Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, system and apparatus for controlling a PCR detector that improves the accuracy of the thermostatic control of the PCR detector relative to conventional methods of thermostatic control of PCR detectors:
In a first aspect, an embodiment of the present application provides a method for controlling a constant temperature applied to a PCR detector, the method including the steps of:
Collecting temperature data of any test solution at each collecting moment;
taking the time required by each cycle in the PCR detection process as a period, and acquiring each constant temperature interval and each non-constant temperature interval in each period based on the change condition of temperature data in each period;
determining fluctuation values of the temperature data in each constant temperature interval based on the distribution condition of the difference between all extreme values of the temperature data in each constant temperature interval and the preset temperature and the variation amplitude of the temperature data in each constant temperature interval;
Determining the smoothness of the temperature data in each non-constant temperature interval based on the smoothness of the temperature data change in each non-constant temperature interval;
Determining an abnormality factor of each period based on the difference of the fluctuation values in the corresponding constant temperature interval and the difference of the smoothness in the corresponding non-constant temperature interval between each period and the adjacent period, and combining the similarity degree of the temperature data in the corresponding constant temperature interval;
And determining the optimized proportional term parameters of the PID controller by combining the abnormal factor of the current period and the preset initial proportional term parameters, and controlling the temperature of any test solution.
In one embodiment, the acquiring process of each constant temperature interval and each non-constant temperature interval is as follows:
the temperature data of all the acquisition moments in any period are arranged according to time sequence to form a temperature sequence;
a mutation point detection algorithm is adopted to obtain mutation points in the temperature sequence, and the temperature sequence is divided into temperature subsequences according to the mutation points;
arranging the discrete degrees of all the temperature subsequences from small to large, taking the time interval of the temperature subsequence corresponding to the first three discrete degrees as each constant temperature interval, and respectively corresponding to denaturation, annealing and extension reaction stages according to time sequences;
The time interval in which the rest of the temperature subsequences are located is taken as each non-constant temperature interval.
In one embodiment, the determining of the fluctuation value is:
for the temperature subsequence of any constant temperature interval, calculating the difference value of the preset temperature of the reaction stage corresponding to the temperature subsequence and each extreme value in the temperature subsequence;
obtaining a jump value of an extremum in the temperature subsequence based on the number of data which is continuously positive or negative in the temperature subsequence corresponding to all the difference values;
a trend checking algorithm is adopted to acquire trend slope of temperature data between any two adjacent extreme values in the temperature subsequence, and difference between any two adjacent extreme values in the temperature subsequence is calculated and recorded as extreme value difference;
Calculating the product of the absolute value of the trend slope and the difference of the corresponding extreme values, and calculating the accumulated value of the product between any two adjacent extreme values in the temperature subsequence;
and the fluctuation value of the temperature data in any constant temperature interval is the fusion result of the jump value and the accumulated value.
In one embodiment, the jump value is the maximum value of the number of data.
In one embodiment, the smoothness determination process is:
Acquiring a fitting curve of all temperature data in any non-constant temperature interval;
And taking the average value of the curvature absolute values of the fitted curve at all temperature data as the smoothness of the temperature data in any non-constant temperature interval.
In one embodiment, the determining process of the anomaly factor is:
Arranging the fluctuation values in all constant temperature intervals and the smoothness in all non-constant temperature intervals in each period according to time sequence to form a temperature state characteristic sequence;
calculating the average value of the absolute values of the similarity of the temperature subsequences of all corresponding constant temperature intervals between each period and the adjacent previous period;
And obtaining the abnormal factors of each period based on the distance between each period and the temperature state characteristic sequence between the adjacent period and the previous period and the average value.
In one embodiment, the expression of the anomaly factor is:
In the formula (I), in the formula (II),An anomaly factor representing an i-th period; representing the distance between the ith period and the temperature state characteristic sequence of the adjacent previous period; the average value of the absolute values of the similarity of the temperature subsequences of all corresponding constant temperature intervals between the ith period and the adjacent previous period is represented, and gamma represents a preset value larger than 0.
In one embodiment, the expression of the optimized proportional term parameter is:
In the formula (I), in the formula (II),The method comprises the steps of representing optimized proportional term parameters, wherein L represents preset initial proportional term parameters, H represents an abnormal factor of a current period, norm () represents a normalization function, and T represents a preset numerical value larger than 0.
