This paper is a short review of classical and recent results on Marshall–Olkin shock models and their applications in reliability analysis. The classical Marshall–Olkin shock model was introduced in Marshall and Olkin (J Am Stat Assoc 62:30–44, 1967). The model describes a joint distribution of lifetimes of two components of a system subjected to three types of shocks. The distribution has absolutely continuous and singular parts. The Marshall–Olkin copula also aroused the interest of researchers working on the theory of copulas as an example of a copula having absolutely continuous and singular parts. There are some recent papers considering general models and modifications constructed on the basic idea of Marshall and Olkin (1967). These works find wide applications in reliability analysis in the case of a general system having n (\(n > 2\)) components and shocks coming from m (\(m > 3\)) sources. Some applications can also be seen in the theory of credit risk, where instead of lifetimes of the components, one considers the times to the default of two counter-parties subject to three independent underlying economic or financial events. In this work, we analyze and describe the results dealing with the generalization and modification of the Marshall–Olkin model.
Background Electrocardiogram (ECG) is a method of recording the electrical activity of the heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any irregularity of the heartbeat that causes an abnormality in the heart rhythm. Early detection of arrhythmia has great importance to prevent many diseases. Manual analysis of ECG recordings is not practical for quickly identifying arrhythmias that may cause sudden deaths. Hence, many studies have been presented to develop computer-aided-diagnosis (CAD) systems to automatically identify arrhythmias. Methods This paper proposes a novel deep learning approach to identify arrhythmias in ECG signals. The proposed approach identifies arrhythmia classes using Convolutional Neural Network (CNN) trained by two-dimensional (2D) ECG beat images. Firstly, ECG signals, which consist of 5 different arrhythmias, are segmented into heartbeats which are transformed into 2D grayscale images. Afterward, the images are used as input for training a new CNN architecture to classify heartbeats. Results The experimental results show that the classification performance of the proposed approach reaches an overall accuracy of 99.7%, sensitivity of 99.7%, and specificity of 99.22% in the classification of five different ECG arrhythmias. Further, the proposed CNN architecture is compared to other popular CNN architectures such as LeNet and ResNet-50 to evaluate the performance of the study. Conclusions Test results demonstrate that the deep network trained by ECG images provides outstanding classification performance of arrhythmic ECG signals and outperforms similar network architectures. Moreover, the proposed method has lower computational costs compared to existing methods and is more suitable for mobile device-based diagnosis systems as it does not involve any complex preprocessing process. Hence, the proposed approach provides a simple and robust automatic cardiac arrhythmia detection scheme for the classification of ECG arrhythmias.
This study investigates the relationship between the climate of authenticity and work engagement and the mediating role of surface acting that is directed at work unit members in this relationship. In a sample of 246 employees from 56 work units, multilevel structural equation modeling results show that a climate of authenticity is negatively associated with surface acting at the individual and work unit levels of analysis. Moreover, the relationship between the climate of authenticity and work engagement is mediated by surface acting in workplace interactions at the individual level of analysis. Our results suggest employee perceptions of a work unit affect climate that welcomes expressions of authentic emotions contributes to work engagement which is underlain by a low need to suppress the felt and fake the displayed emotions in workplace interactions.
Background: The frequency of genotype 4 hepatitis C virus infection is significantly higher in a city compared to other provinces in Turkey. In this study, we aimed to investigate the epidemiology and risk factors of hepatitis C virus genotype 4 infection in Kayseri province of Turkey. Methods: A case-control study was conducted with 61 hepatitis C virus genotype 4-infected patients and 71 controls. A questionnaire was administered to the patients and controls, asking for information about the risk factors of hepatitis C virus transmission. Core/ E1 and NS5B regions of hepatitis C virus genome were amplified and sequenced by Sanger method. Phylogenetic analysis and molecular clock analysis were performed. The risk was determined by calculating the odds ratio and 95% CI. Logistic regression analysis was performed to determine the effect of risk factors by controlling for confounding variables. Results: Kayseri isolates were closely related to type 4d sequences but formed a separate cluster. According to the molecular clock analysis, hepatitis C virus genotype 4d entered Kayseri province probably between 1941 and 1988. Blood transfusion and surgical intervention were found to be significant risk factors for the infection. Conclusion: Epidemiological data showed that hepatitis C virus genotype 4d infections are significantly associated with unsafe medical procedures.
The aim of this study is to use Gegenbauer wavelets in the solution of fractional integro-differential equations. The method is applied to several problems with different values of resolution parameter and the degree of the truncated polynomial. The results are compared with those obtained from other numerical methods. We observe that the current method is very effective and gives accurate results. One of the reasons for that is it enables us to improve accuracy by increasing resolution parameter, while keeping the degree of polynomial fixed. Another reason is nonlinear terms do not require linearization. Hence the method can be directly implemented and results in a the system of algebraic equations which solved by Wolfram Mathematica. It can be asserted that this is the first application of the Gegenbauer wavelet method to the aforementioned types of problems.
