Targeted next-generation sequencing of dedifferentiated chondrosarcoma in the skull base reveals combined TP53 and PTEN mutations with increased proliferation index, an implication for pathogenesis

Dedifferentiated chondrosarcoma (DDCS) is a rare disease with a dismal prognosis. DDCS consists of two morphologically distinct components: the cartilaginous and noncartilaginous components. Whether the two components originate from the same progenitor cells has been controversial. Recurrent DDCS commonly displays increased proliferation compared with the primary tumor. However, there is no conclusive explanation for this mechanism. In this paper, we present two DDCSs in the sellar region. Patient 1 exclusively exhibited a noncartilaginous component with a TP53 frameshift mutation in the pathological specimens from the first surgery. The tumor recurred after radiation therapy with an exceedingly increased proliferation index. Targeted next-generation sequencing (NGS) revealed the presence of both a TP53 mutation and a PTEN deletion in the cartilaginous and the noncartilaginous components of the recurrent tumor. Fluorescence in situ hybridization and immunostaining confirmed reduced DNA copy number and protein levels of the PTEN gene as a result of the PTEN deletion. Patient 2 exhibited both cartilaginous and noncartilaginous components in the surgical specimens. Targeted NGS of cells from both components showed neither TP53 nor PTEN mutations, making Patient 2 a naïve TP53 and PTEN control for comparison. In conclusion, additional PTEN loss in the background of the TP53 mutation could be the cause of increased proliferation capacity in the recurrent tumor

Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model

Short-term load forecasting (STLF) for industrial customers has been an essential task to reduce the cost of energy transaction and promote the stable operation of smart grid throughout the development of the modern power system. Traditional STLF methods commonly focus on establishing the non-linear relationship between loads and features, but ignore the temporal relationship between them. In this paper, an STLF method based on ensemble hidden Markov model (e-HMM) is proposed to track and learn the dynamic characteristics of industrial customer’s consumption patterns in correlated multivariate time series, thereby improving the prediction accuracy. Specifically, a novel similarity measurement strategy of log-likelihood space is designed to calculate the log-likelihood value of the multivariate time series in sliding time windows, which can effectively help the hidden Markov model (HMM) to capture the dynamic temporal characteristics from multiple historical sequences in similar patterns, so that the prediction accuracy is greatly improved. In order to improve the generalization ability and stability of a single HMM, we further adopt the framework of Bagging ensemble learning algorithm to reduce the prediction errors of a single model. The experimental study is implemented on a real dataset from a company in Hunan Province, China. We test the model in different forecasting periods. The results of multiple experiments and comparison with several state-of-the-art models show that the proposed approach has higher prediction accuracy

Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model

Short-term load forecasting (STLF) for industrial customers has been an essential task to reduce the cost of energy transaction and promote the stable operation of smart grid throughout the development of the modern power system. Traditional STLF methods commonly focus on establishing the non-linear relationship between loads and features, but ignore the temporal relationship between them. In this paper, an STLF method based on ensemble hidden Markov model (e-HMM) is proposed to track and learn the dynamic characteristics of industrial customer’s consumption patterns in correlated multivariate time series, thereby improving the prediction accuracy. Specifically, a novel similarity measurement strategy of log-likelihood space is designed to calculate the log-likelihood value of the multivariate time series in sliding time windows, which can effectively help the hidden Markov model (HMM) to capture the dynamic temporal characteristics from multiple historical sequences in similar patterns, so that the prediction accuracy is greatly improved. In order to improve the generalization ability and stability of a single HMM, we further adopt the framework of Bagging ensemble learning algorithm to reduce the prediction errors of a single model. The experimental study is implemented on a real dataset from a company in Hunan Province, China. We test the model in different forecasting periods. The results of multiple experiments and comparison with several state-of-the-art models show that the proposed approach has higher prediction accuracy

Role of inflammation and immunity in vascular calcification: a bibliometric and visual analysis, 2000–2022

BackgroundIn recent years, a great deal of research has been done on vascular calcification (VC), and inflammation and immunity have been displayed to play important roles in the mechanism of VC. However, to date, no comprehensive or systematic bibliometric analyses have been conducted on this topic.MethodsArticles and reviews on the roles of inflammation and immunity in VC were obtained from the Web of Science Core Collection on August 5, 2022. Four scientometric software packages—HistCite, CiteSpace, VOSviewer, and R-bibliometrix—were used for the bibliometric and knowledge mapping analyses.ResultsThe obtained 1,868 papers were published in 627 academic journals by 9,595 authors of 2,217 institutions from 69 countries. The annual number of publications showed a clear growth trend. The USA and China were the most productive countries. Karolinska Institutet, Harvard University, and the University of Washington were the most active institutions. Stenvinkel P published the most articles, whereas Demer LL received the most citations. Atherosclerosis published the most papers, while Circulation was the most highly cited journal. The largest cluster among the 22 clusters, based on the analysis of co-citations, was osteo-/chondrogenic transdifferentiation. “Vascular calcification,” “inflammation,” “chronic kidney disease,” and “expression” were the main keywords in the field. The keyword “extracellular vesicle” attracted great attention in recent years with the strongest citation burst.ConclusionsOsteo-/chondrogenic transdifferentiation is the primary research topic in this field. Extracellular vesicles are expected to become a new research focus for exploring the inflammatory and immune mechanisms of VC

