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What is the place of architecture in the history of art? Why has it been at times central to the discipline, and at other times seemingly so marginal? What is its place now? Many disciplines have a stake in the history of architecture – sociology, anthropology, human geography, to name a few. This book deals with perhaps the most influential tradition of all – art history – examining how the relation between the disciplines of art history and architectural history has waxed and waned over the last one hundred and fifty years. In this highly original study, Mark Crinson and Richard J. Williams point to a decline in the importance attributed to the role of architecture in art history over the last century – which has happened without crisis or self-reflection. The book explores the problem in relation to key art historical approaches, from formalism, to feminism, to the social history of art, and in key institutions from the Museum of Modern Art, to the journal October. Among the key thinkers explored are Banham, Baxandall, Giedion, Panofsky, Pevsner, Pollock, Riegl, Rowe, Steinberg, Wittkower and Wölfflin. The book will provoke debate on the historiography and present state of the discipline of art history, and it makes a powerful case for the reconsideration of architecture.

Satellite‐based optical video sensors are poised as the next frontier in remote sensing. Satellite video offers the unique advantage of capturing the transient dynamics of floods with the potential to supply hitherto unavailable data for the assessment of hydraulic models. A prerequisite for the successful application of hydraulic models is their proper calibration and validation. In this investigation, we validate 2D flood model predictions using satellite video‐derived flood extents and velocities. Hydraulic simulations of a flood event with a 5‐year return period (discharge of 722 m3 s−1) were conducted using Hydrologic Engineering Center—River Analysis System 2D in the Darling River at Tilpa, Australia. To extract flood extents from satellite video of the studied flood event, we use a hybrid transformer‐encoder, convolutional neural network (CNN)‐decoder deep neural network. We evaluate the influence of test‐time augmentation (TTA)—the application of transformations on test satellite video image ensembles, during deep neural network inference. We employ Large Scale Particle Image Velocimetry (LSPIV) for non‐contact‐based river surface velocity estimation from sequential satellite video frames. When validating hydraulic model simulations using deep neural network segmented flood extents, critical success index peaked at 94% with an average relative improvement of 9.5% when TTA was implemented. We show that TTA offers significant value in deep neural network‐based image segmentation, compensating for aleatoric uncertainties. The correlations between model predictions and LSPIV velocities were reasonable and averaged 0.78. Overall, our investigation demonstrates the potential of optical space‐based video sensors for validating flood models and studying flood dynamics. Plain Language Summary Videos of the Earth surface recorded by satellites can enable us to observe and characterize dynamic moving features, such as floods, that would otherwise be very difficult or dangerous to investigate from the ground. Hydrologists often rely on using physics‐based computer models to simulate flood events, but require observational data to make sure these reflect reality accurately. We use artificial intelligence techniques to automatically detect flood extents from satellite video, and track surface features from frame to frame in order to measure how fast the water surface is flowing. Satellite video was collected during opportunistically clear skies in January 2022, along a 6.5 km length of the River Darling in Australia. The flood extent and flow velocities were used to improve numerical model predictions of the flood event. Our findings demonstrate the considerable promise of satellite video to complement existing flood mapping and modeling approaches, and to provide insight into the earth's hydrosphere, particularly in remote locations and during extreme conditions. Key Points Satellite video derived flood extents and velocities successfully validate 2D hydraulic model predictions Test‐time augmentation during deep learning inference improved flood extent delineation and enhanced 2D model validation metrics Incorporating characterization of discharge uncertainty into hydraulic model predictions resulted in more accurate model validation

Introduces Meriwether Lewis and William Clark and their expedition of 1804-6 through the Louisiana Territory, opening the land from the Mississippi River to the Pacific Ocean.

