Miracle-dz/UniFAR-FTUs huggingface dataset:
The integrated stress response remodels the microtubule-organizing center to clear unfolded proteins following proteotoxic stress

When cells encounter stressful situations, they activate the integrated stress response (ISR), which limits total protein synthesis and redirects translation to proteins that help the cells to cope. The ISR has also been implicated in cancers, but redundancies in the stress-sensing kinases that trigger the ISR have posed hurdles to dissecting physiological relevance. To overcome this challenge, we targeted the regulatory node of these kinases, namely the S51 phosphorylation site of eukaryotic translation initiation factor eIF2α and genetically replaced eIF2α with eIF2α-S51A in squamous cell carcinoma (SCC) stem cells. While inconsequential under normal growth conditions, the vulnerability of this ISR-null state was unveiled when SCC stem cells experienced proteotoxic stress. Seeking mechanistic insights into the protective roles of the ISR, we combined ribosome profiling and functional approaches to identify and probe the functional importance of translational differences between ISR-competent and ISR-null SCC stem cells when exposed to proteotoxic stress. In doing so, we learned that the ISR redirects translation to centrosomal proteins that orchestrate the microtubule dynamics needed to efficiently concentrate unfolded proteins at the microtubule organizing center so that they can be cleared by the perinuclear degradation machinery. Thus, rather than merely maintaining survival during stress, the ISR also functions in promoting cellular recovery once the stress has subsided. This finding exposes a vulnerability to SCC stem cells that could be exploited therapeutically.
Magnetic tile image classification based on PCA preprocessing broad learning

There is an urgent need for efficient magnetic tile image classification methods in the complex industrial background. Broad learning system (BLS) is a network structure that does not require depth. In order to further reduce the number of parameters that need to be calculated and improve the training efficiency of the network, the data set is enhanced and then the principal component analysis (PCA) is performed. Compared with broad learning, the method we proposed reduces the dimensionality of the data set and shortens the training and testing time from 2.93 to 0.63s while the classification accuracy is not much different.
Dynamic event-triggered integral sliding mode control for consensus tracking of multi-agent systems with disturbances

This paper proposed a novel dynamic event-triggered integral sliding mode control (ISMC) method for the consensus tracking problem of multi-agent systems (MASs) with disturbances. A mixed event-triggered scheme with two different dynamic trigger parts is addressed with the ISMC controller to avoid data consumption and reduce the trigger times. Two types of integral sliding mode switching functions are developed to analyze the reachability of sliding mode surface and design the dynamic event-triggered integral sliding mode controller. Finally, the validity of obtained theoretical results are verified by simulation.
Comparison of Clinical Effects of the Modified Masquelet Technique and Kirschner Wire External Fixation-Assisted Autogenous Bone Transplantation in the Treatment of Segmental Metacarpophalangeal Bone Defects

Objective The present study aims to explore the (1) clinical effects of the modified Masquelet technique, whose improved Masquelet technique innovates the in vitro plasticity of the bone cement module and prefabricated hollow design, and the Kirschner wire external fixation-assisted autologous bone transplantation technique in the treatment of segmental metacarpophalangeal bone defects and (2) the differences between the two techniques. Methods The clinical data of 32 patients with segmental metacarpophalangeal bone defects (15 patients treated with the modified Masquelet technique and 17 patients treated with the self-made Kirschner wire external fixation technique) admitted to our department between January 2012 and January 2020 were retrospectively analyzed. The postoperative bone healing time, hand function, and complications were compared between the two groups. Results The two groups were comparable; there were no significant differences in age, sex, length of bone defect, and time from injury to operation between the two groups (P > 0.05). All patients were followed up with for 6–24 months (average = 13.7 months), and all patients with segmental metacarpophalangeal bone defects achieved fracture healing. The postoperative hospital stay, fracture healing time, functionary scores of the affected limb, and incidence of severe complications were better in the modified group than in the external fixation group (P < 0.05). Conclusion Compared with the Kirschner wire external fixation stent assisted autologous bone transplantation, the improved Masquelet technique has the advantages of simple operation, fast healing, accurate effect, wide indications, and less complications, making it more worthy of clinical promotion.
Executive Functions in Alzheimer’s Disease: A Systematic Review

