Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychological health and socioeconomic status among non-Hispanic whites

Much attention has been paid of late to drug abuse and mortality among less-educated whites. This group, often referred to as the “white working class,” is typically defined as non-Hispanic whites who do not have Bachelor of Arts or Bachelor of Science college degrees (1). Mortality rates have risen for members of the white working class in midlife, mainly due to increases in drug overdoses, suicides, and alcohol-related liver mortality (2⇓⇓–5). In contrast, mortality rates for African Americans and Hispanics have continued to decline (4). These findings have contributed to a popular narrative of rising malaise among the white working class, leading to a surge in “deaths of despair” (6, 7) and to support for Donald Trump in the 2016 Presidential election (8). But has there really been an increase in feelings of despair among the white working class? In PNAS, Goldman et al. (9) examine changes since the mid-1990s in the psychological health of the non-Hispanic white population. The authors use data from the Midlife in the United States (MIDUS) study, an interdisciplinary investigation of psychological well-being and physical health among Americans ages 25–74 y (10). It comprises two national surveys conducted in 1995–1996 and 2011–2014. The surveys contained a comprehensive set of questions about positive and negative mental health, which were combined into scales with known psychometric properties. A study that linked the 1995–1996 MIDUS data to National Death Index records found that positive mental health was associated with a lower risk of subsequent mortality (11). Goldman et al. (9) find a decline in psychological health between the earlier and later MIDUS survey samples for white non-Hispanics as a whole. In addition, the authors find that the … [↵][1]1Email: cherlin{at}jhu.edu. [1]: #xref-corresp-1-1
High and Low Levels of an NTRK2-Driven Genetic Profile Affect Motor- and Cognition-Associated Frontal Gray Matter in Prodromal Huntington’s Disease

This study assessed how BDNF (brain-derived neurotrophic factor) and other genes involved in its signaling influence brain structure and clinical functioning in pre-diagnosis Huntington’s disease (HD). Parallel independent component analysis (pICA), a multivariate method for identifying correlated patterns in multimodal datasets, was applied to gray matter concentration (GMC) and genomic data from a sizeable PREDICT-HD prodromal cohort (N = 715). pICA identified a genetic component highlighting NTRK2, which encodes BDNF’s TrkB receptor, that correlated with a GMC component including supplementary motor, precentral/premotor cortex, and other frontal areas (p < 0.001); this association appeared to be driven by participants with high or low levels of the genetic profile. The frontal GMC profile correlated with cognitive and motor variables (Trail Making Test A (p = 0.03); Stroop Color (p = 0.017); Stroop Interference (p = 0.04); Symbol Digit Modalities Test (p = 0.031); Total Motor Score (p = 0.01)). A top-weighted NTRK2 variant (rs2277193) was protectively associated with Trail Making Test B (p = 0.007); greater minor allele numbers were linked to a better performance. These results support the idea of a protective role of NTRK2 in prodromal HD, particularly in individuals with certain genotypes, and suggest that this gene may influence the preservation of frontal gray matter that is important for clinical functioning.
Accelerated Particle Filter for Real-Time Visual Tracking With Decision Fusion

Correlation-filter-based trackers, showing strong discrimination ability in challenging situations, have recently achieved superior performance in visual tracking. However, because the model treats the tracker's predictions in new frames as training data, the filter can be contaminated by small incorrect predictions, which cause model drift. Particle-filter-based trackers usually produce more accurate results due to the richer image representations used in prediction, but suffer when the environments are complex throughout an image sequence. In this letter, we propose an innovative real-time algorithm, which combines the particle filter with correlation filters in the prediction stage, enabling accurate predictions by the particle filter and alleviating model drift. Moreover, an effective decision fusion strategy is proposed to get more precise object predictions, thus further enhancing the overall tracking performance. Extensive evaluations on the OTB-2013 benchmark demonstrate that the proposed tracker is very promising compared with the state-of-the-art trackers, while operating over 85 frames/s.
Image tone mapping approach using essentially non-oscillatory bi-quadratic interpolations combined with a weighting coefficients strategy

This paper proposes a Tone Mapping Operator (TMO) to convert High Dynamic Range (HDR) images into Low Dynamic Range (LDR) images. It is related on two consecutive stages. The first one decomposes the HDR image on several resolution levels according to a non-separable multiresolution approach using Essentially Non-Oscillatory (ENO) strategy where adapted bi-quadratic interpolations are exploited. The underlying idea of this decomposition is to better represent the details of the complex HDR image preserving as much as possible the HDR image quality. In the second stage, a weighting coefficients process is deployed to reduce judiciously the dynamic range of each subband's coefficient. The weights are deduced from Gaussian filtering operations combined with a monotonic decreasing function where its parameters depend on the statistical properties of each HDR subband, neighborhood and resolution level. Simulation results show that the proposed image TMO has the ability to better represent the details compared to other operators.
Single image super resolution: An efficient approach using auto-learning and filter pooling

