Miracle-dz/UniFAR-FTUs huggingface dataset:
MIT Open Access

Background: Compensating for the effect of gravity by providing arm-weight support (WS) is a technique often utilized in the rehabilitation of patients with neurological conditions such as stroke to facilitate the performance of arm movements during therapy. Although it has been shown that, in healthy subjects as well as in stroke survivors, the use of arm WS during the performance of reaching movements leads to a general reduction, as expected, in the level of activation of upper limb muscles, the effects of different levels of WS on the characteristics of the kinematics of motion and of the activity of upper limb muscles have not been thoroughly investigated before. Methods: In this study, we systematically assessed the characteristics of the kinematics of motion and of the activity of 14 upper limb muscles in a group of 9 healthy subjects who performed 3-D arm reaching movements while provided with different levels of arm WS. We studied the hand trajectory and the trunk, shoulder, and elbow joint angular displacement trajectories for different levels of arm WS. Besides, we analyzed the amplitude of the surface electromyographic (EMG) data collected from upper limb muscles and investigated patterns of coordination via the analysis of muscle synergies. Results: The characteristics of the kinematics of motion varied across WS conditions but did not show distinct trends with the level of arm WS. The level of activation of upper limb muscles generally decreased, as expected, with the increase in arm WS. The same eight muscle synergies were identified in all WS conditions. Their level of activation depended on the provided level of arm WS. Conclusions: The analysis of muscle synergies allowed us to identify a modular organization underlying the generation of arm reaching movements that appears to be invariant to the level of arm WS. The results of this study provide a normative dataset for the assessment of the effects of the level of arm WS on muscle synergies in stroke survivors and other patients who could benefit from upper limb rehabilitation with arm WS.
Ibuprofen: Discovery, Development and Therapeutics

Ibuprofen has become one of the foremost pain-relieving medications world-wide with its proven safety and efficacy in a wide variety of painful and inflammatory conditions. It has also been widely investigated for application in a variety of painful and non-pain inflammatory states including cancer and neurodegenerative conditions, reflecting the unique and novel properties of the drug that would never have been foreseen from knowledge of the properties when it was initially discovered.
High-Accuracy Neuro-Navigation with Computer Vision for Frameless Registration and Real-Time Tracking

For the past three decades, neurosurgeons have utilized cranial neuro-navigation systems, bringing millimetric accuracy to operating rooms worldwide. These systems require an operating room team, anesthesia, and, most critically, cranial fixation. As a result, treatments for acute neurosurgical conditions, performed urgently in emergency rooms or intensive care units on awake and non-immobilized patients, have not benefited from traditional neuro-navigation. These emergent procedures are performed freehand, guided only by anatomical landmarks with no navigation, resulting in inaccurate catheter placement and neurological deficits. A rapidly deployable image-guidance technology that offers highly accurate, real-time registration and is capable of tracking awake, moving patients is needed to improve patient safety. The Zeta Cranial Navigation System is currently the only non-fiducial-based, FDA-approved neuro-navigation device that performs real-time registration and continuous patient tracking. To assess this system’s performance, we performed registration and tracking of phantoms and human cadaver heads during controlled motions and various adverse surgical test conditions. As a result, we obtained millimetric or sub-millimetric target and surface registration accuracy. This rapid and accurate frameless neuro-navigation system for mobile subjects can enhance bedside procedure safety and expand the range of interventions performed with high levels of accuracy outside of an operating room.
Integration of Lipid-Functionalized Epigallocatechin-3-gallate into PLGA Matrix as a Novel Polyphenol-Based Nanoantioxidant

The search for polyphenol-based materials with antioxidant activity is a growing research area in the biomedical field. To obtain an efficient and stable nanoantioxidant, a novel biosystem was designed by integrating a lipophilic derivative of epigallocatechin-3-gallate (named EGCG-C18) on the surface of poly(lactic-co-glycolic acid) (PLGA). Poly(vinyl alcohol) (PVA) and 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-poly(ethylene glycol) (DSPE-PEG2000) were selected as polymeric and lipidic stabilizers, respectively, and their influence on both physical properties and the antioxidant activity of nanoantioxidant was investigated by a combined in silico and experimental approach. Full-atom molecular dynamics (MD) simulations were carried out to describe the different self-assembly processes of all components and the interactions that guided the EGCG-C18 insertion inside the PLGA matrix. Together with infrared spectroscopy results, the formation of an antioxidant lipid shell on the PLGA surface was clear. Dynamic light scattering and transmission electron microscopy showed that in the presence of DSPE-PEG2000, NPs were smaller than those treated with PVA. In addition, the different stabilizers used strongly influenced the ROS-scavenging ability of nanomaterials and this effect was strictly related to the molecular organization of EGCG-C18. MD showed that the apolar interaction between the alkyl chains of DSPE-PEG2000 and EGCG-C18 oriented the phenolic groups of the polyphenol toward the solvent, providing an ability of NP to scavenge hydroxyl radicals over to free EGCG-C18 and PLGA/PVA NPs. Finally, the ability of nanoantioxidants to protect human dermal fibroblasts from cell death induced by oxidative stress has been tested, revealing the high potential of these novel NPs as polyphenol-based materials.
Using the Omega Index for Evaluating Abstractive Community Detection

