In order to solve the problems of weak prediction stability and generalization ability of a neural network algorithm model in the yarn quality prediction research for small samples,a prediction model based on an AdaBo...In order to solve the problems of weak prediction stability and generalization ability of a neural network algorithm model in the yarn quality prediction research for small samples,a prediction model based on an AdaBoost algorithm(AdaBoost model) was established.A prediction model based on a linear regression algorithm(LR model) and a prediction model based on a multi-layer perceptron neural network algorithm(MLP model) were established for comparison.The prediction experiments of the yarn evenness and the yarn strength were implemented.Determination coefficients and prediction errors were used to evaluate the prediction accuracy of these models,and the K-fold cross validation was used to evaluate the generalization ability of these models.In the prediction experiments,the determination coefficient of the yarn evenness prediction result of the AdaBoost model is 76% and 87% higher than that of the LR model and the MLP model,respectively.The determination coefficient of the yarn strength prediction result of the AdaBoost model is slightly higher than that of the other two models.Considering that the yarn evenness dataset has a weaker linear relationship with the cotton dataset than that of the yarn strength dataset in this paper,the AdaBoost model has the best adaptability for the nonlinear dataset among the three models.In addition,the AdaBoost model shows generally better results in the cross-validation experiments and the series of prediction experiments at eight different training set sample sizes.It is proved that the AdaBoost model not only has good prediction accuracy but also has good prediction stability and generalization ability for small samples.展开更多
When conducting a literature review,medical authors typically search for relevant keywords in bibliographic databases or on search engines like Google.After selecting the most pertinent article based on the title’s r...When conducting a literature review,medical authors typically search for relevant keywords in bibliographic databases or on search engines like Google.After selecting the most pertinent article based on the title’s relevance and the abstract’s content,they download or purchase the article and cite it in their manuscript.Three major elements influence whether an article will be cited in future manuscripts:the keywords,the title,and the abstract.This indicates that these elements are the“key dissemination tools”for research papers.If these three elements are not determined judiciously by authors,it may adversely affect the manuscript’s retrievability,readability,and citation index,which can negatively impact both the author and the journal.In this article,we share our informed perspective on writing strategies to enhance the searchability and citation of medical articles.These strategies are adopted from the principles of search engine optimization,but they do not aim to cheat or manipulate the search engine.Instead,they adopt a reader-centric content writing methodology that targets well-researched keywords to the readers who are searching for them.Reputable journals,such as Nature and the British Medical Journal,emphasize“online searchability”in their author guidelines.We hope that this article will encourage medical authors to approach manuscript drafting from the perspective of“looking inside-out.”In other words,they should not only draft manuscripts around what they want to convey to fellow researchers but also integrate what the readers want to discover.It is a call-to-action to better understand and engage search engine algorithms,so they yield information in a desired and self-learning manner because the“Cloud”is the new stakeholder.展开更多
The auto industry, in cooperation over the past 23 years, is embracing new changes. Various new forms are finding use there which used to be dominated by introduced technology, brand name or funds.
AIM: To characterize the influence of location, species and treatment upon RNA degradation in tissue samples from the gastrointestinal tract. METHODS: The intestinal samples were stored in different medium for differe...AIM: To characterize the influence of location, species and treatment upon RNA degradation in tissue samples from the gastrointestinal tract. METHODS: The intestinal samples were stored in different medium for different times under varyingconditions: different species(human and rat), varying temperature(storage on crushed ice or room temperature), time point of dissection of the submucous-mucous layer from the smooth muscle(before or after storage), different rinsing methods(rinsing with Medium, PBS, RNALater or without rinsing at all) and different regions of the gut(proximal and distal small intestine, caecum, colon and rectum). The total RNA from different parts of the gut(rat: proximal and distal small intestine, caecum, colon and rectum, human: colon and rectum) and individual gut layers(muscle and submucosal/mucosal) was extracted. The quality of the RNA was assessed by micro capillary electrophoresis. The RNA quality was expressed by the RNA integrity number which is calculated from the relative height and area of the 18 S and 28 S RNA peaks. From rat distal small intestine q PCR was performed for neuronal and glial markers.RESULTS: RNA obtained from smooth muscle tissue is much longer stable than those from submucosal/mucosal tissue. At RT muscle RNA degrades after one day, on ice it is stable at least three days. Cleaning and separation of gut layers before storage and use of RNALater, maintains the stability of muscle RNA at RT for much longer periods. Different parts of the gut show varying degradation periods. RNA obtained from the submucosal/mucosal layer always showed a much worse amplification rate than RNA from muscle tissue. In general RNA harvested from rat tissue, either smooth muscle layer or submucosal/mucosal layer is much longer stable than RNA from human gut tissue, and