Primary Modulation Research Articles

PIMFP: An accurate tool for the prediction of intramuscular fat percentage in live pigs using ultrasound images based on deep learning

Intramuscular fat (IMF) percentage is the proportion of fat between the muscle fibers of the loin eye, which contributes significantly to the flavor and tenderness of the pork. Traditional IMF percentage is measured after slaughter, which requires a high cost, and the slaughtered pig with high IMF percentage can not be directly retained for breeding. Instead of slaughter, using ultrasound images to predict IMF percentage has been gradually proposed as an alternative measurement. However, due to the high cost of dataset construction and limited feature extraction capability of traditional image analysis methods, there is still a lack of an accurate and robust method based on the large-scale dataset for predicting IMF percentage. Here, to establish an accurate model for non-invasive prediction of IMF percentage in pigs, a total number of 4,552 ultrasound images and their corresponding IMF percentage of 945 pigs are collected in this study. We proposed a general-purpose model, PIMFP, to accurately predict IMF percentage based on the deep convolutional neural network. The PIMFP model comprises three primary modules: image preprocessing, feature extraction, and IMF percentage prediction. The preprocessing procedures for the raw ultrasound images include channel conversion, region of interest selection, and contrast enhancement. The feature extraction module characterizes the preprocessed images using the residual neural network as the backbone to extract features. The image features are then delivered to the IMF percentage prediction module depending on the fully connected layer. Our proposed model exhibits significant advantages over existing models in predictive performance, computational speed, classification ability, robustness, and interpretability. For easier use, we also provide a user-friendly web tool for predicting IMF percentage automatically in a single step. In conclusion, our study demonstrates the power of the PIMFP model for predicting IMF percentage, which has great potential in future breeding and highlights the application of artificial intelligence algorithms in livestock.

Therapeutic In Situ Cancer Vaccine Using Pulsed Stereotactic Body Radiotherapy-A Translational Model.

Both radiation and cancer therapeutic vaccine research are more than 100 years old, and their potential is likely underexplored. Antiangiogenics, nanoparticle targeting, and immune modulators are some other established anticancer therapies. In the meantime, immunotherapy usage is gaining momentum in clinical applications. This article proposes the concept of a pulsed/intermittent/cyclical endothelial-sparing single-dose in situ vaccination (ISVRT) schedule distinguishable from the standard therapeutic stereotactic body radiotherapy (SBRT) and stereotactic radiosurgery (SRS) plans. This ISVRT schedule can repeatedly generate tumor-specific neoantigens and epitopes for primary and immune modulation effects, augment supplementary immune enhancement techniques, activate long-term memory cells, avoid extracellular matrix fibrosis, and essentially synchronize with the vascular normalized immunity cycle. The core mechanisms of ISVRT impacting in situ vaccination would be optimizing cascading antigenicity and adjuvanticity. The present proposed hypothesis can be validated using the algorithm presented. The indications for the proposed concept are locally progressing/metastatic cancers that have failed standard therapies. Immunotherapy/targeted therapy, chemotherapy, antiangiogenics, and vascular-lymphatic normalization are integral to such an approach.

Symptoms experienced after transcatheter arterial chemoembolization in patients with primary liver cancer: A network analysis

ObjectiveThis study aimed to establish a symptom network for patients with primary liver cancer posttranscatheter arterial chemoembolization (TACE), identifying core and bridge symptoms. The goal is to provide a foundation for precise and comprehensive nursing interventions. MethodsA total of 1207 post-TACE patients were included using a consecutive sampling method. Data collection involved a general information questionnaire, the Anderson Symptom Assessment Scale, and a primary liver cancer–specific symptom module. The symptom network was constructed using the R language. ResultsIn the overall network, distress exhibited the highest strength (rs = 1.31) and betweenness (rb = 62). Fatigue had the greatest closeness (rc = 0.0043), while nausea and vomiting (r = 0.76 ± 0.02) had the highest marginal weights. Nausea had the highest bridge strength (rbs = 5.263). In the first-time TACE-treated symptom network, sadness (rbs = 5.673) showed the highest bridge strength, whereas in the non-first-time symptom network, fever (rbs = 3.061) had the highest bridge strength. ConclusionsDistress serves as a core symptom, and nausea acts as a bridge symptom after TACE treatment in liver cancer patients. Interventions targeting bridge symptoms should be tailored based on the number of treatments, enhancing the quality of symptom management.

