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Associative learning is a fundamental cognitive capacity that allows animals and humans to learn the predictive relationship between behavioral events and rewarding outcomes. While the process of learning is commonly conceptualized as a prospective strategy (learning how behavioral events predict future rewards), here we provide behavioral and neurophysiological evidence to show that animals may instead employ a retrospective and stepwise learning strategy (learning how the reward is predicted by preceding behavioral events). In rats learning a new association in which the reward was paired with a sequence of behavioral events, learning started from the event closest to the reward and sequentially incorporated earlier events into animals' internal model. The learning of each behavioral event as a new reward predictor was accompanied by the emergence of basal forebrain (BF) neuronal responses toward that event. BF activities quantitatively conveyed a reward prediction error signal associated with the behavioral event, and promoted reward-seeking behavioral sequences containing the newly learned event. As the internal model incorporated more behavioral events as reward predictors, non-rewarded behavioral sequences that were once compatible with the internal model during early stages of learning became incompatible and were sequentially eliminated. Together, these results demonstrate how the retrospective and stepwise learning strategy can effectively establish animals' internal model during the learning process and lead to the sequential refinement of reward-seeking behaviors. These results also highlight the functional significance of BF neuronal activities, which provided unique insights into the covert dynamics of the learning process in single trials.
Associative learning is essential for survival and allows animals and humans to predict future reward based on behavioral events, such as environmental stimuli1,2 or their own actions3,4. Understanding the algorithmic principles of associative learning has been a central question in psychology and neuroscience5–12, and has broad implications in machine learning13 and artificial intelligence14. Much evidence supports the view that animals form internal models of associative relationships between behavioral events and rewarding outcomes5–8. However, it remains unclear how internal reward-prediction models are established in the first place, and how such models dynamically evolve during the early phase of new associative learning.
The learning strategies for building the internal model can be conceptualized under two broad categories: prospective8,9 and retrospective strategies15,16. Under the prospective strategy, animals learn about how the current behavioral state predicts events and rewards in the future. This forward-looking prospective strategy is widely used in well-learned contexts as an effective means to guide decision making through the prediction of future rewards. An alternative, but not mutually exclusive, strategy that has been proposed recently is the backward-looking retrospective strategy15,16. Under the retrospective strategy, learning starts from the rewarded state and animals learn about how the rewarded state is predicted by preceding behavioral events. Recent studies support that animals do learn about retrospective associative relationships between behavioral events17, and the retrospective strategy might be especially powerful during the learning process for discovering the connection between rare rewards with preceding behavioral events15. The current study seeks to provide empirical support for the use of retrospective strategy during the learning process.
To investigate whether animals use the retrospective strategy to build their internal reward-prediction models during the learning process, an ideal setting to test this idea is when animals must learn the association between the reward and a sequence of behavioral events. Under this setting, the retrospective strategy predicts that learning would be triggered by the unexpected reward and animals would first learn about the last event closest to the reward as the reward predictor, and subsequently learn about earlier behavioral events. Moreover, as the internal model expands and sequentially incorporates more behavioral events, behavioral responses should evolve accordingly and follow specific sequences. The current study tested these predictions and sought to identify the behavioral signatures of the retrospective learning strategy.
In order to provide an independent validation for the behavioral observations, we further focused on a learning-related neural activity in the basal forebrain18–24 (BF), which conveys a reward-prediction error signal associated with rewards and reward-predicting behavioral events23–27. Specifically, we focused on a special subset of noncholinergic BF neurons, which will be referred to as BF bursting neurons18–24, that show highly robust phasic bursting responses to reward-predicting sensory stimuli irrespective of their sensory modalities. Moreover, such responses only emerge after reward-based associative learning23, and are tightly coupled with behavioral performance and promotes faster decision speeds22–24. By observing the temporal evolution of BF activities throughout the learning process, we will be able to determine which behavioral events animals have learned to predict reward, and when such learning takes place. In the context of retrospective learning strategy, we predict that BF responses should first emerge toward the last behavioral event closest to the reward, and subsequently develop toward the earlier behavioral events. In other words, the temporal evolution of BF activities should mirror the behavioral learning process, and reveal the dynamic expansion of the internal reward-prediction model during the learning process.
To better understand the strategy the animals adopt during associative learning, we first trained adult Long-Evans rats in an auditory discrimination task. Rats entered the fixation port to initiate each trial, where they encountered three trial types (Sleft; Sright; catch) with equal probabilities that respectively indicated sucrose water reward in the left or right port, or no reward in the case of catch trials (no stimulus) (Figure 1A). During the initial auditory discrimination phase, Sleft and Sright were two distinct sound stimuli. After reaching asymptotic performance, rats entered the new learning phase (denoted as the D0 session), in which the Sright stimulus was switched to a novel light stimulus that rats never encountered before (Figure 1B, Table 1). This enabled rats to learn the new stimulus-reward association while maintaining stable levels of performance toward the previously-learned Sleft sound stimulus (Figure 1C). All rats (N=7) began responding in the new light trials within the first three sessions, and reached >90% correct responses within fourth sessions of new learning and maintained stable performance afterwards (Figure 1C, Table 2).
Detailed examination of behavioral response patterns, however, revealed that the new learning experience not only changed behaviors in the new light trials, but also led to the emergence of two other types of behaviors toward the right reward port: catch licks and no-fixation licks (Figure 1C). Catch licks refers to licking responses toward the right reward port in catch trials when no sensory stimuli were presented. No-fixation licks refers to the situation in which rats directly licked at the right reward port without properly initiating the trial by nosepoking in the center fixation port. These two types of behaviors were largely absent before the new learning phase.
The emergence of no-fixation licks and catch licks in the new learning phase was curious because both behaviors were not rewarded. Moreover, the frequency of both behaviors peaked during early sessions of the new learning phase, and subsequently diminished in later sessions (Figure 1C). Their temporal sequence was highly consistent across animals despite individual behavioral variabilities: the peak of no-fixation licks occurred earlier than the peak of catch licks in most animals (N=6/7) or in the same session (N=1/7). We will denote the peak of no-fixation licks as the D1 session, and the peak of catch licks as the D2 session in each animal. The D1 and D2 sessions allowed us to identify similar learning stages across animals despite their individual differences in learning dynamics.
These behavioral observations indicate that the new learning not only induced behavioral changes in the new light trials that were rewarded, but also led to the emergence of non-rewarded licking behaviors toward the same reward port. Both the rewarded and non-rewarded licking behaviors all emerged in the D1 session (Figure 1C, middle panel, no response in the D1-1 session), suggesting that the D1 session was likely when the initial learning took place.
Based on these observations, new learning could be characterized as the sequential refinement of reward-seeking behaviors toward the right reward port, in which non-rewarded licking behaviors were sequentially eliminated until licking was selectively present in the new light trial.
The overall pattern of reward-seeking behaviors toward the right reward port was not easily compatible with the prospective learning strategy of decision making (Figure 1D). In the prospective model, the three types of rightward licking behaviors were scattered at different branches of the decision tree, which made it difficult to explain the conserved pattern of sequential refinement. There was also little evidence that animals explored other branches of the decision trees during learning.
On the other hand, the retrospective learning strategy provided a simple and parsimonious model that directly linked the three types of rightward licking behaviors in sequential orders (Figure 1E). We hypothesize that the sequential refinement of reward-seeking behaviors might result from the stepwise expansion of the internal model under the retrospective learning strategy. During the initial stage of learning, the internal model would only incorporate the behavioral event closest to the reward (lick right) as the reward predictor. Since all three types of rightward licking behaviors contained this behavioral event, and therefore would all predict reward, animals would engage in all three types of reward-seeking behaviors at this initial stage of learning. As the internal model subsequently expanded and incorporated earlier events (fix-out and then the light stimulus), no-fixation licks and catch licks would become incompatible with the expanded internal model because they did not contain all the reward-predicting events. As a result, no-fixation licks and catch licks would no longer predict reward in later stages of learning, and would be subsequently eliminated. We suggest that the sequential refinement of reward-seeking behaviors might represent the behavioral signature of the retrospective and stepwise learning strategy.
