Miracle-dz/UniFAR-FTUs huggingface dataset:
Pioneer transcription factors are associated with the modulation of DNA methylation patterns across cancers

Methylation of cytosines on DNA is a prominent modification associated with gene expression regulation. Aberrant DNA methylation patterns have recurrently been linked to dysregulation of the regulatory program in cancer cells. To shed light on the underlying molecular mechanism driving this process, we hypothesised that aberrant methylation patterns could be controlled by the binding of specific transcription factors (TFs) across cancer types. By combining DNA methylation arrays and gene expression data with TF binding sites (TFBSs), we explored the interplay between TF binding and DNA methylation in 19 cancer types. We performed emQTL (expression–methylation quantitative trait loci) analyses independently in each cancer type and identified 13 TFs whose expression levels are correlated with local DNA methylation patterns around their binding sites in at least 2 cancer types. The 13 TFs are mainly associated with local demethylation and are enriched for pioneer function, suggesting a specific role for these TFs in modulating chromatin structure and transcription in cancer patients. Furthermore, we confirmed that de novo methylation is precluded across cancers at CpGs lying in genomic regions enriched for TF binding signatures associated with SP1, CTCF, NRF1, GABPA, KLF9, and/or YY1. The modulation of DNA methylation associated with TF binding was observed at cis-regulatory regions controlling immune- and cancer-associated pathways, corroborating that the emQTL signals were derived from both cancer and tumor-infiltrating cells. As a case example, we experimentally confirmed that FOXA1 knock-down is associated with higher methylation in regions bound by FOXA1 in breast cancer MCF-7 cells. Finally, we reported physical interactions between FOXA1 with TET1 and TET2 both in an in vitro setup and in vivo at physiological levels in MCF-7 cells, adding further support for FOXA1 attracting TET1 and TET2 to induce local demethylation in cancer cells.
HelR is a helicase-like protein that protects RNA polymerase from rifamycin antibiotics

Rifamycin antibiotics such as rifampin are widely used for the management of tuberculosis and other bacterial infections. These drugs inhibit prokaryotic RNA polymerase (RNAP) by preventing elongation of mRNA resulting in cell death. Rifamycin resistance in the clinic is manifested primarily through amino acid substitutions in RNAP that decrease target affinity for the antibiotics. In contrast, environmental bacteria possess a wide variety of highly specific rifamycin enzyme-mediated resistance mechanisms that modify and inactivate the antibiotics by glycosylation, phosphorylation, ADP-ribosylation, or hydroxylation. Expression of rifamycin resistance is controlled by a common 19bp cis-acting rifamycin associated element (RAE) upstream of inactivating genes. Guided by the presence of RAE sequences, we identify an unprecedented ATP-dependent mechanism of rifamycin resistance that acts not by antibiotic inactivation but by protecting the RNAP target. We show that Streptomyces venezuelae encodes a helicase-like protein, HelR, which confers broad spectrum rifamycin resistance. Furthermore, HelR is essential for promoting rifamycin tolerance at inhibitory concentrations, enabling bacterial evasion of the toxic properties of these antibiotics. HelR forms a complex with RNAP in vivo and rescues transcription inhibition by rifampin in vitro. We synthesized a rifamycin photoprobe and demonstrated that HelR directly displaces rifamycins from RNAP. HelR-encoding genes associated with RAEs are broadly distributed in actinobacteria, including many opportunistic Mycobacterial pathogens, which cannot currently be treated with rifamycins. This first report of an RNAP protection protein conferring antibiotic resistance and offers guidance for developing new rifamycin antibiotics that can avoid this mechanism.
Crystal dissolution and precipitation in porous media flow: variable geometry

