Dry season habitat use by lizards in a tropical deciduous forest of western Mexico

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Adagrasib in Non-Small-Cell Lung Cancer Harboring a KRAS(G12C) Mutation

BACKGROUND: Adagrasib, a KRAS(G12C) inhibitor, irreversibly and selectively binds KRAS(G12C), locking it in its inactive state. Adagrasib showed clinical activity and had an acceptable adverse-event profile in the phase 1-1b part of the KRYSTAL-1 phase 1-2 study. METHODS: In a registrational phase 2 cohort, we evaluated adagrasib (600 mg orally twice daily) in patients with KRAS(G12C) -mutated non-small-cell lung cancer (NSCLC) previously treated with platinum-based chemotherapy and anti-programmed death 1 or programmed death ligand 1 therapy. The primary end point was objective response assessed by blinded independent central review. Secondary end points included the duration of response, progression-free survival, overall survival, and safety. RESULTS: As of October 15, 2021, a total of 116 patients with KRAS(G12C) -mutated NSCLC had been treated (median follow-up, 12.9 months); 98.3% had previously received both chemotherapy and immunotherapy. Of 112 patients with measurable disease at baseline, 48 (42.9%) had a confirmed objective response. The median duration of response was 8.5 months (95% confidence interval [CI], 6.2 to 13.8), and the median progression-free survival was 6.5 months (95% CI, 4.7 to 8.4). As of January 15, 2022 (median follow-up, 15.6 months), the median overall survival was 12.6 months (95% CI, 9.2 to 19.2). Among 33 patients with previously treated, stable central nervous system metastases, the intracranial confirmed objective response rate was 33.3% (95% CI, 18.0 to 51.8). Treatment-related adverse events occurred in 97.4% of the patients - grade 1 or 2 in 52.6% and grade 3 or higher in 44.8% (including two grade 5 events) - and resulted in drug discontinuation in 6.9% of patients. CONCLUSIONS: In patients with previously treated KRAS(G12C) -mutated NSCLC, adagrasib showed clinical efficacy without new safety signals

AIM 2020: Scene Relighting and Illumination Estimation Challenge

We review the AIM 2020 challenge on virtual image relighting and illumination estimation. This paper presents the novel VIDIT dataset used in the challenge and the different proposed solutions and final evaluation results over the 3 challenge tracks. The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i.e., light source position). The goal of the second track was to estimate illumination settings, namely the color temperature and orientation, from a given image. Lastly, the third track dealt with any-to-any relighting, thus a generalization of the first track. The target color temperature and orientation, rather than being pre-determined, are instead given by a guide image. Participants were allowed to make use of their track 1 and 2 solutions for track 3. The tracks had 94, 52, and 56 registered participants, respectively, leading to 20 confirmed submissions in the final competition stage