Pathogen detection via inductively coupled plasma mass spectrometry analysis with nanoparticles.

Infections caused by viruses and bacteria pose a significant threat to global public health, emphasizing the critical importance of timely and precise detection methods. Inductively coupled plasma mass spectrometry (ICP-MS), a contemporary approach for pathogen detection, offers distinct advantages such as high sensitivity, a wide linear range, and multi-index capabilities. This review elucidates the underexplored application of ICP-MS in conjunction with functional nanoparticles (NPs) for the identification of viruses and bacteria. The review commences with an elucidation of the underlying principles, procedures, target pathogens, and NP requirements for this innovative approach. Subsequently, a thorough analysis of the advantages and limitations associated with these techniques is provided. Furthermore, the review delves into a comprehensive examination of the challenges encountered when utilizing NPs and ICP-MS for pathogen detection, culminating in a forward-looking assessment of the potential pathways for advancement in this domain. Thus, this review contributes novel perspectives to the field of pathogen detection in biomedicine by showcasing the promising synergy of ICP-MS and NPs.

Engineering the protein corona: Strategies, effects, and future directions in nanoparticle therapeutics.

Nanoparticles (NPs) serve as versatile delivery systems for anticancer, antibacterial, and antioxidant agents. The manipulation of protein-NP interactions within biological systems is crucial to the application of NPs in drug delivery and cancer nanotherapeutics. The protein corona (PC) that forms on the surface of NPs is the interface between biomacromolecules and NPs and significantly influences their pharmacokinetics and pharmacodynamics. Upon encountering proteins, NPs undergo surface alterations that facilitate their clearance from circulation by the mononuclear phagocytic system (MPS). PC behavior depends largely on the biological microenvironment and the physicochemical properties of the NPs. This review describes various strategies employed to engineer PC compositions on NP surfaces. The effects of NP characteristics such as size, shape, surface modification and protein precoating on PC performance were explored. In addition, this study addresses these challenges and guides the future directions of this evolving field.

A Distributed Proximal Consensus Algorithm for Energy Saving in Ethylene Production.

This article presents a distributed optimization framework in order to solve the plant-wide energy-saving problem of an ethylene plant. First, the ethylene production process is abstracted into a distributed network, and then, a new distributed consensus algorithm is proposed, which is called adaptive step-size-based distributed proximal consensus algorithm (ASS-DPCA). This algorithm can dynamically adjust the step size and automatically abandon the irrational evolutionary route while eliminating the dependence of optimization algorithms on model gradient information. Moreover, the designed algorithm is able to converge to an optimal solution for any convex cost functions and approach to a convex constraint set of agents over an undirected connected graph. Finally, the results of numerical simulation and industrial experiments show that the algorithm can reduce the total energy consumption of an ethylene plant with less computing time and assured consensus.

The mechanisms of action of mitochondrial targeting agents in cancer: inhibiting oxidative phosphorylation and inducing apoptosis.

The tumor microenvironment affects the structure and metabolic function of mitochondria in tumor cells. This process involves changes in metabolic activity, an increase in the amount of reactive oxygen species (ROS) in tumor cells compared to normal cells, the production of more intracellular free radicals, and the activation of oxidative pathways. From a practical perspective, it is advantageous to develop drugs that target mitochondria for the treatment of malignant tumors. Such drugs can enhance the selectivity of treatments for specific cell groups, minimize toxic effects on normal tissues, and improve combinational treatments. Mitochondrial targeting agents typically rely on small molecule medications (such as synthetic small molecules agents, active ingredients of plants, mitochondrial inhibitors or autophagy inhibitors, and others), modified mitochondrial delivery system agents (such as lipophilic cation modification or combining other molecules to form targeted mitochondrial agents), and a few mitochondrial complex inhibitors. This article will review these compounds in three main areas: oxidative phosphorylation (OXPHOS), changes in ROS levels, and endogenous oxidative and apoptotic processes.

Toxic Epidermal Necrolysis Induced by Sintilimab: A Case Report.

