Miracle-dz/UniFAR-FTUs huggingface dataset:
Microglia and astrocytes underlie neuroinflammation and synaptic susceptibility in autism spectrum disorder

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder with onset in childhood. The mechanisms underlying ASD are unclear. In recent years, the role of microglia and astrocytes in ASD has received increasing attention. Microglia prune the synapses or respond to injury by sequestrating the injury site and expressing inflammatory cytokines. Astrocytes maintain homeostasis in the brain microenvironment through the uptake of ions and neurotransmitters. However, the molecular link between ASD and microglia and, or astrocytes remains unknown. Previous research has shown the significant role of microglia and astrocytes in ASD, with reports of increased numbers of reactive microglia and astrocytes in postmortem tissues and animal models of ASD. Therefore, an enhanced understanding of the roles of microglia and astrocytes in ASD is essential for developing effective therapies. This review aimed to summarize the functions of microglia and astrocytes and their contributions to ASD.
Assimilating Retrievals of Sea Surface Temperature from VIIRS and AMSR2

AbstractExperiments are carried out to assess the potential contributions of two new satellite datasets, derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi–National Polar-Orbiting Partnership satellite and the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission–Water (GCOM-W) satellite, to the quality of global sea surface temperature (SST) analyses at the Canadian Meteorological Centre (CMC). The new datasets are assimilated both separately and together. Verification of the analyses against independent data shows that the VIIRS and AMSR2 datasets yield analyses with similar global average errors, with the VIIRS analysis performing better during some seasons and the AMSR2 analysis performing better in others. Seasonal cloudiness in some regions diminishes the availability of VIIRS retrievals, resulting in better performance by the AMSR2 analysis. Both datasets were assimilated together along with data from the Advanced Very Hig.
Schauder estimates under incomplete Hölder continuity assumptions

Abstract : The possibility of obtaining a priori pointwise estimates involving Holder difference quotients is explored with respect to some of the independent variables. The first step is the derivation of certain potential theoretic results. These are applied to functions given convolutions with a fundamental solution to an elliptic operator as kernel, and to solutions of the basic boundary value problem with compact support when the operators have constant coefficients and the domain is a half-space. The results are extended to a class of problems with variable coefficients and domains with curved boundaries. (Author)
An Underestimated Cybersecurity Problem: Quick-Impact Time Synchronization Attacks and a Fast-Triggered Detection Method

For cybersecurity concerns in smart girds, this paper has focused on the vulnerability analysis and defense technique against the Time Synchronization Attack (TSA). This kind of attack can spoof satellite-signal-based time synchronization processes that are widely utilized in modern power systems. A quick-impact TSA experiment is presented in this paper, based on live satellite signals and a commercial time server used in current smart grids. Experiment results are alarming. Intolerable timing errors have been imposed on the server within a few tens of seconds, and it hasn’t reported any alarms. So a new cybersecurity concern for distributed power systems is revealed: stealthy TSAs must be detected at the early stage due to their quick impacts, yet current studies have underestimated this problem. In response to this potential threat, a fast-triggered detection method is proposed in this paper. It is named the Rapid Detection of Signal Distortions (RDSD) and can detect early stage signal distortions caused by TSAs at the beginning. Apart from this ability, we have also verified, via simulations and real-world comparative experiments, that the proposed method can provide improved detection and false alarm rejection capacity compared with existing methods of this type. Overall, this paper can raise attention to the cybersecurity problem caused by TSAs and facilitate the defense against them in modern smart grids.
The Allelopathic Effects of Trewia nudiflora Leaf Extracts and Its Identified Substances

Trewia nudiflora Linn. is a woody plant of the Euphorbiaceae family. It is well known for its use as a folk remedy, but its potential for phytotoxicity has not been explored. Therefore, this study investigated the allelopathic potential and the allelopathic substances in T. nudiflora leaves. The aqueous methanol extract of T. nudiflora was found to have a toxic effect on the plants used in the experiment. The shoot and root development of lettuce (Lactuca sativa L.) and foxtail fescue (Vulpia myuros L.) were significantly (p ≤ 0.05) reduced by the T. nudiflora extracts. The growth inhibition by the T. nudiflora extracts was proportional to the extract concentration and varied with the test plant species. The chromatographic separation of the extracts resulted in the isolation of two substances, identified as loliolide and 6,7,8-trimethoxycoumarin based on their respective spectral analyses. Both substances significantly inhibited lettuce growth at a concentration of 0.01 mM. To inhibit 50% of the growth of the lettuce, the required concentration of loliolide was 0.043 to 0.128 mM, while that of 6,7,8-trimethoxycoumarin was 0.028 to 0.032 mM. Comparing these values, the lettuce growth was more sensitive to 6,7,8-trimethoxycoumarin than loliolide, suggesting that 6,7,8-trimethoxycoumarin was more effective than loliolide. Therefore, the inhibition of the growth of the lettuce and foxtail fescue suggests that loliolide and 6,7,8-trimethoxycoumarin are responsible for the phytotoxicity of the T. nudiflora leaf extracts. Thus, the growth-inhibitory effectiveness of the T. nudiflora extracts and the identified loliolide and 6,7,8-trimethoxycoumarin may be used to develop bioherbicides that restrict the growth of weeds.
The Music Generation Road from Statistical Method to Deep Learning

