Browse Articles

A dual self-regulatory platform for programmable biosynthesis of L-lysine-derived alkaloids

L-lysine-derived alkaloids represent a structurally diverse and pharmacologically significant class of nitrogen-containing natural products with broad utility as pharmaceutical agents and industrial platform chemicals. However, their microbial biosynthesis has been persistently hindered by precursor imbalance and the rigid regulatory architecture of native metabolic pathways. Here, we report a programmable biosynthetic platform for the de novo production of L-lysine-derived alkaloids directly from glucose in Corynebacterium glutamicum . Through systematic metabolic rewiring, we developed a high-flux cadaverine-producing chassis that achieves robust accumulation of the central nitrogenous precursor required for downstream alkaloid assembly. Leveraging this chassis, we designed a dual metabolite-responsive regulatory circuit that autonomously orchestrates malonate assimilation, malonyl-CoA supply, and alkaloid formation without external inducers. This self-regulating strategy enabled the production of pelletierine at 6.11 ± 0.06 g/L in fed-batch fermentation, representing the highest reported microbial titer to date. Extending this platform through selective short-chain dehydrogenase/reductase catalysis, we further achieved the stereoselective conversion of 1-piperideine and pelletierine into piperidine (8.60 ± 0.16 g/L) and sedridine (3.71 ± 0.19 g/L), respectively—constituting a heterologous microbial production of either compound. Collectively, this work establishes a versatile, programmable, and industrially tractable platform for L-lysine-derived alkaloid biosynthesis, providing a compelling foundation for the sustainable microbial production of diverse alkaloid intermediates and drug leads.

Associations between estimated pulse wave velocity trajectories and all-cause mortality in participants with cardiovascular-kidney-metabolic syndrome stages 0–3

Continuity and change in US legal tradition: Evidence from judicial citation communities

Legal doctrine is often described as evolving case by case while preserving coherent legal traditions. We introduce a method for identifying and tracing such traditions by applying the Louvain community-detection algorithm to the complete U.S. federal judicial citation graph (1790–2024; 760,000 opinions linked by 8.3 million citations). The resulting citation communities are unsupervised, blind to court labels and topical taxonomies, yet they form meaningful empirical groupings: In validation exercises, they recover known institutional structure and capture coherent substantive and procedural approaches to law. Using these communities as empirical units, we study how legal traditions evolve and respond to legislative change. We show that the introduction of pivotal federal statutes shifts citation patterns toward newer precedent, indicating that legislation can disrupt established traditions and redirect the development of case law.

Development of sustainable green geopolymer concrete incorporating sugarcane bagasse ash

Abstract Sugarcane bagasse ash (SCBA) is increasingly being considered as an alternative aluminosilicate source for geopolymer binders. however, limited studies have comprehensively evaluated the combined effects of high SCBA replacement levels, alkaline activator molarity, thermal performance, microstructural characteristics, and economic feasibility. Therefore, this study investigates the use of SCBA as a partial and near-complete replacement of the total binder in geopolymer concrete. Fifteen mixtures were prepared with SCBA replacement levels of 0, 25, 50, 75, and 95% at NaOH molarities of 12, 14, and 16 M. Fresh, mechanical, durability, thermal, and microstructural properties, along with cost analysis, were comprehensively evaluated. The mixture containing 25% SCBA and activated with 14 M NaOH showed the best overall performance, with a compressive strength of 30.13 MPa, a splitting tensile strength of 3.02 MPa, and a sorptivity of 2.449 × 10⁻³ g/cm·s⁰·⁵ at 28 days. After exposure to 200 °C, its compressive strength increased by 39.30%, before declining at higher temperatures. Mixtures with higher SCBA contents showed slower strength loss, suggesting improved thermal stability. SEM–EDX observations were consistent with these results, as the optimum mixture showed a dense geopolymeric matrix, while higher SCBA contents and elevated temperatures led to increased porosity and microcracking. The cost analysis also showed that production cost decreased considerably as the SCBA content increased. Overall, the results indicate that moderate SCBA replacement can provide a practical balance between mechanical performance, durability, thermal resistance, and cost, supporting the use of SCBA as a sustainable binder component in geopolymer concrete.

