Maximum Likelihood Detection for Cooperative Molecular Communication

In this paper, symbol-by-symbol maximum likelihood (ML) detection is proposed for a cooperative diffusion-based molecular communication (MC) system. In this system, a fusion center (FC) chooses the transmitter's symbol that is more likely, given the likelihood of the observations from multiple receivers (RXs). We propose three different ML detection variants according to different constraints on the information available to the FC, which enables us to demonstrate trade-offs in their performance versus the information available. The system error probability for one variant is derived in closed form. Numerical and simulation results show that the ML detection variants provide lower bounds on the error performance of the simpler cooperative variants and demonstrate that majority rule detection has performance comparable to ML detection when the reporting is noisy.Comment: 7 pages, 4 figurs. This work has been accepted by the IEEE ICC 201

Influence of Message Theme on Consumer Perceptions of Lab Grown Meat

Lab grown meat is a new technology being developed as a potential alternative protein source. Although some research has been done about public perception of lab grown meat, no studies to date have observed the effects of message themes on public perception of lab grown meat. The study sought to better understand measures of uncertainty and risk and benefit perceptions after viewing a themed blog post about lab grown meat. Participants were randomly assigned one of three themed blog posts - against lab grown meat, neutral, or support lab grown meat. Perception questions were asked after viewing the blog post, and a total of 238 responses were collected. Results indicated the message theme had a statistically significant effect on risk perception, benefit perception, and intention to consume, but not on message evaluation or measures of uncertainty. Further discussion as well as suggestions for future research are included

Convex optimization of distributed cooperative detection in multi-receiver molecular communication

In this paper, the error performance achieved by cooperative detection among K distributed receivers in a diffusion-based molecular communication system is analyzed and optimized. In this system, the receivers first make local hard decisions on the transmitted symbol and then report these decisions to a fusion center (FC). The FC combines the local hard decisions to make a global decision using an N -out-of- K fusion rule. Two reporting scenarios, namely, perfect reporting and noisy reporting, are considered. Closed-form expressions are derived for the expected global error probability of the system for both reporting scenarios. New approximated expressions are also derived for the expected error probability. Convex constraints are then found to make the approximated expressions jointly convex with respect to the decision thresholds at the receivers and the FC. Based on such constraints, suboptimal convex optimization problems are formulated and solved to determine the optimal decision thresholds which minimize the expected error probability of the system. Numerical and simulation results reveal that the system error performance is greatly improved by combining the detection information of distributed receivers. They also reveal that the solutions to the formulated suboptimal convex optimization problems achieve near-optimal global error performance

Convex optimization of distributed cooperative detection in multi-receiver molecular communication

In this paper, the error performance achieved by cooperative detection among K distributed receivers in a diffusion-based molecular communication system is analyzed and optimized. In this system, the receivers first make local hard decisions on the transmitted symbol and then report these decisions to a fusion center (FC). The FC combines the local hard decisions to make a global decision using an N -out-of- K fusion rule. Two reporting scenarios, namely, perfect reporting and noisy reporting, are considered. Closed-form expressions are derived for the expected global error probability of the system for both reporting scenarios. New approximated expressions are also derived for the expected error probability. Convex constraints are then found to make the approximated expressions jointly convex with respect to the decision thresholds at the receivers and the FC. Based on such constraints, suboptimal convex optimization problems are formulated and solved to determine the optimal decision thresholds which minimize the expected error probability of the system. Numerical and simulation results reveal that the system error performance is greatly improved by combining the detection information of distributed receivers. They also reveal that the solutions to the formulated suboptimal convex optimization problems achieve near-optimal global error performance

Symbol-by-Symbol Maximum Likelihood Detection for Cooperative Molecular Communication

In this paper, symbol-by-symbol maximum likelihood (ML) detection is proposed for a cooperative diffusion-based molecular communication (MC) system. In this system, the transmitter (TX) sends a common information symbol to multiple receivers (RXs) and a fusion center (FC) chooses the TX symbol that is more likely, given the likelihood of its observations from all RXs. The transmission of a sequence of binary symbols and the resultant intersymbol interference are considered in the cooperative MC system. Three ML detection variants are proposed according to different RX behaviors and different knowledge at the FC. The system error probabilities for two ML detector variants are derived, one of which is in closed form. The optimal molecule allocation among RXs to minimize the system error probability of one variant is determined by solving a joint optimization problem. Also for this variant, the equal distribution of molecules among two symmetric RXs is analytically shown to achieve the local minimal error probability. Numerical and simulation results show that the ML detection variants provide lower bounds on the error performance of simpler, non-ML cooperative variants and demonstrate that these simpler cooperative variants have error performance comparable to ML detectors.Comment: 15 pages, 7 figures; submission for possible IEEE publication. arXiv admin note: text overlap with arXiv:1704.0562

