Privacy-preserving data publishing based on sanitized probability matrix using transactional graph for improving the security in medical environment

The Journal of Supercomputing - Privacy is an important factor that hospitals should preserve while publishing data that involve sensitive information of individuals. Research seeks to find...     

Low-loss YIG thick films for microwave applications

We report the fabrication, densification and characterization of polycrystalline free-standing yttrium iron garnet Y₃Fe₅O₁₂ (YIG) thick films in this paper. The thick films were fabricated using a double doctor blade technique from co-precipitated nanocrystalline YIG powders. The sintering temperature of YIG thick films was varied from 1000 to 1400°C. A volume diffusion mechanism is seen to govern the densification process. A high relative density of ~99% could be achieved in a YIG thick film, with a thickness of 80?µm, sintered at a relatively low temperature of 1300?°C. The magnetization value ~1750?Oe, near to the YIG bulk magnetization value and an acceptable low ferromagnetic resonance (FMR) linewidth (?H) of 80?Oe resulted in these high density YIG thick films with microwave device possibilities.     

Genetic analysis of pathogen-specific intramammary infections in dairy cows

Mastitis is one of the most common diseases in dairy cattle, causing severe economic losses to dairy farmers. Mastitis usually occurs due to intramammary infection (IMI) caused by a variety of pathogenic bacteria. Although good progress has been made in understanding genetics of pathogen-specific clinical mastitis, studies involving genetic analysis of pathogen-specific IMI are scarce. The overall objective of this study was, therefore, to assess genetic variation of overall and pathogen-specific IMI in nonclinical primiparous and multiparous cows using bacterial culture. Data and milk samples were collected over a 2-yr interval as part of the Canadian Bovine Mastitis Research Network. The final data set contained records of 46,900 quarter milk samples from 3,382 clinically healthy primiparous and multiparous Holstein cows from 84 dairy herds. For the genetic analysis, we considered the following 7 traits: overall IMI, non-aureus staphylococci (NAS) IMI, contagious pathogen IMI, environmental pathogen IMI, major pathogen IMI, minor pathogen IMI and somatic cell score (SCS). Data were analyzed at the quarter level using a threshold-probit model via Gibbs sampling in BLUPF90. Prevalence of IMI traits at the quarter level in multiparous cow from 0 to 400 DIM ranged from 6.8 to 45.5%. Posterior mean of quarter heritability estimates (on the underlying scale, posterior SD in brackets) of overall IMI and pathogen-specific IMI traits ranged from 0.017 to 0.073 (±0.009 to 0.030). Weak to strong genetic correlations [ranging from 0.18 to 0.97 (±0.01 to 0.29)] among pathogen-specific IMI traits and with overall IMI indicated that not all of these traits were genetically similar. Weak to moderate Spearman rank correlations between estimated breeding values for overall IMI and pathogen-specific IMI traits (from 0.31 to 0.87) indicated possible substantial reranking of sires. The percentage of daughters with IMI caused by various pathogen groups ranged from 13 to 80% and from 38 to 94% for the best (10% decile) and worst sires (90% decile) according to their IMI trait-specific estimated breeding values, respectively. Pathogen-specific IMI traits and overall IMI had weak to moderate positive genetic correlations [ranging from 0.11 to 0.81 (±0.11 to 0.22)] with SCS. Therefore, selection for lower SCS will improve resistance to IMI. However, based on the observed weak to moderate rank correlations (0.04 to 0.47) between pathogen-specific IMI traits and SCS, selection for lower SCC will not improve resistance to IMI from every pathogen-specific IMI group in the same manner. Therefore, despite low heritability estimates, there was sizeable genetic variation for pathogen-specific IMI traits, indicating that long-term direct genetic selection for pathogen-specific IMI can improve pathogen-specific IMI resistance.     