To validate the behavioral observations and understand the underlying neural dynamics, we recorded BF neuronal activity throughout the learning process (Figure 2A) and used the consistent Sleft sound as the control stimulus to identify stable populations of BF bursting neurons (Figure 2B, Figure S1). A total of 1453 BF single units were recorded over 45 sessions (N=7 rats), of which 70% (1013/1453) were classified as BF bursting neurons based on their stereotypical phasic response to the Sleft sound (22.5±7.3 neurons per session, mean±std) (Figure 2B). The population response of BF bursting neurons were highly consistent across animals (Figure 2C), and remained remarkably stable in Sleft trials throughout the learning process (Figure 2D). The inclusion of Sleft trials therefore allowed us to record from stable and representative populations of BF bursting neurons throughout the learning process, and to investigate how their activities dynamically evolved in other trial types during learning at single trial resolution (Figure 2E).
We first applied this approach to understand the behavioral and neural dynamics in the D1 session because all three types of rightward licks emerged in this session (Figure 1C, middle panel). Detailed analysis of behavioral responses from a representative session (Figure 2E) revealed that the three types of rightward reward-seeking behaviors in fact emerged abruptly after a transition point (see Methods for definition). Rightward licking behaviors were mostly absent before the transition point, and rapidly switched to almost 100% licking after the transition point. This pattern was consistently observed across all animals (Figure 3A1).
At the neuronal level, there was a corresponding increase in the activity of BF bursting neurons that rapidly emerged after the abrupt transition in reward-seeking behaviors (Figure 2E, 3B1). This increase in BF activity was most prominent in the epoch before the trial outcome (Figure 2E2). In contrast, in trials before the transition point, BF bursting neurons did not show similar activity increases in the corresponding time window after exiting the fixation port (Figure 2E, 3B1). We will refer to the BF activity in this window as the BF evaluation response (see Methods for definition) because it reflected animals' internal evaluation when no additional sensory stimuli were presented during this epoch.
We further examined the respective contributions of the three types of rightward licking behaviors during the transition process in D1 sessions (Figure 3). We noted that the first trial after the behavioral transition was typically a rewarded light lick trial (Figure 3A2 inset). This observation is consistent with the retrospective learning strategy, which predicts that learning would be triggered by the receipt of an unexpected reward.
Immediately after the abrupt transition, all three types of rightward licking behaviors emerged (Figure 3A). No-fixation licks, in particular, were most frequently observed within the first 60 trials after the transition point, before declining subsequently in the next 60 trials and occurred less frequently than light licks and catch licks (Figure 3A3).
At the neuronal activity level, BF evaluation responses quickly increased after the transition point in all three types of rightward licking behaviors (Figure 3B). The amplitudes of BF evaluation responses were similar between the three types of rightward licking behaviors within the first 60 trials after the transition. Subsequently, the BF evaluation response in no-fixation licks declined in the next 60 trials, relative to the other two types of rightward licking behaviors (Figure 3B2-3).
These results support that the first step of retrospective learning occurred at the transition point in the D1 session (Figure 1E): the unexpected reward triggered the learning of the preceding state, i.e. licking toward the right reward port, as the reward predictor and led to the abrupt behavioral transition. During this initial stage (i.e. the first 60 trials after the transition point in the D1 session), all three types of rightward licking behaviors contained the right lick event and were therefore compatible with this internal model. BF activity was elevated whenever animals approached and licked the right reward port regardless of whether they had exited from the center fixation port.
At roughly 60 trials after the transition point, the second step of the retrospective learning took place and animals expanded their internal model and incorporated a second reward predictor: exiting the fixation port (Figure 1E). As a result, no-fixation licks were no longer compatible with this model (because they did not contain the fixation exit event), resulting in diminished BF evaluation responses and the elimination of this behavior. The other two types of rightward licks, light licks and catch licks, remained compatible with this expanded model and maintained high levels of BF evaluation responses.
The similarities between light licks and catch licks in the D1 session, at both behavioral and neural activity levels (Figure 3), suggested that animals likely treated these two trial types as the same during the D1 session. Such a possibility would predict that the new light stimulus was not the cause of reward-seeking behavior in the D1 session and would not elicit responses in BF bursting neurons.
We tested this prediction by comparing BF activities between light and catch trials, as well as between lick and no lick trials, in D1 sessions (Figure 4). Indeed, BF activities in light trials were highly similar to those in catch trials, in the epochs before exiting the fixation port. This was true regardless of whether animals subsequently licked at the right reward port. This observation confirmed the prediction that the new light stimulus did not activate BF bursting neurons in the D1 session, despite the near-perfect behavioral performance in light trials after the transition point.
In contrast, after fixation port exit, there were similar increases in BF activity when animals licked at the right reward port in both light and catch trials (Figure 4), which corresponded to the BF evaluation response described earlier (Figure 3B). Increases in BF activity after fixation port exit reliably distinguished between lick and no lick trials, but not between light and catch trials (Figure 4C). These observations support the idea that light and catch trials were treated as the same in the D1 session, and that the light stimulus was not used as a reward predictor at this stage of learning.
When did animals learn about the light stimulus as a reward predictor? We noted that the pattern of licking behavior was grossly similar in light and catch trials before the D2 session, which was the session when catch licks peaked (Figure 1C). Closer examinations revealed that behavioral performances in light and catch trials began to diverge and showed a small but significant difference in the D2 session (Figure 5A). This suggested that D2 might be the first session that the light stimulus was used to guide reward-seeking behavior. Such a possibility would predict that BF bursting neurons should begin to respond to the light stimulus in the D2 session.
We tested this prediction by comparing BF activities between light and catch lick trials in the D2 session, and found significant differences in all epochs, including the phasic response to the light onset (Figure 5B). This pattern was distinct from the pattern in D1 sessions, when there was no difference in BF activities in the epochs before exiting the fixation port (Figure 4). We further investigated whether this difference in BF activities between light and catch lick trials emerged within the D2 session. To test this idea, we compared BF activities at the beginning (first 20 trials) and the end (last 20 trials) of this session (Figure 5C). We found that, at the beginning of the D2 session, BF activities patterns were similar to the pattern in the D1 session (Figure 4A), showing no difference between light and catch trials before exiting the fixation port.
However, by the end of the D2 session, BF responses to the light stimulus had developed in the epochs before exiting the fixation port (Figure 5C). These results support the idea that animals first learned about the light as a reward predictor in the D2 session.
The divergence between light and catch trials in the D2 session would correspond to the third step of the retrospective learning process, in which the internal model further expanded and incorporated the light stimulus as a reward predictor (Figure 1E). As a result, the light stimulus acquired the ability to reliably activate BF bursting neurons. After this model expansion, catch lick trials became incompatible with this internal model, and diminished over subsequent sessions.
After the D2 session, we noted that BF phasic responses to light onset continued to grow stronger over sessions (Figure 6A), even though hit rates in light trials already plateaued (Figure 1C). What was the functional significance of BF activity in light trials, and more generally on behavioral performance overall? The results described earlier consistently supported the idea that increased BF activities were associated with increased reward-seeking behaviors (Figures 2–5). Here we provide three additional lines of evidence to further support this idea:
First, in light trials, stronger phasic bursting responses to light onset predicted faster reaction times (RTs) (Figure 6B). As the phasic response to light onset grew stronger over sessions, RTs to light onset decreased accordingly and showed a strong negative correlation at the per session level. Second, in light trials where BF responses to the light stimulus had yet to develop (pre-D2 sessions) and also in catch trials, stronger BF activities after exiting the fixation port predicted reward-seeking behavior (licking) within the same trial type (Figure 6C). In other words, increased BF activities discriminated lick trials from no lick trials within the same trial type. Third, increased BF activities before the start of licking in catch lick trials predicted longer durations of licking in individual sessions (Figure 6D). In other words, animals licked for longer durations in the absence of reward when BF activity was higher prior to the start of licking. Together, these observations provided additional support for the idea that the activity of BF bursting neurons promoted reward-seeking behaviors in quantitative ways.
