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101 results for “anticipation”
Data Set related to Synaptic inhibition in the lateral habenula shapes reward anticipation.
<p>The lateral habenula (LHb) supports learning processes enabling the prediction of upcoming rewards. While reward-related stimuli decrease the activity of LHb neurons, whether this anchors on synaptic inhibition to guide reward-driven behaviors remains poorly understood. Here, we combine in vivo two-photon calcium imaging with Pavlovian conditioning in mice and report that anticipatory licking emerges along with decreases in cue-evoked calcium signals in individual LHb neurons. In vivo multiunit recordings and pharmacology reveal that the cue-evoked reduction in LHb neuronal firing relies on GABA<sub>A</sub>-receptor activation. In parallel, we observe a postsynaptic potentiation of GABA<sub>A</sub>-receptor-mediated inhibition, but not excitation, onto LHb neurons together with the establishment of anticipatory licking. Finally, strengthening or weakening postsynaptic inhibition with optogenetics and GABA<sub>A</sub>-receptor manipulations enhances or reduces anticipatory licking, respectively. Hence, synaptic inhibition in the LHb shapes reward anticipation.</p>
Processed proteomic and phosphoproteomic timeseries from Ostreococcus tauri, with Gene Ontology enrichment, from "A phospho-dawn of protein modification anticipates light onset in the picoeukaryote O. tauri"
<p>Diel regulation of protein levels and protein modification had been less studied than transcript rhythms. These data tables in .XLSX format report partial proteome (Table_S1) and phosphoproteome data (Table_S2), assayed using shotgun mass-spectrometry, from cultures of the alga <em>Ostreococcus tauri </em>under light-dark cycles, sampled at Zeitgeber times (ZT, hours) 0, 4, 8, 12, 16 and 20. 10% of quantified proteins but two-thirds of phosphoproteins were rhythmic. Gene Ontology enrichment analysis was applied to infer the functional enrichment of the proteins or phosphoproteins, grouped by their loadings in PCA analysis (Table_S3), by hierarchical clustering (Table_S4) or by the peak time of their rhythmic profile (Table_S5).Prompted by night-peaking and apparently dark-stable proteins, we also tested the proteome of cultures transferred to prolonged darkness for 24, 48, 72 or 96h (Table_S6), where the proteome changed less than under the diel cycle. The raw data are available from ProteomeXchange, with identifiers PXD001734, PXD001735 and PXD002909.</p>
Figure 3. Screenshot of the MQTT broker, publisher, and two subscribers.-Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology
<p>As it was mentioned above, IoT needs the appropriate lightweight protocols to transmit the<br> info because web-protocols (e.g. TCP) generate several times more traffic usually for IoT (e.g.<br> remote connection to the Arduino weather station). MQTT (Message Queuing Telemetry Transport)<br> and CoAP (Constrained Application Protocol) IoT protocols are mainly in use nowadays<br> (http://postscapes.com/internet-of-things-protocols). In this activity,Arduino Ethernet Shield and C#<br> console app are connected by MQTT Mosquitto open source software (http://mosquitto.org).Similar<br> work presented against https://iotguys.wordpress.com/2014/11/13/arduino-with-mqtt/.The activity<br> consists of the following steps:<br> 1. Download and installation of Mosquitto software gainst http://mosquitto.org/download/.<br> 2. Download and installation of the latest Arduino software against<br> http://arduino.cc/en/main/software.<br> 3. Development of the MQTT subscriber based on C programming language in Arduino<br> IDE.<br> 3. Development of the MQTT subscriber based on C# console app (laptop HP ProBook 650<br> G1 and Windows 10 are used) in Visual Studio.<br> Screen shot of the software is shown in Fig. 3.</p>
Figure 2. Screenshot of the Google Earth web-site's prototype on the visualization of heat/cold waves-Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology-