Elevated expression of glycolytic genes as a prominent feature of early-onset preeclampsia: insights from integrative transcriptomic analysis

Introduction: Preeclampsia (PE), a notable pregnancy-related disorder, leads to 40,000+ maternal deaths yearly. Recent research shows PE divides into early-onset (EOPE) and late-onset (LOPE) subtypes, each with distinct clinical features and outcomes. However, the molecular characteristics of various subtypes are currently subject to debate and are not consistent.Methods: We integrated transcriptomic expression data from a total of 372 placental samples across 8 publicly available databases via combat algorithm. Then, a variety of strategies including Random Forest Recursive Feature Elimination (RF-RFE), differential analysis, oposSOM, and Weighted Correlation Network Analysis were employed to identify the characteristic genes of the EOPE and LOPE subtypes. Finally, we conducted in vitro experiments on the key gene HK2 in HTR8/SVneo cells to explore its function.Results: Our results revealed a complex classification of PE placental samples, wherein EOPE manifests as a highly homogeneous sample group characterized by hypoxia and HIF1A activation. Among the core features is the upregulation of glycolysis-related genes, particularly HK2, in the placenta-an observation corroborated by independent validation data and single-cell data. Building on the pronounced correlation between HK2 and EOPE, we conducted in vitro experiments to assess the potential functional impact of HK2 on trophoblast cells. Additionally, the LOPE samples exhibit strong heterogeneity and lack distinct features, suggesting a complex molecular makeup for this subtype. Unsupervised clustering analysis indicates that LOPE likely comprises at least two distinct subtypes, linked to cell-environment interaction and cytokine and protein modification functionalities.Discussion: In summary, these findings elucidate potential mechanistic differences between the two PE subtypes, lend support to the hypothesis of classifying PE based on gestational weeks, and emphasize the potential significant role of glycolysis-related genes, especially HK2 in EOPE

Effects of a Flaxseed-Derived Lignan Supplement in Type 2 Diabetic Patients: A Randomized, Double-Blind, Cross-Over Trial

Flaxseed consumption has been shown to improve blood lipids in humans and flaxseed-derived lignan has been shown to enhance glycemic control in animals. The study aimed to investigate the effect of a flaxseed-derived lignan supplement on glycemic control, lipid profiles and insulin sensitivity in type 2 diabetic patients.This was a randomized, double-blind, placebo-controlled, cross-over trial and it was conducted between April and December 2006 in Shanghai, China. Seventy-three type 2 diabetic patients with mild hypercholesterolemia were enrolled into the study. Patients were randomized to supplementation with flaxseed-derived lignan capsules (360 mg lignan per day) or placebo for 12 weeks, separated by an 8-week wash-out period. HbA1c, lipid profiles, insulin resistance index and inflammatory factors were measured. Sixty-eight completed the study and were included in the analyses. The lignan supplement significantly improved glycemic control as measured by HbA(1c) (-0.10+/-0.65 % vs. 0.09+/-0.52 %, P = 0.001) compared to placebo; however, no significant changes were observed in fasting glucose and insulin concentrations, insulin resistance and blood lipid profiles. Urinary excretion of lignan metabolites (enterodiol and enterolactone) was significantly higher after the lignan supplement intervention compared to baseline (14.2+/-18.1 vs. 1.2+/-2.4 microg/mL, P<0.001). Data also suggested minimal competition between lignan and isoflavones for bioavailability when measured by the excretion concentrations.Daily lignan supplementation resulted in modest, yet statistically significant improvements in glycemic control in type 2 diabetic patients without apparently affecting fasting glucose, lipid profiles and insulin sensitivity. Further studies are needed to validate these findings and explore the efficacy of lignans on type 2 diabetes.ClinicalTrials.gov NCT00363233