Sintilimab is an anti-programmed cell death receptor-1 antibody. The phase III clinical trial ORIENT-12 confirmed the safety of sintilimab combined with pemetrexed/platinum in the treatment of advanced squamous non-small cell lung cancer. Skin reactions are the most commonly reported adverse events of immune checkpoint inhibitors and are rarely severe. We describe a case of toxic epidermal necrolysis related to sintilimab in an elderly oncologic patient. 3 weeks after immunotherapy, the patient developed an extensive rash and diffuse itching, rapidly evolving into macules, blisters, bullae and erosions. Causal evaluation was performed based on the algorithm of drug causality for epidermal necrolysis and national Food and Drug Administration qualitative analysis. The patient responded to high-dose glucocorticosteroid and supportive therapy, alongside with local wound care. If immune checkpoint inhibitors need to be extrapolated clinically, strictly following evidence-based research, promptly detecting and treating adverse reactions is crucial.

Quantifying fluctuations for dynamical power systems with stochastic excitations: A power spectral density-based method.

Fluctuations of state variables play a pivotal role in analyzing small signal stability of the power system due to the integration of renewable energy sources. This paper develops a theoretical analysis methodology by using the power spectral density (PSD) for capturing the frequency and amplitude of state variable fluctuations in heterogeneous power systems with stochastic excitations. The fluctuations in generation and consumption occurring simultaneously are modeled by stochastic Ornstein-Uhlenbeck processes. The PSDs of the state variable fluctuations can be analytically calculated. PSD-based quantities have been proposed to evaluate angle and frequency deviations. Moreover, a global performance metric has been presented to measure the synchronization stability and calculated using the PSDs of frequency deviations. The underlying mathematical relationship between the metric and the primary control effort mimicking the H2-norm performance is explained in detail. Finally, the proposed analysis methodology is numerically illustrated on the IEEE RTS-96 test case. We investigate the impact of auto-correlations of stochastic processes on stability. Our results show the metric can be an alternative quantitative index of stability. We further find that the inertia allocation does not provide significant grid stability gain under small stochastic power fluctuations.

Solution Set Augmentation for Knee Identification in Multiobjective Decision Analysis.

In multiobjective decision making, most knee identification algorithms implicitly assume that the given solutions are well distributed and can provide sufficient information for identifying knee solutions. However, this assumption may fail to hold when the number of objectives is large or when the shape of the Pareto front is complex. To address the above issues, we propose a knee-oriented solution augmentation (KSA) framework that converts the Pareto front into a multimodal auxiliary function whose basins correspond to the knee regions of the Pareto front. The auxiliary function is then approximated using a surrogate and its basins are identified by a peak detection method. Additional solutions are then generated in the detected basins in the objective space and mapped to the decision space with the help of an inverse model. These solutions are evaluated by the original objective functions and added to the given solution set. To assess the quality of the augmented solution set, a measurement is proposed for the verification of knee solutions when the true Pareto front is unknown. The effectiveness of KSA is verified on widely used benchmark problems and successfully applied to a hybrid electric vehicle controller design problem.

Multilabel Convolutional Network With Feature Denoising and Details Supplement.

In multilabel images, the changeable size, posture, and position of objects in the image will increase the difficulty of classification. Moreover, a large amount of irrelevant information interferes with the recognition of objects. Therefore, how to remove irrelevant information from the image to improve the performance of label recognition is an important problem. In this article, we propose a convolutional network based on feature denoising and details supplement (FDDS) to address this issue. In FDDS, we first design a cascade convolution module (CCM) to collect spatial details of upper features, in order to enhance the information expression of features. Second, the feature denoising module (FDM) is further put forward to reallocate the weight of the feature semantic area, in order to enrich the effective semantic information of the current feature and perform denoising operations on object-irrelevant information. Experimental results show that the proposed FDDS outperforms the existing state-of-the-art models on several benchmark datasets, especially for complex scenes.

Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey.

Autonomous systems possess the features of inferring their own state, understanding their surroundings, and performing autonomous navigation. With the applications of learning systems, like deep learning and reinforcement learning, the visual-based self-state estimation, environment perception, and navigation capabilities of autonomous systems have been efficiently addressed, and many new learning-based algorithms have surfaced with respect to autonomous visual perception and navigation. In this review, we focus on the applications of learning-based monocular approaches in ego-motion perception, environment perception, and navigation in autonomous systems, which is different from previous reviews that discussed traditional methods. First, we delineate the shortcomings of existing classical visual simultaneous localization and mapping (vSLAM) solutions, which demonstrate the necessity to integrate deep learning techniques. Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks. Then, we focus on the visual navigation based on learning systems, mainly including reinforcement learning and deep reinforcement learning. Finally, we examine several challenges and promising directions discussed and concluded in related research of learning systems in the era of computer science and robotics.

