The submissions for three challenges were correct, and for eight challenges, the submissions were not correct; some submissions showed similarity to the correct structures, while others showed no similarity. The low number of correct submissions for category 2 was a result of applying the assumption that all chemicals were derived from biological samples and highlights the importance of knowing the origin of biological or chemical samples studied and the metabolites expected to be present to define the correct chemical space to search in annotation processes.The Critical Assessment of Small Molecule Identification (CASMI) contest was developed to provide a systematic comparative evaluation of strategies applied for the annotation and identification of small molecules. The authors participated in eleven challenges in both category 1 (to deduce a molecular formula) and category 2 (to deduce a molecular structure) related to high resolution LC-MS data. For category 1 challenges, the PUTMEDID_LCMS workflows provided the correct molecular formula in nine challenges; the two incorrect submissions were related to a larger mass error in experimental data than expected or the absence of the correct molecular formula in a reference file applied in the PUTMEDID_LCMS workflows. For category 2 challenges, MetFrag was applied to construct  Metabolites are the building blocks for a range of chemicals  and cellular components  in biological systems and are involved in many biological processes, including metabolism and regulatory processes . The untargeted and holistic study of the metabolite composition of biological samples  is defined as metabolomics and is applied to provide a sensitive and dynamic measure of the phenotype ,2.These a priori. In studies applying chromatography-mass spectrometry as the analytical platform, hundreds to thousands of metabolite features are detected where a single metabolite is related to multiple metabolite features  and match the feature to molecular formula(e) with the same m/z ratio (or associated mass of the non-charged metabolite) within a specified mass error . Followin GC-MS ). When aple, see ,14,15,16ple, see , which iple, see  or PubChple, see ) or metaple, see , HMDB − ions or as isotopic peaks (for example 13C and 34S), applying retention time (RT), correlation coefficient analysis, m/z differences and median peak areas. The molecular mass of the uncharged metabolite was calculated from these data and matched to a large reference file containing accurate molecular masses and their associated molecular formula . A mass tolerance range of 5 ppm was applied, unless stated otherwise. Where more than one molecular formula was reported, the relative isotopic abundances (RIA) for carbon and sulfur were calculated using response data and accurate mass differences to filter the number of molecular formulae.Workflows previously developed by one of the authors (W.D.) and colleagues were applied to compete in category 1. Workflows 1 and 2 of the PUTMEDID-LCMS workflow series  were appin silico fragmentation patterns and compare these data to experimental MS/MS data, because MetFrag software is freely available. Here, the molecular formula or formulae reported in category 1 of the same challenge were inputted on a single and manual basis in to the on-line MetFrag software, followed by searching for the molecular formula in the KEGG and/or ChemSpider databases and reporting of all molecular structures with the defined molecular formula. In the second stage, in silico fragmentation of each putative molecular structure was performed applying MetFrag and matched to the experimental MS/MS data provided. The match scores provided by MetFrag were applied to report putative molecular structures after manual assessment by the authors to ensure that the match scores reflected the different structures reported.The authors have an interest in performing annotation of metabolites not present in mass spectral libraries. We applied MetFrag  to constThe processes followed to construct the results submitted to the CASMI open contest, for each challenge in categories 1 and 2, are described below. We describe the data provided for challenge 1 to inform the readers of the typical data available.m/z values defined in the challenge as being detected in positive ion mode were analysed, applying workflows 1 and 2 of the PUTMEDID_LCMS collection of workflows with a mass accuracy of 5 ppm . The results showed that the [M+H]+ ion was detected and reported a single molecular formula of C18H36N4O11, which was present in the trimMMD_sortAmass.txt file applied in workflow 2. The correct molecular formula was submitted. This molecular formula and the fragmentation mass spectrum acquired at 30eV were submitted to MetFrag applying KEGG as the chosen database. MS/MS data were provided at three different collision energies, and the data acquired at 30eV was chosen, as these MS/MS data provided the greatest number of product ions to allow structural information to be deduced most accurately. Two metabolites were reported, Kanamycin A and C, with in silico fragmentation data matching to 10 experimentally derived product ions for the former and eight for the latter metabolite. As a greater number of product ions were matched for Kanamycin A, this metabolite was submitted as the molecular structure. The correct molecular structure was submitted.For challenge 1, four data files were available; (i) the MS1 raw data (mzXML and netCDF formats), (ii) the MS1 peak list (txt format), (iii) MS2 raw data acquired at three different collision energies (mzXML format) and (iv) the MS2 peak lists for each collision energy at which MS/MS data were acquired/applied. Information on the instrument applied to acquire the data, the mass resolution and expected mass accuracy and retention time were also provided to assist the contestants. Similar data and further information were available for all other challenges. The three m/z values defined in the challenge as being detected in negative ion mode were analysed applying workflows 1 and 2 of the PUTMEDID_LCMS collection of workflows with a mass accuracy of 5 ppm . The results showed that the [M-H]− ion was detected and reported no matches to a molecular formula present in the trimMMD_sortAmass.txt file applied in workflow 2. The search was repeated with a mass accuracy of 10 ppm, but no matches were reported. The m/z of the uncharged metabolite (592.1969 Da) was manually calculated and submitted to MetFrag with a mass accuracy of 5 ppm, where applying the KEGG database provided no hits and where applying the ChemSpider database provided 193 hits related to twenty-nine possible molecular formula. When all molecular formula containing F, Cl, Si or Br were removed (as it was not expected that the correct metabolite would contain these elements), 12 molecular formula remained. The data showed no evidence for the presence of sulfur in the molecular formula (as defined by relative isotopic abundance), and eight molecular formula containing sulfur were removed to leave four molecular formulae. Applying the relative isotopic abundance for carbon showed that 29 carbons were present in the molecular formula and one molecular formula was removed (C21H32N6O14). Three molecular formula remained; C32H32O11, C33H28N4O7 and C38H28N2O5. The correct molecular formula was not submitted, as the experimentally derived mass error (>30 ppm) was greater than the mass error reported with the data and expected for the mass spectrometer applied. The CASMI organisers have now provided data following recalibration; this provides an accurate result as defined by them. Submitting the fragmentation mass spectrum acquired at 20eV (MS/MS data at one collision energy of 20eV were provided) to MetFrag and applying ChemSpider as the chosen database reported six metabolites, and these were submitted to the contest with the MetFrag reported scores. The correct molecular structure was not submitted, because the correct molecular formula was not applied. One important point was observed in this challenge; although the mass accuracy of a specific mass spectrometer can be reported as a specific ppm range (+/− × ppm), this may not always be true for a subset of metabolites . The four m/z values defined in the challenge as being detected in negative ion mode were analysed applying workflows 1 and 2 of the PUTMEDID_LCMS collection of workflows with a mass accuracy of 5 ppm . The results showed that the [M-H]− ion was detected and reported no matches to a molecular formula present in the trimMMD_sortAmass.txt file applied in workflow 2. The process was repeated with a mass accuracy of 10 ppm, and one molecular formula was reported, C13H19N7O7S2. On assessing the sulfur relative isotopic abundance, it was calculated that three sulfur atoms were present in the molecular formula and, therefore, that this molecular formula may be incorrect. The mass (or molecular weight) of the uncharged metabolite (449.0826 Da) was manually calculated, assuming a [M−H]− ion was detected (448.0754 + 1.0077 − 0.00055) and submitted to MetFrag with a mass accuracy of 5 ppm, where applying the KEGG database provide one molecular formula, C14H27N1O9S3. This molecular formula matched to the experimental relative isotopic abundance for sulfur and was submitted. The correct molecular formula was submitted. Submitting the fragmentation mass spectrum acquired at 20eV  to MetFrag, applying KEGG as the chosen database and performing in silico fragmentation, reported a single metabolite, glucolesquerellin (6-methylthiohexyl glucosinolate), with three product ions being matched to in silico-derived fragmentation ions. This single metabolite was submitted to the contest. The InChI submitted to the contest did not match the correct InChI provided by the organisers. However, the InChI submitted to the contest almost matched the correct structure, differing only in the structural configuration of the hexose substructure.The five m/z values defined in the challenge as being detected in positive ion mode were analysed applying workflows 1 and 2 of the PUTMEDID_LCMS collection of workflows with a mass accuracy of 5 ppm . The results showed that the [M+H]+ ion was detected and reported a single molecular formula of C16H21NO4S. However, the relative isotopic abundance observed in the data showed no evidence of a sulfur-containing molecular formula. The experimental information provided defined that mass accuracy “should be below 5 ppm”, though did not guarantee this mass accuracy in the view of the authors. Therefore, the workflows were operated with a mass accuracy of 10 ppm and produced a second molecular formula (C19H17NO4), which was present in the trimMMD_sortAmass.txt file applied in workflow 2. This molecular formula was submitted. The correct molecular formula was submitted. The fragmentation mass spectrum acquired at 30eV was submitted to MetFrag, applying KEGG as the chosen database. MS/MS data at three collision energies were provided, data acquired at 30eV was chosen, as they provided the greatest number of product ions to most accurately define the structure of the metabolite. Two metabolites were reported, rutacridone epoxide and stylopine, with in silico fragmentation data matching to 20 experimentally-derived product ions for the former and 10 for the latter metabolite. Both of these metabolites were submitted to the challenge with scores of 1.0 and 0.5, respectively. The correct molecular structure was not submitted.The three m/z values defined in the challenge as being detected in positive ion mode were analysed applying workflows 1 and 2 of the PUTMEDID_LCMS collection of workflows with a mass accuracy of 10 ppm . The results showed that the [M+H]+ ion was detected and reported two molecular formula of C19H23NO4 and C16H27NO4S, which were present in the trimMMD_sortAmass.txt file applied in workflow 2. However, the relative isotopic abundance data showed no evidence of a sulfur-containing molecular formula; so, C16H27NO4S was removed, and C19H23NO4 was submitted to the contest. The correct molecular formula was submitted. This molecular formula and the fragmentation mass spectrum acquired at 10 eV were submitted to MetFrag, applying KEGG as the chosen database. MS/MS data were acquired at two collision energies; the data acquired at 20eV appeared to be inaccurate, as the highest m/z reported was greater than the molecular weight of the metabolite, and therefore, the data acquired at 10 eV data was applied. Five metabolites were reported; four metabolites matched two experimentally-derived product ions (of a possible 16) to in silico-derived product ions, and the one metabolite reported one product ion match. The latter metabolite was removed, because of the lower number of matches, and the four metabolites were submitted to the contest. As confidence in these four metabolites was not high, because only two of 16 product ions were matched, all were reported with the same score, as no discrimination in confidence could be obtained. The correct molecular structure was submitted.The four m/z values defined in the challenge as being detected in positive ion mode were analysed applying workflows 1 and 2 of the PUTMEDID_LCMS collection of workflows with a mass accuracy of 10 ppm . The results showed that the [M+H]+ ion was detected and reported two molecular formula of C21H21NO6 and C14H25NO11, which were present in the trimMMD_sortAmass.txt file applied in workflow 2. An error by our team was not to assess the carbon relative isotopic abundance, as had been performed in other challenges and which would have removed the C14H25NO11 option. Instead, C21H21NO6 and C14H25NO11 were submitted to the contest with scores of 0.5 and 1.0. The correct molecular formula was