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Users of forced-choice questionnaires (FCQs) to measure personality commonly assume statement parameter invariance across contexts – between Likert and forced-choice (FC) items and between different FC items that share a common statement. In this paper, an empirical study was designed to check these two assumptions for an FCQ assessment measuring interpersonal and intrapersonal skills. We compared parameters of common statements between two Likert forms and two FCQ forms with a block size of two (statement pairs) and among five FCQ pair forms. In three of the five FCQ forms, statements were paired only with a statement they had appeared with in a triplet block. In the other two FCQ forms, statements were paired with statements they had not been paired with in a triplet block. This design allows us to evaluate statement parameter changes due to changes in context. The results do not support the statement parameter invariance assumption between Likert and FC items or the assumption between FC items when recombining statements form new items. However, the assumption between FC items was generally held for pairs formed by dropping a statement from a triplet item. There were some suggestions for sources of context effects, but the analyses were not definitive. Implications of the findings for test practice are discussed. 

The evolution of reproductive mode is expected to have profound impacts on the genetic composition of populations. At the same time, ecological interactions can generate close associations among species, which can in turn generate a high degree of overlap in their spatial distributions. Caenorhabditis elegans is a hermaphroditic nematode that has enabled extensive advances in developmental genetics. Caenorhabditis inopinata, the sister species of C. elegans, is a gonochoristic nematode that thrives in figs and obligately disperses on fig wasps. Here, we describe patterns of genomic diversity in C. inopinata. We performed RAD-seq on individual worms isolated from the field across three Okinawan island populations. C. inopinata is about five times more diverse than C. elegans. Additionally, C. inopinata harbors greater differences in diversity among functional genomic regions (such as between genic and intergenic sequences) than C. elegans. Conversely, C. elegans harbors greater differences in diversity between high-recombining chromosome arms and low-recombining chromosome centers than C. inopinata. FST is low among island population pairs, and clear population structure could not be easily detected among islands, suggesting frequent migration of wasps between islands. These patterns of population differentiation appear comparable with those previously reported in its fig wasp vector. These results confirm many theoretical population genetic predictions regarding the evolution of reproductive mode and suggest C. inopinata population dynamics may be driven by wasp dispersal. This work sets the stage for future evolutionary genomic studies aimed at understanding the evolution of sex as well as the evolution of ecological interactions.

 

Soft robots promise improved safety and capability over rigid robots when deployed near humans or in complex, delicate, and dynamic environments. However, infinite degrees of freedom and the potential for highly nonlinear dynamics severely complicate their modeling and control. Analytical and machine learning methodologies have been applied to model soft robots but with constraints: quasi-static motions, quasi-linear deflections, or both. Here, we advance the modeling and control of soft robots into the inertial, nonlinear regime. We controlled motions of a soft, continuum arm with velocities 10 times larger and accelerations 40 times larger than those of previous work and did so for high-deflection shapes with more than 110° of curvature. We leveraged a data-driven learning approach for modeling, based on Koopman operator theory, and we introduce the concept of the static Koopman operator as a pregain term in optimal control. Our approach is rapid, requiring less than 5 min of training; is computationally low cost, requiring as little as 0.5 s to build the model; and is design agnostic, learning and accurately controlling two morphologically different soft robots. This work advances rapid modeling and control for soft robots from the realm of quasi-static to inertial, laying the groundwork for the next generation of compliant and highly dynamic robots.

 

We demonstrate entangled-state swapping, within the Hermite–Gaussian (HG) basis of first-order modes, directly from the process of spontaneous parametric downconversion within a nonlinear crystal. The method works by explicitly tailoring the spatial structure of the pump photon such that it resembles the product of the desired entangled spatial modes exiting the crystal. Importantly, the result is an entangled state of balanced HG modes, which may be beneficial in applications that depend on symmetric accumulations of geometric phase through optics or in applications of quantum sensing and imaging with azimuthal sensitivity. Furthermore, the methods are readily adaptable to other spatial mode bases.

 

Over the past several decades, forests worldwide have experienced increases in biotic disturbances caused by insects and plant pathogens – a trend that is expected to continue with climate warming. Whereas the causes and effects of individual biotic disturbances are well studied, spatiotemporal interactions among multiple biotic disturbances are less so, despite their importance to ecosystem function and resilience. Here, we highlight an emerging phenomenon of “hotspots” of biotic disturbances (that is, two or more biotic disturbances that overlap in space and time), documenting trends in recent decades in temperate conifer forests of the western US. We also explore potential mechanisms behind and effects of biotic disturbance hotspots, with particular focus on how altered post‐disturbance recovery (successional pathways) can have profound consequences for ecosystem resilience and biodiversity conservation. Finally, we propose research directions that can elucidate drivers of biotic disturbance hotspots and their ecological effects at various spatial scales, and provide insight into this new knowledge frontier.

