Miracle-dz/UniFAR-FTUs huggingface dataset:
Feasibility Study of a Fiber Ring Laser Working on the SLM Regime in a BOTDA Sensor

A feasibility study of the employment of a fiber laser working as the laser source of a Brillouin distributed sensor is presented in this paper. The erbium fiber ring laser structure designed works on the single longitudinal mode regime. The main parameters of the ring cavity are analyzed in terms of the laser requirements and the performance of a Brillouin optical time domain analysis sensor using the fiber laser as light source.
Identification and analysis of key genes associated with ulcerative colitis based on DNA microarray data

Abstract This study aimed to identify genes that may play a role in development of ulcerative colitis (UC) and gain insight into its pathogenesis. Gene expression profiling data, including samples collected from 13 early-stage UC (EUC), 8 advanced-stage UC (AUC), and 5 control subjects, were downloaded from the Gene Expression Omnibus database under the accession number of GSE9452. Differentially expressed genes (DEGs) were identified in EUC and AUC compared with controls. DEGs for EUC and AUC, as well as AUC-specific DEGs were subjected to pathway enrichment analysis. Random Walk with Restart (RWR) was used to identify DEGs that are critical in UC based on a protein-to-protein interaction (PPI) network and the inflammatory bowel disease (IBD) pathway downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. IL17 and transforming growth factor beta TGF-&bgr;) expression levels in colonic tissue from patients with UC and normal colonic mucosa from healthy adults were analyzed by immunohistochemistry (IHC). A total of 3511 and 911 DEGs were identified in AUC and EUC, respectively. The overlapping DEGs and the AUC-specific DEGs were both enriched in pathways related to immunity, such as antigen processing and presentation. AUC-specific DEGs were related to cell migration, such as ECM-receptor interaction. Following DEG prioritization, TLR4 and STAT1 were linked with EUC, AUC, and CD. The upregulated gene TGFB increased the number of Th17 cells, as verified by IHC. Furthermore, PIK3R1, CREBBP, and STAT1 were part of high-degree nodes in the PPI sub-network. The upregulated gene TGFB may regulate IL17 expression in UC. PIK3R1 may participate in immunity and CREBBP may interact with STAT1 in the development and progression of UC.
Pharmacokinetics of Active Components From Guhong Injection in Normal and Pathological Rat Models of Cerebral Ischemia: A Comparative Study

Background and Objectives: Guhong Injection (GHI) is usually administered for the treatment of stroke in clinics. Aceglutamide and hydroxyl safflower yellow A (HSYA) are its key ingredients for brain protective effect. To investigate the pharmacokinetics of aceglutamide and HSYA under pathological and normal conditions, the pharmacokinetic parameters and characteristics of middle cerebral artery occlusion (MCAO) and normal rats given the same dosage of GHI were studied compared. Methods: 12 SD rats were divided into two groups, namely, MCAO and normal groups. Both groups were treated with GHI in the same dosage. Plasma samples were collected from the jaw vein at different time points and subsequently tested by high-performance liquid chromatography (HPLC). Results: After administration of GHI, both aceglutamide and HSYA were immediately detected in the plasma. Ninety percent of aceglutamide and HSYA was eliminated within 3 h. For aceglutamide, statistically significant differences in the parameters including AUC(0−t), AUC(0−∞), AUMC(0−t), AUMC(0−∞), Cmax (P < 0.01), and Vz (P < 0.05). Meanwhile, compared with the MCAO group, in the normal group, the values of AUC(0−t), AUMC(0−t), VRT(0−t), and Cmax (P < 0.01) for HSYA were significantly higher, whereas the value of MRT(0−t) was significantly lower in the normal group. Conclusions: The in vivo trials based on the different models showed that, the pharmacokinetic behaviors and parameters of aceglutamide and HSYA in GHI were completely different. These results suggest that the pathological damage of ischemia-reperfusion has a significant impact on the pharmacokinetic traits of aceglutamide and HSYA.
General-purpose software tool for serial segmentation of stacked images

Many medical imaging modalities produce spatial or temporal stacks of image data. Segmentation of such image stacks has many applications ranging from quantitative measurements to surgical and radiation treatment planning. The key idea presented in this paper is that of propagating information serially from one slice to the next within an interactive framework. Since information on adjacent slices is very similar, segmentation on one slice can be propagated with slight modification to adjacent slices. The segmentation algorithms that we have developed within this framework are all based on energy minimization principles with an additional constraint that the segmentation on a given image slice is similar to the segmentation predicted from the previous image slice. An optical flow approach is used to predict segmentation from one slice to the next. Three types of algorithms have been developed within the above paradigm for different applications --(1) A Mumford and Shah energy- minimizing algorithm combining edge and region information in a region-growing framework, (2) an active contour model-based tracking method, and (3) an algorithm based on pixel classification and Markov random fields. We recognize the fact that interactivity is very important in medical image segmentation. Therefore, our segmentation tools are available in a Java-based graphical user interface (GUI), allowing users to initialize various segmentation algorithms or to edit the results of automatic segmentation, if desired.
MRI slice stacking using manifold alignment and wave kernel signatures