Previous studies have shown that serum estradiol (E2) levels can predict mortality in intensive care unit patients. Our study investigated the predictive role of admission estradiol level on patient mortality and development of acute kidney injury in medical intensive care unit patients with a wide range of diagnoses. We conducted a prospective cohort study using serum samples from hospitalized patients in medical, cardiac, and pulmonary intensive care units at the Ege University Hospital within 6 months. Serum estradiol levels from 118 adult patients were collected within 48 h of hospitalization. Receiver operating curves and multiple logistic regression analyses were performed to investigate its relationship with acute kidney injury development and mortality. Serum estradiol levels were significantly higher in non-survivor patients than in survivor patients [85 (19–560) pg/mL vs. 32 (3–262) pg/mL, p < 0.001]. Admission estradiol levels were significantly higher in patients with AKI on admission than in patients with chronic kidney disease (p = 0.002) and normal renal function (p = 0.017). Serum E2 levels were higher in patients with renal deterioration during follow-up than patients with stable renal functions [62 (11–560) pg/mL vs. 38 (3–456) pg/mL, p = 0.004]. An admission estradiol level of 52.5 pg/mL predicted follow-up renal deterioration with 63% sensitivity and 74% specificity. A combined (APACHE II-E) score using APACHE II and serum estradiol level predicted overall mortality with 66% sensitivity and 82% specificity. Admission estradiol level is a good marker to predict the development of acute kidney injury and mortality in medical intensive care unit patients.
Objectives Virtual laboratory simulations (VLSs) are computer-based tools that offer unlimited application options in scientific, medical, and engineering fields. The aim of this study was to evaluate whether VLSs are efficient learning tools and how these simulations can be integrated into laboratory practice in medical laboratory education. Methods In this pre-test/post-test control group study, 32 volunteers were randomly assigned to either experimental or control groups. The experimental group performed laboratory simulations based on biochemistry and microbiology and then completed a self-report survey to evaluate their satisfaction and beliefs about simulations. Results In the experimental group, post-test scores of each simulation were significantly elevated compared to pre-test scores; however, pre- and post-test scores of control group were statistically the same. The experimental group agreed that these simulations should be applied before theoretical lectures and laboratory practices. They also highlighted that translating from English to their native language creates difficulties in applying and understanding the simulation. Conclusions We emphasized that VLSs are excellent learning tools that increase not only the knowledge but also the self-motivation and focus of the students. Based on feedbacks, native language options are necessary to enable the students to achieve equality of opportunity in education.
Acetylcholinesterase inhibitors (AChE-I) are the core treatment of mild to severe Alzheimer’s disease (AD). However, the efficacy of AChE-I treatment on electroencephalography (EEG) and cognition remains unclear. We aimed to investigate the EEG power and coherence changes, in addition to neuropsychological performance, following a one-year treatment. Nine de-novo AD patients and demographically-matched healthy controls (HC) were included. After baseline assessments, all AD participants started cholinergic therapy. We found that baseline and follow-up gamma power analyzes were similar between groups. Yet, within the AD group after AChE-I intake, individuals with AD displayed higher gamma power compared to their baselines ( P < .039). Also, baseline gamma coherence analysis showed lower values in the AD than in HC ( P < .048), while these differences disappeared with increased gamma values of AD patients at the follow-up. Within the AD group after AChE-I intake, individuals with AD displayed higher theta and alpha coherence compared to their baselines (all, P < .039). These increased results within the AD group may result from a subclinical epileptiform activity. Even though AChE-I is associated with lower mortality, our results showed a significant effect on EEG power yet can increase the subclinical epileptiform activity. It is essential to be conscious of the seizure risk that treatment may cause.
Semantic priming in Turkish was examined in 36 right-handed healthy participants in a delayed lexical decision task via taxonomic relations using EEG. Prime–target relations included related- unrelated- and pseudo-words. Taxonomically related words at long stimulus onset asynchrony (SOA) were shown to modulate N400 and late positive component (LPC) amplitudes. N400 semantic priming effect in the time window of 300–500 ms was the largest for pseudo-words, intermediate for semantically-unrelated targets, and smallest for semantically-related targets as a reflection of lexical-semantic retrieval. This finding contributes to the ERP literature showing how remarkably universal the N400 brain potential is, with similar effects across languages and orthography. The ERP data also revealed different influences of related, unrelated, and pseudo-word conditions on the amplitude of the LPC. Attention scores and mean LPC amplitudes of related words in parietal region showed a moderate correlation, indicating LPC may be related to “relationship-detection process”
Approaches and methodologies based on technology are becoming more widespread in education. One of these technologies, Lecture Capture (LC), is particularly beneficial in higher education, since it allows students to improve learning via flexible access to video-recorded lectures. However, while LC is becoming more widespread in higher education, research on its impact on learning and teaching have prompted further discussion regarding its usage. The purpose of this study is to explore in depth the perceptions of students, faculty members, and administrators on the usage of LC in higher education, and to improve the system by identifying its key benefits and drawbacks. In the study, an embedded single-case study methodology was employed, focusing on a university’s use of LC. Analysis of the findings identified three main themes: purposes for which the system was used; changes occurred through usage of the system; plus, concerns and issues. The study highlights the importance of the pedagogical, methodological, and technical aspects of the LC system.