Numerous NLP tasks rely on clustering or community detection algorithms. For many of these tasks, the solutions are disjoint, and the relevant evaluation metrics assume nonoverlapping clusters. In contrast, the relatively recent task of abstractive community detection (ACD) results in overlapping clusters of sentences. ACD is a sub-task of an abstractive summarization system and represents a twostep process. In the first step, we classify sentence pairs according to whether the sentences should be realized by a common abstractive sentence. This results in an undirected graph with sentences as nodes and predicted abstractive links as edges. The second step is to identify communities within the graph, where each community corresponds to an abstractive sentence to be generated. In this paper, we describe how the Omega Index, a metric for comparing non-disjoint clustering solutions, can be used as a summarization evaluation metric for this task. We use the Omega Index to compare and contrast several community detection algorithms.
Navigating Shadows: The Impact of Social Stigma on the Mental Health of the Transgender Community in South Asia

This qualitative study explores the intricate relationship between social stigma and mental health among the transgender community in Lahore, Pakistan, and Delhi, India. Through 16 in-depth interviews, the research employs thematic analysis to uncover the core themes that emerge from the personal narratives of transgender individuals in these culturally diverse yet challenging environments. The thematic analysis revealed several key themes: the pervasive impact of societal rejection, the psychological toll of discrimination, resilience in the face of adversity, and the quest for identity and acceptance. Participants' accounts vividly portray the harsh realities of living as a transgender individual in societies where deep-seated stigma is prevalent. Many described experiences of ostracization from family and community, leading to feelings of isolation and psychological distress. The stigma associated with their gender identity often resulted in discrimination in various spheres of life, including employment, healthcare, and social interactions, further exacerbating their mental health challenges. Despite these adversities, a recurring theme of resilience emerged. Participants shared coping strategies and support systems that helped them navigate these challenges. The stories highlighted not only their struggles but also their strength and determination to live authentically. This study sheds light on the urgent need for societal change and policy reform to support the mental health and well-being of the transgender community in Lahore and Delhi. The findings provide valuable insights for mental health professionals, policymakers, and advocates, emphasizing the importance of creating inclusive and supportive environments that respect and affirm transgender identities.
Spinal GABA transporter 1 contributes to evoked-pain related behavior but not resting pain after incision injury

The inhibitory function of GABA at the spinal level and its central modulation in the brain are essential for pain perception. However, in post-surgical pain, the exact mechanism and modes of action of GABAergic transmission have been poorly studied. This work aimed to investigate GABA synthesis and uptake in the incisional pain model in a time-dependent manner. Here, we combined assays for mechanical and heat stimuli-induced withdrawal reflexes with video-based assessments and assays for non-evoked (NEP, guarding of affected hind paw) and movement-evoked (MEP, gait pattern) pain-related behaviors in a plantar incision model in male rats to phenotype the effects of the inhibition of the GABA transporter (GAT-1), using a specific antagonist (NO711). Further, we determined the expression profile of spinal dorsal horn GAT-1 and glutamate decarboxylase 65/67 (GAD65/67) by protein expression analyses at four time points post-incision. Four hours after incision, we detected an evoked pain phenotype (mechanical, heat and movement), which transiently ameliorated dose-dependently following spinal inhibition of GAT-1. However, the NEP-phenotype was not affected. Four hours after incision, GAT-1 expression was significantly increased, whereas GAD67 expression was significantly reduced. Our data suggest that GAT-1 plays a role in balancing spinal GABAergic signaling in the spinal dorsal horn shortly after incision, resulting in the evoked pain phenotype. Increased GAT-1 expression leads to increased GABA uptake from the synaptic cleft and reduces tonic GABAergic inhibition at the post-synapse. Inhibition of GAT-1 transiently reversed this imbalance and ameliorated the evoked pain phenotype.
Master Computer Science An empirical analysis of transfer learning for link prediction in real-world networks

