FTKHUIM: A fast and efficient method for mining top-k high-utility itemsets

High-utility itemset mining (HUIM) is an important task in the field of knowledge data discovery. The large search space and huge number of HUIs are the consequences of applying HUIM algorithms with an inappropriate user-defined minimum utility threshold value. Determining a suitable threshold value to obtain the expected results is not a simple task and requires spending a lot of time. For common users, it is difficult to define a minimum threshold utility for exploring the right number of HUIs. On the one hand, if the threshold is set too high then the number of HUIs would not be enough. On the other hand, if the threshold is set too low, too many HUIs will be mined, thus wasting both time and memory. The top-k HUIs mining problem was proposed to solve this issue, and many effective algorithms have since been introduced by researchers. In this research, a novel approach, namely FTKHUIM (Fast top k HUI Mining), is introduced to explore the top-k HUIs. One new threshold-raising strategy called RTU, a transaction utility (TU)-based threshold-raising strategy, has also been shown to rapidly increase the speed of top-k HUIM. The study also proposes a global structure to store utility values in the process of applying raising-threshold strategies to optimize these strategies. The results of experiments on various datasets prove that the FTKHUIM algorithm achieves better results with regard to both the time and search space needed.Web of Science1110480510478

Pharmacists’ Perspectives on the Use of Telepharmacy in Response to COVID-19 Pandemic in Ho Chi Minh City, Vietnam

Introduction: Telepharmacy, the application of information and communication technologies in healthcare services, has been adopted in many countries to provide patients with pharmaceutical care. However, it has yet to be widely used in Vietnam. This study was conducted to assess the current status of use and the factors associated with the willingness to use telepharmacy of pharmacists in Vietnam. Methods: A descriptive cross-sectional study was conducted from February to July 2021; 414 pharmacists were recruited to fill in an online survey. Results: Overall, 86.7% of participants have used telepharmacy application and 87.2% of them were willing to apply telepharmacy in pharmacy practice. According to our multivariate analysis, the level of readiness was associated with positive attitude (odds ratio [OR] = 4.67; 95% confidence interval [CI]: 2.26-9.66), and a good behavior (OR = 11.34; 95% CI: 3.84-33.45). Discussion: Developing a telepharmacy system with appropriate features is essential to meet the requirements of pharmacy practice amid the spread of the COVID-19 pandemic

Discovery of the First Insect Nidovirus, a Missing Evolutionary Link in the Emergence of the Largest RNA Virus Genomes

Nidoviruses with large genomes (26.3–31.7 kb; ‘large nidoviruses’), including Coronaviridae and Roniviridae, are the most complex positive-sense single-stranded RNA (ssRNA+) viruses. Based on genome size, they are far separated from all other ssRNA+ viruses (below 19.6 kb), including the distantly related Arteriviridae (12.7–15.7 kb; ‘small nidoviruses’). Exceptionally for ssRNA+ viruses, large nidoviruses encode a 3′-5′exoribonuclease (ExoN) that was implicated in controlling RNA replication fidelity. Its acquisition may have given rise to the ancestor of large nidoviruses, a hypothesis for which we here provide evolutionary support using comparative genomics involving the newly discovered first insect-borne nidovirus. This Nam Dinh virus (NDiV), named after a Vietnamese province, was isolated from mosquitoes and is yet to be linked to any pathology. The genome of this enveloped 60–80 nm virus is 20,192 nt and has a nidovirus-like polycistronic organization including two large, partially overlapping open reading frames (ORF) 1a and 1b followed by several smaller 3′-proximal ORFs. Peptide sequencing assigned three virion proteins to ORFs 2a, 2b, and 3, which are expressed from two 3′-coterminal subgenomic RNAs. The NDiV ORF1a/ORF1b frameshifting signal and various replicative proteins were tentatively mapped to canonical positions in the nidovirus genome. They include six nidovirus-wide conserved replicase domains, as well as the ExoN and 2′-O-methyltransferase that are specific to large nidoviruses. NDiV ORF1b also encodes a putative N7-methyltransferase, identified in a subset of large nidoviruses, but not the uridylate-specific endonuclease that – in deviation from the current paradigm - is present exclusively in the currently known vertebrate nidoviruses. Rooted phylogenetic inference by Bayesian and Maximum Likelihood methods indicates that NDiV clusters with roniviruses and that its branch diverged from large nidoviruses early after they split from small nidoviruses. Together these characteristics identify NDiV as the prototype of a new nidovirus family and a missing link in the transition from small to large nidoviruses

Koronarsykdom hos kvinner. Forekomst og diagnostikk.

