Ent-eudesmane sesquiterpenoids with anti-neuroinflammatory activity from the marine-derived fungus Eutypella sp. F0219.

In this study, twenty-three ent-eudesmane sesquiterpenoids (1-23) including fifteen previously undescribed ones, named eutypelides A-O (1-15) were isolated from the marine-derived fungus Eutypella sp. F0219. Their planar structures and relative configurations were established by HR-ESIMS and extensive 1D and 2D NMR investigations. The absolute configurations of the previously undescribed compounds were determined by single-crystal X-ray diffraction analyses, modified Mosher's method, and ECD calculations. Structurally, eutypelide A (1) is a rare 1,10-seco-ent-eudesmane, whereas 2-15 are typically ent-eudesmanes with 6/6/-fused bicyclic carbon nucleus. The anti-neuroinflammatory activity of all isolated compounds (1-23) was accessed based on their ability to NO production in LPS-stimulated BV2 microglia cells. Compound 16 emerged as the most potent inhibitor. Further mechanistic investigation revealed that compound 16 modulated the inflammatory response by decreasing the protein levels of iNOS and increasing ARG 1 levels, thereby altering the iNOS/ARG 1 ratio and inhibiting macrophage polarization. qRT-PCR analysis showed that compound 16 reversed the LPS-induced upregulation of pro-inflammatory cytokines, including iNOS, TNF-α, IL-6, and IL-1ß, at both the transcriptional and translational levels. These effects were linked to the inhibition of the NF-κB pathway, a key regulator of inflammation. Our findings suggest that compound 16 may be a potential structure basis for developing neuroinflammation-related disease therapeutic agents.

Marine fungus Aspergillus c1. sp metabolite activates the HSF1/PGC-1α axis, inducing a thermogenic program for treating obesity.

Background and aims: Obesity is one of the most prevalent diseases worldwide with less ideal approved agents in clinic. Activating the HSF1/PGC-1α axis in adipose tissues has been reported to induce thermogenesis in mice, which presents a promising therapeutic avenue for obesity treatment. The present study aimed to identified novel natural HSF1 activator and evaluated the therapeutic effects of the newly discovered compound on obesity-associated metabolic disorders and the molecular mechanisms of these effects. Methods: Our previous reported HSF1/PGC-1α activator screening system was used to identify novel natural HSF1 activator. The PGC-1α luciferase activity, immunoblot, protein nuclear-translocation, immunofluorescence, chromatin immunoprecipitation assays were used to evaluate the activity of compound HN-001 in activating HSF1. The experiments of mitochondrial number measurement, TG assay and imaging, cellular metabolic assay, gene assays, and CRISPR/Cas 9 were applied for investigating the metabolic effect of HN-001 in C3H10-T1/2 adipocytes. The in vivo anti-obesity efficacies and beneficial metabolic effects of HN-001 were evaluated by performing body and fat mass quantification, plasma chemical analysis, GTT, ITT, cold tolerance test, thermogenesis analysis. Results: HN-001 dose- and time-dependently activated HSF1 and induced HSF1 nuclear translocation, resulting in an enhancement in binding with the gene Pgc-1α. This improvement induced activation of adipose thermogenesis and enhancement of mitochondrial oxidation capacity, thus inhibiting adipocyte maturation. Deletion of HSF1 in adipocytes impaired mitochondrial oxidation and abolished the above beneficial metabolic effects of HN-001, including adipocyte browning induction, improvements in mitogenesis and oxidation capacity, and lipid-lowering ability. In mice, HN-001 treatment efficiently alleviated diet-induced obesity and metabolic disorders. These changes were associated with increased body temperature in mice and activation of the HSF1/PGC-1α axis in adipose tissues. UCP1 expression and mitochondrial biogenesis were increased in both white and brown adipose tissues of HN-001-treated mice. Conclusion: These data indicate that HN-001 may have therapeutic potential for obesity-related metabolic diseases by increasing the capacity of energy expenditure in adipose tissues through a mechanism involving the HSF1/PGC-1α axis, which shed new light on the development of novel anti-obesity agents derived from marine sources.

