The use of droplet-based microfluidic technologies for accelerated selection of Yarrowia lipolytica and Phaffia rhodozyma yeast mutants.

Microorganisms are widely used for the industrial production of various valuable products, such as pharmaceuticals, food and beverages, biofuels, enzymes, amino acids, vaccines, etc. Research is constantly carried out to improve their properties, mainly to increase their productivity and efficiency and reduce the cost of the processes. The selection of microorganisms with improved qualities takes a lot of time and resources (both human and material); therefore, this process itself needs optimization. In the last two decades, microfluidics technology appeared in bioengineering, which allows for manipulating small particles (from tens of microns to nanometre scale) in the flow of liquid in microchannels. The technology is based on small-volume objects (microdroplets from nano to femtolitres), which are manipulated using a microchip. The chip is made of an optically transparent inert to liquid medium material and contains a series of channels of small size (<1 mm) of certain geometry. Based on the physical and chemical properties of microparticles (like size, weight, optical density, dielectric constant, etc.), they are separated using microsensors. The idea of accelerated selection of microorganisms is the application of microfluidic technologies to separate mutants with improved qualities after mutagenesis. This article discusses the possible application and practical implementation of microfluidic separation of mutants, including yeasts like Yarrowia lipolytica and Phaffia rhodozyma after chemical mutagenesis will be discussed.

Elucidating Evolutionary Mechanisms and Variants of the Hammerhead Ribozyme Using In Vitro Selection.

The Hammerhead Ribozyme (HHR) is a ubiquitous RNA enzyme that catalyzes site-specific intramolecular cleavage. While mutations to its catalytic core have traditionally been viewed as detrimental to its activity, several discoveries of naturally occurring variants of the full-length ribozyme challenge this notion, suggesting a deeper understanding of HHR evolution and functionality. By systematically introducing mutations at key nucleotide positions within the catalytic core, we generated single-, double-, and triple-mutation libraries to explore the sequence requirements and evolution of a full-length HHR. In vitro selection revealed many novel hammerhead variants, some of which possess mutations at nucleotides previously considered to be essential. We also demonstrate that the evolutionary trajectory of each nucleotide in the catalytic core directly correlates with their functional importance, potentially giving researchers a novel method to assess the sequence requirements of functional nucleic acids.

Improved mapping of heavy metals in agricultural soils using machine learning augmented with spatial regionalization indices.

The accurate spatial mapping of heavy metal levels in agricultural soils is crucial for environmental management and food security. However, the inherent limitations of traditional interpolation methods and emerging machine-learning techniques restrict their spatial prediction accuracy. This study aimed to refine the spatial prediction of heavy metal distributions in Guangxi, China, by integrating machine learning models and spatial regionalization indices (SRIs). The results demonstrated that random forest (RF) models incorporating SRIs outperformed artificial neural network and support vector regression models, achieving R2 values exceeding 0.96 for eight heavy metals on the test data. Hierarchical clustering for feature selection further improved the model performance. The optimized RF models accurately predicted the heavy metal distributions in agricultural soils across the province, revealing higher levels in the central-western regions and lower levels in the north and south. Notably, the models identified that 25.78 % of agricultural soils constitute hotspots with multiple co-occurring heavy metals, and over 6.41 million people are exposed to excessive soil heavy metal levels. Our findings provide valuable insights for the development of targeted strategies for soil pollution control and agricultural soil management to safeguard food security and public health.

SMAD4 mutations causing Myhre syndrome are under positive selection in the male germline.

