Miracle-dz/UniFAR-FTUs huggingface dataset:
The Use of Baclofen as a Treatment for Alcohol Use Disorder: A Clinical Practice Perspective

Alcohol use disorder (AUD) is a brain disorder associated with high rates of mortality and morbidity worldwide. Baclofen, a selective gamma-aminobutyric acid-B (GABA-B) receptor agonist, has emerged as a promising drug for AUD. The use of this drug remains controversial, in part due to uncertainty regarding dosing and efficacy, alongside concerns about safety. To date there have been 15 randomized controlled trials (RCTs) investigating the use of baclofen in AUD; three using doses over 100 mg/day. Two additional RCTs have been completed but have not yet been published. Most trials used fixed dosing of 30–80 mg/day. The other approach involved titration until the desired clinical effect was achieved, or unwanted effects emerged. The maintenance dose varies widely from 30 to more than 300 mg/day. Baclofen may be particularly advantageous in those with liver disease, due to its limited hepatic metabolism and safe profile in this population. Patients should be informed that the use of baclofen for AUD is as an “off-label” prescription, that no optimal fixed dose has been established, and that existing clinical evidence on efficacy is inconsistent. Baclofen therapy requires careful medical monitoring due to safety considerations, particularly at higher doses and in those with comorbid physical and/or psychiatric conditions. Baclofen is mostly used in some European countries and Australia, and in particular, for patients who have not benefitted from the currently used and approved medications for AUD.
Short Intracortical Inhibition During Voluntary Movement Reveals Persistent Impairment Post-stroke

Objective: Short intracortical inhibition (SICI) is a GABAA-mediated phenomenon, argued to mediate selective muscle activation during coordinated motor activity. Markedly reduced SICI has been observed in the acute period following stroke and, based on findings in animal models, it has been posited this disinhibitory phenomenon may facilitate neural plasticity and contribute to early motor recovery. However, it remains unresolved whether SICI normalizes over time, as part of the natural course of stroke recovery. Whether intracortical inhibition contributes to motor recovery in chronic stroke also remains unclear. Notably, SICI is typically measured at rest, which may not fully reveal its role in motor control. Here we investigated SICI at rest and during voluntary motor activity to determine: (1) whether GABAA-mediated inhibition recovers, and (2) how GABAA-mediated inhibition is related to motor function, in the chronic phase post-stroke. Methods: We studied 16 chronic stroke survivors (age: 64.6 ± 9.3 years; chronicity: 74.3 ± 52.9 months) and 12 age-matched healthy controls. We used paired-pulse transcranial magnetic stimulation (TMS) to induce SICI during three conditions: rest, submaximal grip, and performance of box-and-blocks. Upper-extremity Fugl-Meyer Assessment and Box-and-Blocks tests were used to evaluate motor impairment in stroke survivors and manual dexterity in all participants, respectively. Results: At rest, SICI revealed no differences between ipsilesional and contralesional hemispheres of either cortical or subcortical stroke survivors, or healthy controls (P's > 0.05). During box-and-blocks, however, ipsilesional hemisphere SICI was significantly reduced (P = 0.025), especially following cortical stroke (P < 0.001). SICI in the ipsilesional hemisphere during box-and-blocks task was significantly related to paretic hand dexterity (r = 0.56, P = 0.039) and motor impairment (r = 0.56, P = 0.037). Conclusions: SICI during motor activity, but not rest, reveals persistent impairment in chronic stroke survivors indicating that inhibitory brain circuits responsible for motor coordination do not fully normalize as part of the natural history of stroke recovery. Observation that reduced SICI (i.e., disinhibition) is associated with greater motor impairment and worse dexterity in chronic hemiparetic individuals suggests the response considered to promote neuroplasticity and recovery in the acute phase could be maladaptive in the chronic phase post-stroke.
Supplementary Motor Area and Superior Parietal Lobule Restore Sensory Facilitation Prior to Stepping When a Decrease of Afferent Inputs Occurs

