Abstract: Methods can be conducted to prevent disulfide scrambling in liquid chromatography-mass spectrometry analysis of a protein of interest. In particular, methods can be conducted by employing a NEM analog, such as maleimide, and a heavy isotope counterpart of the NEM analog, such as a heavy isotope substituted maleimide, to alkylate a protein of interest and to alkylate a peptide digest of the protein of interest under conditions that prevent disulfide scrambling and subjecting the samples to a liquid chromatography-mass spectrometry analysis.

Abstract: A training data processing method and an electronic device are provided. The method includes: obtaining medical history data including at least one first disease suffered by a user; setting a plurality of disease types according to a target disease; setting a time interval; obtaining at least one second disease in the time interval from the medical history data; performing a pre-processing operation on the second disease according to the disease types to obtain processed data; and inputting the processed data to a neural network to train the neural network.

Abstract: A method for analyzing an electrocardiography (ECG) signal is provided. The ECG signal is measured through a sensor. One or more specified waveforms in the ECG signal are detected. Multiple ECG features from each specified waveform are captured. The ECG features are input into multiple candidate models to obtain multiple candidate scores. Each candidate score is compared with a standard value to obtain an ideal score among the candidate scores. One of the candidate models corresponding to the ideal score is selected as a risk prediction model to predict disease risk through the risk prediction model.

Abstract: A training data processing method and an electronic device are provided. The method includes: obtaining medical history data including at least one first disease suffered by a user; setting a plurality of disease types according to a target disease; setting a time interval; obtaining at least one second disease in the time interval from the medical history data; performing a pre-processing operation on the second disease according to the disease types to obtain processed data; and inputting the processed data to a neural network to train the neural network.

Abstract: A method and a system for mental index prediction are provided. The method includes the following steps. A plurality of images of a subject person are obtained. A plurality of emotion tags of the subject person in the images are analyzed. A plurality of integrated emotion tags in a plurality of predetermined time periods are calculated according to the emotion tags respectively corresponding to the images. A plurality of preferred features are determined according to the integrated emotion tags. A mental index prediction model is established according to the preferred features to predict a mental index according to the emotional index prediction model.

Abstract: A reamer includes a shank portion extending from a rear end and a cutting portion extending from the shank portion to a front end. The cutting portion includes a plurality of cutting teeth separated by gashes. Each cutting tooth having a cutting edge extending in a longitudinal direction from the front end to proximate the shank portion. The cutting portion includes a margin trailing each cutting edge and extends along each cutting edge to proximate the shank portion. Each cutting tooth also has a primary relief formed between the cutting edge and the front end and a secondary relief extending longitudinally between the margin of one cutting tooth to the cutting edge of an adjacent cutting tooth. A width, W, of each cutting tooth increases in a radially outward direction with respect to the central, longitudinal axis when viewed from the front end of the reamer.

Abstract: A method and an electronic device for selecting influence indicators by using an automatic mechanism are provided. The method includes following steps. Raw data is obtained, where the raw data includes a body-related variable and a plurality of to-be-measured indicators corresponding to the body-related variable. The body-related variable is set as a target parameter. The body-related variable and the to-be-measured indicators are input into a plurality of validation models, and the to-be-measured indicators are sorted according an output result of the validation models to obtain ranking data. Importance of the to-be-measured indicators is calculated by using a screening condition according to the ranking data, so as to select a candidate indicator from the to-be-measured indicators. An influence indicator is determined by calculating a correlation between the candidate indicator and the body-related variable.

Abstract: A method and a system for mental index prediction are provided. The method includes the following steps. A plurality of images of a subject person are obtained. A plurality of emotion tags of the subject person in the images are analyzed. A plurality of integrated emotion tags in a plurality of predetermined time periods are calculated according to the emotion tags respectively corresponding to the images. A plurality of preferred features are determined according to the integrated emotion tags. A mental index prediction model is established according to the preferred features to predict a mental index according to the emotional index prediction model.

Abstract: A method for analyzing an electrocardiography (ECG) signal is provided. The ECG signal is measured through a sensor. One or more specified waveforms in the ECG signal are detected. Multiple ECG features from each specified waveform are captured. The ECG features are input into multiple candidate models to obtain multiple candidate scores. Each candidate score is compared with a standard value to obtain an ideal score among the candidate scores. One of the candidate models corresponding to the ideal score is selected as a risk prediction model to predict disease risk through the risk prediction model.

Abstract: A training data processing method and an electronic device are provided. The method includes: obtaining medical history data including at least one first disease suffered by a user; setting a plurality of disease types according to a target disease; setting a time interval; obtaining at least one second disease in the time interval from the medical history data; performing a pre-processing operation on the second disease according to the disease types to obtain processed data; and inputting the processed data to a neural network to train the neural network.

Abstract: A method and an electronic device for selecting influence indicators by using an automatic mechanism are provided. The method includes following steps. Raw data is obtained, where the raw data includes a body-related variable and a plurality of to-be-measured indicators corresponding to the body-related variable. The body-related variable is set as a target parameter. The body-related variable and the to-be-measured indicators are input into a plurality of validation models, and the to-be-measured indicators are sorted according an output result of the validation models to obtain ranking data. Importance of the to-be-measured indicators is calculated by using a screening condition according to the ranking data, so as to select a candidate indicator from the to-be-measured indicators. An influence indicator is determined by calculating a correlation between the candidate indicator and the body-related variable.

