Renal sensory nerves increase sympathetic nerve activity and blood pressure in 2-Kidney 1-Clip hypertensive mice

Renal denervation lowers arterial blood pressure (ABP) in multiple clinical trials and some experimental models of hypertension. These antihypertensive effects have been attributed to the removal of renal afferent nerves. The purpose of the present study was to define the function, anatomy, and contribution of mouse renal sensory neurons to a renal-nerve dependent model of hypertension. First, electrical stimulation of mouse renal afferent nerves produced frequency-dependent increases in ABP that were eliminated by ganglionic blockade. Stimulus-triggered averaging revealed renal afferent stimulation significantly increased splanchnic, renal, and lumbar sympathetic nerve activity (SNA). Second, kidney injection of wheat germ agglutinin into male C57Bl6 mice (12-14 weeks, Jackson Laboratories) produced ipsilateral labeling in the T11-L2 dorsal root ganglia. Next, 2K1C hypertension was produced in male C57Bl6 mice (12-14 weeks, Jackson Laboratories) by placement of a 0.5mm length of PTFE tubing around the left renal artery. 2K1C mice displayed an elevated ABP measured via telemetry and greater fall in mean ABP to ganglionic blockade at day 14 or 21 vs day 0. Renal afferent discharge was significantly higher in 2K1C-clipped versus 2K1C-unclipped or sham kidneys. In addition, 2K1C-clipped versus 2K1C-unclipped or sham kidneys had lower renal mass and higher mRNA levels of several pro-inflammatory cytokines. Finally, both ipsilateral renal denervation (10% phenol) or selective denervation of renal afferent nerves (periaxonal application of 33 mM capsaicin) at time of clipping resulted in lower ABP of 2K1C mice at Days 14 or 21. These findings suggest mouse renal sensory neurons are activated to increase SNA and ABP in 2K1C hypertension

Parametric tuning of the Gielis Superformula for non-target based automated evolution of 3D Printable objects

3D printing is an emerging trend fuelled by the rapid technology advancements in 3D printing technology. Printing out 3D designs is something new and interesting but the process of designing 3D objects is far from effortless. Researchers have recently forged ahead in conducting numerous studies on using mathematical formulas to create objects and shapes in 3D space. A mathematical encoding for geometric shapes called the Superformula was proposed by Johan Geilis through the generalization of the Supereclipse formula to generate 3D shapes and objects by extending its spherical products. The focus of this study is to investigate the ideal range of parametric values supplied to the Superformula in order to automatically generate 3D shapes and objects through the use of Evolution Algorithms (EAs). Thus, Evolutionary Programming was used as the EA in this study which serves as the main evolution component that uses a fitness function tailored in a way that it is able to evaluate the 3D objects and shapes generated by the Superformula. The values require by the Superformula to generate 3D objects or shapes are . To obtain the ideal range of values for the afore mentioned parameters, five different sets of experiments were carried out within the range set of {0 - 20}, {0 - 40}, {0 - 60}, {0 - 120}, and {0 - 240}.Each range set of numbers will be tested five times and the final objects from each of the runs were then analysed. From the observations obtained, the range set of {0- 20}, {0- 60}, and {0- 120} shows the most promising results as the final objects produced were unique and it was surmised that within these range of numbers contain highly unique and novel 3D objects and shapes

A Time-Critical Investigation of Parameter Tuning in Differential Evolution for Non-Linear Global Optimization

Parameter searching is one of the most important aspects in getting favorable results in optimization problems. It is even more important if the optimization problems are limited by time constraints. In a limited time constraint problems, it is crucial for any algorithms to get the best results or near-optimum results. In a previous study, Differential Evolution (DE) has been found as one of the best performing algorithms under time constraints. As this has help in answering which algorithm that yields results that are near-optimum under a limited time constraint. Hence to further enhance the performance of DE under time constraint evaluation, a throughout parameter searching for population size, mutation constant and f constant have been carried out. CEC 2015 Global Optimization Competition’s 15 scalable test problems are used as test suite for this study. In the previous study the same test suits has been used and the results from DE will be use as the benchmark for this study since it shows the best results among the previous tested algorithms. Eight different populations size are used and they are 10, 30, 50, 100, 150, 200, 300, and 500. Each of these populations size will run with mutation constant of 0.1 until 0.9 and from 0.1 until 0.9. It was found that population size 100, Cr = 0.9, F=0.5 outperform the benchmark results. It is also observed from the results that good higher Cr around 0.8 and 0.9 with low F around 0.3 to 0.4 yields good results for DE under time constraints evaluatio

When Disinformation Studies Meets Production Studies: Social Identities and Moral Justifications in the Political Trolling Industry