While it is widely thought that de novo mutations (DNMs) occur randomly, we previously showed that some DNMs are enriched because they are positively selected in the testes of aging men. These "selfish" mutations cause disorders with a shared presentation of features, including exclusive paternal origin, significant increase of the father's age, and high apparent germline mutation rate. To date, all known selfish mutations cluster within the components of the RTK-RAS-MAPK signaling pathway, a critical modulator of testicular homeostasis. Here, we demonstrate the selfish nature of the SMAD4 DNMs causing Myhre syndrome (MYHRS). By analyzing 16 informative trios, we show that MYHRS-causing DNMs originated on the paternally derived allele in all cases. We document a statistically significant epidemiological paternal age effect of 6.3 years excess for fathers of MYHRS probands. We developed an ultra-sensitive assay to quantify spontaneous MYHRS-causing SMAD4 variants in sperm and show that pathogenic variants at codon 500 are found at elevated level in sperm of most men and exhibit a strong positive correlation with donor's age, indicative of a high apparent germline mutation rate. Finally, we performed in vitro assays to validate the peculiar functional behavior of the clonally selected DNMs and explored the basis of the pathophysiology of the different SMAD4 sperm-enriched variants. Taken together, these data provide compelling evidence that SMAD4, a gene operating outside the canonical RAS-MAPK signaling pathway, is associated with selfish spermatogonial selection and raises the possibility that other genes/pathways are under positive selection in the aging human testis.

A meta-analysis of whole-body and heart mass effect sizes from a long-term artificial selection experiment for high voluntary exercise.

Selection experiments play an increasingly important role in comparative and evolutionary physiology. However, selection experiments can be limited by relatively low statistical power, in part because replicate line is the experimental unit for analyses of direct or correlated responses (rather than number of individuals measured). One way to increase the ability to detect correlated responses is through a meta-analysis of studies for a given trait across multiple generations. To demonstrate this, we applied meta-analytic techniques to two traits (body mass and heart ventricle mass, with body mass as a covariate) from a long-term artificial selection experiment for high voluntary wheel-running behavior. In this experiment, all 4 replicate High Runner (HR) lines reached apparent selection limits around generations 17-27, running approximately 2.5-3-fold more revolutions/day than the 4 non-selected Control (C) lines. Although both traits would also be expected to change in HR lines (relative heart size expected to increase, expected direction for body mass is less clear), the statistical significance has varied, despite repeated measurements. We compiled information from 33 unique studies and calculated a measure of effect size (Pearson's R). Our results indicate that, despite a lack of statistical significance in most generations, HR mice have evolved larger hearts and smaller bodies relative to Controls. Moreover, plateaus in effect sizes for both traits coincides with the generational range during which the selection limit for wheel-running behavior was reached. Finally, since the selection limit, absolute effect sizes for body mass and heart ventricle mass have gotten smaller (i.e., closer to 0).

Developing guidelines for selection-deselection in high performance sport for athletes, coaches, and organisations: A delphi study.

The process of athlete selection and deselection in sport involves not only athletes but also coaches, managers, performance directors, talent pathway coordinators and the wider organisation. Athlete selection and deselection can be viewed as the opposite sides of the same coin in that the process may be the same for all athletes but the outcome is very different. The outcome of this process can evoke extremely powerful emotions ranging from elevation to devastation. While selection and deselection are part of competitive sport regardless of type, level, gender or age, research is scarce. Employing the Delphi method, a total of 20 participants comprised the expert panel (coaches, athletes and other key personnel in high performance) from various sports, and ages ranged from 21 to 59 years old. Following three rounds, 60 items reached the pre-determined consensus level of 75%. The 60 items were then further content analysed and grouped with respect to the three key stakeholders: athlete (14), coach (21) and organisation (25). Within each of these categories, subcategories emerged: personal, interpersonal, procedural, educational, supportive, communicative and reviewing behaviours and actions that athletes, coaches and organisations can take to ease the navigation, apply consistency and establish a common ground during this challenging situation.

GRU-powered sleep stage classification with permutation-based EEG channel selection.

We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods. By systematically permuting the electroencephalographic (EEG) channels, different combinations of EEG channels are evaluated to identify the most informative subset for the classification of the 5-class sleep stage. For analysis, we used an EEG dataset that was collected at the International Institute for Integrative Sleep Medicine (WPI-IIIS) at the University of Tsukuba in Japan. The results of these explorations provide many new insights such as the (1) drastic decrease in performance when channels are fewer than 3, (2) 3-random channels selected by permutation provide the same or better prediction than the 3 channels recommended by the American Academy of Sleep Medicine (AASM), (3) N1 class suffers the most in prediction accuracy as the channels drop from 128 to 3 random or 3 AASM, and (4) no single channel provides acceptable levels of accuracy in the prediction of 5 classes. The results obtained show the GRU's ability to retain essential temporal information from EEG data, which allows capturing the underlying patterns associated with each sleep stage effectively. Using permutation-based channel selection, we enhance or at least maintain as high model efficiency as when using high-density EEG, incorporating only the most informative EEG channels.

