The invention relates to a planar ATR and evanescently exited luminescence optical sensor platform, consisting of a transducer and a recognition layer, wherein changes in the effective refractive index of the recognition layer are converted into a measurable variable in accordance with the integrated-optical light pointer principle. The invention relates also to the use of the method and to the method itself using the sensor platform, for example in label-free biosensory analysis.
Molecular recognition-based electronic sensor, which is gateless, depletion mode field effecttransistor consisting of source and drain diffusions, a depletion-mode implant, and insulating layer chemically modified by immobilized molecular receptors that enables miniaturized label-free molecular detection amenable to high-density array formats. The conductivity of the active channel modulates current flow through the active channel when a voltage is applied between the source and drain diffusions. The conductivity of the active channel is determined by the potential of the sample solution in which the device is immersed and the device-solution interfacial capacitance. The conductivity of the active channel modulates current flow through the active channel when a voltage is applied between the source and drain diffusions. The interfacial capacitance is determined by the extent of occupancy of the immobilized receptor molecules by target molecules. Target molecules can be either charged or uncharged. Change in interfacial capacitance upon target molecule binding results in modulation of an externally supplied current through the channel.
This document describes an optical sensor unit and a procedure for the specific detection and identification of biomolecules at high sensitivity in real fluids and tissue homogenates. High detection limits are reached by the combination of i) label-free integrated optical detection of molecular interactions, ii) the use of specific bioconstituents for sensitive detection and iii) planar optical transducer surfaces appropriately engineered for suppression of non-specific binding, internal referencing and calibration. Applications include the detection of prion proteins and identification of those biomolecules which non-covalently interact with surface immobilized prion proteins and are intrinsically involved in the cause of prion related disease.
Molecular recognition-based electronic sensor, which is gateless, depletion mode field effecttransistor consisting of source and drain diffusions, a depletion-mode implant, and insulating layer chemically modified by immobilized molecular receptors that enables miniaturized label-free molecular detection amenable to high-density array formats. The conductivity of the active channel modulates current flow through the active channel when a voltage is applied between the source and drain diffusions. The conductivity of the active channel is determined by the potential of the sample solution in which the device is immersed and the device-solution interfacial capacitance. The conductivity of the active channel modulates current flow through the active channel when a voltage is applied between the source and drain diffusions. The interfacial capacitance is determined by the extent of occupancy of the immobilized receptor molecules by target molecules. Target molecules can be either charged or uncharged. Change in interfacial capacitance upon target molecule binding results in modulation of an externally supplied current through the channel.
The devices and method are provided for detecting labeled and label-free analytes, such as nucleic acids and proteins, employing conjugated cationic, anionic and neutral polymers. The analytes can be immobilized on a solid support material. The solid support material can be disposed in the pre-fabricated patterns on a substrate. The chemical structures of the polymers employed by the various embodiments of the present invention are described herein.
Photonic crystal (PC) sensors, and sensor arrays and sensing systems incorporating PC sensors are described which have integrated fluid containment and / or fluid handling structures. The PC sensors are further integrated into a sample handling device such as a microwell plate. Sensors and sensing systems of the present disclosure are capable of high throughput sensing of analytes in fluid samples, bulk refractive index detection, and label-free detection of a range of molecules, including biomolecules and therapeutic candidates. The present disclosure also provides a commercially attractive fabrication platform for making photonic crystal sensors and systems wherein an integrated fluid containment structure and a photonic crystal structure are fabricated in a single molding or imprinting processing step amendable to high throughputprocessing.
The invention discloses an online traditional Chinese medicine text named entity identifying method based on deep learning. The method includes the steps that online traditional Chinese medicine text data are obtained through a web crawler, and named entities of the obtained online traditional Chinese medicine text data are labeled with existing terminological dictionaries and human assistance; a word2vec tool is used for carrying out learning on large-scale label-free linguistic data, and word vectors with fixed length are obtained and used for forming a corresponding glossary; word segmentation is carried out on the online traditional Chinese medicine text data, words are converted into the word vectors with the fixed length by searching for the glossary, the word vectors serve as input of a convolutional neural network, and a blank character is used for filling when sentence length is insufficient; output of the convolutional neural network serves as input of a bidirectional long-short-time memory recurrent neural network, and an identification result of the online traditional Chinese medicine text data words to be identified is output. Compared with a traditional method for named entity identifying, the method reduces complexity and workload of feature extraction, simplifies the processing process and remarkably improves identification efficiency.
Provided are methods and devices for label-free detection of nucleic acids that are amplified by polymerasechain reaction. A solution containing the components necessary for a PCR is introduced to a microfluidic amplification chamber and an electric field applied to a confined region in which PCR occurs. PCR product generated in the confined region is detected by measuring an electrical parameter that is, for example, solution impedance. The devices and methods provided herein are used, for example, in assays to detect one or more pathogens or for point-of-care tests. In an aspect, the PCR product is confined to droplets and the assay relates to detecting an electrical parameter of a flowing droplet, thereby detecting PCR product without a label. In an aspect, the PCR occurs in the droplet.
