Miracle-dz/UniFAR-FTUs huggingface dataset:
The Virtual Hero: the influence of narrative on affect and presence in a VR game

Entertainment media, such as video-games, often make use of a narrative to invoke an emotional reaction and draw people into the experience. A narrative can add layers of emotional complexity to even quite simple forms of game-play. In immersive virtual reality people are surrounded by the game environment, making their sense of presence and immersion highly salient to the perceived quality of the game. This pilot study investigates how game performance, satisfaction, arousal and sense of presence are affected when a pre-game background story is introduced to a virtual reality zombie shooter game. Several trends were observed, indicating that people might be more involved in the game-world and more prepared for the virtual experience. Participants who were exposed to a short pre-game back-story tended to achieve higher game scores and experience a higher sense of presence, although these differences were not significant. We examine the measurements used in this study and theorize how affect and presence interact with respect to the use of narrative devices in virtual reality games.
Human Action Segmentation using 3D Fully Convolutional Network

Detailed action analysis, such as action detection, localization and segmentation, has received more and more attention in recent years. Compared to action classification, action segmentation and localization are more useful in many practical applications that require precise spatio-temporal information of the actions. However, performing action segmentation and localization is more challenging, because determining the pixel-level locations of action not only requires a strong spatial model that captures the visual appearances for the actions, but also calls for a temporal model that characterizes the dynamics of the actions. Most existing methods either use hand-crafted spatial models, or can only extract short-term motion information. In this paper, we propose a 3D fully convolutional deep network to jointly exploit spatial and temporal information in a unified framework for action segmentation and localization. The proposed deep network is trained to combine both information in an end-to-end fashion. Extensive experimental results have shown that the proposed method outperforms state-of-the-art methods by a large margin.
Semisynthetic sensor proteins enable metabolic assays at the point of care

A protein designed to sense metabolites Many diseases cause characteristic changes in blood metabolites. Yu et al. describe a paper-based assay in which a chosen metabolite can be oxidized to generate reduced nicotinamide adenine dinucleotide phosphate (NADPH). Color changes in a designed NADPH sensor protein are then quantified by a digital camera. The sensor system successfully generated point-of-care measurements of phenylalanine, glucose, and glutamate. Concentrations of phenylalanine in the blood of phenylketonuria patients were analyzed within minutes with only half a microliter of blood. Science, this issue p. 1122 Sensor proteins allow a paper-based color-change assay of important metabolites, including phenylalanine. Monitoring metabolites at the point of care could improve the diagnosis and management of numerous diseases. Yet for most metabolites, such assays are not available. We introduce semisynthetic, light-emitting sensor proteins for use in paper-based metabolic assays. The metabolite is oxidized by nicotinamide adenine dinucleotide phosphate, and the sensor changes color in the presence of the reduced cofactor, enabling metabolite quantification with the use of a digital camera. The approach makes any metabolite that can be oxidized by the cofactor a candidate for quantitative point-of-care assays, as shown for phenylalanine, glucose, and glutamate. Phenylalanine blood levels of phenylketonuria patients were analyzed at the point of care within minutes with only 0.5 microliters of blood. Results were within 15% of those obtained with standard testing methods.
Pressure Injury Prevention Practices in the Intensive Care Unit: Real-world Data Captured by a Wearable Patient Sensor.

BACKGROUND Compliance with turning protocols in the intensive care unit (ICU) is low; however, little is known about the quality of turning, such as turn angle magnitude or depressurization time. Wearable sensors are now available that provide insight into care practices. OBJECTIVE This secondary descriptive study describes the turning practices of nurses from 2 ICUs at an academic medical center among consecutive ICU patients. MATERIALS AND METHODS A wearable patient sensor was applied to patients on hospital admission. The sensor continuously recorded position data but was not visible to staff. A qualified turn was one that reached > 20° angle and was held for 1 minute after turning. The institution's clinical research repository provided clinical data. RESULTS A total of 555 patients were analyzed over a 5-month period (September 2015-January 2016); 44 870 hours of monitoring data (x- = 73 hours ± 97/patient) and 27 566 individual turns were recorded. Compliant time was recorded as 54%, with 39% of observed turns reaching the minimum angle threshold and 38% of patients remaining in place for > 15 minutes (depressurization). Turn magnitude was similar for medical and surgical patients. Factors associated with lower compliant time included male sex, high body mass index, and low Braden score. Patients were supine for 72% of the observed time. CONCLUSIONS The investigators found dynamically measured turning frequency, turn magnitude, and tissue depressurization time to be suboptimal. This study highlights the need to reinforce best practices related to preventive turning and to consider staff and patient factors when developing individualized turn protocols.
Measuring the interdisciplinarity of Big Data research: a longitudinal study

