Miracle-dz/UniFAR-FTUs huggingface dataset:
Phylogenomic subsampling and the search for phylogenetically reliable loci

Phylogenomic subsampling is a procedure by which small sets of loci are selected from large genome-scale datasets and used for phylogenetic inference. This step is often motivated by either computational limitations associated with the use of complex inference methods, or as a means of testing the robustness of phylogenetic results by discarding loci that are deemed potentially misleading. Although many alternative methods of phylogenomic subsampling have been proposed, little effort has gone into comparing their behavior across different datasets. Here, I calculate multiple gene properties for a range of phylogenomic datasets spanning animal, fungal and plant clades, uncovering a remarkable predictability in their patterns of covariance. I also show how these patterns provide a means for ordering loci by both their rate of evolution and their relative phylogenetic usefulness. This method of retrieving phylogenetically useful loci is found to be among the top performing when compared to alternative subsampling protocols. Relatively common approaches such as minimizing potential sources of systematic bias or increasing the clock-likeness of the data are found to fare worse than selecting loci at random. Likewise, the general utility of rate-based subsampling is found to be limited: loci evolving at both low and high rates are among the least effective, and even those evolving at optimal rates can still widely differ in usefulness. This study shows that many common subsampling approaches introduce unintended effects in off-target gene properties, and proposes an alternative multivariate method that simultaneously optimizes phylogenetic signal while controlling for known sources of bias.
Response of net carbon exchange rate of soybean to ozone at different stages of growth and its relation to yield

High concentrations of ozone (O₃) occur episodically during the growing season, but the impacts of O₃ stress on photosynthesis and crop yield when O₃ episodes occur at various stages of growth are not known. Also, quantitative relationships between reduction of photosynthesis and yield suppresion by O₃ have not been established for most species. Field experiments with soybean [Glycine max (Merr.) cv. Davis] were performed to determine the impact of O₃ exposure at different stages of plant development on net carbon exchange rate (NCER) and to establish the relationship between NCER and yield for different O₃ exposure periods. The growing season was divided into four quarters (Q1–Q4) of 31 d each for O₃ treatment. In each quarter, O₃ treatments in open-top chambers were either charcoal-filtered air (C) or nonfiltered air (N) to which O₃ was added to achieve approximately 100 nL L⁻¹ O₃ (mean 7 h d⁻¹ average for the quarter). The NCER was measured in eight O₃ treatments (CCCC, NNCC, CNCN, NCCN, CNNC, NCNC, CCNN, and NNNN) where each letter indicates the O₃ treatment for successive quarters. The NCER measurements during Q4 (R6–R7) provided the best relationship to yield in a linear regression model (R² = 0.919). A significant relationship with yield was obtained also with NCER measurements from Q3 (R2–R5) (R² = 0.626), whereas measurments during Q2 (V10-R1) were poorly related to yield (R₂ = 0.072). Good linear relationships of yield and NCER were found also when Q3 and Q4 or Q2, Q3, and Q4 data were combined. In all cases, however, Q4 data were essential to a good correlation between NCER and yield. It is likely that NCER measurements taken during Q4 correlated well with yield, because that was the period when the requirement for energy from photosynthesis is most critical for yield. Cooperative investigations of the USDA-ARS and the North Carolina State Univ. The research reported in this publication was funded in part by the North Carolina Agric. Res. Service. The use of trade names in this publication does not imply endorsement by the North Carolina Agric. Res. Service or the USDA of the products named, nor criticism of similar ones not mentioned.
Improving range shift predictions: enhancing the power of traits