The weighting of the sensory inputs is not uniform during movement preparation and execution. For instance, a transient increase in the transmission to the cortical level of cutaneous input ~700 ms was observed before participants initiated a step forward. The sensory facilitation occurred at a time when feet cutaneous information is critical for setting the forces to be exerted onto the ground to shift the center of mass toward the supporting side prior to foot-off. Despite clear evidence of task-dependent modulation of the early somatosensory signal transmission, the neural mechanisms are mainly unknown. One hypothesis suggests that during movement preparation the premotor cortex and specifically the supplementary motor area (SMA) can be the source of an efferent signal that facilitates the somatosensory processes irrespectively of the amount of sensory inputs arriving at the somatosensory areas. Here, we depressed mechanically the plantar sole cutaneous transmission by increasing pressure under the feet by adding an extra body weight to test whether the task-dependent modulation is present during step preparation. Results showed upregulation of the neural response to tactile stimulation in the extra-weight condition during the stepping preparation whereas depressed neural response was still observed in standing condition. Source localization indicated the SMA and to a lesser extent the superior parietal lobule (SPL) areas as the likely origin of the response modulation. Upregulating cutaneous inputs (when mechanically depressed) at an early stage by efferent signals from the motor system could be an attempt to restore the level of sensory afferents to make it suitable for setting the anticipatory adjustments prior to step initiation.
A P300-Based Brain-Computer Interface for Improving Attention

A Brain-computer Interface (BCI) can be used as a neurofeedback training tool to improve cognitive performance. BCIs aim to improve the effectiveness and efficiency of the conventional neurofeedback methods by focusing on the self-regulation of individualized neuromarkers rather than generic ones in a graphically appealing training environment. In this work, for the first time, we have modified a widely used P300-based speller BCI and used it as an engaging neurofeedack training game to enhance P300. According to the user's performance the game becomes more difficult in an adaptive manner, requiring the generation of a larger and stronger P300 (i.e., in terms of total energy) in response to target stimuli. Since the P300 is generated naturally without conscious effort in response to a target trial, unlike many rhythm-based neurofeedback tools, the ability to control the proposed P300-based neurofeedback training is obtained after a short calibration without undergoing tedious trial and error sessions. The performance of the proposed neurofeedback training was evaluated over a short time scale (approximately 30 min training) using 28 young adult participants who were randomly assigned to either the experimental group or the control group. In summary, our results show that the proposed P300-based BCI neurofeedback training yielded a significant enhancement in the ERP components of the target trials (i.e., 150–550 ms after the onset of stimuli which includes P300) as well as attenuation in the corresponding ERP components of the non-target trials. In addition, more centro-parietal alpha suppression was observed in the experimental group during the neurofeedback training as well as a post-training spatial attention task. Interestingly, a significant improvement in the response time of a spatial attention task performed immediately after the neurofeedback training was observed in the experimental group. This paper, as a proof-of-concept study, suggests that the proposed neurofeedback training tool is a promising tool for improving attention particularly for those who are at risk of attention deficiency.
The C/EBP Homologous Protein (CHOP) Transcription Factor Functions in Endoplasmic Reticulum Stress-Induced Apoptosis and Microbial Infection

Apoptosis is a form of cell death by which the body maintains the homeostasis of the internal environment. Apoptosis is an initiative cell death process that is controlled by genes and is mainly divided into endogenous pathways (mitochondrial pathway), exogenous pathways (death receptor pathway), and apoptotic pathways induced by endoplasmic reticulum (ER) stress. The homeostasis imbalance in ER results in ER stress. Under specific conditions, ER stress can be beneficial to the body; however, if ER protein homeostasis is not restored, the prolonged activation of the unfolded protein response may initiate apoptotic cell death via the up-regulation of the C/EBP homologous protein (CHOP). CHOP plays an important role in ER stress-induced apoptosis and this review focuses on its multifunctional roles in that process, as well as its role in apoptosis during microbial infection. We summarize the upstream and downstream pathways of CHOP in ER stress induced apoptosis. We also focus on the newest discoveries in the functions of CHOP-induced apoptosis during microbial infection, including DNA and RNA viruses and some species of bacteria. Understanding how CHOP functions during microbial infection will assist with the development of antimicrobial therapies.
Exploiting Parts-of-Speech for Improved Textual Modeling of Code-Switching Data