submitted as the second ranked possible molecular formula. These molecular formulae and the fragmentation mass spectrum acquired at 20 eV were submitted to MetFrag, applying KEGG as the chosen database. MS/MS data for three collision energies were available; data acquired at 20 eV data was chosen, as this included a m/z peak representing the molecular ion and which the authors prefer to observe in MS/MS data. Seven metabolites were reported; four metabolites were reported with a molecular formula of C21H21NO6, and three metabolites were reported with a molecular formula of C14H25NO11. The three latter metabolites matched a greater number of experimentally-derived product ions to in silico-derived product ions. These three metabolites were submitted to the contest with the MetFrag calculated scores. The correct molecular structure was not submitted.The four m/z values defined in the challenge as being detected in positive ion mode were analysed applying workflows 1 and 2 of the PUTMEDID_LCMS collection of workflows with a mass accuracy of 5 ppm (as would be expected with a hybrid LTQ-Orbitrap mass spectrometer). The results showed that the [M+H]+ ion was detected and reported a single molecular formula of C14H9NO2, which was present in the trimMMD_sortAmass.txt file applied in workflow 2. The correct molecular formula was submitted. This molecular formula and the fragmentation mass spectrum (MS/MS data were only acquired at one collision energy of 10eV data) were submitted to MetFrag, applying KEGG as the chosen database. Three metabolites were reported; one metabolite matched in silico fragmentation data to two experimentally-derived product ions, whereas two metabolites matched one product ion. As the number of matches was low and none were conclusive, all three metabolites were submitted to the contest. The correct molecular structure was not submitted.The three m/z values defined in the challenge as being detected in positive ion mode were analysed applying workflows 1 and 2 of the PUTMEDID_LCMS collection of workflows with a mass accuracy of 5 ppm (as would be expected with a hybrid Orbitrap mass spectrometer). The results showed that the [M+H]+ ion was detected and reported a single molecular formula of C9H16N4O7, which was present in the trimMMD_sortAmass.txt file applied in workflow 2. The correct molecular formula was not submitted. Further research after the results were released shows that the correct molecular formula (C19H17OP) is not present in the reference file applied in workflow 2. The molecular formula C9H16N4O7 and the fragmentation mass spectrum collected applying collision-induced dissociation (CID) at a normalized collision energy (NCE) of 45% were submitted to MetFrag, applying KEGG as the chosen database. Four MS/MS datasets were available, CID at 45 and 75% and higher-energy C-trap dissociation (HCD) at 45 and 75%. CID at 45% was chosen, as it provided as many product ions as the other data provided, though HCD at 45% provided the same number of product ions. No matches were reported, and the process was repeated applying ChemSpider. Three metabolites were reported; one metabolite matched in silico fragmentation data to five experimentally-derived product ions, whereas two metabolites matched two product ions. The former metabolite (N-hydroxy-6-(hydroxyamino)-5,6-dihydrocytidine) was submitted to the contest, as this showed a significantly better score in MetFrag. The correct molecular structure was not submitted, because the correct molecular formula was not applied.The three m/z values defined in the challenge as being detected in positive ion mode were analysed applying workflows 1 and 2 of the PUTMEDID_LCMS collection of workflows with a mass accuracy of 5 ppm (as would be expected with a hybrid Orbitrap mass spectrometer). The results showed that the [M+H]+ ion was detected and reported a single molecular formula of C12H9N, which was present in the trimMMD_sortAmass.txt file applied in workflow 2. The correct molecular formula was submitted. To the team, this appeared to be related to a chemical rather than a metabolite, and therefore, ChemSpider, and not KEGG, was applied in MetFrag. This molecular formula and the fragmentation mass spectrum collected applying HCD at 180V were submitted to MetFrag applying ChemSpider as the chosen database. MS/MS data were provided at two different collision energies, and the data acquired at 120 V was chosen, as these MS/MS data provided the greatest number of product ions to allow structural information to be deduced most accurately. Sixty-five chemicals were reported; many of these were defined as chemically unusual, as they contained C-N triple covalent bonds or C-C triple covalent bonds or three fused benzene rings or two fused C-C double bonds. These chemicals were removed to leave 23 possible molecular structures. The 23 molecular structures were submitted to the contest with MetFrag scores. The correct molecular structure was submitted and was ranked as 12th in possible molecular structures.The two m/z values defined in the challenge as being detected in positive ion mode were analysed applying workflows 1 and 2 of the PUTMEDID_LCMS collection of workflows with a mass accuracy of 5 ppm (as would be expected with a hybrid Orbitrap mass spectrometer). The results showed that the [M+H]+ ion was detected and reported a single molecular formula of C12H13NO2, which was present in the trimMMD_sortAmass.txt file applied in workflow 2. The correct molecular formula was submitted. This molecular formula and the fragmentation mass spectrum collected, applying HCD at a 120V, were submitted to MetFrag, applying KEGG as the chosen database. MS/MS data were acquired at two different collision energies; both provided the same number of product ions, and the data acquired at 120V was chosen. Three metabolites were reported; one metabolite matched in silico fragmentation data to 10 experimentally derived product ions, whereas the other two metabolites matched one and no product ions. The former metabolite (indole-3-butyric acid) was submitted to the contest, as this showed a significantly higher score in MetFrag. The correct molecular structure was not submitted. The submitted and correct structure had the same sub-structure (indole), the additional substructures were different for the correct and submitted structures.The two m/z values defined in the challenge as being detected in positive ion mode were analysed applying workflows 1 and 2 of the PUTMEDID_LCMS collection of workflows with a mass accuracy of 5 ppm (as would be expected with a hybrid Orbitrap mass spectrometer). The results showed that the [M+H]+ ion was detected and reported a single molecular formula of C13H13N3, which was present in the trimMMD_sortAmass.txt file applied in workflow 2. The correct molecular formula was submitted. This molecular formula and the fragmentation mass spectrum collected applying HCD 90V  were submitted to MetFrag, applying KEGG as the chosen database. MS/MS data were provided applying two different fragmentation techniques (CID and HCD); the data acquired applying HCD was chosen, as these MS/MS data provided the greatest number of product ions to allow structural information to be deduced most accurately. Three metabolites were reported; only one metabolite matched in silico fragmentation data to experimentally-derived product ions, whereas the other two metabolites matched no product ions. The former metabolite  was submitted to the contest. The correct molecular structure was not submitted. The correct structure was similar to the submitted structure, as both contained two sub-structures that were identical (benzene and aniline), though the difference between both structures was the chemical sub-structure connecting these two sub-structures. The three 2O, HCO2H and NH3). The results submitted to category 1 have shown that the PUTMEDID_LCMS operates very well in defining the molecular formula; in only two of thirteen submissions were the results not correct, one due to a limitation of the reported data and one due to a limitation of a reference file applied in PUTMEDID_LCMS.Applying two separate workflows to putatively annotate metabolites was an enjoyable process and tested the authors’ knowledge of chemistry, metabolites and metabolite annotation applying automated workflows and manual interpretation. The results presented here were acquired, applying one workflow for each challenge. However, different workflows were also assessed, but were not submitted to the CASMI contest, because of the opportunity to submit only one result for each challenge. Other workflows investigated included MI-Pack  and MassThe authors’ accuracy in defining chemical structures in category 2 was significantly lower than for category 1; they submitted results to 11 challenges, of which their highest probability match was correct in one challenge , their submission was not correct in eight challenges and, in two challenges, the correct structure was ranked by the authors as fourth  and 12th . All challenges were performed, with the belief that all chemicals were endogenous or exogenous metabolites, and this logic was applied in the processes employed to define molecular structure. In the eight challenges where the submission was not correct, the authors applied only a metabolite-specific database (KEGG) or in challenges where there were no matches to KEGG, ChemSpider was applied, but results were filtered to remove chemicals  not believed to be derived from endogenous and exogenous metabolism. This logic is applied in all metabolomics studies by the authors, though the contest does not state anywhere that chemicals are endogenous or exogenous metabolites, and so, applying this logic was not appropriate. Here, the application of chemical rather than metabolite-specific libraries when integrated with the application of MetFrag would be expected to provide greater accuracy in the annotation of metabolites, though this has not been experimentally assessed by the authors.This observation highlights an important aspect of the annotation process. The search space for chemicals is very large; PubChem contains more than 31 million entries . The metin silico fragmentation to aid in their putative annotation process, as they are interested in this process for the annotation of metabolites not present in mass spectral libraries and the appropriateness of applying this process. MetFrag was chosen to perform in silico fragmentation, as it was freely available to the academic research community. The authors also applied Mass Frontier, a commercial software package available from HighChem, though did not submit any results from these data, as only single submissions were available for each challenge. A second process, which the authors apply, is to submit experimentally-derived MS/MS data to freely available mass spectral MS/MS libraries, including METLIN )The molecular formula or formulae chosen by the authors were manually copied to a .txt file, and a score was applied, ranging from 0.0 to 1.0, with values of 1.0 describing the highest probability of reporting an accurate putative annotation.in silico fragmentation of these molecular structures and matching of these in silico data to experimentally-derived gas-phase fragmentation mass spectra provided in the contest. KEGG was applied as the only database in cases where matches to the inputted molecular formula were observed and biologically-related metabolites were suspected to be present. ChemSpider was applied in challenges where no matches were observed to the KEGG database or where chemicals, which were not biologically-related, were suspected to be present. Search ppm was set at 5 ppm, unless stated otherwise; the “Only biological compound” option was applied; the “Limit # of structures” was set at 2,000; and m/z ppm was set at 10 ppm, unless stated otherwise. Experimentally-derived MS/MS data was manually copied into MetFrag; where multiple sets of MS/MS data were available, one was chosen as optimal, based on providing a range of product ions, but not greater than 30 product ions. The reported metabolites and the number of product ions matched were manually assessed to determine whether all were potential molecular structures matching to the data; structures were removed, which the authors defined as not being biologically-related or where structures showed lower numbers of matches to fragment ions compared to other molecular structures.MetFrag v0.9, ,36) was  was 36])The InChI for each molecular structure was manually acquired from one of two databases (ChemSpider  and PubCResearchers at The University of Birmingham took part in the CASMI open contest and submitted responses to 11 challenges, each in category 1 and 2. When applying the PUTMEDID_LCMS workflows to category 1 challenges, a high level of accuracy was observed, with nine of eleven submissions correct; the two incorrect submissions being related to a significantly lower mass accuracy being observed experimentally than was expected and reported and being related to the absence of the correct molecular formula in the molecular formula reference file applied in the workflows. When employing processes applied by the authors in metabolomic studies to eleven category 2 challenges, a low level of accuracy was reported, with eight challenges not submitting the correct structure and three challenges submitting the correct structure ranked by the authors as first, fourth and twelfth. The results for category 2 highlight the importance of knowing the organism or biological sample from which data has been acquired; this aids in focusing the chemical or metabolite search space applied; the authors here assumed all metabolites were of a biological origin, which was not the case.


