Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Alangium lamarckii Bark for Antiplasmodial and Cytotoxic Constituents and Isolation of a Novel Tubulosine Analogue

Alangium lamarkii is traditionally used in India for the treatment of malaria. Partial activity guided-fractionation of the basic chloroform fraction of the methanolic extract of the bark led to the isolation of a novel tubulosine analogue, 10-demethyl-9'-N-methyltubulosine (1), for which the trivial name tubulosatine is suggested. In addition, the known A. lamarkii constituents, tubulosine (2), cephaeline and emetine were isolated as potent antiplasmodial and cytotoxic constituents, but 1 was up to 1000-fold less potent than the former alkaloids against both malaria parasites and human cancer cell lines. The compounds were active against both chloroquine-sensitive and chloroquine-resistant malaria parasites, but no selective toxicity was observed towards malaria parasites compared with cancer cells with any of the alkaloids. Further work to explore the basis for the relatively weak biological activities of 1 would be worthwhile. Betulinic acid, stigmasterol and its 3-O-glucoside were isolated from the neutral chloroform fraction of the methanolic extract.
An Open-Source FPGA Platform for Shared-Memory Heterogeneous Many-Core Architecture Exploration

Many-core architectures, especially those with heterogeneous components, are gaining momentum due to the benefits of having an Open Source Instruction Set Architecture (ISA), such as RISC-V. In this paper we present a new computing platform for developing and analysing future heterogeneous architectures where specific custom accelerators are integrated with more standard RISC-V computing cores. The platform implements a coherent shared memory model which simplifies programmability and enables efficient communication support to all heterogeneous components. We detail the network and memory subsystems and provide preliminary evaluation results showing the benefits when using two systolic accelerators managed by two computing cores.
Emotional Climate Recognition in Conversations using Peers’ Speech-based Bispectral Features and Affect Dynamics *

Emotion recognition in conversations using artificial intelligence (AI) has recently gained a lot of attention, as it can provide additional emotion cues that can be correlated with human social behavior. An extension towards an AI-based emotional climate (EC) recognition, i.e., the recognition of the joint emotional atmosphere dynamically created and perceived by the peers throughout a conversation, is proposed here. In our approach, namely MLBispeC (Machine Learning Based Bispectral Classification), the peers’ speech signals during their conversation are subjected to time-windowed bispectral analysis, allowing for feature extraction related to dynamic harmonics nonlinear interactions. In addition, peers’ affect dynamics, derived from their same time-windowed emotion labeling, are combined to form an extended feature vector, inputted into two well-known machine learning classifiers (Support Vector Machine, K-Nearest Neighbor). MLBispeC was evaluated on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) open access dataset, which contains 2D emotions, i.e., Arousal (A) and valence (V) that are divided into (low/high) classes. The experimental results have shown that MLBispeC outperforms previous state-of-the-art techniques, achieving an accuracy of 0.826A/0.754V, sensitivity of 0.864A/0.774V, and area under the curve (AUC) of 0.821A/0.799V. This demonstrates the effectiveness of MLBispeC to objectively recognize peers’ EC during their conversation, allowing for insights into their emotional and social interactions.Clinical relevance—Unobtrusive, objective and dynamic recognition of the EC built during peers’ conversation can scaffold effective assessment of patients with physiological, psychological, and mental diseases, at various age ranges (children, adults, and older adults)
Normalization of Flow-mediated Dilation to Brachial Artery Material Property: A Feasibility Study*

Endothelial reactivity (ER) is widely measured using flow-mediated dilation (FMD) of brachial artery. Conventional measurement of FMD is influenced by factors such as input shear stress, arterial transmural pressure, diameter and thereby arterial material properties (ε). Thus, for a reliable interpretation of FMD, it has to be normalized with respect to the above confounding factors. Normalization of FMD with shear stress at the time of measurement has been reported to reduce measurement variability. However, its widespread usage among the research community is limited. In this work, we examine the feasibility of normalizing the brachial FMD index (FMD%) to ε : extrema (εp), baseline (εb) and extrema change (∆ε) post-ischemia using its inter-day variability against FMD. In-vivo measurements were performed on 10 participants for 2 consecutive days and simultaneous pressure-diameter cycles were collected to estimate the material properties during reactive hyperemia (RH). The box-whisker plot reveals differences in the mean and deviation of FMD to FMD|εb. A significant value for repeatability (ICC ≥ 0.6) was obtained for normalized FMD (FMD|εb) for specific stiffness index (β), pressure-strain elastic modulus (Ep), and local pulse wave velocity (PWV) as compared to FMD. Hence, normalization of FMD% to arterial ε can potentially improve the measurement reliability of ER assessment.Clinical Relevance— This pilot study demonstrates the feasibility of brachial artery stiffness assessment during FMD and its potential use for normalizing the standard FMD measurement.
Millennial-scale hydroclimate variations in southwest China linked to tropical Indian Ocean since the Last Glacial Maximum

