Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel combined level set model for automatic MR image segmentation

Abstract Medical image processing comes along with object segmentation, which is one of the most important tasks in that field. Nevertheless, noise and intensity inhomogeneity in magnetic resonance images challenge the segmentation procedure. The level set method has been widely used in object detection. The flexible integration of energy terms affords the level set method to deal with variable difficulties. In this paper, we introduce a novel combined level set model that mainly cooperates with an edge detector and a local region intensity descriptor. The noise and intensity inhomogeneities are eliminated by the local region intensity descriptor. The edge detector helps the level set model to locate the object boundaries more precisely. The proposed model was validated on synthesized images and magnetic resonance images of in vivo wrist bones. Comparing with the ground truth, the proposed method reached a Dice similarity coefficient of > 0.99 on all image tests, while the compared segmentation approaches failed the segmentations. The presented combined level set model can be used for the object segmentation in magnetic resonance images.
EEG-Based Classification of the Driver Alertness State

Abstract GMLVQ (Generalized Matrix Relevance Learning Vector Quantization) is a method of machine learning with an adaptive metric. While training, the prototype vectors as well as the weight matrix of the metric are adapted simultaneously. The method is presented in more detail and compared with other machine learning methods employing a fixed metric. It was investigated how accurately the methods can assign the 6-channel EEG of 25 young drivers, who drove overnight in the simulation lab, to the two classes of mild and severe drowsiness. Results of cross-validation show that GMLVQ is at 81.7 ± 1.3 % mean classification accuracy. It is not as accurate as support-vector machines (SVM) and gradient boosting machines (GBM) and cannot exploit the potential of learning adaptive metrics in the case of EEG data. However, information is provided on the relevance of each signal feature from the weighting matrix.
Exploring an Ensemble of Textual Machine Learning Methodologies for Traffic Event Detection and Classification

Abstract Late research has established the critical environmental, health and social impacts of traffic in highly populated urban regions. Apart from traffic monitoring, textual analysis of geo-located social media responses can provide an intelligent means in detecting and classifying traffic related events. This paper deals with the content analysis of Twitter textual data using an ensemble of supervised and unsupervised Machine Learning methods in order to cluster and properly classify traffic related events. Voluminous textual data was gathered using innovative Twitter APIs and managed by Big Data cloud methodologies via an Apache Spark system. Events were detected using a traffic related typology and the clustering K-Means model, where related event classification was achieved applying Support Vector Machines (SVM), Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks. We provide experimental results for 2-class and 3-class classification examples indicating that the ensemble performs with accuracy and F-score reaching 98.5%.
Screens, Teens, and Psychological Well-Being: Evidence From Three Time-Use-Diary Studies

The notion that digital-screen engagement decreases adolescent well-being has become a recurring feature in public, political, and scientific conversation. The current level of psychological evidence, however, is far removed from the certainty voiced by many commentators. There is little clear-cut evidence that screen time decreases adolescent well-being, and most psychological results are based on single-country, exploratory studies that rely on inaccurate but popular self-report measures of digital-screen engagement. In this study, which encompassed three nationally representative large-scale data sets from Ireland, the United States, and the United Kingdom (N = 17,247 after data exclusions) and included time-use-diary measures of digital-screen engagement, we used both exploratory and confirmatory study designs to introduce methodological and analytical improvements to a growing psychological research area. We found little evidence for substantial negative associations between digital-screen engagement—measured throughout the day or particularly before bedtime—and adolescent well-being.
Two Number-guessing Problems Plus Applications in Cryptography

The article ﬁrst puts forward two number-guessing problems that is to guess if an odd integer or one of its divisors is a divisor of another odd integer that is contained in an given odd sequence consisting in consecutive odd integers, then solves the two problems through an investigation on the global intrinsic properties of the odd sequence. Several criteria are proved to determine if a special term is contained in an odd sequence. Based on the proved criteria, algorithms are designed to detect if a interval contains a divisor-host that has a common divisor with a given number and to ﬁnd out the divisor-host by means of probabilistic search. The theory and the algorithms are helpful in cracking a password or some encryption codes.
Spatio-temporal variation in the distribution and abundance of marine cladocerans in relation to environmental factors in a productive lagoon (Güllük Bay, SW Aegean Sea, Turkey)