Quantitative selection of sample structures in small-angle scattering using Bayesian methods.

Small-angle scattering (SAS) is a key experimental technique for analyzing nanoscale structures in various materials. In SAS data analysis, selecting an appropriate mathematical model for the scattering intensity is critical, as it generates a hypothesis of the structure of the experimental sample. Traditional model selection methods either rely on qualitative approaches or are prone to overfitting. This paper introduces an analytical method that applies Bayesian model selection to SAS measurement data, enabling a quantitative evaluation of the validity of mathematical models. The performance of the method is assessed through numerical experiments using artificial data for multicomponent spherical materials, demonstrating that this proposed analysis approach yields highly accurate and interpretable results. The ability of the method to analyze a range of mixing ratios and particle size ratios for mixed components is also discussed, along with its precision in model evaluation by the degree of fitting. The proposed method effectively facilitates quantitative analysis of nanoscale sample structures in SAS, which has traditionally been challenging, and is expected to contribute significantly to advancements in a wide range of fields.

Assessing the carotenoid profiles and allelic diversity of yellow maize inbred lines adapted to mid-altitude subhumid maize agroecology in Ethiopia.

Biofortification of provitamin A in maize is an attractive and sustainable remedy to the problem of vitamin A deficiency in developing countries. The utilization of molecular markers represents a promising avenue to facilitate the development of provitamin A (PVA)-enriched maize varieties. We screened 752 diverse tropical yellow/orange maize lines using kompetitive allele-specific PCR (KASP) makers to validate the use of KASP markers in PVA maize breeding. To this end, a total of 161 yellow/orange inbred lines, selected from among the 752 lines, were evaluated for their endosperm PVA and other carotenoid compounds levels in two separate trials composed of 63 and 98 inbred lines in 2020 and 2021, respectively. Significant differences (p < 0.001) were observed among the yellow maize inbred lines studied for all carotenoid profiles. An inbred line TZMI1017, introduced by the International Institute of Tropical Agriculture (IITA) showed the highest level of PVA (12.99 µg/g) and ß-carotene (12.08 µg/g). The molecular screening showed 43 yellow maize inbred lines carrying at least three of the favorable alleles of the KASP markers. TZMI1017 inbred line also carried the favorable alleles of almost all markers. In addition, nine locally developed inbred lines had medium to high PVA concentrations varying from 5.11 µg/g to 10.76 µg/g and harbored the favorable alleles of all the KASP PVA markers. Association analysis between molecular markers and PVA content variation in the yellow/orange maize inbred lines did not reveal a significant, predictable correlation. Further investigation is warranted to elucidate the underlying genetic architecture of the PVA content in this germplasm. However, we recommend strategic utilization of the maize-inbred lines with higher PVA content to enhance the PVA profile of the breeding program's germplasm.

Sparse testing designs for optimizing predictive ability in sugarcane populations.

Sugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcane's yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (G×E) interaction. An efficient breeding of sugarcane demands an accurate assessment of the genotype stability through multi-environment trials (METs), where genotypes are tested/evaluated across different environments. However, phenotyping all genotype-in-environment combinations is often impractical due to cost and limited availability of propagation-materials. This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic prediction models. This approach was applied to a dataset comprising 186 genotypes across six environments (6×186=1,116 phenotypes). Our study employed three predictive models, including environment, genotype, and genomic markers as main effects, as well as the G×E to predict saccharose accumulation (SA) and tons of cane per hectare (TCH). Calibration sets sizes varying between 72 (6.5%) to 186 (16.7%) of the total number of phenotypes were composed to predict the remaining 930 (83.3%). Additionally, we explored the optimal number of common genotypes across environments for G×E pattern prediction. Results demonstrate that maximum accuracy for SA ( ρ = 0.611 ) and for TCH ( ρ=0.341 ) was achieved using in training sets few (3) to no common (0) genotype across environments maximizing the number of different genotypes that were tested only once. Significantly, we show that reducing phenotypic records for model calibration has minimal impact on predictive ability, with sets of 12 non-overlapped genotypes per environment (72=12×6) being the most convenient cost-benefit combination.