MRI slice stacking involves retrospective combination of 2D MRI images to form pseudo 3D volumes. It is useful because physical constraints limit the temporal/spatial resolutions with which dynamic 3D MRI volumes can be acquired and so stacking fast highresolution 2D images can yield pseudo 3D volumes with high inplane spatial and temporal resolution. However, it is important that the stacked 2D images were acquired at consistent motion states. Assessing motion state consistency between slices representing different anatomy is challenging as the image contents are not easily comparable. Manifold alignment (MA) is a technique which provides a solution to this problem by embedding the 2D images for all slices into one globally consistent low-dimensional space. One successful approach to MA involves forming graphs from each slice dataset and using graph descriptors to find correspondences between datasets. Here we propose a new graph descriptor for the slice stacking problem, inspired by work in the computer vision literature, and evaluate it with two experiments. First, using a highly realistic synthetic MRI dataset in which reconstructed volumes can be compared to a ground truth, we find our method significantly outperforms the state of the art. Second, we use in vivo MRI data and show that the volumes reconstructed by our method have a higher degree of self-consistency.
SemEval-2018 Task 3: Irony Detection in English Tweets

This paper presents the first shared task on irony detection: given a tweet, automatic natural language processing systems should determine whether the tweet is ironic (Task A) and which type of irony (if any) is expressed (Task B). The ironic tweets were collected using irony-related hashtags (i.e. #irony, #sarcasm, #not) and were subsequently manually annotated to minimise the amount of noise in the corpus. Prior to distributing the data, hashtags that were used to collect the tweets were removed from the corpus. For both tasks, a training corpus of 3,834 tweets was provided, as well as a test set containing 784 tweets. Our shared tasks received submissions from 43 teams for the binary classification Task A and from 31 teams for the multiclass Task B. The highest classification scores obtained for both subtasks are respectively F1= 0.71 and F1= 0.51 and demonstrate that fine-grained irony classification is much more challenging than binary irony detection.
Performing and interpreting individual pharmacokinetic profiles in patients with Hemophilia A or B: Rationale and general considerations

In a separate document, we have provided specific guidance on performing individual pharmacokinetic (PK) studies using limited samples in persons with hemophilia with the goal to optimize prophylaxis with clotting factor concentrates. This paper, intended for clinicians, aims to describe how to interpret and apply PK properties obtained in persons with hemophilia.
The effect of competitor presence and relative competitive ability on male mate choice

Recent studies suggest that males might respond in an adaptive manner to an elevated likelihood of male--male competition for mates by facultatively altering their mating preferences. Little is known, however, about how male choice is influenced by a male's relative competitiveness or by the risk posed by increased physical competition for access to mates compared with greater risk of sperm competition. We investigated how the relative size and presence of a competitor influences male choice using 2-choice experiments in the mosquito fish, Gambusia holbrooki. We varied the size of the focal male, the size of a competitor, and whether the competitor remained alongside the preferred female (physical and sperm competition) or was removed (only greater sperm competition). Prior to viewing a competitor, males, regardless of size, significantly preferred larger females. After a male had viewed a competitor alongside his preferred female, however, he subsequently spent significantly less time associating with her. This change in male choice was not influenced by focal male size, competitor size, or whether or not the competitor was physically present during the choice trial. After viewing a competitor, however, larger males spent significantly more time than did smaller males near their preferred female. Our results suggest that male mating preferences are sensitive to the risk of increased physical or indirect sperm competition, but there was no evidence that males adjust their mate choice in response to the relative size (and therefore presumed competitiveness) of rivals. Copyright 2011, Oxford University Press.
Performance Enhancement for Multihop Harvest-to-Transmit WSNs With Path-Selection Methods in Presence of Eavesdroppers and Hardware Noises

Energy-harvesting-based physical-layer security has become a promising technique, as it not only secures information from eavesdropping without upper layer data encryption but also improves the energy efficiency of wireless networks. However, it imposes new challenges, as adversary parties can overhear the transmission of confidential information between the source and destination via a relay. Therefore, the transmit power of the signals must be large enough for energy harvesting, but it must also be small enough to avoid eavesdropping. This is even more challenging with multihop multipath wireless networks. Motivated by these observations, this paper proposes three innovative protocols, namely, the shortest path selection protocol, random path selection protocol, and best path selection protocol. These will enhance the security of multihop multipath randomize-and-forward cooperative wireless sensor networks under the presence of eavesdroppers and hardware impairment, wherein the source node and relay nodes are capable of harvesting energy from beacon for data transmission. Furthermore, we derive exact closed-form expressions and the asymptotic outage probability for each protocol under multiple eavesdropping attacks. The simulation results validate the theoretical results.
ECNU at SemEval-2018 Task 2: Leverage Traditional NLP Features and Neural Networks Methods to Address Twitter Emoji Prediction Task

