Miracle-dz/UniFAR-FTUs huggingface dataset:
Involvement of TRPM7 in Alcohol-Induced Damage of the Blood–Brain Barrier in the Presence of HIV Viral Proteins

Ethanol (EtOH) exerts its effects through various protein targets, including transient receptor potential melastatin 7 (TRPM7) channels, which play an essential role in cellular homeostasis. We demonstrated that TRPM7 is expressed in rat brain microvascular endothelial cells (rBMVECs), the major cellular component of the blood–brain barrier (BBB). Heavy alcohol drinking is often associated with HIV infection, however mechanisms underlying alcohol-induced BBB damage and HIV proteins, are not fully understood. We utilized the HIV-1 transgenic (HIV-1Tg) rat to mimic HIV-1 patients on combination anti-retroviral therapy (cART) and demonstrated TRPM7 expression in rBMVECs wass lower in adolescent HIV-1Tg rats compared to control animals, however control and HIV-1Tg rats expressed similar levels at 9 weeks, indicating persistent presence of HIV-1 proteins delayed TRPM7 expression. Binge exposure to EtOH (binge EtOH) decreased TRPM7 expression in control rBMVECs in a concentration-dependent manner, and abolished TRPM7 expression in HIV-1Tg rats. In human BMVECs (hBMVECs), TRPM7 expression was downregulated after treatment with EtOH, HIV-1 proteins, and in combination. Next, we constructed in vitro BBB models using BMVECs and found TRPM7 antagonists enhanced EtOH-mediated BBB integrity changes. Our study demonstrated alcohol decreased TRPM7 expression, whereby TRPM7 could be involved in the mechanisms underlying BBB alcohol-induced damage in HIV-1 patients on cART.
Hyperspectral Imaging and Chemometrics for Authentication of Extra Virgin Olive Oil: A Comparative Approach with FTIR, UV-VIS, Raman, and GC-MS

Limited information on monitoring adulteration in extra virgin olive oil (EVOO) by hyperspectral imaging (HSI) exists. This work presents a comparative study of chemometrics for the authentication and quantification of adulteration in EVOO with cheaper edible oils using GC-MS, HSI, FTIR, Raman and UV-Vis spectroscopies. The adulteration mixtures were prepared by separately blending safflower oil, corn oil, soybean oil, canola oil, sunflower oil, and sesame oil with authentic EVOO in different concentrations (0–20%, m/m). Partial least squares-discriminant analysis (PLS-DA) and PLS regression models were then built for the classification and quantification of adulteration in olive oil, respectively. HSI, FTIR, UV-Vis, Raman, and GC-MS combined with PLS-DA achieved correct classification accuracies of 100%, 99.8%, 99.6%, 96.6%, and 93.7%, respectively, in the discrimination of authentic and adulterated olive oil. The overall PLS regression model using HSI data was the best in predicting the concentration of adulterants in olive oil with a low root mean square error of prediction (RMSEP) of 1.1%, high R2pred (0.97), and high residual predictive deviation (RPD) of 6.0. The findings suggest the potential of HSI technology as a fast and non-destructive technique to control fraud in the olive oil industry.
Three-dimensional simulation of a flapping flag in a uniform flow