Genomic prediction for rust resistance in pea.

Genomic selection (GS) has become an indispensable tool in modern plant breeding, particularly for complex traits. This study aimed to assess the efficacy of GS in predicting rust (Uromyces pisi) resistance in pea (Pisum sativum), using a panel of 320 pea accessions and a set of 26,045 Silico-Diversity Arrays Technology (Silico-DArT) markers. We compared the prediction abilities of different GS models and explored the impact of incorporating marker × environment (M×E) interaction as a covariate in the GBLUP (genomic best linear unbiased prediction) model. The analysis included phenotyping data from both field and controlled conditions. We assessed the predictive accuracies of different cross-validation strategies and compared the efficiency of using single traits versus a multi-trait index, based on factor analysis and ideotype-design (FAI-BLUP), which combines traits from controlled conditions. The GBLUP model, particularly when modified to include M×E interactions, consistently outperformed other models, demonstrating its suitability for traits affected by complex genotype-environment interactions (GEI). The best predictive ability (0.635) was achieved using the FAI-BLUP approach within the Bayesian Lasso (BL) model. The inclusion of M×E interactions significantly enhanced prediction accuracy across diverse environments in GBLUP models, although it did not markedly improve predictions for non-phenotyped lines. These findings underscore the variability of predictive abilities due to GEI and the effectiveness of multi-trait approaches in addressing complex traits. Overall, our study illustrates the potential of GS, especially when employing a multi-trait index like FAI-BLUP and accounting for M×E interactions, in pea breeding programs focused on rust resistance.

The proactive and reactive mechanisms of learned spatial suppression.

Selection history refers to the notion that previous allocations of attention or suppression have the potential to elicit lingering and enduring selection biases that are isolated from goal-driven or stimulus-driven attention. However, in the singleton detection mode task, manipulating the selection history of distractors cannot give rise to pure proactive inhibition. Therefore, we employed a combination of a working memory task and a feature search mode task, simultaneously recording cortical activity using EEG, to investigate the mechanisms of suppression guided by selection history. The results from event-related potential and reaction times showed an enhanced inhibitory performance when the distractor was presented at the high-probability location, along with instances where the target appeared at the high-probability location of distractors. These findings demonstrate that a generalized proactive inhibition bias is learned and processed independent of cognitive resources, which is supported by selection history. In contrast, reactive rejection toward the low-probability location was evident through the Pd component under varying cognitive resource conditions. Taken together, our findings indicated that participants learned proactive inhibition when the distractor was at the high-probability location, whereas reactive rejection was involved at low-probability location.

Maternal manipulation of offspring size can trigger the evolution of eusociality in promiscuous species.

Eusocial organisms typically live in colonies with one reproductive queen supported by thousands of sterile workers. It is widely believed that monogamous mating is a precondition for the evolution of eusociality. Here, we present a theoretical model that simulates a realistic scenario for the evolution of eusociality. In the model, mothers can evolve control over resource allocation to offspring, affecting offspring's body size. The offspring can evolve body-size-dependent dispersal, by which they disperse to breed or stay at the nest as helpers. We demonstrate that eusociality can evolve even if mothers are not strictly monogamous, provided that they can constrain their offspring's reproduction through manipulation. We also observe the evolution of social polymorphism with small individuals that help and larger individuals that disperse to breed. Our model unifies the traditional kin selection and maternal manipulation explanations for the evolution of eusociality and demonstrates that-contrary to current consensus belief-eusociality can evolve despite highly promiscuous mating.

Ensemble of machine learning techniques to predict survival in kidney transplant recipients.

