Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessments of tumor metabolism with CEST MRI

Chemical exchange saturation transfer (CEST) is a relatively new contrast mechanism for MRI. CEST MRI exploits a specific MR frequency (chemical shift) of a molecule while generating an image with good spatial resolution using standard MRI techniques, combining the specificity of MRS with the spatial resolution of MRI. Many CEST MRI acquisition methods have been developed to improve analyses of tumor metabolism. GluCEST, CrCEST, and LATEST can map glutamate, creatine, and lactate, which are important metabolites involved in tumor metabolism. GlucoCEST MRI tracks the pharmacokinetics of glucose transport and cell internalization within tumors. CatalyCEST MRI detects enzyme catalysis that changes a substrate CEST agent. AcidoCEST MRI measures extracellular pH of the tumor microenvironment by exploiting a ratio of two pH‐dependent CEST signals. This review describes each technique, the technical issues involved with CEST MRI and each specific technique, and the merits and challenges associated with applying each CEST MRI technique to study tumor metabolism.
Health-related quality of life in intensive care survivors: Associations with social support, comorbidity, and pain interference

Background Experiences during a stay in the intensive care unit (ICU), including pain, delirium, physical deterioration, and the critical illness itself, may all influence survivors’ health-related quality of life (HRQOL). However, few studies have examined the influence of social support, comorbidity, and pain interference on ICU survivors’ HRQOL. Objectives To investigate possible associations between social support, number of comorbidities, and pain interference on HRQOL in ICU survivors. Methods ICU survivors responded to a survey 3 months (n = 118) and 1 year (n = 89) after ICU discharge. HRQOL was measured using the Short Form Health Survey-12 (v1), social support using the revised Social Provision Scale, pain interference using the Brief Pain Inventory–Short Form, and comorbidities using the Self-Administered Comorbidity Questionnaire. Results Physical and mental HRQOL were reduced at both 3 months and 1 year in ICU survivors compared with the general population. This reduction was more pronounced at 3 months for physical HRQOL, while a small reduction in mental HRQOL was not clinically relevant. Social support was statistical significantly positively associated with mental HRQOL at 3 months, while number of comorbidities was statistical significantly associated with a reduction in physical HRQOL at 3 months and 1 year and mental HRQOL at 1 year. Lastly pain interference was significantly associated with a reduction in physical HRQOL at 3 months and 1 year. Conclusions ICU survivors primarily report reduced physical HRQOL. Social support was positively associated with mental HRQOL, while number of comorbidities, and pain interference were all significantly associated with a reduction in HRQOL. Pain interference was associated with the largest reduction in HRQOL.
Cluster and dynamic-TrAdaBoost-based transfer learning for text classification

Transfer learning can use the knowledge of closed fields to enhance complete the learning tasks in the target field. After several years of development, it has been widely used in research, data mining and deep learning. The problem that the sample weight of TrAdaBoost is prone to polarization in the source domain and the target domain when is in the transfer learning process. So we propose a transfer learning algorithm which based on dynamic-TrAdaBoost and k-means algorithm. This experiment proved to be a good solution to this problem.
Using convolutional neural networks to network intrusion detection for cyber threats

In practice, Defenders need a more efficient network detection approach which has the advantages of quick-responding learning capability of new network behavioural features for network intrusion detection purpose. In many applications the capability of Deep Learning techniques has been confirmed to outperform classic approaches. Accordingly, this study focused on network intrusion detection using convolutional neural networks (CNNs) based on LeNet-5 to classify the network threats. The experiment results show that the prediction accuracy of intrusion detection goes up to 99.65% with samples more than 10,000. The overall accuracy rate is 97.53%.
TRIM37 deficiency induces autophagy through deregulating the MTORC1-TFEB axis

