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For the state estimation problem of a multi-source localization nonlinear system with unknown and bounded noise, a distributed sequential ellipsoidal intersection fusion estimation algorithm based on the dual set-membership filtering method is proposed to ensure the reliability of the localization system. First, noise with unknown and bounded characteristics is modeled by using bounded ellipsoidal regions. At the same time, local estimators are designed at the sensor link nodes to filter out the noise interference in the localization system. The local estimator is designed using the dual set-membership filtering algorithm. It uses the dual principle to find the minimizing ellipsoid that can contain the nonlinear function by solving the optimization problem with semi-infinite constraints, and a first-order conditional gradient algorithm is used to solve the optimization problem with a low computational complexity. Meanwhile, the communication confusion among multiple sensors causes the problem of unknown correlation. The obtained estimates of local filters are fused at the fusion center by designing a distributed sequential ellipsoid intersection fusion estimation algorithm to obtain more accurate fusion localization results with lower computational cost. Finally, the stability and reliability of the proposed distributed fusion algorithm are verified by designing a simulation example of a multi-source nonlinear system.

For state estimation of multi-source asynchronous measurement systems with measurement missing phenomena, this paper proposes a distributed sequential inverse covariance intersection (DSICI) fusion algorithm based on conditional Kalman filtering method. It is mainly divided into synchronized state space module, local filtering module and fusion estimation module. The missing measurements occurring in the system are modelled and described by a set of random variables obeying a Bernoulli distribution. The synchronized state space module uses a state iteration method to synchronize the asynchronous measurement system at the moment of measurement update and it ensures the integrity of the measurement information. The local filtering module uses a conditional Kalman filtering algorithm for filter estimation. The reliability of the local filtering results is guaranteed because the local estimator designs a method to interact information with the domain sensors. The fusion estimation module designs a DSICI fusion algorithm with higher accuracy and satisfying consistency, which fuses the filtering results provided by each sensor when the relevant information between multiple sensors is unknown. Simulation examples demonstrate the excellent performance of the proposed algorithm, with a 33% improvement in accuracy over existing algorithms and an iteration time of less than 3 ms.

Xenotransplantation holds great promise as a solution to address the critical shortage of organs, but it raises concerns regarding the potential transmission of porcine viruses to recipients, leading to infections and even zoonotic diseases. Data used in this review were mainly from literature of Pubmed database. Keywords included xenotransplantation, infection, virus, and epidemiology. The original articles and critical reviews selected were relevant to this review’s theme. We review the major viral infections of concern in xenotransplantation, their risk of transmission, diagnosis, treatment, and ways to prevent infection. Then, we pivot to a comprehensive overview of the current status of xenotransplantation. In addition, we offer our own insights and recommendations for propelling xenotransplantation forward, transitioning from preclinical experiments to the critical phase of clinical trials. Viral infections pose considerable safety concerns within xenotransplantation, particularly with the possibility of emerging or currently unidentified viruses. Clinical trials serve as a crucial platform to progress the safety standards of xenotransplantation. However, further studies and dedicated efforts are required to effectively translate findings into practical applications that can improve safety measures in this field.

This paper investigates the problem of distributed ellipsoidal intersection (DEI) fusion estimation for linear time-varying multi-sensor complex systems with unknown input disturbances and measurement data transmission delays. For the problem with external unknown input disturbance signals, a non-informative prior distribution is used to model the problem. A set of independent random variables obeying Bernoulli distribution is also used to describe the situation of measurement data transmission delay caused by network channel congestion, and appropriate buffer areas are added at the link nodes to retrieve the delayed transmission data values. For multi-sensor systems with complex situations, a minimum mean square error (MMSE) local estimator is designed in a Bayesian framework based on the maximum a posteriori (MAP) estimation criterion. In order to deal with the unknown correlations among the local estimators and to select the fusion estimator with lower computational complexity, the fusion estimator is designed using ellipsoidal intersection (EI) fusion technique, and the consistency of the estimator is demonstrated. In this paper, the difference between DEI fusion and distributed covariance intersection (DCI) fusion and centralized fusion estimation is analyzed by a numerical example, and the superiority of the DEI fusion method is demonstrated.

Both constitutive and inducible immune mechanisms are employed by hosts for defense against infection. Constitutive immunity allows for a faster response, but it comes with an associated cost that is always present. This trade-off between speed and fitness costs leads to the theoretical prediction that constitutive immunity will be favored where parasite exposure is frequent. We selected populations of Drosophila melanogaster under high parasite pressure from the parasitoid wasp Leptopilina boulardi . With RNA sequencing, we found the evolution of resistance in these populations was associated with them developing constitutively active humoral immunity, mediated by the larval fat body. Furthermore, these evolved populations were also able to induce gene expression in response to infection to a greater level, which indicates an overall more activated humoral immune response to parasitization. The anti-parasitoid immune response also relies on the JAK/STAT signaling pathway being activated in muscles following infection, and this induced response was only seen in populations that had evolved under high parasite pressure. We found that the cytokine Upd3, which induces this JAK/STAT response, is being expressed by immature lamellocytes. Furthermore, these immune cells became constitutively present when populations evolved resistance, potentially explaining why they gained the ability to activate JAK/STAT signaling. Thus, under intense parasitism, populations evolved resistance by increasing both constitutive and induced immune defenses, and there is likely an interplay between these two forms of immunity.

