Miracle-dz/UniFAR-FTUs huggingface dataset:
Spike-based analysis of brain injured anesthetized animals undergoing closed-loop intracortical stimulation

Acquired brain injuries, such as stroke, are a major cause of long-term disability worldwide. When an injury occurs within primary motor cortex (M1) there is both a loss of descending information to the spinal cord and a disruption in the communication within and between spared regions, which can lead to hemiparesis and other motor dysfunction. Recently, activity-dependent stimulation (ADS), a closed loop intracortical neurostimulation technique, was shown to significantly improve behavioral recovery in rodent models following a unilateral traumatic brain injury in the primary motor cortex. While behavioral benefits have been described, the neurophysiological changes in spared areas in response to this type of stimulation have not been fully characterized. In this study, we investigated how neuronal activity is modified 1) following an injury to primary motor cortex and 2) in response to an ADS protocol. Intracortical microelectrode array were inserted in the ipsilesional rostral forelimb area (RFA) to record spike activity and to trigger intracortical microstimulation in the somatosensory cortex (S1). Following a rodent (Long Evans) model of ischemic injury using endothelin-1, a potent vasoconstrictor, was injected unilaterally through the extent of the forelimb area of primary motor cortex. We found that an ischemic injury in M1 led to an overall increase in spike activity within RFA and S1. Subsequent treatment with ADS promoted the generation of synchronized patterns in both RFA and S1.
Unified Optimal Power Flow Incorporating Full AC Control Variables

This paper introduces a particle swarm optimization based unified optimal power flow (PSO-UOPF). The main objective of the proposed method is to minimize the total system generation cost considering full AC control variables. In the proposed PSO-UOPF, the IEEE 30 buses system is used for base case and extended SVCs case. The results showed that the proposed PSO-UOPF method can efficiently and effectively provide the minimum total system generation cost comparing to the existing methods.
Hierarchical Neighbor Propagation With Bidirectional Graph Attention Network for Relation Prediction

The graph attention network (GAT) [1] has started to become a mainstream neural network architecture since 2018, yielding remarkable performance gains in various natural language processing (NLP) tasks. Although GAT has reached the state-of-the-art (SOTA) performance as a recent success in relation prediction in knowledge graph, the current model is still limited by the following two aspects: (1) the existing model only considers the neighbors from the inbound-direction of the given entity, but ignores the rich neighborhood information from outbound-directions; (2) the existing model only uses the $k$-th hop output to learn the multi-hop embeddings, which leads to the loss of a large amount of early-stage embedding information (e.g., one-hop) at the graph attention step. In this study, we propose a novel bidirectional graph attention network (BiGAT) to learn the hierarchical neighbor propagation. In our proposed BiGAT, an inbound-directional GAT and an outbound-directional GAT are introduced to capture sufficient neighborhood information before propagating the bidirectional neighborhood information to learn the multi-hop feature embeddings in a hierarchical manner. Experiments conducted on the four publicly available datasets show that BiGAT achieves the competitive results in comparison to other SOTA methods.
Inter-muscular coherence features to classify upper limb simple tasks

The application of Hybrid Brain-Computer Interfaces (BCI) for post-stroke hand motor rehabilitation requires the investigation of new electromyographic (EMG) features, potentially able to identify pathological synergies to be discouraged. Inter-muscular coherence (IMC) is gaining attention as a descriptor of the mechanisms behind abnormal motor control in stroke patients. With the ultimate goal to exploit IMC features to control BCIs, this work aims at (a) characterizing finger extension and grasping tasks by IMC features, (b) assessing IMC feature performance in classifying different conditions. Classification results (accuracy equal to 0.81 ± 0.19) pave the way for IMC feature application in hybrid BCI control.
Comparison of Cell-type Specific Optogenetic Cortical Stimulation Targeting Distinct Neural Populations for the Restoration of Vision