The task of predicting missing links in real-world networks has been thoroughly studied in the field of network science. Powerful algorithms have been developed that have shown remarkable performance in identifying unconnected pairs of nodes in a network likely to be connected. However, these algorithms often come at the cost of preprocessing and training time. Transfer learning has emerged as a viable solution to this problem, reusing previously trained models to predict on different data than they were trained on. However, limited research has been done on transfer learning for link prediction and how to efficiently and correctly employ a pre-trained link prediction model. In this thesis we propose a transfer learning model for link prediction, with a particular focus on two important issues in this regard. The first is how to adequately test transfer learning between different train and test networks. The second objective is to get a detailed understanding of which networks perform well, and why. The latter is investigated by assessing the relationship between similarity in network structure and performance in terms of AUC scores. For this, we perform experiments on 49 real-world networks of varying size and domain in a supervised setting. We find that the transfer learning performance of a model is related to network statistics associated to how clustered the network used to train the model is. Additionally, we observed that transfer learning performance in general decreases when employed for networks with divergent topological properties.
Water undersaturated mantle plume volcanism on present‐day Mars

Based on meteorite evidence, the present‐day Martian mantle has a combined abundance of up to a few hundred ppm of H2O, Cl, and F, which lowers the solidus and enhances the magma production rate. Adiabatic decompression melting in upwelling mantle plumes is the best explanation for young (last 200 Myr) volcanism on Mars. We explore water undersaturated mantle plume volcanism using a finite element mantle convection model coupled to a model of hydrous peridotite melting. Relative to a dry mantle, the reduction in solidus temperature due to water increases the magma production rate by a factor of 1.3–1.7 at 50 ppm water and by a factor of 1.9–3.2 at 200 ppm water. Mantle water also decreases the viscosity and increases the vigor of convection, which indirectly increases the magma production rate by thinning the thermal boundary layer and increasing the flow velocity. At conditions relevant to Mars, these indirect effects can cause an order of magnitude increase in the magma production rate. Using geologic and geophysical observations of the Late Amazonian magma production rate and geochemical observations of melt fractions in shergottite meteorites, present‐day Mars is constrained to have a core–mantle boundary temperature of ~1750 to 1800 °C and a volume‐averaged thermal Rayleigh number of 2 × 106 to 107, indicating that moderately vigorous mantle convection has persisted to the present day. Melting occurs at depths of 2.5–6 GPa and is controlled by the Rayleigh number at the low pressure end and by the mantle water concentration at high pressure.
MIMO-NOMA-Aided Healthcare IoT Networking: Automated Massive Connectivity Protocol

This paper addresses the design of multiple access protocol for healthcare Internet of things (HIoT) networking, where devices are massively deployed to facilitate ubiquitous service provision. While non-orthogonal multiple access (NOMA) has been considered a major breakthrough for next generation smart healthcare network and beyond, automated random access enabled massive connectivity remains a challenge. To this end, automated massive connectivity protocol is developed, where multiple devices compete for a power level to gain access whenever needed. The power level is selected randomly from a predetermined set derived using large dimensional analysis presuming user-load $K$ significantly outnumbers the antennas equipped at access point (AP). As a contention-based random access protocol, the contention is resolved using ALOHA protocol. Moreover, simple forward error corrections (FECs) are introduced to enable massive connectivity in finite blocklength regime as well as to mitigate severe interference. In contrast to existing multiple-input multiple-output NOMA (MIMO-NOMA), the proposed scheme avoids spectral efficiency penalty due to preamble and the potentially long latency due to infinite blocklength as a result of density evolution method. The simulated results demonstrate that quality of service (QoS) in terms of enhanced random access, improved spectral efficiency and reduced power consumption, which justify the proposed MIMO-NOMA design evidently.
University of Memphis Digital Commons University of Memphis Digital Commons

In an attempt to illustrate the application of cognitive science principles to hard AI problems in machine learning we propose the LIDA technology, a cognitive science based architecture capable of more human-like learning. A LIDA based software agent or cognitive robot will be capable of three fundamental, continuously active, human-like learning mechanisms: 1) perceptual learning , the learning of new objects , categories , relations , etc., 2) episodic learning of events, the what , where , and when , 3) procedural learning , the learning of new actions and action sequences with which to accomplish new tasks. The paper argues for the use of modular components, each specializing in implementing individual facets of human and animal cognition, as a viable approach towards achieving general intelligence.
Framing Blame: Toward a Better Understanding of the Effects of Populist Communication on Populist Party Preferences

