Son preference has been shown to influence the childbearing behavior of women, especially in China. Existing research has largely focused on this issue using cross-sectional data of urban or rural populations in China, while evidence from the rural-urban migrant women is relatively limited. Based on the data of China Migrants Dynamic Survey in 2015, we used logistic regression models to explore the relationship of son preference and reproductive behavior of rural-urban migrant women in China. The results show that the son preference of migrant women is still strong, which leads women with only daughters to have significantly higher possibility of having another child and results in a higher imbalance in the sex ratio with higher parity. Migrant women giving birth to a son is a protective factor against having a second child compared to women whose first child was a girl. Similarly, the effects of the gender of the previous child on women’s progression from having two to three children showed the same result that is consistent with a preference for sons. These findings have implications for future public strategies to mitigate the son preference among migrant women and the imbalance in the sex ratio at birth. A strong preference for sons over daughters is common in East and Southeast Asia ,2, notabSignificantly different from that in other countries, the family planning policy in China is one of the limiting factors affecting fertility practices in the country ,12,13,14Another special policy in China, the household registration policy, also has a significant impact on women’s reproductive behavior. The dual structure in urban and rural areas in China leads to significant differences in economic development, children-bearing concepts, and policy supervision ,18,19. TTo a large extent, the birth conception and behavior of migrant women have a profound impact on the population changes in the places of domicile and migration, which will further affect the implementation of China’s family planning policy, the overall fertility level and the changes in the gender structure . MoreoveStudies on women’s reproductive behavior in the existing literature focus on family reproductive intention, family planning, number of sons and contraceptive usage ,14,32,33The influence of son preference on the number of children and fertility behavior remains a controversial topic. Some studies suggest that a strong preference for sons will lead to the increases in fertility level desired by the state ,35. OtheRegarding the research on the son preferences of migrant women, this paper conducts a retrospective study based on the 2015 national monitoring survey data of the China Migrants Dynamic Survey (CMDS) and focuses on two aspects. The first is the pursuit of the subsequent parity progression. The second is the relationship between the sex composition of existing children and subsequent childbearing behavior. That is, in the context of China’s special fertility policy, what are the factors that influence migrant women to have a higher number of children? If the gender composition of existing children fails to satisfy migrant women’s son preference, will the women violate national policies and have more children? As the number of children increases, will the likelihood of having boys be increased through prenatal sex identification technology? Having more children can reduce the quality of childcare, while choosing to have boys following prenatal sex identification may cause severe gender imbalances, both of which pose challenges to improving the quality of the population and ensuring the gender balance in China.The data in this paper are derived from the China Migrants Dynamic Survey in 2015 (CMDS2015). This is an annual large-scale national migrant population sampling survey initiated by the National Health Commission of the People’s Republic of China and coordinated by the China Population and Development Research Center. CMDS2015 covers 31 provinces  in mainland China. The stratified, multistage and proportional PPS method is adopted for sampling. The survey covers the basic information of the migrant individuals and their family members, the mobility range of the migrant women, employment and social security, income and expenditure, residence, basic public health services, management of marriage and family planning services, children’s mobility and education, etc. The survey questionnaire was presented through direct interviews by investigators who had been trained uniformly and reviewed by professional instructors. After the questionnaire was completed, it was properly kept in each city (district) and randomly checked by a panel of experts. Finally, the quality of the submitted questionnaire was monitored by logical verification and telephone return visits. The CMDS2015 was characterized by the full coverage and representativeness of the migrant population, and the questionnaire has good authenticity and reliability.Cross-sectional data of rural-to-urban migrant women with one child were derived from CMDS2015 to explore the complex relationship of son preference and reproductive behavior. Given the research question of our study, we limited our sample to migrant women of childbearing age. Specifically, the sample selection criteria in this article are being married (first marriage or remarriage), having had at least one child, being aged 15–49 years old, having rural household registration, and being a non-local resident. Following the exclusion of non-qualifying individuals and invalid data samples resulting from refusal to answer, the number of valid samples was 36,182.The dependent variable in this study is the fertility behavior of migrant women, that is, whether each respondent progressed from having one, two and three children to having two, three and four children, respectively. The independent variable is the gender of existing children. The control variables involved in this study are grouped into two categories. The first category elicited sociodemographic characteristics of the participants, including age, ethnic group, marriage duration, individual education level, spouse’s education level, employment, mobility range and residence intention. The second category assessed the participants’ fertility information based on the following items: the number of existing children and the birthplace of the first child. The proportion of boys being born should be around 51 percent without son preference, and if the proportion in our data is higher than this ratio, it means that the boy preference exists. Since the outcome variable of this study is a binary variable, we used logistics regression analysis to explore the effects of motivation and influencing factors, especially the sex composition of current children, on the subsequent childbearing behavior of migrant women.p-values < 0.05. Data analysis was conducted using the STATA 15.0. .A descriptive analysis was used to describe the characteristics of the participants and their reproductive behaviors. Variables that were significantly associated with fertility behavior