Miracle-dz/UniFAR-FTUs huggingface dataset:
Passive Wrist Stiffness: The Influence of Handedness

Objective: This paper reports on the quantification of passive wrist joint stiffness and investigates the potential influence of handedness and gender on stiffness estimates. Methods: We evaluated the torque–angle relationship during passive wrist movements in 2 degrees of freedom (into flexion–extension and radial–ulnar deviation) in 13 healthy subjects using a wrist robot. Experimental results determined intrasubject differences between dominant and nondominant wrist and intersubject differences between male and female participants. Results: We found differences in the magnitude of passive stiffness of left- and right-hand dominant males and right-hand dominant females suggesting that the dominant hand tends to be stiffer than the nondominant hand. Left-hand stiffness magnitude was found to be 37% higher than the right-hand stiffness magnitude in the left-handed male group and the right-hand stiffness magnitude was 11% and 40% higher in the right-handed male and female groups, respectively. Other joint stiffness features such as the orientation and the anisotropy of wrist stiffness followed the expected pattern from previous studies. Conclusion: The observed difference in wrist stiffness between the dominant and nondominant limb is likely due to biomechanical adaptations to repetitive asymmetric activities (such as squash, tennis, basketball, or activities of daily living such as writing, teeth brushing, etc.). Significance: Understanding and quantifying handedness influence on stiffness may have critical implication for the optimization of surgical and rehabilitative interventions.
Changes in Sleep Characteristics and Breathing Parameters During Sleep in Early and Late Pregnancy.

STUDY OBJECTIVES Few studies have objectively evaluated sleep characteristics during pregnancy or investigated the relationship between altered spectral electroencephalogram (EEG) bands and sleep-disordered breathing (SDB). The study aimed to describe changes in sleep as measured by polysomnography (PSG) and spectral EEG bands during pregnancy and to examine the relationship between delta power in non-rapid eye movement (NREM) sleep and SDB. METHODS This is a secondary analysis of a prospective study. One hundred twenty-three women underwent full PSG in early pregnancy, and 97 repeated PSG in late pregnancy. Spectral analysis of the EEG in NREM sleep was performed. We used linear and logistic mixed-model regression to analyze the sleep measures and linear regression to explore the association between delta power and apnea-hypopnea index (AHI) changes during pregnancy. RESULTS In late pregnancy, women had shorter sleep duration, poorer sleep efficiency, more awakenings, more stage N2 sleep, less slow wave sleep, less REM sleep, higher AHI, and higher periodic limb movement index compared to early pregnancy. The percentage of stage N1 sleep, sleep latency, REM sleep latency, and arousal index frequency did not change. Regarding EEG-spectra, delta and theta powers decreased, but beta-2 power increased during pregnancy. In multivariable analyses, greater reduction of delta power was associated with larger increases in AHI (β [95% confidence interval] = -0.038 [-0.073, -0.002], P = .040). Estimates suggest that each one-unit increase in AHI reduces delta power by 4% in late pregnancy. CONCLUSIONS PSG-measured sleep characteristics change during pregnancy. Delta power decreases when the severity of SDB increases during pregnancy. COMMENTARY A commentary on this article appears in this issue on page 1095.
A Comparative Study of Network Motifs in the Integrated Transcriptional Regulation and Protein Interaction Networks of Shewanella

The Shewanella species shows a remarkable respiratory versatility with a great variety of extracellular electron acceptors (termed Extracellular Electron Transfer, EET). To explore relevant mechanisms from the network motif view, we constructed the integrated networks that combined transcriptional regulation interactions (TRIs) and protein-protein interactions (PPIs) for 13 Shewanella species, identified and compared the network motifs in these integrated networks. We found that the network motifs were evolutionary conserved in these integrated networks. The functional significance of the highly conserved motifs was discussed, especially the important ones that were potentially involved in the Shewanella EET processes. More importantly, we found that: 1) the motif co-regulated PPI took a role in the “standby mode” of protein utilization, which will be helpful for cells to rapidly response to environmental changes; and 2) the type II cofactors, which involved in the motif TRI interacting with a third protein, mainly carried out a signalling role in Shewanella oneidensis MR-1.
Is EMG a Viable Alternative to BCI for Detecting Movement Intention in Severe Stroke?

