EC-KitY: Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integration

EC-KitY is a comprehensive Python library for doing evolutionary computation (EC), licensed under the BSD 3-Clause License, and compatible with scikit-learn. Designed with modern software engineering and machine learning integration in mind, EC-KitY can support all popular EC paradigms, including genetic algorithms, genetic programming, coevolution, evolutionary multi-objective optimization, and more. This paper provides an overview of the package, including the ease of setting up an EC experiment, the architecture, the main features, and a comparison with other libraries.Comment: 6 pages, 1 figure, 1 table. Published in Elsevier Software

Accuracy in predicting repetitions to task failure in resistance exercise: A scoping review and exploratory meta-analysis

BackgroundPrescribing repetitions relative to task failure is an emerging approach to resistance training. Under this approach, participants terminate the set based on their prediction of the remaining repetitions left to task failure. While this approach holds promise, an important step in its development is to determine how accurate participants are in their predictions. That is, what is the difference between the predicted and actual number of repetitions remaining to task failure, which ideally should be as small as possible.ObjectiveThe aim of this study was to examine the accuracy in predicting repetitions to task failure in resistance exercises.DesignScoping review and exploratory meta-analysis.Search and InclusionA systematic literature search was conducted in January 2021 using the PubMed, SPORTDiscus, and Google Scholar databases. Inclusion criteria included studies with healthy participants who predicted the number of repetitions they can complete to task failure in various resistance exercises, before or during an ongoing set, which was performed to task failure. Sixteen publications were eligible for inclusion, of which 13 publications covering 12 studies, with a total of 414 participants, were included in our meta-analysis.ResultsThe main multilevel meta-analysis model including all effects sizes (262 across 12 clusters) revealed that participants tended to underpredict the number of repetitions to task failure by 0.95 repetitions (95% confidence interval [CI] 0.17–1.73), but with considerable heterogeneity (Q(261) = 3060, p < 0.0001, I2 = 97.9%). Meta-regressions showed that prediction accuracy slightly improved when the predictions were made closer to set failure (β =  − 0.025, 95% CI − 0.05 to 0.0014) and when the number of repetitions performed to task failure was lower (≤ 12 repetitions: β = 0.06, 95% CI 0.04–0.09; > 12 repetitions: β = 0.47, 95% CI 0.44–0.49). Set number trivially influenced prediction accuracy with slightly increased accuracy in later sets (β =  − 0.07 repetitions, 95% CI − 0.14 to − 0.005). In contrast, participants’ training status did not seem to influence prediction accuracy (β =  − 0.006 repetitions, 95% CI − 0.02 to 0.007) and neither did the implementation of upper or lower body exercises (upper body – lower body =  − 0.58 repetitions; 95% CI − 2.32 to 1.16). Furthermore, there was minimal between-participant variation in predictive accuracy (standard deviation 1.45 repetitions, 95% CI 0.99–2.12).ConclusionsParticipants were imperfect in their ability to predict proximity to task failure independent of their training background. It remains to be determined whether the observed degree of inaccuracy should be considered acceptable. Despite this, prediction accuracies can be improved if they are provided closer to task failure, when using heavier loads, or in later sets. To reduce the heterogeneity between studies, future studies should include a clear and detailed account of how task failure was explained to participants and how it was confirmed

Epitope mapping using combinatorial phage-display libraries: a graph-based algorithm

A phage-display library of random peptides is a combinatorial experimental technique that can be harnessed for studying antibody–antigen interactions. In this technique, a phage peptide library is scanned against an antibody molecule to obtain a set of peptides that are bound by the antibody with high affinity. This set of peptides is regarded as mimicking the genuine epitope of the antibody's interacting antigen and can be used to define it. Here we present PepSurf, an algorithm for mapping a set of affinity-selected peptides onto the solved structure of the antigen. The problem of epitope mapping is converted into the task of aligning a set of query peptides to a graph representing the surface of the antigen. The best match of each peptide is found by aligning it against virtually all possible paths in the graph. Following a clustering step, which combines the most significant matches, a predicted epitope is inferred. We show that PepSurf accurately predicts the epitope in four cases for which the epitope is known from a solved antibody–antigen co-crystal complex. We further examine the capabilities of PepSurf for predicting other types of protein–protein interfaces. The performance of PepSurf is compared to other available epitope mapping programs

Displaced femoral neck fracture in a pregnant patient diagnosed with transient osteoporosis of the hip

Transient osteoporosis of pregnancy (TOP) is a self-limiting pathology with unspecified etiology. It is typically found in women in late pregnancy or early postpartum. A femoral neck fracture is an infrequent complication. Herein, we describe a TOP case in a 38-year-old female who suffered a displaced sub-capital femoral neck fracture without obvious trauma at 28 weeks of gestation. The patient underwent operative treatment using closed reduction and internal fixation (CRIF), using cannulated screws, with no intraoperative complications. The postoperative radiograph revealed a collapse and further displacement of the femoral neck. A decision was made to postpone a definitive treatment to a postpartum date. The patient underwent a cesarean section at 38-week of gestation with no complications. At her latest follow-up, 24 months postoperatively, the patient was asymptomatic. Pelvic and hip radiographs demonstrated consolidation of the fracture. Level of evidence: IV

Stochasticity constrained by deterministic effects of diet and age drive rumen microbiome assembly dynamics

How complex communities assemble through the animal’s life, and how predictable the process is remains unexplored. Here, we investigate the forces that drive the assembly of rumen microbiomes throughout a cow’s life, with emphasis on the balance between stochastic and deterministic processes. We analyse the development of the rumen microbiome from birth to adulthood using 16S-rRNA amplicon sequencing data and find that the animals shared a group of core successional species that invaded early on and persisted until adulthood. Along with deterministic factors, such as age and diet, early arriving species exerted strong priority effects, whereby dynamics of late successional taxa were strongly dependent on microbiome composition at early life stages. Priority effects also manifest as dramatic changes in microbiome development dynamics between animals delivered by C-section vs. natural birth, with the former undergoing much more rapid species invasion and accelerated microbiome development. Overall, our findings show that together with strong deterministic constrains imposed by diet and age, stochastic colonization in early life has long-lasting impacts on the development of animal microbiomes

The tumor microenvironment shows a hierarchy of cell-cell interactions dominated by fibroblasts

Abstract The tumor microenvironment (TME) is comprised of non-malignant cells that interact with each other and with cancer cells, critically impacting cancer biology. The TME is complex, and understanding it requires simplifying approaches. Here we provide an experimental-mathematical approach to decompose the TME into small circuits of interacting cell types. We find, using female breast cancer single-cell-RNA-sequencing data, a hierarchical network of interactions, with cancer-associated fibroblasts (CAFs) at the top secreting factors primarily to tumor-associated macrophages (TAMs). This network is composed of repeating circuit motifs. We isolate the strongest two-cell circuit motif by culturing fibroblasts and macrophages in-vitro, and analyze their dynamics and transcriptomes. This isolated circuit recapitulates the hierarchy of in-vivo interactions, and enables testing the effect of ligand-receptor interactions on cell dynamics and function, as we demonstrate by identifying a mediator of CAF-TAM interactions - RARRES2, and its receptor CMKLR1. Thus, the complexity of the TME may be simplified by identifying small circuits, facilitating the development of strategies to modulate the TME