Effectiveness of the holistic primary school-based intervention MindMatters: study protocol for a cluster-randomised controlled trial

BackgroundThe prevalence of mental health problems in childhood and adolescence has increased significantly, not least due to the COVID-19 pandemic in Germany and other countries worldwide. Although holistic school interventions to promote mental health and prevent mental health problems are considered promising, there is currently uncertainty about their effectiveness due to evaluation studies with heterogeneous methodological quality. This paper presents the study protocol for the evaluation of the primary school module of MindMatters.MethodsAs part of a universal mental health intervention, the MindMatters primary school module ‘Learning Together with Emotions’ aims to promote social-emotional learning (SEL) in the classroom across five skill areas. In addition to classroom activities, the intervention includes a school development module to help primary schools create structures and processes to maintain and promote mental health. To evaluate the effectiveness of the intervention, a two-arm cluster-randomised controlled trial will be conducted, including schools implementing MindMatters over a 12-month period and a control group with no access to the intervention. Data will be collected before and 18 months after initiation of the intervention. Controlled for baseline conditions, multilevel regression analysis will be used to examine primary intervention outcomes at the pupil level (i.e. reductions in mental and behavioural problems). Further mediation and moderation analyses will examine whether proximal outcomes predict changes in mental health outcomes and whether school-level factors influence the effectiveness of the intervention.DiscussionThis study will contribute to strengthen the evidence base for holistic school (mental) health promotion interventions using a study design with high internal validity. Based on an intervention model, the results will not only provide insights into the relationship between proximal and distal outcomes, but will also allow conclusions to be drawn about how the implementation of the intervention affects its effectiveness. Finally, the findings also address the question of whether improved mental health has a positive effect on primary school pupils’ academic performance.Trial registrationGerman Clinical Trials Register DRKS00023762. Registered on 5 January 2021.

Genetic Correlation, Shared Loci, and Causal Association Between Sex Hormone-Binding Globulin and Bone Mineral Density: Insights From a Large-Scale Genomewide Cross-Trait Analysis.

Although the impact of sex hormones on bone metabolism is well-documented, effect of their primary modulator, sex hormone-binding globulin (SHBG), remains inconclusive. This study aims to elucidate the genetic overlap between SHBG and heel estimated bone mineral density (eBMD), a widely-accepted tool for osteoporosis management and fracture risk assessment. Using summary statistics from large-scale genomewide association studies conducted for SHBG (N = 370,125), SHBG adjusted for body mass index (SHBGa, N = 368,929), and eBMD (N = 426,824), a comprehensive genomewide cross-trait approach was performed to quantify global and local genetic correlations, identify pleiotropic loci, and infer causal associations. A significant overall inverse genetic correlation was found for SHBG and eBMD (rg = -0.11, p = 3.34 × 10-10 ), which was further supported by the significant local genetic correlations observed in 11 genomic regions. Cross-trait meta-analysis revealed 219 shared loci, of which seven were novel. Notably, four novel loci (rs6542680, rs8178616, rs147110934, and rs815625) were further demonstrated to colocalize. Mendelian randomization identified a robust causal effect of SHBG on eBMD (beta = -0.22, p = 3.04 × 10-13 ), with comparable effect sizes observed in both men (beta = -0.16, p = 1.99 × 10-6 ) and women (beta = -0.19, p = 2.73 × 10-9 ). Replacing SHBG with SHBGa, the observed genetic correlations, pleiotropic loci and causal associations did not change substantially. Our work reveals a shared genetic basis between SHBG and eBMD, substantiated by multiple pleiotropic loci and a robust causal relationship. Although SHBG has been implicated in preventing and screening aging-related diseases, our findings support its etiological role in osteoporosis. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Single-shot quantitative x-ray imaging using a primary modulator and dual-layer detector.