<italic>Objective</italic>: In light of the shortcomings of current restorative brain–computer interfaces (BCI), this study investigated the possibility of using EMG to detect hand/wrist extension movement intention to trigger robot-assisted training in individuals without residual movements. <italic>Methods</italic>: We compared movement intention detection using an EMG detector with a sensorimotor rhythm based EEG-BCI using only ipsilesional activity. This was carried out on data of 30 severely affected chronic stroke patients from a randomized control trial using an EEG-BCI for robot-assisted training. <italic>Results</italic>: The results indicate the feasibility of using EMG to detect movement intention in this severely handicapped population; probability of detecting EMG when patients attempted to move was higher (<italic>p</italic> <inline-formula><tex-math notation="LaTeX">$ <$</tex-math> </inline-formula> 0.001) than at rest. Interestingly, 22 out of 30 (or 73%) patients had sufficiently strong EMG in their finger/wrist extensors. Furthermore, in patients with detectable EMG, there was poor agreement between the EEG and EMG intent detectors, which indicates that these modalities may detect different processes. <italic>Conclusion </italic>: A substantial segment of severely affected stroke patients may benefit from EMG-based assisted therapy. When compared to EEG, a surface EMG interface requires less preparation time, which is easier to don/doff, and is more compact in size. <italic>Significance</italic>: This study shows that a large proportion of severely affected stroke patients have residual EMG, which yields a direct and practical way to trigger robot-assisted training.
Inhibition of Biofilm and Virulence Factors of Candida albicans by Partially Purified Secondary Metabolites of Streptomyces chrestomyceticus Strain ADP4.

BACKGROUND Despite several advancements in antifungal drug discovery, fungal diseases like Invasive Candidiasis (IC) still remain associated with high rates of morbidity and mortality worldwide. Thus there is an enormous need for anti-Candida drugs. OBJECTIVE The main objectives of the work included: 1. To investigate therapeutically significant classes of secondary metabolites produced by S. chrestomyceticus strain ADP4. 2. To investigate and analyze inhibition of significant virulence attributes of C. albicans, such as, biofilm and secretory hydrolytic enzymes by ADP4 secondary metabolites. 3. Mechanistic analysis of probable compounds for their site of action on Secretary Aspartyl Proteinase 3 (Sap3). METHODS Metabolite extract-SDB (MESDB) of S. chrestomyceticus strain ADP4 was fractionated on silica gel column chromatography. Fractions were analyzed for anti-Candida activity by disc diffusion assay. Active fractions were further purified by differential solvent treatment. MIC90 values were determined by broth dilution method. MFC was based on counting viable cells. Inhibition of yeast to hyphae transition and that of production of hydrolytic enzymes were estimated by plate assays. GC-MS of MESDB and Partially Purified Metabolite preparations (PPMs) was done. GRIP docking studies with Sap 3 of C. albicans was done using VLife MDS 4.6 software. RESULTS Chemical profiling showed that ADP4 secondary metabolites contained alkaloids, flavonoids, polyphenols, terpenoids and triterpenes. The MESDB and the PPMs showed low or no cytotoxicity but were able to effectively contain virulence attributes of Candida pathogen. Docking studies revealed that some of the probable compounds have affinity for aspartic acid residue in Sap3 enzyme of C. albicans. CONCLUSION Secondary metabolite of strain ADP4 included important classes of therapeutically important compounds. Their anti-Candida activity was mediated by inhibition of critical virulence factors of the pathogen.
Loss Mechanisms Determining the Quality Factors in Quartz Tuning Forks Vibrating at the Fundamental and First Overtone Modes

