A Novel Screw Modeling Approach to Study the Effects of Screw Parameters on Pullout Strength.

Screw loosening remains a prominent problem for osteoporotic patients undergoing pedicle screw fixation surgeries and is affected by screw parameters (e.g., diameter, pitch, and thread angle). However, the individual and interactive effects of these parameters on screw fixation are not fully understood. Furthermore, the current finite element modeling of a threaded screw is less computationally efficient. To address these issues, we (1) explored a novel "simulated threaded screw" approach (virtual threads assigned to the contact elements of a simplified screw) and compared its performance with threaded and simplified screws, and (2) examined this approach the individual and interactive effects of altering screw diameter (5.5-6.5 mm), pitch (1-2 mm) and half-thread angle (20-30 deg) on pullout strength of normal vertebrae. Results demonstrated that the "simulated threaded screw" approach equivalently predicted pullout strength compared to the "threaded screw" approach (R2 = 0.99, slope = 1). We further found that the pullout strength was most sensitive to the change in screw diameter, followed by thread angle, pitch, and interactions of diameter*pitch or diameter*angle. In conclusion, the "simulated threaded screw" approach can achieve the same predictive capability compared to threaded modeling of the screw. The current findings may serve as useful references for planning of screw parameters, so as to improve the complication of screw loosening.

Optimal time-varying postural control in a single-link neuromechanical model with feedback latencies.

Maintaining balance during quiet standing is a challenging task for the neural control mechanisms due to the inherent instabilities involved in the task. The feedback latencies and the lowpass characteristics of skeletal muscle add to the difficulty of regulating postural dynamics in real-time. Inverted-pendulum (IP) type robotic models have served as a popular paradigm to investigate control of postural balance. In this study, an in-depth neuromechanical postural control model is developed from physiological principles. The model comprises a single-segment IP robotic model, Hill-type muscle model, and proprioceptive feedback from the muscle spindle (MS) and golgi tendon organ (GTO). An optimal proportional-integral-derivative (PID) controller is proposed to realize effective postural control amid latencies in sensory feedback. The neural commands for postural stabilization are generated by a time-varying PID controller, tuned using linear quadratic regulator (LQR) principles. Computer simulations are used to assess the efficacy of the tuned PID-LQR controller. Sensitivity analysis of the controlled system shows a delay tolerance of 300ms. Preliminary empirical data in support of the mathematical model were obtained from perturbation experiments. The model response to perturbation torque, measured in terms of the center of mass (COM) excursion in the anterior-posterior (AP) direction, displays a high degree of correlation with the empirical data ([Formula: see text]).

Local edge-enhanced active contour for accurate skin lesion border detection.

BACKGROUND: Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions. Accurate skin lesion border detection is the key to extract important dermoscopic features of the skin lesion. In current clinical settings, border delineation is performed manually by dermatologists. Operator based assessments lead to intra- and inter-observer variations due to its subjective nature. Moreover it is a tedious process. Because of aforementioned hurdles, the automation of lesion boundary detection in dermoscopic images is necessary. In this study, we address this problem by developing a novel skin lesion border detection method with a robust edge indicator function, which is based on a meshless method. RESULT: Our results are compared with the other image segmentation methods. Our skin lesion border detection algorithm outperforms other state-of-the-art methods. Based on dermatologist drawn ground truth skin lesion borders, the results indicate that our method generates reasonable boundaries than other prominent methods having Dice score of 0.886 ±0.094 and Jaccard score of 0.807 ±0.133. CONCLUSION: We prove that smoothed particle hydrodynamic (SPH) kernels can be used as edge features in active contours segmentation and probability map can be employed to avoid the evolving contour from leaking into the object of interest.

How does green human resource management foster employees' environmental commitment: A sequential mediation analysis.

