The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation.

The hippocampal-entorhinal system is important for spatial and relational memory tasks. We formally link these domains, provide a mechanistic understanding of the hippocampal role in generalization, and offer unifying principles underlying many entorhinal and hippocampal cell types. We propose medial entorhinal cells form a basis describing structural knowledge, and hippocampal cells link this basis with sensory representations. Adopting these principles, we introduce the Tolman-Eichenbaum machine (TEM). After learning, TEM entorhinal cells display diverse properties resembling apparently bespoke spatial responses, such as grid, band, border, and object-vector cells. TEM hippocampal cells include place and landmark cells that remap between environments. Crucially, TEM also aligns with empirically recorded representations in complex non-spatial tasks. TEM also generates predictions that hippocampal remapping is not random as previously believed; rather, structural knowledge is preserved across environments. We confirm this structural transfer over remapping in simultaneously recorded place and grid cells.

Synthesis, biological activities and mechanism studies of 1,3,4-oxadiazole analogues of petiolide A as anticancer agents.

In order to develop new natural product-based anticancer agents, a series of 1,3,4-oxadiazole analogues based on petiolide A were prepared and evaluated for their anticancer activities by MTT method. The structures of all analogues were characterized by various spectral analyses, and B9 was further confirmed by X-ray crystallography. Among all the synthesized compounds, B1 displayed the most promising growth inhibitory effect on colon cancer cells (HCT116) with the IC50 value of 8.53 µM. Flow cytometric analysis exhibited that B1 arrested the cell cycle at G2 phase and induced apoptosis. Additionally, network pharmacology analysis calculated that B1 might target several key proteins, including AKT serine/threonine kinase 1 (AKT1), SRC proto-oncogene, non-receptor tyrosine kinase (SRC) and epidermal growth factor receptor (EGFR). Furthermore, molecular docking study indicated that B1 had potentially high binding affinity to these three target proteins. Given these results, analogue B1 could be deeply developed as potential anticancer agents.

Contract-Optimization Approach (COA): A New Approach for Optimizing Service Caching, Computation Offloading, and Resource Allocation in Mobile Edge Computing Network.

An optimal method for resource allocation based on contract theory is proposed to improve energy utilization. In heterogeneous networks (HetNets), distributed heterogeneous network architectures are designed to balance different computing capacities, and MEC server gains are designed based on the amount of allocated computing tasks. An optimal function based on contract theory is developed to optimize the revenue gain of MEC servers while considering constraints such as service caching, computation offloading, and the number of resources allocated. As the objective function is a complex problem, it is solved utilizing equivalent transformations and variations of the reduced constraints. A greedy algorithm is applied to solve the optimal function. A comparative experiment on resource allocation is conducted, and energy utilization parameters are calculated to compare the effectiveness of the proposed algorithm and the main algorithm. The results show that the proposed incentive mechanism has a significant advantage in improving the utility of the MEC server.

Enhancing Data Freshness in Air-Ground Collaborative Heterogeneous Networks through Contract Theory and Generative Diffusion-Based Mobile Edge Computing.

Mobile edge computing is critical for improving the user experience of latency-sensitive and freshness-based applications. This paper provides insights into the potential of non-orthogonal multiple access (NOMA) convergence with heterogeneous air-ground collaborative networks to improve system throughput and spectral efficiency. Coordinated resource allocation between UAVs and MEC servers, especially in the NOMA framework, is addressed as a key challenge. Under the unrealistic assumption that edge nodes contribute resources indiscriminately, we introduce a two-stage incentive mechanism. The model is based on contract theory and aims at optimizing the utility of the service provider (SP) under the constraints of individual rationality (IR) and incentive compatibility (IC) of the mobile user. The block coordinate descent method is used to refine the contract design and complemented by a generative diffusion model to improve the efficiency of searching for contracts. During the deployment process, the study emphasizes the positioning of UAVs to maximize SP effectiveness. An improved differential evolutionary algorithm is introduced to optimize the positioning of UAVs. Extensive evaluation shows our approach has excellent effectiveness and robustness in deterministic and unpredictable scenarios.

