Mass-spectrometry-based proteomics has become an important component of biological research. Numerous proteomics methods have been developed to identify and quantify the proteins in biological and clinical samples1, identify pathways affected by endogenous and exogenous perturbations2, and characterize protein complexes3. Despite successes, the interpretation of vast proteomics datasets remains a challenge. There have been several calls for improvements and standardization of proteomics data analysis frameworks, as well as for an application-programming interface for proteomics data access4,5. In response, we have developed the ProteoWizard Toolkit, a robust set of open-source, software libraries and applications designed to facilitate proteomics research. The libraries implement the first-ever, non-commercial, unified data access interface for proteomics, bridging field-standard open formats and all common vendor formats. In addition, diverse software classes enable rapid development of vendor-agnostic proteomics software. Additionally, ProteoWizard projects and applications, building upon the core libraries, are becoming standard tools for enabling significant proteomics inquiries.

In order to best understand a visual system one should attempt to characterize the natural images it processes. We gather images from the woods and find that these scenes possess an ensemble scale invariance. Further, they are highly non-Gaussian, and this non-Gaussian character cannot be removed through local linear filtering. We find that including a simple "gain control" nonlinearity in the filtering process makes the filter output quite Gaussian, meaning information is maximized at fixed channel variance. Finally, we use the measured power spectrum to place an upper bound on the information conveyed about natural scenes by an array of receptors.

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AbstracLRecently there has been a resurgence of interest in the properties of natural images. Their statistics are important not only in image compression but also far the study of sensory processing in biology, which can be viewed as satisfying cettain 'design criteria'. This review summarizes previous work on image statistics and presents our own data Perhaps the most notable property of natural images is an invariance to scale. We present data to support this claim as well 35 evidence for a hierarchical invariance in natural scenes. These symmetries provide a powerful description of natunl images as they g~d y resttiit the class of allowed d i s Vib ut ions.

Simple cells in the primary visual cortex process incoming visual information with receptive ¢elds localized in space and time, bandpass in spatial and temporal frequency, tuned in orientation, and commonly selective for the direction of movement. It is shown that performing independent component analysis (ICA) on video sequences of natural scenes produces results with qualitatively similar spatio-temporal properties. Whereas the independent components of video resemble moving edges or bars, the independent component ¢lters, i.e. the analogues of receptive ¢elds, resemble moving sinusoids windowed by steady Gaussian envelopes. Contrary to earlier ICA results on static images, which gave only ¢lters at the ¢nest possible spatial scale, the spatio-temporal analysis yields ¢lters at a range of spatial and temporal scales. Filters centred at low spatial frequencies are generally tuned to faster movement than those at high spatial frequencies.

One of the most robust qualities of our visual world is the scale invariance of natural images. Not only has scaling been found in different visual environments, but the phenomenon also appears to be calibration-independent. This paper proposes a simple property of natural images which explains this robustness: they are collages of regions corresponding to statistically independent "objects". Evidence is provided for these objects having a power-law distribution of sizes within images, from which follows scaling in natural images. It is commonly suggested that scaling instead results from edges, each with power spectrum 1/k2. This hypothesis is refuted by example.

AbstracLRecently there has been a resurgence of interest in the properties of natural images. Their statistics are important not only in image compression but also far the study of sensory processing in biology, which can be viewed as satisfying cettain 'design criteria'. This review summarizes previous work on image statistics and presents our own data Perhaps the most notable property of natural images is an invariance to scale. We present data to support this claim as well 35 evidence for a hierarchical invariance in natural scenes. These symmetries provide a powerful description of natunl images as they g~d y resttiit the class of allowed d i s Vib ut ions.

For newly diagnosed breast cancer, estrogen receptor status (ERS) is a key molecular marker used for prognosis and treatment decisions. During clinical management, ERS is determined by pathologists from immunohistochemistry (IHC) staining of biopsied tissue for the targeted receptor, which highlights the presence of cellular surface antigens. This is an expensive, time-consuming process which introduces discordance in results due to variability in IHC preparation and pathologist subjectivity. In contrast, hematoxylin and eosin (H&E) staining—which highlights cellular morphology—is quick, less expensive, and less variable in preparation. Here we show that machine learning can determine molecular marker status, as assessed by hormone receptors, directly from cellular morphology. We develop a multiple instance learning-based deep neural network that determines ERS from H&E-stained whole slide images (WSI). Our algorithm—trained strictly with WSI-level annotations—is accurate on a varied, multi-country dataset of 3,474 patients, achieving an area under the curve (AUC) of 0.92 for sensitivity and specificity. Our approach has the potential to augment clinicians’ capabilities in cancer prognosis and theragnosis by harnessing biological signals imperceptible to the human eye.