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"Xia, Yi"
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Machine fault detection model based on MWOA-BiLSTM algorithm
This paper proposes the Modulated Whale Optimization Algorithm(MWOA), an innovative metaheuristic algorithm derived from the classic WOA and tailored for bionics-inspired optimization. MWOA tackles common optimization problems like local optima and premature convergence using two key methods: shrinking encircling and spiral position updates. In essence, it prevents algorithms from settling for suboptimal solutions too soon, encouraging exploration of a broader solution space before converging, by incorporating cauchy variation and a perturbation term, MWOA achieve optimization over a wide search space. After that, comparisons were conducted between MWOA and seven recently proposed metaheuristics, utilizing the CEC2005 benchmark functions to assess MWOA’s optimization performance. Moreover, the Wilcoxon rank sum test is used to verify the effectiveness of the proposed algorithm. Eventually, MWOA was juxtaposed with the BiLSTM classifier and six other meta-heuristics combined with the BiLSTM classifier. The aim was to affirm that MWOA-BiLSTM outperforms its counterparts, showcasing superior performance across crucial metrics such as accuracy, precision, recall, and F1-Score. The study results unequivocally demonstrate that MWOA showcases exceptional optimization capabilities, adeptly striking a harmonious balance between exploration and exploitation.
Journal Article
Exosomes From miR-19b-3p-Modified ADSCs Inhibit Ferroptosis in Intracerebral Hemorrhage Mice
Objectives: Effective treatments for intracerebral hemorrhage (ICH) are limited until now. Ferroptosis, a novel form of iron-dependent cell death, is implicated in neurodegeneration diseases. Here, we attempted to investigate the impact of exosomes from miR-19b-3p-modified adipose-derived stem cells (ADSCs) on ferroptosis in ICH. Methods: Collagenase was used to induce a mouse model of ICH and hemin was used to induce ferroptosis in cultured neurons. Exosomes were isolated from mimic NC- or miR-19b-3p mimic-transfected ADSCs (ADSCs-MNC-Exos or ADSCs-19bM-Exos, respectively) and then administered to ICH mice or hemin-treated neurons. ICH damage was evaluated by assessing the neurological function of ICH mice and cell viability of neurons. Ferroptosis was evaluated in mouse brains or cultured neurons. The interaction between miR-19b-3p and iron regulatory protein 2 (IRP2) 3′-UTR was analyzed by performing luciferase reporter assay. Results: Ferroptosis occurred in ICH mice, which also exhibited decreased miR-19b-3p and increased IRP2 expression. IRP2 was a direct target of miR-19b-3p, and IRP2 expression was repressed by ADSCs-19bM-Exos. Importantly, ADSCs-19bM-Exos effectively attenuated hemin-induced cell injury and ferroptosis. Moreover, ADSCs-19bM-Exos administration significantly improved neurologic function and inhibited ferroptosis in ICH mice. Conclusion: Exosomes from miR-19b-3p-modified ADSCs inhibit ferroptosis in ICH mice.
Journal Article
Role of astrocytes, microglia, and tanycytes in brain control of systemic metabolism
Astrocytes, microglia, and tanycytes play active roles in the regulation of hypothalamic feeding circuits. These non-neuronal cells are crucial in determining the functional interactions of specific neuronal subpopulations involved in the control of metabolism. Recent advances in biology, optics, genetics, and pharmacology have resulted in the emergence of novel and highly sophisticated approaches for studying hypothalamic neuronal–glial networks. Here we summarize the progress in the field and argue that glial–neuronal interactions provide a core hub integrating food-related cues, interoceptive signals, and internal states to adapt a complex set of physiological responses operating on different timescales to finely tune behavior and metabolism according to metabolic status. This expanding knowledge helps to redefine our understanding of the physiology of food intake and energy metabolism.
Journal Article
Dynamics of Interacting Diseases
Current modeling of infectious diseases allows for the study of complex and realistic scenarios that go from the population to the individual level of description. However, most epidemic models assume that the spreading process takes place on a single level (be it a single population, a metapopulation system, or a network of contacts). In particular, interdependent contagion phenomena can be addressed only if we go beyond the scheme-one pathogen-one network. In this paper, we propose a framework that allows us to describe the spreading dynamics of two concurrent diseases. Specifically, we characterize analytically the epidemic thresholds of the two diseases for different scenarios and compute the temporal evolution characterizing the unfolding dynamics. Results show that there are regions of the parameter space in which the onset of a disease’s outbreak is conditioned to the prevalence levels of the other disease. Moreover, we show, for the susceptible-infected-susceptible scheme, that under certain circumstances, finite and not vanishing epidemic thresholds are found even at the limit for scale-free networks. For the susceptible-infected-removed scenario, the phenomenology is richer and additional interdependencies show up. We also find that the secondary thresholds for the susceptible-infected-susceptible and susceptible-infected-removed models are different, which results directly from the interaction between both diseases. Our work thus solves an important problem and paves the way toward a more comprehensive description of the dynamics of interacting diseases.
Journal Article
The hypothalamus for whole-body physiology: from metabolism to aging
Obesity and aging are two important epidemic factors for metabolic syndrome and many other health issues, which contribute to devastating diseases such as cardiovascular diseases, stroke and cancers. The brain plays a central role in controlling metabolic physiology in that it integrates information from other metabolic organs, sends regulatory projections and orchestrates the whole-body function. Emerging studies suggest that brain dysfunction in sensing various internal cues or processing external cues may have profound effects on metabolic and other physiological functions. This review highlights brain dysfunction linked to genetic mutations, sex, brain inflammation, microbiota, stress as causes for whole-body pathophysiology, arguing brain dysfunction as a root cause for the epidemic of aging and obesity-related disorders. We also speculate key issues that need to be addressed on how to reveal relevant brain dysfunction that underlines the development of these disorders and diseases in order to develop new treatment strategies against these health problems.
