Identification of sugarcane hybrids is difficult when selections are based solely on morphological traits. Our objective was to combine morphological traits and molecular marker analysis to select F1 hybrids from two separate crosses between Djatiroto, a clone of Saccharum spontaneum, and elite sugarcane clones, LCP 85-384 (Cross 97-3144) and CP 62-258 (Cross 97-3146). The maternal inflorescences of Djatiroto were emasculated by submersion in a circulating 45°C hot-water tank for 10 min to minimize self-fertilization. Cross 97-3144 produced 4.7 g of seeds with 338 viable seeds per gram and Cross 97-3146 produced 2.4 g of seeds with 166 viable seeds per gram. After greenhouse germination, 96 progeny from each cross were evaluated in a field plot. Evaluations were conducted on the ratoon crops for stalk diameter (mm), juice Brix (percentage soluble solids), and a randomly amplified polymorphic DNA (RAPD) marker OPA-11-366 that was reproducibly amplified through PCR from the elite clones, but not the maternal S. spontaneum clone. Fifty progeny (52.1%) from Cross 97-3144 and 36 progeny (37.5%) from Cross 97-3146 inherited the RAPD marker. Five putative F1 progeny were selected from each cross, namely US 99-43, US 99-44, US 99-45, US 99-46 and US 99-47 from Cross 97-3144, and US 99-48, US 99-49, US 99-50, US 99-51 and US 99-52 from Cross 97-3146, based on their relatively larger stalk diameter, higher Brix and inheritance of the RAPD marker. The hybrid nature of these selected progeny was verified with sugarcane microsatellite markers. This is the first report of the development of Saccharum hybrids with the cytoplasm of S. spontaneum for breeding purpose through a combination of conventional and molecular breeding approaches. Availability of these F1 hybrids could enhance the genetic diversity of Saccharum germplasm and has enabled sugarcane geneticists and breeders to explore the possible contribution of S. spontaneum cytoplasm in the development of new sugarcane cultivars.

Recent arguments claim that behavioral science has focused – to its detriment – on the individual over the system when construing behavioral interventions. In this commentary, we argue that tackling economic inequality using both framings in tandem is invaluable. By studying individuals who have overcome inequality, “positive deviants,” and the system limitations they navigate, we offer potentially greater policy solutions.

The U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) has been a leader in weed science research covering topics ranging from the development and use of integrated weed management (IWM) tactics to basic mechanistic studies, including biotic resistance of desirable plant communities and herbicide resistance. ARS weed scientists have worked in agricultural and natural ecosystems, including agronomic and horticultural crops, pastures, forests, wild lands, aquatic habitats, wetlands, and riparian areas. Through strong partnerships with academia, state agencies, private industry, and numerous federal programs, ARS weed scientists have made contributions to discoveries in the newest fields of robotics and genetics, as well as the traditional and fundamental subjects of weed–crop competition and physiology and integration of weed control tactics and practices. Weed science at ARS is often overshadowed by other research topics; thus, few are aware of the long history of ARS weed science and its important contributions. This review is the result of a symposium held at the Weed Science Society of America’s 62nd Annual Meeting in 2022 that included 10 separate presentations in a virtual Weed Science Webinar Series. The overarching themes of management tactics (IWM, biological control, and automation), basic mechanisms (competition, invasive plant genetics, and herbicide resistance), and ecosystem impacts (invasive plant spread, climate change, conservation, and restoration) represent core ARS weed science research that is dynamic and efficacious and has been a significant component of the agency’s national and international efforts. This review highlights current studies and future directions that exemplify the science and collaborative relationships both within and outside ARS. Given the constraints of weeds and invasive plants on all aspects of food, feed, and fiber systems, there is an acknowledged need to face new challenges, including agriculture and natural resources sustainability, economic resilience and reliability, and societal health and well-being.

The target backsheath field acceleration mechanism is one of the main mechanisms of laser-driven proton acceleration (LDPA) and strongly depends on the comprehensive performance of the ultrashort ultra-intense lasers used as the driving sources. The successful use of the SG-II Peta-watt (SG-II PW) laser facility for LDPA and its applications in radiographic diagnoses have been manifested by the good performance of the SG-II PW facility. Recently, the SG-II PW laser facility has undergone extensive maintenance and a comprehensive technical upgrade in terms of the seed source, laser contrast and terminal focus. LDPA experiments were performed using the maintained SG-II PW laser beam, and the highest cutoff energy of the proton beam was obviously increased. Accordingly, a double-film target structure was used, and the maximum cutoff energy of the proton beam was up to 70 MeV. These results demonstrate that the comprehensive performance of the SG-II PW laser facility was improved significantly.