Indian summer monsoon (ISM) variations have been linked to the orbital-scale boreal summer insolation and millennial-scale North Atlantic climates. Recent studies show the critical role of Indian Ocean sea-surface temperatures (SSTs) in affecting deglacial millennial-scale monsoon oscillations. However, it is unclear whether SSTs can affect monsoon rainfall and terrestrial hydroclimate during the Holocene. Here we report multiproxy evidence of hydroclimate changes in southwest China since the Last Glacial Maximum. Similar to the often-documented gradual decrease in Holocene monsoon rainfall with superimposed millennial-scale variations, our records particularly show pronounced hydroclimate fluctuations including wet conditions at ∼5000-4000 yr ago, and perhaps over the past 1000 yr. We also find coherent variations between our records and sea-surface salinities in the eastern Indian Ocean, suggesting that terrestrial hydroclimate and resultant continental drainage have affected surface ocean conditions. These fluctuations are likely linked to changes in SSTs downstream of the monsoon source in the tropical western Indian Ocean, i.e., a warmer ocean and more monsoon rainfall. We conclude that the influence of both insolation and tropical SSTs on the ISM has persisted from the last deglaciation into Holocene.
Long term sustained attention alters dynamic functional connectivity patterns

Mental fatigue has attracted much attention from researchers as it plays a key role in performance efficiency and safety situations. Functional connectivity analysis using graph theory is an effective method for revealing changes in cognition resources influenced by mental fatigue. Previous studies have revealed that functional networks are dynamically reorganized. Therefore, it is critical to explore dynamic timescales of networks related to specific cognitive abilities. In this study, we used an open EEG dataset of twenty-one subjects recorded in a 60-minutes sustained attention task. After preprocessing, we constructed connectivity matrices using the weighted phase lag index (wPLI) in the theta band and characterized them with dynamic graph measures, namely characteristic path length (CPL) and clustering coefficient (CC). The results show that the frontal-parietal brain networks in theta band are involved in a sustaining attention task. When averaging from temporal and spatial activations, CPL and CC decreased with time-on-task. Our results indicate that mental fatigue results in deteriorations in sustaining attention, and graph theory analysis can provide support for mental fatigue analysis.Clinical Relevance— Identification of the effects of long term sustained attention on dynamic brain networks may be potential for mechanism study and detection of mental states and attentional deficits caused by mental diseases.
Closed-Loop Feedback Control of Human Step Width During Walking by Mediolaterally Acting Robotic Hip Exoskeleton

Maintaining balance during gait in the mediolateral direction requires more active motor control than in the anteroposterior direction. Step width modulation is a key strategy used by healthy individuals to achieve mediolateral walking balance, but it can be disrupted in populations with poor sensorimotor integration and weak hip abductors, such as the elderly, stroke patients, and people with lower limb amputation. Wearable hip exoskeletons have the potential to serve as assistive or rehabilitation devices for these populations, but there has been limited research on their appropriate usage. In this study, we successfully demonstrated the feasibility of controlling step width using a mediolaterally acting robotic hip exoskeleton. We were able to effectively adjust the user's step width by increasing or decreasing it to predefined targets through the regulation of admittance control parameters governing the device. The maximum average error to increase or decrease the step width was 1.2 cm. This research has the potential to facilitate the development of assistive and rehabilitation applications focused on enhancing the mediolateral gait balance of individuals with neurological impairments, elderly individuals, and amputees via the control of step width.
Design and Simulation of a Surface Acoustic Wave Based Micro Pressure Sensor

Among electromechanical systems, sensors based on surface acoustic waves (SAW) are of particular importance due to their fast response, high sensitivity, compact structure, small size, low cost of mass production as well as their wide range of applications. This paper presents design simulation and optimization of a SAW based micro pressure sensor using finite element method. Using the mathematical model of Delta function and various analysis like time-dependent analysis and frequency-domain analysis, several methods for calculating the central frequency of this kind of sensors have been presented. Determination of central frequency is then followed by calculation of changes in surface acoustic wave velocities for applied pressures. This lead to obtain the sensitivity of microsensor. By optimization of the affecting parameters, sensitivity of this sensor has been improved up to -101 ppm/ bar.
Incipient Slip Detection with a Biomimetic Skin Morphology