Drawing on the social identity theory, this study aims to examine the impact of organizational pride and organizational identification as sequential mediators in the association between green HRM and employee environmental commitment. The study extended prior research by incorporating the sequential mediators of organizational pride and organizational identification. The researchers gathered data from 267 employees of telecommunications companies in Pakistan. They used Smart PLS software version 3.0 to carry out partial least squares structural equation modeling to verify the hypotheses. The results indicate that green HRM leads to organizational pride, which, in turn, leads to increased organizational identification and, consequently, enhances environmental commitment. The findings hold significant value to practitioners and HR managers striving to develop HR practices that support sustainability, contribute to a culture of environmental responsibility, and lead to positive green outcomes for employees.

Craniocaudal toggling increases the risk of screw loosening in osteoporotic vertebrae.

BACKGROUND AND OBJECTIVE: Screw loosening remains a prominent problem for osteoporotic patients undergoing pedicle screw fixation surgeries but its underlying mechanisms are not fully understood. This study sought to examine the interactive effect of craniocaudal or axial cyclic loading (toggling) and osteoporosis on screw fixation. METHODS: QCT-based finite element models of normal (n = 7; vBMD = 156 ± 13 mg/cm3) and osteoporotic vertebrae (n = 7; vBMD = 72 ± 6 mg/cm3) were inserted with pedicle screws and loaded with or without craniocaudal toggling. Among them, a representative normal vertebra (age: 55; BMD: 140 mg/cm3) and an osteoporotic vertebra (age: 64; BMD: 79 mg/cm3) were also loaded with or without axial toggling. The individual and interactive effects of craniocaudal toggling and osteoporosis on screw fixation strength (the force when the pull-up displacement of the screw head reached 1 mm) and bone tissue failure (characterized by equivalent plastic strain) were examined by repeated measure ANOVA. RESULTS: A significant interactive effect between craniocaudal toggling and osteoporosis on screw fixation strength was detected (p = 0.008). Specifically, craniocaudal toggling led to a marked decrease in the fixation strength (68%, p < 0.05) and stiffness (83%, p < 0.05) only in the osteoporotic vertebrae but had no effect on screw fixation strength and stiffness of the normal vertebrae (p > 0.05). Likewise, most of the bone tissues around the screw in the osteoporotic vertebrae yielded following craniocaudal toggling whereas this result was not seen in the normal vertebrae. The axial toggling had no significant effect on bone tissue failure as well as pedicle screw fixation in normal or osteoporotic vertebrae. CONCLUSIONS: Craniocaudal toggling substantially reduces the screw fixation strength of the osteoporotic vertebrae by progressively increasing tissue failure around the screw, and therefore may contribute to the higher rates of screw loosening in osteoporotic compared to normal patients, whereas axial toggling is not a risk factor for pedicle screw loosening in normal or osteoporotic patients.

Automatic seizure detection with different time delays using SDFT and time-domain feature extraction.

Automatic seizure detection is important for fast detection of the seizure because the way that the expert denotes and searches for seizure in the long signal takes time. The most common way to detect seizures automatically is to use an electroencephalogram (EEG). Many studies have used feature extraction that needs time for calculation. In this study, sliding discrete Fourier transform (SDFT) was applied for conversion to a frequency domain without using a window, which was compared with using window for feature selection. SDFT was calculated for each time series sample directly without any delay by using a simple infinite impulse response (IIR) structure. The EEG database of Bonn University was used to test the proposed method, and two cases were defined to examine a two-classifier feedforward neural network and an adaptive network-based fuzzy inference system. Results revealed that the maximum accuracies were 93% without delay and 99.8% with a one-second delay. This delay accrued because the average was taken for the results with a one-second window.

Improved Classification Accuracy of Hand Movements Using Softmax Classifier and Kalman Filter.

Accurate identification of the intended hand movement from the surface Electromyography (sEMG) data is desired for effective control of myoelectric lower arm prostheses. This study improves the classification accuracy of hand gestures by using feature arrays, Kalman filter (KF), and a Softmax classifier. We use data from BioPatRec database to classify ten hand movements performed by 17 participants. The proposed classifier achieved 95.3% accuracy without KF, and 99.3% accuracy when KF was used to smooth the training data.

