Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative analysis of the 2016 ACR-EULAR and the 2002 AECG classification criteria for Sjögren's syndrome: Findings from the NIH cohort.

INTRODUCTION The introduction of new classification criteria for Sjögren's syndrome, known as the 2016 American College of Rheumatology/European League against Rheumatism Classification Criteria (ACR-EULAR), created a need for the evaluation of its performance in an external cohort. The purpose of this study was to compare the performance of the 2016 ACR-EULAR classification set with the widely used American-European Consensus Group Classification criteria (AECG) in the cohort at the National Institutes of Health, USA, and to compare the performance of the sets in classifying both primary and secondary Sjögren's syndrome (pSS and sSS). METHODS The study cohort at the NIH (N = 1,303) was enrolled for clinical suspicion of SS. Participants were classified as SS, pSS, and sSS according to both classification sets. Performance of 2016 ACR-EULAR and AECG sets was compared holding each as gold standard to the other. Statistical analysis of test diagnostics and agreement between the two sets were undertaken. RESULTS By the AECG set, 701 were classified as having SS (627 pSS, 74 sSS) and 714 were classified with SS (647 pSS, 67 sSS) by the 2016 ACR-EULAR set. Sensitivity and specificity of the two sets were comparable in classifying SS, pSS, and sSS. There was high agreement between the two sets for classifying SS (κ = 0.79), pSS (κ = 0.81), and sSS (κ = 0.87). The specificity of the 2016 ACR-EULAR set was significantly higher for classifying sSS than pSS, while the sensitivity was similar for the two disease groups. However, this pattern was also exhibited by the AECG set. CONCLUSION There was high agreement between the two classification sets with comparable performance diagnostics. There was no evidence of superior performance value by the new 2016 ACR-EULAR set over the AECG set, and the two sets were found to be equivalent. Findings from our cohort indicate that 2016 ACR-EULAR classification could be extended to classification of sSS.
B-Myb Is Up-Regulated and Promotes Cell Growth and Motility in Non-Small Cell Lung Cancer

B-Myb is a transcription factor that is overexpressed and plays an oncogenic role in several types of human cancers. However, its potential implication in lung cancer remains elusive. In the present study, we have for the first time investigated the expression profile of B-Myb and its functional impact in lung cancer. Expression analysis by quantificational real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry demonstrated that B-Myb expression is aberrantly overexpressed in non-small cell lung cancer (NSCLC), and positively correlated with pathologic grade and clinical stage of NSCLC. A gain-of-function study revealed that overexpression of B-Myb significantly increases lung cancer cell growth, colony formation, migration, and invasion. Conversely, a loss-of-function study showed that knockdown of B-Myb decreases cell growth, migration, and invasion. B-Myb overexpression also promoted tumor growth in vivo in a NSCLC xenograft nude mouse model. A molecular mechanistic study by RNA-sequencing (RNA-seq) analysis showed that B-Myb overexpression causes up-regulation of various downstream genes (e.g., COL11A1, COL6A1, FN1, MMP2, NID1, FLT4, INSR, and CCNA1) and activation of multiple critical pathways (e.g., extracellular signal-regulated kinases (ERK) and phosphorylated-protein kinase B (Akt) signaling pathways) involved in cell proliferation, tumorigenesis, and metastasis. Collectively, our results indicate a tumor-promoting role for B-Myb in NSCLC and thus imply its potential as a target for the diagnosis and/or treatment of NSCLC.
Managing QoS in IoTs: a survey

Internet of Things "IoT" had spread because of the rapid development of technologies and applications. It encompasses heterogeneous objects and underlying technologies to deliver efficient services to its end-users while guarantying certain Quality of Service (QoS) properties. From this perspective, we summarize and discuss in this paper different QoS models used in IoT. Also, we propose a classification scheme that categories QoS attributes of IoT based on resources, performance, and networks properties. This classification opens up further research directions in QoS management of IoT and may serve as a baseline to manage and guarantee QoS in IoT.
Diagnosis of schistosomiasis mansoni: an evaluation of existing methods and research towards single worm pair detection