Stimulation of the visual cortex is a promising method for the restoration of vision, covering a broad range of patients with visual impairments. Optogenetic stimulation has the potential to advance visual restoration. To design better optogenetic implants, an appropriate combination of specific cell types and optogenetic proteins should be identified. Here, we developed a photo-stimulator system and characterized two combinations in the primary visual cortex: targeting excitatory neurons with the red-light-sensitive opto-stimulator chrimsonR and targeting parvalbumin-positive inhibitory neurons with the red-light-sensitive opto-silencer eNpHR-3.0. A custom-developed system consisting of a mini-LED device emitting red-shift light and two convex lenses was installed above the V1 for light focus. We recorded and compared the electrophysiological responses to both visual stimuli and photostimulation. The optogenetic stimulations resulted in a slightly larger number of silence-evoked neurons than excitation-evoked neurons, with a similar number of neurons that responded to visual stimuli.
Using Machine Learning to Predict Injury Severity of Road Traffic Accidents During New Year Festivals From Thailand’s Open Government Data

The fast-rising number of vehicles on the road has resulted in a matching number of traffic accidents, especially during long holidays when people travel back home. A better understanding of the risk factors leading to traffic accidents may help decrease the number and prevent severe injuries and deaths. This paper addresses the imbalanced binary classification problem of predicting injury severity of road traffic accidents during Thailand’s new year festivals using Thailand’s open government data. Random undersampling, oversampling with SMOTE, and the combination of both undersampling and oversampling were used to rebalance training instances before fitting any Random Forests models. The results show that random undersampling gives the best performance at 83 percent with regards to both recall and accuracy. Important factors driving the prediction of severe injuries are delivering method, age, and alcohol drinking. Our work could benefit public authorities in analyzing accidents' hot spots and crucial factors that discourage road safety.
Use Of Conflict Space Depiction for Detect and Avoid: History, Rationale and State-Of-The-Art

In 2008, the depiction of the predicted ‘conflict space’ was proposed as a means to provide the pilot with the information needed to remain Well Clear.In this paper, it is discussed how results from flight testing, MOPS development and new ideas were the basis for the evolution of the concept of conflict space depiction as first presented in 2008 to today’s implementation in Conflict Prediction and Display System (CPDS) and the DELPHINS Synthetic Vision System (SVS).
TLATR: Automatic Topic Labeling Using Automatic (Domain-Specific) Term Recognition

Topic modeling is a probabilistic graphical model for discovering latent topics in text corpora by using multinomial distributions of topics over words. Topic labeling is used to assign meaningful labels for the discovered topics. In this paper, we present a new topic labeling method that uses automatic term recognition to discover and assign relevant labels for each topic, i.e., TLATR (Topic Labeling using Automatic Term Recognition). TLATR uses domain-specific multi-terms that appear in the set of documents belonging to a topic. The multi-term having the highest score as determined by the automatic term recognition algorithm is chosen as the label for that topic. To evaluate TLATR, we use two real, publicly available datasets that contain scientific articles’ abstracts. The topic label evaluation is done both automatically and using human annotators. For the automatic evaluation, we use Pointwise Mutual Information, Normalized Pointwise Mutual Information, and document similarity. For human evaluation, we employ the average rating method. Furthermore, we also evaluate the quality of the topic models using the Adjusted Rand Index. To prove that our novel method extracts relevant topic labels, we compare TLATR with two state-of-the-art methods, one supervised and one unsupervised, provided by the NETL Automatic Topic Labelling system. The experimental results show that our method outperforms or provides similar results with both NETL’s supervised and unsupervised approaches.
Network to Network Functional Connectivity Modulated by Transcranial Alternating Current Stimulation in Chronic Stroke

Transcranial alternating current stimulation (tACS) is an emerging non-invasive neuro-modulation technique which has been proved to enhance neuro-rehabilitation in stroke. Simultaneous acquisition of neuroimaging data with brain stimulation allow a noninvasive investigation of the brain dynamic changes during the process. In the concurrent tACS-fMRI study, fMRI data were collected in 13 chronic stroke individuals under different tACS protocols (10 Hz, 20 Hz and sham). Resting-state fMRI was acquired before, during and after the stimulation to investigate the dynamic process of neural response. In this preliminary analysis, ICA across conditions and subjects was used to extracted typical brain networks. The extracted networks were used as masks to do network to network functional connectivity analysis. Functional connectivity between networks was found to be significantly modulated for default-mode network, contralesional frontoparietal network and executive control network, and this modulation effect varied for different stimulation frequency. 10 Hz tACS exhibited stronger modulation effects in the brain network interactions.
A TCAD Compatible SONOS Trapping Layer Model for Accurate Programming Dynamics

