experiment 2 - example subject (16) - Go-RTs

The Stop Signal Task is a laboratory task assessing inhibitory control. In the task, participants were seated in a comfortable desk chair behind a personal computer of which the screen was placed at approximately 60 cm distance. The experimenter was seated next to the participant and held track of performance through a hand computer. The participant held a button box in each hand and was instructed to press buttons with the thumb. The task consisted of 64 practice trials and 256 experimental trials, of which 64 stop-trials and 192 go-trials. On both go and stop-trials, participants saw a fixation point presented for 500 ms. The fixation point was followed by a go-stimulus (a picture of an airplane) that was displayed for 1,000 ms and that was presented in the center of the screen. In response to the go-stimulus, participants were required to press a response button that corresponded to the direction the plane was flying in (left or right). On stop-trials, a white cross was superimposed on the go-stimulus and acted as stop-signal. Participants were instructed not to press any button when a trial contained a stop-signal. Trials were presented in a semi-random fixed order. The longer the delay between go-signal (plane) and stop-signal (white cross; the stop-signal-delay, SSD), the more difficult it was to inhibit the response. To ensure that the percentage of inhibited responses approached 50% for each individual, SSD was systematically varied. If participants inhibited correctly, SSD lengthened by 50 ms; if participants failed to inhibit their response, SSD shortened by 50 ms (see Luman et al., 2004). In order to avoid non-responses or highly delayed responses, the experimenter provided instructions to react as fast as possible after an omission error and after four highly delayed responses on go-trials. Mean reaction time (xcorrect = MRT) over all correct go-trials, Stop Signal Reaction Time (SSRT) and the number of commission and omission errors were assessed.

About this dataset

Context

The dataset tabulates the population of Signal Mountain by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Signal Mountain. The dataset can be utilized to understand the population distribution of Signal Mountain by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Signal Mountain. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Signal Mountain.

Key observations

Largest age group (population): Male # 5-9 years (615) | Female # 40-44 years (573). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Signal Mountain population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Signal Mountain is shown in the following column.
• Population (Female): The female population in the Signal Mountain is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Signal Mountain for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Signal Mountain Population by Gender. You can refer the same here

Analysis of ‘Traffic Signals Status’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/15a2b0ac-3a77-4893-8433-4391674798e4 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset reports on the operation state of traffic signals in Austin, TX. Traffic signals enter flash mode when something is preventing the signal from operating normally. This is typically the result of a power surge, power outage, or damage to signal equipment. A signal may also be intentionally placed into flash mode for maintenance purposes or be scheduled to flash overnight.

You can view an interactive map of flashing traffic signals here: http://transportation.austintexas.io/signals-on-flash

Approximately 90% of the City’s signals communicate with our Advanced Trasnportation Management System. When these signals go on flash, they will be reported in this dataset. Although we are extending communications to all signals, approximately 10% are not currently captured in this dataset. It also occasionally happens that the event that disables a traffic signal also disables network communication to the signal, in which case the signal outage will not be reported here.

In this dataset the distinction between scheduled and unscheduled flash is identified by the 'operation state' column. A signal that is in unscheduled flash mode will have a status of 2 or 7. A signal that is in in scheduled flash mode will have a status of 1.

This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. It does not represent an on-the-ground survey and represents only the approximate relative location of traffic signals.

--- Original source retains full ownership of the source dataset ---

This dataset reports on the operation state of traffic signals in Austin, TX. Traffic signals enter flash mode when something is preventing the signal from operating normally. This is typically the result of a power surge, power outage, or damage to signal equipment. A signal may also be intentionally placed into flash mode for maintenance purposes or be scheduled to flash overnight.

You can view an interactive map of flashing traffic signals here:

https://data.mobility.austin.gov/signal-monitor

Approximately 90% of the City’s signals communicate with our Advanced Transportation Management System. When these signals go on flash, they will be reported in this dataset. Although we are extending communications to all signals, approximately 10% are not currently captured in this dataset. It also occasionally happens that the event that disables a traffic signal also disables network communication to the signal, in which case the signal outage will not be reported here.

In this dataset the distinction between scheduled and unscheduled flash is identified by the 'operation state' column. A signal that is in unscheduled flash mode will have a status of 2 or 7. A signal that is in in scheduled flash mode will have a status of 1.

This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. It does not represent an on-the-ground survey and represents only the approximate relative location of traffic signals.

This dataset reports on the operation state of traffic signals in Austin, TX. Traffic signals enter flash mode when something is preventing the signal from operating normally. This is typically the result of a power surge, power outage, or damage to signal equipment. A signal may also be intentionally placed into flash mode for maintenance purposes or be scheduled to flash overnight.

You can view an interactive map of flashing traffic signals here: http://transportation.austintexas.io/signals-on-flash

Approximately 90% of the City’s signals communicate with our Advanced Trasnportation Management System. When these signals go on flash, they will be reported in this dataset. Although we are extending communications to all signals, approximately 10% are not currently captured in this dataset. It also occasionally happens that the event that disables a traffic signal also disables network communication to the signal, in which case the signal outage will not be reported here.