RNA obtained from smooth muscle tissue shows an increased stability compared to RNA from submucosal/mucosal tissue. At RT muscle RNA degrades after one day, while the stability on ice lasts at least three days. Cleaning and separation of gut layers before storage and use of RNALater, maintains the stability of muscle RNA at RT for much longer periods. Different parts of the gut show varying degradation periods. The RNA from muscle and submucosal/mucosal tissue of the proximal small intestine degrades much faster than the RNA of distal small intestine, caecum or colonwith rectum. RNA obtained from the submucosal/mucosal layer always showed a much more reduced amplification rate than RNA from muscle tissue [β-Tubulin Ⅲ for muscle quantification cycle(Cp): 22.07 ± 0.25, for β-Tubulin Ⅲ submucosal/mucosal Cp: 27.42 ± 0.19].CONCLUSION: Degradation of intestinal m RNA depends on preparation and storage conditions of the tissue. Cooling, rinsing and separating of intestinal tissue reduce the degradation of m RNA.展开更多
Sampling plays an important role in acquiring precise soil information required in modern agricultural production worldwide, which determines both the cost and quality of final soil mapping products. For sampling desi...Sampling plays an important role in acquiring precise soil information required in modern agricultural production worldwide, which determines both the cost and quality of final soil mapping products. For sampling design, it has been proposed possibile to transfer the relationships between kriging variance and sampling grid spacing from an area with existing information to other areas with similar soil-forming environments. However, this approach is challenged in practice because of two problems: i) different population vaxiograms among similar areas and ii) sampling errors in estimated variograms. This study evaluated the effects of these two problems on the transferability of the relationships between kriging variance and sampling grid spacing, by using spatial data simulated with three variograms and soil samples collected from four grasslands in Ireland with similar soil-forming environments. Results showed that the variograms suggested by different samples collected with the same grid spacing in the same or similar areas were different, leading to a range of mean kriging variance (MKV) for each grid spacing. With increasing grid spacing, the variation of MKV for a specific grid spacing increased and deviated more from the MKV generated using the population variograms. As a result, the spatial transferability of the relationships between kriging variance and grid spacing for sampling design was limited.展开更多
文摘In order to solve the problems of weak prediction stability and generalization ability of a neural network algorithm model in the yarn quality prediction research for small samples,a prediction model based on an AdaBoost algorithm(AdaBoost model) was established.A prediction model based on a linear regression algorithm(LR model) and a prediction model based on a multi-layer perceptron neural network algorithm(MLP model) were established for comparison.The prediction experiments of the yarn evenness and the yarn strength were implemented.Determination coefficients and prediction errors were used to evaluate the prediction accuracy of these models,and the K-fold cross validation was used to evaluate the generalization ability of these models.In the prediction experiments,the determination coefficient of the yarn evenness prediction result of the AdaBoost model is 76% and 87% higher than that of the LR model and the MLP model,respectively.The determination coefficient of the yarn strength prediction result of the AdaBoost model is slightly higher than that of the other two models.Considering that the yarn evenness dataset has a weaker linear relationship with the cotton dataset than that of the yarn strength dataset in this paper,the AdaBoost model has the best adaptability for the nonlinear dataset among the three models.In addition,the AdaBoost model shows generally better results in the cross-validation experiments and the series of prediction experiments at eight different training set sample sizes.It is proved that the AdaBoost model not only has good prediction accuracy but also has good prediction stability and generalization ability for small samples.
文摘When conducting a literature review,medical authors typically search for relevant keywords in bibliographic databases or on search engines like Google.After selecting the most pertinent article based on the title’s relevance and the abstract’s content,they download or purchase the article and cite it in their manuscript.Three major elements influence whether an article will be cited in future manuscripts:the keywords,the title,and the abstract.This indicates that these elements are the“key dissemination tools”for research papers.If these three elements are not determined judiciously by authors,it may adversely affect the manuscript’s retrievability,readability,and citation index,which can negatively impact both the author and the journal.In this article,we share our informed perspective on writing strategies to enhance the searchability and citation of medical articles.These strategies are adopted from the principles of search engine optimization,but they do not aim to cheat or manipulate the search engine.Instead,they adopt a reader-centric content writing methodology that targets well-researched keywords to the readers who are searching for them.Reputable journals,such as Nature and the British Medical Journal,emphasize“online searchability”in their author guidelines.We hope that this article will encourage medical authors to approach manuscript drafting from the perspective of“looking inside-out.”In other words,they should not only draft manuscripts around what they want to convey to fellow researchers but also integrate what the readers want to discover.It is a call-to-action to better understand and engage search engine algorithms,so they yield information in a desired and self-learning manner because the“Cloud”is the new stakeholder.