Background: In Alzheimer’s disease, in addition to memory, attention has been given to cognitive testing due to its interface and connection with memory. Objective: The aim of this study is to take a global view of executive functions and place the concept within the theoretical framework of Alzheimer’s disease dementia, verifying their role in the cognitive functioning of the human mind, as well as how they are compromised in this pathology. Methods: An initial search was carried out in databases such as PubMed, ScienceDirect, and Web of Science. The guiding question presented at the end of the introduction was elaborated from the PICO/PIO/PEO strategy. The selected articles, therefore, answered the guiding question, were made available in full, and published in the period from 2000 to 2020. Studies without specific methodology and which correlated with other diseases or other types of dementia were excluded. To meet the objective, an integrative literature review was adopted. Results: The results indicate that, although the tests to verify the performance of cognitive functions have their limitations, they bring some evidence that they have been compromised, especially when analyzed periodically during the development of dementia. Conclusion: It is concluded that there is an interference of executive functions in function of Alzheimer’s and that memory and attention are the most evident in this type of dementia.
Profile of the brain arteriovenous malformation at National Brain Center hospital in 2015 – 2016: Epidemiological study

Introduction: Brain arteriovenous malformation (AVM) is a congenital vascular lesion in which arterial blood flows directly into draining veins without capillary networking. The estimated incidence rate is approximately 1 case for 100,000 people a year. Clinical presentations are headache, seizure, neurological deficit, and others that are caused by space-occupying lesions. The treatment of brain AVM consists of microsurgery resection, embolization, stereotactic radiosurgery to conservative treatment. Methods: The design of the study was a descriptive non-analytical study. Data were taken from patients’ medical records, radiological records, and catheter lab data records at National Brain Center Hospital in 2015 – 2016. Results: There were 32 patients with AVM. The characteristics of brain AVM in our study were mostly men (62.5%), productive age ranging from 20 – 29 years (31.25%), manifested as seizure (43.75%), presence with Glasgow Coma Scale (GCS) score 15 (87.5%), and rupture of brain AVM nidus (59.38%). Conclusion: Brain AVM predominantly occurred at a young age and the management varied on the Spetzler-Martin grading. In addition, the likelihood of rupture was influenced by the characteristics of the lesion, such as size, age, location of the nidus, and history of rupture of brain AVM nidus.
Network Representation Lightening From Hashing to Quantization

Information network embedding is an important way to enable efficient graph analytics. However, it still faces with computational challenges in problems such as link prediction and node recommendation, particularly with the increasing scale of networks. Both hashing and quantization are promising approaches for accelerating these problems by orders of magnitude. In the preliminary work, we have proposed to learn binary codes for information networks, but graph analytics may suffer from large accuracy degradation. To reduce information loss while achieving memory and search efficiency, we further propose to learn quantized codes for information networks. In particular, each node is represented by compositing multiple latent vectors, each of which is optimally selected from a distinct set. Since (generalized) matrix factorization unifies several well-known embedding methods with high-order proximity preserved, we propose a Network Representation Lightening framework based on Matrix Factorization (NRL-MF) to learn binary and quantized codes. We also propose an alternating optimization algorithm for efficient parameter learning, even for the generalized matrix factorization case. We finally evaluate NRL-MF on four real-world information network datasets with respect to the tasks of node classification and node recommendation. The results show that NRL-MF significantly outperforms competing baselines in both tasks, and that quantized representations indeed incur much smaller information loss than binarized codes.
Classifying COVID-19 Disinformation on Twitter using a Convolutional Neural Network

Disinformation regarding COVID-19 is spreading rapidly on social media platforms and can cause undesirable consequences for people who rely on such content. To combat disinformation, several platform providers have implemented intelligent systems to detect disinformation and provide measurements that apprise users of the quality of information being disseminated on social media platforms. For this purpose, intelligent systems employing deep learning approaches are often applied, hence, their effectivity requires closer analysis. The study begins with a thorough literature review regarding the concept of disinformation and its classification. This paper models and evaluates a disinformation detector that uses a convolutional neural network to classify samples of social media content. The evaluation of the proposed deep learning model showed that it performed well overall in discriminating the fake-labelled tweets from the real-labelled tweets;the model yielded an accuracy score of 97.2%, a precision score of 95.7% and a recall score of 99.8%. Consequently, the paper contributes an effective disinformation detector, which can be used as a tool to combat the substantial volume of disinformation scattered throughout social media platforms. A more standardised feature extraction for disinformation cases should be the subject of subsequent research.
Monitoring Molecular Properties of a Fluorescence Light-Up Aptamer Using Fluorescence Cross-Correlation Spectroscopy

Fluorescence light-up aptamers (FLAPs) are tools for RNA imaging, wherein the RNA of interest is appended with a FLAP sequence that can bind to a corresponding small-molecule fluorogen and enhance its fluorescence. The fluorescence properties of FLAPs have mostly been analyzed in bulk and described as the average of a large number of RNA–fluorogen complexes. In this study, we evaluated the feasibility of fluorescence correlation spectroscopy (FCS)- and fluorescence cross-correlation spectroscopy (FCCS)-based quantifications of FLAPs in a solution using Broccoli, a common FLAP, and its corresponding fluorogen, DFHBI-1T. We investigated the folding efficiency, photostability, and photophysical properties of the Broccoli–DFHBI-1T complex using their FCS/FCCS characteristics. With FCS, we observed that the fluorescence was affected by the affinity between Broccoli and DFHBI-1T and the folding (maturation) state of Broccoli RNA. Moreover, the FCCS measurement of ATTO647N-labeled Broccoli and its complex with DFHBI-1T revealed the proportion of the mature Broccoli–DFHBI-1T complex. The current FCS/FCCS-based study of Broccoli–DFHBI-1T provides a model for analyzing FLAPs and their fluorogen pairs at the single-molecule level.
Trapdoor Privacy in Public Key Encryption With Keyword Search: A Review