To determine the comparative effectiveness and costs of coronary CT angiography (CCTA) and stress cardiac magnetic resonance imaging (CMR) for diagnosing coronary artery disease (CAD).A Markov micro-simulation model for 60-year-old patients with stable chest pain was developed, analyzing the perspective of the United States (US), United Kingdom (UK), and the Netherlands (NL).CCTA, CMR, and CCTA+CMR  were considered and compared to direct catheter-based angiography (CAG) and no testing. The strategies were considered both as conservative strategy (patients with mildly-positive test results are not referred for CAG), and as invasive strategy . Outcome measures included lifetime costs, quality-adjusted life years (QALY), and radiation exposure.Differences in effectiveness (QALYs) across diagnostic strategies were very small (range 0.001-0.016). For 60-year old men and women with a pre-test probability of 30% , the CCTA, CMR, and CAG strategies were dominated, because the CCTA+CMR-conservative strategy was slightly more effective, and less expensive. Compared to the CCTA+CMR-conservative strategy, the CCTA+CMR-invasive strategy was slightly more costly and slightly more effective. The CCTA+CMR-invasive strategy was cost-effective for the US and NL, but not for the UK. When patients with false-negative test results were assumed to remain false-negative for 3 years, differences between strategies increased, and the CCTA-invasive strategy became cost-effective for UK and NL.Quality-adjusted life expectancy was similar across strategies. The CCTA+CMR strategy was cost-effective up to a pre-test probability of 70-90%, depending on the country. Above these thresholds, the CMR-strategy was cost-effective.This research was supported by a Health Care Efficiency grant from the Erasmus University Medical Centre, Rotterdam. S.E.P. and F.P. were directly funded by the Barts and the London National Institute for Health Research Cardiovascular Biomedical Research Unit. A.G.D. was directly funded through the Barts and The London Charity (437/1412). K.E.F. was directly funded through a grant from NIH (NIH-NHLBI award no. R21HL112255).


The neuromodulator dopamine is centrally involved in reward, approach behavior, exploration, and various aspects of cognition. Variations in dopaminergic function appear to be associated with variations in personality, but exactly which traits are influenced by dopamine remains an open question. This paper proposes a theory of the role of dopamine in personality that organizes and explains the diversity of findings, utilizing the division of the dopaminergic system into value coding and salience coding neurons (Bromberg-Martin et al.,  Personality neuroscience is an interdisciplinary approach to understanding mechanisms in the brain that produce relatively stable patterns of behavior, motivation, emotion, and cognition that differ among individuals  Personality traits can be explained as relatively stable responses to broad classes of stimuli , with very different distributions in the brain. Why should not this diversity have evolved to serve several independent functions, with no unifying higher-order function? The simple reason this seems unlikely is evolutionary path-dependency. If dopamine served a particular function in a phylogenetically early organism, then it would be easier for evolution to co-opt the dopaminergic system to perform additional functions if they were not incompatible with the first function, and easier still if the new functions were influenced by some broad selective pressure that also influenced the older function, which is to say, if they shared some more general function. This is because any factor that affects synthesis of dopamine, whether genetic, metabolic, or dietary/digestive, is likely to influence all aspects of dopaminergic function, no matter how diverse, as it will tend to increase or decrease available dopamine in all branches of the system. The maintenance of some overarching consistency of dopaminergic function by evolution is likely because it would avoid conflict between different branches of the system when global levels of dopamine are raised or lowered. Note that this is an argument about what is evolutionarily The nature of evolutionary path-dependency suggests a hierarchical organization of functions of the dopaminergic system. The different functions carried out by different branches and components of the dopaminergic system are posited, in the present theory, to have one higher-order function in common, and that function is exploration. The release of dopamine, anywhere in the dopaminergic system, increases motivation to explore and facilitates cognitive and behavioral processes useful in exploration.Different forms of exploration exist, however, and these are governed by different subsystems of the dopaminergic system. Further, different branches of the dopaminergic system are likely to have different effects on different brain regions  in order to adjust neural populations in those regions to particular functional demands. Thus, the dopaminergic system can be considered to carry out multiple distinct functions, which may appear extremely diverse or even incompatible when considered at the level of specific brain structures, but which nonetheless possess a larger functional unity.Before providing evidence that this functional unity reflects exploration, the definition of exploration as “any behavior or cognition motivated by the incentive reward value of uncertainty” must be explained. To explore is to transform the unknown into the known or the known into the unknown  and for behavior, at any given time  desired end states or goals, (2) the current state, largely comprising evaluations and representations of the world as it is relevant to those goals, and (3) a set of In explicating EMU, Hirsh et al.  describeUlysses.) Importantly, because of the nested nature of goals, in which superordinate goals are achieved through the accomplishment of more immediate subgoals, a single stimulus can be simultaneously a punishment and a threat (of further punishment) or simultaneously a consummatory reward  and an incentive reward .In cybernetic terms, rewards are any stimuli that indicate progress toward or attainment of a goal, whereas punishments are any stimuli that disrupt progress toward a goal. These definitions are generally compatible with the behaviorist definition of rewards and punishments as stimuli that increase or decrease, respectively, the frequency of the behaviors leading up to them. Two classes of reward can be distinguished: consummatory rewards, which represent the actual attainment of a goal, and incentive rewards, also called cues of reward or promises, which indicate an increase in the probability of achieving a goal. Similarly, one can distinguish between punishments, which represent definite inability to reach a goal, and threats, or cues of punishment, which indicate a decrease in the likelihood of achieving a goal.  Another way to say this is that everything both good and bad comes initially out of the unknown, so that an unpredicted event may signal an obstacle or an opportunity , and which of these possibilities is signaled is often not immediately evident . In the case of reward, psychological entropy may be increased relatively little, and the optimal response is often straightforward: First, in all cases of unpredicted reward, learning should take place, both so that the behavior that led to the reward is reinforced and so that environmental cues that may predict the reward are remembered. This learning constitutes a very basic form of cognitive exploration, transforming the unknown into the known and the unpredictable into the predictable. Second, if the unpredicted stimulus is an incentive reward rather than a consummatory reward, additional approach behavior will often be necessary to attempt to attain the consummatory reward that is signaled. The effort expended in this attempt is exploratory (and accompanied by heightened dopamine release) to the degree that attainment of the reward remains uncertain following the cue Schultz, . The oneIn the case of unpredicted stimuli that signal a specific punishment, determination of what to do is more complicated, primarily because punishments or negative goals are repulsors rather than attractors , they trigger an alerting or orienting response, which involves the involuntary direction of attention toward the stimulus, so as to aid in identifying its significance . The magnitude of their activation reflects the degree to which the value of the stimulus over- or under-shoots expectations. They thus provide a signal of the value of unpredicted stimuli. Salience coding neurons are activated by unpredicted punishments as well as unpredicted rewards and thus provide an index of the salience, or degree of motivational significance, of stimuli. In addition to value and salience signals, a third type of input, consisting of alerting signals, excites both value coding and salience coding neurons . Alerting signals are responses to any “unexpected sensory cue that captures attention based on a rapid assessment of its potential importance”  and the substantia nigra pars compacta (SNc). , but only to those that are unpredicted or uncertain. Thus, dopamine is not simply an energizer of all behavior. Indeed, Ikemoto and Panksepp , p 24 arFollowing Peterson , I argueAnother case in which some might consider my use of the term “exploration” too broad comes in contexts where exploration has been contrasted with exploitation  to learn how stimuli and actions predict reward and to exert vigorous effort to reach goals. Activity in the salience coding system should be accompanied by motivation to learn what predicts reward or punishment and to engage cognitive effort to understand the correlational and causal structure of relevant stimuli. When both systems are activated together by an alerting stimulus, they should produce strong motivation to learn what just happened and to exert cognitive and motor effort to classify the unpredicted event.Note that in the case of unexpected reward, both value and salience coding dopaminergic neurons will typically be activated. This is sensible because of the potential benefit from exploring both the possibility of acquiring the specific reward in question  and the possibility of gaining information about the reward and its context . In the case of unexpected punishment, however, salience neurons will be activated, whereas value neurons will be suppressed. This should facilitate general motivation to cope with the threat and cognitive and perceptual exploration of the situation, while suppressing behavioral exploration that might be risky. The general motivation produced by the salience system may, in the presence of aversive stimuli, aid in overcoming the cost of effort to explore possible coping strategies for dealing with the threat. Overcoming the cost of effort appears to be an important function of dopamine, probably attributable to the value system as well as the salience system. This was demonstrated by a recent study showing that individual differences in dopaminergic function in the striatum and VMPFC predicted willingness to expend effort to seek reward, particularly when probability of receiving the reward was low  or to discover information. This desire is not necessarily pleasant. When working hard for a reward that is highly uncertain, for example, or when progress is frustratingly slow, the desire that is driven by dopamine may involve little pleasure in and of itself, and may even be experienced as unpleasant. This is true as well of the desire for information associated with the salience system. People sometimes describe themselves as “dying of curiosity” or “dying” to reach a particular goal—it is safe to assume that the use of “dying” as a metaphor rarely signals straightforward enjoyment. To be extremely eager can be emotionally painful. Of course, the desire for specific rewards or information can be accompanied by intense pleasure when progress toward the goal is satisfactory  However, the importance of the opioid system for pleasure does highlight the fact that dopaminergic emotions are not simply pleasant and that they reflect wanting more specifically than liking. They are likely to include a variety of emotions oriented toward future acquisition of reward or information: desire, determination, eagerness, interest, excitement, hope, curiosity  global levels of dopamine, determined by genetic and metabolic processes that influence availability of dopamine throughout the dopaminergic system, (2) level of activity in the value coding dopaminergic system, and (3) level of activity in the salience coding dopaminergic system. Obviously, some individual differences in behavior and experience are likely to be associated with additional parameters more fine-grained than these three, such as the density of different dopaminergic receptors in different brain structures, or the efficiency of different mechanisms of synaptic dopamine clearance. Nonetheless, the extent of available evidence is not yet conducive to compelling theory at that level of detail, and I will only occasionally speculate about such effects, when it is particularly relevant to the evidence in question.An important premise in many theories of the biological basis of personality is that traits reflect relatively stable responses to broad classes of stimuli Gray, .  