Accurately predicting species’ range shifts in response to environmental change is a central ecological objective and applied imperative. In synthetic analyses, traits emerge as significant but weak predictors of species’ range shifts across recent climate change. These studies assume linearity in the relationship between a trait and its function, while detailed empirical work often reveals unimodal relationships, thresholds, and other nonlinearities in many trait-function relationships. We hypothesize that the use of linear modeling approaches fails to capture these nonlinearities and therefore may be under-powering traits to predict range shifts. We evaluate the predictive performance of four different machine learning approaches that can capture nonlinear relationships (ridge-regularized linear regression, ridge-regularized kernel regression, support vector regression, and random forests). We validate our models using four multi-decadal range shift datasets in montane plants, montane small mammals, and marine fish. We show that nonlinear approaches perform substantially better than least-squares linear modeling in reproducing historical range shifts. In addition, using novel model observation and interrogation techniques, the trait classes (e.g. dispersal-or diet-related traits) that we identify as primary drivers of model predictions are consistent with expectations. However, disagreements among models in the directionality of trait predictors suggests limits to trait-based statistical predictive frameworks.
Domain-adaptive neural networks improve cross-species prediction of transcription factor binding

The intrinsic DNA sequence preferences and cell-type specific cooperative partners of transcription factors (TFs) are typically highly conserved. Hence, despite the rapid evolutionary turnover of individual TF binding sites, predictive sequence models of cell-type specific genomic occupancy of a TF in one species should generalize to closely matched cell types in a related species. To assess the viability of cross-species TF binding prediction, we train neural networks to discriminate ChIP-seq peak locations from genomic background and evaluate their performance within and across species. Cross-species predictive performance is consistently worse than within-species performance, which we show is caused in part by species-specific repeats. To account for this domain shift, we use an augmented network architecture to automatically discourage learning of training species-specific sequence features. This domain adaptation approach corrects for prediction errors on species-specific repeats and improves overall cross-species model performance. Our results demonstrate that cross-species TF binding prediction is feasible when models account for domain shifts driven by species-specific repeats.
Enhanced Angiogenesis in Salivary Duct Carcinoma Ex-Pleomorphic Adenoma

Salivary duct carcinoma (SDC) is morphologically similar to breast cancer, with HER2-overexpression reported. With regard to the pattern of disease onset, SDC can arise from de novo or carcinoma ex-pleomorphic adenoma (Ca-ex-PA). Recently, multiple molecular profiles of SDC as well as breast cancer have been reported, with significant differences in HER2 expression between Ca-ex-PA and de novo. We assessed the differences in gene expression between onset classifications. We conducted immunohistochemical analysis and HER2-DISH for 23 patients and classified SDCs into three subtypes as follows: “HER2-positive” (HER2+/any AR), “Luminal-AR” (HER2-/AR+), and “Basal-like” (HER2-/AR-). We assessed the expression levels of 84 functional genes for 19 patients by using a qRT-PCR array. Ten cases were classified as HER2-positive, seven cases as Luminal-AR, and six cases as Basal-like. The gene expression pattern was generally consistent with the corresponding immunostaining classification. The expression levels of VEGFA, ERBB2(HER2), IGF1R, RB1, and XBP1 were higher, while those of SLIT2 and PTEN were lower in Ca-ex-PA than in de novo. The functions of those genes were concentrated in angiogenesis and AKT/PI3K signaling pathway (Fisher’s test: p-value = 0.025 and 0.004, respectively). Multiple machine learning methods, OPLS-DA, LASSO, and RandomForest, also show that VEGFA can be a candidate for the characteristic differences between Ca-ex-PA and de novo. In conclusion, the AKT/PI3K signaling pathway leading to angiogenesis was hyper-activated in all SDCs, particularly in those classified into the Ca-ex-PAs. VEGFA was over-expressed significantly in the Ca-ex-PA, which can be a crucial factor in the malignant conversion to SDC.
Case Report: A Case of Acute Cellular Rejection Due to Atopic Dermatitis Exacerbation 3 Years After Heart Transplantation

