Unsupervised Feature Selection with Adaptive Structure Learning

The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data. However, the estimated intrinsic structures are unreliable/inaccurate when the redundant and noisy features are not removed. Therefore, we face a dilemma here: one need the true structures of data to identify the informative features, and one need the informative features to accurately estimate the true structures of data. To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously. The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data. By leveraging the interactions between these two essential tasks, we are able to capture accurate structures and select more informative features. Experimental results on many benchmark data sets demonstrate that the proposed method outperforms many state of the art unsupervised feature selection methods

Extracting scientific trends by mining topics from Call for Papers

© 2019, Emerald Publishing Limited. Purpose: The purpose of this paper is to present a novel approach for mining scientific trends using topics from Call for Papers (CFP). The work contributes a valuable input for researchers, academics, funding institutes and research administration departments by sharing the trends to set directions of research path. Design/methodology/approach: The authors procure an innovative CFP data set to analyse scientific evolution and prestige of conferences that set scientific trends using scientific publications indexed in DBLP. Using the Field of Research code 804 from Australian Research Council, the authors identify 146 conferences (from 2006 to 2015) into different thematic areas by matching the terms extracted from publication titles with the Association for Computing Machinery Computing Classification System. Furthermore, the authors enrich the vocabulary of terms from the WordNet dictionary and Growbag data set. To measure the significance of terms, the authors adopt the following weighting schemas: probabilistic, gram, relative, accumulative and hierarchal. Findings: The results indicate the rise of “big data analytics” from CFP topics in the last few years. Whereas the topics related to “privacy and security” show an exponential increase, the topics related to “semantic web” show a downfall in recent years. While analysing publication output in DBLP that matches CFP indexed in ERA Core A* to C rank conference, the authors identified that A* and A tier conferences not merely set publication trends, since B or C tier conferences target similar CFP. Originality/value: Overall, the analyses presented in this research are prolific for the scientific community and research administrators to study research trends and better data management of digital libraries pertaining to the scientific literature

An experimental study of the intrinsic stability of random forest variable importance measures

BACKGROUND: The stability of Variable Importance Measures (VIMs) based on random forest has recently received increased attention. Despite the extensive attention on traditional stability of data perturbations or parameter variations, few studies include influences coming from the intrinsic randomness in generating VIMs, i.e. bagging, randomization and permutation. To address these influences, in this paper we introduce a new concept of intrinsic stability of VIMs, which is defined as the self-consistence among feature rankings in repeated runs of VIMs without data perturbations and parameter variations. Two widely used VIMs, i.e., Mean Decrease Accuracy (MDA) and Mean Decrease Gini (MDG) are comprehensively investigated. The motivation of this study is two-fold. First, we empirically verify the prevalence of intrinsic stability of VIMs over many real-world datasets to highlight that the instability of VIMs does not originate exclusively from data perturbations or parameter variations, but also stems from the intrinsic randomness of VIMs. Second, through Spearman and Pearson tests we comprehensively investigate how different factors influence the intrinsic stability. RESULTS: The experiments are carried out on 19 benchmark datasets with diverse characteristics, including 10 high-dimensional and small-sample gene expression datasets. Experimental results demonstrate the prevalence of intrinsic stability of VIMs. Spearman and Pearson tests on the correlations between intrinsic stability and different factors show that #feature (number of features) and #sample (size of sample) have a coupling effect on the intrinsic stability. The synthetic indictor, #feature/#sample, shows both negative monotonic correlation and negative linear correlation with the intrinsic stability, while OOB accuracy has monotonic correlations with intrinsic stability. This indicates that high-dimensional, small-sample and high complexity datasets may suffer more from intrinsic instability of VIMs. Furthermore, with respect to parameter settings of random forest, a large number of trees is preferred. No significant correlations can be seen between intrinsic stability and other factors. Finally, the magnitude of intrinsic stability is always smaller than that of traditional stability. CONCLUSION: First, the prevalence of intrinsic stability of VIMs demonstrates that the instability of VIMs not only comes from data perturbations or parameter variations, but also stems from the intrinsic randomness of VIMs. This finding gives a better understanding of VIM stability, and may help reduce the instability of VIMs. Second, by investigating the potential factors of intrinsic stability, users would be more aware of the risks and hence more careful when using VIMs, especially on high-dimensional, small-sample and high complexity datasets

Global Perspectives on Task Shifting and Task Sharing in Neurosurgery.

