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Raman spectroscopy is a powerful technique that enables fingerprinting of materials, molecules, and chemical environments by probing vibrational resonances. In many applications, the desired Raman signals are masked by fluorescence, either from the molecular system being studied, or from adjacent metallic nanostructures. Here, it is shown that wavelength‐modulated Raman spectroscopy provides a powerful way to significantly reduce the strength of the fluorescence background, thereby allowing the desired Raman signals to be clearly recorded. This approach is made use of to explore Raman scattering in the context of vibrational strong coupling, an area that has thus far been problematic to visualise. Specifically, strong coupling between the vibrational modes in a polymer and two types of confined light field, the fundamental mode of a metal‐clad microcavity, and the surface‐plasmon modes of an adjacent thin metal film are looked at. While clear advantages in using the wavelength‐modulated Raman approach are found, these results on strong coupling are inconclusive, and highlight the need for more work in this exciting topic area. [ABSTRACT FROM AUTHOR]

In the construction of intelligent transportation systems, short-term traffic flow prediction is of great significance for the advancement of traffic network management. But due to the presence of many complex factors in both spatial and temporal domains, it remains a complex and challenging task. Existing literature usually employs the convolutional neural network (CNN)-based methods in capturing spatiotemporal correlations. These CNN-based methods often use a single-channel data structure to represent different periodic patterns, which makes the model susceptible to over-parameterization when capturing periodic dependencies and prone to information loss after convolution. To overcome these limitations, this paper presents a hybrid deep learning method for short-term traffic flow prediction. In this method, a video-shaped multi-channel data structure is designed to represent different periodic patterns more efficiently. Next, a new mixed-pointwise convolution is introduced for capturing periodic dependencies without the negative impacts mentioned above. Lastly, an improved channel attention mechanism is proposed to learn channel inter-dependencies with controllable parameter usage. The proposed method is lightweight, yet highly effective. Compared to the state-of-the-art baseline method, it reduces the root mean squared error by up to 6.7% on the PeMSD4 dataset and 13.3% on the PeMSD7 dataset, while also achieving substantial improvement in two additional metrics, exhibiting strong robustness and great scalability across various settings. [ABSTRACT FROM AUTHOR]

Medication recommendation aims to provide clinicians with safe medicine combinations for the treatment of patients. Existing medication recommendation models are built based on the temporal structured code data of electronic health records (EHRs) in the medical database; nevertheless, unstructured data in EHRs, such as textual data containing rich information, are underexploited. To fill the gap, a novel multimodal interactive fusion network (MIFNet) is proposed for medication recommendation, which integrates both structured code information and unstructured text information in EHRs. Our model first extracts a series of informative feature representations to encode comprehensive patient health history and control potential drug–drug interactions (DDI), including medical code, clinical notes, and the DDI knowledge graph. Next, a novel cross-modal interaction extraction block is proposed to capture the intricate interaction information between the two modalities. Finally, a multimodal fusion block is adopted to fuse the constructed features and generate a medication combination list. Experiments are conducted on the public MIMIC-III dataset, and the results demonstrate that the proposed model outperforms the state-of-the-art medication recommendation methods on main evaluation metrics. [ABSTRACT FROM AUTHOR]

• Investigated a large-scale heterogeneous container loading problem. • Considered weight limits and suspension constraints in this problem. • Integrated ALNS and heuristic packing algorithm for the presented problem. • Performed experiments on real-world instances. • Verified the superiority of HALNS by comparing with existing methods. This paper aims to solve the large-scale heterogeneous container loading problem (HCLP), which is an extensive form of the multiple container loading problem, in a limited time. The target is to choose a set of containers of different sizes to accommodate all products and minimize the wasted space rate. Although the heterogeneous container selection problem is a general problem in the logistics industry, few related studies have been conducted. This study also considers some practical constraints, such as weight limits and suspension constraints. A hybrid adaptive large neighborhood search (HALNS) algorithm, which includes a set of original destroy-repair operators, especially for heterogeneous container selection problems, and integrates a heuristic packing algorithm, is proposed to solve the problem in an acceptable time. To verify the efficiency of the proposed algorithm, computational experiments on real-world instances from a multinational logistics company are performed, and the results are compared with those of other existing algorithms. The results indicate that the proposed algorithm outperforms other algorithms for the HCLP. [ABSTRACT FROM AUTHOR]

