Warmer climates are strongly correlated with lower diatom diversity . Diatoms were 20% less diverse in the early late Miocene, when temperatures and pCO2 were only moderately higher than today. Diversity is strongly correlated to both ∂13C and pCO2 over the last 15 my , but only weakly over the earlier Cenozoic, suggesting increasingly strong linkage of diatom and climate evolution in the Neogene. Our results suggest that many living marine planktonic diatom species may be at risk of extinction in future warm oceans, with an unknown but potentially substantial negative impact on the ocean biologic pump and oceanic carbon sequestration. We cannot however extrapolate our my-scale correlations with generic climate proxies to anthropogenic time-scales of warming without additional species-specific information on proximate ecologic controls.Marine planktonic diatoms export carbon to the deep ocean, playing a key role in the global carbon cycle. Although commonly thought to have diversified over the Cenozoic as global oceans cooled, only two conflicting quantitative reconstructions exist, both from the Neptune deep-sea microfossil occurrences database. Total diversity shows Cenozoic increase but is sample size biased; conventional subsampling shows little net change. We calculate diversity from a separately compiled new diatom species range catalog, and recalculate Neptune subsampled-in-bin diversity using new methods to correct for increasing Cenozoic geographic endemism and decreasing Cenozoic evenness. We find coherent, substantial Cenozoic diversification in both datasets. Many living cold water species, including species important for export productivity, originate only in the latest Miocene or younger. We make a first quantitative comparison of diatom diversity to the global Cenozoic benthic ∂ Marine planktonic diatoms (hereafter ‘diatoms’) are major components of the phytoplankton and are most common in regions of high productivity (upwelling zones) and in high latitudes Diatom fossils in Cenozoic pelagic sediments While diversity estimates compiled from raw data can have major data size biases, subsampling can also easily produce misleading results when underlying assumptions are violated , producing in such cases artificially ‘flattened’ diversity vs time curves. The Rabosky and Sorhannus results have already been questioned for this reason Our analytic strategy to estimate diatom diversity history is two-fold. First, we re-analyze the Neptune data. In contrast to prior studies, we explicitly test for sample size bias, and correct subsampled diversity estimates for both changing geographic endemism and changing mean evenness. Second, we use the complementary nature of catalog-derived diversity estimates 18O and ∂13C of Zachos et al. 18O is an often used proxy for Cenozoic climate in studies of climate and evolution, e.g. 13C reflects many different factors, but Cenozoic changes are usually interpreted as reflecting change in either the fraction of carbon sequestered in organic form into sediments, or changes in global organic isotope fractionation ratios  To test the sensitivity of diatom diversity to climate state, the resultant diversity estimates are compared to the global compilations of Cenozoic marine benthic foraminiferal isotope data for ∂The Neptune database was originally developed in the early 1990s The species range catalog  of the taxa in the BDC have ranges determined in part from the Chronos Neptune database. The two data sources are thus not 100% independent of each other. However virtually all taxa present in the BDC are present as well in the primary literature used in the compilation, so use of NSB at most provides a minor adjustment to the range of a small number of species rather than significantly affecting diversity itself.All marine microfossil diversity data has potential sources of error The large majority of the data in our study, including the NSB data and most published literature data was originally calibrated to the Berggren et al. 1995 timescale As biotas usually consist mostly of rare species, a sample's diversity is usually much less than the total diversity of the sampled biota; thus sampled diversity increases with sample size. If sample size varies in a data series, sampled diversities, and also range-through diversity from catalogs compiled from such samples, can be biased The SQS algorithm was specifically developed to compensate for the inability of classical subsampling methods to adequately capture change in diversity associated with the development of geographic differences in distributions (e.g. endemism) To correct for the effect of changing evenness (degree of similarity in relative abundances of different species) over time, a simple correction factor was computed based on 1) a simple ranked abundance data shape metric ‘D(80)’  that quaThe pooled data for species ranked occurrences used were from two major time intervals . Each data set was scaled to the same total area  for the ranked abundance curves, interpolated down to the same number (100) of virtual species, and these two virtual data sets - having the same diversity and total sample size, differing only in the relative frequencies of taxa - were subsampled using simple rarefaction at a range of sample sizes. Each sample size was repeated 50 times and averaged. The mean diversity found by subsampling for a given subsample size in each of the two virtual populations was used to calculate the degree to which the diversity of the low evenness Neogene population was being underestimated compared to the Paleogene population. The results for a wide range of sample sizes are shown in Outliers can have major effects on range-through calculations of diversity In comparing time-series data, one problem occurs when both variables have a trend, resulting in a correlation being seen even if there is no causal relationship between them. A second problem arises when one variable cannot respond fully to changes in the forcing variable due to internal limits (e.g. stochastic effects), which will affect the observed correlation between variables. There is considerable a-priori evidence from diatom biology and biogeography to expect diatom diversity to respond to climate, and thus to expect the long-term as well as short term correlations to be at least in part causal, not chance. There is also no evidence in our diversity-climate comparisons for significant mis-correlation due to limitations in the freedom of response in the diatom diversity data: even short-term changes in climate state are partially mirrored by diatom diversity change, and maximum bin-to-bin step size changes in diatom diversity, both increasing and decreasing, match in amplitude those of the presumed climate forcing function. Such observations however cannot fully exclude the existence of correlation artifacts. One established method of testing for the robustness of a correlation is to use linear regression to detrend, or to first-difference the time series and compare only residuals. This has the major disadvantages of potentially removing one of the main (long-term) components of the signal, and by using only residuals, unless errors in individual values (noise) are low, a significant loss of ability to detect correlations, and thus correctly evaluate a hypothesis.Given the existence of a variety of errors limiting individual bin estimates for diversity (described above under Data Sources) we do not feel that first-differencing the data would yield a time-series with a usable signal. Hannisdal Although the primary justification given by Rabosky and Sorhannus By contrast, similar analysis shows that there is only a weak overall correlation between the BDC diversity and the number of papers used in its compilation . Most imIn both data sources it is also clear that the quantity of data declines with time, and in particular, very little data is available to estimate diatom diversity below ca. 40 Ma (late middle Eocene), and particularly between ca 42–55 Ma.Our analyses yielded three different estimates of Cenozoic diatom diversity, using different methods and different data sources: the SQS and PTR estimates from the NSB database, and the BDC estimate from the range catalog . For comThe first occurrences of all living species with origins in the Neogene from the BDC are shown in a range plot in The diversity curve is strongly visually correlated to climate , with bo13C  however is strongly correlated to the ∂13C record . Diatom diversity is also strongly correlated to the pCO2 record over the last 15 my  results are summarized along with undetrended analyses in We argue that we have been able to extract a coherent diversity signal from incomplete, biased occurrence data, as demonstrated by the similarity between diversity estimates made using different methods, and using very different types of source data . An essential component of our reconstruction method from occurrence data is the use not only of SQS subsampling to address changing geographic structure in the occurrence data, but also the application of an evenness correction factor based on modeling. This latter correction is not normally used in paleodiversity studies, but in our data is shown to be very significant and necessary in order to obtain a coherent diversity signal. In a recent study of similar deep-sea microfossil data, Renaudie and Lazarus Despite obtaining a coherent diversity signal, our results may still be inaccurate if there are systematic biases in the data which would produce coherent biases in our diversity estimates. We specifically consider changes in preservation over time which might bias our results. The height of the late Eocene diversity peak may in part be a preservation artifact: older Paleogene diatoms are often diagenetically altered Our results have implications for several fields. Many of these implications depend not on diatom diversity itself but diatom export productivity. We distinguish two distinct types of relationship between diversity and export productivity: naturally occurring behavior that has developed over evolutionary time; and perturbed relationships due to rapid extirpation or extinction, in which evolution has not had time to operate.The relationship between diversity and productivity in living natural systems is controversial, and scale dependent. However there are several reasons to believe, on the global scale and over longer (but not geologic) time-scales, that biotic diversity and productivity are in general, positively related, even for groups where local diversity-productivity relationships show low diversity with very high productivity Perturbed systems - To our knowledge, no experimental studies of short-term  pelagic ecosystem responses to species loss exist. However, hundreds of experimental studies with a variety of other systems, including not only terrestrial but also aquatic environments, have shown that ecosystem services and diversity are significantly correlated, with diversity loss reducing productivity and nutrient cycling Having an independently derived estimate (LBR) for Cenozoic diatom diversity history, which we argue is reliable, we can compare this to prior estimates of diversity: the CSC and RS curves.The RS diversity estimate is based, as the LBR estimate, on subsampling of the same occurrence database. Between ca 40 and 20 Ma the two estimates are quite similar, with an initial rapid rise from 40 Ma to a peak at the Eocene-Oligocene boundary, a decline throughout most of the Oligocene, and a more modest increase in the early Miocene. The curves are very different over remainder of the Neogene. The LBR curve shows a dramatic, rapid rise beginning at the base of the mid Miocene, reaching diversity levels, at their peak in the early Pliocene, ca 50% higher than the E/O peak, while the RS curve shows only a very gradual increase in diversity throughout the mid Miocene-Recent, reaching values only ca 75% of the E/O peak diversity in the Recent. As we have shown, the differences are due to the failure of the assumptions underlying the RS analysis in the mid-Miocene to Recent, e.g. no major changes in biogeographic endemism, or changes in relative abundance structure in the sampled populations.The CSC estimate is a range through estimate of raw NSB data. It is similar in overall shape to the LBR curve, though this varies with time interval. The Eocene and older parts of the curves differ substantially: compared to the LBR curve the CSC curve shows no major peak in the late Eocene vs diversity fall in the late Oligocene, and a much steeper rise in the mid Eocene from low diversity values of the Paleocene-early Eocene. However over the last ca 30 Ma both curves show a plateau in diversity between ca 30 and 20 Ma, a rapid rise beginning ca 18-15 Ma reaching a brief peak in diversity in the early Pliocene slightly more than twice the prior plateau values, and a slight decline into the later Pliocene-Recent. The two curves differ substantially however in estimates of absolute diversity. For much of the Cenozoic, and in particular the last 30 Ma, the CSC diversity values are as much as 50% greater than those in the LBR curve.Higher absolute diversity values in the CSC vs the LBR curves are difficult to explain as a simple calculation artifact: the LBR curve derives its absolute values via calibration to the BDR catalog