Ecological interactions shape the evolution of multiple species traits in populations. These traits are often linked to each other through genetic correlations, affecting how each trait evolves through selection imposed by interacting partners. Here, we integrate quantitative genetics, coevolutionary theory and network science to explore how trait correlations affect the coevolution of mutualistic species not only in pairs of species but also in species‐rich networks across space. We show that genetic correlations may determine the pace of coevolutionary change, affect species abundances and fuel divergence among populations of the same species. However, this trait divergence promoted by genetic correlations is partially buffered by the nested structure of species‐rich mutualisms. Our study, therefore, highlights how coevolution and its ecological consequences may result from conflicting processes at different levels of organisation, ranging from genes to communities.

Flower color has been studied in different ecological levels of organization, from individuals to communities. However, it is unclear how color is structured at the intrafloral level. In bee-pollinated flowers, the unidirectional gradient in color purity and pollen mimicry are two common processes to explain intrafloral color patterns. Considering that floral traits are often integrated, usually reflecting evolutionary modules under pollinator-mediated selection, we hypothesize that such intrafloral color patterns are structured by intrafloral color modules as perceived by bee color vision system. Here, we studied the tropical bee-pollinated orchid Cattleya walkeriana , given its intrafloral color complexity and variation among individuals. Considering bee color vision, we investigated if intrafloral color modules arose among intrafloral patches (tip or base of the sepals, petals, and labellum). We expected a separate color module between the labellum patches (the main attractive structure in orchids) and petals and sepals. We measured the color reflectance and calculated the photoreceptor excitation, spectral purity, hue, and the chromatic contrast of the floral structures in the hexagon color model. Spectral purity (saturation) was higher in the labellum tip in comparison to petals and sepals, generating a unidirectional gradient. Labellum base presented a less saturated yellow UV-absorbing color, which may reflect a pollen mimicry strategy. C. walkeriana presented three intrafloral color modules corresponding to the color of petals and sepals, the color of the labellum tip, and the color of labellum base. These color modules were unrelated to the development of floral structures. Given the importance of intrafloral color patterns in bee attraction and guidance, our results suggest that intrafloral patterns could be the outcome of evolutionary color modularization under pollinator-mediated selection.

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Studies on gender disparity in academia generate constructive discussions to promote equality. In a recently published study, AlShebli et al. 2020 analyzed the role of informal mentorship in supporting early-career scientists and how gender may shape scientific careers. Besides presenting methodological flaws, the study culminates in the authors' conclusion that mentoring quality is determined by the mentor's gender, suggesting that female protégés reap more benefits when mentored by males rather than equally-impactful females. Despite acknowledging that possible causal factors were not considered in their analyses, they attest that the success of female scientists' careers relies on opposite-gender mentorships in terms of publication and impact. Although the authors state that these findings add a new perspective to the policy debate on the best ways to elevate the women in science, their conclusions reinforce the traditional patriarchal, biased scientific structure that stimulates a poorly diversified hierarchical chain in STEM. Here we highlight the study's methodological weaknesses and major issues that must be addressed to avoid the perpetuation of gender disparity in science.

Este é o primeiro relatório do Observatório COVID19 - Grupo: Redes de Contágio – Laboratório de Estudos de Defesa para a região Sul do Brasil. Combinamos dados de casos confirmados do novo coronavírus (SARS-CoV-2) para o Sul, disponíveis até o dia 17/04/2020, com análises estruturais da rede de rotas rodoviárias intra e interestaduais para estimarmos a vulnerabilidade e potencial influência das microrregiões sulinas na propagação da doença.