Abstract In order to understand the effects of some physical (temperature and salinity) and chemical (dissolved oxygen, nitrate and chlorophyll a) factors on the abundance and distribution of cladoceran species, zooplankton samples were seasonally collected between August 2015 and July 2016 from three stations near the entrance to the Boğaziçi Lagoon in Güllük Bay (Aegean Sea, Turkey) using a WP2 plankton net with a mesh size of 200 μm. Four cladoceran species – Penilia avirostris, Pseudevadne tergestina, Evadne spinifera and Pleopis polyphemoides – were found during all sampling events throughout the study period. They showed high abundance in August (4774 ind. m−3) and October (10 706 ind. m−3) as the dominant zooplankton group. The abundance of Penilia avirostris – the dominant cladoceran at all sampling locations – was estimated up to 10 871 ind. m−3 in October. Pseudevadne tergestina was the second dominant cladoceran. In September, only Pseudevadne tergestina and Pleopis polyphemoides were found in samples in small numbers. The abundance of cladocerans varied significantly throughout the seasons. Two physicochemical factors, temperature and dissolved oxygen, were the main drivers of changes in the cladoceran composition.
Sensor Fusion and State Estimation of IoT Enabled Wind Energy Conversion System

The use of renewable energy has increased dramatically over the past couple of decades. Wind farms, consisting of wind turbines, play a vital role in the generation of renewable energy. For monitoring and maintenance purposes, a wind turbine has a variety of sensors to measure the state of the turbine. Sensor measurements are transmitted to a control center, which is located away from the wind farm, for monitoring and maintenance purposes. It is therefore desirable to ensure reliable wireless communication between the wind turbines and the control center while integrating the observations from different sensors. In this paper, we propose an IoT based communication framework for the purpose of reliable communication between wind turbines and control center. The communication framework is based on repeat-accumulate coded communication to enhance reliability. A fusion algorithm is proposed to exploit the observations from multiple sensors while taking into consideration the unpredictable nature of the wireless channel. The numerical results show that the proposed scheme can closely predict the state of a wind turbine. We also show that the proposed scheme significantly outperforms traditional estimation schemes.
A Deep-Learning Approach to Detect Fiducials in Planar X-Ray Images for Undistortion of Conventional C-Arm Images

Abstract In some applications such as 2D-3D registration, undistorted images are required to achieve optimal results. These types of images can be obtained from a distortion-free C-arm (flat-panel detector) or by undistorting the images given from a conventional C-arm (analogue image intensifier.) Undistorting images require a plate with fiducials connected to the C-arm detector. Detecting fiducials is affected by differences in the image contrast due to elements in the background. Therefore, the results vary from image to image and could require manual tuning of parameters. We propose a deep-learning approach for detecting undistortion-platefiducials in X-ray images to overcome the drawbacks previously stated. With an undistortion plate, we took 1120 XRays using a C-arm in different poses. Every X-ray is afterward cut into 60 sub-images. We used these sub-images for training a convolutional neural network (CNN). Comparing the CNN and a traditional image processing method based on the Hough Circle algorithm, we found that the detected fiducials using the traditional method give a similar fiducial positioning error. Nevertheless, the fiducial detection rate goes from 89.7% using the traditional method to 100% with the developed CNN. The results show that the detection rate and precision of our deep-learning approach guarantee the undistortion of conventional C-Arm images.
Plasma CADM1 promoter hypermethylation and D‐dimer as novel metastasis predictors of cervical cancer

