Function Network Research Articles

In vivo perturb-seq of cancer and microenvironment cells dissects oncologic drivers and radiotherapy responses in glioblastoma.

Genetic perturbation screens with single-cell readouts have enabled rich phenotyping of gene function and regulatory networks. These approaches have been challenging in vivo, especially in adult disease models such as cancer, which include mixtures of malignant and microenvironment cells. Glioblastoma (GBM) is a fatal cancer, and methods of systematically interrogating gene function and therapeutic targets in vivo, especially in combination with standard of care treatment such as radiotherapy, are lacking. Here, we iteratively develop a multiplex in vivo perturb-seq CRISPRi platform for single-cell genetic screens in cancer and tumor microenvironment cells that leverages intracranial convection enhanced delivery of sgRNA libraries into mouse models of GBM. Our platform enables potent silencing of drivers of in vivo growth and tumor maintenance as well as genes that sensitize GBM to radiotherapy. We find radiotherapy rewires transcriptional responses to genetic perturbations in an in vivo-dependent manner, revealing heterogenous patterns of treatment sensitization or resistance in GBM. Furthermore, we demonstrate targeting of genes that function in the tumor microenvironment, enabling alterations of ligand-receptor interactions between immune and stromal cells following in vivo CRISPRi perturbations that can affect tumor cell phagocytosis. In sum, we demonstrate the utility of multiplexed perturb-seq for in vivo single-cell dissection of adult cancer and normal tissue biology across multiple cell types in the context of therapeutic intervention, a platform with potential for broad application.

An MPPT Controller with a Modified Four-Leg Interleaved DC/DC Boost Converter for Fuel Cell Applications

A fuel cell system can produce electricity and water more efficiently while emitting near-zero emissions. Internal constraints and operating parameters such as hydrogen, temperature, humidity levels, and oxygen gas partial pressures trigger a nonlinear power characteristic in a typical fuel cell stack, resulting in reduced overall system efficiency. Consequently, it's critical to get the most power out of the fuel cell stack while minimizing fuel use. This study examines and proposes a radial basis function network (RBFN) based maximum power point tracking technique (MPPT) for a 6-kW proton exchange membrane fuel cell (PEMFC) system. The proposed MPPT algorithm modulates the duty cycle of the modified four-leg interleaved DC/DC boost converter (MFLIBC) to extricate the maximum power from the fuel cell system. To validate the execution of the proposed controller, the outcome is related to the various MPPT control strategies such as PID & Mamdani fuzzy inference systems. Finally, it was observed that the proposed RBFN controller has achieved an enhanced efficiency of 83.2 % relative to the PID and fuzzy logic controllers of 75.5 % and 77.4 % respectively. The efficiency of the proposed configuration is analysed using the MATLAB/Simulink platform.

Wind Speed Modeling under Quasi‐Linear Autoregressive Neural Network Model for Prediction of Production Power

Accurate wind speed modeling is beneficial for the design of wind energy conversion systems. Models of wind speed are used to assess the adequacy and dependability of a power supply. However, precise wind speed modeling is challenging due to the sporadic availability of wind speed. In this note, we propose a wind speed model with an autoregressive (AR) structure. A hybrid model is developed under linear and nonlinear parts based on a quasi‐linear autoregressive exogenous neural network (Q‐ARX‐NN). The model's structure is composed of a regression vector and its coefficients. The coefficients are divided into linear and nonlinear coefficients. A set of linear coefficients is identified under the algorithm of least square error (LSE), and a set of nonlinear coefficients is modeled by using a neural network to refine the residual error of the nonlinear part. Some artificial neural network (ANN) models can be set as nonlinear part sub‐models to sharpen the model's accuracy. The proposed model is tested for wind speed modeling to estimate wind energy production. Various nonlinear parts of the sub‐model are tested, such as neural networks, radial basis function networks, and ANN networks. Moreover, we evaluate the effects of the order of the model by varying hidden and output nodes, which can be summarized as the number of coefficients of the regression vector. Using specific wind turbine performance data, prediction models estimate production power. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Key bacterial taxa with specific metabolisms and life history strategies sustain soil microbial network stability exposed to carbendazim and deoxynivalenol

