Miracle-dz/UniFAR-FTUs huggingface dataset:
Narcissism and stress-reactivity: a biobehavioural health perspective

ABSTRACT From a perspective broadly informed by Stress and Coping Theory, this review examined whether theoretically distinct and important dimensions of narcissism (grandiosity and vulnerability) associate with health-related stress-reactivity. Literature searches were conducted and articles were included if they contained a validated baseline assessment of narcissism, a stressor, and a within-person assessment of stress-reactivity (i.e., a baseline and post-stress assessment of a health-related psychological, biological, or behavioural process). Additionally, narcissism measures had to be systematically categorised as assessing grandiosity or vulnerability (see Grijalva, E., Newman, D. A., Tay, L., Donnellan, M. B., Harms, P. D., Robins, R. W., & Yan, T. (2015). Gender differences in narcissism: A meta-analytic review. Psychological Bulletin, 141(2), 261–310. doi:10.1037/a0038231), and narcissism dimensions had to be assessed independently of other constructs. Findings were narratively synthesised within three broad dimensions of stress-reactivity (psychological, biological, and behavioural). Overall, there appear to be relatively consistent patterns that grandiosity and vulnerability are related to altered stress-reactivity. Additionally, grandiosity and vulnerability may differentially influence stress-reactivity depending on the type of stressor and/or indicator of stress-reactivity (e.g., under certain conditions, grandiosity may confer some level of resilience). This review highlights important theoretical and empirical gaps in the emerging narcissism and health literature. Furthermore, this review may help inform methodological considerations for future research, and may also point to physical health outcomes that could conceivably be affected by narcissism over time (e.g., overweight/obesity, cardiovascular disease, HIV/AIDS).
A new human delayed‐matching‐to‐place test in a virtual environment reverse‐translated from the rodent watermaze paradigm: Characterization of performance measures and sex differences

Watermaze tests of place learning and memory in rodents and corresponding reverse‐translated human paradigms in real or virtual environments are key tools to study hippocampal function. In common variants, the animal or human participant has to find a hidden goal that remains in the same place over many trials, allowing for incremental learning of the place with reference to distal cues surrounding the circular, featureless maze. Although the hippocampus is involved in incremental place learning, rodent studies have shown that the delayed‐matching‐to‐place (DMP) watermaze test is a more sensitive assay of hippocampal function. On the DMP test, the goal location changes every four trials, requiring the rapid updating of place memory. Here, we developed a virtual DMP test reverse‐translated from the rat watermaze DMP paradigm. In two replications, participants showed 1‐trial place learning, evidenced by marked latency and path length savings between Trials 1 and 2 to the same goal location, and by search preference for the vicinity of the goal when Trial 2 was run as probe trial (during which the goal was removed). The performance was remarkably similar to rats' performance on the watermaze DMP test. In both replications, male participants showed greater savings and search preferences compared to female participants. Male participants also showed better mental rotation performance, although mental rotation scores did not consistently correlate with DMP performance measures, pointing to distinct neurocognitive mechanisms. The remarkable similarity between rodent and human DMP performance suggests similar underlying neuro‐psychological mechanisms, including hippocampus dependence. The new virtual DMP test may, therefore, provide a sensitive tool to probe human hippocampal function.
The Temporal Dynamic Relationship Between Attention and Crowding: Electrophysiological Evidence From an Event-Related Potential Study

Visual crowding is the difficulty experienced in identifying a target flanked by other objects within the peripheral visual field. Despite extensive research conducted on this topic, the precise relationship between attention and crowding is still debatable. One perspective suggests that crowding is a bottom-up and pre-attentive process, while another suggests that crowding is top-down and attentional. A third perspective proposes that crowding is a combination of bottom-up and top-down processes. To address this debate, the current study manipulated the attention and distance between targets and flankers, while simultaneously measuring event-related potentials, in human participants. Results indicated that, compared to uncrowded targets, crowded targets elicited more negative frontal N1 and P2 activity and a less negative occipital N1 activity, regardless of whether targets were attended or unattended, and a more positive occipital P2 activity when they were attended. Furthermore, the crowded minus uncrowded difference amplitude was more negative over the frontal region and more positive over the occipital region when the targets were attended, compared to when they were unattended during the N1 and P2 stages. This suggests that crowding, a concept that originates from Gestalt grouping, occurs automatically and can be modulated by attention.
MPEG-DASH SRD based 360 VR Tiled Streaming System for Foveated Rendering

