Miracle-dz/UniFAR-FTUs huggingface dataset:
How Does Embodying a Transgender Narrative Influence Social Bias? An Explorative Study in an Artistic Context

Virtual reality (VR) protocols inducing illusory embodiment of avatars have shown a positive impact on participants’ perception of outgroup members, in line with the idea that the simulation of another’s sensorimotor states might underlie prosocial behavior. These studies, however, have been mostly confined to laboratory settings with student populations and the use of artificial avatars. In an interdisciplinary effort benefiting from the heterogeneous sample within a museum, we aimed at quantifying changes in interpersonal perception induced by embodying a transgender man narrating his life. We compared an artistic methodology mixing VR and elaborate sensorimotor stimulation to a more conventional primarily audiovisual VR experience. We tested how these affect embodiment and the perception of transgender men as measured by a brief implicit association test and a questionnaire. Neither significant difference in embodiment nor changes in implicit or explicit bias was found, the latter potentially due to the initially low bias in the group. We further assessed participants’ illusory embodiment as a function of age, finding a negative correlation between these. The results are discussed with respect to current theories of embodiment, differences between laboratory and real-life settings, and the intersection of art and science.
Identifying a Comprehensive ceRNA Network to Reveal Novel Targets for the Pathogenesis of Parkinson's Disease

Parkinson's disease (PD) is the second commonest progressive neurodegenerative disease worldwide. Increasing evidence reveals that non-coding RNAs play roles in the pathophysiological process of PD. The notion called competing endogenous RNAs (ceRNAs) network is used to describe the roles of non-coding RNAs. According to this theory, long non-coding RNAs (lncRNAs) act as microRNAs (miRNAs) sponges by miRNA response elements or miRNA binding sites to control the availability of endogenous miRNA for binding to their target mRNAs. This study aimed to construct a ceRNA network in PD, which might have the potential to clarify the pathogenesis of PD. We investigated differential expression (DE) lncRNAs and mRNAs in substantia nigra array data GSE7621 between PD patients and healthy controls from the Gene Expression Omnibus database. And we used starBase 2.0 and miRWalk 2.0 databases to predict miRNAs that have interactions with DElncRNAs and DEmRNAs. Based on DElncRNAs, DEmRNAs and predicted miRNAs, two ceRNA networks were constructed. The first one was based on lncRNA-miRNA interactions and miRNA-mRNA interactions that shared the same miRNAs that we predicted, on which function annotation and PPI analysis were performed to identify hub genes. Hereby the second ceRNA network was generated to explore the core section in the first ceRNA network and was validated in external datasets. As a result, we identified 31 DE lncRNAs and 1,828 DEmRNAs, and finally constructed the first ceRNA network associated with PD, including 9 lncRNAs, 18 miRNAs, and 185 mRNAs. mRNAs in the first ceRNA network focused on autophagy, DNA repair and vesicle transport, which were critical pathological processes in PD. Nineteen hub genes in the first ceRNA network identified through PPI analysis, the second ceRNA network was constructed to annotate the core part of the first one. Moreover, the core subnetwork was validated in external datasets, of which several nodes including FBXL7, PTBP2, and lncRNA NEAT1 were verified. In conclusion, a ceRNA network was constructed based on the differential expression profiles of whole substantia nigra tissues of normal and PD patients, and the network was subsequently identified which revealed its association with autophagy, DNA repair and vesicle transport. The core subnetwork of the ceRNA network was identified and validated in external data. Our findings offered novel insights into the roles of ceRNAs in the pathogenesis of PD and provided promising diagnostic biomarkers.
The Association Between Substrate Variability and Bryophyte and Lichen Diversity in Eastern Australian Forests