Co-contamination of carbendazim (CBD) and deoxynivalenol (DON) is common in agricultural soils, yet their ecological impact on soil microbiome remains poorly assessed. Here, we investigated the influence of CBD and DON on the structure, function, and co-occurrence networks of soil microbiome. The combined treatment of CBD and DON significantly exacerbated the negative impacts on soil microbial diversity, functional diversity, and microbial network stability compared to individual treatments. Specifically, Lysobacter, Gemmatimonas, Nitrospira, Massilia, and Bacillus were identified as indicator species for CBD and DON. Simultaneously, the abundance of genes involved in key ecological functions, such as nitrification (amoA) and organic phosphorus mineralization (phoAD), was significantly reduced. Notably, key bacterial taxa Nitrospira and Gemmatimonas, with K-life history strategy and capabilities for nitrification and organic nitrogen mineralization, played crucial roles in promoting positive interactions in networks. Furthermore, variance partitioning analysis (VPA) and structural equation modeling (SEM) demonstrated that the abundance and niche breadth of key bacterial taxa were the primary drivers of microbial network stability. In conclusion, our study provides new insights into how soil microbiomes and networks respond to pesticides and mycotoxins, aiding in a more comprehensive assessment of exposure risks.

Adaptive sliding mode control of petrochemical flare combustion process based on radial basis function network

Steam-assisted combustion elevated flares are currently the most widely used type of petrochemical flares. Due to the complex and variable composition of the waste gas they handle, the combustion environment is severely affected by meteorological conditions. Key process parameters such as intake composition, flow rate, and real-time data of post-combustion residues are difficult to measure or exhibit lag in data availability. As a result, the control methods for these flares are limited, leading to poor control effectiveness. To address this issue, this paper proposes an adaptive sliding mode control method based on the Radial Basis Function (RBF) network. Firstly, the operational characteristics of the petrochemical flare combustion process are analyzed, and a control model for the combustion process is established based on carbon dioxide detection. Secondly, an RBF neural network-based unknown function approximator is designed to identify the nonlinear part of the actual operating system. Finally, by combining the control model of the petrochemical flare combustion and designing the RBF sliding mode controller with its adaptive control law, fast and stable control of the flare combustion state is achieved. Simulation results demonstrate that the designed control strategy can achieve tracking control of the petrochemical flare combustion state, and the adaptive law also accomplishes system identification.

Association of Social Network with Physical Function Among Community-Dwelling Older Adults in Rural Thailand: A Cross-Sectional Study.

As the number of older adults in society increases, their social roles and networks, as well as their physical function, decrease. This study aimed to clarify the association between social networks and physical function among people aged ≥ 60 years in rural Thailand. This cross-sectional study was conducted in the Photharam District, Ratchaburi Province, Thailand. Participants were required to be at least 60 years old and be able to walk to the health center. Social networks were surveyed using the Thai version of Lubben Social Network Scores-6. Four physical function measures, namely hand grip strength, five-times-sit-to-stand test, timed up-and-go (TUG) test, and one-leg standing, were considered. Regression analysis was conducted with Lubben Social Network Scores-6 as the dependent variable and the four types of physical function as independent variables. A total of 497 older adults aged 60 years or more were enrolled; 82 were males, and 412 were females. The mean Lubben Social Network Scores-6 was 14.9 ± 5.7. Only the TUG test was associated in a single and multiple regression analysis with the Lubben Social Network Scores-6 as the dependent variable and the four physical function assessments as independent variables. The TUG test assessed the smoothness of normal standing and walking, which are essential physical functions for maintaining a social network and meeting people. This suggests a relationship between physical function and social network.

A Target Collision Algorithm of Autonomous Mobile Robot for Two-player Competition

Abstract A variety of robot competitions are held to train and attract students everywhere. This paper studies the algorithm of autonomous robot with two-player competition. The autonomous robots in a two-player competition takes the scoring form of collision and wins first. The impact object is the enemy target. The key algorithms in a two-player competition are target detection, target path decision, and collision target motion control. First, the vehicle target data set was established and enhanced, the lightweight detection algorithm model was screened and trained, the model pruning optimization experiment and the model deployment optimization experiment were carried out, and then the TensorRT framework was used for inter-layer fusion and data quantification processing. After hitting the target path decision algorithm after detecting the target target, it finally integrates the decision in simple and complex situations. The collision target motion control is the scheme of more intelligent and self-adjustable parameters of fuzzy Radial Basis Function (RBF) network Proportional Integral Derivative (PID) controller. Finally, the collision target implementation algorithm is tested in a real environment, and the results show that the task requirements can be met.

