Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Alterations in Resting-State Neural Networks on the Severity of Neuropathic Pain after Spinal Cord Injury

Neuropathic pain (NP) following spinal cord injury (SCI) is refractory to pain control strategies, and the underlying neuronal mechanisms remain poorly understood. This study aimed to determine the brain regions engaged in maintaining a spontaneous resting state and the link between those regions and the severity of NP in patients with incomplete SCI. Seventy-three subjects (41 patients and 32 age- and sex-matched healthy controls) participated in this retrospective study. Regarding the neurological level of injury, patients with incomplete SCI experienced at-level or below-level NP. The severity of NP was evaluated using a visual analog scale (VAS), and patients were divided into mild and moderate–severe NP groups based on VAS scores. Graph theory and fractional amplitude of low-frequency fluctuation (fALFF) analyses were performed to compare resting-state functional magnetic resonance imaging (fMRI) analysis results among the three groups. Graph theory analysis was performed through a region of interest (ROI)-to-ROI analysis and then fALFF analysis was performed in the brain regions demonstrating significant differences among the three groups analyzed using the graph theory. We evaluated whether the brain regions showing significant differences using graph theory and fALFF correlated with the VAS scores. Patients with moderate–severe NP showed reduced node degree and fALFF in the left middle frontal gyrus compared with those with mild NP and healthy controls. Furthermore, patients with severe NP demonstrated increased average path lengths and reduced fALFF values in the posterior cingulate gyrus. This study found that changes in intrinsic oscillations of fMRI signals in the middle frontal gyrus and posterior cingulate gyrus were significant considering the severity of NP.
Blockage of S100A8/A9 ameliorates septic nephropathy in mice

Septic acute kidney injury (AKI) is the commonest cause of complication of sepsis in intensive care units, but its pathophysiology remains unclear. Calprotectin (S100A8/A9), which is a damage-associated molecular patterns (DAMPs) molecule, exerts a critical role in modulating leukocyte recruitment and inflammatory response during various diseases. However, role of S100A8/A9 in septic AKI is largely unknown. In this research, Septic AKI was triggered by cecal ligation and puncture (CLP) operation in wild-type mice, which treated with or without an S100A9 inhibitor, Paquinimod (Paq, 10 mg/kg) that prevents S100A8/A9 to bind to Toll-like receptor 4 (TLR4). Renal function, pathological changes, cell death, and oxidative stress were evaluated. Our research indicated that the mRNA and protein expression of S100A9 are time-dependently elevated in the kidney following CLP. Moreover, the administration of Paq for 24 h significantly improved CLP-induced renal dysfunction and pathological alterations compared with vehicle treatment in mice. These beneficial effects were associated with the inhibition of CLP-triggered renal tubular epithelial cell apoptosis, inflammation, superoxide production, and mitochondrial dynamic imbalance. What’s more, we further confirmed the above findings by cell co-culture experiments. Our study demonstrates that S100A9 is a prominent protein to lead to septic AKI, and the selective inhibition of S100A9 could represent a new therapeutic approach which can treat septic AKI.
Machine learning prediction model for the rupture status of middle cerebral artery aneurysm in patients with hypertension: a Chinese multicenter study

