Miracle-dz/UniFAR-FTUs huggingface dataset:
Behavior of isolated floating pile adjacent to deep excavation reinforced by dual-row batter pile wall

Abstract The construction of modern city consists of many underground facilities, and one major concern is how to construct them safely and efficiently. Thus, the excavation-induced pile response needs to be evaluated. Dual-row batter pile wall (DRBPW) exhibits better performance than traditional dual-row vertical pile wall (DRVPW) in foundation pit engineering. However, the study of single pile subjected to excavation-induced soil movement behind DRVPW is rather limited, needless to say that of the DRBPW. An explicit finite difference method (FDM) was utilized to obtain the responses of isolated floating single pile (IFSP) near deep excavations braced by DRBPW. The responses of IFSP in the vicinity of foundation pit reinforced by DRBPW were compared to that of the DRVPW. The pile length, distance of pile from wall, excavation depth, working load, and Young’s modulus of pile were considered in the parametric study. The conclusions were as follows: (1) The DRBPW can minimize the influence on the existing pile; thus, the induced lateral displacement and bending moment of pile retained by DRBPW are smaller than that of DRVPW. (2) The excavation depth, pile location, and pile length have significant influence on the responses of IFSP adjacent excavation, while the working load and Young’s modulus of pile have diminutive influence. (3) The influence zone induced by excavation can be divided into two areas. The primary influence zone is the severely affected area where the induced deformation and bending moment are remarkable. The secondary influence zone is the moderately affected area where the induced deformation and bending moment are mild. The primary influence zone is within twice of the excavation depth. The secondary influence zone is 2.0–3.0 times that of the excavation depth. (4) The pile toe of IFSP has considerable lateral displacement during excavation, and attentions should be paid to the whole pile shaft during construction.
Assessment of long-term trends in a threatened grassland bird community using daily bird lists

Open Natural Ecosystems (ONEs), such as tropical grasslands, are among the most threatened habitats on Earth today. The long-term monitoring of ONEs is an important research domain that is essential for understanding anthropogenic impacts and facilitating conservation action. Using a simple day-listing method over a 13-year period, we studied species trends in a central Indian grassland-agriculture mosaic experiencing several land-use changes. Our results indicate that some grassland species (such as the Great Indian Bustard Ardeotis nigriceps) show steep declines during the study period, while other generalist species (such as the Indian Peafowl Pavo cristatus) show an increasing trend. Daily listing also reveals distinct seasonal patterns, and we discuss the Great Indian Bustard and Western Marsh Harrier Circus aeruginosus as examples. Our study highlights the utility of consistent checklist surveys to monitor population trends of bird communities within a changing landscape.
TP53-dependent CRISPR-Cas9 sex bias across cancer types affects MYC, PIK3CA, and SUV39H1 mediated by factors including SOX9, FOXO4, and PRC1

CRISPR-Cas9 system has emerged as the dominant technology for gene editing and has great potential for large-scale clinical applications. One major concern is its off-target issue and other potential side effects after the introduction of exogenous CRISPR-Cas9 into cells. Several previous studies investigated CRISPR-Cas9 interactions with p53 mainly in non-transformed cells, such as RPE1 (retinal pigmented epithelium cells) and H9 (embryonic stem cells [ESC]). Recently, it has been reported that Cas9 alone can activate the p53 pathway and select for p53-inactivating mutations after studying hundreds of cancer cell lines. We reanalyzed the reported data of Cas9-associated p53-inactivating mutations and observed large significant sex difference when comparing Cas9 activities in p53-wildtype and p53-mutant cell lines. To expand the impact of this finding, we further examined all protein-coding genes screening by the CRISPR-Cas9 system in a large-scale dataset from the DepMap project. We highlight the p53 status-dependent sex bias of CRISPR-Cas9 effect across cancer cell types (genes including MYC, PIK3CA, KAT2B, KDM4E, SUV39H1, FANCB, TLR7, and APC2) and potential mechanisms (mediated by transcriptional factors including SOX9, FOXO4, LEF1, and RYBP) underlying this phenomenon, which suggest that the p53-dependent sex bias effect may need to be considered in future clinical applications, especially in cancer, when using this genome editing system.
Mag-Net: Rapid enrichment of membrane-bound particles enables high coverage quantitative analysis of the plasma proteome

