This article traces the shifting fortunes of for-profit psychedelic medicine through two phases: a boom from 2016 to late 2021, followed by a bust that continued through late 2024. It argues that the forces driving this cycle are best understood through the concept of capitalization, which links present valuations to investor expectations about future earnings. Engaging the capital-as-power framework, the article situates psychedelic companies within the broader biopharmaceutical sector, showing how the volatility of drug development is intensified by the unruliness of these substances as capitalized assets. This unruliness stems from a range of factors, including murky intellectual property claims, unpredictable and intense subjective experiences, and lingering cultural stigma. During the boom, firms attracted significant interest from venture capital and other investors by promising revolutionary breakthroughs in mental health treatment. As expectations rose, so did valuations. But disappointing results from clinical trials, regulatory setbacks, and deepening doubts about the ability to control and standardize psychedelic therapies led to sharp declines in investor confidence. Analyzing financial performance alongside investor narratives, the article underscores the tensions involved in subjecting these unruly substances to the logic of capitalist power.

Non-native languages tend to be acquired through a combination of explicit and implicit learning, where implicit learning requires coordination of language information with referents in the environment. In this study, we examined how learners use both language input and environmental cues to acquire vocabulary and morphology in a novel language and how their language background influences this process. We trained 105 adults with native languages (L1s) varying in morphological richness (English, German, Mandarin) on an artificial language comprising nouns and verbs with morphological features (number, tense, and subject-verb [SV] agreement) appearing alongside referential visual scenes. Participants were able to learn both word stems and morphological features from cross-situational statistical correspondences between language and the environment, without any instruction. German-speakers learned SV agreement worse than other morphological features, which were acquired equally effectively by English or Mandarin speakers, indicating the subtle and varied influence of L1 morphological richness on implicit non-native language learning.

Recentneuro-symbolic approaches have successfully extracted symbolic rule-sets from Convolutional Neural Network-based models to enhance interpretability. However, applying similar techniques to Vision Transformers (ViTs) remains challenging due to their lack of modular concept detectors and reliance on global self-attention mechanisms. We propose a framework for symbolic rule extraction from ViTs by introducing a sparse concept layer inspired by Sparse Autoencoders (SAEs). This linear layer operates on attention-weighted patch representations and learns a disentangled, binarized representation in which individual neurons activate for high-level visual concepts. To encourage interpretability, we apply a combination of L1 sparsity, entropy minimization, and supervised contrastive loss. These binarized concept activations are used as input to the FOLD-SE-M algorithm, which generates a rule-set in the form of a logic program. Our method achieves a better classification accuracy than the standard ViT while enabling symbolic reasoning. Crucially, the extracted rule-set is not merely post-hoc but acts as a logic-based decision layer that operates directly on the sparse concept representations. The resulting programs are concise and semantically meaningful. This work is the first to extract executable logic programs from ViTs using sparse symbolic representations, providing a step forward in interpretable and verifiable neuro-symbolic AI.

With the end of the slave trade and the general emancipation of slaves, the problems of race did not disappear but simply were transformed.1

We augment the ‘quasisisymmetric stellarator repository’ (QUASR) to include vacuum field stellarators with quasihelical symmetry using a globalized optimization workflow. The database now has over 300 000 quasisaxisymmetry and quasihelically symmetric devices along with coil sets, optimized for a variety of aspect ratios, rotational transforms and discrete rotational symmetries. This paper outlines a couple of ways to explore and characterize the data set. We plot devices on a near-axis quasisymmetry (QS) landscape, revealing close correspondence to this predicted landscape. We also use principal component analysis (PCA) to reduce the dimensionality of the data so that it can easily be visualized in two or three dimensions. The PCA also gives a mechanism to compare the new devices here with previously published ones in the literature. We are able to characterize the structure of the data, observe clusters and visualize the progression of devices in these clusters. The topology of the data are governed by the interplay of the design constraints and valleys of the QS objective. These techniques reveal that the data has structure, and that typically one, two or three principal components are sufficient to characterize it. The latest version of QUASR is archived at https://zenodo.org/doi/10.5281/zenodo.10050655 and can be explored online at quasr.flatironinstitute.org.