Finally, the last question we sought to address was: what kind of information did BF bursting neurons encode, which allowed us to use their activity as a proxy for animals' internal model? We noted that the response of BF bursting neurons to trial outcomes (reward or the absence of reward) often showed the opposite trend as their responses in earlier epochs, both at the per session level (Figure 6A) as well as in single trials (Figure 3B).
We therefore investigated this effect further at the per session level, and found that the amplitude of BF phasic bursting response to the light onset was strongly and negatively correlated with the amplitude of BF responses to the reward in light trials (Figure 7A). Moreover, at the single trial level, we found that the BF evaluation response was negatively correlated with BF responses to the trial outcome, both when the reward was delivered or absent (Figure 7B, 7C).
The presence of strong negative correlations between BF responses to the trial outcome and BF activities in earlier epochs (stimulus onset or evaluation window) is a hallmark feature of prediction error encoding28. These observations therefore support the idea that BF bursting neurons encoded reward prediction error in the current behavioral setting, and such information was strongly and quantitatively coupled with reward-seeking behavior.
Together, results from the current study support that animals used a retrospective and stepwise learning strategy, which provided a parsimonious framework that can account for the observed behavioral and neurophysiological dynamics (Figure 8). Under this framework, learning was initiated by the unexpected reward at the right reward port (Figure 8, 1st row), which led to the learning of how the reward was predicted by preceding behavioral events in several distinct steps (Figure 8, 2nd-4th row). Behavioral events were sequentially incorporated into the animal's internal model as reward predictors, which started from the event closest to the reward (rightward lick) and sequentially expanded to earlier events (fixation port exit, and then the light stimulus). The manifestation of this stepwise expansion process at the behavioral level was the abrupt emergence of three types of rightward licking behaviors, followed by the sequential elimination of non-rewarded licking behaviors (no-fixation licks and catch licks) (Figures 1C & 3A). At the neuronal level, there were corresponding increases in the activity of BF bursting neurons, which were initially present in all three types of rightward licks (Figures 2E & 3B) and subsequently decreased in non-rewarded licks as those behaviors were sequentially eliminated (Figures 3B & 6A). Moreover, increased BF activities first emerged in the time window closest to the reward (Figures 2E & 3B), while responses to the earlier event (light stimulus) developed later (Figures 4 & 5). Throughout the learning process, the activity of BF bursting neurons encoded reward prediction error signals (Figure 7) and their increased activities consistently predicted reward-seeking behaviors and quantitatively predicted faster reaction times and longer licking durations (Figure 6). These properties enabled us to use BF activities to reconstruct the dynamics of new learning in single trials without perturbing the learning process, and provided an independent validation of the retrospective learning strategy.
Under this retrospective learning framework, learning about each behavioral event as a new reward predictor was accompanied by the emergence of BF responses toward that event, which conveyed animals' reward prediction toward that event and promoted the types of rewardseeking behaviors that were compatible with animals' internal model at that step. The stepwise learning strategy therefore offered a parsimonious explaination of why animals committed certain types of 'errors' (non-rewarded licks) at earlier stages of learning, and how such ineffective reward-seeking behaviors were sequentially eliminated in later stages of learning. This highlights the importance of considering all types of reward-seeking behaviors in order to understand the learning process, regardless of whether those behaviors were rewarded or not. The observation of the abrupt behavioral transition in D1 sessions also suggests that the retrospective learning strategy may underlie the abrupt behavioral transitions observed in other learning contexts29.
The retrospective and stepwise learning strategy offers three key insights about the learning process. First, the same overt behavior can be driven by different internal models at different stages of learning. For example, while the licking response in the new light trial may appear to be the same throughout the learning process, it was in fact driven by different internal models at different stages of learning, which were associated with different activity patterns in the BF (Figure 8). Such covert changes in the internal model underlies a curious observation during early stages of learning (Figure 4): while the light stimulus was clearly-perceptible and had been consistently paired with reward over many trials, the reward-seeking behavior in light trials was not driven by the light stimulus but by other behavioral events (fixation port exit and lick right). The light stimulus had yet to be incorporated into animals' internal reward prediction model at this stage of learning. This observation highlights the possible mismatch between behavioral events in the physical world (such as the light stimulus) and the behavioral events represented in the internal model (the light stimulus was not represented). This is especially a concern during the early stages of learning when the internal reward prediction model may undergo rapid and drastic revisions.
Second, the stepwise expansion of animals' internal models highlights the possibility that the reward-predicting events themselves may change during the learning process. In other words, the learning process not only can incrementally change the weighting of each reward predictor, the reward predictors themselves may also be added or eliminated during the learning process. Such structural revisions of the internal reward prediction model pose a fundamental challenge to theories and models of learning that assume a static set of reward predictors. Using models with incorrect reward predictors will lead to incorrect interpretations, no matter how well the model fits the behavioral performance.
Third, the retrospective and stepwise learning strategy offers an efficient strategy to build the internal reward prediction model: starting from the unexpected reward event and expanding the model by adding one reward predictor at a time. In contrast to the prospective decision tree that contained many state transitions that animals rarely explored (Figure 1D), the retrospective decision tree was much more compact and contained only the relevant subspace that animals did explore (Figures 1E & 8). The compact retrospective model, coupled with the stepwise expansion strategy, likely minimized the cognitive burden of the animal to build the internal reward prediction model during learning.
While our results demonstrated that, at least in the behavioral contexts tested in this study, the retrospective learning strategy was effective and commonly adopted by animals, we do not believe that all associative learning scenarios must involve the retrospective learning strategy. Animals could also employ the prospective learning strategy if, based on past experience, behavioral events could be regarded as potential reward predictors. For example, in the current behavioral context, if animals had previously learned that similar light stimuli predicted reward, the new light stimulus could immediately and prospectively elicit a reward prediction signal and promoted reward-seeking behavior in those trials. The use of prospective strategy, either through generalizations of past experience or resulting from individual variabilities, could facilitate the learning process. Such strategies perhaps underlie the accelerated learning dynamics that we observed in a subset of animal (animal #7, Figure 1C), which also featured the strongest BF responses to the light stimulus during the initial encounter of the new light (Figure 5B).
The results from the current study also highlight the advantages of our approach in understanding the learning dynamics through directly observing the activities of BF bursting neurons. First, BF activities corroborated behavioral observations and provided an independent validation of the retrospective learning strategy. Second, BF activities revealed the covert dynamics of the learning process with single trial temporal resolution. Most importantly, observing BF activities did not perturb the learning process itself. In contrast, many behavioral tests that are commonly used to interrogate the content of learning are invasive in the sense that they inevitably change the learning process that we seek to understand30,31. The activities of BF bursting neurons therefore offer a unique opportunity to understand the covert dynamics of the internal model during learning.
Finally, the current study extends our understanding about the roles of BF bursting neurons in the encoding of reward prediction error and in promoting reward-seeking behaviors. Previous studies have demonstrated that BF responses to rewards are negatively modulated by reward expectation23–26 and support the idea that BF neurons encode a reward prediction error signal26,27. The current study further extends this idea and shows that BF bursting neurons also encode reward prediction error in the context of new learning, and such encoding is robust even at the single trial level (Figure 7). This robust encoding of reward prediction error signals therefore quantitatively conveys the amount of reward prediction associated with each behavioral event. The behavioral consequence of this BF reward prediction error signal is the promotion of reward-seeking behaviors. Previous studies have established that BF bursting neurons serve as a bidirectional gain modulation mechanism for reward-seeking behaviors, where increased BF activities promote faster reaction times23,24 while the inhibition of BF activities leads to rapid behavioral stopping22. The current study extends this idea to the context of new learning, by showing that increased BF activities were tightly coupled with rewardseeking behaviors at multiple levels throughout the entire learning process (Figures 2–5), and quantitatively predicted faster reaction times and longer licking durations (Figure 6). Together, these results highlight the functional significance of BF bursting neurons in reward-seeking behaviors. While the neurochemical identity of BF bursting neurons remains unknown, previous studies suggest that BF bursting neurons are likely a subset of GABAergic neurons20,23,32. Their neurochemical identity, as well as their specific cellular marker(s), remains to be identified in future studies.
