Regulation of apoptosis of rbf mutant cells during Drosophila development

AbstractInactivation of the retinoblastoma gene Rb leads to defects in cell proliferation, differentiation, or apoptosis, depending on specific cell or tissue types. To gain insights into the genes that can modulate the consequences of Rb inactivation, we carried out a genetic screen in Drosophila to identify mutations that affected apoptosis induced by inactivation of the Retinoblastoma-family protein (rbf) and identified a mutation that blocked apoptosis induced by rbf. We found this mutation to be a new allele of head involution defective (hid) and showed that hid expression is deregulated in rbf mutant cells in larval imaginal discs. We identified an enhancer that regulates hid expression in response to developmental cues as well as to radiation and demonstrated that this hid enhancer is directly repressed by RBF through an E2F binding site. These observations indicate that apoptosis of rbf mutant cells is mediated by an upregulation of hid. Finally, we showed that bantam, a miRNA that regulates hid translation, is expressed in the interommatidial cells in the larval eye discs and modulates the survival of rbf mutant cells

Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization

BackgroundSepsis-associated acute kidney injury (S-AKI) is considered to be associated with high morbidity and mortality, a commonly accepted model to predict mortality is urged consequently. This study used a machine learning model to identify vital variables associated with mortality in S-AKI patients in the hospital and predict the risk of death in the hospital. We hope that this model can help identify high-risk patients early and reasonably allocate medical resources in the intensive care unit (ICU).MethodsA total of 16,154 S-AKI patients from the Medical Information Mart for Intensive Care IV database were examined as the training set (80%) and the validation set (20%). Variables (129 in total) were collected, including basic patient information, diagnosis, clinical data, and medication records. We developed and validated machine learning models using 11 different algorithms and selected the one that performed the best. Afterward, recursive feature elimination was used to select key variables. Different indicators were used to compare the prediction performance of each model. The SHapley Additive exPlanations package was applied to interpret the best machine learning model in a web tool for clinicians to use. Finally, we collected clinical data of S-AKI patients from two hospitals for external validation.ResultsIn this study, 15 critical variables were finally selected, namely, urine output, maximum blood urea nitrogen, rate of injection of norepinephrine, maximum anion gap, maximum creatinine, maximum red blood cell volume distribution width, minimum international normalized ratio, maximum heart rate, maximum temperature, maximum respiratory rate, minimum fraction of inspired O2, minimum creatinine, minimum Glasgow Coma Scale, and diagnosis of diabetes and stroke. The categorical boosting algorithm model presented significantly better predictive performance [receiver operating characteristic (ROC): 0.83] than other models [accuracy (ACC): 75%, Youden index: 50%, sensitivity: 75%, specificity: 75%, F1 score: 0.56, positive predictive value (PPV): 44%, and negative predictive value (NPV): 92%]. External validation data from two hospitals in China were also well validated (ROC: 0.75).ConclusionsAfter selecting 15 crucial variables, a machine learning-based model for predicting the mortality of S-AKI patients was successfully established and the CatBoost model demonstrated best predictive performance

Identification of common molecular signatures of SARS-CoV-2 infection and its influence on acute kidney injury and chronic kidney disease

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the main cause of COVID-19, causing hundreds of millions of confirmed cases and more than 18.2 million deaths worldwide. Acute kidney injury (AKI) is a common complication of COVID-19 that leads to an increase in mortality, especially in intensive care unit (ICU) settings, and chronic kidney disease (CKD) is a high risk factor for COVID-19 and its related mortality. However, the underlying molecular mechanisms among AKI, CKD, and COVID-19 are unclear. Therefore, transcriptome analysis was performed to examine common pathways and molecular biomarkers for AKI, CKD, and COVID-19 in an attempt to understand the association of SARS-CoV-2 infection with AKI and CKD. Three RNA-seq datasets (GSE147507, GSE1563, and GSE66494) from the GEO database were used to detect differentially expressed genes (DEGs) for COVID-19 with AKI and CKD to search for shared pathways and candidate targets. A total of 17 common DEGs were confirmed, and their biological functions and signaling pathways were characterized by enrichment analysis. MAPK signaling, the structural pathway of interleukin 1 (IL-1), and the Toll-like receptor pathway appear to be involved in the occurrence of these diseases. Hub genes identified from the protein–protein interaction (PPI) network, including DUSP6, BHLHE40, RASGRP1, and TAB2, are potential therapeutic targets in COVID-19 with AKI and CKD. Common genes and pathways may play pathogenic roles in these three diseases mainly through the activation of immune inflammation. Networks of transcription factor (TF)–gene, miRNA–gene, and gene–disease interactions from the datasets were also constructed, and key gene regulators influencing the progression of these three diseases were further identified among the DEGs. Moreover, new drug targets were predicted based on these common DEGs, and molecular docking and molecular dynamics (MD) simulations were performed. Finally, a diagnostic model of COVID-19 was established based on these common DEGs. Taken together, the molecular and signaling pathways identified in this study may be related to the mechanisms by which SARS-CoV-2 infection affects renal function. These findings are significant for the effective treatment of COVID-19 in patients with kidney diseases

