Miracle-dz/UniFAR-FTUs huggingface dataset:
Metabolites profiling of Limonium tubiflorum (Delile) Kuntze var tubiflorum via UPLC-qTOF-MS technique in relation to its cytotoxic activity

Ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-qTOF-MS) technique led to the detection of 55 metabolites in Limonium tubiflorum (Delile) Kuntze var tubiflorum flowers extract for the first time including 38 flavonoids, 7 phenolic acids and 10 anthocyanins. The total phenolic content was (231.225 mg GAE g DW) and the total flavonoid content was (136.66 mg rutin g DW) in L. tubiflorum (Delile) Kuntze var tubiflorum. The flower plant powder has been prepared by successive fractionation. The successive extracts have been tested for in vitro cytotoxic activity against MCF7, HEPG2, and HCT116 and exhibited moderate to weak cytotoxic activities compared to Doxorubicin (positive control) which showed in vitro cytotoxic effect with IC50 values of 2.97 ± 0.9, 4.57 ± 0.5 and 3.73 ± 0.6 μg/mL, respectively. The results also showed that the 95% ethanolic successive extract exhibited the strongest cytotoxic activity among all the tested extracts against HEPG2 and HCT116 cell lines with IC50 values of 21 ± 0.5 and 21 ± 0.8 μg/mL, respectively, while 70% ethanolic successive extract showed the highest cytotoxic activity against MCF7 cell line with IC50 value (26.6 ± 0.7 μg/mL). The hydro-ethanolic plant flower extracts are enriched in phenolic compounds (flavonoids, phenolic acids and anthocyanins) which are likely to mediate for cytotoxic activity.
A Continuous Time Quantum Monte Carlo as an Impurity Solver for Strongly Correlated System

We assess the continuous-time quantum Monte Carlo (CT-QMC) technique with hybridization expansion for solvingthe electronic structure of the strongly correlated system LaxSr1−xVO3 . The impurity solver method implemented here shows the fair agreement with the other available Monte Carlo techniques. From the study, it is found that the CT-QMC technique clearly distinguishes metallic phase, quasiparticle phase and insulating phases of the system depending upon the strength of the correlation. In case of La0.33Sr0. 67VO3 system the metal-insulator transition is found to be at U = 4.5 eV for β = 6(eV)−1. The value of U depends with the value of β, and also the value of Hund’s coupling (J) and bandwidth (W). This technique allows the particle to exchange with the reservoir of the particles and the impurity sites, which is accounted numerically to treat the temporal fluctuation of the fermionic systems termed as dynamical mean field theory (DMFT). This theory is used to explain the phenomena of MottHubbard metal insulator transition of the materials which are applicable for designing the Mottronics, Neuromorphic computing, Quantum computing and resistive memory devices.
The Minimal Important Difference of the Fecal Incontinence Quality of Life (FIQL) Questionnaire for Patients with Posterior Compartment Prolapse: A Prospective Cohort Study

Objectives: The minimally important difference (MID) of the Fecal Incontinence Quality of Life (FIQL) scale has never been determined. Thus, in this study, we aimed to estimate the MID of the Japanese FIQL for patients with posterior compartment prolapse (PCP). Methods: For 3-months after surgery, we followed a prospective cohort of 136 patients with PCP combined with fecal incontinence (FI) who had undergone ventral rectopexy between 2012 and 2018. Usable data from 114 patients were analyzed. Patients have both completed the FIQL and the 36-Item Short Forum Health Survey (SF-36) before and after surgery. Distribution-based MID values were estimated at 1/2 SD and the standard error of measurement (SEM) for domain and total scores across time points. Changes in the domain scores anchored to changes in a SF-36 overall health assessment question were used to estimate anchor-based MID. To be interpreted as true change, the median, anchor-based MID values that were greater than the corresponding SEM were proposed as estimates of the MID for the FIQL. Results: Distribution-based MID of 1/2 SD for each domain and total score ranged between 0.3 and 0.4, whereas SEM ranges were between 0.2 and 0.3. The anchor-based approach resulted in the median MID estimates of 0.4 to 1.0. Final estimates of MID for each FIQL and total score were as follows: lifestyle (0.6-1.1), coping/behavior (0.8-1.4), depression/self-perception (0.4-0.8), embarrassment (1.0-1.6), and total score (0.7-1.1). Conclusions: The results provide a basis for clinically important differences in FIQL scores after surgery for patients with PCP and FI.
When negativity is the fuel. Bots and Political Polarization in the COVID-19 debate