Ethics Statement
All experimental procedures were conducted in accordance with the National Institutes of Health (NIH) Guide for Care and Use of Laboratory Animals and approved by the National Institute on Aging Animal Care and Use Committee.
Seven male Long Evans rats (Charles River, NC), aged 3–6 months and weighing 300–400 grams were used for this experiment. Rats were housed in a 12/12 day/night cycle and were provided with 10 to 12 dry pellets per day and unrestricted access to water. Rats were first trained in daily sessions lasting 60-90 minutes.
Twelve plexiglass operant chambers (11" L×8 ¼" W×13" H), custom-built by Med Associates Inc. (St. Albans, VT), were contained in sound-attenuating cubicles (ENV-018MD) each with an exhaust fan that helped mask external noise. Each chamber's front panel was equipped with an illuminated nose poke port (ENV-114M) located in the center (horizontal axis) as the fixation port, which was equipped with an infrared (IR) sensor to detect the entry of the animals' snout into the port. On each side of the center nose-poke port there were two reward ports (CT-ENV-251L-P). Two IR sensors were positioned to detect reward-port entry and sipper-tube licking, respectively.
Sucrose solution (13.3%) was used as reward and delivered through the sipper tubes located in the reward ports. Reward delivery was controlled by solenoid valves (Parker Hannifin Corp #003-0111-900, Hollis, NH) and calibrated to provide 10 μl of solution per drop. Each chamber was equipped with a ceiling-mounted speaker (ENV-224BM) to deliver auditory stimuli, and a stimulus light (ENV-221) positioned above the center fixation port to serve as the new light stimulus. Behavioral training protocols were controlled by Med-PC software (Version IV, Med Associates Inc.), which stored all event timestamps at 2 ms resolution and also sent out TTL signals to the neurophysiology recording systems.
Behavioral Training Procedures
Food-restricted adult Long-Evans rats were trained in operant chambers that were dimly lit. Rats were first trained in an auditory discrimination task. After an inter-trial interval (4-6 s), each trial started with the illumination of the center fixation port, which was turned off when rats poked the fixation port. Rats were required to maintain fixation in the center nose poke port for a variable amount of foreperiod. Four different foreperiods (0.35, 0.5, 0.65, and 0.8 s) were used, pseudorandomly across trials, to minimize temporal expectation of stimulus onset. After the foreperiod, one of three conditions was randomly presented with equal probabilities: a Sright sound stimulus indicated reward on the right port; a different Sleft sound stimulus indicated reward on the left port; or the absence of stimulus (catch trial) indicated no reward. The specific sound stimuli chosen as the Sright and Sleft for each animal are listed in TABLE 1. An internal timestamp was recorded in catch trials to mark the onset of the would-be stimulus. Early fixation port exit before the foreperiod or licking in the incorrect side port resulted in no reward delivery and reset the timer for the inter-trial-interval. Licking in the correct port within a 3 s window led to three drops of sucrose water reward, delivered starting at the 3rd lick. The delivery of reward at the 3rd lick created an expectation for trial outcome at this time point, which was dissociated in time from the initiation of licking (1st lick). We will therefore refer to the time point of the 3rd lick also as the trial outcome event.
After reaching asymptotic behavioral performance in the auditory discrimination task, rats underwent stereotaxic surgery to implant moveable electrode bundles into bilateral BF. After surgery, rats were re-trained in the auditory discrimination task to asymptotic performance level. Once stable recording of BF neuronal activity was achieved, a new learning phase was introduced by replacing the Sright stimulus from the sound used in the auditory discrimination task by the central light above the fixation port to indicate reward on the right port. All other aspects of the task remained the same. See TABLE 1 for details of the Sright and Sleft stimuli for each animal.
Stereotaxic Surgery and Electrode
Surgery was performed under isoflurane anesthesia as previously described24. Multiple skull screws were inserted to anchor the implant, with one screw over the cerebellum serving as the common electrical reference and a separate screw over the opposite cerebellum hemisphere serving as the electrical ground. Craniotomies were opened to target bilateral BF (AP –0.6 mm, ML ±2.25 mm relative to Bregma)33. The electrode contained two bundles of 16 polyimide-insulated tungsten wires (38 μm diameter; California Fine Wire, CA), each bundle ensheathed in a 28-gauge stainless steel cannula and controlled by a precision microdrive. The impedance of individual wire was ~ 0.1 MΩ measured at 1 kHz (niPOD, NeuroNexusTech, MI). During surgery, the cannulae were lowered to DV 6.5 mm below cortical surface using a micropositioner (Model 2662, David Kopf Instrument) at a speed of 50 μm/s. After reaching target depth, the electrode and screws were covered with dental cement (Hygenic Denture Resin), and electrodes further advanced to 7.5 mm below the cortical surface. Rats received ibuprofen and topical antibiotics after surgery for pain relief and prevention of infection, and were allowed one week to recover with ad libitum food and water. Cannulae and electrode tip locations were verified with cresyl violet staining of histological sections at the end of the experiment. All electrodes were found at expected positions between AP [−0.2, −1.2] mm, ML [1.5, 3] mm, relative to Bregma, and DV [7.5, 8.5] mm relative to cortical surface (Figure 2A).
Data acquisition and spike sorting
Electrical signals were referenced to a common skull screw placed over the cerebellum. Electrical signals were filtered (0.3 Hz–7.5 kHz) and amplified using Cereplex M digital headstages and recorded using a Neural Signal Processor (Blackrock Microsystems, UT). Single unit activity was further filtered (250 Hz–5 kHz) and recorded at 30 kHz. Spike waveforms were sorted offline to identify single units using custom software written in Python. Only single units with clear separation from the noise cluster and with minimal (<0.1%) spike collisions (spikes with less than 1.5 ms interspike interval) were used for further analyses, consistent with previous studies of BF bursting neurons19–24. Additional cross-correlation analysis was used to remove duplicate units recorded simultaneously across multiple electrodes19–24.
Recording during the new learning phase
After surgery, BF neuronal activity was monitored while rats were re-trained in the auditory discrimination task to asymptotic performance level. During this re-training phase, BF electrode depths were adjusted slightly (by advancing electrodes at 125 μm increment) until a stable population of BF single units can be recorded. At this point, the new learning phase with the light as the new Sright stimulus was introduced and rats were trained and recorded daily with BF electrodes remained at the same depth. This approach allowed us to monitor the activity of a large population of BF neurons and follow its temporal evolution across sessions.
Data analysis
Data were analyzed using custom Matlab (MATLAB The MathWorks Inc., Natick, MA) scripts.
Define different behavioral response types
Licking responses were defined for stimulus (Sleft and Sright) and catch trials if rats licked at least three times in the reward port within the 3 sec window after stimulus onset (or the corresponding timestamp for the would-be stimulus in catch trials). Licking responses to the correct reward port were rewarded with three drops of sucrose water, delivered starting at the 3rd lick (referred to as trial outcome event). During the new learning phase, licking responses in light and catch trials were predominantly to the right reward port. Responses to the left reward port were negligible and therefore not analyzed.
No-fixation licks corresponded to licking responses to the right reward port that were not preceded by poking the center fixation port. Specifically, no-fixation licks were defined based on three criteria: (1) rats made at least three consecutive licks in the right reward port; (2) the interval between the last exit from the fixation port and the first lick must be greater than 2 sec; (3) the interval between the last exit from the reward port and the subsequent first lick in the same reward port must be greater than 1 sec. These duration thresholds were determined based on the empirical licking patterns across animals. No-fixation licks to the left reward port occurred at much lower frequencies and therefore not analyzed. In the analyses of learning dynamics in the D1 session (Figures 2 & 3), no-fixation licks were treated as rightward licking trials, even though such behaviors were self-initiated and not imposed by the task design.
Reaction time (RT) in light trials in a session (Figure 6B) was defined as the median of the interval between the onset of the light stimulus and the exit from the fixation port in light lick trials. Lick duration in catch trials in a session (Figure 6D) was defined as the median of the interval between the first and the last lick in catch lick trials.