Hand gesture-based systems are one of the most effective technological advances and continue to develop with improvements in the field of human–computer interaction. Surface electromyography (sEMG) is utilized as a popular input data for gesture classification with elevated accuracy and advanced control capability. This paper presents a comparative hand gesture classification approach using time–frequency (TF) images of the spontaneous sEMG signals and the transfer learning method. 4-channel sEMG signals are collected from 30 subjects performing 7 specific hand gestures. After the required pre-processing, segmentation, and windowing steps, three TF analysis methods, namely Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), and Hilbert-Huang Transform (HHT), are applied to EMG signals to obtain TF images. Spectrograms from STFT, scalograms from CWT, and Hilbert-Huang spectra (HHS) from HHT obtained from multi-channel sEMG data are separately fused. TF images are then utilized to extract distinct features using seven state-of-the-art, pre-trained Convolutional Neural Network (CNN) architectures and classify seven hand gestures. Two different robust cross-validation strategies are conducted to evaluate the proposed method; stratified k-fold cross-validation (SKCV) and leave-one-subject-out cross-validation (LOOCV). We also investigate the effect of window size and the combination of Intrinsic Mode Functions (IMFs) on classification performance. The results demonstrated that the HHT utilizing IMFs obtained by Empirical Mode Decomposition (EMD) provided improved TF resolution and better results than STFT and CWT in the classification of sEMG signals. Finally, the best average accuracies (93.75% for SKCV) and (94.41% for LOOCV) are obtained by the HHT method with the pre-trained ResNet-50 model.
Since the exploration of sequencing began in 2005, third and next-generation sequencing (TGS and NGS) technologies have fundamentally changed metagenomics research. These platforms provide essential benefits regarding speed, cost, quality and precision in the never-ending search for microorganisms’ genetic material, regardless of location on earth. TGS are typically represented by technologies driven from power generation by semiconductor chips and utilization of enzymatic reactions by SOLiD/Ion Torrent PGM™ from Life Sciences, sequencing by synthesis using fluorescent labels on HiSeq/MiSeq™ from Illumina, pyrosequencing by GS FLX Titanium/GS Junior from Roche and nanopore-based sequencing by MinION™/GridION™/PromethION™ from Oxford Nanopore Technologies. The evolution of this technology enabled researchers to continually broaden their knowledge of the microbial world. This review presents a comprehensive overview of the recent literature on the utilization of both TGS and NGS technologies for the investigation of microbial metagenomics, their benefits and limitations with real-time examples of novel applications in clinical microbiology and public health, food and agriculture, energy and environment, arts and space.
Objectives The present study aims to investigate the effects of age, gender, and level of education on P300 in a healthy population, aged 50 years and over; and determine the reliability metrics for different conditions and measurement methods. Method Auditory and visual oddball recordings of 171 healthy adults were investigated. A fully automated preprocessing was applied to elicit ERP P300. Maximum peak amplitude, latency and mean amplitudes were measured. Data were stratified by age, gender, and education to determine group-level differences by using repeat measures of ANOVA. The internal consistency of P300 was calculated by a split-half method using odd-even segments. Test-retest reliability was assessed by calculating the intraclass correlation coefficient (ICC). Results Maximum peak P300 amplitudes were higher in the 50–64 years age group compared to the >65 years age group; and females showed increased P300 amplitudes compared to males. P300 measures showed fair to good internal consistency and poor to good test-retest reliability. Conclusion Age and gender should be taken into account when designing ERP studies with elderly individuals. P300 showed good internal consistency in general, between gender groups and age groups. Long-term test-retest reliability was lower but acceptable. These findings can be interpreted as the strength of P300 by being an objective and reliable method independent of cultural differences. Here we underline several factors that may affect P300 measures and discuss other possible factors that should be standardized for P300 to be used in clinical settings.