Conventional x-ray imaging and fluoroscopy have limitations in quantitation due to several challenges, including scatter, beam hardening, and overlapping tissues. Dual-energy (DE) imaging, with its capability to quantify area density of specific materials, is well-suited to address such limitations, but only if the dual-energy projections are acquired with perfect spatial and temporal alignment and corrected for scatter. In this work, we propose single-shot quantitative imaging (SSQI) by combining the use of a primary modulator (PM) and dual-layer (DL) detector, which enables motion-free DE imaging with scatter correction in a single exposure. The key components of our SSQI setup include a PM and DL detector, where the former enables scatter correction for the latter while the latter enables beam hardening correction for the former. The SSQI algorithm allows simultaneous recovery of two material-specific images and two scatter images using four sub-measurements from the PM encoding. The concept was first demonstrated using simulation of chest x-ray imaging for a COVID patient. For validation, we set up SSQI geometry on our tabletop system and imaged acrylic and copper slabs with known thicknesses (acrylic: 0-22.5cm; copper: 0-0.9mm), estimated scatter with our SSQI algorithm, and compared the material decomposition (MD) for different combinations of the two materials with ground truth. Second, we imaged an anthropomorphic chest phantom containing contrast in the coronary arteries and compared the MD with and without SSQI. Lastly, to evaluate SSQI in dynamic applications, we constructed a flow phantom that enabled dynamic imaging of iodine contrast. Our simulation study demonstrated that SSQI led to accurate scatter correction and MD, particularly for smaller focal blur and finer PM pitch. In the validation study, we found that the root mean squared error (RMSE) of SSQI estimation was 0.13cm for acrylic and 0.04mm for copper. For the anthropomorphic phantom, direct MD resulted in incorrect interpretation of contrast and soft tissue, while SSQI successfully distinguished them quantitatively, reducing RMSE in material-specific images by 38%-92%. For the flow phantom, SSQI was able to perform accurate dynamic quantitative imaging, separating contrast from the background. We demonstrated the potential of SSQI for robust quantitative x-ray imaging. The integration of SSQI is straightforward with the addition of a PM and upgrade to a DL detector, which may enable its widespread adoption, including in techniques such as radiography and dynamic imaging (i.e., real-time image guidance and cone-beam CT).

A Parent Authentication System for the Security of Pre-basic School Pupils

The safety of pupils is of uttermost concern to school authorities and especially parents in this age of rampant kidnappings and other malicious activities by unscrupulous people within the human ecosystem. In this paper, a robust security system, referred to as a Parent Authentication System (PAS) is designed and implemented using bi-modal authentication and/or verification mechanisms for the safety of Pre-Basic School pupils. The primary module for authentication and verification is Fingerprint (i.e. biometrics) with an alternative module of a Password which, can only be used if the primary module fails or rejects the biometrics of a particular user. The Agile System Development Life Cycle Prototyping model was leveraged to design the system; all key components responsible for the functioning of the system have been appropriately implemented and integrated resulting in a very successful and robust system. The developed system was deployed and tested at Garu Senior High School in the Upper East Region of Ghana. During the test implementation, It was affirmed that students with issues such as scratches and dirt on their fingers were denied access and were not able to be authenticated and verified. However, after three attempts via this primary authentication module, they were able to use the alternative module, which gave them the opportunity to be authenticated and verified. As a result, a full implementation of this system will make sure only rightful parents pick up their wards from school. This system can also be deployed in other areas for attendance monitoring or personality tracking.

Research on frequency modulation capacity configuration and control strategy of multiple energy storage auxiliary thermal power unit