There are three regions when watching 360 VR video on HMD, which are Region of Interest viewer is watching, Non-Region of Interest, and shaded region. It is not efficient that streaming entire 360 VR video having over 8k resolution. In this paper, we extracted Field of View by using HMD Tracking and developed Low-Latency Tiled Streaming System based on MPEG-DASH SRD to improve efficiency of computing process and streaming bandwidth by rendering Region of Interest in high quality tiles and the rest of the regions in low quality tiles.
Engineering ‘designer’ glycomodules for boosting recombinant protein secretion in tobacco hairy root culture and studying hydroxyproline‐O‐glycosylation process in plants

Summary The key technical bottleneck for exploiting plant hairy root cultures as a robust bioproduction platform for therapeutic proteins has been low protein productivity, particularly low secreted protein yields. To address this, we engineered novel hydroxyproline (Hyp)‐O‐glycosylated peptides (HypGPs) into tobacco hairy roots to boost the extracellular secretion of fused proteins and to elucidate Hyp‐O‐glycosylation process of plant cell wall Hyp‐rich glycoproteins. HypGPs representing two major types of cell wall glycoproteins were examined: an extensin module consisting of 18 tandem repeats of ‘Ser‐Hyp‐Hyp‐Hyp‐Hyp’ motif or (SP4)18 and an arabinogalactan protein module consisting of 32 tandem repeats of ‘Ser‐Hyp’ motif or (SP)32. Each module was expressed in tobacco hairy roots as a fusion to the enhanced green fluorescence protein (EGFP). Hairy root cultures engineered with a HypGP module secreted up to 56‐fold greater levels of EGFP, compared with an EGFP control lacking any HypGP module, supporting the function of HypGP modules as a molecular carrier in promoting efficient transport of fused proteins into the culture media. The engineered (SP4)18 and (SP)32 modules underwent Hyp‐O‐glycosylation with arabino‐oligosaccharides and arabinogalactan polysaccharides, respectively, which were essential in facilitating secretion of the fused EGFP protein. Distinct non‐Hyp‐O‐glycosylated (SP4)18‐EGFP and (SP)32‐EGFP intermediates were consistently accumulated within the root tissues, indicating a rate‐limiting trafficking and/or glycosylation of the engineered HypGP modules. An updated model depicting the intracellular trafficking, Hyp‐O‐glycosylation and extracellular secretion of extensin‐styled (SP4)18 module and AGP‐styled (SP)32 module is proposed.
Representation based and Attention augmented Meta learning

Deep learning based computer vision fails to work when labeled images are scarce. Recently, Meta learning algorithm has been confirmed as a promising way to improve the ability of learning from few images for computer vision. However, previous Meta learning approaches expose problems: 1) they ignored the importance of attention mechanism for the Meta learner; 2) they didn't give the Meta learner the ability of well using the past knowledge which can help to express images into high representations, resulting in that the Meta learner has to solve few shot learning task directly from the original high dimensional RGB images. In this paper, we argue that the attention mechanism and the past knowledge are crucial for the Meta learner, and the Meta learner should be trained on high representations of the RGB images instead of directly on the original ones. Based on these arguments, we propose two methods: Attention augmented Meta Learning (AML) and Representation based and Attention augmented Meta Learning(RAML). The method AML aims to improve the Meta learner's attention ability by explicitly embedding an attention model into its network. The method RAML aims to give the Meta learner the ability of leveraging the past learned knowledge to reduce the dimension of the original input data by expressing it into high representations, and help the Meta learner to perform well. Extensive experiments demonstrate the effectiveness of the proposed models, with state-of-the-art few shot learning performances on several few shot learning benchmarks. The source code of our proposed methods will be released soon to facilitate further studies on those aforementioned problem.
Clustering Player Behavioral Data and Improving Performance of Churn Prediction from Mobile Game

