Learning and climate change

Learning – i.e. the acquisition of new information that leads to changes in our assessment of uncertainty – plays a prominent role in the international climate policy debate. For example, the view that we should postpone actions until we know more continues to be influential. The latest work on learning and climate change includes new theoretical models, better informed simulations of how learning affects the optimal timing of emissions reductions, analyses of how new information could affect the prospects for reaching and maintaining political agreements and for adapting to climate change, and explorations of how learning could lead us astray rather than closer to the truth. Despite the diversity of this new work, a clear consensus on a central point is that the prospect of learning does not support the postponement of emissions reductions today.Learning; Uncertainty; Climate change; Decision analysis

The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection

Artificial intelligence represents a new frontier in human medicine that could save more lives and reduce the costs, thereby increasing accessibility. As a consequence, the rate of advancement of AI in cancer medical imaging and more particularly tissue pathology has exploded, opening it to ethical and technical questions that could impede its adoption into existing systems. In order to chart the path of AI in its application to cancer tissue imaging, we review current work and identify how it can improve cancer pathology diagnostics and research. In this review, we identify 5 core tasks that models are developed for, including regression, classification, segmentation, generation, and compression tasks. We address the benefits and challenges that such methods face, and how they can be adapted for use in cancer prevention and treatment. The studies looked at in this paper represent the beginning of this field and future experiments will build on the foundations that we highlight

Improving Long-Range Energy Modeling: A Plea for Historical Retrospectives

One of the most striking things about forecasters is their lack of historical perspective. They rarely do retrospectives, even though looking back at past work can both illuminate the reasons for its success or failure, and improve the methodologies of current and future forecasts. One of the best and most famous retrospectives is that by Hans Landsberg, which investigates work conducted by Landsberg, Sam Schurr, and others. In this article, written mainly for model users, we highlight Landsberg s retrospective as a uniquely valuable contribution to improving forecasting methodologies. We also encourage model users to support such retrospectives more frequently. Finally, we give the current generation of analysts the kind of guidance we believe Landsberg and Sam Schurr would have offered about how to do retrospectives well.

A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channels

Here we present a structural similarity index measure (SSIM) guided conditional Generative Adversarial Network (cGAN) that generatively performs image-to-image (i2i) synthesis to generate photo-accurate protein channels in multiplexed spatial proteomics images. This approach can be utilized to accurately generate missing spatial proteomics channels that were not included during experimental data collection either at the bench or the clinic. Experimental spatial proteomic data from the Human BioMolecular Atlas Program (HuBMAP) was used to generate spatial representations of missing proteins through a U-Net based image synthesis pipeline. HuBMAP channels were hierarchically clustered by the (SSIM) as a heuristic to obtain the minimal set needed to recapitulate the underlying biology represented by the spatial landscape of proteins. We subsequently prove that our SSIM based architecture allows for scaling of generative image synthesis to slides with up to 100 channels, which is better than current state of the art algorithms which are limited to data with 11 channels. We validate these claims by generating a new experimental spatial proteomics data set from human lung adenocarcinoma tissue sections and show that a model trained on HuBMAP can accurately synthesize channels from our new data set. The ability to recapitulate experimental data from sparsely stained multiplexed histological slides containing spatial proteomic will have tremendous impact on medical diagnostics and drug development, and also raises important questions on the medical ethics of utilizing data produced by generative image synthesis in the clinical setting. The algorithm that we present in this paper will allow researchers and clinicians to save time and costs in proteomics based histological staining while also increasing the amount of data that they can generate through their experiments

Unique modifications with phosphocholine and phosphoethanolamine define alternate antigenic forms of Neisseria gonorrhoeae type IV pili

Several major bacterial pathogens and related commensal species colonizing the human mucosa express phosphocholine (PC) at their cell surfaces. PC appears to impact host–microbe biology by serving as a ligand for both C-reactive protein and the receptor for platelet-activating factor. Type IV pili of Neisseria gonorrhoeae (Ng) and Neisseria meningitidis, filamentous protein structures critical to the colonization of their human hosts, are known to react variably with monoclonal antibodies recognizing a PC epitope. However, the structural basis for this reactivity has remained elusive. To address this matter, we exploited the finding that the PilE pilin subunit in Ng mutants lacking the PilV protein acquired the PC epitope independent of changes in pilin primary structure. Specifically, we show by using mass spectrometry that PilE derived from the pilV background is composed of a mixture of subunits bearing O-linked forms of either phosphoethanolamine (PE) or PC at the same residue, whereas the wild-type background carries only PE at that same site. Therefore, PilV can influence pilin structure and antigenicity by modulating the incorporation of these alternative modifications. The disaccharide covalently linked to Ng pilin was also characterized because it is present on the same peptides bearing the PE and PC modifications and, contrary to previous reports, was found to be linked by means of 2,4-diacetamido-2,4,6-trideoxyhexose. Taken together, these findings provide new insights into Ng type IV pilus structure and antigenicity and resolve long-standing issues regarding the nature of both the PC epitope and the pilin glycan

Defining a Standard Metric for Electricity Savings

The growing investment by governments and electric utilities in energy efficiency programs highlights the need for simple tools to help assess and explain the size of the potential resource. One technique that is commonly used in this effort is to characterize electricity savings in terms of avoided power plants, because it is easier for people to visualize a power plant than it is to understand an abstraction such as billions of kilowatt-hours. Unfortunately, there is no standardization around the characteristics of such power plants. In this letter we define parameters for a standard avoided power plant that have physical meaning and intuitive plausibility, for use in back-of-the-envelope calculations. For the prototypical plant this article settles on a 500 MW existing coal plant operating at a 70% capacity factor with 7% T&D losses. Displacing such a plant for one year would save 3 billion kWh/year at the meter and reduce emissions by 3 million metric tons of CO2 per year. The proposed name for this metric is the Rosenfeld, in keeping with the tradition among scientists of naming units in honor of the person most responsible for the discovery and widespread adoption of the underlying scientific principle in question—Dr Arthur H Rosenfeld