The Hot (Invisible?) Hand: Can Time Sequence Patterns of Success/Failure in Sports Be Modeled as Repeated Random Independent Trials?

The long lasting debate initiated by Gilovich, Vallone and Tversky in is revisited: does a “hot hand” phenomenon exist in sports? Hereby we come back to one of the cases analyzed by the original study, but with a much larger data set: all free throws taken during five regular seasons () of the National Basketball Association (NBA). Evidence supporting the existence of the “hot hand” phenomenon is provided. However, while statistical traces of this phenomenon are observed in the data, an open question still remains: are these non random patterns a result of “success breeds success” and “failure breeds failure” mechanisms or simply “better” and “worse” periods? Although free throws data is not adequate to answer this question in a definite way, we speculate based on it, that the latter is the dominant cause behind the appearance of the “hot hand” phenomenon in the data.     

The hot hand phenomenon as a cognitive adaptation to clumped resources

The hot hand phenomenon refers to the expectation of “streaks” in sequences of hits and misses whose probabilities are, in fact, independent (e.g., coin tosses, basketball shots). Here we propose that the hot hand phenomenon reflects an evolved psychological assumption that items in the world come in clumps, and that hot hand, not randomness, is our evolved psychological default. In two experiments, American undergraduates and Shuar hunter–horticulturalists participated in computer tasks in which they predicted hits and misses in foraging for fruits, coin tosses, and several other kinds of resources whose distributions were generated randomly. Subjects in both populations exhibited the hot hand assumption across all the resource types. The only exception was for American students predicting coin tosses where hot hand was reduced. These data suggest that hot hand is our evolved psychological default, which can be reduced (though not eliminated) by experience with genuinely independent random phenomena like coin tosses.     

Predicting signatures of "synthetic associations" and "natural associations" from empirical patterns of human genetic variation

Genome-wide association studies (GWAS) have in recent years discovered thousands of associated markers for hundreds of phenotypes. However, associated loci often only explain a relatively small fraction of heritability and the link between association and causality has yet to be uncovered for most loci. Rare causal variants have been suggested as one scenario that may partially explain these shortcomings. Specifically, Dickson et al. recently reported simulations of rare causal variants that lead to association signals of common, tag single nucleotide polymorphisms, dubbed "synthetic associations". However, an open question is what practical implications synthetic associations have for GWAS. Here, we explore the signatures exhibited by such "synthetic associations" and their implications based on patterns of genetic variation observed in human populations, thus accounting for human evolutionary history -a force disregarded in previous simulation studies. This is made possible by human population genetic data from HapMap 3 consisting of both resequencing and array-based genotyping data for the same set of individuals from multiple populations. We report that synthetic associations tend to be further away from the underlying risk alleles compared to "natural associations" (i.e. associations due to underlying common causal variants), but to a much lesser extent than previously predicted, with both the age and the effect size of the risk allele playing a part in this phenomenon. We find that while a synthetic association has a lower probability of capturing causal variants within its linkage disequilibrium block, sequencing around the associated variant need not extend substantially to have a high probability of capturing at least one causal variant. We also show that the minor allele frequency of synthetic associations is lower than of natural associations for most, but not all, loci that we explored. Finally, we find the variance in associated allele frequency to be a potential indicator of synthetic associations.     

The interaction of soil micromycetes with "hot" particles in a model system]

A model system which permits observing for a long time and fixing interaction of fungi with a radiation source has been created on the basis of an isolated "hot" particle, deficient mineral medium (saccharose content 60 mg/l) and suspension of fungal conidia. Five species (six strains) of micromycetes isolated from radionuclide-contaminated soils and fifteen "hot" particles have been tested. It has been found out for the first time that Cladosporium cladosporioides and Penicillium roseo-purpureum are able actively overgrow "hot" particles whose radioactivity did not exceed 3.1-1.0(-7) Ci by gamma-spectrum and to destroy them 50-150 days later. Certain changes in morphology of fungi-destructors of "hot" particles are revealed. A problem on ecological significance of the found phenomenon is discussed.     