Abstract The difference in species diversity on different substrates and the effect of both substrate (ground, logs, rocks, trees, and fallen branches) and site variation (such as disturbance history, aspect, slope) on bryophyte and lichen diversity were investigated in eastern Australian forests. Thirty-five sites in forest types ranging from dry sclerophyll to riparian were surveyed using a 50 m × 20 m area. Despite strong differences between the different substrates in species richness and composition for both bryophytes and lichens, differences within each substrate were limited. On examining each substrate separately, we found that variation in substrate quality, such as degree of log decay, was not strongly correlated with species diversity, explaining no more than 16% of richness and 5% of composition. Despite both bryophytes and lichens showing high fidelity for particular substrates, the quality of that substrate was not an important factor in determining species diversity in this study. Site environmental variables explained larger variation in both bryophyte and lichen species diversity, in particular species richness, with individual site variables explaining up to 41% of richness (topographic position) and 6% of composition (time since fire). For some site variables, notably some of the disturbance variables, there were no overall trends, but significant results for particular substrates, such as logging being significant only on logs and trees.
Mitigating the Impacts of False Data Injection Attacks in Smart Grids using Deep Convolutional Neural Networks

The smart grid is vulnerable to cyberattacks due to the integration of information and communication technologies (ICT). The false data injection attack (FDIA) is a type of cyberattack that is against the state estimation of the power grid. It is imperative to mitigate the impacts of such a stealthy attack. In this paper, a deep convolutional neural network scheme was proposed. It has been evaluated on the IEEE 39-bus system using real-world load data and performs better than existed approaches.
The INALT family – a set of high-resolution nests for the Agulhas Current system within global NEMO ocean/sea-ice configurations

Abstract. The Agulhas Current, the western boundary current of the South Indian Ocean, has been shown to play an important role in the connectivity between the Indian and Atlantic oceans. The greater Agulhas Current system is highly dominated by mesoscale dynamics. To investigate their influence on the regional and global circulations, a family of high-resolution ocean general circulation model configurations based on the NEMO code has been developed. Horizontal resolution refinement is achieved by embedding “nests” covering the South Atlantic and the western Indian oceans at 1/10∘ (INALT10) and 1/20∘ (INALT20) within global hosts with coarser resolutions. Nests and hosts are connected through two-way interaction, allowing the nests not only to receive boundary conditions from their respective host but also to feed back the impact of regional dynamics onto the global ocean. A double-nested configuration at 1/60∘ resolution (INALT60) has been developed to gain insights into submesoscale processes within the Agulhas Current system. Large-scale measures such as the Drake Passage transport and the strength of the Atlantic meridional overturning circulation are rather robust among the different configurations, indicating the important role of the hosts in providing a consistent embedment of the regionally refined grids into the global circulation. The dynamics of the Agulhas Current system strongly depend on the representation of mesoscale processes. Both the southward-flowing Agulhas Current and the northward-flowing Agulhas Undercurrent increase in strength with increasing resolution towards more realistic values, which suggests the importance of improving mesoscale dynamics as well as bathymetric slopes along this narrow western boundary current regime. The exploration of numerical choices such as lateral boundary conditions and details of the implementation of surface wind stress forcing demonstrates the range of solutions within any given configuration.
Eco-friendly Caching and Forwarding in Named Data Networking

Green networking, by making the network more energy efficient and helping reduce environmental impact, is receiving more and more attraction for sustainable ICT. In this paper, we propose a new green approach for Named Data Networking (NDN) where content requests and caching perform towards green content delivery. We design a forwarding and caching strategy, where we first define the greenness of nodes, a quantitative metric for measuring the environmental footprint of the network, based on which we identify corresponding green paths and encourage traffic to aggregate on green paths powered by more eco-friendly renewable energy. We validate our approach with a variety of simulations using real network topology and renewable energy datasets from the US, and the results show that applying the proposed green NDN achieves significant ecofriendly gains.
Cardioncological Approach for Trastuzumab Therapy in Breast Cancer Patients With Cardiotoxicity: Impact on Adherence and Clinical Outcome