Purpose The purpose of this paper is to measure the degree of interdisciplinary collaboration in Big Data research based on the co-occurrences of subject categories using Stirling’s diversity index and specialization index. Design/methodology/approach Interdisciplinarity was measured utilizing the descriptive statistics of disciplines, network indicators showing relationships between disciplines and within individual disciplines, interdisciplinary communities, Stirling’s diversity index and specialization index, and a strategic diagram revealing the development status and trends of discipline communities. Findings Comprehensively considering all results, the degree of interdisciplinarity of Big Data research is increasing over time, particularly, after 2013. There is a high level of interdisciplinarity in Big Data research involving a large number of disciplines, but it is unbalanced in distribution. The interdisciplinary collaborations are not intensive on the whole; most disciplines are aggregated into a few distinct communities with computer science, business and economics, mathematics, and biotechnology and applied microbiology as the core. Four major discipline communities in Big Data research represent different directions with different development statuses and trends. Community 1, with computer science as the core, is the most mature and central to the whole interdisciplinary network. Accounting for all network indicators, computer science, engineering, business and economics, social sciences, and mathematics are the most important disciplines in Big Data research. Originality/value This study deepens our understanding of the degree and trend of interdisciplinary collaboration in Big Data research through a longitudinal study and quantitative measures based on two indexes. It has practical implications to study and reveal the interdisciplinary phenomenon and characteristics of related developments of a specific research area, or to conduct comparative studies between different research areas.
PKCι and YAP1 are crucial in promoting pancreatic tumorigenesis

Pancreatic ductal adenocarcinoma (PDAC) is a fatal malignant disease with 5-year survival rate of less than 6%. Activating mutations of Kras (mu-Kras) are often detected in most of PDAC patients. Although it has been known that oncogenic Kras is the driver of pancreatic cancer initiation and development, the underlying mechanisms by which mu-Kras promotes PDAC remain poorly understood. Here, we identify that PKCι is one of the crucial factors for supporting the survival of pancreatic cancer cells expressing mu-Kras. Our study demonstrates that after the knockdown of PKCι, the expression of the transcriptional co-activator YAP1 is decreased, which hinders the expression of the downstream target gene Mcl-1, and subsequently sensitizes pancreatic cancer MiaPaCa and PANC-1 cells experssing mu-Kras to apoptosis. In comparison, the suppression of PKCι has little impact on the viability of non-neoplastic pancreatic HPDE6-C7 cells. Moreover, the transient overexpression of oncogenic Kras in HPDE6-C7 elevates the expression of PKCι and YAP1 concomitantly. The upregulated YAP1 in HPDE6-C7/ mu-Kras cells is abolished once PKCι is suppressed, suggesting the linear relationship among mu-Kras, PKCι and YAP1. This phenomenon is further proven by the co-upregulation of PKCι and YAP1 in HPDE6-C7 cells stably transfected with mu-Kras. Taken together, our findings suggest that PKCι acts through promoting YAP1 function to promote the survival of pancreatic cancer cells expressing mu-Kras. It appears that targeting PKCι-YAP1 signaling is a feasible strategy for developing new therapeutics for treating pancreatic cancer patients.
Extent of coronary atherosclerosis and arterial remodelling in women: the NHLBI-sponsored Women's Ischemia Syndrome Evaluation.

Background Information regarding the pathogenesis of ischemic heart disease (IHD) in women is limited. Sex-specific responses to atherosclerosis and coronary arterial remodelling in women versus men have been hypothesized, but limited study exists. Methods Case-matched study of 174 women with suspected ischemia referred for coronary angiography: 87 with non-obstructive coronary artery disease (CAD) (no luminal diameter stenosis >20% in any coronary artery) and 87 age and ethnicity matched women with obstructive CAD. Groups were compared with regard to atheroma burden and coronary arterial remodelling assessed by coronary artery intravascular ultrasound (IVUS). Results IVUS revealed more extensive atheroma with obstructive CAD vs. those without obstructive CAD, with greater percent atheroma volume (PAV) (36.1%±9.8% vs. 25.4%±9.1%, P<0.0001), total atheroma volume (TAV) (140.8±58.7 vs. 98.8±46.9 mm3, P<0.0001) and percentage of images containing plaque (70.0%±30.5% vs. 35.7%±32.6%, P<0.0001). Adjusting for risk factors, PAV (35%±1% vs. 28%±1%, P=0.0008), TAV (131±7 vs. 115±7 mm3, P=0.110) and percentage of images containing plaque (66%±4% vs. 45%±5%, P=0.0008) remained greater with obstructive CAD. Obstructive CAD was associated with smaller lumen volumes (251.9±92.8 vs. 289.7±91.8 mm3, P=0.005), but surprisingly, the external elastic membrane (EEM) volume was very similar comparing the groups (392.7±128.1 vs. 388.6±113.7 mm3, P=0.910). Conclusions Our findings suggest that women referred to angiography for suspected ischemia, have differing patterns of coronary arterial response to injury with regard to accumulation of atherosclerosis and compensatory remodelling related to the presence and absence of obstructive CAD. Preservation and cultivation of compensatory arterial remodelling may be a novel CAD therapeutic target.
Video to fully automatic 3D hair model

