Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of novel, functional, long noncoding RNAs involved in programmed, large-scale genome rearrangements

Noncoding RNAs (ncRNAs) make up to ∼98% percent of the transcriptome of a given organism. In recent years, one relatively new class of ncRNAs, long noncoding RNAs (lncRNAs), were shown to be more than mere by-products of gene expression and regulation. The unicellular eukaryote Paramecium tetraurelia is a member of the ciliate phylum, an extremely heterogeneous group of organisms found in most bodies of water across the globe. A hallmark of ciliate genetics is nuclear dimorphism and programmed elimination of transposons and transposon-derived DNA elements, the latter of which is essential for the maintenance of the somatic genome. Paramecium and ciliates in general harbor a plethora of different ncRNA species, some of which drive the process of large-scale genome rearrangements, including DNA elimination, during sexual development. Here, we identify and validate the first known functional lncRNAs in ciliates to date. Using deep-sequencing and subsequent bioinformatic processing and experimental validation, we show that Paramecium expresses at least 15 lncRNAs. These candidates were predicted by a highly conservative pipeline, and informatic analyses hint at differential expression during development. Depletion of two lncRNAs, lnc1 and lnc15, resulted in clear phenotypes, decreased survival, morphological impairment, and a global effect on DNA elimination.
Angiotensin II‐induced endothelial dysfunction: Impact of sex, genetic background, and rho kinase

The renin‐angiotensin system (RAS) contributes to vascular disease with multiple cardiovascular risk factors including hypertension. As a major effector within the RAS, angiotensin II (Ang II) activates diverse signaling mechanisms that affect vascular biology. Despite the impact of such vascular pathophysiology, our understanding of the effects of Ang II in relation to the function of endothelial cells is incomplete. Because genetic background and biological sex can be determinants of vascular disease, we performed studies examining the direct effects of Ang II using carotid arteries from male and female mice on two genetic backgrounds, C57BL/6J and FVB/NJ. Although FVB/NJ mice are much less susceptible to atherosclerosis than C57BL/6J, the effects of Ang II on endothelial cells in FVB/NJ are poorly defined. Overnight incubation of isolated arteries with Ang II (10 nmol/L), impaired endothelial function in both strains and sexes by approximately one‐half (p < 0.05). To examine the potential mechanistic contribution of Rho kinase (ROCK), we treated arteries with SLX‐2119, an inhibitor with high selectivity for ROCK2. In both male and female mice of both strains, SLX‐2119 largely restored endothelial function to normal, compared to vessels treated with vehicle. Thus, Ang II‐induced endothelial dysfunction was observed in both FVB/NJ and C57BL/6J mice. This effect was sex‐independent. In all groups, effects of Ang II were reversed by inhibition of ROCK2 with SLX‐2119. These studies provide the first evidence that ROCK2 may be a key contributor to Ang II‐induced endothelial dysfunction in both sexes and in mouse strains that differ in relation to other major aspects of vascular disease.
Adaptive Dynamic Programming-Based Event-Triggered Robust Control for Multiplayer Nonzero-Sum Games With Unknown Dynamics

In this article, the event-triggered robust control of unknown multiplayer nonlinear systems with constrained inputs and uncertainties is investigated by using adaptive dynamic programming. To relax the requirement of system dynamics, a neural network-based identifier is constructed by using the system input-output data. Subsequently, by designing a nonquadratic value function, which contains the bounded functions, the system states, and the control inputs of all players, the event-triggered robust stabilization problem is converted into an event-triggered constrained optimal control problem. To obtain the approximate solution of the event-triggered Hamilton–Jacobi (HJ) equation, a critic network for each player is established with a novel weight updating law to relax the persistence of excitation condition based on the experience replay technique. Furthermore, according to the Lyapunov stability theorem, the present event-triggered robust optimal control ensures the multiplayer system to be uniformly ultimately bounded. Finally, two simulation examples are employed to show the effectiveness of the present method.
Predicting resilience through the lens of competing adjustments to vegetation function.

