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The most active phases of star formation and black hole accretion are strongly affected by dust extinction, making far-infrared (FIR) observations the best way to disentangle and study the co-evolution of galaxies and super massive black holes. The plethora of fine-structure lines and emission features from dust and ionised and neutral atomic and warm molecular gas in the rest-frame mid-infrared (MIR) and FIR provide unmatched diagnostic opportunities to determine the properties of gas and dust, measure gas-phase metallicities, and map cold galactic outflows in even the most obscured galaxies. By combining multi-band photometric surveys with low- and high-resolution FIR spectroscopy, the PRobe far-Infrared Mission for Astrophysics (PRIMA), a 1.8 m diameter, cryogenically cooled FIR observatory currently at the conception stage, will revolutionise the field of galaxy evolution by taking advantage of this IR toolkit to find and study dusty galaxies across galactic time. In this work, we make use of the phenomenological simulation SPRITZand the Santa Cruz semi-analytical model to describe how a moderately deep multi-band PRIMA photometric survey can easily reach beyond previous IR missions to detect and study galaxies down to 10¹¹ L_⊙beyond cosmic noon and at least up toz = 4, even in the absence of gravitational lensing. By decomposing the spectral energy distribution (SED) of these photometrically selected galaxies, we show that PRIMA can be used to accurately measure the relative AGN power, the mass fraction contributed by polycyclic aromatic hydrocarbons (PAHs), and the total IR luminosity. At the same time, spectroscopic follow up with PRIMA will allow us to trace both the star formation and black hole accretion rates (SFRs and BHARs), the gas-phase metallicities, and the mass-outflow rates of cold gas in hundreds to thousands of individual galaxies toz = 2. 

ABSTRACT The PRobe far-Infrared Mission for Astrophysics (PRIMA) concept aims to perform mapping with spectral coverage and sensitivities inaccessible to previous FIR space telescopes. PRIMA’s imaging instrument, PRIMAger, provides unique hyperspectral imaging simultaneously covering 25–235 µm. We synthesize images representing a deep, 1500 h deg−2 PRIMAger survey, with realistic instrumental and confusion noise. We demonstrate that we can construct catalogues of galaxies with a high purity (>95 per cent) at a source density of 42 k deg−2 using PRIMAger data alone. Using the XID+ deblending tool, we show that we measure fluxes with an accuracy better than 20 per cent to flux levels of 0.16, 0.80, 9.7, and 15 mJy at 47.4, 79.7, 172, and 235 µm, respectively. These are a factor of ∼2 and ∼3 fainter than the classical confusion limits for 72–96 and 126–235 µm, respectively. At $$1.5 \le z \le 2$$, we detect and accurately measure fluxes in 8–10 of the 10 channels covering 47–235 µm for sources with $$2 \lesssim \log ({\rm SFR}) \lesssim 2.5$$, a 0.5 dex improvement on what might be expected from the classical confusion limit. Recognizing that PRIMager will operate in a context where high-quality data will be available at other wavelengths, we investigate the benefits of introducing additional prior information. We show that by introducing even weak prior flux information when employing a higher source density catalogue (more than one source per beam), we can obtain accurate fluxes an order of magnitude below the classical confusion limit for 96–235 µm. 

BackgroundPredicting the likelihood of success of weight loss interventions using machine learning (ML) models may enhance intervention effectiveness by enabling timely and dynamic modification of intervention components for nonresponders to treatment. However, a lack of understanding and trust in these ML models impacts adoption among weight management experts. Recent advances in the field of explainable artificial intelligence enable the interpretation of ML models, yet it is unknown whether they enhance model understanding, trust, and adoption among weight management experts. ObjectiveThis study aimed to build and evaluate an ML model that can predict 6-month weight loss success (ie, ≥7% weight loss) from 5 engagement and diet-related features collected over the initial 2 weeks of an intervention, to assess whether providing ML-based explanations increases weight management experts’ agreement with ML model predictions, and to inform factors that influence the understanding and trust of ML models to advance explainability in early prediction of weight loss among weight management experts. MethodsWe trained an ML model using the random forest (RF) algorithm and data from a 6-month weight loss intervention (N=419). We leveraged findings from existing explainability metrics to develop Prime Implicant Maintenance of Outcome (PRIMO), an interactive tool to understand predictions made by the RF model. We asked 14 weight management experts to predict hypothetical participants’ weight loss success before and after using PRIMO. We compared PRIMO with 2 other explainability methods, one based on feature ranking and the other based on conditional probability. We used generalized linear mixed-effects models to evaluate participants’ agreement with ML predictions and conducted likelihood ratio tests to examine the relationship between explainability methods and outcomes for nested models. We conducted guided interviews and thematic analysis to study the impact of our tool on experts’ understanding and trust in the model. ResultsOur RF model had 81% accuracy in the early prediction of weight loss success. Weight management experts were significantly more likely to agree with the model when using PRIMO (χ2=7.9; P=.02) compared with the other 2 methods with odds ratios of 2.52 (95% CI 0.91-7.69) and 3.95 (95% CI 1.50-11.76). From our study, we inferred that our software not only influenced experts’ understanding and trust but also impacted decision-making. Several themes were identified through interviews: preference for multiple explanation types, need to visualize uncertainty in explanations provided by PRIMO, and need for model performance metrics on similar participant test instances. ConclusionsOur results show the potential for weight management experts to agree with the ML-based early prediction of success in weight loss treatment programs, enabling timely and dynamic modification of intervention components to enhance intervention effectiveness. Our findings provide methods for advancing the understandability and trust of ML models among weight management experts. 