 

We present a new upper limit on the cosmic molecular gas density atz= 2.4–3.4 obtained using the first year of observations from the CO Mapping Array Project (COMAP). COMAP data cubes are stacked on the 3D positions of 243 quasars selected from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS) catalog, yielding a 95% upper limit for flux from CO(1–0) line emission of 0.129 Jy km s⁻¹. Depending on the balance of the emission between the quasar host and its environment, this value can be interpreted as an average CO line luminosity$L_{\mathrm{CO}}^{\prime }$of eBOSS quasars of ≤1.26 × 10¹¹K km pc²s⁻¹, or an average molecular gas density${\rho }_{{\mathrm{H}}_2}$in regions of the Universe containing a quasar of ≤1.52 × 10⁸M_⊙cMpc⁻³. The$L_{\mathrm{CO}}^{\prime }$upper limit falls among CO line luminosities obtained from individually targeted quasars in the COMAP redshift range, and the${\rho }_{{\mathrm{H}}_2}$value is comparable to upper limits obtained from other line intensity mapping (LIM) surveys and their joint analyses. Further, we forecast the values obtainable with the COMAP/eBOSS stack after the full 5 yr COMAP Pathfinder survey. We predict that a detection is probable with this method, depending on the CO properties of the quasar sample. Based on the achieved sensitivity, we believe that this technique of stacking LIM data on the positions of traditional galaxy or quasar catalogs is extremely promising, both as a technique for investigating large galaxy catalogs efficiently at high redshift and as a technique for bolstering the sensitivity of LIM experiments, even with a fraction of their total expected survey data.

 

A series of 1,2-dimethylimidazolium ionic liquids bearing a hexadecyl alkyl chain are thoroughly examined via X-ray crystallography. The crystal structures reveal several key variations in the non-covalent interactions in the lipid-like salts. Specifically, distinct cation–cation π interactions are observed when comparing the bromide and iodide structures. Changing the anion to bis(trifluoromethane)sulfonimide (Tf 2 N − ) changes these cation–cation π interactions with anion⋯π interactions. Additionally, several well-defined geometries of the cations are noted based on torsion and core-plane angles of the alkyl chains. Hirshfeld surface analysis is used to distinguish the interactions and geometries in the solid state, helping to reveal characteristic structural fingerprints for the compounds. The solid-state structures of the ionic liquids are correlated with the solution-state structures through UV-vis spectroscopic studies, further emphasizing the importance of the π interactions in the formation of aggregates. Finally, we investigated the thermal properties of the ionic liquids, revealing complex phase transitions for the iodide-containing species. These phase transitions are further rationalized via the analysis of the data gathered from the structures of the other crystallized salts. 

Abstract High-resolution optical imaging systems are quickly becoming universal tools to characterize and quantify microbial diversity in marine ecosystems. Automated classification systems such as convolutional neural networks (CNNs) are often developed to identify species within the immense number of images (e.g., millions per month) collected. The goal of our study was to develop a CNN to classify phytoplankton images collected with an Imaging FlowCytobot for the Palmer Antarctica Long-Term Ecological Research project. A relatively small CNN (~2 million parameters) was developed and trained using a subset of manually identified images, resulting in an overall test accuracy, recall, and f1-score of 93.8, 93.7, and 93.7%, respectively, on a balanced dataset. However, the f1-score dropped to 46.5% when tested on a dataset of 10,269 new images drawn from the natural environment without balancing classes. This decrease is likely due to highly imbalanced class distributions dominated by smaller, less differentiable cells, high intraclass variance, and interclass morphological similarities of cells in naturally occurring phytoplankton assemblages. As a case study to illustrate the value of the model, it was used to predict taxonomic classifications (ranging from genus to class) of phytoplankton at Palmer Station, Antarctica, from late austral spring to early autumn in 2017‐2018 and 2018‐2019. The CNN was generally able to identify important seasonal dynamics such as the shift from large centric diatoms to small pennate diatoms in both years, which is thought to be driven by increases in glacial meltwater from January to March. This shift in particle size distribution has significant implications for the ecology and biogeochemistry of these waters. Moving forward, we hope to further increase the accuracy of our model to better characterize coastal phytoplankton communities threatened by rapidly changing environmental conditions. 

We show that annihilation dynamics between oppositely charged optical vortex pairs can be manipulated by the initial size of the vortex cores, consistent with hydrodynamics. When sufficiently close together, vortices with strongly overlapped cores annihilate more quickly than vortices with smaller cores that must wait for diffraction to cause meaningful core overlap. Numerical simulations and experimental measurements for vortices with hyperbolic tangent cores of various initial sizes show that hydrodynamics governs their motion, and reveal distinct phases of vortex recombination; decreasing the core size of an annihilating pair can prevent the annihilation event.

 

Line-intensity mapping (LIM) is a promising technique to constrain the global distribution of galaxy properties. To combine LIM experiments probing different tracers with traditional galaxy surveys and fully exploit the scientific potential of these observations, it is necessary to have a physically motivated modeling framework. As part of developing such a framework, in this work, we introduce and model the conditional galaxy property distribution (CGPD), i.e., the distribution of galaxy properties conditioned on the host halo mass and redshift. We consider five galaxy properties, including the galaxy stellar mass, molecular gas mass, galaxy radius, gas-phase metallicity, and star formation rate (SFR), which are important for predicting the emission lines of interest. The CGPD represents the full distribution of galaxies in the five-dimensional property space; many important galaxy distribution functions and scaling relations, such as the stellar mass function and SFR main sequence, can be derived from integrating and projecting it. We utilize two different kinds of cosmological galaxy simulations, a semi-analytic model and the IllustrisTNG hydrodynamic simulation, to characterize the CGPD and explore how well it can be represented using a Gaussian mixture model (GMM). We find that with just a few (approximately three) Gaussian components, a GMM can describe the CGPD of the simulated galaxies to high accuracy for both simulations. The CGPD can be mapped to LIM or other observables by constructing the appropriate relationship between galaxy properties and the relevant observable tracers, which will be discussed in future works.