The Impact of Starting Positions and Breathing Rhythms on Cardiopulmonary Stress and Post-Exercise Oxygen Consumption after High-Intensity Metabolic Training: A Randomized Crossover Prospective Study.

Background: The exploration of optimizing cardiopulmonary function and athletic performance through high-intensity metabolic exercises (HIMEs) is paramount in sports science. Despite the acknowledged efficacy of HIMEs in enhancing cardiopulmonary endurance, the high metabolic stress imposed on the cardiopulmonary system, especially for amateurs, necessitates a scaled approach to training. Objective: The aim of this study is to ascertain whether adjustments in the initiation posture and the adoption of an appropriate breathing strategy can effectively mitigate the cardiopulmonary stress induced by HIMEs without compromising training efficacy. Methods: Twenty-two subjects were recruited into this study. The post-exercise heart rate (PHR) and post-exercise oxygen consumption rate (POCR) were collected within 30 min after exercise. A two-way ANOVA, multi-variable Cox regression, and random survival forest machine learning algorithm were used to conduct the statistical analysis. Results: Under free breathing, only the maximum POCR differed significantly between standing and prone positions, with prone positions showing higher stress (mean difference = 3.15, p < 0.001). In contrast, the regulated breathing rhythm enhanced performance outcomes compared to free breathing regardless of the starting position. Specifically, exercises initiated from prone positions under regulated breathing recorded a significantly higher maximum and average PHR than those from standing positions (maximum PHR: mean difference = 13.40, p < 0.001; average PHR: mean difference = 6.45, p < 0.001). The multi-variable Cox regression highlighted the starting position as a critical factor influencing the PHR and breathing rhythm as a significant factor for the POCR, with respective variable importances confirmed by the random survival forest analysis. These results underscore the importance of controlled breathing and starting positions in optimizing HIME outcomes. Conclusions: Regulated breathing in high-intensity exercises enhances performance and physiological functions, emphasizing the importance of breathing rhythm over starting position. Effective training should balance exercise volume and technique to optimize performance and minimize stress, reducing overtraining and injury risks.

Evolutionary origin and population diversity of a cryptic hybrid pathogen.

Cryptic fungal pathogens pose disease management challenges due to their morphological resemblance to known pathogens. Here, we investigated the genomes and phenotypes of 53 globally distributed isolates of Aspergillus section Nidulantes fungi and found 30 clinical isolates-including four isolated from COVID-19 patients-were A. latus, a cryptic pathogen that originated via allodiploid hybridization. Notably, all A. latus isolates were misidentified. A. latus hybrids likely originated via a single hybridization event during the Miocene and harbor substantial genetic diversity. Transcriptome profiling of a clinical isolate revealed that both parental subgenomes are actively expressed and respond to environmental stimuli. Characterizing infection-relevant traits-such as drug resistance and growth under oxidative stress-revealed distinct phenotypic profiles among A. latus hybrids compared to parental and closely related species. Moreover, we identified four features that could aid A. latus taxonomic identification. Together, these findings deepen our understanding of the origin of cryptic pathogens.

A taxon-rich and genome-scale phylogeny of Opisthokonta.

Ancient divergences within Opisthokonta-a major lineage that includes organisms in the kingdoms Animalia, Fungi, and their unicellular relatives-remain contentious. To assess progress toward a genome-scale Opisthokonta phylogeny, we conducted the most taxon rich phylogenomic analysis using sets of genes inferred with different orthology inference methods and established the geological timeline of Opisthokonta diversification. We also conducted sensitivity analysis by subsampling genes or taxa from the full data matrix based on filtering criteria previously shown to improve phylogenomic inference. We found that approximately 85% of internal branches were congruent across data matrices and the approaches used. Notably, the use of different orthology inference methods was a substantial contributor to the observed incongruence: analyses using the same set of orthologs showed high congruence of 97% to 98%, whereas different sets of orthologs resulted in somewhat lower congruence (87% to 91%). Examination of unicellular Holozoa relationships suggests that the instability observed across varying gene sets may stem from weak phylogenetic signals. Our results provide a comprehensive Opisthokonta phylogenomic framework that will be useful for illuminating ancient evolutionary episodes concerning the origin and diversification of the 2 major eukaryotic kingdoms and emphasize the importance of investigating effects of orthology inference on phylogenetic analyses to resolve ancient divergences.