Lately, the problem of code-switching has gained a lot of attention and has emerged as an active area of research. In bilingual communities, the speakers commonly embed the words and phrases of a non-native language into the syntax of a native language in their day-to-day communications. The code-switching is a global phenomenon among multilingual communities, still very limited acoustic and linguistic resources are available as yet. For developing effective speech-based applications, the ability of the existing language technologies to deal with the code-switched data cannot be over emphasized. The code-switching is broadly classified into two modes: inter-sentential and intra-sentential code-switching. In this work, we have studied the intrasentential problem in the context of code-switching language modeling task. The salient contributions of this paper includes: (i) the creation of Hindi-English code-switching text corpus by crawling a few blogging sites educating about the usage of the Internet, and (ii) the exploration of the parts-of-speech features towards more effective modeling of Hindi-English code-switched data by the monolingual language models trained on native (Hindi) language data.
The Optimized Deep Belief Networks With Improved Logistic Sigmoid Units and Their Application in Fault Diagnosis for Planetary Gearboxes of Wind Turbines

Efficient and accurate planetary gearbox fault diagnosis is the key to enhance the reliability and security of wind turbines. Therefore, an intelligent and integrated approach based on deep belief networks (DBNs), improved logistic Sigmoid (Isigmoid) units, and impulsive features is proposed in this paper. The vanishing gradient problem is an inherent drawback of conventional Sigmoid units, and it usually occurs in the backpropagation process of DBNs, resulting in that the training is considerably slowed down and the classification rate is reduced. To solve this problem, Isigmoid units are designed to combine the merits of unsaturation from leaky rectified linear (LReL) units. The results of handwritten digit recognition experiments show the superiority of Isigmoid over Sigmoid on convergence speed and classification accuracy. Since impulses contain much useful fault information, especially for early failures, an integrated approach using the optimized Morlet wavelet transform, kurtosis index, and soft-thresholding is applied to extract impulse components from original signals to improve the diagnosis accuracy. Then, the features extracted from original signals and impulsive signals are employed to train and test the DBNs with Isigmoid, Sigmoid, and LReL units for comparison. Finally, the results of planetary gearbox fault diagnosis show that Isigmoid has higher comprehensive performance than conventional sigmoid and LReL.
Electromagnetic Scattering of an Object Using a Modified High Order FDTD

A M(2,4)compatible conformal scheme is proposed to accurately simulate electromagnetic scatterings of two-dimensional (2D)perfectly electric conducting (PEC)objects. We remain the updating equations of electric fields as same with those in the M(2,4)and modify the updating magnetic field components by using three integration loops of the Faraday's law, instead of two integration loops in the FDTD (2,4)conformal scheme. Therefore, more accurate results can be obtained. Numerical experiments including accuracy testing for a PEC model and scattering problem of a two-dimensional PEC model are performed. The accuracy and efficiency of the proposed novel modified FDTD (2,4)-compatible conformal scheme with existing methods are demonstrated as well.
Emotion Detection Using Multivariate Synchrosqueezing Transform via 2D Circumplex Model

Emotion detection by utilizing signal processing methods is a challenging area. An open issue in emotional modeling is to obtain an optimum feature set to use for the classification process. This study proposes an approach for emotional state classification by the investigation of EEG signals via multivariate synchrosqueezing transform (MSST). MSST is a post-processing technique to compose a localized time-frequency representation yielding multivariate syncyrosqueezing coefficients. After obtaining these coefficients from EEG signals for 18 subjects from DEAP dataset, coefficients and self-assessment-mannequins (SAM) labels of those subjects are used for emotional state classification by using support vector machines (SVM) nearest neighbor, decision tree, and ensemble methods. The accuracy rate is 70.6% for high valence high arousal (HVHA), 75.4% for low valence high arousal (LVHA), 77.8% for high valence low arousal (HVLA), and 77.2% for low valence low arousal (LVLA) cases using SVM.
Adaptive Subsurface Visualization System Using Phase Retrieval Method and Complex-Valued Self-Organizing Map

