42 results about "Hippocampus proper" patented technology

The invention discloses an environment-emotion gradient association memristive neural network circuit based on a TAP cell regulation mechanism. The environment-emotion gradient association memristive neural network circuit is composed of a hippocampus module and an amygdala kernel module. The hippocampus module comprises an environment recognition neuron module and an emotion generation neuron module; the environment recognition neuron module and the emotion generation neuron module are each composed of a weight regulation and control module adj based on a TAP cell regulation mechanism, an adder, a memristor, a resistor, an operational amplifier, a function module ABM1 and a sampling holder. Wherein the weight regulation and control module adj is composed of a voltage-controlled switch, a summator, a function module ABM2 and a function module ABM3. The almond kernel module is composed of a weight regulation and control module adj, a memristor, a resistor, an operational amplifier and a sampling holder. The provided circuit simulates the hippocampus and amygdala nucleus of the brain in structure, the regulation principle of TAP cells on neural stem cells is used for reference in a weight regulation mechanism, and bidirectional gradient associated memory of the environment and the emotion can be achieved.

The invention belongs to the technical field of nutritious food, and particularly discloses a composition for promoting brain development and application thereof. The composition for promoting brain development provided by the invention comprises bifidobacterium longum subsp. Infantis i18 and sn-2 site docosahexaenoic acid. According to the composition provided by the invention, all the components have a synergistic effect, so that the accumulation of DHA in cerebral cortex and hippocampus can be remarkably improved, and the purpose of promoting brain development is achieved. The composition is suitable for health food, formula milk powder or fermented milk and other products for improving cognition and promoting brain development.

The invention relates to application of bifidobacterium animalis subsp. Lactis BLa80 in preparation of a preparation for relieving depression or anxiety. The bifidobacterium animalis subsp. Lactis BLa80 is a strain of the bifidobacterium animalis subsp. Lactis BLa80 with the preservation number of CGMCC (China General Microbiological Culture Collection Center) No.22547, and the strain of the bifidobacterium animalis subsp. Lactis BLa80 is a strain of the bifidobacterium animalis subsp. Lactis BLa80 with the preservation number of CGMCC No.22547. The invention develops a brand new application of the bifidobacterium animalis subsp. Lactis BLa80 strain, namely, the bifidobacterium animalis subsp. Lactis BLa80 strain is used for relieving depression or anxiety symptoms, specifically, the bifidobacterium animalis subsp. Lactis BLa80 strain can stimulate RIN14B cells to secrete 5-HT, can relieve depression and anxiety-like behaviors of mice suffering from depression and anxiety, can relieve hyperfunction of hypothalamic-pituitary-adrenal axis (HPA axis), and can relieve depression and anxiety-like behaviors of mice suffering from depression and anxiety. The inflammatory response of mice with depression and anxiety is relieved, the expression level of brain-derived neurotrophic factors in hippocampus of the mice with depression and anxiety is improved, the abundance of intestinal beneficial flora of the mice with depression and anxiety is improved, and the diversity of intestinal flora is increased.

The invention discloses a multi-modal feature fusion prediction method and system for early screening of Alzheimer's disease, and belongs to the technical field of brain disease prediction. The method comprises the following steps: acquiring multi-dimensional data containing cognitive test scores, brain image scanning results and biomarker concentration levels from a patient record database, and performing standardization processing to obtain a multi-dimensional data set in a unified format; key feature vectors are extracted through a dimensionality reduction analysis method to capture the covariant relation between cognitive test scores and brain image changes; when the reduction range of the cognitive test score exceeds a preset threshold value and the brain image displays an atrophy sign, a classification model is constructed through an integrated learning method to preliminarily classify abnormal signals; and fusing the biomarker concentration level to obtain an abnormal signal vector. According to the method, the accuracy and efficiency of early screening of brain diseases such as Alzheimer's disease are remarkably improved through accurate evaluation of key parts of the human brain, such as hippocampus.