and Plasticity and hypothesized that they reflect the primary manifestations in personality of individual differences in serotonergic and dopaminergic function, respectively . Agreeableness reflects differences in the various forms of altruistic social behavior. The relations among Extraversion and Agreeableness can be clarified by noting that these two traits define the interpersonal circumplex (IPC), a two-dimensional model widely used to describe social behavior  Extraversion has been shown to predict better learning under conditions of reward in reinforcement learning paradigms , rendering their findings inconclusive  and COMT genes in three samples  is an enzyme that degrades dopamine and is important for synaptic clearance. Because D4 receptors are localized primarily in the cortex  has been found to predict intelligence in these studies , and overinclusive thinking  In neuroimaging studies, schizotypy has predicted D2 receptor density and dopamine release in response to amphetamine  that reflects subclinical levels of symptoms of schizophrenia-spectrum disorders in the general population, and it is a major liability factor for those disorders. Dopamine has long been implicated in schizophrenia, and most anti-psychotic medications are dopamine antagonists. Importantly, excess dopamine seems to be involved specifically in the psychotic, or Inclusion of positive schizotypy or apophenia as a facet of Openness is nearly as controversial as inclusion of intelligence as a facet of Intellect, in part because apophenia is weakly negatively correlated with intelligence and nearly uncorrelated with questionnaire measures of Intellect. Nonetheless, we have shown that both apophenia and intelligence load positively on the general Openness/Intellect factor, and that when Openness and Intellect are separated, then apophenia loads strongly with Openness  rather than positive schizotypy  For example, Plasticity was found to predict self-reported moral conformity negatively, based on the premise that those who conform to societal moral expectations are less likely to be exploratory or to rely on their own adaptive capacity  and Beta (Plasticity) and proposed that the latter reflects a tendency toward personal growth. Olson , the Achievement scale showed its strongest correlations with Industriousness (0.30), Assertiveness (0.32), and Intellect (0.35). . Confidence, ambition, and agency seem to be at the core of manifestations of Plasticity, and they are related not only to Extraversion (particularly Assertiveness), but also to Intellect and Industriousness and to a lack of Withdrawal.  The present theory posits that all of these traits are influenced by dopamine.This cluster of traits has been detected in slightly different guises in previous personality research. First, these aspect-level traits are all related to the lexical Dynamism factor  and the jangle fallacy . Perhaps the most confusion has been created by use of the word “impulsivity” to refer to a number of related but importantly distinct traits. Impulsivity-related constructs have been substantially clarified by the development of the UPPS model , then it may be associated with increased activity in the salience system, whereas more spontaneous sensation seeking seems less likely to be related to salience. The effect of dopamine on behavior can either facilitate long-term goal pursuit or hinder it, depending on other factors that are likely to include not only the ability of DLPFC to maintain a stable focus on long-term goals but also differential influence of different parts of the dopaminergic system . This observation may account for the fact that some Extraversion-related traits are positively related to Conscientiousness, whereas others are negatively related.Aggression is another trait, like lack of premeditation, that might be influenced in opposite directions by the value and salience systems. Salience system deficits are suggested by the negative association of working memory and intelligence with aggression . The grouping of depression and anxiety in a single trait dimension is consistent with clinical research showing that risks for diagnosis of depression and generalized anxiety disorder overlap very strongly, forming a more general factor that has been labeled “Distress” . In this case, variation in the salience system in response to threat would merely influence the likelihood that someone who responds with anxiety will engage in active or “problem-focused” coping . Mania is linked to heightened exploratory behavior . For example, a dopaminergic value-system parameter that predicts sensation seeking need not necessarily predict Extraversion. What should be the case, however, is that some parameter of the value system could be found that is related to both Extraversion and sensation seeking—because the theory presumes that any trait influenced by dopamine will be related to Extraversion or Openness/Intellect partly through dopaminergic mechanisms. Because of the many different parameters that may vary in the dopaminergic system, Extraversion and Openness/Intellect need not account for (or fully mediate) every association of some other trait with dopaminergic function, but any trait associated with dopaminergic function should be associated with Extraversion and/or Openness/Intellect or one of their subtraits.Because Extraversion and Openness/Intellect are considered to be the primary manifestations of dopaminergic function in personality, one should always test whether an association between a dopaminergic parameter and some other personality trait is mediated by these two traits, and particularly by their Assertiveness and Intellect aspects, which are hypothesized to be most strongly related to dopamine. Further, when demonstrating an association of any phenomenon with Extraversion or Assertiveness, one should always test whether the effect might be due to variance shared with Intellect, and vice versa. For example, any positive association of working memory capacity or intelligence with Extraversion is likely to be merely an artifact, due to the association of these cognitive abilities with Intellect (DeYoung et al., The list of traits in Table One should not fall victim to the jangle fallacy and assume that because a scale has a different name it cannot be measuring one of the traits already on the list. For example, the MPQ, which is often used in research on dopamine, contains a Social Potency that is a good measure of Assertiveness (DeYoung et al., Another important caveat is that variations in the dopaminergic system are not presumed to be solely responsible for variation in any of the traits listed here. Even traits like Assertiveness and Intellect that are hypothesized to be strongly influenced by dopaminergic function are undoubtedly influenced by non-dopaminergic neurobiological parameters as well. Further, because multiple biological systems will influence most, if not all, traits, the mere fact that a trait is associated with Extraversion or Openness/Intellect does not guarantee that it is influenced by dopamine. Some other biological system or process may be responsible for the trait associations in question.In recent years, the most prominent theory of the role of dopamine in personality has linked it to Extraversion, reward sensitivity, and approach behavior (Depue and Collins, This theory about the nature of dopaminergic function and its role in personality is an extension of the entropy model of uncertainty (EMU; Hirsh et al., The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The objective of this paper is to apply the Translog Stochastic Frontier production model (SFA) and Data Envelopment Analysis (DEA) to estimate efficiencies over time and the Total Factor Productivity (TFP) growth rate for Bangladeshi rice crops  throughout the most recent data available comprising the period 1989–2008. Results indicate that technical efficiency was observed as higher for Boro among the three types of rice, but the overall technical efficiency of rice production was found around 50%. Although positive changes exist in TFP for the sample analyzed, the average growth rate of TFP for rice production was estimated at almost the same levels for both Translog SFA with half normal distribution and DEA. Estimated TFP from SFA is forecasted with ARIMA  model. ARIMA  model is used to forecast TFP of Aman from DEA estimation. Total factor productivity (TFP) is a widely used measure to calculate productivity. There are two kinds of methods to calculate TFP: a) Stochastic Frontier Analysis (SFA), which is parametric, and b) Data Envelopment Analysis (DEA), which is nonparametric. The two alternative approaches have different strengths and weaknesses. The main advantage of DEA is that it does not require any information more than input and output quantities. The efficiency is measured relative to the highest observed performance rather than an average et alAgricultural development has recently returned to the forefront of development issues, drawing attention to the impacts of agricultural productivity change on economic growth and poverty reduction in both rural and urban areas. Agricultural productivity and its determinants are important but have not always been well measured or well understood. Bangladesh currently uses 75% of its arable land to produce 94% of country's total food grain requirement. Few researchers have done studies on the TFP growth rate of Bangladesh crop agriculture. Most studies consider the rice production as one crop, for example, Ahmed Productivity growth from new agricultural technology was declining, and that trend was a threat to sustainable economic development of Bangladesh in 1990s Measures of efficiency indicate the possibility of improvement in total productivity. Nguyen et al. itY = Output in the i- th firm in the t- th periodA stochastic frontier production model with time-varying inefficiency is used in panel data. Functional form of translog production model in SFA can be defined asitX = input variables in the i- th firm in the t- th period0, βi, βii = the unknown parameter to be estimated.