Stimulated by recent approaches on internal database driven image super resolution, we propose a super resolution algorithm based on auto learning and filter pooling approach. This paper presents an algorithm where auto-trained high frequency cluster of patches are used to reconstruct a high resolution image. The auto-trained features are extracted using filter pooling approach combining the advantages of Sobel and Gabor filters. The key feature of this approach is preserving features at different frequencies with different orientations using self-similarity to avoid the use of multiple images. The experimental analysis shows that the proposed technique gives better results when compared to existing state-of-the-art internal database methods for image super resolution in terms of quantitative and qualitative performance measures.
A novel approach for clustering hindi tweets

Social media online platforms have critical data. The information accessible on the Social Networking platforms isn't just enormous in volume, yet in addition in assorted variety of subject and quality. Such factors make it infeasible to productively rummage helpful data from it. Twitter is an online news and person to person communication benefit where user posts and co-operate with messages, “Tweets”. Tweets on Twitter contains diverse news and events. Thus, this paper intends to order these posts into various classes utilizing WSD (Word Sense Disambiguation) with the goal that the user will have the capacity to get to the class of tweets he needs to search for. For grouping the tweets, we utilize knn (K — Nearest Neighbour) algorithm. Word sense disambiguation is a process of automatically figuring out the intended meaning of such words when used in a sentence. For this purpose, we used LESK algorithm based on two assumptions. First assumption is, when two words are used in closed proximity in a sentence. Second, if one sense each of the two words can be used to talk of the same topic.
Local structure of liquid–vapour interfaces

The structure of a simple liquid may be characterized in terms of ground state clusters of small numbers of atoms of that same liquid. Here we use this sensitive structural probe to consider the effect of a liquid–vapour interface upon the liquid structure. At higher temperatures (above around half the critical temperature) we find that the predominant effect of the interface is to reduce the local density, which significantly suppresses the local cluster populations. At lower temperatures, however, pronounced interfacial layering is found. This appears to be connected with significant orientational ordering of clusters based on three- and five-membered rings, with the rings aligning perpendicular and parallel to the interface respectively. At all temperatures, we find that the population of fivefold symmetric structures is suppressed, rather than enhanced, close to the interface.
Driver interaction with vulnerable road users: Understanding and modelling driver behaviour for the design and evaluation of intelligent safety systems

Every year, more than 5000 pedestrians and 2000 cyclists die on European roads. These vulnerable road users (VRU) are at especially high risk when interacting with motorised vehicles. Safety systems designed to mitigate or avoid crashes with VRU started to enter the market a few years ago and still need to be improved to be effective in all scenarios. Understanding how drivers interact with VRU is crucial to improve the development and the evaluation of safety systems. Today, however, there is a lack of knowledge about driver behaviour in interactions with VRU, which keeps active safety measures from expressing their full potential. This thesis has multiple objectives: 1) to provide new knowledge about driver behaviour in crossing interactions with VRU, 2) to present this knowledge to assessment programs such as Euro NCAP with the goal of improving their system-evaluation scenarios, and 3) to include this knowledge in a counterfactual analysis framework for safety-benefit evaluation. Results showed that the moment in which a VRU becomes visible to the driver had the largest influence on the driver braking response process in driver-VRU interactions. This thesis contributes to experimental methodologies by comparing the steps of the response process in test-track and in driving-simulator studies. Additionally, the thesis describes a driver braking response model and uses the information gained from it to suggest improvements in the design and evaluation of safety systems. Finally, a framework for counterfactual simulations was developed which is suitable for evaluating safety benefits and refining intelligent safety systems (such as autonomous emergency braking and frontal collision warning). This thesis addressed some of the research gaps in the understanding of driver behaviour that have hindered the improvement of driver models and their application to the design and evaluation of safety systems.
Synthesis of N6‐Substituted Adenosines as Cytokinin Nucleosides

This unit describes preparation of N6‐substituted adenosines (cytokinin nucleosides), a unique class of compounds with a wide spectrum of biological activities. Regioselective alkylation of N6‐acetyl‐2′,3′,5′‐tri‐O‐acetyladenosine with alkyl halides under basic conditions or alcohols under Mitsunobu conditions followed by deprotection are the methods of choice for the preparation of the cytokinin nucleosides. The attractive feature of this strategy is the possibility of using a broad library of commercially available alkyl halides and alcohols under mild reaction conditions. © 2018 by John Wiley & Sons, Inc.
iLoc‐lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC

Motivation: Long non‐coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. They have important functions in cell development and metabolism, such as genetic markers, genome rearrangements, chromatin modifications, cell cycle regulation, transcription and translation. Their functions are generally closely related to their localization in the cell. Therefore, knowledge about their subcellular locations can provide very useful clues or preliminary insight into their biological functions. Although biochemical experiments could determine the localization of lncRNAs in a cell, they are both time‐consuming and expensive. Therefore, it is highly desirable to develop bioinformatics tools for fast and effective identification of their subcellular locations. Results: We developed a sequence‐based bioinformatics tool called ‘iLoc‐lncRNA’ to predict the subcellular locations of LncRNAs by incorporating the 8‐tuple nucleotide features into the general PseKNC (Pseudo K‐tuple Nucleotide Composition) via the binomial distribution approach. Rigorous jackknife tests have shown that the overall accuracy achieved by the new predictor on a stringent benchmark dataset is 86.72%, which is over 20% higher than that by the existing state‐of‐the‐art predictor evaluated on the same tests. Availability and implementation: A user‐friendly webserver has been established at http://lin‐group.cn/server/iLoc‐LncRNA, by which users can easily obtain their desired results. Supplementary information: Supplementary data are available at Bioinformatics online.
Understanding road traffic risks from the street hawker’s perspective

Abstract This study explored street hawkers’ lived experiences and risks in traffic using the phenomenological case study design. Data from 24 hawkers at three hawking spots and field observations form the basis of our study. A stratified purposeful sampling scheme was employed to ensure a balance of gender and age. Thematic data analytic strategy was employed in the data analysis. We found that participants’ traffic incidents were mainly self-induced, attributable to their negligence on the road, indiscriminate road crossing and car-following behaviour. Our results suggest a perceived relationship between socio-demographic characteristics and personal traffic accident encounters. Also, we noted that the ability to manage traffic risk seem to influence hawkers’ traffic risk perceptions, with gender and age as possible mediators. As this study is exploratory, we recommend further scientific enquiry to explain these issues and to provide the basis for appropriate interventions to manage the road safety implications of this activity.
Optimal power flow algorithm based on self-adaptive filter-trust region method

The Optimal Power Flow (OPF) problem for power systems could be expressed by a Non-Linear Programming (NLP) model, which is difficult to be solved. This study presents a practical algorithm to solve OPF through a successive linear programming (SLP), in which conventional power flow and linearized Sub-Problem (LSP) are solved by alternating iteration. A Self-Adaptive Filter-Trust Region (SAIFT) method is proposed to solve the LSP, in which the acceptance of the current iteration point is decided by a filter, and the step size is controlled by the trust region. The Wolfe linear search method is used to provide the direction of optimization when the consequence is dominated by filter. Furthermore, the linearization strategy of transformer ratio and the dynamic adjustment strategy of step size are proposed, which simplifies the formulation and improves the convergence of the algorithm. Case studies based on Ward-Hale 6-bus system demonstrate the efficacy of the new method in solving the OPF problems.
A call admission control scheme in cache-enabled satellite-terrestrial communication networks

Satellite-terrestrial communication network has been proposed in recent years, for its great potential in larger coverage and better load balancing ability for wireless communication networks. However, with the number of users and the traffic load in the network rapidly increasing, the resource of the system becomes more intense. Thus, a proper and high-efficient call admission control scheme is needed to balance the traffic load and properly allocate the limited wireless resources. This paper presents a call admission control scheme with additional consideration of users' Quality of Service (QoS) and cache technique. Firstly, a system model of the satellite-terrestrial communication network is introduced. After that, an objective function of the system throughput is derived and laterly optimized with genetic algorithm. Simulation result shows that higher system throughput can be achieved with the proposed scheme, while the QoS of users remains uninfluenced. Besides, the Call Blocking Probability (CBP) of the system is reduced efficiently, and the resource utilization is improved at the same time.
A real time packet classification and allocation approach for C-RAN implementation in 5G network

Cloud-RAN (C-RAN) is a centralized architecture which is able to solve most of the challenges of mobile operators and to fulfill the needs of ever-growing end-users in the fifth generation (5G) cellular network. The main idea behind C-RAN is to split the functionalities of the traditional base stations (BS) into a radio unit known as Remote Radio Head (RRH) and a Centralized and virtualized Baseband Unit (BBUs). The Virtualization of BBU provides more scalability and flexibility to the BBU unit, along with that this also gives a number of benefits in terms of cost and capacity. There are many research articles published earlier which described the C-RAN network architecture briefly, but no one describes the actual packet flow and processing in the C-RAN network. In this paper, we try to solve this issue in two consecutive steps. The first step is to classify the incoming packets, and then allocate that packet based on the destination address to the proper virtual baseband unit (VB). Then the second step is to schedule the packets based on the priority value and also provide the load balancing mechanism in VB if required.
Intraprocedural predictors of post-stent retriever thrombectomy subarachnoid hemorrhage in middle cerebral artery stroke

