"Learning in reverse: Dopamine errors drive excitatory and inhibitory components of backward conditioning in an outcome-specific manner".

For over two decades, midbrain dopamine was considered synonymous with the prediction error in temporal-difference reinforcement learning. Central to this proposal is the notion that reward-predictive stimuli become endowed with the scalar value of predicted rewards. When these cues are subsequently encountered, their predictive value is compared to the value of the actual reward received allowing for the calculation of prediction errors. Phasic firing of dopamine neurons was proposed to reflect this computation, facilitating the backpropagation of value from the predicted reward to the reward-predictive stimulus, thus reducing future prediction errors. There are two critical assumptions of this proposal: 1) that dopamine errors can only facilitate learning about scalar value and not more complex features of predicted rewards, and 2) that the dopamine signal can only be involved in anticipatory learning in which cues or actions precede rewards. Recent work has challenged the first assumption, demonstrating that phasic dopamine signals across species are involved in learning about more complex features of the predicted outcomes, in a manner that transcends this value computation. Here, we tested the validity of the second assumption. Specifically, we examined whether phasic midbrain dopamine activity would be necessary for backward conditioning- when a neutral cue reliably follows a rewarding outcome. Using a specific Pavlovian-to-Instrumental Transfer (PIT) procedure, we show rats learn both excitatory and inhibitory components of a backward association, and that this association entails knowledge of the specific identity of the reward and cue. We demonstrate that brief optogenetic inhibition of ventral tegmental area dopamine (VTA DA) neurons timed to the transition between the reward and cue, reduces both of these components of backward conditioning. These findings suggest VTA DA neurons are capable of facilitating associations between contiguously occurring events, regardless of the content of those events. We conclude that these data are in line with suggestions that the VTA DA error acts as a universal teaching signal. This may provide insight into why dopamine function has been implicated in a myriad of psychological disorders that are characterized by very distinct reinforcement-learning deficits.

Unlock Condensate Reserves by Peripheral Development of Giant Condensate

This paper addresses the development of peripheral area in order to maximize the condensate production in a giant Recycle Gas-Condensate Reservoir in UAE. The condensate reservoir is producing many years under recycling mode to maintain the pressure and maximize the gas condensate recovery. The producers and injector wells are in a line drive pattern where the injected fluid is lean gas to maintain 100% VRR. The condensate production declined through the years due to gradual pressure decrease as well as injected lean gas/N2 breakthrough. Several studies were done to increase condensate recovery and extend gas production plateau. The methodology adopted for this study is the developing of the peripheral area in the giant recycle reservoir as part of its full field development plan (FFDP) in order to provide more pressure support initially and to increase the sweep efficiency for more condensate recovery. In addition; it is worth to mention that peripheral wells will provide production relaxation from some gas produces which have lean gas and N2 breakthroughs. Pilot wells were drilled to examine and confirm the strategy assumed by analyzing the performance of those wells in terms of location, condensate production, CGR values and trajectory. Simulation modeling was as well used for matching purposes and future prediction and forecasting. Pilot wells were drilled in deferent peripheral area in the reservoir and completed as horizontal gas producers. By analyzing the current wells performance it has been approved that the wells are producing high condensate about (2000-2500) MMstb and producing high CGR values about (80-100). Simulation modeling were utilize for future prediction and confirmed that the development of peripheral area by drilling additional wells enhances the sweeping efficiency and participated in expected gain a multimillions of barrels of additional condensate with maintaining the same business plan gas production target. It was promising to have more incremental in case of ramping up the production. The paper discussed in detail about methodology adopted in order to unlock the condensate reserves by peripheral development and confirmed the results of the gain of condensate production and CGR from actual data and simulation modeling. The provided information is quite informative to be widely used and applied in similar reservoirs.

Investigation of the Stochastic Modeling of COVID-19 with Environmental Noise from the Analytical and Numerical Point of View

In this article, we propose a novel mathematical model for the spread of COVID-19 involving environmental white noise. The new stochastic model was studied for the existence and persistence of the disease, as well as the extinction of the disease. We noticed that the existence and extinction of the disease are dependent on R0 (the reproduction number). Then, a numerical scheme was developed for the computational analysis of the model; with the existing values of the parameters in the literature, we obtained the related simulations, which gave us more realistic numerical data for the future prediction. The mentioned stochastic model was analyzed for different values of σ1,σ2 and β1,β2, and both the stochastic and the deterministic models were compared for the future prediction of the spread of COVID-19.

Association between the number of visual fields and the accuracy of future prediction in eyes with retinitis pigmentosa

PurposeTo evaluate the minimum number of visual fields (VFs) required to precisely predict future VFs in eyes with retinitis pigmentosa (RP).MethodsA series of 12 VFs (Humphrey Field Analyzer 10–2 test (8.9 years in average) were analysed from 102 eyes of 52 patients with RP. The absolute error to predict the 12th VF using the prior 11 VFs was calculated in a pointwise manner, using the linear regression, and the 95% CI range was determined. Then, using 3–10 initial VFs, next VFs (4th to 11th VFs, respectively) were also predicted. The minimum number of VFs required for the mean absolute prediction error to reach the 95% CI was identified. Similar analyses were iterated for the second and third next VF predictions. Similar analyses were conducted using mean deviation (MD).ResultsIn the pointwise analysis, the minimum number of VFs required to reach the 95% CI for the 12th VF was five (first and second next VF predictions) and six (third next VF prediction). For the MD analysis, three (first and second next VF predictions) and four (third next VF prediction) VFs were required to reach 95% CI for the 12th VF.ConclusionsThe minimum number of VFs required to obtain accurate predictions of the future VF was five or six in the pointwise analysis and three or four in the analysis with MD.

