171 research outputs found
Optimal Sensing and Transmission in Energy Harvesting Sensor Networks
Sensor networks equipped with energy harvesting (EH) devices have attracted great attentions recently. Compared with conventional sensor networks powered by batteries, the energy harvesting abilities of the sensor nodes make sustainable and environment-friendly sensor networks possible. However, the random, scarce and non-uniform energy supply features also necessitate a completely different approach to energy management.
A typical EH wireless sensor node consists of an EH module that converts ambient energy to electrical energy, which is stored in a rechargeable battery, and will be used to power the sensing and transmission operations of the sensor. Therefore, both sensing and transmission are subject to the stochastic energy constraint imposed by the EH process. In this dissertation, we investigate optimal sensing and transmission policies for EH sensor networks under such constraints.
For EH sensing, our objective is to understand how the temporal and spatial variabilities of the EH processes would affect the sensing performance of the network, and how sensor nodes should coordinate their data collection procedures with each other to cope with the random and non-uniform energy supply and provide reliable sensing performance with analytically provable guarantees. Specifically, we investigate optimal sensing policies for a single sensor node with infinite and finite battery sizes in Chapter 2, status updating/transmission strategy of an EH Source in Chapter 3, and a collaborative sensing policy for a multi-node EH sensor network in Chapter 4.
For EH communication, our objective is to evaluate the impacts of stochastic variability of the EH process and practical battery usage constraint on the EH systems, and develop optimal transmission policies by taking such impacts into consideration. Specifically, we consider throughput optimization in an EH system under battery usage constraint in Chapter 5
SIMULATION OF MICROBUBBLE RESISTANCE REDUCTION ON A SUBOFF MODEL
This paper presents a mixture-model based computational fluid dynamics (CFD) simulation on the two-phase microbubble flow over the hull of a SUBOFF model, aimed at assessing the roles of air-injection-to-freestream velocity ratio and air volume fraction in microbubble resistance reduction. The numerical framework consists of the Reynolds-average Navier-Stokes (RANS) equations and the standard turbulence model with standard wall function treatment, which is validated, without microbubbles, by existing experimental data of the same SUBOFF model. The effect of velocity ratio is then investigated by comparing different types of the resistance reduction at various water speeds, and the effect of air volume fraction on the friction resistance reduction is also studied with various air injection velocities. This study confirms that both the velocity ratio and air volume fraction play important roles in the microbubble resistance reduction phenomenon
An information-flow-based model with dissipation, saturation and direction for active pathway inference
<p>Abstract</p> <p>Background</p> <p>Biological systems process the genetic information and environmental signals through pathways. How to map the pathways systematically and efficiently from high-throughput genomic and proteomic data is a challenging open problem. Previous methods design different heuristics but do not describe explicitly the behaviours of the information flow.</p> <p>Results</p> <p>In this study, we propose new concepts of dissipation, saturation and direction to decipher the information flow behaviours in the pathways and thereby infer the biological pathways from a given source to its target. This model takes into account explicitly the common features of the information transmission and provides a general framework to model the biological pathways. It can incorporate different types of bio-molecular interactions to infer the signal transduction pathways and interpret the expression quantitative trait loci (eQTL) associations. The model is formulated as a linear programming problem and thus is solved efficiently. Experiments on the real data of yeast indicate that the reproduced pathways are highly consistent with the current knowledge.</p> <p>Conclusions</p> <p>Our model explicitly treats the biological pathways as information flows with dissipation, saturation and direction. The effective applications suggest that the three new concepts may be valid to describe the organization rules of biological pathways. The deduced linear programming should be a promising tool to infer the various biological pathways from the high-throughput data.</p
