Mechanism of Dopaminergic Nerve Transmission in Different Doses of Morphine Addiction and Stress-Induced Depression

Depression not only threatens the health and quality of life of patients but also brings a huge mental and economic burden to the patients’ families. This paper mainly studies the mechanism of dopaminergic neurotransmission in different doses of morphine addiction and stress-induced depression. In the experiment, 40 male SD rats were selected. The experiment established a rat model of chronic stress depression. The rats used in this model are all raised in a single cage, and there will be various stimuli every day for 21 days, but high-intensity continuous stimuli must be avoided, and the same stimuli will not appear continuously. The experiment established a depression animal model through chronic unpredictable mild stress (CUMS), combined with the conditioned position preference (CPP) model of morphine addiction to detect the establishment of CPP in such animals, so as to explore certain stress stimuli or depression, the influence on morphine addiction, and the relationship between them. The second or third branches of pyramidal neurons were selected to analyze the PL and CA3 regions. When analyzing the density of dendrites, each animal selected at least 8 dendrites in order to count the number of dendrites and selected a length of 20 μm on each branch to record the number of dendrites. All measured values are expressed as average ± standard deviation and analyzed by SPSS17.0 statistical software, and Levene test is used in the scattered consistency test. The average NIV of PEN before injection was 11.92 ± 2.90 Hz, and the average latency was 0.16 ± 0.03 s. The results indicate that CUMS may reduce the conditioned learning and memory ability by damaging the learning loop, rather than affecting the reward loop to weaken the establishment of morphine-dependent CPP.

Distinct neuronal populations contribute to trace conditioning and extinction learning in the hippocampal CA1

Trace conditioning and extinction learning depend on the hippocampus, but it remains unclear how neural activity in the hippocampus is modulated during these two different behavioral processes. To explore this question, we performed calcium imaging from a large number of individual CA1 neurons during both trace eye-blink conditioning and subsequent extinction learning in mice. Our findings reveal that distinct populations of CA1 cells contribute to trace conditioned learning versus extinction learning, as learning emerges. Furthermore, we examined network connectivity by calculating co-activity between CA1 neuron pairs and found that CA1 network connectivity patterns also differ between conditioning and extinction, even though the overall connectivity density remains constant. Together, our results demonstrate that distinct populations of hippocampal CA1 neurons, forming different sub-networks with unique connectivity patterns, encode different aspects of learning.

Food Entrainment, Arousal, and Motivation in the Neonatal Rabbit Pup

In the newborn rabbit, the light entrainable circadian system is immature and once a day nursing provides the primary timing cue for entrainment. In advance of the mother’s arrival, pups display food anticipatory activity (FAA), and metabolic and physiological parameters are synchronized to this daily event. Central structures in the brain are also entrained as indicated by expression of Fos and Per1 proteins, GFAP, a glial marker, and cytochrome oxidase activity. Under fasting conditions, several of these rhythmic parameters persist in the periphery and brain, including rhythms in the olfactory bulb (OB). Here we provide an overview of these physiological and neurobiological changes and focus on three issues, just beginning to be examined in the rabbit. First, we review evidence supporting roles for the organum vasculosum of lamina terminalis (OVLT) and median preoptic nucleus (MnPO) in homeostasis of fluid ingestion and the neural basis of arousal, the latter which also includes the role of the orexigenic system. Second, since FAA in association with the daily visit of the mother is an example of conditioned learning, we review evidence for changes in the corticolimbic system and identified nuclei in the amygdala and extended amygdala as part of the neural substrate responsible for FAA. Third, we review recent evidence supporting the role of oxytocinergic cells of the paraventricular hypothalamic nucleus (PVN) as a link to the autonomic system that underlies physiological events, which occur in preparation for the upcoming next daily meal. We conclude that the rabbit model has contributed to an overall understanding of food entrainment.

Assessment of Alternative Ways to Integrate Weather Predictions in Photovoltaic Generation Forecasting.

<div> <p>In recent years, the share of photovoltaic (PV) power in Europe has grown: the installed capacity increased from around 10 GW in 2008 to nearly 119 GW in 2018 [1]. Due to the intermittent nature of PV generation, new challenges arise regarding economic profitability and the safe operation of the power network. To overcome these issues, a special effort is made to develop efficient PV generation forecasting tools.</p> <p> </p> <p>For short-term PV production forecasting, past production observations are typically the main drivers. In addition, spatio-temporal (ST) inputs such as Satellite-Derived Surface Irradiance (SDSI) provide relevant information regarding the weather situation in the vicinity of the farm. Moreover, the literature shows us that Numerical Weather Predictions (NWPs) provide relevant information regarding weather trends.</p> <p> </p> <p>NWPs can be integrated in the forecasting process in two different ways. The most straightforward approach considers NWPs as explanatory input variables to the forecasting models. Thus, the atmosphere dynamics are directly carried by the NWPs. The alternative considers NWPs as state variables: weather information is used to filter the training data set to obtain a coherent subset of PV production observations measured under similar weather conditions as the PV production to be predicted. This approach is based on analog methods and makes the weather dynamics to be implicitly contained in the PV production observations. This conditioned learning approach permits to perform local regressions and is adaptive in the sense that the model training is conditioned to the weather situation.</p> <p>The specialized literature focuses on spot NWPs which permits to find situations that evolve in the same way but does not preserve ST patterns. In this context, the addition of SDSI features cannot make the most of the conditioning process. Ref. [3] proposes to use geopotential fields, which are wind drivers, as analog predictors.</p> <p> </p> <p>In this work, we propose the following contributions to the state of the art:</p> <p>We investigate the influence of spot NWPs on the performances of an auto-regressive (AR) and a random forest models according to the two above-mentioned approaches: either as additional explanatory features and/or as analog features. The analogy score proposed by [2] is used to find similar weather situations, then the model is trained over the associated PV production observations. The results highlight that the linear model performs better with the conditioned approach while the non-linear model obtains better performances when fed with explanatory features.</p> <p>Then, the similarity score is extended to gridded NWPs data through the use of a principal component analysis. This method allows to condition the learning to large-scale weather information. A comparison between spot and gridded NWPs conditioned approaches applied with AR model highlights that gridded NWPs improves the influence of SDSI over forecasting performances.</p> <p> </p> <p>The proposed approaches are evaluated using 9 PV plants in France and for a testing period of 12 months.</p> <p> </p> <strong>References</strong> <p>[1]      IRENA - https://www.irena.org/Statistics/Download-Data</p> <p>[2]      Alessandrini, Delle Monache, et al. An analog ensemble for short-term probabilistic solar power forecast. Applied Energy, 2015. https://doi.org/10.1016/j.apenergy.2015.08.011</p> <p>[3]      Bellinguer, Girard, Bontron, Kariniotakis. Short-term Forecasting of Photovoltaic Generation based on Conditioned Learning of Geopotential Fields. 2020, UPEC. https://doi.org/10.1109/UPEC49904.2020.9209858</p> </div>