ABSTRACT TRIM37 gene mutations cause mulibrey (muscle-liver-brain-eye) nanism, a severe growth disorder with prenatal onset. Although TRIM37 depletion normally induces apoptosis, patients with TRIM37 mutations have a high risk of developing tumors, suggesting that there may be an alternative pro-survival mechanism for TRIM37-deficient tumor cells. We find that TRIM37 interacts with MTOR and RRAGB proteins, enhances the MTOR-RRAGB interaction and promotes lysosomal localization of MTOR, thereby activating amino acid-stimulated MTORC1 signaling. In response to loss of TRIM37 functions, phosphorylation of TFEB is significantly reduced, resulting in its translocation into the nucleus enabling its transcriptional activation of genes involved in lysosome biogenesis and macroautophagy/autophagy. The enhanced autophagy depends on the inhibition of MTORC1 signaling and may serve as an alternative mechanism to survive the loss of TRIM37 functions. Our study unveils a positive role of TRIM37 in regulating the MTORC1–TFEB axis and provides mechanistic insights into the pathogenesis of mulibrey nanism, as well as potential therapeutic treatment. Abbreviations: ACTB: actin beta; ATG: autophagy related; CASP3: caspase3; CLEAR: coordinated lysosomal expression and regulation; CQ: chloroquine; CTS: cathepsin proteases; CTSL: cathepsin L; EIF4EBP1: eukaryotic translation initiation factor 4E binding protein 1; LAMP1: lysosomal associated membrane protein 1; LAMP2: lysosomal associated membrane protein 2; LMNB1: lamin B1; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; MTOR: mechanistic target of rapamycin kinase; MTORC1: MTOR complex 1; mulibrey: muscle–liver–brain–eye; NAC: N-acetyl-L-cysteine; PARP1: poly(ADP-ribose) polymerase 1; RAP2A: member of RAS oncogene family; RHEB: Ras homolog enriched in brain; ROS: reactive oxygen species; RPS6KB1: ribosomal protein S6 kinase B1; RRAGB: Ras related GTP binding B; SQSTM1: sequestosome 1; TFEB: transcription factor EB; TRIM37: tripartite motif containing 37.
segment_liftover : a Python tool to convert segments between genome assemblies

The process of assembling a species’ reference genome may be performed in a number of iterations, with subsequent genome assemblies differing in the coordinates of mapped elements. The conversion of genome coordinates between different assemblies is required for many integrative and comparative studies. While currently a number of bioinformatics tools are available to accomplish this task, most of them are tailored towards the conversion of single genome coordinates. When converting the boundary positions of segments spanning larger genome regions, segments may be mapped into smaller subsegments if the original segment’s continuity is disrupted in the target assembly. Such a conversion may lead to a relevant degree of data loss in some circumstances such as copy number variation (CNV) analysis, where the quantitative representation of a genomic region takes precedence over base-specific accuracy. segment_liftover aims at continuity-preserving remapping of genome segments between assemblies and provides features such as approximate locus conversion, automated batch processing and comprehensive logging to facilitate processing of datasets containing large numbers of structural genome variation data.
Risks and risk control of wind power enterprises

Accidents happened in wind power enterprises can lead to severe consequences. Based on the statistical analysis on the collection of the accident of world wind power enterprises for nearly 20 years, the proportion of accidents' type can be discovered. With the SWOT analysis method and the AHP analytic hierarchy process, the risk evaluation model, the judgment matrix and the weight of each factor can be established. Take wind power company as an example, human factor is determined to be most significant of it's safe production. Afterwards, using the Monte Carlo method and to simulate, operate, count the results of Software-Hardware-Environment-Liveware (SHEL) model. The result shows the significance of the human factors in accident consequence and accident types, which provides the foundation for the analysis of wind power control for the electric wind power company. Finally, put forward security measures in technology and management, scientific, effective, reasonable and specific ways to provide technical support for the scientific prevention of accidents for wind power generation enterprises.
Frictional Realities: Enabling Immersion in Mixed-Reality Performances