Peak Experience and the Insula in Health and Illness

AbstractThree major neural networks exist in the human central nervous system—the executive function network, the default mode network and the salience network. A key component of the latter is the insula. The insula and associated structures is implicated in peak experiences such as romantic love, profound spirituality and engagement with the arts which can impart to those experiencing them a sublime sense of connection with something beyond themselves which helps give their lives meaning. This mental state can also occur in rare neuropsychiatric disorders; here, Dr Nicholson explores the research.

An adaptive learning strategy for surrogate modeling of high-dimensional functions - Application to unsteady hypersonic flows in chemical nonequilibrium

Many engineering applications rely on the evaluation of expensive, non-linear high-dimensional functions. In this paper, we propose the RONAALP algorithm (Reduced Order Nonlinear Approximation with Adaptive Learning Procedure) to incrementally learn, on-the-fly as the application progresses, a fast and accurate reduced-order surrogate model of a target function. First, a combination of nonlinear auto-encoder, community clustering, and radial basis function networks allows us to learn an efficient and compact surrogate model with limited training data. Secondly, an active learning procedure overcomes any extrapolation issues during the online stage by adapting the surrogate model with high-fidelity evaluations that fall outside its current validity range. This approach results in generalizable, fast, and accurate reduced-order models of high-dimensional functions. The method is demonstrated on three direct numerical simulations of hypersonic flows in chemical nonequilibrium. Accurate simulations of these flows rely on detailed thermochemical gas models that dramatically increase the cost of such calculations. Using RONAALP to learn a reduced-order thermodynamic model surrogate on-the-fly, the cost of such simulations was reduced by up to 75% while maintaining an error of less than 10% on relevant quantities of interest.

Solution of the Forward Problem of ECG using Radial basis Function Network

ECG inverse problem is a mathematical formulation of cardiac electrical activity on the heart surface that can be solved to determine the electric potential on the heart surface. In this paper, a forward problem to ECG will be formulated using a mathematical formulation similar to the ECG forward problem. The forward problem will be solved using radial basis collocation techniques by taking different discretization points of N=9 and N=25 points to predict the solution u(x,t) to the forward problem.

What can the psychoneuroimmunology of yoga teach us about depression's psychopathology?

Depression, the most prevailing mental health condition, remains untreated in over 30% of patients. This cluster presents with sub-clinical inflammation. Investigations trialling anti-inflammatory medications had mixed results. The lack of results may result from inflammation's complexity and targeting only a few of depression's abnormal pathways. Mind-body therapies' biological and neuro-imaging studies offer valuable insights into depression psychopathology. Interestingly, mind-body therapies, like yoga, reverse the aberrant pathways in depression. These aberrant pathways include decreased cognitive function, interoception, neuroplasticity, salience and default mode networks connectivity, parasympathetic tone, increased hypothalamic-pituitary-adrenal (HPA) axis activity, and metabolic hyper/hypofunction. Abundant evidence found yogic techniques improving self-reported depressive symptoms across various populations. Yoga may be more effective in treating depression in conjunction with pharmacological and cognitive therapies. Yoga's psychoneuroimmunology teaches us that reducing allostatic load is crucial in improving depressive symptoms. Mind-body therapies promote parasympathetic tone, downregulate the HPA axis, reduce inflammation and boost immunity. The reduced inflammation promotes neuroplasticity and, subsequently, neurogenesis. Improving interoception resolves the metabolic needs prediction error and restores homeostasis. Additionally, by improving functional connectivity within the salience network, they restore the dynamic switching between the default mode and central executive networks, reducing rumination and mind-wandering. Future investigations should engineer therapies targeting the mechanisms mentioned above. The creation of multi-disciplinary health teams offering a combination of pharmacological, gene, neurofeedback, behavioural, mind-body and psychological therapies may treat treatment-resistant depression.