Unique trajectory of gene family evolution from genomic analysis of nearly all known species in an ancient yeast lineage.

Gene gains and losses are a major driver of genome evolution; their precise characterization can provide insights into the origin and diversification of major lineages. Here, we examined gene family evolution of 1,154 genomes from nearly all known species in the medically and technologically important yeast subphylum Saccharomycotina. We found that yeast gene family and genome evolution are distinct from plants, animals, and filamentous ascomycetes and are characterized by small genome sizes and smaller gene numbers but larger gene family sizes. Faster-evolving lineages (FELs) in yeasts experienced significantly higher rates of gene losses-commensurate with a narrowing of metabolic niche breadth-but higher speciation rates than their slower-evolving sister lineages (SELs). Gene families most often lost are those involved in mRNA splicing, carbohydrate metabolism, and cell division and are likely associated with intron loss, metabolic breadth, and non-canonical cell cycle processes. Our results highlight the significant role of gene family contractions in the evolution of yeast metabolism, genome function, and speciation, and suggest that gene family evolutionary trajectories have differed markedly across major eukaryotic lineages.

The Influence of the Number of Tree Searches on Maximum Likelihood Inference in Phylogenomics.

Maximum likelihood (ML) phylogenetic inference is widely used in phylogenomics. As heuristic searches most likely find suboptimal trees, it is recommended to conduct multiple (e.g., 10) tree searches in phylogenetic analyses. However, beyond its positive role, how and to what extent multiple tree searches aid ML phylogenetic inference remains poorly explored. Here, we found that a random starting tree was not as effective as the BioNJ and parsimony starting trees in inferring the ML gene tree and that RAxML-NG and PhyML were less sensitive to different starting trees than IQ-TREE. We then examined the effect of the number of tree searches on ML tree inference with IQ-TREE and RAxML-NG, by running 100 tree searches on 19,414 gene alignments from 15 animal, plant, and fungal phylogenomic datasets. We found that the number of tree searches substantially impacted the recovery of the best-of-100 ML gene tree topology among 100 searches for a given ML program. In addition, all of the concatenation-based trees were topologically identical if the number of tree searches was ≥10. Quartet-based ASTRAL trees inferred from 1 to 80 tree searches differed topologically from those inferred from 100 tree searches for 6/15 phylogenomic datasets. Finally, our simulations showed that gene alignments with lower difficulty scores had a higher chance of finding the best-of-100 gene tree topology and were more likely to yield the correct trees.

Genomic factors shape carbon and nitrogen metabolic niche breadth across Saccharomycotina yeasts.

Organisms exhibit extensive variation in ecological niche breadth, from very narrow (specialists) to very broad (generalists). Two general paradigms have been proposed to explain this variation: (i) trade-offs between performance efficiency and breadth and (ii) the joint influence of extrinsic (environmental) and intrinsic (genomic) factors. We assembled genomic, metabolic, and ecological data from nearly all known species of the ancient fungal subphylum Saccharomycotina (1154 yeast strains from 1051 species), grown in 24 different environmental conditions, to examine niche breadth evolution. We found that large differences in the breadth of carbon utilization traits between yeasts stem from intrinsic differences in genes encoding specific metabolic pathways, but we found limited evidence for trade-offs. These comprehensive data argue that intrinsic factors shape niche breadth variation in microbes.

Giants among Cnidaria: Large Nuclear Genomes and Rearranged Mitochondrial Genomes in Siphonophores.

