UC Berkeley

Background: The European Medicines Agency (EMA) implemented 'fast-track' programmes, like conditional marketing authorisations (CMA), where the benefits of immediate drugs' availability outweigh the risks associated with incomplete evidence. However, payers in the European Union (EU) decide on medicines' coverage based on clinical benefits assessment, cost-effectiveness and/or budget impact. We investigated differences in the time-to-access of drugs approved via CMA vis-à-vis standard marketing authorisation (SMA) in Italy, Germany, and Spain. Methods: CMA-licenced drugs from 2006 to 2022 were retrieved and matched with comparable SMA drugs. Collected data were as follows: marketing authorisation details, drug characteristics, pivotal trials' characteristics and national reimbursement decision dates. Data sources included European Public Assessment Reports, and country-specific databases (Farmadati®, Lauer-Taxe®, BIFIMED). Results: CMA drugs take longer, in days, to reach reimbursement compared to SMA drugs both in Italy (CMA: median 523, mean 635, standard deviation (SD 364; SMA: median 455, mean 497, SD 242) and Spain (CMA: median 691, mean 779, SD 456; SMA: median 534, mean 568, SD 273). Cox regressions and Kaplan-Meier survival analyses corroborate these findings. Conclusions: The EMA's intent to accelerate access to promising medicines may be offset by longer timelines to secure national reimbursement in major EU nations.

Computed Axial Lithography (CAL) represents a significant advancement in the emerging field of Volumetric Additive Manufacturing (VAM). CAL addresses key limitations of traditional photopolymer additive manufacturing technologies, by eliminating the need for layering and support structures. Unlike conventional methods, CAL prints components by illuminating all points within a desired geometry simultaneously, using tomographic reconstruction to form the object in a single step. This unique approach eliminates the relative motion between the object and the precursor material, enabling faster printing speeds and reducing the waste associated with support structures. However, CAL parts require post-processing steps before they can be utilized. CAL's core attributes make it particularly suited for In-Space Manufacturing (ISM), due to its fast fabrication times, wide breadth of materials it can use, and minimized footprint. CAL has been successfully demonstrated in microgravity during parabolic flight experiments. However to fully validate and understand CAL's behaviour in microgravity, all manufacturing and post-processing steps must be integrated. In June 2024, we conducted SpaceCAL Mission 3, testing this entire workflow on a suborbital flight aboard Virgin Galactic's SpaceShipTwo. During ∼140 s of microgravity, the system autonomously manufactured and post-processed four parts using PEGDA700 resin. Post-flight analysis showed that 2/4 parts were recognisable, while others were distorted due to bubble formation from residual water droplets, off-axis optical aberrations, and non-uniform solvent rinsing. Despite these limitations, this study represents the first integrated CAL workflow in space, providing an initial experimental demonstration and analysis for closed-loop in-space manufacturing.

Structural design is deeply expertise-driven: engineers rely on intuition, experience, and both explicit and tacit knowledge of load paths, structural typologies, first principles and in-depth calculations to arrive at a design solution. Current computational workflows in structural design largely focus on optimization, form-finding and dimensioning of members, where the design space is predefined and algorithms iteratively refine a solution. Machine learning, however, opens the possibility to explore broader design spaces in which geometry and topology are not fixed in advance. Imitation learning (IL), a paradigm closely related to reinforcement learning, offers a pathway to integrate skills from design examples, rather than explicit rulesets. Recently, flow matching, a generative framework that learns a vector field transporting noise toward data over time, has emerged as a prominent method for modeling complex, multimodal action distributions that standard one-shot predictors often fail to capture. By learning from engineers’ demonstrations with a flow-based imitation policy, we transfer structural intuition into a design agent without dense reward engineering or computationally expensive finite element analysis at every decision step. To study this idea, we create a 2D structural testing environment. Coupled with a finite element analysis solver to track and rank design outcomes, we create a training ground for the IL algorithm. Using the environment we tested building of pin-jointed steel truss bridges as graphs and trained an imitation policy to predict chunks of continuous placement actions and capture longer-term design intent. Our results show that the flow-based policies can learn structural intuition, generating diverse feasible bridge designs that reflect established engineering principles. The work introduces a reproducible benchmark for assembly-constrained structural design and examines the limitation and the potential of generative imitation learning as an alternative framework for structural design exploration beyond top-down optimization.

Structural member sizing directly influences material consumption, embodied carbon, and overall structural efficiency. Recent advances in graph representations have opened new possibilities for structural optimization, with applications such as topology optimization and surrogate modeling. We propose a graph-based representation learning and clustering pipeline that uses structural simulation results to support member sizing and cross-section optimization. In this representation, connection points and linear members in the structural system are modeled as two node types in a heterogeneous graph. Mechanical responses, geometric properties, and topological information are encoded as features, and structural context is propagated through message passing. We then train a Heterogeneous Graph Attention Network (HeteroGAT) encoder with a contrastive objective constructed from mechanically similar and topologically adjacent member pairs. Finally, we cluster the learned embeddings with Gaussian Mixture Models (GMM). The resulting clusters provide a practical basis for structural optimization. Specifically, we use the clustering results as grouping rules for cross-section optimization in Karamba3D, such that members within the same cluster share a common profile. Using steel member sizing as a case study, we evaluate the cross-section assignment strategies on a whole-building structural system and demonstrate the method through multiple application examples. The results show that the proposed method achieves a rationalized section system with significantly fewer cross-section types and stable convergence, while maintaining structural performance comparable to the original design, making it a practical tool for early-stage steel structural design.