A three-dimensional computational model is developed for simulating the flag motion in a uniform flow. The nonlinear dynamics of the coupled fluid–flag system after setting up of flapping is investigated by a series of numerical tests. At low Reynolds numbers, the flag flaps symmetrically about its centreline when gravity is excluded, and the bending in the spanwise direction is observed near the corners on the trailing edge. As the Reynolds number increases, the spanwise bending is flattened due to the decrease of the positive pressure near the side edges as well as the viscous force of the fluid. At a certain critical Reynolds number, the flag loses its symmetry about the centreline, which is shown to be related to the coupled fluid–flag instability. The three-dimensional vortical structures shed from the flag show a significant difference from the results of two-dimensional simulations. Hairpin or O-shaped vortical structures are formed behind the flag by connecting those generated at the flag side edges and the trailing edge. Such vortical structures have a stabilization effect on the flag by reducing the pressure difference across the flag. Moreover, the positive pressure near the side edges is significantly reduced as compared with that in the center region, causing the spanwise bending. The Strouhal number defined based on the flag length is slightly dependent on the Reynolds number and the flag width, but scales with the density ratio as St ~ ρ−1/2). On the other hand, the flapping-amplitude-based Strouhal number remains close to 0.2, consistent with the values reported for flying or swimming animals. A flag flapping under gravity is then simulated, which is directed along the negative spanwise direction. The sagging down of the flag and the rolling motion of the upper corner are observed. The dual effects of gravity are demonstrated, i.e. the destabilization effect like the flag inertia and the stabilization effect by increasing the longitudinal tension force.
Molecular gas in galaxies

Molecular gas is critical in determining both the morphology and evolution of galactic disks. It is within the giant molecular clouds that interstellar gas is cycled into the next generation of stars, and the most massive of these young stars produce a major part of the galactic luminosity. In addition, the dense interstellar medium, as it is highly dissipative, probably plays a fundamental part in determining the outcome of galactic interactions. Over the last two decades, CO observations have been used to probe the molecular component of hundreds of galaxies from the Local Group to the Virgo cluster, and in luminous galaxies with recession velocities up to cz = 45,000 km s^(-1).
Computerized Adaptive Testing for the Random Weights Linear Logistic Test Model

This article discusses four-item selection rules to design efficient individualized tests for the random weights linear logistic test model (RWLLTM): minimum posterior-weighted D -error ( D B ) , minimum expected posterior-weighted D -error ( E D B ) , maximum expected Kullback–Leibler divergence between subsequent posteriors (KLP), and maximum mutual information (MUI). The RWLLTM decomposes test items into a set of subtasks or cognitive features and assumes individual-specific effects of the features on the difficulty of the items. The model extends and improves the well-known linear logistic test model in which feature effects are only estimated at the aggregate level. Simulations show that the efficiencies of the designs obtained with the different criteria appear to be equivalent. However, KLP and MUI are given preference over D B and E D B due to their lesser complexity, which significantly reduces the computational burden.
Attack Graph Model for Cyber-Physical Power Systems Using Hybrid Deep Learning

Electrical power grids are vulnerable to cyber attacks, as seen in Ukraine in 2015 and 2016. However, existing attack detection methods are limited. Most of them are based on power system measurement anomalies that occur when an attack is successfully executed at the later stages of the cyber kill chain. In contrast, the attacks on the Ukrainian power grid show the importance of system-wide, early-stage attack detection through communication-based anomalies. Therefore, in this paper, we propose a novel method for online cyber attack situational awareness that enhances the power grid resilience. It supports power system operators in the identification and localization of active attack locations in Operational Technology (OT) networks in near real-time. The proposed method employs a hybrid deep learning model of Graph Convolutional Long Short-Term Memory (GC-LSTM) and a deep convolutional network for time series classification-based anomaly detection. It is implemented as a combination of software defined networking, anomaly detection in communication throughput, and a novel attack graph model. Results indicate that the proposed method can identify active attack locations, e.g., within substations, control center, and wide area network, with an accuracy above 96%. Hence, it outperforms existing state-of-the-art deep learning-based time series classification methods.
RFPad: Enabling Device-Free Handwriting Recognition With a Tag Square