There is a pressing need to better understand ecosystem resilience to droughts and heatwaves. Eco-evolutionary optimisation approaches have been proposed as means to build this understanding in land surface models and improve their predictive capability, but competing approaches are yet to be tested together. Here, we coupled approaches that optimise canopy gas-exchange and leaf nitrogen investment, respectively, extending both approaches to account for hydraulic impairment. We assessed model predictions using observations from a native Eucalyptus woodland that experienced repeated droughts and heatwaves between 2013 and 2020, whilst exposed to an elevated [CO2 ] treatment. Our combined approaches improved predictions of transpiration and enhanced the simulated magnitude of the CO2 fertilisation effect on gross primary productivity. The competing approaches also worked consistently along axes of change in soil moisture, leaf area, and [CO2 ]. Despite predictions of significant percentage loss of hydraulic conductivity due to embolism (PLC) in 2013, 2014, 2016, and 2017 (99th percentile PLC > 45%), simulated hydraulic legacy effects were small and short-lived (2 months). Our analysis suggests that leaf shedding and/or suppressed foliage growth formed a strategy to mitigate drought risk. Accounting for foliage responses to water availability has the potential to improve model predictions of ecosystem resilience. This article is protected by copyright. All rights reserved.
Contribution of Metabolic Tumor Volume and Total Lesion Glycolysis to Predict Prognosis in Early-Stage Lung Cancer at Preoperative Staging.

OBJECTIVE To evaluate the effect of metabolic tumor volume (MTV) and total lesion glycolysis (TLG) values of the primary tumor measured by preoperative positron emission tomography/computed tomography (FDG-PET/CT) on survival in patients with operated non-small cell lung cancer (NSCLC). STUDY DESIGN Cohort study. PLACE AND DURATION OF STUDY Recep Tayyip Erdogan University, Faculty of Medicine, Department of Medical Oncology from January 2017 to June 2020, Turkey. METHODOLOGY Patients with operated NSCLC were reviewed retrospectively. Metabolic parameters of FDG-PET/CT such as pathological tumor features, type of operation, MTV/TLG values, and whether they received adjuvant therapy were evaluated. Disease-free survival (DFS) and overall survival (OS) times were calculated. RESULTS Most of the 77 patients (96.1%) were male. The mean age is 64±8 years. Lobectomy was performed in 66 (85.7%) patients, and pneumonectomy was performed in 11 (14.3%) patients. The mean tumor diameter was 3.7±2.015cm. Squamous cell carcinoma was detected in 37 patients (48.1%) and adenocarcinoma in 35 patients (45.5%). Thirty-eight patients (49.4%) received adjuvant chemotherapy. SUVmax, MTV, and TLG values of the primary tumor were high in patients under 65 years of age and with a tumor diameter of ≥3cm. DFS was nine months (4.5-18), and OS was 19 months (11-29). The 2-year survival rate was 75.6%. It was observed that patients with adenocarcinoma relapsed more frequently, which negatively affected survival (p=0.023, and p=0.024 respectively). High MTV (p=0.01) and TLG (p=0.015) values were associated with poor prognosis. CONCLUSION NSCLC is a heterogeneous disease, and survival is affected by many factors. Our study showed that the subtype of adenocarcinoma and high MTV and TLG values of the primary tumor are poor prognostic factors in operated early-stage lung cancers. KEY WORDS Early-stage, Non-small cell lung cancer, MTV, TLG, NSCLC, Survival, FDG-PET/CT.
Automatic Detection of Aortic Valve Events Using Deep Neural Networks on Cardiac Signals From Epicardially Placed Accelerometer

Background: Miniaturized accelerometers incorporated in pacing leads attached to the myocardium, are used to monitor cardiac function. For this purpose functional indices must be extracted from the acceleration signal. A method that automatically detects the time of aortic valve opening (AVO) and aortic valve closure (AVC) will be helpful for such extraction. We tested if deep learning can be used to detect these valve events from epicardially attached accelerometers, using high fidelity pressure measurements to establish ground truth for these valve events. Method: A deep neural network consisting of a CNN, an RNN, and a multi-head attention module was trained and tested on 130 recordings from 19 canines and 159 recordings from 27 porcines covering different interventions. Due to limited data, nested cross-validation was used to assess the accuracy of the method. Result: The correct detection rates were 98.9% and 97.1% for AVO and AVC in canines and 98.2% and 96.7% in porcines when defining a correct detection as a prediction closer than 40 ms to the ground truth. The incorrect detection rates were 0.7% and 2.3% for AVO and AVC in canines and 1.1% and 2.3% in porcines. The mean absolute error between correct detections and their ground truth was 8.4 ms and 7.2 ms for AVO and AVC in canines, and 8.9 ms and 10.1 ms in porcines. Conclusion: Deep neural networks can be used on signals from epicardially attached accelerometers for robust and accurate detection of the opening and closing of the aortic valve.
HSFs drive transcription of distinct genes and enhancers during oxidative stress and heat shock