The public key encryption with keyword search (PEKS) scheme allows searches to be performed over ciphertext by a server in a public-key setting. The PEKS scheme suffers from a major drawback which is keyword guessing attack. A keyword guessing attack (KGA) allows the attacker to successfully guess the correct keyword encrypted in a searchable ciphertext and trapdoor. To overcome this vulnerability, security notions, such as keyword privacy and trapdoor privacy were introduced. Keyword privacy prevents any information leaked from the keyword itself, and similarly trapdoor privacy prevents any information leaked from the trapdoor side. A PEKS scheme that is secure against KGA should satisfy trapdoor privacy. In this paper, we compare various types of PEKS schemes in terms of their underlying computational hardness, system model, search function, security properties of keyword privacy and trapdoor privacy, and security against offline KGA and online KGA. From the comparison analysis, we highlight that trapdoor privacy and keyword privacy are essential for a PEKS scheme to be secure against KGA. Lastly, we draw some potential research directions.
Trudinger–Moser‐type inequality with logarithmic convolution potentials

We establish Moser–Trudinger‐type inequalities in the presence of a logarithmic convolution potential when the domain is a ball or the entire space R2$\mathbb {R}^2$ . Moreover, we characterize critical nonlinear growth rates for these inequalities to hold and for the existence of corresponding extremal functions. In addition, we show that extremal functions satisfy corresponding Euler–Lagrange equations, and we derive general symmetry and uniqueness results for solutions of these equations.
A fast parameter estimation method for attributed scattering centers based on complex field separation

This paper presents focus on physical feature extraction and parameter estimation for satellites in space. In the context of increasingly prominent space security issues, it is necessary to improve the security and protection capabilities of the space system, especially the refined feature extraction and recognition technology of space targets. However, the feature extraction methods based on optical or radar imagery are limited by resolution and observation conditions, making it difficult to extract refined features. This paper is based on the attribute scattering center theory, using high-frequency electromagnetic wave detection method. Through the attribute parameter estimation result of the scattering center, the scattering structure on the space target can be identified and its distribution position can be analyzed, and the structure and size of the typical parts in the space target can be extracted. Aiming at the problem of high parameter dimension and large amount of calculation, an attributed scattering centers parameter estimation method with amplitude and phase separation is proposed, which reduces the time complexity of parameter estimation by an order of magnitude, saves system resources and increases the parameter estimation rate, and the estimation accuracy is better than 90%. In terms of the efficiency and accuracy of parameter estimation from simulation and experimental data, the effectiveness of the proposed method in this paper is verified.
Polarimetric radar interferometry in the presence of differential Faraday rotation

Faraday rotation (FR) affects the low-frequency transionospheric radar by creating cross-talk between polarizations. The baseline part of FR can be compensated for by applying an appropriate linear transformation—rotation with a known FR angle. Yet the differential Faraday rotation (dFR), which is a frequency-dependent part of FR, persists and introduces distortions into the observations. We build a simplified model with two polarimetric scattering channels that allows us to evaluate the effect of dFR on the accuracy of PolInSAR reconstruction. We also assess the severity of distortions due to dFR for the future BIOMASS mission and several other spaceborne radar systems.
Long COVID in Children and Adolescents

Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) has caused significant mortality and morbidity worldwide. In children, the acute SARS-CoV-2 infection is often asymptomatic or paucisymptomatic, and life-threatening complications are rare. Nevertheless, there are two long-term consequences of SARS-CoV-2 infection in children that raise concern: multisystem inflammatory syndrome in children (MIS-C) and long COVID. While the understanding and the experience regarding the acute phase of SARS-CoV-2 infection have remarkably increased over time, scientific and clinical research is still exploring the long-term effects of COVID-19. In children, data on long COVID are scant. Reports are conflicting regarding its prevalence, duration and impact on daily life. This narrative review explored the latest literature regarding long COVID-19 in the pediatric population. We showed that long COVID in children might be a relevant clinical problem. In most cases, the prognosis is good, but some children may develop long-term symptoms with a significant impact on their daily life. The paucity of studies on long COVID, including a control group of children not infected by SARS-CoV-2, prevents us from drawing firm conclusions. Whether the neuropsychiatric symptoms widely observed in children and adolescents with long COVID are the consequence of SARS-CoV-2 infection or are due to the tremendous stress resulting from the restrictions and the pandemics is still not clear. In both cases, psychological support can play a fundamental role in managing COVID pandemics in children. More knowledge is needed to share a standardized definition of the syndrome and improve its management and treatment.
Six-Dimensional Target Pose Estimation for Robot Autonomous Manipulation: Methodology and Verification