curve, which is in principle also a range through estimate of diversity, derived even in part from the same deep-sea occurrence data, even if using different literature sources and additional onshore section data. The absolute values for the LBR curve should be thus similar, or, given the use of additional sources, somewhat higher than the CSC curve; the results however are the opposite. We speculate that the CSC curve contains substantial amounts of erroneous diversity due to data outliers and incomplete identification of synonyms in the taxonomy. The diatom taxonomy used in the Neptune database was only provisional when first compiled, and recent revision  and CSC curves over the last 30 Ma can only be due to a strong correlation between LBR diversity and sample availability over the last 30 Ma. This is shown in We group our discussion of implications into those related to the diversity history and its correlation to the evolution of other groups of organisms; and those related to the correlation between diversity and climate. In the first category we cite three examples.3 year) differences in net rates of silicate weathering, yet grasslands may not actually increase long-term weathering rates vs. other vegetation types Rabosky and Sorhannus Our results weaken support for the hypothesis that Cenozoic cooling and the consequent concentration of high export productivity in upwelling regions, dominated by efficient, short food-chain diatoms, provided high levels of large zooplankton/nekton food supply that supported the radiation of cetaceans Lazarus et al. 2 and rates of weathering (which provide nutrients for new diatom productivity) were changing. Although still very poorly constrained, with different proxies often yielding very different estimates, Cenozoic pCO2 showed a largely parallel trend, from low initial values in the Paleocene-early Eocene, maximum values of ca 1000 ppm in the mid-late Eocene, and declining values in the Oligocene-Recent  2, are argued, based on various geochemical proxies, to either have increased substantially in the Oligocene-Recent As we wish to consider how the diversity history of diatoms is correlated to changing environments it is useful to review the most relevant aspects of Cenozoic ocean change. The main features are well known 2 have been implicated as the primary driver of Cenozoic climate change Cenozoic climatic-ocean history was long believed to have been primarily driven by the tectonic opening of gateways that altered ocean circulation and polar heat transport, such as the development of a circumpolar Antarctic current near the Eocene-Oligocene boundary 2 concentrations of the early Pliocene may not be representative of more extensive future warming, particularly beyond 2100 2 (via atmospheric temperature and wind fields) and unique, non-recurring past events such as the tectonically driven partial or complete closure of several oceanic gateways. The Panama gateway in particular closed gradually in the late Miocene-early Pliocene, which redirected tropical Atlantic waters northwards, warming the North Atlantic, and increasing pole-to-pole (Norwegian-Greenland Sea to Southern Ocean) surface-to-deep circulation and Southern Ocean diatom productivity 2 estimates from epsilon-p plankton alkenones  2 were primarily responsible for late Miocene ocean conditions.Late Miocene climate was probably only moderately warmer than the Present  only a secondary impact on global climate 22 or due to changes in gateways or other boundary conditions More recent work however calls this tectonic ‘gateway’ model of late Miocene oceanography into question. Several ocean-atmosphere models have found Cenozoic gateways to have had  of other microfossil records with complementary ecologies and shell mineralogy  might provide more insight. Currently, the best available estimate for Cenozoic calcareous phytoplankton diversity 2. Until quite recently Much of the earth-sciences interest in global Cenozoic diatom diversity lies in the presumed correlation of increasing Cenozoic diatom diversity to increasing Cenozoic export productivity by diatoms in the oceans, which in turn may have substantially affected the Cenozoic evolution of the global carbon cycle, including atmospheric pCO2 and climate. As noted earlier, ∂13C is often used as a proxy for global carbon cycle behavior. Late Neogene ∂13C shows a substantial shift (ca 1 per mil), which has been interpreted as reflecting an increase in the fraction of organic carbon sequestered by less 13C depleted groups of organisms, specifically C4 terrestrial plants and marine diatoms 13C since the early Oligocene. These comparisons thus provide qualitative support for the idea that increasing Cenozoic diatom diversity is correlated to increasing fractions of diatom export productivity, and increasing relative abundance of diatom opal in sediment, since the mid Miocene. Together with evidence from living ecosystems and the limited independent evidence for Cenozoic opal productivity (reviewed above) our results suggest that diatom diversity can be used to some degree as a proxy for the influence of diatoms on marine export productivity and the carbon cycle. Diatom diversity is further strongly correlated to pCO2 over the last 15 my. Since the pCO2 record is derived from the ∂18O record 2 is stronger than between diversity and ∂18O, implying that the observed correlation to pCO2 is not simply one inherited from the observed correlation of diversity and ∂18O. Our analysis thus also suggests that increasing diversity and abundance of diatoms are closely linked to late Neogene changes in the global carbon cycle. Whether this latter link is due to both diatom diversity and pCO2 being driven by a third factor, such as increasing late Neogene silicate weathering, is however beyond the scope of our current study .While export productivity is important to understanding many aspects of the marine ecosystem, of particular interest is the effect that marine export may have had on the global carbon cycle, and thus on atmospheric pCO2->global climate->marine ocean conditions->diatom diversity->the carbon pump.Our analysis shows that warmer oceans are associated with lower diatom diversity, suggesting the possibility that future warmer oceans due to anthropogenic warming may result in lower diatom diversity, i.e. extinctions, with possibly substantial consequences for the functioning of the ocean carbon pump. This is suggested both by the correlation of increasing diversity with increasingly cold climate in the Neogene, and the reduction of diversity that occurred with warming ocean conditions in the late Oligocene. The broad uniformity of response over such a long range of time, and under such a wide variety of ocean conditions (as reviewed above) suggest that response will be similar also in future climate change. There are however significant problems in converting these general correlations into concrete predictions of diatom response to future warming. Oligocene floras were, as we document, almost entirely composed of species not present today, and even many genera were different. It is thus difficult to be sure that the response behavior of the modern, taxonomically different flora would be the same. The diatom floras of the Late Miocene ocean were much more similar taxonomically to those of the present, and the diatom diversity-climate relationship is particularly sensitive in this time interval. Diatom diversity in the late Miocene was, in our estimate, up to ca. 20% lower than modern values , and important cold-water polar species, such as F. kerguelensis and Neodenticula seminae, which play a major role in export productivity in the modern ocean Although still uncertain, current paleoceanographic studies suggest that late Miocene ocean conditions might be a relevant analog for future marine plankton diatoms due to global climate warming, particularly on longer (post 2100) time scales. Reconstructions indicate higher temperatures and reduced sea-ice extent in polar regions, factors that are known to play a major role in the distribution of living diatom species and other phytoplankton, and diatom export productivity 2 climate change scenario, although only a moderate impact on the ocean biologic pump. In Bopp et al. 2.The effect of loss of diatom diversity on future ocean productivity is unknown but potentially significant. Bopp et al. 18O to identify truly analogous conditions in the past that can be used to predict future diatom behavior in a warmer world. In order to do this, we need much more information on the proximate controls on individual species distributions, both for now extinct species, and for modern species, whose ecology also is often only poorly known.While the above presents arguments why the correlation of diatom diversity to climate state should be considered in thinking about future climate change, there are limits as well. We argue that while our results provide a useful perspective on eventual system response, a variety of issues, including temporal scaling and other potential casual factors, limit our ability to use our results to make any direct estimate of extinction risk due to anthropogenic change. As reviewed above, Late Miocene oceans, although largely very similar to the modern ocean, were still dissimilar in details of circulation and climatic context. The proximate conditions that affect diatom distributions - nutrient concentrations, water column stability, frontal structures; and the variability of these on seasonal to multi-decadal scales, could have been significantly different: not only from those of the modern ocean, but also to the oceans that anthropogenic global warming will produce. Our understanding of these proximate controls in Late Miocene oceans is very sketchy, particularly in high latitude regions which are most important to understanding diversity and productivity response. Also important is the temporal scaling between our data and future global change in how global nutrient are input to the system, which at the time scales of our study may exert a rate limiting effect on climate change, since the residence times of key elements in the oceans are much less than the scale of our analyses  and the complexity of cause-effect relationships however mean that we cannot evaluate from our data alone the likelihood of future extinction over the next decades or centuries as a possible consequence of anthropogenic global warming.Cenozoic diatom diversity and its correlation to climate change is important to understanding how evolution in pelagic systems functions, and how biotic diversity may regulate climate change itself over various time scales - from millions of years, to those expected for future anthropogenic global warming. Existing estimates of diatom diversification have been contradictory and have methodologic problems. We derive a new, internally coherent estimate using multiple methodologies and datasets, using a newly created, first-ever comprehensive catalog of Cenozoic marine diatom species ranges as well as a new version of the Neptune marine microfossil database, while explicitly controlling for prior problems in diversity reconstruction such as changing sample sizes, evenness of population frequencies - by use of a novel modeling-based correction function, and other factors. We show that diatoms diversified strongly over the Cenozoic, with major increases near the Eocene/Oligocene boundary, and in the mid Miocene. Diversification occurs primarily by increasing diversity of endemic high latitude floras, and the disjunct development of high productivity regions in Cenozoic oceans is suggested to be a primary reason for diversification of the group. Diatom diversity is strongly correlated to evolutionary characteristics of other groups of organisms such as radiolaria; and to the oxygen isotope proxy record of global climate change: diversity not only increases with intervals of cooling, but also significantly decreases during intervals of past warming such as the late Oligocene-early Miocene. Over the last 15 my diatom diversity is strongly correlated to both the global carbon isotope record and to estimated past atmospheric pCOFigure S1Number of diatom species vs biomass in water column samples from Atlantic ocean transect, from data presented in .(TIFF)Click here for additional data file.Figure S2Detrended numbers of occurrences in time intervals in the NSB database vs detrended LBR diatom diversity estimate. Data point symbols age coded according to main paper (TIFF)Click here for additional data file.Figure S3Comparison of species diversity in Neogene cetaceans (red: sampled in bin and range-through) to diatom diversity of this study . Cetacean data from (TIFF)Click here for additional data file.Figure S4Diatom diversity  vs radiolarian shell silicification, from . Silicification data binned to 1 my intervals, intervals with no silicification data excluded. Age scale: (TIFF)Click here for additional data file.Table S1The BDC . a) Data. b) List of publications/sources used.(TXT)Click here for additional data file.Table S2Pearson correlation coefficient r and color coded p value intervals for diatom diversity, Neoceti diversity (from ) and geologic age.(TIFF)Click here for additional data file.Table S3Main data and results of this study in numeric form.(TXT)Click here for additional data file.


