Browse Articles

Green synthesis, one-pot reaction, biological evaluation, and molecular docking studies of new pyrazolo[3,4-d]tetrazolo[1,5-a]pyrimidine derivatives

Linking species local trends from assemblage monitoring to global extinction risk

Abstract While biodiversity is being reshaped across the globe, extinction risk assessments are lacking for most species, and a major challenge remains in understanding whether global threat status aligns with local population trends. Here, we assess whether population temporal prevalence trends are consistent with a species’ global extinction risk, using over 60,000 populations of 2362 species across 978 marine and terrestrial assemblages (sampled for at least 20 years, mostly from temperate regions). We assign each population to one of five categories of temporal prevalence dynamics, and retrieve each species’ extinction risk from the International Union for Conservation of Nature (IUCN) Red List. Fewer than 10% of local populations show consistent increasing or decreasing prevalence over time, with most exhibiting random patterns of temporal change, especially marine populations. Overall, higher extinction risk is associated with a higher frequency of decreasing local prevalence, and vice-versa for increasing prevalence, against a backdrop of complex links between extinction risk and local temporal dynamics. Our results suggest that directional changes in species local prevalence could be harbingers of future changes in global threat status, and highlight how leveraging assemblage monitoring data can aid conservation efforts and extinction assessments.

Video games help push the boundaries of AI

Design and synthesis of 2,5-di(pyridin-4-yl)-1,3,4-oxadiazole: structural elucidation, DFT insights, and preliminary in silico biological evaluation

Self-assembly Monte Carlo reveals localized entanglement in giant polymer melts

Genome-edited rice variety with low-cadmium accumulation in the grain

Cadmium (Cd) is a toxic and carcinogenic heavy metal, and rice, as a staple food, is a major source of dietary Cd intake. Therefore, limiting the transfer of Cd from soil to rice grain without compromising grain yield is a critical issue for human health. In this study, through base-editing-mediated mutagenesis screening targeting OsNramp5 , a major transporter gene for manganese (Mn) and Cd uptake, we identified a single amino acid substitution at position 441 (Ile to Thr) that significantly reduced Cd accumulation in both shoots and grains without affecting the accumulation of other essential metals. Functional analysis revealed that this point mutation did not alter gene expression, protein abundance, subcellular localization, or Cd and Mn transport activity in yeast. However, we found that OsNramp5 also transports zinc (Zn), and the point mutation increased its selectivity for Zn. It is likely that elevated Zn levels in root cells competitively inhibit Cd release into the xylem, thereby reducing root-to-shoot Cd translocation. A field trial confirmed that the mutated OsNramp5 did not affect grain yield or essential micronutrient concentration but significantly decreased Cd accumulation in grains. Our findings suggest that precise editing of this key residue in OsNramp5 offers an effective strategy to reduce Cd transfer from soil to rice grain without yield penalty.

Unified GPU-based simulation of porous flow, absorption, and diffusion in cloth–liquid coupling

Structural basis for selective and potent degradation of IRAK4 by KT-474

Persistent selection on size explains micro- and macroevolutionary alignments in fly wings

The alignment among mutational variance ( M ), standing genetic variance ( G ), and macroevolutionary divergence ( R ) in Drosophila wing shape poses a rate paradox under a simple constraint hypothesis: Evolution follows mutational lines of least resistance, yet proceeds orders of magnitude slower than the abundant genetic variation would permit. This is difficult to reconcile with a simple constraint view in which long-term evolution merely tracks the amount of available variation in each direction. Previous explanations invoke deleterious pleiotropy on unmeasured traits or correlational selection on trait combinations, but recent empirical work finds little evidence of fitness costs beyond flight performance. Here, by reanalyzing published data, I show that wing size shows the hallmark of the primary selection target: Among all wing traits, size exhibits the lowest ratio of standing genetic to mutational variance, indicating the strongest selective depletion. Based on this empirical observation, I develop a single-axis selection model in which natural selection targets only a single trait while all other traits evolve as correlated byproducts via within-module pleiotropy. This minimal model reproduces both the observed M – G – R alignment and slower-than-neutral divergence rates, explaining micro- and macroevolutionary patterns in fly wings without invoking complex adaptive landscapes.

Driving dynamics of a dual-vehicle serial anchoring machine with a quadruple crawler tracked system

Framework-templated gas lattices in metal-organic frameworks

Conformity to popular, not average, opinions: Models, data, and evolution

In assessing how individuals in a network estimate a quantity, it is often assumed that they take an average or weighted average of the estimates of others they observe, as in the French–DeGroot learning model. However, individuals may actually copy clusters of similar opinions among their peers, a process that we call conformity. For example, if ten individuals estimate a quantity to be 0 and ten individuals guess 100, then although the average estimate is 50, one may conform by choosing either 0 or 100. Here, we a) extend a recent model of conformity to include both personal beliefs and social information, b) evaluate this model and two models of French–DeGroot averaging on human decision-making data, and c) simulate evolutionary dynamics of each model under different kinds of population structure, including static and adaptive networks. Under many of the conditions we analyze, the conformity model provides a significantly better fit to the data than either of the French–DeGroot models. In addition, evolutionary simulations reveal considerable differences among the models; for example, compared to the French–DeGroot models, the conformity model can produce faster shifts in populations toward “extreme” opinions—where individuals’ estimates of a quantity are either very large or very small—and can reduce individuals’ average estimation accuracy. These findings have implications for research on consensus formation, polarization, and wisdom of crowds.