Diet variation of common buzzards in Finland supports the alternative prey hypothesis

The regional synchrony of short-term population fluctuations of small rodents and small game has usually been explained by varying impacts of generalist predators subsisting on both voles and small game (the "alternative prey hypothesis" APH). APH says that densities of predators increase as a response to increasing vole densities and then these predators shift their diet from the main prey to the alternative prey when the main prey decline and vice versa. We studied the diet composition of breeding common buzzards Buteo buteo during 1985-92 in western Finland. Microtus voles were the main prey and water voles, shrews, forest grouse, hares and small birds the most important alternative prey. Our data from the between-year variation in the diet composition of buzzards fulfilled the main predictions of APH. The yearly proportion of main prey (Microtus voles) in the diet was higher in years of high than low vole abundance. The proportion of grouse in the diet of buzzards was negatively related to the abundance of Microtus voles in the field and was nearly independent of grouse abundance in the field. In addition, buzzards mainly took grouse chicks and young hares which is consistent with the prediction of APH. Therefore, we conclude that buzzards are able to shift their diet in the way predicted by the APH and that buzzards, together with other generalist predators, may reduce the breeding success of small game in the decline phase of the vole cycle, and thus substantially contribute to the existence of short-term population cycles of small game.     

Cross‐validation strategies for data with temporal,spatial, hierarchical,or phylogenetic structure

Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross‐validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross‐validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also provides ample opportunity for overfitting with non‐causal predictors. This problem can persist even if remedies such as autoregressive models, generalized least squares, or mixed models are used. Block cross‐validation, where data are split strategically rather than randomly, can address these issues. However, the blocking strategy must be carefully considered. Blocking in space, time, random effects or phylogenetic distance, while accounting for dependencies in the data, may also unwittingly induce extrapolations by restricting the ranges or combinations of predictor variables available for model training, thus overestimating interpolation errors. On the other hand, deliberate blocking in predictor space may also improve error estimates when extrapolation is the modelling goal. Here, we review the ecological literature on non‐random and blocked cross‐validation approaches. We also provide a series of simulations and case studies, in which we show that, for all instances tested, block cross‐validation is nearly universally more appropriate than random cross‐validation if the goal is predicting to new data or predictor space, or for selecting causal predictors. We recommend that block cross‐validation be used wherever dependence structures exist in a dataset, even if no correlation structure is visible in the fitted model residuals, or if the fitted models account for such correlations.     

Characterization of "native" apomyoglobin by molecular dynamics simulation.

We have used molecular dynamics simulation methods to study the structure and fluctuations of "native" apomyoglobin in aqueous solution for a period of greater than 0.5 nanosecond. This work was motivated by the recent attempts of Hughson et al. to characterize the structure and motion of both this molecule and the less compact, acid stabilized I stage, using methods of pulsed H/2H exchange. The study of these systems provides new insights into protein folding intermediates and our simulation has yielded a detailed model for structure and fluctuations in apomyoglobin which complements the experimental studies. We find that local (short-time) fluctuations agree well with fluctuations observed for the holoprotein in aqueous solution, as well as results from the crystallographic B-factors. In addition, the structural features we observe for native apomyoglobin are very similar to the holoprotein, in basic agreement with the findings of Hughson et al. By examining larger-scale motions, developing only over timescales in excess of a 100 picoseconds, we are able to identify conformationally "labile" and "non-labile" regions within native apomyoglobin. These regions correspond extremely well to those identified in the nuclear magnetic resonance experiments as unstable and stable "folding subdomains" in the I state of apomyoglobin. Overall we find that helices A, B, E, G and H show the least amount of motion and helices C, D and F move substantially over the timescales examined. The major motions, and the primary difference between the holo and apo structures as we have observed them, are due to the shifting motion of helices C, D and F into the vacant heme cavity. We also find that motions at the interface of helical segments can be large, with one important exception being the chain segment connecting helices G and H. This segment of chain interacts with the conformationally "non-labile" helix A to form a relatively rigid subdomain composed of helices A, G and H. We believe that these findings provide direct support for the suggestion of Hughson et al. that helices A, G and H constitute a compact subdomain that remains in a native-like conformation as the protein begins to unfold in environments of decreasing pH.     

