Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Social and Emotional Representations Used by the Elderly to Deal With the COVID-19 Pandemic

Spain has become one of the European epicenters of coronavirus (COVID-19), a virus that particularly affects the elderly, since this group accounts for the majority of hospitalized cases and has the highest mortality rates. Therefore, the aim of this research is to understand how elderly people represent and emotionally cope with COVID-19 during the days when the pandemic emerged in Spain. Using a qualitative methodology, a free association exercise elicited by the word “COVID-19” was completed by 115 participants (age range: 60–85 years) from the North of Spain. Lexical analysis was used to analyze the content. The results revealed that the government and the mass media are criticized for failing to communicate a clear message, and for giving out information that is both insufficient and contradictory. However, participants are clear that it is essential to follow the guidelines of the scientists and doctors, which are represented as credible sources. However, when the state of alarm and the lockdown of all citizens was declared, most of the participants represented the risk as being associated with the elderly and the pandemic became something that might also affect their families. Due to these circumstances, negative emotions appear such as fear, nervousness, uncertainty, restlessness, and insecurity. Feelings of solitude and loneliness also emerged, and these are represented as being linked to death. These results indicate the need for governments to manage the current situation with the elderly by placing greater emphasis on social and inclusive policies to help alleviate the possible effects of the pandemic and the lockdown.
Neuroprotective derivatives of tacrine that target NMDA receptor and acetyl cholinesterase - Design, synthesis and biological evaluation

The complex and multifactorial nature of neuropsychiatric diseases demands multi-target drugs that can intervene with various sub-pathologies underlying disease progression. Targeting the impairments in cholinergic and glutamatergic neurotransmissions with small molecules has been suggested as one of the potential disease-modifying approaches for Alzheimer’s disease (AD). Tacrine, apotent inhibitor of acetylcholinesterase (AChE) is the first FDA approved drug for the treatment of AD. Tacrine is also a low affinity antagonist of N-methyl-D-aspartate receptor (NMDAR). However, tacrine was withdrawn from its clinical use later due to its hepato-toxicity. With an aim to develop novel high affinity multi-target directed ligands (MTDLs) against AChE and NMDAR, with reduced hepatotoxicity, we performed in silico structure-based modifications on tacrine, chemical synthesis of the derivatives and in vitro validation of their activities. Nineteen such derivatives showed inhibition with IC50 values in the range of 18.53±2.09 to 184.09±19.23 nM against AChE and 0.27±0.05 to 38.84±9.64 μM against NMDAR. Some of the selected compounds also protected rat primary cortical neurons from glutamate induced excitotoxicity. Two of the tacrine derived MTDLs, 201 and 208 exhibited in vivo efficacy in rats by protecting against behavioral impairment induced by administration of the excitotoxic agent, monosodium glutamate. Additionally, several of these synthesized compounds also exhibited promising inhibitory activities against butyrylcholinesterase and β-secretase. Given the therapeutic potential of MTDLs in disease-modifying therapy, our studies revealed several promising MTDLs of which 201 appears to be a potential candidate for immediate preclinical and clinical evaluations.
Global analysis of cell behavior and protein localization dynamics reveals region-specific functions for Shroom3 and N-cadherin during neural tube closure

Failures of neural tube closure are common and serious birth defects, yet we have a poor understanding of the interaction of genetics and cell biology during neural tube closure. Additionally, mutations that cause neural tube defects (NTDs) tend to affect anterior or posterior regions of the neural tube but rarely both, indicating a regional specificity to NTD genetics. To better understand the regional specificity of cell behaviors during neural tube closure, we analyzed the dynamic localization of actin and N-cadherin via high-resolution tissue-level time-lapse microscopy during Xenopus neural tube closure. To investigate the regionality of gene function, we generated mosaic mutations in shroom3, a key regulator or neural tube closure This approach elucidates new differences between cell behaviors during cranial/anterior and spinal/posterior neural tube closure, provides mechanistic insight into the function of shroom3 and demonstrates the ability of tissue-level imaging and analysis to generate cell-biological mechanistic insights into neural tube closure.
Microglial NKCC1 shapes microglial phenotype, cerebral inflammatory responses and brain injury

1 Momentum Laboratory of Neuroimmunology, Institute of Experimental Medicine, Budapest, H-1083 Hungary 2 János Szentágothai Doctoral School of Neuroscience, Semmelweis University, Budapest, H-1083 Hungary 3 Department of Physiology, Faculty of Medicine, University of Debrecen, Debrecen, H-4012 Hungary 4 Doctoral School of Molecular Cellular and Immune Biology, Faculty of Medicine, University of Debrecen, Debrecen, H-4012 Hungary
United by Hope, Divided by Access: Country Mapping of COVID-19 Information Accessibility and Its Consequences on Pandemic Eradication

