MobiFuzzyTrust: An efficient fuzzy trust inference mechanism in mobile social networks

PublishedJournal Article© 2014 IEEE. Mobile social networks (MSNs) facilitate connections between mobile users and allow them to find other potential users who have similar interests through mobile devices, communicate with them, and benefit from their information. As MSNs are distributed public virtual social spaces, the available information may not be trustworthy to all. Therefore, mobile users are often at risk since they may not have any prior knowledge about others who are socially connected. To address this problem, trust inference plays a critical role for establishing social links between mobile users in MSNs. Taking into account the nonsemantical representation of trust between users of the existing trust models in social networks, this paper proposes a new fuzzy inference mechanism, namely MobiFuzzyTrust, for inferring trust semantically from one mobile user to another that may not be directly connected in the trust graph of MSNs. First, a mobile context including an intersection of prestige of users, location, time, and social context is constructed. Second, a mobile context aware trust model is devised to evaluate the trust value between two mobile users efficiently. Finally, the fuzzy linguistic technique is used to express the trust between two mobile users and enhance the human's understanding of trust. Real-world mobile dataset is adopted to evaluate the performance of the MobiFuzzyTrust inference mechanism. The experimental results demonstrate that MobiFuzzyTrust can efficiently infer trust with a high precision.This work was partly supported by the National Nature Science Foundation of China under grant 61201219 and the EU FP7 CLIMBER project under Grant Agreement No. PIRSES-GA-2012-318939

A tensor-based approach for big data representation and dimensionality reduction

PublishedJournal Article© 2013 IEEE. Variety and veracity are two distinct characteristics of large-scale and heterogeneous data. It has been a great challenge to efficiently represent and process big data with a unified scheme. In this paper, a unified tensor model is proposed to represent the unstructured, semistructured, and structured data. With tensor extension operator, various types of data are represented as subtensors and then are merged to a unified tensor. In order to extract the core tensor which is small but contains valuable information, an incremental high order singular value decomposition (IHOSVD) method is presented. By recursively applying the incremental matrix decomposition algorithm, IHOSVD is able to update the orthogonal bases and compute the new core tensor. Analyzes in terms of time complexity, memory usage, and approximation accuracy of the proposed method are provided in this paper. A case study illustrates that approximate data reconstructed from the core set containing 18% elements can guarantee 93% accuracy in general. Theoretical analyzes and experimental results demonstrate that the proposed unified tensor model and IHOSVD method are efficient for big data representation and dimensionality reduction

Molecular composition of particulate matter emissions from dung and brushwood burning household cookstoves in Haryana, India

Emissions of airborne particles from biomass burning are a significant source of black carbon (BC) and brown carbon (BrC) in rural areas of developing countries where biomass is the predominant energy source for cooking and heating. This study explores the molecular composition of organic aerosols from household cooking emissions with a focus on identifying fuel-specific compounds and BrC chromophores. Traditional meals were prepared by a local cook with dung and brushwood-fueled cookstoves in a village in Palwal district, Haryana, India. Cooking was done in a village kitchen while controlling for variables including stove type, fuel moisture, and meal. Fine particulate matter (PM2.5) emissions were collected on filters, and then analyzed via nanospray desorption electrospray ionization-high-resolution mass spectrometry (nano-DESI-HRMS) and high-performance liquid chromatography-photodiode array-high-resolution mass spectrometry (HPLC-PDA-HRMS) techniques. The nano-DESI-HRMS analysis provided an inventory of numerous compounds present in the particle phase. Although several compounds observed in this study have been previously characterized using gas chromatography methods a majority of the species in the nano-DESI spectra were newly observed biomass burning compounds. Both the stove (chulha or angithi) and the fuel (brushwood or dung) affected the composition of organic aerosols. The geometric mean of the PM2.5 emission factor and the observed molecular complexity increased in the following order: brushwood-chulha (7.3±1.8 g kg-1 dry fuel, 93 compounds), dung-chulha (21.1±4.2 g kg-1 dry fuel, 212 compounds), and dung-angithi (29.8±11.5 g kg-1 dry fuel, 262 compounds). The mass-normalized absorption coefficient (MACbulk) for the organic-solvent extractable material for brushwood PM2.5 was 3.7±1.5 and 1.9±0.8m2 g-1 at 360 and 405 nm, respectively, which was approximately a factor of two higher than that for dung PM2.5. The HPLC-PDA-HRMS analysis showed that, regardless of fuel type, the main chromophores were CxHyOz lignin fragments. The main chromophores accounting for the higher MACbulk values of brushwood PM2.5 were C8H10O3 (tentatively assigned to syringol), nitrophenols C8H9NO4, and C10H10O3 (tentatively assigned to methoxycinnamic acid)

