Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine-mapping of nuclear compartments using ultra-deep Hi-C shows that active promoter and enhancer elements localize in the active A compartment even when adjacent sequences do not

Megabase-scale intervals of active, gene-rich and inactive, gene-poor chromatin are known to segregate, forming the A and B compartments. Fine mapping of the contents of these A and B compartments has been hitherto impossible, owing to the extraordinary sequencing depths required to distinguish between the long-range contact patterns of individual loci, and to the computational complexity of the associated calculations. Here, we generate the largest published in situ Hi-C map to date, spanning 33 billion contacts. We also develop a computational method, dubbed PCA of Sparse, SUper Massive Matrices (POSSUMM), that is capable of efficiently calculating eigenvectors for sparse matrices with millions of rows and columns. Applying POSSUMM to our Hi-C dataset makes it possible to assign loci to the A and B compartment at 500 bp resolution. We find that loci frequently alternate between compartments as one moves along the contour of the genome, such that the median compartment interval is only 12.5 kb long. Contrary to the findings in coarse-resolution compartment profiles, we find that individual genes are not uniformly positioned in either the A compartment or the B compartment. Instead, essentially all (95%) active gene promoters localize in the A compartment, but the likelihood of localizing in the A compartment declines along the body of active genes, such that the transcriptional termini of long genes (>60 kb) tend to localize in the B compartment. Similarly, nearly all active enhancers elements (95%) localize in the A compartment, even when the flanking sequences are comprised entirely of inactive chromatin and localize in the B compartment. These results are consistent with a model in which DNA-bound regulatory complexes give rise to phase separation at the scale of individual DNA elements.
Reconstruction of distinct vertebrate gastrulation modes via modulation of key cell behaviors in the chick embryo

The morphology of gastrulation driving the internalisation of the mesoderm and endoderm differs dramatically among vertebrate species. It ranges from involution of epithelial sheets of cells through a circular blastopore in amphibians to ingression of mesenchymal cells through a primitive streak in amniotes. By targeting signalling pathways controlling critical cell behaviours in the chick embryo, we generated crescent- and ring-shaped mesendoderm territories in which cells can or cannot ingress. These alterations subvert the formation of the chick primitive streak into the gastrulation modes seen in amphibians, reptiles and teleost fish. Our experimental manipulations are supported by a theoretical framework linking cellular behaviors to self-organized multi-cellular flows in the accompanying paper. All together, this suggests that the evolution of gastrulation movements are largely determined by the shape of and cell behaviours in the mesendoderm territory across different species, and controlled by a relatively small number of signalling pathways.
Dynamic DNA Methylation Changes in the COMT Gene Promoter Region in Response to Mental Stress and Its Modulation by Transcranial Direct Current Stimulation

Changes in epigenetic modifications present a mechanism how environmental factors like the experience of stress can alter gene regulation. While stress-related disorders have consistently been associated with differential DNA methylation, little is known about the time scale in which these alterations emerge. We investigated dynamic DNA methylation changes in whole blood of 42 healthy male individuals in response to a stressful cognitive task, its association with concentration changes in cortisol and its modulation by transcranial direct current stimulation (tDCS). We observed a continuous increase in COMT promotor DNA methylation which correlated with higher saliva cortisol levels and was still detectable one week later. However, this lasting effect was suppressed by concurrent activity-enhancing anodal tDCS to the dorsolateral prefrontal cortex. Our findings support the significance of gene-specific DNA methylation in whole blood as potential biomarkers for stress-related disorders. Moreover, they suggest alternative molecular mechanisms possibly involved in lasting behavioral effects of tDCS.
Atractylenolide-I Sensitizes Triple-Negative Breast Cancer Cells to Paclitaxel by Blocking CTGF Expression and Fibroblast Activation