Kidney transplant recipients face a high cardiovascular risk, which is a leading cause of death in this patient group. This article proposes the application of clustering techniques and feature selection to predict the survival outcomes of kidney transplant recipients based on machine learning techniques and mainstream statistical methods. First, feature selection techniques (Boruta, Random Survival Forest and Elastic Net) are used to detect the most relevant variables. Subsequently, each set of variables obtained by each feature selection technique is used as input for the clustering algorithms used (Consensus Clustering, Self-Organizing Map and Agglomerative Clustering) to determine which combination of feature selection, clustering algorithm and number of clusters maximizes intercluster variability. Next, the mechanism called False Clustering Discovery Reduction is applied to obtain the minimum number of statistically differentiable populations after applying a control metric. This metric is based on a variance test to confirm that reducing the number of clusters does not generate significant losses in the heterogeneity obtained. This approach was applied to the Organ Procurement and Transplantation Network medical dataset (n = 11,332). The combination of Random Survival Forest and consensus clustering yielded the optimal result of 4 clusters starting from 8 initial ones. Finally, for each population, Kaplan-Meier survival curves are generated to predict the survival of new patients based on the predictions of the XGBoost classifier, with an overall multi-class AUC of 98.11%.

The HSCT procedure (I): Mobilization, collection, manipulation, and cryopreservation of a HSC graft.

Most hematopoietic stem cell transplants performed for an autoimmune disease of the nervous system, use the patient's hematopoietic stem cells (HSCs). Obtaining an HSC graft is the first step of the process. This typically involves mobilization of bone marrow HSCs into the circulation using high-dose cyclophosphamide followed by filgrastim, a drug based on granulocyte colony-stimulating factor. Toxicity from these agents is usually manageable and adverse events are less severe and less frequent than those experienced during the hematopoietic stem cell transplant. Following mobilization, HSCs are collected from the circulation by leukapheresis. Some centers deplete the graft of lymphocytes using an ex vivo immunomagnetic selection procedure. HSC grafts are cryopreserved until required for the stem cell transplant. Quality testing of the graft ensures sterility and it contains sufficient HSCs and hematopoietic progenitors. The clinical and laboratory aspects of HSC graft mobilization, collection, and storage must meet standards set by national regulatory bodies and accredited by international professional standards organizations. Experienced stem cell transplant teams are important for minimizing procedural toxicity and enhancing successful collection.

Improving selection procedures in health professions education from the applicant perspective: an interview study.

INTRODUCTION: Applicant perceptions of selection impact motivation and performance during selection, and student diversity. However, in-depth insight into which values underly these perceptions is lacking, creating challenges for aligning selection procedures with applicant perceptions. This qualitative interview study aimed to identify values applicants believe should underlie selection, and how, according to applicants, these values should be used to make specific improvements to selection procedures in undergraduate health professions education (HPE). METHODS: Thirty-one applicants to five undergraduate HPE programs in the Netherlands participated in semi-structured interviews using Appreciative Inquiry, an approach that focuses on what goes well to create vision for improvement, to guide the interviews. Transcriptions were analyzed using thematic analysis, adopting a constructivist approach. RESULTS: Applicants' values related to the aims of selection, the content of selection, and the treatment of applicants. Applicants believed that selection procedures should aim to identify students who best fit the training and profession, and generate diverse student populations to fulfill societal needs. According to applicants, the content of selection should be relevant for the curriculum and profession, assess a comprehensive set of attributes, be of high quality, allow applicants to show who they are, and be adapted to applicants' current developmental state. Regarding treatment, applicants believed that selection should be a two-way process that fosters reflection on study choice, be transparent about what applicants can expect, safeguard applicants' well-being, treat all applicants equally, and employ an equitable approach by taking personal circumstances into account. Applicants mentioned specific improvements regarding each value. DISCUSSION: Applicants' values offer novel insights into what they consider important preconditions for the design of selection procedures. Their suggested improvements can support selection committees in better meeting applicants' needs.

COMSE: analysis of single-cell RNA-seq data using community detection-based feature selection.