In the contemporary business-to-business (B2B) context, marked by technological, economic, and geopolitical turbulence, creating and maintaining Customer Engagement (CE) is both challenging and necessary for buyers and suppliers. However, while prior studies have already investigated how suppliers are adapting their practices to retain and attract customers, the buyers’ perspective is largely unexplored in existing literature. Therefore, drawing on the Paradox Theory as an interpretative lens, this research investigates the tensions that characterize CE through interviews with buyers from medium to large companies across various sectors. Results highlight that buyers are not merely passive recipients but active participants in the generation and management of tensions related to CE. At a managerial level, the study proposes an operational framework to support suppliers in adapting their engagement practices. Finally, the study suggests future research directions.

We are in the early stage of a revolution in the field of comparative genomics. Within the past five years, thousands of animal, plant, and fungal genomes have been sequenced and assembled to high quality. There is even serious discussion around sequencing the genomes of every eukaryotic species on earth. Here, I explain why this genomic revolution is happening and discuss the feasibility of sequencing genomes on a massive scale. Having a very wide diversity of genome sequences will accelerate applied research in biomedicine, biotechnology, aquaculture, agriculture, and conservation, and facilitate fundamental research in areas such as ecology, physiology, developmental biology, and evolutionary biology. In this article, I explore new findings and new questions in evolutionary biology emerging from animal genome analyses. Examples are drawn from marine animals such as polychaetes, bivalves, cephalopods, fish, and bryozoans, plus unusual terrestrial groups such as gerbils, moths, and bee-flies. I highlight patterns of mutation, the dynamics of gene families, and chromosomal organisation of genomes as areas ripe for further research. An even wider diversity of genome sequences will be needed to fill the knowledge gaps or investigate emerging puzzles, and a case is made for sequencing the genomes of over 100,000 species.

Precision weed detection and mapping in vegetable crops are beneficial for improving the effectiveness of weed control. This study proposes a novel method for indirect weed detection and mapping using a detection network based on the You-Only-Look-Once-v8 (YOLOv8) architecture. This approach detects weeds by first identifying vegetables and then segmenting weeds from the background using image processing techniques. Subsequently, weed mapping was established and innovative path planning algorithms were implemented to optimize actuator trajectories along the shortest possible path. Experimental results demonstrated significant improvements in both precision and computational efficiency compared with the original YOLOv8 network. The mean average precision at 0.5 (mAP50) increased by 0.2, while the number of parameters, giga floating-point operations per second (GFLOPS), and model size decreased by 0.57 million, 1.8 GFLOPS, and 1.1 MB, respectively, highlighting enhanced accuracy and reduced computational costs. Among the analyzed path planning algorithms, including Christofides, Dijkstra, and dynamic programming (DP), the Dijkstra algorithm was the most efficient, producing the shortest path for guiding the weeding system. This method enhances the robustness and adaptability of weed detection by eliminating the need to detect diverse weed species. By integrating precision weed mapping and efficient path planning, mechanical actuators can target weed-infested areas with optimal precision. This approach offers a scalable solution that can be adapted to other precision weeding applications.

This surveillance report describes the epidemiology and clinical outcomes of carbapenem-resistant Enterobacterales (CRE) infections in Tennessee from 2016 to 2022, analysing 570 cases and 406 isolates. The incidence of CRE infections per 100 000 population showed an upward trend. Enterobacter species were the most common organisms, whereas Klebsiella species were the main carbapenemase-producing CRE (CP-CRE). Klebsiella pneumoniae carbapenemase was the most common mechanism contributing to this resistance. Demographic characteristics of patients with identified isolates demonstrated a median age of 69.5 years. There were no significant differences in CP-CRE infection by sex or race. Patients with CP-CRE were more likely to be hospitalized than those with non-CP-CRE, at 60.9% and 43.9%, respectively. Multivariable analysis indicated that patients with CP-CRE had significantly higher odds of 90-day mortality (odds ratio, 2.22; 95% confidence interval, 1.12–4.42; p < 0.0001) than non-CP-CRE patients. Individuals with a higher Charlson Comorbidity Index score exhibited an increased odds of dying within 30- and 90-day post-specimen collection and had a greater likelihood of requiring intensive care unit admission. This report underscores the need to understand the epidemiology and risk factors linked to CRE infections to improve prevention strategies and patient care.