Abstract The inadequacy of current diagnostics for the detection of low worm burdens in humans means that schistosomiasis mansoni is more widespread than previously acknowledged. With the inception of mass drug treatment programmes aimed at disease elimination and the advent of human vaccine trials, the need for more sensitive diagnostics is evident. In this review, we evaluate the merits and limitations of the principal diagnostic methods, namely detection of eggs in faeces; anti-schistosome antibodies in serum; parasite-derived proteins and glycans in serum or urine; parasite DNA in blood, faeces or urine. Only in the baboon model, where actual worm burden is determined by portal perfusion, have faecal smear and circulating antigen methods been calibrated, and shown to have thresholds of detection of 10–19 worm pairs. There is scope for improvement in all the four methods of detection, e.g. the identification of single targets for host antibodies to improve the specificity of enzyme linked immunosorbent assay. Despite recent advances in the definition of the schistosome secretome, there have been no comprehensive biomarker investigations of parasite products in the urine of infected patients. Certainly, the admirable goal of eliminating schistosomiasis will not be achieved unless individuals with low worm burdens can be diagnosed.
Findings from the hackathon on understanding euroscepticism through the lens of textual data

We present an overview and the results of a shared-task hackathon that took place as part of a research seminar bringing together a variety of experts and young researchers from the fields of political science, natural language processing and computational social science. The task looked at ways to develop novel methods for political text scaling to better quantify political party positions on European integration and Euroscepticism from the transcript of speeches of three legislations of the European Parliament.
Aggregates and the Deformation Properties of Concrete

This paper presents results from two comprehensive series of tests spanning a 5-year period that investigated the influence of 23 different aggregate types on the properties of hardened concrete. The information given is concerned with concrete stiffness and deformation. Tables of design values for estimating the elastic modulus of concrete are given. The influence of aggregate type on concrete shrinkage and creep is discussed with reference to the experimental work. Guidance is provided on the adjustment of code values for the effects of aggregates. The results provide evidence that aggregates exert a profound and important influence on the deformation properties of concrete.
Association of body size with health status in patients beginning dialysis.

BACKGROUND Greater weight-for-height has been associated with prolonged survival in patients with end-stage renal disease (ESRD) but not in the general population. The association between body size and health status has not been carefully evaluated. OBJECTIVES We compared the self-reported health status of 2467 participants in the Dialysis Morbidity and Mortality Study Wave 2 by using body mass index (BMI; in kg/m2) to approximate body size and composition. DESIGN BMI was categorized into 4 groups (<19, 19 to <25, 25 to <30, and > or = 30) corresponding to World Health Organization criteria for underweight, normal-weight, overweight, and obese status. We adjusted for demographic, clinical, and laboratory factors that may have confounded the association between body size and health status. RESULTS Scores on the physical component summary and the physical functioning scale were significantly lower for obese subjects than for those with normal weight or moderately high BMI after adjustment for demographic factors, comorbidity, and laboratory markers of nutritional status. Mental component summary and symptom scores were unrelated to BMI. The underweight group scored lower on many Medical Outcomes Study 36-Item Short Form scales than did the normal-weight group. CONCLUSIONS Whereas higher BMI has consistently been associated with enhanced dialysis-related survival, health status-particularly physical function-may be impaired by obesity. Additional longitudinal studies of body weight and composition are needed for a better understanding of the complex effects of obesity and undernutrition in persons with ESRD and advanced chronic kidney disease.
Magnetic imaging of micrometer and nanometer-size magnetic structures and their flux-pinning effects on superconducting thin films

Magnetic Imaging of Micrometer and Nanometer-size Magnetic Structures and Their Flux-Pinning Effects on Superconducting Thin Films. (May 2009) Ali Esad Özmetin, B.S., Bogazici University; M.S., Texas A&M University Chair of Advisory Committee: Dr. Donald G. Naugle In this work the interactions between neighboring superconducting thin film and ferromagnetic structures, i.e. superconductor-ferromagnet hybrid systems, were studied. A type-II superconducting thin film (Pb82Bi12), was deposited in close proximity to various ferromagnetic structures. These magnetic structures include: (i) alternating iron-brass shims of 275μm period, (ii) an array of 4μm wide Co stripes with smaller period (9μm), (iii) a square array of 50nm diameter, high aspect ratio (5-7) Ni rods with 250nm period. Measurements of critical transport current (IC), resistance (RH(T )) and second critical field (HC2) are reported. A variety of novel effects (enhancement of (IC) and (HC2), matching field effect, field compensation effect, and large hysteresis) are also reported. Using measurements on thin superconducting films atop a Co stripe array with a 9μm period, a superconductor-ferromagnet hybrid device (a mechanical superconducting persistent switch) is proposed. In addition, scanning Hall probe microscopy (SHPM) and other imaging techniques were used to characterize the magnetic properties of the systems mentioned. The SHPM was also used to acquire B-H and M-H curves. An additional sharp magnetic needle and electromagnetic coil assembly intended for micromanipulation of small magnetic particles and individual cells was also characterized.
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application