Abstract Reprogramming of transcription is critical for the survival under cellular stress. Heat shock has provided an excellent model to investigate nascent transcription in stressed cells, but the molecular mechanisms orchestrating RNA synthesis during other types of stress are unknown. We utilized PRO-seq and ChIP-seq to study how Heat Shock Factors, HSF1 and HSF2, coordinate transcription at genes and enhancers upon oxidative stress and heat shock. We show that pause-release of RNA polymerase II (Pol II) is a universal mechanism regulating gene transcription in stressed cells, while enhancers are activated at the level of Pol II recruitment. Moreover, besides functioning as conventional promoter-binding transcription factors, HSF1 and HSF2 bind to stress-induced enhancers to trigger Pol II pause-release from poised gene promoters. Importantly, HSFs act at distinct genes and enhancers in a stress type-specific manner. HSF1 binds to many chaperone genes upon oxidative and heat stress but activates them only in heat-shocked cells. Under oxidative stress, HSF1 localizes to a unique set of promoters and enhancers to trans-activate oxidative stress-specific genes. Taken together, we show that HSFs function as multi-stress-responsive factors that activate distinct genes and enhancers when encountering changes in temperature and redox state.
Impact of medium cut‐off membranes on S100A12 and soluble receptor for advanced glycation end products

Of the most remarkable molecules associated with atherosclerosis and the cardiovascular outcome are S100A12 (10,379.5 Da) and soluble receptor for advanced glycation end products (sRAGE‐42,803 Da) in the hemodialysis (HD) population. We designed a study investigating the effects of the medium cut‐off (MCO) dialyzers focusing on S100A12 and sRAGE in HD patients compared with low‐flux and high‐flux dialyzers.
Profiles of patients using emergency departments or hospitalized for suicidal behaviors.

OBJECTIVES This study identified profiles of patients with suicidal behaviors, their sociodemographic and clinical correlates, and assessed the risk of death within a 12-month follow-up period. METHODS Based on administrative databases, this 5-year study analyzed data on 5064 patients in Quebec who used emergency departments (ED) or were hospitalized for suicidal behaviors over a 2-year period. Latent class analysis was used for patient profiles, bivariate analysis for patient correlates over 2 years, and survival analysis for risk of death within a 12-month follow-up. RESULTS Four profiles were identified: high suicidal behaviors and high service use (Profile 1: 23%); low suicidal behaviors and moderate service use (Profile 2: 46%); low suicidal behaviors and low service use (Profile 3: 25%); and high suicidal behaviors and high acute care, but low outpatient care (Profile 4: 6%). Profiles 1 and 4 patients had more serious conditions, with a higher risk of death in Profile 1 versus Profiles 2 and 3. Profile 2 patients had relatively more common mental disorders, and Profile 3 patients had less severe conditions. Profiles 3 and 4 included more men and younger patients. CONCLUSION Programs better adapted to patient profiles should be deployed after ED use and hospitalization in coordination with outpatient services.
Resveratrol Binds Nuclear Receptor 4A1 (NR4A1) and Acts as an NR4A1 Antagonist in Lung Cancer Cells