Reduction of leukocyte microvascular adherence and preservation of blood-brain barrier function by superoxide-lowering therapies in a piglet model of neonatal asphyxia

Background: Asphyxia is the most common cause of brain damage in newborns. Substantial evidence indicates that leukocyte recruitment in the cerebral vasculature during asphyxia contributes to this damage. We tested the hypothesis that superoxide radical (O2⋅_) promotes an acute post-asphyxial inflammatory response and blood-brain barrier (BBB) breakdown. We investigated the effects of removing O2⋅_ by superoxide dismutase (SOD) or C3, the cell-permeable SOD mimetic, in protecting against asphyxia-related leukocyte recruitment. We also tested the hypothesis that xanthine oxidase activity is one source of this radical.Methods: Anesthetized piglets were tracheostomized, ventilated, and equipped with closed cranial windows for the assessment of post-asphyxial rhodamine 6G-labeled leukocyte-endothelial adherence and microvascular permeability to sodium fluorescein in cortical venules. Asphyxia was induced by discontinuing ventilation. SOD and C3 were administered by cortical superfusion. The xanthine oxidase inhibitor oxypurinol was administered intravenously.Results: Leukocyte-venular adherence significantly increased during the initial 2 h of post-asphyxial reperfusion. BBB permeability was also elevated relative to non-asphyxial controls. Inhibition of O2⋅_ production by oxypurinol, or elimination of O2⋅_ by SOD or C3, significantly reduced rhodamine 6G-labeled leukocyte-endothelial adherence and improved BBB integrity, as measured by sodium fluorescein leak from cerebral microvessels.Conclusion: Using three different strategies to either prevent formation or enhance elimination of O2⋅_ during the post-asphyxial period, we saw both reduced leukocyte adherence and preserved BBB function with treatment. These findings suggest that agents which lower O2⋅_ in brain may be attractive new therapeutic interventions for the protection of the neonatal brain following asphyxia

SeqAssist: A Novel Toolkit For Preliminary Analysis of Next-Generation Sequencing Data

Background: While next-generation sequencing (NGS) technologies are rapidly advancing, an area that lags behind is the development of efficient and user-friendly tools for preliminary analysis of massive NGS data. As an effort to fill this gap to keep up with the fast pace of technological advancement and to accelerate data-to-results turnaround, we developed a novel software package named SeqAssist ( Sequencing Assistant or SA). Results: SeqAssist takes NGS-generated FASTQ files as the input, employs the BWA-MEM aligner for sequence alignment, and aims to provide a quick overview and basic statistics of NGS data. It consists of three separate workflows: (1) the SA_RunStats workflow generates basic statistics about an NGS dataset, including numbers of raw, cleaned, redundant and unique reads, redundancy rate, and a list of unique sequences with length and read count; (2) the SA_Run2Ref workflow estimates the breadth, depth and evenness of genome-wide coverage of the NGS dataset at a nucleotide resolution; and (3) the SA_Run2Run workflow compares two NGS datasets to determine the redundancy (overlapping rate) between the two NGS runs. Statistics produced by SeqAssist or derived from SeqAssist output files are designed to inform the user: whether, what percentage, how many times and how evenly a genomic locus (i.e., gene, scaffold, chromosome or genome) is covered by sequencing reads, how redundant the sequencing reads are in a single run or between two runs. These statistics can guide the user in evaluating the quality of a DNA library prepared for RNA-Seq or genome (re-)sequencing and in deciding the number of sequencing runs required for the library. We have tested SeqAssist using a synthetic dataset and demonstrated its main features using multiple NGS datasets generated from genome re-sequencing experiments. Conclusions: SeqAssist is a useful and informative tool that can serve as a valuable assistant to a broad range of investigators who conduct genome re-sequencing, RNA-Seq, or de novo genome sequencing and assembly experiments

Discrete element modelling of flexible fibre packing

This paper presents Discrete Element Model simulations of packing of non-cohesive flexible fibres in a cylindrical vessel. No interstitial fluid effects are modelled. Each fibre-particle is modelled as a series of connected sphero-cylinders. In an initial study each particle is modelled a single rigid sphero-cylinder; the method has been used before but this study considers higher aspect ratios up to 30. This posed some modelling challenges in terms of stability which were overcome by imposing limits on the particle angular velocity. The results show very good agreement with experimental data in the literature and more detailed in-house experiments for packing volume fraction. Model results on particle orientation are also shown. The model is developed to include flexibility by connecting sphero-cylinders as sub-elements to describe a particle. Some basic tests are shown for the joint model that connects the sub-elements. The simulation results show similar trends to the rigid particle results with increased packing fraction. The effects of number of sub-elements, joint properties and contact friction are examined. The model has the potential for predicting packing of fibrous particles and fibre bundles relevant to the preparation of preforms for the production of discontinuously-reinforced polymer, ceramic and metallic matrix composites