Abstract: The present invention relates to a method for preparing a bactericidal phage vector, (pharmaceutical) compositions comprising such phage vectors, also for use in treating diseases, particularly those caused by (antimicrobial resistance) bacterial cells.

Abstract: The present disclosure provides a multi-wave and multi-domain adaptive seismic horizon auto-tracking method and apparatus. The multi-wave and multi-domain adaptive seismic horizon auto-tracking method comprises: importing seismic data, and setting a seed point on the seismic data; determining a seismic relative resolution according to a seismic waveform of a seismic trace where the seed point is located; determining horizon tracking parameters according to the seismic relative resolution; determining a horizon point according to the horizon tracking parameters and the seismic trace where the seed point is located; replacing the seed point with the horizon point, and performing corresponding iterative calculation to obtain a plurality of horizon points in one-to-one correspondence with the seismic traces in the seismic data; and combining horizon values corresponding to the plurality of horizon points into seismic horizon data and exporting the seismic horizon data.

Abstract: A method and apparatus for stratigraphic sedimentary cycle analysis in chronostratigraphic domain are provided. The method includes: performing multi-horizon tracking on seismic volumes by utilizing a horizon tracking technology so as to obtain a chronostratigraphic volume; transforming all horizons in the chronostratigraphic volume from time to the chronostratigraphic domain; extracting a sedimentary discontinuity or stratum erosion amount sequence based on end point positions of respective horizons in the chronostratigraphic domain; converting the sedimentary discontinuity or stratum erosion amount sequence into a stratigraphic sedimentary cycle curve; and obtaining a stratigraphic sedimentary cycle sequence based on the stratigraphic sedimentary cycle curve.

Abstract: The present application relates to a compound containing cyclohexyl, in particular to a compound of formula (I-AA), a stereoisomer or pharmaceutically acceptable salt thereof, a preparation method therefor, a pharmaceutical composition containing the compound, and a use thereof in treating related diseases (such as cancer).

Abstract: A processing unit can train a model as a joint multi-domain recurrent neural network (JRNN), such as a bi-directional recurrent neural network (bRNN) and/or a recurrent neural network with long-short term memory (RNN-LSTM) for spoken language understanding (SLU). The processing unit can use the trained model to, e.g., jointly model slot filling, intent determination, and domain classification. The joint multi-domain model described herein can estimate a complete semantic frame per query, and the joint multi-domain model enables multi-task deep learning leveraging the data from multiple domains. The joint multi-domain recurrent neural (JRNN) can leverage semantic intents (such as, finding or identifying, e.g., a domain specific goal) and slots (such as, dates, times, locations, subjects, etc.) across multiple domains.

Abstract: In some examples, a computing device refines feature information of query text. The device repeatedly determines attention information based at least in part on feature information of the image and the feature information of the query text, and modifies the feature information of the query text based at least in part on the attention information. The device selects at least one of a predetermined plurality of outputs based at least in part on the refined feature information of the query text. In some examples, the device operates a convolutional computational model to determine feature information of the image. The device network computational models (NCMs) to determine feature information of the query and to determine attention information based at least in part on the feature information of the image and the feature information of the query. Examples include a microphone to detect audio corresponding to the query text.

Abstract: Provided herein is an injectable neutral pH collagen-based composition for mesotherapy to improve skin appearance, treat skin disease, promote hair growth or reduce cellulite. The composition comprises neutral pH collagen or modified collagen solution; and optionally, biocompatible active ingredient(s) capable of increasing moisturization, preventing skin fine lines formation, enhancing skin brightening, providing anti-aging functions, promoting lipolysis, nourishing follicle cell, promoting blood supply and/or preventing hair loss.

Abstract: A recombinant armed oncolytic virus composition for conversion of tumor cells into APCs, specifically herpes simplex oncolytic virus composition. The oncolytic virus composition infects tumor cells and expresses trimeric OX40L and IL-12 and optionally a PD-1 inhibitor. Also provided is the use of the oncolytic virus composition for enhancing antigen presentation of tumor cells, and for enhancing the anti-tumor effect of a tumor infiltrating lymphocyte (TIL) in cancer therapy. Further provided are a pharmaceutical composition, a kit, and a combination product for the methods and uses.

Abstract: A method and system for identifying the contact between electrodes and a tissue includes acquiring an impedance between the electrodes on a body cavity invasion device at different frequencies, determining, according to the acquired impedance between the electrodes, an impedance frequency response coefficient between the electrodes, and determining, according to the impedance frequency response coefficient, the contact condition between the electrodes and a tissue in a body cavity. The body cavity invasion device includes a catheter. A plurality of electrodes are disposed at a distal end of the invasive catheter. The contact condition includes whether the electrodes are in contact with the surface of the body cavity and the extent of the contact.