3D bipedal model with holonomic constraints for the decoupled optimal controller design of the biomechanical sit-to-stand maneuver.

Human voluntary movements are complex physical phenomena due to the complex control mechanism for coordination of limbs in the presence of physiological constraints. In this study, we propose a nonlinear human bipedal model with thirteen generalized coordinates to model sit-to-stand (STS) transfer. The model has three position based holonomic constraints and at the first stage, we decouple the translational variables (constrained system) from rotational variables (unconstrained systems). The unconstrained rotational degrees consist of seven sagittal and three frontal plane angles, which are controlled through their respective joint torques. We further decouple these angles in sagittal and frontal plane systems for a better control strategy. In this scheme, there are three decoupled controllers working together to stabilize the nonlinear model for a STS maneuver while satisfying the holonomic constraints. We adopt H(infinity) and H(2) controller designs for feedback torques in sagittal and frontal planes, respectively, and provide simulation results to show the improvement in the angular profiles. We further adopt this modeling strategy to study and analyze the neuromuscular disorders by decoupling healthy and neurodeficient extremities. Our study indicates that the decoupling of the bipedal model improves the controllability of the system and produces better angular profiles for a bipedal STS maneuver. This modeling scheme is useful for analysis of neuromuscular disorders and other relevant physiological motor control models.

The Impact of Transformational Leadership on Nurses' Organizational Commitment: A Multiple Mediation Model.

This study proposed that the transformational style of nursing staff supervisors inculcate commitment amongst nurses. Moreover, psychological empowerment and psychological well-being were posited as multiple meditators in the above-mentioned association, based on the tenant of conservation of resource (COR) theory. The authors have collected the survey data from the sample of (n = 299) hospital nurses working in Sargodha district of Pakistan. The bootstrap results have supported the direct, as well as indirect relationships. These findings imply that when the nursing staff perceives their leader as transformational, their psychological empowerment is enhanced, and they have higher well-being, which in turn increases their commitment to their hospitals. This study offers a better understanding of psychological states that mediate transformational leadership and organizational commitment linkage.

Defining the Characteristics of Chronic Hypoparathyroidism Not Adequately Controlled on Conventional Therapy: Consensus Findings of Three European Delphi Panels.

INTRODUCTION: European Society of Endocrinology (ESE) guidelines provide goals for hypoparathyroidism management but do not define characteristics of chronic hypoparathyroidism that is not adequately controlled. Three European country-specific Delphi panels were conducted to gain consensus on these characteristics. METHODS: Delphi panels were conducted in the UK, Sweden, and Portugal using similar methodology. At each round, panellists considered patients with chronic hypoparathyroidism whose disease is not adequately controlled on conventional therapy according to a matrix of four presentations of patients with chronic hypoparathyroidism: normal biochemical levels/well (group 1), abnormal biochemical levels/well (group 2), normal biochemical levels/unwell (group 3), and abnormal biochemical levels/unwell (group 4), with wellness defined by the patient's persistent symptoms, comorbidities, and complications. For groups 2-4, panellists rated characteristics in five categories (patient characteristics, family history, comorbidities, biochemistry, and symptoms/impact on quality of life [QoL]) with respect to defining a patient as having chronic hypoparathyroidism that was not adequately controlled on conventional therapy. Consensus was achieved when more than 80% of respondents agreed. RESULTS: Among the three countries, panellists agreed that characteristics within four of the five categories (patient characteristics, comorbidities, biochemistry, and symptoms/impact on QoL) were important for defining inadequate control. Characteristics deemed important in groups 2-4 included a history of compliance problems and chronic kidney disease stages 4 and 5. In groups 2 and 4, the biochemical parameters deemed important were serum calcium, urinary calcium, and serum creatinine. In groups 3 and 4, tingling or numbness in the hands/feet and face was the only symptom deemed important in all three countries. CONCLUSION: Delphi panels conducted in three European countries provided national consensus on key parameters of patient characteristics, biochemistry, comorbidities, and symptoms/impact on QoL that define not adequately controlled chronic hypoparathyroidism. These characteristics should be tested more widely for their applicability in clinical practice. FUNDING: Shire International GmbH, Zug, Switzerland, a member of the Takeda group of companies.

