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The history of modern art owes a great debt to William Merritt Chase (1849-1916), one of America's influential artists and educators. Chase was a leading member of the international artistic avant-garde and was best known for his mastery of a wide range of subjects in oil and pastel, including figures, landscapes, urban park scenes, interiors, and portraits. As a teacher and founder of the Shinnecock Summer School of Art and the New York School of Art, Chase mentored a new generation of modernists, including Edward Hopper, Georgia O'Keeffe, and Joseph Stella. A century after his death, the breadth and richness of Chase's career are celebrated in this beautifully illustrated publication. Five essays by prominent scholars of American art offer new insights into Chase's multi-faceted artistic practice and his position in the international cultural climate at the turn of the 20th century.

Abstract Microorganisms are physiologically diverse, possessing disparate genomic features and mechanisms for adaptation (functional traits), which reflect on their associated life strategies and determine at least to some extent their prevalence and distribution in the environment. Unlike animals and plants, there is an unprecedented diversity and intractable metabolic versatility among bacteria, making classification or grouping these microorganisms based on their functional traits as has been done in animal and plant ecology challenging. Nevertheless, based on representative pure cultures, microbial traits distinguishing different life strategies had been proposed, and had been the focus of previous reviews. In the environment, however, the vast majority of naturally occurring microorganisms have yet to be isolated, restricting the association of life strategies to broad phylogenetic groups and/or physiological characteristics. Here, we reviewed the literature to determine how microbial life strategy concepts (i.e. copio- and oligotrophic strategists, and competitor–stress tolerator–ruderals framework) are applied in complex microbial communities. Because of the scarcity of direct empirical evidence elucidating the associated life strategies in complex communities, we rely heavily on observational studies determining the response of microorganisms to (a)biotic cues (e.g. resource availability) to infer microbial life strategies. Although our focus is on the life strategies of bacteria, parallels were drawn from the fungal community. Our literature search showed inconsistency in the community response of proposed copiotrophic- and oligotrophic-associated microorganisms (phyla level) to changing environmental conditions. This suggests that tracking microorganisms at finer phylogenetic and taxonomic resolution (e.g. family level or lower) may be more effective to capture changes in community response and/or that edaphic factors exert a stronger effect in community response. We discuss the limitations and provide recommendations for future research applying microbial life strategies in environmental studies. Qualitative and quantitative measures are important when designating microbial life strategies at finer phylogenetic resolution over time.

Quality of life (QoL) in early breast cancer (BC) treatment may be affected by acute and late toxicities. This study evaluated the impact of radiotherapy (RT) schedules, treatment-related toxicities, hormone therapy (HT) and age on QoL. Ninety-five patients answered the FACT-B 4.0 questionnaire. Acute or late toxicities were recorded at each follow-up visit. The median trend of the QoL subscales was stable during all questionnaires. HT negatively impacted on Functional Assessment of Cancer Therapy-General-Total, functional and emotional wellbeing. No difference was recorded between RT schedules and toxicity. No significant differences for age were detected in QoL. RT seems not to influence QoL of BC patients, in terms of fractionation regimen or RT-related side-effects. Moreover, women having systemic HT experienced a QoL worse than patients treated with RT only. Further and long-term protocols are needed to improve the validity of the tool.

Cancer cells within a tumour have heterogeneous phenotypes and exhibit dynamic plasticity. How to evaluate such heterogeneity and its impact on outcome and drug response is still unclear. Here, we transcriptionally profile 35,276 individual cells from 32 breast cancer cell lines to yield a single cell atlas. We find high degree of heterogeneity in the expression of biomarkers. We then train a deconvolution algorithm on the atlas to determine cell line composition from bulk gene expression profiles of tumour biopsies, thus enabling cell line-based patient stratification. Finally, we link results from large-scale in vitro drug screening in cell lines to the single cell data to computationally predict drug responses starting from single-cell profiles. We find that transcriptional heterogeneity enables cells with differential drug sensitivity to co-exist in the same population. Our work provides a framework to determine tumour heterogeneity in terms of cell line composition and drug response. The impact of tumour heterogeneity on drug response in breast cancer is not fully understood. Here, the authors characterise cell lines from all main breast cancer subtypes using single-cell RNA-seq and train a deconvolution algorithm to predict drug responses in heterogeneous tumour cell populations.