Background Treatment with Trastuzumab is associated with cardiotoxicity. If Trastuzumab could be administered in a safe manner to patients who develop a reduced left ventricular ejection fraction (EF) of < 50% remains poorly understood. Objective To evaluate the impact of a cardioncological approach in terms of adherence and continuation of oncological therapy with Trastuzumab. Methods and Results Internal databases of candidates for trastuzumab chemotherapy with evidence of cardiotoxicity according to echocardiographic criteria were retrospectively evaluated. Eighty-four female patients (age 51.7 years, 95% CI 49.5–53.8), were finally included. Patients were divided to receive a standard (n 27) or cardioncological (n 57) scheme. Baseline EF values were within normal limits (60.9, 95% CI 60 - 61.9%; p=0.5 between groups). The nadir of EF observed during trastuzumab therapy was more pronounced in the standard care group (40.6, 95% CI 37.3–43.9% vs. 46.3, 95% CI 44.3–48.3%; p=0.002). At re-challenge, after cardiotoxicity detection, all patients in the cardioncological arm resumed and completed trastuzumab therapy (p<0.0001). An overall reduction of EF was observed at the final evaluation (p <0.0001 vs. baseline). Cardioncological approach was the only independent determinant of ΔEF from baseline to final evaluation (R20.12; p=0.004). We observed a total of 13 (15%) HF events, seven (26%) in the standard, and six (10%) in the cardioncological approach group (p =0.1). Patients in the cardioncological approach arm had a better outcome (Log Rank Chi-squared 4.89; p=0.02). Conclusions A targeted cardioncological approach, in patients with evidence of cardiotoxicity during HER-2 inhibitor therapy, could favorably influence the oncological management of breast cancer patients, reducing the adverse cardiovascular impact of chemotherapy.
A Cross-block Connection Network for Retinal Vessel Segmentation

The appearance and structure of retinal vessels is one of the diagnostic criterias for ophthalmic, cardiovascular and cerebrovascular diseases, which makes the segmentation of retinal vessels worthwhile in clinical medicine. Retinal images have low contrast, complex tissue structures, and diverse pathological conditions. Due to the difficulty to accurately and effectively identify these information, existing segmentation methods often have neglection on subtle vessels and have blurry segmentation on overlap area. This paper proposed a new vascular segmentation method based on deep learning. Considering the necessity of feature extraction from multi-aspect and multi-level, a cross-block dense connection was proposed to improve the feature identify and extract ability. It use additional mapping to connect and share features between different blocks of the network. Further, the residual connection is used inside the block to solve the degradation problem. Powerful extraction capability enables the network to obtain ideal segmentation results with less parameters, which immensely reduces the computing resource consumption of the network. Extensive experiments demonstrate that our approach achieves better performance based on multiple criteria with a much faster speed.
Remote photoplethysmography combining color channels with SNR maximization for respiratory rate assessment

Monitoring of breathing rate considerably improves healthcare quality by providing information about the patient’s state. Yet, the current methods used to assess the respiratory rate have some drawbacks that could be improved. Recent developments of the non-contact based measurement of the respiratory rate brought the remote Photoplethysmography (rPPG) technique. This technique allows physiological parameters monitoring with a camera and ambient light. It measures the light absorption variation by the blood to extract several physiological parameters. The main issue with the rPPG method is that its signal quality is lower compared with the contact-based methods. Several ways to improve this quality may be explored to obtain a robust rPPG pipeline. One of these is the combination of the multidimensional temporal traces generated by a camera into a single temporal trace. The literature methods aimed to extract a signal with the maximal information using smart estimation of the combination coefficients. These coefficients are computed using statistical or physiological/optical properties of the traces. In this paper, we developed an algorithm that estimates a better respiratory signal than with regular methods. The algorithm named Energy Variance Maximization (EVM) estimates the linear combination that maximizes the Signal Noise Ratio (SNR) of the output trace. We compare our contribution with three state-of-the-art method: CHROM, PBV and PVM. The results obtained with this method are better than the state of the art methods, with the Mean Absolute Error (MAE) being 0.87 rpm better than CHROM, the best of compared literature methods.
Model of Misalignment Tolerant Inductive Power Transfer System for EV Charging