<p>A non-anticipative analog method consists of four main steps:<br> 1. Generation of the prediction rules.<br> 2. Analysis of the prediction rules. The rules with time slots, which are not concentrated at<br> the same frame, are excluded.<br> 3. Generation of possible extremes.<br> 4. Analysis of the generated possible extremes. The extremes with time slots, which do not<br> correspond to the time slots of the appropriate rules, are excluded.<br> The results of the heat/cold waves’ prediction from 2011 to 2014 at different locations<br> (places are selected randomly) are presented in Table 2.</p>
Figure 1. Azure management portal and VM with two Delphi desktop apps-Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology
<p><br> Nowadays, only D-Wave Systems Company produces commercially the 2nd generation<br> adiabatic quantum computer with up to 512 flux qubits (project code name ″Vesuvius″). They are<br> microscopic loops of niobium metal that are capable of quantum behavior at low temperatures.<br> Hence, electrical currents in the loops can flow in clockwise (+1) or counterclockwise (-1)<br> direction, or both, when in quantum superposition. Qubits are connected to neighbors according to<br> the topology of quantum processor. The hardware is controlled by a framework of Josephson<br> junctions that allow individual qubit values to be stored and read, and to influence the states of<br> neighboring qubits.</p>
Data set for "Measuring synchronization and anticipation between individual investors from their daily performance"
<p>The data stored here is used as a support of the paper "Measuring<br> synchronization and anticipation between individual investors from their<br> daily performance" where a measure based on Mutual Information and<br> Transfer of Entropy is used in order to map investors' behaviour and which<br> ones are following same behavioural patterns.</p> <p>The study linked to this data is published on pre-print Arxiv.org<br> with the following citation:</p> <p><br> Mario Gutiérrez-Roig, Javier Borge-Holthoeffer, Alex Arenas and<br> Josep Perelló. Measuring synchronization and anticipation between<br> individual investors from their daily performance (2018)</p>
A Serious Game to Anticipate Handwriting Difficulties Screening Through Visual Perception Assessment - DATASET
<p>Each row in the dataset represents a subject. It contains:</p> <ul> <li>The answers to a characterization questionnaire</li> <li>The performance in the game described in the article</li> </ul>
Can Free Drawing Anticipate Handwriting Difficulties? A Longitudinal Study - DATASET
<p>Data to support the conference paper:</p> <p>Dui, L. G., Toffoli, S., Speziale, C., Termine, C., Matteucci, M., & Ferrante, S. (2022, September). Can Free Drawing Anticipate Handwriting Difficulties? A Longitudinal Study. In <em>2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)</em> (pp. 1-4). IEEE.</p> <ul> <li>BHI22_risk.xlsx: an Excel file with information about: <ul> <li>risk: the risk for handwriting delay, 0=no risk, 1=risk</li> <li>hand: right or left</li> <li>sex: M=male, F=female</li> <li>age: computed at the beginning of the longitudinal study</li> </ul> </li> <li>drawing_features.mat: a Matlab file with five datasets, one for each time point of the longitudinal study, with rows=children, columns=features</li> <li>metadata.mat: features names and type</li> </ul> <p> </p>
Concurrent anticipation of two object dimensions during grasping in 10-month-old infants: A quantitative analysis
<p>Data set of Concurrent anticipation of two object dimensions during grasping in 10-month-old infants: A quantitative analysis</p>
Feeling the future: A meta-analysis of 90 experiments on the anomalous anticipation of random future events
<p>The research that created this data is described in:</p> <ul> <li>Bem D, Tressoldi P, Rabeyron T and Duggan M. Feeling the future: A meta-analysis of 90 experiments on the anomalous anticipation of random future events. F1000Research 2015, 4:1188 (<a href="https://f1000research.com/articles/4-1188">doi: 10.12688/f1000research.7177.1</a>)</li> </ul>
Predictive physiological anticipation preceding seemingly unpredictable stimuli: a meta-analysis
<p>The research that created this data is described in:</p> <ul> <li>Mossbridge, J., Tressoldi, P.E. and Utts, J. (2012). Predictive physiological anticipation preceding seemingly unpredictable stimuli: a meta-analysis. Frontiers in Psychology 3:390. doi: 10.3389/fpsyg.2012.00390.</li> </ul> <p>Two files are included:</p> <ul> <li>PPAA_MA.xlsx - The original file, Excel format</li> <li>PPAA_MA.csv - The file converted to CSV format by Adrian Ryan; comments converted to columns.</li> </ul>