Cervical cancer (CC) is the fourth malignant tumor in women worldwide. The metastasis is still the major reason for the treatment failures of most CC patients. Cell adhesion molecule 1 (CADM1) promoter methylation and plasma D‐dimer levels have been reported to be increased in many types of cancers. The purpose of this study was to investigate the value of combinatorial assay of plasma CADM1 promoter hypermethylation and D‐dimer as a metastasis marker in CC.
A Study on the Stress Concentration Factor Induced in Double Countersunk Holes Due to Uniaxial Tension

Abstract Finite element and response surface methods were utilized to investigate the stress concentration factor induced in isotropic rectangular plates with two identical countersunk rivet holes due to uniaxial tension. In this investigation, the finite element model was constructed using ANSYS software and used to produce stress concentration factor (SCF) data. Additionally, the response surface method (RSM) was implemented to characterize the influence of the problem geometric parameters on the SCF. Besides, RSM combined with least squares regression methods were employed to formulate a simple and effective equation to mathematically compute the stress concentration factor (Kt) value. This equation was consequently verified with finite element analysis (FEA) results. Lastly, an optimum plate and holes configuration that minimizes the SCF was suggested and hence recommended.
Saccadic suppression measured by steady-state visual evoked potentials (SSVEPs).

Visual sensitivity is severely impaired during the execution of saccadic eye movements. This phenomenon has been extensively characterized in human psychophysics and non-human primate single-neuron studies, but a physiological characterization in humans is less established. Here, we used a method based on steady-state visually evoked potential (SSVEP), an oscillatory brain response to periodic visual stimulation, to examine how saccades affect visual sensitivity. Observers made horizontal saccades back and forth, while horizontal black-and-white gratings flickered at 5-30 Hz in the background. We analyzed EEG epochs with a length of 0.3s either centered at saccade onset (saccade epochs) or centered at fixations half a second before the saccade (fixation epochs). Compared with fixation epochs, saccade epochs showed a broadband power increase, which most likely resulted from saccade-related EEG activity. The execution of saccades, however, led to an average reduction of 57% in the SSVEP amplitude at the stimulation frequency. This result provides additional evidence for an active saccadic suppression in the early visual cortex in humans. Compared to previous fMRI and EEG studies, an advantage of this approach lies in its capability to trace the temporal dynamics of neural activity throughout the time course of a saccade. In contrast to previous electrophysiological studies in non-human primates, we did not find any evidence for post-saccadic enhancement, even though simulation results show that our method would have been able to detect it. We conclude that SSVEP is a useful technique to investigate the neural correlates of visual perception during saccadic eye movements in humans.
The evaluation of synthetic datasets on training AlexNet for surgical tool detection

Abstract Surgical tool recognition is a key task to analyze surgical workflow, in order to improve the efficiency and safety of laparoscopic surgeries. The laparoscopic videos are important sources to conduct this task, However, there are some challenges to analyze these videos. Focus on the imbalanced dataset problem, data augmentation method based on generate different synthetic datasets and evaluate their performance training on a convolutional neural network model are investigated in this research. The results show the effect on the model with different background patterns. A better performance was achieved when the model was trained by a structure background dataset. Further research will be needed to understand why the original background patterns support the correct classification. It is assumed that this is an overlearning effect, that will not hold if other procedures were included into the test set.
Clinical Characteristics of PMS Co-Morbid with MDD and Effectiveness of SSRIs in its Treatment