As a natural interaction approach, handwriting acts as an essential role in human–computer interaction. In order to achieve ubiquitous and reliable applications, a handwriting recognition system should be easy to use without any intrusion and robust to environment changes, which cannot be satisfied in most of existing approaches. In this article, we present RFPad, a device-free handwriting recognition system with a tag square consisting of four low-cost passive radio frequency identification (RFID) tags. With such an ingenious tag square, we transform the flat surface into a handwriting platform, and build a geometry-based theoretical model between the finger positions and the tags' phase variations so that the finger trajectory can be accurately tracked and handwriting can be recognized with the phases segmented from continuous signals. We implement a prototype of RFPad using commercial off-the-shelf RFID devices and conduct extensive experiments to evaluate its performance. Experiment results show that our RFPad can track finger movement with an average error of 1 cm and achieve average recognition accuracy of 94.08$\%$ for all 26 handwriting capital letters.
An approach to determining the thicknesses of shear bands with an echelon‐crack structure

In this paper, the internal structure of shear band is investigated, and a model of the shear band with an echelon crack structure is developed. The model assumes the shear band to be composed of two conjugate sets of echelon cracks, such that the smaller echelon cracks are embedded in the space of the larger ones. The additional strain induced by the echelon cracks and the anisotropic development of the compliance tensor in the shear band zone are analyzed. The critical crack density at the onset of shear band is obtained by applying the bifurcation condition. Deviating from previous approaches, the new procedure evaluates the thickness of shear band based on the geometrical characteristics of echelon crack arrays and the failure probability of grain boundaries in the longitudinal direction at the onset of shear band. Parametric analysis shows that grain size, internal friction angle, dilation angle, and failure probability of grain boundaries are the dominant factors that account for the shear band thickness. The calculated results are consistent with the experimental data available in the literature. The model soundly explains that the measurements of the shear band thickness are generally scattered, ranging from 4 to 30 (or even more) times the grain size. Copyright © 2011 John Wiley & Sons, Ltd.
Terahertz spectroscopy and detection of brain tumor in rat fresh-tissue samples

Terahertz (THz) spectroscopy and imaging of biomedical samples is expected to be an important application of THz analysis techniques. Identification and localization of tumor tissue, imaging of biological samples, and analysis of DNA by THz spectroscopy have been reported. THz time-domain spectroscopy (TDS) is useful for obtaining the refractive index over a broad frequency range. However, THz-TDS spectra of fresh tissue samples are sensitive to procedures such as sample preparation, and a standardized measurement protocol is required. Therefore, in this work, we establish a protocol for measurements of THz spectra of fresh tissue and demonstrate reliable detection of rat brain tumor tissue. We use a reflection THz-TDS system to measure the refractive index spectra of the samples mounted on a quartz plate. The tissue samples were measured immediately after sectioning to avoid sample denaturalization during storage. Special care was taken in THz data processing to eliminate parasitic reflections and reduce noise. The error level in our refractive index measurements was as low as 0.02 in the frequency range 0.8–1.5 THz. With increasing frequency, the refractive index in the tumor and normal regions monotonically decreased, similarly to water, and it was 0.02 higher in the tumor regions. The spectral data suggest that the tumor regions have higher water content. Hematoxylin-eosin stained images showed that increased cell density was also responsible for the observed spectral features. A set of samples from 10 rats showed consistent results. Our results suggest that reliable tumor detection in fresh tissue without pretreatment is possible with THz spectroscopy measurements. THz spectroscopy has the potential to become a real-time in vivo diagnostic method.
Differences in interference processing and frontal brain function with climate trauma from California’s deadliest wildfire

As climate change accelerates extreme weather disasters, the mental health of the impacted communities is a rising concern. In a recent study of 725 Californians we showed that individuals that were directly exposed to California’s deadliest wildfire, the Camp Fire of 2018, had significantly greater chronic symptoms of post-traumatic stress disorder, anxiety and depression than control individuals not exposed to the fires. Here, we study a subsample of these individuals: directly exposed (n = 27), indirectly exposed (who witnessed the fire but were not directly impacted, n = 21), versus age and gender-matched non-exposed controls (n = 27). All participants underwent cognitive testing with synchronized electroencephalography (EEG) brain recordings. In our sample, 67% of the individuals directly exposed to the fire reported having experienced recent trauma, while 14% of the indirectly exposed individuals and 0% of the non-exposed controls reported recent trauma exposure. Fire-exposed individuals showed significant cognitive deficits, particularly on the interference processing task and greater stimulus-evoked fronto-parietal activity as measured on this task. Across all subjects, we found that stimulus-evoked activity in left frontal cortex was associated with overall improved interference processing efficiency, suggesting the increased activity observed in fire exposed individuals may reflect a compensatory increase in cortical processes associated with cognitive control. To the best of our knowledge this is the first study to examine the cognitive and underlying neural impacts of recent climate trauma.
On the use of nonparametric regression for model checking

