments other than the mean parameter on statistical operating characteristics in estimation and testing of treatment effect can be substantial. As an example, the ordinary least squares based test detects a treatment difference even if the expected treatment effects in placebo non-responders and the entire population are both zero.
Data Fusion for Hybrid and Autonomous Time-of-Flight Positioning

Existing mobile devices such as smartphones rely on a multi-radio access technology (RAT) architecture to provide pervasive location information in various environmental contexts as the user is moving. Yet, existing architectures consider the different localization technologies as monolithic entities and choose the final navigation position from the RAT that is expected to provide the highest accuracy. In contrast, we propose to fuse timing range measurements of diverse radio technologies in order to circumvent the limitations of the individual radio access technologies. We take a first step in this direction and propose to fuse timing measurements of satellite navigation systems and WiFi networks. We introduce different novel methods such as a data fuser, an estimator of WiFi ToF distance and a geometrical-statistical approach to best fuse the set of ranges in presence of a rich set of measurements. Experimental results show that our solution allows the mobile device to efficiently position itself in diverse challenging scenarios.
A Supporting Tool to Identify both Satisfied Requirements and Tolerant Threats for a Java Mobile Code Application

A mobile code application can be easily integrated by using existing software components, thus it is one of the promising ways to develop software efficiently. However, using a mobile code application sometimes follows harmful effects on valuable resources of users because malicious codes in such an application can be activated. Therefore, users of mobile code applications have to identify both benefits and risks by the applications and to decide which benefits should be gotten and which risks should be tolerated. In this paper, we introduce a tool to support such users. By using this tool, the users can identify security related functions embedded in each mobile code automatically. The users can also relate these functions to each benefit or risk. By defining a security policy for mobile codes, some functions are disabled, thus some benefits and risks are also disabled. By adjusting the security policy, the users can make decision about the benefits and the risks.
Attention-Gated Networks for Improving Ultrasound Scan Plane Detection

In this work, we apply an attention-gated network to real-time automated scan plane detection for fetal ultrasound screening. Scan plane detection in fetal ultrasound is a challenging problem due the poor image quality resulting in low interpretability for both clinicians and automated algorithms. To solve this, we propose incorporating self-gated soft-attention mechanisms. A soft-attention mechanism generates a gating signal that is end-to-end trainable, which allows the network to contextualise local information useful for prediction. The proposed attention mechanism is generic and it can be easily incorporated into any existing classification architectures, while only requiring a few additional parameters. We show that, when the base network has a high capacity, the incorporated attention mechanism can provide efficient object localisation while improving the overall performance. When the base network has a low capacity, the method greatly outperforms the baseline approach and significantly reduces false positives. Lastly, the generated attention maps allow us to understand the model's reasoning process, which can also be used for weakly supervised object localisation.
Abdominal aortic aneurysm as an IgG4‐related disease

The objectives of this study were to evaluate patients with aortic abdominal aneurysm (AAA) with regard to immunoglobulin (Ig)G4‐related disease (IgG4‐RD). IgG4‐RD represents a recently defined condition comprised of a collection of disorders characterized by IgG4 hypergammaglobulinemia, the presence of IgG4‐positive plasma cells in organs affected with fibrotic or sclerotizing changes and typical histopathological features. It was identified as a possible cause of vasculitis in large vessels. Studies have been published on a possible association between inflammatory aortic or cardiovascular disease and IgG4‐RD. We examined 114 patients with AAA requiring surgery in order to identify findings which are characteristic of IgG4‐RD. Aneurysm samples from seven patients showed histopathological features consistent with IgG4‐RD and the presence of IgG4+ plasma cells. Only two of these seven patients showed elevated IgG4 serum levels higher 1·35 g/l. In five of the patients, the concentration of serum IgG4 was lower than 1·20 g/l, with the number of IgG4+ plasma cells being higher than 50/high‐power field. These findings were consistent with AAA being a heterogeneous group of inflammatory diseases with different pathogenesis.
Nucleosides for the treatment of respiratory RNA virus infections

Influenza virus, respiratory syncytial virus, human metapneumovirus, parainfluenza virus, coronaviruses, and rhinoviruses are among the most common viruses causing mild seasonal colds. These RNA viruses can also cause lower respiratory tract infections leading to bronchiolitis and pneumonia. Young children, the elderly, and patients with compromised cardiac, pulmonary, or immune systems are at greatest risk for serious disease associated with these RNA virus respiratory infections. In addition, swine and avian influenza viruses, together with severe acute respiratory syndrome-associated and Middle Eastern respiratory syndrome coronaviruses, represent significant pandemic threats to the general population. In this review, we describe the current medical need resulting from respiratory infections caused by RNA viruses, which justifies drug discovery efforts to identify new therapeutic agents. The RNA polymerase of respiratory viruses represents an attractive target for nucleoside and nucleotide analogs acting as inhibitors of RNA chain synthesis. Here, we present the molecular, biochemical, and structural fundamentals of the polymerase of the four major families of RNA respiratory viruses: Orthomyxoviridae, Pneumoviridae/Paramyxoviridae, Coronaviridae, and Picornaviridae. We summarize past and current efforts to develop nucleoside and nucleotide analogs as antiviral agents against respiratory virus infections. This includes molecules with very broad antiviral spectrum such as ribavirin and T-705 (favipiravir), and others targeting more specifically one or a few virus families. Recent advances in our understanding of the structure(s) and function(s) of respiratory virus polymerases will likely support the discovery and development of novel nucleoside analogs.
Minimax Optimal Bayes Mixtures for Memoryless Sources over Large Alphabets

