Miracle-dz/UniFAR-FTUs huggingface dataset:
Human skeletal muscle methylome after low carbohydrate energy balanced exercise

We aimed to investigate the human skeletal muscle (SkM) DNA methylome after exercise in low carbohydrate (CHO) energy balance (with high fat) compared with exercise in low-CHO energy deficit (with low fat) conditions. The objective to identify novel epigenetically regulated genes and pathways associated with ‘train-low sleep-low’ paradigms. The sleep-low conditions included 9 males that cycled to deplete muscle glycogen while reaching a set energy expenditure. Post-exercise, low-CHO meals (protein-matched) completely replaced (using high-fat) or only partially replaced (low-fat) the energy expended. The following morning resting baseline biopsies were taken and the participants then undertook 75 minutes of cycling exercise, with skeletal muscle biopsies collected 30 minutes and 3.5 hours post exercise. Discovery of genome-wide DNA methylation was undertaken using Illumina EPIC arrays and targeted gene expression analysis was conducted by RT-qPCR. At baseline participants under energy balance (high fat) demonstrated a predominantly hypermethylated (60%) profile across the genome compared to energy deficit-low fat conditions. However, post exercise performed in energy balance (with high fat) elicited a more prominent hypomethylation signature 30 minutes post-exercise in gene regulatory regions important for transcription (CpG islands within promoter regions) compared with exercise in energy deficit (with low fat) conditions. Such hypomethylation was enriched within pathways related to: IL6-JAK-STAT signalling, metabolic processes, p53 / cell cycle and oxidative / fatty acid metabolism. Hypomethylation within the promoter regions of genes: HDAC2, MECR, IGF2 and c13orf16 were associated with significant increases in gene expression in the post-exercise period in energy balance compared with energy deficit. Furthermore, histone deacetylase, HDAC11 was oppositely regulated at the gene expression level compared with HDAC2, where HDAC11 was hypomethylated yet increased in energy deficit compared with energy balance conditions. Overall, we identify some novel epigenetically regulated genes associated with train-low sleep-low paradigms.
Recursive Prefix-Free Parsing for Building Big BWTs

Prefix-free parsing is useful for a wide variety of purposes including building the BWT, constructing the suffix array, and supporting compressed suffix tree operations. This linear-time algorithm uses a rolling hash to break an input string into substrings, where the resulting set of unique substrings has the property that none of the substrings’ suffixes (of more than a certain length) is a proper prefix of any of the other substrings’ suffixes. Hence, the name prefix-free parsing. This set of unique substrings is referred to as the dictionary. The parse is the ordered list of dictionary strings that defines the input string. Prior empirical results demonstrated the size of the parse is more burdensome than the size of the dictionary for large, repetitive inputs. Hence, the question arises as to how the size of the parse can scale satisfactorily with the input. Here, we describe our algorithm, recursive prefix-free parsing, which accomplishes this by computing the prefix-free parse of the parse produced by prefix-free parsing an input string. Although conceptually simple, building the BWT from the parse-of-the-parse and the dictionaries is significantly more challenging. We solve and implement this problem. Our experimental results show that recursive prefix-free parsing is extremely effective in reducing the memory needed to build the run-length encoded BWT of the input. Our implementation is open source and available at https://github.com/marco-oliva/r-pfbwt.
Informing generative replay for continual learning with long-term memory formation in the fruit fly

Continual learning without catastrophic forgetting is a challenge for artificial systems but it is done naturally across a range of biological systems, including in insects. A recurrent circuit has been identified in the fruit fly mushroom body to consolidate long term memories (LTM), but there is not currently an algorithmic understanding of this LTM formation. We hypothesize that generative replay is occurring to consolidate memories in this recurrent circuit, and find anatomical evidence in synapse-level connectivity that supports this hypothesis. Next, we introduce a computational model which combines an initial experience phase and LTM phase to perform generative replay based continual learning. When evaluated on a CIFAR-100 class-incremental continual learning task, the modeled LTM phase increases classification performance by 20% and approaches within 2% of the performance for a non-incremental upper bound baseline. Unique elements of the proposed generative replay model include: 1) coupling high dimensional sparse activation patterns with generative replay and 2) sampling and reconstructing higher level representations for training generative replay (as opposed to reconstructing sensory-level or processed sensory-level representations). Additionally, we make the experimentally testable prediction that a specific set of synapses would need to undergo experience-dependent plasticity during LTM formation to support our generative replay based model.
Stochastic motion and transcriptional dynamics of pairs of distal DNA loci on a compacted chromosome