Resveratrol is a polyphenolic phytochemical found in fruits, nuts, and vegetables that contributes to the remarkable dietary effects of polyphenolic as inhibitors of aging and multiple aging related diseases. In addition, resveratrol has been extensively investigated as an inhibitor of inflammatory diseases including cancer; however, the underlying mechanisms of these chemotherapeutic effects of resveratrol are not completely understood. In cancer cells, resveratrol inhibits cell growth, survival, migration, and invasion, and many of the effects of resveratrol resemble those observed for bis-indole derived (CDIM) compounds that bind the pro-oncogenic nuclear receptor 4A1 (NR4A1, Nur77) and act as receptor antagonists. Using an isothermal titration calorimetry binding assay, we observed that resveratrol bound to the ligand binding domain of NR4A1 with a KD value of 2.4 µM and a ΔG of -32.2 kJ/mol. Resveratrol also inhibited NR4A1-dependent transactivation in H460 and H1299 lung cancer cells, suggesting that resveratrol is an NR4A1 antagonist. This observation was confirmed in a series of functional (cell proliferation, survival, migration, and invasion) and gene expression assays in H460 and H1299 cells, showing that treatment with resveratrol mimicked the effects of NR4A1 knockdown and were similar to results of previous studies using CDIM/NR4A1 antagonists. These data indicate that applications of resveratrol may be more effective in patients that overexpress NR4A1, which is a negative prognostic factor for patients with some solid tumor-derived cancers. SIGNIFICANCE STATEMENT This study has examined the mechanism of action of resveratrol and shows binding to NR4A1 (KD = 2.4 µM) and inhibition of NR4A1-dependent transactivation in lung cancer cells. Treatment of H460 and H1299 lung cancer cells with resveratrol inhibits cell growth, survival, migration/invasion, and related genes and acts as an NR4A1 antagonist. Resveratrol can now be used more effectively in cancer chemotherapy by targeting patients that overexpress NR4A1 in lung cancer.
Transfer-Learning-Based Gaussian Mixture Model for Distributed Clustering

Distributed clustering based on the Gaussian mixture model (GMM) has exhibited excellent clustering capabilities in peer-to-peer (P2P) networks. However, more iterative numbers and communication overhead are required to achieve the consensus in existing distributed GMM clustering algorithms. In addition, the truth that it cannot find a closed form for the update of parameters in GMM causes the imprecise clustering accuracy. To solve these issues, by utilizing the transfer learning technique, a general transfer distributed GMM clustering framework is exploited to promote the clustering performance and accelerate the clustering convergence. In this work, each node is treated as both the source domain and the target domain, and these nodes can learn from each other to complete the clustering task in distributed P2P networks. Based on this framework, the transfer distributed expectation–maximization algorithm with the fixed learning rate is first presented for data clustering. Then, an improved version is designed to obtain the stable clustering accuracy, in which an adaptive transfer learning strategy is adopted to adjust the learning rate automatically instead of a fixed value. To demonstrate the extensibility of the proposed framework, a representative GMM clustering method, the entropy-type classification maximum-likelihood algorithm, is further extended to the transfer distributed counterpart. Experimental results verify the effectiveness of the presented algorithms in contrast with the existing GMM clustering approaches.
Assessing Progress Toward the Vision of a Comprehensive, Shared Electronic Care Plan: Scoping Review

Background Care plans are central to effective care delivery for people with multiple chronic conditions. But existing care plans—which typically are difficult to share across care settings and care team members—poorly serve people with multiple chronic conditions, who often receive care from numerous clinicians in multiple care settings. Comprehensive, shared electronic care (e-care) plans are dynamic electronic tools that facilitate care coordination and address the totality of health and social needs across care contexts. They have emerged as a potential way to improve care for individuals with multiple chronic conditions. Objective To review the landscape of e-care plans and care plan–related initiatives that could allow the creation of a comprehensive, shared e-care plan and inform a joint initiative by the National Institutes of Health and the Agency for Healthcare Research and Quality to develop e-care planning tools for people with multiple chronic conditions. Methods We conducted a scoping review, searching literature from 2015 to June 2020 using Scopus, Clinical Key, and PubMed; we also searched the gray literature. To identify initiatives potentially missing from this search, we interviewed expert informants. Relevant data were then identified and extracted in a structured format for data synthesis and analysis using an expanded typology of care plans adapted to our study context. The extracted data included (1) the perspective of the initiatives; (2) their scope, (3) network, and (4) context; (5) their use of open syntax standards; and (6) their use of open semantic standards. Results We identified 7 projects for e-care plans and 3 projects for health care data standards. Each project provided critical infrastructure that could be leveraged to promote the vision of a comprehensive, shared e-care plan. All the e-care plan projects supported both broad goals and specific behaviors; 1 project supported a network of professionals across clinical, community, and home-based networks; 4 projects included social determinants of health. Most projects specified an open syntax standard, but only 3 specified open semantic standards. Conclusions A comprehensive, shared, interoperable e-care plan has the potential to greatly improve the coordination of care for individuals with multiple chronic conditions across multiple care settings. The need for such a plan is heightened in the wake of the ongoing COVID-19 pandemic. While none of the existing care plan projects meet all the criteria for an optimal e-care plan, they all provide critical infrastructure that can be leveraged as we advance toward the vision of a comprehensive, shared e-care plan. However, critical gaps must be addressed in order to achieve this vision.
Imparting Multifunctional Properties to Materials