An optimized computational model for multi-community-cloud social collaboration

PublishedCommunity Cloud Computing is an emerging and promising computing model for a specific community with common concerns, such as security, compliance and jurisdiction. It utilizes the spare resources of networked computers to provide the facilities so that the community gains services from the cloud. The effective collaboration among the community clouds offers a powerful computing capacity for complex tasks containing the subtasks that need data exchange. Selecting the best group of community clouds that are the most economy-efficient, communication-efficient, secured, and trusted to accomplish a complex task is very challenging. To address this problem, we first formulate a computational model for multi-community-cloud collaboration, namely MG3. The proposed model is then optimized from four aspects: minimizing the sum of access cost and monetary cost, maximizing the security-level agreement and trust among the community clouds. Furthermore, an efficient and comprehensive selection algorithm is devised to extract the best group of community clouds in MG3. Finally, the extensive simulation experiments and performance analysis of the proposed algorithm are conducted. The results demonstrate that the proposed algorithm outperforms the minimal set coverings based algorithm and the random algorithm. Moreover, the proposed comprehensive community clouds selection algorithm can guarantee good global performance in terms of access cost, monetary cost, security level and trust between user and community clouds

Dehydroepiandrosterone (DHEA) supplementation improves in vitro fertilization outcomes of poor ovarian responders, especially in women with low serum concentration of DHEA-S: a retrospective cohort study

Background: Dehydroepiandrosterone (DHEA) is now widely used as an adjuvant for in vitro fertilization (IVF) cycles in poor ovarian responders (PORs). Several studies showed that DHEA supplementation could improve IVF outcomes of PORs. However, most of the PORs do not respond to DHEA clinically. Therefore, the aim of this study is to confirm the beneficial effects of DHEA on IVF outcomes of PORs and to investigate which subgroups of PORs can best benefit from DHEA supplementation. Methods: This retrospective cohort study was performed between January 2015 and December 2017. A total of 151 PORs who fulfilled the Bologna criteria and underwent IVF cycles with the gonadotropin-releasing hormone antagonist protocol were identified. The study group (n = 67) received 90 mg of DHEA daily for an average of 3 months before the IVF cycles. The control group (n = 84) underwent the IVF cycles without DHEA pretreatment. The basic and cycle characteristics and IVF outcomes between the two groups were compared using independent t-tests, Chi-Square tests and binary logistic regression. Results: The study and control groups did not show significant differences in terms of basic characteristics. The study group demonstrated a significantly greater number of retrieved oocytes, metaphase II oocytes, fertilized oocytes, day 3 embryos and top-quality embryos at day 3 and a higher clinical pregnancy rate, ongoing pregnancy rate and live birth rate than those measures in the control group. The multivariate analysis revealed that DHEA supplementation was positively associated with clinical pregnancy rate (OR = 4.93, 95% CI 1.68–14.43, p = 0.004). Additionally, in the study group, the multivariate analysis showed that serum dehydroepiandrosterone-sulfate (DHEA-S) levels < 180 μg/dl were significantly associated with a rate of retrieved oocytes > 3 (OR = 5.92, 95% CI 1.48–23.26, p = 0.012). Conclusions: DHEA supplementation improves IVF outcomes of PORs. In PORs with DHEA pretreatment, women with lower DHEA-S level may have greater possibility of attaining more than 3 oocytes

First-Principles Study of the Band Gap Structure of Oxygen-Passivated Silicon Nanonets

A net-like nanostructure of silicon named silicon nanonet was designed and oxygen atoms were used to passivate the dangling bonds. First-principles calculation based on density functional theory with the generalized gradient approximation (GGA) were carried out to investigate the energy band gap structure of this special structure. The calculation results show that the indirect–direct band gap transition occurs when the nanonets are properly designed. This band gap transition is dominated by the passivation bonds, porosities as well as pore array distributions. It is also proved that Si–O–Si is an effective passivation bond which can change the band gap structure of the nanonets. These results provide another way to achieve a practical silicon-based light source

STUDY ON TOTAL LUMINESCENCE SPECTRA - APPLICATION OF THE MONTE-CARLO METHOD TO 3-DIMENSIONAL SYNCHRONOUS FLUORESCENCE SPECTROMETRY