In this work we propose a pore scale model for crystal dissolution and precipitation in a porous medium. Our investigations are a first step towards an upscaled model for crystal formation in reactive porous media flows and are motivated by the work of P. Knabner et al. (Adv. Water Res. 1995) and C.J. van Duijn et al. (J. reine angew. Math. 2004). We consider a porous medium that is fully saturated by a fluid in which cations (e.g. sodium ions) and anions (e.g. chlorine ions) are dissolved. In a precipitation reaction, n cations and m anions can precipitate in the form of one particle of crystalline solid (e.g. sodium chloride) attached to the surface of the grains (the porous matrix). The reverse reaction of dissolution is also possible. As a result of the precipitation and dissolution of crystals the geometry of the flow domain may change. The model we propose consists of a system of coupled partial differential equations on a variable domain. The changes in the flow domain, which occur due to the dissolution and precipitation of crystals are modeled by a Stefan-like boundary condition. This describes the movement of the interface between the fluid and the crystal layer. For simple geometries we show that solutions exist and we study their qualitative behavior using both analytic and numerical techniques.
Protein Extraction From FFPE Kidney Tissue Samples: A Review of the Literature and Characterization of Techniques

Most tissue biopsies from patients in hospital environments are formalin-fixed and paraffin-embedded (FFPE) for long-term storage. This fixation process produces a modification in the proteins called “crosslinks”, which improves protein stability necessary for their conservation. Currently, these samples are mainly used in clinical practice for performing immunohistochemical analysis, since these modifications do not suppose a drawback for this technique; however, crosslinks difficult the protein extraction process. Accordingly, these modifications make the development of a good protein extraction protocol necessary. Due to the specific characteristics of each tissue, the same extraction buffers or deparaffinization protocols are not equally effective in all cases. Therefore, it is necessary to obtain a specific protocol for each tissue. The present work aims to establish a deparaffinization and protein extraction protocol from FFPE kidney samples to obtain protein enough of high quality for the subsequent proteomic analysis. Different deparaffination, protocols and protein extraction buffers will be tested in FFPE kidney samples. The optimized conditions will be applied in the identification by LC-MS/MS analysis of proteins extracted from 5, 10, and 15 glomeruli obtained through the microdissection of FFPE renal samples.
Usefulness of the Neutrophil-to-Lymphocyte Ratio as a Predictor of Pneumonia and Urinary Tract Infection Within the First Week After Acute Ischemic Stroke

Background: A high Neutrophil-to-Lymphocyte ratio (NLR) in patients with acute ischemic stroke (AIS) has been associated with post-stroke infections, but it's role as an early predictive biomarker for post-stroke pneumonia (PSP) and urinary tract infection (UTI) is not clear. Aim: To investigate the usefulness of NLR obtained within 24 h after AIS for predicting PSP and UTI in the first week. Methods: Clinical and laboratory data were retrieved from the University Hospital Brussels stroke database/electronic record system. Patients were divided into those who developed PSP or UTI within the first week after stroke onset and those who didn't. Receiver operating characteristics (ROC) curves and logistic regression analysis were used to identify independent predictors. Results: Five hundred and fourteen patients were included, of which 15.4% (n = 79) developed PSP and 22% (n = 115) UTI. In univariate analysis, NLR was significantly higher in patients who developed PSP (4.1 vs. 2.8, p < 0.001) but not in those who developed UTI (3.3 vs. 2.9, p = 0.074). Multiple logistic regression analysis for PSP showed that NLR, male gender, dysphagia, and stroke severity measured by the National Institutes of Health Stroke Scale (NIHSS), were independent predictors of PSP. For NLR alone, the area under the curve (AUC) in the ROC curve was 0.66 (95% CI = 0.59–0.73). When combining NLR ≥ 4.7 with age >75 years, male gender, NIHSS > 7, and dysphagia, the AUC increased to 0.84 (95% CI = 0.79–0.89). Conclusion: The NLR within 24 h after AIS appears to have no predictive value for post-stroke UTI, and is only a weak predictor for identifying patients at high risk for PSP. Its predictive value for PSP appears to be much stronger when incorporated in a prediction model including age, gender, NIHSS score, and dysphagia.
Lipid Secretion by Parasitic Cells of Coccidioides Contributes to Disseminated Disease