This paper describes our submissions to Task 2 in SemEval 2018, i.e., Multilingual Emoji Prediction. We first investigate several traditional Natural Language Processing (NLP) features, and then design several deep learning models. For subtask 1: Emoji Prediction in English, we combine two different methods to represent tweet, i.e., supervised model using traditional features and deep learning model. For subtask 2: Emoji Prediction in Spanish, we only use deep learning model.
A Dynamic Placement Policy of Virtual Machine Based on MOGA in Cloud Environment

Currently cloud data centers exist problems of load imbalance and high power consumption. This paper studies the virtual machine placement policy in cloud environment by applying live migration techniques, and proposes a target host selection algorithm MOGA-THSA based on MOGA. As a heuristic algorithm, through designing excellent genetic operators and fitness functions, it optimizes the load balance and power consumption of the data center with smaller SLA violation rate. The algorithm is implemented on simulation platform CloudSim, and experiments show that it can improve the load balance of cloud data center and decrease total power consumption effectively, thus having a certain guiding significance for researching the virtual machine placement policy
The Evolution of Eriksonian and, Neo-Eriksonian Identity Theory and Research: A Review and Integration

The evolution of the study of identity development is traced from Freud's early writings to Erikson's extrapolations and theoretical expositions, Marcia's empirical operationalization, and finally to 6 alternative theories that have been introduced since 1987 (Berzonsky, Grotevant, Waterman, Kurtines, Adams, and Côté. The issues of measurement and intervention, which have been crucial to the evolution of identity as a researchable construct, are also reviewed. The alternative theories are conceptualized as addressing aspects of Erikson's work that were not addressed by Marcia's identity status model. To facilitate the broadening of identity theory to include more of Erikson's ideas, taxonomies for understanding relations among the alternative theories, and a system for integrating them, is introduced. Finally, suggestions for the development of future identity theory and research are offered.
A Movement Decomposition and Machine Learning-Based Fall Detection System Using Wrist Wearable Device

Falls in the elderly is a world health problem. Although many fall detection solutions were presented in literature, few of them are wrist-wearable devices, mainly due to typical processing and classification challenges to achieve accuracy greater than 95%. Considering the wrist as a more comfortable, discrete and acceptable place for an elderly wearable device, this paper presents the development and evaluation of a wrist-worn fall detection solution. Different sensors (accelerometer, gyroscope, and magnetometer), signals (acceleration, velocity, and displacement), and direction components (vertical and non-vertical) were combined and a comprehensive set of threshold-based and machine learning methods were applied in order to define the best approach for fall detection. Data was acquired for fall and non-fall movements from 22 volunteers. For threshold-based methods, a maximum accuracy of 91.1% was achieved with 95.8% and 86.5% of sensitivity and specificity, respectively, using Madgwick’s decomposition. With the same movement decomposition and machine learning methods in the classification stage, an impressive accuracy of 99.0% was achieved, with 100% of sensitivity and 97.9% of specificity in our data set. Prolonged tests with a volunteer wearing the fall detector also demonstrate the advantages of machine learning methods in terms of practical applications.
Location Prediction Based on User Mobile Behavior Similarity

In practical application, GPS trajectories are often sparse due to the sampling points lost or a new user appearing, which makes the accuracy of location prediction low based on a single user data. To solve this problem, this paper proposes a novel Markov location prediction approach based on user mobile behavior similarity clustering. First, this paper proposes a region partitioning method based on Voronoi diagram, transforms the GPS trajectories into region trajectories and predicts the locations over region trajectories. Second, this paper proposes a new approach to measure the similarity of users’ mobile behavior by considering users’ transferring features and regional features. Third? based on the mobile behavior similarity, this paper divides users into various groups and employs the first-order Markov model on the groups for location prediction. The experiments over real GPS trajectory dataset indicate that the proposed method is effective for location prediction.
Nanoparticle-Mediated Therapeutic Agent Delivery for Treating Metastatic Breast Cancer—Challenges and Opportunities

Breast cancer (BC) is the second leading cause of cancer-related death in American women and more than 90% of BC-related death is caused by metastatic BC (MBC). This review stresses the limited success of traditional therapies as well as the use of nanomedicine for treating MBC. Understanding the biological barriers of MBC that nanoparticle in vivo trafficking must overcome could provide valuable new insights for translating nanomedicine from the bench side to the bedside. A view about nanomedicine applied in BC therapy has been summarized with their present status, which is gaining attention in the clinically-applied landscape. The progressions of drug/gene delivery systems, especially the status of their preclinical or clinical trials, are also discussed. Here we highlight that the treatment of metastasis, in addition to the extensively described inhibition of primary tumor growth, is an indispensable requirement for nanomedicine. Along with more innovations in material chemistry and more progressions in biology, nanomedicine will constantly supply more exciting new approaches for targeted drug/gene delivery against MBC.
An Analysis of the Charging Characteristics of Electric Vehicles Based on Measured Data and Its Application