Comparison of a linear discrimination function and artificial neural networks approach to discriminate the seismic events in Ankara (Turkey)

In this study, natural and blast-induced ground vibrations (namely earthquakes and quarry blasts) in Ankara (Turkey) were analysed and distinguished from each other. A total of 156 digitized vertical component velocity seismograms of seismic events of 2009-2014 with Md≤3.5 were obtained from the Lodumlu broadband station (LOD) controlled by Boğaziçi University, Kandilli Observatory, and Earthquake Research Institute Regional Earthquake-Tsunami Monitoring Center (BU-KOERIRETMC). We examined the following variables: the ratio of the highest amplitude value of the S-wave and the highest amplitude value of the P-wave (Amplitude ratio) of vertical component velocity seismograms, the power ratio (Complexity), the logarithmical value of the amplitude of the S-wave of the seismogram (Log S), and total signal duration of the seismogram. It is the first time that natural and blast-induced ground vibrations were separated from each other using the Fisher’s Linear Discriminate Analysis (FLDA) technique and Artificial Neural Networks (ANNs) approach together in Ankara (The capital of Turkey). Ninety-two (59%) of the 156 seismic events studied were identified as earthquakes and sixty-four (41%) of them were described as blast-induced ground vibrations. Then the obtained accuracy percentage results of three pairs of some variables were compared. The amplitude ratio versus complexity and the amplitude ratio versus total signal duration had a high classification accuracy determination coefficient for the LOD dataset (94% for the FLDA technique and 100% for ANNs approach). ANNs approach is more successful than the FLDA technique.

Mapless navigation via Hierarchical Reinforcement Learning with memory-decaying novelty

Hierarchical Reinforcement Learning (HRL) has shown superior performance for mapless navigation tasks. However, it remains limited in unstructured environments that might contain terrains like long corridors and dead corners, which can lead to local minima. This is because most HRL-based mapless navigation methods employ a simplified reward setting and exploration strategy. In this work, we propose a novel reward function for training the high-level (HL) policy, which contains two components: extrinsic reward and intrinsic reward. The extrinsic reward encourages the robot to move towards the target location, while the intrinsic reward is computed based on novelty, episode memory and memory decaying, making the agent capable of accomplishing spontaneous exploration. We also design a novel neural network structure that incorporates an LSTM network to augment the agent with memory and reasoning capabilities. We test our method in unknown environments and specific scenarios prone to the local minimum problem to evaluate the navigation performance and local minimum resolution ability. The results show that our method significantly increases the success rate when compared to advanced RL-based methods, achieving a maximum improvement of nearly 28%. Our method demonstrates effective improvement in addressing the local minimum issue, especially in cases where the baselines fail completely. Additionally, numerous ablation studies consistently confirm the effectiveness of our proposed reward function and neural network structure.

Multiplexed spatial mapping of chromatin features, transcriptome, and proteins in tissues.

The phenotypic and functional states of a cell are modulated by a complex interactive molecular hierarchy of multiple omics layers, involving the genome, epigenome, transcriptome, proteome, and metabolome. Spatial omics approaches have enabled the capture of information from different molecular layers directly in the tissue context. However, current technologies are limited to map one to two modalities at the same time, providing an incomplete representation of cellular identity. Such data is inadequate to fully understand complex biological systems and their underlying regulatory mechanisms. Here we present spatial-Mux-seq, a multi-modal spatial technology that allows simultaneous profiling of five different modalities, including genome-wide profiles of two histone modifications and open chromatin, whole transcriptome, and a panel of proteins at tissue scale and cellular level in a spatially resolved manner. We applied this technology to generate multi-modal tissue maps in mouse embryos and mouse brains, which discriminated more cell types and states than unimodal data. We investigated the spatiotemporal relationship between histone modifications, chromatin accessibility, gene and protein expression in neuron differentiation revealing the relationship between tissue organization, function, and gene regulatory networks. We were able to identify a radial glia spatial niche and revealed spatially changing gradient of epigenetic signals in this region. Moreover, we revealed previously unappreciated involvement of repressive histone marks in the mouse hippocampus. Collectively, the spatial multi-omics approach heralds a new era for characterizing tissue and cellular heterogeneity that single modality studies alone could not reveal.