This paper employs Swedish data on households' stock holdings to investigate how consumption responds to changes in stock market returns. We instrument the actual capital gains and dividend payments with past portfolio weights. Unrealized capital gains lead to a marginal propensity to consume of 23% for the bottom 50% of the wealth distribution and about 3% for the top 30% of the wealth distribution. Household consumption is significantly more responsive to dividend payouts across all parts of the wealth distribution. Our findings are consistent with households treating capital gains and dividends as separate sources of income.

Since the early days of Dirac flux quantization, magnetic monopoles have been sought after as a potential corollary of quantized electric charge. As opposed to magnetic monopoles embedded into the theory of electromagnetism, Weyl semimetals (WSM) exhibit Berry flux monopoles in reciprocal parameter space. As a function of crystal momentum, such monopoles locate at the crossing point of spin-polarized bands forming the Weyl cone. Here, we report momentum-resolved spectroscopic signatures of Berry flux monopoles in TaAs as a paradigmatic WSM. We carried out angle-resolved photoelectron spectroscopy at bulk-sensitive soft X-ray energies (SX-ARPES) combined with photoelectron spin detection and circular dichroism. The experiments reveal large spin- and orbital-angular-momentum (SAM and OAM) polarizations of the Weyl-fermion states, resulting from the broken crystalline inversion symmetry in TaAs. Supported by first-principles calculations, our measurements image signatures of a topologically non-trivial winding of the OAM at the Weyl nodes and unveil a chirality-dependent SAM of the Weyl bands. Our results provide directly bulk-sensitive spectroscopic support for the non-trivial band topology in the WSM TaAs, promising to have profound implications for the study of quantum-geometric effects in solids. Weyl semimetals exhibit Berry flux monopoles in momentum-space, but direct experimental evidence has remained elusive. Here, the authors reveal topologically non-trivial winding of the orbital-angular-momentum at the Weyl nodes and a chirality-dependent spin-angular-momentum of the Weyl bands, as a direct signature of the Berry flux monopoles in TaAs.

The topological classification of electronic band structures is based on symmetry properties of Bloch eigenstates of single-particle Hamiltonians. In parallel, topological field theory has opened the doors to the formulation and characterization of non-trivial phases of matter driven by strong electron-electron interaction. Even though important examples of topological Mott insulators have been constructed, the relevance of the underlying non-interacting band topology to the physics of the Mott phase has remained unexplored. Here, we show that the momentum structure of the Green’s function zeros defining the “Luttinger surface\" provides a topological characterization of the Mott phase related, in the simplest description, to the one of the single-particle electronic dispersion. Considerations on the zeros lead to the prediction of new phenomena: a topological Mott insulator with an inverted gap for the bulk zeros must possess gapless zeros at the boundary, which behave as a form of “topological antimatter” annihilating conventional edge states. Placing band and Mott topological insulators in contact produces distinctive observable signatures at the interface, revealing the otherwise spectroscopically elusive Green’s function zeros. Topological classification of interacting electronic states has emerged as an important topic recently. Wagner at al. show that the momentum structure of the zeros of the electron Green’s function can be used to identify a topological Mott insulator phase, similarly to the single-particle dispersion.

For many governments, enacting green policies is a priority, but such policies often impose on citizens substantial and uneven costs. How does the introduction of green policies affect voting? We study this question in the context of a major ban on polluting cars introduced in Milan, which was strongly opposed by the populist right party Lega. Using several inferential strategies, we show that owners of banned vehicles—who incurred a median loss of €3,750—were significantly more likely to vote for Lega in the subsequent elections. Our analysis indicates that this electoral change did not stem from a broader shift against environmentalism, but rather from disaffection with the policy’s uneven pocketbook implications. In line with this pattern, recipients of compensation from the local government were not more likely to switch to Lega. The findings highlight the central importance of distributive consequences in shaping the political ramifications of green policies.