When children shape their families: Evocative effects analysis of 40,000 trios from the Norwegian Mother, Father and Child Cohort Study

Family demographic behavior is often studied as a response to structural conditions or parental characteristics, yet children may themselves shape key family trajectories through evocative gene–environment correlations. This study is based on the Norwegian Mother, Father and Child Cohort Study (MoBa). By analyzing ~ 40,000 genotyped mother–father–child trios, we test whether children’s polygenic indices (PGIs) predict parents’ subsequent fertility, partnership dissolution, and transition to marriage. Accounting for parental PGIs, we found limited evidence for evocative effects across most traits. The exception was that higher child ADHD PGI predicted increased parental partnership dissolution (7% [CI = 2.9 to 11.8%] per SD), especially in families with daughters (11% [CI = 4.9 to 18%] per SD), higher socioeconomic status (15% [CI = 7.7 to 23.2%] per SD), and older maternal age at first birth (10% [CI = 3 to 17%] per SD). The main source of variation is the socioeconomic dimension where child-driven effects are stronger in higher-SES families. The study provides evidence for evocative gene–environment correlations in family demography, demonstrating the importance of child-to-parent effects in shaping family trajectories.

A pilot study on the assessment of fetal lung and liver elasticity by two-dimensional shear wave elastography in congenital heart disease

A phosphorylation cascade involving ZmSnRK2.10–ZmRIPK2–ZmWRKY38 attenuates drought response by derepressing <i>ZmSUS2</i> in maize

Drought stress severely limits crop productivity, with transcription factors (TFs) playing pivotal roles in plant adaptation. Here, we identify the maize TF ZmWRKY38 as a positive regulator of drought tolerance. CRISPR/Cas9-mediated knockout of ZmWRKY38 increased plant sensitivity to drought compared with the wild type. We demonstrate that ZmWRKY38 binds to the promoter of the sucrose synthase gene ZmSUS2 and represses its transcription. Conversely, ZmSUS2 functions as a negative regulator of drought tolerance, as zmsus2 mutants displayed enhanced drought resistance. Moreover, we identified ZmRIPK2 (RPM1-induced protein kinase 2), a plasma membrane-localized receptor-like cytoplasmic kinase that also negatively regulates drought responses. Under abscisic acid (ABA) or drought stress, ZmSnRK2.10 phosphorylates and activates ZmRIPK2, triggering its partial translocation into the nucleus. Within the nucleus, ZmRIPK2 interacts with and phosphorylates ZmWRKY38, impairing its DNA-binding capacity and thereby alleviating transcriptional repression of ZmSUS2 . Together, our results reveal a negative feedback loop in which ZmRIPK2-mediated phosphorylation of ZmWRKY38 fine-tunes the drought stress response through modulation of ZmSUS2 expression. This mechanism illustrates how ABA signaling attenuates drought responses to balance stress adaptation with normal growth.

A novel method for predicting gallstones based on ensemble feature selection method

Abstract Gallstone disease is a major health problem worldwide, affecting millions of people. Although conventional diagnostic methods such as magnetic resonance imaging and endoscopic ultrasound are still in use, they are expensive as well as require significant expertise, and can produce variable results. In this study, we introduce an artifial intelligence based approach to predict gallstone disease using bioimpedance measurements and laboratory biomarkers as an alternative to imaging techniques. The dataset comprises 38 parameters, including demographic information, biochemical indicators such as glucose, lipid profiles, triglycerides, and bioimpedance data. We introduce a novel ensemble feature selection framework that combines four statistical methods of minimum redundancy maximum relevance, chi-square test, analysis of variance, and Kruskal–Wallis with four metaheuristic algorithms of grey wolf optimization, whale optimization algorithm, Harris hawk optimization, and particle swarm optimization for the purpose of enhancing predictive performance. The study findings denote that the features retained commonly by seven of the eight methods are deemed the most discriminative. Next, we evaluate the selected features using several classifiers, including logistic regression, Naive Bayes, support vector machine, decision tree, k-nearest neighbor, gradient boosting, Adaboost, random forest, and multilayer perceptron. Experimental results show that eliminating redundant features significantly improves model accuracy, where the random forest classifier achieved the highest accuracy rate of 90.62% using only 11 features instead of 38. The proposed ensemble method reduces computational complexity, enhances classification accuracy, and provides systematic evidence of how feature selection impacts different classifiers. Its superiority is also demonstrated through comparisons with state-of-the-art approaches for gallstone prediction.

