Evaluating spatially enabled machine learning approaches to depth to bedrock mapping, Alberta, Canada.

Maps showing the thickness of sediments above the bedrock (depth to bedrock, or DTB) are important for many geoscience studies and are necessary for many hydrogeological, engineering, mining, and forestry applications. However, it can be difficult to accurately estimate DTB in areas with varied topography, like lowland and mountainous terrain, because traditional methods of predicting bedrock elevation often underestimate or overestimate the elevation in rugged or incised terrain. Here, we describe a machine learning spatial prediction approach that uses information from traditional digital elevation model derived estimates of terrain morphometry and satellite imagery, augmented with spatial feature engineering techniques to predict DTB across Alberta, Canada. First, compiled measurements of DTB from borehole lithologs were used to train a natural language model to predict bedrock depth across all available lithologs, significantly increasing the dataset size. The combined data were then used for DTB modelling employing several algorithms (XGBoost, Random forests, and Cubist) and spatial feature engineering techniques, using a combination of geographic coordinates, proximity measures, neighbouring points, and spatially lagged DTB estimates. Finally, the results were contrasted with DTB predictions based on modelled relationships with the auxiliary variables, as well as conventional spatial interpolations using inverse-distance weighting and ordinary kriging methods. The results show that the use of spatially lagged variables to incorporate information from the spatial structure of the training data significantly improves predictive performance compared to using auxiliary predictors and/or geographic coordinates alone. Furthermore, unlike some of the other tested methods such as using neighbouring point locations directly as features, spatially lagged variables did not generate spurious spatial artifacts in the predicted raster maps. The proposed method is demonstrated to produce reliable results in several distinct physiographic sub-regions with contrasting terrain types, as well as at the provincial scale, indicating its broad suitability for DTB mapping in general.

Peptidoglycan Remodeling Enables Escherichia coli To Survive Severe Outer Membrane Assembly Defect.

Gram-negative bacteria have a tripartite cell envelope with the cytoplasmic membrane (CM), a stress-bearing peptidoglycan (PG) layer, and the asymmetric outer membrane (OM) containing lipopolysaccharide (LPS) in the outer leaflet. Cells must tightly coordinate the growth of their complex envelope to maintain cellular integrity and OM permeability barrier function. The biogenesis of PG and LPS relies on specialized macromolecular complexes that span the entire envelope. In this work, we show that Escherichia coli cells are capable of avoiding lysis when the transport of LPS to the OM is compromised, by utilizing LD-transpeptidases (LDTs) to generate 3-3 cross-links in the PG. This PG remodeling program relies mainly on the activities of the stress response LDT, LdtD, together with the major PG synthase PBP1B, its cognate activator LpoB, and the carboxypeptidase PBP6a. Our data support a model according to which these proteins cooperate to strengthen the PG in response to defective OM synthesis.IMPORTANCE In Gram-negative bacteria, the outer membrane protects the cell against many toxic molecules, and the peptidoglycan layer provides protection against osmotic challenges, allowing bacterial cells to survive in changing environments. Maintaining cell envelope integrity is therefore a question of life or death for a bacterial cell. Here we show that Escherichia coli cells activate the LD-transpeptidase LdtD to introduce 3-3 cross-links in the peptidoglycan layer when the integrity of the outer membrane is compromised, and this response is required to avoid cell lysis. This peptidoglycan remodeling program is a strategy to increase the overall robustness of the bacterial cell envelope in response to defects in the outer membrane.

Evolution of Imaging in Breast Cancer.

The following topics are discussed in this article. A historical review of the evolution of breast cancer imaging from thermography through digital breast tomosynthesis, molecular breast imaging, and advanced breast magnetic resonance imaging. Discussion of multiple clinical trials, their strengths, and weaknesses. Historical perspective on the Mammography Quality Standards Act and its relationship with development and implementation of the Breast Imaging-Reporting and Data System (BI-RADS).

Current breast imaging modalities, advances, and impact on breast care.

Mammography will continue as the breast cancer screening imaging study of choice for the foreseeable future. Ultrasound and magnetic resonance imaging (MRI) are widely available adjunctive studies for women with suspicious mammographic or clinical findings, and MRI is a screening tool for women with specific increased risks for breast cancer. Options for diagnosis will continue to evolve and progress. This article discusses a wide variety of imaging options currently used and in development, their strengths, limitations, and potential future roles in the continuing pursuit of early breast cancer diagnosis, treatment, and follow-up.

Medicolegal considerations in breast health: the benefits of collaboration between OB/GYNs and radiologists.

A common endeavor shared by physicians practicing in specialty fields of Radiology and Obstetrics and Gynecology is the comprehensive care and diagnosis of women with breast problems and breast disease. Because each specialty provides its respective clinical expertise in breast health, each also shares a concern, which is the high risk of litigation associated with a missed or delayed diagnosis of breast cancer. This shared concern is well documented for both specialties. Instead, it is argued that physicians are better prepared by engaging in the practice of evidence-based breast care in their respective specialties.

The relationship between counterfactual thinking and emotional reactions to event outcomes: does one account fit all?

By enabling a comparison between what is and what might have been, counterfactual thoughts amplify our emotional responses to bad outcomes. Well-known demonstrations such as the action effect (the tendency to attribute most regret to a character whose actions brought about a bad outcome) and the temporal order effect (the tendency to undo the last in a series of events leading up to a bad outcome) are often explained in this way. An important difference between these effects is that outcomes are due to decisions in the action effect, whereas in the temporal order effect outcomes are achieved by chance. In Experiment 1, we showed that imposing time pressure leads to a significant reduction in the action but not in the temporal order effect. In Experiment 2, we found that asking participants to evaluate the protagonists ("who ought to feel worse?") led to a significant reduction in the temporal order but not in the action effect. The results suggest that the action and temporal order effects require different explanations and are consistent with other work that suggests that when decisions lead to bad outcomes a comparison of decision quality is an important determinant of the emotional response attributed to the protagonists. The stimulus materials used in our experiments may be downloaded from pbr.psychonomic-journals.org/content/supplemental.