Anticipated versus Actual Effects of Platform Design Change: A Case Study of Twitter's Character Limit
<p>The design of online platforms is both critically important and challenging, as any changes may lead to unintended consequences, and it can be hard to predict how users will react. Here we conduct a case study of a particularly important real-world platform design change: Twitter's decision to double the character limit from 140 to 280 characters to soothe users' need to "cram'' or "squeeze'' their tweets, informed by modeling of historical user behavior.<br> In our analysis, we contrast Twitter's anticipated pre-intervention predictions about user behavior with actual post-intervention user behavior: Did the platform design change lead to the intended user behavior shifts, or did a gap between anticipated and actual behavior emerge?<br> Did different user groups react differently?<br> We find that even though users do not "cram'' as much under 280 characters as they used to under 140 characters, emergent "cramming'' at the new limit seems to not have been taken into account when designing the platform change. Furthermore, investigating textual features, we find that, although post-intervention ``crammed'' tweets are longer, their syntactic and semantic characteristics remain similar and indicative of "squeezing''. Applying the same approach as Twitter policy-makers, we create updated counterfactual estimates and find that the character limit would need to be increased further to reduce cramming that re-emerged at the new limit.<br> We contribute to the rich literature studying online user behavior with an empirical study that reveals a dynamic interaction between platform design and user behavior, with immediate policy and practical implications for the design of socio-technical systems. </p>
User Driving Recordings in Simulated Environment for Manoeuvre Anticipation
<p>This is a dataset acquired with the Euro Track Simulator 2 for the evaluation of driver intention based on face tracking and vehicular data. For each subject we collected the environment video and the face looking video plus the telemetry expressed in JSON.</p> <p>For each manoeuvre we provide the 5 seconds before. </p> <p>This dataset has been used for evaluating a technique of Domain Adversarial Recurrent Neural Network in the paper "Adaptive Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural Networks"</p>
Code for Manuscript - Near-term lake water temperature forecasts can be used to anticipate the ecological dynamics of freshwater species -
<p>Code for Manuscript - Near-term lake water temperature forecasts can be used to anticipate the ecological dynamics of freshwater species -</p>
Anticipating fluctuations of bigeye tuna in the Pacific Ocean from three-dimensional ocean biogeochemistry
<p>1. Subseasonal to decadal ocean forecasting can make significant contributions to achieving effective management of living marine resources in a changing ocean. Most applications rely on indirect proxies, however, often measured at the ocean surface and lacking a direct mechanistic link to the dynamics of marine populations.</p> <p>2. Here we take advantage of three-dimensional, dynamical reconstructions and forecasts of ocean biogeochemistry based on a global Earth System Model to hindcast and assess the capacity to anticipate fluctuations in the dynamics of bigeye tuna (<em>Thunnus obesus</em> Lowe) in the Pacific Ocean during the last six decades. We reconstructed spatial patterns in catch per unit effort (CPUE) through the combination of physiological indices capturing both habitat preferences and physiological tolerance limits in bigeye tuna.</p> <p>3. Our analyses revealed a sequence of four distinct regimes characterized by changes in the zonal distribution and average CPUE of bigeye tuna in the Pacific Ocean. Habitat models accounting for basin-wide fluctuations in the thermal structure and oxygen concentration throughout the water column captured interannual fluctuations in CPUE and regime switches that models based solely on surface information were unable to reproduce. Decade-long forecast experiments further suggested that forecasts of three-dimensional biogeochemical information might enable anticipation of fluctuations in bigeye tuna several years ahead.