Relation of skinfold thickness with the serum lipids, glucose and blood pressure among Indian sedentary office workers.

Background: Serum glucose, cholesterol, triglycerides and high-density lipoproteins (HDL) are established cardiovascular disease (CVD) markers, however accessibility to these markers is less in individuals from low-middle income countries. The non-invasive CVD risk marker especially skinfold measured fat percentages are less explored for its relevance with established serum biochemistry markers.

Methods: A cross-sectional study was conducted in 70 sedentary office workers (aged 30-40 years) who were healthy.

ProGen2: Exploring the boundaries of protein language models.

Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial-intelligence-driven protein design. However, we lack a sufficient understanding of how very large-scale models and data play a role in effective protein model development. We introduce a suite of protein language models, named ProGen2, that are scaled up to 6.

Large language models generate functional protein sequences across diverse families.

Deep-learning language models have shown promise in various biotechnological applications, including protein design and engineering. Here we describe ProGen, a language model that can generate protein sequences with a predictable function across large protein families, akin to generating grammatically and semantically correct natural language sentences on diverse topics. The model was trained on 280 million protein sequences from >19,000 families and is augmented with control tags specifying protein properties.

Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials.

Prostate cancer is the most frequent cancer in men and a leading cause of cancer death. Determining a patient's optimal therapy is a challenge, where oncologists must select a therapy with the highest likelihood of success and the lowest likelihood of toxicity. International standards for prognostication rely on non-specific and semi-quantitative tools, commonly leading to over- and under-treatment.

Deep learning-enabled medical computer vision.

A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields-including medicine-to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques-powered by deep learning-for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare.

Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains.

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.

Computer vision uncovers predictors of physical urban change.

Which neighborhoods experience physical improvements? In this paper, we introduce a computer vision method to measure changes in the physical appearances of neighborhoods from time-series street-level imagery. We connect changes in the physical appearance of five US cities with economic and demographic data and find three factors that predict neighborhood improvement. First, neighborhoods that are densely populated by college-educated adults are more likely to experience physical improvements-an observation that is compatible with the economic literature linking human capital and local success.

Pose estimation using time-resolved inversion of diffuse light.

We present a novel approach for evaluation of position and orientation of geometric shapes from scattered time-resolved data. Traditionally, imaging systems treat scattering as unwanted and are designed to mitigate the effects. Instead, we show here that scattering can be exploited by implementing a system based on a femtosecond laser and a streak camera.

Estimating wide-angle, spatially varying reflectance using time-resolved inversion of backscattered light.

Imaging through complex media is a well-known challenge, as scattering distorts a signal and invalidates imaging equations. For coherent imaging, the input field can be reconstructed using phase conjugation or knowledge of the complex transmission matrix. However, for incoherent light, wave interference methods are limited to small viewing angles.