Complexity of avian evolution revealed by family-level genomes.

Despite tremendous efforts in the past decades, relationships among main avian lineages remain heavily debated without a clear resolution. Discrepancies have been attributed to diversity of species sampled, phylogenetic method and the choice of genomic regions1-3. Here we address these issues by analysing the genomes of 363 bird species4 (218 taxonomic families, 92% of total). Using intergenic regions and coalescent methods, we present a well-supported tree but also a marked degree of discordance. The tree confirms that Neoaves experienced rapid radiation at or near the Cretaceous-Palaeogene boundary. Sufficient loci rather than extensive taxon sampling were more effective in resolving difficult nodes. Remaining recalcitrant nodes involve species that are a challenge to model due to either extreme DNA composition, variable substitution rates, incomplete lineage sorting or complex evolutionary events such as ancient hybridization. Assessment of the effects of different genomic partitions showed high heterogeneity across the genome. We discovered sharp increases in effective population size, substitution rates and relative brain size following the Cretaceous-Palaeogene extinction event, supporting the hypothesis that emerging ecological opportunities catalysed the diversification of modern birds. The resulting phylogenetic estimate offers fresh insights into the rapid radiation of modern birds and provides a taxon-rich backbone tree for future comparative studies.

Simulations of Sequence Evolution: How (Un)realistic They Are and Why.

MOTIVATION: Simulating multiple sequence alignments (MSAs) using probabilistic models of sequence evolution plays an important role in the evaluation of phylogenetic inference tools and is crucial to the development of novel learning-based approaches for phylogenetic reconstruction, for instance, neural networks. These models and the resulting simulated data need to be as realistic as possible to be indicative of the performance of the developed tools on empirical data and to ensure that neural networks trained on simulations perform well on empirical data. Over the years, numerous models of evolution have been published with the goal to represent as faithfully as possible the sequence evolution process and thus simulate empirical-like data. In this study, we simulated DNA and protein MSAs under increasingly complex models of evolution with and without insertion/deletion (indel) events using a state-of-the-art sequence simulator. We assessed their realism by quantifying how accurately supervised learning methods are able to predict whether a given MSA is simulated or empirical. RESULTS: Our results show that we can distinguish between empirical and simulated MSAs with high accuracy using two distinct and independently developed classification approaches across all tested models of sequence evolution. Our findings suggest that the current state-of-the-art models fail to accurately replicate several aspects of empirical MSAs, including site-wise rates as well as amino acid and nucleotide composition.

Working from home and management controls.

The Covid-19 pandemic and the corresponding shift toward working from home (WFH) amplifies control problems within organizations and poses severe challenges for management control as employees' tasks are difficult to observe under WFH conditions. We examine the association between WFH and action controls. Based on a survey among employees in a large international corporation, we find that under WFH conditions the organization more intensively uses standardization and planning participation. We also examine the association between WFH and employee outcomes. The findings suggest that WFH is associated with more time employees spend in meetings and a higher job focus. Overall, the study adds to the literature by exploring the association between WFH and the use of management controls in organizations.

Adaptive RAxML-NG: Accelerating Phylogenetic Inference under Maximum Likelihood using Dataset Difficulty.

Phylogenetic inferences under the maximum likelihood criterion deploy heuristic tree search strategies to explore the vast search space. Depending on the input dataset, searches from different starting trees might all converge to a single tree topology. Often, though, distinct searches infer multiple topologies with large log-likelihood score differences or yield topologically highly distinct, yet almost equally likely, trees. Recently, Haag et al. introduced an approach to quantify, and implemented machine learning methods to predict, the dataset difficulty with respect to phylogenetic inference. Easy multiple sequence alignments (MSAs) exhibit a single likelihood peak on their likelihood surface, associated with a single tree topology to which most, if not all, independent searches rapidly converge. As difficulty increases, multiple locally optimal likelihood peaks emerge, yet from highly distinct topologies. To make use of this information, we introduce and implement an adaptive tree search heuristic in RAxML-NG, which modifies the thoroughness of the tree search strategy as a function of the predicted difficulty. Our adaptive strategy is based upon three observations. First, on easy datasets, searches converge rapidly and can hence be terminated at an earlier stage. Second, overanalyzing difficult datasets is hopeless, and thus it suffices to quickly infer only one of the numerous almost equally likely topologies to reduce overall execution time. Third, more extensive searches are justified and required on datasets with intermediate difficulty. While the likelihood surface exhibits multiple locally optimal peaks in this case, a small proportion of them is significantly better. Our experimental results for the adaptive heuristic on 9,515 empirical and 5,000 simulated datasets with varying difficulty exhibit substantial speedups, especially on easy and difficult datasets (53% of total MSAs), where we observe average speedups of more than 10×. Further, approximately 94% of the inferred trees using the adaptive strategy are statistically indistinguishable from the trees inferred under the standard strategy (RAxML-NG).

The Free Lunch is not over yet-systematic exploration of numerical thresholds in maximum likelihood phylogenetic inference.