Hence, any improvement in performance is only a consequence of topological changes. Two local selection methods presenting opposite selection pressures are used in order to evaluate the influence of the proposed techniques. Problems ranging from continuous to real world and combinatorial are tackled. Empirical results are supported statistically in terms of efficiency and efficacy.Cellular Genetic Algorithms (cGAs) have attracted the attention of researchers due to their high performance, ease of implementation and massive parallelism. Maintaining an adequate balance between exploitative and explorative search is essential when studying evolutionary optimization techniques. In this respect, cGAs inherently possess a number of structural configuration parameters that are able to sustain diversity during evolution. In this study, the internal reconfiguration of the lattice is proposed to constantly or adaptively control the exploration-exploitation trade-off. Genetic operators are characterized in their simplest form since algorithmic performance is assessed on implemented reconfiguration mechanisms. Moreover,  Evolutionary Algorithms (EAs) are stochastic search techniques based on the principles of evolution. Amongst EAs, Genetic Algorithms (GAs) provide a competitive approach that has been successfully applied to a wide variety of difficult optimization problems From the theoretical to the real world and particularly in the hardware implementation arena, GAs feasibility has been investigated. From the Very Large Scale Integration (VLSI) point of view, GAs possess important characteristics that makes them suitable for hardware implementation targeting real time applications. Parallelized versions of GAs have been used as optimizers and adapted for hardware implementation targeting real-time performance. Among problems solved by parallel GAs are: the image registration problem In An image processing architecture was developed to tackle the image registration problem that presents real-time constraints In A cellular GA architecture tackling the GPS attitude determination problem was developed by Xu et al. In Stefatos et al. extended the GPS attitude architecture presenting a novel approach from a fault tolerance perspective to deal with Single Hard Errors (SHEs) and implicitly with Single Event Upsets (SEUs), both are Single Event Effects (SEEs) subclasses The fault tolerant architecture is a double layer approach, each layer implements a cellular GA. A computational layer to determine the GPS attitude parameters and a control layer to determine the best configuration of individuals or Processor Elements (PEs) to overcome a faulty scenario. Several experiments were carried out, first only the computational layer was tested in order to observe how the cGA deals with the faults. Then the control layer was executed and a performance comparison is carried out. Results showed a significant improvement in the system’s performance when the control layer was active, in the worst case scenarios with 30% and 40% faulty PEs. Although, using the control layer allows an improvement in the system performance, there is a significant increment in the use of hardware resources. Another disadvantage is that the control layer was considered free of faults.In internally reconfigures a traditional square topology into smaller sub-cellular structures is investigated.This article aims to provide a deeper insight into the great flexibility which cellular GAs inherently possess. When appropriately exploited, cGAs’ unique properties as regards the interaction of individuals enhance their searching abilities. In EAs, the main interest is to preserve population diversity through satisfactorily balancing the exploration and exploitation of solutions throughout the search space; in order to successfully converge to the global optimum in the minimum number of generations Initial studies on Genetic Algorithms were mainly based on panmictic populations In order to validate the importance of decentralized GAs, authors compared their results to those of standard GAs, and frequently referred to modified or improved versions of standard GAs, such as steady state GA or generational GA. In several cases, cGAs were proved to perform better in terms of algorithmic efficiency and efficacy Alba et al. have widely studied decentralized GAs In cellular GAs, the population is normally distributed in a torus like grid structure with wraparound edges. One individual is placed in each grid position and can only interact with nearby neighbours. Several parameters must be configured in order to reach the best algorithm performance, such as, shape and size of local neighbourhood and population topology, migration rate and frequency (in case explicit migration occurs), local selection and replacement policies, among others Initially, the study of cGAs has been developed following empirical knowledge through manually tuning and configuring the parameters mentioned above and through considering specific areas of application. However, there has also been an effort to provide a mathematical basis to explain their behaviour. For example, in order to provide a numerical relationship between grid structure and local neighbourhood, the dispersion of a points pattern has been defined as an appropriate measure known as the Neighbourhood to Grid Ratio (NGR) external lattice reconfiguration. Three main scenarios were evaluated: static (fixed topology shape), pre-programmed (reconfiguration at a predefined time slot) and adaptive (dynamic reconfiguration) criteria. Statistically significant results were obtained for several difficult benchmark problems. The mapping of individuals, after grid re-arrangement, induces a loss of the natural adjacency of individuals, due to toroidal connection. In this research, adjacency among individuals is maintained while constant or adaptive lattice reconfiguration is carried out by subdividing the entire population into smaller square, rectangular or linear toroidal arrays, with no induced migration among them apart from neighbourhoods overlapping. Proposed internal reconfiguration mechanisms are applied to a set of continuous, real and combinatorial problems covering characteristics such as multi-modality, epistasis, deceptiveness and non-regularity.This article aims to investigate how selection pressure can be constantly or adaptively controlled in order to improve the performance of cellular GAs, as a result of the population topology and the local neighbourhood configuration. Previously, researchers have proposed dynamically reconfiguring the population topology internal reconfiguration mechanisms.In the following section, a detailed explanation of the effect of controlling selection pressure through structural properties in cellular GAs is presented, together with proposed internal reconfiguration of the population topology. Internal lattice reconfiguration means that there is no alteration to the natural adjacency of individuals. Measuring selection pressure in cellular GAs has been fundamental and has allowed a more accurate study of their behaviour. Selection pressure in cGAs, is measured through the local selection of individuals, without intervention from other genetic operators; and it is empirically calculated as the number of generations the best individual of an initial population needs in order to spread its solution throughout the entire grid, concept also known as take-over time Mechanisms proposed in this article control, constantly or adaptively, the induced selection pressure during evolution through the externally modify the induced selection pressure in order to improve the performance of cGAs Previously, Dorronsoro et al. proposed several constant and adaptive criteria to Selection pressure in cGAs provides important information about the evolutionary process showing levels of exploration or exploitation that certain population topology contribute to the search. In Researchers have demonstrated that the exploration-exploitation trade-off can be implicitly controlled in cGAs through the configuration of the population topology and the neighbourhood where In For most topologies in internal lattice reconfiguration criterion.Different ratios means having higher or lower selection pressures. Therefore, decentralized GAs are structurally capable of modifying selective pressure while changing the population’s structural configuration. Lower ratios means more exploration is performed while higher ratios imply more exploitation. However, the NGR is a representation of the selective pressure in a fully connected structure or sub-structure. Therefore, it does not reflect the overall induced selective pressure in topologies configurations formed by several cellular sub-structures, like those shown in Take-over time or proportional growth of the best individual reflects how long it takes for the best individual to spread its solution throughout the whole lattice, applying only local selection. Thus, longer take-over times represent lower selection pressure and therefore more explorative behaviour. In contrast, shorter take-over times correspond to higher selection pressure, equivalent to a more exploitative search.At a local level, two different selection methods are applied; widely known binary tournament selection and, ad-hoc for cellular GAs, anisotropic selection proposed by internal lattice reconfiguration is not subject to take-over time analysis since it depends on phenotypic or genotypic diversity; information not available