In a second aspect, embodiments of the present application further provide a thermostatic control system applied to a PCR detector, the system including:
The temperature data acquisition module is used for acquiring temperature data of any test solution at each acquisition moment;
The temperature data analysis module is used for taking the time required by each cycle in the PCR detection process as a period and acquiring each constant temperature interval and each non-constant temperature interval in each period based on the change condition of temperature data in each period;
determining fluctuation values of the temperature data in each constant temperature interval based on the distribution condition of the difference between all extreme values of the temperature data in each constant temperature interval and the preset temperature and the variation amplitude of the temperature data in each constant temperature interval;
Determining the smoothness of the temperature data in each non-constant temperature interval based on the smoothness of the temperature data change in each non-constant temperature interval;
Determining an abnormality factor of each period based on the difference of the fluctuation values in the corresponding constant temperature interval and the difference of the smoothness in the corresponding non-constant temperature interval between each period and the adjacent period, and combining the similarity degree of the temperature data in the corresponding constant temperature interval;
and the constant temperature control module is used for determining the optimized proportional term parameters of the PID controller by combining the abnormal factors of the current period and preset initial proportional term parameters and controlling the temperature of any test solution.
In a third aspect, an embodiment of the present application further provides a thermostatic control device applied to a PCR detector, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor executes the computer program to implement the steps of any one of the above-mentioned thermostatic control methods applied to a PCR detector.
The application has at least the following beneficial effects:
By analyzing the jump condition and the change amplitude of the temperature in the constant temperature interval in the PCR detection, the fluctuation value of the temperature data in the constant temperature interval is determined, the change condition of the actual temperature relative to the preset temperature is fully considered, and the accuracy of the analysis of the temperature fluctuation condition in the constant temperature interval is improved;
Further, the non-constant temperature interval, namely the instantaneous change degree of the temperature in the heating interval and the cooling interval is analyzed to obtain the smoothness of the temperature in the non-constant temperature interval, the fluctuation value and the smoothness can reflect the change characteristics of the temperature of the test solution in different reaction stages, and further, the abnormal factors of each period are determined according to the difference of the change characteristics of the temperature of the test solution in different periods and the similarity degree of the temperature in the PCR detection process, the influence degree of the temperature of the test solution in each period due to the change of the ambient temperature around the PCR detector, the heat conduction between adjacent PCR reaction tubes and the heating non-uniformity of the heating device is reflected, the parameters of the PID controller are optimized according to the abnormal factors, and the accuracy of the constant temperature control of the PCR detector is improved.
Detailed Description
In describing embodiments of the present application, words such as "exemplary," "or," "such as," and the like are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "or," "such as," and the like are intended to present related concepts in a concrete fashion.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It is to be understood that, unless otherwise indicated, a "/" means or.
It should be further noted that the terms "first" and "second" are used herein to distinguish similar objects from each other and are not used to describe a particular order or sequence.
The following specifically describes a specific scheme of a constant temperature control method, a system and a device applied to a PCR detector provided by the application with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for controlling a constant temperature applied to a PCR detector according to an embodiment of the present application is shown, and the method includes the following steps:
step S1, collecting temperature data of any test solution at each collecting moment.
The constant temperature control is one of the cores of the PCR detector, and directly influences the efficiency of the polymerase chain reaction and the accuracy of the result. The working principle of the PCR detector is based on three basic reaction steps, namely denaturation, annealing and extension, which are cyclically performed to achieve amplification of DNA. Since the temperature required for each reaction step is different, different reaction modules are provided in the PCR detector to perform the three steps, respectively. A schematic of a portion of a PCR detector is shown in FIG. 2.
In the figure, 1 and 4 are denaturation reaction modules, 2 and 5 are annealing reaction modules, 3 and 6 are extension reaction modules, 7 and 8 are fluorescence detection holes, and 9 is a heating base. A red copper heating block and a temperature control circuit are arranged below the heating base, and each reaction module is stabilized at a set temperature by using the temperature control circuit. A rotating platform capable of accommodating a plurality of PCR reaction tubes is placed above the heating base.