βit, is assumed to be independently and identically distributed random error having normal distribution with mean zero and variance σv2, that is, N. and uit stands for technical inefficiency and can be predicted by the following equation:it is measured as the ratio of observed output to the corresponding stochastic frontier output. It takes a value between zero and 1. uit = (ui*exp(-ç(t-T))) where uit is the non-negative random variables, which are assumed to account for technical inefficiency in production and are assumed to be independent and identically distributed as truncations at zero. It may be follow truncated normal, half normal, or exponential. ç is a parameter to be estimated, and the panel of data need not be complete ; t is the period of calculation inefficiency, whereas T is the end period.The systematic error component, vTechnically, we use the methodology to calculate TFP from The functional form of translog stochastic frontier production model is defined as follows:i = 1, 2, 3 ; t = 1, 2, 3……..20itY = production in the i-th rice with t-th period.itArea = area in the i-th rice with t-th perioditSeed = the quantity of seed of the -th rice in the i-th rice with t-th perioditFer = the amount of fertilizer used in the i-th rice with t-th periodTime = the periods vary from 1 to 20.ln = the natural logarithmi = the number of rice groupThe Malmquist Productivity Index (MPI), a nonparametric DEA model under time-dependent situations, is used for the evaluating the TFP growth of a Decision Making Unit (DMU). The concept of MPI that was introduced by Malmquist t,yt) and  ast = 1/Ft is the reciprocal of Farrel technical efficiency and the proportion of the production point , which is compressed to the ideal minimum input point. Xj =  and Yj =  represent the input and output vector, respectively. Ft is the Farrel technical efficiency, and  is the input set on the production with input strong disposability.The Malmquist index in terms of the input and output data in t and t+1 time is denoted by sets  and the technical change (TC), which is as follows:Sections 2.1 and 2.3 provide TFP according to parametric and nonparametric estimation, respectively. TFP is a univariate time series data. ARIMA  model can be used to forecast TFP where p is the order of autoregressive (AR) part, d is the number of difference to get stationary data, and q is the order of moving average (MA). ARIMA model building has the following steps:STEP 1: Model IdentificationCorrelogram, the graph of Autocorrelation function (ACF) and Partial Autocorrelation function (PACF), is used to find the number of parameters for the ARIMA model.STEP 2: Model Estimationt = Yt-1.General form of ARIMA  can be expressed ast is the time series data at time t, μ is the mean of time series data, εt is the error at time t, φp is the p-th parameter for AR, and θq is q-th the parameter for MA.YSTEP 3: Model CheckingWe have two types of information criterion: Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC). The best model has minimum value of AIC and BIC.STEP 4: Forecasting with ModelSelected best model can be used for forecasting.The data on rice production in Bangladesh are obtained from The Year Book of Agricultural Statistics published by the Bangladesh Bureau of Statistics (BBS) every year. The Agriculture branch of BBS is responsible for collecting, compiling, and disseminating crop statistics and other agriculture-related data. Additionally, secondary data from other organizations are also included in the year book.Information about the three types of rice are generally available, such Aus, Aman, and Boro in The Yearbook of Agriculture Statistics, Bangladesh. The dependent variable is production of rice, and independent variables are the area, amount of seed, and amount of fertilizer for each type of rice collected from Yearbook 2009. For this study, we consider 20-year period from 1989 to 2008. Because Bangladesh produces rice Aus, Aman, and Boro simultaneously, it is necessary to know the efficiency level of each type of rice separately, which motivates the use of the data set. To become efficient in rice production, it needs to improve the production of each of the three types of rice. To the best of our knowledge, it is the only existing comprehensive farm level data set available for the three types of Bangladeshi rice.Among the three types of rice, Boro is the most important in Bangladesh because of its huge production. The favorable weather condition, electricity management, and stable market price helped the farmers to bring more area under the Boro crop in Bangladesh. Total Boro production has been estimated in thousand metric tons. Estimation of Boro production includes all the general varieties such as the local Boro and the hybrid Boro. Total production of Aus has been estimated in thousand metric tons. Varieties of Aus are local Aus and hybrid Aus, which are included in the data set. In case of Aman production, we include the amount of broadcast Aman, local transplant Aman, and HYV Aman in the data set.The total areas of Boro, Aus, and Aman rice have been estimated in thousand hectares. In this research, we considered using the land of all varieties of each crop as area.Seed is a very important input to increase rice production. Therefore, it is very much required that the farmers must use pure, healthy seeds as per the minimum certification standards, which have standard percentage. In fact, the seed is the foundation of farming. Highly good-quality seeds are those that have genetic purity, physical purity, health standards, and moisture percentage in accordance with the minimum seed certification standards. If the seed is of bad or low quality/sub standard, then the labor and other expenses, which the farmer does, are in vain. For this study, we measured the amount of seed in thousand metric tons. Amount of improved seed for each crop is separately given in the Year Book of Agriculture.Fertilizer is kingpin in enhancing crop production. No country has been able to increase agricultural productivity without expanding the use of chemical industry. Fertilizer application mainly depends on soil type, growing season, irrigation applications, and cultivars used. Demand for fertilizer is also affected by agro climatic conditions. High yielding varieties of rice are highly responsive and need adequate supply of fertilizer to achieve targeted production. Urea (nitrogen), Muriate of Potash (MOP), and Triple Supper Phosphate (TSP) are the major fertilizers, which were applied in agricultural land in various proportions for rice production in Bangladesh. Type of fertilizer depends on the type of rice; for the limitation of methodology, we combine all the types of fertilizer quantity together with same unit (‘000 MT)u2/(σu2+σv2) a measure of level of inefficiency. Range of inefficiency is between 0 and 1. Ratio of specific variability to total variability (γ) is positive and significant at 1% level of significance in both consideration  of SFA, implying that specific technical efficiency is important in explaining the total variability of the output. In SFA with half-normal and truncated normal distribution, the value of γ is 0.996 and 0.955, respectively. That is, 99.6% and 95.5% of random variation occurred in production because of inefficiency measured by SFA with half-normal and truncated normal distribution, respectively. Estimated parameters of time-varying inefficiency model indicate that the technical inefficiency effects tend to decline over time because the estimate for the parameter eta (η) is positive in both consideration of SFA. Eta (η) is highly significant when considering inefficiency following truncated normal distribution but insignificant when considering half normal distribution in SFA. The value of mu (μ) is zero in SFA with half-normal, indicating that the distribution of the inefficiency effects is more concentrated than that of SFA with truncated normal. The second order coefficients  and  are significant in SFA with half normal and truncated normal distributions respectively.Inefficiency of time-varying production function is calculated by the error term. Composed error term of a stochastic frontier model is defined as γ = σIt is clear from the annual technical efficiency of rice that the efficiency is increasing over time. Technical efficiency of Aus is increased from 0.462 in 1989 to 0.557 in 2008 and from 0.40 in 1989 to 0.539 in 2008 for half-normal and truncated normal distributions, respectively. The efficiency of Aman is increased from 0.372 to 0.473 and 0.304 to 0.448 for half normal and truncated normal distribution, respectively, and that of Boro is almost constant for both half-normal (0.98) and truncated normal (0.85) distributions.There is wide variation in technical efficiencies among the different types of rice. Average efficiencies over time are 0.51, 0.423, and 0.983 for Aus, Aman, and Boro, respectively, from SFA with half normal. These values indicate that Aus, Aman, and Boro can improve their output level more by 49%, 57.7%, and 1.7% from the current level, respectively, using the same inputs and technology. Average technical efficiencies are found to be 0.47, 0.376, and 0.870 for Aus, Aman, and Boro, respectively, from SFA with truncated normal. These values indicate that Aus, Aman, and Boro can improve their output level more by 53%, 62.4%, and 13% from the current level, respectively, using the same inputs and technology. In both cases, half-normal and truncated normal of SFA, the lowest technical efficiency is observed for Aus production, and the highest technical efficiency is recorded for Boro production. The result is supported by SFA with truncated normal shows that efficiency for Aus is negative but improving but constant from DEA (at 1.0) and SFA with half-normal (at 1.01) estimate. Efficiency change has fluctuation in certain years only for Aman in all methods of estimation. Fluctuation in efficiency change of Aman is observed in years 1990–1991 and 2004–2007 by DEA estimation. Efficiency change of Aman is always positive but decreasing in SFA with half-normal distribution, whereas SFA with truncated normal estimation is negative. Estimation based on DEA, SFA with half normal, and SFA with truncated normal shows that there is no efficiency change for Boro.There are fluctuations in technical change for all types of rice by DEA estimation. Both methods of SFA  shows positive technical change. That is, technical change is always higher than that in the preceding year. For SFA estimation, technical progress is higher than the efficiency progress as shown in the graph; technical change shows upper line compared with efficiency change, except Aman in SFA with half normal. Technical change is less than the efficiency change in the first few years for Aman but otherwise in the following years. In this study, technical change, rather than the modest TFP growth, is the driving force in rice production in Bangladesh, whereas Nin and Yu In SFA and DEA is used to estimate the growth rate of TFP and efficiency of rice production in Bangladesh. We assumed two distributions  of inefficiency for translog stochastic frontier production model.This paper presented some important findings for policy implication. Although the efficiency of Boro is followed by Aus and Aman, if we rank the average growth rate of TFP of rice production, SFA with half normal and DEA provide the same ranking (Aus>Aman>Boro). According to the performance of various types of rice, Aus is the major performer with an average TFP growth of 5.2% from SFA with half normal and 7.6% from DEA. Boro seems to be the weakest performer with negative TFP growth from DEA and 3.2% from SFA with half normal. We can conclude that Aus and Aman are improving their performance. Both SFA and DEA showed positive growth rate of TFP for Aus, indicating the improvement in technical efficiency. The TFP growth rate of Aman is negative in SFA with truncated normal estimation but positive in SFA with half normal and DEA estimation. Boro showed negative average growth rate of TFP in DEA estimation. Technical inefficiency is decreasing overtime, which is a great advantage for Bangladesh. Technical change is the driving force to improve TFP for rice production in Bangladesh. By increasing the efficiency level, the production of Aman and Aus can be increased. ARIMA  model is presented to forecast TFP of Aus, Aman, and Boro from SFA with half-normal and truncated normal assumption, and ARIMA  model is used to forecast the TFP of Aman from DEA estimation. TFP of Aus and Boro from DEA estimation cannot be forecasted by ARIMA.


