A sequencing method, system and kit of low molecular weight heparin (LMWH) oligosaccharides are provided. The sequencing method includes: a sample preparation step: isolating or preparing a group of LMWH oligosaccharide mixture samples; a sample treatment step: performing complete enzymatic digestion and nitrous acid degradation on the LMWH oligosaccharide mixture samples to obtain an enzymatically digested eight-common-heparin-disaccharide array, a 3-O-sulfate group array, a 1,6-anhydro structure array, a nitrous acid degradation array, respectively; a data processing step: obtaining a disaccharide isomeric unit array according to the enzymatically digested eight-common-heparin-disaccharide array and the nitrous acid degradation array; a sequence database building step: building a sequence database according to the degree of polymerization of the oligosaccharide mixture, the disaccharide isomeric unit array, the 3-O-sulfate group array, and the 1,6-anhydro structure array; and a specific result output step: screening the sequence database according to input qualification information and then outputting a specific result file.

A processor executable method, system, and computer-readable media expedite the process of entering equations for use in developing simulations of chemical processes. The process of entering equations is expedited by dynamically inferring the dimensions of variables. The process infers the dimensions of all variables in user-added equations, and infers the dimensions of each variable in a user-added equation sequentially. The process automatically creates variables with unassigned dimensions in response to indications, such as inputs from a user, to declare new equations. The process assigns dimensions to variables based on relations between variables, such as logical relations between the dimensions of variables.

A processor executable method, system, and computer-readable media expedite the process of entering equations for use in developing simulations of chemical processes. The process of entering equations is expedited by dynamically inferring the dimensions of variables. The process infers the dimensions of all variables in user-added equations, and infers the dimensions of each variable in a user-added equation sequentially. The process automatically creates variables with unassigned dimensions in response to indications, such as inputs from a user, to declare new equations. The process assigns dimensions to variables based on relations between variables, such as logical relations between the dimensions of variables.

Described herein are systems, methods, and apparatus for electronically drawing and editing representations of chemical structures using an intuitive user interface. This user interface, the context-aware virtual keyboard, makes it faster and easier to draw and edit chemical structure representations by guiding the user through the sequence of steps required to produce the representation in a context-based, non-linear fashion. The context-based virtual keyboard allows a user to quickly create graphical representations of complex chemical structures by using the structure itself as a basis for presenting efficient options for subsequent drawing/editing steps. Different possible and/or likely actions (e.g., edits to a chemical structure being drawn) are presented to the user based on a selected navigation position on the drawing. Thus, a user can efficiently and intuitively modify a chemical structure drawing without the tedious manual selection of portions of the chemical structure and without searching through complicated menus.

Described herein are systems, methods, and apparatus for electronically drawing and editing representations of chemical structures using an intuitive user interface. This user interface, the context-aware virtual keyboard, makes it faster and easier to draw and edit chemical structure representations by guiding the user through the sequence of steps required to produce the representation in a context-based, non-linear fashion. The context-based virtual keyboard allows a user to quickly create graphical representations of complex chemical structures by using the structure itself as a basis for presenting efficient options for subsequent drawing/editing steps. Different possible and/or likely actions (e.g., edits to a chemical structure being drawn) are presented to the user based on a selected navigation position on the drawing. Thus, a user can efficiently and intuitively modify a chemical structure drawing without the tedious manual selection of portions of the chemical structure and without searching through complicated menus.

Techniques and apparatus for ion source devices with minimized post-column volumes are described. In one embodiment, for example, an apparatus may include at least one memory, and logic coupled to the at least one memory. The logic may be configured to receive analytical information from at least one analytical instrument, and generate at least one record in a distributed ledger having a transaction associated with at least a portion of the analytical information. Other embodiments are described.

Effective compound feature quantities reflecting expert knowledge are efficiently generated to thereby accurately predict physical properties of an unknown compound with a device for predicting a material property using a case-by-case material database storing a plurality of case databases. The case databases include a plurality of records that record structural information about material structures in association with material properties about properties of materials. This device is includes a chemical space designation unit that receives a designation of at least one case database; an autoencoder learning unit that generates an autoencoder for converting structural information corresponding to the case database received by the chemical space designation unit to multi-variables; and a material property prediction unit that predicts material properties using the multi-variables converted by the autoencoder generated by the autoencoder learning unit.

Effective compound feature quantities reflecting expert knowledge are efficiently generated to thereby accurately predict physical properties of an unknown compound with a device for predicting a material property using a case-by-case material database storing a plurality of case databases. The case databases include a plurality of records that record structural information about material structures in association with material properties about properties of materials. This device is includes a chemical space designation unit that receives a designation of at least one case database; an autoencoder learning unit that generates an autoencoder for converting structural information corresponding to the case database received by the chemical space designation unit to multi-variables; and a material property prediction unit that predicts material properties using the multi-variables converted by the autoencoder generated by the autoencoder learning unit.

The system includes a material property prediction presenting unit, a cross-task compatible feature value generating unit, and a material property predicting unit. The material property prediction presenting unit accepts a specification of first task data that includes a record in which a material property is unknown and is to be a target of material property prediction through a first predictive model. The cross-task compatible feature value generating unit predicts feature values from material compositions in the first task data by using a second predictive model. The material property predicting unit generates the first predictive model by using the material compositions, experimental condition, feature values, and the known material property in the first task data. Also, the material property predicting unit inputs the material composition, experimental condition, and feature value in a record in which the material property is unknown in the first task data and predicts the unknown material property.

Disclosed is a method for predicting at least one aspect of an enzymatically catalyzed chemical reaction. The method comprises providing a trained machine learning model, and inputting one or two input strings into the training model. Each input string is selected from a group of strings consisting of: a string representation of at least one educts of the chemical reaction, a string representation of at least one product of the chemical reaction, and/or a string representation of amino acids of an enzyme which is supposed to transform the educts into the products in the reaction. The trained machine learning model predicts at least the one or more strings which were not provided as input and the prediction is performed as a function of the one or two strings provided as input. The method outputting the prediction result for predicting or optimizing the chemical reaction.