Abstract: A network system comprises a first control unit, a second control unit, and a subscriber unit. The first control unit periodically sends a heartbeat message to the subscriber unit. the subscriber unit forwards received heartbeat messages to a second control unit. The second control unit makes a master/backup decision based on the heartbeat messages and whether a heartbeat message is received in a respective period.

Abstract: Provided are a fused imide derivative as shown in formula I, a preparation method therefor, a pharmaceutical composition containing same, and the use thereof in the treatment of relevant diseases (such as cancers).

Abstract: A text adjustment method includes: obtaining a to-be-processed text; determining whether to adjust the to-be-processed text according to the to-be-processed text and a context text of the to-be-processed text; in response to determining to adjust the to-be-processed text, determining adjustment character information and adjustment position information of the to-be-processed text according to the context text; and determining an adjusted to-be-processed text according to the adjustment character information and the adjustment position information.

Abstract: The present invention relates to a method for separating and purifying tetrahydrocannabivarin (THCV) by means of high-speed countercurrent chromatography, the method comprising: sufficiently shaking a solvent system, leaving same to stand, and separately collecting upper and lower phases; dissolving full-spectrum refined hemp oil in the upper phase, taking the upper phase as a stationary phase and the lower phase as a mobile phase, performing separation by using high-speed countercurrent chromatography to obtain a mixed solution of tetrahydrocannabivarin and the mobile phase, and removing the mobile phase to obtain the tetrahydrocannabivarin. According to the present invention, THCV with a purity of >95% is obtained by separation and purification from full-spectrum refined hemp oil by using high-speed countercurrent chromatography technology for the first time.

Abstract: In one aspect, a method of speech signal processing using artificial intelligence, includes receiving, with at least one processor, a digital speech signal, converting, with the at least one processor, the digital speech signal to text, performing self-alignment pre-training of an encoder on entities and their synonyms, label attention training the pretrained encoder by aligning label-text joint representation to that of label synonym-text joint representations, and identifying, with the trained encoder, entities in a span in the text.

Abstract: A horizontal sampling soil drilling rig and a control method are used to solve the problem that the soil sampling device in the prior art has difficulty in horizontal sampling. The horizontal sampling soil drilling rig includes a rack, a connection assembly, a drive mechanism, a spiral drill rod, and a sampler. The rack is provided with a spline shaft and a screw rod in parallel. The screw rod and the spline shaft are connected through a first transmission assembly in transmission connection. The drive mechanism includes a rotation driver, a second transmission assembly, and a spline shaft sleeve. The spline shaft sleeve is rotationally connected to the rack, and the spline shaft sleeve and the spline shaft mesh with each other. The rotation driver is connected with the spline shaft through the second transmission assembly in transmission connection.

Abstract: A processing unit can train a model as a joint multi-domain recurrent neural network (JRNN), such as a bi-directional recurrent neural network (bRNN) and/or a recurrent neural network with long-short term memory (RNN-LSTM) for spoken language understanding (SLU). The processing unit can use the trained model to, e.g., jointly model slot filling, intent determination, and domain classification. The joint multi-domain model described herein can estimate a complete semantic frame per query, and the joint multi-domain model enables multi-task deep learning leveraging the data from multiple domains. The joint multi-domain recurrent neural (JRNN) can leverage semantic intents (such as, finding or identifying, e.g., a domain specific goal) and slots (such as, dates, times, locations, subjects, etc.) across multiple domains.

Abstract: A first control unit sends a heartbeat message to a subscriber unit, the subscriber unit forwards the heartbeat message to a second control unit, and the second control unit makes a master/backup decision based on the heartbeat message.

Abstract: Systems and methods are disclosed for inquiry-based deep learning. In one implementation, a first content segment is selected from a body of content. The content segment includes a first content element. The first content segment is compared to a second content segment to identify a content element present in the first content segment that is not present in the second content segment. Based on an identification of the content element present in the first content segment that is not present in the second content segment, the content element is stored in a session memory. A first question is generated based on the first content segment. The session memory is processed to compute an answer to the first question. An action is initiated based on the answer. Using deep learning, content segments can be encoded into memory. Incremental questioning can serve to focus various deep learning operations on certain content segments.

Abstract: A processing unit can train a model as a joint multi-domain recurrent neural network (JRNN), such as a bi-directional recurrent neural network (bRNN) and/or a recurrent neural network with long-short term memory (RNN-LSTM) for spoken language understanding (SLU). The processing unit can use the trained model to, e.g., jointly model slot filling, intent determination, and domain classification. The joint multi-domain model described herein can estimate a complete semantic frame per query, and the joint multi-domain model enables multi-task deep learning leveraging the data from multiple domains. The joint multi-domain recurrent neural network (JRNN) can leverage semantic intents (such as, finding or identifying, e.g., a domain specific goal) and slots (such as, dates, times, locations, subjects, etc.) across multiple domains.