Magnetotaxis in an anaerobic ciliate via tripartite syntrophy

Magnetotaxis has evolved independently numerous times in bacteria, whereby genetically controlled biomineralization of nano-crystalline magnets results in swimming along Earth’s magnetic field lines. Compared to magnetotactic bacteria (MTB), evolutionary mechanisms of magnetotaxis as a trait in eukaryotes remain poorly understood. Here, we report a magnetotactic ciliate, Tropidoatractus magnetotacticus sp. nov., that acquires magnetotaxis via syntrophy. T. magnetotacticus exhibits magnetotaxis due to the magnetic moment of internal ferrimagnetic magnetite (Fe 3 O 4 ) nanoparticles forming ellipsoidal “necklace-shaped” parallel chains. Electron microscopy revealed T. magnetotacticus hosts numerous internal rod-shaped bacteria containing these magnetosome chains. Consistent with this, a genomic population of MTB (Thermodesulfobacteriota) in magnetically sorted T. magnetotacticus cells was found that encoded and expressed a magnetosome gene cluster responsible for magnetosome Fe 3 O 4 biomineralization closely related to that of the ectosymbiont “ Candidatus Desulfarcum epimagneticum.” T. magnetotacticus also housed a second genomic population affiliated with the endosymbiotic methanogen Methanoregula . Metatranscriptomes of sorted T. magnetotacticus cells show eukaryotic hydrogenosomal Fe-hydrogenase gene expression, and expression of genes encoding proteins in an electron transport chain indicative of H 2 -producing mitochondria-related organelles. Active gene expression of energy metabolism pathways indicates a tripartite syntrophic network whereby anaerobic fermentation products from T. magnetotacticus are consumed by two syntrophic partners: MTB producing the magnetosome chains and hydrogenotrophic methanogens. Our findings show how magnetotaxis can emerge as a trait in eukaryotes via syntrophic cooperation.

Adaptive level modification via player skill classification and large language models

Abstract Maintaining player engagement in video games requires a careful balance between challenge and player competence. Static difficulty settings fail to account for individual skill variation, while existing dynamic difficulty adjustment systems are limited to tuning low-level game parameters rather than restructuring level content. This paper presents an adaptive level modification framework that personalizes gameplay by continuously inferring player skill and applying targeted structural modifications to level content in real-time. A hybrid behavioral dataset is constructed by combining agent-generated trajectories, produced by Proximal Policy Optimization (PPO) agents, a reinforcement learning approach, trained at three distinct skill levels, with manually collected human gameplay data labeled through clustering. A classifier trained on this dataset categorizes players into expert, normal, and beginner skill levels, achieving an overall accuracy of 97.82%. The classifier output drives a two-stage large language model (LLM) pipeline guided by prompt engineering, which expands a skill-conditioned prompt into a structured modification instruction applied to the current level chunk. A physics-constrained verifier based on a graph-based shortest path method ensures all modified levels remain traversable. Evaluated on Super Mario Bros. levels, the framework achieves a post-modification playability rate of 74.1% at the full-level granularity and 83.5% at the isolated-chunk granularity, closely matching the 80.0% baseline of the original levels.