An Efficient Implementation Method for Distributed Fusion in Sensor Networks Based on CPHD Filters.

A highly efficient implementation method for distributed fusion in sensor networks based on CPHD filters is proposed to address the issues of unknown cross-covariance fusion estimation and long fusion times in multi-sensor distributed fusion. This method can effectively and efficiently fuse multi-node information in multi-target tracking applications. Discrete gamma cardinalized probability hypothesis density (DG-CPHD) can effectively reduce the computational burden while ensuring computational accuracy similar to that of CPHD filters. Parallel inverse covariance intersection (PICI) can effectively avoid solving high-dimensional weight coefficient convex optimization problems, reduce the computational burden, and efficiently implement filtering fusion strategies. The effectiveness of the algorithm is demonstrated through simulation results, which indicate that PICI-GM-DG-CPHD can substantially reduce the computational time compared to other algorithms and is more suitable for distributed sensor fusion.

Gaussian Mixture Cardinalized Probability Hypothesis Density(GM-CPHD): A Distributed Filter Based on the Intersection of Parallel Inverse Covariances.

A distributed GM-CPHD filter based on parallel inverse covariance crossover is designed to attenuate the local filtering and uncertain time-varying noise affecting the accuracy of sensor signals. First, the GM-CPHD filter is identified as the module for subsystem filtering and estimation due to its high stability under Gaussian distribution. Second, the signals of each subsystem are fused by invoking the inverse covariance cross-fusion algorithm, and the convex optimization problem with high-dimensional weight coefficients is solved. At the same time, the algorithm reduces the burden of data computation, and data fusion time is saved. Finally, the GM-CPHD filter is added to the conventional ICI structure, and the generalization capability of the parallel inverse covariance intersection Gaussian mixture cardinalized probability hypothesis density (PICI-GM-CPHD) algorithm reduces the nonlinear complexity of the system. An experiment on the stability of Gaussian fusion models is organized and linear and nonlinear signals are compared by simulating the metrics of different algorithms, and the results show that the improved algorithm has a smaller metric OSPA error than other mainstream algorithms. Compared with other algorithms, the improved algorithm improves the signal processing accuracy and reduces the running time. The improved algorithm is practical and advanced in terms of multisensor data processing.

Hydroxide-Mediated SNAr Rearrangement for Synthesis of Novel Depside Derivatives Containing Diaryl Ether Skeleton as Antitumor Agents.

A simple and efficient hydroxide-mediated SNAr rearrangement was reported to synthesize new depside derivatives containing the diaryl ether skeleton from the natural product barbatic acid. The prepared compounds were determined using 1H NMR, 13C NMR, HRMS, and X-ray crystallographic analysis and were also screened in vitro for cytotoxicity against three cancer cell lines and one normal cell line. The evaluation results showed that compound 3b possessed the best antiproliferative activity against liver cancer HepG2 cell line and low toxicity, which made it worth further study.

Role of lncRNA-MEG8/miR-296-3p axis in gestational diabetes mellitus.

AIM: Gestational diabetes mellitus (GDM) is the most common complication in pregnancy. This study aimed to investigate the potential mechanism and effects of long-noncoding RNA maternally expressed 8 (lncRNA-MEG8) in GDM. METHODS: Targeted interactions involving lncRNA-MEG8 and miR-296-3p were initially predicted using starBase software and then confirmed using dual-luciferase reporter gene analysis. The expression levels of lncRNA-MEG8 and miR-296-3p in peripheral blood samples from patients with GDM were measured using reverse transcription-quantitative polymerase chain reaction. Enzyme-linked immunosorbent assay was used to evaluate the overall levels of insulin and insulin secretion. Additionally, MTT and flow cytometric methods were used to detect cell viability and apoptosis. Cell apoptosis-associated proteins were determined by western blotting. RESULTS: Our results indicated that lncRNA-MEG8 is a potential target of miR-296-3p. lncRNA-MEG8 level was higher, whereas that of miR-296-3p was lower in patients with GDM than in healthy individuals. LncRNA-MEG8-siRNA promoted insulin content and secretion. Furthermore, MEG8-siRNA increased cell viability and decreased apoptosis. However, these changes were reversed by an miR-296-3p inhibitor. Moreover, a miR-296-3p mimic had the same effect on INS-1 cells as MEG8-siRNA, as evidenced by enhanced insulin secretion, cell viability, and reduced apoptosis. CONCLUSION: LncRNA-MEG8-siRNA promotes pancreatic ß-cell function by upregulating miR-296-3p.