The invention provides a senile dementia early diagnosis method based on hippocampus magnetic resonance imaging, which comprises the following steps that: according to a segmented image, a contour extraction algorithm is adopted to obtain multi-layer two-dimensional contour data to obtain a contour sequence, and the contour extraction algorithm carries out gradient analysis and threshold filtering on geometric features of a hippocampus, and captures boundary details; the contour sequence is subjected to space alignment processing, an alignment contour set is obtained, displacement deviation between different layers is eliminated through a transformation correction method in the space alignment processing, and the alignment contour set is generated; calculating volume and morphological indexes according to the preliminary three-dimensional model, and determining the atrophy degree; and comparing the refined three-dimensional structure with a preset reference model to generate difference mapping, judging the diagnosis precision, and adopting a geometric feature deviation measurement method to evaluate the structural difference in comparison to generate the difference mapping. According to the method, the fidelity and diagnosis precision of three-dimensional reconstruction of the hippocampus are remarkably improved, and reliable technical support is provided for early diagnosis of neurodegenerative diseases.

This invention discloses a method for identifying and processing abnormal hippocampal atrophy in functional MRI images of the brain, comprising: acquiring functional MRI image data of the brain to be processed; preprocessing the functional MRI image data of the brain to obtain preprocessed functional MRI image data of the brain; extracting the hippocampal region from the preprocessed functional MRI image data of the brain; constructing a three-dimensional image of the hippocampus based on the hippocampal region; performing feature extraction on the three-dimensional image of the hippocampal region based on a deep learning network to obtain feature extraction results; and classifying and identifying abnormal atrophy of the hippocampal region according to the feature extraction results to obtain classification and identification results.

The invention belongs to the technical field of disease control in aquaculture, and particularly relates to a hippocampus enteritis pathogen separation and identification and medication screening method based on a high-throughput sequencing technology, which comprises the following steps: collecting intestinal tissues of a diseased hippocampus and a healthy hippocampus, respectively preparing tissue homogenate, and freezing for preservation; the method comprises the following steps: extracting DNA (Deoxyribose Nucleic Acid) of microorganisms of an intestinal tissue sample, performing 16SrDNA high-throughput sequencing, analyzing intestinal flora composition, and determining dominant bacteria; selecting and screening a culture medium according to dominant bacteria, and performing pathogenic bacterium separation and purification on intestinal tissues of the diseased hippocampus; dNA of the purified strain is extracted, 16S rDNA amplification and sequencing are carried out, and the species of pathogenic bacteria are identified through sequence comparison; according to the method, the diversity and abundance differences of microorganisms in the sick hippocampus and the healthy hippocampus are compared through a high-throughput sequencing technology, and targeted separation, identification and drug sensitivity screening are further carried out by taking statistically significant difference bacteria as the guidance of potential pathogenic bacteria.

Systems and methods for neural modeling are disclosed. A perfusable microfluidic platform with human hippocampal organoid integration and functional vasculature is disclosed. Vascularized human brain organoids can be developed on a microphysiological platform. The system can include a membrane configured to enable perfusion throughout the membrane. The membrane can couple with a carrier. The system can include the carrier including channels. The channel can be configured to deliver a first set of cells to the membrane through a lumen of the membrane; and deliver a second set of cells to the membrane through an exterior of the membrane. The system can include monitoring components to continuously and non-invasively measure the onset and development of neural oscillations.

To provide a gene construct for use in the treatment of a metabolic disorder in a mammal, particularly a human.SOLUTION: A gene construct provided comprising a nucleotide sequence encoding fibroblast growth factor 21 (FGF21) for use in the treatment and / or prevention of a metabolic disorder, wherein the therapy involves expression of the gene construct in the central nervous system (CNS), preferably in the brain, more preferably in the hypothalamus and / or cortex and / or hippocampus and / or cerebellum and / or olfactory bulb, and most preferably in the hypothalamus.SELECTED DRAWING: None

The present application provides a kind of nutritional composition in the application of the product for improving learning, memory and cognitive ability.The nutritional composition includes animal bifidobacterium lactis subsp.CP-9 and 3'-sialyllactose.Animal bifidobacterium lactis subsp.CP-9 and 3'-sialyllactose can synergistically enhance the memory retention ability of the body, reduce the degree of learning and memory errors, and up-regulate the expression of oligodendrocyte transcription factor 2 coding gene, myelin basic protein coding gene and brain-derived neurotrophic factor coding gene, realize the double regulation of myelin formation and neurotrophic in hippocampus region of brain, synergistically improve the neural plasticity of central nervous system through gut-brain axis pathway, and then double improve the learning, memory and cognitive function of the body from the behavior and molecular level.