Automatic music generation is regarded as one of the most concerning research areas. However, the current music generation AI model lacks mainstream algorithms and norms, including model evaluation standards and objective music quality measurements. In this paper, we conducted experiments on traditional statistical models, machine learning models and deep learning models to generate music, respectively involving Markov Chain, SVM, Random Forest, LSTM, GAN models and their variants. In addition, we also proposed three new quantitative methods to measure the quality of computer-generated music using technical metrics: the Key Confidence Test, the Random Walk Test and the Word2Vec Similarity Test. These methods could be used to measure the quality of generated music and avoid the subjectivity of manual evaluation, thus providing reference for future music evaluation tasks.
Transmittance of MCF-7 breast tumor cell line through visible and near infrared spectrum

In this study, light transmittance of MCF-7 tumor cells from 450 nm to 1100 nm has been measured in their growing medium and evaluated. Transmittance differences have been tried to be put forward in cancer cell line on visible (VIS) and near infrared (NIR) spectrum as well as in between different numbers of cells in medium. An absorption-reflection spectrophotometer was used in the experiments. System has a tungsten light source, optical chopper, a monochromator, sample chamber, silicon detectors, lock-in amplifier and computer. System was controlled by software in order to adjust scan range, scan steps and grating configuration. Cells were grown in medium, and measurements were taken from cells while they were in 5 ml medium. According to our findings, there are significant differences between VIS and NIR regions for the same number of cells. There were found no statistical difference among different numbers of cells. Increasing number of cells has not affected the transmittance. Transmittance of medium is not significantly different from different concentration of cells.
Evaluation of 12-lead ECG synthesis using analysis measurements in 240 patients

Synthesis of the 12-lead electrocardiogram (ECG) has been investigated recently as a method for improving patient monitoring in situations where three leads are recorded routinely. The authors present a recent study which analyzes ECG-estimation accuracy by comparing twelve diagnostic parameters from the recorded and synthesized QRS and ST-T segments of 240 patients. The results reveal strong correlations between the synthesized and recorded measurements. These correlations are present for some measurements despite statistically-significant absolute differences between the two sets. Spurious deflections were observed at the beginning of the QRS complex in leads V/sub 1/ or V/sub 3/ in a small number of cases. The measurements taken from the Q and R waves in leads V/sub 1/ and V/sub 3/ indicate that these spurious deflections cause erroneous measurements in about 9 to 17% of the patients. <<ETX>>
Experimental Study on Heat Transfer of Heated Falling Film Under Gas–Liquid Cross-Flow Condition

With the influence of the different gas Reynolds numbers and liquid Reynolds numbers on heated falling film heat transfer, an experiment was performed by noncontact thermal infrared imaging technology under the gas–liquid cross-flow condition. The results indicated that during the increase of liquid Reynolds number the thickness and thermal resistance of liquid film increased in the determined temperature of the heating water, which weakened the heat transfer of the liquid film. However, the increase of liquid Reynolds number strengthened liquid film turbulence and therefore enhanced heat transfer. Under the synergistic effect of these two factors, there should be an optimal liquid Reynolds number that minimizes thermal resistance and maximizes the heat transfer coefficient of the liquid film. Temperature plays an important role in heat transfer of laminar liquid film flow. However, the heat transfer of turbulent liquid film flow is not sensitive to liquid film inlet temperature.
A novel time domain-based multiple breathing crack diagnosis technique using Quadratic-Teager Kaiser Energy (Q-TKE)

This paper presents a generalized time domain-based multiple breathing crack localization technique in beams with an unknown number of cracks based on Quadratic-Teager Kaiser Energy (Q-TKE) using vibration data. Localization of multiple breathing cracks is a highly challenging inverse problem, as all these breathing cracks (more than two) might be in a similar state (either opening or closing state) at any particular time instant or in contrasting states (while some breathing cracks are opening, others are in closing state) during vibration. The level of nonlinearity of vibration response is strongly dependent not only on each crack size and location but also on the application of driving force along the cracked beam. The concept of varied input frequency excitation sources is employed in the present work for multiple breathing crack identification over the tedious traditional approach of varying input force application positions. The major advantage of using Q-TKE in the present work is that it can reliably isolate and extract the very low-amplitude nonlinear sensitive components (superharmonics and intermodulation) being buried in the total response and enhance them in the time domain signal itself. Investigations have been carried out by varying the number, spatial locations, and also intensities of the breathing cracks. Sensitivities associated with measurement noise and also with limited sensors is also investigated. The results of both numerical and experimental investigations carried out in this paper concluded that the proposed Q-TKE can effectively localize closely spaced or sparsely spaced (i.e., spatially far apart) multiple cracks in the beam. The proposed Q-TKE approach is data-driven, works with limited sensors, and does not need reference healthy state measurements.
Polymeric Micellar Systems—A Special Emphasis on “Smart” Drug Delivery