We present a TCAD model for the accurate description of charging kinetics of trapping layers in 3D gate-all-around VNAND SONOS devices. We build on the established description of the trapping layer as trap-rich semiconductor and show that a strongly field-dependent carrier mobility greatly improves the reproduction of experimental incremental step pulse programming (ISPP) slopes. The resulting model was implemented in a commercial TCAD simulator and shown to accurately predict ISPP and retention loss measurement data with one consistent parameter set.
An Overview of Organoid and 3-Dimensional Models in Multiple Myeloma

Abstract Multiple myeloma is a hematological malignancy of differentiated B cells that resides primarily in bone marrow niches. Its interaction with the microenvironment is known to provide a survival advantage and plays an important role in drug resistance. Despite the increased efficacy of new treatment drugs, clinical results oftentimes fall short of in vitro observations, and this disease remains incurable. Conventional 2-dimensional cultures used to perform chemosensitivity assays and the established multiple myeloma cells lines commonly used do not replicate the conditions seen in vivo. This review presents various 3-dimensional culture platforms for myeloma that attempt to overcome this obstacle by incorporating aspects of the tumor microenvironment. The unique features of each model and contributions they have provided in personalized medicine, tumor physiology, and chemosensitivity assays will be summarized.
[Studies on the purity of Chinese megestrol acetate: separation and identification of epimeric isomers of 6-hydroxy-6-methyl-17 alpha-acetoxy-progesterone].

Two impurities are separated from Chinese megestrol acetate by column chromatography and thin-layer chromatography. On the basis of UV, IR, ~1HNMR, ~(13)CNMR spectroscopy and EI mass spectrometry, these two impurities were identified as 6α-hydroxy-6β-methyl-17α-acetoxy-progesterone and its epimeric isomer 6β-hydroxy-6α-methyl-17α-acetoxy-progesterone. we report here the optimal TLC conditions (silica GF_(254), ethyl acetate—toluene=7:3) and the spectral data.
In�uence of maintaining apical patency in post-endodontic pain

Background The concept of instrumentation beyond the apical foramen by small �exible �le to prevent apical blockage is apical patency. However, this procedure might endow postoperative pain, thus to maintain apical patency or not is the matter of dilemma. Hence, the primary objective of this study was to compare postoperative pain between apical patency and non-patency groups and secondary objective was to evaluate the in�uence of number of visits, vitality of teeth, group of teeth and preoperative pain on post-operative pain. Methods Preselected (n=178) patients based on group of teeth and status of pulp were randomly divided into 2 groups, apical patency and non-patency which was further treated in either single or multiple visit. After exclusion, 160 patients were included. Each group (n=80) was subdivided in single visit (n = 40) and multiple visits (n = 40), including vital (n=20) and non-vital teeth (n=20) and single-rooted (n=10) and multiple-rooted teeth (n=10). Apical patency was maintained with a size 10 K-le during conventional hand �ling step-back shaping procedure. Intensity of pain was recorded before treatment and on days 1, 2, and 7 after treatment using Numerical Rating Scale (NRS-11). Statistical analysis was done using Mann -Whitney U test, Spearman correlation and Multiple linear regression analysis. Results The primary outcome of this study showed statistically signi�cant difference (p<0.05) in postoperative pain scores between patency and non-patency groups with higher pain scores in patency group on 1 st , 2 nd and 7 th day follow up. The secondary outcome showed postoperative pain in patency maintained group was in�uenced by status of the pulp and preoperative pain only. Vital teeth of patency-maintained group treated in multiple visits showed statistically signi�cant (p=0.02) post-operative pain in day 1 follow up. Pre-operative pain showed positive correlation with postoperative pain with statistically signi�cant difference. Conclusions Our study concluded that maintenance of apical patency increased postoperative pain. Evaluation of in�uence of number of visits, status of pulp, group of tooth and preoperative pain revealed
Evaluating the Impact of Electric Vehicles on the Smart Grid