This paper presents a case study of a Mixed-Reality Performance employing 360-degree video for a virtual reality experience. We repurpose the notions of friction to illustrate the different threads at which priming is enacted during the performance to create an immersive audience experience. We look at aspects of friction between the different layers of the Mixed-Reality Performance, namely: temporal friction, friction between the physical and virtual presence of the audience, and friction between realities. We argue that Mixed-Reality Performances that employ immersive technology, do not need to rely on its presumed immersive nature to make the performance an engaging or coherent experience. Immersion, in such performances, emerges from the audience' transition towards a more active role, and the creation of various fictional realities through frictions.
Named entity recognition for Chinese microblog with convolutional neural network

Named Entity Recognition (NER) has usually focused on traditional formal text. we consider the task of NER on microblog text. In this paper, we propose a Convolutional Neural Network for NER in Chinese microblog text. Instead of traditional machine learning needing man-made input features carefully optimized for NER task, our system learns the words feature by itself. Our network uses a sliding window of word context to predict tags. Experimental results show that our model achieved 80% accuracy on this task.
Terrain classification of hyperspectral remote sensing images based on kernel maximum margin criterion

Hyperspectral remote sensing images have brought abundant spectral information for terrain classification. But the terrain classification of hyperspectral remote sensing images is confronted with the problems of high dimensionality and nonlinear separability, which lead to unsatisfied terrain classification rate. In order to raise the terrain classification recognition rate of hyperspectral remote sensing images, a new terrain classification method is presented based on kernel maximum margin criterion (KMMC), i.e., KMMC subspace method. Firstly, the original data are mapped to a high-dimensional kernel space by kernel method, and then the maximum margin criterion (MMC) is used to extract the nonlinear discriminant features of original data in the kernel space. Finally, the minimum Euclidean distance classifier is used to classify in the resulting KMMC feature subspace. Recognition results on account of an airborne visible / infrared imaging spectrometer (AVIRIS) hyperspectral remote sensing image show that, compared with the original space method, linear discriminant analysis (LDA) subspace method, MMC subspace method and kernel linear discriminant analysis (KLDA) subspace method, the proposed KMMC subspace method can significantly raise the recognition rate while reducing the dimensionality of the data.
Functional acclimation across microgeographic scales in Dodonaea viscosa

We studied a native Australian shrub—Dodonaea viscosa, or sticky hop bush—in the wild and in a gardening experiment and found that the species can readily adapt to different environments. Our findings are interesting because the plants we used came from sites with quite different environmental conditions, although they were only short distances apart. Our findings indicate that the potential risks associated with moving plants between sites with different environmental conditions are not likely to cause negative outcomes for restoration projects using this species, which is commonly used for restoration in southern Australia.
Two-Side Agreement Learning for Non-Parametric Template Matching

We address the problem of measuring matching similarity in terms of template matching. A novel method called two-side agreement learning (TAL) is proposed which learns the implicit correlation between two sets of multi-dimensional data points. TAL learns from a matching exemplar to construct a symmetric tree-structured model. Two points from source set and target set agree to form a two-side agreement (TA) pair if each point falls into the same leaf cluster of the model. In the training stage, unsupervised weak hyper-planes of each node are learned at first. After then, tree selection based on a cost function yields final model. In the test stage, points are propagated down to leaf nodes and TA pairs are observed to quantify the similarity. Using TAL can reduce the ambiguity in defining similarity which is hard to be objectively defined and lead to more convergent results. Experiments show the effectiveness against the state-of-the-art methods qualitatively and quantitatively. key words: template matching, visual similarity, similarity learning
Practical Multichannel SAR Imaging in the Maritime Environment