when applying only local selection to a single individual. On the other hand, due to the stopping criterion which evaluates the average fitness score for the whole population, and in order to assess selective pressure on an internally different grid configuration; reconfiguration occurs between a fully connected In For both local selection methods in internal (proposed in this article) and externalexternal lattice reconfiguration, evolution starts on a square topology and continue on a rectangular grid, making the search going from more exploitation to more exploration. On the other hand, for internal reconfiguration, initially the population evolves on an internal arrangement of four square cellular sub-structures  with highest probability and constant mutation probability. Both genetic operators have been applied in their simplest form, as it is not the objective of this research to evaluate the effect of either of them but to widely investigate the effect of dynamically changing selection pressure through cGAs structural properties. Although it is outwith the scope of this paper, particular attention should be paid to mutation as its effect has been modelled for cellular GAs as an essential control parameter to lead or mislead the search In CLR consists in constant cyclic reconfiguration among internal lattice topologies shown in Grid reconfiguration based on phenotypic diversity (PLR procedure) evaluates differences in phenotypic entropy, where In On the other hand, determining genotypic diversity requires to calculate the Hamming distance among chromosomes. The distance between current  operates pairs formed by a central individual and an individual selected from the local neighbourhood with probability only replace if better policy is followed. On the other hand, synchronous population updating is also considered. Synchronous updating requires current and next generations to be stored separately; once all individuals have evolved, new offspring will entirely replace the current population. Asynchronous updating has also been investigated and in several problems outperforms synchronous updating in terms of convergence time although the accuracy of the results and their hit rate are negatively affected Replacement policies also play an important role in GAs. In cGAs, an offspring can replace a current individual in the following three ways: 1) always, 2) only if it has a better fitness score or 3) following some other condition. In this research an The following experimental constraints are evaluated:A population size of 400 individuals was used for most problems. Due to their size, GPS and MTTP problems are tackled using a population size of 64 and 100 individuals respectively.Binary encoding is used in all problems. In continuous and real problems, each variable is encoded in 10 bits, except for the GPS attitude problem where each variable is encoded in 14, 10, 8 bits respectively. Binary encoding is implicit in combinatorial problems.Local neighbourhood configuration is Von Neumann or linear composed by four individuals plus a central individual.One hundred independent runs are carried out per experimental reconfiguration criterion.A problem specific threshold based on population average fitness is used as a stop condition.A limit of 500 generations is used in most problems, except for the Langerman function, the SLE and the MMDP problems with a limit of 700 and the ECC problem with 2500 generations.The same reconfiguration mechanisms are applied to all problems and results are analysed based on convergence time and hit rate. The accuracy of results is determined by problem specific thresholds, see mean absolute deviation and italics are used to highlight these cases in the results tables in the next section.In order to statistically support results so far obtained, the Lilliefors normality test, at 5% of significance, is performed on each set of convergence time results. This test is suitable when a fully-specified null distribution is unknown, in contrast to the Kolmorov-Smirnov test. Once normality of results has been established, an Analysis of Variance (ANOVA) is applied among results that follow a normal distribution whereas the Kruskal-Wallis test is applied to results that are not normally distributed. For sets of results with non-normal distributions, dispersion of data is calculated using the In the next section, results are summarized in two tables, one for static, pre-programmed and constant reconfiguration criteria and a second one for adaptive approaches. Then, in order to prove that a certain approach outperforms others, in terms of convergence time, a multiple comparison test is carried out, after a statistical analysis is done independently for each table of results. In contrast, if statistical proof is obtained only in one of the two tables, a multiple comparison test is applied separately.In the results tables, where a statistically significant difference at The hit rate has been statistically evaluated as a Bernoulli trial. A random experiment whose result is either success or failure can be considered a Bernoulli trial where Results analysis aims to empirically support the main hypothesis of this study: dynamic modification of the internal configuration of the population’s topology would lead to a significant improvement in cGAs’ performance. At a local level, as explained previously in Section, two different local selection methods have been applied. The main difference between them consists of inducing higher (binary tournament) or lower (anisotropic) selection pressure. These two methods were chosen because of their considerably different selection intensity. Thus, it is possible to separately evaluate 1) the effect of having a highly exploitative or explorative cellular GA, due to local selection, and 2) the added flexibility of dynamically reconfiguring the population topology in order to supply the search process with a more convenient and balanced exploitation-exploration trade-off.external) reconfiguration and constant  reconfiguration. The second group presents adaptive approaches for both external and internal lattice reconfiguration approaches.For presentation, results are divided by local selection method. Two tables of results are organized: the first includes static, pre-programmed  and mean absolute deviation  as dispersion measures. Next to the hit rates, corresponding Bernoulli trial standard deviations are shown in small numbers.In the tables, numbers in internal reconfiguration. On the other hand, adaptive approaches do not show significant difference in convergence time.internal reconfiguration with respect to static and pre-programmed criteria. However, in terms of success search rate, pre-programmed square internal reconfiguration with statistical difference. The best hit rate is 88% . In contrast, the best adaptive square internal phenotypic and genotypic based criteria reach a hit rate of 35%, with no meaningful difference due to From these two sets of results, it is worth noticing that the best convergence times in efficacy 9%. In cointernal reconfiguration. However, the best hit rate of 92%  where genotypic based external adaptive approaches outperformed constant ones. Efficacy achieved is very similar when applying adaptive or constant mechanisms with no statistical significant difference. Next section provides final conclusions of this study.In Internal lattice reconfiguration has been experimentally assessed and compared with external reconfiguration proposed in internal lattice reconfiguration while it is lost in the external approach because it induces an implicit migration mechanism. Making difficult to distinguish if performance improvements are a consequence of the added effect of the lattice reconfiguration mechanism and the induced migration.The aim of this research has been to extend previous studies on how structural properties in cGAs can be a factor in adequately tuning diversity during evolution. internal lattice reconfiguration is performed. Thus, a significant effect on selective pressure to tune diversity and consequently in performance was not expected. In contrast, take-over times for anisotropic selection showed a wider span to affect selective pressure when the population’s topology is internally reconfigured; for most of the problems statistical difference was proved in terms of convergence time.Binary tournament showed a narrow span for modifying selective pressure when Internal adaptive approaches outperformed external ones in six out of ten benchmark problems when applying anisotropic local selection. In order to statistically compare the best adaptive criteria, tests were carried out versus static, pre-programmed and constant reconfiguration mechanisms. In three out of ten benchmark problems, adaptive approaches  outperformed constant internal reconfiguration.In cGAs, exploration is carried out globally throughout the grid while exploitation is mostly promoted locally within neighbourhoods. The proposed mechanism for diversity tuning works at a global level through the internal reconfiguration of the population’s topology as a way of modifying the induced selective pressure and thus promote a more balanced exploration-exploitation trade-off. Therefore, its effect does not need to be a prompt response to changes in the solutions or the representation spaces; this is why constant reconfiguration of the topology induces a similar effect in performance as measuring phenotypic or genotypic entropy and is less computationally expensive.