Chromosomes in the eukaryotic nucleus are highly compacted. However, for many functional processes, including transcription initiation, the 3D pair-wise motion of distal chromosomal elements, such as enhancers and promoters, is essential and necessitates dynamic fluidity. Therefore, the interplay of chromosome organization and dynamics is crucial for gene regulation. Here, we use a live imaging assay to simultaneously measure the positions of pairs of enhancers and promoters and their transcriptional output in the developing fly embryo while systematically varying the genomic separation between these two DNA loci. Our analysis reveals a combination of a compact globular organization and fast subdiffusive dynamics. These combined features cause an anomalous scaling of polymer relaxation times with genomic separation and lead to long-ranged correlations compared to existing polymer models. This scaling implies that encounter times of DNA loci are much less dependent on genomic separation than predicted by existing polymer models, with potentially significant consequences for eukaryotic gene expression.
lmeEEG: Mass linear mixed-effects modeling of EEG data with crossed random effects

Background Mixed-effects models are the current standard for the analysis of behavioral studies in psycholinguistics and related fields, given their ability to simultaneously model crossed random effects for subjects and items. However, they are hardly applied in neuroimaging and psychophysiology, where the use of mass univariate analyses in combination with permutation testing would be too computationally demanding to be practicable with mixed models. New method Here, we propose and validate an analytical strategy that enables the use of linear mixed models (LMM) with crossed random intercepts in mass univariate analyses of EEG data (lmeEEG). It avoids the unfeasible computational costs that would arise from massive permutation testing with LMM using a simple solution: removing random-effects contributions from EEG data and performing mass univariate linear analysis and permutations on the obtained marginal EEG. Results lmeEEG showed excellent performance properties in terms of power and false positive rate. Comparison with existing methods lmeEEG overcomes the computational costs of standard available approaches (our method was indeed more than 300 times faster). Conclusions lmeEEG allows researchers to use mixed models with EEG mass univariate analyses. Thanks to the possibility offered by the method described here, we anticipate that LMM will become increasingly important in neuroscience. Data and codes are available at osf.io/kw87a. The codes and a tutorial are also available at github.com/antovis86/lmeEEG.
Comparative phylogeography of Ponto-Caspian amphipods throughout the invaded Baltic and native NW Black Sea donor ranges – can introduction mode affect genetic diversity?

The Baltic countries harbor a diverse assemblage of alien amphipods of Ponto-Caspian origin. The composition of this fauna was shaped by three invasion waves: 1) pre-20th century dispersals via watershed-connecting canals, 2) deliberate introductions in the 1960s, and 3) new dispersals during the last decade via shipping and pre-existing canals. Given this rich invasion history, we test whether genetic diversity (mitochondrial and nuclear) differs between the native and invaded ranges and between the deliberately introduced species and the ones that dispersed on their own. Our results show a significant decrease in mitochondrial but not nuclear genetic diversity in the invaded Baltic range. We also find that in the invaded range the introduced species exhibit a higher mitochondrial and nuclear genetic diversity than the species that dispersed on their own, while in the native range only the nuclear diversity is higher in introduced species. Mitochondrial diversity was more structured geographically in the native range and the dominant invasive haplotypes were detected in the native populations of all but one species, further highlighting the utility of this marker in tracing invasion sources. Our comparative approach provides important insight into the inter-range genetic diversity of Ponto-Caspian invaders, highlighting the role of introduction mode.
Simple Causal Relationships in Gene Expression Discovered through Deep Learned Collective Variables

Developments in high-content phenotypic screening with single-cell read-out hold the promise of revealing interactions and functional relationships between genes at the genomic scale scale. However, the high-dimensionality and noisiness of gene expression makes this endeavor highly challenging when treated as a conventional problem in causal machine learning, both because of the statistical power required and because of the limits on computational tractability. Here we take different tack, and propose a deep-learning approach that finds low-dimensional representations of gene expression in which the response to genetic perturbation is highly predictable. We demonstrate that the interactions between genes that are cooperative in these representations are highly consistent with known ground-truth in terms of causal ordering, functional relatedness, and synergistic impact on cell growth and death. Our novel, statistical physics-inspired approach provides a tractable means through which to examine the response the living cell to perturbation, employing coarse graining that reduces data requirements and focuses on identifying simple relationships between groups of genes. Author summary Understanding the causal relationships between genes and the functions of a cell’s molecular components has long been a challenge in biology and biomedicine. With recent advancements in technologies that manipulate and measure the activity of thousands of genes at once at the single-cell level, scientists are now afforded with the opportunity to interrogate such relationships at scale. However, extracting useful information from the vast readouts of these technologies is non-trivial, in part due to their many-dimensional and noisy nature. Here we develop a machine learning model that allows for the interpretation of complex genetic perturbations in terms of a simple set of causal relations. By analyzing cooperative groups of genes identified by our model, we demonstrate the model can group genes accurately based on their biological function, their relative ordering up- or downstream in the flow of causation, and how their activities combine to affect cell growth and death. Our approach complements existing machine learning methods in providing a simple way to interpret causal mechanism governing genetic interactions and functional states of cells.
The phosphodiesterase 2A regulates lymphatic endothelial development via cGMP-mediated control of Notch signaling