Microwave-Assisted Oxidation of Electrospun Turbostratic Carbon Nanofibers for Tailoring Energy Storage Capabilities
We report the systematic structural manipulation of turbostratic electrospun carbon nanofibers (ECNFs) using a microwave-assisted oxidation process, which is extremely rapid and highly controllable and affords controlled variation of the capacitive energy storage capabilities of ECNFs. We find a nonmonotonic relationship between the oxidation degree of ECNFs and their electrocapacitive performance and present a detailed study on the electronic and crystalline structures of ECNFs to elucidate the origin of this nonmonotonic relation. The ECNFs with an optimized oxidation level show ultrahigh capacitances at high operation rates, exceptional cycling performance, and an excellent energyâpower combination. We have identified three key factors required for optimal energy storage performance for turbostratic carbon systems: (i) an abundance of surface oxides, (ii) microstructural integrity, and (iii) an appropriate interlayer spacing
Differential expression of six genes and correlation with fatness traits in a unique broiler population
AbstractPrevious results from genome wide association studies (GWASs) in chickens divergently selected for abdominal fat content of Northeast Agricultural University (NEAUHLF) showed that many single nucleotide polymorphism (SNP) variants were associated with abdominal fat content. Of them, six top significant SNPs at the genome level were located within SRD5A3, SGCZ, DLC1, GBE1, GALNT9 and DNAJB6 genes. Here, expression levels of these six candidate genes were investigated in abdominal fat and liver tissue between fat and lean broilers from the 14th generation population of NEAUHLF. The results showed that expression levels of SRD5A3, SGCZ and DNAJB6 in the abdominal fat and SRD5A3, DLC1, GALNT9, DNAJB6 and GBE1 in the liver tissue differed significantly between the fat and lean birds, and were correlated with abdominal fat traits. The findings will provide important references for further function investigation of the six candidate genes involved in abdominal fat deposition in chickens
Discovering cooperative biomarkers for heterogeneous complex disease diagnoses
Biomarkers with high reproducibility and accurate prediction
performance can contribute to comprehending the underlying
pathogenesis of related complex diseases and further facilitate
disease diagnosis and therapy. Techniques integrating gene
expression profiles and biological networks for the
identification of network-based disease biomarkers are
receiving increasing interest. The biomarkers for heterogeneous
diseases often exhibit strong cooperative effects, which
implies that a set of genes may achieve more accurate outcome
prediction than any single gene. In this study, we evaluated
various biomarker identification methods that consider gene
cooperative effects implicitly or explicitly, and proposed the
gene cooperation network to explicitly model the cooperative
effects of gene combinations. The gene cooperation network-
enhanced method, named as MarkRank, achieves superior
performance compared with traditional biomarker identification
methods in both simulation studies and real data sets. The
biomarkers identified by MarkRank not only have a better
prediction accuracy but also have stronger topological
relationships in the biological network and exhibit high
specificity associated with the related diseases. Furthermore,
the top genes identified by MarkRank involve crucial biological
processes of related diseases and give a good prioritization
for known disease genes. In conclusion, MarkRank suggests that
explicit modeling of gene cooperative effects can greatly
improve biomarker identification for complex diseases,
especially for diseases with high heterogeneity
Evaluation of the Observational Associations and Shared Genetics Between Glaucoma With Depression and Anxiety
