The hidden structure of overimitation.
Ontology highlight
ABSTRACT:
SUBMITTER: Lyons DE
PROVIDER: S-EPMC2148370 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Overimitation - the copying of another's unnecessary or irrelevant actions toward a goal - is largely considered to be uniquely human. Recent studies, however, have found evidence of this behavior in dogs. Humans seem to overimitate more or less depending on social factors, such as the cultural origin of the demonstrator. Like humans, dogs may have social motivations behind their overimitation, since they have been shown to copy irrelevant actions more from their caregivers than from strangers. By using priming methodology, this study aimed to investigate whether dogs' overimitation can be facilitated via the experimental manipulation of their attachment-based motivations. To test this, we invited caregivers to demonstrate goal-irrelevant and relevant actions to their dog, following either a dog-caregiver relationship prime, a dog-caregiver attention prime, or no prime. Our results showed no significant main effect of priming on copying behavior for either relevant or irrelevant actions, but we found a trend that unprimed dogs copied the least actions overall. Additionally, dogs copied their caregiver's relevant actions more often and more faithfully as the number of trials increased. Our final finding was that dogs were much more likely to copy irrelevant actions after (rather than before) already achieving the goal. This study discusses the social motivations behind dog imitative behavior, and has potential methodological implications regarding the influence of priming on dog behavioral studies.
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Project description:Enamel is the hardest and most resilient tissue in the human body. Enamel includes morphologically aligned, parallel, ∼50 nm wide, microns-long nanocrystals, bundled either into 5-μm-wide rods or their space-filling interrod. The orientation of enamel crystals, however, is poorly understood. Here we show that the crystalline c-axes are homogenously oriented in interrod crystals across most of the enamel layer thickness. Within each rod crystals are not co-oriented with one another or with the long axis of the rod, as previously assumed: the c-axes of adjacent nanocrystals are most frequently mis-oriented by 1°-30°, and this orientation within each rod gradually changes, with an overall angle spread that is never zero, but varies between 30°-90° within one rod. Molecular dynamics simulations demonstrate that the observed mis-orientations of adjacent crystals induce crack deflection. This toughening mechanism contributes to the unique resilience of enamel, which lasts a lifetime under extreme physical and chemical challenges.
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Project description:A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we attempt to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit, using cascaded linear-nonlinear (LN-LN) models. We combine non-smooth regularization with proximal consensus algorithms to overcome difficulties in fitting such models that arise from the high dimensionality of their parameter space. We apply this framework to retinal ganglion cell processing, learning LN-LN models of retinal circuitry consisting of thousands of parameters, using 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. Internal nonlinear subunits of the model match properties of retinal bipolar cells in both receptive field structure and number. Subunits have consistently high thresholds, supressing all but a small fraction of inputs, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model's parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space, a central tenet of efficient coding theory, originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our methods are statistically and computationally efficient, enabling us to rapidly learn hierarchical non-linear models as well as efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. This general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data.
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Project description:Population structure influences genealogical patterns, however, data pertaining to how populations are structured are often unavailable or not directly observable. Inference of population structure is highly important in molecular epidemiology where pathogen phylogenetics is increasingly used to infer transmission patterns and detect outbreaks. Discrepancies between observed and idealized genealogies, such as those generated by the coalescent process, can be quantified, and where significant differences occur, may reveal the action of natural selection, host population structure, or other demographic and epidemiological heterogeneities. We have developed a fast non-parametric statistical test for detection of cryptic population structure in time-scaled phylogenetic trees. The test is based on contrasting estimated phylogenies with the theoretically expected phylodynamic ordering of common ancestors in two clades within a coalescent framework. These statistical tests have also motivated the development of algorithms which can be used to quickly screen a phylogenetic tree for clades which are likely to share a distinct demographic or epidemiological history. Epidemiological applications include identification of outbreaks in vulnerable host populations or rapid expansion of genotypes with a fitness advantage. To demonstrate the utility of these methods for outbreak detection, we applied the new methods to large phylogenies reconstructed from thousands of HIV-1 partial pol sequences. This revealed the presence of clades which had grown rapidly in the recent past and was significantly concentrated in young men, suggesting recent and rapid transmission in that group. Furthermore, to demonstrate the utility of these methods for the study of antimicrobial resistance, we applied the new methods to a large phylogeny reconstructed from whole genome Neisseria gonorrhoeae sequences. We find that population structure detected using these methods closely overlaps with the appearance and expansion of mutations conferring antimicrobial resistance. [Antimicrobial resistance; coalescent; HIV; population structure.].
