Hybrid NOMA-TDMA for Multiple Access Channels with Non-Ideal Batteries and Circuit Cost

We consider a multiple-access channel where the users are powered from batteries having non-negligible internal resistance. When power is drawn from the battery, a variable fraction of the power, which is a function of the power drawn from the battery, is lost across the internal resistance. Hence, the power delivered to the load is less than the power drawn from the battery. The users consume a constant power for the circuit operation during transmission but do not consume any power when not transmitting. In this setting, we obtain the maximum sum-rates and achievable rate regions under various cases. We show that, unlike in the ideal battery case, the TDMA (time-division multiple access) strategy, wherein the users transmit orthogonally in time, may not always achieve the maximum sum-rate when the internal resistance is non-zero. The users may need to adopt a hybrid NOMA-TDMA strategy which combines the features of NOMA (non-orthogonal multiple access) and TDMA, wherein a set of users are allocated fixed time windows for orthogonal single-user and non-orthogonal joint transmissions, respectively. We also numerically show that the maximum achievable rate regions in NOMA and TDMA strategies are contained within the maximum achievable rate region of the hybrid NOMA-TDMA strategy

IDENTIFICATION OF CIS-ACTING ELEMENTS CONTROLLING GENE EXPRESSION IN S. neurona

Sarcocystis neurona is an apicomplexan parasite that is a major cause of equine protozoal myeloencephalitis (EPM). During intracellular development of S. neurona, many genes are temporally regulated. To better understand gene regulation, it is important to identify and characterize regulatory elements controlling gene expression in S. neurona. To perform this study, it was essential to establish transfection system for this parasite. Hence, the 5 flanking region of the SnSAG1 gene was isolated from a genomic library and used to construct expression plasmids. In transient assays, the reporter molecules -galactosidase (-gal) and yellow fluorescent protein (YFP) were expressed by electroporated S. neurona, thereby confirming the feasibility of performing molecular genetic experiments in this organism. Stable transformation of S. neurona was achieved using a mutant dihydrofolate reductase thymidylate synthase (DHFR-TS) gene of T. gondii that confers resistance to pyrimethamine. This selection system was used to create transgenic S. neurona that stably express -gal and YFP. These transgenic clones were shown to be useful for analyzing growth rate of parasites in-vitro and for assessing drug sensitivities. To uncover possible sequence elements involved in promoter activity, the 5 flanking regions of five S. neurona genes were subjected to comparative analysis. This revealed the presence of a 7-base conserved motif GCGTCTC. Using a dual luciferase assay system, the SnSAG1 promoter was subjected to functional analysis. The motif GAGACGC located between -136 and -129 upstream of the transcription start site was found to be essential for SnSAG1 expression. This motif functions in an orientation dependent manner and was shown to play a role in binding nuclear proteins of S. neurona

Controversy trend detection in social media

In this research, we focus on the early prediction of whether topics are likely to generate significant controversy (in the form of social media such as comments, blogs, etc.). Controversy trend detection is important to companies, governments, national security agencies, and marketing groups because it can be used to identify which issues the public is having problems with and develop strategies to remedy them. For example, companies can monitor their press release to find out how the public is reacting and to decide if any additional public relations action is required, social media moderators can moderate discussions if the discussions start becoming abusive and getting out of control, and governmental agencies can monitor their public policies and make adjustments to the policies to address any public concerns. An algorithm was developed to predict controversy trends by taking into account sentiment expressed in comments, burstiness of comments, and controversy score. To train and test the algorithm, an annotated corpus was developed consisting of 728 news articles and over 500,000 comments on these articles made by viewers from CNN.com. This study achieved an average F-score of 71.3% across all time spans in detection of controversial versus non-controversial topics. The results suggest that it is possible for early prediction of controversy trends leveraging social media