Microbial dysbiosis has been linked to environmental enteropathy (EE) and alterations in nutrient absorption; however, compositional modifications following exposure to supplementary nutrients are poorly understood. Here, we report the effect of amino acid and micronutrient supplementation on the gut microbiome of adults with EE.

In the AMAZE trial, adults with EE were randomized to amino acids (AA) and/or micronutrients (MM) for 16 weeks in a 2 × 2 factorial design against placebo. Endoscopy was performed before and after intervention, during which duodenal aspirates were collected as well as fecal samples. 16S rRNA amplicon sequencing was performed on both these samples, and differences in bacterial community composition before and after interventions were investigated using differential abundance analysis, corrected using false discovery rate, plus alpha and beta diversity measurements.

HIV seropositive participants exhibited lower alpha and beta diversity at baseline. AA and/or MM supplementation did not show significant changes in abundance or diversity of genera post-intervention compared to placebo. Micronutrient supplementation resulted in an increase in the pyruvate fermentation to acetone MetaCyc pathways compared to the placebo arm.

This study provides insights into the responsiveness of the gut microbiome to micronutrient and amino acid supplementation in adults with EE.

Servitization is a key strategy for enhancing competitiveness in manufacturing, yet the managerial drivers behind this transformation remain underexplored. This study investigates the impact of top executives’ service cognition on servitization using a novel index derived from text-mined disclosures of Chinese listed manufacturing firms (2007–2020). Results show that executives’ service cognition significantly promotes servitization, even after controlling for endogeneity using instrumental variables and Heckman’s two-stage model. Mechanism analysis reveals that this cognitive orientation enhances human capital accumulation and R&D investment, which in turn drive higher service levels. Furthermore, the relationship is moderated by executive power concentration and regional internet penetration. Heterogeneity tests indicate stronger effects in high-tech industries, state-owned enterprises, and large firms. These findings highlight the critical role of executive cognition in shaping strategic transformation and offer practical implications for firms and policymakers aiming to foster servitization through leadership development and supportive digital infrastructure.

My parents, immigrants from Haiti, settled in Canada. I grew up in Sept-Ⓘles, a town with an Indigenous community. We were the first Black family to put down roots there. One day, Steve, an Indigenous friend, invited me to his home, a brand-new house on the reserve.1 I was shocked. The walls were covered with graffiti of despair, “red sacrifice,” and “black mourning.” As an Indigenous person assigned a house by the Federal Government, it “was living in prison.” For Steve, an Indian residential school (IRS) survivor’s descendant, it was the symbol of the “civilizing” society that wiped out his Indigenous values and culture, eradicating the foundation of his identity. The graffiti was a form of resistance.

Employers purchase health benefits for more than 60% of the nonelderly population, making employers both important custodians of employee well-being and important actors in the health care ecosystem. Because employers typically have unilateral control over health and retirement benefits, the federal Employee Retirement Income Security Act (ERISA), enacted in 1974, imposes fiduciary obligations on employers when they manage or administer benefits. We provide evidence, from a novel survey of respondents who administer or oversee health benefits for their companies, that many employers appear to neglect even the most basic of their fiduciary obligations to their employees. This neglect may help explain the poor performance of employer plans in controlling costs and providing access to health care, and it suggests that many employers may be vulnerable to liability from ERISA lawsuits.

“If, therefore, we are going to sin, we must sin quietly.”1

This article investigates sample selection bias in early-stage investment. We use comprehensive administrative data on the universe of new firm starts in Norway, allowing us to compare venture-backed firms with ex ante similar firms that do not receive venture funding. The valuation premium for venture backing is sizeable at firm birth and doubles over the first 5 years, implying a substantial upward bias in venture capital (VC) returns relative to comparable firms. In contrast, the premium for firms receiving multiple rounds of outside equity emerges only after the first year and remains significantly smaller than the VC premium throughout the firm life cycle.