</p> <p>4. <em>Synthesis and applications.</em> Together, our results reveal the impact of variability of biogeochemical conditions in the ocean interior on the dynamics of bigeye tuna in the Pacific Ocean, raising concerns about the future impact of ocean warming and deoxygenation. The results also lend support to incorporating subsurface biogeochemical information into ecological forecasts to implement efficient dynamic management strategies and promote the sustainable use of marine living resources.</p>
Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation
<p>Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation</p> <p>Megan E. Young, Camille Spencer-Salmon, Clayton Mosher, Sarita Tamang, Kanaka Rajan, and Peter H. Rudebeck</p> <p>This dataset contains peripheral physiology (heart rate) and single neuron activity data from the paper entitled “Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation” by Young, Spencer-Salmon and colleagues.</p> <p>The study investigated how neurons in macaque subcallosal anterior cingulate cortex, basolateral amygdala, and rostromedial striatum encoded anticipated reward during Pavlovian and instrumental tasks.</p> <p>Heart rate data were pre-processed using methods described in the paper and were downsampled to 50 Hz for analysis. Neural activity data were pre-processed using steps as described in the paper.</p> <p>The dataset is saved as .MAT files.</p> <p>Files included:</p> <p>‘Pavlovian_task_neurons.mat’ – single neuron data from the Pavlovian task. Each of the 656 rows represents a single neuron and its associated information.</p> <p>‘Instrumental_task_neurons.mat’ – single neuron data from the instrumental task. Each of the 425 rows represents a single neuron and its associated information.</p> <p>“heart_rate.mat” – heart rate data from monkeys D and H.</p> <p><br> File structure and information:</p> <p>Pavlovian_task_neurons.mat</p> <p>Structure “Pavlovian_task_neurons”<br> - “Pavlovian_task_neurons.unit_name” – neuron specific identifier<br> - “Pavlovian_task_neurons.monkeynumber” – subject specific #<br> - “Pavlovian_task_neurons.monkeyname” – subject specific name<br> - “Pavlovian_task_neurons.date” – date on which data were recorded<br> - “Pavlovian_task_neurons.session” – session identifier from date (a-d)<br> - “Pavlovian_task_neurons.channel” – recording channel data recorded from<br> - “Pavlovian_task_neurons.wavemark” – waveform number (a-e)<br> - “Pavlovian_task_neurons.brainarea” – brain area where neuron recorded (SC = subcallosal ACC, AMY = basolateral amygdala, VS = rostromedial striatum).<br> - “Pavlovian_task_neurons.areanum” – # brain area (subcallosal ACC = 1, BLA = 2, rostromedial striatum = 3)<br> - “Pavlovian_task_neurons.condition” – condition # from Monkey Logic for each of the trials (1 by n trials)<br> - “Pavlovian_task_neurons.stimspikes” – smoothed spike rate from -600 to 2500ms after stimulus onset (trials by time matrix)<br> - “Pavlovian_task_neurons.rewardspikes” – smoothed spike rate from -600 to 2500ms after reward onset (trials by time matrix)<br> - “Pavlovian_task_neurons.stimID” – stimulus shown on that trial (1 = neutral, 3 = CS+ juice, 4 = CS+ water, 5 = CS-) (1 by n trials).</p> <p><br> Instrumental_task_neurons.mat</p> <p>Structure “Instrumental_task_neurons”<br> - “instrumental_task_neurons.unit_name” – neuron specific identifier<br> - “instrumental_task_neurons.monkeynumber” – subject specific #<br> - “instrumental_task_neurons.monkeyname” – subject specific name<br> - “instrumental_task_neurons.date” – date on which data were recorded<br> - “instrumental_task_neurons.session” – session identifier from date (a-d)<br> - “instrumental_task_neurons.channel” – recording channel data recorded from<br> - “instrumental_task_neurons.wavemark” – waveform number (a-e)<br> - “instrumental_task_neurons.brainarea” – brain area where neuron recorded (SC = subcallosal ACC, AMY = basolateral amygdala, VS = rostromedial striatum).<br> - “instrumental_task_neurons.areanum” – # brain area (subcallosal ACC = 1, BLA = 2, rostromedial striatum = 3)<br> - “instrumental_task_neurons.condition” – condition 7-18 from Monkey Logic for each of the trials (1 by n trials). CNDs 7,8,13,14= CS+ juice vs CS+ water; CNDs 9,10,15,16= CS+ juice vs CS-; CNDs 11,12,17,18= CS+ water vs CS-.