Radiation monitors as a vacuum diagnostic in the room temperature parts of the LHC straight sections

In the absence of collisions, inelastic interactions between protons and residual gas molecules are the main source of radiation in the room temperature parts of the LHC long straight sections. In this case the variations in the radiation levels will reflect the dynamics of the residual pressure distribution. Based on the background simulations for the long straight section of the LHC IP5 and on the current understanding of the residual pressure dynamics, we evaluate the possibility to use the radiation monitors for the purpose of the vacuum diagnostic, and we present the first estimates of the predicted monitor counts for different scenarios of the machine operation

Validating the Water Vapor Variance Similarity Relationship in the Interfacial Layer Using Observations and Large-Eddy Simulations

In previous work, the similarity relationship for the water vapor variance in the interfacial layer (IL) at the top of the convective boundary layer (CBL) was proposed to be proportional to the convective velocity scale and the gradients of the water vapor mixing ratio and the Brunt‐Vaisala frequency in the entrainment zone. In the presence of wind shear in the IL, the similarity relationship was hypothesized to also include a dependence on the gradient Richardson number. Simultaneous measurements of the surface buoyancy flux, wind‐shear profiles from a radar wind profiler, water vapor mixing ratio and temperature measurements and their gradients from a Raman lidar provide a unique opportunity to thoroughly examine the function used in defining the variance and validate it. These observations were made over the Atmospheric Radiation Measurement Southern Great Plains site. We identified 19 cases from 2016 during which the CBL was quasi‐stationary and well mixed for at least 2 hr in the afternoon. Furthermore, we simulated the CBL using a large‐eddy simulation (LES) model for these cases and derived the water vapor variance and other profiles to test the similarity function. Utilizing this unique combination of observations and LES, we demonstrate that the water vapor variance in the IL has little‐to‐no dependence on wind shear. Furthermore, we demonstrate that the predicted variance using the original similarity function matches the observed and LES‐modeled variance very well, with linear correlations between the two variances of 0.82 and 0.95, respectively

Validating the Water Vapor Variance Similarity Relationship in the Interfacial Layer Using Observations and Large-Eddy Simulations

In previous work, the similarity relationship for the water vapor variance in the interfacial layer (IL) at the top of the convective boundary layer (CBL) was proposed to be proportional to the convective velocity scale and the gradients of the water vapor mixing ratio and the Brunt‐Vaisala frequency in the entrainment zone. In the presence of wind shear in the IL, the similarity relationship was hypothesized to also include a dependence on the gradient Richardson number. Simultaneous measurements of the surface buoyancy flux, wind‐shear profiles from a radar wind profiler, water vapor mixing ratio and temperature measurements and their gradients from a Raman lidar provide a unique opportunity to thoroughly examine the function used in defining the variance and validate it. These observations were made over the Atmospheric Radiation Measurement Southern Great Plains site. We identified 19 cases from 2016 during which the CBL was quasi‐stationary and well mixed for at least 2 hr in the afternoon. Furthermore, we simulated the CBL using a large‐eddy simulation (LES) model for these cases and derived the water vapor variance and other profiles to test the similarity function. Utilizing this unique combination of observations and LES, we demonstrate that the water vapor variance in the IL has little‐to‐no dependence on wind shear. Furthermore, we demonstrate that the predicted variance using the original similarity function matches the observed and LES‐modeled variance very well, with linear correlations between the two variances of 0.82 and 0.95, respectively

From Fossils to Living Canids:Two Contrasting Perspectives on Biogeographic Diversification