Define the D0, D1 and D2 learning landmarks
During the new learning phase, three sessions (D0, D1, D2) were identified in individual animals as landmarks that demarcated distinct stages of new learning (Figure 1C). The D0 session was defined as the very first session the new light stimulus was introduced. The D1 session was defined as the first session when animals began to respond correctly in light trials, which also corresponded to the session in which no-fixation licks occurred most frequently. The D2 session was defined as the session in which catch licks occurred most frequently. The D1 and D2 landmarks allowed us to identify similar learning stages across animals despite their individual differences in learning dynamics. The specific timing of the three landmark sessions in each animal are provided in TABLE 2 (also see Figure 1C). One animal (ID#7) with accelerated learning dynamics, in which D1 and D2 occurred in the same session, was excluded from analyses of D1 neural dynamics (Figure 3–4) to ensure that neural activities associated with D2 did not confound the neural dynamics in the D1 session. The BF neuronal activity in this animal was included in the analysis of D2 neural dynamics (Figure 5) and showed the strongest phasic response to the light onset among all animals, consistent with its accelerated learning dynamics.
Identification of the behavioral transition point in the D1 session
The behavioral transition points in D1 sessions (Figures 2 & 3) were identified based on behavioral response patterns in three trial types combined: light trials, catch trials and no-fixation licks. The behavioral response pattern in each trial was coded as either 1 or 0 based on whether animals licked in the right reward port in that trial. The behavioral transition point was defined as the point with the largest difference in licking responses between the 20 trials before and the 20 trials after that point. In 5/7 animals, the first trial after the behavioral transition was a rewarded light lick trial. In the other two animals, the transition point was adjusted to the closest light lick trial by 2 or 4 trials, respectively.
Identification of BF bursting neurons
BF bursting neurons were defined as BF single units whose average firing rates during the [0.05, 0.2]s window after stimulus onset increased by more than 2 spikes/s in the Sleft sound trials compared to the corresponding window in catch trials (Figure S1). This contrast between sound trials and catch trials was necessary because many BF neurons changed their activity during the foreperiod while waiting for stimulus onset. This subtraction procedure removed the nonstationary baseline before stimulus onset and allowed us to ask whether BF neurons truly responded to the sound stimulus. In addition, BF bursting neurons should have baseline firing rates (during the [−1, 0]s window relative to the trial start signal) less than 10 spikes/s.
A total of 1453 BF single units were recorded over 45 sessions (N=7 rats), of which 70% (1013/1453) were classified as BF bursting neurons based on their stereotypical phasic response to the Sleft sound (22.5±7.3 neurons per session, mean±std) (Figure S1, TABLE 2). One session with only one BF bursting neuron was excluded from the analysis of BF population activities. The large number of BF bursting neurons recorded in each session allowed us to treat them as a representative sample of all BF bursting neurons, whose responses to the Sleft sound were highly stable throughout the learning process (Figure 2B-D). This strategy ensured that we were following functionally the same neuronal ensemble and could track how BF bursting neurons acquired responses to the new light during learning, regardless of whether the identities of these BF neurons were exactly the same in each session.
Population BF responses to behavioral events
The spike timestamps of all BF bursting neurons in a single session were pooled together to approximate the population activity of all BF bursting neurons. Population peri-stimulus time histograms (PSTHs) were calculated with 10-ms bins, and normalized by the number of BF bursting neurons in a session.
To properly assess whether BF bursting neurons responded to the onset of the new light stimulus (Figures 4–6), it was important to disambiguate such stimulus-onset responses from the increased BF activities after fixation port exit (Figure 4). To achieve this goal, PSTHs to the stimulus onset were calculated based only on spikes that occurred before fixation port exit in individual trials, resulting in different interval lengths (between stimulus onset to fixation port exit) across trials. Accordingly, the calculation of the mean PSTH across all trials in a session was adjusted for the different number of trials at different interval lengths. The mean PSTHs were further truncated at the median interval length of that session to reduce noisy estimates of PSTHs at long interval lengths due to lower number of trials. When PSTHs were averaged across animals, the averaged PSTHs were further truncated at the mean of median interval latencies across animals. This truncation procedure resulted in the uneven lengths of PSTHs across individual animals (Figures 4 & 5) and across sessions (Figure 6). This procedure was also applied to calculating the BF responses before fixation port exit to include only spikes that occurred after stimulus onset (Figures 4 & 5), and for calculating BF responses during licking in catch lick trials (Figure 6D).
The time windows used to quantify average BF activity in different epochs were indicated in respective figures, and corresponded to the following: [0.05, 0.2]s after Sleft sound onset; [0.1, 0.3]s after Sright light stimulus onset; [0.1, 0.3]s after the timestamp for the would-be stimulus in catch trials; [0.05, 0.35]s after the 3rd lick for outcome responses; [−0.3, 0]s and [0, 0.3]s relative to the fixation port exit. The epoch for calculating evaluation response is described below.
Evaluation response (Figures 2E, 3B, 7B, 7C) refers to the increased BF activity after exiting the fixation port and before the trial outcome (3rd lick). The evaluation response reflected animals' internal evaluation because no additional sensory stimuli were presented during this epoch. Specifically, the evaluation response was calculated in individual trials and defined as the maximum firing rate of any 500ms window during the evaluation epoch, which corresponded to the interval between [fix-out, outcome], with additional adjustments according to trial types. In light lick and catch lick trials, the evaluation epoch was defined as [fix-out, outcome] in each trial. The epoch durations in light lick and catch lick trials within each session were used as the reference point for other trial types as described next. In no-fixation licks, in which the fix-out event was absent, the duration of evaluation epoch was set as the 95th percentile of the evaluation epoch durations in light lick and catch lick trials. In light no lick and catch no lick trials, in which the 3rd lick event was absent, the duration of the evaluation epoch was set as the median of the evaluation epoch durations in light lick and catch lick trials. These adjustments in the definition of evaluation epochs, as well as its calculation of maximum firing rate within the epoch, took into consideration the behavioral variability across trial types, learning stages and individual animals.
To evaluate the dynamic changes of BF activities around the transition point in the D1 session (Figures 2E & 3B), single trial evaluation and outcome responses were smoothed using moving median over 10 trials. The smoothed trends were aligned at the transition point and then averaged across all animals. Only trials with smoothed trend data from at least 4 animals were plotted in the group average (Figure 3B).
Statistical comparisons were conducted using the Statistics and Machine Learning Toolbox (version 11.3) in MATLAB (R2018a) (https://www.mathworks.com/). Paired t-test (ttest.m) was used to compare behavioral and neural activity differences between two groups (Figures 2D, 3A3, 3B3, 5A, 6A). Repeated measures analysis of variance (ranova.m) was used for comparisons involving more than 2 groups, by specifying the appropriate within-subject models (Figures 3A3, 3B3, 4B). Comparisons of PSTHs between two groups (Figures 4A, 5B, 5C) was conducted for each 100 ms sliding window (10 ms step) using paired t-test. Significance level was set at p<0.01 for three consecutive bins. Pearson correlation (corrcoef.m) was used to determine the relationship between neuronal activities and/or behavior (Figures 6B, 6D, 7).
Receiver operating characteristic (ROC) and area under curve (AUC) analysis
To determine whether the activity of BF bursting neurons differentiated between trial types within each D1 session (Figure 4C), we compared BF activity for each 100ms sliding window (10ms step) using the AUC measure of ROC analysis (auc.m by Alois Schloegl). At each sliding window, BF population activity was calculated for each light and catch trial, and distributions of BF activities were compared between light vs catch trials or between lick vs no lick trials. Significance level was set at p<0.001 using 10,000 trial-shuffled random permutations.
To determine whether BF activity differentiated between lick and no lick trials within the same trial type (light or catch trials) (Figure 6C), we compared BF activity in the [0, 500] ms window after exiting the fixation port. For each session and each trial type, lick and no lick trials must each constitute at least 10% of that trial type to be included in the analysis. Catch trials from all sessions were included in this analysis. Only light trials before the D2 session (pre-D2) were included in this analysis because BF responses to the onset of the light stimulus had not developed in those sessions. Significance level was set at p<0.05 using 1,000 trial-shuffled random permutations.