Siphonophores (Cnidaria: Hydrozoa) are abundant predators found throughout the ocean and are important constituents of the global zooplankton community. They range in length from a few centimeters to tens of meters. They are gelatinous, fragile, and difficult to collect, so many aspects of the biology of these roughly 200 species remain poorly understood. To survey siphonophore genome diversity, we performed Illumina sequencing of 32 species sampled broadly across the phylogeny. Sequencing depth was sufficient to estimate nuclear genome size from k-mer spectra in six specimens, ranging from 0.7 to 2.3 Gb, with heterozygosity estimates between 0.69% and 2.32%. Incremental k-mer counting indicates k-mer peaks can be absent with nearly 20× read coverage, suggesting minimum genome sizes range from 1.4 to 5.6 Gb in the 25 samples without peaks in the k-mer spectra. This work confirms most siphonophore nuclear genomes are large relative to the genomes of other cnidarians, but also identifies several with reduced size that are tractable targets for future siphonophore nuclear genome assembly projects. We also assembled complete mitochondrial genomes for 33 specimens from these new data, indicating a conserved gene order shared among nonsiphonophore hydrozoans, Cystonectae, and some Physonectae, revealing the ancestral mitochondrial gene order of siphonophores. Our results also suggest extensive rearrangement of mitochondrial genomes within other Physonectae and in Calycophorae. Though siphonophores comprise a small fraction of cnidarian species, this survey greatly expands our understanding of cnidarian genome diversity. This study further illustrates both the importance of deep phylogenetic sampling and the utility of k-mer-based genome skimming in understanding the genomic diversity of a clade.

Enhancing neural encoding models for naturalistic perception with a multi-level integration of deep neural networks and cortical networks.

Cognitive neuroscience aims to develop computational models that can accurately predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to leverage the representation power of deep neural networks (DNNs) to predict the brain response and suggest a correspondence between artificial and biological neural networks in their feature representations. However, typical voxel-wise encoding models tend to rely on specific networks designed for computer vision tasks, leading to suboptimal brain-wide correspondence during cognitive tasks. To address this challenge, this work proposes a novel approach that upgrades voxel-wise encoding models through multi-level integration of features from DNNs and information from brain networks. Our approach combines DNN feature-level ensemble learning and brain atlas-level model integration, resulting in significant improvements in predicting whole-brain neural activity during naturalistic video perception. Furthermore, this multi-level integration framework enables a deeper understanding of the brain's neural representation mechanism, accurately predicting the neural response to complex visual concepts. We demonstrate that neural encoding models can be optimized by leveraging a framework that integrates both data-driven approaches and theoretical insights into the functional structure of the cortical networks.

Cryptic diversity begets challenges and opportunities in biodiversity research.

How many species of life are there on Earth? This is a question that we want to know but cannot yet answer. Some scholars speculate that the number of species may reach 2.2 billion when considering cryptic diversity and that each morphology-based insect species may contain an average of 3.1 cryptic species. With nearly two million described species, such high estimates of cryptic diversity would suggest that cryptic species are widespread. The development of molecular species delimitation has led to the discovery of a large number of cryptic species, and cryptic biodiversity has gradually entered our field of vision and attracted more attention. This paper introduces the concept of cryptic species, how they evolve, and methods by which they may be discovered and confirmed, and provides theoretical and methodological guidance for the study of hidden species. A workflow of how to confirm cryptic species is provided. In addition, the importance and reliability of multi-evidence-based integrated taxonomy are reaffirmed as a way to better standardize decision-making processes. Special focus on cryptic diversity and increased funding for taxonomy is needed to ensure that cryptic species in hyperdiverse groups are discoverable and described. An increased focus on cryptic species in the future will naturally arise as more difficult groups are studied, and thereby, we may finally better understand the rules governing the evolution and maintenance of cryptic biodiversity.

Multiple Origins of Bioluminescence in Beetles and Evolution of Luciferase Function.

