诺和诺德

Mechanistic chromatography models are valuable tools in process systems engineering, but their use in gradient-based optimization can be hindered by computational cost and the effort required to obtain accurate objective-function gradients. To address this concern, a fully differentiable Julia chromatography solver, ChromOps.jl, is presented that combines high-order spatial discretization with discrete forward sensitivity analysis (DFSA). Two established high-order spatial discretizations, finite difference summation-by-parts (FD-SBP) and the discontinuous Galerkin spectral element method (DG-SEM), are compared on a 6-component ion-exchange chromatography problem with steric mass action kinetics. Both attain their theoretical convergence rates on manufactured problems, while FD-SBP offers better work-precision performance for both forward (primal) and dual-valued simulations. Moreover, its derivation and implementation require substantially less effort. The measured DFSA runtime follows t (DFSA) approximate to 0.9 ( 1 + N (p) ) t fwd for up to N p = 24 parameters propagated in a single ForwardDiff.jl chunk, corresponding to an incremental cost of approximately 0.9 forward solves per additional parameter. By using the same code path for primal and dual-valued simulations, DFSA computes gradients of user-defined objectives through the discrete chromatography model without hand-derived sensitivity equations or chain-rule expressions. This reduces the implementation effort required to integrate mechanistic chromatography models into gradient-based optimization workflows in process systems engineering.

BACKGROUND:Evidence, including animal, clinical, and real-world studies in individuals with type 2 diabetes and/or obesity, suggests reduced risk of dementia and Alzheimer's disease after GLP-1 receptor agonist exposure. The evoke and evoke+ trials aimed to investigate the efficacy and safety of oral semaglutide in individuals with early Alzheimer's disease. METHODS:evoke and evoke+ were multicentre, randomised, double-blind, placebo-controlled phase 3 trials conducted across 566 sites in 40 countries. The trials assessed the efficacy and safety of oral semaglutide up to 14 mg once daily in participants with amyloid-confirmed Alzheimer's disease, aged 55-85 years, with mild cognitive impairment or mild dementia due to Alzheimer's disease. In evoke+, participants with significant small vessel pathology were included. Participants were randomly assigned (1:1) to once-daily semaglutide 14 mg (flexible dose) or placebo for up to 156 weeks. The primary endpoint was change in Clinical Dementia Rating-Sum of Boxes (CDR-SB) score from baseline to week 104, assessed in all randomised participants. Safety was assessed in all randomised participants and reported for those receiving at least one dose of study drug. These trials were registered at ClinicalTrials.gov (NCT04777396 and NCT04777409); both trials have been discontinued due to negative clinical outcome. FINDINGS:Between May 18, 2021, and Sept 8, 2023, 9981 participants were screened, of whom 3808 were randomly assigned; 1855 in evoke (semaglutide, n=928; placebo, n=927) and 1953 in evoke+ (semaglutide, n=976; placebo, n=977). Mean age was 72·2 years (SD 7·1), and mean CDR-SB score was 3·7 (SD 1·6) at baseline. In evoke+, 54 (2·8%) participants had small vessel pathology. In evoke and evoke+, mean changes in CDR-SB score from baseline to week 104 were 2·3 (SE 0·1) and 2·2 (0·1) with semaglutide, compared with 2·3 (0·1) and 2·1 (0·1) with placebo (estimated difference -0·08 [95% CI -0·35 to 0·20], p=0·57 in evoke and 0·10 [-0·17 to 0·38], p=0·46 in evoke+). Treatment-emergent adverse events were reported in 1729 (91·2%) of 1896 participants receiving semaglutide versus 1613 (84·8%) of 1902 receiving placebo. There were five fatalities considered treatment-related by the investigators (one in the semaglutide group and four in the placebo group). INTERPRETATION:Oral semaglutide was not efficacious in slowing clinical progression in participants with early Alzheimer's disease. Safety and tolerability of semaglutide in early Alzheimer's disease is consistent with studies in other indications. FUNDING:Novo Nordisk.

Electrochemical gradients are essential to the functioning of cells and form across membranes using active transporters. Here we show in contrast that condensed biomolecular systems-often termed condensates-sustain pH gradients without any external energy input. By studying individual condensates on the micrometre scale using a microdroplet platform, we reveal dense-phase pH shifts towards conditions of minimal electrostatic repulsion. We demonstrate that protein condensates can drive substantial alkaline and acidic gradients, which are compositionally tunable and can extend to complex architectures sustaining multiple unique pH conditions simultaneously. Through in silico characterization of human proteomic condensate networks, we further highlight potential wide-ranging electrochemical properties emerging from condensation in nature, while correlating intracellular condensate pH gradients with complex biomolecular composition. Together, the emergent nature of condensation shapes distinct pH microenvironments, thereby creating a regulatory mechanism to modulate biochemical activity in living and artificial systems.

As data-science agents shift from co-pilots to auto-pilots, silent misframing becomes a critical failure mode. Agents quietly commit to plausible but unintended task framings, producing clean, executable artifacts that hide their incorrect assessment of the task. Existing benchmarks score whether the pipeline runs, ignoring whether the agent recognized the task was underspecified. We introduce Ambig-DS, two diagnostic suites: one for prediction-target ambiguity (Ambig-DS-Target, 51 tasks built on DSBench, a tabular modeling benchmark) and one for evaluation-objective ambiguity (Ambig-DS-Objective, 61 tasks built on MLE-bench, a Kaggle-style ML competition benchmark), constructed so that scoring uses each source benchmark's original evaluator. For every task we pair the original, fully specified version with an ambiguous variant produced by controlled edits; a human-and-LLM verification pipeline confirms each variant admits multiple plausible interpretations with decision-relevant consequences. The suites are analyzed independently and ambiguity lowers performance in both. Across five agents spanning efficient to frontier-class models, we find in our controlled diagnostic setting: (i) failures are silent commitments: wrong-target submissions on Target, wrong-metric or non-committal baseline submissions on Objective, rather than execution errors; (ii) allowing the agent to ask one clarifying question recovers much of the loss under idealized conditions, suggesting missing framing information drives a substantial part of the observed degradation; but (iii) agents cannot reliably tell when to use it: permissive prompts induce over-asking on clear tasks, while conservative prompts induce silent defaulting on ambiguous ones. Recognizing target and objective underspecification, not pipeline execution, is the bottleneck missing from standard DS-agent evaluations.

The development of continuous biomanufacturing processes requires robust and early anomaly detection, since even minor deviations can compromise yield and stability, leading to disruptions in scheduling, reduced weekly production, and diminished economic performance. These processes are inherently complex and exhibit non-linear dynamics with intricate relationships between process variables, thus making advanced methods for anomaly detection essential for efficient operation. In this work, we present a novel framework for unsupervised anomaly detection in continuous biomanufacturing based on an ensemble of generative adversarial networks (GANs). We first establish a benchmark dataset simulating both normal and anomalous operation regimes in a continuous process for the production of a small molecule. We then demonstrate the effectiveness of our GAN-based framework in detecting anomalies caused by sudden feedstock variability. Finally, we evaluate the impact of using a hybrid quantum/classical GAN approach with both a simulated quantum circuit and a real photonic quantum processor on anomaly detection performance. We find that the hybrid approach yields improved anomaly detection rates. Our work shows the potential of hybrid quantum/classical approaches for solving real-world problems in complex continuous biomanufacturing processes.