text:
This study investigated how neuronal activity in orbitofrontal cortex related to the expectation of reward changed while monkeys repeatedly learned to associate new instruction pictures with known behavioral reactions and reinforcers. In a delayed go-nogo task with several trial types, an initial picture instructed the animal to execute or withhold a reaching movement and to expect a liquid reward or a conditioned auditory reinforcer. When novel instruction pictures were presented, animals learned according to a trial-and-error strategy. After experience with a large number of novel pictures, learning occurred in a few trials, and correct performance usually exceeded 70% in the first 60–90 trials. About 150 task-related neurons in orbitofrontal cortex were studied in both familiar and learning conditions and showed two major forms of changes during learning. Quantitative changes of responses to the initial instruction were seen as appearance of new responses, increase of existing responses, or decrease or complete disappearance of responses. The changes usually outlasted initial learning trials and persisted during subsequent consolidation. They often modified the trial selectivities of activations. Increases might reflect the increased attention during learning and induce neuronal changes underlying the behavioral adaptations. Decreases might be related to the unreliable reward-predicting value of frequently changing learning instructions. The second form of changes reflected the adaptation of reward expectations during learning. In initial learning trials, animals reacted as if they expected liquid reward in every trial type, although only two of the three trial types were rewarded with liquid. In close correspondence, neuronal activations related to the expectation of reward occurred initially in every trial type. The behavioral indices for reward expectation and their neuronal correlates adapted in parallel during the course of learning and became restricted to rewarded trials. In conclusion, these data support the notion that neurons in orbitofrontal cortex code reward information in a flexible and adaptive manner during behavioral changes after novel stimuli.
The adaptation of behavioral reactions to novel environmental events appears to constitute an important process in the control of goal-directed behavior. It is conceivable that brain systems involved in controlling behavior also are engaged in learning and adaptation of this behavior. The frontal lobe controls several aspects of voluntary behavior at a very high level (Fuster 1989;Passingham 1993; Roberts et al. 1998). Previous studies have shown that neurons in several areas of the frontal lobe are activated in close relation to changes in voluntary behavior during learning (Chen and Wise 1995a,b;Mitz et al. 1991; Niki et al. 1990;Rolls et al. 1996; Watanabe 1990).
Rewards play a central role in mediating voluntary behavior (Dickinson and Balleine 1994). The orbital part of prefrontal cortex is involved importantly in the processing of reward information and the motivational control of behavior (Damasio 1994; Rolls 1996). Patients with lesions of orbitofrontal cortex show impairments when making decisions about the expected outcome of actions (Bechara et al. 1998). Monkeys with orbitofrontal lesions respond abnormally to changes in reward contingencies (Dias et al. 1996; Iversen and Mishkin 1970), and neurons in this area show activity related to the delivery and expectation of reward (Thorpe et al. 1983; Tremblay and Schultz 2000).
The present study investigated how reward-related activity in the orbitofrontal cortex changed during the repeated learning of novel, reward-predicting visual stimuli. We employed a learning set situation in which only a single task component changed and animals learned the new reward contingencies within a few trials (Gaffan et al. 1988; Harlow 1949). Similar to a learning study with striatal neurons (Tremblay et al. 1998), this procedure allowed us to investigate single neurons during complete courses of learning in comparison with familiar performance. The learning experiment involved a delayed go-nogo paradigm that typically tests the functions of the frontal cortex in behavioral control, working memory, and movement preparation and inhibition (Iversen and Mishkin 1970; Jacobsen and Niessen 1937). An instruction picture predicted the reinforcer to be expected and the behavioral reaction to be performed. Novel instruction pictures were presented in learning trials, and animals learned their reward prediction and behavioral significance by trial and error. These data were presented previously as an abstract (Tremblay and Schultz 1996).
We used the same two Macaca fascicularis monkeys (A and B) with the mostly same experimental procedures as described in the preceding report (Tremblay and Schultz 2000). Animal A had served before for recordings in the striatum in the same task (animal B ofHollerman et al. 1998).
In the behavioral task, one of three colored instruction pictures was presented on a computer monitor in front of the animal for 1.0 s and specifically indicated one of three trial types (rewarded movement, rewarded nonmovement, and unrewarded movement). A red trigger stimulus, identical in each trial type, was presented at random 1.5–2.5 s after instruction offset and elicited the movement or nonmovement reaction as instructed. In rewarded-movement trials, the animal released a resting key and touched a small lever below the trigger to receive a small quantity of apple juice (0.15–0.20 ml) after a delay of 1.5 s. In rewarded-nonmovement trials, the animal remained motionless on the resting key for 2.0 s after the trigger and received the same liquid reward after a further 1.5 s. In unrewarded-movement trials, the animal reacted as in rewarded-movement trials, but correct performance was followed by a 1-kHz sound. The sound constituted a conditioned auditory reinforcer, as it visibly helped the animal to perform the task, but it was not an explicit reward, hence the simplifying term "unrewarded" movements. Thus each instruction indicated in each trial the behavioral reaction (execution or withholding of movement) and predicted the reinforcer (liquid or sound). Correctly performed unrewarded movements were followed by one of the rewarded trials. Apart from that, trial types alternated semirandomly with consecutive trials of the same type restricted to three rewarded movement trials, two nonmovement trials, and one unrewarded movement trial. Trials lasted 11–13 s, intertrial intervals were 4–7 s.
During learning, one new instruction picture was presented in each of the three trial types, whereas all other task events and the global task structure remained unchanged. Learning consisted of associating each new instruction picture with the execution or withholding of movement and with liquid reward versus the conditioned auditory reinforcer. Whereas each familiar instruction consisted of a single fractal image (Fig. 1, top), each learning instruction was composed of two partly superimposed, simple geometric shapes that were drawn randomly from 256 images (64 shapes having 1 of the colors red, yellow, green, or blue), resulting in a total of 65,536 possible stimuli (Fig. 1, middle andbottom). A color subtraction mode produced composite pictures with up to five colors on the computer monitor.
Animals first learned all three trial types with familiar pictures at >90% correct. Then the instruction stimuli for each of the three trial types were replaced successively by learning stimuli. When >90% correct performance was reached with a set of three learning stimuli, those stimuli were discarded and three new stimuli were introduced successively, until a total of ∼15 new images had been learned. This stage lasted ∼1.5 mo. Subsequently, new learning stimuli for all three trial types were introduced at once as a new problem, andmonkeys A and B were trained for a further 1.5 and 0.5 mo, respectively. Previously presented learning stimuli never were reused with the animal. Errors in behavioral performance cancelled all further signals in a trial, including reward. Learning was facilitated by repeating erroneous trials until correct performance was obtained.
Activity of single neurons was recorded with moveable microelectrodes from histologically reconstructed positions in left orbitofrontal cortex during contralateral task performance, together with activity from the right forearm extensor digitorum communis and biceps muscles. Every neuron was tested in 40–80 trials with the three familiar instructions and, in separate blocks, with >60 learning trials using three novel instruction pictures. The sequence of testing varied randomly between familiar and learning blocks. Task-related activations in individual neurons were assessed with the one-tailed Wilcoxon test (P < 0.01) incorporated in the evaluation software. Neurons not activated in familiar or learning trials are not reported. Differences in magnitudes and latencies of task-related changes between familiar and learning trials were assessed in individual neurons with the two-tailed Mann-Whitney U test (P < 0.05). Movement parameters were evaluated in terms of reaction time (from trigger onset to key release), movement time (from key release to lever touch), and return time (from lever touch back to touch of resting key). They were compared on a trial-by-trial basis with the Kolmogorov-Smirnov test (P < 0.001).
Learning behavior
The repeated learning of new stimuli within the same task structure resulted in a learning set in which each problem of three new stimuli was learned rapidly. At the onset of neuronal recordings,animals A and B had learned 625 and 77 problems, respectively. The first instruction in a block of new pictures could be of any of the three trial types. Monkey A reacted to this entirely unknown instruction randomly or by withholding the movement, whereas monkey B reacted preferentially with a movement. The trial outcome after the reaction to the first instruction (reward or sound if correct, no reinforcement if erroneous) influenced the animals' reaction to the subsequent instruction, and trial outcomes with each subsequent instruction influenced the following behavioral reactions. This learning behavior resulted, in average, in above chance performance already with the first new instruction of each trial type (Fig. 2 A), as two of three instructions of a new set already were preceded by at least one instruction and outcome. Thus learning occurred largely within the first trials and approached an asymptote within 5–10 trials of each type (Fig. 2 A). Medians of correct performance in the first 15 trials in the three trial types were, respectively, 87, 80, and 93% (monkey A) and 73, 73, and 87% (monkey B). Performance in familiar trials exceeded 98% in both animals. Learning curves were stable during the period of neuronal recordings inanimal A (Fig. 2 B) but became steeper with increasing learning experience in animal B.