Membrane-bound particles in plasma are composed of exosomes, microvesicles, and apoptotic bodies and represent ∼1-2% of the total protein composition. Proteomic interrogation of this subset of plasma proteins augments the representation of tissue-specific proteins, representing a “liquid biopsy,” while enabling the detection of proteins that would otherwise be beyond the dynamic range of liquid chromatography-tandem mass spectrometry in unfractionated plasma. We have developed a one-step enrichment strategy (Mag-Net) using hyper-porous strong-anion exchange magnetic microparticles to sieve membrane-bound particles from plasma. The Mag-Net method is robust, reproducible, inexpensive, and requires <100 μL plasma input. Coupled to a quantitative data-independent mass spectrometry analytical strategy, we demonstrate that we can routinely collect results for >37,000 peptides from >4,000 plasma proteins with high precision. We demonstrate excellent quantitative accuracy and analytical reproducibility of the protocol.
Comparing kinetic proofreading and kinetic segregation for T cell receptor activation

The T cell receptor (TCR) is a key component of the adaptive immune system, recognizing foreign antigens and triggering an immune response. Competing models exist to explain the high sensitivity and selectivity of the TCR in discriminating ‘self’ from ‘non-self’ antigens, particularly models using kinetic proofreading (KP), kinetic segregation (KS), and combinations of the two. In this paper, we consider the role and importance of KS in TCR activation, using two models: classic KP (cKP), without KS, where antigen-TCR binding is required for activation, and a combination of KP and KS (KS-KP), where only residence within a close contact is required for activation. Building on previous work, our computational model is the first to permit a head-to-head comparison of these models in silico. While we find that both models can be used to explain the probability of TCR activation across much of the parameter space, we find biologically important regions in the parameter space where significant differences in performance can be expected. Furthermore, we show that the available experimental evidence may favour the KS-KP model over cKP. Our results may be used to motivate and guide future experiments to determine highly accurate computational models for the TCR. Author summary The T cell receptor (TCR) is a master of reliable sensing: it detects faint ‘signals’ (rare ligands derived from foreign proteins) over high ‘noise’ (abundant ligands derived from the body’s own proteins) to set T cells on a course to exterminate pathogens and tumours, a process that is central to our immune response. Despite decades of studying TCR signalling, we still do not know how the TCR can be so exceptionally sensitive and accurate. It is widely believed that kinetic proofreading (KP), in which the TCR binds to an antigen and triggers a series of phosphorylation steps prior to activation, plays an important role. However, recent results suggest that kinetic segregation (KS), in which binding is not required, is also important. These models are mutually exclusive, and yet both appear to explain various aspects of T cell activation. Our work directly addresses this puzzle. We develop a computational modeling framework which can simulate TCR activation by both KP-based and KS-based models, making it possible to compare them in silico for the first time. Using this framework, we find conditions under which the two models provide different responses, and we show that the limited experimental evidence to date is consistent with KS, which should motivate further investigation.
Plant-mycorrhizal associations may explain the latitudinal gradient of plant community assembly

Biogeographical variation in community assembly patterns forms the basis of large-scale biodiversity gradients. Previous studies on community assembly have suggested the dominant role of environmental filtering at higher latitudes. However, recent evidence indicates that plant community distributions at higher latitudes exhibit more spatial clustering, suggesting the importance of spatial assembly processes. In this study, we propose a hypothesis that resolves this paradox by incorporating biogeographic variations in dominant mycorrhizal types: the increasing prevalence of ectomycorrhizal (EcM) trees with latitude contributes to the spatially clustered distribution of plants, as EcM trees tend to exhibit positive plant-soil feedback. We analyzed a large-scale standardized dataset of Japanese forests covering a latitudinal gradient of >10° and found that (i) the proportion of EcM trees was higher at higher latitudes, and (ii) EcM tree-rich communities exhibited more spatially clustered distributions. Consequently, (iii) the tree species composition at higher latitudes was better explained by spatial variables. Moreover, consistent with predictions derived from the plant-soil feedback theory, these findings were particularly evident in the understory rather than the canopy communities. This study demonstrates that plant-soil feedback influences plant community distributions at metacommunity scales, thereby giving rise to a paradox within the latitudinal gradient of plant community assembly.
Genome analysis of Legionella pneumophila ST901 an Italian endemic strain

Legionella pneumophila typing, based on SBT analysis, has been allowing to identify sequence types (ST) responsible for many Legionnaires’ disease cases. ST901 has so far been identified only in a town of the northern Italy, where it caused numerous travel-associated Legionnaires’ disease cases over a 32-year period. In this study, ST901 were analyzed by whole genome sequencing in looking for both differences and similarities inside this ST and with other unrelated Legionella strains. To this aim core genome multi locus sequence type, single nucleotide polymorphisms and pangenome analyses were performed. ST901 resulted highly similar each other, characterized by I-C CRISPR-cas system, located on the chromosome and by a putative plasmid, highly similar to that found in Lp strain Lens, which contains I-F CRISPR-cas system. Accessory genomic islands, either already described or specifically found in ST901, were also shown. In conclusion ST901 has been quite preserved over the time. The co-occurrence of two phage resistance systems and genomic islands of virulence could have made the ST901 enough virulent to cause such a high number of cases. Summary blurb ST901 appears as endemic of the Northern Italy, with virulence traits similar to those in Alcoy and Corby, and with two CRISPR-cas systems which probably preserved them in their environmental niche.
Select EZH2 inhibitors enhance the viral mimicry effects of DNMT inhibition through a mechanism involving calcium-calcineurin-NFAT signaling

