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Extracting knowledge from data streams received from observed objects through data mining is required in various domains. However, there is a lack of any kind of guidance on which techniques can or should be used in which contexts. Meta mining technology can help build processes of data processing based on knowledge models taking into account the specific features of the objects. This paper proposes a meta mining ontology framework that allows selecting algorithms for solving specific data mining tasks and build suitable processes. The proposed ontology is constructed using existing ontologies and is extended with an ontology of data characteristics and task requirements. Different from the existing ontologies, the proposed ontology describes the overall data mining process, used to build data processing processes in various domains, and has low computational complexity compared to others. The authors developed an ontology merging method and a sub-ontology extraction method, which are implemented based on OWL API via extracting and integrating the relevant axioms.

Cloud computing has been used for providing services for many years already, but now it is seen that it is not a solution for big modern networks that contain multiple distributed devices and produce huge amount of data. In these conditions existing data processing models based on cloud technologies suffer from low latency. To overcome this problem, many researchers are focusing on fog computing. By placing fog nodes in the proximity of IoT devices, many problems have been solved. But the majority of the proposed fog-based models are oriented on data processing in static networks. They cannot be effectively used in the networks of mobile devices, when the structure of the network changes in time. In the paper we propose a new data processing model that allow build mobile IoT systems

Objective Ultra-high-field 7-Tesla (7T) magnetic resonance imaging (MRI) offers increased signal-to-noise and contrast-to-noise ratios, which may improve visualization of cortical malformations. We aim to assess the clinical value of in vivo structural 7T MRI and its post-processing for the noninvasive identification of epileptic brain lesions in patients with pharmacoresistant epilepsy and nonlesional 3T MRI who are undergoing presurgical evaluation. Methods Sixty-seven patients were included who had nonlesional 3T MRI by official radiology report. Epilepsy protocols were used for the 3T and 7T acquisitions. Post-processing of the 7T T1-weighted magnetization-prepared two rapid acquisition gradient echoes sequence was performed using the morphometric analysis program (MAP) with comparison to a normal database consisting of 50 healthy controls. Review of 7T was performed by an experienced board-certified neuroradiologist and at the multimodal patient management conference. The clinical significance of 7T findings was assessed based on intracranial electroencephalography (ICEEG) ictal onset, surgery, postoperative seizure outcomes, and histopathology. Results Unaided visual review of 7T detected previously unappreciated subtle lesions in 22% (15/67). When aided by 7T MAP, the total yield increased to 43% (29/67). The location of the 7T-identified lesion was identical to or contained within the ICEEG ictal onset in 13 of 16 (81%). Complete resection of the 7T-identified lesion was associated with seizure freedom (P = .03). Histopathology of the 7T-identified lesions encountered mainly focal cortical dysplasia (FCD). 7T MAP yielded 25% more lesions (6/24) than 3T MAP, and showed improved conspicuity in 46% (11/24). Significance Our data suggest a major benefit of 7T with post-processing for detecting subtle FCD lesions for patients with pharmacoresistant epilepsy and nonlesional 3T MRI.

Fog computing has become the key solution that allows reduce latency and energy consumption when collecting and processing data in IoT systems. The advantages of fog computing can also be used to collect and process data in new mobile IoT systems. It is a challenging task because these systems contain multiple mobile devices that generate huge amounts of heterogeneous data. In many cases, the data is redundant. The same data can be sent several times. Such data is not useful for end users. Therefore, we propose a new model for data collecting and processing in fog-enabled mobile IoT systems. Instead of collecting and processing huge amounts of data using cloud technologies, the data is collected and processed on fog nodes. The new model is based on using classification algorithms, in particular, K-nearest neighbor algorithm. It is used to classify heterogeneous data generated by mobile devices. This allows reduce the amount of data that is transmitted to the cloud for further processing and storage. The proposed model can be considered as, an effective intermediary that allows collect and process data in mobile IoT systems.

In recent years, an alarming number of cases of lethal acute hemorrhagic disease have occurred in Asian elephant calves raised in logging camps in Myanmar. To determine whether these deaths were associated with infection by elephant endotheliotropic herpesvirus (EEHV), we conducted diagnostic PCR subtype DNA sequencing analysis on necropsy tissue samples collected from 3 locations. We found that EEHV DNA from 7 PCR loci was present at high levels in all 3 calves and was the same EEHV1A virus type that has been described in North America, Europe, and other parts of Asia. However, when analyzed over 5,610 bp, the strains showed major differences from each other and from all previously characterized EEHV1A strains. We conclude that these 3 elephant calves in Myanmar died from the same herpesvirus disease that has afflicted young Asian elephants in other countries over the past 20 years.

Objective Keyhole craniotomy is a minimally invasive approach for the treatment of middle cerebral artery (MCA) aneurysms. The aim of this study was to compare the clinical outcome between supraorbital keyhole approach and pterional keyhole approach (PKA) in Chinese patients with MCA aneurysm. Methods Consecutive patients with MCA aneurysms were reviewed between January 2013 and December 2017. Efficacy and safety between PKA and supraorbital keyhole approach were compared. Poor outcome was defined as modified Rankin Scale score of 3–6 at discharge. Results This study enrolled 260 patients; 222 (85.4%) had ruptured aneurysm, and 183 (70.4%) received PKA. The distribution of PKA in unruptured and ruptured aneurysms showed no significant difference (P > 0.05). In subgroup analyses, PKA was more likely associated with poor outcome at discharge in patients with unruptured aneurysms (odds ratio = 5.500, 95% confidence interval = 1.013–29.850, P = 0.048), whereas approach selection was not an independent factor predicting poor outcome in patients with ruptured aneurysms (P > 0.05). Conclusions In a Chinese population, supraorbital keyhole approach was superior to PKA in improving outcome in patients with unruptured MCA aneurysms, but the 2 approaches showed comparable outcomes at discharge in patients with ruptured aneurysms.