Background Little evidence has been presented about the association between previous atopic/allergic disease and graft rejection after solid organ transplantation. Thus, we present a case wherein acute cellular rejection (ACR) after heart transplantation (HTx) was noted along with exacerbation of atopic disease. Case Summary A 32-year-old man was admitted at our hospital for regular monitoring of graft rejection. He had undergone heart transplant 3 years prior due to dilated cardiomyopathy. Echocardiogram revealed good biventricular function, and no abnormal findings were found in blood sampling tests. However, biopsy showed moderate ACR [Grade 2R(ISHLT 2004)/3A(ISHLT 1990)], which required twice-repeated steroid pulses with intensified immunosuppression. Meanwhile, his atopic dermatitis, which was diagnosed before having heart failure, was getting worse for the past 6 months. The exacerbation of atopic dermatitis was presumed to be related to the development of the intractable cellular rejection. Discussion This case suggested the association of atopic disease and graft rejection after HTx. We examined 76 patients from a cohort of previous studies who underwent HTx at our hospital, which suggested that patients with atopic/allergic disorders such as atopic dermatitis and asthma tended to have a significantly higher frequency of moderate rejection than non-allergic patients. (p = 0.012; Fisher’s exact test). Our case also suggests that exacerbation of atopic dermatitis might cause graft rejection of the transplanted organ, so that it is important to carefully evaluate the risk of graft rejection if there is a previous history of atopic/allergic disease.
Akkermansia muciniphila secretome promotes α-synuclein aggregation in enteroendocrine cells

The notion that the gut microbiota play a role in neurodevelopment, behavior and outcome of neurodegenerative disorders is recently taking place. A number of studies have consistently reported a greater abundance of Akkermansia muciniphila in Parkinson’s disease (PD) fecal samples. Nevertheless, a functional link between A.muciniphila and sporadic PD remained unexplored. Here, we investigated whether A.muciniphila secretome could initiate the misfolding process of α-synuclein (αSyn) in enteroendocrine cells (EECs), which are part of the gut epithelium and possess many neuron-like properties. We found that A.muciniphila secretome is directly modulated by mucin, induces intracellular calcium (Ca2+) release, and causes increased mitochondrial Ca2+ uptake in EECs, which in turn leads to production of reactive oxygen species (ROS) and αSyn aggregation. However, these events were efficiently inhibited once we buffered mitochondrial Ca2+. Thereby, these molecular insights provided here offer evidence that bacterial secretome is capable of inducing αSyn aggregation in enteroendocrine cells. SYNOPSIS FIGURE DESCRIPTION The secretome isolated from the commensal gut bacterium Akkermansia muciniphila triggers intracellular Ca2+ signaling in enteroendocrine cells, leading to increased mitochondrial Ca2+ uptake. Mitochondrial Ca2+ overload leads to ROS generation culminating with αSyn phosphorylation and aggregation (left panel). All these events were inhibited once mitochondrial Ca2+ is buffered (right panel). Gram-negative gut bacterium Akkermansia muciniphila is consistently found more abundant in Parkinson’s disease patients. Akkermansia muciniphila protein secretome composition is directly modulated by mucin and induces an IP3-independent endoplasmic reticulum (ER)-calcium release in enteroendocrine cells. This Ca2+ release is triggered by direct activation of Ryanodine Receptors leading to increased mitochondrial Ca2+ uptake. Mitochondrial Ca2+ overload leads to ROS generation culminating with αSyn aggregation. Buffering mitochondrial Ca2+ efficiently inhibits A.muciniphila-induced αSyn aggregation in enteroendocrine cells.
Astragali Radix Isoflavones Synergistically Alleviate Cerebral Ischemia and Reperfusion Injury Via Activating Estrogen Receptor-PI3K-Akt Signaling Pathway