The U.S. Naval Research Laboratory (NRL) recently developed an X-band airborne multichannel synthetic aperture radar (MSAR) test bed system that consists of 32 along-track phase centers. This system was deployed in September 2014 and again in October 2015 to perform extensive and systematic data collections on a variety of land and maritime scenes under different environmental conditions. This paper presents a detailed experimental analysis of imaging in the maritime domain using data captured by the NRL MSAR system. After presenting some of the important details of our NRL MSAR system, we demonstrate velocity-based imaging of a variety of moving backscatter sources including ships and shoaling ocean waves. Our analysis is based on the velocity SAR (VSAR) technique, which was originally conceived by Friedlander and Porat. Practical application of this algorithm in the maritime domain requires a number of pre- and postprocessing stages, which are described here in detail. Our results are then benchmarked against the traditional along-track interferometry, where it is demonstrated that VSAR processing is better able to correctly compensate motion-induced distortion.
Design and implement of a rehabilitation system with surface electromyography technology

This study investigated the effect of surface electromyography (sEMG) combined with comprehensive rehabilitation and physical fitness training. Myo armband is a wearable used for gesture recognition. It transmits raw electromyography, gyroscope and accelerometer signals to devices. This study presented a protocol to perform a flexion-extension wrist motion assessment, develop sEMG as biofeedback in the management and treatment of frozen shoulder syndrome. The results can be used to support the development of interactive analysis and use for home healthcare.
Analysis of quality-related competencies within the European e-Competence Framework (e-CF)

Competences are the main foundation, element of each profession. Among professional competences those related to quality are crucial for delivering a service or product/result of a professional activity. Quality-related competencies are/should be incorporated into all other competences because "quality is everything". Professional models and frameworks, as well as Bodies of knowledge dedicate important parts to quality area(s) and competencies. The paper presents the results of analysis of quality-related competencies within the European e-Competence Framework (e-CF) showing how and where competencies directly related to quality are defined, and, consequently, recognized and implemented within other competencies.
Explaining Data Integration

Explanations are an integral part of human behavior: people provide explanations to justify choices and actions, and seek explanations to understand the world around them. The need for explanations extends to technology, as semi-automated and fully-automated systems support crucial activities and increasingly important societal functions. The interpretability of these systems and the ability to explain their decision processes are crucial in developing trust in the systems’ function. Further, explanations provide opportunities for systems to interact with human users and obtain feedback, improving their operation. Finally, explanations allow domain experts and system developers to debug erroneous system decisions, diagnose unexpected outcomes, and improve system function. In this paper, we study and review existing data integration systems with respect to their ability to derive explanations. We present a new classiﬁcation of data integration systems by their explainability and discuss the characteristics of systems within these classes. We review the types of explanations derived by the various data integration systems within each explainability class. Finally, we present a vision of the desired properties of future data integration systems with respect to explanations and discuss the challenges in pursuing this goal.
A Free-Sketch Recognition Method for Chemical Structural Formula

Chemical Structural Formula(CSF) recognition plays an important role in the molecular design and component retrieval. However, sketch-based CSF recognition remains an obstacle in current retrieval systems. This paper introduces a system for sketch CSF recognition on smart mobile devices. A dual-mode-based method is proposed to distinguish the gestures for character inputs and non-character inputs instead of the ordinary segmentation approaches. An attribute graph model is established to describe effectively all necessary information of a sketched CSF. Chemical knowledge is adopted to refine the candidates of structure relationship among elements. The experiments results demonstrate that the proposed method outperforms the existing methods for free-sketch CSFs on effectiveness and flexibility.
Validation and reliability of the rapid diagnostic test ‘SD Bioeasy Dengue Duo’ for dengue diagnosis in Brazil: a phase III study