This invention discloses a novel application of Bifidobacterium breve CCFM1078 in relieving intestinal colic, belonging to the fields of microbial technology and pharmaceutical technology. The Bifidobacterium breve CCFM1078 provided by this invention can relieve intestinal colic in infants and young children, specifically in the following ways: (1) increasing the visceral pain threshold and reducing the frequency of defecation in rats to a certain extent; (2) reducing the number of chromaffin cells and 5-HT content in the colon of rats with intestinal colic; (3) reducing the serum TNF-α and IL-1β content in rats with intestinal colic; (4) reducing the hippocampal TNF-α and IL-6 content in rats with intestinal colic; and (5) increasing the propionic acid content in the cecal contents of rats with intestinal colic.

The invention discloses a near-infrared illumination treatment device for treating Alzheimer's disease, and relates to the technical field of medical equipment. The device comprises a treatment table, a support is movably connected to the top of the treatment table, a supporting shell is arranged at the other end of the support, and a first infrared lamp panel is installed in the supporting shell. By arranging the rotatable circular ring and the second infrared lamp panel installed in the circular ring and cooperating with a limiting assembly composed of a supporting frame, a moving rod, a rubber pad and a screw rod, accurate adjustment and reliable fixation of the irradiation angle of near-infrared light are achieved, and medical staff can accurately adjust the irradiation angle of the near-infrared light according to the structural difference of brain regions of patients. The irradiation direction of the second infrared lamp panel is dynamically adjusted, particularly, accurate directional irradiation can be conducted on key areas such as hippocampus, and compared with a fixed-angle irradiation mode in the prior art, the device remarkably improves treatment pertinence and adaptability and effectively solves the problem of irradiation blind areas caused by head shape difference.

The application discloses a kind of brain-like neural network multi-vehicle coordination decision-making method and system fusing hippocampus playback mechanism, method includes: vehicle-mounted terminal receives the environment data from vehicle-mounted sensor and traffic signal data transmitted by roadside end, utilize pulse coding conversion into space-time pulse sequence;Time-space pulse sequence is input into pulse neural network to carry out prediction, and based on the feedback data of predicted road condition and vehicle-mounted sensor, calculate prediction error, and the physical feedback of real-time monitoring vehicle;When prediction error or physical feedback exceeds prediction mismatch degree threshold value, activate hippocampus memory mechanism, intercept all perception pulse sequence and self state data in the preset time window before current time, and as high-risk scenario memory write vehicle-mounted short-term memory buffer area;When vehicle is idle or charging state, use counterfactual reasoning mechanism to read high-risk scenario memory, reconstruct high-risk scene in virtual environment, and generate candidate strategy through counterfactual reasoning mechanism to carry out virtual trial and error.

A functional evaluation system of hippocampus that includes: an acquisition unit that acquires image data representing a hippocampus of a subject; and an identification unit that identifies volume data representing a volume of a left hippocampal-amygdaloid transition area of the subject, in accordance with the image data.

Disclosed is an ActiOmega® composition (unsaturated fatty acids and phospholipids) comprising Bacopa monnieri extracts. The composition in the present invention can improve concentration, enhance cognition, improve learning and memory abilities, increase the release and transmission rate of neurotransmitters, enhance hippocampal activity, promote the absorption and utilization of DHA in the human body and the brain development. The DHA in the present invention achieves efficient purification and impurity removal through ultra-high vacuum molecular distillation technology (without the need for solvents), and ensures almost no fishy odourthrough the high vacuum steam deodorization process, with higher DHA content and purity, natural green colour, easy access to the brain, and high bioavailability.