The effect of temperature and solvent composition on transformation of β- to α-glycine as monitored in situ by FBRM and PVM

The application of in situ focused beam reflectance measurement (FBRM) and particle vision measurement (PVM) in monitoring transformation of glycine polymorphs is introduced. The effect of solvent composition and temperature on the transformation from β- to α-glycine was investigated. It is noted that the transformation kinetics are highly sensitive to both the solvent composition and temperature and the transformation rate is a function of ethanol content in aqueous ethanol mixtures. At 303 K, high initial ethanol concentration accounts for a steady transformation. At the same ethanol content, the transformation rate decreases with decrease in temperature. A smoother transformation was observed at 293 K. The results are consistent with the solvent-mediated transformation mechanism in which β-glycine dissolves and α-glycine nucleates and grows. The thermodynamically stable γ-glycine was not observed. Understanding these effects can aid optimization and improve process control

Reliability Assessment and Residual Life Estimation of Concrete Girder Bridges Strengthened by Carbon Fiber during the Service Stage

In recent decades, carbon fiber reinforced plastics (CFRP) have been widely used to repair and maintain concrete structures around the world. Since the parameter uncertainties of load and resistance are very important for the reliability assessment of RC bridge strengthened by CFRP, this paper presents a method to estimate the reliability and residual life of RC bridges strengthened by CFRP. In the proposed method, uncertainties of material properties, geometry parameters, load model, and time-dependent resistance model are taken into account. The proposed method combines the inverse reliability method and the calculation method of load and resistance of RC bridge strengthened by CFRP and is illustrated by an example RC bridge strengthened by CFRP during the service stage. The results indicate that the proposed approach can provide valid information regarding parameter uncertainties for the reliability of RC bridge strengthened by CFRP during the service stage. Additionally, the effects of parameter uncertainty of the reliability and residual life of RC bridge strengthened by CFRP during the service stage are analyzed and discussed. The proposed method is more robust and reliable than the traditional method

A Data-Driven Method to Monitor Carbon Dioxide Emissions of Coal-Fired Power Plants

Reducing CO2 emissions from coal-fired power plants is an urgent global issue. Effective and precise monitoring of CO2 emissions is a prerequisite for optimizing electricity production processes and achieving such reductions. To obtain the high temporal resolution emissions status of power plants, a lot of research has been done. Currently, typical solutions are utilizing Continuous Emission Monitoring System (CEMS) to measure CO2 emissions. However, these methods are too expensive and complicated because they require the installation of a large number of devices and require periodic maintenance to obtain accurate measurements. According to this limitation, this paper attempts to provide a novel data-driven method using net power generation to achieve near-real-time monitoring. First, we study the key elements of CO2 emissions from coal-fired power plants (CFPPs) in depth and design a regression and physical variable model-based emission simulator. We then present Emission Estimation Network (EEN), a heterogeneous network-based deep learning model, to estimate CO2 emissions from CFPPs in near-real-time. We use artificial data generated by the simulator to train it and apply a few real-world datasets to complete the adaptation. The experimental results show that our proposal is a competitive approach that not only has accurate measurements but is also easy to implement

A Data-Driven Method to Monitor Carbon Dioxide Emissions of Coal-Fired Power Plants

