Testing the ΛCDM model requires cosmological probes spanning the wide redshift interval between Type Ia Supernovae (SNe Ia, z ≲ 2.9) and the Cosmic Microwave Background (CMB, z ≈ 1100). Gamma-Ray Bursts (GRBs), observed up to redshift z=9.2, offer the opportunity to explore part of this regime. Here, we investigate how many GRBs are needed to become a useful cosmological probe capable of independently testing deviations from ΛCDM suggested by the recent DESI BAO observations. We develop forecasts based on the two-dimensional X-ray and optical Dainotti relations, between the luminosity at the end of the plateau phase and its rest-frame duration. Using simulated GRB samples constructed from the observed population, we evaluate the constraining power of GRBs on cosmological parameters within the wCDM and w0waCDM models, both independently and in combination with CMB observations. Our results show that GRB samples containing several tens to hundreds of well-characterized plateaus can already approach the precision currently achieved by CMB measurements on the Dark Energy (DE) Equation of State (EoS) parameter w. Particularly, a sample of ∼ 66 optical GRBs can reach a precision σw ≈ 0.47, comparable to that obtained from Planck within the wCDM framework. Such sample sizes are already attainable through machine-learning techniques that double the number of GRBs using inferred redshifts. These forecasts indicate that future GRB observations, when combined with next-generation transient missions and improved statistical techniques, will provide an independent high-redshift probe of cosmic expansion and will play an important role in testing the robustness of potential Dynamical DE signals suggested by other cosmological datasets.
Among the microorganisms involved in bovine respiratory disease (BRD), certain Mycoplasma species have been associated with the severity and chronicity of infections. This study aimed to determine the frequency of Mycoplasma species in calves with respiratory disease from selected dairy herds in the states of Hidalgo and Mexico, located in Central Mexico. Nasal swabs were collected from 150 clinically affected calves from dairy herds: 35 from Melchor Ocampo, State of Mexico (Herd A); 30 from Actopan, State of Hidalgo (Herd B); and 85 from 3 herds in Tizayuca, State of Hidalgo (Herds C, D, and E). Samples were analyzed by culture and PCR targeting the uvrC gene of Mycoplasma bovis and the rpoB gene of Mycoplasma dispar and Mycoplasma bovirhinis. Specifically, M. bovis was more frequently detected in Herds A and B, with PCR positivity rates of 57.7% and 33.3% and corresponding culture positivity rates of 51.4% and 23.3%, respectively. In contrast, M. dispar predominated in Herds C, D and E, with PCR positivity rates ranging from 30% to 48% and corresponding culture positivity rates ranging from 26.6% to 36.6%. M. bovirhinis was detected at low frequencies across all herds. Sequencing analysis confirmed the identity of the detected species. The detection of M. bovis in some clinically affected calves in this study is consistent with previous evidence recognizing it as an important respiratory pathogen in cattle, whereas the detection of M. dispar and M. bovirhinis may reflect opportunistic behavior or their presence as part of the respiratory microbiota, although their role in bovine respiratory disease remains unclear.
This study addresses the inverse problem of parameter estimation for Stochastic Differential Equations by minimizing a regularized discrepancy functional via Stochastic Gradient Descent. To achieve computational efficiency, we leverage the Wiener Chaos Expansion, a spectral decomposition technique that projects the stochastic solution onto an orthogonal basis of Hermite polynomials. This transformation effectively maps the stochastic dynamics into a hierarchical system of deterministic functions, termed the propagator. By reducing the stochastic inference task to a deterministic optimization problem, our framework circumvents the heavy computational burden and sampling requirements of traditional simulation-based methods like Markov chain Monte Carlo or Maximum Likelihood Estimation. The robustness and scalability of the proposed approach are demonstrated through numerical experiments on various non-linear Stochastic Differential Equations, including models for individual biological growth. Results show that the Wiener Chaos Expansion-Stochastic Gradient Descent framework provides accurate parameter recovery even from discrete, noisy observations, offering a significant paradigm shift in the efficient modeling of complex stochastic systems.
Predictive language comprehension allows children to use prior context to anticipate upcoming information before it becomes available in speech. Although young children use sentence meaning to guide real-time comprehension, less is known about when they generate more specific expectations about upcoming words without direct cues. Across two visual-world eye-tracking experiments, we used a grammatical contrast in Spanish as a test case to examine whether children aged 24, 30, and 36 months could use sentence context to guide attention before the expected word was heard. Highly predictable sentence contexts were followed by a silent interval before the final noun, allowing us to distinguish anticipatory looking from later word recognition. In Experiment 1, visual displays included a gender-matched object that shared grammatical gender with the expected noun and also carried transparent surface-form cues. In Experiment 2, those cues were removed, providing a stricter test of whether children could use internally generated expectations before word onset. Results showed anticipatory looks to the expected target across age groups, indicating context-based prediction. However, looks to the gender-matched object followed a more selective developmental pattern. With surface cues, the Gender-matched > Unrelated contrast was reliable at 30 and 36 months and larger at 36 than at 24 months. Without surface cues, it was reliable only at 36 months. These findings suggest that children’s predictions develop from broad expectations about upcoming words toward more specific, linguistically structured expectations between 24 and 36 months. More generally, early predictive processing becomes increasingly precise as language representations become more stable and accessible.
To holistically understand the biology of animals, we must unravel the complexities and specificities of host-microbe interactions across animal taxa. Birds represent enigmatic and scientifically compelling hosts in which to understand these interactions. Here, we present a brief summary of a series of conversations among avian microbiome researchers regarding methodological challenges facing the avian microbiome field, where most research to date has focused on bacterial communities of the gut. Collectively, we acknowledged a commonly shared but underreported issue facing the avian microbiome field: that of difficulty in obtaining high-quality and high-yield microbial DNA from avian fecal samples. We discuss some of the potential reasons underlying low DNA yields, such as inhibitory compounds and rapid DNA degradation, and provide recommendations for how researchers in the avian microbiome field might cope with these methodological challenges. Collective and dedicated efforts to address these challenges will be required for a robust understanding of host-microbe interactions in avian systems.
