An IoT-oriented data placement method with privacy preservation in cloud environment

© 2018 Elsevier Ltd IoT (Internet of Things) devices generate huge amount of data which require rich resources for data storage and processing. Cloud computing is one of the most popular paradigms to accommodate such IoT data. However, the privacy conflicts combined in the IoT data makes the data placement problem more complicated, and the resource manager needs to take into account the resource efficiency, the power consumption of cloud data centers, and the data access time for the IoT applications while allocating the resources for the IoT data. In view of this challenge, an IoT-oriented Data Placement method with privacy preservation, named IDP, is designed in this paper. Technically, the resource utilization, energy consumption and data access time in the cloud data center with the fat-tree topology are analyzed first. Then a corresponding data placement method, based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II), is designed to achieve high resource usage, energy saving and efficient data access, and meanwhile realize privacy preservation of the IoT data. Finally, extensive experimental evaluations validate the efficiency and effectiveness of our proposed method

Psychotic Symptom, Mood, and Cognition-associated Multimodal MRI Reveal Shared Links to the Salience Network Within the Psychosis Spectrum Disorders

Schizophrenia (SZ), schizoaffective disorder (SAD), and psychotic bipolar disorder share substantial overlap in clinical phenotypes, associated brain abnormalities and risk genes, making reliable diagnosis among the three illness challenging, especially in the absence of distinguishing biomarkers. This investigation aims to identify multimodal brain networks related to psychotic symptom, mood, and cognition through reference-guided fusion to discriminate among SZ, SAD, and BP. Psychotic symptom, mood, and cognition were used as references to supervise functional and structural magnetic resonance imaging (MRI) fusion to identify multimodal brain networks for SZ, SAD, and BP individually. These features were then used to assess the ability in discriminating among SZ, SAD, and BP. We observed shared links to functional and structural covariation in prefrontal, medial temporal, anterior cingulate, and insular cortices among SZ, SAD, and BP, although they were linked with different clinical domains. The salience (SAN), default mode (DMN), and fronto-limbic (FLN) networks were the three identified multimodal MRI features within the psychosis spectrum disorders from psychotic symptom, mood, and cognition associations. In addition, using these networks, we can classify patients and controls and distinguish among SZ, SAD, and BP, including their first-degree relatives. The identified multimodal SAN may be informative regarding neural mechanisms of comorbidity for psychosis spectrum disorders, along with DMN and FLN may serve as potential biomarkers in discriminating among SZ, SAD, and BP, which may help investigators better understand the underlying mechanisms of psychotic comorbidity from three different disorders via a multimodal neuroimaging perspective

Mutation screening of the SLC26A4 gene in a cohort of 192 Chinese patients with congenital hypothyroidism

ABSTRACT Objective: Pendred syndrome (PS) is an autosomal recessive disorder characterised by sensorineural hearing loss and thyroid dyshormonogenesis. It is caused by biallelic mutations in the SLC26A4 gene encoding for pendrin. Hypothyroidism in PS can be present from birth and therefore diagnosed by neonatal screening. The aim of this study was to examine the SLC26A4 mutation spectrum and prevalence among congenital hypothyroidism (CH) patients in the Guangxi Zhuang Autonomous Region of China and to establish how frequently PS causes hearing impairment in our patients with CH. Subjects and methods: Blood samples were collected from 192 CH patients in Guangxi Zhuang Autonomous Region, China, and genomic DNA was extracted from peripheral blood leukocytes. All exons of the SLC26A4 gene together with their exon-intron boundaries were screened by nextgeneration sequencing. Patients with SLC26A4 mutations underwent a complete audiological evaluation including otoscopic examination, audiometry and morphological evaluation of the inner ear. Results: Next generation sequencing analysis of SLC26A4 in 192 CH patients revealed five different heterozygous variations in eight individuals (8/192, 4%). The prevalence of SLC26A4 mutations was 4% among studied Chinese CH. Three of the eight were diagnosed as enlargement of the vestibular aqueduct (EVA), no PS were found in our 192 CH patients. The mutations included one novel missense variant p.P469S, as well as four known missense variants, namely p.V233L, p.M147I, p.V609G and p.D661E. Of the eight patients identified with SLC26A4 variations in our study, seven patients showed normal size/location of thyroid gland, and one patients showed a decreased size one. Conclusions: The prevalence of SLC26A4 pathogenic variants was 4% among studied Chinese patients with CH. Our study expanded the SLC26A4 mutation spectrum, provided the best estimation of SLC26A4 mutation rate for Chinese CH patients and indicated the rarity of PS as a cause of CH. Arch Endocrinol Metab. 2016;60(4):323-

Mutation screening of the SLC26A4 gene in a cohort of 192 Chinese patients with congenital hypothyroidism