Hitting Is Contagious in Baseball: Evidence from Long Hitting Streaks

Data analysis is used to test the hypothesis that “hitting is contagious”. A statistical model is described to study the effect of a hot hitter upon his teammates’ batting during a consecutive game hitting streak. Box score data for entire seasons comprising streaks of length games, including a total observations were compiled. Treatment and control sample groups () were constructed from core lineups of players on the streaking batter’s team. The percentile method bootstrap was used to calculate confidence intervals for statistics representing differences in the mean distributions of two batting statistics between groups. Batters in the treatment group (hot streak active) showed statistically significant improvements in hitting performance, as compared against the control. Mean for the treatment group was found to be to percentage points higher during hot streaks (mean difference increased points), while the batting heat index introduced here was observed to increase by points. For each performance statistic, the null hypothesis was rejected at the significance level. We conclude that the evidence suggests the potential existence of a “statistical contagion effect”. Psychological mechanisms essential to the empirical results are suggested, as several studies from the scientific literature lend credence to contagious phenomena in sports. Causal inference from these results is difficult, but we suggest and discuss several latent variables that may contribute to the observed results, and offer possible directions for future research.     

Macroscopic fluctuations: periodicity of the manifestation of "near-zone" effect

The "near-zone effect" one of the main manifestations of the phenomenon of macroscopic fluctuations, was further investigated. It was shown that the statistical significance of the near-zone effect varies with time with a period of about 3.5 days. A phenomenological model was proposed to describe the results obtained.     

A possible genetic basis for vulnerability in <Emphasis Type="Italic">Euphydryas maturna</Emphasis> (Lepidoptera: Nymphalidae)

Nearly all of the known populations of Scarce Fritillary, Euphydryas maturna (Linnaeus, 1758), are declining in Western and Central Europe. In order to identify the possible reasons for its vulnerability we surveyed the population genetics of this butterfly species using multi-locus genotype data. Females of our target species lay lots of eggs in one or two batches only and pre-hibernation caterpillars live and feed gregariously in a nest. As a consequence, a random unfavourable event can eliminate most offspring of a particular female resulting in a strong genetic drift effect combined with inbreeding. Thus, our hypothesis regarding the genetic composition of Scarce Fritillary populations suggests that: (1) there will be random fluctuations in allele frequencies from generation to generation; (2) populations should exhibit small effective sizes and a relatively high level of heterozygote deficiency, and; (3) the majority of the individuals in a population will be composed of the offspring of just a few females. In order to test these hypotheses, fine-scale genetic structure was studied in two subpopulations of a Hungarian Scarce Fritillary population for 4 consecutive years (generations) using enzyme polymorphism data. The results supported all of our assumptions. We detected random fluctuation in the frequency of several alleles, small effective population size and the index of heterozygote deficiency (F _IS) indicated a considerable level of inbreeding in most samples. Furthermore, average values of relatedness were also fairly high, and we were able to identify 17 putative sib families in total with the two subpopulations based on estimation of individual gametic phases. Thus, the present study suggests that intrinsic factors (e.g. specific life history) might increase the sensitivity of a species to various threatening factors (e.g. habitat loss or fragmentation) and result in the vulnerability of the given species.     

Change of dynamic regimes in the population of species with short life cycles: Results of an analytical and numerical study

There is a phenomenon of multiregimism found in the elementary mathematical model of population dynamics, meaning the possibility for different dynamic regimes to exist under the same conditions, with transition to these regimes dependent on the initial numerical values. The effect in question comes into existence in the model which has several different limiting regimes (attractors): equilibrium, regular fluctuations, and chaotic attractor. The revealed phenomenon of multiregimism lets us explain the initiation of fluctuations as well as disappearance of fluctuations. Adequacy of the model's dynamic regimes is depicted by their correlation with the actual dynamics of population size of bank vole (Myodes glareolus). It is shown that the impact of climatic factors on a reproductive process of a population noticeably extends the range of possible dynamic regimes and, in fact, leads to random migration over attraction basins of these regimes.     

Noise-Driven Causal Inference in Biomolecular Networks

Single-cell RNA and protein concentrations dynamically fluctuate because of stochastic ("noisy") regulation. Consequently, biological signaling and genetic networks not only translate stimuli with functional response but also random fluctuations. Intuitively, this feature manifests as the accumulation of fluctuations from the network source to the target. Taking advantage of the fact that noise propagates directionally, we developed a method for causation prediction that does not require time-lagged observations and therefore can be applied to data generated by destructive assays such as immunohistochemistry. Our method for causation prediction, "Inference of Network Directionality Using Covariance Elements (INDUCE)," exploits the theoretical relationship between a change in the strength of a causal interaction and the associated changes in the single cell measured entries of the covariance matrix of protein concentrations. We validated our method for causation prediction in two experimental systems where causation is well established: in an E. coli synthetic gene network, and in MEK to ERK signaling in mammalian cells. We report the first analysis of covariance elements documenting noise propagation from a kinase to a phosphorylated substrate in an endogenous mammalian signaling network.     

Cytogerontology at the beginning of the third millenium: from "correlative" to "gist" models

For the most part, research in the area of cytogerontology, i.e., investigation of the mechanisms of aging in the experiments on cultured cells, is carried out using the "Hayflick's model". More than forty years have passed since the appearance of that model, and during this period of time, very much data were obtained on its basis. These data contributed significantly to our knowledge of the behavior of both animal and human cultured cells. Specifically, we already know of the mechanisms underlying the aging in vitro. On the other hand, in my opinion, little has changed in our knowledge of the aging of the whole organism. In all likelihood, this can be explained by that the Hayflick's model is, like many others used in the experimental gerontology, correlative, i.e. based on a number of detected correlations. In the case of Hayflick's model, these are correlations between the mitotic potential of cells (cell population doubling potential) and some "gerontological" parameters and indices: species life-span, donor age, evidence of progeroid syndromes, etc., as well as various changes of normal (diploid) cells during long-term cultivation and during aging of the organism. It is, however, well known that very frequently a good correlation has nothing to do with the essence (gist) of the phenomenon. For example, we do know that the amount of gray hair correlates quite well with the age of an individual but is in no way related to the mechanisms of his/her aging and probability of death. In this case, the absence of cause-effect relationships is evident, which are, at the same time, indispensable for the development of gist models. These models, as distinct from the correlative ones, are based on a certain concept of aging. In the case of Hayflick's model, such a concept is absent: we cannot explain, using the "Hayflick's limit", why our organism ages. This conclusion was convincingly confirmed by the discovery of telomere mechanism which determines the aging of cells in vitro. That discovery initiated the appearance of theories attempting to explain the process of aging in vivo also on its basis. However, it has become clear that the mechanisms of aging of the entire organism, located, apparently, in its postmitotic cells, such as neurons or cardiomyocytes, cannot be explained in the framework of this approach. Hence, we believe that it is essential to develop "gist" models of aging using cultured cells. The mechanisms of cell aging in such models should be similar to the mechanisms of cell aging in the entire organism. Our "stationary phase aging" model could be one of such models, which is based on the assumption of the leading role of cell proliferation restriction in the processes of aging. We assume that the accumulation of "senile" damage is caused by the restriction of cell proliferation either due to the formation of differentiated cell populations during development (in vivo) or to the existence of saturation density phenomenon (in vitro). Cell proliferation changes themselves do not induce aging, they only lead to the accumulation of macromolecular defects, which, in turn, lead to the deterioration of tissues, organs, and, eventually, of the entire organism, increasing the probability of its death. Within the framework of our model, we define cell aging as the accumulation in a cell population of various types of damage identical to the damage arising in senescing multicellular organism. And, finally, it is essential to determine how the cell is dying and what the death of the cell is. These definitions will help to draw real parallels between the "genuine" aging of cells (i.e., increasing probability of their death with "age") and the aging of multicellular organisms.     

Genealogy of wine grape cultivars: "Pinot" is related to "Syrah"

Since the domestication of wild grapes ca 6000 years ago, numerous cultivars have been generated by spontaneous or deliberate crosses, and up to 10 000 are still in existence today. Just as in human paternity analysis, DNA typing can reveal unexpected parentage of grape cultivars. In this study, we have analysed 89 grape cultivars with 60 microsatellite markers in order to accurately calculate the identity-by-descent (IBD) and relatedness (r) coefficients among six putatively related cultivars from France ("Pinot", "Syrah" and "Dureza") and northern Italy ("Teroldego", "Lagrein" and "Marzemino"). Using a recently developed likelihood-based approach to analyse kinship in grapes, we provide the first evidence of a genetic link between grapes across the Alps: "Dureza" and "Teroldego" turn out to be full-siblings (FS). For the first time in grapevine genetics we were able to detect FS without knowing one of the parents and identify unexpected second-degree relatives. We reconstructed the most likely pedigree that revealed a third-degree relationship between the worldwide-cultivated "Pinot" from Burgundy and "Syrah" from the Rhone Valley. Our finding was totally unsuspected by classical ampelography and it challenges the commonly assumed independent origins of these grape cultivars. Our results and this new approach in grape genetics will (a) help grape breeders to avoid choosing closely related varieties for new crosses, (b) provide pedigrees of cultivars in order to detect inheritance of disease-resistance genes and (c) open the way for future discoveries of first- and second-degree relationships between grape cultivars in order to better understand viticultural migrations.     