Bioluminescence in beetles has long fascinated biologists, with diverse applications in biotechnology. To date, however, our understanding of its evolutionary origin and functional variation mechanisms remains poor. To address these questions, we obtained high-quality reference genomes of luminous and nonluminous beetles in 6 Elateroidea families. We then reconstructed a robust phylogenetic relationship for all luminous families and related nonluminous families. Comparative genomic analyses and biochemical functional experiments suggested that gene evolution within Elateroidea played a crucial role in the origin of bioluminescence, with multiple parallel origins observed in the luminous beetle families. While most luciferase-like proteins exhibited a conserved nonluminous amino acid pattern (TLA346 to 348) in the luciferin-binding sites, luciferases in the different luminous beetle families showed divergent luminous patterns at these sites (TSA/CCA/CSA/LVA). Comparisons of the structural and enzymatic properties of ancestral, extant, and site-directed mutant luciferases further reinforced the important role of these sites in the trade-off between acyl-CoA synthetase and luciferase activities. Furthermore, the evolution of bioluminescent color demonstrated a tendency toward hypsochromic shifts and variations among the luminous families. Taken together, our results revealed multiple parallel origins of bioluminescence and functional divergence within the beetle bioluminescent system.

Quantification of Hypsarrhythmia in Infantile Spasmatic EEG: A Large Cohort Study.

Infantile spasms (IS) is a neurological disorder causing mental and/or developmental retardation in many infants. Hypsarrhythmia is a typical symptom in the electroencephalography (EEG) signals with IS. Long-term EEG/video monitoring is most frequently employed in clinical practice for IS diagnosis, from which manual screening of hypsarrhythmia is time consuming and lack of sufficient reliability. This study aims to identify potential biomarkers for automatic IS diagnosis by quantitative analysis of the EEG signals. A large cohort of 101 IS patients and 155 healthy controls (HC) were involved. Typical hypsarrhythmia and non-hypsarrhythmia EEG signals were annotated, and normal EEG were randomly picked from the HC. Root mean square (RMS), teager energy (TE), mean frequency, sample entropy (SamEn), multi-channel SamEn, multi-scale SamEn, and nonlinear correlation coefficient were computed in each sub-band of the three EEG signals, and then compared using either a one-way ANOVA or a Kruskal-Wallis test (based on their distribution) and the receiver operating characteristic (ROC) curves. The effects of infant age on these features were also investigated. For most of the employed features, significant ( ) differences were observed between hypsarrhythmia EEG and non-hypsarrhythmia EEG or HC, which seem to increase with increased infant age. RMS and TE produce the best classification in the delta and theta bands, while entropy features yields the best performance in the gamma band. Our study suggests RMS and TE (delta and theta bands) and entropy features (gamma band) to be promising biomarkers for automatic detection of hypsarrhythmia in long-term EEG monitoring. The findings of our study indicate the feasibility of automated IS diagnosis using artificial intelligence.

Neural control of lexical tone production in human laryngeal motor cortex.

In tonal languages, which are spoken by nearly one-third of the world's population, speakers precisely control the tension of vocal folds in the larynx to modulate pitch in order to distinguish words with completely different meanings. The specific pitch trajectories for a given tonal language are called lexical tones. Here, we used high-density direct cortical recordings to determine the neural basis of lexical tone production in native Mandarin-speaking participants. We found that instead of a tone category-selective coding, local populations in the bilateral laryngeal motor cortex (LMC) encode articulatory kinematic information to generate the pitch dynamics of lexical tones. Using a computational model of tone production, we discovered two distinct patterns of population activity in LMC commanding pitch rising and lowering. Finally, we showed that direct electrocortical stimulation of different local populations in LMC evoked pitch rising and lowering during tone production, respectively. Together, these results reveal the neural basis of vocal pitch control of lexical tones in tonal languages.

Dissecting neural computations in the human auditory pathway using deep neural networks for speech.

The human auditory system extracts rich linguistic abstractions from speech signals. Traditional approaches to understanding this complex process have used linear feature-encoding models, with limited success. Artificial neural networks excel in speech recognition tasks and offer promising computational models of speech processing. We used speech representations in state-of-the-art deep neural network (DNN) models to investigate neural coding from the auditory nerve to the speech cortex. Representations in hierarchical layers of the DNN correlated well with the neural activity throughout the ascending auditory system. Unsupervised speech models performed at least as well as other purely supervised or fine-tuned models. Deeper DNN layers were better correlated with the neural activity in the higher-order auditory cortex, with computations aligned with phonemic and syllabic structures in speech. Accordingly, DNN models trained on either English or Mandarin predicted cortical responses in native speakers of each language. These results reveal convergence between DNN model representations and the biological auditory pathway, offering new approaches for modeling neural coding in the auditory cortex.