The autonomous and precise grasping operation of robots is considered challenging in situations where there are different objects with different shapes and postures. In this study, we proposed a method of 6-D target pose estimation for robot autonomous manipulation. The proposed method is based on: 1) a fully convolutional neural network for scene semantic segmentation and 2) fast global registration to achieve target pose estimation. To verify the validity of the proposed algorithm, we built a robot grasping operation system and used the point cloud model of the target object and its pose estimation results to generate the robot grasping posture control strategy. Experimental results showed that the proposed method can achieve a six-degree-of-freedom pose estimation for arbitrarily placed target objects and complete the autonomous grasping of the target. Comparative experiments demonstrated that the proposed target pose estimation method achieved a significant improvement in average accuracy and real-time performance compared with traditional methods.
Optimization Strategy Using Dynamic Radial Basis Function Metamodel Based on Trust Region

To improve the design quality and optimization efficiency of complicated engineering systems such as flight vehicle, metamodel based optimization is applied widely. Trust region is imported into the metamodel optimization and the updating strategy for sampling space using trust region is proposed and then the optimization strategy using dynamic radial basis function metamodel based on trust region is proposed. The metamodel is constructed with radial basis function and initial sampling points selected by Maximin latin hypercube design method. Global optimization algorithm is employed to optimize current metamodel to find the potential global optimum of the true optimization problem. According to the current known information, the trust region sampling space is updated. During optimization process, the new sampling points in the trust region sampling space are added, and the metamodel is updated, until the potential global optimum of the true optimization problem is satisfied the convergence conditions. The optimization strategy is validated by using five benchmark numerical test problems and an I-beam design problem. As the optimization results shown, the capability of TR-DRBF in both aspects of optimization efficiency and global convergency is good compared with the study fruit inland and overseas at present. Especially for high dimension problems, the performance of TR-DRBF is appealing.
Proposing an Open-Sourced Tool for Computational Framing Analysis of Multilingual Data

Abstract We propose a five-step computational framing analysis framework that researchers can use to analyze multilingual news data. The framework combines unsupervised and supervised machine learning and leverages a state-of-the-art multilingual deep learning model, which can significantly enhance frame prediction performance while requiring a considerably small sample of manual annotations. Most importantly, anyone can perform the proposed computational framing analysis using a free, open-sourced system, created by a team of communication scholars, computer scientists, web designers and web developers. Making advanced computational analysis available to researchers without a programming background to some degree bridges the digital divide within the communication research discipline in particular and the academic community in general.
Sensitive and rapid spectrophotometric methods for sertraline monitoring in pharmaceutical formulations

Purpose: To develop simple, rapid, and selective spectrophotometric methods for the assay of sertraline in a pharmaceutical formulation. Method: These methods depend on the formation of colored ion-pair complexes between the drug and five different reagents; methyl blue (MB), bromophenol red (BPR), methyl green (MG), phenol red (PR), and methyl orange (MO) in B-R buffer solutions of pH ranging from 2.0 – 8.0. The colored products were measured at 668, 747, 647, 717, and 553 nm, respectively. Results: The calibration graphs were linear over the concentration range of 2 – 18 μg/mL for MB and BPR, and 2 – 16 μg/mL for MG, PR, and MO. In all cases, the reaction stoichiometry was 1:1. The proposed methods were successfully applied to solid-dose pharmaceutical preparations (tablets). Excipients in the commercial formulation did not interfere with the analysis. Conclusion: The investigated methods can be recommended for routine analysis and quality control where cost-effectiveness, high specificity of the analytical technique, and time are of great importance.
The State of Knowledge in Rural Gifted Education: A Systematic Literature Review

To outline the current state of empirical knowledge in rural gifted education, a systematic review of the international empirical literature was conducted with peer-reviewed journal articles published from 2000 to 2020. Six inclusion/exclusion criteria guided the searches that were undertaken of the ERIC and PsycINFO databases, six peer-reviewed journals in gifted education, and three peer-reviewed journals in rural education, along with the reference lists of the journal articles identified from the database and journal searches. The 103 journal articles that were eventually identified as meeting search criteria were thereafter analyzed to document key details, including the countries/regions of origin, publication outlets, authorship, approaches to data collection and analyses, and the major findings. A discussion of the key issues and trends in the identified studies, along with areas for focus in future investigations, concludes the review.