Stress granules (SGs), formed by untranslated messenger ribonucleoproteins (mRNPs) during cellular stress in eukaryotes, have been linked to flavivirus interference without clear understanding. This study reveals the role of Zika virus (ZIKV) NS2B as a scaffold protein mediating interaction between protein phosphatase 1α (PP1α) and eukaryotic initiation factor 2α (eIF2α). This interaction promotes eIF2α dephosphorylation by PP1α, inhibiting SG formation. The NS2B-PP1α complex exhibits remarkable stability, resisting ubiquitin-induced degradation and amplifying eIF2α dephosphorylation, thus promoting ZIKV replication. In contrast, the NS2BV35A mutant, interacting exclusively with eIF2α, fails to inhibit SG formation, resulting in reduced viral replication and diminished impact on brain organoid growth. These findings reveal PP1α's dual role in ZIKV infection, inducing interferon production as an antiviral factor and suppressing SG formation as a viral promoter. Moreover, we found that NS2B also serves as a versatile mechanism employed by flaviviruses to counter host antiviral defenses, primarily by broadly inhibiting SG formation. This research advances our comprehension of the complex interplay in flavivirus-host interactions, offering potential for innovative therapeutic strategies against flavivirus infections. Author summary: Zika virus (ZIKV) belongs to vector-borne flaviviruses, associated with Guillain-Barré syndrome and neonatal microcephaly. Stress granules (SGs) which forms under stress in eukaryotic cells can interfere with viral replication. Although previous studies have indicated that ZIKV can interfere with SG formation, the precise molecular mechanism and function remain unclear. Exploration of the mechanisms by which ZIKV antagonizes SG formation may help to identify new antiviral approaches. Here, we found that ZIKV NS2B bridge PP1α and eIF2α to promote eIF2α dephosphorylation and SG inhibition. Meanwhile, Reciprocal inhibition of K48-linked ubiquitination stabilized NS2B and PP1α to further facilitate the dephosphorylation of eIF2α and the concurrent inhibition of SG formation. Conversely, we demonstrated that a mutant form of NS2B, NS2BV35A, which interacted solely with eIF2α and not PP1α, attenuated virus replication and mitigated neurotoxicity in brain organoid development. The NS2B-PP1α-eIF2α axis reveals a novel biological function of NS2B as a general mechanism for flavivirus evasion of host antiviral effects, providing new therapeutic strategies for antiviral study. [ABSTRACT FROM AUTHOR]

Task scheduling is important in cloud manufacturing because of customers' increasingly individualized demands. However, when various changes occur, a previous optimal schedule may become non-optimal or even infeasible owing to the uncertainty of the real manufacturing environment where dynamic task arrival over time is a vital source. In this paper, we propose a novel collaborative task scheduling (CTS) model dealing with new task arrival which considers multi-supply chain collaboration. We present an improved multi-population biogeography-based optimization (IMPBBO) algorithm that uses a matrix-based solution representation and integrates the multi-population strategy, local search for the best solution, and the collaboration mechanism, for determining the optimal schedule. A series of experiments are conducted for verifying the effectiveness of the IMPBBO algorithm for solving the CTS model by comparing it with five other algorithms. The experimental results concerning average best values obtained by the IMPBBO algorithm are better than that obtained by comparison algorithms for 41 out of 45 cases, showing its superior performance. Wilcoxon-test has been employed to strengthen the fact that IMPBBO algorithm performs better than five comparison algorithms. [ABSTRACT FROM AUTHOR]

Population aging and a shortage of medical resources are driving the growth of home health care (HHC). Many HHC studies have focused on developing an optimal routing plan to reduce operation costs; however, few have addressed the attendant long-term sustainability concerns, such as HHC center locations and carbon emissions from transportation. This paper proposes a home health care location-routing problem with a mixed fleet of electric and conventional vehicles that considers battery swapping stations. It aims to simultaneously determine the locations of HHC centers, the scheduling of caregivers with respect to skill requirements, and a routing plan for a mixed fleet under specific time windows, load capacities, synchronized visits, and driving ranges. To address this problem, the paper proposes a novel competitive simulated annealing (CSA) algorithm in which a series of problem-specific effective local search operators expand the solution space of the CSA algorithm, with a competitive mechanism to adaptively adjust these operators to accelerate convergence speed and improve exploration ability. To enhance the exploitation ability, it employs a modified simulated annealing algorithm with a heating strategy and variable neighborhood descent. Comparison experiments are conducted to verify the superior performance of the CSA algorithm in comparison to existing algorithms and the Gurobi solver. Finally, sensitivity analyses are performed on key factors and provide some new managerial insights. [ABSTRACT FROM AUTHOR]

We previously found that, among human parainfluenza virus type 3 (HPIV3) proteins, the interaction of nucleoprotein (N) and phosphoprotein (P) provides the minimal requirement for the formation of cytoplasmic inclusion bodies (IBs), which are sites of RNA synthesis, and that acetylated α-tubulin enhances IB fusion and viral replication. In this study, using immunoprecipitation and mass spectrometry assays, we determined that vimentin (VIM) specifically interacted with the N–P complex of HPIV3, and that the head domain of VIM was responsible for this interaction, contributing to the inhibition of IB fusion and viral replication. Furthermore, we found that VIM promoted the degradation of α-tubulin acetyltransferase 1 (α-TAT1), through its head region, thereby inhibiting the acetylation of α-tubulin, IB fusion, and viral replication. In addition, we identified a 20-amino-acid peptide derived from the head region of VIM that participated in the interaction with the N–P complex and inhibited viral replication. Our findings suggest that VIM inhibits the formation of HPIV3 IBs by downregulating α-tubulin acetylation via enhancing the degradation of α-TAT1. Our work sheds light on a new mechanism by which VIM suppresses HPIV3 replication. Author summary: Human parainfluenza virus type 3 (HPIV3) is one of the most common pathogenic respiratory RNA viruses, and its replication sites, known as inclusion bodies (IBs), which are formed by nucleoprotein (N) and phosphoprotein (P), are critical in viral RNA synthesis. Exploration of the mechanisms by which viral inclusion bodies are formed in cells may help to identify new antiviral approaches. Our aim was to identify cellular proteins with regulatory effects on the formation of viral IBs. The formation of HPIV3 IBs relies on acetylated α-tubulin, and we found that VIM negatively regulated α-tubulin acetylation by enhancing the degradation of α-TAT1, thereby suppressing the fusion and maturation of viral IBs. Based on the interaction between IBs and VIM, the peptide derived from amino-acid residues 61 to 80 in the VIM head region exerted antiviral effects, providing a new direction for our antiviral study. [ABSTRACT FROM AUTHOR]