文摘The auto industry, in cooperation over the past 23 years, is embracing new changes. Various new forms are finding use there which used to be dominated by introduced technology, brand name or funds.
文摘AIM: To characterize the influence of location, species and treatment upon RNA degradation in tissue samples from the gastrointestinal tract. METHODS: The intestinal samples were stored in different medium for different times under varyingconditions: different species(human and rat), varying temperature(storage on crushed ice or room temperature), time point of dissection of the submucous-mucous layer from the smooth muscle(before or after storage), different rinsing methods(rinsing with Medium, PBS, RNALater or without rinsing at all) and different regions of the gut(proximal and distal small intestine, caecum, colon and rectum). The total RNA from different parts of the gut(rat: proximal and distal small intestine, caecum, colon and rectum, human: colon and rectum) and individual gut layers(muscle and submucosal/mucosal) was extracted. The quality of the RNA was assessed by micro capillary electrophoresis. The RNA quality was expressed by the RNA integrity number which is calculated from the relative height and area of the 18 S and 28 S RNA peaks. From rat distal small intestine q PCR was performed for neuronal and glial markers.RESULTS: RNA obtained from smooth muscle tissue is much longer stable than those from submucosal/mucosal tissue. At RT muscle RNA degrades after one day, on ice it is stable at least three days. Cleaning and separation of gut layers before storage and use of RNALater, maintains the stability of muscle RNA at RT for much longer periods. Different parts of the gut show varying degradation periods. RNA obtained from the submucosal/mucosal layer always showed a much worse amplification rate than RNA from muscle tissue. In general RNA harvested from rat tissue, either smooth muscle layer or submucosal/mucosal layer is much longer stable than RNA from human gut tissue, and RNA obtained from smooth muscle tissue shows an increased stability compared to RNA from submucosal/mucosal tissue. At RT muscle RNA degrades after one day, while the stability on ice lasts at least three days. Cleaning and separation of gut layers before storage and use of RNALater, maintains the stability of muscle RNA at RT for much longer periods. Different parts of the gut show varying degradation periods. The RNA from muscle and submucosal/mucosal tissue of the proximal small intestine degrades much faster than the RNA of distal small intestine, caecum or colonwith rectum. RNA obtained from the submucosal/mucosal layer always showed a much more reduced amplification rate than RNA from muscle tissue [β-Tubulin Ⅲ for muscle quantification cycle(Cp): 22.07 ± 0.25, for β-Tubulin Ⅲ submucosal/mucosal Cp: 27.42 ± 0.19].CONCLUSION: Degradation of intestinal m RNA depends on preparation and storage conditions of the tissue. Cooling, rinsing and separating of intestinal tissue reduce the degradation of m RNA.
基金?nancially supported by the National Natural Science Foundation of China (Nos. 41541006 and 41771246)co-funded by Enterprise Ireland and the European Regional Development Fund (ERDF) under the National Strategic Reference Framework (NSRF) 2007–2013
文摘Sampling plays an important role in acquiring precise soil information required in modern agricultural production worldwide, which determines both the cost and quality of final soil mapping products. For sampling design, it has been proposed possibile to transfer the relationships between kriging variance and sampling grid spacing from an area with existing information to other areas with similar soil-forming environments. However, this approach is challenged in practice because of two problems: i) different population vaxiograms among similar areas and ii) sampling errors in estimated variograms. This study evaluated the effects of these two problems on the transferability of the relationships between kriging variance and sampling grid spacing, by using spatial data simulated with three variograms and soil samples collected from four grasslands in Ireland with similar soil-forming environments. Results showed that the variograms suggested by different samples collected with the same grid spacing in the same or similar areas were different, leading to a range of mean kriging variance (MKV) for each grid spacing. With increasing grid spacing, the variation of MKV for a specific grid spacing increased and deviated more from the MKV generated using the population variograms. As a result, the spatial transferability of the relationships between kriging variance and grid spacing for sampling design was limited.