| S-EPMC8559910 | biostudies-literature
Project description:Children 'overimitate' causally irrelevant actions in experiments where both irrelevant and relevant actions involve a single common tool. This study design may make it harder for children to recognize the irrelevant actions, as the perceived functionality of the tool during the demonstration of the relevant action may be carried over to the irrelevant action, potentially increasing overimitation. Moreover, little is known how overimitation is affected by the demonstrator's expressed emotions and the child's prior success with the task. Here, 131 nine- to ten-year-old French and German children first engaged in a tool-based task, being successful or unsuccessful, and then watched an adult demonstrating the solution involving one irrelevant and one relevant action before smiling or remaining neutral. These actions were performed with the same tool or with two separate tools, testing potential carry-over effects of the functionality of the relevant action on the irrelevant action. We show that overimitation was higher when the same tool was used for both actions and when children were previously unsuccessful, but was not affected by the demonstrator's displayed emotion. Our results suggest that future overimitation research should account for the number of tools used in a demonstration and participants' previous task experience.
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Project description:BackgroundOne of the most powerful methods for the prediction of protein structure from sequence information alone is the iterative construction of profile-type models. Because profiles are built from sequence alignments, the sequences included in the alignment and the method used to align them will be important to the sensitivity of the resulting profile. The inclusion of highly diverse sequences will presumably produce a more powerful profile, but distantly related sequences can be difficult to align accurately using only sequence information. Therefore, it would be expected that the use of protein structure alignments to improve the selection and alignment of diverse sequence homologs might yield improved profiles. However, the actual utility of such an approach has remained unclear.ResultsWe explored several iterative protocols for the generation of profile hidden Markov models. These protocols were tailored to allow the inclusion of protein structure alignments in the process, and were used for large-scale creation and benchmarking of structure alignment-enhanced models. We found that models using structure alignments did not provide an overall improvement over sequence-only models for superfamily-level structure predictions. However, the results also revealed that the structure alignment-enhanced models were complimentary to the sequence-only models, particularly at the edge of the "twilight zone". When the two sets of models were combined, they provided improved results over sequence-only models alone. In addition, we found that the beneficial effects of the structure alignment-enhanced models could not be realized if the structure-based alignments were replaced with sequence-based alignments. Our experiments with different iterative protocols for sequence-only models also suggested that simple protocol modifications were unable to yield equivalent improvements to those provided by the structure alignment-enhanced models. Finally, we found that models using structure alignments provided fold-level structure assignments that were superior to those produced by sequence-only models.ConclusionWhen attempting to predict the structure of remote homologs, we advocate a combined approach in which both traditional models and models incorporating structure alignments are used.
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Project description:The clinical presentation of patients with schizophrenia has long been described to be very heterogeneous. Coherent symptom profiles can probably be directly derived from behavioral manifestations quantified in medical questionnaires. The combination of machine learning algorithms and an international multi-site dataset (n = 218 patients) identified distinctive patterns underlying schizophrenia from the widespread PANSS questionnaire. Our clustering approach revealed a negative symptom patient group as well as a moderate and a severe group, giving further support for the existence of schizophrenia subtypes. Additionally, emerging regression analyses uncovered the most clinically predictive questionnaire items. Small subsets of PANSS items showed convincing forecasting performance in single patients. These item subsets encompassed the entire symptom spectrum confirming that the different facets of schizophrenia can be shown to enable improved clinical diagnosis and medical action in patients. Finally, we did not find evidence for complicated relationships among the PANSS items in our sample. Our collective results suggest that identifying best treatment for a given individual may be grounded in subtle item combinations that transcend the long-trusted positive, negative, and cognitive categories.
