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Fig. 3 in Androgenic Modulation in the Primary Ovarian Growth of the Japanese eel, .

Fig. 3. The ovarian composition of female eel undergoing hormonal induction in three stages: IA, IB, and IC (the grey tint from light to dark). Stage was categorized as the characteristics for follicle stage calculation. GSI was measured and is shown in each individual. The ovary composition is shown in each percentage bar. The stage composition is shown among female eels in (A) the control group, (B) weekly SPH injection for three weeks and (C) weekly SPH + MT injection for three weeks.

Fig. 1 in Androgenic Modulation in the Primary Ovarian Growth of the Japanese eel, .

Fig. 1. Histological comparison of ovarian development among the control and three weekly SPH- and SPH + MT- injected groups. Histological analyses are shown in controls (A, GSI = 0.42%; B, GSI = 0.46%; C, GSI = 0.5%; D, GSI = 0.75%), weekly SPH injection for three weeks (E, GSI = 0.63%; F, GSI = 1.02%; G, GSI = 1.07%) and weekly SPH + MT for three weeks (H, GSI = 0.93%; I, GSI = 1.34%; J, GSI = 1.38%; K, GSI = 1.41%). Stage IA follicles are labeled as A; Stage IB follicles are labeled as B; Stage IC follicles are labeled as C. Ovarian tissue was sampled 72 hours after the third injection. Sections underwent HE staining. 10-fold magnified LM images were obtained by digital camera photography. Scale bar = 100 μm.

Fig. 8 in Androgenic Modulation in the Primary Ovarian Growth of the Japanese eel, .

Fig. 8. in vitro detection of arα and arβ expression during ovarian tissue culture with and without hormone treatment. mRNA expression was detected after FSH, MT, or FSH + MT treatments. ara expression is shown after (A) 1 and (B) 12 hours of hormonal treatments; arβ expression is shown after (C) 1 and (D) 12 hours of hormonal treatments. Relative mRNA expression data are presented as mean ± SD, and statistically significant differences were determined by one-way ANOVA and LSD post hoc tests (p <0.05).

Fig. 5 in Androgenic Modulation in the Primary Ovarian Growth of the Japanese eel, .

Fig. 5. Correlations between gene (arα, arβ, and fshr) expression and GSI. Correlations between gene expression and GSI are displayed. (A, B, and C) are correlations between fshr expression and GSI. (D, E and F) are correlations between arα expression and GSI. (G, H, and I) are correlations between arβ expression and GSI. The three groups displayed are the control (A, D, and G; n = 6), weekly SPH injection for three weeks (B, E and H; n = 9) and weekly SPH+MT injection for three weeks (C, F and I; n = 6). The correlation between mRNA expression and previtellogenic ovary growth condition was analyzed using Spearman's rho correlations. GSI is plotted against the mRNA expression level. p <0.05 indicates significant correlation, and r-value represents the positive or negative correlation coefficient.

Fig. 2 in Androgenic Modulation in the Primary Ovarian Growth of the Japanese eel, .

Fig. 2. Calculation of follicle stage among female eels undergoing hormonal induction of ovary development. (A) Ovarian development was demonstrated by GSI, and GSI percentage was calculated as mean ± SD (control, n = 4; SPH, n = 3 and SPH + MT, n = 4). Stage IA, IB, and IC follicles were categorized as the characteristics for follicle stage calculation among female eels in the control, weekly SPH-injected, and weekly SPH + MT-injected groups. Each stage calculation is displayed in (B) stage IA follicles, (C) stage IB follicles, and (D) stage IC follicles. Significant differences are compared using one-way ANOVA and LSD post hoc tests; p <0.05.

Fig. 4 in Androgenic Modulation in the Primary Ovarian Growth of the Japanese eel, .

Fig. 4. in vivo mRNA expression of arα, arβ, and fshr after weekly hormone injection for three weeks. Relative mRNA expression of (A) arα, (B) arβ, and (C) fshr among the control (n = 6), SPH (n = 9) and SPH + MT (n = 6) groups. The mRNA of 18S rRNA was used as the internal standard for relative mRNA normalized quantification. The mRNA expression data from female eels' ovaries were collected and calculated as mean ± SD. Statistically significant differences are identified by one-way ANOVA and LSD post hoc tests. p <0.05 is considered significant.

opencc-by-4.0Oct 2013View details →

BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 18. Different Modules involved in the Autonomous Decision-Making Process

The basic functioning of this architecture is now described in the following step by step using Figure 18a-f, where always the relevant modules of the model are highlighted for better comprehension.