With the 23% of total global GreenHouse Gas (GHG) emissions coming from the transport sector, many initiatives have been suggested to address this emissions sector, including reducing the number of vehicles on the roads using ridesharing programs, and switching from Internal Combustion Engine Vehicles (ICEV) to battery powered Electrical Vehicles (EV). While many governments around the world have started various programs to help increase the adoption of EVs, including incentive to purchase, introducing new policies, increasing public awareness, and installing charging infrastructures, the impact of EVs on the electric grid (load, harmonic, line and transformer lifespan) is still not completely understood. In this paper, we present a literature review about the impact of EV on the electric grid, and the evaluation results carried on the grid on a small community in the Greater Toronto Area (GTA). The results of the evaluation show a 12.5% increase in overloaded transformers within the simulated city. Keywords-Electrical vehicle; Energy management; Smart
Analysis of clinical and demographic characteristics for the differential diagnosis of pseudothrombocytopenia

INTRODUCTION: EDTA-dependent pseudothrombocytopenia (EDTA-PTCP) is an in-vitro phenomenon of platelet clumping that leads to artificial low platelet counts by automatic hematology analyzers. Knowledge of PTCP is important for the accuracy of a clinical assessment and to avoid unnecessary treatment. The aim of this study was to determine easily available predictive markers which can be used for suspicion of PTCP in case of
Effect of organic manures on soil organic matter quality in groundnut growing soils

A study on changes in organic matter quality as influenced by continuous application of organic manures was carried out during Kharif, 2016 in an ongoing long-term (9-years old) experiment at RARS, Tirupati. It is essential to know about the organic manure quality and transformation of soil OM which inturn influenced by the humic fraction. The continuous application of organic manures significantly increased humic acid fraction and oxygen containing functional groups of humic acid but decreased optical density (E4/E6 ratio) over control and RDF (Recommended dose of fertilizer i.e.20:40:50 N, P2O5, K2O kg ha for groundnut crop in kharif season). This indicated more aromatic character of humic acid. Different treatments recorded the following ranges of humic acid content from 0.23 to 1.26 g kg,total acidity (3.4 to 10.1 me g), carboxyl groups (1.8 to 3.7 me g), phenolic-OH groups (1.6 to 6.5 me g) and E4/E6 ratio with narrow range (3.45 to 5.63) at harvest of crop. Potentiometric titrations curves of humic acid obtained from different organic manure treatments were sigmoid in nature, indicating their weak acidic nature. The conductometric titration curves showed slow conductance and the UV spectra were feature less i.e. similar in nature inspite of differences in composition and showed decreased optical density with increased wavelength.
The Effect of Artificial Intelligence (AI) on the Quality and Interpretation of Financial Statements in the Hotels Classified in the AQABA Special Economic Zone (ASEZA)

The study aims to examine the effects of artificial intelligence (AI) on the consistency and analysis of financial statements in hotels in ASEZA, Jordan. This research is an exploratory, empirical study, which uses the methodology of data collection and interpretation to draw conclusions. The researchers used the arithmetic mean, standard deviation, T-test and ANOVA test to calculate the degree of significance of the study questions. The findings of a basic linear regression study of the impact of AI implemented in Jordanian hotels on the integration of accounting information systems and the association between AI and the integration of accounting information systems (R = 59.6%) also indicate that the fixed limit value amounted to (2.060) and the value of (Beta) for T-test
On the Evaluation of Semi-Supervised 2D Segmentation for Volumetric 3D Computed Tomography Baggage Security Screening