Coccidioides is a soil-borne fungal pathogen and causative agent of a human respiratory disease (coccidioidomycosis) endemic to semi-desert regions of southwestern United States, Mexico, Central and South America. Aerosolized arthroconidia inhaled by the mammalian host first undergo conversion to large parasitic cells (spherules, 80–100 μm diameter) followed by endosporulation, a process by which the contents of spherules give rise to multiple endospores. The latter are released upon rupture of the maternal spherules and establish new foci of lung infection. A novel feature of spherule maturation prior to endosporulation is the secretion of a lipid-rich, membranous cell surface layer shed in vivo during growth of the parasitic cells and secretion into liquid culture medium during in vitro growth. Chemical analysis of the culture derived spherule outer wall (SOW) fraction showed that it is composed largely of phospholipids and is enriched with saturated fatty acids, including myristic, palmitic, elaidic, oleic, and stearic acid. NMR revealed the presence of monosaccharide- and disaccharide-linked acylglycerols and sphingolipids. The major sphingolipid components are sphingosine and ceramide. Primary neutrophils derived from healthy C57BL/6 and DBA/2 mice incubated with SOW lipids revealed a significant reduction in fungicidal activity against viable Coccidioides arthroconidia compared to incubation of neutrophils with arthroconidia alone. Host cell exposure to SOW lipids had no effect on neutrophil viability. Furthermore, C57BL/6 mice that were challenged subcutaneously with Coccidioides arthroconidia in the presence of the isolated SOW fraction developed disseminated disease, while control mice challenged with arthroconidia alone by the same route showed no dissemination of infection. We hypothesize that SOW lipids contribute to suppression of inflammatory response to Coccidioides infection. Studies are underway to characterize the immunosuppressive mechanism(s) of SOW lipids.
The Cholinergic and ACE-2-Dependent Anti-Inflammatory Systems in the Lung: New Scenarios Emerging From COVID-19

The renin angiotensin system and the cholinergic anti-inflammatory pathway have been recently shown to modulate lung inflammation in patients with COVID-19. We will show how studies performed on this disease are starting to provide evidence that these two anti-inflammatory systems may functionally interact with each other, a mechanism that could have a more general physiological relevance than only COVID-19 infection.
Dysfunction of Decidual Macrophages Is a Potential Risk Factor in the Occurrence of Preeclampsia

Preeclampsia is a multi-factorial and multi-genetic disorder that affects more than eight million mother and baby pairs each year. Currently, most of the attention to the pathogenesis of preeclampsia has been focused on placenta, but recent progresses suggest that excellent decidualization lays foundation for placentation and growth. Moreover, preeclampsia is associated with an imbalance in immunoregulatory mechanisms, however, how the immune regulatory system in the decidua affects preeclampsia is still unclear. In our study, after intersecting the genes of differentially expressed between preeclampsia and the control gotten by conventional expression profile analysis and the genes contained in the ligand receptor network, we found eight differentially expressed genes in a ligand-receptor relationship, and the eight genes have a characteristic: most of them participate in the interaction between decidual macrophages and other decidual immune cells. The results of single-cell sequencing of decidual cells further demonstrated that decidual macrophages affect the functions of other immune cells through export. As a result, abnormal gene expression affects the export function of decidual macrophages, which in turn affects the interaction of decidual macrophages with other immune cells, thereby destroying the original immune regulation mechanism, and ultimately leading to the occurrence of preeclampsia.
Development and Validation of a Nomogram to Predict the Individual Future Stroke Risk for Adult Patients With Moyamoya Disease: A Multicenter Retrospective Cohort Study in China