Meat quality is not only influenced by breed but also rearing environment. The aim of this study was to evaluate the influence of different housing environments on growth performance, carcase traits, meat quality, physiological response pre-slaughter and fatty acid composition in two pig breeds. A total of 120 growing pigs at 60-70 days of age were arranged in a 2 × 2 factorial design with the breeds (Duroc × Landrace × Large White [D × L × LW] and Duroc × Landrace × Min pig [D × L × M]) and environmental enrichment (barren concrete floor or enriched with straw bedding) as factors. Each treatment was performed in triplicate with ten pigs per replicate. The pigs housed in the enriched environment exhibited a higher average daily gain, average daily feed intake, saturated fatty acid percentage and backfat depth than the pigs reared in the barren environment. Plasma cortisol levels were lower and growth hormone higher in enriched compared to barren pens. The D × L × M pigs showed lower cooking loss compared with the D × L × LW pigs. Moreover, the D × L × M pigs exhibited poor growth performance but had a better water-holding capacity. Only carcase traits and meat quality interaction effects were observed. We concluded that an enriched environment can reduce preslaughter stress and improve the growth performance of pigs and modulate the fatty acid composition of pork products.

Caregiving experiences are implicated in children’s depression risk; however, children’s neural reactivity to positive and negative feedback from mothers, a potential mediator of depression risk, is poorly understood. In a sample of 81 children (Mage = 11.12 years, SDage = 0.63), some of whom were recruited based on a maternal history of depression (n = 29), we used fMRI to characterize children’s neural responses to maternal praise and criticism. Maternal history of depression was unrelated to children’s brain activity during both the praise and criticism conditions; however, ROI analyses showed that children’s self-reported depressive symptoms were negatively associated with functional activity in the left anterior insula and right putamen while hearing maternal criticism. Whole-brain analyses showed that children’s depressive symptoms were positively associated with left inferior frontal gyrus activity while listening to maternal praise. These findings complement past work implicating these brain regions in the processing of emotionally salient stimuli, reward processing, and internal speech. Given associations between early depressive symptoms and later disorder, findings suggest that maladaptive neural processing of maternal feedback may contribute to children’s early emerging risk for depression.

Over the last 25 years, radiowave detection of neutrino-generated signals, using cold polar ice as the neutrino target, has emerged as perhaps the most promising technique for detection of extragalactic ultra-high energy neutrinos (corresponding to neutrino energies in excess of 0.01 Joules, or 1017 electron volts). During the summer of 2021 and in tandem with the initial deployment of the Radio Neutrino Observatory in Greenland (RNO-G), we conducted radioglaciological measurements at Summit Station, Greenland to refine our understanding of the ice target. We report the result of one such measurement, the radio-frequency electric field attenuation length $L_\alpha$. We find an approximately linear dependence of $L_\alpha$ on frequency with the best fit of the average field attenuation for the upper 1500 m of ice: $\langle L_\alpha \rangle = ( ( 1154 \pm 121) - ( 0.81 \pm 0.14) \, ( \nu /{\rm MHz}) ) \,{\rm m}$ for frequencies ν ∈ [145 − 350] MHz.

Reduced-order models (ROMs) are computationally inexpensive simplifications of high-fidelity complex ones. Such models can be found in computational fluid dynamics where they can be used to predict the characteristics of multiphase flows. In previous work, we presented a ROM analysis framework that coupled compression techniques, such as autoencoders, with Gaussian process regression in the latent space. This pairing has significant advantages over the standard encoding–decoding routine, such as the ability to interpolate or extrapolate in the initial conditions’ space, which can provide predictions even when simulation data are not available. In this work, we focus on this major advantage and show its effectiveness by performing the pipeline on three multiphase flow applications. We also extend the methodology by using deep Gaussian processes as the interpolation algorithm and compare the performance of our two variations, as well as another variation from the literature that uses long short-term memory networks, for the interpolation.

Cognitive theories of depression contend that biased cognitive information processing plays a causal role in the development of depression. Extensive research shows that deeper processing of negative and/or shallower processing of positive self-descriptors (i.e., negative and positive self-schemas) predicts current and future depression in adults and children. However, the neural correlates of the development of self-referent encoding are poorly understood. We examined children's self-referential processing using the self-referent encoding task (SRET) collected from 74 children at ages 6, 9, and 12; around age 10, these children also contributed structural magnetic resonance imaging data. From age 6 to age 12, both positive and negative self-referential processing showed mean-level growth, with positive self-schemas increasing relatively faster than negative ones. Further, voxel-based morphometry showed that slower growth in positive self-schemas was associated with lower regional gray matter volume (GMV) in ventrolateral prefrontal cortex (vlPFC). Our results suggest that smaller regional GMV within vlPFC, a critical region for regulatory control in affective processing and emotion development, may have implications for the development of depressogenic self-referential processing in mid-to-late childhood.