The application belongs to the technical field of robot intelligent control, and relates to a robot behavior decision method and equipment simulating brain learning and memory mechanism. The method combines a developmental network DN2, reinforcement learning and a self-organizing map SOM, simulates functions of a cerebellum learning system, a basal ganglia and a hippocampus learning system, and obtains a self-organizing reinforcement learning model based on the developmental network DN2. The equipment comprises one or more processors and a computer readable medium storing one or more computer readable instructions, and is used for implementing the above robot behavior decision method. The application properly utilizes functions of the cerebellum learning system, the basal ganglia and the hippocampus learning system. The fusion algorithm combines reinforcement learning and supervised learning methods, enhances robustness of the self-organizing reinforcement learning model based on the developmental network DN2, improves the ability of the algorithm to obtain an effective obstacle avoidance strategy, and can help an intelligent agent to quickly learn an efficient solution.

The embodiment of the application discloses a hippocampus segmentation method and device based on deep learning and a storage medium, wherein the hippocampus segmentation method based on deep learning comprises the following steps: S1, a first image generated by nuclear magnetic resonance imaging is acquired, a target shape in the first image is inferred by using a trained deep belief network, and a second image containing the target shape is obtained; S2, an energy function is constructed according to the second image, a deep belief network driven lattice Boltzmann model is obtained based on the energy function, curve evolution is performed on the first image by using the deep belief network driven lattice Boltzmann model, and a third image and a fourth image after segmentation are obtained, and the fourth image contains the target shape; S3, steps S1 to S2 are executed for multiple times, multiple groups of third images and fourth images obtained are fed back to an error correction convolutional neural network model, multiple fifth images are obtained, the multiple fifth images are averaged and fused, and a segmentation result is obtained.

The present disclosure discloses a method, system, composition, and kit for diagnosis and differential diagnosis of Alzheimer's disease (AD) based on spatial transcriptomics of a human hippocampus. In the present disclosure, the rapid and efficient early diagnosis and differential diagnosis of AD-associated cognitive impairment is achieved based on one or more of cholecystokinin (CCK), neurogranin (NRGN), and peripheral myelin protein 2 (PMP2) that are carried by plasma extracellular vesicles (EVs). The present disclosure enables the high-sensitivity and high-throughput detection of nervous system-derived EVs in peripheral blood, and demonstrates advantages such as high efficiency and low cost. The present disclosure provides a new technical means and method for the clinical application and large-scale screening related to the accurate diagnosis of AD-associated cognitive impairment.

The application provides a multi-functional operant conditioning reflex neural network circuit with blocking and competition effect, wherein a stimulating signal is connected with an input end of a hippocampus module, an exciting signal and an output signal are connected with input ends of a cerebellum module, the exciting signal, the hippocampus module and the cerebellum module are connected with an input end of a voltage control module, an output end of the voltage control module is connected with an experience module and a feedback module respectively, an output end of the experience module is connected with a decision module and the feedback module respectively, an output end of the feedback module is connected with the voltage control module and the experience module respectively, and an output end of the decision module is connected with the feedback module and obtains the output signal. The application realizes learning of operant conditioning reflex under multiple input signals, can simulate decision judgment of animals in a complex natural environment, and widens the possibility of the existing operant conditioning reflex circuit.

The application provides a robot positioning mapping method based on a visual cortex and hippocampus space coding, and the method comprises the following steps: acquiring linear velocity and angular velocity through a wheel encoder and an inertial measurement unit, and obtaining robot pose information through path integration; collecting scene images by using a camera and extracting GIST features, obtaining a winning cell in a view cell network through Euclidean distance matching, updating weights, and introducing a posteriori entropy adaptive threshold to perform robust scene matching; storing the pose information and the successfully matched weight vector into a hippocampus CA1 area location cell, eliminating cumulative errors through iterative optimization, controlling the map size through space density redundancy pruning, and adopting a hierarchical switching strategy to perform scale stretching optimization, so that fast mapping is realized. Through simulating a biological space coding mechanism, the application improves scene recognition robustness, positioning consistency and mapping efficiency, and is suitable for intelligent navigation of a mobile robot.