The contexts of social and political polarization are generating new forms of communication that affect the digital public sphere. In these environments, different social and political actors contribute to extreme their positions, using bots to create spaces for social distancing where hate speech and incivility have a place, a phenomenon that worries scientists and experts. The main objective of this research is to analyze the role that these automated agents played in the debate on social networks about the Spanish Government’s management of the global COVID-19 pandemic. For this, “Social Big Data Analysis” techniques were applied: “machine learning algorithms to know the positioning of users; bot detection algorithms; “topic modeling” techniques to learn about the topics of the debate on the web, and sentiment analysis. We used a database comprised of Twitter messages published during the confinement, as a result of the Spanish state of alarm. The main conclusion is that the bots could have served to design a political propaganda campaign initiated by traditional actors with the aim of increasing tension in an environment of social emergency. It is argued that, although these agents are not the only actors that increase polarization, they do contribute to deepening the debate on certain key issues, increasing negativity. Los contextos de polarización social y política están generando nuevas formas de comunicar que inciden en la esfera pública digital. En estos entornos, distintos actores sociales y políticos estarían contribuyendo a extremar sus posicionamientos, utilizando «bots» para crear espacios de distanciamiento social en los que tienen cabida el discurso del odio y la «incivility», un fenómeno que preocupa a científicos y expertos. El objetivo principal de esta investigación es analizar el rol que desempeñaron estos agentes automatizados en el debate en redes sociales sobre la gestión del Gobierno de España durante la pandemia global de COVID-19. Para ello, se han aplicado técnicas de «Social Big Data Analysis»: algoritmos de «machine learning» para conocer el posicionamiento de los usuarios; algoritmos de detección de «bots»; técnicas de «topic modeling» para conocer los temas del debate en la red, y análisis de sentimiento. Se ha utilizado una base de datos compuesta por mensajes de Twitter publicados durante el confinamiento iniciado a raíz del estado de alarma español. La principal conclusión es que los «bots» podrían haber servido para diseñar una campaña de propaganda política iniciada por actores tradicionales con el objetivo de aumentar la crispación en un ambiente de emergencia social. Se sostiene que, aunque dichos agentes no son los únicos actores que aumentan la polarización, sí coadyuvan a extremar el debate sobre determinados temas clave, incrementando la negatividad.
Modeling Thermal Environment Responses to Terrain and Urbanization of Metropolis

Thermal environment responses to terrain and urbanization of Beijing, a metropolis of China, were simulated by the Weather Research and Forecasting model (WRF) with the single-layer Urban Canopy model (UCM). Four numerical experiments were designed: (1) the control experiment (Control exp), (2) no-terrain sensitive experiment (No-Terrain exp), (3) no-urban surface sensitive experiment (No-Urban exp), and (4) real land surface sensitive experiment (LU exp). Results indicate that better near-surface air temperature simulations were obtained after using MODIS land surface type product data MCD12Q1. The foehn effect due to northwestern mountains in Beijing were simulated to intensify the high temperature intensity in the plain area. Urban heat island effect existed in this area day and night, and the heat island intensity in the nighttime was obviously greater than that in the daytime. As a result of urbanization, the nighttime temperature had risen by 1.5∼3.5 °C and the expansion range of nighttime high temperature areas was basically consistent with the urban expansion range.
Simulation of Wide Band BAW Filter for N78 Application

Based on the requirements of large bandwidth filters for 5G communications, finite element models for material analysis and filter design are established. Characteristics of lithium niobate under different cut angle are simulated, and 36° Y cut lithium niobate is used in resonator with SiO2/Mo acoustic reflector. The influence of top electrode is evaluated and the area of the electrode is designed. A method of co-simulation using finite element model and modified Butterworth van Dyke model is introduced. A wide band filter with relative bandwidth of 10.1% is designed while the insertion loss is less than 2 dB, which can satisfy the requirement of N78 band.
Strong γc - γcl H∞ Stabilization for Networked Control System Based on Lyapunov Function and DoS Attacks

This paper is concerned with the strong γc - γcl H∞ stabilization for networked control system under DoS attacks with limited communication bandwidths. The quantizer is employed to the network transmission channel from sensor-to-controller in order to reduce the unnecessary data transmissions. In the network environment, it is assumed that there exist DoS attacks in the transmission channel from controller-to-actuator. By describing DoS attacks as the probability models subject to the Bernoulli distribution, the network control system is modeled as the system with random parameters. With this model, a dynamic output feedback controller is designed, which satisfies the given H∞ bound norm γc . Under the action of the controller, the closed-loop system is exponential stable in the mean-square sense and satisfies H∞ bound norm γcl . Especially, a Lyapunov function based on quantization error and attack probability is proposed in the stability analysis. Finally, the effectiveness of the proposed method is verified by a simulation example.
Research on Multi-modal Medical Image Fusion Based on Multi-Scale Attention Mechanism