Abstract Background: Data on the clinical characteristics of premenstrual syndrome (PMS) with co-morbid major depressive disorder (MDD) are scarce. Although selective serotonin re-uptake inhibitors (SSRIs) are widely used to treat both PMS and MDD there is little information on their efficacy in PMS in patients with co-morbid MDD. Objective: To describe the clinical picture of PMS co-morbid with current depressive episode (DE) and evaluate its dynamics under a 6-month course of SSRIs treatment. Materials and methods: We present a longitudinal observational study, conducted in out- and inpatient conditions. Thirty-one women eligible to antidepressant treatment for a current DE in the course of MDD were evaluated for PMS and those of them suffering from both conditions were re-evaluated twice during a 6-month medication intake period. Results: The pre-treatment clinical picture of PMS co-morbid with MDD was dominated by moderately to severely expressed mood swings, anxiety, fatigue, breast tenderness, palpitations, abdominal bloating, and headache. After six months of SSRIs intake the syndrome was characterised by nearly the same symptoms (with the addition of irritability and appetite changes) but mildly expressed. Conclusion: Untreated PMS co-morbid with MDD is characterised by mainly moderately severe psychological and around three times less commonly – somatic symptoms. SSRIs treatment alleviate both symptom types at month three and even further, although less pronouncedly at month six.
Stress measurement using speech: Recent advancements, validation issues, and ethical and privacy considerations

Abstract Life stress is a well-established risk factor for a variety of mental and physical health problems, including anxiety disorders, depression, chronic pain, heart disease, asthma, autoimmune diseases, and neurodegenerative disorders. The purpose of this article is to describe emerging approaches for assessing stress using speech, which we do by reviewing the methodological advantages of these digital health tools, and the validation, ethical, and privacy issues raised by these technologies. As we describe, it is now possible to assess stress via the speech signal using smartphones and smart speakers that employ software programs and artificial intelligence to analyze several features of speech and speech acoustics, including pitch, jitter, energy, rate, and length and number of pauses. Because these digital devices are ubiquitous, we can now assess individuals’ stress levels in real time in almost any natural environment in which people speak. These technologies thus have great potential for advancing digital health initiatives that involve continuously monitoring changes in psychosocial functioning and disease risk over time. However, speech-based indices of stress have yet to be well-validated against stress biomarkers (e.g., cortisol, cytokines) that predict disease risk. In addition, acquiring speech samples raises the possibility that conversations intended to be private could one day be made public; moreover, obtaining real-time psychosocial risk information prompts ethical questions regarding how these data should be used for medical, commercial, and personal purposes. Although assessing stress using speech thus has enormous potential, there are critical validation, privacy, and ethical issues that must be addressed.
Insulin receptor substrate 1 gene expression is strongly up-regulated by HSPB8 silencing in U87 glioma cells

Abstract Objective. The aim of the present investigation was to study the expression of genes encoding IRS1 (insulin receptor substrate 1) and some other functionally active proteins in U87 glioma cells under silencing of polyfunctional chaperone HSPB8 for evaluation of the possible significance of this protein in intergenic interactions. Methods. Silencing of HSPB8 mRNA was introduced by HSPB8 specific siRNA. The expression level of HSPB8, IRS1, HK2, GLO1, HOMER3, MYL9, NAMPT, PER2, PERP, GADD45A, and DEK genes was studied in U87 glioma cells by quantitative polymerase chain reaction. Results. It was shown that silencing of HSPB8 mRNA by specific to HSPB8 siRNA led to a strong down-regulation of this mRNA and significant modification of the expression of IRS1 and many other genes in glioma cells: strong up-regulated of HOMER3, GLO1, and PERP and down-regulated of MYL9, NAMPT, PER2, GADD45A, and DEK gene expressions. At the same time, no significant changes were detected in the expression of HK2 gene in glioma cells treated by siRNA, specific to HSPB8. Moreover, the silencing of HSPB8 mRNA enhanced the glioma cells proliferation rate. Conclusions. Results of this investigation demonstrated that silencing of HSPB8 mRNA affected the expression of IRS1 gene as well as many other genes encoding tumor growth related proteins. It is possible that the dysregulation of most of the studied genes in glioma cells after silencing of HSPB8 is reflected by a complex of intergenic interactions and that this polyfunctional chaperone is an important factor for the stability of genome function and regulatory mechanisms contributing to the tumorigenesis control.
A research on the protein expression of p53, p16, and MDM2 in endometriosis