Identification of a natural PLA2 inhibitor from the marine fungus Aspergillus sp. c1 for MAFLD treatment that suppressed lipotoxicity by inhibiting the IRE-1α/XBP-1s axis and JNK signaling.

Lipotoxicity is a pivotal factor that initiates and exacerbates liver injury and is involved in the development of metabolic-associated fatty liver disease (MAFLD). However, there are few reported lipotoxicity inhibitors. Here, we identified a natural anti-lipotoxicity candidate, HN-001, from the marine fungus Aspergillus sp. C1. HN-001 dose- and time- dependently reversed palmitic acid (PA)-induced hepatocyte death. This protection was associated with IRE-1α-mediated XBP-1 splicing inhibition, which resulted in suppression of XBP-1s nuclear translocation and transcriptional regulation. Knockdown of XBP-1s attenuated lipotoxicity, but no additional ameliorative effect of HN-001 on lipotoxicity was observed in XBP-1s knockdown hepatocytes. Notably, the ER stress and lipotoxicity amelioration was associated with PLA2. Both HN-001 and the PLA2 inhibitor MAFP inhibited PLA2 activity, reduced lysophosphatidylcholine (LPC) level, subsequently ameliorated lipotoxicity. In contrast, overexpression of PLA2 caused exacerbation of lipotoxicity and weakened the anti-lipotoxic effects of HN-001. Additionally, HN-001 treatment suppressed the downstream pro-apoptotic JNK pathway. In vivo, chronic administration of HN-001 (i.p.) in mice alleviated all manifestations of MAFLD, including hepatic steatosis, liver injury, inflammation, and fibrogenesis. These effects were correlated with PLA2/IRE-1α/XBP-1s axis and JNK signaling suppression. These data indicate that HN-001 has therapeutic potential for MAFLD because it suppresses lipotoxicity, and provide a natural structural basis for developing anti-MAFLD candidates.

Advanced QuEChERS Method Using Core-Shell Magnetic Molecularly Imprinted Polymers (Fe3O4@MIP) for the Determination of Pesticides in Chlorophyll-Rich Samples.

Graphitized carbon black (GCB) in the traditional QuEChERS (quick, easy, cheap, effective, rugged, and safe) method was used to remove the interfering substance chlorophyll in vegetable and fruit samples for pesticide residues determination. However, it not only adsorbs pigments, but also adsorbs some planar and aromatic pesticides. In order to solve the shortcoming, a core-shell magnetic molecularly imprinted polymer (Fe3O4@MIP) that can specifically recognize and adsorb chlorophyll was synthesized, and an advanced QuEChERS method with the Fe3O4@MIP as a purification material was developed. This advanced method presents detection that is highly sensitive, specific, and reproducible for planar and aromatic pesticides. The limits of detection (LOD) ranged from 0.001-0.002 mg kg-1, and the limit of quantification (LOQ) was 0.005 mg kg-1. The recovery for the planar and aromatic pesticides was within 70-110% with the associated relative standard deviations < 15% in leek samples by the advanced QuEChERS method. However, in the traditional QuEChERS method with GCB, the recovery of most planar and aromatic pesticides was <60%. It may also be useful for the determination of other pesticides in vegetable samples with quick and easy sample purification.

Reducing Urban Traffic Congestion Using Deep Learning and Model Predictive Control.

This article proposes a deep learning (DL)-based control algorithm-DL velocity-based model predictive control (VMPC)-for reducing traffic congestion with slowly time-varying traffic signal controls. This control algorithm consists of system identification using DL and traffic signal control using VMPC. For the training process of DL, we established a modeling error entropy loss as the criteria inspired by the theory of stochastic distribution control (SDC) originated by the fourth author. Simulation results show that the proposed algorithm can reduce traffic congestion with a slowly varying traffic signal control input. Results of an ablation study demonstrate that this algorithm compares favorably to other model-based controllers in terms of prediction error, signal varying speed, and control effectiveness.