And acquiring temperature data of the test solution in each PCR reaction tube through a temperature sensor.
In this embodiment, the acquisition time interval of the temperature data is 0.01 seconds, the value of the acquisition time interval is preset manually, and the operator can set the acquisition time interval by himself, and the application is not particularly limited.
Step S2, a constant temperature interval and a non-constant temperature interval in the PCR detection process are obtained, fluctuation values of temperature data in the constant temperature interval are determined, smoothness of the temperature data in the non-constant temperature interval is determined, and then abnormal factors of each period are determined.
For any test solution, multiple cycles are required to go through three stages of denaturation, annealing and extension during the PCR detection, and temperature control is critical in these three stages. For example, in the denaturation phase, if the temperature is too high, the activity of the polymerase is affected, the amplification efficiency of DNA is reduced, if the temperature is too low, denaturation is insufficient, false negative is likely to occur, and in the annealing phase, the temperature is too high or too low, the specificity of PCR reaction is affected, so that the temperature of each phase in each cycle needs to be ensured to be in a stable state as much as possible, and therefore, the PCR detector needs to meet the requirements of high temperature control precision, small overshoot and short adjustment time.
And 2.1, taking the time required by each cycle in the PCR detection process as a period, and acquiring each constant temperature interval and each non-constant temperature interval in each period based on the change condition of temperature data in each period.
The time required for each cycle in the PCR detection process is taken as one cycle, namely, the time of three stages of denaturation, annealing and extension is taken as one cycle, and the temperature data characteristics in each cycle are analyzed. In the DNA denaturation stage, the temperature needs to be constant at 95 ℃, the annealing stage temperature needs to be constant at 55 ℃, and the extension stage temperature needs to be constant at 72 ℃. Thus, there is a rapid abrupt change in temperature data between the different phases.
Taking any period as an example, the temperature data of all the acquisition moments in the period are arranged according to time sequence to form a temperature sequence. The method comprises the steps of acquiring mutation points in a temperature sequence by adopting a mutation point detection algorithm, taking the mutation points as division points, dividing the temperature sequence into 6 temperature subsequences, and dividing a period into 6 time intervals because of the temperature rising or falling regulation process before each stage.
In this embodiment, a Bernaola Galvan segmentation algorithm is adopted to obtain the mutation points in the temperature sequence, and as other embodiments, on the basis that the mutation points in the temperature sequence can be obtained, an operator can obtain the mutation points in the temperature sequence by using other existing technologies, for example, a bayesian mutation point detection algorithm, a Mann-Kendall mutation point detection algorithm, and the like, and the application is not limited in particular.
Calculating the discrete degree of each temperature subsequence, arranging the discrete degree from small to large, taking the time interval of the temperature subsequence corresponding to the first three discrete degrees as each constant temperature interval, and respectively corresponding to denaturation, annealing and extension reaction stages according to time sequence. Taking the time interval of the rest temperature subsequences as each non-constant temperature interval, including a temperature decreasing interval and a temperature increasing interval. The schematic flow of the acquisition of the constant temperature interval and the non-constant temperature interval is shown in fig. 3.
In this embodiment, the degree of dispersion is a standard deviation, and as other embodiments, on the basis of the measurable degree of non-uniformity of the temperature data distribution in the temperature subsequence, the practitioner may measure by using other statistics, such as variance, coefficient of variation, etc., which is not particularly limited in the present application.
And 2.2, determining the fluctuation value of the temperature data in each constant temperature interval based on the distribution condition of the difference between all extreme values of the temperature data in each constant temperature interval and the preset temperature and the variation amplitude of the temperature data in each constant temperature interval.
Although the test solution is in a constant temperature state in a constant temperature interval in theory, due to the environmental temperature change around the PCR detector, heat conduction between adjacent PCR reaction tubes, heating non-uniformity of a heating device and the like, the temperature data in the constant temperature interval has fluctuation conditions, and particularly shows random and irregular fluctuation at the upper and lower preset temperatures.