In this dataset the distinction between scheduled and unscheduled flash is identified by the 'operation state' column. A signal that is in unscheduled flash mode will have a status of 2 or 7. A signal that is in in scheduled flash mode will have a status of 1.

This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. It does not represent an on-the-ground survey and represents only the approximate relative location of traffic signals.

About this dataset

Context

The dataset tabulates the population of Signal Hill by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Signal Hill. The dataset can be utilized to understand the population distribution of Signal Hill by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Signal Hill. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Signal Hill.

Key observations

Largest age group (population): Male # 30-34 years (619) | Female # 30-34 years (692). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Signal Hill population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Signal Hill is shown in the following column.
• Population (Female): The female population in the Signal Hill is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Signal Hill for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Signal Hill Population by Gender. You can refer the same here

A central experimental task in executive control research is the Stop-signal task, which allows measuring the ability to inhibit dominant responses. A crucial aspect of this task consists of varying the delay between the Go- and Stop-signal. Since the time necessary to administer the task can be long, a method of optimal delay choice was recently proposed: the PSI method. In a behavioral experiment, we show a variant of this method, the PSI marginal method, to be unable to deal with the Go-response slowing often observed in the Stop-signal task. We propose the PSI adjusted method, which is able to deal with this response slowing by correcting the estimation process for the current reaction time. In several sets of behavioral simulations, as well as another behavioral experiment, we document and compare the statistical properties of the PSI marginal method, our PSI adjusted method, and the traditional staircase method, both when reaction times are constant and when they are linearly increasing. The results show the PSI adjusted method’s performance to be comparable to the PSI marginal method in the case of constant Go-response times, and to outperform the PSI marginal method as well as the staircase methods when there is response slowing. The PSI adjusted method thus offers the possibility of efficient estimation of Stop-signal reaction times in the face of response slowing.

The role of autophosphorylation sites on PDGF receptors are to provide docking sites for downstream signal transduction molecules which contain SH2 domains. The SH2 domain is a conserved motif of around 100 amino acids that can bind a phosphorylated tyrosine residue. These downstream molecules are activated upon binding to, or phosphorylated by, the receptor kinases intrinsic to PDGF receptors.
Some of the dowstream molecules are themselves enzymes, such as phosphatidylinositol 3'-kinase (PI3K), phospholipase C (PLC-gamma), the Src family of tyrosine kinases, the tyrosine phosphatase SHP2, and a GTPase activating protein (GAP) for Ras. Others such as Grb2 are adaptor molecules which link the receptor with downstream catalytic molecules.

Inter-animal communication allows signals released by an animal to be perceived by others. Scent-marking is the primary mode of such communication in giant pandas (Ailuropoda melanoleuca). Signal detection theory propounds that animals choose the substrate and location of their scent marks so that the signals released are transmitted more widely and last longer. We believe that pandas trade off scent-marking because they are an energetically marginal species and it is costly to generate and mark chemical signals. Existing studies only indicate where pandas mark more frequently, but their selection preferences remain unknown. This study investigates whether the marking behavior of pandas is consistent with signal detection theory. Feces count, reflecting habitat use intensity, was combined with mark count to determine the selection preference for marking. The results showed that pandas preferred to mark ridges with animal trails and that most marked tree species were locally dominant. In addition, marked plots and species were selected for lower energy consumption and a higher chance of being detected. Over 90% of the marks used were the longest-surviving anogenital gland secretion marks, and over 80% of the marks were oriented toward animal trails. Our research demonstrates that pandas go out of their way to make sure their marks are found. This study not only sheds light on the mechanisms of scent-marking by pandas but also guides us toward more precise conservation of the panda habitat.

What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI), we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA). Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the “go” signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a “countdown” condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in “no-go” conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals.

In this paper, three different methods for brain signal acquisition are presented. All methods deal with feature extraction method of Electroencephalogram (EEG) based P300 waves. The performance of the three methods is investigated through backpropagation neural network classifier. EEG-P300 recordings provide an important means of brain-computer communication, but their classification accuracy and transfer rate are limited by unexpected signal variations due to artifacts and noises. A comparison of extraction methods (i.e., AAR, JADE, and SOBI) entailing time-series EEG signals is proposed. Finally, the promising results reported here reflect the considerable potential of EEG for the continuous classification of mental states. Advanced Science Letters, Volume 20, Number 1, January 2014 , pp. 80-85(6)

Similar to NOTCH1, NOTCH2 is activated by Delta-like and Jagged ligands (DLL/JAG) expressed in trans on a neighboring cell (Shimizu et al. 1999, Shimizu et al. 2000, Hicks et al. 2000, Ji et al. 2004). The activation triggers cleavage of NOTCH2, first by ADAM10 at the S2 cleavage site (Gibb et al. 2010, Shimizu et al. 2000), then by gamma-secretase at the S3 cleavage site (Saxena et al. 2001, De Strooper et al. 1999), resulting in the release of the intracellular domain of NOTCH2, NICD2, into the cytosol. NICD2 subsequently traffics to the nucleus where it acts as a transcription regulator.