The application discloses a kind of based on clinical MRI image human brain hippocampus multiscale morphological measurement method and equipment, comprising: obtaining image sequence;Based on skeleton description method, baseline hippocampus corresponding skeleton representation model is constructed;Subregion label is added in baseline skeleton representation model, and multiscale skeleton representation model is obtained;According to the boundary surface of baseline hippocampus and the hippocampus of each time point, the multiscale skeleton representation model of hippocampus in the magnetic resonance image of each time point is obtained;The morphological measurement of each time point hippocampus of test object is carried out, and the morphological parameter of each time point hippocampus is obtained.The present application constructs the multiscale skeleton representation model of different time points of test object by using skeleton representation method, so as to provide the basis for accurately measuring the time characteristics of hippocampus morphology, solve the problem that the morphological difference between different individual hippocampus is large, and the morphological change in individual is small, improve the precision of hippocampus morphological change measurement.

The invention provides a problem chain generation method and device, equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a query problem of a user; a trained neural symbol inference model is obtained, and the neural symbol inference model comprises a prefrontal cortex classifier and a hippocampus simulator; inputting the query question into the neural symbol inference model, identifying the query question through the prefrontal cortex classifier, and determining a question type; and generating a target question chain of the query question according to the question type through the hippocampus simulator. According to the method, the logicality, the coherence and the scene pertinence of problem chain generation are remarkably improved.

The invention is applicable to the technical field of chemical and biological detection, and provides a method and a system for evaluating microplastic neurotoxicity based on DIA mass spectrometry, and the method comprises the following steps: constructing an animal model by using to-be-evaluated microplastic, and carrying out a behavioral experiment; obtaining hippocampus holoprotein of the animal model and performing enzymolysis to obtain a peptide fragment mixture; carrying out mass spectrum data acquisition on the peptide fragment mixture by adopting a data independent acquisition mode; performing qualitative and quantitative analysis on mass spectrum data, and screening differential expression proteins; carrying out bioinformatics analysis on the differential expression protein; and combining a bioinformatics analysis result with a behavioral experiment result, and evaluating the neurotoxicity of the microplastics. The method combines bioinformatics and behavioral analysis, systematically evaluates the influence degree of exposure of microplastics such as polytetrafluoroethylene on animal nerve function damage, and provides a comprehensive mechanism explanation and effective evaluation method for neurotoxicity of microplastics.

The invention provides a method for extracting and separating unsaturated fatty acid in a hippocampus japonicus body, and belongs to the technical field of biological separation. The method comprises the following steps: grinding a hippocampus japonicus sample, soaking with 75% ethanol, performing reflux extraction on the soaked sample for multiple times with 75% ethanol to obtain an extracting solution, filtering the extracting solution, collecting filtrate, concentrating and recovering to obtain a concentrated extract, dissolving the concentrated extract with ethanol, and performing freeze-drying to obtain the hippocampus japonicus freeze-dried powder. The preparation method comprises the following steps: loading on a silica gel column by a dry method, carrying out gradient elution on the silica gel column by adopting a mixed solvent of normal hexane, ethyl acetate and water to obtain a plurality of eluted parts, and further carrying out reverse silica gel column chromatography purification on the part with high content through thin-layer chromatographic analysis to obtain the unsaturated fatty acid. The EPA-containing unsaturated fatty acid is obtained through multiple reflux extraction of 75% ethanol and repeated purification of a silica gel column, and the method is simple to operate, low in cost and small in pollution, and can efficiently obtain the high-purity EPA-containing unsaturated fatty acid.

The invention discloses a hippocampus three-dimensional segmentation method based on wavelet transform and multi-modal fusion, and the method carries out the three-dimensional segmentation of a hippocampus through the steps of data obtaining, data preprocessing, feature extraction, feature fusion, three-dimensional segmentation, dynamic threshold adjustment, cross-modal fusion and post-processing. According to the method, the multi-scale features of the image are effectively extracted, noise is suppressed, meanwhile, the MPR technology and wavelet transformation are deeply fused, multi-plane images of an axial plane, a coronal plane and a sagittal plane are obtained, and three-dimensional segmentation of the hippocampus is achieved more efficiently and safely.