Isoflavones are major neuroprotective components of a medicinal herb Astragali Radix, against cerebral ischemia-reperfusion injury but the mechanisms of neuroprotection remain unclear. Calycosin and formononetin are two major AR isoflavones while daidzein is the metabolite of formononetin after absorption. Herein, we aim to investigate the synergistic neuroprotective effects of those isoflavones of Astragali Radix against cerebral ischemia-reperfusion injury. Calycosin, formononetin and daidzein were organized with different combinations whose effects observed in both in vitro and in vivo experimental models. In the in vitro study, primary cultured neurons were subjected to oxygen-glucose deprivation plus reoxygenation (OGD/RO) or l-glutamate treatment. In the in vivo study, rats were subjected to middle cerebral artery occlusion to induce cerebral ischemia and reperfusion. All three isoflavones pre-treatment alone decreased brain infarct volume and improved neurological deficits in rats, and dose-dependently attenuated neural death induced by l-glutamate treatment and OGD/RO in cultured neurons. Interestingly, the combined formulas of those isoflavones revealed synergistically activated estrogen receptor (estrogen receptors)-PI3K-Akt signaling pathway. Using ER antagonist and phosphatidylinositol 3-kinase (PI3K) inhibitor blocked the neuroprotective effects of those isoflavones. In conclusion, isoflavones could synergistically alleviate cerebral ischemia-reperfusion injury via activating ER-PI3K-Akt pathway.
A draft genome of Alliaria petiolata (garlic mustard) as a model system for invasion genetics

The emerging field of invasion genetics examines the genetic causes and consequences of biological invasions, but few study systems are available that integrate deep ecological knowledge with genomic tools. Here we report on the de novo assembly and annotation of a genome for the biennial herb Alliaria petiolata (M. Bieb.) Cavara & Grande (Brassicaceae), which is widespread in Eurasia and invasive across much of temperate North America. Our goal was to sequence and annotate a genome to complement resources available from hundreds of published ecological studies, a global field survey, and hundreds of genetic lines maintained in Germany and Canada. We sequenced a genotype (EFCC-3-20) collected from the native range near Venice, Italy and sequenced paired-end and mate pair libraries at ~70 × coverage. A de novo assembly resulted in a highly continuous draft genome (N50 = 121 Mb; L50 = 2) with 99.7 % of the 1.1 Gb genome mapping to scaffolds of at least 50 Kb in length. A total of 64,770 predicted genes in the annotated genome include 99 % of plant BUSCO genes and 98 % of transcriptome reads. Consistent with previous reports of (auto)hexaploidy in western Europe, we found that almost one third of BUSCO genes (390/1440) mapped to two or more scaffolds despite < 2 % genome-wide average heterozygosity. The continuity and gene space quality of our draft assembly will enable molecular and functional genomic studies of A. petiolata to address questions relevant to invasion genetics and conservation strategies.
Evolutionary jumps in bacterial GC content

Genomic GC content is a fundamental molecular trait linked with many key genomic features such as codon and amino acid use. Across bacteria, GC content is surprisingly diverse and has been studied for many decades; yet its evolution remains incompletely understood. Since it is difficult to observe GC content evolve on laboratory time-scales, phylogenetic comparative approaches are instrumental; but this dimension is rarely studied systematically in the case of bacterial GC content. We applied phylogenetic comparative models to analyse GC content evolution in multiple bacterial groups across two major bacterial phyla. We find that GC content diversifies via a combination of gradual evolution and evolutionary “jumps”. Surprisingly, unlike prior reports that solely focused on reductions in GC, we found a comparable number of jumps with both increased and decreased GC content. Overall, many of the identified jumps occur in lineages beyond the well-studied peculiar examples of endosymbiotic and AT-rich marine bacteria, and do not support the predicted role of oxygen dependence. Our analysis of rapid and large shifts in GC content thus identifies new clades and novel contexts to further understand the ecological and evolutionary drivers of this important genomic trait.
Phyletic Distribution and Diversification of the Phage Shock Protein Stress Response System in Bacteria and Archaea