Here we tested the contribution of M5 muscarinic receptors to morphine-induced dopamine elevations by measuring nucleus accumbens dopamine efflux in response to intra-VTA morphine infusion using in vivo chronoamperometry. Intra-VTA morphine increased nucleus accumbens dopamine efflux in urethane-anesthetized wildtype mice starting at 10 min after infusion. These increases were absent in M5 knockout mice and were similarly blocked by pre-treatment with VTA scopolamine in wildtype mice. Furthermore, in wildtype mice electrical stimulation of the PPT evoked an initial, short-lasting increase in striatal dopamine efflux, followed 5 min later by a second prolonged increase in dopamine efflux. In M5 knockout mice, or following systemic pre-treatment with scopolamine in wildtype mice, the prolonged increase in striatal dopamine efflux was absent. The time course of increased accumbal dopamine efflux in wildtype mice following VTA morphine was consistent with both the prolonged M5-mediated excitation of striatal dopamine efflux following PPT electrical stimulation and accumbal dopamine efflux following LDT electrical stimulation. Therefore, M5 receptors appear critical for prolonged PPT excitation of dopamine efflux and for dopamine efflux induced by intra-VTA morphine.Opiates, like other addictive drugs, elevate forebrain dopamine levels and are thought to do so mainly by inhibiting GABA neurons near the ventral tegmental area (VTA), in turn leading to a disinhibition of dopamine neurons. However, cholinergic inputs from the laterodorsal (LDT) and pedunculopontine (PPT) tegmental nucleus to the VTA and substantia nigra (SN) importantly contribute, as either LDT or PPT lesions strongly attenuate morphine-induced forebrain dopamine elevations. Pharmacological blockade of muscarinic acetylcholine receptors in the VTA or SN has similar effects. M Dopamine neurotransmission in the forebrain plays an important role in the reinforcing effects of drugs of abuse Opiate-induced dopamine elevations in the forebrain also depend, however, on cholinergic inputs from the pedunculopontine (PPT) and laterodorsal (LDT) tegmental nuclei that excite midbrain dopamine neurons 5 muscarinic acetylcholine receptor subtype, associated with dopamine neurons, is most important for cholinergic excitation of VTA and SN dopamine neurons 5 knockout mice The M5 muscarinic excitation is also critical for opiate functions. M5 knockout mice show reduced morphine-induced accumbal dopamine efflux as measured by microdialysis, and reduced morphine conditioned place preference 5 knockout mice 5 muscarinic receptors in the VTA, therefore, contribute strongly to both the stimulant and rewarding effects of morphine.M5 receptors in opiate-induced dopamine activation. Increases in accumbal dopamine efflux following VTA infusions of morphine were absent in M5 knockout mice. Prolonged PPT-evoked striatal dopamine efflux, which has not been previously characterized in mice, was also reduced in M5 knockout mice. In both cases, M5-mediated increases in forebrain dopamine efflux followed a similar time course. Our results suggest that VTA opiates may indirectly activate dopamine neurons through a mechanism involving a PPT/LDT cholinergic relay.The goal of the current studies was to better understand the role of the PPT and M5 knockout mice in Experiment 1. Injection sites in the various treatment conditions were confined within the boundaries of the VTA and were spread along its rostro-caudal extent. Recording sites in the nucleus accumbens  in wildtype mice induced a delayed increase in accumbal dopamine efflux that began 16.5±7.9 min after infusion and steadily increased over the course of the following 2 hrs. Subsequently, dopamine efflux leveled off over a 50-min period, but never decreased back to baseline levels . By contl 30 min .5 knockout mice. Post-hoc analysis using Fischer's LSD test showed significantly lower accumbal dopamine efflux in M5 knockout mice at every 30-sec time point between 32 and 165 min (p's<0.05 to p's<0.001).A between-within repeated-measures ANOVA with genotype as the between-subjects factor and time as the within-subjects factor revealed a significant interaction between genotype and time  = 11.59, p<0.0001), confirming that the temporal profile of accumbal dopamine efflux induced by intra-VTA morphine was different between wildtype and M5 knockout mice  prevented the increase in accumbal dopamine efflux typically observed following intra-VTA morphine (50 ng) . Most inout mice  vs. 2C. 5 knockout mice were distributed along the rostro-caudal extent of the PPT as shown on sagittal sections . By comparison however, the second increase was markedly attenuated in M5 knockout mice, with peak dopamine efflux significantly lower compared to wildtype mice (t(14) = 3.38, p<0.01). The minimum of the trough was always above baseline in both wildtype and M5 knockout mice.PPT stimulation in Ma trough . Neither5 contribution to PPT-evoked striatal dopamine efflux, the average PPT-evoked change in basal dopamine oxidation current in M5 knockout mice was subtracted from the average change in wildtype mice. This shows that following electrical stimulation of the PPT, M5 receptors mediate a delayed increase in striatal dopamine efflux with the greatest rise occurring between 5 and 22 min, and a peak around 30 min  = 1.00, p>0.3), or the time (t(3) = 0.39, p>0.7) of the first increase in striatal dopamine efflux. The trough was slightly enhanced, with the minimum now below baseline, resulting in a statistically significant difference relative to the pre-scopolamine baseline (t(3) = 3.94, p<0.05). Pre-treatment with scopolamine almost completely abolished the second increase in striatal dopamine efflux (t(3) = 7.53, p<0.01) in wildtype mice .5 knockout mice  or the time of the minimum (p>0.2). Scopolamine had no effect on the already significantly reduced second increase in dopamine efflux in M5 knockout mice (p>0.9).In Mout mice , systemi5 knockout mice and blocked by VTA scopolamine pre-treatment in wildtype mice. In a similar manner, the second increase in striatal dopamine efflux following electrical stimulation of the PPT was absent in M5 knockout mice and blocked by systemic scopolamine pre-treatment in wildtype mice. In both cases M5 receptors mediated a delayed onset, prolonged increase in accumbal and striatal dopamine efflux. Together, these data suggest that M5 receptor activation in the VTA/SN is critical for delayed, long-lasting excitation of dopamine neurons by either intra-VTA morphine or PPT electrical stimulation.Increases in accumbal dopamine efflux following intra-VTA morphine were absent in M5 knockout mice. By contrast, nicotine or carbachol in rats has been shown to increase basal dopamine efflux in the accumbens within 1 min following VTA infusion In wildtype mice, morphine infusion into the VTA induced a slow and long-lasting (>2 hrs) increase in accumbal dopamine efflux that began 10–20 min after infusion. This effect was entirely absent in MAccumbal dopamine efflux rose steadily from 10–150 min following VTA morphine infusion and then leveled off between 2.5 to 3 hrs post-infusion. Previous chronoamperometric measurement of both i.v. morphine-induced accumbal and striatal dopamine efflux in urethane-anesthetized rats using similar recording electrodes showed a shorter-latency increase in dopamine efflux that peaked at ∼40 min and fully returned to pre-injection baseline levels between 2–3 hours The increased accumbal dopamine efflux induced by 50 ng intra-VTA morphine observed in urethane-anesthetized mice is thought to have functional relevance, as mice self-administer a 50 ng dose of morphine into the VTA, and this self-administration has been shown to be reduced by pre-treatment with 4 mg/kg (i.p.) of the non-selective opioid receptor antagonist naloxone 5 knockout mice. The second, prolonged increase began 5–6 min following stimulation, and in marked contrast to wildtype mice, the second increase in striatal dopamine efflux was significantly reduced in M5 knockout mice. In addition, scopolamine pre-treatment in wildtype mice prevented the second increase in striatal dopamine efflux. Together, these data suggest that M5 receptors in the SN Our previous chronoamperometry study showed that electrical stimulation of the LDT in wildtype mice evoked an initial brief increase in accumbal dopamine efflux, followed by a brief decrease to below baseline levels, and thereafter by a prolonged second increase in accumbal dopamine efflux 2-like autoreceptors at the level of the LDT 5-mediated second increase in prolonged striatal dopamine efflux. Inspection of 5 contribution to increased striatal dopamine efflux following PPT stimulation began during the trough (i.e. between 5 and 22 min), thus likely masking the inhibitory effect of the trough.The trough in striatal dopamine efflux following PPT stimulation was reduced compared to LDT stimulation. Following LDT stimulation in rats, the trough in accumbal dopamine has been shown to be mediated by M5 receptor-dependent increase in dopamine efflux occurring ∼5 min after stimulation  increased striatal dopamine efflux with a relatively rapid onset (<30 sec) followed by an Mtion see . By comption see . If opiators see . OpiatesOur morphine infusions sites tended to be in the caudal half of the VTA , so it i5 muscarinic receptors in the midbrain. Consistent with previous studies 5 receptors in modulating cholinergic excitation of both nigrostriatal and mesolimbic dopaminergic transmission. Previous studies have shown that the PPT/LDT 5 knockout mice. Taken together, these data suggest an important role for increased cholinergic input to the VTA in mediating dopamine activation induced by morphine and specifically suggest that M5 receptors are a critical target through which opiates in the VTA activate mesencephalic dopaminergic systems.In conclusion, the present data show that prolonged increases in accumbal dopamine efflux induced by intra-VTA morphine, and prolonged increases in striatal dopamine efflux induced by PPT electrical stimulation both critically depend on M5 knockout mice were homozygous mutants for the M5 muscarinic acetylcholine receptor gene. Mice were created using recombinant DNA methods on a mixed 129 SvJ×CD1 background as described by Takeuchi et al. 5 knockout and wildtype mice were age-matched (4–8 months at the time of testing). Eighteen male wildtype and 8 male M5 knockout mice were used in Experiment 1. Seven wildtype and 7 M5 knockout mice were used in Experiment 2. These studies were approved by the University of Toronto Animal Care Committee under protocol # 20007696.MStearate-modified carbon paste electrodes were constructed according to the methods of Blaha and Jung Mice were anesthetized with urethane . Each mouse was mounted in a stereotaxic frame  using rat earbars  and a mouse head-holder . Temperature was maintained at 37±0.5°C with a temperature-regulated heating pad . A single 26 gauge guide cannula  was implanted 1 mm dorsal to the left VTA of each mouse . For VTA infusions a 33 gauge cannula was inserted 1 mm past the tip of the guide cannula, and was connected to a Hamilton microsyringe via Tygon tubing. A single stearate-modified carbon paste recording electrode was implanted into the left nucleus accumbens of each mouse . A combination Ag/AgCl reference and stainless-steel auxiliary electrode was placed into contact with the contralateral parietal cortex.in vivo implantation of all electrodes and cannulae, the working characteristics of the recording electrode were evaluated by recording several voltammetric sweeps in the striatum  using an electrometer . After confirming the presence of a dopamine peak in the voltammogram, repetitive chronoamperometric measurements of oxidation current were made by applying a potential pulse from −0.15 V to +0.25 V to the recording electrode for 1 sec at 30-sec intervals and monitoring the current for the final 50 ms of each 1-sec pulse 5 knockout mice. In an additional 6 wildtype mice, changes in accumbal dopamine efflux in response to 50 ng intra-VTA morphine were measured following VTA pre-treatment with 50 µg scopolamine 10 min prior to morphine. Changes in accumbal dopamine efflux following 50 ng intra-VTA morphine were also measured in an additional 4 wildtype mice following systemic pre-treatment with naltrexone hydrochloride  5 min prior to morphine. Finally, changes in accumbal dopamine oxidation current in response to 0.3 µl VTA saline were measured in 4 wildtype and 4 M5 knockout mice.After 5 knockout mice, or following combinations of intra-VTA