The invention discloses a pedestrian re-identification method and device based on unsupervised learning and a medium, and the method comprises the steps: obtaining a target image and a comparison image, and identifying whether a pedestrian exists in the target image in the comparison image through a pedestrian re-identification model based on unsupervised learning; outputting a recognition result; establishing a pedestrian re-identification model: carrying out initial training on the visual classifier according to the labeled source data set to obtain a visual classifier; learning the label-free target data set by using the vision classifier after initial training to obtain a matching probability and space-time information; obtaining a Bayesian fusion model according to the matching probability and the space-time information; carrying out similarity matching on pedestrian images in the unlabeled target data set by the Bayesian fusion model according to the comparison target pedestrian images to obtain a similarity score; sorting the similarity scores according to a preset threshold value to obtain a sorting result; when it is detected that the current model training optimization frequency is smaller than or equal to a preset optimization threshold value, performing parameter updating on the visual classifier.
The invention discloses a firework identification method and a firework identification system based on deep learning of an image. The firework identification method comprises the following steps of step 1, acquiring a label-free sample image set and a labelsample image set; step 2, obtaining a label-free training data set and a label training data set; step 3, performing whitening preliminary processing on training data; step 4, based on the label-free training data subjected to the whitening preliminary processing, constructing a deep neutral network based on sparse self coding by adopting unsupervised learning, and extracting a basic image feature set of the label-free training data; step 5, convolving basic image features and pooling image data; step 6, training a Softmax classifier based on the convolved and pooled label training data set; step 7, inputting the convolved and pooled images to be identified into the trained Softmax classifier to obtain the identification result. According to the firework identification method and the firework identification system disclosed by the invention, the visual identification rate of fireworks and a similar object can be effectively improved, and automatic identification with higher precision for the fireworks can be realized.
Quantification of a target analyte is performed using a single sample to which amounts of the target analyte are added. Calibration is performed as part of quantification on the same sample. The target analyte is detectable and quantifiable using label free reagents and requiring no sample preparation. Target analytes include biomarkers such as cancer biomarkers, pathogenic Escherichia coli, single stranded DNA, and staphylococcal enterotoxin. The quantification process includes determining a sensor response of a sensor exposed to the sample and configured to detect the target analyte. Sensor responses are determined after sequential additions of the target analyte to the sample. The amount of target analyte detected by the sensor when first exposed to the sample is determined in accordance with the multiple sensor responses.
A chip-based capillary electrophoresisassembly including: a holder including a frame for removably receiving a chip; a capillary electrophoresis microchip dimensioned to fit onto the holder, and comprising a body and a separation channel defined in the body; and a pair of adhesive detection electrodes integrated with an electronic conductometric detection circuit, wherein the assembly is assemblable by disposing the capillary electrophoresis microchip on the holder and removably placing the adhesive electrodes on the microchip body near the separation channel. A label-free analyte conductometric detection method and a label-free capillary electrophoresisimmunoassay method are also described.
The devices and method are provided for detecting labeled and label-free analytes, such as nucleic acids and proteins, employing conjugated cationic, anionic and neutral polymers. The analytes can be immobilized on a solid support material. The solid support material can be disposed in the pre-fabricated patterns on a substrate. The chemical structures of the polymers employed by the various embodiments of the present invention are described herein.
Photonic crystal (PC) sensors, and sensor arrays and sensing systems incorporating PC sensors are described which have integrated fluid containment and / or fluid handling structures. Sensors and sensing systems of the present disclosure are capable of high throughput sensing of analytes in fluid samples, bulk refractive index detection, and label-free detection of a range of molecules, including biomolecules and therapeutic candidates. The present disclosure also provides a commercially attractive fabrication platform for making photonic crystal sensors and systems wherein an integrated fluid containment structure and a photonic crystal structure are fabricated in a single molding or imprinting processing step amendable to high throughputprocessing.
Surface enhanced Raman Scattering (SERS) and related modalities offer greatly enhanced sensitivity and selectivity for detection of molecular species through the excitation of plasmonmodes and their coupling to molecular vibrational modes. One of the chief obstacles to widespread application is the availability of suitable nanostructured materials that exhibit strong enhancement of Raman scattering, are inexpensive to fabricate, and are reproducible. I describe nanostructured surfaces for SERS and other photonic sensing that use semiconductor and metal surfaces fabricated using femtosecondlaserprocessing. A noble metal film (e.g., silver or gold) is evaporated onto the resulting nanostructured surfaces for use as a substrate for SERS. These surfaces are inexpensive to produce and can have their statistical properties precisely tailored by varying the laserprocessing. Surfaces can be readily micropatterned and both stochastic and self-organized structures can be fabricated. This material has application to a variety of genomic, proteomic, and biosensing applications including label free applications including binding detection. Using this material, monolithic or arrayed substrates can be designed. Substrates for cell culture and microlabs incorporating microfluidics and electrochemical processing can be fabricated as well. Laser processing can be used to form channels in the substrate or a material sandwiched onto it in order to introduce reagents and drive chemical reactions. The substrate can be fabricated so application of an electric potential enables separation of materials by electrophoresis or electro-osmosis.
โA label-free imaging method to monitor stem cellmetabolism discriminates different states of stem cell as they differentiate in a living tissues. We use intrinsic fluorescence biomarkers and the phasor approach to Fluorescence Lifetime Imaging Microscopy (FLIM). We identify and map intrinsic fluorophores such as collagen, retinol, retinoic acid, flavins, nicotinamide adenine dinucleotide (NADH) and porphyrin. We measure the phasor values of germ cells in C. Elegans germ line. Their metabolic fingerprint cluster according to their differentiation state, reflecting changes in FAD concentration and NADH binding during the differentiation pathway. The phasor approach to lifetime imaging provides a label-free, fit-free and sensitive method to identify different metabolic state of cells during differentiation, to sense small changes in the redox state of cells and may identify symmetric and asymmetric divisions and predict cell fate.โ