Extensive intragenomic variation in the internal transcribed spacer region of fungi.

Fungi are among the most biodiverse organisms in the world. Accurate species identification is imperative for studies on fungal ecology and evolution. The internal transcribed spacer (ITS) rDNA region has been widely accepted as the universal barcode for fungi. However, several recent studies have uncovered intragenomic sequence variation within the ITS in multiple fungal species. Here, we mined the genome of 2414 fungal species to determine the prevalence of intragenomic variation and found that the genomes of 641 species, about one-quarter of the 2414 species examined, contained multiple ITS copies. Of those 641 species, 419 (∼65%) contained variation among copies revealing that intragenomic variation is common in fungi. We proceeded to show how these copies could result in the erroneous description of hundreds of fungal species and skew studies evaluating environmental DNA (eDNA) especially when making diversity estimates. Additionally, many genomes were found to be contaminated, especially those of unculturable fungi.

Evolutionary origin, population diversity, and diagnostics for a cryptic hybrid pathogen.

Cryptic fungal pathogens pose significant identification and disease management challenges due to their morphological resemblance to known pathogenic species while harboring genetic and (often) infectionrelevant trait differences. The cryptic fungal pathogen Aspergillus latus, an allodiploid hybrid originating from Aspergillus spinulosporus and an unknown close relative of Aspergillus quadrilineatus within section Nidulantes, remains poorly understood. The absence of accurate diagnostics for A. latus has led to misidentifications, hindering epidemiological studies and the design of effective treatment plans. We conducted an in-depth investigation of the genomes and phenotypes of 44 globally distributed isolates (41 clinical isolates and three type strains) from Aspergillus section Nidulantes. We found that 21 clinical isolates were A. latus; notably, standard methods of pathogen identification misidentified all A. latus isolates. The remaining isolates were identified as A. spinulosporus (8), A. quadrilineatus (1), or A. nidulans (11). Phylogenomic analyses shed light on the origin of A. latus, indicating one or two hybridization events gave rise to the species during the Miocene, approximately 15.4 to 8.8 million years ago. Characterizing the A. latus pangenome uncovered substantial genetic diversity within gene families and biosynthetic gene clusters. Transcriptomic analysis revealed that both parental genomes are actively expressed in nearly equal proportions and respond to environmental stimuli. Further investigation into infection-relevant chemical and physiological traits, including drug resistance profiles, growth under oxidative stress conditions, and secondary metabolite biosynthesis, highlight distinct phenotypic profiles of the hybrid A. latus compared to its parental and closely related species. Leveraging our comprehensive genomic and phenotypic analyses, we propose five genomic and phenotypic markers as diagnostics for A. latus species identification. These findings provide valuable insights into the evolutionary origin, genomic outcome, and phenotypic implications of hybridization in a cryptic fungal pathogen, thus enhancing our understanding of the underlying processes contributing to fungal pathogenesis. Furthermore, our study underscores the effectiveness of extensive genomic and phenotypic analyses as a promising approach for developing diagnostics applicable to future investigations of cryptic and emerging pathogens.

Latent neural dynamics encode temporal context in speech.

Direct neural recordings from human auditory cortex have demonstrated encoding for acoustic-phonetic features of consonants and vowels. Neural responses also encode distinct acoustic amplitude cues related to timing, such as those that occur at the onset of a sentence after a silent period or the onset of the vowel in each syllable. Here, we used a group reduced rank regression model to show that distributed cortical responses support a low-dimensional latent state representation of temporal context in speech. The timing cues each capture more unique variance than all other phonetic features and exhibit rotational or cyclical dynamics in latent space from activity that is widespread over the superior temporal gyrus. We propose that these spatially distributed timing signals could serve to provide temporal context for, and possibly bind across time, the concurrent processing of individual phonetic features, to compose higher-order phonological (e.g. word-level) representations.

Genomic and ecological factors shaping specialism and generalism across an entire subphylum.