Concurrent developments in anticancer nanotechnological treatments have been observed as the burden of cancer increases every year. The 21st century has seen a transformation in the study of medicine thanks to the advancement in the field of material science and nanomedicine. Improved drug delivery systems with proven efficacy and fewer side effects have been made possible. Nanoformulations with varied functions are being created using lipids, polymers, and inorganic and peptide-based nanomedicines. Therefore, thorough knowledge of these intelligent nanomedicines is crucial for developing very promising drug delivery systems. Polymeric micelles are often simple to make and have high solubilization characteristics; as a result, they seem to be a promising alternative to other nanosystems. Even though recent studies have provided an overview of polymeric micelles, here we included a discussion on the “intelligent” drug delivery from these systems. We also summarized the state-of-the-art and the most recent developments of polymeric micellar systems with respect to cancer treatments. Additionally, we gave significant attention to the clinical translation potential of polymeric micellar systems in the treatment of various cancers.
A Kopp-Pneuman-like Picture of Coronal Mass Ejections

A new coronal mass ejection (CME) picture is described that utilizes a number of attributes commonly found operating during magnetotail reconnection events. We first present key observational constraints any final model of CMEs must explain. We then describe how three-dimensional reconnection occurs in the magnetotail and how magnetotail reconnection helps explain a variety of observed CME attributes. We then argue why reconnection, as usually described in the literature, cannot explain the particle acceleration process that occurs during the CME/flare process. Instead we argue that it is the flow fields that are driven by the relaxation of the magnetic stresses due to reconnection that are ultimately the cause of particle acceleration. In particular, it is the electrons that make up the discharging field-aligned currents, which connect flow field-driven cross field inertial currents in the high corona with the chromosphere, that are in fact the high-energy electrons needed to explain flare ribbons and other high-energy emissions. We compute the expected electron fluxes from these current systems and find that they are of order those required. In addition, we discuss betatron acceleration during the dipolarization process that occurs when the flux rope/CME is ejected and how the hot particles generated during the dipolarization process can lead to traps in solar loops, thereby helping to explain long-duration events. Further, we examine whether particle acceleration by shocks can contribute to the mix. We also note that our new picture eliminates a number of paradoxes, specifically elimination of magnetic flux from the Sun and how the Aly conjecture is not of consequence in our picture. Finally, we examine what will be needed to numerically simulate our picture of a CME.
Carbon Nano tube based Thermoelectric generator and Graphite Nanoparticle based supercapacitor for smart wearable sensors

This research paper outlines the design of a self-powered smart wearable sensor, that utilizes a Nano particle based Thermoelectric generator as well as a graphite Nano particle Based super capacitor. Utilizing this combo this research aims to showcase the feasibility of the use for smart wearable sensor, in this case a glucose monitor serves as the sensor. Humans are under increasing strain in modern life, which has led to an increase in a variety of health conditions. Unfortunately, this consequence either lowers life quality or prematurely shortens it. Utilizing sensors to track various bodily activities and predict the risk of a disease at an early stage might be a solution to this issue.
Neurobiology of Aggression—Review of Recent Findings and Relationship with Alcohol and Trauma