Taking any constant temperature interval in any period as an example, all extremum values, including maximum value and minimum value, in the temperature subsequence in any constant temperature interval are obtained, if the temperature is more stable in the reaction process, the obtained extremum value is more close to the preset temperature, and the obtained extremum values alternately appear above and below the preset temperature, for example, the mth extremum value is higher than the preset temperature, the (m+1) th extremum value is lower than the preset temperature, and the (m+2) th extremum value is higher than the preset temperature. If a plurality of continuous extremum values are higher or lower than the preset temperature, the greater the instability degree of the temperature in the corresponding time period of the extremum values.
Based on the characteristics, calculating the difference value between the preset temperature of the reaction stage corresponding to the temperature sub-sequence and each extreme value in the temperature sub-sequence, and counting the maximum value of the number of data which is positive or negative continuously in all the difference values corresponding to the temperature sub-sequence, wherein the maximum value is used as the jump value of the extreme value in the temperature sub-sequence, and the larger the jump value is, the larger the degree of deviation of the temperature data in the temperature sub-sequence from the preset temperature is indicated.
The trend change degree of the temperature data between the adjacent extreme values can reflect the change speed characteristic of the temperature data in a short period. Therefore, a trend checking algorithm is adopted to acquire trend slopes of temperature data between any two adjacent extremum values in the temperature subsequence, differences between any two adjacent extremum values in the temperature subsequence are calculated and recorded as extremum differences, products of absolute values of the trend slopes and the corresponding extremum differences are calculated, accumulated values of all the products of the temperature subsequence are calculated, and the larger the accumulated values are, the larger the variation amplitude of the temperature data in any constant temperature interval is indicated.
In this embodiment, the trend Slope is obtained by using Theil-SEN MEDIAN Slope estimation method, and as other embodiments, on the basis of the trend Slope that can be obtained, the practitioner can obtain the trend Slope by using other existing technologies, for example, mann-Kendall trend test, sen's Slope test, etc., and the application is not limited in particular.
Based on the analysis, the fusion result of the jump value of the extreme value in the temperature subsequence and the accumulated value is used as the fluctuation value of the temperature data in any constant temperature interval.
It is to be understood that fusion refers to combining multiple independent variables together in a manner that enhances the overall effect, such as additive relationships, multiplicative relationships, etc., and the practitioner may define himself or herself depending on the situation.
In this embodiment, the sum of the jump value of the extremum in the temperature sub-sequence and the accumulated value is used as the fluctuation value of the temperature data in any constant temperature interval.
In another embodiment, the product of the jump value of the extremum in the temperature sub-sequence and the accumulated value is used as the fluctuation value of the temperature data in any constant temperature interval.
The larger the fluctuation value is, the larger the fluctuation degree of the temperature data in any constant temperature interval is, and the more unstable is. A schematic diagram of the flow of the fluctuation value determination is shown in fig. 4.
And 2.3, determining the smoothness of the temperature data in each non-constant temperature interval based on the smoothness of the temperature data change in each non-constant temperature interval.
The rate of heating and cooling of the PCR detector has a direct effect on the DNA amplification results, and improper rates may lead to insufficient DNA melting or non-specific amplification. Therefore, temperature data in the non-constant temperature section is analyzed as follows:
And for any non-constant temperature interval in any period, acquiring a fitting curve of all temperature data in any non-constant temperature interval, and calculating the curvature of the fitting curve at each temperature data. During the warming and cooling process, the magnitude of the curvature can reflect the degree of abrupt changes in temperature, especially the beginning and ending portions. When the PCR detector works well, the curvature of the fitted curve at all temperature data is small, the average value of the curvature absolute values of the fitted curve at all temperature data is used as the smoothness of the temperature data in any non-constant temperature interval, and the smaller the obtained smoothness is, the more gradual the change of the temperature data in any non-constant temperature interval is indicated. The curvature calculation is a well-known technique, and the present application is not repeated.
In this embodiment, the fitted curve is obtained by using a least square method, and as other embodiments, on the basis of the obtained fitted curve, an implementer may obtain the fitted curve by using other existing technologies, for example, local weighted regression, K-nearest neighbor regression, etc., and the present application is not limited in particular.
And 2.4, determining an abnormality factor of each period based on the difference of the fluctuation values in the corresponding constant temperature interval and the difference of the smoothness in the corresponding non-constant temperature interval between each period and the adjacent period, and combining the similarity degree of the temperature data in the corresponding constant temperature interval.