Background Stent retriever thrombectomy (SRT) in acute thromboembolic stroke can result in post-thrombectomy subarachnoid hemorrhage (PTSAH). Intraprocedural findings associated with PTSAH are not well defined. Objective To identify angiographic findings and procedural factors during SRT that are associated with PTSAH. Materials and methods This was a retrospective, observational cohort study of consecutive patients with middle cerebral artery (MCA) acute ischemic stroke treated with SRT. Inclusion criteria were: (1) age ≥18 years; (2) thromboembolic occlusion of the MCA; (3) at least one stent retriever pass beginning in an M2 branch; (4) postprocedural CT or MRI scan within 24 hours; (5) non-enhanced CT Alberta Stroke Program Early CT Score >5. Exclusion criteria included multi-territory stroke before SRT. Results Eighty-five patients were enrolled; eight patients had PTSAH (group 1) and 77 did not (group 2). Baseline demographic and clinical characteristics were comparable between the two groups. In group 1, a significantly greater proportion of patients had more than two stent retriever passes (62.5% vs 18.2%, P=0.01), a stent retriever positioned ≥2 cm along an M2 branch (100% vs 30.2%, P=0.002), and the presence of severe iatrogenic vasospasm before SRT pass (37.5% vs 5.2%, P=0.02). One patient with PTSAH and associated mass effect deteriorated clinically. Conclusions An increased number of stent retriever passes, distal device positioning, and presence of severe vasospasm were associated with PTSAH. Neurological deterioration with PTSAH can occur.
Generating FN3‐Based Affinity Reagents Through Phage Display

Antibodies are useful tools for detecting individual proteins in complex samples and for learning about their location, amount, binding partners, and function in cells. Unfortunately, generating antibodies is time consuming and laborious, and their affinity and/or specificity is often limited. This protocol offers a fast and inexpensive alternative to generate antibody surrogates through phage display of a library of fibronectin type III (FN3) monobody variants and affinity selection for binders. © 2018 by John Wiley & Sons, Inc.
Injectable OPF/graphene oxide hydrogels provide mechanical support and enhance cell electrical signaling after implantation into myocardial infarct

After myocardial infarction (MI), the scar tissue contributes to ventricular dysfunction by electrically uncoupling viable cardiomyocytes in the infarct region. Injection of a conductive hydrogel could not only provide mechanical support to the infarcted region, but also synchronize contraction and restore ventricular function by electrically connecting isolated cardiomyocytes to intact tissue. Methods: We created a conductive hydrogel by introducing graphene oxide (GO) nanoparticles into oligo(poly(ethylene glycol) fumarate) (OPF) hydrogels. The hydrogels were characterized by AFM and electrochemistry workstation. A rat model of myocardial infarction was used to investigate the ability of OPF/GO to improve cardiac electrical propagation in the injured heart in vivo. Echocardiography (ECHO) was used to evaluate heart function 4 weeks after MI. Ca2+ imaging was used to visualize beating cardiomyocytes (CMs). Immunofluorescence staining was used to visualize the expression of cardiac-specific markers. Results: OPF/GO hydrogels had semiconductive properties that were lacking in pure OPF. In addition, the incorporation of GO into OPF hydrogels could improve cell attachment in vitro. Injection of OPF/GO 4 weeks after myocardial infarction in rats enhanced the Ca2+ signal conduction of cardiomyocytes in the infarcted region in comparison with PBS or OPF alone. Moreover, the injection of OPF/GO hydrogel into the infarct region enhanced the generation of cytoskeletal structure and intercalated disc assembly. Echocardiography analysis showed improvement in load-dependent ejection fraction/fractional shortening of heart function 4 weeks after injection. Conclusions: We prepared a conductive hydrogel (OPF/GO) that provide mechanical support and biological conduction in vitro and in vivo. We found that injected OPF/GO hydrogels can provide mechanical support and electric connection between healthy myocardium and the cardiomyocytes in the scar via activating the canonical Wnt signal pathway, thus upregulating the generation of Cx43 and gap junction associated proteins. Injection of OPF/GO hydrogel maintained better heart function after myocardial infarction than the injection of a nonconductive polymer.
Par4Sim – Adaptive Paraphrasing for Text Simplification

Learning from a real-world data stream and continuously updating the model without explicit supervision is a new challenge for NLP applications with machine learning components. In this work, we have developed an adaptive learning system for text simplification, which improves the underlying learning-to-rank model from usage data, i.e. how users have employed the system for the task of simplification. Our experimental result shows that, over a period of time, the performance of the embedded paraphrase ranking model increases steadily improving from a score of 62.88% up to 75.70% based on the NDCG@10 evaluation metrics. To our knowledge, this is the first study where an NLP component is adaptively improved through usage.