Contributors:Lilian Franco-Belussi, Diogo Borges Provete, Rinneu Borges, Classius de Oliveira, Lia Raquel Santos
This dataset comprehends data and and associated R code used to run the analysis for the paper. We also include an R Markdown Dynamic document. We tested whether the amount of melanomacrophages and hepatic cellular catabolism substances are influenced by land use changes in the Brazilian Cerrado. Data contains the Environmental matrix (Q) composed of the land use classes for each samplimg site, species trait (R) matrix with content of each pigment in cells, averaged from all individuals, and species composition matrix (L) with the species incidence in all sampling sites.
The present dataset includes data and figures relative to the submitted paper " Detection of formation boundary using transient multicomponent electromagnetic logging while drilling method " currently under review in Journal of Petroleum Science and Engineering.
Contributors:Dominik Borawski, Anna Siudak, Anna Pawelec, Bartłomiej Rozpara, Mateusz Zawada
The aim of the study was to investigate the interplay between loneliness and mindfulness in predicting presence of meaning (POM ), taking into consideration the additional role of the search for meaning (SFM). A sample of 415 participants from Poland aged 18 - 55 (M = 27.88; SD = 8.66) completed a set of 3 questionnaires: the De Jong-Gierveld Loneliness Scale, the Mindful Attention and Awareness Scale, and Meaning in Life Questionnaire. Our results suggest that mindfulness partially mediated the relationship between loneliness and POM. This effect was moderated by SFM. Specifically, an indirect effect was found among participants with medium- and high-levels of search for meaning but not in the participants with a low level of this variable. Furthermore, it turned out that SFM enhanced the relationship between mindfulness and POM. These results are discussed in the context of the evolutionary theory of loneliness, meaning-making mechanisms of mindfulness and schema-like properties of SFM.
This data set can be used to plot bar charts of COVID 19 incidence and estimate time dependent reproduction number in Nigeria and the six geo - political zones in the country. The data was collated from the daily COVID – 19 infections situation report in Nigeria numbers 01 – 89 from the 28th of February to 27th of May 2020 downloaded from the country’s Nigeria Centre for Disease Control (NCDC) website (https://ncdc.gov.ng/diseases/sitreps/?cat=14&name=An%20update%20of%20COVID-19%20outbreak%20in%20Nigeria ). The data files in .csv are presented with r codes that can be used to recreate the analysis. We used the R software version 3.6.1 to draw a combine bar plot of COVID 19 incidence in Nigeria and across the six geo - political zones. We estimated the TD - R0 for the country and the six geo - political zones in the country using the EpiEstim package and tested the fitness in the pattern of distribution of the estimated TD - R0 across the six geo - political zones in the country using the Kolmogorov - Smirnov test adjusting for Type 1 error.
This paper explored the coordination-like phenomena observed in collective dissipative structures. We observed the coordination of two self-organized electrical structures that oscillated together in a shared electrical field. The system is called the Electrical Self-Organized Foraging Implementation (E-SOFI). The structures are coupled by this field and settle into stable oscillatory regimes defined by their relative phase. We also observed the oscillatory dynamics of simulated electrical structures, using a computational model called the Charge Depletion Model (CDM; De Bari et al., 2019). We predicted that both in-phase (zero radians relative phase) and anti-phase (pi radians relative phase) would be stable oscillatory modes for the physical and simulated system. Previous work has demonstrated that this system self-selects for states of maximal current. We predicted that the model would demonstrate differential stability of in-phase and anti-phase modes, in accordance with whichever mode produced the greater current.
This data set includes the digitally tracked positions of the physical structures, gathered from video data. One data set demonstrated in-phase coordination, and another demonstrated anti-phase coordination. Also included are corresponding data from simulations of the physical model. Simulations tested the existence of the in-phase and anti-phase modes, and the current conducted during the coordination. Simulations were conducted across a parameter space that varied the stability of the in- and anti-phase coordination modes.
We demonstrated stability in the in-phase coordination mode, and potential meta-stability of the anti-phase mode in the physical system. Simulations suggested that within a reasonable parameter space the anti-phase mode can be made stable. Simulations also revealed that within a given parameter space, whichever mode produces greater current is also more stable.
An R Markdown file includes the code to compute the relative phase of both the physical and simulated data, as well as the code used to produce the plots in the published manuscript.
Contributors:John Palmieri, Kevin Spiegler, Kevin Pang, Catherine Myers
The purpose of this experiment was to compare between-strain differences regarding avoidance behavior. Active avoidance data were collected from 40 Wistar-Kyoto (WKY) and 40 Sprague Dawley (SD) rats during a foot-shock experiment. The experiment consisted of 12 sessions each composed of 25 trials. Each trial consisted of a warning period, a possible shock period (if avoidance did not occur), and an intertrial interval (ITI) representing a safety period. During warning periods, rats had an option to press a lever in order to avoid future shock. However, if a lever press did not occur, then a shock period began during which the rats could press the lever to escape the foot-shock. After an escape or an avoidance, a 180-second safe period began (ITI). The maximum time of a warning period and a shock were both 72-seconds each if the rat failed to lever press during both periods. Every session also began with a 60-second habituation period during which the rat could familiarize itself with the experimental cage.
Lever presses during ITI periods were classified as inter-trial responses (ITRs) whereas lever presses during the habituation periods were classified as anticipatory responses (ARs). Lever presses during shock periods were labeled as escapes “E” whereas lever presses during warning periods were labeled as avoidances “A”.
Lever press data were discretized to 12-second periods. Frequency counts for lever presses during each 12-second timestep can be found in the raw data files (located in the “ratRL_datafiles_Model_InputOutput” folder). An example name of this file is “S09.csv” representing an SD rat or “W09.csv” representing a WKY rat.
A reinforcement learning, actor-critic model was applied to this raw data to determine different learning parameters for each rat. The code for this model fitting can be found in the folder titled “ratRL_ModelFitting_Code”.
Based on learning parameters determined from the reinforcement learning model code, a simulation of theoretical rats running through the experimental protocol was created. This simulation can be found in the “ratRL_Simulation_Code” folder. Inputs to this simulation are found in the “ratRL_codedTrials_Sim_InputOutput” folder. The summaries for each experimental rat’s performance during the foot-shock avoidance experiment are labeled “Sum” files (ie S09sum.csv) and the learning parameters for each rat are stored in the file called “parm_listfile.csv”. Output files are labeled “out” (ie S09out.csv) and represent the performance of a theoretical simulated rat.
The differences in learning parameters between the two rat strains can provide insight into anxiety-disorders as the WKY rats have been used as an animal model for anxiety. Learning parameters can also be mapped to certain brain regions in order to explore possible neurological differences in the two strains in future experiments.