Fractal properties of human muscle sympathetic nerve activity

Muscle sympathetic nerve activity (MSNA) in resting humans is characterized by cardiac-related bursts of variable amplitude that occur sporadically or in clusters. The present study was designed to characterize the fluctuations in the number of MSNA bursts, interburst interval, and burst amplitude recorded from the peroneal nerve of 15 awake, healthy human subjects. For this purpose, we used the Allan and Fano factor analysis and dispersional analysis to test whether the fluctuations were time-scale invariant (i.e., fractal) or random in occurrence. Specifically, we measured the slopes of the power laws in the Allan factor, Fano factor, and dispersional analysis curves. In addition, the Hurst exponent was calculated from the slope of the power law in the Allan factor curve. Whether the original time series contained fractal fluctuations was decided on the basis of a comparison of the values of these parameters with those for surrogate data blocks. The results can be summarized as follows. Fluctuations in the number of MSNA bursts and interburst interval were fractal in each of the subjects, and fluctuations in burst amplitude were fractal in four of the subjects. We also found that fluctuations in the number of heartbeats and heart period (R-R interval) were fractal in each of the subjects. These results demonstrate for the first time that apparently random fluctuations in human MSNA are, in fact, dictated by a time-scale-invariant process that imparts "long-term memory" to the sequence of cardiac-related bursts. Whether sympathetic outflow to the heart also is fractal and contributes to the fractal component of heart rate variability remains an open question.     

Stochastic fluctuations in frequency-dependent selection: a one-locus, two-allele and two-phenotype model

Stochastic fluctuations in a simple frequency-dependent selection model with one-locus, two-alleles and two-phenotypes are investigated. The steady-state statistics of allele frequencies for an interior stable phenotypic equilibrium are shown to be similar to the stochastic fluctuations in standard evolutionary game dynamics [Tao, Y., Cressman, R., 2007. Stochastic fluctuations through intrinsic noise in evolutionary game dynamics. Bull. Math. Biol. 69, 1377-1399]. On the other hand, for an interior stable phenotypic or genotypic equilibrium, our main results show that the deterministic model cannot be used to predict the expectation of phenotypic frequency. The variance of phenotypic frequency for an interior stable genotypic equilibrium is more sensitive to the expected population size than for an interior stable phenotypic equilibrium. Furthermore, the stochastic fluctuations of allele frequency and phenotypic frequency can be considered approximately independent of each other for these genotypic equilibria, but not for phenotypic.     

Ecological assembly rules in plant communities—approaches,patterns and prospects

Understanding how communities of living organisms assemble has been a central question in ecology since the early days of the discipline. Disentangling the different processes involved in community assembly is not only interesting in itself but also crucial for an understanding of how communities will behave under future environmental scenarios. The traditional concept of assembly rules reflects the notion that species do not co‐occur randomly but are restricted in their co‐occurrence by interspecific competition. This concept can be redefined in a more general framework where the co‐occurrence of species is a product of chance, historical patterns of speciation and migration, dispersal, abiotic environmental factors, and biotic interactions, with none of these processes being mutually exclusive. Here we present a survey and meta‐analyses of 59 papers that compare observed patterns in plant communities with null models simulating random patterns of species assembly. According to the type of data under study and the different methods that are applied to detect community assembly, we distinguish four main types of approach in the published literature: species co‐occurrence, niche limitation, guild proportionality and limiting similarity. Results from our meta‐analyses suggest that non‐random co‐occurrence of plant species is not a widespread phenomenon. However, whether this finding reflects the individualistic nature of plant communities or is caused by methodological shortcomings associated with the studies considered cannot be discerned from the available metadata. We advocate that more thorough surveys be conducted using a set of standardized methods to test for the existence of assembly rules in data sets spanning larger biological and geographical scales than have been considered until now. We underpin this general advice with guidelines that should be considered in future assembly rules research. This will enable us to draw more accurate and general conclusions about the non‐random aspect of assembly in plant communities.