This investigation was conducted to elucidate whether atractylenolide-I (ATL-1), which is the main component of Atractylodes macrocephala Koidz, can sensitize triple-negative breast cancer (TNBC) cells to paclitaxel and investigate the possible mechanism involved. We discovered that ATL-1 could inhibit tumor cell migration and increase the sensitivity of tumor cells to paclitaxel. ATL-1 downregulated the expression and secretion of CTGF in TNBC cells. Apart from inhibiting TNBC cell migration via CTGF, ATL-1 downregulated the expression of CTGF in fibroblasts and decreased the ability of breast cancer cells to transform fibroblasts into cancer-associated fibroblasts (CAFs), which in turn increased the sensitivity of TNBC cells to paclitaxel. In a mouse model, we found that ATL-1 treatments could enhance the chemotherapeutic effect of paclitaxel on tumors and reduce tumor metastasis to the lungs and liver. Primary cultured fibroblasts derived from inoculated tumors in mice treated with ATL-1 combined with paclitaxel expressed relatively low levels of CAF markers. Collectively, our data indicate that ATL-1 can sensitize TNBC cells to paclitaxel by blocking CTGF expression and fibroblast activation and could be helpful in future research to determine the value of ATL-1 in the clinical setting.
Qualitative Histopathological Classification of Primary Bone Tumors Using Deep Learning: A Pilot Study

Background Histopathological diagnosis of bone tumors is challenging for pathologists. We aim to classify bone tumors histopathologically in terms of aggressiveness using deep learning (DL) and compare performance with pathologists. Methods A total of 427 pathological slides of bone tumors were produced and scanned as whole slide imaging (WSI). Tumor area of WSI was annotated by pathologists and cropped into 716,838 image patches of 256 × 256 pixels for training. After six DL models were trained and validated in patch level, performance was evaluated on testing dataset for binary classification (benign vs. non-benign) and ternary classification (benign vs. intermediate vs. malignant) in patch-level and slide-level prediction. The performance of four pathologists with different experiences was compared to the best-performing models. The gradient-weighted class activation mapping was used to visualize patch’s important area. Results VGG-16 and Inception V3 performed better than other models in patch-level binary and ternary classification. For slide-level prediction, VGG-16 and Inception V3 had area under curve of 0.962 and 0.971 for binary classification and Cohen’s kappa score (CKS) of 0.731 and 0.802 for ternary classification. The senior pathologist had CKS of 0.685 comparable to both models (p = 0.688 and p = 0.287) while attending and junior pathologists showed lower CKS than the best model (each p < 0.05). Visualization showed that the DL model depended on pathological features to make predictions. Conclusion DL can effectively classify bone tumors histopathologically in terms of aggressiveness with performance similar to senior pathologists. Our results are promising and would help expedite the future application of DL-assisted histopathological diagnosis for bone tumors.
Investigating Data Cleaning Methods to Improve Performance of Brain–Computer Interfaces Based on Stereo-Electroencephalography

Stereo-electroencephalography (SEEG) utilizes localized and penetrating depth electrodes to directly measure electrophysiological brain activity. The implanted electrodes generally provide a sparse sampling of multiple brain regions, including both cortical and subcortical structures, making the SEEG neural recordings a potential source for the brain–computer interface (BCI) purpose in recent years. For SEEG signals, data cleaning is an essential preprocessing step in removing excessive noises for further analysis. However, little is known about what kinds of effect that different data cleaning methods may exert on BCI decoding performance and, moreover, what are the reasons causing the differentiated effects. To address these questions, we adopted five different data cleaning methods, including common average reference, gray–white matter reference, electrode shaft reference, bipolar reference, and Laplacian reference, to process the SEEG data and evaluated the effect of these methods on improving BCI decoding performance. Additionally, we also comparatively investigated the changes of SEEG signals induced by these different methods from multiple-domain (e.g., spatial, spectral, and temporal domain). The results showed that data cleaning methods could improve the accuracy of gesture decoding, where the Laplacian reference produced the best performance. Further analysis revealed that the superiority of the data cleaning method with excellent performance might be attributed to the increased distinguishability in the low-frequency band. The findings of this work highlighted the importance of applying proper data clean methods for SEEG signals and proposed the application of Laplacian reference for SEEG-based BCI.
Development and Verification of the Hypoxia- and Immune-Associated Prognostic Signature for Pancreatic Ductal Adenocarcinoma