In E-commerce platforms such as Amazon and TaoBao , ranking items in a search session is a typical multi-step decision-making problem. Learning to rank (LTR) methods have been widely applied to ranking problems. However, such methods often consider different ranking steps in a session to be independent, which conversely may be highly correlated to each other. For better utilizing the correlation between different ranking steps, in this paper, we propose to use reinforcement learning (RL) to learn an optimal ranking policy which maximizes the expected accumulative rewards in a search session. Firstly, we formally define the concept of search session Markov decision process (SSMDP) to formulate the multi-step ranking problem. Secondly, we analyze the property of SSMDP and theoretically prove the necessity of maximizing accumulative rewards. Lastly, we propose a novel policy gradient algorithm for learning an optimal ranking policy, which is able to deal with the problem of high reward variance and unbalanced reward distribution of an SSMDP. Experiments are conducted in simulation and TaoBao search engine. The results demonstrate that our algorithm performs much better than the state-of-the-art LTR methods, with more than 40% and 30% growth of total transaction amount in the simulation and the real application, respectively.
Feature selection for high dimensional imbalanced class data based on F-measure optimization

Feature selection is designed to eliminate redundant attributes and improve classification accuracy. This is a challenging problem, especially in the case of imbalanced data. The traditional feature selection methods ignores the problem of class imbalance, making the selected features biased towards the majority class and neglecting the significant features for the minority class. Due to the advantage of F-measure in imbalanced data classification, we propose to use F-measure rather than accuracy as the optimization target in feature selection algorithm. This paper introduces a novel feature selection method SSVM-FS which is based on an optimal F-measure structural support vector machine classifier. Features will be selected according to the weight vector of SSVM which takes class imbalance problem into account. Based on this, we developed a comprehensive feature ranking method which integrate weight vector of SSVM and symmetric uncertainty. We use the comprehensive score to reduce the features to a suitable size and then use a harmony search to find the optimal combination of features to predict the target class label. The feature subset selected by the proposed method can represent both majority and minority class, in addition, it is less redundant. The experimental results on six high dimensional class imbalanced microarray data sets show that this method is a better method to solve the unbalanced classification.
Multi-task Learning with Gradient Guided Policy Specialization

We present a method for efficient learning of control policies for multiple related robotic motor skills. Our approach consists of two stages, joint training and specialization training. During the joint training stage, a neural network policy is trained with minimal information to disambiguate the motor skills. This forces the policy to learn a common representation of the different tasks. Then, during the specialization training stage we selectively split the weights of the policy based on a per-weight metric that measures the disagreement among the multiple tasks. By splitting part of the control policy, it can be further trained to specialize to each task. To update the control policy during learning, we use Trust Region Policy Optimization with Generalized Advantage Function (TRPOGAE). We propose a modification to the gradient update stage of TRPO to better accommodate multi-task learning scenarios. We evaluate our approach on three continuous motor skill learning problems in simulation: 1) a locomotion task where three single legged robots with considerable difference in shape and size are trained to hop forward, 2) a manipulation task where three robot manipulators with different sizes and joint types are trained to reach different locations in 3D space, and 3) locomotion of a two-legged robot, whose range of motion of one leg is constrained in different ways. We compare our training method to three baselines. The first baseline uses only joint training for the policy, the second trains independent policies for each task, and the last randomly selects weights to split. We show that our approach learns more efficiently than each of the baseline methods.
An efficient platform for generating somatic point mutations with germline transmission in the zebrafish by CRISPR/Cas9-mediated gene editing

Homology-directed recombination (HDR)–mediated genome editing is a powerful approach for both basic functional study and disease modeling. Although some studies have reported HDR-mediated precise editing in nonrodent models, the efficiency of establishing pure mutant animal lines that carry specific amino acid substitutions remains low. Furthermore, because the efficiency of nonhomologous end joining (NHEJ)-induced insertion and deletion (indel) mutations is normally much higher than that of HDR-induced point mutations, it is often difficult to identify the latter in the background of indel mutations. Using zebrafish as the model organism and Y box–binding protein 1 (Ybx1/ybx1) as the model molecule, we have established an efficient platform for precise CRISPR/Cas9-mediated gene editing in somatic cells, yielding an efficiency of up to 74% embryos. Moreover, we established a procedure for screening germline transmission of point mutations out of indel mutations even when germline transmission efficiency was low (<2%). To further improve germline transmission of HDR-induced point mutations, we optimized several key factors that may affect HDR efficiency, including the type of DNA donor, suppression of NHEJ, stimulation of HDR pathways, and use of Cas9 protein instead of mRNA. The optimized combination of these factors significantly increased the efficiency of germline transmission of point mutation up to 25%. In summary, we have developed an efficient procedure for creating point mutations and differentiating mutant individuals from those carrying knockouts of entire genes.
Complex Neutrosophic Graphs of Type 1