ABSTRACT Objective: Pendred syndrome (PS) is an autosomal recessive disorder characterised by sensorineural hearing loss and thyroid dyshormonogenesis. It is caused by biallelic mutations in the SLC26A4 gene encoding for pendrin. Hypothyroidism in PS can be present from birth and therefore diagnosed by neonatal screening. The aim of this study was to examine the SLC26A4 mutation spectrum and prevalence among congenital hypothyroidism (CH) patients in the Guangxi Zhuang Autonomous Region of China and to establish how frequently PS causes hearing impairment in our patients with CH. Subjects and methods: Blood samples were collected from 192 CH patients in Guangxi Zhuang Autonomous Region, China, and genomic DNA was extracted from peripheral blood leukocytes. All exons of the SLC26A4 gene together with their exon-intron boundaries were screened by next-generation sequencing. Patients with SLC26A4 mutations underwent a complete audiological evaluation including otoscopic examination, audiometry and morphological evaluation of the inner ear. Results: Next generation sequencing analysis of SLC26A4 in 192 CH patients revealed five different heterozygous variations in eight individuals (8/192, 4%). The prevalence of SLC26A4 mutations was 4% among studied Chinese CH. Three of the eight were diagnosed as enlargement of the vestibular aqueduct (EVA), no PS were found in our 192 CH patients. The mutations included one novel missense variant p.P469S, as well as four known missense variants, namely p.V233L, p.M147I, p.V609G and p.D661E. Of the eight patients identified with SLC26A4 variations in our study, seven patients showed normal size/location of thyroid gland, and one patients showed a decreased size one. Conclusions: The prevalence of SLC26A4 pathogenic variants was 4% among studied Chinese patients with CH. Our study expanded the SLC26A4 mutation spectrum, provided the best estimation of SLC26A4 mutation rate for Chinese CH patients and indicated the rarity of PS as a cause of CH

The incidence of congenital hypothyroidism (CH) in Guangxi, China and the predictors of permanent and transient CH

Background: The incidence of congenital hypothyroidism (CH) differs significantly among different ethnicities and regions, and early differentiation of transient CH is important to avoid unnecessary prolonged treatment with L-T4. Objective: To investigate the incidence of CH based on the newborn screening program in Guangxi Zhuang Autonomous Region, China, and to analyze the predictors that might allow for an early differentiation between permanent (P) and transient (T) CH. Design and methods Data from newborn screening program over a seven-year period (January 2009 to January 2016) at Guangxi Maternal and Child Health Hospital are analyzed. Blood samples were collected on filter paper between 3 and 7 days after birth, and TSH level was measured by time-resolved fluorescence assay. Individuals with increased TSH (TSH ≥ 8 IU/L) levels detected by newborn screening were recalled for further evaluation. Serum TSH, FT3 and FT4 were determined by electrochemiluminescence assay using venous blood samples. Diagnosis of CH is based on elevated TSH levels (>10 IU/L) and decreased FT4 levels (30 μg/day) has the highest predictive value for PCH. Earlier differentiation of PCH and TCH helps to determine appropriate treatment course

A Weakly Supervised Gas-Path Anomaly Detection Method for Civil Aero-Engines Based on Mapping Relationship Mining of Gas-Path Parameters and Improved Density Peak Clustering

Gas-path anomalies account for more than 90% of all civil aero-engine anomalies. It is essential to develop accurate gas-path anomaly detection methods. Therefore, a weakly supervised gas-path anomaly detection method for civil aero-engines based on mapping relationship mining of gas-path parameters and improved density peak clustering is proposed. First, the encoder-decoder, composed of an attention mechanism and a long short-term memory neural network, is used to construct the mapping relationship mining model among gas-path parameters. The predicted values of gas-path parameters under the restriction of mapping relationships are obtained. The deviation degree from the original values to the predicted values is regarded as the feature. To force the extracted features to better reflect the anomalies and make full use of weakly supervised labels, a weakly supervised cross-entropy loss function under extreme class imbalance is deployed. This loss function can be combined with a simple classifier to significantly improve the feature extraction results, in which anomaly samples are more different from normal samples and do not reduce the mining precision. Finally, an anomaly detection method is deployed based on improved density peak clustering and a weakly supervised clustering parameter adjustment strategy. In the improved density peak clustering method, the local density is enhanced by K-nearest neighbors, and the clustering effect is improved by a new outlier threshold determination method and a new outlier treatment method. Through these settings, the accuracy of dividing outliers and clustering can be improved, and the influence of outliers on the clustering process reduced. By introducing weakly supervised label information and automatically iterating according to clustering and anomaly detection results to update the hyperparameter settings, a weakly supervised anomaly detection method without complex parameter adjustment processes can be implemented. The experimental results demonstrate the superiority of the proposed method

Black Box Traceable Ciphertext Policy Attribute-Based Encryption Scheme

In the existing attribute-based encryption (ABE) scheme, the authority (i.e., private key generator (PKG)) is able to calculate and issue any user’s private key, which makes it completely trusted, which severely influences the applications of the ABE scheme. To mitigate this problem, we propose the black box traceable ciphertext policy attribute-based encryption (T-CP-ABE) scheme in which if the PKG re-distributes the users’ private keys for malicious uses, it might be caught and sued. We provide a construction to realize the T-CP-ABE scheme in a black box model. Our scheme is based on the decisional bilinear Diffie-Hellman (DBDH) assumption in the standard model. In our scheme, we employ a pair (ID, S) to identify a user, where ID denotes the identity of a user and S denotes the attribute set associated with her