We address the automatic contraband material detection problem within volumetric 3D Computed Tomography (CT) data for baggage security screening. Distinct from the prohibited item detection using object detection techniques, contraband material detection is usually formulated as a segmentation problem due to the variations of their potential appearances and shapes. Previous studies have employed either morphological operation based traditional methods or 3D Convolutional Neural Networks (CNN) for 3D segmentation towards target material detection within volumetric 3D CT baggage security screening imagery. In this work, we investigate the effectiveness of 2D semantic segmentation techniques in this 3D CT segmentation problem. Specifically, we extract 2D slices from three planes of the 3D CT volumes and train a 2D segmentation model which is subsequently used to predict segmentation results for all the slices from a given test CT volume. Moreover, we also evaluate how the performance is affected when using a reduced number of annotated slices for training. As a result, it is demonstrated reasonable performance can be achieved with very limited annotated slices (1–2) per CT volume during training. Finally, we propose a semi-supervised learning framework for 3D CT segmentation. Using only 1/128 of the total number of annotated slices, our framework can achieve comparable performance with full supervision.
European Labor Relations and Welfare-State Regimes: A Comparative Analysis of their „Elective Affinities“

While recent comparative research revealed different welfare-state regimes and labour relations systems in Europe, we need more systematical investigations of their interrelations. Indeed, the literature is largely divided into two separate research areas, though the recent debates on a European Social Dimension and on global regime competition show how much social and labor policies are related. The paper attempts to bridge this gap by mapping out the „elective affinities“ between welfare-state regimes and labor relations systems in Europe by comparing four types of regimes (Anglo-Irish, Continental, Latin, and Scandinavian country clusters). On the one hand, it will look at the cross-national differences in the relations between employers, organized labor, and the state, and on the other, at the different welfare-state regimes, in particular, with respect to employment-related social policies. While the role of organized interests for the expansion of welfare states has been documented, the impact of social policy systems on employment relations and the role of the social partners in shaping social and labor policy still needs more consideration. Address: Dr. Bernhard Ebbinghaus Max Planck Institute for the Study of Societies (MPIfG) Lothringer Str. 78 D-50677 Köln (Germany) phone: ++49-221-33605-27 fax: ++49-221-33605-55 email: ebbinghaus@mpi-fg-koeln.mpg. de
The Psychometric Properties of the Metacognitive Awareness Inventory in the Japanese EFL Context

The measurement properties of the Metacognitive Awareness Inventory (MAI; Schraw & Dennison, 1994) as well as three versions of the instrument prominent in the literature are reported in this study. Responses from university students at four universities in Western Japan (N = 729) comprised the data-set. Normality of score distributions for test items was examined, and reliability estimates (Cronbach’s alpha) were calculated for the various subscales reported for each of the versions tested. Confirmatory factor analysis (CFA) was employed to examine the structural validity of four versions of the MAI: 1) the correlated, two-factor structure originally hypothesized by Schraw and Dennison (1994); 2) the hierarchical, three-factor structure proposed by Abe and Ida (2010); 3) the correlated, three-factor structure proposed by Niwa and Yamaji (2017); and, 4) the correlated, three-factor structure proposed by Teo and Lee (2012). All of the models performed less than satisfactorily, displaying a very poor degree of fit with the structure of the scores in the dataset. These results, by providing negative evidence for a twoor three-construct structure underlying scores on the MAI, positively assist with future theorization of the structure of metacognitive awareness.
Policy and Macro Signals from Central Bank Announcements

How do private agents understand central bank actions and communication? This paper investigates private agents’ interpretation of central bank signals about future policy and the macroeconomic outlook that are conveyed by both policy decisions and macroeconomic communication. Using U.K. data, we assess the effects of these two types of central bank announcements on inflation swaps and stock returns at daily and monthly frequencies. We find that policy decisions convey signals about the macroeconomic outlook at the daily frequency, but that policy signals dominate at the monthly frequency such that asset prices respond negatively to contractionary policy, consistent with the usual transmission mechanism. We also find that inflation expectations respond positively to the Bank of England’s macroeconomic information surprises, consistent with agents taking a signal about the economic outlook.