We propose an adaptive subsurface visualization system based on a complex-valued self-organizing map (CSOM). Conventionally buried things can be detected in so-called B-scan images obtained by a ground penetrating radar. In contrast, our proposed method is able not only to detect their presence, but also to classify the targets by the self-organizing dynamics in the CSOM. Instead of utilizing only the amplitude information in the time domain, we use both the amplitude and phase information to obtain the scattering coefficients of the targets by use of the phase retrieval method. The CSOM classifies the obtained scattering coefficients to realize an automatic categorization of the targets that scatter the radar electromagnetic wave.
Cell-Phone Identification from Recompressed Audio Recordings

Many audio forensic applications would benefit from the ability to classify audio recordings, based on characteristics of the originating device, particularly in social media platforms where an enormous amount of data is posted every day. This paper utilizes passive signatures associated with the recording devices, as extracted from recorded audio itself, in the absence of any extrinsic security mechanism such as digital watermarking, to identify the source cell-phone of recorded audio. It uses device-specific information present in low as well as high-frequency regions of the recorded audio. On the only publicly available dataset in this field, MOBIPHONE, the proposed system gives a closed set accuracy of 97.2 % which matches the state of art accuracy reported for this dataset. On audio recordings which have undergone double compression, as typically happens for a recording posted on social media, the proposed system outperforms the existing methods (4% improvement in average accuracy).
Filtered Image Forensics Based on Frequency Domain Features

Image filtering has been widely used in image tampering for denoising and anti-forensics. The detection of image filtering has attracted much attention in recent years, and several methods have been done. However, there are still some challenging works to do. In this paper, a novel method is proposed for two kinds of image filtering detection. The forensics algorithm is based on DCT-LQPAT (local quadruple pattern based on discrete cosine transform) feature in frequency domain. Firstly, the spatial image is converted into the frequency domain by block DCT transform. Secondly, the DCT-LQPAT feature is calculated by local quadruple pattern in the frequency domain. Finally, the extracted DCT-LQPAT feature set is fed into a SVM (support vector machine) classifier for training and testing. A series of experiments are carried out for filtered image forensics. The experimental results show that this algorithm has a better performance compared with other classic methods even in detecting filtering in high compressed images.
Plasmonically Enhanced Spectrally-Sensitive Coatings for Gradient Heat Flux Sensors

The spectral response and directional scattering of semiconductor-oxide core-shell spherical microparticles embedded in an insulating medium at low volume fraction are computed using Mie Theory and Multiscale Modelling methods. The surface plasmon resonances of low-bandgap semiconductor microinclusions have excellent and tunable scattering properties. By adjusting the size, material, shell thickness, and dielectric environment of the particles, the energies of the localized surface resonances are tuned to match the discrete solar spectrum. Near-IR solar reflectance efficiency factors of up to 78% are observed. Further the transmittance of broadband or specific wavelengths could be blocked. These spectrally-sensitive coatings have application as a back-reflector for solar devices, high temperature thermal insulator, and optical filters in Gradient Heat Flux Sensors (GHFS)for fire safety applications.
A Simple Unified Model for Generic Operation of Dual Active Bridge Converter

This paper presents a simple and generic model of a dual active bridge converter valid throughout its range of operation. It is suitable for all operating modes and modulation strategies such as phase-shift, extended phase-shift, dual phase-shift, or triple phase-shift modulation. It hypothesizes that any mode of its operation characterized by the duty ratios (<inline-formula><tex-math notation="LaTeX">$d_1$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$d_2$</tex-math></inline-formula>) of the two full-bridge converter ac voltages and the phase shift (<inline-formula><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula>) between them can be decomposed into four parallel dual active bridge circuits operating in simple phase-shift modulation. The hypothesis is proven mathematically; the average and small-signal models are derived and validated through simulations and experiments on a hardware prototype.
Mutation Relation Extraction and Genes Network Analysis in Colon Cancer