Imagine taking a selfie video with your mobile phone and getting as output a 3D model of your head (face and 3D hair strands) that can be later used in VR, AR, and any other domain. State of the art hair reconstruction methods allow either a single photo (thus compromising 3D quality) or multiple views, but they require manual user interaction (manual hair segmentation and capture of fixed camera views that span full 360°). In this paper, we describe a system that can completely automatically create a reconstruction from any video (even a selfie video), and we don't require specific views, since taking your -90°, 90°, and full back views is not feasible in a selfie capture. In the core of our system, in addition to the automatization components, hair strands are estimated and deformed in 3D (rather than 2D as in state of the art) thus enabling superior results. We provide qualitative, quantitative, and Mechanical Turk human studies that support the proposed system, and show results on a diverse variety of videos (8 different celebrity videos, 9 selfie mobile videos, spanning age, gender, hair length, type, and styling).
Spleen thickness can predict significant liver pathology in patients with chronic hepatitis B with persistently normal alanine aminotransferase or minimally raised alanine aminotransferase: a retrospective study

Objective Liver biopsy is the gold standard test for assessment of liver pathology. This study was performed to assess the predictive value of spleen thickness for liver pathology and the role of routine follow-up procedures in significant liver pathology for patients with chronic hepatitis B (CHB) with persistently normal alanine aminotransferase (PNALT) or minimally raised alanine aminotransferase (ALT). Methods Patients with CHB who underwent percutaneous liver biopsy were retrospectively reviewed. The relationship of liver pathology with age, ALT, hepatitis B e-antigen, and spleen thickness was statistically analyzed, and the predictive accuracy of spleen thickness was evaluated. Results In total, 80.65% of patients had significant necroinflammation and/or fibrosis. Nearly 60% of patients had splenomegaly, of which 89.12% had a histopathological grade of ≥G2 and/or S2. Spleen thickness was predictive of liver pathology, and significant histological findings increased as the hepatitis B virus (HBV) DNA level increased. Conclusions Spleen thickness is an effective predictor of liver pathology in patients with PNALT or minimally raised ALT. Additionally, the prevalence of significant histological findings tended to increase as the HBV DNA level increased. Patients with CHB and splenomegaly and a high HBV DNA level should be treated early with antivirals to improve liver pathology.
Do-It-Yourself Single Camera 3D Pointer Input Device

We present a new algorithm for single camera 3D reconstruction, or 3D input for human-computer interfaces, based on precise tracking of an elongated object, such as a pen, having a pattern of colored bands. To configure the system, the user provides no more than one labelled image of a handmade pointer, measurements of its colored bands, and the camera's pinhole projection matrix. Other systems are of much higher cost and complexity, requiring combinations of multiple cameras, stereocameras, and pointers with sensors and lights. Instead of relying on information from multiple devices, we examine our single view more closely, integrating geometric and appearance constraints to robustly track the pointer in the presence of occlusion and distractor objects. By probing objects of known geometry with the pointer, we demonstrate acceptable accuracy of 3D localization.
Are Object Detection Assessment Criteria Ready for Maritime Computer Vision?

Maritime vessels equipped with visible and infrared cameras can complement other conventional sensors for object detection. However, application of computer vision techniques in maritime domain received attention only recently. The maritime environment offers its own unique requirements and challenges. Assessment of the quality of detections is a fundamental need in computer vision. However, the conventional assessment metrics suitable for usual object detection are deficient in the maritime setting. Thus, a large body of related work in computer vision appears inapplicable to the maritime setting at the first sight. We discuss the problem of defining assessment metrics suitable for maritime computer vision. We consider new bottom edge proximity metrics as assessment metrics for maritime computer vision. These metrics indicate that existing computer vision approaches are indeed promising for maritime computer vision and can play a foundational role in the emerging field of maritime computer vision.
Sequential Coordination of Deep Models for Learning Visual Arithmetic

Achieving machine intelligence requires a smooth integration of perception and reasoning, yet models developed to date tend to specialize in one or the other; sophisticated manipulation of symbols acquired from rich perceptual spaces has so far proved elusive. Consider a visual arithmetic task, where the goal is to carry out simple arithmetical algorithms on digits presented under natural conditions (e.g. hand-written, placed randomly). We propose a two-tiered architecture for tackling this problem. The lower tier consists of a heterogeneous collection of information processing modules, which can include pre-trained deep neural networks for locating and extracting characters from the image, as well as modules performing symbolic transformations on the representations extracted by perception. The higher tier consists of a controller, trained using reinforcement learning, which coordinates the modules in order to solve the high-level task. For instance, the controller may learn in what contexts to execute the perceptual networks and what symbolic transformations to apply to their outputs. The resulting model is able to solve a variety of tasks in the visual arithmetic domain, and has several advantages over standard, architecturally homogeneous feedforward networks including improved sample efficiency.
A Convolutional Temporal Encoder for Video Caption Generation