BACKGROUND: Neurosurgical task shifting and task sharing (TS/S), delegating clinical care to non-neurosurgeons, is ongoing in many hospital systems in which neurosurgeons are scarce. Although TS/S can increase access to treatment, it remains highly controversial. This survey investigated perceptions of neurosurgical TS/S to elucidate whether it is a permissible temporary solution to the global workforce deficit. METHODS: The survey was distributed to a convenience sample of individuals providing neurosurgical care. A digital survey link was distributed through electronic mailing lists of continental neurosurgical societies and various collectives, conference announcements, and social media platforms (July 2018-January 2019). Data were analyzed by descriptive statistics and univariate regression of Likert Scale scores. RESULTS: Survey respondents represented 105 of 194 World Health Organization member countries (54.1%; 391 respondents, 162 from high-income countries and 229 from low- and middle-income countries [LMICs]). The most agreed on statement was that task sharing is preferred to task shifting. There was broad consensus that both task shifting and task sharing should require competency-based evaluation, standardized training endorsed by governing organizations, and maintenance of certification. When perspectives were stratified by income class, LMICs were significantly more likely to agree that task shifting is professionally disruptive to traditional training, task sharing should be a priority where human resources are scarce, and to call for additional TS/S regulation, such as certification and formal consultation with a neurosurgeon (in person or electronic/telemedicine). CONCLUSIONS: Both LMIC and high-income countries agreed that task sharing should be prioritized over task shifting and that additional recommendations and regulations could enhance care. These data invite future discussions on policy and training programs

Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology

Artificial intelligence (AI) is a broad, umbrella term that encompasses the theory and development of computer systems able to perform tasks normally requiring human intelligence. The aim of this study is to assess the radiology community’s attitude in Saudi Arabia toward the applications of AI. Methods: Data for this study were collected using electronic questionnaires in 2019 and 2020. The study included a total of 714 participants. Data analysis was performed using SPSS Statistics (version 25). Results: The majority of the participants (61.2%) had read or heard about the role of AI in radiology. We also found that radiologists had statistically different responses and tended to read more about AI compared to all other specialists. In addition, 82% of the participants thought that AI must be included in the curriculum of medical and allied health colleges, and 86% of the participants agreed that AI would be essential in the future. Even though human–machine interaction was considered to be one of the most important skills in the future, 89% of the participants thought that it would never replace radiologists. Conclusion: Because AI plays a vital role in radiology, it is important to ensure that radiologists and radiographers have at least a minimum understanding of the technology. Our finding shows an acceptable level of knowledge regarding AI technology and that AI applications should be included in the curriculum of the medical and health sciences colleges

Control of charged species dynamics in atmospheric pressure plasmas using tailored voltage waveforms

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Impact of COVID-19 Pandemic Lockdown on the Prognosis, Morbidity, and Mortality of Patients Undergoing Elective and Emergency Abdominal Surgery: A Retrospective Cohort Study in a Tertiary Center, Saudi Arabia

The SARS-CoV-2 pandemic’s main concerns are limiting the spread of infectious diseases and upgrading the delivery of health services, infrastructure, and therapeutic provision. The goal of this retrospective cohort study was to evaluate the emergency experience and delay of elective abdominal surgical intervention at King Abdul-Aziz University Hospital from October 2019 to October 2020, with a focus on post-operative morbidity and mortality before and during the COVID-19 pandemic. This study compares two groups of patients with emergent and elective abdominal surgical procedures between two different periods; the population was divided into two groups: the control group, which included 403 surgical patients, and the lockdown group, which included 253 surgical patients. During the lockdown, surgical activity was reduced by 37.2% (p = 0.014), and patients were more likely to require reoperations and blood transfusions during or after surgery (p= 0.002, 0.021, and 0.018, respectively). During the lockdown period, the average length of stay increased from 3.43 to 5.83 days (p = 0.002), and the patients who developed complications (53.9%) were more than those in the control period (46.1%) (p = 0.001). Our tertiary teaching hospital observed a significant decline in the overall number of surgeries performed during the COVID-19 pandemic and lockdown period. During the lockdown, abdominal surgery was performed only on four patients; they were positive for COVID-19. Three of them underwent exploratory laparotomy; two of the three developed shock post-operative; one patient had colon cancer (ASA score 3), one had colon disease (ASA score 2), and two had perforated bowels (ASA scores 2 and 4, respectively). Two out of four deaths occurred after surgery. Our results showed the impact of the COVID-19 lockdown on surgical care as both 30-day mortality and total morbidity have risen considerably