We aim to construct a hypoxia- and immune-associated risk score model to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). By unsupervised consensus clustering algorithms, we generate two different hypoxia clusters. Then, we screened out 682 hypoxia-associated and 528 immune-associated PDAC differentially expressed genes (DEGs) of PDAC using Pearson correlation analysis based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx) dataset. Seven hypoxia and immune-associated signature genes (S100A16, PPP3CA, SEMA3C, PLAU, IL18, GDF11, and NR0B1) were identified to construct a risk score model using the Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which stratified patients into high- and low-risk groups and were further validated in the GEO and ICGC cohort. Patients in the low-risk group showed superior overall survival (OS) to their high-risk counterparts (p < 0.05). Moreover, it was suggested by multivariate Cox regression that our constructed hypoxia-associated and immune-associated prognosis signature might be used as the independent factor for prognosis prediction (p < 0.001). By CIBERSORT and ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and immune checkpoint expression such as PD-L1, and different immunocyte infiltration states compared with those low-risk patients. The mutation spectrum also differs between high- and low-risk groups. To sum up, our hypoxia- and immune-associated prognostic signature can be used as an approach to stratify the risk of PDAC.
Pyrophosphate and Irreversibility in Evolution, or why PPi Is Not an Energy Currency and why Nature Chose Triphosphates

The possible evolutionary significance of pyrophosphate (PPi) has been discussed since the early 1960s. Lipmann suggested that PPi could have been an ancient currency or a possible environmental source of metabolic energy at origins, while Kornberg proposed that PPi vectorializes metabolism because ubiquitous pyrophosphatases render PPi forming reactions kinetically irreversible. To test those ideas, we investigated the reactions that consume phosphoanhydride bonds among the 402 reactions of the universal biosynthetic core that generates amino acids, nucleotides, and cofactors from H2, CO2, and NH3. We find that 36% of the core’s phosphoanhydride hydrolyzing reactions generate PPi, while no reactions use PPi as an energy currency. The polymerization reactions that generate ~80% of cell mass – protein, RNA, and DNA synthesis – all generate PPi, while none use PPi as an energy source. In typical prokaryotic cells, aminoacyl tRNA synthetases (AARS) underlie ~80% of PPi production. We show that the irreversibility of the AARS reaction is a kinetic, not a thermodynamic effect. The data indicate that PPi is not an ancient energy currency and probably never was. Instead, PPi hydrolysis is an ancient mechanism that imparts irreversibility, as Kornberg suggested, functioning like a ratchet’s pawl to vectorialize the life process toward growth. The two anhydride bonds in nucleoside triphosphates offer ATP-cleaving enzymes an option to impart either thermodynamic control (Pi formation) or kinetic control (PPi formation) upon reactions. This dual capacity explains why nature chose the triphosphate moiety of ATP as biochemistry’s universal energy currency.
Editorial: Interplay Between Autophagy and Metabolic Syndrome: Causes, Consequences and Therapeutic Challenges

1 Laboratory of Vascular Nanopharmacology, Department of Pharmacology, Institute of Biomedical Sciences, University of São Paulo (ICB/USP), São Paulo, Brazil, Department of Molecular and Cellular Physiology, Louisiana State University Health Shreveport, Shreveport, LA, United States, Department of Physiology and Biophysics, Institute of Biomedical Sciences, University of São Paulo (ICB/USP), São Paulo, Brazil, 4 Laboratory of Immunoendocrinology, Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences of University São Paulo (FCF/USP), São Paulo, Brazil
Interaction of Phytophthora sojae Effector Avr1b With E3 Ubiquitin Ligase GmPUB1 Is Required for Recognition by Soybeans Carrying Phytophthora Resistance Rps1-b and Rps1-k Genes