Abstract: A reaming tool includes a tool body having an axially rearward shank portion and an axially forward cutting portion, the forward cutting portion having at least one peripherally arranged cutting insert. The reaming tool also includes an inlet coolant channel formed in the tool body and an inlet opening at an axially rearward end of the shank portion. An outlet coolant channel is formed in the tool body and is in fluid communication with the inlet coolant channel, wherein the outlet coolant channel defines an outlet opening proximate to the cutting insert. In one particular aspect, at least a portion of the outlet coolant channel is non-linear. A method of making a component of the reaming tool by performing a printing operation on a substrate to form the component is also provided.

Abstract: A disease suffering probability prediction method and an electronic apparatus are provided. The method includes: determining a path length; obtaining a plurality of first paths conforming to the path length from a plurality of history data of a specific disease; obtaining a plurality of second paths positively related to the specific disease from the plurality of first paths; filtering the plurality of second paths to obtain a plurality of third paths, and establishing a prediction model according to the plurality of third paths; and inputting a path to be predicted to the prediction model and outputting a probability of suffering the specific disease.

Abstract: One-piece reamer cutting discs including integral engagement and cutting portions are disclosed. The engagement portion may allow for torque transmission and alignment of the reamer cutting disc on a reamer tool body. The integral engagement portion and cutting portion allows for the cutting portion to be provided closer to the end of the tool body. The one-piece reamer cutting discs may be mounted on a tool body having coolant outlets that direct coolant from the tool body to the exterior surface of the cutting disc.

Abstract: A reamer head for a reamer includes one or more blades separated by flutes. Each flute has a front core region proximate a front end of the cutting portion, a rear portion proximate a rear end of the cutting portion and the middle core region therebetween. The front core region has a thickness, T1, the middle core region has a thickness, T2, and the rear core region has a thickness, T3. The thickness, T2, of the middle core region is greater than the thickness, T1, of the front core region and the thickness, T3, of the rear core region. A method of making the reamer head is also disclosed.

Abstract: A statistical performance evaluation method for different grouping sets includes setting a plurality of first grouping ranges of a first grouping set corresponding to a sample space, setting a plurality of second grouping ranges of a second grouping set corresponding to the sample space, generating a plurality of first probability values and a plurality of first standard deviations corresponding to the plurality of first grouping ranges at each sampling time according to the sample space, generating a plurality of second probability values and a plurality of second standard deviations corresponding to the plurality of second grouping ranges at the each sampling time according to the sample space, and generating a plurality of statistical indicators corresponding to the first grouping set and the second grouping set and outputting a statistical performance ranking result of the first grouping set and the second grouping set accordingly.

Abstract: A reaming tool includes a tool body having an axially rearward shank portion and an axially forward cutting portion, the forward cutting portion having at least one peripherally arranged cutting insert. The reaming tool also includes an inlet coolant channel formed in the tool body and an inlet opening at an axially rearward end of the shank portion. An outlet coolant channel is formed in the tool body and is in fluid communication with the inlet coolant channel, wherein the outlet coolant channel defines an outlet opening proximate to the cutting insert. In one particular aspect, at least a portion of the outlet coolant channel is non-linear. A method of making a component of the reaming tool by performing a printing operation on a substrate to form the component is also provided.

Abstract: A modular cutting tool includes a cutting head, a shank and a mechanism which assists in assembling and locks the cutting head to the shank. The cutting head includes a cutting portion rotatable about a central longitudinal axis and a cylindrical skirt extending axially from the cutting portion and terminating at a contact face disposed perpendicular to the longitudinal axis. The skirt has a number of curved inner surfaces which generally define a bore disposed about the longitudinal axis. The shank includes a cylindrical portion of a first diameter disposed about the central longitudinal axis and a central protrusion disposed in the bore of the cutting portion. The central protrusion has a second diameter less than the first diameter and extends longitudinally from a contact face disposed perpendicular to the longitudinal axis at a leading end of the cylindrical portion.

Abstract: A reaming tool includes a tool body having an axially rearward shank portion and an axially forward cutting portion, the forward cutting portion having at least one peripherally arranged cutting insert. The reaming tool also includes an inlet coolant channel formed in the tool body and an inlet opening at an axially rearward end of the shank portion. An outlet coolant channel is formed in the tool body and is in fluid communication with the inlet coolant channel, wherein the outlet coolant channel defines an outlet opening proximate to the cutting insert. In one particular aspect, at least a portion of the outlet coolant channel is non-linear. A method of making a component of the reaming tool by performing a printing operation on a substrate to form the component is also provided.

Abstract: A modular rotary cutting tool for conducting cutting operations on a work piece is described. The modular rotary cutting tool includes a tool shank having a pocket and a cutting head replaceably mounted in the pocket of the tool shank. The cutting head has a leading end and trailing end opposite the leading end. The drilling portion extends from the leading end of the modular rotary cutting tool to the reaming portion. The reaming portion extends from the drilling portion to a coupling portion of the cutting head. The coupling portion extends from the reaming portion to the trailing end of the cutting head.