Foliar symptoms of grapevine leaf stripe disease (GLSD, a disease within the esca complex) are linked to drastic alteration of photosynthetic function and activation of defense responses in affected grapevines several days before the appearance of the first visible symptoms on leaves. The present study suggests a methodology to investigate the relationships between high-resolution multispectral images (0.05 m/pixel) acquired using an Unmanned Aerial Vehicle (UAV), and GLSD foliar symptoms monitored by ground surveys. This approach showed high correlation between Normalized Differential Vegetation Index (NDVI) acquired by the UAV and GLSD symptoms, and discrimination between symptomatic from asymptomatic plants. High-resolution multispectral images were acquired during June and July of 2012 and 2013, in an experimental vineyard heavily affected by GLSD, located in Tuscany (Italy), where vines had been surveyed and mapped since 2003. Each vine was located with a global positioning system, and classified for appearance of foliar symptoms and disease severity at weekly intervals from the beginning of each season. Remote sensing and ground observation data were analyzed to promptly identify the early stages of disease, even before visual detection. This work suggests an innovative methodology for quantitative and qualitative analysis of spatial distribution of symptomatic plants. The system may also be used for exploring the physiological bases of GLSD, and predicting the onset of this disease.

Background:Rheumatoid arthritis (RA) disease activity is often evaluated with composite disease activity scores like the 28 joint Disease Activity Score (DAS28). However, by combining different subjective and objective measures into a one-dimensional score, composite metrics run the risk of mixing patient-related vs disease-related factors, hiding potential subgroups of the disease, and may thus introduce noise in e.g., studies of the relation between biomarkers and RA disease phenotype, and in clinical decision-making.Objectives:To derive clusters of RA patients at diagnosis based on disease and patient characteristics.Methods:We included patients from the Swedish Rheumatology Quality Register (SRQ). We selected the baseline visit (defined as ± 30 days from inclusion into the SRQ, thus representing untreated RA disease activity at diagnosis) among early RA patients aged 18-100 years, between 2012 and 2022.We clustered patients using the K-means algorithm and compared results for three to ten clusters using the elbow method and silhouette plots. We evaluated cluster stability using the coefficient of variation. The algorithm required patients to have complete data on all input variables (28 swollen joint count (SJC28), 28 tender joint count (TJC28), patient global health (PGH), physician global health (PHGH), pain, fatigue, Health Assessment Questionnaire (HAQ), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), time to diagnosis). To describe the clusters, we calculated medians of the inputs, as well as age, sex, seropositivity, and DAS28ESR.Results:Among 14 626 early RA patients, 7417 with complete data on all inputs were included in the analysis.The best cluster solution featured 5 clusters (see Figure 1, cluster order follows DAS28ESR median). Cluster 1 (n=1856) contained patients with very low scores across all inputs, while Cluster 5 (n=1562) contained patients with very high scores across all inputs. Cluster 2 (n=1626) captured patients with high values on subjective measures (median pain, fatigue, PGH all > 60 on 0-100 visual analogue scale (VAS)) but low values on objective measures (median CRP < 10 mg/L, ESR < 20 mm/hour). Cluster 3 (n=1413) captured patients with high objective measures (median CRP > 20, ESR > 40) and moderate values on subjective measures (median pain, fatigue, PGH all ≈ 50). Lastly, cluster 4 (n=960) contained patients, proportionally more males, with strong joint involvement (median SJC28 and TJC28 > 10 joints each) and poor PHGH (3 on a 0-4 scale).Conclusion:In line with DAS28ESR, our unsupervised clustering revealed clusters of patients with very high and very low scores on all variables. In contrast, however, it unraveled distinct clusters of patients with different combinations of objective-, subjective- and joint-dominated phenotypes, which otherwise form a single category under the DAS28ESR. Our approach thus offers a more nuanced mean of describing RA disease activity, necessary for precision medicine in RA research and management.REFERENCES:NIL.Acknowledgements:NIL.Disclosure of Interests:Simon Steiger: None declared, Helga Westerlind: None declared, Daniela Di Giuseppe: None declared, Johan Askling Abbvie, BMS, Eli Lilly, Galapagos, MSD, Pfizer, Roche, Samsung Bioepis, Sanofi.