Colon cancer (Cc) is the third high-malignancy cancer in the world, and there are abundant publications about colon cancer. A Support Vector Machine classifier is used to extract the relation about mutated genes, microRNAs (miRNA) in Cc cells by mining scientific literature containing experimentally validated data. Three topology networks are constructed based on the extracted genes and miRNAs. The feed-back loops and self-adaption relations are found within these networks, and used for identifying the mechanism of colon cancer by comparing and analyzing the difference and relatedness among the networks. This study provides a fundamental hypothesis for further analysis of colon tumorigenesis.
Does NATO Need a Blockchain?

Distributed ledgers and blockchains have been heralded as a new and universally applicable architecture for management of trust. In this article we investigate what are the possible advantages of using distributed ledger technology within the North Atlantic Treaty Organization (NATO) operations and what are possible use cases for such use. We also analyze different types of distributed ledgers proposed in the literature in respect to their applicability in context of federated military operations. Finally, we describe in more detail a specific use case, related to use of blockchain as new approach to implementing trusted binding mechanism for detached labels as stipulated in the NATO Standardization Agreement (STANAG) 4778, which is now undergoing a ratification process within NATO.
Matching Game Based User Access Algorithm in Energy-Harvesting Small Cell Network

In order to increase the capacity and improve the spectrum efficiency of wireless communication system, this paper proposes a rate-based iterative one-to-one matching game algorithm to realize multi users access an energy-harvesting small cells considering NOMA (non-orthogonal multiple access) in heterogeneous cellular networks. First, we use a heuristic clustering based channel allocation algorithm to assign channels to small cells and reduce the interference. Then we model user access problem as an iterative one-to-one matching game with rate as its utility, where one user matches with one small cell at each matching game and so is the small cell. After that, we propose an algorithm to iteratively enable users to access small cells in terms of proposed matching game and prove its stability. Finally, a power allocation algorithm to reallocate transmission power for each user is presented to fully use the harvesting power. Simulation results show that this algorithm outperforms OMA (orthogonal multiple access) system in efficiency besides improving the system capacity.
Measurement and Modeling of Multi-Frequency Microwave Emission of Soil Freezing and Thawing Processes

A ground-based microwave remote sensing experiment was carried out in 2016 using a multi-frequency microwave radiometer at L, C and X bands (1.4, 6.925 and 10.65 GHz). The aim of the experiment was to explore multi-frequency microwave emission characteristics of the soil freeze-thaw process for model and algorithm development for the future Water Cycle Observation Mission (WCOM). Measurements were carried out on pastureland in Chengde, Hebei Province, which belongs to seasonally frozen ground of China. Together with brightness temperature, soil temperature and soil moisture profiles, the frost depth, and meteorological observations were synchronously collected. Measurement and modelling results show that microwave brightness temperature has different responses to soil freezing and thawing process at different frequencies.
Mixed Discretization of CFIE in the Framework of MLFMA

The conventional combined-field integral equation (CFIE)using a Galerkin scheme suffers from inaccuracy issues due to the incorrect testing of the identity operator in the magnetic-field integral equation (MFIE). In this contribution, a mixed discretization scheme is used for correct testing of MFIE in the context of CFIE. The projection of testing spaces of EFIE and MFIE onto each other is required while solving CFIE numerically with the mixed discretization scheme. For this purpose, computations of the Gram matrix inversions are required to perform the projection operations. Such an operation can easily become computationally expensive, especially when solving large-scale problems using accelerated algorithms, such as the multilevel fast multipole algorithm (MLFMA). In this work, matrix decomposition methods and iterative solvers are used to solve Gram systems while solving CFIE with the mixed discretization scheme in the framework of MLFMA. The accuracy and efficiency of the results are compared, in the context of large-scale problems.