Inverse input optimization for tuning two-ply yarn processing parameters using feedforward neural network

Author Correction: Magnetoreception in a freshwater ciliate arises from endosymbiosis

TL1A/DR3 signaling deletion attenuates mucosal inflammation and alveolar bone loss in a murine model of spontaneous periodontitis

TL1A and its receptor DR3 are key regulators of mucosal immune responses, but their role in periodontal disease is unknown. Herein, we investigated whether TL1A/DR3 signaling contributes to mucosal immune amplification and tissue-destructive inflammation in periodontitis using SAMP1/YitFc (SAMP) mice, which develop spontaneous ileitis and periodontal disease. DR3 deficiency markedly attenuated alveolar bone loss and improved periodontal architecture, restoring a phenotype comparable to healthy AKR (parental) controls. Gingival tissues from wild-type SAMP mice exhibited increased expression of both Tnfsf15 (encoding TL1A) and Tnfrsf25 (encoding DR3), with both positively correlating with disease severity. This was accompanied by elevated levels of IL-17, TNF-α, and IL-1β, and by increased numbers of CD4 + T helper cells and neutrophils. Conversely, SAMPxDR3 −/− mice exhibited reduced inflammatory cytokine production and immune cell accumulation. These findings support a model in which the TL1A/DR3 axis is associated with amplification of mucosal immune responses in periodontal disease, linking effector T cell activation, increased cytokine production, and recruitment of innate immune cells. Altogether, our data identify the TL1A/DR3 cytokine-receptor pair as a potential regulator of inflammatory circuits that drive periodontal pathology. Blocking this pathway may provide a therapeutic modality for patients affected by chronic periodontitis.

Parametric modeling of random polygonal aggregates in concrete and nusssmerical simulation of uniaxial compression using SPH

Abstract Random polygonal aggregates represent the most realistic aggregate morphology in the mesostructure of concrete. Their angular characteristics and spatial distribution have a decisive influence on the stress concentration, crack initiation, and ultimate failure mode of the material under uniaxial compression. Therefore, in-depth research in this area is essential for revealing the essence of the macroscopic mechanical behavior of concrete. To this end, this paper presents an SPH-based simulation framework for uniaxial compression failure of concrete by integrating the Mohr–Coulomb criterion with tensile cutoff and an improved smoothing kernel function. A key component is the development of an in-house particle generation program for random polygonal aggregates, which achieves automatic generation satisfying geometric constraints and precise particle attribute classification based on the separating axis theorem and the ray casting method. Through three sets of comparative numerical examples, the effects of the number of aggregates (30, 40, 50), aggregate size range (1–8 mm, 1–10 mm, 1–12 mm), and shape complexity (number of edges: 8–15, 8–20, 8–25) on the failure mode of concrete are systematically investigated. The findings reveal that an increase in aggregate density or expansion of the size range promotes a transition from localized shear failure to more distributed damage patterns, accompanied by enhanced material brittleness. In contrast, increasing the shape complexity of aggregates (i.e., a larger number of edges) shifts the failure mode from “angularity-dominated concentrated shear failure” to “interface-dominated fine diffuse failure,” significantly improving material ductility. Comparison and validation with existing results show that the proposed method achieves good agreement in terms of failure morphology and crack evolution. This study provides an efficient modeling approach and theoretical support for the mesomechanical analysis of concrete with irregular aggregates.

Neuron type-specific translatomes in dorsal hippocampus during early memory consolidation

Genetic structure of Biscogniauxia mediterranea fungus populations in Zagros forests of Iran

A rhizobium-induced FT-FD module locally activates nodule stem cell gene to promote nodulation

In search of exotic pairing in the Hubbard model: Many-body computation and quantum gas microscopy

Finite-momentum pairing, exemplified by Fulde–Ferrell–Larkin–Ovchinnikov (FFLO) states, represents a paradigmatic form of unconventional superfluidity driven by Fermi-surface mismatch, but its detection in two-dimensional systems has remained elusive. Here we study a doped, spin-imbalanced attractive Hubbard model using a combined experimental and computational approach, based on quantum gas microscopy and constrained-path auxiliary-field quantum Monte Carlo, with direct comparisons showing quantitative agreement for short-range correlations at experimentally accessible temperatures. We identify broad regimes in density and magnetization where FFLO correlations emerge, and establish their finite-temperature evolution, with clear signatures of finite-momentum pairing already appearing at experimentally accessible temperatures. Spin–XY correlations are identified as a robust, directly measurable proxy for FFLO physics. Quantitative characterizations are obtained on the temperature dependence of a variety of observables and correlations, which elucidate the interplay of pairing with competing orders.