Migratory Birds Monitoring of India’s Largest Shallow Saline Ramsar Site with Big Geospatial Data Using Google Earth Engine for Restoration

Globally, saline lakes occupying 23% by area 44% by volume among all the lakes might desiccate by 2025 due to agricultural diversion, illegal encroachment, pollution, and invasive species. India’s largest saline lake, Sambhar is currently shrinking at the rate of 4.23% due to illegal saltpan en-croachment. This research article aims to identify the trend of migratory birds and monthly wetland status. Birds survey was conducted for 2019, 2020 and 2021 and combined with literature data of 1994, 2003, and 2013 for visiting trend, feeding habit, migratory and resident ratio, and ecological diversity index analysis. Normalized Difference Water Index was scripted in Google Earth Engine. Results state that it has been suitable for 97 species. Highest NDWI values for the was whole study period was 0.71 in 2021 and lowest 0.008 in 2019 which is highly fluctuating. The decreasing trend of migratory birds coupled with decreasing water level indicates the dubious status for its existence. If the causal factors are not checked, it might completely desiccate by 2059 as per its future prediction. Certain steps are suggested that might help conservation. Least, the cost of restoration might exceed the revenue generation.

Predicting Future Geographic Hotspots of Potentially Preventable Hospitalisations Using All Subset Model Selection and Repeated K-Fold Cross-Validation

Long-term future prediction of geographic areas with high rates of potentially preventable hospitalisations (PPHs) among residents, or “hotspots”, is critical to ensure the effective location of place-based health service interventions. This is because such interventions are typically expensive and take time to develop, implement, and take effect, and hotspots often regress to the mean. Using spatially aggregated, longitudinal administrative health data, we introduce a method to make such predictions. The proposed method combines all subset model selection with a novel formulation of repeated k-fold cross-validation in developing optimal models. We illustrate its application predicting three-year future hotspots for four PPHs in an Australian context: type II diabetes mellitus, heart failure, chronic obstructive pulmonary disease, and “high risk foot”. In these examples, optimal models are selected through maximising positive predictive value while maintaining sensitivity above a user-specified minimum threshold. We compare the model’s performance to that of two alternative methods commonly used in practice, i.e., prediction of future hotspots based on either: (i) current hotspots, or (ii) past persistent hotspots. In doing so, we demonstrate favourable performance of our method, including with respect to its ability to flexibly optimise various different metrics. Accordingly, we suggest that our method might effectively be used to assist health planners predict excess future demand of health services and prioritise placement of interventions. Furthermore, it could be used to predict future hotspots of non-health events, e.g., in criminology.

A High Latitude Model for the E Layer Dominated Ionosphere

Under certain conditions, the ionization of the E layer can dominate over that of the F2 layer. This phenomenon is called the E layer dominated ionosphere (ELDI) and occurs mainly in the auroral regions. In the present work, we model the variation of the ELDI for the Northern and Southern Hemispheres. Our proposed Neustrelitz ELDI Event Model (NEEM) is an empirical, climatological model that describes ELDI characteristics by means of four submodels for selected model observables, considering the dependencies on appropriate model drivers. The observables include the occurrence probability of ELDI events and typical E layer parameters that are important to describe the propagation medium for High Frequency (HF) radio waves. The model drivers are the geomagnetic latitude, local time, day of year, solar activity and the convection electric field. During our investigation, we found clear trends for the model observables depending on the drivers, which can be well represented by parametric functions. In this regard, the submodel NEEM-N characterizes the peak electron density NmE of the E layer, while the submodels NEEM-H and NEEM-W describe the corresponding peak height hmE and the vertical width wvE of the E layer electron density profile, respectively. Furthermore, the submodel NEEM-P specifies the ELDI occurrence probability %ELDI. The dataset underlying our studies contains more than two million vertical electron density profiles covering a period of almost 13 years. These profiles were derived from ionospheric GPS radio occultation observations on board the six COSMIC/FORMOSAT-3 satellites (Constellation Observing System for Meteorology, Ionosphere and Climate/Formosa Satellite Mission 3). We divided the dataset into a modeling dataset for determining the model coefficients and a test dataset for subsequent model validation. The normalized root mean square deviation (NRMS) between the original and the predicted model observables yields similar values across both datasets and both hemispheres. For NEEM-N, we obtain an NRMS varying between 36.1% and 47.1% and for NEEM-H, between 6.1% and 6.3%. In the case of NEEM-W, the NRMS varies between 38.5% and 41.1%, while it varies between 56.5% and 60.3% for NEEM-P. In summary, the proposed NEEM utilizes primary relationships with geophysical and solar wind observables, which are useful for describing ELDI occurrences and the associated changes of the E layer properties. In this manner, the NEEM paves the way for future prediction of the ELDI and of its characteristics in technical applications, especially from the fields of telecommunications and navigation.