Recognizing change of players’ behavior is crucial as video game evolves. Therefore, clustering players’ behavioral data has become an important issue. In this paper, we propose a clustering method from behavior log data. Using the proposed method, we present two experimental results: one is to analyze change of the clustered behavior pattern to in-game event and the other is to use the clustering result as a feature to improve a churn supervised learning model. Experimental results show that the proposed method has applied to analyze influence of in-game event on players’ behavior as well as predict churn for real-world application.
B-Cache: A Behavior-Level Caching Framework for the Programmable Data Plane

By enabling operators to program behaviors of the packet processing pipeline, P4, a domain-specific language, unleashes new opportunities for offloading network functions onto the programmable data plane (PDP) and enhancing network performance. However, recent research shows that as P4 programs and the corresponding packet processing pipeline grow in size and complexity, the performance of the PDP will decrease significantly, which compromises the programmability and flexibility brought by P4. To overcome this performance degradation, we propose B-Cache, a general behavior-level caching framework for both stateful and stateless behaviors on the PDP. The basic idea of B-Cache is to compile packet processing behaviors that were once distributed across multiple tables into one synthetic cache table, thus guarantee the performance on various P4 targets. Our experiment results indicate that B-Cache comparably yields significant performance benefits including a 49% delay decrease and a 200% throughput increase on the software target, and a 60% throughput increase on the hardware target.
Exploring photo sharing to engage intergenerational families on health

Sharing photos is a common practice for family members to inform and connect with each other. Sharing photos inspires recollection, reminiscence, and feelings of connectedness. Could it also be an effective channel for sharing health information and supporting healthy activity? In this paper, we examine how photo-sharing practices can help family members, specifically elderly parents and adult children, share health information. We present preliminary results from an interview study investigating photo-sharing benefits for non-collocated elderly parents and their adult children to encourage healthy lifestyles within the family. We discuss photo sharing as a viable strategy to engage family members in sharing health information.
Exosomes Derived From miR-133b-Modified Mesenchymal Stem Cells Promote Recovery After Spinal Cord Injury

Dysregulation of microRNAs (miRNAs) has been found in injured spinal cords after spinal cord injury (SCI). Previous studies have shown that miR-133b plays an important role in the differentiation of neurons and the outgrowth of neurites. Recently, exosomes have been used as novel biological vehicles to transfer miRNAs locally or systemically, but little is known about the effect of the delivery of exosome-mediated miRNAs on the treatment of SCI. In the present study, we observed that mesenchymal stem cells, the most common cell types known to produce exosomes, could package miR-133b into secreted exosomes. After SCI, tail vein injection of miR-133b exosomes into rats significantly improved the recovery of hindlimb function when compared to control groups. Additionally, treatment with miR-133b exosomes reduced the volume of the lesion, preserved neuronal cells, and promoted the regeneration of axons after SCI. We next observed that the expression of RhoA, a direct target of miR-133b, was decreased in the miR-133b exosome group. Moreover, we showed that miR-133b exosomes activated ERK1/2, STAT3, and CREB, which are signaling pathway proteins involved in the survival of neurons and the regeneration of axons. In summary, these findings demonstrated that systemically injecting miR-133b exosomes preserved neurons, promoted the regeneration of axons, and improved the recovery of hindlimb locomotor function following SCI, suggesting that the transfer of exosome-mediated miRNAs represents a novel therapeutic approach for the treatment of SCI.
Point-of-Care Compatibility of Ultra-Sensitive Detection Techniques for the Cardiac Biomarker Troponin I—Challenges and Potential Value