Inversion of Soil Organic Matter Content Based on Improved Convolutional Neural Network.

Soil organic matter (SOM) is an important source of nutrients required during crop growth and is an important component of cultivated soil. In this paper, we studied the possibility of using deep learning methods to establish a multi-feature model to predict SOM content. Moreover, using Nong'an County of Changchun City as the study area, Sentinel-2A remote sensing images were taken as the data source to construct the dataset by using field sampling and image processing. The LeNet-5 convolutional neural network model was chosen as the deep learning model, which was improved based on the basic model. The evaluation metrics were selected as the root mean square error (RMSE) and the coefficient of determination R2. Through comparison, the R2 of the improved model was found to be higher than that of the linear regression method, Support Vector Machines (SVM) (RMSE = 2.471, R2 = 0.4035), and Random Forest (RF) (RMSE = 2.577, R2 = 0.4913). The result shows that: (1) It is feasible to use the multispectral data extracted from remote sensing images for soil organic matter content inversion based on the deep learning model with a minimum RMSE of 2.979 and with the R2 reaching 0.89. (2) The choice of features has an impact on the prediction of the model to a certain extent. After ranking the importance of features, selecting the appropriate number of features for inversion provides better results than full feature inversion, and the computational speed is improved.

Theta Rhythmic Clock-Like Activity of Single Units in the Mouse Hippocampus.

Theta rhythmic clock-like activity was observed in a small group of hippocampal CA1 neurons in freely behaving mice. These neurons were only persistently activated during theta states of waking exploration and rapid eye movement sleep, but were almost silent during the non-theta state of slow-wave sleep. Interestingly, these cells displayed a theta clock-like simple-spike firing pattern, and were capable of firing one spike per theta cycle during theta states. This is the first report of a unique class of hippocampal neurons with a clock-like firing pattern at the theta rhythm. We speculate that these cells may act as a temporal reference to participate in the theta-related temporal coding in the hippocampus. SIGNIFICANCE STATEMENT: Theta oscillations, as the predominant rhythms in the hippocampus during waking exploration and rapid eye movement sleep, may be critical for temporal coding/decoding of neuronal information, and theta-phase precession in hippocampal place cells is one of the best demonstrations of such temporal coding. Here, we show that a unique small class of hippocampal CA1 neurons fired with a theta rhythmic clock-like firing pattern during theta states. These firing characteristics support the notion that these neurons may play a critical role in theta-related temporal coding in the hippocampus.

How vision and movement combine in the hippocampal place code.

How do external environmental and internal movement-related information combine to tell us where we are? We examined the neural representation of environmental location provided by hippocampal place cells while mice navigated a virtual reality environment in which both types of information could be manipulated. Extracellular recordings were made from region CA1 of head-fixed mice navigating a virtual linear track and running in a similar real environment. Despite the absence of vestibular motion signals, normal place cell firing and theta rhythmicity were found. Visual information alone was sufficient for localized firing in 25% of place cells and to maintain a local field potential theta rhythm (but with significantly reduced power). Additional movement-related information was required for normally localized firing by the remaining 75% of place cells. Trials in which movement and visual information were put into conflict showed that they combined nonlinearly to control firing location, and that the relative influence of movement versus visual information varied widely across place cells. However, within this heterogeneity, the behavior of fully half of the place cells conformed to a model of path integration in which the presence of visual cues at the start of each run together with subsequent movement-related updating of position was sufficient to maintain normal fields.

Visual boundary cues suffice to anchor place and grid cells in virtual reality.