Three-dimensional synchronous fluorescence spectrometry (TDSFS, a combination of synchronous fluorescence spectrometry and three-dimensional fluorescence spectrometry) is a new method which has been developed recently. The method has usually been used as an efficient tool to select the best Delta lambda value for synchronous fluorescence spectra. This paper studies the sensitivity of the method, which was not been done in the past. The total fluorescence intensity has been used instead of the conventional single point intensity, calculated by the Monte-Carlo method, as the experimental parameter to determine fluorescein and tryptophan. The sensitivity of the total fluorescence method is nearly one hundred times better than that of the single point method. The new method has been used to simultaneously determine naphthalene, pyrene and perylene successfully. The mechanism of the method has also been studied

Challenges and directions: an analysis of Genetic Analysis Workshop 17 data by collapsing rare variants within family data

Recent studies suggest that the traditional case-control study design does not have sufficient power to discover rare risk variants. Two different methods—collapsing and family data—are suggested as alternatives for discovering these rare variants. Compared with common variants, rare variants have unique characteristics. In this paper, we assess the distribution of rare variants in family data. We notice that a large number of rare variants exist only in one or two families and that the association result is largely shaped by those families. Therefore we explore the possibility of integrating both the collapsing method and the family data method. This combinational approach offers a potential power boost for certain causal genes, including VEGFA, VEGFC, SIRT1, SREBF1, PIK3R3, VLDLR, PLAT, and FLT4, and thus deserves further investigation

The highly rearranged mitochondrial genomes of the crabs Maja crispata and Maja squinado (Majidae) and gene order evolution in Brachyura

Abstract We sequenced the mitochondrial genomes of the spider crabs Maja crispata and Maja squinado (Majidae, Brachyura). Both genomes contain the whole set of 37 genes characteristic of Bilaterian genomes, encoded on both \u3b1- and \u3b2-strands. Both species exhibit the same gene order, which is unique among known animal genomes. In particular, all the genes located on the \u3b2-strand form a single block. This gene order was analysed together with the other nine gene orders known for the Brachyura. Our study confirms that the most widespread gene order (BraGO) represents the plesiomorphic condition for Brachyura and was established at the onset of this clade. All other gene orders are the result of transformational pathways originating from BraGO. The different gene orders exhibit variable levels of genes rearrangements, which involve only tRNAs or all types of genes. Local homoplastic arrangements were identified, while complete gene orders remain unique and represent signatures that can have a diagnostic value. Brachyura appear to be a hot-spot of gene order diversity within the phylum Arthropoda. Our analysis, allowed to track, for the first time, the fully evolutionary pathways producing the Brachyuran gene orders. This goal was achieved by coupling sophisticated bioinformatic tools with phylogenetic analysis

Blockade of Hsp90 by 17AAG antagonizes MDMX and synergizes with Nutlin to induce p53-mediated apoptosis in solid tumors

Strategies to induce p53 activation in wtp53-retaining tumors carry high potential in cancer therapy. Nutlin, a potent highly selective MDM2 inhibitor, induces non-genotoxic p53 activation. Although Nutlin shows promise in promoting cell death in hematopoietic malignancies, a major roadblock is that most solid cancers do not undergo apoptosis but merely reversible growth arrest. p53 inhibition by unopposed MDMX is one major cause for apoptosis resistance to Nutlin. The Hsp90 chaperone is ubiquitously activated in cancer cells and supports oncogenic survival pathways, many of which antagonize p53. The Hsp90 inhibitor 17-allylamino-17-demethoxygeldanamycin (17AAG) is known to induce p53-dependent apoptosis. We show here that in multiple difficult-to-kill solid tumor cells 17AAG modulates several critical components that synergize with Nutlin-activated p53 signaling to convert Nutlin's transient cytostatic response into a cytotoxic killing response in vitro and in xenografts. Combined with Nutlin, 17AAG destabilizes MDMX, reduces MDM2, induces PUMA and inhibits oncogenic survival pathways, such as PI3K/AKT, which counteract p53 signaling at multiple levels. Mechanistically, 17AAG interferes with the repressive MDMX–p53 axis by inducing robust MDMX degradation, thereby markedly increasing p53 transcription compared with Nutlin alone. To our knowledge Nutlin+17AAG represents the first effective pharmacologic knockdown of MDMX. Our study identifies 17AAG as a promising synthetic lethal partner for a more efficient Nutlin-based therapy