Cardiac biomarkers are frequently measured to provide guidance on the well-being of a patient in relation to cardiac health with many assays having been developed and widely utilised in clinical assessment. Effectively treating and managing cardiovascular disease (CVD) relies on swiftly responding to signs of cardiac symptoms, thus providing a basis for enhanced patient management and an overall better health outcome. Ultra-sensitive cardiac biomarker detection techniques play a pivotal role in improving the diagnostic capacity of an assay and thus enabling a better-informed decision. However, currently, the typical approach taken within healthcare depends on centralised laboratories performing analysis of cardiac biomarkers, thus restricting the roll-out of rapid diagnostics. Point-of-care testing (POCT) involves conducting the diagnostic test in the presence of the patient, with a short turnaround time, requiring small sample volumes without compromising the sensitivity of the assay. This technology is ideal for combatting CVD, thus the formulation of ultra-sensitive assays and the design of biosensors will be critically evaluated, focusing on the feasibility of these techniques for point-of-care (POC) integration. Moreover, there are several key factors, which in combination, contribute to the development of ultra-sensitive techniques, namely the incorporation of nanomaterials for sensitivity enhancement and manipulation of labelling methods. This review will explore the latest developments in cardiac biomarker detection, primarily focusing on the detection of cardiac troponin I (cTnI). Highly sensitive detection of cTnI is of paramount importance regarding the rapid rule-in/rule-out of acute myocardial infarction (AMI). Thus the challenges encountered during cTnI measurements are outlined in detail to assist in demonstrating the drawbacks of current commercial assays and the obstructions to standardisation. Furthermore, the added benefits of introducing multi-biomarker panels are reviewed, several key biomarkers are evaluated and the analytical benefits provided by multimarkers-based methods are highlighted.
Evaluation of Food Insecurity in Adults and Children With Cystic Fibrosis: Community Case Study

Advances in the care and treatment of cystic fibrosis (CF) have led to improved mortality rates; therefore, considerably more individuals with CF are living into adulthood. With an increased number of CF patients advancing into adulthood, there is the need for more research that surrounds the aging adult CF patient. It is important to conduct research and collect results on the aging CF population to help better prepare the CF patient, who is dealing with the heavy treatment and financial burden of their disease, build autonomy and increase their quality of life. Of note, research has found that social, behavioral, and physical factors influence the ability of those with CF to follow dietary recommendations. A primary treatment goal in CF is a high calorie, high protein, and high fat diet. A socio-economic factor that has not been adequately investigated with regards to dietary compliance of individuals with CF is food insecurity. The aim of this community case study was to document the experiences and estimate the prevalence of food insecurity among CF patients residing in Idaho. The correlation between food insecurity and health outcomes (lung function and body mass index) was also examined. Participants included adult patients and parents of pediatric patients with CF. Food insecurity rates among CF patients of all ages were found to be significantly higher than that seen in the overall community; however, no specific correlation between food insecurity and body mass index (BMI) or lung function emerged. This case study highlights the need for continued research around food access issues in this patient population. The data resulting from this study shows the value of CF advocacy organizations promoting efforts to build resources and provide education around food insecurity issues.
How to Hold Your Phone When Tapping: A Comparative Study of Performance, Precision, and Errors

We argue that future mobile interfaces should differentiate between various contextual factors like grip and active fingers, adjusting screen elements and behaviors automatically, thus moving from merely responsive design to responsive interaction. Toward this end we conducted a systematic study of screen taps on a mobile device to find out how the way you hold your device impacts performance, precision, and error rate. In our study, we compared three commonly used grips and found that the popular one-handed grip, tapping with the thumb, yields the worst performance. The two-handed grip, tapping with the index finger, is the most precise and least error-prone method, especially in the upper and left halves of the screen. In landscape orientation (two-handed, tapping with both thumbs) we found the best overall performance with a drop in performance in the middle of the screen. Additionally, we found differentiated trade-off relationships and directional effects. From our findings we derive design recommendations for interface designers and give an example how to make interactions truly responsive to the context-of-use.
CNN based Indoor Localization using RSS Time-Series