Quartz tuning forks (QTFs) are piezo-transducers that have been implemented for numerous applications, such as chemical gas sensing, atomic force microscopy, rheology, and industrial process control. The most important parameter for QTFs’ sensing application is the resonance quality factor (Q-factor). An experimental investigation and theoretical analysis of the influence of QTFs’ geometries on the Q-factor of the flexural fundamental and first overtone resonance modes are reported. The resonance frequencies and related Q-factors for five different QTFs have been measured. The QTF response was recorded at different air pressures to investigate the influence of the surrounding medium on the Q-factor. A data analysis demonstrated that air viscous damping is the dominant energy dissipation mechanism for both flexural modes. Thermoelastic and support losses are additional contributions that depend on the QTF geometry. A study of the QTF damping mechanism dependence upon the prong geometry is also provided.
Deep Neuro-Cognitive Co-Evolution for Fuzzy Attribute Reduction by Quantum Leaping PSO With Nearest-Neighbor Memeplexes

Attribute reduction with many patterns and indicators has been regarded as an important approach for large-scale data mining and machine learning tasks. However, it is extremely difficult for researchers to inadequately extract knowledge and insights from multiple overlapping and interdependent fuzzy datasets from the current changing and interconnected big data sources. This paper proposes a deep neuro-cognitive co-evolution for fuzzy attribute reduction (DNCFAR) that contains a combination of quantum leaping particle swarm optimization with nearest-neighbor memeplexes. A key element of DNCFAR resides in its deep neuro-cognitive cooperative co-evolution structure, which is explicitly permitted to identify interdependent variables and adaptively decompose them in the same neuro-subpopulation, with minimizing the complexity and nonseparability of interdependent variables among different fuzzy attribute subsets. Next DNCFAR formalizes to the different types of quantum leaping particles with nearest-neighbor memeplexes to share their respective solutions and deeply cooperate to evolve the assigned fuzzy attribute subsets. The experimental results demonstrate that DNCFAR can achieve competitive performance in terms of average computational efficiency and classification accuracy while reinforcing noise tolerance. Furthermore, it can be well applied to clearly identify different longitudinal surfaces of infant cerebrum regions, which indicates its great potential for brain disorder prediction based on fMRI.
Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises

Discriminative methods commonly produce models with relatively good generalization abilities. However, this advantage is challenged in real-world applications (e.g., medical image analysis problems), in which there often exist outlier data points (sample-outliers) and noises in the predictor values (feature-noises). Methods robust to both types of these deviations are somewhat overlooked in the literature. We further argue that denoising can be more effective, if we learn the model using all the available labeled and unlabeled samples, as the intrinsic geometry of the sample manifold can be better constructed using more data points. In this paper, we propose a semi-supervised robust discriminative classification method based on the least-squares formulation of linear discriminant analysis to detect sample-outliers and feature-noises simultaneously, using both labeled training and unlabeled testing data. We conduct several experiments on a synthetic, some benchmark semi-supervised learning, and two brain neurodegenerative disease diagnosis datasets (for Parkinson’s and Alzheimer’s diseases). Specifically for the application of neurodegenerative diseases diagnosis, incorporating robust machine learning methods can be of great benefit, due to the noisy nature of neuroimaging data. Our results show that our method outperforms the baseline and several state-of-the-art methods, in terms of both accuracy and the area under the ROC curve.
Physical activity and weight gain after smoking cessation in postmenopausal women

Objective: Weight gain frequently occurs after smoking cessation. The objective of this study was to examine whether weight gain after smoking cessation was attenuated by physical activity (PA) in postmenopausal women. Methods: A total of 4,717 baseline smokers from the Women's Health Initiative were followed for 3 years. One thousand two hundred eighty-two women quit smoking, and 3,435 continued smoking. Weight was measured at baseline and at the year 3 visit. PA was assessed at both times by self-report, summarized as metabolic equivalent task-hours per week. Multiple linear regression models were used to assess the association between PA and postcessation weight gain, adjusting for potential confounding factors. Results: Compared with continuing smokers, quitters gained an average of 3.5 kg (SD = 5.6) between the baseline and year 3 visit. Quitters with decreased PA had the highest amount of weight gain (3.88 kg, 95% CI: 3.22-4.54); quitters with increased PA (≥15 metabolic equivalent task-hours /week) had the lowest weight gain (2.55 kg, 95% CI: 1.59-3.52). Increased PA had a stronger beneficial association for postcessation weight gain for women with obesity compared to normal weight women. Quitters who had low PA at baseline and high PA at year 3 and were also enrolled in a dietary modification intervention had nonsignificant weight gain (1.88 kg, 95% CI: −0.21-3.96) compared with continuing smokers. Conclusions: Our data demonstrate that even a modest increase in PA (equivalent to current recommendations) can attenuate weight gain after quitting smoking among postmenopausal women, especially in combination with improved diet.
Trajectory Optimization of Space Maneuver Vehicle Using a Hybrid Optimal Control Solver