Rewarded movements in familiar trials showed consistently shorter reaction times and longer return times (from lever touch back to resting key), as compared with unrewarded movements (Fig.3, top). This allowed identification of typical rewarded versus unrewarded movements. Thus animals kept their hand on the lever until the reward was delivered with rewarded movements, whereas they left the lever before the sound after unrewarded movements. With new stimuli, median reaction times in the first movement trial were intermediate between the two movement trial types. By contrast, median return times were initially typical for rewarded movements in both types of movement trials. All parameters became distinguishable and typical for the two types of movement trial after the first few correct trials of each type (P < 0.001) (Fig. 3, bottom). Erroneous movements in nonmovement trials usually were performed with return times typical for rewarded movements.
In rewarded movement trials, forearm muscle activity between key release and return to the resting key resembled that seen in familiar trials and remained stable over consecutive learning trials (Fig.4, left). In unrewarded-movement trials, muscle activity was frequently typical of rewarded movements in initial learning trials and later approached a pattern typical for unrewarded movements with shorter return times (Fig. 4, bottom right). Thus movement parameters and muscle activity in initial learning trials were typical for rewarded movements and subsequently differentiated between the two reinforcers.
Overview of neuronal changes
Performance of the task with familiar instruction pictures resulted in three main types of task relationships in orbitofrontal neurons, namely responses to instruction stimuli, activations preceding the reinforcers, and responses to the reinforcers (Fig.5) (Tremblay and Schultz 2000). Most activations occurred in both rewarded trial types irrespective of movement but not in unrewarded-movement trials or only in one of the three trial types.
A total of 148 neurons with statistically significant activations of one or two of the three main types were recorded in rostral area 13, entire area 11, and lateral area 14 of orbitofrontal cortex and were tested in both familiar and learning trials. Other task relationships occurred rarely and were not tested during learning. Responses to instructions or reinforcers had median latencies and durations ranging from 90 to 110 ms and from 280 to 700 ms, respectively. Activations preceding reinforcers began 1,188–1,282 ms before these events and subsided <500 ms after them. These values varied insignificantly between familiar performance and learning (P >0.05; Mann-Whitney U test).
Responses to instructions
A total of 93 neurons responded to instructions in familiar or learning trials. Responses to familiar instructions were stable during repeated testing blocks in all 15 neurons examined.
Response magnitudes in 56 neurons increased significantly during learning as compared with familiar trials (Table1). In a first form, increases consisted of appearance of activations in 18 neurons that did not respond in any familiar trial type (Fig. 6). In a second form, increases consisted of additional responses in one or two trial types in 24 neurons that responded selectively in one or two familiar trial types. This reduced the trial selectivity in these neurons. The additional activations began with the first learning trial. They were reproducible in 12 of 13 neurons tested in two learning problems, the exceptional neuron showing varying increases and decreases. In the example of Fig. 7, the neuron showed a solid response in familiar rewarded-movement trials, but only mild or no responses in the other familiar trial types (A–C). During learning, response latency shortened, and additional responses occurred in nonmovement trials (E). The neuron also showed strong responses in initial unrewarded-movement trials, which were performed with parameters of rewarded movements (F). Responses decreased progressively during learning, whereas movement parameters became appropriate for unrewarded movements. The increases were reproduced in a second learning problem with more rapid learning (G–I).
In a third form seen in 14 neurons responding unselectively to all three familiar instructions, existing responses increased significantly by a median of 54%, and latencies shortened occasionally (Fig.8).
Response magnitudes in 26 neurons decreased significantly during learning, as compared with familiar trials (by a median of 55%; Table1). The decreases were reproducible in three of four neurons tested in two learning problems (Fig. 9), the exceptional neuron showing varying increases and decreases. As in 10 other neurons, responses occurred only in unrewarded movement trials and were lost during learning. Response decreases persisted beyond the adaptation of movement parameters. Decreases also were seen in six other neurons responding in both rewarded trial types during familiar performance.
Average population histograms indicated that existing instruction responses mainly increased in duration after the initial peak during learning (Fig. 10, top), whereas decreases resulted only in a slightly shortened population response (middle). The averaging of both increases and decreases resulted only in a minor learning effect (bottom).
Activations preceding reinforcers
Activations preceding reinforcers were observed in 39 neurons. They began after the trigger stimulus and lasted until liquid reward or sound was delivered. These activations occurred either in one or both rewarded trial types and not in unrewarded movement trials or exclusively in unrewarded movement trials (Table 1).
Most of these activations were maintained during learning (23 neurons). Activations occurred also in initial unrewarded movement trials that were performed with parameters of rewarded movements and subsided once the animals performed the unrewarded movement with appropriate parameters (Fig. 11). Activations occurred also with erroneous movements in nonmovement trials, consistent with the frequent treatment of all initial movements as rewarded. Comparable adaptations in the opposite direction were observed with activations occurring exclusively in unrewarded movement trials (Fig. 12). Activations were absent in initial unrewarded movement trials during learning and appeared only after a few trials. Activations in this neuron were also absent in error trials.
In different forms of changes, nine neurons showed significantly increased activations irrespective of the adaptation of movement parameters (by a median of 47%), including five neurons that were activated exclusively during learning. Seven neurons showed decreased activations (median 67%).
Responses to reinforcers
During familiar performance, 44 neurons responded to liquid reward in either or both rewarded trials and none to the auditory reinforcer in unrewarded movement trials (Table 1). Most reward responses were maintained unaltered during learning and occurred in both rewarded trials but not in unrewarded movement trials (28 neurons) (Fig.13). Responses in 10 neurons were significantly increased (by a median of 80%) and in 6 decreased (median 67%). Four neurons responded to rewards exclusively during learning.
These data show that task-related neuronal activations in the orbitofrontal cortex undergo changes when animals rapidly learn new stimuli. Responses to the reward-predicting instructions were frequently increased or decreased. Some neurons responded exclusively during learning or, conversely, became entirely unresponsive. The changes began during the initial learning period and extended into the subsequent consolidation phase. Activations preceding the reward, apparently related to reward expectation, adapted together with the animals' reward expectations, as evidenced by their behavioral reactions. Although reinforcers play different roles during learning than during established task performance, responses following the reinforcers were largely unchanged. Thus orbitofrontal neurons show modified activity when expectations of upcoming rewards change.
Changes in reward expectation
In most learning situations, only a part of the environment changes, whereas the other task components remain unmodified and learning advances relatively rapidly. Our learning paradigm was based on a conditional, delayed go-nogo task and consisted of changing a single task component, the reward-predicting and movement preparatory instruction stimulus. Thus animals learned new associations between visual stimuli and behavioral reactions from their general task experience and without explicit sensorimotor guidance. Similar conditional delay tasks were used in learning set situations in prefrontal and premotor cortex (Mitz et al. 1991;Niki et al. 1990; Watanabe 1990) and supplementary and frontal eye fields (Chen and Wise 1995a,b,1996). Whereas the previous studies concerned mainly changes of behavior-related activity during learning, our present experiments and our previous study on striatum (Tremblay et al. 1998) particularly investigated the changes of reward expectation during learning.
During initial learning trials, unrewarded movements showed often parameters and muscle activity typical for rewarded movements and distinguished only after a few trials between rewarded and unrewarded movements. This suggests that animals initially expected a reward with all movements. Apparently animals had an existing set of expectations derived from their previous experience in the familiar task. When confronted with a novel instruction, they could draw on these expectations to test the novel stimulus and later adapt their expectations and behavior to the new situation. Inappropriate or "erroneous" behavioral performance thus would reflect inappropriate but otherwise unchanged expectations evoked by the new instructions. The simplicity of evoking existing expectations and matching them to the experienced meaning of new instructions may explain the remarkable speed of learning.