Abstract This study aims to examine the expression of p53, p16, and murine double minute 2 (MDM2) protein in normal endometrium and endometriosis, in order to discuss the role of p53, p16, and MDM2 protein and apoptosis in the pathogenesis and development of endometriosis, and provide a theoretical basis for clinical diagnosis and treatment. The immunohistochemical streptavidin-biotin peroxidase method was used to detect the expression of p53, p16, and MDM2 in tissue samples obtained from 30 women with pathologically confirmed ovarian endometriosis and 29 women with pathologically confirmed normal endometrium. The relationship between p53, p16, and MDM2 expression and apoptosis was analyzed. In normal endometrium, the positive rate of p53 in the secretory phase was higher than that in the proliferative phase (P < .05). Furthermore, the positive rate of p53 in normal endometrium was higher than that in ovarian endometriosis (P < .05). There was a significant difference between normal endometrium and ovarian endometriosis. The positive rate of p16 in normal endometrium was higher than that in ovarian endometriosis (P < .05). Furthermore, there was a significant difference between normal endometrium and ovarian endometriosis. The positive rate of MDM2 in normal endometrium was lower than that in ovarian endometriosis (P < .05). In ovarian endometriosis, the expression of p53 and p16 was positively correlated with each other (r = 0.611, P < .01). However, the expression of p53 and MDM2 was negatively correlated with each other (r = −0.541, P < .01). Furthermore, the expression of p16 and MDM2 might not be relevant in the endometriosis (r = 0.404, P > .05). As important apoptosis regulatory genes, p53, p16, and MDM2 might be involved in the pathogenesis and development of endometriosis.
Automatic Detection of Pediatric Craniofacial Deformities using Convolutional Neural Networks

Abstract The geometric shape of our skull is very important, not only from an esthetic perspective, but also from medical viewpoint. However, the lack of designated medical experts and wrong positioning is leading to an increasing number of abnormal head shapes in newborns and infants. To make screening and therapy monitoring for these abnormal shapes easier, we develop a mobile application to automatically detect and quantify such shapes. By making use of modern machine learning technologies like deep learning and transfer learning, we have developed a convolutional neural network for semantic segmentation of bird’s-eye view images of child heads. Using this approach, we have been able to achieve a segmentation accuracy of approximately 99 %, while having sensitivity and specificity of above 98 %. Given these promising results, we will use this basis to calculate medical parameters to quantify the skull shape. In addition, we will integrate the proposed model into a mobile application for further validation and usage in a real-world scenario.
Morality and the Brain: The Right Hemisphere and Doing Right

Morality, the set of shared attitudes and practices that regulate individual behavior to facilitate cohesion and well-being, is a function of the brain, yet its localization is uncertain. Neuroscientific study of morality has been conducted by examining departures from moral conduct after neurologic insult and by functional neuroimaging of moral decision-making in cognitively intact individuals. These investigations have yielded conflicting results: Acquired sociopathy, a syndromic surrogate for acquired immorality, has been reported predominantly after right frontotemporal lesions, whereas functional neuroimaging during moral decision-making has demonstrated bilateral activation. Although morality is bilaterally represented, the right hemisphere is clinically more critical in light of focal lesion data suggesting that moral behavior is subserved by a network of right frontotemporal structures and their subcortical connections. Evolution may have endowed the brain with bilaterally represented but unilaterally right-dominant morality. The unilateral dominance of morality permits concentration of an essential social cognitive function to support the perceptual and executive operations of moral behavior within a single hemisphere; the bilateral representation of morality allows activation of reserve tissue in the contralateral hemisphere in the event of an acquired hemispheric injury. The observed preponderance of right hemisphere lesions in individuals with acquired immorality offers a plausible hypothesis that can be tested in clinical settings. Advances in the neuroscience of morality promise to yield potentially transformative clinical and societal benefits. A deeper understanding of morality would help clinicians address disordered conduct after acquired neurologic insults and guide society in bolstering public health efforts to prevent brain disease.