In this paper, a constrained space maneuver vehicles trajectory optimization problem is formulated and solved using a new three-layer-hybrid optimal control solver. To decrease the sensitivity of the initial guess and enhance the stability of the algorithm, an initial guess generator based on a specific stochastic algorithm is applied. In addition, an improved gradient-based algorithm is used as the inner solver, which can offer the user more flexibility to control the optimization process. Furthermore, in order to analyze the quality of the solution, the optimality verification conditions are derived. Numerical simulations were carried out by using the proposed hybrid solver and the results indicate that the proposed strategy can have better performance in terms of convergence speed and convergence ability when compared with other typical optimal control solvers. A Monte-Carlo simulation was performed and the results show a robust performance of the proposed algorithm in dispersed conditions.
Learning and Guaranteed Cost Control With Event-Based Adaptive Critic Implementation

This paper focuses on the event-triggered guaranteed cost control design of nonlinear systems via a self-learning technique. In brief, an event-based guaranteed cost control strategy of nonlinear systems subjects to matched uncertainties is developed, thereby balancing the performance of guaranteed cost and the actuality of limited communication resource. The original control design is transformed into an optimal control problem with an event-based mechanism, where the relationship of guaranteed cost performance compared to the time-based formulation is discussed. A critic neural network is constructed for implementing the event-based optimal control design with stability guarantee. Simulation experiments are carried out to verify the theoretical results in detail.
Pathogenesis of Thalassemia Major–Associated Osteoporosis: Review of the Literature and Our Experience

Due to increasing life expectancy in thalassemia major (TM), osteoporosis is emerging as a significant problem. Its aetiology is multifactorial, culminating in increased bone resorption and remodelling. Hypogonadism and marrow expansion seem to play an important role, but iron overload, deferoxamine toxicity, a defective GH-IGF-1 axis and multiple endocrinopathies may represent additional causes of bone damage. Many of these patients, though under appropriate treatment programs, do not achieve normal peak bone mass. The RANK/RANKL/OPG and the Wnt/βCatenin systems work as major mediators of imbalanced bone turnover and bone loss. Additional genetic factors, such as collagen type I alpha 1 and vitamin D receptor gene polymorphisms, may exert some influence on the enhanced fracture risk observed in TM. To date, in spite of adequate hormone replacement, chelating therapy, and acceptable haemoglobin levels, subjects with TM display impaired bone density and imbalanced bone turnover, so the puzzle of the pathogenesis of thalassemia major–induced osteoporosis remains far from being solved.
Exponential Stability Analysis for Delayed Semi-Markovian Recurrent Neural Networks: A Homogeneous Polynomial Approach

This paper investigates the exponential stability analysis issue for a class of delayed recurrent neural networks (RNNs) with semi-Markovian parameters. By constructing a stochastic Lyapunov functional and using some zoom techniques to estimate its weak infinitesimal operator, the exponential mean square stability criteria have been proposed for the Markovian neural networks with certain transition probabilities. We then generalize the homogeneous polynomial approach for the delayed Markovian RNNs with uncertain transition probabilities during the stability analysis. Theoretical results have obtained by introducing an appropriate technique for dealing with a large number of complex homogeneous polynomial matrix inequalities. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed technique.
Reaching for the stars: Iranian nurses’ perceptions of career success