Frame-Level Teacher-Student Learning With Data Privacy for EEG Emotion Recognition.

Recently, electroencephalogram (EEG) emotion recognition has gradually attracted a lot of attention. This brief designs a novel frame-level teacher-student framework with data privacy (FLTSDP) for EEG emotion recognition. The framework first proposes a teacher-student network without prior professional information for automated filtering of useful frame-level features by a gated mechanism and extracting high-level features by using knowledge distillation to capture the results of EEG emotion recognition from a teacher network and student networks. Then, the results from subnetworks are integrated by using the novel decision module, which, motivated by the voting mechanism, adjusts the composition of feature vectors and improves the weight of accurate prediction to optimize the integration effect. During training, an innovative data privacy protection mechanism is applied for avoiding data sharing, where each student network only inherits weights from all trained networks and does not inherit the training dataset. Here, the framework can be repeatedly optimized and improved by only training the next student subnetwork on new EEG signals. Experimental results show that our framework improves the accuracy of EEG emotion recognition by more than 5% and gets state-of-the-art performance for EEG emotion recognition in the subject-independent mode.

Data-Driven Tabulation for Chemistry Integration Using Recurrent Neural Networks.

Due to the wide range of time scales involved in the ordinary differential equations (ODEs) describing chemical reaction kinetics, multidimensional numerical simulation of chemical reactive flows using detailed combustion mechanisms is computationally expensive. To confront this issue, this article presents an economic data-driven tabulation algorithm for fast combustion chemistry integration. It uses the recurrent neural networks (RNNs) to construct the tabulation from a series of current and past states to the next state, which takes full advantage of RNN in handling long-term dependencies of time series data. The training data are first generated from direct numerical integrations to form an initial state space, which is divided into several subregions by the K-means algorithm. The centroid of each cluster is also determined at the same time. Next, an Elman RNN is constructed in each of these subregions to approximate the expensive direct integration, in which the integration routine obtained from the centroid is regarded as the basis for a storing and retrieving solution to ODEs. Finally, the alpha-shape metrics with principal component analysis (PCA) are used to generate a set of reduced-order geometric constraints that characterize the applicable range of these RNN approximations. For online implementation, geometric constraints are frequently verified to determine which RNN network to be used to approximate the integration routine. The advantage of the proposed algorithm is to use a set of RNNs to replace the expensive direct integration, which allows to reduce both the memory consumption and computational cost. Numerical simulations of a H2/CO-air combustion process are performed to demonstrate the effectiveness of the proposed algorithm compared to the existing ODE solver.

Generalized Nonconvex Nonsmooth Low-Rank Matrix Recovery Framework With Feasible Algorithm Designs and Convergence Analysis.

Decomposing data matrix into low-rank plus additive matrices is a commonly used strategy in pattern recognition and machine learning. This article mainly studies the alternating direction method of multiplier (ADMM) with two dual variables, which is used to optimize the generalized nonconvex nonsmooth low-rank matrix recovery problems. Furthermore, the minimization framework with a feasible optimization procedure is designed along with the theoretical analysis, where the variable sequences generated by the proposed ADMM can be proved to be bounded. Most importantly, it can be concluded from the Bolzano-Weierstrass theorem that there must exist a subsequence converging to a critical point, which satisfies the Karush-Kuhn-Tucher (KKT) conditions. Meanwhile, we further ensure the local and global convergence properties of the generated sequence relying on constructing the potential objective function. Particularly, the detailed convergence analysis would be regarded as one of the core contributions besides the algorithm designs and the model generality. Finally, the numerical simulations and the real-world applications are both provided to verify the consistence of the theoretical results, and we also validate the superiority in performance over several mostly related solvers to the tasks of image inpainting and subspace clustering.

Miniaturized microfluidic-based nucleic acid analyzer to identify new biomarkers of biopsy lung cancer samples for subtyping.