The PspA protein domain is found in all domains of life, highlighting its central role in Psp networks. To date, all insights into the core functions of Psp responses derive mainly from protein network blueprints representing only three bacterial phyla. ABSTRACT Maintaining cell envelope integrity is of vital importance for all microorganisms. Not surprisingly, evolution has shaped conserved protein protection networks that connect stress perception, transmembrane signal transduction, and mediation of cellular responses upon cell envelope stress. The phage shock protein (Psp) stress response is one such conserved protection network. Most knowledge about the Psp response derives from studies in the Gram-negative model bacterium Escherichia coli, where the Psp system consists of several well-defined protein components. Homologous systems were identified in representatives of the Proteobacteria, Actinobacteria, and Firmicutes. However, the Psp system distribution in the microbial world remains largely unknown. By carrying out a large-scale, unbiased comparative genomics analysis, we found components of the Psp system in many bacterial and archaeal phyla and describe that the predicted Psp systems deviate dramatically from the known prototypes. The core proteins PspA and PspC have been integrated into various (often phylum-specifically) conserved protein networks during evolution. Based on protein domain-based and gene neighborhood analyses of pspA and pspC homologs, we built a natural classification system for Psp networks in bacteria and archaea. We validate our approach by performing a comprehensive in vivo protein interaction study of Psp domains identified in the Gram-positive model organism Bacillus subtilis and found a strong interconnected protein network. Our study highlights the diversity of Psp domain organizations and potentially diverse functions across the plethora of the microbial landscape, thus laying the ground for studies beyond known Psp functions in underrepresented organisms. IMPORTANCE The PspA protein domain is found in all domains of life, highlighting its central role in Psp networks. To date, all insights into the core functions of Psp responses derive mainly from protein network blueprints representing only three bacterial phyla. Despite large overlaps in function and regulation, the evolutionary diversity of Psp networks remains largely elusive. Here, we present an unbiased protein domain- and genomic context-centered approach that describes and classifies Psp systems. Our results suggest so-far-unknown Psp-associated roles with other protein networks giving rise to new functions. We demonstrate the applicability of our approach by dissecting the Psp protein network present in Bacillus subtilis and demonstrate Psp domains working in concert with other cell envelope stress response systems. We find that the Psp-like protein universe reflects a surprising diversity within the bacterial and archaeal microbial world.
Individual variability of neural computations in the primate retina

Variation in the neural code contributes to making each individual unique. We probed neural code variation using ∼100 neural population recordings from major ganglion cell types in the macaque retina, combined with an interpretable computational representation of individual variability using machine learning. This representation captured individual variation and covariation in properties such as nonlinearity, temporal dynamics, and spatial receptive field size, while preserving invariances, such as asymmetries between ON and OFF cells. The covariation of response properties in different cell types was associated with the proximity of lamination of their synaptic inputs. Surprisingly, male retinas exhibited higher firing rates and faster temporal integration than female retinas. Exploiting data from previously recorded macaque retinas enabled efficient characterization of a new macaque retina, and of a human retina. Simulations indicated that combining a vast dataset of healthy macaque recordings with behavioral feedback could be used to identify the neural code and thus improve retinal implants for vision restoration.
Optimal Reinforcement Learning with Asymmetric Updating in Volatile Environments: a Simulation Study

The ability to predict the future is essential for decision-making and interaction with the environment to avoid punishment and gain reward. Reinforcement learning algorithms provide a normative way for interactive learning, especially in volatile environments. The optimal strategy for the classic reinforcement learning model is to increase the learning rate as volatility increases. Inspired by optimistic bias in humans, an alternative reinforcement learning model has been developed by adding a punishment learning rate to the classic reinforcement learning model. In this study, we aim to 1) compare the performance of these two models in interaction with different environments, and 2) find optimal parameters for the models. Our simulations indicate that having two different learning rates for rewards and punishments increases performance in a volatile environment. Investigation of the optimal parameters shows that in almost all environments, having a higher reward learning rate compared to the punishment learning rate is beneficial for achieving higher performance which in this case is the accumulation of more rewards. Our results suggest that to achieve high performance, we need a shorter memory window for recent rewards and a longer memory window for punishments. This is consistent with optimistic bias in human behavior.