During vascular development endothelial junctions mature and vessel integrity is established to form the endothelial barrier. The molecular mechanisms by which lymphatic vessels induce cell contact inhibition are not understood. Here, we uncover the cGMP-dependent phosphodiesterase 2A (PDE2A) as a selective regulator of lymphatic, but not blood endothelial contact inhibition. Conditional deletion of Pde2a in mouse embryos reveals severe lymphatic dysplasia, while large blood vessel architecture remains unaltered. In the absence of PDE2A, human lymphatic endothelial cells fail to induce mature junctions and cell cycle arrest, while cGMP levels, but not cAMP levels, are increased. Loss of PDE2A-mediated cGMP hydrolysis leads to downregulation of NOTCH signaling. Vice versa, DLL4-induced NOTCH activation restores junctional maturation in PDE2A-deficient lymphatic endothelial cells. Our data demonstrate that PDE2A selectively modulates a crosstalk between cGMP and NOTCH signaling to finetune lymphatic development and suggest that PDE2A may be a druggable target to control lymphatic leakage and regeneration.
SARS-CoV-2 spike HexaPro formulated in aluminium hydroxide and administered in an accelerated vaccination schedule partially protects Syrian Hamsters against viral challenge despite low neutralizing antibody responses

SARS-CoV-2 continues to pose a threat to human health as new variants emerge and thus a diverse vaccine pipeline is needed. We evaluated SARS-CoV-2 HexaPro spike protein formulated in Alhydrogel® (aluminium oxyhydroxide) in Syrian hamsters, using an accelerated two dose regimen (given 10 days apart) and a standard regimen (two doses given 21 days apart). Both regimens elicited spike- and RBD-specific IgG antibody responses of similar magnitude, but in vitro virus neutralization was low or undetectable. Despite this, the accelerated two dose regimen offered reduction in viral load and protected against lung pathology upon challenge with homologous SARS-CoV-2 virus (Wuhan-Hu-1). This highlights that vaccine-induced protection against SARS-CoV-2 disease can be obtained despite low neutralizing antibody levels and suggests that accelerated vaccine schedules may be used to confer rapid protection against SARS-CoV-2 disease.
A mixed integer linear programming model for minimum backbone grid

Developing a minimum backbone grid in the power system planning is beneficial to improve the power system’s resilience. To obtain a minimum backbone grid, a mixed integer linear programming (MILP) model with network connectivity constraints for a minimum backbone grid is proposed. In the model, some constraints are presented to consider the practical application requirements. Especially, to avoid islands in the minimum backbone grid, a set of linear constraints based on single-commodity flow formulations is proposed to ensure connectivity of the backbone grid. The simulations on the IEEE-39 bus system and the French 1888 bus system show that the proposed model can be solved with higher computational efficiency in only about 30 min for such a large system and the minimum backbone grid has a small scale only 52% of the original grid. Compared with the improved fireworks method, the minimum backbone grid from the proposed method has fewer lines and generators.
Editorial: Agile data-oriented research tools to support smallholder farm system transformation

COPYRIGHT © 2023 Hammond, Pagella, van Etten, Ghosh and van Wijk. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Agile data-oriented research tools to support smallholder farm system transformation
Shared sex hormone metabolism-related gene prognostic index between breast and endometrial cancers