Identifying new biomarkers is necessary and important to diagnose and treat malignant lung cancer. However, existing protein marker detection methods usually require complex operation steps, leading to a lag time for diagnosis. Herein, we developed a rapid, minimally invasive, and convenient nucleic acid biomarker recognition method, which enabled the combined specific detection of 11 lung cancer typing markers in a microliter reaction system after only one sampling. The primers for the combined specific detection of 11 lung cancer typing markers were designed and screened, and the microfluidic chip for parallel detection of the multiple markers was designed and developed. Furthermore, a miniaturized microfluidic-based analyzer was also constructed. By developing a microfluidic chip and a miniaturized nucleic acid analyzer, we enabled the detection of the mRNA expression levels of multiple biomarkers in rice-sized tissue samples. The miniaturized nucleic acid analyzer could detect ≥10 copies of nucleic acids. The cell volume of the typing reaction on the microfluidic chip was only 0.94 µL, less than 1/25 of that of the conventional 25-µL Eppendorf tube PCR method, which significantly reduced the testing cost and significantly simplified the analysis of multiple biomarkers in parallel. With a simple injection operation and reverse transcription loop-mediated isothermal amplification (RT-LAMP), real-time detection of 11 lung cancer nucleic acid biomarkers was performed within 45 min. Given these compelling features, 86 clinical samples were tested using the miniaturized nucleic acid analyzer and classified according to the cutoff values of the 11 biomarkers. Furthermore, multi-biomarker analysis was conducted by a machine learning model to classify different subtypes of lung cancer, with an average area under the curve (AUC) of 0.934. This method shows great potential for the identification of new nucleic acid biomarkers and the accurate diagnosis of lung cancer.

Vancomycin dosing in an obese patient with acute renal failure: A case report and review of literature.

BACKGROUND: Vancomycin is the most commonly used drug for methicillin-resistant Staphylococcus aureus. The empirical clinical doses of vancomycin based on non-obese patients may not be optimal for obese ones. CASE SUMMARY: This study reports a case of vancomycin dosing adjustment in an obese patient (body mass index 78.4 kg/m2) with necrotizing fasciitis of the scrotum and left lower extremity accompanied with acute renal failure. Dosing adjustment was performed based on literature review and factors that influence pharmacokinetic parameters are analyzed. The results of the blood drug concentration monitoring confirmed the successful application of our dosing adjustment strategy in this obese patient. Total body weight is an important consideration for vancomycin administration in obese patients, which affects the volume of distribution and clearance of vancomycin. The alterations of pharmacokinetic parameters dictate that vancomycin should be dose-adjusted when applied to obese patients. At the same time, the pathophysiological status of patients, such as renal function, which also affects the dose adjustment of the patient, should be considered. CONCLUSION: Monitoring vancomycin blood levels in obese patients is critical to help adjust the dosing regimen to ensure that vancomycin concentrations are within the effective therapeutic range and to reduce the incidence of renal injury.

Evolving Dual-Threshold Bienenstock-Cooper-Munro Learning Rules in Echo State Networks.

The strengthening and the weakening of synaptic strength in existing Bienenstock-Cooper-Munro (BCM) learning rule are determined by a long-term potentiation (LTP) sliding modification threshold and the afferent synaptic activities. However, synaptic long-term depression (LTD) even affects low-active synapses during the induction of synaptic plasticity, which may lead to information loss. Biological experiments have found another LTD threshold that can induce either potentiation or depression or no change, even at the activated synapses. In addition, existing BCM learning rules can only select a set of fixed rule parameters, which is biologically implausible and practically inflexible to learn the structural information of input signals. In this article, an evolved dual-threshold BCM learning rule is proposed to regulate the reservoir internal connection weights of the echo-state-network (ESN), which can contribute to alleviating information loss and enhancing learning performance by introducing different optimal LTD thresholds for different postsynaptic neurons. Our experimental results show that the evolved dual-threshold BCM learning rule can result in the synergistic learning of different plasticity rules, effectively improving the learning performance of an ESN in comparison with existing neural plasticity learning rules and some state-of-the-art ESN variants on three widely used benchmark tasks and the prediction of an esterification process.

Chinese Medicinal Herb-Derived Carbon Dots for Common Diseases: Efficacies and Potential Mechanisms.

The management of hemorrhagic diseases and other commonly refractory diseases (including gout, inflammatory diseases, cancer, pain of various forms and causes) are very challenging in clinical practice. Charcoal medicine is a frequently used complementary and alternative drug therapy for hemorrhagic diseases. However, studies (other than those assessing effects on hemostasis) on charcoal-processed medicines are limited. Carbon dots (CDs) are quasi-spherical nanoparticles that are biocompatible and have high stability, low toxicity, unique optical properties. Currently, there are various studies carried out to evaluate their efficacy and safety. The exploration of using traditional Chinese medicine (TCM) -based CDs for the treatment of common diseases has received great attention. This review summarizes the literatures on medicinal herbs-derived CDs for the treatment of the difficult-to-treat diseases, and explored the possible mechanisms involved in the process of treatment.