Increased and decreased instruction responses
Most responses to instructions were either increased or decreased during learning. The new appearance or loss of responses during learning resulted in changes in trial selectivity. The present learning increases resembled the sustained increases in the presupplementary motor area during learning of sequential movements (Nakamura et al. 1998) and the "learning-static" increases in frontal and supplementary eye fields during the preparation of eye movements (Chen and Wise 1995b). The exclusive responses during learning resembled "learning-selective" activations in the supplementary eye field (Chen and Wise 1995a) and comparable changes in hippocampus (Cahusac et al. 1993). Reduced trial selectivity by increased responsiveness also was observed in prefrontal cortex (Watanabe 1990) and is compatible with the modification or breakdown of directional selectivity in the supplementary eye field (Chen and Wise 1996). These comparisons indicate a number of common neuronal learning mechanisms in different areas of frontal cortex.
The present decreases resembled the learning-static decreases outlasting the learning phase in frontal eye field and supplementary eye field neurons during the preparation of eye movements (Chen and Wise 1995b). Similar changes were seen in prefrontal (Niki et al. 1990) and hippocampal neurons (Cahusac et al. 1993).
The increased or decreased responses to the instructions during learning might simply reflect differences in visual stimulus features. However, learning changes were similar with different sets of instruction pictures. This suggests that many of the observed changes were not primarily related to differences in visual features. Responses dependent on visual features were found in the tail of caudate (Brown et al. 1995).
In contrast to visual stimulus features, mechanisms related to general arousal and attention may have contributed to increased responses. Most task events probably were processed with heightened general attention during learning. The instruction stimuli were novel and had unknown behavioral significance, and the other task events were processed in a less automatic manner. Environmental stimuli and behavioral reactions during spatial attention were accompanied by enhanced neuronal activations in parietal and temporal cortex (Bushnell et al. 1981; Mountcastle et al. 1981; Sakata et al. 1983; Treue and Maunsell 1996), although their potential contribution to the present changes is unclear.
The increased instruction responses during learning might influence the storage of new stimulus-response associations in memory. Associating new instructions with known behavioral reactions and reinforcers would require modifications of neuronal processing, most likely involving synaptic changes. These should take place during the acquisition of new instructions and subsequent consolidation. Neuronal activations presently were increased during both periods. The increased orbitofrontal responses were most likely due to increased synaptic input that might possibly lead to long-lasting changes at cortical synapses, as indeed found in dorsolateral prefrontal cortex (Otani et al. 1998).
The decreases outlasted the initial learning phase. The familiar instructions consisted of highly structured, fractal images which were used without changes for many months and thus constituted reliable reward predictors. By contrast, the learning pictures were simple geometric forms which were usually presented for <100 trials and then replaced by new pictures. Thus the reward prediction of these "disposable" stimuli was only transient. Some neurons might have been sensitive to the different characters of reward prediction among the two stimulus classes and react with decreases to the disposable instructions during learning.
Adaptation of reward expectation-related activity
The changes of reward expectations during learning, as evidenced by the modifications of behavioral reactions, were paralleled by changes in neuronal responses to the reward-predicting instructions and expectation-related activations preceding the reward. However, the reward expectation-related activations were not related to overt behavioral reactions, like arm movement or licking (Tremblay and Schultz 2000), and their changes during learning should not in any simple manner reflect the behavioral changes during learning. Thus it appears that initially inappropriate neuronal activations reflected inappropriate reward expectations. The activations adapted and became appropriate for the trial type during learning when expectations were matched to the new task contingencies. In analogy, the few activations reflecting the expectation of the auditory reinforcer were rare in initial learning trials and reappeared subsequently. Adaptations of reward expectation-related activity also were found in the orbitofrontal cortex and amygdala of rats (Schoenbaum et al. 1998). These data suggest a mechanism for adaptive learning in which existing expectation-related neuronal activity is matched to the new conditions rather than acquiring all relationships to task contingencies from scratch.
Neuronal changes in parallel with behavioral changes were found also in orbitofrontal cortex during reversals of visual stimuli (Rolls et al. 1996; Thorpe et al. 1983). In similar go-nogo learning set tasks as employed presently, some dorsolateral prefrontal neurons changed movement preparatory activity in close correspondence with changes in actually executed behavioral responses (Niki et al. 1990; Watanabe 1990). Further experiments may assess how inputs from these frontal areas might mediate the adaptations of reward expectation-related activity in orbitofrontal cortex.
Largely maintained responses to reinforcers
As a negative but puzzling finding, most reinforcer responses were unchanged between familiar and learning trials. Only few responses to reward or auditory reinforcement occurred exclusively during learning. This contrasts strongly with the differences in function of reinforcers in learning versus established task performance (Schultz 1998). These results might indicate that orbitofrontal neurons use reinforcer information for the consolidation of learned reactions rather than for early learning. However, some orbitofrontal neurons responded particularly well to unpredictable rewards (Tremblay and Schultz 2000), a situation in which the learning of new task contingencies is particularly efficient (Rescorla and Wagner 1972). It would be interesting to resolve this conflicting issue concerning the use of reinforcer information during learning.
Comparison with learning-related changes in striatum
Neurons in the striatum (caudate nucleus, putamen, and ventral striatum) showed a considerable spectrum of activity related to various behavioral events, such as expectation of external events including rewards, preparation of movement, responses to reward-predicting or movement-eliciting stimuli, and responses to rewards (Alexander and Crutcher 1990; Apicella et al. 1992;Hikosaka et al. 1989; Schultz 1995). A large fraction of these activities reflected the expected reward (Hollerman et al. 1998). By contrast, orbitofrontal neurons showed a narrower range of task-related activities that predominantly reflected reinforcement processes (Rolls et al. 1996; Thorpe et al. 1983; Tremblay and Schultz 2000; see also Fig. 5). Nevertheless, there were considerable parallels between learning-related changes in both structures in exactly the same learning situation (Tremblay et al. 1998). Striatal neurons also showed adaptation of reward expectation-related activity. They also showed new responses or increases of existing responses to novel instructions, although increases lasted frequently shorter during learning in striatum as compared with orbitofrontral cortex. The fractions of decreases or complete abolition of instruction responses were about similar in the two structures. By contrast, orbitofrontal neurons showed twice as many increases than decreases. Although orbitofrontal inputs to ventral striatum (Eblen and Graybiel 1995; Haber et al. 1995; Selemon and Goldman-Rakic 1985) may not induce the large variety of task-related activities throughout the striatum, the presently observed orbitofrontal activities could influence some of the reward expectation-related changes in the ventral striatum during learning.
We thank B. Aebischer, J. Corpataux, A. Gaillard, A. Pisani, A. Schwarz, and F. Tinguely for expert technical assistance.
The study was supported by Swiss National Science Foundation Grants 31-28591.90, 31.43331.95, and NFP38.4038-43997. L. Tremblay received a postdoctoral fellowship from the Fondation pour la Recherche Scientifique of Quebec.
Present address of L. Tremblay: INSERM Unit 289, Hôpital de la Salpetri re, 47 Boulevard de l'Hôpital, F-75651 Paris, France.
Address reprint requests to W. Schultz.
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
- Copyright © 2000 The American Physiological Society


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text:
You can still redeem your Pepsi/iTunes code
Although the redemption deadline for the Pepsi/iTunes song giveaway has officially expired as of Friday, April 30, the "Redeem Song" page is still available via iTunes. For those still hanging on to winning codes redeem them quickly, before it's too late.
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Hmm… Any word on whether or not you can USE the codes that you can sneak onto the system?
And… Any bets on how long (short) this stays up? ;-)
Posted by Jerrod H. in Irvine, CA on May 2, 2004 at 12:22 PM (CDT)
You can't use the codes any more. I tried yesterday morning (may 1), and the free songs in my account disappeared.
Posted by yahblo in Irvine, CA on May 2, 2004 at 12:29 PM (CDT)
Actually, what I meant was even if you can sneak the codes in, it won't do any good because the free songs aren't there any more.