Summary: Maximum likelihood (ML) is a widely used phylogenetic inference method. ML implementations heavily rely on numerical optimization routines that use internal numerical thresholds to determine convergence. We systematically analyze the impact of these threshold settings on the log-likelihood and runtimes for ML tree inferences with RAxML-NG, IQ-TREE, and FastTree on empirical datasets. We provide empirical evidence that we can substantially accelerate tree inferences with RAxML-NG and IQ-TREE by changing the default values of two such numerical thresholds. At the same time, altering these settings does not significantly impact the quality of the inferred trees. We further show that increasing both thresholds accelerates the RAxML-NG bootstrap without influencing the resulting support values. For RAxML-NG, increasing the likelihood thresholds ϵLnL and ϵbrlen to 10 and 103, respectively, results in an average tree inference speedup of 1.9 ± 0.6 on Data collection 1, 1.8 ± 1.1 on Data collection 2, and 1.9 ± 0.8 on Data collection 2 for the RAxML-NG bootstrap compared to the runtime under the current default setting. Increasing the likelihood threshold ϵLnL to 10 in IQ-TREE results in an average tree inference speedup of 1.3 ± 0.4 on Data collection 1 and 1.3 ± 0.9 on Data collection 2. Availability and implementation: All MSAs we used for our analyses, as well as all results, are available for download at https://cme.h-its.org/exelixis/material/freeLunch_data.tar.gz. Our data generation scripts are available at https://github.com/tschuelia/ml-numerical-analysis.

From Easy to Hopeless-Predicting the Difficulty of Phylogenetic Analyses.

Phylogenetic analyzes under the Maximum-Likelihood (ML) model are time and resource intensive. To adequately capture the vastness of tree space, one needs to infer multiple independent trees. On some datasets, multiple tree inferences converge to similar tree topologies, on others to multiple, topologically highly distinct yet statistically indistinguishable topologies. At present, no method exists to quantify and predict this behavior. We introduce a method to quantify the degree of difficulty for analyzing a dataset and present Pythia, a Random Forest Regressor that accurately predicts this difficulty. Pythia predicts the degree of difficulty of analyzing a dataset prior to initiating ML-based tree inferences. Pythia can be used to increase user awareness with respect to the amount of signal and uncertainty to be expected in phylogenetic analyzes, and hence inform an appropriate (post-)analysis setup. Further, it can be used to select appropriate search algorithms for easy-, intermediate-, and hard-to-analyze datasets.

Progression of cerebral infarction before and after thrombectomy is modified by prehospital pathways.

BACKGROUND: Evidence of the consequences of different prehospital pathways before mechanical thrombectomy (MT) in large vessel occlusion stroke is inconclusive. The aim of this study was to investigate the infarct extent and progression before and after MT in directly admitted (mothership) versus transferred (drip and ship) patients using the Alberta Stroke Program Early CT Score (ASPECTS). METHODS: ASPECTS of 535 consecutive large vessel occlusion stroke patients eligible for MT between 2015 to 2019 were retrospectively analyzed for differences in the extent of baseline, post-referral, and post-recanalization infarction between the mothership and drip and ship pathways. Time intervals and transport distances of both pathways were analyzed. Multiple linear regression was used to examine the association between infarct progression (baseline to post-recanalization ASPECTS decline), patient characteristics, and logistic key figures. RESULTS: ASPECTS declined during transfer (9 (8-10) vs 7 (6-9), p<0.0001), resulting in lower ASPECTS at stroke center presentation (mothership 9 (7-10) vs drip and ship 7 (6-9), p<0.0001) and on follow-up imaging (mothership 7 (4-8) vs drip and ship 6 (3-7), p=0.001) compared with mothership patients. Infarct progression was significantly higher in transferred patients (points lost, mothership 2 (0-3) vs drip and ship 3 (2-6), p<0.0001). After multivariable adjustment, only interfacility transfer, preinterventional clinical stroke severity, the degree of angiographic recanalization, and the duration of the thrombectomy procedure remained predictors of infarct progression (R 2=0.209, p<0.0001). CONCLUSIONS: Infarct progression and postinterventional infarct extent, as assessed by ASPECTS, varied between the drip and ship and mothership pathway, leading to more pronounced infarction in transferred patients. ASPECTS may serve as a radiological measure to monitor the benefit or harm of different prehospital pathways for MT.

Continuous Feature-Based Tracking of the Inner Ear for Robot-Assisted Microsurgery.

Robotic systems for surgery of the inner ear must enable highly precise movement in relation to the patient. To allow for a suitable collaboration between surgeon and robot, these systems should not interrupt the surgical workflow and integrate well in existing processes. As the surgical microscope is a standard tool, present in almost every microsurgical intervention and due to it being in close proximity to the situs, it is predestined to be extended by assistive robotic systems. For instance, a microscope-mounted laser for ablation. As both, patient and microscope are subject to movements during surgery, a well-integrated robotic system must be able to comply with these movements. To solve the problem of on-line registration of an assistance system to the situs, the standard of care often utilizes marker-based technologies, which require markers being rigidly attached to the patient. This not only requires time for preparation but also increases invasiveness of the procedure and the line of sight of the tracking system may not be obstructed. This work aims at utilizing the existing imaging system for detection of relative movements between the surgical microscope and the patient. The resulting data allows for maintaining registration. Hereby, no artificial markers or landmarks are considered but an approach for feature-based tracking with respect to the surgical environment in otology is presented. The images for tracking are obtained by a two-dimensional RGB stream of a surgical microscope. Due to the bony structure of the surgical site, the recorded cochleostomy scene moves nearly rigidly. The goal of the tracking algorithm is to estimate motion only from the given image stream. After preprocessing, features are detected in two subsequent images and their affine transformation is computed by a random sample consensus (RANSAC) algorithm. The proposed method can provide movement feedback with up to 93.2 µm precision without the need for any additional hardware in the operating room or attachment of fiducials to the situs. In long term tracking, an accumulative error occurs.