Indoor localization of mobile nodes is receiving great interest due to the recent advances in mobile devices and the increasing number of location-based services. Fingerprinting based on Wifi received signal strength (RSS) is widely used for indoor localization due to its simplicity and low hardware requirements. However, its positioning accuracy is significantly affected by random fluctuations of RSS values caused by fading and multi-path phenomena. This paper presents a convolutional neural network (CNN) based approach for indoor localization using RSS time-series from wireless local area network (WLAN) access points. Applying CNN on a time-series of RSS readings is expected to reduce the noise and randomness present in separate RSS values and hence improve the localization accuracy. The proposed model is implemented and evaluated on a multi-building and multi-floor dataset, UJIIndoorLoc dataset. The proposed approach provides 100% accuracy for building prediction, 100% accuracy for floor prediction and the mean error in coordinates estimation is 2.77 m.
Molecular Mechanism for the Regulation of ABA Homeostasis During Plant Development and Stress Responses

The plant hormone abscisic acid (ABA) play essential roles in numerous physiological processes such as seed dormancy, seed germination, seeding growth and responses to biotic and abiotic stresses. Such biological processes are tightly controlled by a complicated regulatory network including ABA homoeostasis, signal transduction as well as cross-talking among other signaling pathways. It is known that ABA homoeostasis modulated by its production, inactivation, and transport pathways is considered to be of great importance for plant development and stress responses. Most of the enzymes and transporters involved in ABA homoeostasis have been largely characterized and they all work synergistically to maintain ABA level in plants. Increasing evidence have suggested that transcriptional regulation of the genes involved in either ABA production or ABA inactivation plays vital roles in ABA homoeostasis. In addition to transcription factors, such progress is also regulated by microRNAs and newly characterized root to shoot mobile peptide-receptor like kinase (RLKs) mediated long-distance signal transduction. Thus, ABA contents are always kept in a dynamic balance. In this review, we survey recent research on ABA production, inactivation and transport pathways, and summarize some latest findings about the mechanisms that regulate ABA homoeostasis.
An Initial Investigation of Unmanned Aircraft Systems (UAS) and Real-Time Organ Status Measurement for Transporting Human Organs

Organ transportation has yet to be substantially innovated. If organs could be moved by drone, instead of ill-timed commercial aircraft or expensive charter flights, lifesaving organs could be transplanted more quickly. A modified, six-rotor UAS was used to model situations relevant to organ transportation. To monitor the organ, we developed novel technologies that provided the real-time organ status using a wireless biosensor combined with an organ global positioning system. Fourteen drone organ missions were performed. Temperatures remained stable and low (2.5 °C). Pressure changes (0.37–0.86 kPa) correlated with increased altitude. Drone travel was associated with less vibration (<0.5 G) than was observed with fixed-wing flight (>2.0 G). Peak velocity was 67.6 km/h (42 m/h). Biopsies of the kidney taken prior to and after organ shipment revealed no damage resulting from drone travel. The longest flight was 3.0 miles, modeling an organ flight between two inner city hospitals. Organ transportation may be an ideal use-case for drones. With the development of faster, larger drones, long-distance drone organ shipment may result in substantially reduced cold ischemia times, subsequently improved organ quality, and thousands of lives saved.
Interweaving Physical Artifacts with Visualization on Digital Media in Museums

Museums and libraries store a huge number of data related to their exhibition objects. Often, these data cannot be presented in exhibitions because of limited space or limited time during guided tours. Interweaving these physical artifacts with data visualization on digital media can help to make these data visible and searchable for visitors independently. Concepts and best practices for such situated visualizations are still rare. Our approach is to utilize the visitor's own device to enhance the museum visit through interactive data visualization. Thus, the visitor gets the ability to interactively gain more information on topics of interest or can link different topics.