DNA methyltransferase (DNMT) inhibitors are FDA-approved for various hematological malignancies but have limited efficacy in solid tumors. DNA hypomethylation with these drugs is associated with elevated lysine 27 tri-methylation on histone H3 (H3K27me3). We hypothesized that this EZH2-dependent repressive mark limits the full potential of DNMT inhibition. Here, we show in cell line and tumoroid models of colorectal cancer, that low-dose DNMT inhibition sensitizes cells to selective EZH2 inhibitors that have limited single agent toxicity, and that EZH2 inhibition enhances DNMT inhibitor-driven molecular and therapeutic effects. Through integrative epigenomic analyses, we reveal that DNMT inhibition induces H3K27me3 accumulation at genomic regions poised with EZH2. Unexpectedly, combined treatment alters the epigenome landscape to promote transcriptional upregulation of the calcium-calcineurin-NFAT signaling pathway. Blocking this pathway limits the transcriptional activating effects of the drug combination, including expression of transposable elements and innate immune response genes within a viral defense pathway. Consistently, we demonstrate positive correlations between DNMT inhibitor- and innate immune response-associated transcription profiles and calcium signal activation in primary human colon cancer specimens. Collectively, our study demonstrates that compensatory EZH2 activity following DNA hypomethylation presents a barrier to the therapeutic action of DNMT inhibition in colon cancer, reveals a new application of EZH2 inhibitors beyond cancers associated with PRC2 hyperactivity, and links calcium-calcineurin-NFAT signaling to epigenetic therapy-induced viral mimicry. Highlights Select EZH2 inhibitors enhance the transcriptional activating and antiproliferative effects of DNA hypomethylating agents in colon cancer cells. The mechanism involves blockade of H3K27me3 accumulation in regions of the genome poised for PRC2 activity. DNMT inhibitor + EZH2 inhibitor treatment transcriptionally upregulates calcium-calcineurin- NFAT signaling, and this pathway is necessary for complete induction of viral mimicry and innate immune response pathways. The therapeutic utility of EZH2 inhibitors may be extended beyond cancers with PRC2 hyperactivity in combination regimens with DNMT inhibitors.
Weak local adaptation to drought in seedlings of a widespread conifer

There is an urgent need for better understanding how populations of trees will respond to predictable changes in climate and the intensification of extreme weather events such as droughts. The distribution of adaptive traits in seedlings is a crucial component of population adaptive potential and its characterization is important for development of management approaches mitigating the effects of climate change on forests. In this study, we used a large-scale common garden drought experiment to characterize the variation in drought tolerance, growth, and plastic responses to extreme summer drought in seedlings of 73 natural provenances of the two main varieties of Douglas-fir (Pseudotsuga menziesii var. menziesii and var. glauca), sampled across most of their extensive natural ranges. We detected large differences between the two Douglas-fir varieties for all traits assessed, with var. glauca showing higher tolerance to drought but slower height growth and less plasticity than var. menziesii. Surprisingly, signals of local adaptation to drought within varieties were weak within var. glauca and nearly absent within var. menziesii. Temperature-related variables were identified as the main climatic drivers of clinal variation in drought tolerance and height growth species-wide, and in height growth within var. menziesii. Furthermore, our data indicate that higher plasticity under extreme droughts could be maladaptive in var. menziesii. Overall, our study suggests that genetic variation for drought tolerance in seedlings is maintained primarily within rather than among provenances within varieties and does not compromise growth at early stages of plant development. Given these results, assisted gene flow is unlikely to help facilitate adaptation to drought within Douglas-fir varieties, but selective breeding within provenances could accelerate adaptation.
Microbiome Features Associated with Performance Measures in Non-Athletic and Athletic Individuals