Glucocorticoid management of adrenal insufficiency in the United Kingdom: assessment using real-world data.

Background and objectives Glucocorticoids are used to manage adrenal insufficiency (AI). We describe treatments used in the United Kingdom and real-world clinical outcomes for each treatment. Methods We used 2010-2016 primary care data from The Health Improvement Network (THIN). Descriptive analyses were conducted, and differences in variables between patients prescribed immediate-release hydrocortisone (IR HC), prednisolone or modified-release hydrocortisone (MR HC) were assessed using Fisher's exact test. Results Overall, 2648 patients were included: 1912 on IR HC (72%), 691 on prednisolone (26%) and 45 (2%) on MR HC. A total of 1174 (44.3%) had primary and 1150 (43.4%) had secondary AI. Patients on prednisolone were older (P < 0.001) and had a greater history of smoking (292/691, P < 0.001) and CVD (275/691, P < 0.001). Patients on MR HC had more PCOS (3/45, P = 0.001) and diabetes (27/45, P = 0.004). The number of GP visits/patient/year was 6.50 in IR HC, 9.54 in prednisolone and 9.11 in MR HC cohorts. The mean number of A&E visits and inpatient and outpatient hospital admissions ranged from 0.42 to 0.93 visits/patient/year. The mean number of adrenal crises/patient/year was between 0.02 and 0.03 for all cohorts. Conclusion IR HC is most commonly used for the management of AI in the United Kingdom, followed by prednisolone. Few patients receive MR HC. The prednisolone and MR HC cohorts displayed a greater prevalence of vascular risk factors compared with IR HC. The occurrence of AC and primary and secondary resource use were similar between treatment cohorts, and they indicate significant resource utilisation. Improved treatment and management of patients with AI is needed.

A Systematic Review on Muscle Synergies: From Building Blocks of Motor Behavior to a Neurorehabilitation Tool.

The central nervous system (CNS) is believed to utilize specific predefined modules, called muscle synergies (MS), to accomplish a motor task. Yet questions persist about how the CNS combines these primitives in different ways to suit the task conditions. The MS hypothesis has been a subject of debate as to whether they originate from neural origins or nonneural constraints. In this review article, we present three aspects related to the MS hypothesis: (1) the experimental and computational evidence in support of the existence of MS, (2) algorithmic approaches for extracting them from surface electromyography (EMG) signals, and (3) the possible role of MS as a neurorehabilitation tool. We note that recent advances in computational neuroscience have utilized the MS hypothesis in motor control and learning. Prospective advances in clinical, medical, and engineering sciences and in fields such as robotics and rehabilitation stand to benefit from a more thorough understanding of MS.

A Method for Locomotion Mode Identification Using Muscle Synergies.

Active lower limb transfemoral prostheses have enabled amputees to perform different locomotion modes such as walking, stair ascent, stair descent, ramp ascent and ramp descent. To achieve seamless mode transitions, these devices either rely on neural information from the amputee's residual limbs or sensors attached to the prosthesis to identify the intended locomotion modes or both. We present an approach for classification of locomotion modes based on the framework of muscle synergies underlying electromyography signals. Neural information at the critical instances (e.g., heel contact and toe-off) was decoded for this purpose. Non-negative matrix factorization was used to extract the muscles synergies from the muscle feature matrix. The estimation of the neural command was done using non-negative least squares. The muscle synergy approach was compared with linear discriminant analysis (LDA), support vector machine (SVM), and neural network (NN) and was tested on seven able-bodied subjects. There was no significant difference ( p > 0.05 ) in transitional and steady state classification errors during stance phase. The muscle synergy approach performed significantly better ( ) than NN and LDA during swing phase while results were similar to SVM. These results suggest that the muscle synergy approach can be used to discriminate between locomotion modes involving transitions.