Multicomponent polymer systems are of interest in organic photovoltaic and drug delivery applications, among others where diverse morphologies influence performance. An improved understanding of morphology classification, driven by composition-informed prediction tools, will aid polymer engineering practice. We use a modified Cahn–Hilliard model to simulate polymer precipitation. Such physics-based models require high-performance computations that prevent rapid prototyping and iteration in engineering settings. To reduce the required computational costs, we apply machine learning (ML) techniques for clustering and consequent prediction of the simulated polymer-blend images in conjunction with simulations. Integrating ML and simulations in such a manner reduces the number of simulations needed to map out the morphology of polymer blends as a function of input parameters and also generates a data set which can be used by others to this end. We explore dimensionality reduction, via principal component analysis and autoencoder techniques, and analyze the resulting morphology clusters. Supervised ML using Gaussian process classification was subsequently used to predict morphology clusters according to species molar fraction and interaction parameter inputs. Manual pattern clustering yielded the best results, but ML techniques were able to predict the morphology of polymer blends with ≥90% accuracy.

Gravitational waves from coalescing neutron stars encode information about nuclear matter at extreme densities, inaccessible by laboratory experiments. The late inspiral is influenced by the presence of tides, which depend on the neutron star equation of state. Neutron star mergers are expected to often produce rapidly rotating remnant neutron stars that emit gravitational waves. These will provide clues to the extremely hot post-merger environment. This signature of nuclear matter in gravitational waves contains most information in the 2–4 kHz frequency band, which is outside of the most sensitive band of current detectors. We present the design concept and science case for a Neutron Star Extreme Matter Observatory (NEMO): a gravitational-wave interferometer optimised to study nuclear physics with merging neutron stars. The concept uses high-circulating laser power, quantum squeezing, and a detector topology specifically designed to achieve the high-frequency sensitivity necessary to probe nuclear matter using gravitational waves. Above 1 kHz, the proposed strain sensitivity is comparable to full third-generation detectors at a fraction of the cost. Such sensitivity changes expected event rates for detection of post-merger remnants from approximately one per few decades with two A+ detectors to a few per year and potentially allow for the first gravitational-wave observations of supernovae, isolated neutron stars, and other exotica.

In chemical process engineering, surrogate models of complex systems are often necessary for tasks of domain exploration, sensitivity analysis of the design parameters, and optimization. A suite of computational fluid dynamics (CFD) simulations geared toward chemical process equipment modeling has been developed and validated with experimental results from the literature. Various regression-based active learning strategies are explored with these CFD simulators in-the-loop under the constraints of a limited function evaluation budget. Specifically, five different sampling strategies and five regression techniques are compared, considering a set of four test cases of industrial significance and varying complexity. Gaussian process regression was observed to have a consistently good performance for these applications. The present quantitative study outlines the pros and cons of the different available techniques and highlights the best practices for their adoption. The test cases and tools are available with an open-source license to ensure reproducibility and engage the wider research community in contributing to both the CFD models and developing and benchmarking new improved algorithms tailored to this field.

Development of high energy density solid-state batteries with Li metal anodes has been limited by uncontrollable growth of Li dendrites in liquid and solid electrolytes (SEs). This, in part, may be caused by a dearth of information about mechanical properties of Li, especially at the nano- and microlength scales and microstructures relevant to Li batteries. We investigate Li electrodeposited in a commercial LiCoO2/LiPON/Cu solid-state thin-film cell, grown in situ in a scanning electron microscope equipped with nanomechanical capabilities. Experiments demonstrate that Li was preferentially deposited at the LiPON/Cu interface along the valleys that mimic the domain boundaries of underlying LiCoO2 (cathode). Cryogenic electron microscopy analysis of electrodeposited Li revealed a single-crystalline microstructure, and in situ nanocompression experiments on nano-pillars with 360–759 nm diameters revealed their average Young's modulus to be 6.76 ± 2.88 GPa with an average yield stress of 16.0 ± 6.82 MPa, ~24x higher than what has been reported for bulk polycrystalline Li. We discuss mechanical deformation mechanisms, stiffness, and strength of nano-sized electrodeposited Li in the framework of its microstructure and dislocation-governed nanoscale plasticity of crystals, and place it in the parameter space of existing knowledge on small-scale Li mechanics. The enhanced strength of Li at small scales may explain why it can penetrate and fracture through much stiffer and harder SEs than theoretically predicted.