The aim of the study is to characterize synthesized spherical morphological polymeric hydrogel membranes (SMPHMs), especially their swelling properties, and to show the usability of these SMPHMs in the biomedical applications such as drug delivery systems. Insulin used in the treatment of diabetes mellitus disease was chosen as a model drug to demonstrate the usability of these SMPHMs as a drug delivery system. For this purpose, poly(hydroxyethyl methacrylate-co-glycidyl methacrylate) [P(HEMA-GMA)] SMPHMs were prepared by photopolymerization technique using different monomers mole ratios. Characterization of SMPHMs was carried out with SEM and FTIR analyses. Swelling experiments were conducted in water. Equilibrium percentage swelling values of SMPHMs were calculated and found as in the range of 40–122%, depending on hydrophilic structure of SMPHMs. Swelling kinetic parameters were determined, and the diffusion behaviour of water was also investigated. Water diffusion into the SMPHMs was found to shift from non-Fickian diffusion to Fickian diffusion when HEMA/GMA mole ratio was decreased in the structure of SMPHMs. In the final part of study, insulin release conditions from SMPHMs were optimized. For this purpose, insulin release studies were carried out to investigate the effect of monomer ratios, pH, temperature, and initial insulin concentration. The amount of maximum cumulative insulin release was found as 3747.73 µg/g in pH 7.4, at 25 °C, in the 0.5 mg/mL insulin concentration from SMPHMs-3 in seven hours. According to these obtained results, these SMPHMs can be used as alternative systems for biotechnological applications such as swelling-controlled drug delivery systems.
Background: Neck pain is a common musculoskeletal problem in adults. Clinical pilates exercises can be beneficial managing the pain and improving the risk factors. Aims: The aim of this study was to investigate the effects of clinical pilates exercises on pain and disability, deep neck flexor endurance (DNFE), posture, cervical range of motion (CROM), and proprioception in patients with chronic nonspecific neck pain. Methods: Fifty patients with chronic nonspecific neck pain were randomized into two groups. The clinical pilates exercise group (CPEG) received clinical pilates exercises for 6 weeks. The control group (CG) received posture education. Pain and disability, DNFE, forward head angle (FHA) and forward shoulder angle (FSA) degrees, CROM, and joint position error (JPE) were measured before and after 6 weeks. Results: Clinical pilates exercises improved pain, Neck Disability Index (NDI), DNFE, posture, CROM (except extension), and JPE (p < 0.05). We found significant differences between CPEG and CG in the comparisons for the change of pain, NDI, DNFE, FSA, CROM (except extension), and JPE (except left rotation) (p < 0.05). NDI, FSA, and rotational JPE also improved in CG (p < 0.05). However, these improvements were significantly better in CPEG than CG (p < 0.05) except JPE in left rotation (p = 0.118). Conclusion: In patients with chronic nonspecific neck pain, clinical pilates exercise is a safe and effective method to improve pain and disability, DNFE, posture, ROM, and proprioception. Clinical trial registration: NCT03782584 retrospectively registered December 20 2018.
Live fluorescence imaging has demonstrated the dynamic nature of dendritic spines, with changes in shape occurring both during development and in response to activity. The structure of a dendritic spine correlates with its functional efficacy. Learning and memory studies have shown that a great deal of the information stored by a neuron is contained in the synapses. High precision tracking of synaptic structures can give hints about the dynamic nature of memory and help us understand how memories evolve both in biological and artificial neural networks. Experiments that aim to investigate the dynamics behind the structural changes of dendritic spines require the collection and analysis of large time-series datasets. In this paper, we present an open-source software called SpineS for automatic longitudinal structural analysis of dendritic spines with additional features for manual intervention to ensure optimal analysis. We have tested the algorithm on in-vitro, in-vivo, and simulated datasets to demonstrate its performance in a wide range of possible experimental scenarios.
Pathogens, which are the source of infectious diseases, have imposed a strong selection pressure on human evolution as one of the most important causes of human death during the natural selection process. As a result of this, it is assumed that a variety of adaptations have evolved against infection threats and one of these adaptations is the physiological immune system. However, activation of the physiological immune system can be quite costly for organisms in some cases, and therefore it has been recently proposed in evolutionary psychology that an adaptive system called behavioral immune system may have evolved in association with the proactive functional processes against pathogen threats. Furthermore, it was hypothesized that a number of psychopathologies might develop as a result of maladaptive processes affecting the functionality of this system, and one of these psychopathologies might be trypophobia. Trypophobia refers to a psychological disorder in which individuals experience aversion and disgust at excessive levels toward clusters of small objects such as holes and bumps. Following this, the current review was established within the framework of three distinct goals. Firstly, this review aimed to discuss the evolutionary basis and mechanisms of the behavioral immune system. Secondly, the review aimed to discuss the characteristic features and the etiological explanations of trypophobia. Finally, the review aimed to discuss how potential changes in the behavioral immune system might lead to the development of trypophobia.
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