Impact of Ghost Introgression on Coalescent-based Species Tree Inference and Estimation of Divergence Time

The species studied in any evolutionary investigation generally constitute a very small proportion of all the species currently existing or that have gone extinct. It is therefore likely that introgression, which is widespread across the tree of life, involves "ghosts," i.e., unsampled, unknown, or extinct lineages. However, the impact of ghost introgression on estimations of species trees has been rarely studied and is thus poorly understood. In this study, we use mathematical analysis and simulations to examine the robustness of species tree methods based on a multispecies coalescent model under gene flow sourcing from an extant or ghost lineage. We found that very low levels of extant or ghost introgression can result in anomalous gene trees (AGTs) on three-taxon rooted trees if accompanied by strong incomplete lineage sorting (ILS). In contrast, even massive introgression, with more than half of the recipient genome descending from the donor lineage, may not necessarily lead to AGTs. In cases involving an ingroup lineage (defined as one that diverged no earlier than the most basal species under investigation) acting as the donor of introgression, the time of root divergence among the investigated species was either underestimated or remained unaffected, but for the cases of outgroup ghost lineages acting as donors, the divergence time was generally overestimated. Under many conditions of ingroup introgression, the stronger the ILS was, the higher was the accuracy of estimating the time of root divergence, although the topology of the species tree is more prone to be biased by the effect of introgression.

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.

Bridge Response and Heavy Truck Classification Framework Based on a Two-Step Machine Learning Algorithm

Collecting information on heavy trucks and monitoring the bridges which they regularly cross is important for many facets of infrastructure management. In this paper, a two-step algorithm is developed using bridge and truck data, by deploying sequentially unsupervised and supervised machine learning techniques. Longitudinal clustering of bridge data, concerning strain waveforms, is adopted to perform the first step of the algorithm, while image visual inspection and classification tree methods are applied to truck data concurrently in the second step. Both bridge and truck traffic must be monitored for a limited, yet significant, amount of time to calibrate the algorithm, which is then used to build a classification framework. The framework provides the same benefits of two data collection systems while only one needs to be operative. Depending on which monitoring system remains available, the framework enables the use of bridge data to identify the truck’s profile which generated it, or to estimate bridge response given the truck’s information. As a result, the present study aims to provide decision-makers with an effective way to monitor the whole bridge-traffic system, bridge managers to plan effective maintenance, and policymakers to develop ad hoc regulations.

Multi-Domain Informative Coverage Path Planning for A Hybrid Aerial Underwater Vehicle in Dynamic Environments

Hybrid aerial underwater vehicles (HAUV) are a new frontier for vehicles. They can operate both underwater and aerially, providing enormous potential for a wide range of scientific explorations. Informative path planning is essential to vehicle autonomy. However, covering an entire mission region is a challenge to HAUVs because of the possibility of a multidomain environment. This paper presents an informative trajectory planning framework for planning paths and generating trajectories for HAUVs performing multidomain missions in dynamic environments. We introduce the novel heuristic generalized extensive neighborhood search GLNS–k-means algorithm that uses k-means to cluster information into several sets; then through the heuristic GLNS algorithm, it searches the best path for visiting these points, subject to various constraints regarding path budgets and the motion capabilities of the HAUV. With this approach, the HAUV is capable of sampling and focusing on regions of interest. Our method provides a significantly more optimal trajectory (enabling collection of more information) than ant colony optimization (ACO) solutions. Moreover, we introduce an efficient online replanning scheme to adapt the trajectory according to the dynamic obstacles during the mission. The proposed replanning scheme based on KD tree enables significantly shorter computational times than the scapegoat tree methods.

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical fault diagnosis of bearings

The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.

Barriers to cognitive screening in acute stroke units

Cognitive impairment is common after stroke. However, not all patients with stroke undergo cognitive screening, despite recommendations. The aim of this retrospective, explorative study was to examine the barriers to cognitive screening in acute stroke units. Data were retrieved from two Swedish Stroke registries. The outcome variable was cognitive screening during the stay at acute stroke units. Forty-three candidate explanatory variables were considered for analysis, encompassing sociodemographic factors and stroke-related outcomes during the stay at acute stroke units. The Least Absolute Shrinkage and Selection Operator and decision-tree methods were used. Of the 1120 patients (56% male, mean age: 72 years, 50% with mild stroke), 44% did not undergo cognitive screening. Walking 10 m post-stroke was the most important attribute for decisions regarding cognitive screening. The classification accuracy, sensitivity, and specificity of the model were 70% (95% CI 63–75%), 71% (63–78%), and 67% (55–77%), respectively. Patient-related parameters that influenced cognitive screening with a valid and reliable screening instrument in acute stroke units included new stroke during the hospitalisation, aphasia at admission, mobility problems, impaired verbal output skills, and planned discharge to another care facility. The barriers to cognitive screening were both patient- and organisation-related, suggesting the need for patient-tailored cognitive screening tools as well as the implementation and systematic adherence to guidelines.

Statistical Modelling of the Market Value of Dwellings, on the Example of the City of Kraków

The analysis of a city’s spatial development, in terms of a location that meets the needs of its inhabitants, requires many approaches. The preliminary assessment of the collected material showed that there was real estate in the database whose price did not have market characteristics. For the correct formulation of the valuation model, it is necessary to detect and eliminate or reduce the impact of these properties on the valuation results. In this study, multivariate analysis was used and three methods of detecting outliers were verified. The database of 8812 residential premises traded on the primary market in Kraków was analyzed. In order to detect outliers, the following indices were determined: projection matrix, Mahalanobis distances, standardized chi test and Cook distances. Critical values were calculated based on the formulas proposed in the publication. The probability level was P = 0.95. The article shows that the selected methods of eliminating outliers—the methods of standardized residuals and the Cook’s distance method give similar regression models. Further analysis (with the use of classification tree methods) made it possible to distinguish zones that are homogeneous in terms of price dispersion. In these zones, a set of features influencing real estate prices were determined.

DETERMINATION OF THE PERIOD OF EXISTENCE OF THE BOUNDARIES OF A LAND AREA WHEN FIXING THE BOUNDARIES WITH TREES

The article presents a solution to the problem of determining the age of a land plot, if its border is fixed by a tree. Methods will be considered with the help of which it is possible to confirm the age of the existence of the boundaries of the land plot, marked with trees. This method involves determining the age of the tree itself, using four different methods that will determine their approximate age, which means that the approximate age of the boundaries of the land plot will also be known. The main condition that satisfies the law will be that the age of the object that sets the boundaries must be over 20 years.

Decision Tree and AHP Methods Application for Projects Assessment: A Case Study

This research is dedicated to the modelling of decision process occurring during the implementation of construction projects. Recent studies generally do not assess the robustness of the decisions regarding the possible changes during the construction project implementation. However, such an assessment might increase the reliability of the decision-making process. We addressed this gap through a new model that combines the decision-making process modelling with the AHP method and includes the analysis of model stability concerning stakeholders’ behaviour. We used the Analytic Hierarchy Process (AHP) and Decision tree methods to model the decision-making process. The proposed model was validated on a case study of multiple construction projects. The assessment was performed from individual investor’s and independent expert’s perspectives. The criteria for the assessment were selected according to the principles of sustainability. We performed the sensitivity analysis, making it possible to assess the possible changes of the decisions depending on the potential patterns of the decision-makers’ behaviour. The results of the study show that, sometimes, small fluctuations in the project factors affect the project selection indicating the possible lack of the robustness of the project decisions.