This paper presents a novel image fusion method designed to enhance the integration of infrared and visible images through the use of a residual attention mechanism. The primary objective is to generate a fused image that effectively combines the thermal radiation information from infrared images with the detailed texture and background information from visible images. To achieve this, we propose a multi-level feature extraction and fusion framework that encodes both shallow and deep image features. In this framework, deep features are utilized as queries, while shallow features function as keys and values within a residual cross-attention module. This architecture enables a more refined fusion process by selectively attending to and integrating relevant information from different feature levels. Additionally, we introduce a dynamic feature preservation loss function to optimize the fusion process, ensuring the retention of critical details from both source images. Experimental results demonstrate that the proposed method outperforms existing fusion techniques across various quantitative metrics and delivers superior visual quality.

Cytochrome P450 monooxygenases are versatile heme-thiolate enzymes that catalyze a wide range of reactions. Self-sufficient cytochrome P450 enzymes contain the redox partners in a single polypeptide chain. Here, we present the crystal structure of full-length CYP116B46, a self-sufficient P450. The continuous polypeptide chain comprises three functional domains, which align well with the direction of electrons traveling from FMN to the heme through the [2Fe-2S] cluster. FMN and the [2Fe-2S] cluster are positioned closely, which facilitates efficient electron shuttling. The edge-to-edge straight-line distance between the [2Fe-2S] cluster and heme is approx. 25.3 Å. The role of several residues located between the [2Fe-2S] cluster and heme in the catalytic reaction is probed in mutagenesis experiments. These findings not only provide insights into the intramolecular electron transfer of self-sufficient P450s, but are also of interest for biotechnological applications of self-sufficient P450s. Self-sufficient cytochrome P450 monooxygenases, which contain all redox partners in a single polypeptide chain, are of interest for biotechnological applications. Here, the authors present the crystal structure of full-length Thermobispora bispora CYP116B46 and discuss the potential electron transfer pathway.

When an animal is infected, the expression of a large suite of genes is changed, resulting in an immune response that can defend the host. Despite much evidence that the sequence of proteins in the immune system can evolve rapidly, the evolution of gene expression is comparatively poorly understood. We therefore investigated the transcriptional response to parasitoid wasp infection in Drosophila simulans and D. sechellia . Although these species are closely related, there has been a large scale divergence in the expression of immune-responsive genes in their two main immune tissues, the fat body and hemocytes. Many genes, including those encoding molecules that directly kill pathogens, have cis regulatory changes, frequently resulting in large differences in their expression in the two species. However, these changes in cis regulation overwhelmingly affected gene expression in immune-challenged and uninfected animals alike. Divergence in the response to infection was controlled in trans . We argue that altering trans -regulatory factors, such as signalling pathways or immune modulators, may allow natural selection to alter the expression of large numbers of immune-responsive genes in a coordinated fashion.

Efficiently fabricating a cavity that can achieve strong interactions between terahertz waves and matter would allow researchers to exploit the intrinsic properties due to the long wavelength in the terahertz waveband. Here we show a terahertz detector embedded in a Tamm cavity with a record Q value of 1017 and a bandwidth of only 469 MHz for direct detection. The Tamm-cavity detector is formed by embedding a substrate with an Nb 5 N 6 microbolometer detector between an Si/air distributed Bragg reflector (DBR) and a metal reflector. The resonant frequency can be controlled by adjusting the thickness of the substrate layer. The detector and DBR are fabricated separately, and a large pixel-array detector can be realized by a very simple assembly process. This versatile cavity structure can be used as a platform for preparing high-performance terahertz devices and opening up the study of the strong interactions between terahertz waves and matter. Here the authors report a terahertz detector with a Q value of 1017, embedded in a Tamm cavity and offers a 469 MHz bandwidth. It features an Nb5N6 microbolometer in an Si/air DBR and metal reflector, with tunable resonant frequency via substrate layer thickness.

Terpenoid cyclases (TCs) account for the synthesis of the most widespread and diverse natural compounds. A sesquiterpene cyclase termed BcABA3 from an abscisic acid-producing fungus Botrytis cinerea that yields (2 Z ,4 E )-α-ionylideneethane but lacks signature feature of canonical TCs represents a distinct type of TCs. Here, we report the crystal structures of BcABA3, a closely related RuABA3 from Rutstroemia sp. and a bacterial SkABA3 from Shimazuella kribbensis . These ABA3 proteins adopt an all-α-helix fold and bind pyrophosphate moiety of farnesyl pyrophosphate by Glu-chelated Mg 2+ ion cluster. We conduct mutagenesis experiments to validate the role of the substrate-binding residues. SkABA3 appears to yield compounds that are distinct from (2 Z ,4 E )-α-ionylideneethane. These results not only provide the molecular insight into ABA3 proteins that serve as an important basis to the future investigation of this class of TCs, but also reveal the existence of more uncharacterized terpenoids synthesized via dedicated machineries. BcABA3 catalyzes farnesyl pyrophosphate cyclization but lacks features to be recognized as a terpene cyclase. Here, authors report crystal structures of BcABA3 and homologues to reveal the molecular basis of this distinct type of enzyme.