In this paper, we introduced a new neutrosophic graphs called complex neutrosophic graphs of type1 (CNG1) and presented a matrix representation for it and studied some properties of this new concept. The concept of CNG1 is an extension of generalized fuzzy graphs of type 1 (GFG1) and generalized single valued neutrosophic graphs of type 1 (GSVNG1).
A New Active Visual System for Humanoid Robots

In this paper, a new active visual system is developed, which is based on bionic vision and is insensitive to the property of the cameras. The system consists of a mechanical platform and two cameras. The mechanical platform has two degrees of freedom of motion in pitch and yaw, which is equivalent to the neck of a humanoid robot. The cameras are mounted on the platform. The directions of the optical axes of the two cameras can be simultaneously adjusted in opposite directions. With these motions, the object's images can be located at the centers of the image planes of the two cameras. The object's position is determined with the geometry information of the visual system. A more general model for active visual positioning using two cameras without a neck is also investigated. The position of an object can be computed via the active motions. The presented model is less sensitive to the intrinsic parameters of cameras, which promises more flexibility in many applications such as visual tracking with changeable focusing. Experimental results verify the effectiveness of the proposed methods.
Time-domain channel equalization for subcarrier spacing compressed FDM with SC-MMSE turbo equalization receiver

Subcarrier spacing compressed frequency division multiplexing (SSC-FDM) has been widely researched as one of the digital multi-carrier modulation schemes for the beyond 5th generation mobile communication system. The SSC-FDM arranges subcarriers non-orthogonally in the frequency domain and improves spectral efficiency by compressing the channel bandwidth compared to the conventional orthogonal frequency division multiplexing (OFDM). On the other hand, the inter-carrier inference (ICI) mainly caused by overlapped adjacent subcarriers deteriorates the communication quality. The SSC-FDM using soft canceller followed by minimum square error filter (SC-MMSE) turbo equalization has been reported to remove the ICI in the additive white Gaussian noise (AWGN) environment, while no meaningful result in the multipath fading environment has been reported at present. In addition, the SSC-FDM poses considerable difficulties in proper channel estimation and equalization in the frequency domain due to the non-orthogonal subcarrier arrangement. In this paper, we propose a receiver architecture in which time-domain equalization schemes are applied to the SSC-FDM using SC-MMSE turbo equalization, assuming the mobile communication system. In addition, the usefulness of the proposed scheme is discussed with regard to the block error rate (BLER) and throughput performance in the multipath fading environment by computer simulation.
Smartphone Sensing Meets Transport Data: A Collaborative Framework for Transportation Service Analytics

We advocate for and introduce TRANSense, a framework for urban transportation service analytics that combines participatory smartphone sensing data with city-scale transportation-related transactional data (taxis, trains, etc.). Our work is driven by the observed limitations of using each data type in isolation: (a) commonly-used anonymous city-scale datasets (such as taxi bookings and GPS trajectories) provide insights into the aggregate behavior of transport infrastructure, but fail to reveal individual-specific transport experiences (e.g., wait times in taxi queues); while (b) mobile sensing data can capture individual-specific commuting-related activities, but suffers from accuracy and energy overhead challenges due to usage artefacts and lack of appropriate sensing triggers. TRANSense demonstrates how a judicious fusion of such disparate data sources can overcome these challenges and offer novel insights. We detail two examples: (a) Taxi Service Analyzer that provides accurate detection of commuter queuing for taxis and estimates their wait time, by using taxi trip records to identify potential taxi locations with high demand and subsequently selectively triggering mobile sensing-based queuing analytics on nearby commuters; and (b) Subway Boarding Analyzer that identifies instances when passengers fail to board arriving trains, by first estimating train arrivals from temporal patterns of passenger egress at station gantries, and then using mobile sensing-based analysis of commuter movement behavior on platforms. Experiments with real-world datasets (from over 20,000 taxis and 1.7 million commuters in Singapore) show the power of this approach: the taxi service analyzer detects commuter queuing with over 90 percent accuracy with negligible energy overhead and estimates wait times with error margins below 15 percent, whereas the subway boarding analyzer can detect failed boarding events with a precision of over 90 percent (more than thrice what is achievable through purely mobile sensing).