The field of disinformation studies remains relatively silent about questions of identity, motivation, labor, and morality. Drawing from a one-year ethnographic study of disinformation producers employed in digital black ops campaigns in the Philippines, this article proposes that approaches from production studies can address gaps in disinformation research. We argue that approaching disinformation as a culture of production opens inquiry into the social conditions that entice people to this work and the creative industry practices that normalize fake news as a side gig. This article critically reflects on the methodological risks and opportunities of ethnographic research that subverts expectations of the exceptionally villainous troll and instead uses narratives of creative workers’ complicity and collusion to advance holistic social critique and local-level disinformation interventions

Anomalous conductivity tensor in the Dirac semimetal Na_3Bi

Na3Bi is a Dirac semimetal with protected nodes that may be sensitive to the breaking of time-reversal invariance in a magnetic field B. We report experiments which reveal that both the conductivity and resistivity tensors exhibit robust anomalies in B. The resistivity $\rho_{xx}$ is B-linear up to 35 T, while the Hall angle exhibits an unusual profile approaching a step-function. The conductivities $\sigma_{xx}$ and $\sigma_{xy}$ share identical power-law dependences at large B. We propose that these significant deviations from conventional transport result from an unusual sensitivity of the transport lifetime to B. Comparison with Cd3As2 is made.Comment: 8 pages, 5 figure

Automated synthesis of mobile game environments and rulesets using a hybridized interactive evolutionary programming approach

By hybridizing Evolutionary Programming (EP) with Interactive Evolutionary Algorithm (IEA), game rules and its playing environment will be automatically generated for an arcade-type game that can be played on the Android mobile platform. In this study, mutation rates of 0.7 and 0.9 are used to generate both the game rules and the game environment for the mobile game. Players are used as the evaluator instead of the conventional mathematical fitness functions and hence the motivation for using high mutation rate is that they are able to generate higher levels of diversity during the optimization runs. This interactive mode of game-playing cum evaluation will enable the creation of games that can fit the user’s preferences as well as styles of game-playing. Experiments show a very positive result where very good evaluation scores were obtained from the users. This shows that with a high mutation rate, the hybridized EP with IEA approach can generate rules and environments that are well-accepted and liked by human players

A time-critical investigation of parameter tuning in differential evolution for non-linear global optimization

Parameter searching is one of the most important aspects in getting favorable results in optimization problems. It is even more important if the optimization problems are limited by time constraints. In a limited time constraint problems, it is crucial for any algorithms to get the best results or near-optimum results. In a previous study, Differential Evolution (DE) has been found as one of the best performing algorithms under time constraints. As this has help in answering which algorithm that yields results that are near-optimum under a limited time constraint. Hence to further enhance the performance of DE under time constraint evaluation, a throughout parameter searching for population size, mutation constant and f constant have been carried out. CEC 2015 Global Optimization Competition’s 15 scalable test problems are used as test suite for this study. In the previous study the same test suits has been used and the results from DE will be use as the benchmark for this study since it shows the best results among the previous tested algorithms. Eight different populations size are used and they are 10, 30, 50, 100, 150, 200, 300, and 500. Each of these populations size will run with mutation constant of 0.1 until 0.9 and from 0.1 until 0.9. It was found that population size 100, Cr = 0.9, F=0.5 outperform the benchmark results. It is also observed from the results that good higher Cr around 0.8 and 0.9 with low F around 0.3 to 0.4 yields good results for DE under time constraints evaluatio

Exploration of mutation step sizes in the automated evolution of printable free-form 3D objects

3D printing is a comparatively new technology that is becoming ever more attractive to everyday practitioners and hobbyists due to its low start-up cost as well as making significant advancements in its printing process as well resolution and material variety. Implementation of EAs in the field of 3D printing is still in its infancy since 3D printing itself is a relatively new technology that has only become main stream due to its significant decrease in acquisition cost in the past 2-3 years. In this study, an EA in the form of Evolutionary Programming (EP) is used to automatically evolve 3D objects generated by Gielis’ Superformula. Objective: The focus of this study is to explore the mutation step size in hoping to create more diverse populations in the evolution of the generated 3D printable objects. In EP, the operator responsible for offspring generation is through the mutation process solely. Hence, the mutation step size has a direct and very significant impact on the diversity of the offspring generated. A fitness function was designed to evaluate the 3D objects and shapes generated by the Superformula. The parameters for the Superformula to generate 3D objects or shapes are m_1, m_2, n_(1,1), n_(1,2), n_(1,3), n_(2,1), n_(2,2), and n_(2,3). These parameters serve as a representation in EP and the mutation step size will affect the chances of these parameters’ values to change. To carry out this study, ten different mutation step sizes ranging from 0.1 to 1.0 in increments of 0.1 were used and run for five times. Results: The results indicate that the most aesthetically-pleasing as well as machine-printable results were obtained using the smallest mutation size of 0.1. Conclusion: Optimal setting for mutation rate can successful generate 3D-printable shapes that are aesthetically-pleasing using the proposed Gielis Superformula-based methodology