Exploring a COVID-19 Endemic Scenario: High-Resolution Agent-Based Modeling of Multiple Variants.

Our efforts as a society to combat the ongoing COVID-19 pandemic are continuously challenged by the emergence of new variants. These variants can be more infectious than existing strains and many of them are also more resistant to available vaccines. The appearance of these new variants cause new surges of infections, exacerbated by infrastructural difficulties, such as shortages of medical personnel or test kits. In this work, a high-resolution computational framework for modeling the simultaneous spread of two COVID-19 variants: a widely spread base variant and a new one, is established. The computational framework consists of a detailed database of a representative U.S. town and a high-resolution agent-based model that uses the Omicron variant as the base variant and offers flexibility in the incorporation of new variants. The results suggest that the spread of new variants can be contained with highly efficacious tests and mild loss of vaccine protection. However, the aggressiveness of the ongoing Omicron variant and the current waning vaccine immunity point to an endemic phase of COVID-19, in which multiple variants will coexist and residents continue to suffer from infections.

Hainanxylogranolides A-F: New Limonoids isolated from the seeds of Hainan mangrove plant Xylocarpus granatum.

Six new limonoids, named hainanxylogranolides A-F (1-6), together with nineteen known ones (7-25) were isolated from the seeds of a Hainan mangrove Xylocarpus granatum. The structures of the new compounds were established by extensive NMR spectroscopic data combined with the DFT and TDDFT calculated electronic circular dichroism spectra. Hainanxylogranolide A (1) is the aromatic B-ring limonoid containing a central pyridine ring and a C-17 substituted γ(21)-hydroxybutenolide moiety. Hainanxylogranolide B (2) belongs to the small group of mexicanolides containing a C3-O-C8 bridge, whereas hainanxylogranolides C and D (3 and 4) are mexicanolides comprising a C1-O-C8 bridge. Compounds 9 and 25 posed obvious inhibition effect on the tube formation of HUVECs. There are only about 25% tube-like structures were observed at the concentration of 40.0 µM of compound 25. The antiviral activities of the isolates against herpes simplex virus-1 (HSV-1) and severe fever with thrombocytopenia syndrome virus (SFTSV) were tested in vitro. Compound 23 exhibited moderate anti-SFTSV activity with the IC50 value of 29.58 ± 0.73 µM. This is the first report of anti-angiogenic effect and anti-SFTSV activity of limonoids from the genus Xylocarpus.

Activity-driven network modeling and control of the spread of two concurrent epidemic strains.

The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from unavoidable mutations. The existing network toolbox to study epidemic spreading cannot be readily adapted to the study of multiple, coexisting strains. In this context, particularly lacking are models that could elucidate re-infection with the same strain or a different strain-phenomena that we are seeing experiencing more and more with COVID-19. Here, we establish a novel mathematical model to study the simultaneous spreading of two strains over a class of temporal networks. We build on the classical susceptible-exposed-infectious-removed model, by incorporating additional states that account for infections and re-infections with multiple strains. The temporal network is based on the activity-driven network paradigm, which has emerged as a model of choice to study dynamic processes that unfold at a time scale comparable to the network evolution. We draw analytical insight from the dynamics of the stochastic network systems through a mean-field approach, which allows for characterizing the onset of different behavioral phenotypes (non-epidemic, epidemic, and endemic). To demonstrate the practical use of the model, we examine an intermittent stay-at-home containment strategy, in which a fraction of the population is randomly required to isolate for a fixed period of time.

Urban Determinants of COVID-19 Spread: a Comparative Study across Three Cities in New York State.