This work on colorectal cancer shows that secondary mutations in KRAS that confer resistance to panitumumab, an anti-EGFR monoclonal antibody, are already present when antibody treatment begins; the apparent inevitability of resistance suggests that combinations of drugs targeting at least two different oncogenic pathway will be needed for treatment. Acquired resistance in anti-EGFR therapy Antibodies targeting epidermal growth factor receptor (EGFR) have become an established treatment for colorectal cancer, but they are contraindicated in patients carrying mutations in the KRAS oncogene. Drug resistance can also arise in initially responsive patients, and two papers in this issue of Nature present unequivocal evidence that mutations in KRAS underlie acquired resistance to anti-EGFR antibodies in many patients and that KRAS mutations can be detected in the serum of patients before the clinical emergence of resistance and relapse. Misale et al . show in cell-line models that KRAS mutations can confer resistance to cetuximab. And in colorectal cancer patients treated with cetuximab or panitumumab, resistance is associated with KRAS mutations selected from pre-existing subclones or acquired during treatment. Diaz et al . also find KRAS mutations accumulating in patients becoming resistant to panitumumab. Their mathematical models suggest that KRAS mutations pre-existed in tumour cells before therapy, which may explain why clinical recurrence is usually seen after about six months of treatment, by which time the resistant subpopulations of tumour cells with KRAS mutations has expanded. The apparent inevitability of resistance suggests that combinations of drugs targeting more than one oncogenic pathway will be needed if resistance is to be avoided. Colorectal tumours that are wild type for KRAS are often sensitive to EGFR blockade, but almost always develop resistance within several months of initiating therapy 1 , 2 . The mechanisms underlying this acquired resistance to anti-EGFR antibodies are largely unknown. This situation is in marked contrast to that of small-molecule targeted agents, such as inhibitors of ABL, EGFR, BRAF and MEK, in which mutations in the genes encoding the protein targets render the tumours resistant to the effects of the drugs 3 , 4 , 5 , 6 . The simplest hypothesis to account for the development of resistance to EGFR blockade is that rare cells with KRAS mutations pre-exist at low levels in tumours with ostensibly wild-type KRAS genes. Although this hypothesis would seem readily testable, there is no evidence in pre-clinical models to support it, nor is there data from patients. To test this hypothesis, we determined whether mutant KRAS DNA could be detected in the circulation of 28 patients receiving monotherapy with panitumumab, a therapeutic anti-EGFR antibody. We found that 9 out of 24 (38%) patients whose tumours were initially KRAS wild type developed detectable mutations in KRAS in their sera, three of which developed multiple different KRAS mutations. The appearance of these mutations was very consistent, generally occurring between 5 and 6 months following treatment. Mathematical modelling indicated that the mutations were present in expanded subclones before the initiation of panitumumab treatment. These results suggest that the emergence of KRAS mutations is a mediator of acquired resistance to EGFR blockade and that these mutations can be detected in a non-invasive manner. They explain why solid tumours develop resistance to targeted therapies in a highly reproducible fashion.

The niche model has been widely used to model the structure of complex food webs, and yet the ecological meaning of the single niche dimension has not been explored. In the niche model, each species has three traits, niche position, diet position and feeding range. Here, a new probabilistic niche model, which allows the maximum likelihood set of trait values to be estimated for each species, is applied to the food web of the Benguela fishery. We also developed the allometric niche model, in which body size is used as the niche dimension. About 80% of the links in the empirical data are predicted by the probabilistic niche model, a significant improvement over recent models. As in the niche model, species are uniformly distributed on the niche axis. Feeding ranges are exponentially distributed, but diet positions are not uniformly distributed below the predator. Species traits are strongly correlated with body size, but the allometric niche model performs significantly worse than the probabilistic niche model. The best-fit parameter set provides a significantly better model of the structure of the Benguela food web than was previously available. The methodology allows the identification of a number of taxa that stand out as outliers either in the model's poor performance at predicting their predators or prey or in their parameter values. While important, body size alone does not explain the structure of the one-dimensional niche.

When Herbie Jones gets the dreaded girl disease and becomes lovesick for Annabelle Hodgekiss, it threatens his friendship with his good pal Raymond.

Projected effects of climate change across many ecosystems globally include more frequent disturbance by fire and reduced plant growth due to warmer (and especially drier) conditions. Such changes affect species - particularly fire-intolerant woody plants - by simultaneously reducing recruitment, growth, and survival. Collectively, these mechanisms may narrow the fire interval window compatible with population persistence, driving species to extirpation or extinction. We present a conceptual model of these combined effects, based on synthesis of the known impacts of climate change and altered fire regimes on plant demography, and describe a syndrome we term \"interval squeeze\". This model predicts that interval squeeze will increase woody plant extinction risk and change ecosystem structure, composition, and carbon storage, especially in regions projected to become both warmer and drier. These predicted changes demand new approaches to fire management that will maximize the in situ adaptive capacity of species to respond to climate change and fire regime change.