Although previous research has argued that the media play a crucial role in populism’s success, we know too little about how populist messages affect preferences for populist parties. To advance this knowledge, we conducted an experiment in which the core of populist rhetoric – constructing the people as innocent in-group opposed to the establishment as culprit out-group – was manipulated in newspaper articles. The findings indicate that when political elites are blamed for a salient national problem, people are more likely to vote for a populist party and less likely to vote for the largest party in government. Populist vote intentions are indirectly affected via blame perceptions. These findings offer important insights into the media’s role in the electoral success of populism.
Techniques to determine the kinematics of synsedimentary normal faults and implications for fault growth models

Abstract Normal faults grow via a sympathetic increase in their displacement and length (‘isolated model’) or by rapid establishment of their near-final length prior to significant displacement accumulation (‘constant-length model’). The isolated model has dominated the structural geology literature for >30 years, although some 3D seismic data-based studies support the constant-length model. Because they make different predictions regarding rift development, and earthquake size and recurrence intervals in areas of continental extension, it is critical to test these models with data from natural examples. Here we outline a range of techniques that constrain the kinematics of synsedimentary normal faults and thus test competing fault growth models. We then apply these techniques to three seismically imaged faults, showing that, in general, they grew in accordance with the constant-length model, although periods of relatively minor tip propagation and coeval displacement accumulation, characteristics more consistent with the isolated model, also occurred. We argue that analysis of growth strata represents the best way to test competing fault growth models; most studies utilizing this approach support the constant-length fault model, suggesting it may be more widely applicable than is currently assumed. It is plausible that the very early development of large faults is, however, characterized by the development of faults that, pre-linkage, grow in accordance with the isolated model; we may simply lack the data resolution, especially in the subsurface, to resolve this very early stage of fault growth.
Left-Behind Experiences and Cyberbullying Behavior in Chinese College Students: The Mediation of Sense of Security and the Moderation of Gender

Left-behind children seem to be more sensitive in interpersonal communication, find it more difficult to establish a stable, safe relationship with surrounding people, and have fewer positive coping styles when encountering problems, thus the aim of the present study was to explore the association between left-behind experiences and cyberbullying behavior among Chinese college students through the mediation of sense of security and the moderation of gender. A questionnaire survey comprised 553 college students with left-behind experiences and 526 college students without such experiences. The results showed that, firstly, cyberbullying behavior was significantly higher in college students with left-behind experiences than those without such experiences; secondly, left-behind experiences and cyberbullying behavior in college students was partially mediated by a sense of security; and finally, that gender moderated the mediation of the sense of security between left-behind experiences and cyberbullying behavior. This study suggests the family environment is important for individual growth and illustrates how the influence of childhood left-behind experience persists in individuals.
Market potential for processed sweet potato products in Tanzania: a case of Coast, Mwanza and Shinyanga Regions

Prices of sweet potato had drastically dropped due to its glut in the market during harvesting season. This lead to stumpy margin revenues to famers as compared to production costs. Hence solution to help farmers earn more is to add value to the produce in Tanzania. The objective of the study was to investigate and create awareness on the sweet potato processed products marketing potential in the smallholder production system. Specifically the study sought (i) to characterize and describe the sweet potato value chain (ii) to estimate market analysis for sweet potato processed products (iii) to analyse social economic factors affecting consumption of various processed sweet potato products and (iv) to analyses social economic factors influences farmer’s profitability. The study used cross-sectional research design combining both qualitative and quantitative approaches. It employed Regression analysis, Analysis of Variance (ANOVA), Market Potential Analysis, Profit Margin Analysis and Market Margin Analysis. The findings revealed that overall chain for sweet potato was characterized by low value addition, informal marketing system and poor coordination. Likewise, nutritional value and packaging were among a factor that influences the decision of consumers towards purchasing the products. Furthermore, results found that there was significant mean different in profit margin among actors in the chain at p < 0.01. Similarly, farmer’s profit margin was statistically significant different from the wholesaler’s profit margin at p < 0.01. The empirical data also suggested that variables included in the model explained about 64% of the variations in the dependent variable, hence farm gate price, occupation of the household and education level were statistically significant at p < 0.01. The industry still faces a number of developmental challenges, the most serious are chronic shortage of improved vines (33.1%), lack of capital (26.8%) and unpredicted weather (20.6%). Value addition approach is now a systematic focus of