Ligand regulation and function of preformed EGFR dimers

Receptor tyrosine kinases (RTKs) are key therapeutic targets in cancer, diabetes, and other diseases. With only one transmembrane α-helix—compared with seven in G-protein-coupled receptors—RTKs are thought to be activated by ligand-induced dimerization. Complicating this view, however, one of the best-studied RTKs, the insulin receptor (IR), forms allosterically regulated covalent dimers. Moreover, noncovalent “preformed” dimers have frequently been reported for the sequence-related epidermal growth factor receptor (EGFR), one of the first RTKs for which ligand-induced dimerization was described. Here, we describe a detailed structural view of a preformed EGFR dimer. Using cryo-EM, we describe how the Caenorhabditis elegans EGFR (LET-23) dimerizes without ligand. We show that preformed dimer formation modulates ligand sensitivity in vivo, but is not required for signaling itself. We also elucidate substantial ligand-induced conformational changes in LET-23 required for signaling. Our structures reveal unexpected similarities between regulation of LET-23 and the IR, suggesting that LET-23 may represent an evolutionary “missing link” between the IR and EGFR families. In the absence of ligand, intermolecular interactions within preformed receptor dimers hold the extracellular juxtamembrane regions far apart to separate the intracellular kinase domains so that they remain inactive. Ligand binding disrupts these interactions to remove the restraints on the kinase domains, which then can associate to become activated. Our analysis further suggests a unified model for the allosteric activation of preformed RTK dimers that has important implications for understanding cell-surface EGFR.

OZAIANet a fusion based explainable deep learning framework for thermal image based diabetes classification

There is no free benchmark: An institutional view of legal AI benchmarking

Despite substantial excitement around the use of AI in law, little information exists on the performance and associated risks of the domain’s widely marketed tools. Recent work, for instance, has demonstrated the significant potential for “hallucinations”—wherein models make up facts, law, and precedent—leading Chief Justice Roberts to spotlight this risk in his annual report on the judiciary. We argue that there is a need for public AI benchmarking in law. First, relative to other AI application domains, the legal AI ecosystem lacks legibility—there is little information about the design and performance of many commercial legal AI systems. Legal AI has not benefited from the types of benchmarking that have catalyzed, measured, and informed AI innovation and responsible use in other domains. Second, we articulate the challenges of the institutional design of benchmarking. We illustrate how benchmarks can be captured, watered down, and abused. Careful institutional design around the why, who, what, and how of benchmarking will be critical to navigate difficult tradeoffs of transparency, objectivity, expertise, and resources. Third, addressing legal AI’s illegibility requires matching institutional models to available resources and constraints. Rather than advocating for a single “best” approach to benchmarking, we show how benchmarking strategies depend on available resources.

Association between thoracic hyperkyphosis and sagittal skeletal malocclusion patterns: a cross-sectional study

Measuring disparate impact in human and machine decisions

Empirical analyses have grown increasingly important in discrimination litigation with the greater availability of detailed data on individuals and decisions. A popular analytic strategy is to estimate disparities after adjusting for observed covariates, typically with a regression model, in hopes of ferreting out discriminatory intent. This approach, however, is ill-suited to auditing algorithms that are now commonly used to aid decisions, which typically do not include race or other legally protected factors as inputs. Motivated by legal understandings of disparate impact, we introduce an approach that aims to measure “unjustified” disparities in both human and machine decisions. Our method, which we call risk-adjusted regression, proceeds in three steps. In the first step, we combine all available information in a machine learning model to estimate the value, or inversely, the risk, of taking a certain action, such as approving a loan application or hiring a job candidate. Second, we measure disparities in decisions after adjusting for these risk estimates alone. Finally, in the third step, we assess the sensitivity of results to potential mismeasurement of risk. We demonstrate this approach on a detailed dataset of 2.2 million police stops of pedestrians in New York City, and show that traditional statistical tests of discrimination can substantially understate the magnitude of (risk-adjusted) racial disparities.