The influence of human gut microbiota on health and disease is now well established. Therefore, it is not surprising that microbiome research has found applications within the sports community, hoping to improve health and optimize performance. Every comparison study found new species or pathways that were more enriched in elites than in sedentary controls. In addition, sport-specific and performance-level-specific microbiome features have been identified. However, the results remain inconclusive and indicate the need for further assessment. In our study, we conducted two interventions, anaerobic (Wingate Anaerobic Tests (WAnTs)) and aerobic (Bruce Treadmill Test), on fit non-athletic but physically active controls and athletic strength (training professional sports which increase anaerobic efforts such as weightlifting, powerlifting, and bodybuilding) and endurance (training professional sports which increase aerobic efforts such as race walking, long-distance running, or ski running) individuals to compare sport-specific and training-specific responses. While we did not identify any differences in alpha and beta diversity or significant differential abundance of microbiome components at baseline, we noted that one-third of the species identified were unique to either group. Longitudinal analysis of samples (pre- and post-intervention) revealed an abundance of Alistipes communis in the strength group during the WAnT intervention and 88 species with notable differences between groups during the Bruce Test intervention. SparCC recognized Bifidobacterium longum and Bifidobacterium adolescentis, short-chain fatty acid producers with probiotic properties, as species strongly associated with VO2max. Ultimately, we identified several taxa with different baseline abundances and longitudinal changes when comparing individuals based on their VO2max, average power, and maximal power parameters. Our results confirmed the health status of the individuals involved in the study, based on previous assumptions about microbiome health. Furthermore, our findings indicate that microbiome features are associated with better performance measures previously identified in elite athletes.
zol & fai: large-scale targeted detection and evolutionary investigation of gene clusters

Many universally and conditionally important genes are genomically aggregated within clusters. Here, we introduce fai and zol, which together enable large-scale comparative analysis of different types of gene clusters and mobile-genetic elements (MGEs), such as biosynthetic gene clusters (BGCs) or viruses. Fundamentally, they overcome a current bottleneck to reliably perform comprehensive orthology inference at large scale across broad taxonomic contexts and thousands of genomes. First, fai allows the identification of orthologous or homologous instances of a query gene cluster of interest amongst a database of target genomes. Subsequently, zol enables reliable, context-specific inference of protein-encoding ortholog groups for individual genes across gene cluster instances. In addition, zol performs functional annotation and computes a variety of statistics for each inferred ortholog group. These programs are showcased through application to: (i) longitudinal tracking of a virus in metagenomes, (ii) discovering novel population-genetic insights of two common BGCs in a fungal species, and (iii) uncovering large-scale evolutionary trends of a virulence-associated gene cluster across thousands of genomes from a diverse bacterial genus.
From gonadogenesis to testicular cancer: Unravelling the novel regulators and identification of drug candidates against FGF9 and PTGDS

Sex determination is the preliminary step toward gonadogenesis in mammals. Antagonistic interactions of key regulators have been only fragmentarily mentioned so far. Therefore, exploring regulators underlying the phenomena is required to solve questions, especially regarding female gonad development and gonadal disorders in congenital or adults. Inhibiting discrepancies in PPI pathways and combating related disorders are of urgent necessity, for which novel drugs are constantly required. Here, we performed in silico analysis using robust bioinformatics methods, which is unprecedented work in sex determination studies, providing large-scale analysis without exorbitant wet lab procedures. Analyzed regulators were overlapped with our RNA-seq data for authentication, to obtain differentially expressed elements. Additionally, CADD approach was used to discover inhibitors for FGF9 and PTGDS to search for potential drugs combating gonadal disorders in adults. Along with druggable properties, only FGF9 and PTGDS had full-length protein structures available, among 25 key genes under investigation. Our large-scale analysis of PPIN, produced highly interacting hub-bottleneck nodes as novel genes. Further, functional enrichment analysis revealed importance of these regulators in gonadogenesis. We identified sex-specific novel genes, miRNAs-target pairs, and lncRNAs-target pairs, which appear to play an important role in regulation of ovary development. CADD with molecular docking, MD simulations, and molecular mechanics confirmed stability of two novel compounds, DB12884 and DB12412 that could potentially inhibit FGF9 and PTGDS respectively. Taken together our study provides valuable information regarding involvement of crucial regulators in antagonistic mechanism of gonadogenesis and their related disorders, which will further assist in refining wet lab experiments.
Similarity metric learning on perturbational datasets improves functional identification of perturbations

Analysis of high-throughput perturbational datasets, including the Next Generation Connectivity Map (L1000) and the Cell Painting projects, uses similarity metrics to identify perturbations or disease states that induce similar changes in the biological feature space. Similarities among perturbations are then used to identify drug mechanisms of action, to nominate therapeutics for a particular disease, and to construct bio-logical networks among perturbations and genes. Standard similarity metrics include correlations, cosine distance and gene set enrichment methods, but these methods operate on the measured features without refinement by transforming the measurement space. We introduce Perturbational Metric Learning (PeML), a weakly supervised similarity metric learning method to learn a data-driven similarity function that maximizes discrimination of replicate signatures by transforming the biological measurements into an intrinsic, dataset-specific basis. The learned similarity functions show substantial improvement for recovering known biological relationships, like mechanism of action identification. In addition to capturing a more meaningful notion of similarity, data in the transformed basis can be used for other analysis tasks, such as classification and clustering. Similarity metric learning is a powerful tool for the analysis of large biological datasets.