The normalized maximum likelihood (NML) distribution achieves minimax log loss and coding regret for the multinomial model. In practice other nearly minimax distributions are used instead as calculating the sequential probabilities needed for coding and prediction takes exponential time with NML. The Bayes mixture obtained with the Dirichlet prior Dir(1/2, . . . , 1/2) and asymptotically minimax modifications of it have been widely studied in the context of large sample sizes. Recently there has also been interest in minimax optimal coding distributions for large alphabets. We investigate Dirichlet priors that achieve minimax coding regret when the alphabet size m is finite but large in comparison to the sample size n. We prove that a Bayes mixture with the Dirichlet prior Dir(1/3, . . . , 1/3) is optimal in this regime (in particular, when m > 52n + 4 n−2 + 3 2 ). The worst-case regret of the resulting distribution approaches the NML regret as the alphabet size grows.
PeyeDF: an Eye-Tracking Application for Reading and Self-Indexing Research

PeyeDF is a Portable Document Format (PDF) reader with eye tracking support, available as free and open source software. It is especially useful to researchers investigating reading and learning phenomena, as it integrates PDF reading-related behavioural data with gaze-related data. It is suitable for short and long-term research and supports multiple eye tracking systems. We utilised it to conduct an experiment which demonstrated that features obtained from both gaze and reading data collected in the past can predict reading comprehension which takes place in the future. PeyeDF also provides an integrated means for data collection and indexing using the DiMe personal data storage system. It is designed to collect data in the background without interfering with the reading experience, behaving like a modern lightweight PDF reader. Moreover, it supports annotations, tagging and collaborative work. A modular design allows the application to be easily modified in order to support additional eye tracking protocols and run controlled experiments. We discuss the implementation of the software and report on the results of the experiment which we conducted with it.
Comparison of 2 Automated Pupillometry Devices in Critically III Patients

Supplemental Digital Content is available in the text. Background: Automated pupillometry may help detect early cerebral disturbances in critically ill patients. It remains unclear whether different automated pupillometry devices can detect pupillary abnormalities with similar accuracy. The aim of this study was to compare the performance of 2 commercially available automated pupillometry devices—Neurolight Algiscan (NL) and NPi-200 (NP) versus standard pupillary light reflex (PLR) examination in an unselected cohort of critically ill patients. Materials and Methods: This prospective study included all adult (>18 y) patients admitted to the intensive care unit of a university hospital over a 20-day period. Measurements were made consecutively with each method once during the intensive care unit stay in each patient. To assess sensitivity and specificity, we calculated areas under the curve of the receiver operating characteristic curve. Results: A total of 112 patients were included in the study. There was a significant correlation between the 2 automated pupillometry devices for pupil size, constriction to light stimulation, and constriction velocity but not for pupillary latency. The mean bias for pupil size measured by the NL and the NP devices was −0.12 (limit of agreement [LoA], −1.29 to 1.06) mm, for pupil constriction −1.0% (LoA, −9.3% to 7.2%), and for latency 0.02 (LoA, −0.22 to 0.25) ms. There was a significant correlation between pupil size evaluated by clinical examination and that using the NL or NP. The areas under the curves for pupil constriction measured by NL and NP were 0.93 and 0.91, respectively, to detect clinically reactive pupils. Conclusions: Although there was a significant correlation between NL and NP values as well as with clinical examination of the PLR, the 2 devices were not always interchangeable, especially for the evaluation of pupillary latency.
Effect of magnetic flux advection on the dynamics of shock in accretion flow around a rotating black hole

We investigate the dynamical behavior of a magnetized, dissipative accretion flow around a rapidly rotating black hole. We solve the magnetohydrodynamic equations and calculate the transonic accretion solutions which may contain discontinuous shock transitions. We investigate the effect of ζ–parameter (parametrizing the radial variation of the toroidal magnetic flux advection rate) on the dynamical behavior of shocks. For a rapidly rotating black hole and for fixed injection parameters at the outer edge, we show that stationary shocks are sustained in the global magnetized accretion solutions for a wide range of ζ and accretion rate (ṁ). To investigate the observational implications, we consider dissipative shocks and estimate the maximum accessible energy from the post-shock corona (PSC) for nine stellar mass black hole candidates. We compare this with the observed radio jet kinetic power reported in the literature, whenever available. We find close agreement between the estimated values from our model and those reported in the literature.
Fully Memristive Neural Network Merging Image Preprocessing and Pattern Recognition

With the development of research on novel memristor model and device, fully memristive neural networks have become a hot research topic recently. However, some other devices besides memristors are still needed in state-of-the-art works about fully memristive neural networks. In this paper, a novel kind of fully memristive neural network is introduced, in which diffusion and drift memristor models are applied to construct neural network for both image preprocessing and pattern recognition, respectively. Specifically, the entire network consists of two diffusion memristive cellular layers and one drift memristive feedforward layer. Experimental results show that a good recognition accuracy of noisy MNIST is obtained due to the fusion of image preprocessing and pattern recognition. Moreover, owing to high-efficiency in-memory computing and brief spiking encoding methods, high processing speed and few hardware resources of the entire network are achieved.