Dynamics and microevolution of Vibrio parahaemolyticus populations in shellfish farms

Vibrio parahaemolyticus is becoming the leading cause of acute bacterial gastroenteritis, but its population dynamics in aquafarms have received limited attention. To address this research gap, we selected three shellfish farms to examine the impacts of ocean currents and the transport of live aquatic animals on the transmission and microevolution of V. parahaemolyticus by using multilocus sequence typing (MLST) and whole-genome sequencing. MLST and genomic analysis revealed that the community structure of V. parahaemolyticus in Dalian and Donggang was relatively stable in the presence of ocean currents; however, horizontal gene transfer of mobile genetic elements (MGEs) between Dalian and Donggang was very common. Further analysis indicated that the transport of live aquatic animals from Dalian to Xiamen not only introduced new V. parahaemolyticus populations but also allowed the exchange of genetic material between the two sites. More interestingly, Dalian-originated strain ST722 was introduced to Xiamen farms, resulting in one MLST allele change and the acquisition of two genomic islands from indigenous isolates in Xiamen within 8 months; such alterations are thought to promote the adaptation of V. parahaemolyticus. These results provide direct observations of how ocean currents and the transport of live aquatic animals contribute to the dissemination and genetic mixture of V. parahaemolyticus, which provides insights into the dynamics and microevolution of V. parahaemolyticus in aquacultural environments. IMPORTANCE Globally, V. parahaemolyticus-related gastroenteritis outbreaks caused by seafood consumption represent an increasing threat to human health. Despite advances in our understanding of the global epidemiology of pandemic V. parahaemolyticus, fundamental questions about the key driving forces for the spread of V. parahaemolyticus at regional and national scales remain unanswered. This study revealed that the transregional transport of aquatic animals and the movement of ocean currents both contributed to the mixing of V. parahaemolyticus populations. More importantly, this study demonstrated how genetic mixture occurred between introduced and endemic V. parahaemolyticus populations via the transport of aquatic animals, which accelerated bacterial adaptation by transferring ecologically important functions. These results suggest that human activities entail a risk of the emergence of new virulent populations for both aquatic animals and humans by horizontal gene transfer and provide important insights into the microevolution and population mixing of V. parahaemolyticus

Boundary-to-Solution Mapping for Groundwater Flows in a Toth Basin

In this paper, the authors propose a new approach to solving the groundwater flow equation in the Toth basin of arbitrary top and bottom topographies using deep learning. Instead of using traditional numerical solvers, they use a DeepONet to produce the boundary-to-solution mapping. This mapping takes the geometry of the physical domain along with the boundary conditions as inputs to output the steady state solution of the groundwater flow equation. To implement the DeepONet, the authors approximate the top and bottom boundaries using truncated Fourier series or piecewise linear representations. They present two different implementations of the DeepONet: one where the Toth basin is embedded in a rectangular computational domain, and another where the Toth basin with arbitrary top and bottom boundaries is mapped into a rectangular computational domain via a nonlinear transformation. They implement the DeepONet with respect to the Dirichlet and Robin boundary condition at the top and the Neumann boundary condition at the impervious bottom boundary, respectively. Using this deep-learning enabled tool, the authors investigate the impact of surface topography on the flow pattern by both the top surface and the bottom impervious boundary with arbitrary geometries. They discover that the average slope of the top surface promotes long-distance transport, while the local curvature controls localized circulations. Additionally, they find that the slope of the bottom impervious boundary can seriously impact the long-distance transport of groundwater flows. Overall, this paper presents a new and innovative approach to solving the groundwater flow equation using deep learning, which allows for the investigation of the impact of surface topography on groundwater flow patterns

Weighted Multimodel Predictive Function Control for Automatic Train Operation System

Train operation is a complex nonlinear process; it is difficult to establish accurate mathematical model. In this paper, we design ATO speed controller based on the input and output data of the train operation. The method combines multimodeling with predictive functional control according to complicated nonlinear characteristics of the train operation. Firstly, we cluster the data sample by using fuzzy-c means algorithm. Secondly, we identify parameter of cluster model by using recursive least square algorithm with forgetting factor and then establish the local set of models of the process of train operation. Then at each sample time, we can obtain the global predictive model about the system based on the weighted indicators by designing a kind of weighting algorithm with error compensation. Thus, the predictive functional controller is designed to control the speed of the train. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm

Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey

Transformer-based Large Language Models (LLMs) have been applied in diverse areas such as knowledge bases, human interfaces, and dynamic agents, and marking a stride towards achieving Artificial General Intelligence (AGI). However, current LLMs are predominantly pretrained on short text snippets, which compromises their effectiveness in processing the long-context prompts that are frequently encountered in practical scenarios. This article offers a comprehensive survey of the recent advancement in Transformer-based LLM architectures aimed at enhancing the long-context capabilities of LLMs throughout the entire model lifecycle, from pre-training through to inference. We first delineate and analyze the problems of handling long-context input and output with the current Transformer-based models. We then provide a taxonomy and the landscape of upgrades on Transformer architecture to solve these problems. Afterwards, we provide an investigation on wildly used evaluation necessities tailored for long-context LLMs, including datasets, metrics, and baseline models, as well as optimization toolkits such as libraries, frameworks, and compilers to boost the efficacy of LLMs across different stages in runtime. Finally, we discuss the challenges and potential avenues for future research. A curated repository of relevant literature, continuously updated, is available at https://github.com/Strivin0311/long-llms-learning.Comment: 40 pages, 3 figures, 4 table

A Human Intestinal Infection Caused by a Novel Non-O1/O139 Vibrio cholerae Genotype and Its Dissemination Along the River

Non-O1/O139 Vibrio cholerae is increasingly reported in the clinical settings. However, intestinal infections via the consumption of non-O1/O139 V. cholerae-carrying seafood are rarely documented in China. In this study, we reported a case of mild watery diarrhea in a young male, caused by non-O1/O139 V. cholerae in the downstream of Liaohe River. Epidemiological investigation showed that this intestinal infection potentially associated with the raw consumption of mollusc. Prior to this finding, we conducted a 6-month pathogen surveillance of three locations along the Liaohe River and identified three environmental non-O1/O139 V. cholerae strains. To confirm the epidemiological links between clinical and environmental strains, high-resolution genomic typing was employed and revealed that V. cholerae isolated from human stool sample was genomically related to the one found in local mollusc and shared a common ancestor with other environmental strains obtained in the upstream sites of the Liaohe River. This fact suggests that the river is a natural reservoir for non-O1/O139 V. cholerae which poses a potential threat to the public health. In summary, our results deepened the insights on the transmission of non-pandemic V. cholerae strains and underscored the significance of genomic surveillance for drinking water along the river sites

Tumor‐derived exosomal PD-L1: a new perspective in PD-1/PD-L1 therapy for lung cancer

Exosomes play a crucial role in facilitating intercellular communication within organisms. Emerging evidence indicates that a distinct variant of programmed cell death ligand-1 (PD-L1), found on the surface of exosomes, may be responsible for orchestrating systemic immunosuppression that counteracts the efficacy of anti-programmed death-1 (PD-1) checkpoint therapy. Specifically, the presence of PD-L1 on exosomes enables them to selectively target PD-1 on the surface of CD8+ T cells, leading to T cell apoptosis and impeding T cell activation or proliferation. This mechanism allows tumor cells to evade immune pressure during the effector stage. Furthermore, the quantification of exosomal PD-L1 has the potential to serve as an indicator of the dynamic interplay between tumors and immune cells, thereby suggesting the promising utility of exosomes as biomarkers for both cancer diagnosis and PD-1/PD-L1 inhibitor therapy. The emergence of exosomal PD-L1 inhibitors as a viable approach for anti-tumor treatment has garnered significant attention. Depleting exosomal PD-L1 may serve as an effective adjunct therapy to mitigate systemic immunosuppression. This review aims to elucidate recent insights into the role of exosomal PD-L1 in the field of immune oncology, emphasizing its potential as a diagnostic, prognostic, and therapeutic tool in lung cancer

Titanium Nitride Film on Sapphire Substrate with Low Dielectric Loss for Superconducting Qubits

Dielectric loss is one of the major decoherence sources of superconducting qubits. Contemporary high-coherence superconducting qubits are formed by material systems mostly consisting of superconducting films on substrate with low dielectric loss, where the loss mainly originates from the surfaces and interfaces. Among the multiple candidates for material systems, a combination of titanium nitride (TiN) film and sapphire substrate has good potential because of its chemical stability against oxidization, and high quality at interfaces. In this work, we report a TiN film deposited onto sapphire substrate achieving low dielectric loss at the material interface. Through the systematic characterizations of a series of transmon qubits fabricated with identical batches of TiN base layers, but different geometries of qubit shunting capacitors with various participation ratios of the material interface, we quantitatively extract the loss tangent value at the substrate-metal interface smaller than $8.9 \times 10^{-4}$ in 1-nm disordered layer. By optimizing the interface participation ratio of the transmon qubit, we reproducibly achieve qubit lifetimes of up to 300 $\mu$s and quality factors approaching 8 million. We demonstrate that TiN film on sapphire substrate is an ideal material system for high-coherence superconducting qubits. Our analyses further suggest that the interface dielectric loss around the Josephson junction part of the circuit could be the dominant limitation of lifetimes for state-of-the-art transmon qubits