Phytophthora sojae is an oomycete that causes stem and root rot disease in soybean. P. sojae delivers many RxLR effector proteins, including Avr1b, into host cells to promote infection. We show here that Avr1b interacts with the soybean U-box protein, GmPUB1-1, in yeast two-hybrid, pull down, and bimolecular fluorescence complementation (BIFC) assays. GmPUB1-1, and a homeologous copy GmPUB1-2, are induced by infection and encode 403 amino acid proteins with U-Box domains at their N-termini. Non-synonymous mutations in the Avr1b C-terminus that abolish suppression of cell death also abolished the interaction of Avr1b with GmPUB1-1, while deletion of the GmPUB1-1 C-terminus, but not the U box, abolished the interaction. BIFC experiments suggested that the GmPUB1-1-Avr1b complex is targeted to the nucleus. In vitro ubiquitination assays demonstrated that GmPUB1-1 possesses E3 ligase activity. Silencing of the GmPUB1 genes in soybean cotyledons resulted in loss of recognition of Avr1b by gene products encoded by Rps1-b and Rps1-k. The recognition of Avr1k (which did not interact with GmPUB1-1) by Rps1-k plants was not, however, affected following GmPUB1-1 silencing. Furthermore, over-expression of GmPUB1-1 in particle bombardment experiments triggered cell death suggesting that GmPUB1 may be a positive regulator of effector-triggered immunity. In a yeast two-hybrid system, GmPUB1-1 also interacted with a number of other RxLR effectors including Avr1d, while Avr1b and Avr1d interacted with a number of other infection-induced GmPUB proteins, suggesting that the pathogen uses a multiplex of interactions of RxLR effectors with GmPUB proteins to modulate host immunity.
Changes in Metabolite Patterns During Refrigerated Storage of Lamb's lettuce (Valerianella locusta L. Betcke)

Lamb's lettuce is a popular winter salad, often grown in private vegetable plots, small local farms or in intensive vegetable production. It is usually marketed as a ready-to-eat produce in supermarkets. The aim of the study was to evaluate the changes in biochemical composition and degradation of bioactive compounds during consumer-relevant time of home-grown and store-bought Valerianella locusta “Vit” salad. Primary metabolites, assimilatory pigments as well as secondary metabolites were monitored during 1 week of refrigerated storage. Home-grown lamb's lettuce exhibited highest levels of total sugars, total organic acids, vitamin C, and total phenolic content as well as enhanced levels of most individual phenolic compounds and chloroplast pigments. Locally produced samples of lamb's lettuce also contained high levels of analyzed bioactive components. All samples retained most bioactive components during the entire period of refrigerated storage. The results underline the instability of vitamin C during refrigerated storage of lamb's lettuce and pinpoint this parameter as being the most affected by storage.
Hypoxia-Inducible Factors as Key Players in the Pathogenesis of Non-alcoholic Fatty Liver Disease and Non-alcoholic Steatohepatitis

Non-alcoholic fatty liver disease (NAFLD) and its more severe form non-alcoholic steatohepatitis (NASH) are a major public health concern with high and increasing global prevalence, and a significant disease burden owing to its progression to more severe forms of liver disease and the associated risk of cardiovascular disease. Treatment options, however, remain scarce, and a better understanding of the pathological and physiological processes involved could enable the development of new therapeutic strategies. One process implicated in the pathology of NAFLD and NASH is cellular oxygen sensing, coordinated largely by the hypoxia-inducible factor (HIF) family of transcription factors. Activation of HIFs has been demonstrated in patients and mouse models of NAFLD and NASH and studies of activation and inhibition of HIFs using pharmacological and genetic tools point toward important roles for these transcription factors in modulating central aspects of the disease. HIFs appear to act in several cell types in the liver to worsen steatosis, inflammation, and fibrosis, but may nevertheless improve insulin sensitivity. Moreover, in liver and other tissues, HIF activation alters mitochondrial respiratory function and metabolism, having an impact on energetic and redox homeostasis. This article aims to provide an overview of current understanding of the roles of HIFs in NAFLD, highlighting areas where further research is needed.
Cognitive Impairment in Late-Life Depression: A Comparative Study of Healthy Older People, Late-Life Depression, and Mild Alzheimer's Disease Using Multivariate Base Rates of Low Scores