Comparative evaluation of six machine learning models for multi-fuel variable compression ratio diesel engine emission prediction under leave-one-out cross-validation

Continuity in geometric intuition between humans and monkeys

Geometric intuition is regarded as a hallmark of human intelligence. Humans hold a unique advantage in recognizing geometric shapes with Euclidean features (e.g., parallelism, symmetry) and have more sophisticated geometric concepts than nonhuman primates. A popular explanation for this human distinctiveness is that humans rely on discrete, symbolic features to encode geometric shapes, while other primates depend only on continuous perceptual features. The current study challenges this view. We used a match-to-sample task to test whether monkeys share geometric representations with humans. We found that monkeys combined high-level perceptual representations with abstract symbolic representations like humans, suggesting a graded, quantitative continuity in geometric intuition across species rather than a categorical, qualitative divide. Importantly, monkeys showed stronger reliance on symbolic features when processing rotated geometric shapes than preschoolers did with unrotated shapes, indicating that what appears to be symbolic representation may largely emerge from rotation-invariant encoding. These results challenge the hypothesis of human uniqueness in geometric intuition and suggest that its origins in humans are rooted in abstractions shared with primates.

Securing the modern power grid with hybrid deep learning against cyber threats in renewable-integrated smart grids

Quantitative guiding of developmental cell fate patterns using a dynamical landscape model

During development, cells gradually assume specialized fates via changes of transcriptional dynamics in thousands of genes. Landscape modeling approaches, which abstract from the underlying gene regulatory networks and reason in a low-dimensional phenotypic space, have been remarkably successful in explaining terminal fate outcomes. The success of these models also prompts their application toward inferring dynamic perturbations of multicellular patterning that alter cell fate outcomes in predictable ways, a task that is otherwise highly challenging due to the complex dynamics of the underlying gene circuits. Here, we accomplish this task by combining a landscape model for Caenorhabditis elegans vulval fate patterning with temporally controlled perturbations of EGF and Notch signaling in vivo using temperature-sensitive mutant alleles. We find that nonintuitive fate outcomes that emerge in combinations of these alleles at static temperature conditions through pathway epistasis are correctly predicted by the model. We then show that short pulses of signaling in these genetic backgrounds, delivered via temperature shifts, can be used to guide both the fraction of induced precursor cells and the specific fates they adopt with quantitative precision. Analysis of the underlying cellular landscapes indicates that cell fate guidance via pulses of signaling effectively redesigns the decision structure into one that has no equivalent in normal development, namely, a conversion of the three-way cell fate decision topology into two sequential binary fate decisions. Our results highlight the predictive power of landscape models and illustrate a method to quantitatively guide cell fate acquisition in a developmental context.

Analysis of the intensive phase of biowaste composting process with the addition of sour buttermilk

Abstract The sustainable management of dairy industry waste remains a major environmental challenge, particularly for sour buttermilk, which is characterized by low pH and high concentrations of slowly degradable biogenic compounds. Its discharge to wastewater treatment systems contradicts circular economy principles and highlights the need for alternative valorization pathways. In this research, the feasibility of incorporating waste sour buttermilk into green biomass during composting was evaluated, and its effects on the intensive phase, selected physicochemical and microbiological properties, and the phytotoxicity of the resulting material were assessed. Sour buttermilk was applied at proportions ranging from 0 to 30% (w/w). Its addition reduced ammonia concentrations in emitted gases from maximum values exceeding 1000 ppm in the control to approximately 600–700 ppm in treatments with 9% and higher additions. At levels above 6%, the addition of sour buttermilk limited the extent of drying during the thermophilic phase. However, additions exceeding 12% adversely affected process performance, as thermophilic conditions (&gt; 45 °C) were achieved only briefly and at relatively low temperatures. This inhibition was associated with decreased pH, increased moisture content, and reduced air-filled porosity. Higher application rates also increased the phytotoxicity of the obtained materials. No potentially pathogenic bacteria were detected in any treatment. Overall, the results indicate that controlled incorporation of sour buttermilk may influence composting dynamics, with 9% (w/w) showing the most balanced process performance under the tested conditions.