Author Contributions
H.E.M. and S.L. designed the study. H.E.M. and K.V. performed experiments and collected data. H.E.M. and S.L. analyzed data. H.E.M. and S.L. wrote the manuscript with inputs from K.V.
Author Information
The authors declare no competing financial interests. Correspondence and requests for materials should be addressed to S.L. (shihchieh.lin{at}nycu.edu.tw).
Supplementary Figure
We thank P.R. Rapp, A. Scaglione, S.W. Wu and K.L. Hsieh for critical discussions of the manuscript; B.M. Brock for technical support. This research was funded by the Intramural Research Program of the National Institute on Aging (NIH, USA) and by NARSAD Young Investigator Award to S.L.. Additional supports came from the Ministry of Science and Technology (MOST, Taiwan) grants (107-2320-B-010-028-MY3; 108-2638-B-010-002-MY2; 110-2628-B-A49A-505), and from the Brain Research Center, National Yang Ming Chiao Tung University from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.


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subset: commoncrawl


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VA – Public's assistance requested with missing person | Royal Examiner
July 26, 2022 Leave a comment
On February 23, 2022, Shannon Lee Fox, a 30-year-old white female, 5'7", 135 pounds was last seen by family and friends. Shannon Fox was reported missing on March 3, 2022. Currently, there is no evidence of foul play. It is felt that Shannon would not ignore her family and friends for this amount of time.


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The auditing process is the evaluation or evaluation of different books of accounts by an auditor followed by physical checking of inventory to make certain that all departments are adhering to documented system of taping deals. It is done to ascertain the precision of monetary declarations offered by the organisation.
Audits can be done internally by staff members or heads of a specific department and also externally by an outside company or an independent auditor. The suggestion is to examine and confirm the accounts by an independent authority to ensure that all account books are carried out in a reasonable way and there is no misrepresentation or fraud that is being performed. All the public recognized firms need to get auditing management software their accounts examined by an independent auditor prior to they proclaim their results for any kind of quarter.
There are 4 major action in the bookkeeping process. The very first one is to define the auditor's role and also the terms of engagement which is generally in the form of a letter which is properly authorized by the customer. The second action is to prepare the audit which would certainly include details of deadlines and also the divisions the auditor would cover.
Is it a solitary division or entire organisation which the auditor would certainly be covering. The audit can last a day and even a week depending upon the nature of the audit.
The next crucial action is assembling the info from the audit. When an auditor audits the accounts or checks crucial financial statements of a business, the findings are generally put out in a report or compiled in a methodical way. The last and also most important aspect of an audit is reporting the outcome.
The outcomes are recorded in the auditor's report.
Bookkeeping is the detailed assessment of the financial records of a company as well as is utilized to supply confidence for all stakeholders that the company's accounting reports are precise.
In accounting, we consider the various accounting guidelines, journal access, monetary declarations, and various other accounting tasks. All these tasks are important due to the fact that, with these abilities, accountants can then be associated with an engagement group to carry out an audit on both interior or exterior clients. The most common audits are performed by the Big 4 accounting companies for large publicly-traded business around the globe. The monetary declarations in the very first box, which include the balance sheet, earnings declaration, declaration of cash flows, and note disclosures, are assessed versus some type of audit standards. Various regions around the world comply with various guidelines. Some typical standards may be adopted. The bottom line is that these are recognized criteria that are recognized publicly. Finally, the work culminates in an audit record where the searchings for are connected to the customers.
More officially, auditing is referred to as the buildup and also evaluation of proof to figure out as well as report on the level of communication in between the information offered like financial declarations and also the recognized standards. Auditing needs to be done by a skilled, independent person or entity. In general, auditing is an extra specialized area of bookkeeping however the two go hand in hand. This suggests that auditors can not be entirely not aware of accounting rules. In fact, auditors should be certified and also experienced in audit in order to appropriately carry out an audit. There are essentially two sorts of auditors: external auditors and interior auditors.
Outside auditors describe accountants who handle different clients and also execute the audit along with an involvement team. As discussed in the past, these are the typical public accounting companies such as the Big Four firms that audit huge public firms along with huge private business. Exterior auditors are workers of the accountancy company they are related to and also just connect with their customers with the audit process.Internal auditors, on the other hand, are actual staff members of the firm. Their role is to do general bookkeeping treatments all year to guarantee that all accountancy as well as record-keeping are being done properly to make sure that the outside audit ends up being a lot more feasible. Interior auditors normally exist just in big firms.
Bookkeeping drops under a wider umbrella of assurance. An assurance engagement describes those executed by an auditor to enhance the reliability of the situation. Apart from audit interaction, there are other kinds of assurance that an accountant can provide. The kinds of assurance may differ in regards to levels and also tasks. In all these circumstances, the general public accountant needs to acquire an agreement from the customer prior to beginning any work.
Operational Audits Profile
The auditing process is the evaluation or evaluation of different books of accounts by an auditor followed by physical checking of inventory to make certain that all departments are adhering to documented system of taping deals. It is done to ascertain the precision of monetary declarations offered by the organisation.


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Biden Backs Off Facebook Comments With New Spin
Biden Backs Off Facebook Comments With New Spin
Well, Joe Biden is once again having to "explain" himself, because what he DID say ended up sounding really, really bad.
In the latest edition of "Biden Unscripted," the president of the United States seemed to be fully on board with his administration's desires for UNLIMITED POWER (cue Emperor Palpatine here) over the social media platforms. "You have a nice platform here, be a shame if something happened to it because information got out that we didn't like" was the message coming out of Jen Psaki's mouth. Those comments led to this "Biden Unscripted" moment with the press on Friday.
These unscripted moments, as scary and authoritarian as they are, are actually a gift. The saying "when they tell you who they are, believe them" applies here. Joe Biden may look like a senile and frail old man in decline, and he is, but he is a RADICAL senile and frail old man in decline. That "kindly moderate Grandpa Joe" was a complete wash by Democrats and the media (but I repeat myself) in order to make sure that he beat Bernie in the primary, and then inflated beyond imagining during the general election. Accusing Facebook of "killing people" is insane, and Facebook itself took umbrage at that remark, as Nina covered here.
While the media showed their glee by trotting out guests that agreed with Biden and wrote op-eds in concurrence that this was all Facebook's fault, one can only speculate on the level of communication on the backchannels between Facebook and Team Biden. Did Facebook suddenly remember that they have this thing called the First Amendment on their side, and tell the Biden administration to go stuff their demands where the sun don't shine? I wish, but that likely didn't happen, because that would require Facebook growing a spine – something that they have decidedly lacked when it has come to the demands of the left to "play Calvinball with us, OR ELSE." But something clearly did happen, because Joe Biden is now out in front of the press again, with a prepared statement about what he "meant" by "Facebook is killing people." He did not mean what you think it means.
Joe Biden: It's not Facebook that is killing people, but Facebook users who are spreading "outrageous misinformation." pic.twitter.com/qEifueCmva
— Townhall.com (@townhallcom) July 19, 2021
And we all know that this kind of spin has to wait until Monday, because Grandpa Joe doesn't work on weekends.
"Facebook isn't killing people, these 12 people are out there giving misinformation. Anyone listening to it is getting hurt by it. It's killing people. It's bad information," Biden told reporters after giving remarks about the economy, referencing a study that showed a dozen accounts on Facebook are responsible for the majority of misinformation about the coronavirus vaccine seen on the platform."
"My hope is that Facebook, instead of taking it personally, that somehow I'm saying Facebook is killing people, that they would do something about the misinformation, the outrageous misinformation about the vaccine," Biden added. "That's what I meant."
Asked if he felt Facebook has done enough to curb misinformation on its platform, Biden said he didn't think they had as of the last few days, but did not know for sure."
"I'm trying to make people look at themselves," he said when asked how he might hold Facebook and other platforms accountable. "Look in the mirror. Think about that misinformation going to your son, your daughter. That's all I'm asking."
That's a level of spin that obviously got workshopped over the weekend. The president of the United States telling Facebook to essentially look into their hearts and do what is right, is still a level of authoritarian control that stinks to high heaven. Soft-pedaling back the killing accusations was the obvious thing to do, but the implicit threat remains. (Though if Facebook ends up banning Robert F. Kennedy Jr. for being an anti-vaccine lunatic, I will personally find that hilarious, because a Democrat overreach into information suppression that ends up with a Kennedy being banned would be amazing.)