Organisms exhibit extensive variation in ecological niche breadth, from very narrow (specialists) to very broad (generalists). Paradigms proposed to explain this variation either invoke trade-offs between performance efficiency and breadth or underlying intrinsic or extrinsic factors. We assembled genomic (1,154 yeast strains from 1,049 species), metabolic (quantitative measures of growth of 843 species in 24 conditions), and ecological (environmental ontology of 1,088 species) data from nearly all known species of the ancient fungal subphylum Saccharomycotina to examine niche breadth evolution. We found large interspecific differences in carbon breadth stem from intrinsic differences in genes encoding specific metabolic pathways but no evidence of trade-offs and a limited role of extrinsic ecological factors. These comprehensive data argue that intrinsic factors driving microbial niche breadth variation.

Incongruence in the phylogenomics era.

Genome-scale data and the development of novel statistical phylogenetic approaches have greatly aided the reconstruction of a broad sketch of the tree of life and resolved many of its branches. However, incongruence - the inference of conflicting evolutionary histories - remains pervasive in phylogenomic data, hampering our ability to reconstruct and interpret the tree of life. Biological factors, such as incomplete lineage sorting, horizontal gene transfer, hybridization, introgression, recombination and convergent molecular evolution, can lead to gene phylogenies that differ from the species tree. In addition, analytical factors, including stochastic, systematic and treatment errors, can drive incongruence. Here, we review these factors, discuss methodological advances to identify and handle incongruence, and highlight avenues for future research.

Decoding and synthesizing tonal language speech from brain activity.

Recent studies have shown that the feasibility of speech brain-computer interfaces (BCIs) as a clinically valid treatment in helping nontonal language patients with communication disorders restore their speech ability. However, tonal language speech BCI is challenging because additional precise control of laryngeal movements to produce lexical tones is required. Thus, the model should emphasize the features from the tonal-related cortex. Here, we designed a modularized multistream neural network that directly synthesizes tonal language speech from intracranial recordings. The network decoded lexical tones and base syllables independently via parallel streams of neural network modules inspired by neuroscience findings. The speech was synthesized by combining tonal syllable labels with nondiscriminant speech neural activity. Compared to commonly used baseline models, our proposed models achieved higher performance with modest training data and computational costs. These findings raise a potential strategy for approaching tonal language speech restoration.

The Transcriptional Repressor PerR Senses Sulfane Sulfur by Cysteine Persulfidation at the Structural Zn2+ Site in Synechococcus sp. PCC7002.

Cyanobacteria can perform both anoxygenic and oxygenic photosynthesis, a characteristic which ensured that these organisms were crucial in the evolution of the early Earth and the biosphere. Reactive oxygen species (ROS) produced in oxygenic photosynthesis and reactive sulfur species (RSS) produced in anoxygenic photosynthesis are closely related to intracellular redox equilibrium. ROS comprise superoxide anion (O2●-), hydrogen peroxide (H2O2), and hydroxyl radicals (●OH). RSS comprise H2S and sulfane sulfur (persulfide, polysulfide, and S8). Although the sensing mechanism for ROS in cyanobacteria has been explored, that of RSS has not been elucidated. Here, we studied the function of the transcriptional repressor PerR in RSS sensing in Synechococcus sp. PCC7002 (PCC7002). PerR was previously reported to sense ROS; however, our results revealed that it also participated in RSS sensing. PerR repressed the expression of prxI and downregulated the tolerance of PCC7002 to polysulfide (H2Sn). The reporter system indicated that PerR sensed H2Sn. Cys121 of the Cys4:Zn2+ site, which contains four cysteines (Cys121, Cys124, Cys160, and Cys163) bound to one zinc atom, could be modified by H2Sn to Cys121-SSH, as a result of which the zinc atom was released from the site. Moreover, Cys19 could also be modified by polysulfide to Cys19-SSH. Thus, our results reveal that PerR, a representative of the Cys4 zinc finger proteins, senses H2Sn. Our findings provide a new perspective to explore the adaptation strategy of cyanobacteria in Proterozoic and contemporary sulfurization oceans.