Objective We review the current state-of-the-art in team cognition research, but more importantly describe the limitations of existing theories, laboratory paradigms, and measures considering the increasing complexities of modern teams and the study of team cognition. Background Research on, and applications of, team cognition has led to theories, data, and measures over the last several decades. Method This article is based on research questions generated in a spring 2022 seminar on team cognition at Arizona State University led by the first author. Results Future research directions are proposed for extending the conceptualization of teams and team cognition by examining dimensions of teamness; extending laboratory paradigms to attain more realistic teaming, including nonhuman teammates; and advancing measures of team cognition in a direction such that data can be collected unobtrusively, in real time, and automatically. Conclusion The future of team cognition is one of the new discoveries, new research paradigms, and new measures. Application Extending the concepts of teams and team cognition can also extend the potential applications of these concepts. 

Workplace research suggests that roughly equal communication between teammates is positively associated with team effectiveness. A distinction between teams in these studies and distributed action teams is the degree of role specialization and context-driven communication which may entail unequal degrees of communication. Yet, distributed action teams may have more equal footing to provide inputs in contexts such as mission planning or briefings. Twenty-two ad hoc teams participated in a simulated ground combat vehicle task in which teams conducted six-missions and briefed before each mission. We used team performance, team situation awareness, team workload, and team resilience as team effectiveness criteria. Balanced degrees of communication in mission briefs were correlated with performance and resilience measures, and largely uncorrelated with situation-awareness and workload measures. The overall amount of communication was also largely uncorrelated with all effectiveness measures. The results suggest that communication balance in mission briefs may help predict effectiveness in action teams. 

Abstract Potato ( Solanum tuberosum L.) is the world’s most important non-cereal food crop, and the vast majority of commercially grown cultivars are highly heterozygous tetraploids. Advances in diploid hybrid breeding based on true seeds have the potential to revolutionize future potato breeding and production 1–4 . So far, relatively few studies have examined the genome evolution and diversity of wild and cultivated landrace potatoes, which limits the application of their diversity in potato breeding. Here we assemble 44 high-quality diploid potato genomes from 24 wild and 20 cultivated accessions that are representative of Solanum section Petota , the tuber-bearing clade, as well as 2 genomes from the neighbouring section, Etuberosum . Extensive discordance of phylogenomic relationships suggests the complexity of potato evolution. We find that the potato genome substantially expanded its repertoire of disease-resistance genes when compared with closely related seed-propagated solanaceous crops, indicative of the effect of tuber-based propagation strategies on the evolution of the potato genome. We discover a transcription factor that determines tuber identity and interacts with the mobile tuberization inductive signal SP6A. We also identify 561,433 high-confidence structural variants and construct a map of large inversions, which provides insights for improving inbred lines and precluding potential linkage drag, as exemplified by a 5.8-Mb inversion that is associated with carotenoid content in tubers. This study will accelerate hybrid potato breeding and enrich our understanding of the evolution and biology of potato as a global staple food crop. 

Abstract The southern Coast Mountain batholith was episodically active from Jurassic to Eocene time and experienced four distinct high magmatic flux events during that period. Similar episodicity has been recognized in arcs worldwide, yet the mechanism(s) driving such punctuated magmatic behavior are debated. This study uses zircon Hf and O isotopes, with whole-rock and mineral geochemistry, to track spatiotemporal changes in southern Coast Mountains batholith melt sources and to evaluate models of flare-up behavior and crust formation in Cordilleran arc systems. Zircon Hf isotope analysis yielded consistently primitive values, with all zircon grains recording initial εHf between +6 and +16. The majority (97%) of zircons analyzed yielded δ18O values between 4.2‰ and 6.5‰, and only five grains recorded values of up to 8.3‰. These isotopic results are interpreted to reflect magmatism dominated by mantle melting during all time periods and across all areas of the southern batholith, which argues against the periodic input of more melt-fertile crustal materials as the driver of episodic arc magmatism. They also indicate that limited crustal recycling is needed to produce the large volumes of continental crust generated in the batholith. Although the isotopic character of intrusions is relatively invariant through time, magmas emplaced during flare-ups record higher Sr/Y and La/Yb(N) and lower zircon Ti and Yb concentrations, which is consistent with melting in thickened crust with garnet present as a fractionating phase. Flare-ups are also temporally associated with periods when the southern Coast Mountains batholith both widens and advances inboard. We suggest that the landward shift of the arc into more fertile lithospheric mantle domains triggers voluminous magmatism and is accompanied by magmatic and/or tectonic thickening. Overall, these results demonstrate that the magmatic growth of Cordilleran arcs can be spatially and temporally complex without requiring variability in the contributions of crust and/or mantle to the batholith.