Image fusion technology has been widely used and researched in the fields of medicine, aviation, military and civil. Compared with the traditional image fusion technology, the fusion image technology based on intelligent algorithm generates more realistic, clear and reliable images, and has a lot of detailed information. Deep neural networks have rapidly developed into a research hotspot in medical image analysis, which can automatically extract hidden pathological information from medical image data. Multi-scale and attention mechanism are two important modules in neural networks, which can significantly enhance the features extracted by the network. The problems that need to be solved for medical image fusion based on multi-scale attention mechanism include: how to build a multi-scale module, how to build an attention module, and how to combine multi-scale and attention mechanisms. Construct a deep learning network through a multi-scale attention mechanism, and then adjust the parameters and train the network to achieve multi-modal medical image fusion. In the process of building neural networks, multi-scale and attention mechanisms are incorporated, and multi-scale features of multi-modal medical images are extracted and enhanced for fusion. Through a large number of experiments: (1) The edge strength of the fused image is expected to increase by 10%-20% based on the average value of the existing algorithm; (2) The fused image can achieve high color fidelity and rich detailed information; (3) ) The time required for the fusion algorithm is expected to be reduced by 1%-10% based on the average value of the existing algorithm.
An Inverse Solution Algorithm for Industrial Robot

The inverse solution process of industrial robot is complex. Because the traditional matrix algorithm involves a lot of matrix operation, its process is complex. Numerical algorithms usually do not yield all the solutions, generally, the selection of initial value and the search algorithm have great influence on the convergence and accuracy. The geometric algorithms is simple and intuitionistic with simple expression and small computation. In this paper, a hybrid algorithm combining the geometric method and the inverse transformation matrix method is proposed for puma 560 industrial robots. The geometric algorithms is used to solve the first three joints and the matrix algorithm is used to obtain the final three joint angles. This paper introduces the inverse solution algorithm for this kind of robot in detail, and verifies the effectiveness of the proposed algorithm through matlab simulation.
An Improved Dark Channel Prior Method for Dusty Image Restoration

For the images which were obtained in a dusty environment, because of the tiny particles suspended in the atmosphere absorb and scatter the light, there is serious haze in the images. The haze can directly cause colour changes and the loss of image details. As a result, a lot of efficient features in the dusty image were concealed or weakened by the haze, which created difficulties to obtain a natural and clear restored image, and has brought serious impact for aerospace, automatic driving and traffic monitoring in the dusty environment. An improved restoration method based on dark channel prior theory and gradient domain guided filtering was proposed to remove the haze and restore the dusty image. According to the characteristics of the dusty images, a specific guided image was adopted to make gradient domain guided filtering for the dark channel image firstly, so that the transmittance image can be better refined than that in the dark channel prior method, and then the image can be restored according to the image degradation model. Experiments show that the visual effect of the restored image is natural and clear, and can retain most effective features, which lays a foundation for computer vision-based target monitoring and recognition in the dusty environment.
Blockchain-based solutions for smart mobility sustainability assurance

Smart mobility represents the future for many of today’s cities and is part of the smart city concept. Besides all its characteristics, the smart city concept aims to use the IoT (Internet of Things) technology to improve the citizens quality of life through a secure and efficient administration of city resources. The exponential growth of population and urbanization creates many challenges, especially from a mobility perspective. The main challenge in this direction is to ensure a sustainable transportation system. The objective of this paper is to present a survey on the sustainability assurance for smart mobility systems by using the blockchain technology. Moreover, this study proposes a conceptual model for a sustainable smart mobility system that implies the usage of blockchain technology. Widely known through its connection with bitcoin and other cryptocurrencies, blockchain can serve the safety and security requirements that are specific to intelligent transportation systems (ITS). The main advantage of this technology is the capability to face against several security threats by storing securely the information. This approach will streamline and secure the sensors networks data handling by the TMCs (Traffic Monitoring Centers). The process of these data handling is challenging because of possible exposure to cyber-attacks that can lead for example to erroneous traffic lights phases and green-interval settings in crossroads. The conclusions will be issued based on a comparison with the current IoT approaches for ensuring the secure storage of traffic data retrieved from sensors networks.
The methylome in females with adolescent Conduct Disorder: Neural pathomechanisms and environmental risk factors