by chi-square analysis were entered as independent variables in the binary logistic regression, including age, ethnic group, marriage duration, individual education level, spouse’s education level, gender of the first child, mobility range and residence intention. The factors which were significantly related to reproductive behavior were then included in the logistic model analysis. A single-factor regression analysis was used to explore the effect of the gender of existing children on having another child. A multivariable logistic regression model was built to identify the determinants of reproductive behaviors. Through these analyses, crude ORs and 95% confidence intervals (CIs) were estimated for the gender preference and fertility behavior of migrant women. Statistical significance was defined as Variables that were significantly associated with fertility behavior by chi-square analysis were entered as independent variables in the binary logistic regression. We took fertility behavior for the second child as an example and conducted an analysis. As shown in p < 0.05). Women with one son is a protective factor against having a second child compared to women whose first child was a girl . Compared with minority women, a smaller proportion of Han women chose to have a second child due to the restriction of family planning policy. Women over 25 were more likely to have a second child than women aged 24 or younger. Migrant women with age of more than 35 years old increased the chance of progressing to the next parity by more than three times compared to women less than 25 years old  . Migrant women with education is a protective factor; those with higher education levels reported lower possibility of having another child than those who had never gone to school. The marriage duration more than 5 years increased the chance of having a second child by more than seven times compared to a marriage duration less than 5 years . Having a job and the education level of the spouse were negatively correlated with having another child. Women with residence intention were more likely than women without residence intention to have a second child . In addition, those who chose to have their first child at home  or in a private clinic  increased the chance of having a second child than those who had their first child in the hospital.Furthermore, we took fertility behavior for the second child as an example and conducted a multifactor analysis. The results are reported in China’s migrant population is large, and there is a trend of further growth in the future ,24,41. TThe fertility concepts of migrant women tend to be markedly different from those of their rural or urban counterparts, which are influential in women’s preference regarding childbearing. Although the emerging literature indicated that the modern outlook of fertility in urban areas might reduce the parental preference for sons ,42, someOur findings showed the patterns of association between the sex composition of existing children and subsequent childbearing behaviors that are consistent with the preference for sons, which leads women with only daughters to have significantly higher intentions of having another child and results in a higher imbalance in the sex ratio with higher parity. To elaborate, our study found that migrant women whose first child was a girl were more likely to have another child compare with women whose first child was a boy. The same is true for the second and third children. It can be said that although the migrant women live in the city, the traditional concept of son preference had not changed significantly, which motivated them to have more children and give birth to at least one boy . In addiSocioeconomic characteristics can explain why some migrant women were more inclined to have more than one child. The results showed that with the increase in age, the likelihood of having another child gradually increased. A possible reason is that with the increase in age, individuals obtain a certain amount of savings and a stable work and residence environment that ease the pressure of raising another child, on the basis of which son preference can be achieved. Education level was found to have a significant effect on fertility. For better-educated migrants, the likelihood of having another child decreased greatly, and the evidence of a son preference declined. On the one hand, the increase in education improves migrant women’s awareness of and compliance with national policies. On the other hand, highly educated women pay more attention to the quality of childrearing than to the quantity of children . TherefoIn addition, the sex ratio of first children is slightly higher than the sex ratio under natural conditions (1.04–1.06) , indicatOur study makes a possible contribution to the literature regarding the effect that son preference and the sex composition of existing children has on the reproductive behavior of women of childbearing age in the rural-urban migrant population. First, most studies of son preference focus on rural areas or locally representative samples, and there are relatively few studies on migrant women, a special group of women who differ from women in rural and urban areas. This paper expands the research scope of relevant fields. Second, most studies tend to explore fertility behaviors through fertility intentions, which are measured at a particular time point and could be unreliable because intentions are fluid. With data and retrospective birth histories, we can use logistic regression analysis to estimate the parity progression among migrant women conditional on sex composition.The limitations of the study also should be highlighted. First, our study is based on a cross-sectional study that focuses on the number and gender of existing children, which makes it difficult to analyze the dynamics of female fertility. Women’s reproductive behavior could be tracked using more effective methods. If large-scale data covering migrant women can be collected in future studies, through a longitudinal study, a more comprehensive understanding of the gender preference and reproductive behavior will become possible. Second, married women rarely migrate independently. In fact, they often migrant with their husbands . Under tChina’s long-term gender imbalance deserves much attention. Gender imbalance is closely related to son preference. Therefore, the change in the fertility concept of migrant women plays an important role in reversing the gender imbalance. However, under the existing household registration system, migrant women have not been truly integrated into urban society, which makes it difficult for them to adapt to both the urban and the rural cultures. They have become a marginal group that is separated from rural and urban areas but also closely related to these areas, which complicates the transformation of their fertility concept. Therefore, how to carry out selective intervention through public policies, especially aiming to reverse the son preference of migrant women, is a problem worthy of attention in the future.