morphine and intra-VTA scopolamine or systemic naltrexone in wildtype mice were analyzed using mixed-model ANOVA. Significant interactions were further analyzed using Fisher's LSD test.Pre-stimulation baseline chronoamperometric currents were normalized to zero current values, with induced changes in oxidation current, corresponding to changes in dopamine efflux, presented as absolute changes in current (increases as positive and decreases as negative). Changes in dopamine efflux following intra-VTA morphine in wildtype and MSurgical procedures and chronoamperometric recordings followed those described for Experiment A single concentric, bipolar stimulating electrode  was implanted into the left PPT of each mouse  according to the atlas of Paxinos and Franklin (2004), and a single stearate-modified carbon paste electrode was implanted into the left striatum . A combination Ag/AgCl reference and stainless-steel auxiliary electrode was placed into contact with the contralateral parietal cortex.Following at least 30 min of baseline recordings, a series of cathodal monophasic current pulses  were delivered to the concentric, bipolar stimulating electrode implanted in the PPT via a programmable pulse generator  in combination with a constant current stimulus isolation unit . Each stimulation of the PPT consisted of a 1 sec, 35 Hz train of 400 µA pulses  applied over a 60 sec period . These parameters were designed to mimic spontaneous firing patterns of PPT neurons in awake, naturally aroused animals 5 knockouts) was injected with the non-selective muscarinic receptor antagonist scopolamine hydrobromide . Thirty min later, the PPT was again stimulated and changes in striatal dopamine oxidation currents were monitored for an additional 80 min.Subsequent to monitoring the effects of PPT stimulation on striatal dopamine efflux, a sub-set of mice  and peak times (in min) were compared between wildtype and M5 knockout mice (two-tailed unpaired t-test) and before and after scopolamine in all mice (two-tailed paired t-test). The peak time of the second increase in dopamine efflux, which was strongly attenuated in M5 knockout mice, was not statistically analyzed.Pre-stimulation baseline chronoamperometric currents were normalized to zero current values, with stimulated change in oxidation current, corresponding to changes in dopamine efflux, presented as absolute changes in current (increases as positive and decreases as negative). The maximal and minimal PPT-evoked changes in dopamine efflux associated with the two increases and the decrease, respectively, were obtained for individual wildtype and M5 contribution to PPT-evoked striatal dopamine efflux, the average PPT-evoked change in basal dopamine oxidation current in M5 knockout mice was subtracted from the average change in wildtype mice at each 30-sec time point. The calculated differences were then plotted as a function of time following PPT stimulation.To calculate the overall MOn completion of all experiments mice were killed with a 0.25 ml intracardial injection of urethane. Their brains were removed and put into a 10% formalin solution. All brains were then sectioned on a Vibratome, examined under a light microscope, and compared to atlas sections .On completion Experiment 2, a PPT lesion was made by passing direct current (100 µA for 5 sec) through the concentric bipolar stimulating electrode. For these brains 0.1% potassium ferricyanide was included in the formalin solution. The iron deposited at the stimulating electrode tip by the electrolytic lesion reacted with the potassium ferricyanide to form a Prussian blue spot, clearly marking the PPT stimulation site.Morphine sulfate pentahydrate, scopolamine hydrobromide, naltrexone hydrochloride, and urethane were purchased from Sigma Aldrich  and dissolved in sterile 0.9% saline. Drugs were infused into the VTA or systemically injected as their salt weight. Intra-VTA infusions were made at a volume of 0.3 µl, and systemic naltrexone and scopolamine injections at a volume of 10 ml/kg.


Vortex is a topological defect with a quantized winding number of the phase in superfluids and superconductors. Here, we investigate the crystallized  and amorphous vortices in rotating atomic-molecular Bose-Einstein condensates (BECs) by using the damped projected Gross-Pitaevskii equation. The amorphous vortices are the result of the considerable deviation induced by the interaction of atomic-molecular vortices. By changing the atom-molecule interaction from attractive to repulsive, the configuration of vortices can change from an overlapped atomic-molecular vortices to carbon-dioxide-type ones, then to atomic vortices with interstitial molecular vortices, and finally into independent separated ones. The Raman detuning can tune the ratio of the atomic vortex to the molecular vortex. We provide a phase diagram of vortices in rotating atomic-molecular BECs as a function of Raman detuning and the strength of atom-molecule interaction. For example, this system is very suitable for observing the quantized vortex2et al. have explored the structural phase transition of atomic-molecular vortex lattices by increasing the rotating frequency. They observed the Archimedean lattice of vortex with the repulsive atom-molecule interaction. In fact, atom-molecule interaction can be either attractive or repulsive with large amplitude by using the Feshbach resonance2025293031323637The molecular BEC can be created by the magnetoassociation (Feshbach resonance) of cold atoms to molecules27In this report, we study the crystallized and amorphous vortices in rotating atomic-molecular BECs181920212223242526272829303132j denotes the macroscopic wave function of atomic condensate and molecular condensate respectively, the coupling constants are, aM (mM) is the mass of atom (molecule), ω is the trapped frequency, Ω is the rotation frequency, z component of the orbital angular momentum. The parameter χ describes the conversions of atoms into molecules due to stimulated Raman transitions. ε is a parameter to characterize Raman detuning for a two photon resonance27283132We ignore the molecular spontaneous emission and the light shift effect283132173387Rb to generate a molecular BEC of 87Rb87Rb system with mM = 2aM = 2m (m = 144.42 × 10−27 Kg), mg = 2ag , χ = 2 × 10−3, and the trapped frequency ω = 100 × 2π. Note that if the change in energy in converting two atoms into one molecule (ΔU = 2TaU − TmU)ag = mg. The unit of length, time, and energy correspond to ω−1 (≈1.6 × 10−3 s), and In real experiment, it is observed that the coherent free-bound stimulated Raman transition can cause atomic BEC of ω, we study the influence of atom-molecule interaction on the formation of vortices. With rotation frequency Ω = 0.8amg = −0.87ag), vortices form a square lattice . Atomic vortices with interstitial molecular vortices emerge in region (3). In the AMBEC(II) region, atomic vortices and molecular vortices are separated. Certainly, in the region of atomic BEC (molecular BEC), atomic vortices (molecular vortices) favor to form the triangular lattice. The green region indicates that the created atomic and molecular vortices fully match with each other by roughly the ratio 1:2. Above the green region, more atomic vortices occur. Below the green region, more molecular vortices appear. According to above analysis about atomic-molecular vortices and the corresponding atomic-molecular BECs, we calculate lots of other results and finally give a vortex phase diagram to summarize the vortex structures in amg < 0.5ag  Imbalance transition: The increase of Raman detuning causes more atomic BEC. Thus, the pure molecular vortices change into carbon-dioxide-type atomic-molecular vortices , and finally into single atomic vortices in region of 0 < 87Rb in a spherically symmetric trap coupled by stimulated Raman photoassociation process. In fact, the light shift effect almost has the same function as the Raman detuning term. Thus, it can be contributed to the Raman detuning term. This is the reason why we do not consider the light shift term in Hamiltonian like that in Ref. In this report, we focus on the strength of atom-molecule interaction and the Raman detuning term. The form of Hamiltonian in this paper is like that in the Ref. 283132In real experiment, it is believed that the single molecular BEC would occur when the Raman detuning goes to zero28aV(r) and molecular BEC experiences the trap potential mV(r) + ε. Here, our method initially derives from the finite-temperature consideration: the system is divided into the coherent region with the energies of the state below RE and the noncoherent region with the energies of the state above RE42mμ − ε. The molecular BEC will be converted by atoms until the system reaches the equilibrium state. Therefore, a maximum of creating molecular BEC does not occur at the equilibrium state when Raman detuning varies. Instead, molecule number decreases monotonously when Raman detuning increases.It is obvious that the Raman detuning term in the Hamiltonian behaves just like the chemical potential to control the system's energy. The external potential for atomic BEC is fixed to be CO2-type structures do not follow the fixed direction, i.e., long-range order vanishes but there is still short-range order. Thus, we have to view molecular vortices as the amorphous state. In region (3), atomic vortices and molecular vortices can not form the carbon dioxide structure. Because the size of molecular vortices is smaller than that of atomic vortices, it tends to locate at the interval of the lattice of atomic vortices. When the number of one component is much more than that of the other, the vortices of this component dominate over the vortices of the other component. The former is easy to form the regular vortex lattice. The latter has to follow the interaction of the former and forms the vortex lattice. The amorphous state originates from the competition between atomic vortices and molecular vortices, especially when the number of atom and molecule has the considerable proportion [see Why do atomic-molecular vortices display so rich lattice configurations? In fact, atomic vortices and molecular vortices tend to be attractive in region (1) and (2). Otherwise, the overlapped atomic-molecular vortices and the carbon-dioxide-type ones can not occur. The attractive force makes atomic vortices and molecular vortices behave similarly. Thus, both atomic and molecular vortex lattices in region (1) are square. In region (2), atomic vortices display the triangular lattice. Molecular vortices seem to follow the triangular lattice but the interaction among vortices causes the considerable deviation. Obviously, the tion see . In thatχ, the CO2-type structure of vortices are easy to be created. Even the interstitial-composite structure we now obtain in CO2-type structure if χ is big enough. We have also considered the effect of rotation frequency. With the attractive interaction of atom-molecule (amg = −0.87ag), ω, only one molecular vortex is induced. In atomic BEC, the phase indicates no vortex is created although there is a local minimum of density near the center. For Ω = 0.4ω, the phase indicates that there is an atomic vortex. In fact, we find the atomic vortex is overlapped with a molecular vortex. Undoubtedly, more and more vortices emerge when rotation frequency increases. When the rotation frequency is up to Ω = 0.8ω, we can obtain a regular square vortex lattice. Meanwhile, each atomic vortex is overlapped with a corresponding molecular vortex. Obviously, vortices and vortex lattice may not be induced with a slow rotation. This is the reason why we favor to investigate the vortices with a fast rotation in The structural phase transitions of vortex lattices are explored through tuning the atom-molecule coupling coefficient and the rotational frequency of the system87Rb atoms are a candidate for observing the predicted atomic-molecular vortices. By initially using a large atomic BEC of 87Rb  vortices. This study shows rich vortex states and exotic transitions in rotating atomic-molecular BECs.T is the final temperature, Bk is the Boltzmann constant, jμ is the chemical potential, and jγ is the growth rate for the jth component. The projection operator j is generated by sampling the grand canonical ensemble for a free ideal Bose gas with the chemical potential We use the damped projected Gross-Pitaevskii equation (PGPE)W.M.L. conceived the idea and supervised the overall research. C.F.L. and. S.C.G. designed and performed the numerical experiments. C.F.L. and H.F. wrote the paper with helps from all other co-authors.