BACKGROUND: Single-cell RNA sequencing enables studying cells individually, yet high gene dimensions and low cell numbers challenge analysis. And only a subset of the genes detected are involved in the biological processes underlying cell-type specific functions. RESULT: In this study, we present COMSE, an unsupervised feature selection framework using community detection to capture informative genes from scRNA-seq data. COMSE identified homogenous cell substates with high resolution, as demonstrated by distinguishing different cell cycle stages. Evaluations based on real and simulated scRNA-seq datasets showed COMSE outperformed methods even with high dropout rates in cell clustering assignment. We also demonstrate that by identifying communities of genes associated with batch effects, COMSE parses signals reflecting biological difference from noise arising due to differences in sequencing protocols, thereby enabling integrated analysis of scRNA-seq datasets of different sources. CONCLUSIONS: COMSE provides an efficient unsupervised framework that selects highly informative genes in scRNA-seq data improving cell sub-states identification and cell clustering. It identifies gene subsets that reveal biological and technical heterogeneity, supporting applications like batch effect correction and pathway analysis. It also provides robust results for bulk RNA-seq data analysis.

Revisiting Dominance in Population Genetics.

Dominance refers to the effect of a heterozygous genotype relative to that of the two homozygous genotypes. The degree of dominance of mutations for fitness can have a profound impact on how deleterious and beneficial mutations change in frequency over time as well as on the patterns of linked neutral genetic variation surrounding such selected alleles. Since dominance is such a fundamental concept, it has received immense attention throughout the history of population genetics. Early work from Fisher, Wright, and Haldane focused on understanding the conceptual basis for why dominance exists. More recent work has attempted to test these theories and conceptual models by estimating dominance effects of mutations. However, estimating dominance coefficients has been notoriously challenging and has only been done in a few species in a limited number of studies. In this review, we first describe some of the early theoretical and conceptual models for understanding the mechanisms for the existence of dominance. Second, we discuss several approaches used to estimate dominance coefficients and summarize estimates of dominance coefficients. We note trends that have been observed across species, types of mutations, and functional categories of genes. By comparing estimates of dominance coefficients for different types of genes, we test several hypotheses for the existence of dominance. Lastly, we discuss how dominance influences the dynamics of beneficial and deleterious mutations in populations and how the degree of dominance of deleterious mutations influences the impact of inbreeding on fitness.

Predicting Conversion Time from Mild Cognitive Impairment to Dementia with Interval-Censored Models.

Background: Mild cognitive impairment (MCI) patients are at a high risk of developing Alzheimer's disease and related dementias (ADRD) at an estimated annual rate above 10%. It is clinically and practically important to accurately predict MCI-to-dementia conversion time. Objective: It is clinically and practically important to accurately predict MCI-to-dementia conversion time by using easily available clinical data. Methods: The dementia diagnosis often falls between two clinical visits, and such survival outcome is known as interval-censored data. We utilized the semi-parametric model and the random forest model for interval-censored data in conjunction with a variable selection approach to select important measures for predicting the conversion time from MCI to dementia. Two large AD cohort data sets were used to build, validate, and test the predictive model. Results: We found that the semi-parametric model can improve the prediction of the conversion time for patients with MCI-to-dementia conversion, and it also has good predictive performance for all patients. Conclusions: Interval-censored data should be analyzed by using the models that were developed for interval- censored data to improve the model performance.

Regression without regrets -initial data analysis is a prerequisite for multivariable regression.

Statistical regression models are used for predicting outcomes based on the values of some predictor variables or for describing the association of an outcome with predictors. With a data set at hand, a regression model can be easily fit with standard software packages. This bears the risk that data analysts may rush to perform sophisticated analyses without sufficient knowledge of basic properties, associations in and errors of their data, leading to wrong interpretation and presentation of the modeling results that lacks clarity. Ignorance about special features of the data such as redundancies or particular distributions may even invalidate the chosen analysis strategy. Initial data analysis (IDA) is prerequisite to regression analyses as it provides knowledge about the data needed to confirm the appropriateness of or to refine a chosen model building strategy, to interpret the modeling results correctly, and to guide the presentation of modeling results. In order to facilitate reproducibility, IDA needs to be preplanned, an IDA plan should be included in the general statistical analysis plan of a research project, and results should be well documented. Biased statistical inference of the final regression model can be minimized if IDA abstains from evaluating associations of outcome and predictors, a key principle of IDA. We give advice on which aspects to consider in an IDA plan for data screening in the context of regression modeling to supplement the statistical analysis plan. We illustrate this IDA plan for data screening in an example of a typical diagnostic modeling project and give recommendations for data visualizations.