Many government websites and mobile content are inaccessible for people with vision, hearing, cognitive, and motor impairments. The COVID-19 pandemic highlighted these disparities when health authority website information, critical in providing resources for curbing the spread of the virus, remained inaccessible for numerous disabled populations. The Web Content Accessibility Guidelines provide comparatively universally accepted guidelines for website accessibility. We utilized these parameters to examine the number of countries with or without accessible health authority websites. The resulting data indicate a dearth of countries with websites accessible for persons with disabilities. Methods of information dissemination must take into consideration individuals with disabilities, particularly in times of global health crises.
High-Sensitivity Refractive Index Sensor Based on a Cascaded Core-Offset and Macrobending Single-Mode Fiber Interferometer

A high-sensitivity Mach–Zehnder interferometer (MZI) based on the cascaded core-offset and macrobending fiber structure is proposed for refractive index (RI) measurement. The core-offset structure makes the fiber core mode couple to the cladding modes, and some of them recouple back to the fiber core at the macrobending structure forming a model interference effect. The liquid RI can be measured by monitoring the spectral shift of the modal interference. The RI sensing performances for the interferometers with different macrobending radii and core offsets are investigated experimentally. Experimental results show that when the core offset is 2 μm and the macrobending radius is 5.5 mm, the sensitivity can reach 699.95 nm/RIU for the RI of 1.43. The temperature dependence for the proposed sensor is also tested, and a temperature sensitivity of 0.112 nm/°C is obtained.
Optimising expression quantitative trait locus mapping workflows for single-cell studies

Single-cell RNA-sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states, and promises to improve our understanding of genetic regulation across tissues in both health and disease. While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimise sc-eQTL mapping. Here, we evaluate the role of different normalisation and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches and provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.
Upregulation of eIF4E, but not other translation initiation factors, in dendritic spines during memory consolidation

Local translation can provide a rapid, spatially targeted supply of new proteins in distal dendrites to support synaptic changes that underlie learning. Learning and memory are especially sensitive to manipulations of translational control mechanisms, particularly those that target the initiation step, and translation initiation at synapses could be a means of maintaining synapse specificity during plasticity. Initiation predominantly occurs via recruitment of ribosomes to the 5’ mRNA cap by complexes of eukaryotic initiation factors (eIFs), and the interaction between eIF4E and eIF4G1 is a particularly important target of translational control pathways. Pharmacological inhibition of eIF4E-eIF4G1 binding impairs consolidation of memory for aversive Pavlovian conditioning as well as the accompanying increase in polyribosomes in the heads of dendritic spines in the lateral amygdala (LA). This is consistent with a role for initiation at synapses in memory formation, but whether eIFs are even present near synapses is unknown. To determine whether dendritic spines contain eIFs and whether eIF distribution is affected by learning, we combined immunolabeling with serial section transmission electron microscopy (ssTEM) volume reconstructions of LA dendrites after Pavlovian conditioning. Labeling for eIF4E, eIF4G1, and eIF2α – another key target of regulation – occurred in roughly half of dendritic spines, but learning effects were only found for eIF4E, which was upregulated in the heads of dendritic spines. Our results support the possibility of regulated translation initiation as a means of synapse-specific protein targeting during learning and are consistent with the model of eIF4E availability as a central point of control.
Topological analysis of single-cell data reveals shared glial landscape of macular degeneration and neurodegenerative diseases

A novel topological machine learning approach applied to single-nucleus RNA sequencing from human retinas with age-related macular degeneration identifies interacting disease phase-specific glial activation states shared with Alzheimer’s disease and multiple sclerosis. 2 Abstract Neurodegeneration occurs in a wide range of diseases, including age-related macular degeneration (AMD), Alzheimer’s disease (AD), and multiple sclerosis (MS), each with distinct inciting events. To determine whether glial transcriptional states are shared across phases of degeneration, we sequenced 50,498 nuclei from the retinas of seven AMD patients and six healthy controls, generating the first single-cell transcriptomic atlas of AMD. We identified groupings of cells implicated in disease pathogenesis by applying a novel topologically-inspired machine learning approach called ‘diffusion condensation. ’ By calculating diffusion homology features and performing persistence analysis, diffusion condensation identified activated glial states enriched in the early phases of AMD, AD, and MS as well as an AMD-specific proangiogenic astrocyte state promoting pathogenic neovascularization in advanced AMD. Finally, by mapping the expression of disease-associated genes to glial states, we identified key signaling interactions creating hypotheses for therapeutic intervention. Our topological analysis identified an integrated disease-phase specific glial landscape that is shared across neurodegenerative conditions affecting the central nervous system.
Simulating lesion-dependent functional recovery mechanisms