Ultrasound Imaging in Subjects with Sickle Cell Disease: The Saudi Arabia Experiences

Mohamed Adam,1 Mustafa J Musa,2 Saleh M Al-Qahtani,3 Magbool Alelyani,1 Alamin Musa,1 Maisa Elzaki,4 Amel FH Alzain,4 Sarra Ali,5 Afaf Medani,1 Emadeldedin Mohamed Mukhtar,1 Awadia Gareeballah4 1Department of Radiological Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Asir, Saudi Arabia; 2Department of Applied Radiologic Science, University of Jeddah, Kingdom of Saudi Arabia (KSA), Jeddah, Saudi Arabia; 3Department of Child Health, College of Medicine, King Khalid University, Abha, Asir, Saudi Arabia; 4Department of Diagnostic Radiology Technology, College of Applied Medical Sciences, Taibah University, Al-Madianah Al-Munawwarrah, Saudi Arabia; 5Department of Diagnostic Radiography Technology, College of Applied Medical Sciences, Jazan University, Jazan, Saudi ArabiaCorrespondence: Mohamed Adam, Department of Radiological Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Asir, Saudi Arabia, Email [email protected]; [email protected]: Abdominal organ sonography is a crucial part of the workup for treating sickle cell disease (SCD) patients.Objective: The main objective of this study was to evaluate the abdominal organs in SCD patients using ultrasonography.Methodology: A non-interventional descriptive cross-sectional study was carried out in Asir region Saudi Arabia from April 2019 to July 2020. The study was conducted in 78 patients with sickle cell disease (SCD). Data were gathered using a data collection sheet included demographic information, clinical information including medication types, and complications linked to SCD. Furthermore, the study evaluated abdominal ultrasound findings pertaining to the liver, gall bladder, spleen, and kidneys. The data were analyzed using Statistical Package for Social Sciences (SPSS).Results: More than half of the study participants 43 (55.1%) were females. About 53.8% of the study participants received blood transfusions, and (11.5%) receive extra-vaccine. Concerning ultrasound findings, hepatomegaly was found in seventeen (21.8%), focal liver lesions in four (5.1%), gallstones in five (6.4%), splenomegaly in fifteen (19.3%), and the presence of splenic focal lesions was found in seven (9.0%). The most frequent complication associated with SCD was osteomyelitis sepsis in six cases (7.7%). The study revealed a significant correlation between the type of crisis and type of medication used and the size of the spleen (P-value 0.05).Conclusion: Abdominal sonography in SCD patients revealed a wide range of alterations in the liver, gallbladder, and spleen. The most frequently observed complications in SCD were hepatomegaly, splenomegaly, localized lesions in both organs, and the presence of gallstones.Keywords: sickle cell disease, ultrasonography, abdominal organ, echo-textur

The effective adsorption of arsenic from polluted water using modified Halloysite nanoclay

The presented research applied the modified Halloysite nanoclay to boost the adsorption efficacy of heavy metals from the water. To improve As (III) adsorption effectiveness from water, the study assessed the characteristics of the prepared materials and improved the experimental conditions. The study was optimized the experimental condition with a dosage of 1 g/L, contact time of 90 min, the solution pH of 8, and the initial concentration of 5 ppm of As (III). The optimization was performed in distilled water and later the experiments were conducted in the real polluted water. The modified Halloysite nanoclay’s physical characteristics were investigated using techniques like X-ray diffraction, scanning and transmission electron microscopy, Fourier transform infrared spectroscopy, and surface area analysis. The experimental result shows the adsorption efficiency of 82.4 % of As (III) at the optimized condition during the usage of modified Halloysite nanoclay. To create a suitable mathematical model for a better description of the interactions between pollutants and solid adsorbents, it is helpful to analyze the process kinetically. The removal process of As (III) was studied kinetically and the observation shows the pseudo-second order kinetics