The Role of the Thalamus in Post-Traumatic Stress Disorder

Post-traumatic stress disorder (PTSD) has a high lifetime prevalence and is one of the more serious challenges in mental health care. Fear-conditioned learning involving the amygdala has been thought to be one of the main causative factors; however, recent studies have reported abnormalities in the thalamus of PTSD patients, which may explain the mechanism of interventions such as eye movement desensitization and reprocessing (EMDR). Therefore, I conducted a miniature literature review on the potential contribution of the thalamus to the pathogenesis of PTSD and the validation of therapeutic approaches. As a result, we noticed the importance of the retinotectal pathway (superior colliculus−pulvinar−amygdala connection) and discussed therapeutic indicators.

PHARMACOLOGICAL BLOCKADE OF ACID SENSING ION CHANNELS NORMALIZES EMOTIONALLY CONDITIONED LEARNING DISTURBED DUE TO EPILEPTIC STATUS

The effect of pharmacological blockade of ASIC1a channels on emotionally conditioned learning in rats with lithiumpilocarpine model of temporal lobe epilepsy was studied. The development of status epilepticus is known to impair both contextual and stimulated emotional learning. In our experiments, the painful effect was directly accompanied by freezing in animals of all groups, however, under conditions of induced epilepsy, its duration was shorter, and blocking ASIC1a channels neutralized this difference. The induction of epilepsy disrupted both contextual and stimulated emotional learning. Suppression of ASIC1a channels partially restored this process in animals with induced epilepsy. This means that pharmacological blocking of ASIC1a channels can have a positive therapeutic effect in correcting the emotional disorders associated with temporal lobe epilepsy.

Reconceptualising resilience within a translational framework is supported by unique and brain-region specific transcriptional signatures in mice

ABSTRACTChronic social defeat (CSD) in mice has been increasingly employed in experimental resilience research. Particularly, the degree of CSD-induced social avoidance is used to classify animals into resilient (socially non-avoidant) versus susceptible (avoidant). In-spired by human data pointing to threat-safety discrimination and responsiveness to extinction training of aversive memories as characteristics of resilient individuals, we here describe a translationally informed stratification which identified three phenotypic subgroups of mice following CSD: the Discriminating-avoiders, characterised by successful social threat-safety discrimination and successful extinction of social avoidance; the Indis-criminate-avoiders, showing aversive response generalisation, and the Non-avoiders (absence of social avoidance) displaying impaired conditioned learning. Furthermore, and supporting the biological validity of our approach, we uncovered subgroup-specific transcriptional signatures in classical fear conditioning and anxiety-related brain regions. Our reconceptualisation of resilience in mice refines the currently used dichotomous classification and contributes to advancing future translational approaches.

Proximal threats promote enhanced acquisition and persistence of reactive fear-learning circuits

Physical proximity to a traumatic event increases the severity of accompanying stress symptoms, an effect that is reminiscent of evolutionarily configured fear responses based on threat imminence. Despite being widely adopted as a model system for stress and anxiety disorders, fear-conditioning research has not yet characterized how threat proximity impacts the mechanisms of fear acquisition and extinction in the human brain. We used three-dimensional (3D) virtual reality technology to manipulate the egocentric distance of conspecific threats while healthy adult participants navigated virtual worlds during functional magnetic resonance imaging (fMRI). Consistent with theoretical predictions, proximal threats enhanced fear acquisition by shifting conditioned learning from cognitive to reactive fear circuits in the brain and reducing amygdala–cortical connectivity during both fear acquisition and extinction. With an analysis of representational pattern similarity between the acquisition and extinction phases, we further demonstrate that proximal threats impaired extinction efficacy via persistent multivariate representations of conditioned learning in the cerebellum, which predicted susceptibility to later fear reinstatement. These results show that conditioned threats encountered in close proximity are more resistant to extinction learning and suggest that the canonical neural circuitry typically associated with fear learning requires additional consideration of a more reactive neural fear system to fully account for this effect.