High coverage of personal protection measures that kill mosquitoes dramatically reduce malaria transmission where vector populations depend upon human blood. However, most primary malaria vectors outside of sub-Saharan Africa can be classified as “very zoophagic,” meaning they feed occasionally  upon humans, so personal protection interventions have negligible impact upon their survival.We extended a published malaria transmission model to examine the relationship between transmission, control, and the baseline proportion of bloodmeals obtained from humans (human blood index). The lower limit of the human blood index enables derivation of simplified models for zoophagic vectors that (1) Rely on only three field-measurable parameters. (2) Predict immediate and delayed  impacts of personal protection measures upon transmission. (3) Illustrate how appreciable indirect communal-level protection for non-users can be accrued through direct personal protection of users. (4) Suggest the coverage and efficacy thresholds required to attain epidemiological impact. The findings suggest that immediate, indirect, community-wide protection of users and non-users alike may linearly relate to the efficacy of a user’s direct personal protection, regardless of whether that is achieved by killing or repelling mosquitoes. High protective coverage and efficacy (≥80%) are important to achieve epidemiologically meaningful impact. Non-users are indirectly protected because the two most common species of human malaria are strict anthroponoses. Therefore, the small proportion of mosquitoes that are killed or diverted while attacking humans can represent a large proportion of those actually transmitting malaria.Simplified models of malaria transmission by very zoophagic vectors may be used by control practitioners to predict intervention impact interventions using three field-measurable parameters; the proportion of human exposure to mosquitoes occurring when an intervention can be practically used, its protective efficacy when used, and the proportion of people using it. Anopheles gambiae and An. funestus from sub-Saharan Africa An. punctulatus and An. koliensis from the Pacific An. funestus was replaced by An. rivulorum and/or An. parensis following the introduction of IRS on at least three distinct occasions in South Africa, Kenya and Tanzania Indoor residual spraying (IRS) and long-lasting insecticidal nets (LLIN) dramatically reduce malaria transmission However, mosquitoes which feed upon animals (zoophagic) are primary malaria vectors in many tropical countries While LLINs confer personal protection against any mosquitoes attempting to bite while they are in use, it remains unclear whether they confer community-level protection against zoophagic vectors that feed only occasionally upon humans. We therefore extended a previously published static malaria transmission model We extended a static malaria transmission model Before describing how the model simulations were performed, we first present the basic input parameters and their definitions, equations and derived parameters, output from the model, description of simplified models for very zoophagic vectors, and the expression of malaria transmission and control as a function of baseline human blood index.Several subscripts are used in this model; We present equations from previous model de facto protective coverage of humans As previously The mean availability The total availability of all hostsSimilarly, the total availability of blood from all hosts, The human blood index is the proportion of all blood meals obtained from both protected and unprotect humans Changing the mean availabilities of protected humans per seper seFor predominantly animal-feeding mosquito In equation 5, baseline human blood index goes to zeroMalaria transmission intensity is often expressed in terms of the entomologic inoculation rate (EIR) which is a direct, field-measurable indicator of human exposure to bites of mosquitoes infected with transmissible sporozoite stage malaria parasites Killeen et alWe present equations from Hence, The number of infectious bites on humans Thus, absolute EIR for an average community member in a given intervention scenario The relative exposure for non-users An. arabiensis which readily feeds on both humans and cattle Anopheles epiroticus in the Mekong delta of Vietnam. This mosquito population has a >11-fold preference for cattle over humans Initial simulations suggested closer examination of the underlying mechanisms through which personal protection mediates community-level protection against malaria transmission by very zoophagic mosquitoes. We specifically define very zoophagic vectors as those which are not merely zoophagic, such as We express the primary and secondary outputs in terms of human blood index Since Using equation 9 the same approach can be applied for An. Arabiensis, which is both anthropophagic and zoophagic An.gambiaeAn.quadriannulatusAn.gambiae, An.arabiensis and An.quadriannulatus come from a single African species complex (An.gambiae sensu lato); they span the full range of host choice preferences exhibited by anophelines world-wide. Although An.gambiae typically feeds almost exclusively upon humans, and has historically been the most important vector of malaria in the world An.arabiensis is as likely to attack cattle as humans and is a correspondingly less potent but nevertheless significant primary vector An.quadriannulatus is thought to rarely feed upon humans and transmit little, if any malaria, despite being readily infected by Plasmodium falciparumAn.arabiensis is a useful intermediate example because this species has been well studied, feeds readily upon both humans and animals The full possible range of host preference for mosquitoes was simulated by modifying field estimates for cattle and human encounter rate, The first scenario was simulated with no intervention by setting −3 and then decreasing to 1.1×10−4 encounters per day per host-seeking vector per unprotected human, with −3 encounters per day per host-seeking vector per cow. The default value of 1.7×10−3 encounters per day per host-seeking vector per unprotected human, at which these two ranges coincide, is used because it is an intermediate value between field measures for −3 and for −3 encounters per day per host-seeking for An. arabiensisThe model was implemented with a range of values of A represents the absolute entomological inoculation rate (EIR) for average community member in which the dependent values were plotted using equation 12. The y-axes for all other panels were plotted using equations given in brackets representing relative values for mosquito population parameters when compared with those expected in the absence of LLINs: B: Relative exposure for non-users C: Relative probability of surviving one complete feeding cycle D: Relative proportion of blood-meals taken from human E: Relative feeding cycle length F: Relative emergence rate of adult mosquitoes For all panels in Consistent with field observations  reduced , the len reduced , and the reduced  [6], [48By comparison, as previously described Plasmodium falciparum and P. vivax.Personal protection measures can deliver appreciable communal protection against transmission by zoophagic vectors  because squitoes . This isNext, we illustrate how the dependence of transmission and control enables derivation of much simpler models for both immediate and delayed impacts  upon malaria transmission, to be derived that rationalize the reduced, but nevertheless useful, impacts of a personal protection measure upon zoophagic vector systems that are illustrated by the intercepts on the left hand side of So, as ction  , compareBy assuming that We assume that Now, if we substitute the definition of In simple terms, the level of indirect communal protection afforded to all community members is equivalent to the coverage-weighted mean of feeding probabilities (equation 18). This is, in turn, equivalent to the community-wide mean level of person protection obtained as a coverage-weighted mean of personal protection. Relative exposure can also be expressed in terms of personal protection So, by substituting equations 1 and 19 into rearranged equation 18, the impact upon transmission by very zoophagic vector can be expressed in terms of only three field-measurable parameters: the proportion of human exposure to mosquitoes occurring when an intervention can be practically used  and delayed (C and D) impact upon relative exposure experienced by non-users. While the latter assumes that delayed effects upon human-to-mosquito transmission occur if immediate reductions in the ability of mosquitoes to mediate transmission to humans are sustained over a long time A), equation 21 (B), product of values from equations 20 and 21 (C) and equation 23 (D).In all the panels of In Human blood index, defined as the proportion of a mosquito population that feeds upon humans, is clearly as important a determinant of malaria transmission and control  today [2Plasmodium falciparum infections Plasmodium vivax infections An. gambiae, An funestus and An koliensis, that have dominated the thinking behind global malaria control policy An. arabiensis Africa and An. farauti in the Pacific, are both zoophagic and anthropophagic so that they sit between these two extremes. Surveys of human blood indices, or underlying host preference indices such as relative availability The world’s malaria vectors span the full range of baseline human blood indices considered here Where such surveys confirm very low human blood indices, the minimum immediate (equation 21) and delayed (equation 23) impacts of a personal protection measure upon transmission by very zoophagic mosquitoes can be approximately calculated with very simple models. These models use only three parameters which may potentially be measured in the field by National Malaria Control Programmes (NMCPs) and their supporting national institutional partners in developing countries: the maximum proportion of human exposure to mosquitoes that can be directly prevented through personal protection by using a given intervention, its protective efficacy when used, and the demographic coverage of human users. The relationship between entomologic inoculation rate (EIR) which is a direct, field-measurable indicator of human exposure to bites of mosquitoes infected with transmissible sporozoite stage malaria parasites The suggestion that the impact of personal protection upon malaria transmission by very zoophagic vectors may be independent of the mode of action of the product has substantial implications for manufacturers and NMCPs alike. Unlike transmission mediated by anthropophagic vectors We extended a published malaria transmission model to examine the relationship between transmission, control, and the baseline human blood index for very zoophagic vectors. The results from model is very simple and can be used by vector control practitioners to forecast the likely immediate and delayed impacts of personal protection measures using three parameters that may potentially be measured in the field: the proportion of human exposure to mosquitoes occurring when a intervention can be practically used, its protective efficacy when used, and demographic coverage of human users. High levels (≥80%) of protective coverage and efficacy are important to achieve an epidemiologically meaningful impact.