In vivo molecular imaging has a great potential to impact medicine by detecting diseases or screening disease in early stages, identifying extent of disease, selecting disease and patient-specific therapeutic treatment, applying a directed or targeted therapy, and measuring molecular-specific effects of treatment. Current preclinical findings and advances in instrumentation such as endoscopes and microcatheters suggest that these molecular imaging modalities have numerous clinical applications and will be translated into clinical use in the near future.There is an urgent need for early diagnosis in medicine, whereupon effective treatments could prevent irreversible tissue damage. The special structure of the eye provides a unique opportunity for noninvasive light-based imaging of ocular fundus vasculature. To detect endothelial injury at the early and reversible stage of adhesion molecule upregulation, some novel imaging agents that target retinal endothelial molecules were generated.  Molecular imaging permits noninvasive visualization and measurement of molecular and cell biology in living subjects, thereby complementing conventional anatomical imaging. Optical molecular imaging technologies use light emitted through fluorescence or bioluminescence. Molecular imaging is defined as the ability to visualize and quantitatively measure the function of biological and cellular processes in vivo , 2, whilIn vivo visualization techniques of the retinal microcirculation, including conventional fundus fluorescein angiography (FFA) and indocyanine green angiography (ICGA) or the experimental laser-targeted angiography [in vitro techniques, such as immunohistochemistry or PCR [in vivo detection and measurement, these novel tools provide high specificity for their target.iography , 4, are iography . Howeveriography . Using aiography  in live y or PCR . In fluoThis paper briefly describes different molecular imaging techniques and devices used in retinal imaging, as well as potential imaging tools and targets that may be translated into clinical applications in the near future.in vivo imaging of reporter gene expression [in vivo detection of the FLuc reporter gene was demonstrated [The development of molecular imaging is rooted in radiology and nuclear medicine as well as in molecular biology. Since the 1950s, nuclear imaging of radioactive isotope-labeled biomacromolecule has been an integral part of drug development and diagnostic imaging. The broad clinical significance of such approaches remained restricted until positron emission tomography was introduced in 1979 and became an important tool for the detection of metabolic activities in tissues such as the brain and heart, as well as in cancer. It facilitated a biological imaging readout, albeit with limited specificity. Around the same time, magnetic resonance spectroscopy promoted the evolution of molecular imaging. The ability to collect information about specific endogenous molecules by taking advantage of their intrinsic nuclear spin property represented an early example of molecular imaging. These advances paved the way for pioneering molecular imaging studies by demonstrating pression . Concurrnstrated  and the nstrated . Recent nstrated  and cardnstrated . Moleculnstrated  and micrnstrated .in vivo.The use of visible light to examine intraocular processes can be considered the oldest form of imaging in ophthalmology. The unique optical properties of the eye allow direct microscopic observation of the retina. Optical molecular imaging technologies use light emitted through fluorescence or bioluminescence. In fluorescence imaging, light of the excitation wavelengths must penetrate tissues to reach a targeted reporter molecule carrying a fluorochrome, resulting in the emission of light of usually lower wavelength that can be registered by a charge-coupled device (CCD) camera. Fluorescent proteins, such as cyan, green, or yellow fluorescent protein can be introduced into cells of choice by transgenic technology. This technology has greatly facilitated studies on GFP-positive animals. These animals render all cells expressing the fractalkine receptor, such as microglia cells, dendritic cells, and macrophages, intrinsically fluorescent . Certainin vivo. The significantly higher rolling interaction of the double-conjugated MSs compared to the rPSGL-1-conjugated MSs indicates that ICAM-1 may contribute to the rolling of the MSs, once the interaction with the endothelium is initiated. Surprisingly, the rolling velocity of the double-conjugated MSs is significantly higher in the choroidal vessels than in the retina. The absolute number of MS interactions in the choriocapillaris is higher than in the retina. This difference might be explained by the higher vascular density in the choriocapillaris compared to the retina. Also, the inflammatory response in the choroid may differ from retina. Another striking qualitative difference between the two vascular beds is that, in the retina, most rolling initiates from the periphery and continues toward the optic nerve head, suggesting that the rolling interaction mainly occurs in the retinal veins.Our imaging approach is founded on certain aspects of leukocyte-endothelial interaction, a common component in the pathogenesis of various ocular diseases. Leukocytes normally do not interact with the endothelium of blood vessels, save for occasional tethering. However, at sites of inflammation, endothelial cells express adhesion molecules, such as P-selectin and intercellular adhesion molecule-1 (ICAM-1) which facilitate the multistep leukocyte recruitment cascade . The steAcridine orange digital fluorography revealed leucocyte rolling in the retina of animals with experimental autoimmune uveoretinitis. Acridine orange solution was injected continuously through a tail vein at a proper velocity. Retinal images were generated by an SLO connected to a computer-assisted image analysis system. Acridine orange binds to DNA and RNA, and the spectral properties of acridine orange DNA complexes are very similar to those of sodium fluorescein, with a 502 nm excitation maximum and an emission maximum of 522 nm. Results reveal that leukocyte endothelium interaction and extravascular infiltration in the retinal venous vasculature may play significant roles in the early stages of posterior segment inflammation.in vivo expression assays that allow detection of activated T cells in the iris at multiple time points within the same animal with experimental uveitis. Transgenic reporter mice for numerous other cytokines exist. Intravital microscopy has also been used for imaging dendritic cells in the cornea using transgenic mice that express YFP under control of the CD11c promoter (CD11c-YFP) [in vivo, in order to perform longitudinal studies of disease processes such as glaucoma [in vivo retinal images at a cellular level. Adaptive optics SLO was used to image the retinal pigment epithelial (RPE) cells in patients with rod-cone dystrophy and bilateral progressive maculopathy [Xu and colleagues reported another method of investigating leucocytes in the retinal vasculature by SLO. They tried to inject calcein-acetoxymethyl ester- (AM-) labeled T cells into the tail vein of rodents . Leucocy11c-YFP) . cSLO haglaucoma . This teulopathy . “Adaptiulopathy , and theulopathy . Choi anulopathy . Preclinical molecular imaging in small animals is an invaluable part of new molecular targets and contrast agents, as well as developing drugs prior to clinical translation . ResearcTo identify a molecular target beginning with understanding and characterizing the biology, the first step is to find the differences between a healthy and diseased state. For instance, since there is an intricate relationship between cancer and inflammation , the differences between inflammation and cancer states must be characterized. In general, much focus is directed to cancer imaging including retinal and choroidal tumor, and several preclinical studies have identified new molecular targets for imaging cancer. In addition to imaging the cancer phenotype such as increases in metabolism, angiogenesis, proliferation, hypoxia, and apoptosis, agents have been developed to target specific protein markers expressed on cancer cells. Many chemotherapeutic drugs also target these markers; they have been radiolabelled for assessment of biodistribution and pharmacokinetics using noninvasive molecular imaging . ContinuMolecular imaging can be applied to all parts of medical imaging: early detection, screening, diagnosis, therapy delivery, monitoring, and treatment followup. The current status of clinical molecular imaging is limited, with most current applications using visual able imaging and a small number of highly specific applications for MRI and ultrasound. Current demands and trends are calling for new strategies to focus on early disease detection through improved imaging and screening protocols in retina, as well as patient-specific treatment selection delivery and therapy-specific monitoring. It is hoped that these new strategies of early diagnosis and immediate treatment monitoring will improve success rates for curing diseases with high mortality rates such as retinal disease and some types of cancer, as well as providing more specific treatment for other diseases. Preclinical research has resulted in the identification of a large number of molecular targets and the development of novel molecular imaging contrast agents as well as device, hardware, and software technologies. It is expected that molecular imaging in retina with imaging modalities other than our developed MSs, PET, MRI, molecular ultrasound, and photoacoustic tomography will be integrated into more frequent clinical application in the near future.