The ongoing pandemic is laying bare dramatic differences in the spread of COVID-19 across seemingly similar urban environments. Identifying the urban determinants that underlie these differences is an open research question, which can contribute to more epidemiologically resilient cities, optimized testing and detection strategies, and effective immunization efforts. Here, we perform a computational analysis of COVID-19 spread in three cities of similar size in New York State (Colonie, New Rochelle, and Utica) aiming to isolate urban determinants of infections and deaths. We develop detailed digital representations of the cities and simulate COVID-19 spread using a complex agent-based model, taking into account differences in spatial layout, mobility, demographics, and occupational structure of the population. By critically comparing pandemic outcomes across the three cities under equivalent initial conditions, we provide compelling evidence in favor of the central role of hospitals. Specifically, with highly efficacious testing and detection, the number and capacity of hospitals, as well as the extent of vaccination of hospital employees are key determinants of COVID-19 spread. The modulating role of these determinants is reduced at lower efficacy of testing and detection, so that the pandemic outcome becomes equivalent across the three cities.

Predicting the Effects of Waning Vaccine Immunity Against COVID-19 through High-Resolution Agent-Based Modeling.

The potential waning of the vaccination immunity to COVID-19 could pose threats to public health, as it is tenable that the timing of such waning would synchronize with the near-complete restoration of normalcy. Should also testing be relaxed, a resurgent COVID-19 wave in winter 2021/2022 might be witnessed. In response to this risk, an additional vaccine dose, the booster shot, is being administered worldwide. A projected study with an outlook of 6 months explores the interplay between the rate at which boosters are distributed and the extent to which testing practices are implemented, using a highly granular agent-based model tuned on a medium-sized US town. Theoretical projections indicate that the administration of boosters at the rate at which the vaccine is currently administered could yield a severe resurgence of the pandemic. Projections suggest that the peak levels of mid-spring 2021 in the vaccination rate may prevent such a scenario to occur, although exact agreement between observations and projections should not be expected due to the continuously evolving nature of the pandemic. This study highlights the importance of testing, especially to detect asymptomatic individuals in the near future, as the release of the booster reaches full speed.

Human motor learning is robust to control-dependent noise.

Noises are ubiquitous in sensorimotor interactions and contaminate the information provided to the central nervous system (CNS) for motor learning. An interesting question is how the CNS manages motor learning with imprecise information. Integrating ideas from reinforcement learning and adaptive optimal control, this paper develops a novel computational mechanism to explain the robustness of human motor learning to the imprecise information, caused by control-dependent noise that exists inherently in the sensorimotor systems. Starting from an initial admissible control policy, in each learning trial the mechanism collects and uses the noisy sensory data (caused by the control-dependent noise) to form an imprecise evaluation of the performance of the current policy and then constructs an updated policy based on the imprecise evaluation. As the number of learning trials increases, the generated policies mathematically provably converge to a (potentially small) neighborhood of the optimal policy under mild conditions, despite the imprecise information in the learning process. The mechanism directly synthesizes the policies from the sensory data, without identifying an internal forward model. Our preliminary computational results on two classic arm reaching tasks are in line with experimental observations reported in the literature. The model-free control principle proposed in the paper sheds more lights into the inherent robustness of human sensorimotor systems to the imprecise information, especially control-dependent noise, in the CNS.

Learning-Based Adaptive Optimal Control for Connected Vehicles in Mixed Traffic: Robustness to Driver Reaction Time.

Through vehicle-to-vehicle (V2V) communication, both human-driven and autonomous vehicles can actively exchange data, such as velocities and bumper-to-bumper distances. Employing the shared data, control laws with improved performance can be designed for connected and autonomous vehicles (CAVs). In this article, taking into account human-vehicle interaction and heterogeneous driver behavior, an adaptive optimal control design method is proposed for a platoon mixed with multiple preceding human-driven vehicles and one CAV at the tail. It is shown that by using reinforcement learning and adaptive dynamic programming techniques, a near-optimal controller can be learned from real-time data for the CAV with V2V communications, but without the precise knowledge of the accurate car-following parameters of any driver in the platoon. The proposed method allows the CAV controller to adapt to different platoon dynamics caused by the unknown and heterogeneous driver-dependent parameters. To improve the safety performance during the learning process, our off-policy learning algorithm can leverage both the historical data and the data collected in real time, which leads to considerably reduced learning time duration. The effectiveness and efficiency of our proposed method is demonstrated by rigorous proofs and microscopic traffic simulations.