Conclusion: The study of CTCs provides insight into the molecular biology of tumors of prostate origin that will eventually guide the development of tailored therapeutics. These advances are predicated on high yield and accurate isolation techniques that exploit the unique biochemical features of these cells.Circulating tumor cells (CTCs) are tumor cells found in the peripheral blood that putatively originate from established sites of malignancy and likely have metastatic potential. Analysis of CTCs has demonstrated promise as a prognostic marker as well as a source of identifying potential targets for novel therapeutics. Isolation and characterization of these cells for study, however, remain challenging owing to their rarity in comparison with other cellular components of the peripheral blood. Several techniques that exploit the unique biochemical properties of CTCs have been developed to facilitate their isolation. Positive selection of CTCs has been achieved using microfluidic surfaces coated with antibodies against epithelial cell markers or tumor-specific antigens such as EpCAM or prostate-specific membrane antigen (PSMA). Following isolation, characterization of CTCs may help guide clinical decision making. For instance, molecular and genetic characterization may shed light on the development of chemotherapy resistance and mechanisms of metastasis without the need for a tissue biopsy. This paper will review novel isolation techniques to capture CTCs from patients with advanced prostate cancer, as well as efforts to characterize the CTCs. We will also review how these analyzes can assist in clinical decision making.  Tumor metastases are a major cause of cancer morbidity and mortality. The precise mechanisms underlying the development of metastases, however, remain poorly understood. Simply stated, this process requires the migration of malignant cells from a primary tumor to distant sites where these cells establish secondary tumors. Circulating tumor cells (CTCs), which were first detected in the blood of an autopsy patient who died from cancer in 1869, are thought to represent tumor cells in transit, some of which will result in metastases  after treatment of his original tumor in the prostate. Thus, performing a molecular analysis of archived prostate cancer tissue may be complicated by the inability to obtain old pathology specimens and by the possible irrelevance of that tissue sample to the current status of the patient's disease. Ideally, a tumor biopsy for molecular study would be obtained at the time of relapse, but as many men have only bone metastases, it is difficult to obtain adequate and representative tumor cells for study. In a disease for which a blood test measuring prostate specific antigen (PSA) is sufficiently specific to support the diagnosis of prostate cancer, it is difficult clinically to justify a biopsy. Consequently, analysis of peripheral blood overcomes these obstacles by easily providing clinically relevant tumor cells for study.Ideally, a robust CTC capture technique would be highly sensitive, specific, reproducible, and automated  have been used to identify tumor-specific genetic and chromosomal features in order to differentiate CTCs from contaminating cells. Immunofluorescent microscopy is utilized to detect epithelial specific antigens such as epithelial cell adhesion molecule (EpCAM) or cytokeratin (CK), or prostatic antigens such as PSA and prostate-specific membrane antigen (PSMA).Accurate characterization of CTCs is essential to the development of these cells as a clinical biomarker and substrate for laboratory experimentation. There is currently, however, no “gold standard” approach for the specific identification of CTCs. This is essential, in part, because most available CTC enrichment technologies yield samples composed of hematopoietic cells, CTCs, and, in some cases, benign epithelial cells. Genomic analysis and surface antigen detection are the two most commonly used methods for CTC detection. Reverse transcription polymerase chain reaction (RT-PCR) and Fluorescence Reverse transcription-PCR is highly sensitive for identifying the presence of CTCs and is able to detect a single malignant cell among ten million peripheral blood mononuclear cells (PBMCs) , which increases the specificity of mRNA detection by use of transcript-specific probes and enables the determination of mRNA copy number such that above a specific threshold a transcript is thought to be of malignant origin , is a process in which epithelial CTCs assume a mesenchymal phenotype in preparation for extravasation and implantation in metastatic sites (He et al., Despite multiple studies validating the CellSearch system's prognostic value as related to CTC enumeration, several important caveats limit its usefulness. It is both expensive and time consuming to perform. The CellSearch device fixes cells prior to staining them, significantly limiting the ability to perform subsequent functional assays and nucleic acid analysis (Stott et al., Microfluidic devices have demonstrated high capability to enrich CTCs from whole blood. One example, the “CTC-chip” is composed of an array of antibody-coated microscopic posts arranged as equilateral triangles through which a blood sample is flowed. As described, the arrangement of the posts is designed to minimize the shear forces that cells are exposed to while within the device. CTCs within the sample collide with the posts and are specifically captured by the antibodies used to coat them (Nagrath et al., The same investigators developed what they term a “herringbone chip” to use the vortical flow induced by anisotropic surface grooves to generate a device exhibiting chaotic advection (Stroock et al., Despite showing effective capture using cell lines with low levels of EpCAM expression, it is unclear whether chips functionalized with anti-EpCAM antibodies can efficiently capture cells that have undergone EMT in patient samples. CTCs may express lower levels of EpCAM than cultured cells used for these experiments and may evade capture. PSMA based CTC capture may be able overcome this limitation in prostate cancer patients and enable the capture of CTCs that have undergone EMT. PSMA, also known as glutamate carboxypeptidase II, is a type II transmembrane metallopeptidase that is universally expressed in prostatic tumors and may be conserved during EMT. Furthermore, levels of expression correlate with disease severity, suggesting utility as a prognostic marker (Bostwick et al., The FDA approved the CellSearch device for monitoring disease status in patients with metastatic prostate cancer (Wang et al., The number of prostate cancer CTCs has also been studied as a secondary endpoint in a number of clinical trials. Two recent phase II trials examined the efficacy of abiraterone acetate, a CYP17 inhibitor that impairs androgen synthesis, in patients with castration resistant prostate cancer used CTCs as efficacy markers. In one study, CTCs were isolated from patient blood by use of the CellSearch Circulating Tumor Cell Test prior to treatment and every 4 weeks during treatment. The authors found significant declines in CTC counts of treated patients, with 63% of patients having a greater than 50% decrease in CTCs. This decline mirrored the PSA decline in a subset of patients with ERG gene mutations (Reid et al., Although still in its early stages, molecular and genetic analyses of CTCs have also been used to correlate CTC characteristics with treatment outcomes. For example, a study using FISH to detect the fusion gene TMPRSS2-ERG demonstrated a significant association between expression of this marker and PSA response to abiraterone. Furthermore, this study also demonstrated a high degree of concordance between the presence of the fusion gene in CTCs and in primary prostatic tumors, further supporting the utility of CTCs as a “liquid biopsy” (Attard et al., Studies have also examined the predictive value of nuclear and/or cytoplasmic localization of AR in CTCs. The AR plays a key role in the development and progression of prostate cancer. In hormone-sensitive prostate cancer, systemic androgens induce AR-mediated cellular proliferation, which is impaired by androgen deprivation therapy by preventing ligand-dependent nuclear AR translocation. AR signaling can continue to stimulate tumor growth in castrate patients via intra-tumoral androgen synthesis or constitutive AR activation-independent of ligand binding (Chen et al., The science of CTC capture and analysis is evolving and will certainly change as newer technologies are incorporated and validated. The only FDA-cleared device, CellSearch system, has been shown to be an important prognostic tool, providing valuable insights into treatment response and overall survival. Experiments with alternative enrichment methods highlight the poor sensitivity of the CellSearch technique, with multiple studies demonstrating significantly higher capture rates from patient with metastatic castrate-resistant prostate cancer. However, their clinical utility remains to be confirmed. Further studies are needed to improve and validate alternative enrichment in identification techniques.The effect that CTC analysis will have on patient care remains to be determined. As discussed, genetic analysis of CTCs has enabled the detection of abnormalities that influence tumor sensitivity to a variety of prostate cancer therapies. Molecular analysis has helped elucidate the mechanism of taxane anti-tumor effect in prostate cancer, and provides a basis for an assay to assess the likely efficacy of this chemotherapeutic class. Future studies will be aimed at assessing additional markers of treatment sensitivity and resistance, and attempting to ascertain additional drug targets. Additional studies correlating the molecular features of CTCs with those of tissue specimens obtained from primary and metastatic sites are needed. The ultimate goal is to develop technology that will enable periodic monitoring of tumor biology in a way that will enable clinicians to effectively tailor therapy to the individual patient on an ongoing basis in order to maximize patient outcomes.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Social decision-making is often complex, requiring the decision-maker to make inferences of others' mental states in addition to engaging traditional decision-making processes like valuation and reward processing. A growing body of research in neuroeconomics has examined decision-making involving social and non-social stimuli to explore activity in brain regions such as the striatum and prefrontal cortex, largely ignoring the power of the social context. Perhaps more complex processes may influence decision-making in social vs. non-social contexts. Years of social psychology and social neuroscience research have documented a multitude of processes  that occur upon viewing another person. These processes rely on a network of brain regions including medial prefrontal cortex (MPFC), superior temporal sulcus (STS), temporal parietal junction, and precuneus among others. Undoubtedly, these social cognition processes affect social decision-making since mental state inferences occur spontaneously and automatically. Few studies have looked at how these social inference processes affect decision-making in a social context despite the capability of these inferences to serve as predictions that can guide future decision-making. Here we review and integrate the person perception and decision-making literatures to understand how social cognition can inform the study of social decision-making in a way that is consistent with both literatures. We identify gaps in both literatures—while behavioral economics largely ignores social processes that spontaneously occur upon viewing another person, social psychology has largely failed to talk about the implications of social cognition processes in an economic decision-making context—and examine the benefits of integrating social psychological theory with behavioral economic theory. What makes social decision-making unique and different from non-social decision-making? Humans are highly social animals—as such, researchers often take for granted the ease with which humans make social decisions. This begs the question whether social decision-making is a simplified type of decision-making. Yet social decision-making should be a complex process—social decision-makers must engage traditional decision-making processes , as well as infer the mental states of another person. These two tasks have been separately studied in the fields of behavioral economics and social psychology, with behavioral economists studying decision-making in interactive economic games and social psychologists studying spontaneous inferences about other people. Each of these fields has separately made major contributions to the understanding of social behavior. However, a more cohesive theory of social decision-making results when researchers combine these literatures.When talking about social decision-making, many different types of decisions may come to mind—decisions about other people (Is Linda a feminist bank teller?), decisions that are influenced by other people , as well as decisions that are interactive . In this review, we focus on strategic interaction decisions often employed in behavioral economics games  that require thinking about the mental states of another person. Research shows that such decisions may differ depending on whether the interaction partner is another