This invention discloses the preparation and application of a hydrophobic compound high-encapsulation protein carrier, belonging to the field of bionanomaterials and food functional factor delivery technology. This invention provides a hydrophobic lumen-modified ferritin mutant rXHF, obtained by mutating polar residues at positions 68, 76, 141, and 148 on the lumen surface of human heavy chain ferritin rHuHF to tryptophan. Using this mutant as a carrier, lycopene is compounded with lycopene at a molar ratio of 1:200 to prepare the lycopene nanodelivery system rXHF-LYC. This system has a lycopene encapsulation efficiency of 17.8% and a DPPH clearance rate of 30%. Through transferrin receptor-mediated endocytosis, it crosses the blood-brain barrier, delivering lycopene to the hippocampus, inhibiting oxidative stress and neuroinflammation, restoring cholinergic function, and improving age-related cognitive impairment. This invention provides a new carrier for brain-targeted delivery of hydrophobic functional factors and offers a new strategy for the intervention of age-related neurodegenerative diseases.

The present application relates to the technical field of probiotic composition, and provides a probiotic composition with nerve protection and cognitive improvement functions and a preparation method thereof.The composition is composed of Lactobacillus rhamnosus GG HBM11-69, Lactobacillus rhamnosus HBM11-35 and one or more oligosaccharides, and is prepared by fermentation, freeze-drying and mixing with a prebiotic carrier.The composition significantly improves the generation of short-chain fatty acids and gamma-aminobutyric acid through the gut-brain axis regulation mechanism, up-regulates the expression of BDNF to enhance hippocampal synaptic plasticity, protects the integrity of the blood-brain barrier, thereby improving D-galactose-induced cognitive impairment and relieving brain neuroinflammation, and enhancing learning and memory ability.

This invention provides a method, apparatus, device, storage medium, and program product for generating question chains, relating to the field of artificial intelligence technology. The method includes: acquiring a user's query question; acquiring a trained neural symbolic reasoning model, wherein the neural symbolic reasoning model includes a prefrontal cortex classifier and a hippocampal simulator; inputting the query question into the neural symbolic reasoning model, identifying the query question through the prefrontal cortex classifier to determine the question type; and generating a target question chain for the query question based on the question type using the hippocampal simulator. This invention significantly improves the logic, coherence, and scenario-specificity of question chain generation.

The invention relates to the technical field of industrial process quality prediction, and discloses a complementary lifelong learning adaptive quality prediction system and method. The system comprises a hippocampus pathway, a new cortex pathway, a complementary lifelong learning mechanism, a semi-supervised enhancement strategy and an online reasoning updating module, and the hippocampus pathway is rapidly updated sample by sample by adopting a layered fuzzy rule system to cope with concept drift. A new cortex path adopts a semi-supervised quality-related stacked auto-encoder to perform block updating and introduces topology protection to prevent forgetting, and two-way knowledge interaction is realized through a complementary lifelong learning mechanism. According to the semi-supervised enhancement strategy, the prediction performance in a low-label environment is improved by using a pseudo label, and the online reasoning module realizes prediction and continuous optimization. The method gives consideration to short-term adaptation and long-term stability under the conditions of label scarcity and distribution drift, has high prediction precision and interpretability, and is suitable for quality prediction and control of manufacturing processes of semiconductors, lithium batteries, display panels, precision optics and the like.

This application discloses an automatic hippocampal segmentation method based on HAU-Net and gated spatial attention. The method includes: acquiring a three-dimensional MRI brain image of the target object to be segmented; sampling at least one target slice from the three-dimensional MRI brain image, and constructing a multi-channel two-dimensional input feature map for each target slice, wherein the multi-channel two-dimensional input feature map is formed by stacking multiple consecutive slices containing the target slice along the channel dimension; inputting the multi-channel two-dimensional input feature map into a pre-trained hippocampal segmentation network to output a hippocampal segmentation probability map; and performing thresholding processing on the hippocampal segmentation probability map to obtain the final binarized hippocampal segmentation result. This achieves an optimized balance between segmentation accuracy and computational efficiency, significantly improving the accuracy and continuity of hippocampal segmentation while maintaining the computational efficiency of the two-dimensional network.