Posted by yahblo in Irvine, CA on May 2, 2004 at 12:30 PM (CDT)
I tried to redeem them and it dosn't work. It says you have to put in a credit card. Darn
Posted by Kayle12 in Irvine, CA on May 2, 2004 at 3:19 PM (CDT)
Shouldnt of waited ;P
Posted by Pablo in Irvine, CA on May 2, 2004 at 3:37 PM (CDT)
If you don't want your codes can i have them?
Posted by NxC429 in Irvine, CA on May 2, 2004 at 4:30 PM (CDT)
how can you get the songs???
Posted by m. in Irvine, CA on May 2, 2004 at 7:10 PM (CDT)
Too late. I had 12 codes already stored on iTunes, I went to use them up today and it updated my cart and said my codes were outdated and it deleted all of them. Pepsi and iTunes suck!
Posted by Slaughter in Irvine, CA on May 2, 2004 at 7:37 PM (CDT)
OR DO YOU SUCK?! Just kidding, but you should have redeemed them earlier.
Posted by Jim Dawg in Irvine, CA on May 2, 2004 at 8:09 PM (CDT)
LOL I have yet to see an iTunes/Pepsi bottle in person. Good job Pepsi!
Posted by Biff in Irvine, CA on May 3, 2004 at 7:42 AM (CDT)
Funny, I just saw a cooler stocked full of them yesterday. Go pepsi!
Posted by Carl in Irvine, CA on May 3, 2004 at 8:15 AM (CDT)
I had two songs free songs in my account too, but they vanished as well. Ah well…
Posted by Jason Kessel in Irvine, CA on May 3, 2004 at 11:59 AM (CDT)
Yea I thought Redeem just meant typing in the code and crediting my account, did not realize I needed to download or loose the credits, considering they did not give away nearly what they expected with the promotion you'd think they would let you keep the credits in your account…
Ah well..
Posted by Scott in Irvine, CA on May 3, 2004 at 12:59 PM (CDT)
I had a code or two lying around, and I still see several of them winning Pepsis every day. I hate Pepsi, because no matter how many times a drank one of their products during the promotion, they were totally lame when it came to getting out those bottles to the public, and I don't think I will ever be spending my money to buy a Pepsi again.. I mean, honestly, isn't there some sort of way that Apple can sue them??? They must of had some sort of agreement. Even after making the TV ad and everything, it was as if Pepsi didn't even car. It's their fault that Apple only had a 5% redemption rate, and it's their fault that we are still sitting here with unused codes everywhere.
Posted by MacFanatic90 in Irvine, CA on May 3, 2004 at 3:04 PM (CDT)
Interesting, my credited freebies showed up again today, after being gone since Mon.
Posted by Scott in Irvine, CA on May 5, 2004 at 11:35 AM (CDT)
You guys are such complainers. This was Pepsi's promotion, not Apple's.
Why in the world would Pepsi want to extend the promotion when they have to pay Apple every time someone redeems a code?
It's still a business, folks. They aren't throwing cash at us.
Posted by Steve Jobs in Irvine, CA on May 6, 2004 at 9:16 PM (CDT)
I bothered to download the itunes player which won't let me move my songs to Windows Media Player and then found out the stupid code isn't redeemable. So Pepsi actually only gave out a few songs as opposed to the bazillion they promised. I'm staying Windows and I'm staying PC.
Sniff you later Apple.
Posted by Down with Macs & IPODS in Irvine, CA on May 28, 2004 at 7:54 PM (CDT)
That damn ITunes sucks totally,
The biggest piece o shit you can create with bytes. And don't trink pepsi. Being impotent isn't as funny, as you think. Leave that to popfans, and hope that evolution prefers intelligent life.
Posted by Snu in Irvine, CA on June 17, 2004 at 4:12 AM (CDT)
Mann why cant itune music be free
Posted by Patrick in Irvine, CA on February 17, 2006 at 9:30 PM (CST)
What do they mean bi drinkin pepsi, I trank so much but i see no itune codes
Posted by Patrick in Irvine, CA on February 17, 2006 at 9:33 PM (CST)
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Last Updated: 2/24/2017 4:19 AM
Description for 26 Timber Trail Sandown, NH 03873
Are you Ready to enjoy the less demanding life style you have been dreaming about ? Snow plowing ,lawn maintenance, trash pick-up, Club House, walking Trails and more.Inside you will find one level living at it's finest. Fully applianced kitchen . Dining room , large living room . A gas fireplace in the living room. Two bedrooms, with a third room that could be a bedroom. Two baths. First floor laundry. Three season enclosed porch. Ample closet space.A 2 car oversized garage that is over 27 feet long.the master bedroom is larger than other homes in the neighborhood due to optional 4 feet added to the length of the building.Move in ready !! No waiting.
Listing Information
Mill Pine Village
Community Water, Gas Water Heater
100 Amps, Circuit Breaker Box, Propane
Room Information
Kitchen, Living Room, Sun Room, Office, Utility Room, Living/Dining Combo
Master Bathroom, Master Suite
Dining Area, Formal
pantry walk-in
Interior Features
1,584 Sq. Ft. Above Ground
Dishwasher, Microwave, Gas Range/Cooktop, Refrigerator
Tile, Vinyl, Carpet
Central Air
Gas Heat, Forced Air
Concrete, Crawl
Alarm -Smoke/Fire
Smoke Alarm, Gas Fireplace, Skylight, Walk-in Closets
Exterior / Lot Features
2 Garage Spaces, Attached Garage, Paved Driveway, RV Parking
Porch Covered, Enclosed Porch
Composition Shingle
Irrigation System, Garden, Paved Road, Wooded
Financial Considerations
Inside AccessTM
Current market conditions for 26 Timber Trail
Active Market Trends
Source:MLS DATA
Median list price$327,400
Days on market103
Properties for sale12
Sold Market Trends
Source:MLS DATA
Median sold price$251,500
Sales to list price9,708.0%
Properties sold29
Median sq ft price$145
Arrows indicate 90 day trend
Listing Price History for 26 Timber Trail
Sandown Real Estate
Sandown is located in New Hampshire. Sandown, New Hampshire 03873 has a population of 5,997.
The median household income in Sandown, New Hampshire 03873 is $84,628. The median household income for the surrounding county is $77,939 compared to the national median of $53,046. The median age of people living in Sandown 03873 is 36.9 years.
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text:
BLOOMINGTON, Ill. (AP)–The Illinois High School Association says it will start posting full-length video of past state tournament events on YouTube in the coming months.
The nonprofit association is in charge of high school sports in the state. The IHSA says it could upload even more of the 1,600 state final events on film and video in its library if the project is successful.
Video from the IHSA fall calendar, such as cross country, swimming and diving, soccer, volleyball and football, is already up on the site. The association plans to continue publishing clips weekly throughout winter and spring.
The Herald & Review reports IHSA has also posted several older videos to its YouTube channel, including highlights of a 1989 boys basketball tournament and footage of the 1952 state championship.


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Pete Arden
Chief Marketing Officer
Pete is an experienced entertainment marketer who brings many years of MMO expertise to Disruptor Beam and its portfolio of games. A dedicated tabletop RPG player, he prefers to multi-class whenever possible.
Angela Bull
Angela co-founded Disruptor Beam and continues to guide its development by managing an efficient, talented network of global artists. She is known for co-creating one of the first commercial MUDs (Legends of Future Past) and one of the world's first blogging engines (Eprise).
Dave Cham
VP Engineering
Dave architects and codes for client and server, helping to coordinate development work on all our games. He's worked on everything from Medal of Honor to Boom Blox. When he's not putting out fires, Dave draws, plays drums and guitar, and keeps trying to conquer the piano.
Matt Fuller
VP Finance
Matt manages our Iron Bank like a Ferengi. Not one bar of gold-pressed latinum goes out without his approval. A Fuller always pays his debts.
Hank Howie
COO & VP Business Development
Hank hustles to license the latest IPs, works with our current and future partners, manages various aspects of our business as we grow, and ensures the fridges have adequate supplies of beer and wine.
Joey Lapegna
Creative Director
Joey leads the design and strategic direction of our games. When he has extra time, he's nerding out on psychology, coding new game ideas, or apologizing for his polite Canadian accent.
Jon Radoff
Chief Executive Officer
Before founding Disruptor Beam, Jon built one of the first commercial games on the Internet, took a web content management company public, and grew a game advertising network to ten million people. He continues to disrupt the mobile game publishing industry through creative thinking and rapid iteration.
Andy Rappaport
Chief Data Officer
Andy joined the Disruptor Beam team in 2016 as Chief Data Officer. In this role, Andy digs deep into player behavior and other data, helping to ensure our games keep players coming back for more fun.
Jennifer Ramcharan
VP of People Operations
Jenn Ramcharan leads the People Operations team. She is responsible for overseeing human resources, talent management and recruiting, as well as supporting Disruptor Beam's culture and overall employee experience.
The Team
Marketing Art Director
Adam has the task of making sure our business cards, t-shirts, and convention booths look as awesome as our games. In no particular order, Adam would most love: self-driving cars, repairing old pinball machines, or playing Mega Man 2.
Full Stack Mobile Engineer
Adam works on both the backend and frontend code for Game of Thrones Ascent, making sure the two work smoothly and cooperate with each other. Outside work, he trains in various martial arts and enjoys the occasional D&D campaign.
Product Manager
Alex, aka Nod, is deeply committed to supporting both new and veteran players, drawing on his experience in half a dozen massively multiplayer games. He is an avid video game collector, bicyclist, and outdoors enthusiast.
Principal Software Engineer
Alex codes whatever is needed to help bring Star Trek Timelines to life on the screen, specializing in tools that make developers' lives easier. He cooks, juggles, runs, tumbles, and collects other hobbies.
Senior Software Engineer
Ali is part of the old guard who helped develop Game of Thrones Ascent from inception to launch. In his second act, he builds next generation platform systems for all of Disruptor Beam's games, and enjoys a good brunch.
Art Director
Allen defines the visual direction for all of Disruptor Beam's products, striving to push the envelope for mobile and tablet gaming. He has an extensive background in theatre carpentry, so he's also handy around the house.
Executive Assistant
As executive assistant to the CEO, Amy is like Jon's own personal ninja, ensuring he has everything he needs to run Disruptor Beam before he even knows he needs it. After a weekend spent chasing her son and his friends on outdoor adventures or cheering them on at sporting events, assisting our rowdy office of game developers during the week is a piece of cake.
Player Support Representative
Andrea, called Andrea both in RL and on the forums, helps our players get the most out of their gaming experience. In between support tickets, she can be found writing about games on the internet or hitting people with foam swords.
Player Experience Specialist
Anne, aka Shan, helps our support and community teams by listening to and learning from our players on social media and our forums. She is Belgium-born and a native French speaker who enjoys crafting and herding cats.
Ben tackles the wordsmithing for the Marketing department, creating blog posts, video scripts, social media posts, and anything else that might need a narrative touch. When not at work, he dads and he writes, mostly about dadding.
Principal QA Tester
Brian is part of the QA team, deftly pivoting between Game of Thrones Ascent and Star Trek Timelines as needed. He can be counted on to find bugs and periodically dispel myths about the random number generator.
Market Research Analyst
Chen tackles countless bugs in Game of Thrones Ascent across all platforms, ensuring that the game is responsive and engaging. When he's not improving Westeros, he enjoys bowling and following Apple products.
Senior UI/UX Designer
Colleen focuses on making UI so immersive, you forget it's even there. She is a talented digital artist capable of everything from asset creation to interactive screens and effects. In all elections, she votes for Cthulhu.
QA Tester
Dave is a member of the QA team of bug hunters at Disruptor Beam. He enjoys volunteering in his community, creating art, and going on local adventures!
Senior Systems Engineer
Dave orchestrates, builds and maintains our technical infrastructure while continously researching new ways to further enhance our games and services. He is a skilled tennis player who not only excelled at the collegiate level but continues to play in tournaments.
Cinematic Artist
David collaborates with multiple departments to create exciting videos and other media that gets you energized for what's coming next! Outside the office, he enjoys camping, going to the beach, and camping *on* the beach, which he has done everywhere from Padre Island to Elephant Butte Lake.
Business Intelligence Analyst
Ehsan designs intelligent algorithms using math, statistics and machine learning to help us understand how people are enjoying our games. His favorite metric to track is how players express their feelings about game features in chat.
Director of Marketing and Communications
Elicia has worked in games PR and marketing for over a decade. She helps keep the entire marketing team on task, while spreading word of our awesomeness via events, media relations, and more.
Director of Player Experience
Glen, aka Nights Watch Brother, leads our support and community teams for all products, and works hard to ensure the player experience is a great one. He's known by his team as "The Wombat," because he is adorable and from Australia.
Performance Marketing Manager
James uses creative strategies to bring new players into our games and communities. The best part of his job is that he gets to spend most of his time at work on Facebook.
UX Designer
Jared crafts deceptively simple, intuitive UI for Star Trek Timelines, bringing his experience in ecommerce to bear wherever relevant. After a decade working on Lord of the Rings Online, he knows more about mithril than any man should.
Lead Technical Designer
Jason codes the client architecture and graphics shaders for Star Trek Timelines. He often serenades the office on his guitar, drawing on past projects Guitar Hero and Rock Band.
Lead Narrative Designer
Jessica is Disruptor Beam's resident wordsmith, responsible for creating exciting and immersive content for all of our products. She is a foodie who can't cook and a fashionista who wears t-shirts.
Interactive Designer
As Interactive Designer, Joe helps the Marketing team create both physical and virtual experiences that promote our games. He's always up for a game of disc golf, a hike through the woods, or a relaxing evening at home with his dogs.
Kristen ensures the Game of Thrones Ascent team has everything they need to work efficiently, and communicates with all of our licensors to keep us in sync. She bakes delicious cakes and loves to color.
Financial Consultant
Lucy tries to predict how much money we will make, where it will come from, and what we need to spend it on to keep our games amazing and our company growing. She enjoys traveling, by sailboat whenever possible.
3D Artist
Marc is a veteran 3D game artist who worked on both The Sims 3 and The Sims 4 before joining Disruptor Beam. His favorite thing he has created for Star Trek Timelines is Cold Station 12 in the N'Vak system.
From the most gruesome sword blow scene to the cutest vector icon, Catalan illustrator Maria puts her illustration and design skills at the service of Disruptor Beam whenever it's needed.
Principal Software Engineer
Matt has beamed from enterprise software onto the Enterprise itself, helping to code the universe of Star Trek Timelines. Unlike everyone else, he genuinely enjoys long walks on the beach, especially when accompanied by his wife and dog.
Chief Technical Officer
Max brings some structure to the chaos that is game development, ensuring that our systems scale and our players have a consistent and fun experience. He enjoys spending time with his kids, golfing, and woodworking, though he never seems to find time for the woodworking anymore.
Senior Game Designer
Michael is busy prototyping for future projects, drawing on his prior experience designing new systems and features for Game of Thrones Ascent. He enjoys playing softball and is in possession of nine replica weapons from the Lord of the Rings films.
Game Designer
Michael brings a near-decade of experience to balancing missions and crafting cool experiences for Star Trek Timelines. When he's not designing games, he's redesigning bonsai... at least, when the bonsai chooses him.
Concept Artist
Mike enjoys the daily challenge of producing art, from concept to completion, for all aspects of Game of Thrones Ascent and Star Trek Timelines. He has a background in commercial Illustration for advertising, publishing and sports marketing clients.
Senior QA Tester
Nicole has been chasing bugs since 2010 to ensure our players have the best possible experience. When she's not knitting, she's traveling, usually to an aquarium because fish are awesome.
Lead Software Engineer
Paul fiddles with the innards of Star Trek Timelines, adding, changing, and removing bits as needed while striving to make each build better than the last. He is a master mixologist.
Lead Software Engineer
Paul has coded many of your favorite or soon-to-be-favorite features in Star Trek Timelines. He has obsessed over translating tabletop game experiences to digital formats ever since cutting his teeth on the first online trading card game, Chron X. Next time you're at a convention, join one of his D&D games!
Associate Producer
Rachel ensures the Star Trek Timelines team can work efficiently. Her specialities include managing art assets, coordinating localization, and spreading sparkly rainbow sunshine over the team.
Executive Producer
Rich plays Game Dev RTS: balancing the team, keeping them focused, and getting them everything they need to make great games. You might recognize him from On the Spot, one of the many shows he produced during his tenure at GameSpot.