OBJECTIVES: Multiple myeloma (MM) is the second most common hematological malignancy with several available therapies and an extensive pipeline. A metanalysis comparing pivotal relapsed/refractory (RRMM) studies that assessed DRd, ERd, KRd and IRd showed that DRd had the best PFS results. The goal of this analysis was to compare the cost per progression-free survival (PFS) for each of the comparators from the perspective of the Brazilian private healthcare system. METHODS: We calculated the cost per PFS individually using the most recent available data from follow-up (FUP) studies of the therapies assessed. PFS values were based on POLLUX 3-year FUP (DRd), ENDEAVOR 4-year FUP (ERd), ASPIRE 3-year FUP (KRd) and TOURMALINE-MM1 (IRd). Only drug acquisition costs (with wastage) were considered in the analysis (according to drug label dosage) and were retrieved from the Brazilian official price list (Jul/19). Cost per PFS was calculated by dividing the total cost in the PFS period by time in PFS (in months). Cost in 18 months was assessed because it is the maximum time that carfilzomib is recommended to be used. RESULTS: DRd showed the best PFS result compared with ERd, KRd and IRd (42.0 vs. 19.4, 26.1, 20.6, respectively). Total costs in the period of PFS for each of the comparators were BRL 2.02 million (DRd), BRL 1.29 million (ERd), BRL 1.14 million (KRd) and BRL 0.98 million (IRd). Costs per PFS- were BRL 48,060, BRL 66,343, BRL 43,822, BRL 47,760 and costs in the 18 first months were BRL 1.01 million, BRL 1,13 million, BRL 0.95 million and BRL 0.84 million for DRd, ERd, KRd and IRd, respectively. CONCLUSIONS: DRd showed the best PFS result, however its cost per PFS is the 3rd lowest among the comparators. As costs are directly dependent on PFS, the longer the PFS the higher are the costs. Additionally, daratumumab has continuous usage meanwhile carfilzomib has label recommended usage for just 18 cycles due to toxicity and tolerability-related events. Disclosures Santana: Janssen pharmaceuticals: Employment. Saad:Janssen pharmaceuticals: Employment. Kolanian:Janssen pharmaceuticals: Employment. Fioratti:Janssen pharmaceuticals: Employment. Junqueira:Janssen pharmaceuticals: Employment. Decimoni:Janssen Pharmaceuticals: Employment.

Parece a parte mais fácil da dissertação, mas talvez seja a mais difícil. Como encontrar palavras que expressem o sentimento de gratidão a todos que compartilharam desta fase da minha vida? Na vida, estamos sempre aprendendo. E, só pode ser assim porque tenho pessoas incríveis que me dão forças. É obvio, e até clichê, mas os que aparecem primeiro nos meus agradecimentos, são os meus pais (Álvaro e Pillar). Foram eles que me deram a vida, que me deram suas vidas, para me criar da melhor maneira que pudessem, com empenho e determinação exemplares. Foram eles que me ensinaram, desde pequena, o que é o amor, palavra que até hoje não sei descrever, mas sentimento que enxergo no olhar de cada um deles. E, à minha família como um todo, irmã e irmãos, avós e avô, tios e tias, primos e primas, que fazendo parte da minha história, me constituíram e me deram suporte para seguir sempre adiante na vida. Minha gratidão fica aqui declarada também para a Prof a. Silvana Buzato. Pessoa que, desde que eu bati em sua sala, pedindo que me ensinasse o que sabia, me recebeu de braços abertos, trabalhando comigo e me orientando em todos os momentos de desenvolvimento deste trabalho. Carregarei por toda a minha vida a sua dedicação e o seu caráter como exemplos a serem seguidos. Meu muito obrigada vai, não só pelos ensinamentos, mas pelas vivências que você me propiciou. Meus agradecimentos vão também a Prof a. Vânia Pivello, pois sem seu apoio este trabalho não poderia ter sido conduzido. Essenciais de serem citados são também os professores que participaram do meu comitê de acompanhamento, Prof. Paulo Guimarães Jr. e Prof. Sérgio Tadeu Meirelles. Seus comentários foram sempre relevantes. É claro que há também aqueles que me ajudaram para que um trabalho de tamanha escala pudesse ser realizado. Àqueles que foram ali e colocaram a mão na massa comigo. A primeira que tenho que citar é a Valéria França, pois foi ela que me ensinou a identificar tais plantas e também foi ela que me contagiou com sua paixão por estas plantas. Peças fundamentais foram os técnicos do laboratório: Geison, Lenilda, Natália, Patrícia e Raimunda. Sem eles certamente este trabalho não seria possível! E também, é claro, os muitos amigos que ou me ajudaram a plantar estas milhares de plantas (Joice, Ju, Cata e Lika), ou que me ajudaram no campo (Miti, Fred, Laize e Natália), ou que me ajudaram a conferir planilhas tão grandes de dados (Miti, Patê). Também tiveram aqueles que contribuíram grandemente para me ajudar a entender como analisar os dados, ou interpretálos (Ayana, Edu, Melina, Karine, Felipe e Paulinha).

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