Figure 10b. The control loop's information displayed by the controller-Designing a Growing Functional Modules "Artificial Brain"

Once performed the design and configuration, the controller and the local application are executed by pressing the corresponding button. In case of a local simulation, two terminal windows are generated. The first one, localized on the left side of the screen (figure 10.a), displays the behavior of the simulation; the second one, localized on the right side of the screen (figure 10.b), displays the behavior of the controller. Both application run concurrently and their respective contents allow the user to observe and monitor the control session. The last text line of figure 10.b displays the set of commands at cycle 201.

opencc-by-4.0Jan 2018View details →

Figure 10.a. Simulation's display: The characters "\__/" represents the front part of the car that should pass through the string "....==..=.==..==" representing the range of obstacles-Designing a Growing Functional Modules "Artificial Brain"

Figure 8. Configuration of the controller-Designing a Growing Functional Modules "Artificial Brain"

Before running the controller, it is necessary to specify the communication's configuration. When pressing the “configure” button, two text windows appear (figure 8). The upper one allows to specify the application's localization in order to run it previously to the controller. Presently, the corresponding simulation will run on the same machine, thus the name of the corresponding executable file must be specified. Otherwise, in the same field, the user should specify the IP address of the server where the controlled system is running.

Figure 7. Adding an Acting Module, its configuration values and its input connections-Designing a Growing Functional Modules "Artificial Brain"

The fourth step consists of adding an Acting Module, its configuration values and input connection as shown in figure 7. A type “CI” is assigned because it functionality will consist of triggering a steering command in accordance with the perception from the Sensing Module and in order to satisfy the input request from the Global Goal. Consequently, the feedback is set to “1 18” where “1” is the reference to the Sensation “free” and “18” to the perception in output of the Sensing Module. The identifier “18” for this perception is computed as at the total number of Sensation plus one (first sensing module). Identifiers and their references are automatically updated when a Sensation is added or deleted.

Figure 6. Adding a Global Goal with its required type-Designing a Growing Functional Modules "Artificial Brain"

The next step consists of adding a Global Goal expressing a motivation required by the controller. The goal is to keep the vehicle's front free of obstacles, thus the Sensation “free” should stay equal to “1”. After adding a new Global Goal, its assigned type should be “Cst” corresponding to a constant output request (see figure 6). In the parameter field, its specified value is “1”.

Figure 4. Adding a Sensing Module and selecting its type-Designing a Growing Functional Modules "Artificial Brain"

GFM controllers learn to satisfy some predefined goals while interacting with the environment and thus should be considered as artificial brains. An example of the design process of a simple controller is provided herein to explain the inherent methodology, to exhibit the components' interconnections and to demonstrate the control process.

Figure 5. Adding connections from Sensations 2-17 to the Sensing Module 1-Designing a Growing Functional Modules "Artificial Brain"

The next step consists of connecting the sixteen sensations in the input of the Sensing Module. To do this, the user must right-click on each Sensation, then on “new connection” and indicate the Sensing Module identifier. The resulting design is presented on figure 5. Finally, the Acting Module's field is set to “1” (indicating the number of the Acting Module that later will assess the correctness of the perception) and the unique extra-parameter set to “20” (related with the module's behavior).

Figure 2. The editor's components from left to right: a) Sensation, b) Sensing Module, c) Global Goal, d) Acting Module.-Designing a Growing Functional Modules "Artificial Brain"

Each “Sensation” corresponds to an integer value corresponding to a specific system's sensor. Sensations are symbolized by a green rectangle on the editor's canvas (figure 2.a). Each newly created sensation is assigned an identifier previously incremented. Its unique field, initially filled with question-marks, allows it to associate a mnemonic in order to facilitate its interpretation.

Figure 1. The GFM controller and its control loop-Designing a Growing Functional Modules "Artificial Brain"

The GFM control loop has many similarities to a standard one as shown in figure 1. The controller, delimited by a dotted line, sends during each cycle an output command in order to trigger some mechanical or virtual actuators and in return, receives a feedback composed of a sequence of sensors' values. However, the concept of reference value is replaced by “Global Goals” which are integrated to the controller.