Functional recovery after brain damage varies widely and depends on many factors, including lesion site and extent. When a neuronal system is damaged, recovery may occur by engaging residual (e.g., perilesional) components. When damage is extensive, recovery depends on the availability of other intact neural structures that can reproduce the same functional output (i.e., degeneracy). A system’s response to damage may occur rapidly, require learning or both. Here, we simulate functional recovery from four different types of lesions, using a generative model of word repetition that comprised a default premorbid system and a less used alternative system. The synthetic lesions (i) completely disengaged the premorbid system, leaving the alternative system intact, (ii) partially damaged both premorbid and alternative systems, and (iii) limited the experience-dependent plasticity of both. The results, across 1000 trials, demonstrate that (i) a complete disconnection of the premorbid system naturally invoked the engagement of the other, (ii) incomplete damage to both systems had a much more devastating long-term effect on model performance and (iii) the effect of reducing learning capacity within each system. These findings contribute to formal frameworks for interpreting the effect of different types of lesions.
A Mechatronic Lock-In Amplifier: Integrating Demodulation in Sensor Electronics for Measuring Mechanical Oscillations

The demodulation of mechanical oscillations is essential for the operation of micro- and nanometer-scale transducer systems, such as resonant MEMS mirrors and cantilever-based sensors. The use of external demodulators, such as lock-in amplifiers and spectrum analyzers is not always desired due to their large size, complexity, and cost. The method presented in this article enables simplified demodulation of the oscillation of mechanical oscillators with integrated deflection sensors. By configuring two deflection sensors in separate bridge circuits, which are supplied with in-phase and quadrature sinusoidal signals, the amplitude, and phase of the mechanical oscillation can directly be measured without additional demodulation. The method is implemented and experimentally verified by demodulating the oscillation of a self-sensing Atomic Force Microscopy cantilever with integrated piezoresistive elements.
Novel Omnidirectional Aerial Manipulator With Elastic Suspension: Dynamic Control and Experimental Performance Assessment

Aerial manipulators may replace industrial manipulators for specific tasks involving large workspaces and high dynamics. However, these robots suffer from a lack of accuracy, autonomy, payload capability and a complex regulatory environment. To overcome some of these limitations, we introduce a novel aerial manipulator suspended by a spring to a robotic carrier. A computed torque controller with exteroceptive feedback is used to control the robot, using singular perturbation theory to prove the stability. We carried out various experiments to assess the workspace, accuracy and dynamics of the manipulator.
Response generation reflecting the personality of a chat dialogue system

This paper investigates the relationship between speakers' personalities and their utterances in dialogue, and propose a response generation method that reflects the personality of a chat dialogue system. We construct a corpus of utterances by persons with specific personality features, and calculate percentages of various types of speech act. From the analysis result, features of utterances by various personalities are extracted. With these utterance features, response generation strategies for specific personality features are designed and implemented in a simple chat dialogue system. The experimental result shows that the personality features of “shyness” and “nurturing parent” are appropriately reflected in our dialogue system.
A Control Strategy for the Safe Navigation of UAVs Among Dynamic Obstacles using Real-Time Deforming Trajectories*

Unmanned aerial vehicles (UAVs) have become very popular in many military and civilian applications where they are required to fly autonomously through unknown environments to perform some tasks without human interaction. Safe strategies are needed to control the motion of UAVs, and navigation decisions should be based mainly on onboard sensors observations for a fully autonomous operation. Developing such strategies is very challenging when considering dynamic environments with moving obstacles and other non-cooperative UAVs as usual path planning methods can be computationally unreliable. Hence, this work proposes a safe navigation control strategy based on the sense-and-avoid paradigm to allow for a reliable solution computational-wise. Bezier splines are used to generate motion paths which are manipulated in realtime reacting to current sensors observations resulting in a deformation-like effect to navigate safely around obstacles. The performance of the proposed approach is evaluated using the Gazebo simulator within a ROS framework, and good results were obtained for different simulation scenarios.
Analysis of D2D-Aided Underlaying Uplink Cellular Networks Using Poisson Hole Process