Reinforcement Learning and Adaptive Optimal Control for Continuous-Time Nonlinear Systems: A Value Iteration Approach.

This article studies the adaptive optimal control problem for continuous-time nonlinear systems described by differential equations. A key strategy is to exploit the value iteration (VI) method proposed initially by Bellman in 1957 as a fundamental tool to solve dynamic programming problems. However, previous VI methods are all exclusively devoted to the Markov decision processes and discrete-time dynamical systems. In this article, we aim to fill up the gap by developing a new continuous-time VI method that will be applied to address the adaptive or nonadaptive optimal control problems for continuous-time systems described by differential equations. Like the traditional VI, the continuous-time VI algorithm retains the nice feature that there is no need to assume the knowledge of an initial admissible control policy. As a direct application of the proposed VI method, a new class of adaptive optimal controllers is obtained for nonlinear systems with totally unknown dynamics. A learning-based control algorithm is proposed to show how to learn robust optimal controllers directly from real-time data. Finally, two examples are given to illustrate the efficacy of the proposed methodology.

Reinforcement Learning-Based Cooperative Optimal Output Regulation via Distributed Adaptive Internal Model.

In this article, a data-driven distributed control method is proposed to solve the cooperative optimal output regulation problem of leader-follower multiagent systems. Different from traditional studies on cooperative output regulation, a distributed adaptive internal model is originally developed, which includes a distributed internal model and a distributed observer to estimate the leader's dynamics. Without relying on the dynamics of multiagent systems, we have proposed two reinforcement learning algorithms, policy iteration and value iteration, to learn the optimal controller through online input and state data, and estimated values of the leader's state. By combining these methods, we have established a basis for connecting data-distributed control methods with adaptive dynamic programming approaches in general since these are the theoretical foundation from which they are built.

Designing the Safe Reopening of US Towns Through High-Resolution Agent-Based Modeling.

As COVID-19 vaccine is being rolled out in the US, public health authorities are gradually reopening the economy. To date, there is no consensus on a common approach among local authorities. Here, a high-resolution agent-based model is proposed to examine the interplay between the increased immunity afforded by the vaccine roll-out and the transmission risks associated with reopening efforts. The model faithfully reproduces the demographics, spatial layout, and mobility patterns of the town of New Rochelle, NY - representative of the urban fabric of the US. Model predictions warrant caution in the reopening under the current rate at which people are being vaccinated, whereby increasing access to social gatherings in leisure locations and households at a 1% daily rate can lead to a 28% increase in the fatality rate within the next three months. The vaccine roll-out plays a crucial role on the safety of reopening: doubling the current vaccination rate is predicted to be sufficient for safe, rapid reopening.

Discovery of benthol A and its challenging stereochemical assignment: opening up a new window for skeletal diversity of super-carbon-chain compounds.

Super-carbon-chain compounds (SCCCs) are marine organic molecules featuring long polyol carbon chains with numerous stereocenters. Polyol-polyene compounds (PPCs) and ladder-frame polyethers (LFPs) are two major families. It is highly challenging to establish the absolute configurations of SCCCs. In this century, few new SCCC families have been reported. Benthol A, an aberrant SCCC, was obtained from a South China Sea benthic dinoflagellate that should belong to a new taxon. Its planar structure and absolute configuration, containing thirty-five carbon stereocenters, were unambiguously established by a combination of extensive NMR spectroscopic investigations, periodate degradation of the 1,2-diol groups, ozonolysis of the carbon-carbon double bonds, J-based configurational analysis, NOE interactions, modified Mosher's MTPA ester method, and DFT-NMR 13C chemical-shift calculations aided by DP4+ statistical analysis. Benthol A displayed potent antimalarial activity against Plasmodium falciparum 3D7 parasites. This new molecule combines extraordinary structural features, particularly eight scattered ether rings on a C72 backbone chain, which places it within a new SCCC family between PPCs and LFPs, herein termed polyol-polyether compounds. This suggestion was strongly supported by principal component analysis. The discovery of benthol A does not only provide new insights into the untapped biosynthetic potential of marine dinoflagellates, but also opens up a new window for skeletal diversity of SCCCs.