This paper presents a study on the behaviour of the currents in a 1-D resonator array of inductive power transfer (IPT) systems that can be used for electric vehicle (EV) charging. The resonator array is used to ease misalignment issues between the receiver and the transmitter and to improve the transmission distance. Besides, IPT systems with resonator arrays may offer the possibility of dynamic charging. The analysis is performed assuming a generic position of the receiver over a 1-D array. A theoretical model - tolerant to the misalignment of the receiver - has been developed and experimentally verified. The model is used to determine the currents in the system. The obtained results highlight significant stresses on the circuit components that must be considered in the design stage of the apparatus, especially when a large power is transferred, such as in the case of EV charging.
Hyper-Activated Brain Resting-State Network and Mismatch Negativity Deficit in Schizophrenia With Auditory Verbal Hallucination Revealed by an Event-Related Potential Evidence

Schizophrenia is a holergasia with unclear mechanism and high heterogeneity. Auditory verbal hallucination (AVH) study might help in understanding schizophrenia from the perspective of individual symptoms. This study aimed to investigate the activities of the resting-state networks (RSN) in the electroencephalogram (EEG) and mismatch negativity (MMN) in task-related state of schizophrenia patients with AVH. We recruited 30 schizophrenia patients without any medication for more than 4 weeks (15 AVH patients and 15 Non-AVH patients) and 15 healthy controls. We recorded the EEG data of the participants in the resting-state for 7 min and the event-related potential (ERP) data under an auditory oddball paradigm. In the resting-state EEG network, AVH patients exhibited a higher clustering coefficient than Non-AVH patients and healthy controls on delta and beta bands and a shorter characteristic path length than Non-AVH patients and healthy controls on all frequency bands. For ERP data, AVH patients showed a lower MMN amplitude than healthy controls (p = 0.017) and Non-AVH patients (p = 0.033). What’s more, MMN amplitude was positively correlated with clustering coefficient, and negatively correlated with characteristic path length on delta, theta, beta and gamma band in AVH patients. Our results indicate that AVH patients showed a hyper-activity in resting-state and may have impaired higher-order auditory expectations in the task-related state than healthy controls and Non-AVH patients. And it seems reasonable to conclude that the formation of AVH may occupy certain brain resources and compete for brain resources with external auditory stimuli.
Estradiol Replacement at the Critical Period Protects Hippocampal Neural Stem Cells to Improve Cognition in APP/PS1 Mice

It has been suggested that there is a critical window for estrogen replacement therapy (ERT) in postmenopausal women with Alzheimer’s disease (AD); however, supporting evidence is lacking. To address this issue, we investigated the effective period for estradiol (E2) treatment using a mouse model of AD. Four-month-old female APPswe/PSEN1dE9 (APP/PS1) mice were ovariectomized (OVX) and treated with E2 for 2 months starting at the age of 4 months (early period), 6 months (mid-period), or 8 months (late period). We then evaluated hippocampal neurogenesis, β-amyloid (Aβ) accumulation, telomerase activity, and hippocampal-dependent behavior. Compared to age-matched wild type mice, APP/PS1 mice with intact ovaries showed increased proliferation of hippocampal neural stem cells (NSCs) at 8 months of age and decreased proliferation of NSCs at 10 months of age; meanwhile, Aβ accumulation progressively increased with age, paralleling the reduced survival of immature neurons. OVX-induced depletion of E2 in APP/PS1 mice resulted in elevated Aβ levels accompanied by elevated p75 neurotrophin receptor (p75NTR) expression and increased NSC proliferation at 6 months of age, which subsequently declined; accelerated reduction of immature neurons starting from 6 months of age, and reduced telomerase activity and worsened memory performance at 10 months of age. Treatment with E2 in the early period post-OVX, rather than in the mid or late period, abrogated these effects, and p75NTR inhibition reduced the overproliferation of NSCs in 6-month-old OVX-APP/PS1 mice. Thus, E2 deficiency in young APP/PS1 mice exacerbates cognitive deficits and depletes the hippocampal NSC pool in later life; this can be alleviated by E2 treatment in the early period following OVX, which prevents Aβ/p75NTR-induced NSC overproliferation and preserves telomerase activity.
Indium Doping of Lead-Free Perovskite Cs2SnI6