Superhydrophobic coatings have a huge impact in various applications due to their extreme water-repellent properties. The main novelty of the current research work lies in the development of cheap, stable, superhydrophobic and self-cleaning coatings with extreme water-repellency. In this work, a composite of hydrothermally synthesized alumina (Al2O3), polymethylhydrosiloxane (PMHS) and polystyrene (PS) was deposited on a glass surface by a dip-coating technique. The Al2O3 nanoparticles form a rough structure, and low-surface-energy PHMS enhances the water-repellent properties. The composite coating revealed a water contact angle (WCA) of 171 ± 2◦ and a sliding angle (SA) of 3◦. In the chemical analysis, Al2p, Si2p, O1s, and C1s elements were detected in the XPS survey. The prepared coating showed a self-cleaning property through the rolling action of water drops. Such a type of coating could have various industrial applications in the future.
The relationships between antihypertensive medications and the overall survival of patients with pancreatic cancer: a systematic review and meta-analysis

ABSTRACT Background Antihypertensive medications may have some impacts on cancer. The influence of antihypertensive medications, including angiotensin converting enzyme inhibitors (ACEIs)/angiotensin II receptor blockers (ARBs), beta-blockers, calcium channel blockers (CCBs) and diuretics, on patients with pancreatic cancer (PC) remains controversial. This meta-analysis was conducted to investigate whether antihypertensive medications had a negative effect on the prognosis of patients with PC. Methods The PubMed, Embase, Web of Science and the Cochrane Library databases were searched up to 30 November 2021. The Newcastle-Ottawa scale was used to evaluate the quality of each study. This meta-analysis was registered with PROSPERO (CRD42021279169) and was carried out by using RevMan 5.3. Results Twelve studies with 120,549 patients were included in this study. ACEIs/ARBs [HR = 0.89, 95% CI (0.70–1.14)], CCB (HR = 0.69, 95% CI (0.47–0.99)], beta-blockers [HR = 0.95, 95% CI (0.84–1.07)] and diuretics [HR = 1.08, 95% CI (0.91–1.29)] use had no effects on overall survival among patients with PC (all P ≥ 0.05). Conclusion Antihypertensive medication will not have a negative effect on overall survival in patients with PC. PC patients with hypertension should continue to use antihypertensive medications to reduce the morbidity and mortality of cardiovascular events.
Survival Impact of Variant Histology Diagnosis in Upper Tract Urothelial Carcinoma

Purpose: Little is known regarding the prognostic implications of variant histology in upper tract urothelial carcinoma (UTUC). We sought to evaluate the impact of variant histology UTUC on patient survival outcomes at our institution. Materials and Methods: We identified 705 patients who underwent nephroureterectomy for UTUC at our institution between January 1995 and December 2018. We tested the association between variant histology and cancer-specific survival (CSS) and overall survival (OS) using separate multivariable Cox models after adjusting for pathological stage. Results: Forty-seven patients (6.7%) had variant histology, with prevalence increasing over time (p=0.003). Other demographic and surgical characteristics were similar between variant histology and pure urothelial carcinoma groups. While patients with variant histology were more likely to receive neoadjuvant chemotherapy (38% vs 15%, p <0.001), they were also more likely to have a higher pathological T stage (p <0.001). Variant histology was associated with significantly worse CSS (HR: 2.14; 95% CI 1.33, 3.44; p=0.002) and OS (HR: 1.74; 95% CI 1.15, 2.63; p=0.008). After adjusting for pathological T stage, variant histology was not significantly associated with CSS (HR: 1.17; 95% CI 0.72, 1.89; p=0.5) or OS (HR: 1.20; 95% CI 0.79, 1.84; p=0.4). Conclusions: Variant histology UTUC is associated with advanced stage and poor survival, and could serve as a useful biomarker for high-risk disease when pathological stage is unknown. However, the inferior CSS and OS with variant histology can be explained by the higher tumor stage on nephroureterectomy. Thus, finding variant histology on surgical pathology does not provide additional prognostic information beyond stage.