Business Analytics Manager
When he's not breaking things in GoTA, Rob updates financial models and turns player and company data into meaningful and actionable items. He enjoys spending time with his family, gaming, drawing, and running. He may also be the biggest Star Wars nerd in the galaxy...
Lead Product Manager
Rob uses his experience making and managing mobile games to help us balance qualitative player feedback with data-based analysis, all with the goal of giving our players the best possible experience. He's also a firm believer in better living through spreadsheets.
Senior Software Engineer
From startups to media giants, from AAA video games to social networks, Russell is a software engineering and gaming veteran who loves to make interactive products from the ground up. He went shark diving in the Bahamas and lived to tell the tale!
Player Support Representative
Ryan, aka Wesley Crushin It, has been in tech support for many years and is excited to finally join the video game industry. When he's not helping players, he's either playing games himself, drawing, or going out with friends.
Player Support Representative
Sean, aka Ruadhan, works with Star Trek Timelines players to answer their questions about the game or help them search for a misplaced coin. In his free time, he is active in several LARPs, among them Fables of Fenorra and Occam's Razor.
Player Support Representative
Yannic is Disruptor Beam's first UK-based employee, supporting the localized versions of Star Trek Timelines from across the pond. An avid anime and Star Trek fan, dressing 'appropriately' for his role is no issue.
Senior Software Engineer
Zach takes the vision of the Star Trek Timelines design team and uses his engineering superpowers to make it a reality. He spends most of his time reading or playing games, though every now and then he'll reenact a space battle using paper clips.


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Good Magento coder needed
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Project Budget$750 - $1500 USD
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Project Description
we are looking for excelent magento coder and long term project . you will be required to work on new magento projects everyday. right now it is psd to magento project which has 5 pages .
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text:
Resize a Clip in Premiere
Edited by Djinu, Alexander Avdeev, Eng, Inukshuk
You're watching VisiHow. In this video, we are going to show how to resize a clip in Adobe Premiere Pro. In the previous video at Change Sequence Size in Premiere, we've shown how to change "Sequence Settings". That left us with some black bars at the top and bottom that we need to fix.
- 1
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- 4
- 5
- 6
- 7
- 8We scale "Scale Height".We will increase just the height so that the video covers the black bars and does not get distorted too much.Advertisement
That was how to change the image or clip size in Adobe Premiere Pro. If you have any questions or comments, please leave them in the space below. Thanks for watching VisiHow!
Video: Change Size of a Clip in Premiere
Questions and Answers
Clip size - I have altered it by mistake?
I have been fiddling with my titles and managed to resize my clip by mistake and there is now a black line down the left hand side of my clip. How can I put it back to how it was?. I have tried: I don't want to fiddle any more. I think it was caused by: I clicked on the screen and altered the size of my clip
VisiHow QnA. This section is not written yet. Want to join in? Click EDIT to write this answer.
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Article Info
Categories : Software
Recent edits by: Eng, Alexander Avdeev, Djinu


text:
Capital inflows, exchange market pressure, and credit growth in four transition economies with fixed exchange rates
With the transition from planned economic systems to membership in the European Union, capital inflows and domestic credit have expanded tremendously in Central and Eastern Europe. Four of these countries--Estonia, Latvia, Lithuania, and Bulgaria--maintain fixed exchange-rate regimes, which may face pressure because of rapid credit growth or during a slowdown. This study uses a Vector Autoregressive (VAR) approach to assess the contribution of capital inflows to exchange market pressure in these four countries, as well as to the growth of domestic credit. Both FDI and non-FDI flows are shown to feed credit growth in Bulgaria, but not the Baltics. Relatively volatile flows, particularly non-FDI inflows, reduce EMP in three of the four countries.
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Volume (Year): 33 (2009)
Issue (Month): 2 (June)
Pages: 155-167
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Web page: http://www.elsevier.com/locate/inca/621171
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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Heather D. Gibson & Euclid Tsakalotos, 2004.
"Capital flows and speculative attacks in prospective EU member states,"
The Economics of Transition,
The European Bank for Reconstruction and Development, vol. 12(3), pages 559-586, 09.
- Heather D Gibson & Euclid Tsakalotos, 2003. "Capital Flows and Speculative Attacks in Prospective EU Member States," Working Papers 06, Bank of Greece.
- Kai Carstensen & Farid Toubal, 2003.
"Foreign Direct Investment in Central and Eastern European Countries: A Dynamic Panel Analysis,"
Kiel Working Papers
1143, Kiel Institute for the World Economy.
- Carstensen, Kai & Toubal, Farid, 2004. "Foreign direct investment in Central and Eastern European countries: a dynamic panel analysis," Journal of Comparative Economics, Elsevier, vol. 32(1), pages 3-22, March.
- Carstensen, Kai & Toubal, Farid, 2004. "Foreign direct investment in Central and Eastern European countries: A dynamic panel analysis," Munich Reprints in Economics 19965, University of Munich, Department of Economics.
- Kai Carstensen & Farid Toubal, 2004. "Foreign Direct Investment in Central and Eastern European Countries: A Dynamic Panel Analysis," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00311585, HAL.
- Carstensen, Kai & Toubal, Farid, 2003. "Foreign Direct Investment in Central and Eastern European Countries: A Dynamic Panel Analysis," Kiel Working Papers 1143, Kiel Institute for the World Economy (IfW).
- Pesaran, H. Hashem & Shin, Yongcheol, 1998.
"Generalized impulse response analysis in linear multivariate models,"
Economics Letters,
Elsevier, vol. 58(1), pages 17-29, January.
- Pesaran, M. H. & Shin, Y., 1997. "Generalised Impulse Response Analysis in Linear Multivariate Models," Cambridge Working Papers in Economics 9710, Faculty of Economics, University of Cambridge.
- Lane, Philip R. & Milesi-Ferretti, Gian Maria, 2007.
"Capital flows to central and Eastern Europe,"
Emerging Markets Review,
Elsevier, vol. 8(2), pages 106-123, May.
- Gian M Milesi-Ferretti & Philip R. Lane, 2006. "Capital Flows to Central and Eastern Europe," IMF Working Papers 06/188, International Monetary Fund.
- Philip R. Lane & Gian Maria Milesi-Ferretti, 2006. "Capital Flows to Central and Eastern Europe," The Institute for International Integration Studies Discussion Paper Series iiisdp161, IIIS.
- Robert Lensink & Oliver Morrissey, 2006. "Foreign Direct Investment: Flows, Volatility, and the Impact on Growth," Review of International Economics, Wiley Blackwell, vol. 14(3), pages 478-493, 08.
- André Van Poeck & Jacques Vanneste & Maret Veiner, 2007. "Exchange Rate Regimes and Exchange Market Pressure in the New EU Member States," Journal of Common Market Studies, Wiley Blackwell, vol. 45, pages 459-485, 06.
- Christoph Duenwald & Nikolay Gueorguiev & Andrea Schaechter, 2005. "Too Much of a Good Thing? Credit Booms in Transition Economies; The Cases of Bulgaria, Romania, and Ukraine," IMF Working Papers 05/128, International Monetary Fund.
- Balázs Égert & Peter Backé & Tina Zumer, 2007. "Private-Sector Credit in Central and Eastern Europe: New (Over)Shooting Stars?," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 49(2), pages 201-231, June.
- Girton, Lance & Roper, Don, 1977. "A Monetary Model of Exchange Market Pressure Applied to the Postwar Canadian Experience," American Economic Review, American Economic Association, vol. 67(4), pages 537-548, September.
- Hans-Peter Lankes & A. J. Venables, 1996. "Foreign direct investment in economic transition: the changing pattern of investments," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 4(2), pages 331-347, October.
- Demekas, Dimitri G. & Horvath, Balazs & Ribakova, Elina & Wu, Yi, 2007. "Foreign direct investment in European transition economies--The role of policies," Journal of Comparative Economics, Elsevier, vol. 35(2), pages 369-386, June.
- Weymark, Diana N, 1997. "Measuring Exchange Market Pressure and Intervention in Interdependent Economies: A Two-Country Model," Review of International Economics, Wiley Blackwell, vol. 5(1), pages 72-82, February.