Journal Article
Heterogeneous Coupling between Interdependent Lattices Promotes the Cooperation in the Prisoner’s Dilemma Game
In the research realm of game theory, interdependent networks have extended the content of spatial reciprocity, which needs the suitable coupling between networks. However, thus far, the vast majority of existing works just assume that the coupling strength between networks is symmetric. This hypothesis, to some extent, seems inconsistent with the ubiquitous observation of heterogeneity. Here, we study how the heterogeneous coupling strength, which characterizes the interdependency of utility between corresponding players of both networks, affects the evolution of cooperation in the prisoner's dilemma game with two types of coupling schemes (symmetric and asymmetric ones). Compared with the traditional case, we show that heterogeneous coupling greatly promotes the collective cooperation. The symmetric scheme seems much better than the asymmetric case. Moreover, the role of varying amplitude of coupling strength is also studied on these two interdependent ways. Current findings are helpful for us to understand the evolution of cooperation within many real-world systems, in particular for the interconnected and interrelated systems.
Journal Article
Dearomative 1,4-difunctionalization of naphthalenes via palladium-catalyzed tandem Heck/Suzuki coupling reaction
Dearomative functionalization reactions represent an important strategy for the synthesis of valuable three-dimensional molecules from simple planar aromatics. Naphthalene is a challenging arene towards transition-metal-catalyzed dearomative difunctionalization reactions. Reported herein is an application of naphthalene as a masked conjugated diene in a palladium-catalyzed dearomative 1,4-diarylation or 1,4-vinylarylation reaction via tandem Heck/Suzuki sequence. Three types of 1,4-dihydronaphthalene-based spirocyclic compounds are achieved in excellent regio- and diastereoselectivities. Key to this transformation is the inhibition of a few competitive side reactions, including intramolecular naphthalenyl C-H arylation, intermolecular Suzuki cross-coupling, dearomative 1,2-difunctionalization, and dearomative reductive-Heck reaction. Density functional theory (DFT) calculations imply that the facile exergonic dearomative insertion of a naphthalene double bond disrupts the sequence of direct Suzuki coupling, leading to the tandem Heck/Suzuki coupling reaction. The observed regioselectivity towards 1,4-difunctionalization is due to the steric repulsions between the introduced aryl group and the spiro-scaffold in 1,2-difunctionalization.
Naphthalene is a challenging arene towards transition-metal-catalyzed dearomative difunctionalization. Here, the authors show that naphthalene may act as a masked conjugated diene in palladium-catalyzed dearomative 1,4-diarylation or 1,4-vinylarylation via a tandem Heck/Suzuki sequence.
Journal Article
Risk factors for inadvertent intraoperative hypothermia in patients undergoing laparoscopic surgery: A prospective cohort study
Inadvertent intraoperative hypothermia is frequent during open surgeries; however, few studies on hypothermia during laparoscopic abdominal surgery have been reported. We aimed to investigate the incidence and risk factors for hypothermia in patients undergoing laparoscopic abdominal surgery. This single-center prospective cohort observational study involved patients undergoing laparoscopic surgery between October 2018 and June 2019. Data on core body temperature and potential variables were collected. A multivariate logistic regression analysis was performed to identify the risk factors associated with hypothermia. A Cox regression analysis was used to verify the sensitivity of the results. In total, 690 patients were included in the analysis, of whom 200 (29.0%, 95% CI: 26%-32%) had a core temperature < 36°C. The core temperature decreased over time, and the incident hypothermia increased gradually. In the multivariate logistic regression analysis, age (OR = 1.017, 95% CI: 1.000-1.034, P = 0.050), BMI (OR = 0.938, 95% CI: 0.880-1.000; P = 0.049), baseline body temperature (OR = 0.025, 95% CI: 0.010-0.060; P < 0.001), volume of irrigation fluids (OR = 1.001, 95% CI: 1.000-1.001, P = 0.001), volume of urine (OR = 1.001, 95% CI: 1.000-1.003, P = 0.070), and duration of surgery (OR = 1.010, 95% CI: 1.006-1.015, P < 0.001) were significantly associated with hypothermia. In the Cox analysis, variables in the final model were age, BMI, baseline body temperature, volume of irrigation fluids, blood loss, and duration of surgery. Inadvertent intraoperative hypothermia is evident in patients undergoing laparoscopic surgeries. Age, BMI, baseline body temperature, volume of irrigation fluids, and duration of surgery are significantly associated with intraoperative hypothermia.
Journal Article
Quantum-Enhanced Data Classification with a Variational Entangled Sensor Network
Variational quantum circuits (VQCs) built upon noisy intermediate-scale quantum (NISQ) hardware, in conjunction with classical processing, constitute a promising architecture for quantum simulations, classical optimization, and machine learning. However, the required VQC depth to demonstrate a quantum advantage over classical schemes is beyond the reach of available NISQ devices. Supervised learning assisted by an entangled sensor network (SLAEN) is a distinct paradigm that harnesses VQCs trained by classical machine-learning algorithms to tailor multipartite entanglement shared by sensors for solving practically useful data-processing problems. Here, we report the first experimental demonstration of SLAEN and show an entanglement-enabled reduction in the error probability for classification of multidimensional radio-frequency signals. Our work paves a new route for quantum-enhanced data processing and its applications in the NISQ era.
Journal Article