SUMMARY The use of nonparametric regression is explored to check the fit of a parametric regression model. The principal aim is to check the validity of the regression curve rather than necessarily to detect outliers. A pseudo likelihood ratio test is developed to provide a global assessment of fit and simulation bands are used to indicate the nature of departures from the model. The types of data considered include discrete response variables, where standard diagnostic techniques are often not appropriate, and first-order autoregressive series. Several numerical examples are given. Nonparametric regression can be used in an informal graphical way to assess the relationship between a response and an explanatory variable. In this paper we aim to develop more formal methods of assessing the assumptions of a parametric model, in particular when regression diagnostics of the type developed for normal linear models are not readily available. The principal aim is to check the validity of the systematic part of the model by comparing a nonparametric estimate of the regression curve with a parametric one. Such a comparison may also identify outliers, although the distinction between outliers and model inadequacy is not always easy. Two techniques are used to assess the fit of a parametric model. In ? 2, confidence bands are constructed around the fitted regression curve by simulation. A comparison of these with the nonparametric curve gives an indication of the nature of any departures from the model. In ? 3, a pseudo likelihood ratio test is developed. This provides a quantitative global assessment of fit. In applying these ideas, special emphasis is given to discrete data, and notably logistic regression, because of the difficulty in applying standard residual-based model checking techniques to this type of response variable. A Poisson regression example is discussed in ? 4. However, the underlying ideas have wider applications. Autoregressive time series of order 1 are discussed in ? 6. Sections 5 and 7 discuss general issues. We first discuss the context of binary regression with a single covariate and the difficulties caused by the discreteness of the response variable. The observed data are assumed to be of the form (xi, yi, ni), where xi is a covariate value, and yi has a binomial
Classifying marine faults for hazard assessment offshore Israel: a new approach based on fault size and vertical displacement

Abstract. For many countries, the methodology for offshore geohazard mitigation lags far behind the well-established onshore methodology. Particularly complicated is the assessment of fault hazard in the marine environment. The determination of whether a fault is active or not requires ultra-high-resolution seismic surveys and multiple coring and, unfortunately, frequently ends with uncertain results. Moreover, if a pipeline must cross a fault, it is not enough to determine whether the fault is active; slip rates are needed for resistant planning. Here we suggest a new approach for fault hazard assessment for the master planning of infrastructure. We provide planners a way to choose a route that will cross the least hazardous faults; these faults will then be investigated in site-specific surveys for slip rates that will allow seismic design. Instead of following the onshore practice that is hard to implement in the marine environment, we suggest taking advantage of the marine environment where seismic data are commonly better in quantity and quality. Based on existing industrial 3D seismic surveys, we measure for each fault in the study area the amount of its recent (in our specific case, 350 ka) vertical displacement and the size of its plane. According to these two independently measured quantities, we classify the faults into three hazard levels. This allows planners to choose infrastructure routes that cross the least hazardous faults at an early stage of planning and direct them to sites that need further investigation. Our case study is the Israeli continental slope, where numerous salt-related, thin-skinned, normal faults dissect the seabed, forming tens of meters high scarps. A particular hazardous zone is the upper slope south of the Dor disturbance, where a series of big listric faults rupture the seabed in an area where the sedimentation rate is 4 times faster than the vertical displacement rate. We suggest that this indicates exceptionally fast creep, seismic rupture, or rapid tremor and slip episodes.
Design and Implementation of a Low Power Wireless Frequency-Division Multiplexed Magnetic 3D Localization Scheme With Sub-mm Precision for Capsule Endoscopy Applications