person or a computer agent. Here, we suggest that such differences in decision-making arise due to differences when processing human and computer agents. Specifically, viewing another person engages the social cognition brain network, allowing for mental state inferences that function as predictions during the decision phase, as well as spontaneous trait inferences that occur when viewing the other person's behavior in the feedback phase.To understand how decision-making in a social context is different than non-social decision-making, it is first important to understand what exactly makes humans unique as social agents. Social psychological theory suggests humans differ from objects in important ways —responsible for creating value signals for food, non-food consumables, and monetary gambles  will do. The combination of the participant's and the partner's decisions determines the outcome. Therefore, in order to maximize payout, the participant has to predict what the partner will do and decide accordingly. What information do participants rely on when making these predictions? Social psychological theory suggests these predictions rely on trait inferences that occur when viewing the person and learning about their past behavior, while also taking the social context into account. Yet discussions of how these predictions are utilized within a decision-making context have eluded social psychology researchers in favor of understanding the processes by which such predictions are made. Below, we discuss these social cognitive processes and how they influence social decision-making in various behavioral economic paradigms involving human and computer agents.Predictions have received much attention when studying social decision-making. Behavioral economics games such as the trust game, ultimatum game, or the prisoner's dilemma game are often used to study social preferences for trustworthiness, fairness, or cooperation, respectively. However, each of these games requires Social decisions are not made within a vacuum; they are made in a social context. A social context involves the actual, imagined, or implied presence of another person—an intentional agent—whose behavior cannot be predicted with certainty. Although humans have developed ways to try to predict what another person will probably do, the other person has the ability to originate their own actions and only they know their true intentions. Therefore, social decision-making is complicated by the uncertainty of the other person's behavior and requires inferences about a person's mental state. Despite these uncertainties, humans are highly motivated to explain and predict others behavior Heider, . To faciBut social predictions are not always formed based on trait inferences alone—social category information  and physical features  can guide initial impressions of a person as well Fiske, . StereotThe basis of these social predictions  are often assessed automatically and efficiently, with only 100 ms of exposure to a person's face leading to accurate assessments  or information about their past behavior, participants are constantly trying to make predictions about what other people will do (even outside of a decision-making context). As such, traits provide a concise schema suggesting how a person will behave, allowing for generalizations across contexts when making predictions about behavior. In general, if a person is thought to be trustworthy in one context, people predict that they will be trustworthy in other contexts. Whether actual consistency across contexts exists depends on the psychological viewpoint one takes—personality psychologists would suggest traits are an enduring quality that stays consistent across situations, however, social psychologists stress the importance of the situation and the interaction between person and environment Lewin, .How does this contribute to our discussion of human and computer agents in an economic game? Do participants use the same brain regions when making predictions about what a human will do vs. what a computer will do? Since each type of agent recruits different brain regions, do social predictions rely on the person perception/social cognition network as we hypothesize above? Below we describe three economic games—the trust game, ultimatum game, and prisoner's dilemma game—often used in the neuroeconomics literature on social decision-making and discuss how social cognition and social psychological theory may be useful when studying these games. We also review research that will help us understand the brain regions underlying these predictions, specifically studies that use non-social agents  as a control and examine activation during the decision phase when participants are making predictions about what the other agent will do  to another person. Alternatively, participants can decide to keep the money for themselves and not invest. If the money is given to the partner, it is multiplied by some factor  and the partner decides whether or not to share the profit with the investor. If the partner shares with the participant, each receives an equal payout ($15). However, if the partner decides to keep the profit ($30), the participant receives nothing. Participants must predict what the partner will do in order to maximize their payout. If they predict the partner will not share, the participants should not invest and keep the money for themselves. However, if participants predict the partner will share, the participants should invest with the partner, risking the chance that they will lose the whole amount.How do participants make these predictions if they have never interacted with their partners before? From a social cognition perspective, spontaneous mental state inferences may guide these predictions, resulting in corresponding activity in social cognition brain regions. In fact, research shows that when making such predictions for human and computer agents in a trust game social cognition brain regions including the prefrontal cortex (PFC) and inferior parietal cortex (IPL) are more active for human compared to computer partners when participants decide to invest  from a single exchange. If this behavior is repeated, the partner will build a reputation (a trait inference) for being trustworthy. When relying on reputation to predict the partner's actions, striatal activation shifts from the feedback phase when processing rewards to the decision phase when viewing pictures of previous cooperators, suggesting that participants are making predictions that previous cooperators will again cooperate in the current trial . While some of these offers are fair ($5 each party), others are unfair ($3 for the participant and $7 for the partner). If the participant decides to accept the offer, the money is divided as proposed. However, if the participant rejects the offer, both parties receive nothing. In an economic sense, any non-zero offer should be accepted in order to maximize payout, especially if partners are not repeated throughout the experiment (one-shot games). However, research suggests that unfair offers are rejected more often when the partner is a human agent than computer agent. Why does the identity of the partner affect decisions if the same economic outcome would result? Perhaps, related to our discussion of flexibility above, participants know that humans respond to the environment and make adaptive decisions. If they see that their unfair offers are being rejected, the participant may predict that the human partner will change their behavior, offering more fair offers. However, a computer may be predicted to propose the same offer regardless of how the participant responds, in which case it would be advantageous to accept any non-zero offer because the participant does not anticipate the computer would respond to his or her rejection of the offers. Rejection may also represent a form of punishment of the partner. If the participant receives a low offer, this suggests that the partner has a negative impression of the participant or is simply a morally bad person . Punishment in this light is action against such mental states. However, since computers do not possess mental states, there is no reason to punish them for similar unfair offers.Research shows that when deciding whether to accept or reject offers proposed by human and computer agents, participants show higher skin conductance responses to unfair offers made by human compared to computer agents  is another economic game exemplifying the role of predictions in social decision-making. In this game, participants must decide whether to cooperate with a partner for a mediocre reward , or defect in order to receive a better reward at the expense of the partner . However, risk is introduced into the game because if the partner also defects, both players end up with the worst possible outcome . In this case it is important for the participant to predict what the partner will do because the payout structure that both parties receive depends on what each chooses.When participants believe they are playing with human rather than computer agents, imaging results show greater activation in regions involved in social cognition, including right posterior STS, PCC, DLPFC, fusiform gyrus, frontal pole, along with decision-making regions like the caudate  vs. following a predetermined response sequence , Singer et al.  observedAlthough these economic games are most often used to study social decision-making, other games also suggest that social cognition brain regions are essential for predicting the actions of others. For instance, when playing a game of Rock-Paper-Scissors with either a human or computer counterpart, Gallagher et al.  observedTogether, across different social decision-making paradigms, there seems to be increasing evidence that human and computer agents engage different brain regions when making predictions. Specifically, making predictions about human agents engages brain regions implicated in the social cognition network, including MPFC, STS, TPJ, along with decision-making regions like the striatum. Next we ask whether these social decision-making paradigms engage different brain circuitry when processing feedback from human and computer agents.While many studies have suggested that social predictions rely on the social cognition brain network, other social decision-making studies have looked at how the outcome of social decision-making, or social feedback, affects traditional decision-making brain regions involved in reward processing and valuation. Initial attempts to study the uniqueness of social decision-making include examining whether social and non-social rewards are processed in the same areas of the brain, and how economic decisions are made in the context of social constructs including trustworthiness, fairness, altruism, and the like. Using behavioral economic games described above  researchers have examined the influence of positive and negative feedback on social decisions. Below, we review the results of such studies in an attempt to continue the comparison between human and computer agents in social decision-making.Social feedback often allows people to infer something about another person as well as receive information about the impression others have formed of them. In the context of receiving direct social feedback about what other people think, research suggests that being labeled trustworthy activates the striatum in much the same way as receiving monetary rewards  with a partner and then receive feedback in the same trial about whether the investment was returned by the partner. However, in the ultimatum game, the outcome phase is less clear—participants already know the outcome of the social interaction when they decide whether to accept or reject the offer made by the agent. However, this does not make the outcome of the social interaction irrelevant. In repeated ultimatum games , feedback about the participant's decision comes on the next trial when the partner proposes the next division of money. For example, if a participant rejects an unfair offer, feedback about whether that rejection was effective in influencing the partner's next proposal comes on the next trial. In other words, offers can be thought of as feedback within the context of this game. However, researchers often use single-shot ultimatum games to avoid effects of repeated interaction just described. In this case, the offers proposed by the partner allow the participant to infer traits about the partner, and their decision still communicates something to the partner, prompting participants to think about impression management.How then do participants respond to offers made by human and computer agents in the context of the ultimatum game? Research suggests that unfair offers made by human agents activate bilateral anterior insula to a greater extent than the same unfair offers made by computer agents, suggesting that there is something about being mistreated specifically by human agents that leads to higher rejection rates . Some brain regions, including ventral striatum and paracingulate cortex (PACC) responded more to positive vs. negative feedback irrespective of whether the agent was a human or computer  would lead people to infer that they were inferior or incompetent compared to other players. On the other hand, losses on the lottery trials were simply relevant to the participants and not their social standing.Converging evidence suggests that common brain regions, particularly the striatum