Structure and properties of an inorganic perovskite Cs2SnI6 demonstrated its potential as a light-harvester or electron-hole transport material; however, its optoelectronic properties are poorer than those of lead-based perovskites. Here, we report the way of light tuning of absorption and transport properties of cesium iodostannate(IV) Cs2SnI6 via partial heterovalent substitution of tin for indium. Light absorption and optical bandgaps of materials have been investigated by UV-vis absorption and photoluminescent spectroscopies. Low-temperature electron paramagnetic resonance spectroscopy was used to study the kind of paramagnetic centers in materials.
The Method Of Calibration Compensation For Fiber Nonlinearity- A Review

Digital signal processing for compensation for fibre nonlinearity Is a key enabler of ever-greater demand higher data rates for unified optical transmissions. A big challenge to the existing techniques that is the nonlinear coefficient of fiber Must be properly scale-up during compensation In order to attain the achieveable improvement in signal quality. In this paper, we have reviewed several papers in Fiber Nonlinear using several algorithms as well as processing the signal transmitting This problem is adaptively optimized using a low-complexity algorithm A soft-decision, bitwise demodulator based metric used for modern applications Decoders from the FEC. The study demonstrates an optimal scaling factor analysis with predictable accuracy.
Being in the Past and Perform the Future in a Virtual World: VR Applications to Assess and Enhance Episodic and Prospective Memory in Normal and Pathological Aging

The process of aging commonly features a gradual deterioration in cognitive performance and, in particular, the decline of memory. Despite the increased longevity of the world’s population, the prevalence of neurodegenerative conditions, such as dementia, continues to be a major burden on public health, and consequently, the latest research has been focused on memory and aging. Currently, the failure of episodic and Prospective memory (PM) is one of the main complaints in the elderly, considered among the early symptoms of dementia. It is therefore increasingly important to define more clearly the boundaries between normal and pathological aging. Recently, researchers have begun to build and apply Virtual Environments (VE) to the explicit purpose of better understanding the performance of episodic and PM in complex and realistic contexts, with the perspective of further developing effective training procedures that depend on reliable cognitive assessment methods. Virtual technology offers higher levels of realism than “pen and paper” testing and at the same time more experimental control than naturalistic settings. In this mini-review article, we examine the outcomes of recently available studies on virtual reality technology applications developed for the assessment and improvement of episodic and/or PM. To consider the latest technology, we selected 29 articles that have been published in the last 10 years. These documents show that VR-based technologies can provide a valid basis for screening and treatment and, through increased sensory stimulation and enriched environments reproducing the scenarios of everyday life, could represent effective stimulating experiences even in pathological aging.
Parameter Self-Configuration and Self-Adaptation in Industrial Wireless Sensor-Actuator Networks

Wireless Sensor-Actuator Network (WSAN) technology is gaining rapid adoption in process industries in recent years. A WSAN typically connects sensors, actuators, and controllers in industrial facilities, such as steel mills, oil refineries, chemical plants, and infrastructures implementing complex monitoring and control processes. IEEE 802.15. 4 based WSANs operate at low-power and can be manufactured inexpensively, which make them ideal where battery lifetime and costs are important. Recent studies have shown that the selection of network parameters has a significant effect on the network performance. However, the current practice of parameter selection is largely based on experience and rules of thumb involving a coarse-grained analysis of expected network load and dynamics or measurements during a few field trials, resulting in non-optimal decisions in many cases. In this work, we develop the Parameter Selection and Adaptation FramEwork (P-SAFE) that optimally configures the network parameters based on the application Quality of Service (QoS) demand and adapts the configuration at runtime to consistently satisfy the dynamic requirements. We implement P-SAFE and evaluate it on three physical testbeds. Experimental results show our solution can significantly better meet the application QoS demand compared to the state of the art.