Aims As sex hormone-dependent tumors, it remains to be clarified whether there is a common genetic signature and its value between breast and endometrial cancers. The aim of this study was to establish the shared sex hormone metabolism-related gene prognostic index (SHMRGPI) between breast and endometrial cancers and to analyze its potential role in the therapeutic and prognostic assessment of endometrial cancers. Methods Using transcriptome data from TCGA, tumor-associated gene modules were identified by weighted gene co-expression network analysis, and the intersection of module genes with female sex hormone synthesis and metabolism genes was defined as sex hormone metabolism-related gene. SHMRGPI was established by the least absolute shrinkage and selection operator and Cox regression. Its prognostic value of patients with endometrial cancer was validated, and a nomogram was constructed. We further investigated the relationship between SHMRGPI groups and clinicopathological features, immune infiltration, tumor mutation burden, and drug sensitivity. Results A total of 8 sex hormone metabolism-related gene were identified as key genes for the construction of prognostic models. Based on SHMRGPI, endometrial cancer patients were divided into high and low SHMRGPI groups. Patients in the low SHMRGPI group had longer overall survival (OS) compared with the high group (P< 0.05). Furthermore, we revealed significant differences between SHMRGPI groups as regards tumor immune cell infiltration, somatic mutation, microsatellite instability and drug sensitivity. Patients with low SHMRGPI may be the beneficiaries of immunotherapy and targeted therapy. Conclusions The SHMRGPI established in this study has prognostic power and may be used to screen patients with endometrial cancer who may benefit from immunotherapy or targeted therapy.
Genetic association of wool quality characteristics in United States Rambouillet sheep

Introduction: Fine wool production is an important source of revenue, accounting for up to 13% of total revenue in extensively managed wool sheep production systems of the United States. The Rambouillet are a predominant breed that excels in wool quality characteristics. Understanding the genetic basis of wool quality characteristics would aid in the development of genomic breeding strategies to facilitate genetic improvement. Methods: Wool characteristics and DNA were collected for rams enrolled in the North Dakota State University and University of Wyoming annual central performance ram tests over a three-year period (2019–2021, N = 313). The relationships of wool quality characteristics including grease fleece weight adjusted 365 days (wt. 365 adj.), clean fleece wt. 365 adj. , staple length 365 adj. , average fiber diameter, face wool cover, amount of skin wrinkles and belly wool were evaluated through genome-wide association studies (GWAS), Pearson correlation and ANOVA. Results: The GWAS identified four genome-wide significant genetic markers (p-value <1.19e-06) and five chromosome-wide significant markers (p-value <1.13e-05) on chromosomes 1, 2, 4, 15, and 19. Significant markers were associated with genes notable for relevant wool biological functions, including the gene ABCC8 which codes for SUR1, an ATP-sensitive potassium channel known to affect hair growth and 60S ribosomal protein L17-like, previously found to be expressed during follicle formation. The strongest Pearson correlation coefficients were identified between clean fleece wt. 365 adj. and grease fleece wt. 365 adj. (r = 0.83) and between clean fleece wt. 365 adj. and staple length 365 adj. (r = 0.53). Additionally, clean fleece wt. 365 adj. was correlated with final body weight (r = 0.35) and scrotal circumference (r = 0.16). Staple length 365 adj. (p-value = 5e-04), average fiber diameter (p-value = .0053) and clean fleece wt. 365 adj. (p-value = .014) were significantly associated with belly wool score. Discussion: The results of this study provide important insight into the relationships between wool quality characteristics and report specific markers that Rambouillet sheep producers may use to help inform selection and breeding decisions for improved wool quality.
Larval settlement and metamorphosis of Mytilus coruscus in response to varying bacterial capsular polysaccharide

Marine invertebrates are the backbone of marine biodiversity and play a pivotal role in the marine ecosystem. The life cycle of most marine invertebrates includes the settlement and metamorphosis stage, which is induced by marine biofilms, but the mechanism is still enigmatic. In the present study, we constructed the capsular polysaccharide (CPS) synthesis gene capC-deleted mutant of Pseudoalteromonas marina by gene knockout and then compared the phenotype, the biofilm-forming ability, the effect on settlement and metamorphosis of Mytilus coruscus, and the exopolysaccharide and CPS levels between the mutant and the wild-type strains to explicate the relationship between bacteria and mussels. The study presented that the phenotype and biofilm-forming ability between the wild-type and ΔcapC strains had no significant difference, but the inducing activity of ΔcapC biofilms on larval settlement and metamorphosis decreased significantly (p < 0.05). Compared with the wild-type, the CPS content of ΔcapC strain significantly decreased by 38.07%, accompanied by the increase of c-di-GMP. Meanwhile, the biomass of α-polysaccharides and β-polysaccharides on ΔcapC biofilms decreased significantly (p < 0.05). Thus, the CPS synthesis gene could modulate c-di-GMP, which regulates bacterial polysaccharide secretion, and then impact larval settlement and metamorphosis of mussels. This work brings an entry point to deeply understand the interaction between bacterial polysaccharide and larval recruitment.