The threat to free speech isn't any less just because Biden had to take back his Facebook comments – he just meant the same thing, but in a nicer way. After all, there are no mean tweets from the Biden White House – just the sound of the First Amendment being flattened by the steamroller of the radical left, and you're not supposed to be paying attention to that.
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Twelve whole people? Oh, Dear Lord, 93% of the population must be completely misinformed about Covid vaccines! Whatever are we going to do? Doesn't this idiot realize that 75% of the adult population is already vaccinated, and another 15% is immune by virtue of having had the disease? I really can't imagine he thinks any significant fraction of people are being influenced by these twelve nutjobs.
I'm just as troubled that they list 12 people they are monitoring. Who are the Biden* Twelve?
Is Fauci one of the 12?
[…] our readers know, Biden had to walk back his "Facebook is killing people" commentary yesterday. Meanwhile Fauci, as noted above, […]
As I keep saying, the media and the "Democrats" sleep in the same bed.


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Main Number: 1-702-258-9895Toll Free: 1-888-258-9895
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The local paper in Mesquite closed in March, an economic victim of the pandemic.
Al Litman, the mayor of Mesquite, delivered his state of the city address this week where he focused on keeping his community a community, while also benefitting from the growth of nearby Apex Indu
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F303 Lounge Chair in Red with Upholstery by Pierre Paulin for Artifort
1 In Stock
Beautiful design. Fabric has some color differences caused by moist and some spots. Also some damages (see pictures). The base has traces of wear as well like some scratches en loss of paint and colored spots. Possibly the chair is painted because of traces of other color underneath. The seat is loose.
Weight 10.5 kg.
More pictures are available on request
- Category:
- Sales; Vintage: All-in price, no VAT mention
- Brand:
- Artifort
- Design Year:
- 1960-1969
- Product year:
- Circa 1970
- Designer:
- Paulin, Pierre
- Dimensions:
- Height: 57 cm
Width: 82 cm
Depth: 72 cm
Sitting height: 34 cm


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The only way to get star coins on spark city without cheating is to pay for them.
- There should be a tab or something that you can click on somewhere on your screen that gives the option of adding coins. Click on this, and you will be asked for your credit card details. You have to pay actual cash for star coins.
- The only other way to get star coins on here is to cheat in the game. To do this, there are several websites online which give you such cheats. Some are free, some ask for a fee. There are also videos on you tube that will talk you through how to cheat the game. The free websites on the Internet will also tell you what you need to do to cheat the game. Not being a player of the game myself, i do not know that much about it and so i am afraid that i cannot help you further.
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- Spark city is a very popular virtual world game that enables you to socialize on the net, make friends and play games. Star coins are a high accolade on this game and so are rare to come by, unless you buy them.


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I'm trying to reduce a particular sound / tonal range in the playback, in this case a saxphone, so that I can play over the track with my own sax. Is this possible and if so, can someone guide me through the steps to do that please?
Windows 10. Spotify version 184.108.40.206 Equalify Pro version 220.127.116.1164


text: The Flagship Course is intended for teams of senior-level and mid-level policy makers working in Health, Health Insurance, Finance, other government officials and development partners and Bank staff. Strong preference is given to teams of 4-10 people from countries working with the World Bank on UHC-related health policies. Usually, country group members are agreed with the World Bank counterpart and task teams with the course team leader Dr Nedim Jaganjac to ensure places for the group. The course will be in English.


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The MSMEs are strong pillars of India's growing economy. They are the engines of economic development due to their contribution towards job creation (especially for women), industrial output, and exports. Hence it becomes essential to enable them and promote the concepts of responsible business. BMOs play an important role in promoting the concepts among MSMEs as per National Voluntary Guidelines (NVGs) and implementing important sustainability linked national agendas including Zero Effect, Kaushal Bharat, and Swachh Bharat.
In line with these efforts, the Foundation for MSME Clusters (FMC) with the support of the Programme for Modernisation and Innovation Promotion in Micro, Small and Medium-Sized Enterprises in India (MSME INNO) hosted the 5th Responsible BMO Awards in New Delhi. The awards were divided into five categories and the participants were shortlisted through an assessment process and methodology.
Mr Giriraj Singh, Minister of State (Independent Charge) for Micro, Small and Medium Enterprises in the Government of India was the chief guest for the event. During the event, Mr Singh emphasised on the cluster development approach and importance of smart industries for India's growth.
Mr Chaman Lal Dhanda, Head of Project, MSME INNO talked about the 60 years of Indo-German cooperation and their focus on sustainable development.
During the event, the project launched an Information Facilitation cell in collaboration with the FMC, that will accelerate innovation through the BMOs, at a national level. In the beginning the project would be connecting with 60 BMOs and later FMC would be reaching to more BMOs through digital initiatives.
The Innovation in MSMEs programme, closely engages with the development actors – government, BMOs, academic institutions and corporates in order to strengthen the competitiveness of Indian MSMEs. In the states of Maharashtra and Punjab, the project has reached more than 3000 MSMEs. That resulted in increased performance and growth, improvements in efficiency, productivity and quality for the MSMEs.
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Are you looking for answers to the issue, "How to become in a marriage? " You may have come towards the right place. This post will address a number of the most important factors that you need to understand if you are ready to start dating an individual. If you want to recognise how to have a relationship, then simply here are some tips you need to follow:
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Introduction: What is an accounting dissertation?
Research question:
What is a research question? A research question is the fundamental part of any research project. It is a question that you are asking in your research in order to find an answer to. The research question should be clear, concise and specific. It is important to make sure that the research question is answerable, otherwise the entire project will be a waste of time. There are many different types of research questions, but all of them share common elements. The following steps can help you create a good research question:
1. State the problem – This can be done by talking to people who are familiar with the issue or by reading about it.
2. Come up with possible solutions to the problem – This can be done by researching similar issues or brainstorming with others.
3. Consider the benefits and disadvantages of each solution.
4. Decide on which solutions should be considered as a possible solution to the research question
5. This can be done in a brainstorming session with other people.
6. Develop hypotheses to test the solutions you have come up with.
7. Test your hypothesis
Literature review:
When it comes to school assignments, a literature review is often one of the most challenging tasks. It can be difficult to know where to start or what information is relevant. This overview will provide an introduction to the literature review process and outline some key steps to help you get started.
The process of research involves many steps, from formulating a research question to analyzing data. The first step in any research project is to develop a plan, or methodology, that outlines how the data will be collected and analyzed. A good methodology will ensure that the research is conducted in an efficient and accurate manner. There are many factors to consider when developing a methodology, including the type of study, the population being studied, and the research design. In order to ensure that the data is reliable and valid, researchers must also consider the ethical implications of their study.
The article discusses the findings of a study on employee productivity. The study found that employees who take breaks are more productive than those who do not. It also found that employees who take short breaks are more productive than those who take long breaks.
One of the most common tasks associated with being a successful accountant is preparing financial reports. To be able to produce accurate and timely reports, an accountant must have strong accounting skills. Unfortunately, many people do not have the necessary training to become accomplished accountants. This is where dissertation help comes in.
Dissertation help can be provided by experienced professionals who have a deep understanding of accounting and the ability to provide effective assistance with your research project. By hiring dissertation help, you can ensure that your research is conducted in a professional manner and that your dissertation meets all required standards.
When it comes to writing, there are a few things that are important to keep in mind. The most important thing is to be clear and concise. This means that your writing should be easy to read and understand. In order to make sure your writing is clear, you should use proper grammar and spelling. Additionally, it is important to be organized and have a clear beginning, middle, and end. When you are finished writing, always take the time to proofread your work.
FAQ + Online Accounting Dissertation Experts
When it comes to accounting dissertations, many students feel overwhelmed. The process of choosing a topic, conducting research, and writing a paper can be daunting. This is why it is important to seek help from qualified professionals. There are many people who can provide assistance with writing an accounting dissertation.