Experimental Investigation on collection efficiency of Deep-Sea hydraulic collection parameters

Hydraulic suction collection has been extensively utilized in deep-sea mining due to its high flow rate and efficiency. This paper introduces a novel hydraulic collection model to investigate the impact of various collection parameters on collection efficiency. We analyze the effect of different ore collection parameters on collection efficiency using a radial basis function network and make predictions. The optimal parameter range was obtained by performing combined parameter experiments. The two-parameter combination give better results compared to a single parameter. A theoretical model of collection efficiency is established based on experimental observations and principles of water jet theory. The accuracy of the model is confirmed through experimental validation, and the research findings can provide valuable insights for the ore collection process in deep-sea mining.

Investigation of interfacial bond strength of circular CFST columns based on ordinary and inorganic polymers through advanced machine learning methods

Concrete-filled steel tube (CFST) is a combination of materials, in which the interfacial bond-slip behavior is the prerequisite to reflect the synergistic loading of external steel tube and core concrete. The inorganic polymer concrete within the concrete-filled steel tube (IPCFST) compensates for the drying shrinkage of the concrete and enhances the adhesion at the interface between the steel tube and the concrete. However, the current code is too simple for the bond strength of CFSTs, and the existing theoretical calculation methods are not universally applicable. This paper aims to develop a practical artificial neural network tool for predicting the interfacial bond strength of ordinary CFSTs and IPCFSTs. An efficient prediction model for the bond strength of CFST interfaces was developed using a radial basis function network (RBFNN), with concrete strength, steel tube size, bond length and other factors as the main parameters. Bond performance test data was collected for a sample database of 322 circular CFST columns, including 56 ordinary CFSTs and 263 IPCFSTs. The results show that the Artificial Neural Network (ANN) algorithm can effectively address the bond strength problem of CFST structures and improve the accuracy and density of prediction in the comparative analyses with the measured values and the results of the semi-theoretical formulations. Based on the prediction results of ANN model, a sensitivity analysis of the parameters using Garson's algorithm shows that the diameter-to-thickness ratio and concrete strength significantly affect bond strength for both ordinary CFSTs and IPCFSTs. This study provides a more reliable prediction method and technical support for the engineering design of CFST structures.

RpoS sigma factor mediates adaptation and virulence in Vibrio mimicus

The alternative sigma factor RpoS functions as a regulator of stress and virulence response in numerous bacterial species. Vibrio mimicus is a critical opportunistic pathogen causing huge losses to aquaculture. However, the exact role of RpoS in V. mimicus remains unclear. In this study, rpoS deletion mutant of V. mimicus was constructed through allelic exchange and the phenotypic and transcriptional changes were investigated to determine the function of RpoS. The abilities of growth, motility, biofilm production, hemolytic activity and pathogenicity were significantly impaired in ΔrpoS strain. Stationary-phase cells of ΔrpoS strain showed lower tolerance to H2O2, heat, ethanol, and starvation stress than the wild-type strain. Transcriptome analyses revealed the involvement of rpoS in various cellular processes, notably bacterial-type flagellum synthesis and assembly, membrane synthesis and assembly and response to various stimuli. Phenotypic and RNA-seq analysis revealed that RpoS is required for biofilm formation, stress resistance, and pathogenicity in V. mimicus. Furthermore, β-galactosidase activity showed that rpoS is essential for optimal transcription of the flgK, fliA, cheA, mcpH mRNA. These results offer significant insight into the function and regulatory network of rpoS/RpoS, thereby improving our understanding and facilitating selection of molecular targets for future prevention strategies against V. mimicus.

