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Analysis of the relationship between chromosomal structural variation (synteny breaks) and 3D-chromatin architectural changes among closely related species has the potential to reveal causes and correlates between chromosomal change and chromatin remodeling. Of note, contrary to extensive studies in animal species, the pace and pattern of chromatin architectural changes following the speciation of plants remain unexplored; moreover, there is little exploration of the occurrence of synteny breaks in the context of multiple genome topological hierarchies within the same model species.

Here we used Hi-C and epigenomic analyses to characterize and compare the profiles of hierarchical chromatin architectural features in representative species of the cotton tribe (Gossypieae), includingGossypium arboreum,Gossypium raimondii, andGossypioides kirkii, which differ with respect to chromosome rearrangements. We found that (i) overall chromatin architectural territories were preserved inGossypioidesandGossypium, which was reflected in their similar intra-chromosomal contact patterns and spatial chromosomal distributions; (ii) the non-random preferential occurrence of synteny breaks in A compartment significantly associate with the B-to-A compartment switch in syntenic blocks flanking synteny breaks; (iii) synteny changes co-localize with open-chromatin boundaries of topologically associating domains, while TAD stabilization has a greater influence on regulating orthologous expression divergence than do rearrangements; and (iv) rearranged chromosome segments largely maintain ancestralin-cisinteractions.

Our findings provide insights into the non-random occurrence of epigenomic remodeling relative to the genomic landscape and its evolutionary and functional connections to alterations of hierarchical chromatin architecture, on a known evolutionary timescale.

 

We propose a test-based elastic integrative analysis of the randomised trial and real-world data to estimate treatment effect heterogeneity with a vector of known effect modifiers. When the real-world data are not subject to bias, our approach combines the trial and real-world data for efficient estimation. Utilising the trial design, we construct a test to decide whether or not to use real-world data. We characterise the asymptotic distribution of the test-based estimator under local alternatives. We provide a data-adaptive procedure to select the test threshold that promises the smallest mean square error and an elastic confidence interval with a good finite-sample coverage property.

 

Ocean spray aerosol formed by bubble bursting are at the core of a broad range of atmospheric processes: they are efficient cloud condensation nuclei and carry a variety of chemical, biological, and biomass material from the surface of the ocean to the atmosphere. The origin and composition of these aerosols is sensibly controlled by the detailed fluid mechanics of bubble bursting. This perspective summarizes our present-day knowledge on how bursting bubbles at the surface of a liquid pool contribute to its fragmentation, namely to the formation of droplets stripped from the pool, and associated mechanisms. In particular, we describe bounds and yields for each distinct mechanism, and the way they are sensitive to the bubble production and environmental conditions. We also underline the consequences of each mechanism on some of the many air-sea interactions phenomena identified to date. Attention is specifically payed at delimiting the known from the unknown and the certitudes from the speculations.

 

A promising approach to solving challenging long-horizon tasks has been to extract behavior priors (skills) by fitting generative models to large offline datasets of demonstrations. However, such generative models inherit the biases of the underlying data and result in poor and unusable skills when trained on imperfect demonstration data. To better align skill extraction with human intent we present Skill Preferences (SkiP), an algorithm that learns a model over human preferences and uses it to extract human-aligned skills from offline data. After extracting human-preferred skills, SkiP also utilizes human feedback to solve downstream tasks with RL. We show that SkiP enables a simulated kitchen robot to solve complex multi-step manipulation tasks and substantially outperforms prior leading RL algorithms with human preferences as well as leading skill extraction algorithms without human preferences. 

Complementary features of randomized controlled trials (RCTs) and observational studies (OSs) can be used jointly to estimate the average treatment effect of a target population. We propose a calibration weighting estimator that enforces the covariate balance between the RCT and OS, therefore improving the trial-based estimator's generalizability. Exploiting semiparametric efficiency theory, we propose a doubly robust augmented calibration weighting estimator that achieves the efficiency bound derived under the identification assumptions. A nonparametric sieve method is provided as an alternative to the parametric approach, which enables the robust approximation of the nuisance functions and data-adaptive selection of outcome predictors for calibration. We establish asymptotic results and confirm the finite sample performances of the proposed estimators by simulation experiments and an application on the estimation of the treatment effect of adjuvant chemotherapy for early-stage non-small-cell lung patients after surgery.