Hybrid model of a cement rotary kiln using an improved attention-based recurrent neural network.

A rotary kiln is core equipment in cement calcination. Significant time delay, time-varying, and nonlinear characteristics cause challenges in the advance process control and operational optimization of the rotary kiln. However, the traditional mechanism model with many assumptions cannot accurately represent the dynamic kiln process because kinetic parameters are difficult to obtain. This paper proposes a novel hybrid strategy to develop a dynamic model of a rotary kiln by combining a process mechanism and a recurrent neural network to address this issue. A time delay mechanism is used to estimate the kiln's residence time to compensate for the time delay. A long short-term memory model that combines an attention mechanism and an ordinary differential equation solver is proposed to capture the time-varying and nonlinear behaviors of the kiln process. Case studies from two real-world cement plants with different processing loads are used to verify the effectiveness of the proposed hybrid modeling strategy. The results show that the proposed method has better accuracy and robustness than the traditional methods. The sensitivity analysis of the proposed model also makes it practical for t control system design and real-time optimization.

A nature-inspired hierarchical branching structure pressure sensor with high sensitivity and wide dynamic range for versatile medical wearables.

Pressure-sensing capability is essential for flexible electronic devices, which require high sensitivity and a wide detection range to simplify the system. However, the template-based pressure sensor is powerless to detect high pressure due to the rapid deformation saturation of microstructures. Herein, we demonstrated that a nature-inspired hierarchical branching (HB) structure can effectively address this problem. Finite element analysis demonstrates that the HB structure permits a step-by-step mobilization of microstructure deformation, resulting in a dramatically improved sensitivity (up to 2 orders of magnitude) when compared with the traditional monolayer structure. Experiments show that the HB structure enables pressure sensors to have a lower elastic modulus (1/3 of that of monolayer sensors), a high sensitivity of 13.1 kPa-1 (almost 14 times higher than the monolayer sensor), and a wide dynamic range (0-800 kPa, the minimum detection pressure is 1.6 Pa). The maximum frequency that the sensor can detect is 250 Hz. The response/recovery time is 0.675/0.55 ms respectively. Given this performance, the HB sensor enables high-resolution detection of the weak radial artery pulse wave characteristics in different states, indicating its potential to noninvasively reveal cardiovascular status and the effectiveness of related interventions, such as exercise and drug intervention. As a proof of concept, we also verified that the HB sensor can serve as a versatile platform to support diverse applications from low to high pressure.

Rapid, Highly Sensitive, and Label-Free Pathogen Assay System Using a Solid-Phase Self-Interference Recombinase Polymerase Amplification Chip and Hyperspectral Interferometry.

Recombinase polymerase amplification (RPA) is a useful pathogen identification method. Several label-free detection methods for RPA amplicons have been developed in recent years. However, these methods still lack sensitivity, specificity, efficiency, or simplicity. In this study, we propose a rapid, highly sensitive, and label-free pathogen assay system based on a solid-phase self-interference RPA chip (SiSA-chip) and hyperspectral interferometry. The SiSA-chips amplify and capture RPA amplicons on the chips, rather than irrelevant amplicons such as primer dimers, and the SiSA-chips are then analysed by hyperspectral interferometry. Optical length increases of SiSA-chips are used to demonstrate RPA detection results, with a limit of detection of 1.90 nm. This assay system can detect as few as six copies of the target 18S rRNA gene of Plasmodium falciparum within 20 min, with a good linear relationship between the detection results and the concentration of target genes (R2 = 0.9903). Single nucleotide polymorphism (SNP) genotyping of the dhfr gene of Plasmodium falciparum is also possible using the SiSA-chip, with as little as 1% of mutant gene distinguished from wild-type loci (m/wt). This system offers a high-efficiency (20 min), high-sensitivity (6 copies/reaction), high-specificity (1% m/wt), and low-cost (∼1/50 of fluorescence assays for RPA) diagnosis method for pathogen DNA identification. Therefore, this system is promising for fast identification of pathogens to help diagnose infectious diseases, including SNP genotyping.

Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems.

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the l0 -norm usually achieve better performance than the convex case (e.g., l1 -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Lojasiewica (KL) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones.