Animal learning as a source of developmental bias

Research supported in part by a grant from the John Templeton Foundation to K. N. L. (“Putting the extended evolutionary synthesis to the test”, ref 60501), by Japan Society for the Promotion of Science KAKENHI to W. T. (ref 17J01559), and a grant from the Netherlands Organization for Scientific Research to T. O. (ref 019.172EN.011).As a form of adaptive plasticity that allows organisms to shift their phenotype toward the optimum, learning is inherently a source of developmental bias. Learning may be of particular significance to the evolutionary biology community because it allows animals to generate adaptively biased novel behavior tuned to the environment and, through social learning, to propagate behavioral traits to other individuals, also in an adaptively biased manner. We describe several types of developmental bias manifest in learning, including an adaptive bias, historical bias, origination bias, and transmission bias, stressing that these can influence evolutionary dynamics through generating nonrandom phenotypic variation and/or nonrandom environmental states. Theoretical models and empirical data have established that learning can impose direction on adaptive evolution, affect evolutionary rates (both speeding up and slowing down responses to selection under different conditions) and outcomes, influence the probability of populations reaching global optimum, and affect evolvability. Learning is characterized by highly specific, path‐dependent interactions with the (social and physical) environment, often resulting in new phenotypic outcomes. Consequently, learning regularly introduces novelty into phenotype space. These considerations imply that learning may commonly generate plasticity first evolution.PostprintPeer reviewe

Human Cell Atlas and cell-type authentication for regenerative medicine

In modern biology, the correct identification of cell types is required for the developmental study of tissues and organs and the production of functional cells for cell therapies and disease modeling. For decades, cell types have been defined on the basis of morphological and physiological markers and, more recently, immunological markers and molecular properties. Recent advances in single-cell RNA sequencing have opened new doors for the characterization of cells at the individual and spatiotemporal levels on the basis of their RNA profiles, vastly transforming our understanding of cell types. The objective of this review is to survey the current progress in the field of cell-type identification, starting with the Human Cell Atlas project, which aims to sequence every cell in the human body, to molecular marker databases for individual cell types and other sources that address cell-type identification for regenerative medicine based on cell data guidelines

Study on the Prognosis of Tuberculous Meningitis Treated with Streptomycin in Children

この論文は国立情報学研究所の学術雑誌公開支援事業により電子化されました

Self-force Regularization in the Schwarzschild Spacetime

We discuss the gravitational self-force on a particle in a black hole space-time. For a point particle, the full (bare) self-force diverges. The metric perturbation induced by a particle can be divided into two parts, the direct part (or the S part) and the tail part (or the R part), in the harmonic gauge, and the regularized self-force is derived from the R part which is regular and satisfies the source-free perturbed Einstein equations. But this formulation is abstract, so when we apply to black hole-particle systems, there are many problems to be overcome in order to derive a concrete self-force. These problems are roughly divided into two parts. They are the problem of regularizing the divergent self-force, i.e., ``subtraction problem'' and the problem of the singularity in gauge transformation, i.e., ``gauge problem''. In this paper, we discuss these problems in the Schwarzschild background and report some recent progress.Comment: 34 pages, 2 figures, submitted to CQG, special volume for Radiation Reaction (CAPRA7

Social learning strategies regulate the wisdom and madness of interactive crowds

This experiment was supported by The John Templeton Foundation (40128 to K.N.L.) and Suntory Foundation research support (2015-311 to W.T.). The computer simulations and computational model analyses were supported by JSPS overseas research fellowships (H27-11 to W.T.). The phenomenological model analyses were supported by JSPS KAKENHI (grant number 17J01559).Why groups of individuals sometimes exhibit collective ‘wisdom’ and other times maladaptive ‘herding’ is an enduring conundrum. Here we show that this apparent conflict is regulated by the social learning strategies deployed. We examined the patterns of human social learning through an interactive online experiment with 699 participants, varying both task uncertainty and group size, then used hierarchical Bayesian model fitting to identify the individual learning strategies exhibited by participants. Challenging tasks elicit greater conformity among individuals, with rates of copying increasing with group size, leading to high probabilities of herding among large groups confronted with uncertainty. Conversely, the reduced social learning of small groups, and the greater probability that social information would be accurate for less-challenging tasks, generated ‘wisdom of the crowd’ effects in other circumstances. Our model-based approach provides evidence that the likelihood of collective intelligence versus herding can be predicted, resolving a long-standing puzzle in the literature.PostprintPeer reviewe

The Epistatic Relationship between BRCA2 and the Other RAD51 Mediators in Homologous Recombination

RAD51 recombinase polymerizes at the site of double-strand breaks (DSBs) where it performs DSB repair. The loss of RAD51 causes extensive chromosomal breaks, leading to apoptosis. The polymerization of RAD51 is regulated by a number of RAD51 mediators, such as BRCA1, BRCA2, RAD52, SFR1, SWS1, and the five RAD51 paralogs, including XRCC3. We here show that brca2-null mutant cells were able to proliferate, indicating that RAD51 can perform DSB repair in the absence of BRCA2. We disrupted the BRCA1, RAD52, SFR1, SWS1, and XRCC3 genes in the brca2-null cells. All the resulting double-mutant cells displayed a phenotype that was very similar to that of the brca2-null cells. We suggest that BRCA2 might thus serve as a platform to recruit various RAD51 mediators at the appropriate position at the DNA–damage site.</p

Theory and application of explicitly correlated Gaussians

The variational method complemented with the use of explicitly correlated Gaussian basis functions is one of the most powerful approaches currently used for calculating the properties of few-body systems. Despite its conceptual simplicity, the method offers great flexibility, high accuracy, and can be used to study diverse quantum systems, ranging from small atoms and molecules to light nuclei, hadrons, quantum dots, and Efimov systems. The basic theoretical foundations are discussed, recent advances in the applications of explicitly correlated Gaussians in physics and chemistry are reviewed, and the strengths and weaknesses of the explicitly correlated Gaussians approach are compared with other few-body technique

The Aquarius Superclusters - I. Identification of Clusters and Superclusters

We study the distribution of galaxies and galaxy clusters in a 10^deg x 6^deg field in the Aquarius region. In addition to 63 clusters in the literature, we have found 39 new candidate clusters using a matched-filter technique and a counts-in-cells analysis. From redshift measurements of galaxies in the direction of these cluster candidates, we present new mean redshifts for 31 previously unobserved clusters, while improved mean redshifts are presented for 35 other systems. About 45% of the projected density enhancements are due to the superposition of clusters and/or groups of galaxies along the line of sight, but we could confirm for 72% of the cases that the candidates are real physical associations similar to the ones classified as rich galaxy clusters. On the other hand, the contamination due to galaxies not belonging to any concentration or located only in small groups along the line of sight is ~ 10%. Using a percolation radius of 10 h^{-1} Mpc (spatial density contrast of about 10), we detect two superclusters of galaxies in Aquarius, at z = 0.086 and at z = 0.112, respectively with 5 and 14 clusters. The latter supercluster may represent a space overdensity of about 160 times the average cluster density as measured from the Abell et al. (1989) cluster catalog, and is possibly connected to a 40 h^{-1} Mpc filament from z ~ 0.11 to 0.14.Comment: LateX text (21 pages) and 12 (ps/eps/gif) figures; figures 5a, 5b and 6 are not included in the main LateX text; to be published in the Astronomical Journal, March issu