and VMPFC, are engaged when viewing outcomes from human and computer agents. However, the activity in these regions seems to be modulated by the social context. In addition to these decision-making regions, the ultimatum game and prisoner's dilemma game also activate regions involved in social cognition, including STS, precuneus, and TPJ. Should it be surprising that social cognition regions are also active during outcomes? Social psychology demonstrates that people infer traits from others' behavior. The outcome of a social interaction allows participants to infer these traits, and what perhaps is even more interesting is that these trait inferences are formed in single-shot games where participants do not interact with the partner again. Essentially, trait inferences in this context are superfluous because the participant will not be interacting with the partner again so there is no need to infer traits that allow for predictions. Yet these social cognition regions are still engaged.So far we have seen that social cognition informs predictions made in social decision-making studies when interacting with human but not (or to a lesser extent) when interacting with computer agents. Social rewards, including being labeled trustworthy by another person  but also differences in brain activity for these two inherently different types agents. Here we have highlighted that these differences lie in engagement of the social cognition/person perception brain regions for human agents. But the underlying mechanisms—the social processes that engage these brain regions and how they interact with decision-making processes—are still being investigated. Social psychological theory can help answer these questions by providing a theoretical background for why human and computers differ in the first place . Keeping this fact in mind will provide future research on social decision-making with the most informed and cohesive theories.Finally, decisions are made in a social context everyday. Whether deciding to do a favor for a friend or close a deal with a potential business partner, decisions have consequences that lead to significant rewards and punishments such as a better relationship with the friend or a poor business transaction. Therefore, it is important to understand how decisions are influenced by the presence or absence of others and how we incorporate social information into our decision-making process. Here we have highlighted differences arising when interacting with human and computer agents and use social psychological theory to provide some explanation for why these differences arise. It is important to point out these differences in social and non-social decision-making because interactions with computers and other machines are becoming more widespread. Businesses often try to find ways to simplify transactions, often replacing human agents with automated computers. However, the decisions made with these different types of agents may affect businesses in unanticipated ways. Financial decisions  are increasingly made through the use of online computers, whereas previously investors had to interact with stockbrokers in an investment firm. Similarly people are able to bid in online auctions for a desired item rather than sitting in a room full of people holding numbered paddles. The decisions to buy and sell stock or possibly overbid in an online auction may be influenced by these different agents, as evidenced by the research described above.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


A coordinate transformation framework for understanding how neurons compute sensorimotor behaviors has generated significant advances toward our understanding of basic brain function. This influential scaffold focuses on neuronal encoding of spatial information represented in different coordinate systems  and how multiple brain regions partake in transforming these signals in order to ultimately generate a motor output. A powerful analogy can be drawn from the coordinate transformation framework to better elucidate how the nervous system computes cognitive variables for social behavior. Of particular relevance is how the brain represents information with respect to oneself and other individuals, such as in reward outcome assignment during social exchanges, in order to influence social decisions. In this article, I outline how the coordinate transformation framework can help guide our understanding of neural computations resulting in social interactions. Implications for numerous psychiatric disorders with impaired representations of self and others are also discussed. The brains of many animals have evolved to deal with an increasing demand for complex social interactions. Interacting with other members in large social groups requires neural representations to be dynamically updated with respect to oneself as well as with respect to other individuals in order to adjust ongoing social behaviors. Even a simple interaction with another individual requires an accurate tracking of actions and outcomes referenced to self and others. Explorations into how the brain computes information necessary to guide social behaviors can thus reveal ecologically valid insights into neural mechanisms underlying complex cognition that might not be tractable otherwise. One might even argue that probing the brain function using socially relevant behavioral tasks is a preferred way to unlock the mystery of “high-level” cognition in highly social species. Furthermore, a failure to accurately represent self and others can result in atypical social behaviors like those that are striking in autism  transform one type of a representation into another , whereas the other focuses on idiosyncratic neuronal representations . For convenience, hereafter I will refer to them as the engineering approach and the connectionist approach, respectively.From the classical engineering perspective, purpose-built networks are designed to compute highly specific quantities under strict rules. This engineering approach emphasizes that every neural representation serves a specific functional purpose using precise quantities. As a classic example, areas 7a neurons not only represent eye-centered target location but also show eye position gain fields  involved in social interactions. Otherwise, normal social interactions simply would not be possible. So, the concept of reference frame is useful for social computations. Accumulating evidence suggests the presence of social reference frames during social behavior  are used to mediate social interactions.In what way can neuronal variables represented during social interactions be considered as having reference frames? Let us consider a simple scenario in which two individuals, agent A and agent B, are playing an afternoon chess at a park. For every move that is made, agent A needs to keep track of the actions of both himself and agent B as well as the outcomes for themselves resulting from each move. Agent B also does the same to have a chance at winning. These actions and outcomes tightly coupled to either agent A or B during their competitive exchanges must be reflected in their neuronal signals. More precisely, these variables with respect to self and others need to be either differentiated or coincided during different stages of computations. Although the above example focused on a competitive interaction, tracking self and others' actions and outcomes is similarly importantly for cooperative transactions, such as when agents A and B need to coordinate steering to the right on a canoe to avoid a rock in their way. Furthermore, it is natural to consider that inaccurate or unstable representations of social variables across self- and other-centered frames of reference may directly underlie many of the social deficits observed in multiple psychiatric conditions (see below). It is worthwhile to emphasize, however, that applying the coordinate transformation framework based on spatial reference frames to cognitive domains is an analogy by nature simply because cognitive computations, like those involved in social cognition, are fundamentally different from the sensorimotor computations using the receptive field or place code. Rather, the analogy is beneficial for understanding how social variables represented in different Reward-guided social learning and decision-making have been critical for investigating neural basis of social behaviors , anterior cingulate sulcus (ACCs), orbitofrontal (OFC) cortices, and the regions in the medial frontal cortex (MFC), have characterized how individual neurons modulate activity with respect to events occurring to self and others  and for other-regarding motivations (WO) to account for the different strengths of modulations with respect to self and others. This relative weighting offers a modulatory control over both-centered representations. For instance, when the two weights are equal (WS = WO), the signals with respect to self and other in the both-centered representations will appear to be mirrored. In contrast, a greater influence of self motivational signals (WS > WO) will result in a stronger representation for the signals with respect to self in the both-centered representation, whereas the opposite pattern is apparent when there is a greater influence of other-regarding motivational signals (WS < WO). Such computations may result in differentially modulated activity corresponding to different social contexts, perhaps similar to what has been reported in OFC neurons  (Servan-Schreiber et al., Similar to the heterogeneity of reference frames found for sensorimotor behaviors (Mullette-Gillman, As mentioned in the earlier section, the engineering and the connectionist approaches describe how neuronal variables are encoded and how they are being computed to result in a desired output during sensorimotor behavior. These two theoretical frameworks could be useful for characterizing how social variables are encoded across different brain regions or different computational stages. For example, highly systematic representations of social variables would suggest that the region serves a specific functional purpose using well-defined social quantities to maximize efficiency. For instance, neurons in the population might be tuned to social status using a shared encoding principle. Under this encoding, population average is particularly meaningful (e.g., preferred direction encoding by individual neurons and population vector averaging for movement direction representations; e.g., Georgopoulos et al., A strikingly large number of neuropsychiatric disorders are accompanied by social deficits Insel, . Many ofGin Figure WS and WO in Figure The model in Figure encoded by individual neurons, and how such encoding may evolve across different computational stages. Therefore, the coordinate transformation approach for social interactions may provide valuable insights into how social information used within various computational models, such as reinforcement learning and game theoretic models, is encoded and transformed across different processing stages. Applied in conjunction with the reinforcement learning framework, it may be especially useful for revealing how the brain assigns reward outcomes across different agencies during social interactions. A bright future lies ahead for social neuroscience. We are now well poised to the test different social psychological theories by directly investigating neural mechanisms. An influential computational scaffold like the coordinate transformation framework will help advance our understanding of social cognition, for which the brains of humans and nonhuman primates have evolved to be specialized.A successful social interaction requires one to track the behaviors of oneself as well as the behaviors of another individual, requiring the brain to integrate both motivational and affective variables across interacting individuals (Schilbach 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.


As a side product, we demonstrate how advanced methods from numerical mathematics could help in future to resolve also other difficult experimental neurobiological issues. Figure To truly appreciate the myriad of events which relate synaptic function and vesicle dynamics, simulations should be done in a spatially realistic environment. This holds true in particular in order to explain the rather astonishing motor patterns presented here which we observed within in vivo recordings which underlie peristaltic contractions at a well characterized synapse, the neuromuscular junction (NMJ) of the Drosophila larva. To this end, we have employed a reductionist approach and generated three dimensional models of single presynaptic boutons at the Drosophila larval NMJ. Vesicle dynamics are described by diffusion-like partial differential equations which are solved numerically on unstructured grids using the uG platform. In our model we varied parameters such as bouton-size, vesicle output probability (Po), stimulation frequency and number of synapses, to observe how altering these parameters effected bouton function. Hence we demonstrate that the morphologic and physiologic specialization maybe a convergent evolutionary adaptation to regulate the trade off between sustained, low output, and short term, high output, synaptic signals. There seems to be a biologically meaningful explanation for the co-existence of the two different bouton types as previously observed at the NMJ (characterized especially by the relation between size and P