Background: Studies exploring the predictive performance of major risk factors associated with future stroke events are insufficient, and a useful tool to predict individual risk is not available. Therefore, personalized advice for preventing future stroke in patients with moyamoya disease (MMD) cannot provide evidence-based recommendations. The aim of this study was to develop a novel nomogram with reliable validity to predict the individual risk of future stroke for adult MMD patients. Methods: This study included 450 patients from seven medical centers between January 2013 and December 2018. Follow-ups were performed via clinical visits and/or telephone interviews from initial discharge to December 2019. The cohort was randomly assigned to a training set (2/3, n = 300) for nomogram development and a test set (1/3, n = 150) for external validation. The Kaplan-Meier analyses and receiver operating characteristic (ROC) curves were applied to assess the clinical benefits of this nomogram. Results: Diabetes mellitus, a family history of MMD, a past history of stroke or transient ischemic attack, clinical manifestation, and treatment were identified as major risk factors via the least absolute shrinkage and selection operator (LASSO) method. A nomogram including these predictors was established via a multivariate Cox regression model, which displayed excellent discrimination [Harrell's concordance index (C-index), 0.85; 95% confidence interval (CI): 0.75–0.96] and calibration. In the external validation, the nomogram was found to have good discrimination (C-index, 0.81; 95% CI: 0.68–0.94) and calibration. In the subgroup analysis, this predictive nomogram also showed great performance in both ischemic-type (C-index, 0.90; 95% CI: 0.77–1.00) and hemorrhagic-type MMD (C-index, 0.72; 95% CI: 0.61–0.83). Furthermore, the nomogram was shown to have potential in clinical practice through Kaplan-Meier analyses and ROC curves. Conclusions: We developed a novel nomogram incorporating several clinical characteristics with relatively good accuracy, which may have considerable potential for evaluating individual future stroke risk and providing useful management recommendations for adult patients with MMD in clinical practice.
Multi-Source Co-adaptation for EEG-Based Emotion Recognition by Mining Correlation Information

Since each individual subject may present completely different encephalogram (EEG) patterns with respect to other subjects, existing subject-independent emotion classifiers trained on data sampled from cross-subjects or cross-dataset generally fail to achieve sound accuracy. In this scenario, the domain adaptation technique could be employed to address this problem, which has recently got extensive attention due to its effectiveness on cross-distribution learning. Focusing on cross-subject or cross-dataset automated emotion recognition with EEG features, we propose in this article a robust multi-source co-adaptation framework by mining diverse correlation information (MACI) among domains and features with l2,1−norm as well as correlation metric regularization. Specifically, by minimizing the statistical and semantic distribution differences between source and target domains, multiple subject-invariant classifiers can be learned together in a joint framework, which can make MACI use relevant knowledge from multiple sources by exploiting the developed correlation metric function. Comprehensive experimental evidence on DEAP and SEED datasets verifies the better performance of MACI in EEG-based emotion recognition.
Comparative Sensitivities of Zooplankton to Ocean Acidification Conditions in Experimental and Natural Settings

Zooplankton can serve as indicators of ecosystem health, water quality, food web structure, and environmental change, including those associated with climate change and ocean acidification (OA). Laboratory studies demonstrate that low pH and high pCO2 associated with OA can significantly affect the physiology and survival of zooplankton, with differential responses among taxa. While laboratory studies can be indicative of zooplankton response to OA, in situ responses will ultimately determine the fate of populations and ecosystems. In this perspective, we compare expectations from experimental studies with observations made in Puget Sound (Washington, United States), a highly dynamic estuary with known vulnerabilities to low pH and high pCO2. We found little association between empirical measures of in situ pH and the abundance of sensitive taxa as revealed by meta-analysis, calling into question the coherence between experimental studies and field observations. The apparent mismatch between laboratory and field studies has important ramifications for the design of long-term monitoring programs and interpretation and use of the data produced. Important work remains to be done to connect traits that are sensitive to OA with those that are ecologically relevant and reliably observable in the field.
Urothelial Cancer Associated 1 (UCA1) and miR-193 Are Two Non-coding RNAs Involved in Trophoblast Fusion and Placental Diseases