BACKGROUND The diagnosis of dengue is complex. Until recently, only specialised laboratories were able to confirm dengue infection. However, this has changed with the newly available immunochromatographic rapid tests. Early diagnosis is of great interest, and point-of-care rapid tests have been increasingly used in Brazil. Most of those tests have not undergone validation in the Brazilian population. In this context, we decided to evaluate a rapid test introduced in the Federal District (FD). OBJECTIVES To estimate the accuracy and reliability of the SD Bioeasy Dengue Duo rapid test and its components to detect dengue infections in a consecutive sample of symptomatic residents in the FD, Brazil. METHODS In total, 1353 venous blood samples were collected between 2013 and 2014. Two hundred and six positive samples (cases) and 246 negative samples (non cases) were required for sensitivity and specificity estimation, respectively; for agreement evaluation, we used 401 samples. The reference standard used was a composite of MAC-ELISA, virus isolation and real-time polymerase chain reaction (RT-qPCR). The evaluation was conducted prospectively under field conditions in the public health units of the FD. FINDINGS The results for the overall accuracy of the rapid test (NS1/IgM combined) showed 76% sensitivity and 98% specificity. The sensitivity for the NS1 component (67%) was better than that for the IgM component (35%). The positive likelihood ratio was 46, and the negative likelihood ratio was 0.24. The reliability of the test (NS1/IgM combined) demonstrated crude agreement of 98% (Kappa index 0.94). MAIN CONCLUSIONS The present phase III, large-scale validation study demonstrates that the rapid test SD Bioeasy Dengue Duo has moderate sensitivity (NS1/IgM combined) and high specificity. Therefore, the test is useful in confirming the diagnosis of dengue, but not enough to rule out the diagnosis. Our results also suggest that Dengue virus (DENV) viral load estimated through the RT-qPCR and antibody level measured through the MAC-ELISA could have had a direct influence on the accuracy of the rapid test.
Utilizing Crowdsourced Asynchronous Chat for Efficient Collection of Dialogue Dataset

In this paper, we design a crowd-powered system to efficiently collect data for training dialogue systems. Conventional systems assign dialogue roles to a pair of crowd workers, and record their interaction on an online chat. In this framework, the pair is required to work simultaneously, and one worker must wait for the other when he/she is writing a message, which decreases work efficiency. Our proposed system allows multiple workers to create dialogues in an asynchronous manner, which relieves workers from time restrictions. We have conducted an experiment using our system on a crowdsourcing platform to evaluate the efficiency and the quality of dialogue collection. Results show that our system can reduce the necessary time to input a message by 68% while maintaining quality.
Classification of rumors and counter-rumors

Rumors are posing a serious threat in this digital era. They spread fast and wide on social media to become viral. While some users are deceived by rumors, others explicitly seek to debunk them. In consequence, two opposite forces of claims, namely, rumors and counter-rumors emerge. Given that rumors and counter-rumors are antithetical and could exhibit contrary patterns, the objective of this paper is to classify the two. For this purpose, messages on a rumoring phenomenon were collected from Twitter, and a set of content- based as well as user-based features were identified to distinguish between rumors and counter-rumors. To ensure methodological rigor, a set of classification algorithms including voting was employed using five-fold cross validation. Furthermore, feature selection techniques were used to identify the top features in classifying rumors and counter-rumors. The implications of the results are discussed.
RNA-directed activation of cytoplasmic dynein-1 in reconstituted transport RNPs

Polarised mRNA transport is a prevalent mechanism for spatial control of protein synthesis. However, the composition of transported ribonucleoprotein particles (RNPs) and the regulation of their movement are poorly understood. We have reconstituted microtubule minus end-directed transport of mRNAs using purified components. A Bicaudal-D (BicD) adaptor protein and the RNA-binding protein Egalitarian (Egl) are sufficient for long-distance mRNA transport by the dynein motor and its accessory complex dynactin, thus defining a minimal transport-competent RNP. Unexpectedly, the RNA is required for robust activation of dynein motility. We show that a cis-acting RNA localisation signal stabilises the interaction of Egl with BicD, which licenses the latter protein to recruit dynein and dynactin. Our data support a model for BicD activation based on RNA-induced occupancy of two Egl-binding sites on the BicD dimer. Scaffolding of adaptor protein assemblies by cargoes is an attractive mechanism for regulating intracellular transport.