Synthesis of hydroxyapatite nanopowders by sol-gel emulsion technique

Hydroxyapatite nanopowders were synthesized by a sol-gel emulsion technique by varying the concentration of a non-ionic surfactant in the organic phases (oil phase) of water-in-oil (w/o) emulsion. Calcium acetate dissolved in distilled water and phosphorous pentoxide dissolved in 2-butanol were used as starting precursors. The prepared sol was emulsified in a support solvent (cyclohexane) containing 2, 4 and 5 volume% of surfactant (Span 80), followed by the addition of triethylamine, for gelation. The gel powders thus obtained were calcined at different temperatures up to 750??C. Characterization was done using XRD, SEM and TEM. Pellets were made from the developed HAP powders and tested for its biocompatibility after their immersion in the simulated body fluid.     

Lactic acid bacteria isolated from raw and fermented pork products: Identification and characterization of catalase-producing Pediococcus pentosaceus

Food Science and Biotechnology - Lactic acid bacteria (LAB) from raw and fermented pork samples were screened for their inhibitory activity by an agar spot test in order to obtain a LAB strain with...     

Important flavonoids and limonin in selected Thai citrus residues

The distributions of important flavonoids and limonin in selected Thai citrus residues were investigated in this study. The residues of interest were peels of pomelo (Citrus grandis (L.) Osbeck cv. Kao Yai and cv. Kao Nampheung), residues after juice extraction of tangerine (Citrus reticulata Blanco cv. Sainamphueng and cv. Bangmod) and peels and residues after juice extraction of kaffir lime (Citrus hystrix DC). Flavonoids were characterized and quantified in terms of flavanones, i.e., naringin and hesperidin, and polymethoxyflavones (PMFs), i.e., nobiletin, sinensetin and tangeretin. Naringin was a dominant flavanone in polmelo peels. Three PMFs were found in all samples with varying contents. Comparing the samples, residues from tangerine cv. Bangmod exhibited the highest value of each PMFs. Limonin was present in small amounts in pomelo peels and residues after juice extraction of tangerine and kaffir lime. All samples were good sources of dietary fibre, with a total dietary fibre content of more than 60 g/100 g on a dry weight basis with high proportion of soluble dietary fibre.     

Energy Efficient Clustering Scheme (EECS) for Wireless Sensor Network with Mobile Sink

The participants in the Wireless Sensor Network (WSN) are highly resource constraint in nature. The clustering approach in the WSN supports a large-scale monitoring with ease to the user. The node near the sink depletes the energy, forming energy holes in the network. The mobility of the sink creates a major challenge in reliable and energy efficient data communication towards the sink. Hence, a new energy efficient routing protocol is needed to serve the use of networks with a mobile sink. The primary objective of the proposed work is to enhance the lifetime of the network and to increase the packet delivered to mobile sink in the network. The residual energy of the node, distance, and the data overhead are taken into account for selection of cluster head in this proposed Energy Efficient Clustering Scheme (EECS). The waiting time of the mobile sink is estimated. Based on the mobility model, the role of the sensor node is realized as finite state machine and the state transition is realized through Markov model. The proposed EECS algorithm is also been compared with Modified-Low Energy Adaptive Clustering Hierarchy (MOD-LEACH) and Gateway-based Energy-Aware multi-hop Routing protocol algorithms (M-GEAR). The proposed EECS algorithm outperforms the MOD-LEACH algorithm by 1.78 times in terms of lifetime and 1.103 times in terms of throughput. The EECS algorithm promotes unequal clustering by avoiding the energy hole and the HOT SPOT issues.     

Hybrid Global Sensitivity Analysis Based Optimal Attribute Selection Using Classification Techniques by Machine Learning Algorithm

In this study, we focus on realizing channel estimation using a fully connected deep neural network. The data aided estimation approach is employed. We assume the transmission channel is Rayleigh and it is constant over the duration of a symbol plus pilot transmission. We develop and tune the deep learning model for various size of pilot data that is known to the receiver and used for channel estimation. The deep learning models are trained on the Rayleigh channel. The performance of the model is discussed for various size of pilot by providing Bit Error Rate of the model. The Bit Error Rate performance of the model is compared to theoretical upper bound which shows that the model successfully estimates the channel.