PURPOSE: Glaucoma, a leading cause of blindness worldwide, is suspected to exhibit a notable association with psychological disturbances. This study aimed to investigate epidemiological associations and explore shared genetic architecture between glaucoma and mental traits, including depression and anxiety.METHODS: Multivariable logistic regression and Cox proportional hazards regression models were employed to investigate longitudinal associations based on UK Biobank. A stepwise approach was used to explore the shared genetic architecture. First, linkage disequilibrium score regression inferred global genetic correlations. Second, MiXeR analysis quantified the number of shared causal variants. Third, specific shared loci were detected through conditional/conjunctional false discovery rate (condFDR/conjFDR) analysis and characterized for biological insights. Finally, two-sample Mendelian randomization (MR) was conducted to investigate bidirectional causal associations.RESULTS: Glaucoma was significantly associated with elevated risks of hospitalized depression (hazard ratio [HR] = 1.54; 95% confidence interval [CI], 1.01-2.34) and anxiety (HR = 2.61; 95% CI, 1.70-4.01) compared to healthy controls. Despite the absence of global genetic correlations, MiXeR analysis revealed 300 variants shared between glaucoma and depression, and 500 variants shared between glaucoma and anxiety. Subsequent condFDR/conjFDR analysis discovered 906 single-nucleotide polymorphisms (SNPs) jointly associated with glaucoma and depression and two associated with glaucoma and anxiety. The MR analysis did not support robust causal associations but indicated the existence of pleiotropic genetic variants influencing both glaucoma and depression.CONCLUSIONS: Our study enhances the existing epidemiological evidence and underscores the polygenic overlap between glaucoma and mental traits. This observation suggests a correlation shaped by pleiotropic genetic variants rather than being indicative of direct causal relationships.</p
Evaluation of the Observational Associations and Shared Genetics Between Glaucoma With Depression and Anxiety
PURPOSE: Glaucoma, a leading cause of blindness worldwide, is suspected to exhibit a notable association with psychological disturbances. This study aimed to investigate epidemiological associations and explore shared genetic architecture between glaucoma and mental traits, including depression and anxiety.METHODS: Multivariable logistic regression and Cox proportional hazards regression models were employed to investigate longitudinal associations based on UK Biobank. A stepwise approach was used to explore the shared genetic architecture. First, linkage disequilibrium score regression inferred global genetic correlations. Second, MiXeR analysis quantified the number of shared causal variants. Third, specific shared loci were detected through conditional/conjunctional false discovery rate (condFDR/conjFDR) analysis and characterized for biological insights. Finally, two-sample Mendelian randomization (MR) was conducted to investigate bidirectional causal associations.RESULTS: Glaucoma was significantly associated with elevated risks of hospitalized depression (hazard ratio [HR] = 1.54; 95% confidence interval [CI], 1.01-2.34) and anxiety (HR = 2.61; 95% CI, 1.70-4.01) compared to healthy controls. Despite the absence of global genetic correlations, MiXeR analysis revealed 300 variants shared between glaucoma and depression, and 500 variants shared between glaucoma and anxiety. Subsequent condFDR/conjFDR analysis discovered 906 single-nucleotide polymorphisms (SNPs) jointly associated with glaucoma and depression and two associated with glaucoma and anxiety. The MR analysis did not support robust causal associations but indicated the existence of pleiotropic genetic variants influencing both glaucoma and depression.CONCLUSIONS: Our study enhances the existing epidemiological evidence and underscores the polygenic overlap between glaucoma and mental traits. This observation suggests a correlation shaped by pleiotropic genetic variants rather than being indicative of direct causal relationships.</p
Pseudonocardians AâC, New Diazaanthraquinone Derivatives from a Deap-Sea Actinomycete Pseudonocardia sp. SCSIO 01299
Pseudonocardians AâC (2â4), three new diazaanthraquinone derivatives, along with a previously synthesized compound deoxynyboquinone (1), were produced by the strain SCSIO 01299, a marine actinomycete member of the genus Pseudonocardia, isolated from deep-sea sediment of the South China Sea. The structures of compounds 1â4 were determined by mass spectrometry and NMR experiments (1H, 13C, HSQC, and HMBC). The structure of compound 1, which was obtained for the first time from a natural source, was confirmed by X-ray analysis. Compounds 1â3 exhibited potent cytotoxic activities against three tumor cell lines of SF-268, MCF-7 and NCI-H460 with IC50 values between 0.01 and 0.21 ÎŒm, and also showed antibacterial activities on Staphylococcus aureus ATCC 29213, Enterococcus faecalis ATCC 29212 and Bacillus thuringensis SCSIO BT01, with MIC values of 1â4 ÎŒg mLâ1
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