As one of the key technologies in 5th generation mobile networks, device-to-device (D2D) communication technology can significantly improve spectrum utilization, throughput and energy efficiency as well as reduce congestion in the networks. However, due to the spectrum sharing between D2D users (DUs) and cellular users (CUs), it may also cause serious interferences. In this paper, we adopt appropriate stochastic geometric tools to model and analyze the mutual interferences, based on which the coverage probabilities of different users are derived to evaluate the network performance. Specifically, we model the locations of CUs as a Poisson point process (PPP), while the locations of DUs are modelled as a Poisson hole process (PHP), which is driven by the PPP of CUs’ locations due to the exclusion between the positions of CUs and DUs. Furthermore, the approximated expressions of user coverage probabilities are obtained by two methods, which are consistent with the simulation results. The first method is to dissolve the hole, and the second method is to approximate the PHP through a thinned PPP. In addition, the user ergodic data rates and the sum data rate of the networks are also derived through the second approach. The impacts of key parameters (such as the density of DUs and the size of the warning radius) on the ergodic rates and sum data rate are analyzed.
The Burden of Artificial Intelligence on Internal Security Detection

Our research team have devoted to extract internal malicious behavior by monitoring the network traffic for many years. We applied the deep learning approach to recognize the malicious patterns within network, but this methodology may lead to more works to examine the results from AI models production. Hence, this paper addressed the scenario to consider the burden of AI, and proposed an idea for long-term reliable detection in the future work.
A Heterogeneous Silicon on Lithium Niobate Modulator for Ultra-Compact and High-Performance Photonic Integrated Circuits

We propose a heterogeneous silicon on lithium niobate (Si-LN) modulator which improves the compactness and modulating performance of large-scale photonic integrated circuits. Two types of configurations are employed on the Si-LN wafer for ultra-compact light-routing waveguides and high-performance light-modulating waveguides, respectively. The low loss taper transfers the optical modes between the two waveguides. In the heterogeneous Si-LN modulator, LN etching is nonessential and thus device processes are supported on a robust wafer. Our design analyzes the influence of the LN thickness on the performance. According to theoretical analysis and numerical simulation, the modulator supports a bend radius of 10 μm and edge-to-edge waveguide separation of 0.7 μm with respect to ∼1 cm beat length. When thickness of LN is 700 nm, the modulation efficiency reaches 1.76 V⋅cm and the bandwidth exceeds 350 GHz. This modulator is potentially suitable for ultra-compact, large-scale, high efficiency, and large bandwidth photonic integrated circuits (PICs).
Two-Stage Joint Optimal Scheduling of a Distribution Network With Integrated Energy Systems

The coordinated operation of an integrated energy system (IES) and a distribution network is the inevitable development trend of the energy Internet of the future. The day-ahead optimal scheduling of the IES is an important way to improve new energy efficiency and the energy economy. When the IES and the distribution network exchange electrical energy, the voltage of the distribution network may be out of limit. This article presents a two-stage joint optimal scheduling method for a distribution network with IESs to improve the economy of the IESs and the safety of the distribution network. In the first stage, the user’s demand response and the electrical energy interaction between IESs are considered, and the schedulable potential of the systems is fully tapped. In the second stage, a bi-level scheduling model is adopted: the upper model takes the distribution network as the control object and reduces the power loss by adjusting the exchange power between the distribution network and the IESs. The lower model takes the IESs as the control objects and obtains the scheme with the lowest cost in each IES through multi-objective particle swarm optimization. Taking the IEEE 33-node distribution system as an example, simulation research is performed to show that the total network loss is 17.01% lower and the total cost is 5.36% lower than the method without two-stage optimal scheduling, which verifies the effectiveness of the proposed method.
UKF-Based Sensor Fusion Method for Position Estimation of a 2-DOF Rope Driven Robot

In this study, the unscented Kalman filter-based method was introduced as a new technique for position estimation of the two-degree-of-freedom façade cleaning robot known as the Dual Ascender Robot (DAR). While other façade cleaning robots use a winch, the DAR uses an ascender, resulting in rope slip inside the ascender. Rope slip does easily cause errors in length data, so DARs cannot be easily controlled based on length data as in the case of most façade cleaning robots. Therefore, the DARs estimate the length data and use it through position estimation to overcome the rope slip for control. DARs use a rope length-based sensor fusion method for position estimation. This method employs position data based on both length data and angle data to estimate the position; however, it is difficult to use for long periods of time owing to the increased error that accumulates with time. Therefore, the use of position data based on angle data is proposed herein via application of the unscented Kalman filter. This unscented Kalman filter-based method is tested to confirm that the positional estimation performance is improved relative to that achieved via the previously used method. The performance improvements are compared in terms of accuracy and repeatability using the double ball bar method, and the errors in accuracy and repeatability are found to be reduced by approximately 2–3 times.