- Weymark, Diana N, 1998. "A General Approach to Measuring Exchange Market Pressure," Oxford Economic Papers, Oxford University Press, vol. 50(1), pages 106-121, January.
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It's Thanksgiving week in the U.S. and alongside a plate full of turkey, stuffing, cranberries and the rest of the holiday
repast, Americans will be downing our share of aerosols.
I don't mean the
spray cans that make holes in the ozone layer. Aerosols are the
scientific name for small airborne particles, like dust, sea salt, and sulfates
from volcanoes and fossil fuel emissions. This new NASA
video visualization of global aerosol data from the GOES-5 satellite
shows, better than any words, how different kinds of aerosols move
around the world on this dusty planet. On any given day, Americans are breathing particles from India, Africa, China, Mexico, you name it.
Keep in mind when you are viewing, that
these are not true colors of aerosols. They are false-colored to
make them easier to distinguish from each other. Dust is red, sea
salt is blue (it swirls inside cyclones), smoke is green and comes
from fires and sulfate particles are white and from volcanoes and
fossil fuel burning. There's a lot going on, so it's worth playing
several times while staring at a particular patch of the globe.
PHOTOS: Dust Storms!
As you can see, everyone on Earth is taking part in the dust feast. It's all a matter of where you are and what time of year it is. One take home message from this is that aerosols are the great equalizer. No country can keep out the air from other places. It's all the same thin shell of gases that we share. So it's best not to muck it up.
A little more info from NASA about the
Satellites, balloon-borne instruments and ground-based devices make 30 million observations of the atmosphere each day. Yet these measurements still give an incomplete picture of the complex interactions within Earth's atmosphere. Enter climate models. Through mathematical experiments, modelers can move Earth forward or backward in time to create a dynamic portrait of the planet. NASA Goddard's Global Modeling and Assimilation Office recently ran a simulation of the atmosphere that captured how winds whip aerosols around the world. Such simulations allow scientists to better understand how these tiny particulates travel in the atmosphere and influence weather and climate. In this visualization, covering August 2006 to April 2007, watch as dust and sea salt swirl inside cyclones, carbon bursts from fires, sulfate streams from volcanoes, and see how these aerosols paint the modeled world.


text:
Randy Mayor; Melanie J. Clarke
The carrots are reminiscent of Hanukkah gelt, money distributed to children during the holiday. Tangerine juice makes the glaze special, but fresh orange juice can also be used.
8 servings (serving size: about 1/2 cup)
Nutritional Information
Calories 72
Caloriesfromfat 13 %
Fat 1 g
Satfat 0.5 g
Monofat 0.2 g
Polyfat 0.2 g
Protein 1.4 g
Carbohydrate 15.4 g
Fiber 3.3 g
Cholesterol 2 mg
Iron 0.5 mg
Sodium 218 mg
Calcium 45 mg
4 cups (1/8-inch-thick) slices carrot (about 1 1/2 pounds)
1/2 cup fresh tangerine juice (about 2 tangerines)
1/2 cup fat-free, less-sodium chicken broth
1 tablespoon maple syrup
1 teaspoon butter
1/8 teaspoon ground cinnamon
1 tablespoon chopped fresh chives
1/4 teaspoon salt
1/4 teaspoon freshly ground black pepper
Combine first 6 ingredients in a large nonstick skillet; bring to a boil. Cover, reduce heat, and simmer 2 minutes. Uncover and cook 10 minutes or until liquid almost evaporates, stirring occasionally. Stir in chives, salt, and pepper.
December 2007


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January 6, 2016
Farewell, In GIF Form
by Matthew Legarreta
Geek Binge is closing up shop, but here's some GIFS to take us out with.
January 4, 2016
Jake's Top 10 Films of 2015
by Jake Wilbanks
Jake Wilbanks chooses the best of the best for 2015.
December 29, 2015
The Big Short Review: In The Land Of The Blind, The One Eyed Man Is King
by Justin Powell
Adam McKay's housing collapse take down is one of the best films of the year.
December 28, 2015
The First Official Images of Benedict Cumberbatch As Doctor Strange Look Exactly As You Would Expect
by Matthew Legarreta
And that's certainly not a bad thing.
December 18, 2015
Screen Bites: Ep. 150 — Star Wars: The Force Awakens
by Jeremy Sollie
On the final episode of Screen Bites, Jeremy, Jared, and Justin bring the show to a close with a review of JJ Abrams' Star Wars: The Force Awakens.


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18 August, 2009 2:28PM AEDT
Local reactions
What are some of the key issues facing your local area in regards to the recommendations coming out of the interim report?
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Several interviews on ABC Ballarat and ABC South-West Victoria were held over the course of this morning.
The reactions of local CFA members, business-holders and residents have been aired after the recommendations from the Victorian Bushfire Royal Commission's interim report were published.
The recommendations are already being discussed by local authorities and residents, and will have an impact on how the upcoming fire season is approached and dealt with.
Listen to the audio on the left, which has been cut up into individual interviews for better access to local content.


text: 301-880-7509 Wheaton Door & Window Request Information 2nd year as a Best Pick Request Information Call 301-880-7509 A-Rated Verified License(s) Verified General Liability Verified Workers' Comp We've been serving homeowners in the Baltimore-Washington corridor for 71 years. Established in 1945, our commitment is to quality you can count on, at prices you can afford. Jack Weber | President Areas Served: Montgomery, Howard, Prince George's, Anne Arundel Services Offered Wheaton Door & Window installs plush, textured, patterned, frieze, Berber, loop, and indoor/outdoor carpet. In addition, the company installs hardwood and laminate flooring in a range of colors, styles, and textures. Wheaton also installs gutters and a variety of windows and doors. Warranty The company provides a lifetime labor warranty on installation and honors all manufacturers' warranties. Awards & Certifications Wheaton Door & Window is an EPA Lead-Safe Certified Firm. Company History Wheaton Door & Window was established in 1945 and has been in continual service for the past 71 years. Product Information The company offers Mohawk SmartStrand carpet, which carries a lifetime soil guarantee. Licenses MHIC #125679


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Woodies Dye-Based Ink TinOther Inks
Fir Forest - View Complete Details
Product Number: NOTM350320
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Woodies Dye-Based Ink Tin - Woodies-Bye Based Ink Tin. This ink tin works perfectly with the Woodies Mounted Rubber Stamps (sold separately). This package contains one 1-1/2 inch round ink tin. Comes in a variety of colors. Each sold separately. Imported.View Product Specs
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Crafted using professional artists' quality soft pastels (highly pigmented, low dust, lightfast) on archival 140lb paper and shipped with a super white mat ready for framing. Signed by the artist.Shipped in durable packing with insurance and requiring signature confirmation for delivery. The artwork will be mounted on a backboard with a white mat.View from my deck in Kansas City. I'm fascinated by the lacy texture of tree leaves against a night sky.
Archival 140lb watercolor paper, professional artists' quality soft pastels (highly pigmented, low dust, lightfast).
Pastel drawing by Michele Fritz
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Indeed! And of course it is a bonus that it one of the clearest and most accurate iems around.
It won't be your last. I started with the ACS T1 and then the C435. Next was the 333 and I am now awaiting my MH334. So, I really don't think this will your last.


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About to start uni? You're about to have the time of your life! Here are a few things I would say to 1st-year me if I could do it all over again.go to blog
You've earned a break from all that study! At the Village we have a calendar jam-packed full of fun events, workshops and free food JUST FOR YOU. Make sure you like us on Facebook to stay up to date with what's happening!go to our facebook events page!


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Smead 12-Pocket Expanding FileExpandable Files
Letter Size - 12 Pockets - 12" Expansion - Polypropylene - Purple, Black - 1 Each - View Complete Details
Product Number: SMD70862
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Estimated Delivery - 1 business day(s) - Tue 2/28 2 business days - Wed 3/1
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Note: Must buy in quantities of 12 starting at 12.
Estimated Delivery 11 business day(s) - Tue 3/14 - The products will be shipped directly from the Manufacturer and will require additional time for delivery. 11 business days - Tue 3/14 - The products will be shipped directly from the Manufacturer and will require additional time for delivery.