Black carbon (BC)–a strong indicator of diesel particulate matter and other sources of incomplete carbonaceous fuel combustion–is an important air pollutant that affects public health, yet low-cost sensors capable of long-term, autonomous BC monitoring remain underdeveloped. We report on the development and evaluation of a novel BC prototype sensor that integrates soot collection on a quartz filter, in-situ optical transmission measurement, and thermal filter regeneration. To enable regeneration at lower temperatures, we evaluated the catalytic effects of various alkali metal salts pre-applied to the filter. Among these, cesium carbonate (Cs2CO3) exhibited the strongest catalytic activity, lowering the temperature required for complete BC removal by up to 190 °C and reducing energy consumption by more than 75% compared to that required for untreated filters. The catalytic effect persisted through 10 BC collection–regeneration cycles. These findings demonstrate the potential of catalytically aided thermal regeneration in BC sensors and suggest a pathway toward energy-efficient and reduced maintenance air quality monitoring suitable for distributed BC monitoring networks.

Bioconversion of lignocellulosic biomass offers a promising alternative to petroleum-based chemical production. However, inefficient xylose utilization and toxic compounds in cellulosic hydrolysate limit microbial fermentation, as the hydrolysate contains substantial amounts of xylose in addition to glucose. To address these challenges, we engineered Issatchenkia orientalis to produce 3-hydroxypropionic acid (3-HP) directly from sorghum hydrolysate under low-pH conditions. A heterologous xylose utilization pathway consisting of XYL1, XYL2, and XYL3 from Scheffersomyces stipitis was introduced into an engineered 3-HP producing strain, enabling efficient conversion of xylose to 3-HP. The engineered strain produced 46.8 g/L 3-HP from sorghum hydrolysate without nutrient supplementation. To eliminate the lag phase under low-pH conditions, fermentation was conducted at pH 6.0 for the first three days, after which pH control was discontinued and in situ 3-HP accumulation buffered the culture. This partial pH control strategy increased 3-HP productivity by 55% from 0.20 to 0.31 g/L∙h, while maintaining low-pH conditions. Introducing an additional copy of XYL2 further increased 3-HP titer to 53.5 g/L and the yield by 33%, from 0.30 to 0.40 g/g sugars, with pH reaching 4.5 at the end of fermentation. This represents one of the highest reported 3-HP titers and yields from cellulosic hydrolysate without additional nutrient supplementation. This work demonstrates a nutrient-independent and low-pH bioprocess for upgrading lignocellulosic hydrolysate into 3-HP, highlighting the industrial potential of engineered xylose-utilizing I. orientalis for sustainable production of platform chemicals from renewable feedstocks.

Anthropogenic heat (AH) from buildings contributes to urban overheating, especially during heat waves, yet building retrofit studies usually evaluate energy savings without assessing impacts on AH. This study quantifies how common building retrofit measures affect both building energy use and AH emissions across the City of Los Angeles. Using a bottom-up urban building energy modeling framework coupled with high-resolution local weather from the Weather Research and Forecasting model with Building Effect Parameterization (WRF-BEP), we evaluate eleven retrofit measures and two multi-measure retrofit packages. HVAC and LED lighting retrofits provide the largest city-wide annual site energy savings, while roof coating is most effective for reducing AH. A package optimized for energy savings reduces summer site energy use by about 32% (2.3 TWh), while a package incorporating AH-focused measures reduces the total AH by over 50% (137 PJ) with minimal difference in energy savings. The AH-aware package produces substantially greater urban cooling, reducing mean near-surface air temperature by up to 0.62 ℃ and peak temperature by up to 3.79 ℃. These results show that retrofit strategies selected only for energy savings may overlook major opportunities for urban heat mitigation. The study provides a framework for integrating AH into building retrofit planning and urban heat resilience policy.

Neutron-induced γ -ray spectra provide key inputs for modern active interrogation applications. A precise modeling of the nuclear reaction and subsequent emission of γ rays is challenging and often impossible due to limitations on evaluated data file formats and nuclear transport simulation codes. We present a framework that addresses these challenges by combining experimental data and reaction-model calculation outputs into an extended candidate version of the Generalized Nuclear Data Structure (GNDS) file, the successor format for the legacy Evaluated Nuclear Data File (ENDF-6). This proposed GNDS hierarchical format contains all the necessary ingredients for inline γ -ray cascade reproduction with event-by-event precision, including continuum–continuum and continuum–discrete transitions following neutron-capture and inelastic neutron scattering reactions. Cascade-event generation based on our approach demonstrates improved energy conservation on an event-by-event basis and permits the use of γ - γ coincidences in applications. This work offers, for the first time, a method to generate neutron-capture and inelastic neutron-scattering γ -ray cascades where energy conservation, correlations, and experimental primaries are fully accounted for.