AIM The aim was to explore nurses' perceptions of career success. INTRODUCTION Career success is a concept which leads to improving professional behaviours. Research that focused on conceptualizing career success found it to be a complex, value-driven construct likely to be perceived differently across work and cultural contexts. It is not yet clear what constitutes career success, and how it is perceived by nurses in Iran. More comprehensive exploration of this concept in nursing is needed, so research and policymaking can advance in informed, data-driven manner. METHOD This is a qualitative descriptive study. The setting was teaching hospitals of Iran. Twenty-seven nurses participated in face-to-face interviews, and six field notes were collected. Data were analysed using conventional content analysis. FINDINGS Analyses identified five themes of career success: providing high-quality care, being exemplary employee, embarking on career growth, having positive personal attributes and being internally satisfied. CONCLUSION Findings confirmed that career success in Iranian nurses was a multidimensional concept that represented five internally and externally driven dimensions set within its cultural and religious context. Policymakers and clinical educators can now plan interventions aimed at fostering career success in nursing staff. IMPLICATIONS FOR EDUCATION, NURSING AND HEALTH POLICIES Future nurses could use these finding to reflect on their developed attributes throughout their studies and clinical placements and adjust their career preparation and learning to bridge any identified gaps. Organizational values and intervention for nursing staff could also be redesigned to align with the aspects of career success.
The Common Time Course of Memory Processes Revealed

Magnetoencephalography (MEG) was used to compare memory processes in two experiments, one involving recognition of word pairs and the other involving recall of newly learned arithmetic facts. A combination of hidden semi-Markov models and multivariate pattern analysis was used to locate brief “bumps” in the sensor data that marked the onset of different stages of cognitive processing. These bumps identified a separation between a retrieval stage that identified relevant information in memory and a decision stage that determined what response was implied by that information. The encoding, retrieval, decision, and response stages displayed striking similarities across the two experiments in their duration and brain activation patterns. Retrieval and decision processes involve distinct brain activation patterns. We conclude that memory processes for two different tasks, associative recognition versus arithmetic retrieval, follow a common spatiotemporal neural pattern and that both tasks have distinct retrieval and decision stages.
Modeling of Agent Cognition in Extensive Games via Artificial Neural Networks

The decision-making process, which is regarded as cognitive and ubiquitous, has been exploited in diverse fields, such as psychology, economics, and artificial intelligence. This paper considers the problem of modeling agent cognition in a class of game-theoretic decision-making scenarios called extensive games. We present a novel framework in which artificial neural networks are incorporated to simulate agent cognition regarding the structure of the underlying game and the goodness of the game situations therein. An algorithmic procedure is investigated to describe the process for solving games with cognition, and then, a new equilibrium concept is proposed as a refinement of the classical one—subgame perfect equilibrium—by involving players’ cognitive reasoning. Moreover, a series of results concerning the computational complexity, soundness, and completeness of the algorithm, as well as the existence of an equilibrium solution, is obtained. This framework, which is shown to be general enough to model the way in which AlphaGo plays Go, may offer a means for bridging the gap between theoretical models and practical problem-solving.
In Vitro Culturing and Screening of Candida albicans Biofilms

Candida albicans is a normal member of the human microbiota that asymptomatically colonizes healthy individuals, however it is also an opportunistic pathogen that can cause severe infections, especially in immunocompromised individuals. The medical impact of C. albicans depends, in part, on its ability to form biofilms, communities of adhered cells encased in an extracellular matrix. Biofilms can form on both biotic and abiotic surfaces, such as tissues and implanted medical devices. Once formed, biofilms are highly resistant to antifungal agents and the host immune system, and can act as a protected reservoir to seed disseminated infections. Here, we present several in vitro biofilm protocols, including protocols that are optimized for high‐throughput screening of mutant libraries and antifungal compounds. We also present protocols to examine specific stages of biofilm development and protocols to evaluate interspecies biofilms that C. albicans forms with interacting microbial partners. © 2018 by John Wiley & Sons, Inc.