Late-Life Depression (LLD) is often associated with cognitive impairment. However, distinction between cognitive impairment due to LLD and those due to normal aging or mild Alzheimer's Disease (AD) remain difficult. The aim of this study was to present and compare the multivariate base rates of low scores in LLD, mild AD, and healthy control groups on a battery of neuropsychological tests. Participants (ages 60–89) were 352 older healthy adults, 390 patients with LLD, and 234 patients with mild AD (i.e., MMSE ≥ 20). Multivariate base rates of low scores (i.e., ≤ 5th percentile) were calculated for each participant group within different cognitive domains (verbal episodic memory, executive skills, mental processing speed, constructional praxis, and language/semantic memory). Obtaining at least one low score was relatively common in healthy older people controls (from 9.4 to 17.6%), and may thus result in a large number of false positives. By contrast, having at least two low scores was unusual (from 0.3 to 4.6%) and seems to be a more reliable criterion for identifying cognitive impairment in LLD. Having at least three low memory scores was poorly associated with LLD (5.9%) compared to mild AD (76.1%) and may provide a useful way to differentiate between these two conditions [χ(1)2 = 329.8, p < 0.001; Odds Ratio = 50.7, 95% CI = 38.2–77.5]. The multivariate base rate information about low scores in healthy older people and mild AD may help clinicians to identify cognitive impairments in LLD patients, improve the clinical decision-making, and target those who require regular cognitive and clinical follow-up.
Spatial Planning Insights for Philippine Coral Reef Conservation Using Larval Connectivity Networks

The marine habitats of the Philippines are recognized to be some of the most biodiverse systems globally yet only 1.7% of its seas are designated as marine protected areas (MPAs) with varying levels of implementation. Many of these MPAs were established based on local-scale conservation and fisheries objectives without considering larger-scale ecological connections. The connectivity of reefs through larval dispersal is important in the regional-scale resilience against anthropogenic disturbances and is considered a significant criterion in planning for MPAs. In this study, we provide insights into the delineation of ecologically connected MPA networks using larval dispersal modeling and network analysis. We characterized the network properties of the Philippine coral reefs, organized as 252 reef nodes, based on the larval connectivity networks of a branching coral, sea urchin, and grouper. We then evaluated the distribution of the existing 1,060 MPAs relative to the connectivity patterns. All reef nodes were found to be highly interconnected with a mean shortest path ranging from 1.96 to 4.06. Reef nodes were then ranked according to their relative importance in regional connectivity based on five connectivity indices. Despite the between-organism and between-index variability in rankings, there were reefs nodes, mostly located offshore and at major straits, which consistently ranked high. We found that the distribution of existing MPAs partially capture some of the regional connectivity functions but there is a spatial mismatch between the primarily coastal MPAs and the high-ranking reef nodes. Furthermore, network partitioning identified subnetworks and dispersal barriers. The existing MPAs were found to be disproportionately distributed to a few subnetworks and that the largest subnetworks do not contain the greatest number of MPAs. Considering these gaps, we suggest expanding the coverage of protected areas especially in underrepresented reef networks to meaningfully capture national-scale connectivity and meet global conservation objectives.
Properties of Communicating Reaction Systems

Communicating reaction systems are new variants of networks of reaction systems where the components communicate with each other by sending products or reactions. Reaction system, a mathematical formalism inspired by the biochemistry of the living cell, focuses on an abstract set-based representation of chemical reactions via facilitation and inhibition. In this paper we examine bio-inspired properties of communicating reaction systems such as steady state and mass conservation.
ADCME MPI: Distributed Machine Learning for Computational Engineering

We propose a framework for training deep neural networks (DNNs) that are coupled with partial differential equations (PDEs) in a parallel computing environment. Unlike most distributed computing frameworks for DNNs, our focus is to parallelize both numerical solvers and DNNs in forward and adjoint computations. Our parallel computing model views data communication as a node in the computational graph for numerical simulations. The advantage of our model is that data communication and computing are cleanly separated, which enables better ﬂexibility, modularity, and testability of the software. We demonstrate our approach on a large-scale problem and show that we can achieve substantial acceleration by using parallel numerical PDE solvers while training DNNs that are coupled with PDEs.