Background Hypertension is a common comorbidity in patients with unruptured intracranial aneurysms and is closely associated with the rupture of aneurysms. However, only a few studies have focused on the rupture risk of aneurysms comorbid with hypertension. This retrospective study aimed to construct prediction models for the rupture of middle cerebral artery (MCA) aneurysm associated with hypertension using machine learning (ML) algorithms, and the constructed models were externally validated with multicenter datasets. Methods We included 322 MCA aneurysm patients comorbid with hypertension who were being treated in four hospitals. All participants underwent computed tomography angiography (CTA), and aneurysm morphological features were measured. Clinical characteristics included sex, age, smoking, and hypertension history. Based on the clinical and morphological characteristics, the training datasets (n=277) were used to fit the ML algorithms to construct prediction models, which were externally validated with the testing datasets (n=45). The prediction performances of the models were assessed by receiver operating characteristic (ROC) curves. Results The areas under the ROC curve (AUCs) of the k-nearest-neighbor (KNN), neural network (NNet), support vector machine (SVM) and logistic regression (LR) models in the training datasets were 0.83 [95% confidence interval (CI): 0.78–0.88], 0.87 (95% CI: 0.82–0.92), 0.91 (95% CI: 0.88–0.95), and 0.83 (95% CI: 0.77–0.88), respectively, and in the testing datasets were 0.74 (95% CI: 0.59–0.89), 0.82 (95% CI: 0.69–0.94), 0.73 (95% CI: 0.58–0.88), and 0.76 (95% CI: 0.61–0.90), respectively. The aspect ratio (AR) was ranked as the most important variable in the ML models except for NNet. Further analysis showed that the AR had good diagnostic performance, with AUC values of 0.75 in the training datasets and 0.77 in the testing datasets. Conclusions The ML models performed reasonably accurately in predicting MCA aneurysm rupture comorbid with hypertension. AR was demonstrated as the leading predictor for the rupture of MCA aneurysm with hypertension.
Substance use and adolescent injuries: a multi-country analysis of the association and mediating effect of interpersonal violence among 122,945 in-school paediatric populations in 29 countries

Background Adolescent use of substances and injury experiences such as head injury have become increasingly prevalent. However, information regarding their association and the potential pathways linking them remains limited. This study examined the association between substance use and injuries, emphasizing the mediating role of interpersonal violence among adolescents. Methods We employed a multi-country analysis of Global School-based Health Surveys of 122,945 in-school adolescents aged 11–18 from 29 countries. This study was a cross-sectional school-based, nationally representative study developed by the World Health Organization and the United States Centers for Disease Control and Prevention, other United Nations allies, and country-specific institutions. Random-effects meta-analysis was performed to estimate the overall prevalence of injury and substance use and the I-square ( I2) statistic was used to investigate the between-country heterogeneity. Logistic regression models were fitted to examine the association between substance use and injuries. A path analysis was used to examine the potential mediation effect of interpersonal violence and employed decomposition of effects into total, direct, and indirect. Results Prevalence of substance use and injuries were 33.6% (95%CI = 28.5, 38.6%) and 41.7% (95%CI = 37.3, 46.1%), respectively. Substance use (37.8% vs. 29.4%, p = 0.001) and injuries (47.3% vs. 36.4%, p = 0.001) were significantly higher among male adolescents than females, respectively. After adjustment, substance users had 40% higher odds of injuries. The path analysis showed a mediation effect of perpetration of and victimization by interpersonal violence on the association of substance use with injuries, with total positive effects of perpetration [β = 0.18; 95%CI = 0.16, 0.19; p = 0.001] and victimization on injuries [β = 0.22; 95%CI = 0.21, 0.24; p = 0.001]. In a further subgroup analysis, tobacco users were 3.98 times more likely to sustain a gunshot wound whiles marijuana users had 2.81 times higher odds of sustaining gunshot wounds. Cigarette smokers had 45% lower odds of sustaining cut/stab wounds. Alcohol users were 53% more likely to sustain concussion/head injury and two and half times more likely to sustain gunshot wound. Conclusion A significant association exists between substance use and severe injuries among adolescents, mediated by interpersonal violence exposure. Our findings may have utility in informing substance use and interpersonal violence control policies and interventions to address adolescent injuries.
Fading Channel Prediction for 5G and 6G Mobile Communication Systems