The accurate modeling of the charging characteristics of electric vehicles (EVs) is the basis for the load forecasting, infrastructure planning, and orderly charging management. While, research based on the measured charging data of EVs is seldom carried out, and the concrete modeling of the correlations of various parameters is a gap in the knowledge. Aiming at this, we carried out an investigation based on operational data, from August 2016 to August 2017, of an EV charging service company in Nanjing, China. The time-energy characteristics of EV charging behavior can be described using the probability distributions and correlations of three charging parameters, i.e., charging start time, charging duration, and charged capacity. In this paper, we fitted the probability densities of these charging parameters using the kernel estimation method and verified the correlations of time parameters of the charging behavior. Multiple copula functions were used to model the correlation between the time and energy parameters of different types of charging behaviors. On this basis, we also carried out stochastic simulation for the load curve of disordered charging and analyzed the potential of the EV charging load participating in the orderly management and its coordination with the output of power generation using renewable energy.
Skeletal maturity determination from hand radiograph by model-based analysis

Derived from a model based segmentation algorithm for hand radiographs proposed in our former work we now present a method to determine skeletal maturity by an automated analysis of regions of interest (ROI). These ROIs including the epiphyseal and carpal bones, which are most important for skeletal maturity determination, can be extracted out of the radiograph by knowledge based algorithms.
Shared identity predicts enhanced health at a mass gathering

Identifying with a group can impact (positively) upon group members’ health. This can be explained (in part) through the social relations that a shared identity allows. We investigated the relationship between a shared identity and health in a longitudinal study of a month-long pilgrimage in north India. Questionnaire data (N = 416) showed that self-reported health (measured before, during, and after the event) was better at the event than before, and although it reduced on returning home, it remained higher than before the event. This trajectory was predicted by data concerning pilgrims’ perceptions of a shared identity with other pilgrims at the event. We also found evidence that a shared identity amongst pilgrims had an indirect effect on changes in self-assessed health via the belief one had closer relations with one’s fellow pilgrims. We discuss the implications of these data for our understandings of the role of shared identity in social relations and health.
The CD40/CD40L system regulates rat cerebral microvasculature after focal ischemia/reperfusion via the mTOR/S6K signaling pathway

ABSTRACT Objective: The role of CD40/CD40 ligand (CD40L) in microvascular thrombosis is now widely accepted. However, the exact mechanisms linking the CD40/CD40L system and the soluble form of CD40L (sCD40L) with microvascular thrombosis are currently a topic of intensive research. The objective of this study was to assess the potential mechanisms in CD40/CD40L system-regulated microvascular thrombosis after focal ischemia/reperfusion (I/R). Methods: Rats were subjected to 60-min transient middle cerebral artery occlusion (MCAO). The experiments were divided into three groups: sham operation, MCAO, and MCAO + CD40 antagonist. Dynamic changes of serum-free sCD40L levels for 0, 1, 3, 5, 6, and 12 h by ELISA detecting kit after focal I/R were observed, and the CD40 expression levels in both platelet surface and vascular endothelial cell surface were measured by flow cytometry and immunofluorescence, respectively. Cerebral infarct volume was analyzed 12 h after reperfusion. mTOR/S6K signaling was determined by Western blot. Results: A comparison of thrombus formation between MCAO and CD40 antagonist treatment rats revealed a role for CD40 and/or CD40L in the inflammation-enhanced thrombosis responses in both of the platelet and vascular endothelial cell. MCAO rats yielded an acceleration of thrombus formation that was accompanied by increased CD40 levels in serum. The brain infarction was significantly decreased in CD40 antagonist treatment group compared to MCAO model group. The mTOR/S6K signaling was activated in MACO model than that of CD40 antagonist treatment group. Conclusions: Our findings indicate that CD40/CD40L system contributes to microvascular thrombosis and brain infarction induced by MCAO and reperfusion. The mTOR/S6K signaling pathway is involved in the regulation of cerebral microvasculature after focal I/R by CD40/CD40L. Abbreviations: AKT: protein kinase B; CD40L: CD40 ligand; CSF: cerebrospinal fluid; FITC: fluorescein isothiocyanate; I/R: ischemia/reperfusion; MCAO: middle cerebral artery occlusion; mTOR: mechanistic target of rapamycin; PE: P-phycoerythrin; sCD40L: soluble form of CD40L; TNF-a: tumor necrosis factor-alpha; WT: wild type.