In the process of constant temperature control of the PCR detector, the temperature state of each period needs to be kept as consistent as possible. To ensure the normal amplification of DNA. In the constant temperature control process, the temperature of different positions on a reaction module of the PCR detector has certain difference due to the heating non-uniformity of the heating device, so that the DNA amplification result is deviated, and the fluctuation degree of the temperature in the constant temperature interval and the lifting speed of the temperature in the non-constant temperature interval are also affected by the non-uniform heating of the device. And arranging the fluctuation values in all constant temperature intervals and the smoothness in all non-constant temperature intervals in any period according to time sequence to form a temperature state characteristic sequence.
And calculating the distance between the temperature state characteristic sequences between any period and the adjacent previous period, wherein the larger the distance is, the larger the difference between the temperature states between any period and the adjacent previous period is. Further, in the temperature control process of the PCR detector, the stability of the temperature in the constant temperature interval is relatively more important, so that the average value of the absolute values of the similarity of the temperature subsequences of all the corresponding constant temperature intervals between any one period and the adjacent previous period is calculated, and the smaller the average value is, the more dissimilar the temperature in the constant temperature interval between the periods is indicated.
In this embodiment, the Distance between the temperature state feature sequences is an SBD (Shape-based Distance) Distance, and as other embodiments, based on the Distance between the temperature state feature sequences, the practitioner may measure the Distance between the temperature state feature sequences by using other existing technologies, such as euclidean Distance, manhattan Distance, etc., and the present application is not limited thereto.
In this embodiment, the similarity between the temperature subsequences is a spearman correlation coefficient, and as other embodiments, based on the similarity between the measurable temperature subsequences, the practitioner may measure the similarity between the temperature subsequences using other prior arts, such as pearson correlation coefficient, cosine similarity, etc., which is not particularly limited in the present application.
Based on the analysis, determining an abnormality factor of any period based on the distance between the temperature state characteristic sequence of any period and the adjacent previous period and the average value, wherein the expression is as follows:
In the formula (I), in the formula (II),An anomaly factor representing an i-th period; representing the distance between the ith period and the temperature state characteristic sequence of the adjacent previous period; The average value of the absolute values of the similarity of all the temperature subsequences in the corresponding constant temperature intervals between the ith period and the adjacent previous period is represented, gamma represents a preset value larger than 0, the value of gamma is preset by human beings, and an implementer can set the value of gamma to be 0.1 in the embodiment.
The greater the abnormality factor of the ith period, the greater the difference of the temperature states between the ith period and the adjacent previous period, and the smaller the similarity of the temperature data in the corresponding constant temperature interval, the more remarkable the abnormal fluctuation of the temperature of the test solution in the ith period, which is caused by the influence of the heating device heating non-uniformity and other reasons. A schematic flow chart of the determination of the anomaly factors is shown in FIG. 5.
And step S3, determining the optimized proportional term parameters of the PID controller by combining the abnormal factors of the current period and preset initial proportional term parameters, and controlling the temperature of any test solution.
The greater the anomaly factor of the current period, the more the response speed of the temperature control of the PCR detector needs to be improved, and in particular, the rate of the temperature control can be improved by increasing the proportional term parameter of the PID controller. If the abnormality factor of the current period is smaller, the temperature of the current period is more stable, and smaller proportion parameter can be adopted for temperature control so as to avoid overshoot.
And optimizing parameters of the PID controller according to the abnormal factors, and manually setting the values of integral term parameters, differential term parameters and initial proportional term parameters of the PID controller. And the constant temperature control is carried out on the parameters of the integral term, the differential term and the initial proportion term in the first two periods of the detection process, and the parameter optimization is carried out from the third period. Based on the anomaly factor of the current period, determining the expression of the optimized proportional term parameter as follows: In the formula (I), in the formula (II),The method comprises the steps of representing optimized proportional term parameters, wherein L represents preset initial proportional term parameters, H represents an abnormal factor of a current period, norm () represents a normalization function, T represents a preset numerical value larger than 0 and is used for adjusting the size of the proportional term parameters, the value of T is preset by people, and an implementer can set the value of T to be 5 in the embodiment.
In this embodiment, the values of the integral term parameter, the differential term parameter and the initial proportional term parameter are respectively 10, 1 and 1, and the values of the integral term parameter, the differential term parameter and the initial proportional term parameter are preset by human beings and can be set by an operator, so that the application is not limited in particular.