—Nowadays, there is a trend to employ adaptive solutions in mobile communication. The adaptive transmission systems seem to answer the need for highly reliable communication that serves high data rates. For efficient adaptive transmission, the future Channel State Information (CSI) has to be known. The various prediction methods can be applied to estimate the future CSI. However, each method has its bottlenecks. The task is even more challenging while considering the future 5G/6G communication where the employment of sub-6 GHz and millimetre waves (mmWaves) in narrow-band, wide-band and ultra-wide-band transmission is considered. Thus, author describes the differences between sub-6 GHz/mmWave and narrow-band/wide-band/ultra-wide-band channel prediction, provide a comprehensive overview of available prediction methods, discuss its performance and analyse the opportunity to use them in sub-6 GHz and mmWave systems. We select Long Short-Term Memory Recurrent Neural Network (RNN) as the most promising technique for future CSI prediction and propose optimising two of its parameters - the number of input features, which was not yet considered as an opportunity to improve the performance of CSI prediction, and the number of hidden layers.
Determining the beam directions for the laser illumination calibration

In the modern world optical sensor systems have a vast number of applications. In this research work the system composed of the two cameras and the laser illumination with the 49 lasers is considered. In the previous research work we proposed the general calibration technique for this system. It was shown that the most complicated subtask in this technique is determining the beam directions for the laser illumination calibration because it couldn’t be solved using the known algorithms. The main stages which are required to execute for determining the beam directions for the laser illumination are: tracking of the laser illumination points in the image sequence of the calibration object; calculation of the coordinates in space for the found laser illumination points; constructing the laser beams in space passing close as much as possible to the found points. Within the scope of the research carried out, all the main stages for determining the beam directions for the laser illumination are considered. But much attention in this work is devoted to the third stage. The origin of the laser beam is known since it coincides with the known location of the laser on the laser illumination. Thus the problem is to find the ray passing through the origin and the least divergent in mean square from the found set of points. Also the algorithms for performing each stage are suggested. Particularly, we developed our own algorithms taking into consideration specifics of the available system with the laser illumination, viz. the algorithms for detecting the laser illumination points, the algorithms for constructing the laser beam from the found set of points. The beam directions in space can be determined for each laser of the illumination. These determined directions can be used as the subset of the calibration parameters for the whole system.
Exact solutions to the KDV-Burgers equation with forcing term using Tanh-Coth method

In this paper, tanh-coth method was applied to derive the exact travelling wave solutions to the Korteweg-de-Vries and Burgers equation with forcing term(fKDVB). Solutions that are linear combination of solitary and shock wave solutions, and periodic wave solutions are obtained, by reducing the equation to the homogeneous type using a wave transformation. The method with the help of symbolic computation tool box provides a systematic way of solving many physical models involving nonlinear partial differential equations in mathematical physics.In this paper, tanh-coth method was applied to derive the exact travelling wave solutions to the Korteweg-de-Vries and Burgers equation with forcing term(fKDVB). Solutions that are linear combination of solitary and shock wave solutions, and periodic wave solutions are obtained, by reducing the equation to the homogeneous type using a wave transformation. The method with the help of symbolic computation tool box provides a systematic way of solving many physical models involving nonlinear partial differential equations in mathematical physics.
Preservation of Mechanical and Morphological Properties of Porcine Cardiac Outflow Vessels after Decellularization and Wet Storage

Widely used storage methods, including freezing or chemical modification, preserve the sterility of biological tissues but degrade the mechanical properties of materials used to make heart valve prostheses. Therefore, wet storage remains the most optimal option for biomaterials. Three biocidal solutions (an antibiotic mixture, an octanediol-phenoxyethanol complex solution, and a glycerol-ethanol mixture) were studied for the storage of native and decellularized porcine aorta and pulmonary trunk. Subsequent mechanical testing and microstructural analysis showed a slight increase in the tensile strength of native and decellularized aorta in the longitudinal direction. Pulmonary trunk elongation increased 1.3–1.6 times in the longitudinal direction after decellularization only. The microstructures of the tested specimens showed no differences before and after wet storage. Thus, two months of wet storage of native and decellularized porcine aorta and pulmonary trunks does not significantly affect the strength and elastic properties of the material. The wet storage protocol using alcohol solutions of glycerol or octanediol-phenoxyethanol mixture may be intended for further fabrication of extracellular matrix for tissue-engineered biological heart valve prostheses.
A regular and improved representation for signed digit constant vector multiplication