A bioinformatics screen for non-coding genes was performed from microarrays analyzing on the one hand trophoblast fusion in the BeWo cell model, and on the other hand, placental diseases (preeclampsia and Intra-Uterine Growth Restriction). Intersecting the deregulated genes allowed to identify two miRNA (mir193b and miR365a) and one long non-coding RNA (UCA1) that are pivotal for trophoblast fusion, and deregulated in placental diseases. We show that miR-193b is a hub for the down-regulation of 135 cell targets mainly involved in cell cycle progression and energy usage/nutrient transport. UCA1 was explored by siRNA knock-down in the BeWo cell model. We show that its down-regulation is associated with the deregulation of important trophoblast physiology genes, involved in differentiation, proliferation, oxidative stress, vacuolization, membrane repair and endocrine production. Overall, UCA1 knockdown leads to an incomplete gene expression profile modification of trophoblast cells when they are induced to fuse into syncytiotrophoblast. Then we performed the same type of analysis in cells overexpressing one of the two major isoforms of the STOX1 transcription factor, STOX1A and STOX1B (associated previously to impaired trophoblast fusion). We could show that when STOX1B is abundant, the effects of UCA1 down-regulation on forskolin response are alleviated.
Characterization of Post-Operative Hemodynamics Following the Norwood Procedure Using Population Data and Multi-Scale Modeling

Children with hypoplastic left heart syndrome (HLHS) must undergo multiple surgical stages to reconstruct the anatomy to a sustainable single ventricle system. Stage I palliation, or the Norwood procedure, provides circulation to both pulmonary and systemic vasculature. The aorta is reconstructed and attached to the right ventricle and a fraction of systemic flow is redirected to the pulmonary arteries (PAs) through a systemic-to-PA shunt. Despite abundant hemodynamic data available 4–5 months after Norwood palliation, data is very scarce immediately following stage I. This data is critical in determining post-operative success. In this work, we combined population data and computational fluid dynamics (CFD) to characterize hemodynamics immediately following stage I (post-stage I) and prior to stage II palliation (pre-stage II). A patient-specific model was constructed as a baseline geometry, which was then scaled to reflect population-based morphological data at both time-points. Population-based hemodynamic data was then used to calibrate each model to reproduce blood flow representative of HLHS patients. The post-stage I simulation produced a PA pressure of 22 mmHg and high-frequency oscillations within the flow field indicating highly disturbed hemodynamics. Despite PA mean pressure dropping to 14 mmHg, the pre-stage II model also produced high-frequency flow components and PA wall shear stress increases. These suboptimal conditions may be necessary to ensure adequate PA flow throughout the pre-stage II period, as the shunt becomes relatively smaller compared to the patient’s somatic growth. In the future, CFD can be used to optimize shunt design and minimize these suboptimal conditions.
Multideep Feature Fusion Algorithm for Clothing Style Recognition

In order to improve recognition accuracy of clothing style and fully exploit the advantages of deep learning in extracting deep semantic features from global to local features of clothing images, this paper utilizes the target detection technology and deep residual network (ResNet) to extract comprehensive clothing features, which aims at focusing on clothing itself in the process of feature extraction procedure. Based on that, we propose a multideep feature fusion algorithm for clothing image style recognition. First, we use the improved target detection model to extract the global area, main part, and part areas of clothing, which constitute the image, so as to weaken the influence of the background and other interference factors. Then, the three parts were inputted, respectively, to improve ResNet for feature extraction, which has been trained beforehand. The ResNet model is improved by optimizing the convolution layer in the residual block and adjusting the order of the batch-normalized layer and the activation layer. Finally, the multicategory fusion features were obtained by combining the overall features of the clothing image from the global area, the main part, to the part areas. The experimental results show that the proposed algorithm eliminates the influence of interference factors, makes the recognition process focus on clothing itself, greatly improves the accuracy of the clothing style recognition, and is better than the traditional deep residual network-based methods.