Incipient slip is defined as the slippage of part, but not all, of the contact surface between a sensor and an object. Reliably detecting incipient slip in artificial tactile sensors would benefit autonomous robot handling capabilities by helping prevent object slippage during manipulation. Here, we present a biomimetic skin morphology based on the human fingerprint with application to marker-based tactile sensors such as the TacTip biomimetic optical tactile sensor. We modify the 3D-printed outer membrane of the TacTip to mimic glabrous skin morphology with the inclusion of external ridges (fingerprint) and internal markers (intermediate ridges), allowing localised shear deformation of the sensor's skin prior to the onset of gross slip. To validate the performance of this skin morphology, we train a random forest classifier (RFC) to identify incipient slip based on the extracted marker displacements from the sensor when it is compressed against an acrylic plate and moved laterally. The RFC model achieves 97.46% accuracy on incipient slip prediction, and is then validated on an unseen pouring task, in which gravity-induced incipient slip is detected on average within $418\pm 753\ \text{ms}$ of its onset, and before gross slip in all trials. This accurate detection of incipient slip enables corrective actions prior to the onset of gross slip, a key capability in robotic manipulation and upper-limb prosthetics.
Fiber Optic Based Leakage Current Measurement and its Possible Length to Achieve the Safety Limit

This article shows a novel approach to measure the electrical Leakage Current or known also as residual current using Fiber Optic (FO). It differs over common Residual Current Device (RCD) device in assuring electrical safety in power system networks as it uses light as its medium. The main challenge of this technique was posed by the requirement of high magnetic fields in FO-based measurements. Therefore, compromising it by increasing the length of the magnetic fields' exposure is certainly logical with FO. Furthermore, the leakage current limit could be as low as 0.5mA by adhering to the IEC60601 standard. The same principle of Magneto Optical Effect (MOE) with Faraday Effect (also known as Faraday rotation) would be utilized. In this study, a common silica core FO with a low Verdet constant characteristic would be used. The proposed technique would be utilizing FO by coiling it around a current-carrying wire so that light experiences the Faraday Effect for a greater distance. From the simulation, FO based measurements with up to 15 km can still be utilized with a power reduction until 50% due to Attenuation Loss from a laser power source of only 100 mW. This implies that more potential applications are able to contribute to advancements in terms of energy efficiency with the many benefits of FO in comparison to the use of electrical wire with its magnetic induction.
The Effect of Fetal Heart Rate Segment Selection on Deep Learning Models for Fetal Compromise Detection

Monitoring the fetal heart rate (FHR) is common practice in obstetric care to assess the risk of fetal compromise. Unfortunately, human interpretation of FHR recordings is subject to inter-observer variability with high false positive rates. To improve the performance of fetal compromise detection, deep learning methods have been proposed to automatically interpret FHR recordings. However, existing deep learning methods typically analyse a fixed-length segment of the FHR recording after removing signal gaps, where the influence of this segment selection process has not been comprehensively assessed. In this work, we develop a novel input length invariant deep learning model to determine the effect of FHR segment selection for detecting fetal compromise. Using this model, we perform five times repeated five-fold cross-validation on an open-access database of 552 FHR recordings and assess model performance for FHR segment lengths between 15 and 60 minutes. We show that the performance after removing signal gaps improves with increasing segment length from 15 minutes (AUC = 0.50) to 60 minutes (AUC = 0.74). Additionally, we demonstrate that using FHR segments without removing signal gaps achieves superior performance across signal lengths from 15 minutes (AUC = 0.68) to 60 minutes (AUC = 0.76). These results show that future works should carefully consider FHR segment selection and that removing signal gaps might contribute to the loss of valuable information.
Improving the Transmission Power of UAVs with Intelligent Reflecting Surfaces in V2X

Unmanned aerial vehicles (UAVs), which can help with high-speed communications and provide better coverage, are an important component of next-generation wireless networks. Because of its high mobility and aerial nature, it is suitable for a wide range of mobile wireless communications-based applications. However, low data rates with limited transmission power constitute a significant difficulty in wireless communication that lowers network performance. To overcome this issue, integrating a UAV with a relay device capable of delivering high data speeds while utilising minimum transmission power is a promising approach. In this research, we presented an edge-cutting framework called UAV-IRS, in which an Intelligent reflective surface (IRS) supports unmanned aerial vehicles (UAVs) that traverse areas with low signal strength. Furthermore, we discussed the applications, challenges and research directions of UAV-IRS in vehicle-to-everything (V2X) communication. We considered a case study of UAV-IRS in V2X communication. The performance evaluation demonstrates how the viable data rate and minimum transmission power decrease with distance as the number of IRS elements increases.
The Kinematic-Muscle Synergies during Infant Crawling: A Pilot Study*

