Abstract: The present disclosure provides a method and a server for platooning. The method provides: obtaining, based on a platooning request message, a first vehicle type and first kinematic information of a vehicle to join a platoon, a second vehicle type and second kinematic information of a current tail of the platoon, first sensor operating status information of the vehicle to join the platoon, and second sensor operating status information of the current tail vehicle; performing vehicle kinematic determination to obtain a kinematic determination result; performing sensor determination to obtain a sensor determination result; transmitting a confirmation request message to a vehicle of the platoon when the determination results are both successful; and controlling, upon receiving a request approval message, the vehicle to join the platoon to establish a V2V communication connection with each vehicle in the platoon. The method can achieve platoon without any road side unit.

Abstract: An identification and an application of an ALV-J MHC-B2 restrictive epitope peptide are provided, which belong to the field of genetic engineering. An amino acid sequence of the provided ALV-J MHC-B2 restrictive epitope peptide is selected from SEQ ID NO:1, SEQ ID NO:2 and SEQ ID NO:3. An application of the ALV-J MHC-B2 restrictive epitope peptide in preparing an ALV epitope-based vaccine is provided. Restrictive motif of B2-haplotype chicken MHC class I molecule binding peptides is identified by in vitro elution assay. Potential epitopes in four proteins expressed by ALV-J are systematically screened by motif. The immunogenic B2-haplotype chicken ALV-J T-cell epitope peptides are identified by functional validation. It provides a material and theoretical basis for the research and development of ALV epitope-based vaccines.

Abstract: A method (100) for standardizing gene nomenclature, comprising: (i) receiving (110) a source; (ii) tokenizing (120) the source; (iii) comparing (130) a first token to a prefix tree structure with a root node, edges, and leaf nodes; (iv) determining (140) which edge extending from the root node to associated first leaf nodes the first token matches; (v) updating (150) an identification pointer with the location of the first leaf node; (vi) determining (160) which of one or more edges that a second token matches; (vii) updating (170) the identification pointer with the location of the second leaf node; (viii) repeating (172) the determining (160) and updating (170) steps with subsequent tokens until a subsequent token fails to match an edge extending from a leaf node or there is no edge extending from the leaf node; and (ix) providing (180) an identification of a canonical gene name.

Abstract: A method (100) for generating a standardized cancer stage from a text-based source using an annotation system (400), comprising: (i) extracting (130), by a stage annotator, information from the text-based source relative to a stage of the patients cancer to generate cancer annotations; (ii) identifying (140), by a disease annotator, information from the text-based source indicative of a type of cancer; (iii) extracting (150), by a stage synonym annotator, information from the text-based source synonymous with a cancer to generate cancer annotations; (iv) converting (160), by a stage canonicalizer, the cancer annotations from the stage annotator and the stage synonym annotator to a standardized cancer stage; and (v) reporting (170) the standardized cancer stage, the report comprising the standardized cancer stage, the cancer annotations extracted sources from the text-based source, and/or the location of each of the cancer annotations within the text-based source.

Abstract: A method (100) for standardizing gene nomenclature, comprising: (i) receiving (110) a source; (ii) tokenizing (120) the source; (iii) comparing (130) a first token to a prefix tree structure with a root node, edges, and leaf nodes; (iv) determining (140) which edge extending from the root node to associated first leaf nodes the first token matches; (v) updating (150) an identification pointer with the location of the first leaf node; (vi) determining (160) which of one or more edges that a second token matches; (vii) updating (170) the identification pointer with the location of the second leaf node; (viii) repeating (172) the determining (160) and updating (170) steps with subsequent tokens until a subsequent token fails to match an edge extending from a leaf node or there is no edge extending from the leaf node; and (ix) providing (180) an identification of a canonical gene name.

Abstract: A method (100) for recruiting a patient for a clinical trial, comprising: receiving (110) a dataset comprising information about one or more clinical trials each including patient eligibility criteria; extracting (120) the patient eligibility criteria from each of the clinical trials; converting (130) the patient eligibility criteria to a standardized patient eligibility criterion using a structured clinical trial mark-up language; storing (140) the patient eligibility criterion in a database (862), each of the criterion associated with one or more clinical trials; receiving (150) patient-specific data values about a patient; querying (160) the clinical trial eligibility criteria database using the patient-specific data values to identify eligibility criterion satisfied by the patient-specific data value; identifying (170) a clinical trial associated with the one or more standardized patient eligibility criterion satisfied by a received patient-specific data value; and providing (180) a report of the identifi

Abstract: A system and method for providing a prioritized list of clinical trials that are relevant to a patient suffering from an illness or disease, such as cancer, are disclosed. Specifically, a method for conducting an automated, real time clinical trial search and a prioritization analysis is described. The method comprises the steps of conducting an automated full-text clinical trial search based on structuralization of clinical trial eligibility data and knowledge-based inference, initiating a query from the patient's side, and providing a prioritized list of all accessible clinical trials fulfilling a particular query. The system provides better sensitivity, precision and negative predictive value than the current most known clinical trial matching tool: clinicaltrials.gov.

Abstract: The present disclosure pertains to computer-assisted linkage of healthcare records. In some embodiments, a first portion of a collection of healthcare records of individuals may be processed using a set of record attributes (corresponding to strong identifiers) that includes one or more record attributes corresponding to strong identifiers. Based on such processing, a first set of matches between healthcare records of the first collection portion may be predicted, and a number of matches in the first set of matches may be determined. At least one other portion of the collection of healthcare records of individuals may be processed using another set of record attributes that includes one or more record attributes different from the strong-identifier-corresponding record attributes. Based on the number of matches in the first set of matches, the processing of the other collection portion with respect to predicting healthcare record matches may be caused to stop.

Abstract: The present disclosure pertains to a system configured to facilitate computer-assisted linkage of healthcare records. The system is configured to process a whole collection of records, using a reference set of record attributes, to generate a first prediction for a first portion of the collection of healthcare records of individuals of which healthcare records of the first collection portion have matching values with respect to the reference set of record attributes; re-process the first portion of records (the already matched portion of records) using other sets of record attributes to determine which ones of the other sets are reliable predictors of matching records; and use the reliable predictors to process a remaining unmatched portion of the healthcare records.

Abstract: A method and an apparatus for accurately predicting and modeling patient events, such as avoidable admissions, within a healthcare network are disclosed herein. The present disclosure provides systems and methods of predicting and modeling patient events with the use of a constantly updated data set, a sliding windows format, and a random survival forest model. Further, the present disclosure provides methods and systems for accurately predicting and modeling patient events and patient flows amongst various facilities within, and outside of, the healthcare network.