A novel improved signed digit representation procedure is proposed to overcome the two fundamental disadvantages of the current multiplier-free techniques: 1) circuit inconsistency and 2) computational redundancy. The fundamental difference between the existing multiplier free strategies and the proposed method could be a novel optimization framework based on vector decomposition. The constant vector is decomposed into two terms: a “private” matrix and a “public” vector which consist of the private operations of each individual entry and public operations shared by all of the entries, the overall data flow can be separated into two regular steps: first multiplied by the “public” vector and then by the “private” matrix. The computational complexity lessening task is then accomplished by minimizing the number of operations within the “private” matrix and the length of the “public” vector. Experimental results illustrate that the proposed strategy outflanks the existing multiplier free strategies in fewer operations and more regular circuit structure.
Are the Nonparametric Person-Fit Statistics More Powerful Than Their Parametric Counterparts? Revisiting the Simulations in Karabatsos (2003)

ABSTRACT Karabatsos compared the power of 36 person-fit statistics using receiver operating characteristics curves and found the HT statistic to be the most powerful in identifying aberrant examinees. He found three statistics, C, MCI, and U3, to be the next most powerful. These four statistics, all of which are nonparametric, were found to perform considerably better than each of 25 parametric person-fit statistics. Dimitrov and Smith replicated part of this finding in a similar study. The present article raises some issues with the comparisons performed in Karabatsos and Dimitrov and Smith and points to literature that suggests that the comparisons could have been performed in a more traditional and more fair manner. The present article then replicates the simulations of Karabatsos and demonstrates in several ways that the parametric person-fit statistics lz and ECI4z (that were also considered by Karabatsos) are as powerful as are HT and U3 in identifying aberrant examinees in more traditional and fair comparisons. Two parametric person-fit statistics are shown to lead to similar results as HT and U3 in a real data example.
Compact U6G Massive MIMO Antenna Arrays With Double-Layer Partial Reflective Decoupling Layers for Mutual Coupling Suppression

In this paper, a systematic decoupling strategy of two partial reflective decoupling layers (PRDLs) and dummy decoupling probes (DDPs) is utilized to suppress the complicated couplings in a ±45° dual-polarized, compact antenna array operating at Upper 6 GHz (U6G, 6425–7125 MHz) bands. Finely engineered neutralization electromagnetic waves are introduced by two PRDLs to counteract the original couplings. Two layers of PRDLs are placed above the antenna array in a step-by-step manner to gradually cancel out several major couplings while DDPs are placed around the antenna elements to reduce the remaining couplings. The measurement results of the $6\times4$ staggered quadruple folded dipole (QFD) array prototype with power combiners show that within the U6G band, the isolations between all ports are improved to more than 21 dB with good port matching performance, while the radiation pattern and envelope correlation coefficient are also in good condition, which verifies the correctness and effectiveness of the proposed decoupling method, especially in very compact U6G Massive MIMO antenna arrays.
Columnar Dendrite Morphology and Solute Concentration of GH3039 Nickel-Based Superalloys during Wire and Laser Additive Manufacturing: Insights from Phase Field Simulations

Wire and laser additive manufacturing (WLaM) can produce outstanding mechanical properties of gh3039 nickel-based superalloys. a quantitative rapid phase field model with solute trapping kinetics has been developed during the rapid solidification process, where a range of process conditions are considered in terms of thermal gradients and pulling speeds. intergranular hot cracking is found to occur at boundaries of tilted columnar dendrite in the gh3039 nickel-based superalloys. The simulations demonstrate that the phase field model considering the interface deflection can represent the dendrite growth during additive manufacturing more realistically. With the aid of numerical simulations, it is determined that dendrite growth morphologies transform from symmetrical columnar dendrite to tilted columnar dendrite as the interface crystallographic deflection is increased, while increasing the deflection angle can lead to uneven composition of material matrix, especially at the columnar dendrite interface. Solute concentrations at the columnar dendrite interface tend to promote hot cracking in additively manufactured Ni-based superalloy.
THz Emission Spectroscopy for THz Spintronics