A Piezoelectric Resonance Pump Based on a Flexible Support

Small volume changes are important factors that restrict the improvement of the performance of a piezoelectric diaphragm pump. In order to increase the volume change of the pump chamber, a square piezoelectric vibrator with a flexible support is proposed in this paper and used as the driving unit of the pump. The pump chamber diaphragm was separated from the driving unit, and the resonance principle was used to amplify the amplitude of the pump diaphragm. After analyzing the working principle of the piezoelectric resonance pump and establishing the motion differential equation of the vibration system, prototypes with different structural parameters were made and tested. The results show that the piezoelectric resonance pump resonated at 236 Hz when pumping air. When the peak-to-peak voltage of the driving power was 220 V, the amplitude of the diaphragm reached a maximum value of 0.43933 mm, and the volume change of the pump was correspondingly improved. When the pump chamber height was 0.25 mm, the output flow rate of pumping water reached a maximum value of 213.5 mL/min. When the chamber height was 0.15 mm, the output pressure reached a maximum value of 85.2 kPa

Bacterial diversity in gut of large yellow croaker Larimichthys crocea and black sea bream Sparus macrocephalus reared in an inshore net pen

Bacterial diversity in the foregut, midgut and hindgut of large yellow croaker Larimichthys crocea and black sea bream Sparus macrocephalus reared in an inshore net pen, formulated feed and seawater were analyzed with Illumina MiSeq high-throughput sequencing platforms. A total of 270 operational taxonomic units (OTUs) were identified from fish guts, formulated feed and seawater, which belonged to 17 phyla and 20 genera. Firmicutes and Proteobacteria dominated at the phylum level, while Bacillus, Lactococcus and Oceanobacillus dominated at the genus level, in fish gut. The similarity in bacterial community between the guts of two fish species was higher than that either between fish gut and formulated feed or between fish gut and seawater. This result indicates that bacterial communities in guts of large yellow croaker and black sea bream were independent on those in the formulated feed and seawater. The common OTUs between fish gut and formulated feed were more than that between fish gut and seawater, suggesting that the impact of the formulated feed on intestinal bacteria of large yellow croaker and black sea bream was greater relative to that of seawater in the net pen. Spatial heterogeneity in bacterial composition along the gut of large yellow croaker and black sea bream was also evaluated

Local Evolution Model of the Communication Network for Reducing Outage Risk of Power Cyber-Physical System

The deep integration of power grids and communication networks is the basis for realizing the complete observability and controllability of power grids. The communication node or link is always built according to the physical nodes. This step is alternatively known as “designing with the same power tower”. However, the communication networks do not form a “one-to-one correspondence” relationship with the power physical network. The existing theory cannot be applied to guide the practical power grid planning. In this paper, a local evolution model of a communication network based on the physical power grid topology is proposed in terms of reconnection probabilities. Firstly, the construction and upgrading of information nodes and links are modeled by the reconnection probabilities. Then, the power flow entropy is employed to identify whether the power cyber-physical system (CPS) is at the self-organized state, indicating the high probability of cascading failures. In addition, on the basis of the cascading failure propagation model of the partially dependent power CPS, operation reliabilities of the power CPS are compared with different reconnection probabilities using the cumulative probability of load loss as the reliable index. In the end, a practical provincial power grid is analyzed as an example. It is shown that the ability of the power CPS to resist cascading failures can be improved by the local growth evolution model of the communication networks. The ability is greater when the probability of reconnection is p = 0.06. By updating or constructing new links, the change in power flow entropy can be effectively reduced

Combined machine learning forecasting method for short-term power load based on the dynamic weight adjustment

The load of power system exhibits evident characteristics of volatility and randomness. The traditional load forecasting algorithm usually studies and trains the historical data to obtain the load model, which makes it difficult to adapt to the load dynamic change situation, and then resulting the unreasonable inaccurate prediction. In this paper, a combinatorial machine learning model is adopted to forecast short-term power load using a dynamic adjustable weight. Firstly, a combined machine learning model is constructed using three types of algorithms including the improved long and short-term neural network, bagging algorithm, and boosting regression algorithm. The weight of each algorithm is determined dynamically by the improved error function. Secondly, the dynamic error function and the optimal weight optimization algorithm are employed so as to balance the contradiction between the speed and accuracy of dynamic adjustment. For different months or different days within a month, different weight adjustment algorithms are selected for enhancement. In addition, a penalty term is introduced to improve the algorithm accuracy and the final prediction outcomes. Finally, a practical load prediction case is simulated and compared with the traditional combined prediction model with fixed weights. It is verified that the proposed model can effectively eliminate the excessive errors caused by the poor dynamic response effect. It has a good dynamic response effect and accurate prediction. The error rate is only 1.24% when the load fluctuation is significant. This study provides a novel approach to forecasting short-term power load