Prediction of internal modification size in glass induced by ultrafast laser scanning.

The modification at the interface between glass plates induced by ultrafast laser is important for the glass welding strength, therefore the relationship between the modification size and processing parameters should be identified. The experimental method has its limitation in understanding the nature of the modification. In this study, a numerical model for the temperature distribution determining the modification size induced by ultrafast laser scanning is developed, in which a three-dimensional steady model for the beam propagation with a transient ionization model is established to estimate the free electron density by the single laser pulse, and then a heat accumulation model for multiple laser pulses is employed to describe energy transportation within the irradiated bulk. The experiment for the internal modifications in single-piece fused silica samples irradiated by a picosecond laser with different pulse energies and scanning velocities is performed to validate the present model.

COVID-19 Modeling: High-Resolution Agent-Based Modeling of COVID-19 Spreading in a Small Town (Adv. Theory Simul. 3/2021).

Since 2020, COVID-19 has wreaked havoc across the planet, taking the lives of more than one million people. The uncertainty and novelty of the current conditions call for the development of theory and simulation tools that can support effective policy-making. In article number 2000277, Agnieszka Truszkowska, Maurizio Porfiri, and co-workers report a high-resolution, agent-based modeling platform to simulate the spreading of COVID-19 in the city of New Rochelle, NY-one of the first outbreaks registered in the United States. Image by Anna Sawulska, Agnieszka Truszkowska, Beata Truszkowska, and Maurizio Porfiri.

High-Resolution Agent-Based Modeling of COVID-19 Spreading in a Small Town.

Amid the ongoing COVID-19 pandemic, public health authorities and the general population are striving to achieve a balance between safety and normalcy. Ever changing conditions call for the development of theory and simulation tools to finely describe multiple strata of society while supporting the evaluation of "what-if" scenarios. Particularly important is to assess the effectiveness of potential testing approaches and vaccination strategies. Here, an agent-based modeling platform is proposed to simulate the spreading of COVID-19 in small towns and cities, with a single-individual resolution. The platform is validated on real data from New Rochelle, NY-one of the first outbreaks registered in the United States. Supported by expert knowledge and informed by reported data, the model incorporates detailed elements of the spreading within a statistically realistic population. Along with pertinent functionality such as testing, treatment, and vaccination options, the model accounts for the burden of other illnesses with symptoms similar to COVID-19. Unique to the model is the possibility to explore different testing approaches-in hospitals or drive-through facilities-and vaccination strategies that could prioritize vulnerable groups. Decision-making by public authorities could benefit from the model, for its fine-grain resolution, open-source nature, and wide range of features.

Distributed Event-Triggered Formation Control of Multiagent Systems via Complex-Valued Laplacian.

Event-triggered formation control of multiagent systems under an undirected communication graph is investigated using complex-valued Laplacian. Both continuous-time and discrete-time models are considered. The dynamics of each agent is described by complex-valued differential or difference equations. For each agent, only the discrete-time information of its neighbors is used in the design of formation controllers and event triggers. Triggering time instants for any agent are determined by certain events that depend on the states of its neighboring agents. Continuous updating of controllers and continuous communication among neighboring agents are avoided. The obtained results show that formation can reach specific but arbitrary formation shape. Furthermore, it is shown that the closed-loop system does not exhibit the Zeno phenomenon for the continuous-time dynamics case or the Zeno-like behavior for the discrete-time dynamics case. Finally, numerical simulations for both the continuous-time and the discrete-time dynamics cases are presented to illustrate the effectiveness of the proposed distributed event-triggered control methods.