Spintronics is used as the standard for the readout of magnetically stored data and also has commercial applications for writing data. The generation, control and detection of spin-polarized currents, spin-dependent electric transport, and pure spin currents on the subpicosecond (10−12 s) timescale are the next challenges in spintronics. Terahertz (THz, 1012 Hz) emission spectroscopy has proven to be an excellent tool for investigating these challenges. In this short review, we outline the functioning of this spectroscopic technique and its recent applications to spintronics.
Recent trends and advances in solving the inverse problem for EEG source localization

ABSTRACT This paper addresses the recent advancements and trends in the field of electroencephalography (EEG) using inverse problem solutions. Using the EEG data of the brain to gather the information regarding the neuronal current source distribution has been a persisting challenge. Since the EEG inverse problem is ill-posed in nature; therefore, it does not offer a unique result. A trivial and precise solution yields a detailed insight regarding the electrical activity as well as the damaged tissue in the brain. Ordinarily, this problem is solved using the regularization techniques, such as minimum norm estimates, mixed-norm estimate, low-resolution electrical tomography, artificial neural networks, and their modified variants. In this paper, the latest algorithmic developments in solving the EEG inverse problem are reviewed. The optimization rendered by these techniques in accurately solving the neural source localization problem is also discussed. The comparative performance analysis of the recent techniques has been presented. Furthermore, a number of future enhancements have also been proposed to further improve the performance of these state-of-the-art techniques.
Doppler coherence imaging of divertor and SOL flows in ASDEX upgrade and Wendelstein 7-X

Measurement of ion dynamics is of great importance to the study of plasma exhaust and impurity particle flows in the scrape-off-layer (SOL) and divertor in magnetic confinement experiments. Doppler coherence imaging spectroscopy (CIS) is a passive optical diagnostic that produces 2D images of line-integrated ion flow velocity. Doppler CIS flow measurements of neutral deuterium and impurities were conducted for the first time in the divertor of the medium-sized tokamak experiment ASDEX Upgrade. A detailed sightline, emission and magnetic field analysis was undertaken to identify location and direction of flows. Under the assumption of toroidal axisymmetry, they revealed mainly parallel impurity flows in the proximity of the X-point and target plates on the order of 20 km s−1. Two Doppler CIS systems are currently set up for the optimized stellarator Wendelstein 7-X (W7-X). The main task of this diagnostic will be the measurement of impurity ion flows in one of the island divertors of W7-X. EMC3-EIRENE simulations have been carried out to estimate the flow behaviour in the W7-X SOL.
Identification of flow structures in fully developed canonical and wavy channels by means of modal decomposition techniques

A Dynamic Mode Decomposition (DMD) of Direct Numerical Simulations (DNS) of fully developed channel flows is undertaken in order to study the main differences in flow features between a plane-channel flow and a passively “controlled” flow wherein the mean friction was reduced relative to the baseline by modifying the geometry in order to generate a streamwise-periodic spanwise pressure gradient, as is the case for an oblique wavy wall. The present analysis reports POD and DMD modes for the plane channel, jointly with the application of a sparsity-promoting method, as well as a reconstruction of the Reynolds shear stress with the dynamic modes. Additionally, a dynamic link between the streamwise velocity fluctuations and the friction on the wall is sought by means of a composite approach both in the plane and wavy cases. One of the DMD modes associated with the wavy-wall friction exhibits a meandering motion which was hardly identifiable on the instantaneous friction fluctuations.