While DLL and JAG ligands are well established, canonical NOTCH2 ligands, there is limited evidence that NOTCH2, similar to NOTCH1, can be activated by CNTN1 (contactin 1), a protein involved in oligodendrocyte maturation (Hu et al. 2003). MDK (midkine), which plays an important role in epithelial to mesenchymal transition, can also activate NOTCH2 signaling and is able to bind to the extracellular domain of NOTCH2, but the exact mechanism of MDK-induced NOTCH2 activation has not been elucidated (Huang et al. 2008, Gungor et al. 2011).

This event has been computationally inferred from an event that has been demonstrated in another species.

The inference is based on the homology mapping from PANTHER. Briefly, reactions for which all involved PhysicalEntities (in input, output and catalyst) have a mapped orthologue/paralogue (for complexes at least 75% of components must have a mapping) are inferred to the other species. High level events are also inferred for these events to allow for easier navigation.

More details and caveats of the event inference in Reactome. For details on PANTHER see also: http://www.pantherdb.org/about.jsp

The dataset contains depth frames and skeleton joints collected using Microsoft Kinect v2 and acceleration samples provided by an IMU during the execution of the timed up and go test.

The Stop-Signal Task - Arrows aims to measure behavioral inhibition by having the child inhibit their reaction in response to a quickly appearing signal. During the task, left- and right-pointing arrows appear on the screen. When the arrow points left, the child has to press the left button, and when the arrow points right, the child has to press the right button. When the arrrow is colored red, the child has to withhold pressing any buttons. The child has to respond to the right signal as quickly and as accurately as possible. Both accuracy and reaction time are calculated in the Go and No Go conditions.

This event has been computationally inferred from an event that has been demonstrated in another species.

The inference is based on the homology mapping from PANTHER. Briefly, reactions for which all involved PhysicalEntities (in input, output and catalyst) have a mapped orthologue/paralogue (for complexes at least 75% of components must have a mapping) are inferred to the other species. High level events are also inferred for these events to allow for easier navigation.

More details and caveats of the event inference in Reactome. For details on PANTHER see also: http://www.pantherdb.org/about.jsp

Go-RTs and error rates means and standard deviations on go trials as a function of picture valence values.

Introduction: Degradation of the condylar cartilage during temporomandibular joint osteoarthritis (TMJ-OA) results in the infiltration of nerves, blood vessels and inflammatory cells from the subchondral bone into the cartilage. The interaction among innervation, angiogenesis and inflammation in the condylar cartilage of TMJ-OA remains largely unknown.Method: In the present study, microarray-based transcriptome analysis was used to detect, and quantitative real-time polymerase chain reaction was used to validate transcriptome changes in the condylar cartilage from a well-established rat TMJ-OA model. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) pathway and protein-protein interaction (PPI) analyses were conducted.Result: There were 1817 differentially expressed genes (DEGs, fold change ≥2, p < 0.05) between TMJ-OA and control cartilages, with 553 up-regulated and 1,264 down-regulated genes. Among those genes, representative DEGs with known/suspected roles in innervation, angiogenesis and inflammation were further validated by enriched GO terms and KEGG pathways. The DEGs related to innervation were predominately enriched in the GO terms of neurogenesis, generation of neurons, and KEGG pathways of cholinergic synapse and neurotrophin signaling. Genes related to angiogenesis were enriched in GO terms of vasculature and blood vessel development, and KEGG pathways of hypoxia-inducible factor 1 (HIF-1) pathway and calcium signaling pathway. For inflammation, the DEGs were enriched in the GO terms of immune system process and immune response, and KEGG pathways of Toll-like receptor and transforming growth factor β (TGFβ) signaling. Analysis with PPI indicated that the aforementioned DEGs were highly-interacted. Several hub genes such as v-akt murine thymoma viral oncogene homolog 1 (Akt1), glycogen synthase kinase 3β (Gsk3b), fibroblast growth factor 2 (Fgf2) and nerve growth factor receptor (Ngfr) were validated.Conclusion: The present study demonstrated, for the first time, that intimate interactions exist among innervation, angiogenesis and inflammation in the condylar cartilage of TMJ-OA.

· Data spatial scope: 14 cities and counties in Jeollabuk-do Special Self-Governing Province · Data temporal scope: As of 2020 · Data source: Kakao Map, Naver Map · Data construction method: After measuring online (road view) using Kakao Map and Naver Map, constructed by applying the traffic light data items of the public data standard dataset · Data construction result: Construction of 35,126 traffic light data items in 14 cities and counties in Jeollabuk-do Special Self-Governing Province completed · Data construction company: Seondosoft Co., Ltd. ※ Note: This was surveyed through the 2021 Public Data New Deal Corporate Matching Support Project, so there may be differences from the actual data, and the data has no legal effect.