The rapid development of new energy sources has had an enormous impact on the existing power grid structure to support the “dual carbon” goal and the construction of a new type of power system, make thermal power units better cope with the impact on the original grid structure under the background of the rapid development of new energy sources, promote the consumption of new energy sources such as solar and wind energy, and further expand the channels for upgrading and flexibility transformation of thermal power energy storage, improve the stability, safety and economy of thermal power unit operation. Therefore, based on the research status at home and abroad, a hybrid energy storage system is added to solve the above problems. The lithium battery-flywheel control strategy and the regional dynamic primary frequency modulation model of thermal power units are proposed, and study the capacity configuration scheme of flywheel‑lithium battery hybrid energy storage system under a certain energy storage capacity, the frequency modulation performance is evaluated by the system frequency fluctuation degree, fluctuation peak range and other indicators. Study under a certain energy storage capacity thermal power unit coupling hybrid energy storage system to participate in a frequency modulation of the optimal capacity configuration scheme, and perform simulation verification using MATLAB/Simulink. When the thermal power unit is coupled with a 10.8612 MW/2.7151 MWh flywheel energy storage system and a 4.1378 MW/16.5491 MWh lithium battery energy storage system, while adaptive variable coefficient droop control is adopted, the system frequency range is 0.00328 p.u.Hz, and the fluctuation degree of the output power of the thermal power units is 0.00498 p.u.MW, compared to the individual frequency regulation of thermal power units, it reduces 52.07 % and. The results show that when the thermal power unit is disturbed by external load, hybrid energy storage assisted thermal power unit frequency modulation reduces the mechanical loss of thermal power unit to a certain extent and extends the service life of the unit, effectively improve the operation stability and economy of thermal power units, further reduce the phenomenon of abandoning wind and light, and better promote the utilization of new energy.

DMFF-Net: Double-stream multilevel feature fusion network for image forgery localization

With the rapid development of image processing technology, it has become increasingly easy to manipulate images, which poses a threat to the stability and security of people’s lives. Recent methods have proposed the fusion of RGB and noise features to uncover tampering traces. However, these approaches overlook the characteristics of features at different levels, leading to insufficient feature fusion. To address this problem, this paper proposes a double-stream multilevel feature fusion network (DMFF-Net). Unlike the traditional feature fusion approach, DMFF-Net adopts a graded feature fusion strategy. It classifies features into primary, intermediate, and advanced levels and introduces the Primary Feature Fusion Module (PFFM) and the Advanced Feature Fusion Module (AFFM) to achieve superior fusion results. Additionally, a multisupervision strategy is employed to decode the fused features into level-specific masks, including boundary, regular, and refined masks. The DMFF-Net is validated on publicly available datasets, including CASIA, Columbia, COVERAGE, and NIST16, as well as a real-life manipulated image dataset, IMD20, and achieves AUCs of 84.7%, 99.6%, 86.6%, 87.4% and 82.8%, respectively. Extensive experiments show that our DMFF-Net outperforms state-of-the-art methods in terms of image manipulation localization accuracy and exhibits improved robustness.

Optimal Allocation of Primary Frequency Modulation Capacity of Battery Energy Storage Based on Antlion Algorithm

Currently, the integration of new energy sources into the power system poses a significant challenge to frequency stability. To address the issue of capacity sizing when utilizing storage battery systems to assist the power grid in frequency control, a capacity optimal allocation model is proposed for the primary frequency regulation of energy storage. Due to the requirement of a large number of actual parameters for the optimal allocation model, a simulation model of energy storage capacity is constructed based on the characteristics of primary frequency control to provide the necessary parameters. Subsequently, the primary frequency modulation output model of energy storage is established by considering the basic action output, the action in the frequency modulation dead zone, and a certain capacity margin. The antlion algorithm is employed to solve the capacity optimal allocation model. Finally, three groups of experiments are designed and compared to demonstrate the effectiveness of the proposed method in setting the capacity margin, which can increase profit to a certain extent.

An improved VSG control strategy based on transient electromagnetic power compensation

Virtual synchronous generator (VSG) not only increases the inertia of grid-connected system, but also brings the problem of active power oscillation under grid disturbance. Therefore, VSG control strategy and system model order reduction method with transient electromagnetic power compensation are proposed. The closed-loop active power small signal model of the system is established, and the influence of transient electromagnetic power compensation on the power stability of VSG is analyzed based on root locus method. By removing the items which have little influence on the stability of the system in the small signal model, the order is reduced to obtain the equivalent second-order model of the system. According to the second-order model, the quantitative design criteria of the system parameters are given. The proposed transient electromagnetic power compensation strategy not only increases the transient equivalent damping of the system, but also does not affect the primary frequency modulation characteristics and will not cause large overshoot of the output active power. The experimental results are consistent with the theoretical analysis, which testify the effectiveness and correctness of the system control strategy and the model reduction method.