<br> - “instrumental_task_neurons.choice” – outcome associated with chosen option (0=nothing, 1=juice, 2=water) (1 by n trials)<br> - “instrumental_task_neurons.unchosen” - outcome associated with unchosen option (0=nothing, 1=juice, 2=water) (1 by n trials)<br> - “instrumental_task_neurons.chosenside” – side of the screen chosen (left [0] or right [1]) (1 by n trials)<br> - “instrumental_task_neurons.stimspikes” – smoothed spike rate from -600 to 2500ms after stimulus onset (trials by time matrix)<br> - “instrumental_task_neurons.rewardspikes” – smoothed spike rate from -600 to 2500ms after reward onset (trials by time matrix)</p> <p>heart_rate.mat</p> <p>Structures – “monkey_d_hr” – monkey D heart rate data<br> – “monkey_h_hr” – monkey H heart rate data</p> <p>Structure of monkey_d/h_hr<br> - “monkey_d/h_hr.trial_type” – trial type presented (1 = neutral, 2 = unsignaled, 3 = CS+ juice, 4 = CS+ water, 5 = CS-) (1 by n trials).<br> - “monkey_d/h_hr.trials” – number of trials in each session by trial type<br> - “monkey_d/h_hr.session – percent change in heart rate for each trial from -200 ms to 3500ms after stimulus onset. Column 1 = session; Column 2 = trial type; Column 3 = trial number; Columns 4 – 3703 = percent change in heart rate.</p> <p> </p>
Supplementary material: for Using anticipation to unveil drivers of local livelihoods in Transfrontier Conservation Areas: a call for more environmental justice
<ol> <li> <span>Calling on the concept of environmental justice in its </span><span>distributive, procedural, and recognition</span><span> dimensions, </span><span>w</span><span>e implemented a collaborative scenario-building approach to explore sustainable livelihood pathways in four sites belonging to two Transfrontier Conservation Areas (TFCAs) in southern Africa. </span> </li> <li><span>Grounded on participation and transdisciplinarity, as a foundation for decolonised anticipatory action research, we aimed at stimulating knowledge exchange and providing insights on the future of local livelihoods by engaging experts living within these TFCAs. </span></li> <li><span>Our results show that wildlife and wildlife-related activities are not seen as the primary drivers of local livelihoods, despite the focus and investments of dominant stakeholders in these sectors. Instead, local governance and land use regulations emerged as key drivers in the four study sites. The state of natural resources, including water, and appropriate farming systems also appeared critical to sustain future livelihoods in TFCAs, together with the recognition of indigenous culture, knowledge, and value systems.</span></li> <li><span>Nature conservation, especially in Africa, is rooted in its colonial past and struggles to free or decolonise itself from the habits of this past despite decades of reconsideration. To date, the enduring coloniality of conservation prevents local citizens from truly participating in the planning and designing of the TFCAs they live in, leaving room for limited benefits to local citizens and often limiting indigenous people's capacity to conserve. </span></li> <li> <span>A practical way forward is to consider environmental justice as a cement between the two pillars of the TFCA concept, i.e.</span><span>nature conservation and socioeconomic development of local or neighbouring communities,</span><span> as part of a more broad and urgent need to rethink the relationships between people in, and with, the rest of nature.</span> </li> </ol>
Diabetes Closed-Loop Project 6 (DCLP6): Fully Automated Closed-Loop Control in Type 1 Diabetes Using Meal Anticipation
ClinicalTrials.gov study NCT04877730. IPD Sharing: YES. Countries: 1. Publications: 1.
Supplementary material: for Using anticipation to unveil drivers of local livelihoods in Transfrontier Conservation Areas: a call for more environmental justice
Open the record for dataset details and reuse information.
Anticipating fluctuations of bigeye tuna in the Pacific Ocean from three-dimensional ocean biogeochemistry
Open the record for dataset details and reuse information.
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