This letter presents a new magnetic localization scheme based on frequency-division multiplexing for capsule endoscopy applications. The approach significantly reduces the reference excitation coil sizes and decreases the required excitation current by three orders of magnitude compared to prior work, making it practical for wearable systems. Different spatial configurations of the reference coils are considered and simulated to provide design insight for maximizing localization accuracy. A fully integrated wireless receiver prototype is implemented in 180-nm bulk CMOS and packaged in an ingestible pill form factor. To the best of our knowledge, the new scheme achieves the best experimentally demonstrated tracking accuracy in both 2D and 3D localization experiments, achieving a sub-mm mean absolute position error and consuming only $336~\mu \text{W}$ while running at 100% duty cycle.
A set‐based approach for hierarchical optimization problem using Bayesian active learning

In the development of complex engineering systems, many engineers from different disciplines collaborate to identify feasible designs that satisfy all system requirements. Analytical target cascading (ATC) is a method for the design optimization of hierarchical, multilevel systems and has been successfully employed in the design of complex engineering systems. In this paper, we propose a novel data‐driven set‐based ATC (SBATC) method for hierarchical design optimization problems using machine learning techniques. The proposed SBATC offers two advantages in engineering processes. First, it decomposes hierarchical design optimization problems into sets of suboptimization problems. The feasible regions of the suboptimization problems are explored and cascaded to lower‐level optimization problems. Using the set‐based approach, couplings between two adjacent levels in the optimization process are not required. Second, an efficient strategy is employed to determine feasible regions based on Bayesian active learning (BAL). In BAL, the Gaussian process (GP) of all cost functions is trained. An acquisition function that combines the probability of feasibility and entropy search is evaluated using posterior distributions of the trained GP. The acquisition function is maximized to generate new sampling points around the feasible regions by balancing the exploitation and exploration of the design space. To verify the effectiveness of the proposed method, numerical examples of hierarchical optimization problems are evaluated.
An experimental investigation of the streamlines in viscous fluid expelled from a tube

Experimental confirmation of some streamline patterns suggested by Taylor (1961) is described. It is shown, also, that when a viscous fluid is expelled from a tube by an inviscid fluid, the interface has a ‘localized’ effect on the fluid ahead of the interface between the two liquids, that is, the usual Hagen–Poiseuille law is obeyd in the viscous fluid except for the region behind a point about 1½ tube diameters ahead of the bubble. Use of a recently developed tungsten-iodide lamp is also described.
Membership and Segregation Effects in the Young Open Cluster NGC 6530

From photographic plate data of Shanghai Astronomical Observatory with a time baseline of 87 years, proper motions and membership probabilities of 364 stars in the open cluster NGC 6530 region are reduced. On the basis of membership determination, luminosity function and segregation effect of the cluster are discussed with details. Spatial mass segregation is obviously present in NGC 6530 while there is no clear evidence for a velocity-mass (or velocity-luminosity) dependence. The observed spatial mass segregation for NGC 6530 might be due to a combination of initial conditions and relaxation process.
Effect of Drawing on the Crystal–Amorphous Interphase, Remanent Polarization and Dielectric Properties of α-PVDF Films

The effect of drawing on the dielectric properties and on the remanent polarization of α-PVDF was investigated. Films were drawing at 150°C and a draw ratio of 4. Calorimetric analyses and X-ray diffraction results indicated that drawing increases the degree of crystallinity and reduces the crystal-amorphous interphase. As a consequence an increase in stable polarization and in the dielectric properties was observed, as well as a strong reduction in the metastable polarization. To explain these results it was suggested that the crystal-amorphous interphase might be responsible for both the metastable polarization and the αc relaxation of the polymer.