Analysis of CYP701A1 genes in Gossypium species and functional characterization through gene silencing

Gibberellins (GA) are known to play crucial roles in various aspects of plant growth and development. The cytochrome P450 enzyme family is recognized for its significance in plant metabolic processes. Specifically, CYP701s, a subgroup of CYP71, encode ent-kaurene oxidase in the gibberellin synthesis pathway. In this study, we analyzed genomic data from 30 Gossypium species, including nine allotetraploid genomes (AD1-AD7, with two each for AD1 and AD2), 21 diploid genomes (A-G, K, with two A-genomes and 12 D-genomes), and Gossypioides kirkii genome as an outgroup for evolutionary analysis, totaling 31 genomes. Subsequently, 40 CYP701A1 genes were identified from various genomes and conducted a comprehensive analysis of their structure and evolution. Virus-induced gene silencing (VIGS) technology was utilized to knock out the GhCYP701A1 gene in Gossypium hirsutum ac TM-1. Subsequent analysis revealed changes in hormone content, with decreased gibberellin levels and notable increases in auxin, cytokinin, and jasmonic acid contents. Conversely, salicylic acid content decreased, while the precursor for ethylene synthesis, 1-aminocyclopropane-1-carboxylic acid (ACC), remained relatively stable. Transcriptome analysis of the gene silencing plants identified 15,962 differentially expressed genes, including 8,376 upregulated and 7,586 downregulated genes. Enrichment analysis through KEGG pathway analysis highlighted 'Plant hormone signal transduction' as a prominent pathway with 234 differentially expressed genes. The study provided insights into the function and regulatory network of the gene.

Data-Driven Multi-Fault Detection in Pipelines Utilizing Frequency Response Function and Artificial Neural Networks

This research presents a data-driven structural health monitoring (SHM) approach for pipeline systems that leverages frequency response function (FRF) signals and artificial neural network (ANN) algorithms to accurately identify and classify diverse pipeline fault conditions. The study focuses on three specific faults: bolt looseness, scale deposits, and crack occurrence at pipeline supports, which were replicated on a pipeline segment located at the Sound and Vibration Research Group (SVRG) at University Putra Malaysia (UPM). The FRF signals were captured using accelerometers to monitor the structural health of the pipeline. The data acquisition stage involved collecting FRF signals from the accelerometers to capture vibrations and responses related to the identified faults using a Siemens LMS SCADAS data acquisition unit. The data underwent preprocessing, including the application of principal component analysis (PCA) for feature selection. The subsequent data processing stage involved the application of an artificial neural network (ANN) algorithm for pattern recognition to analyze and classify the acquired data, identifying patterns associated with the replicated fault conditions. The proposed methodology demonstrated exceptional performance, with the ANN model achieving consistently high overall accuracy (above 99.7%) and remarkably low mean squared error (in the range of 0.0088×10−3to0.3062×10−3) across multiple iterations and sensor datasets. The detailed class-specific metrics, including accuracy, precision, sensitivity, and F1-score, further substantiated the model's effectiveness in identifying the individual fault types with near-perfect or perfect results for the majority of the fault scenarios. The location-invariant performance of the ANN model across different sensor placements demonstrates the robustness of the proposed data-driven SHM methodology. This research highlights the transformative potential of integrating state-of-the-art data-driven techniques to revolutionize the monitoring and assessment of critical pipeline infrastructure, ultimately enhancing the safety, reliability, and longevity of these vital systems.

Machine learning approaches for assessing stability in acid-crude oil emulsions: application to mitigate formation damage

The stability of acid-crude oil emulsion poses manifold issues in the oil industry. Experimentally evaluating this phenomenon may be costly and time-consuming. In contrast, machine learning models have proven effective in predicting and evaluating various phenomena. This research is the first of its kind to assess the stability of acid-crude oil emulsion, employing various classification machine learning models. For this purpose, a data set consisting of 249 experimental data points belonging to 11 different crude oil samples was collected. Three tree-based models, namely decision tree (DT), random forest (RF), and categorical boosting (CatBoost), as well as three artificial neural network models, namely radial basis function (RBF), multi-layer perceptron (MLP) and convolutional neural network (CNN), were developed based on the properties of crude oil, acid, and protective additive. The CatBoost model obtained the highest accuracy with 0.9687, followed closely by the CNN model with 0.9673. In addition, confusion matrix findings showed the superiority of the CatBoost model. Finally, by applying the SHapley Additive exPlanations (SHAP) method to analyze the impact of input parameters, it was found that the crude oil viscosity has the most significant effect on the model's output with the mean absolute SHAP value of 0.88.