We propose a convolutional temporal encoding network for video sequence embedding and caption generation. The mainstream video captioning work is based on recurrent encoder of various forms (e.g. LSTMs and hierarchical encoders). In this work, a multi-layer convolutional neural network encoder is proposed. At the core of this encoder is a gated linear unit (GLU) that performs a linear convolutional transformation of input with a nonlinear gating, which has demonstrated superior performance in natural language modeling. Our model is built on top of this unit for video encoding and integrates several up-to-date tricks including batch normalization, skip connection and soft attention. Experiment on two large-scale benchmark datasets (MSAD and M-VAD) generates strong results and demonstrates the effectiveness of our model.
Robust Hund rule without Coulomb repulsion and exclusion principle in quantum antiferromagnetic chains of composite half spins

Quantum spin chains with composite spins have been used to approximate conventional chains with higher spins. For instance, a spin 1 (or ) chain was sometimes approximated by a chain with two (or three) spin ’s per site. However, little examination has been given as to whether this approximation, effectively assuming the first Hund rule per site, is valid and why. In this paper, the validity of this approximation is investigated numerically. We diagonalize the Hamiltonians of spin chains with a spin 1 and per site and with two and three spin ’s per site. The low energy excitation spectrum for the spin chain with M spin ’s per site is found to coincide with that of the corresponding conventional chain with one spin per site. In particular, we find that as the system size increases, an increasingly larger block of consecutive lowest energy states with maximal spin per site is observed, robustly supporting the first Hund rule even though the exclusion principle does not apply and the system does not possess Coulomb repulsion. As for why this approximation works, we show that this effective Hund rule emerges as a plausible consequence when applying to composite spin systems the Lieb–Mattis theorem, which is originally for the ground state of ferrimagnetic and antiferromagnetic spin systems.
Cohort Profile Cohort Profile : The Finnish Health in Teens ( Fin-HIT ) study : a population-based study

Cohort Profile: The Finnish Health in Teens (Fin-HIT) study: a population-based study Rejane Augusta de Oliveira Figueiredo,* Sabina Simola-Ström, Trine B Rounge, Heli Viljakainen, Johan G Eriksson, Eva Roos and Elisabete Weiderpass Genetic Epidemiology Group, Folkhälsan Research Center, Helsinki, Finland, Faculty of Medicine, University of Helsinki, Helsinki, Finland, Department of Research, Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo, Norway, Department of Food and Environmental Sciences, University of Helsinki, Helsinki, Finland, Department of General Practice and Primary Healthcare, University of Helsinki and Helsinki University Hospital, Helsinki, Finland, Department of Public Health, University of Helsinki, Helsinki, Finland, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden and Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, Arctic University of Norway, Tromsø, Norway
Neurobehavioral disability in stroke patients during subacute inpatient rehabilitation: prevalence and biopsychosocial associations

ABSTRACT Background and Objectives: There are scarce data on post-stroke neurobehavioral disability (NBD). The aim of this study was to identify the prevalence of NBD in a subacute inpatient stroke population and examine potential associations with demographic, stroke-related, functional and psychosocial variables. Methods: 82 survivors of stroke were consecutively recruited during their inpatient rehabilitation admission. Nursing staff rated NBD in patients using the St Andrews –Swansea Neurobehavioral Outcome Scale (SASNOS). Measures of patient functional independence (FIM), cognition (MoCA), and mood symptoms (HADS) were collected in addition to nursing reports of whether observed NBD negatively impacted on the patient or those around them. Results: NBD relating to interpersonal relationships (44.4% of participants) and cognition (52.4%) were highly prevalent within the sample while NBD relating to inhibition (1.2%), aggression (3.6%), and communication (2.5%) were relatively rare. Presence of NBD was significantly associated with reduced functional independence (rs=0.39, p < 0.01) and associated with trends in cognitive impairment (rs=0.29, p = 0.03), increased anxiety (rs=-0.43, p = 0.02) and depressive symptoms (rs=-0.43, p = 0.02). Presence of NBD was significantly correlated with negative impact to the patient and those around them across all SASNOS domains (rs range 0.42 - 0.45, all p ≤ 0.01). Conclusions: NBD is common within a subacute stroke inpatient population, particularly interpersonal and cognitive difficulties and preliminary analyses indicate associations with reduced functional ability, cognition and mood. There is a need to provide education and support to clinicians to facilitate routine assessment and management of NBD following stroke.