The hippocampal formation contains neurons responsive to an animal's current location and orientation, which together provide the organism with a neural map of space.1,2,3 Spatially tuned neurons rely on external landmark cues and internally generated movement information to estimate position.4,5 An important class of landmark cue are the boundaries delimiting an environment, which can define place cell field position6,7 and stabilize grid cell firing.8 However, the precise nature of the sensory information used to detect boundaries remains unknown. We used 2-dimensional virtual reality (VR)9 to show that visual cues from elevated walls surrounding the environment are both sufficient and necessary to stabilize place and grid cell responses in VR, when only visual and self-motion cues are available. By contrast, flat boundaries formed by the edges of a textured floor did not stabilize place and grid cells, indicating only specific forms of visual boundary stabilize hippocampal spatial firing. Unstable grid cells retain internally coherent, hexagonally arranged firing fields, but these fields "drift" with respect to the virtual environment over periods >5 s. Optic flow from a virtual floor does not slow drift dynamics, emphasizing the importance of boundary-related visual information. Surprisingly, place fields are more stable close to boundaries even with floor and wall cues removed, suggesting invisible boundaries are inferred using the motion of a discrete, separate cue (a beacon signaling reward location). Subsets of place cells show allocentric directional tuning toward the beacon, with strength of tuning correlating with place field stability when boundaries are removed.

Global, regional, and national trends in metabolic risk factor-associated mortality among the working-age population from 1990-2019: An age-period-cohort analysis of the Global Burden of Disease 2019 study.

BACKGROUND: Metabolic diseases contribute significantly to premature mortality worldwide, with increasing burdens observed among the working-age population (WAP). This study assessed global, regional, and national trends in metabolic disorders and associated mortality over three decades in WAP. METHODS: Data from the Global Burden of Disease 2019 study were leveraged to assess global metabolism-associated mortality and six key metabolic risk factors in WAP from 1990-2019. An age-period-cohort model was employed to determine the overall percentage change in mortality. RESULTS: The 2019 global metabolic risk-related mortality rate in WAP rose significantly by 50.73%, while the age-standardized mortality rate declined by 21.5%. India, China, Indonesia, the USA, and the Russian Federation were the top contributing countries to mortality in WAP, accounting for 51.01% of the total. High systolic blood pressure (HSBP), high body mass index (HBMI), and high fasting plasma glucose (HFPG) were the top metabolic risk factors for the highest mortality rates. Adverse trends in HBMI-associated mortality were observed, particularly in lower sociodemographic index (SDI) regions. HFPG-related mortality declined globally but increased in older age groups in lower SDI countries. CONCLUSIONS: Despite a general decline in metabolic risk-related deaths in WAP, increasing HBMI- and HFPG-related mortality in lower SDI areas poses ongoing public health challenges. Developing nations should prioritize interventions addressing HBMI and HFPG to mitigate mortality risks in WAP.

On initial Brain Activity Mapping of episodic and semantic memory code in the hippocampus.

It has been widely recognized that the understanding of the brain code would require large-scale recording and decoding of brain activity patterns. In 2007 with support from Georgia Research Alliance, we have launched the Brain Decoding Project Initiative with the basic idea which is now similarly advocated by BRAIN project or Brain Activity Map proposal. As the planning of the BRAIN project is currently underway, we share our insights and lessons from our efforts in mapping real-time episodic memory traces in the hippocampus of freely behaving mice. We show that appropriate large-scale statistical methods are essential to decipher and measure real-time memory traces and neural dynamics. We also provide an example of how the carefully designed, sometime thinking-outside-the-box, behavioral paradigms can be highly instrumental to the unraveling of memory-coding cell assembly organizing principle in the hippocampus. Our observations to date have led us to conclude that the specific-to-general categorical and combinatorial feature-coding cell assembly mechanism represents an emergent property for enabling the neural networks to generate and organize not only episodic memory, but also semantic knowledge and imagination.

The application of advanced computational algorithms used for cooperative communication transmission of vehicular networks: a proposed method.