| S-EPMC6207565 | biostudies-literature
Project description:BackgroundSecondary structure prediction is a useful first step toward 3D structure prediction. A number of successful secondary structure prediction methods use neural networks, but unfortunately, neural networks are not intuitively interpretable. On the contrary, hidden Markov models are graphical interpretable models. Moreover, they have been successfully used in many bioinformatic applications. Because they offer a strong statistical background and allow model interpretation, we propose a method based on hidden Markov models.ResultsOur HMM is designed without prior knowledge. It is chosen within a collection of models of increasing size, using statistical and accuracy criteria. The resulting model has 36 hidden states: 15 that model alpha-helices, 12 that model coil and 9 that model beta-strands. Connections between hidden states and state emission probabilities reflect the organization of protein structures into secondary structure segments. We start by analyzing the model features and see how it offers a new vision of local structures. We then use it for secondary structure prediction. Our model appears to be very efficient on single sequences, with a Q3 score of 68.8%, more than one point above PSIPRED prediction on single sequences. A straightforward extension of the method allows the use of multiple sequence alignments, rising the Q3 score to 75.5%.ConclusionThe hidden Markov model presented here achieves valuable prediction results using only a limited number of parameters. It provides an interpretable framework for protein secondary structure architecture. Furthermore, it can be used as a tool for generating protein sequences with a given secondary structure content.
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Project description:Mutations that add, subtract, rearrange, or otherwise refashion genome structure often affect phenotypes, although the fragmented nature of most contemporary assemblies obscures them. To discover such mutations, we assembled the first new reference-quality genome of Drosophila melanogaster since its initial sequencing. By comparing this new genome to the existing D. melanogaster assembly, we created a structural variant map of unprecedented resolution and identified extensive genetic variation that has remained hidden until now. Many of these variants constitute candidates underlying phenotypic variation, including tandem duplications and a transposable element insertion that amplifies the expression of detoxification-related genes associated with nicotine resistance. The abundance of important genetic variation that still evades discovery highlights how crucial high-quality reference genomes are to deciphering phenotypes.
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Project description:BACKGROUND:Histone H1 is the most mobile histone in the cell nucleus. Defining the positions of H1 on chromatin in situ, therefore, represents a challenge. Immunoprecipitation of formaldehyde-fixed and sonicated chromatin, followed by DNA sequencing (xChIP-seq), is traditionally the method for mapping histones onto DNA elements. But since sonication fragmentation precedes ChIP, there is a consequent loss of information about chromatin higher-order structure. Here, we present a new method, xxChIP-seq, employing antibody binding to fixed intact in situ chromatin, followed by extensive washing, a second fixation, sonication and immunoprecipitation. The second fixation is intended to prevent the loss of specifically bound antibody during washing and subsequent sonication and to prevent antibody shifting to epitopes revealed by the sonication process. In many respects, xxChIP-seq is comparable to immunostaining microscopy, which also involves interaction of the primary antibody with fixed and permeabilized intact cells. The only epitopes displayed after immunostaining are the "exposed" epitopes, not "hidden" by the fixation of chromatin higher-order structure. Comparison of immunoprecipitated fragments between xChIP-seq versus xxChIP-seq should indicate which epitopes become inaccessible with fixation and identify their associated DNA elements. RESULTS:We determined the genomic distribution of histone variants H1.2 and H1.5 in human myeloid leukemia cells HL-60/S4 and compared their epitope exposure by both xChIP-seq and xxChIP-seq, as well as high-resolution microscopy, illustrating the influences of preserved chromatin higher-order structure in situ. We found that xChIP and xxChIP H1 signals are in general negatively correlated, with differences being more pronounced near active regulatory regions. Among the intriguing observations, we find that transcription-related regions and histone PTMs (i.e., enhancers, promoters, CpG islands, H3K4me1, H3K4me3, H3K9ac, H3K27ac and H3K36me3) exhibit significant deficiencies (depletions) in H1.2 and H1.5 xxChIP-seq reads, compared to xChIP-seq. These observations suggest the existence of in situ transcription-related chromatin higher-order structures stabilized by formaldehyde. CONCLUSION:Comparison of H1 xxChIP-seq to H1 xChIP-seq allows the development of hypotheses on the chromosomal localization of (stabilized) higher-order structure, indicated by the generation of "hidden" H1 epitopes following formaldehyde crosslinking. Changes in H1 epitope exposure surrounding averaged chromosomal binding sites or epigenetic modifications can also indicate whether these sites have chromatin higher-order structure. For example, comparison between averaged active or inactive promoter regions suggests that both regions can acquire stabilized higher-order structure with hidden H1 epitopes. However, the H1 xChIP-seq comparison cannot define their differences. Application of the xxChIP-seq versus H1 xChIP-seq method is particularly relevant to chromatin-associated proteins, such as linker histones, that play dynamic roles in establishing chromatin higher-order structure.
| S-EPMC7276084 | biostudies-literature