Coronary heart disease in women. Prevalence and diagnostics. Coronary artery disease (CAD) is a major health care challenge, and is the leading cause of death amongst women. This review of the literature compares prevalence, pathophysiology, risk factors, symptomatology, and diagnostic tools of ischemic heart disease in a gender perspective. CAD is changing from a fatal disease of middle-aged men to more chronic condition of elderly women. The identification of CAD in women is complicated by more diffuse or ‘atypical’ symptoms, a lower initial detection rate of myocardial ischemia by traditional methods, lower rates of interventional procedures, and lastly, potential differences in the pathophysiology of myocardial ischemia. Identification of CAD in women should be by use of multiple risk factor analysis in combination with more aggressive and multiple forms of diagnostic testing. The lower precision of female disease detection is partly due to differences in pre- and post likelihood of disease, but adds to the existing gender bias in diagnosis and referral rates and contributes to the perception that women have less exertional angina than men, despite observations to the contrary. Little is known about potential gender differences in the composition and vulnerability of atherosclerotic plaques. The risk factors are basically the same for both sexes, but with a possible larger relative impact of diabetes and smoking in women than in men. On-going international campaigns focus on increasing the awareness in women and their doctors of female susceptibility to CAD, and that more research in women is urgently needed for optimal treatment of both sexes

Myocardial Function Imaging in Echocardiography Using Deep Learning

Deformation imaging in echocardiography has been shown to have better diagnostic and prognostic value than conventional anatomical measures such as ejection fraction. However, despite clinical availability and demonstrated efficacy, everyday clinical use remains limited at many hospitals. The reasons are complex, but practical robustness has been questioned, and a large inter-vendor variability has been demonstrated. In this work, we propose a novel deep learning based framework for motion estimation in echocardiography, and use this to fully automate myocardial function imaging. A motion estimator was developed based on a PWC-Net architecture, which achieved an average end point error of (0.06±0.04) mm per frame using simulated data from an open access database, on par or better compared to previously reported state of the art. We further demonstrate unique adaptability to image artifacts such as signal dropouts, made possible using trained models that incorporate relevant image augmentations. Further, a fully automatic pipeline consisting of cardiac view classification, event detection, myocardial segmentation and motion estimation was developed and used to estimate left ventricular longitudinal strain in vivo. The method showed promise by achieving a mean deviation of (-0.7±1.6)% compared to a semi-automatic commercial solution for N=30 patients with relevant disease, within the expected limits of agreement. We thus believe that learning-based motion estimation can facilitate extended use of strain imaging in clinical practice

Myocardial Function Imaging in Echocardiography Using Deep Learning

Deformation imaging in echocardiography has been shown to have better diagnostic and prognostic value than conventional anatomical measures such as ejection fraction. However, despite clinical availability and demonstrated efficacy, everyday clinical use remains limited at many hospitals. The reasons are complex, but practical robustness has been questioned, and a large inter-vendor variability has been demonstrated. In this work, we propose a novel deep learning based framework for motion estimation in echocardiography, and use this to fully automate myocardial function imaging. A motion estimator was developed based on a PWC-Net architecture, which achieved an average end point error of (0.06±0.04) mm per frame using simulated data from an open access database, on par or better compared to previously reported state of the art. We further demonstrate unique adaptability to image artifacts such as signal dropouts, made possible using trained models that incorporate relevant image augmentations. Further, a fully automatic pipeline consisting of cardiac view classification, event detection, myocardial segmentation and motion estimation was developed and used to estimate left ventricular longitudinal strain in vivo. The method showed promise by achieving a mean deviation of (-0.7±1.6)% compared to a semi-automatic commercial solution for N=30 patients with relevant disease, within the expected limits of agreement. We thus believe that learning-based motion estimation can facilitate extended use of strain imaging in clinical practice

Myocardial Function Imaging in Echocardiography Using Deep Learning

Deformation imaging in echocardiography has been shown to have better diagnostic and prognostic value than conventional anatomical measures such as ejection fraction. However, despite clinical availability and demonstrated efficacy, everyday clinical use remains limited at many hospitals. The reasons are complex, but practical robustness has been questioned, and a large inter-vendor variability has been demonstrated. In this work, we propose a novel deep learning based framework for motion estimation in echocardiography, and use this to fully automate myocardial function imaging. A motion estimator was developed based on a PWC-Net architecture, which achieved an average end point error of (0.06±0.04) mm per frame using simulated data from an open access database, on par or better compared to previously reported state of the art. We further demonstrate unique adaptability to image artifacts such as signal dropouts, made possible using trained models that incorporate relevant image augmentations. Further, a fully automatic pipeline consisting of cardiac view classification, event detection, myocardial segmentation and motion estimation was developed and used to estimate left ventricular longitudinal strain in vivo. The method showed promise by achieving a mean deviation of (-0.7±1.6)% compared to a semi-automatic commercial solution for N=30 patients with relevant disease, within the expected limits of agreement. We thus believe that learning-based motion estimation can facilitate extended use of strain imaging in clinical practice