he Canidae are an ecologically important group of dog-like carnivores that arose in North America and spread across the planet around 10 million years ago. The current distribution patterns of species, coupled with their phylogenetic structure, suggest that Canidae diversification may have occurred at varying rates across different biogeographic areas. However, such extant-only analyses undervalued the group’s rich fossil history because of a limitation in method’s development. Current State-dependent Speciation and Extinction (SSE) models are (i) often parameter-rich which hinders reliable application to relatively small clades such as the Caninae (the only extant subclade of the Canidae consisting of 36 extant species); and (ii) often assume as possible states only the states that extant species present. Here we extend the SSE method SecSSE to apply to phylogenies with extinct species as well (111 Caninae species) and compare the results to those of analyses with the extant-species-only phylogeny. The results on the extant-species tree suggest that distinct diversification patterns are related to geographic areas, but the results on the complete tree do not support this conclusion. Furthermore, our extant-species analysis yielded an unrealistically low estimate of the extinction rate. These contrasting findings suggest that information from extinct species is different from information from extant species. A possible explanation for our results is that extinct species may have characteristics (causing their extinction), which may be different from the characteristics of extant species that caused them to be extant. Hence, we conclude that differences in biogeographic areas probably did not contribute much to the variation in diversification rates in Caninae

Linking Influenza Virus Tissue Tropism to Population-Level Reproductive Fitness

Influenza virus tissue tropism defines the host cells and tissues that support viral replication and contributes to determining which regions of the respiratory tract are infected in humans. The location of influenza virus infection along the respiratory tract is a key determinant of virus pathogenicity and transmissibility, which are at the basis of influenza burdens in the human population. As the pathogenicity and transmissibility of influenza virus ultimately determine its reproductive fitness at the population level, strong selective pressures will shape influenza virus tissue tropisms that maximize fitness. At present, the relationships between influenza virus tissue tropism within hosts and reproductive fitness at the population level are poorly understood. The selective pressures and constraints that shape tissue tropism and thereby influence the location of influenza virus infection along the respiratory tract are not well characterized. We use mathematical models that link within-host infection dynamics in a spatially-structured human respiratory tract to between-host transmission dynamics, with the aim of characterizing the possible selective pressures on influenza virus tissue tropism. The results indicate that spatial heterogeneities in virus clearance, virus pathogenicity or both, resulting from the unique structure of the respiratory tract, may drive optimal receptor binding affinity-that maximizes influenza virus reproductive fitness at the population level-towards sialic acids with α2,6 linkage to galactose. The expanding cell pool deeper down the respiratory tract, in association with lower clearance rates, may result in optimal infectivity rates-that likewise maximize influenza virus reproductive fitness at the population level-to exhibit a decreasing trend towards deeper regions of the respiratory tract. Lastly, pre-existing immunity may drive influenza virus tissue tropism towards upper regions of the respiratory tract. The propo

Modular Clinical Decision Support Networks (MoDN)-Updatable, interpretable, and portable predictions for evolving clinical environments.

Clinical Decision Support Systems (CDSS) have the potential to improve and standardise care with probabilistic guidance. However, many CDSS deploy static, generic rule-based logic, resulting in inequitably distributed accuracy and inconsistent performance in evolving clinical environments. Data-driven models could resolve this issue by updating predictions according to the data collected. However, the size of data required necessitates collaborative learning from analogous CDSS's, which are often imperfectly interoperable (IIO) or unshareable. We propose Modular Clinical Decision Support Networks (MoDN) which allow flexible, privacy-preserving learning across IIO datasets, as well as being robust to the systematic missingness common to CDSS-derived data, while providing interpretable, continuous predictive feedback to the clinician. MoDN is a novel decision tree composed of feature-specific neural network modules that can be combined in any number or combination to make any number or combination of diagnostic predictions, updatable at each step of a consultation. The model is validated on a real-world CDSS-derived dataset, comprising 3,192 paediatric outpatients in Tanzania. MoDN significantly outperforms 'monolithic' baseline models (which take all features at once at the end of a consultation) with a mean macro F1 score across all diagnoses of 0.749 vs 0.651 for logistic regression and 0.620 for multilayer perceptron (p < 0.001). To test collaborative learning between IIO datasets, we create subsets with various percentages of feature overlap and port a MoDN model trained on one subset to another. Even with only 60% common features, fine-tuning a MoDN model on the new dataset or just making a composite model with MoDN modules matched the ideal scenario of sharing data in a perfectly interoperable setting. MoDN integrates into consultation logic by providing interpretable continuous feedback on the predictive potential of each question in a CDSS questionnaire. The modular design allows it to compartmentalise training updates to specific features and collaboratively learn between IIO datasets without sharing any data