opencc-by-4.0Sep 2018View details →

Temperature-modulated gas sensor signal

Temperature-modulated gas sensor signal Abstract: Data is the conductance of a temperature-modulated gas sensor exposed to several concentrations of several gases. Both classification (gas type) and (selective) quantification are of interest. Source: Creator: Lab for Measurement Technology, Saarland University, 66123 Saarbrücken, Germany Contact: m.bastuck@lmt.uni-saarland.de, info@lmt.uni-saarland.de Dataset: The data set was experimentally obtained from a semiconductor gas sensor (UST GGS 1330) with temperature cycled operation (TCO). The sensor temperature was linearly increased from 200 °C to 400 °C within 20 s, and back to 200 °C within another 20 s. This cycle is repeated during the whole measurement (~18 h). During the measurement, the sensor was exposed to four different gases (carbon monoxide, CO, ammonia, NH3, nitrogen dioxide, NO2, and methane, CH4) in three different concentrations each. The aim is either to classify the type of gas that is currently seen by the sensor independent of its concentration, or the concentration of one specific gas type. Attribute Information: <ul> <li>sensordata.csv The data set consists of the measured conductance in nS (nanosiemens) of the sensor. Each row represents one cycle (200-400-200 °C in 40 s) with 4001 data points. </li> <li>targetvectors.csv Different target vectors have been prepared manually. All of them include an ‘ignore’ label that indicates cycles where the exact gas concentrations are unknown, e.g. due to a change in concentration which can take several cycles. Cycles during an “init peak” in the beginning to test the setup are labeled as ‘ignore’ as well. These cycles should always be discarded. The objectives of the ten target vectors are as follows: <ol> <li>categorical. A short segment at the beginning is labeled as ‘background’, i.e. no relevant test gas is present. In the following, the presence of a test gas is indicated with its name, i.e. CO, NH3, NO2, and CH4</li> <li>continuous. A short segment at the beginning is labeled as ‘0’, CO exposures are labeled with their respective concentrations, everything else is ignored.</li> <li>continuous. A short segment at the beginning is labeled as ‘0’, NH3 exposures are labeled with their respective concentrations, everything else is ignored.</li> <li>continuous. A short segment at the beginning is labeled as ‘0’, NO2 exposures are labeled with their respective concentrations, everything else is ignored.</li> <li>continuous. A short segment at the beginning is labeled as ‘0’, CH4 exposures are labeled with their respective concentrations, everything else is ignored.</li> <li>categorical. Like (1), but the previously ignored background cycles at the beginning and between gas exposures are now labeled as ‘background’.</li> <li>continuous. Like (2), but the previously ignored background cycles at the beginning and between gas exposures are now labeled as ‘0’.</li> <li>continuous. Like (3), but the previously ignored background cycles at the beginning and between gas exposures are now labeled as ‘0’.</li> <li>continuous. Like (4), but the previously ignored background cycles at the beginning and between gas exposures are now labeled as ‘0’.</li> <li>continuous. Like (5), but the previously ignored background cycles at the beginning and between gas exposures are now labeled as ‘0’.</li> </ol> </li> </ul> Target vectors (6)-(10) contain some sensor drift in the background. Additionally, the background class is much larger compared to the gas exposure classes. Target vectors (7)-(10) also label all but one gas as ‘0’, so that a selective quantification of the target gas must be performed. <ul> <li>gasexposures.png A graphical summary of the dataset.</li> <li>temperaturecycle.png A graphical representation of the temperature cycle used when operating the gas sensor.</li> </ul>

opencc-by-4.0May 2019View details →

Data for 'Improved predictability of the Indian Ocean Dipole using seasonally modulated ENSO forcing forecasts'

Abstract of the associated paper: Despite recent progress in seasonal forecast development, the predictive skill for the Indian Ocean Dipole (IOD) remains typically limited to a lead time of one season or less in both dynamical and empirical models. Here we develop a simple stochastic-dynamical model (SDM) to predict the IOD using seasonally modulated El Niño-Southern Oscillation (ENSO) forcing together with a seasonal modulation of the Indian Ocean coupled ocean-atmosphere feedback. The SDM, with either observed or forecasted ENSO forcing, exhibits generally higher skill and longer lead times for predicting IOD events than the operational Climate Forecast System Version 2 and the SINTEX system. These results affirm our hypothesis that operational IOD predictability beyond persistence is largely controlled by ENSO predictability and the signal-to-noise ratio of the system. Therefore, potential future ENSO improvements in models should also translate to more skillful IOD predictions.

opencc-by-4.0Jul 2019View details →

Brain Invaders calibration-less P300-based BCI with modulation of flash duration Dataset (bi2015a)

Summary: This dataset contains electroencephalographic (EEG) recordings of 50 subjects playing to a visual P300 Brain-Computer Interface (BCI) videogame named Brain Invaders. The interface uses the oddball paradigm on a grid of 36 symbols (1 Target, 35 Non-Target) that are flashed pseudo-randomly to elicit the P300 response. EEG data were recorded using 32 active wet electrodes with three conditions: flash duration 50ms, 80ms or 110ms. The experiment took place at GIPSA-lab, Grenoble, France, in 2015. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02172347">https://hal.archives-ouvertes.fr/hal-02172347</a>. Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.BI.EEG.2015a-GIPSA">https://github.com/plcrodrigues/py.BI.EEG.2015a-GIPSA</a>. The ID of this dataset is bi2015a.   Full description of the experiment and dataset: <a href="https://hal.archives-ouvertes.fr/hal-02172347">https://hal.archives-ouvertes.fr/hal-02172347</a>   Investigators: Eng. Louis Korczowski, B. Sc. Martine Cederhout   Technical Support: Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues, M. Sc. Violette Gautheret   Scientific Supervisor: Ph.D. Marco Congedo   ID of the dataset: bi2015a