The research of declared primary frequency capacity and control strategy for electrical fused magnesium furnace load

As a potential control resource of the load side, fused magnesium load can effectively make up for the loss of the primary frequency modulation capacity of the system, in which the conventional units are replaced by new energy sources. However, the capacity of fused magnesium load is closely related to the operating condition and changes dynamically. It is necessary to accurately characterize its capacity and determine the optimal declared capacity of participating in frequency modulation service. According to the technical regulations of primary frequency modulation service, the basic principle is presented, which the electric molten magnesium load participates in primary frequency modulation control in the case of lack of large power. Considering the risk of reward and punishment, the optimal reporting capacity optimization model is constructed. Taking the year as the time scale, a stochastic production simulation method involving primary frequency modulation was proposed. According to its operating characteristics, a real-time control strategy involving primary frequency modulation is designed. Simulation examples show that the proposed method can accurately simulate the production process of molten magnesium load, reasonably realize the operation control of molten magnesium load, and optimize the declared capacity considering the risk.

Research on Control Strategy of Hybrid Energy Storage System Participating in Primary Frequency Regulation of Power Grid

With the ongoing development of China’s power system, there is a gradual increase in the proportion of new energy power generation. However, the randomness and volatility associated with new energy power generation can lead to increased frequency fluctuations in the power grid, posing a significant challenge to power system frequency regulation. In this paper, we investigate the control strategy of a hybrid energy storage system (HESS) that participates in the primary frequency modulation of the system. We analyze the advantages and disadvantages of various types of new energy storage from both technical and economic perspectives and perform an applicability analysis system to select lithium batteries and supercapacitor energy storage for forming a HESS. We propose a virtual droop control strategy to regulate the output of the HESS in the primary frequency regulation of the system. Finally, we build a simulation model that includes the HESS and power grid on the MATLAB/Simulink simulation platform and conduct a simulation analysis. The results show that the proposed strategy is superior and effective in controlling the HESS’s output when participating in the primary frequency modulation of the system.

MFPred: prediction of ncRNA families based on multi-feature fusion.

Non-coding RNA (ncRNA) plays a critical role in biology. ncRNAs from the same family usually have similar functions, as a result, it is essential to predict ncRNA families before identifying their functions. There are two primary methods for predicting ncRNA families, namely, traditional biological methods and computational methods. In traditional biological methods, a lot of manpower and resources are required to predict ncRNA families. Therefore, this paper proposed a new ncRNA family prediction method called MFPred based on computational methods. MFPred identified ncRNA families by extracting sequence features of ncRNAs, and it possessed three primary modules, including (1) four ncRNA sequences encoding and feature extraction module, which encoded ncRNA sequences and extracted four different features of ncRNA sequences, (2) dynamic Bi_GRU and feature fusion module, which extracted contextual information features of the ncRNA sequence and (3) ResNet_SE module that extracted local information features of the ncRNA sequence. In this study, MFPred was compared with the previously proposed ncRNA family prediction methods using two frequently used public ncRNA datasets, NCY and nRC. The results showed that MFPred outperformed other prediction methods in the two datasets.

IoT Orchestration-Based Optimal Energy Cost Decision Mechanism with ESS Power Optimization for Peer-to-Peer Energy Trading in Nanogrid

The Internet of things has revolutionized various domains, such as healthcare and navigation systems, by introducing mission-critical capabilities. However, the untapped potential of IoT in the energy sector is a topic of contention. Shifting from traditional mission-critical electric smart grid systems to IoT-based orchestrated frameworks has become crucial to improve performance by leveraging IoT task orchestration technology. Energy trading cost and ESS power optimization have long been concerns in the scientific community. To address these issues, our proposed architecture consists of two primary modules: (1) a nanogrid energy trading cost and ESS power optimization strategy that utilizes particle swarm optimization (PSO), with two objective functions, and (2) an IoT-enabled task orchestration system designed for improved peer-to-peer nanogrid energy trading, incorporating virtual control through orchestration technology. We employ IoT sensors and Raspberry Pi-based Edge technology to virtually operate the entire nanogrid energy trading architecture, encompassing the aforementioned modules. IoT task orchestration automates the interaction between components for service execution, involving five main steps: task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. Evaluating the proposed model using a real dataset from nanogrid houses demonstrates the significant role of optimization in minimizing energy trading cost and optimizing ESS power utilization. Furthermore, the IoT orchestration results highlight the potential for virtual operation in significantly enhancing system performance.