Conduct Disorder (CD) is an impairing psychiatric disorder of childhood and adolescence characterized by aggressive and dissocial behavior. Environmental factors such as maternal smoking during pregnancy, socio-economic status, trauma, or early life stress are associated with CD. Although the number of females with CD is rising in Western societies, CD is under-researched in female cohorts. We aimed at exploring the epigenetic signature of females with CD and its relation to psychosocial and environmental risk factors. We performed HpaII sensitive genome-wide methylation sequencing of 49 CD girls and 50 matched typically developing controls and linear regression models to identify differentially methylated CpG loci (tags) and regions. Significant tags and regions were mapped to the respective genes and tested for enrichment in pathways and brain developmental processes. Finally, epigenetic signatures were tested as mediators for CD-associated risk factors. We identified a 12% increased methylation 5’ of the neurite modulator SLITRK5 (FDR = 0.0046) in cases within a glucocorticoid receptor binding site. Functionally, methylation positively correlated with gene expression in lymphoblastoid cell lines. At systems-level, genes (uncorr. P < 0.01) were associated with development of neurons, neurite outgrowth or neuronal developmental processes. At gene expression level, the associated gene-networks are activated perinatally and during early childhood in neocortical regions, thalamus and striatum, and expressed in amygdala and hippocampus. Specifically, the epigenetic signatures of the gene network activated in the thalamus during early childhood correlated with the effect of parental education on CD status possibly mediating its protective effect. The differential methylation patterns identified in females with CD are likely to affect genes that are expressed in brain regions previously indicated in CD. We provide suggestive evidence that protective effects are likely mediated by epigenetic mechanisms impairing specific brain developmental networks and therefore exerting a long-term effect on neural functions in CD. Our results are exploratory and thus, further replication is needed.
N-Acetylcysteine as a novel rapidly acting anti-suicidal agent: A pilot naturalistic study in the emergency setting

Objective N-acetylcysteine has a demonstrated role as an adjunctive therapy in psychotic and affective disorders as a treatment to reduce symptoms of Bipolar Affective Disorder, Major Depressive Disorder and Schizophrenia. However, its potential as a rapidly acting anti-suicidal agent has not yet been assessed. This naturalistic study evaluates its effect in thirty patients presenting following intentional medication overdose. Methods Eighteen patients who ingested toxic doses of paracetamol received NAC whilst twelve other patients with other overdoses received standard supportive treatment in the emergency department setting. Symptoms were measured using the Montgomery-Asberg Depression Rating Scale and Clinical Global Impression scale at time of presentation, 24 hours, and seven days. Results Baseline characteristics between groups were similar. Both groups showed a significant reduction in suicidality, as measured by the suicide item of the MADRS, over time (p < 0.001). However, there was a greater reduction in suicidality in the ‘NAC group’ compared to the ‘no-NAC group’ one-week post presentation (p = 0.014). A greater proportion of the ‘no-NAC group’ still exhibited severe depressive symptoms (MADRS >32) compared to the ‘NAC group’ (p = 0.044). Conclusion This naturalistic study suggests NAC may have potential use as a rapidly acting treatment adjunct in major depressive disorder, warranting further investigation of its effects.
Addressing Diverse Petroleum Industry Problems Using Machine Learning Techniques: Literary Methodology—Spotlight on Predicting Well Integrity Failures

Artificial intelligence (AI) and machine learning (ML) are transforming industries, where low-cost, big data can utilize computing power to optimize system performance. Oil and gas (O&G) fields are getting mature, where well integrity (WI) problems become more common and field operations are now more challenging. Hence, they are good candidates for transformation due to the low cost of data storage, highlighting the oil market decline, along with dynamic risk posed during operations. This paper is presenting a comprehensive compilation of different ML applications in diverse disciplines of the petroleum industry. The pool of AI and ML with respect to different areas of applications along with publication years has been categorized. The main focus of this study is classifying well integrity failures where the authors found that the potential of AI and ML in predicting well integrity failures has not been efficiently tapped, and there is an explicit gap in the literature. First, the applications of AI, ML, and data analytics in the O&G industry are discussed thoroughly, so this paper can be a comprehensive reference for readers and future researchers. Then data preprocessing is explained. This includes data gathering, cleaning, and feature engineering. Next, the different ML models are compared and discussed. Finally, model performance evaluation and best model selection are described. This study would be a concrete foundation in the design and construction of ML programs that can be deployed for WI risk management. The developed model can be simply used for any well stock, providing quick and easy assessment instead of subjective and tedious assessment. The layout can be simply adjusted to reflect the risk profile of any well type or any field.
Anti-cyclic citrullinated peptide antibody-positive rheumatoid arthritis caused by bacterial organizing pneumonia in a patient with Sjogren’s syndrome

Abstract A 58-year-old woman with a history of Sjogren’s syndrome was admitted to our hospital with cough, decreased right lung breath sounds and arthralgia in both thumbs. Chest computed tomography showed consolidation with air bronchogram in the right lung. Levels of anti-cyclic citrullinated peptide antibody and rheumatoid factor levels were significantly elevated. She was diagnosed with rheumatoid arthritis induced by bacterial organizing pneumonia. Treatment with salazosulfapyridine was added for rheumatoid arthritis and arthralgia gradually improved. This case highlights that respiratory infections could lead to anti-cyclic citrullinated peptide antibody-positive rheumatoid arthritis in patients with Sjogren’s syndrome.