Simple Summary Aggression can be viewed as any form of behavior, such as physical or verbal, that is intended to cause harm or injury to another living being. This form of behavior has seen its prevalence increase over the past few decades, affecting individuals of all ages and accounting for more than 1.3 million deaths worldwide. As such, the scientific community invested a lot of effort into better understanding its risk factors and mechanisms. Over the past few years, several studies have been conducted in animals and humans for this purpose, with the overall consensus that aggression can be precipitated by several risk factors, including biological and environmental. There is also evidence showing that aggression is linked with alcohol consumption and trauma exposure. However, despite significant progress in research, the mechanisms through which these factors lead to aggressive behaviors are poorly understood. This review provides the current state of knowledge regarding the neurobiology of aggression and highlights recent evidence discussing its relationship with alcohol and trauma. Abstract Aggression can be conceptualized as any behavior, physical or verbal, that involves attacking another person or animal with the intent of causing harm, pain or injury. Because of its high prevalence worldwide, aggression has remained a central clinical and public safety issue. Aggression can be caused by several risk factors, including biological and psychological, such as genetics and mental health disorders, and socioeconomic such as education, employment, financial status, and neighborhood. Research over the past few decades has also proposed a link between alcohol consumption and aggressive behaviors. Alcohol consumption can escalate aggressive behavior in humans, often leading to domestic violence or serious crimes. Converging lines of evidence have also shown that trauma and posttraumatic stress disorder (PTSD) could have a tremendous impact on behavior associated with both alcohol use problems and violence. However, although the link between trauma, alcohol, and aggression is well documented, the underlying neurobiological mechanisms and their impact on behavior have not been properly discussed. This article provides an overview of recent advances in understanding the translational neurobiological basis of aggression and its intricate links to alcoholism and trauma, focusing on behavior. It does so by shedding light from several perspectives, including in vivo imaging, genes, receptors, and neurotransmitters and their influence on human and animal behavior.
Detecting Historical Terrain Anomalies With UAV-LiDAR Data Using Spline-Approximation and Support Vector Machines

The documentation of historical remains and cultural heritage is of great importance to preserve historical knowledge. Many studies use low-resolution airplane-based laser scanning and manual interpretation for this purpose. In this study, a concept to automatically detect terrain anomalies in a historical conflict landscape using high-resolution UAV-LiDAR data was developed. We applied different ground filter algorithms and included a spline-based approximation step in order to improve the removal of low vegetation. Due to the absence of comprehensive labeled training data, a one-class support vector machine algorithm was used in an unsupervised manner in order to automatically detect the terrain anomalies. We applied our approach in a study site with different densities of low vegetation. The morphological ground filter was the most suitable when dense near-ground vegetation is present. However, with the use of the spline-based processing step, all filters used could be significantly improved in terms of the F1-score of the classification results. It increased by up to 42% points in the area with dense low vegetation and by up to 14% points in the area with sparse low vegetation. The completeness (recall) reached maximum values of 0.8 and 1.0, respectively, when taking into account the results leading to the highest F1-score for each filter. Therefore, our concept can support on-site field prospection.
Topology Optimization of Synchronous Reluctance Motors Considering the Optimal Current Reference in the Field-Weakening and Maximum-Torque-Per-Voltage Regions

The torque performance of the synchronous reluctance motors (SynRMs) is primarily determined by the geometric design and current control. The motor structure needs to be designed to yield the maximum output torque under the given current and voltage limitations. The current reference supplied to the stator coils also needs to be determined to maximize the output torque while suppressing the back electromotive force (EMF) to protect a power supply system. However, in the field of design optimization, it has been challenging to simultaneously consider the geometric design and current control due to their complicated relationships. To solve the aforementioned issue, this study proposes a new topology optimization that can cover the maximum-torque-per-ampere (MTPA), field weakening (FW), and maximum-torque-per-voltage (MTPV) controls under both the current and voltage limitations, thereby maximizing the output torque in an entire rotational speed range. To effectively investigate the coupling effects between the geometric design and current reference, this study proposes the design parameterization for the FW and MTPV operation by performing the electromagnetic finite element (FE) analysis and then expresses the optimal current reference in terms of design variables. At every iteration, the structural FE analysis is also performed under the design-dependent loads to obtain a structurally meaningful design. By doing so, both the optimal design and the corresponding optimal current reference can be simultaneously determined. The optimized SynRM is manufactured and experimentally validated to demonstrate the effectiveness of the proposed optimization framework.
Free and moving boundary problems

1. Moving boundary problems: formulation 2. Free boundary problems: formulation 3. Analytical solutions 4. Front-tracking methods 5. Front-fixing methods 6. Fixed-domain methods 7. Analytical solution of seepage problems 8. Numerical solution of free boundary problems References Author index Subject index
Regularizing Label-Augmented Generative Adversarial Networks Under Limited Data

Training generative adversarial networks (GANs) using limited training data is challenging since the original discriminator is prone to overfitting. The recently proposed label augmentation technique complements categorical data augmentation approaches for discriminator, showing improved data efficiency in training GANs but lacks a theoretical basis. In this paper, we propose a novel regularization approach for the label-augmented discriminator to further improve the data efficiency of training GANs with a theoretical basis. Specifically, the proposed regularization adaptively constrains the predictions of the label-augmented discriminator on generated data to be close to the moving averages of its historical predictions on real data, and vice versa. We theoretically establish a connection between the objective function with the proposed regularization and a $f$ -divergence that is more robust than the previous reversed Kullback-Leibler divergence. Experimental results on various datasets and diverse architectures show the significantly improved data efficiency of our proposed method compared to state-of-the-art data-efficient GAN training approaches for training GANs under limited training data regimes.