Predicting Air Quality Index using Ensemble Machine Learning

This study focuses on utilizing Ensemble Machine Learning to predict and forecast the Air Quality Index (AQI). The research is motivated by the adverse effects of industrialization and population growth on air quality, leading to detrimental impacts on human health. While numerous methodologies exist for air quality prediction, it is crucial to anticipate future air conditions to minimize their larger consequences. Hence, this study proposes an air quality evaluation system to facilitate future predictions. The study comprises three primary modules: Data Preparation, AQI Forecasting, and Evaluating Air Quality. The Data Preparation Module involves real-time data collection and formatting to ensure compatibility with subsequent modules. In this research, the Sparse Spectrum GPR (SSGPR) method is employed for AQI forecasting, while the cloud model is adopted for air quality evaluation. The study's findings demonstrate the capability of the proposed model to account for the uncertainty and randomness inherent in air quality prediction. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are employed to evaluate the models' effectiveness. Based on the evaluation results, it can be concluded that the Ensemble Machine Learning method utilized in this study effectively predicts and forecasts the Air Quality Index. These predictions play a crucial role in minimizing the adverse impact of air pollution on human health by providing insights into future air conditions. Overall, this research contributes significantly to comprehending and addressing the increasingly urgent challenges associated with air quality.

Tau tubulin kinase 1 and 2 regulate ciliogenesis and human pluripotent stem cells–derived neural rosettes

Primary cilia are key regulators of embryo development and tissue homeostasis. However, their mechanisms and functions, particularly in the context of human cells, are still unclear. Here, we analyzed the consequences of primary cilia modulation for human pluripotent stem cells (hPSCs) proliferation and differentiation. We report that neither activation of the cilia-associated Hedgehog signaling pathway nor ablation of primary cilia by CRISPR gene editing to knockout Tau Tubulin Kinase 2 (TTBK2), a crucial ciliogenesis regulator, affects the self-renewal of hPSCs. Further, we show that TTBK1, a related kinase without previous links to ciliogenesis, is upregulated during hPSCs-derived neural rosette differentiation. Importantly, we demonstrate that while TTBK1 fails to localize to the mother centriole, it regulates primary cilia formation in the differentiated, but not the undifferentiated hPSCs. Finally, we show that TTBK1/2 and primary cilia are implicated in the regulation of the size of hPSCs-derived neural rosettes.

Abstract P1163: Identifying Novel Intracellular Interactions Driving Membrane-bound Tumour Necrosis Factor-mediated Signalling In The Microvasculature

Elevated total peripheral resistance (TPR) is a hallmark of many cardiovascular diseases and furthers disease progression. We previously demonstrated that myogenic tone, regulated by membrane-bound tumour necrosis factor (mTNF) reverse signalling, is a primary modulator of TPR. Thus, inhibiting mTNF reverse signalling should reduce TPR with the potential to improve disease outcome. However, since mTNF is a ubiquitous protein with critical biological functions, its indiscriminate inhibition can cause widespread side effects. Therefore, therapeutics aiming to reduce myogenic tone and TPR must target other elements of mTNF reverse signalling. To identify these unknown participants of mTNF reverse signalling, we used proximity-dependent biotinylation coupled to mass spectrometry (BioID). Our search identified 42 high confidence hits that interact with and/or are proximal to mTNF’s cytoplasmic domain. Gene ontology (GO) analysis revealed that molecularly, these proteins are structural constituents of the cytoskeleton involved in actin and cell adhesion molecule binding. This aligns with our underlying hypothesis that mTNF functions as a mechanosensor that tethers to its receptors and initiates myogenic vasoconstriction in response to changes in transmural pressure. GO also identified proteins involved in MAPK and RhoA signalling, which are critically involved in myogenic vasoconstriction. Our most abundant hit (fold change = 12.6, p-value = 0.0023) is a cytoskeletal protein that plays a role in maintaining the structural integrity of cells. This protein also interacts with proteins involved in various signalling cascades, namely, RhoA and calcium-dependent signalling pathways. Thus, we hypothesize that this protein is essential for formation of signalling complexes with mTNF and interacts with other proteins to mediate reverse signalling . We will employ a bioluminescence-based approach to quantify the strength of all interactions detected by BioID and identify small molecule inhibitors that can disrupt these interactions. These newly discovered inhibitors could potentially be used as novel therapeutics targeting myogenic tone and hence, elevated TPR to improve disease management and clinical outcome of cardiovascular diseases.