One option is to work with a tutor or consultant who specializes in dissertation writing. These professionals can help you choose a topic, develop a research plan, and write the paper. They can also offer feedback and guidance throughout the process.
Another option is to find a mentor or advisor who has experience in accounting dissertations. This person can help you develop your ideas, offer feedback on your work, and guide you through the process.
If you are looking for help with writing an accounting dissertation, there are many options available to you.
Accounting dissertation help is an online service that provides assistance to students who are working on their dissertations. The service is offered by a team of experts who have years of experience in the field of accounting. The team can help students with topics, research, writing, and editing their dissertations.
When it comes to writing accounting dissertations, many students find themselves struggling. This is because accounting is a complex and difficult field of study, which requires a lot of research and analysis. In order to write an effective accounting dissertation, students need to be able to synthesize their research findings and present them in a clear and concise manner. They also need to be able to correctly apply accounting concepts and theories to real-world situations. Unfortunately, many students lack the necessary skills and knowledge needed to write an effective accounting dissertation. This is where dissertation help can come in handy.


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A group of first graders at an elementary school are honored for acting quickly when their teacher suffers a medical emergency. Tracy Hodges was teaching a small group lesson at Cedar Hill Elementary in Ardmore, Alabama, on the morning of January 20th when she started to feel off. She took a seat and one of her students asked if she was alright. That's the last thing she remembers before she blacked out.
According to her students, Hodges was having tremors and at first they weren't sure if she was joking or not. But soon they realized that she was in trouble. When Hodges became unconscious and unresponsive the students went to find help for her. What no one knew at the time was that Hodges was having a seizure brought on by COVID-19.
Several days after recovering from the episode, Hodges was well and back to teaching. She's grateful for her students saying, "On that day, they were courageous, demonstrated their strength and bravery, and now have a real story to tell." Last week, at a special ceremony attended by their families, classmates, and local first responders, the students, now known as "Hodges' Heroes" were recognized by the school's superintendent and presented with commendation certificates for their act of bravery.


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11 Cool Short Haircuts For Women
In the face of abuse and uncertainty, one affair that will consistently abide connected is the Black community's categorical faculty of actualization and innovation. In the age of amusing media, Black people, accurately Black women, are carefully abstraction out spaces for themselves to affect and animate one addition through the accepting of accustomed Black beauty.
Images of women all-embracing their altered bark tones, senses of style, and of course, beard textures, characterize a aggregate move abroad from akin adorableness standards that historically debilitated Black features. In their own array of aesthetic defiance, Black women abide to advance boundaries and agreement with their curly, coily, and coiled duster to accompany us some of our admired looks.
Keep scrolling for 25 examples of Black women antic artistic hairstyles to get you aflame about the versatility and the adorableness of accustomed hair.
This Tracee Ellis Ross-inspired captivated ponytail actualization is air-conditioned artistic but applied to abrasion out as well. And according to Angela Onuoha, it's accessible to recreate—keep an eye on her IG for the tutorial.
You'd be afraid what you can accomplish with some simple elastic bands. Section the beard at the advanced and defended it in a arrangement with elastic bands for a altered look.
Faux locs acquiesce you to accept the best of both worlds, no abiding commitment. Tiere's ultra blubbery locs accord off a august vibe.
If you appetite to booty your ponytail to the abutting level, we accept one chat for you—extensions. This textured ponytail gives a admirable avalanche aftereffect and the butterfly clips add a nostalgic-summery element.
One breadth area your artistic administration can absolutely flash is with your edges. Rachel's affected and badly laid babyish hairs add aroma to her chichi coiled cut.
Naturals with locs accept so abounding administration options. Create a academic attending by braiding and pinning your locs in an updo like Keisha.
Keke absitively to alteration her mini twists into micro locs, and styled them in this beautiful bifold ponytail, half-up style. Whether you use mini twists as a careful style, or you're transitioning into a loc'ed actualization you can bedrock this actualization to accumulate it adolescent and fresh.
Natural beard can accomplish some amazing gravity-defying shapes. This bunned mohawk is absolute for accidental and academic occasions.
Bandanas as accessories accept fabricated a bland improvement in the aftermost brace of years. Blake uses one to cull aback her diffuse Marley twists.
Flat twists can be a life-saving careful style, abnormally on ablution day. Try this askance updo if you're accessible to get glam for apprehension like Laila-Jean.
Only accept a little time afore your abutting Zoom meeting? Actualization your beard into ample plaits/twists and adorn them with your adopted accessories for a airy look.
Revamp the double-puff attending with accessories and mini twists. Jonell acclimated Afro Sheen's Arrangement Setting Cream Gel for shine, analogue and authority to accomplish this style.
Short cuts attending abundant with color. Bri'andra's air-conditioned low cut makes this platinum albino blush pop.
Having abbreviate beard does not beggarly you can't bedrock braids after extensions. Jaloni's beaded braids attending adventurous and air-conditioned cool.
From its acceptable roots in African cultures, Bantu knots accept become glamorized in contempo years. The actualization has become accepted amid naturalistas for its simplicity, careful value, and altered appearance.
Most of us adulation a acceptable top knot, and Jasmine begin a way to applesauce it up a little. Just collapsed agee the abandon brings a new feel to a archetypal look.
Secure a continued ponytail with some elastic bands forth the breadth of it for a "bubble" aftereffect like Savannah.
Have you anytime dreamt of picture-perfect ringlets? Reshae accomplished this arresting coiled attending with perm rods and Hawaiian Silky Triple Butter.
As a army admired and a bomb careful style, cornrows will consistently accept a appropriate abode aural the accustomed community. Cree's bizarre cornrows are beautiful and will accumulate accident to a minimum.
Sometimes adroitness is all in the accent choice. Add some added ample beard clips that call you and your claimed style.
Who says draft dehydration has to be a footfall in styling? Area your absolute out beard as a actualization or bedrock it as is like Christine.
Goddess braids attending amazing at affecting lengths. Let your braids breeze at thigh or alike knee breadth for a diva-esque look.
Daphne's affected half-up 'do gives off summer bells vibes. Although you ability be blockage low on the amusing alarm this summer, this is a acceptable actualization to accumulate in the basement for that abutting big outing.
Give that ponytail some height. Wrap some gold cord about your ponytail like Isi to approach your close glam genie.
Joyjah's colossal aberration and Bantu bond admixture looks abundantly glassy and fresh. If your beard isn't absolutely as close or as long, go advanced and add some extensions that bout your texture.
Where do you attending for artistic accustomed beard inspo? Let us apperceive some of the influencers you chase in the comments.
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Facebook is talking to exchanges about potentially listing a cryptocurrency.
That's the main finding from a New York Times story published Thursday on several efforts to introduce native cryptocurrencies to widely used messaging platforms. The Times reports that Signal and Telegram are also planning to roll out tokens over the next 12 months.
Citing "four people briefed on the negotiations," the Times reports that Facebook has told exchanges it expects to get a product out in the first half of 2019.
The Facebook project, which had previously been reported by Bloomberg as a stablecoin for its WhatsApp messaging service, would be the most high profile of the bunch. The social media giant has 2.5 billion global users.
One source speaking to CoinDesk on condition of anonymity confirmed that the company is working on a stablecoin, based on past meetings with the Menlo Park company.
Facebook has beefed up its blockchain team in recent months, with a hiring push and at least one talent-minded acquisition. The company is currently seeking to fill nearly 20 blockchain-related positions. According to the Times report, more than 50 Facebook engineers are dedicated to the company's blockchain project.
Increasing the reach of the project, the story notes prior reporting that Facebook has plans to integrate users across its Messenger, WhatsApp and Instagram messaging services.
Brady Dale contributed reporting.
Facebook image via Shutterstock
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Nike SB Dunk Low Pro Dark Russet Cedar
Nike SB Dunk Low Pro Dark Russet Cedar : If you have to do the dunk, you may as well go for Nike. These dark russet and cedar summit white leather SB Dunk Low Pro sneakers are just what you need to slam dunk and alley-oop. Featuring a round toe, a flat rubber sole, a signature Nike swoosh, a lace fastening and a branded insole.