This study comprehensively explored the probiotic and technological attributes, antimicrobial capabilities, safety, and anti-biofilm effects of the Lactiplantibacillus plantarum R2 strain. The strain exhibited robust resilience to gastrointestinal conditions and high colonization potential, characterized by a 13.37% adhesion rate to Caco-2 cells. Its cell-free supernatant (CFS) effectively reduced Listeria monocytogenes biofilm biomass, correlating with the significant downregulation of key virulence genes. Furthermore, the CFS demonstrated potent antioxidant activity (DPPH: 62.56%, ABTS: 65.23%) and notable antiproliferative (anticancer) effects against HT-29, HeLa, and MCF-7 cancer cell lines. Technologically, R2 was identified as a strong acidifier with significant proteolytic activity and high exopolysaccharide (EPS) production. Safety assays confirmed the absence of hemolytic activity, DNase production, and biogenic amine synthesis. In conclusion, L. plantarum R2 represents a multifunctional candidate for the development of targeted functional foods and biotherapeutic formulations aimed at modulating the intestinal microbiota and mitigating pathogen-induced enteric disorders.
Protein secondary structure prediction represents an important intermediate step between a protein’s linear amino acid sequence and its three-dimensional structure, with broad implications for synthetic biology, drug development, and disease research. Although experimental techniques such as X-ray crystallography provide highly accurate structural information, they are labor-intensive, time-consuming, and costly, which has motivated the development of computational alternatives. Early machine-learning approaches to this problem were limited in their ability to capture complex sequence–structure relationships. The introduction of convolutional and recurrent neural networks improved hierarchical feature extraction, and predictive performance advanced further with transformer-based architectures such as AlphaFold2. This review outlines recent advances in hybrid model design, benchmark datasets, and evaluation metrics for protein secondary structure prediction. We also discuss current methodological limitations, including data dependency and dataset bias, and outline future directions such as cross-species validation, uncertainty-aware modeling, and the still-emerging potential of incorporating heterogeneous biological data into next-generation PSSP frameworks.
Line intensity mapping (LIM) has garnered attention as a powerful cosmological probe, with current-generation instruments such as SPHEREx (Spectro-Photometer for the History of the Universe, Epoch of Reionization and Ices Explorer) capable of mapping the evolution of large-scale structure during the epoch of reionization (EoR). Lyman-alpha (Ly alpha) emission in the EoR is strongly shaped by resonant absorption from neutral hydrogen in the diffuse intergalactic medium (IGM), which transforms galactic sources into a low surface-brightness background. In this work, we leverage the state-of-the-art THESAN cosmological simulations to produce high-resolution theoretical predictions for future Ly alpha LIM studies, constructing continuous light-cones for line-of-sight cosmological integrations. We assess the contributions of recombination, collisional excitation, and unresolved HII regions to the total Ly alpha spectral intensity. In addition, we explore the IGM in absorption at different redshifts using damping wing analysis. We produce channel maps exploring spatial fluctuations across redshift bands probe-able by LIM instruments. We find that the slope of the absorption-included Ly alpha fluctuation power spectrum at smaller scales (k greater than or similar to 2 & times; 10(-2) arcsec(-1)) steepens toward lower redshift, and that our emission-only Ly alpha power spectrum lies above the SPHEREx sensitivity, whereas the absorption-included signal is similar to 7-8 orders of magnitude lower-providing a conservative lower limit on inhomogeneity signatures and highlighting the importance of including resonant scattering in the future. We also find that including outflows in a simple toy model boosts power by four orders of magnitude for a channel map spanning z is an element of [5.5, 6.5]. We identify limitations in our analysis and propose next steps, including incorporating the effects of resonant Ly alpha scattering and line interlopers, as well as larger simulation volumes.
Being open-minded about controversial issues and willing to take other perspectives can help reduce political polarization. We investigated the use of perspective taking through the construction of causal models of conflicting viewpoints, aiming to enhance traits associated with an open-minded mindset and reduce aspects of polarization. In a randomized classroom quasi-experiment, 1479 students in introductory courses at two US universities completed measures of open-mindedness at the beginning and end of the semester. Students in the treatment condition (N = 636) completed three assignments modeling opposing perspectives on controversial issues. While participants in the control condition exhibited declines in perspective- taking, open-minded cognition, and intellectual humility, those in the treatment condition did not. Treatment participants were more likely to perceive attitudinally dissimilar others as rational. These findings suggest students may experience declines in open-mindedness over a semester, but modeling different perspectives may help prevent this, with implications for reducing polarization.
Semi-analytic models (SAMs) have been treating galaxy populations as dynamical systems for ≳50 years, but their evolution equations remain poorly constrained. We introduce sapphire, a modular, automatically differentiable, GPU-accelerated SAM written from scratch in JAX. For the first time, we compute exact Jacobian matrices of our nonlinear differential equations and show that they have interpretable, non-random structures, using the Pandya et al. (2023) physical model as an initial example. Both local and global sensitivity analyses reveal that supernova energy loading is a key astrophysical parameter for galaxy evolution. We use gradient descent and Hamiltonian Monte Carlo (HMC) to perform comprehensive mock parameter recovery tests. These indicate that the z=0 stellar-to-halo-mass relation alone does not contain enough information to infer many astrophysical parameters. Using observations of star-forming galaxies from the MaNGA survey and the Behroozi et al. (2019) empirical model as one baseline, we derive multiple posteriors assuming different combinations of data, including z=0 interstellar medium gas fractions and metallicities. The inferred physical parameters suggest that galaxies self-regulate their star formation primarily through preventative rather than ejective feedback. Both Fisher and HMC forecasts demonstrate the potential of sapphire to enable precision inference for galaxy formation, but more work is needed to expand its library of models. We discuss how our unique blend of differentiability, massive GPU parallelization, numerical robustness and principled Bayesian methods sets the stage for hybrid physics-informed, data-driven discovery of galaxy formation astrophysics and cosmology. We make sapphire publicly available at https://github.com/virajpandya/sapphire.