Interspeaker variation in habitual speaking rate: additional evidence.

PURPOSE: The purpose of the present study was to test the hypothesis that talkers previously classified by Y.-C. Tsao and G. Weismer (1997) as habitually fast versus habitually slow would show differences in the way they manipulated articulation rate across the rate continuum. METHOD: Thirty talkers previously classified by Tsao and Weismer (1997) as having habitually slow (n = 15; 7 males, 8 females) and habitually fast (n = 15; 8 males, 7 females) articulation rates produced a single sentence at 7 different rates, using a magnitude production paradigm. Hence, the participants were not randomly assigned to conditions. RESULTS: Quadratic regression functions relating measured to intended articulation rates were all statistically significant, and most important, there were significant differences between the slow and fast groups in the y intercepts of the functions, for both males and females. CONCLUSIONS: This study provides a constructive replication of Tsao and Weismer (1997), showing a difference between slow and fast talkers with a new set of speech materials and in a new task. The findings appear to be consistent with a biological basis for intertalker rate differences.

Recurrent anti-GBM disease with pulmonary artery aneurysms.

We present the first known case of pulmonary artery aneurysms as a feature of Recurrent Anti-Glomerular Basement Membrane disease.

Real-Time Task Discrimination for Myoelectric Control Employing Task-Specific Muscle Synergies.

We present a novel formulation that employs task-specific muscle synergies and state-space representation of neural signals to tackle the challenging myoelectric control problem for lower arm prostheses. The proposed framework incorporates information about muscle configurations, e.g., muscles acting synergistically or in agonist/antagonist pairs, using the hypothesis of muscle synergies. The synergy activation coefficients are modeled as the latent system state and are estimated using a constrained Kalman filter. These task-dependent synergy activation coefficients are estimated in real-time from the electromyogram (EMG) data and are used to discriminate between various tasks. The task discrimination is helped by a post-processing algorithm that uses posterior probabilities. The proposed algorithm is robust as well as computationally efficient, yielding a decision with > 90% discrimination accuracy in approximately 3 ms . The real-time performance and controllability of the algorithm were evaluated using the targeted achievement control (TAC) test. The proposed algorithm outperformed common machine learning algorithms for single- as well as multi-degree-of-freedom (DOF) tasks in both off-line discrimination accuracy and real-time controllability (p < 0.01).

PID controller tuning for the first-order-plus-dead-time process model via Hermite-Biehler theorem.

This paper discusses PID stabilization of a first-order-plus-dead-time (FOPDT) process model using the stability framework of the Hermite-Biehler theorem. The FOPDT model approximates many processes in the chemical and petroleum industries. Using a PID controller and first-order Padé approximation for the transport delay, the Hermite-Biehler theorem allows one to analytically study the stability of the closed-loop system. We derive necessary and sufficient conditions for stability and develop an algorithm for selection of stabilizing feedback gains. The results are given in terms of stability bounds that are functions of plant parameters. Sensitivity and disturbance rejection characteristics of the proposed PID controller are studied. The results are compared with established tuning methods such as Ziegler-Nichols, Cohen-Coon, and internal model control.

Task discrimination for non-weight-bearing movements using muscle synergies.

Myoelectric control of lower limb prostheses requires discrimination of task-specific muscle patterns. In this paper we present a method based on the notion of muscle synergies to discriminate between various non-weight-bearing movements such as knee extension/flexion, femur rotation in/out, tibia rotation in/out and ankle dorsiflexion/plantarflexion. Data is recorded from eight targeted muscle sites on the thigh. Non-negative matrix factorization is used to identify the muscle synergies using multiple features and estimation of electromyographic (EMG) patterns is done using non-negative least squares (NNLS). Classification accuracy for the movements involving the knee joint was higher than the movements involving the ankle joint. The proposed algorithm performs at par with the common machine learning algorithm Linear Discriminant Analysis (LDA) in offline analysis.