Many feature extraction algorithms have been separately used for kinematic or muscle synergy analysis during human movement. However, very few studies focus on the co-extraction of kinematic and muscle synergies. Therefore, the aim of this study was to propose a novel and efficient approach for extracting the kinematic-muscle synergies during infant crawling. Surface electromyography signals and three-dimensional joint trajectories were collected from 20 typically developing infants during self-paced hands-and-knees crawling. Angular accelerations of shoulder, elbow, hip and knee flexion/extension computing from those joint trajectories were divided into two independent directional positive degrees-of-freedom. The kinematic-muscle synergies and corresponding activation coefficients were extracted using the non-negative matrix factorization algorithm based on two selection criteria of synergy number (i.e., criterion 1: the total constraint, criterion 2: a combination of the total constraint and a local constraint for each joint/muscle). Then, the data of each joint/muscle were reconstructed by those synergies and corresponding activation coefficients. Our results indicated that the minimum number of kinematic-muscle synergies based on criterion 1 is less than that based on criterion 2. The data reconstruction of joint flexion/extension based on criterion 2 is better than that based on criterion 1, whereas the data reconstruction of muscles is similar between criterion 1 and 2. These promising results show the feasibility of applying the proposed approach to clinical assessments of motor function for infants.Clinical Relevance— Extracting kinematic-muscle synergies during infant crawling has the potential for professional therapists or rehabilitation physicians to conduct the early assessment and rehabilitation treatment of infants with the central nervous system disorders
Sparse ICA Based Semi-Blind Massive MIMO Channel Estimation without Prior Information of Inter-Cell Interference

Pilot contamination incurred by strong inter-cell interference seriously degrades the performance of channel estimation in massive multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We propose an independent component analysis (ICA) and sparse recovery algorithm based semi-blind channel estimation scheme, referred to as sparse ICA (SICA), for multi-cell massive MIMO-OFDM systems, which does not require any prior information of intercell interference and therefore is more practical. The proposed SICA scheme enables accurate channel estimation by exploiting both the high-order statistics of the received signal and channel sparsity in angle domain. The SICA scheme performs in a semi-blind manner as it is much more robust against pilot overhead than the previous approaches, and requires only one OFDM symbol as pilot to achieve a superior normalized mean square error of channel estimation. Furthermore, the complexity required by SICA is much lower than that required by the previous work, thanks to the negligible complexity of interference sources number estimation based on sparse recovery algorithm.
Robottheory Fitness: GoBot's Engagement Edge for Spurring Physical Activity in Young Children

Children around the world are growing more sedentary over time, which leads to considerable accompanying wellness challenges. Pilot results from our research group have shown that robots may offer something different or better than other developmentally appropriate toys when it comes to motivating physical activity. However, the foundations of this work involved larger-group interactions in which it was difficult to tease apart potential causes of motion, or one-time sessions during which the impact of the robot may have been due to novelty. Accordingly, the work in this paper covers more controlled interactions focused on one robot and one child participant, in addition to considering interactions over longitudinal observation. We discuss the results of a deployment during which $N=8$ participants interacted with our custom GoBot robot over two months of weekly sessions. Within each session, the child users experienced a teleoperated robot mode, a semi-autonomous robot mode, and a control condition during which the robot was present but inactive. Results showed that children tended to be more active when the robot was active and the teleoperated mode did not yield significantly different results than the semi-autonomous mode. These insights can guide future application of assistive robots in child motor interventions, in addition to informing how these robots can be equipped to assist busy human clinicians.
Evaluating Fiber Orientation Dispersion Measures Computed From Single-Shell Diffusion MRI

Fiber orientation dispersion is one of the fundamental features that can be estimated from diffusion magnetic resonance imaging (dMRI) of the brain. Several approaches have been proposed to estimate dispersion from single- and multi-shell dMRI acquisitions. Here, we derive solutions to bring these proposed methods to a standard orientation dispersion index (ODI) with the goal of making them comparable across different dMRI acquisitions. To illustrate the utility of the measures in studying brain aging, we further examined the age-dependent trajectory of the different single- and multi-shell ODI estimates in the white matter across the lifespan.Clinical Relevance— This work computes metrics of brain microstructure that can be adapted for large neuroimaging initiatives that aim to study the brain’s development and aging, and to identify deviations that may serve as biomarkers of brain disease.
“Unconventional” Monetary Policy as Conventional Monetary Policy: A Perspective from the U.S. in the 1920s

To implement monetary policy in the 1920s, the Federal Reserve utilized administered interest rates and conducted open market operations in both government securities and private money market securities, sometimes in fairly considerable amounts. We show how the Fed was able to effectively use these tools to influence conditions in money markets, even those in which it was not an active participant. Moreover, our results suggest that the transmission of monetary policy to money markets occurred not just through changing the supply of reserves but importantly through financial market arbitrage and the rebalancing of investor portfolios. The tools used in the 1920s by the Federal Reserve resemble the extraordinary monetary policy tools used by central banks recently and provide further evidence on their effectiveness even in ordinary times.