In this embodiment, the tanh activation function is used to normalize the abnormal factor of the current period, and as other embodiments, on the basis that the normalization of the abnormal factor of the current period can be achieved, an implementer may use other existing technologies to normalize the abnormal factor of the current period, for example, sigmoid function, decimal scaling normalization, and the like, and the application is not limited in particular.
And the temperature of the test solution is controlled at constant temperature by utilizing the integral term parameter, the differential term parameter and the optimized proportional term parameter and adopting a PID controller. The comparison chart of the temperatures of the test solutions before and after the parameter optimization of the proportional term is shown in FIG. 6, wherein the abscissa of the chart is time in seconds, and the ordinate is temperature in secondsThe three lines in the graph respectively represent the set temperature, the temperature of the test solution before the parameter optimization of the proportion item and the temperature of the test solution after the parameter optimization of the proportion item, wherein the set temperature is the preset temperature of the reaction stage, and compared with the temperature before the parameter optimization of the proportion item, the temperature of the test solution after the parameter optimization of the proportion item is more stable, and the effect of the constant temperature control of the PCR detector is better.
Based on the same inventive concept as the above method, the embodiment of the present application further provides a constant temperature control system applied to a PCR detector, including:
The temperature data acquisition module is used for acquiring temperature data of any test solution at each acquisition moment;
The temperature data analysis module is used for taking the time required by each cycle in the PCR detection process as a period and acquiring each constant temperature interval and each non-constant temperature interval in each period based on the change condition of temperature data in each period;
determining fluctuation values of the temperature data in each constant temperature interval based on the distribution condition of the difference between all extreme values of the temperature data in each constant temperature interval and the preset temperature and the variation amplitude of the temperature data in each constant temperature interval;
Determining the smoothness of the temperature data in each non-constant temperature interval based on the smoothness of the temperature data change in each non-constant temperature interval;
Determining an abnormality factor of each period based on the difference of the fluctuation values in the corresponding constant temperature interval and the difference of the smoothness in the corresponding non-constant temperature interval between each period and the adjacent period, and combining the similarity degree of the temperature data in the corresponding constant temperature interval;
and the constant temperature control module is used for determining the optimized proportional term parameters of the PID controller by combining the abnormal factors of the current period and preset initial proportional term parameters and controlling the temperature of any test solution.
Based on the same inventive concept as the above method, the embodiment of the present application further provides a thermostatic control device applied to a PCR detector, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above methods for controlling a thermostatic control device applied to a PCR detector when executing the computer program.
In summary, the application acquires the constant temperature interval and the non-constant temperature interval in the PCR detection process so as to carry out fine analysis on the change characteristics of the temperature in the PCR detection process, determines the fluctuation value of the temperature data in the constant temperature interval by analyzing the jump condition and the change amplitude of the temperature in the constant temperature interval in the PCR detection, fully considers the change condition of the actual temperature relative to the preset temperature, and improves the accuracy of analyzing the temperature fluctuation condition in the constant temperature interval;
Further, the non-constant temperature interval, namely the instantaneous change degree of the temperature in the heating interval and the cooling interval is analyzed to obtain the smoothness of the temperature in the non-constant temperature interval, the fluctuation value and the smoothness can reflect the change characteristics of the temperature of the test solution in different reaction stages, and further, the abnormal factors of each period are determined according to the difference of the change characteristics of the temperature of the test solution in different periods and the similarity degree of the temperature in the PCR detection process, the influence degree of the temperature of the test solution in each period due to the change of the ambient temperature around the PCR detector, the heat conduction between adjacent PCR reaction tubes and the heating non-uniformity of the heating device is reflected, the parameters of the PID controller are optimized according to the abnormal factors, and the accuracy of the constant temperature control of the PCR detector is improved.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than that disclosed in the description, and sometimes no specific order exists between different operations or steps. For example, two consecutive operations or steps may actually be performed substantially in parallel, they may sometimes be performed in reverse order, which may be dependent on the functions involved. Each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the essential characteristics thereof. The above-described embodiments of the application should therefore be regarded as illustrative in all respects and not restrictive.