Telematics will be one of the critical technologies in the future intelligent transportation system and establish communication between vehicles and vehicles, vehicles and networks, and vehicles and people. Thus, vehicles can sense mobile environments and make rational driving decisions. Therefore, the safety and efficiency of traffic flow would be enhanced. However, due to the unknown nature and higher complexity of the connected network environments of vehicles, the utilization of conventional optimization theory fails to generate satisfying results. To address the problem, this article proposes a methodology for collaborative transmission for communication regarding the Internet of Vehicles (IoV) with the help of advanced computational algorithms. The article employs a multi-intelligence advanced computational algorithm to construct a collaborative communication transmission mechanism in the telematics communication system model. The proposed algorithm fully considers the vehicle mobility and quality-of-service (QoS) of telematics services within the network slice. It adjusts the slice's radio resource allocation and parameter settings on an expanded time scale to improve the QoS of telematics services and increase the system's long-term revenue. The simulation results show that the proposed algorithm has a more significant performance improvement than conventional algorithms using telematics information transmission. For example, when the same load conditions are under consideration, the total capacity of the vehicle-to-infrastructure (V2I) link optimized by the proposed algorithm is still higher than that of the other three baseline strategies.

Hippocampal theta-driving cells revealed by Granger causality.

The two-dipole model of theta generation in hippocampal CA1 suggests that the inhibitory perisomatic theta dipole is generated by local GABAergic interneurons. Various CA1 interneurons fire preferentially at different theta phases, raising the question of how these theta-locked interneurons contribute to the generation of theta oscillations. We here recorded interneurons in the hippocampal CA1 area of freely behaving mice, and identified a unique subset of theta-locked interneurons by using the Granger causality approach. These cells fired in an extremely reliable theta-burst pattern at high firing rates (∼90 Hz) during exploration and always locked to ascending phases of the theta waves. Among theta-locked interneurons we recorded, only these cells generated strong Granger causal influences on local field potential (LFP) signals within the theta band (4-12 Hz), and the influences were persistent across behavioral states. Our results suggest that this unique type of theta-locked interneurons serve as the local inhibitory theta dipole control cells in shaping hippocampal theta oscillations.

Development of a highly sensitive monoclonal antibody-based indirect competitive enzyme-linked immunosorbent assay for the detection of avilamycin in feed.

An indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) was developed using a highly sensitive and specific monoclonal antibody against avilamycin. The immunising antigen synthesised through the carbonyldiimidazole (CDI) method was used to prepare antibodies. The established ic-ELISA, after optimisation of conditions, possessed a half maximum inhibitory concentration (IC50) value of 7.44 ng mL-1 and the detection limit of 0.21 ng mL-1 in the standard curve. The spiked experiments indicated that the limits of detection were 1.86 µg kg-1 and 2.31 µg kg-1 in swine feed and chicken feed, respectively. In addition, the average recoveries ranged from 74.7 to 105.4% with the coefficient of variation less than 11%. The good correlation (R2 = 0.9818) between the result of ic-ELISA and HPLC demonstrated the reliability of the developed ic-ELISA, which showed that the ic-ELISA developed here achieves the aim of strengthening the monitoring of avilamycin residues and the inspection of import and export related products.

High-resolution separation of graphene oxide by capillary electrophoresis.

Separation and purification of graphene oxide (GO) prepared from chemical oxidation of flake graphite and ultrasonication by capillary electrophoresis (CE) was demonstrated. CE showed the ability to provide high-resolution separations of GO fractionations with baseline separation. The GO fractionations after CE were collected for Raman spectroscopy, atomic force microscopy, and transmission electron microscopy characterizations. GO nanoparticles (unexfoliated GO) or stacked GO sheets migrated toward the anode, while the thin-layer GO sheets migrated toward the cathode. Therefore, CE has to be performed twice with a reversed electric field to achieve a full separation of GO. This separation method was suggested to be based on the surface charge of the GO sheets, and a separation model was proposed. This study might be valuable for fabrication of GO or graphene micro- or nanodevices with controlled thickness.