Simulation of Secondary Frequency Modulation Process of Wind Power with Auxiliary of Flywheel Energy Storage

With the rapid increase in the proportion of wind power, the frequency stability problem of power system is becoming increasingly serious. Based on MATLAB/Simulink simulation, the role and effect of secondary frequency modulation assisted by Flywheel Energy Storage System (FESS) in regional power grid with certain wind power penetration rates are studied. First, the linear frequency control of the power system is used to establish the primary frequency modulation control model of FESS assisting wind power, and the frequency characteristics of FESS participating in primary frequency modulation are analyzed according to the transfer function. Then, in the case of step disturbance and continuous disturbance of load power, the frequency characteristics of a regional power grid are simulated and demonstrated through time domain simulation, and conclusions are drawn through comparison; a certain proportion of FESS can quickly respond to the frequency deviation signal. During secondary frequency modulation simulation, the maximum frequency deviation of the system is reduced by 57.1% and the frequency fluctuation range is reduced by 53.8%, effectively improving the frequency quality of the power grid.

The non-specific lethal complex regulates genes and pathways genetically linked to Parkinson's disease.

Genetic variants conferring risks for Parkinson's disease have been highlighted through genome-wide association studies, yet exploration of their specific disease mechanisms is lacking. Two Parkinson's disease candidate genes, KAT8 and KANSL1, identified through genome-wide studies and a PINK1-mitophagy screen, encode part of the histone acetylating non-specific lethal complex. This complex localizes to the nucleus, where it plays a role in transcriptional activation, and to mitochondria, where it has been suggested to have a role in mitochondrial transcription. In this study, we sought to identify whether the non-specific lethal complex has potential regulatory relationships with other genes associated with Parkinson's disease in human brain. Correlation in the expression of non-specific lethal genes and Parkinson's disease-associated genes was investigated in primary gene co-expression networks using publicly-available transcriptomic data from multiple brain regions (provided by the Genotype-Tissue Expression Consortium and UK Brain Expression Consortium), whilst secondary networks were used to examine cell type specificity. Reverse engineering of gene regulatory networks generated regulons of the complex, which were tested for heritability using stratified linkage disequilibrium score regression. Prioritized gene targets were then validated in vitro using a QuantiGene multiplex assay and publicly-available chromatin immunoprecipitation-sequencing data. Significant clustering of non-specific lethal genes was revealed alongside Parkinson's disease-associated genes in frontal cortex primary co-expression modules, amongst other brain regions. Both primary and secondary co-expression modules containing these genes were enriched for mainly neuronal cell types. Regulons of the complex contained Parkinson's disease-associated genes and were enriched for biological pathways genetically linked to disease. When examined in a neuroblastoma cell line, 41% of prioritized gene targets showed significant changes in mRNA expression following KANSL1 or KAT8 perturbation. KANSL1 and H4K8 chromatin immunoprecipitation-sequencing data demonstrated non-specific lethal complex activity at many of these genes. In conclusion, genes encoding the non-specific lethal complex are highly correlated with and regulate genes associated with Parkinson's disease. Overall, these findings reveal a potentially wider role for this protein complex in regulating genes and pathways implicated in Parkinson's disease.