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Smead 12-Pocket Expanding File - Poly Frequency Expanding File offers a fully expanding bottom so it can stand freely on a desk to allow easy access to contents. Unique Frequency embossing pattern adds style, yet remains professional. Durable poly file resists tears and repels moisture for years of use. Material is archival-safe and acid-free. Purple protective cover flap is secured by an elastic cord-and-button closure. Inside pockets with clear indexed dividers distinguish this file from a normal file. File includes 12 expandable pockets and customizable tab inserts. Each pocket expands more than 1".View Product Specs
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Countdown to TechEd Europe 2012: Tulips and TechExpo
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We may not be far from Microsoft HQ, but we're thinking Amsterdam! We recorded this week's show in the RoozenGarde tulip fields in Mount Vernon, WA. We reveal our day 2 keynote speaker and discuss the online schedule builder. Special guest Jeannine joins us to talk about the TechExpo as well as Sponsor and Exhibitor opportunities. Space is limited, so take the opportunity now to sign up while space is still available! Join us 26-29 June in Amsterdam for TechEd Europe 2012 to visit the TechExpo first hand, meet industry experts, and indulge yourself in hundreds of hours of content.
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Countdown to TechEd North America 2012: Session Builder and Tips 'n Tricks
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The Discussion
Many thanks to RoozenGarde for letting us film in their beautiful tulip fields--it definitely got us in the spirit of Amsterdam!
@j9:Nice job rolling those 'r' on 'RoozenGarde' Jeannine! Impressive.
Good video you two - looking forward to TechEd.
Nice video. I love the beautiful location!
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Pope Puts American Priest On Possible Path To SainthoodPope Francis is putting an American priest on the path to possible sainthood by declaring him a martyr.
Pope Francis Allows Priests To Absolve "Grave Sin" Of Abortion- Pope Francis is now allowing all priests to absolve what they consider to be the "grave sin" of abortion.
Pope Taps American To Head New Vatican Office For FamiliesPope Francis has named an American to head the Vatican's office for family and laity. It's a move meant to bring a more pastoral bent to the Vatican's bureaucracy.
Pope To Meet With Russian Church Leader In CubaThe Vatican said on Friday that Pope Francis and the leader of the Russian Orthodox Church will meet in Cuba next week in a major step to heal the 1000-year-old schism that divided Christianity between East and West.
Homestead: One Of Three Cities To Open Children's Immigration ShelterFlorida is set to join Colorado and New Mexico as locations selected for the U.S. government's new shelters to house young Central American immigrants as part of an effort to deal with a renewed influx of unaccompanied children coming across the border.
Pope Francis Approves Miracle For Mother Teresa's SainthoodPope Francis signed off on the miracle needed for Mother Teresa to become a saint - one of the Catholic Church's highest honors - two decades after her death.
Reform Jews Poised To Pass Transgender ResolutionThe Reform Jewish movement is making some major strides for the transgender community and its all coming down to a resolution they want passed.
Man Pushes To Invoke Satan During County MeetingA South Florida man is pushing to be allowed to invoke Satan during a county meeting.
Pope Francis Wraps Up Joyful US Visit With Big Open-Air MassPope Francis urged hundreds of thousands of the faithful gathered Sunday for the biggest event of his U.S. visit to be open to "miracles of love," closing out his joyful six-day trip with a message of hope for families, consolation for victims of child sexual abuse and a warning to America's bishops.
Pope Meets With Family Who Drove 13,000 Miles To See HimPope Francis has met with a family who made a 13,000-mile trip over 194 days from Argentina to Philadelphia in an old Volkswagen van.
Pope Visits Philadelphia, Extols America's Founding IdealsPope Francis arrived in the City of Brotherly Love on Saturday for the final leg of his U.S. visit — a festive weekend devoted to celebrating Catholic families.
Pope Wraps Up NYC Trip With Mass At Madison Square GardenWith a Mass for 18,000 people in a New York City arena, Pope Francis wrapped up a day that took him from the global spotlight of the United Nations to a classroom where he got impromptu coaching from a fourth-grader on using a touch screen.


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STARmeterSEE RANK
Down 11,644 this week
Craig Wedren
Craig Wedren is known for his work on School of Rock (2003), Anchorman: The Legend of Ron Burgundy (2004) and Wet Hot American Summer (2001). He has been married to Meggan Lennon since October 15, 2006. They have one child. See full bio »
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John Henson returns, snags 15 rebounds
Las Vegas — Golden State extended its NBA Summer League winning streak to 11 games with an 84-72 victory over the Milwaukee Bucks on Tuesday at the Thomas & Mack Center.
Milwaukee (2-1) trailed early and never could cut significantly into the Warriors' lead. The Bucks trailed by nine points after one quarter and were down, 42-31, at halftime.
Lance Goulborne paced the Warriors (3-0) with 13 points and Cameron Jones, Kent Bazemore and Ian Clark each added 11.
Power forward John Henson, returning after a one-game injury absence, grabbed 15 rebounds for the Bucks and added nine points but had eight turnovers in 29 minutes.
Guard Dominique Jones scored 11 points and rookie point guard Nate Wolters and John Shurna each added 10 for the Bucks.
Wolters missed his first five shots and finished 2 of 7 but was 6 of 7 at the free throw line and added three rebounds, two assists and two steals. He got the start at point guard and played 28 minutes while Ish Smith played 18 minutes in a backup role.
"I thought as the game went on he improved his understanding of where we need to get the ball," Bucks summer league coach Bob Bender said. "That's what extended minutes need to do for him.
"And he got some offensive success down the stretch and hopefully he can build on that."
Henson was matched up at the start of the game against the Warriors' 6-7 forward Draymond Green, who played a key role off the bench in the recent playoffs as Golden State reached the second round.
"It wouldn't probably be a matchup you would see during the regular season, but it's good for John," Bender said. "Because of his ability to protect the rim, you don't want him to get that far away from the bucket.
"But the reality is there are going to be times, switching out of rotation, he's got to be able to play out there. That's what summer league provided for him. It expands where he feels comfortable, where he gains confidence and what type of players he can play."
Green had seven points and seven rebounds but shot just 2 of 12 from the field.
Milwaukee shot just 28.6% and committed 21 turnovers leading to 21 points by the Warriors.
"They play very well together," Bender said of the Warriors. "They've got a great chemistry and it starts with the defensive identity they've established.
"I think the key was they climbed up into us and got us ahead of ourselves. We had a lot of guys trying to do the right thing. Instead of just staying the course and doing it together, they took us out of a lot of stuff. That's part of what you've got to experience in summer league.
"The good thing is we got a lot of minutes for some guys we wanted to get minutes for, and they'll be better for that."
The Bucks had to wait until late Tuesday to see where they are seeded for the single-elimination tournament that opens Wednesday. Milwaukee won seven of 12 quarters in its three opening-round games, and the number of quarters won is used in the seeding process.


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On average, listings lease in the $10's per square foot per year. The Mountain Home region is experiencing a a boost in rates. In the last 3 months, the median rate went up $0 per sqft per year. That's a 0% increase in rent.
In the last 90 days, 1 listings have come on the market, which means leases most likley are leasing quickly. You'll want to be prepared to tour spaces as quick as possible. There aren't many listings below 2,000 square feet available, so finding a space for a small business might be tough.
Even though the median size is 130,680 sqft, there are still 0 listings below 2,000 sqft. Most buildings in this region were built in 2007 and are Class B.
Trends and status of the commercial real estate market in Mountain Home.
Market Report
Real time and historical data on commercial real estate listings in Mountain Home.


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Are you looking for Pre-owned Cars in VIRGINIA? Hotfrog showcases more than 100 popular businesses related to Pre-owned Cars in VIRGINIA! Find a specific region or suburb using the left navigation.
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Danville, VA
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We sell quality used cars and trucks. We are located in Northern Virginia and primarily serve Virginia, DC and Maryland customers along with West Virginia, Pennsylvania and North Carolina.
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Norfolk, VA
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Covington, VA
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Our professional staff is courteous, knowledgeable and helpful, and is always ready to assist you in finding the right vehicle to fit your transportation needs We also have financing options for those customers…
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Christiansburg, VA
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Suffolk, VA
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Virginia Beach, VA
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Salem, VA
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Chesapeake, VA
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Falls Church, VA
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Chantilly, VA
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