Reducing CO2 emissions from coal-fired power plants is an urgent global issue. Effective and precise monitoring of CO2 emissions is a prerequisite for optimizing electricity production processes and achieving such reductions. To obtain the high temporal resolution emissions status of power plants, a lot of research has been done. Currently, typical solutions are utilizing Continuous Emission Monitoring System (CEMS) to measure CO2 emissions. However, these methods are too expensive and complicated because they require the installation of a large number of devices and require periodic maintenance to obtain accurate measurements. According to this limitation, this paper attempts to provide a novel data-driven method using net power generation to achieve near-real-time monitoring. First, we study the key elements of CO2 emissions from coal-fired power plants (CFPPs) in depth and design a regression and physical variable model-based emission simulator. We then present Emission Estimation Network (EEN), a heterogeneous network-based deep learning model, to estimate CO2 emissions from CFPPs in near-real-time. We use artificial data generated by the simulator to train it and apply a few real-world datasets to complete the adaptation. The experimental results show that our proposal is a competitive approach that not only has accurate measurements but is also easy to implement

Solid–Liquid Phase Equilibrium of Nicotinamide in Different Pure Solvents: Measurements and Thermodynamic Modeling

The solubility of nicotinamide in water, methanol, ethanol, 2-propanol, <i>n</i>-butanol, and ethyl acetate at temperatures ranging from 288.15 K to 318.15 K was measured via a static gravimetric analytic method, and the melting temperature and fusion enthalpy of nicotinamide were obtained using differential scanning calorimetry. The experimental solubility data were correlated by the modified Apelblat equation, the van’t Hoff equation, the λ<i>h</i> (Buchowski) equation, and the Wilson model; the van’t Hoff equation shows the best agreement. Furthermore, the dissolution enthalpy and dissolution entropy of nicotinamide in the corresponding solvents were predicted

Research progress of exosomes in the angiogenesis of digestive system tumour

Abstract Malignant tumours of the digestive system cover a wide range of diseases that affect the health of people to a large extent. Angiogenesis is indispensable in the development, and metastasis of tumours, mainly in two ways: occupation or formation. Vessels can provide nutrients, oxygen, and growth factors for tumours to encourage growth and metastasis, so cancer progression depends on simultaneous angiogenesis. Recently, exosomes have been proven to participate in the angiogenesis of tumours. They influence angiogenesis by binding to tyrosine kinase receptors (VEGFR)-1, VEGFR-2, and VEGFR-3 with different affinities, regulating Yap-VEGF pathway, Akt pathway or other signaling pathway. Additionally, exosomes are potential therapeutic vectors that can deliver many types of cargoes to different cells. In this review, we summarize the roles of exosomes in the angiogenesis of digestive system tumours and highlight the clinical application prospects, directly used as targers or delivery vehicles, in antiangiogenic therapy

Next Decade of Telecommunications Artificial Intelligence

It has been an exciting journey since the mobile communications and artificial intelligence (AI) were conceived in 1983 and 1956. While both fields evolved independently and profoundly changed communications and computing industries, the rapid convergence of 5th generation mobile communication technology (5G) and AI is beginning to significantly transform the core communication infrastructure, network management, and vertical applications. The paper first outlined the individual roadmaps of mobile communications and AI in the early stage, with a concentration to review the era from 3rd generation mobile communication technology (3G) to 5G when AI and mobile communications started to converge. With regard to telecommunications AI, the progress of AI in the ecosystem of mobile communications was further introduced in detail, including network infrastructure, network operation and management, business operation and management, intelligent applications towards business supporting system (BSS) & operation supporting system (OSS) convergence, verticals and private networks, etc. Then the classifications of AI in telecommunication ecosystems were summarized along with its evolution paths specified by various international telecommunications standardization organizations. Towards the next decade, the prospective roadmap of telecommunications AI was forecasted. In line with 3rd generation partnership project (3GPP) and International Telecommunication Union Radiocommunication Sector (ITU-R) timeline of 5G & 6th generation mobile communication technology (6G), the network intelligence following 3GPP and open radio access network (O-RAN) routes, experience and intent-based network management and operation, network AI signaling system, intelligent middle-office based BSS, intelligent customer experience management and policy control driven by BSS & OSS convergence, evolution from service level agreement (SLA) to experience level agreement (ELA), and intelligent private network for verticals were further explored. The paper is concluded with the vision that AI will reshape the future beyond 5G (B5G)/6G landscape, and we need pivot our research and development (R&D), standardizations, and ecosystem to fully take the unprecedented opportunities