The implementation of clinical decision support systems (CDSSs) as an intervention to foster clinical practice change is affected by many factors. Key factors include those associated with behavioral change and those associated with technology acceptance. However, the literature regarding these subjects is fragmented and originates from two traditionally separate disciplines: implementation science and technology acceptance.Our objective is to propose an integrated framework that bridges the gap between the behavioral change and technology acceptance aspects of the implementation of CDSSs.We employed an iterative process to map constructs from four contributing frameworks—the Theoretical Domains Framework (TDF); the Consolidated Framework for Implementation Research (CFIR); the Human, Organization, and Technology-fit framework (HOT-fit); and the Unified Theory of Acceptance and Use of Technology (UTAUT)—and the findings of 10 literature reviews, identified through a systematic review of reviews approach.The resulting framework comprises 22 domains: agreement with the decision algorithm; attitudes; behavioral regulation; beliefs about capabilities; beliefs about consequences; contingencies; demographic characteristics; effort expectancy; emotions; environmental context and resources; goals; intentions; intervention characteristics; knowledge; memory, attention, and decision processes; patient–health professional relationship; patient’s preferences; performance expectancy; role and identity; skills, ability, and competence; social influences; and system quality. We demonstrate the use of the framework providing examples from two research projects.We proposed BEAR (BEhavior and Acceptance fRamework), an integrated framework that bridges the gap between behavioral change and technology acceptance, thereby widening the view established by current models. Every year, significant amounts of resources are invested in medical research globally, an average of 0.19% of the gross domestic product in high-income countries . ReducinCDSSs encode clinical knowledge into computerized algorithms and combine them with patient-specific data to provide clinicians with information and decision guidance . When suAlthough several literature reviews have shown improvements in process measures after the implementation of CDSSs ,22-24, tWe hypothesize that improving the implementation of CDSSs requires attention to factors related to practice change and also to those associated with technology acceptance, defined as the user’s decision to use a technology system routinely . TheorieAnother issue is that there are competing frameworks within the fields of implementation science and technology acceptance. Several authors have proposed theoretical models to explain the determinants of clinical practice change . SimilarTo address these issues, we propose BEAR (BEhavior and Acceptance fRamework), an integrated conceptual framework that bridges the gap between behavioral change and technology acceptance aspects of the implementation of CDSSs. BEAR synthesizes literature about factors influencing both practice change and acceptance of CDSSs from the health professional's perspective. Furthermore, BEAR seeks to capture the variability in the phenomenon of implementing CDSSs while providing an integrated tool that facilitates the design of research and evaluation projects.We developed BEAR by employing an iterative process in which two investigators (JC and MZM) mapped constructs reported in the literature as determinants of behavioral change and acceptance of CDSSs see . In eachThe first iteration comprised the mapping of two well-established frameworks: the TDF ,30 and tSubsequent iterations comprised the mapping of constructs identified through a literature review of recent aggregative studies presenting determinants of the acceptance of CDSSs. To obtain the initial pool of references, we queried Scopus, MEDLINE , Embase, CINAHL , and PsycINFO. Two investigators (JC and MZM) screened the initial reference pool and evaluated their titles and abstracts. For a reference to be selected, it needed to fulfill all of the following inclusion criteria: (1) address the topic of CDSSs, (2) employ literature review methods to obtain its results, and (3) include, among its results, determinants of the acceptance of CDSSs. Exclusion criteria were limited to the following: (1) the publication was not a research article  and (2) the full text was not in English or Spanish.We limited the search strategy to articles published since 2014; we were working under the assumption that, although recent, the aggregate studies found would cover the relevant literature published before that year. That assumption was validated by documenting the period covered by each included review.The investigators (JC and MZM) then extracted the constructs and their definitions from the full text of each article see . In caseThe grouping of constructs into domains was initially informed by the organization and definitions in the base frameworks . Later on, new constructs identified from the literature reviews were contrasted with the domain definitions to choose their locations. In some cases, this process resulted in the creation of new domains or in changing previous definitions.Finally, preliminary versions of the framework were discussed with the other authors  and other colleagues, resulting in the refinement of construct labels, definitions, and grouping.The mapping process comprised 13 iterations, corresponding to 10 reviews and four frameworks. The initial literature search identified 584 references. After removing duplicates, 405 references passed through the screening process see . Of thesThe 10 reviews included in the mapping exercise see  synthesiOur objective was to develop a framework, grounded in the literature about determinants of behavioral change and technology acceptance, that would be useful to researchers investigating the implementation of CDSSs as a strategy to foster the uptake of evidence-based recommendations.facilitating conditions, defined as “the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system” . We are all the time writing in the health record...In fact, some patients get upset. They complain that I do not look them in the eye. I try to look at them while writing in the computer, but I don’t have the ability yet.ParticipantDoes this mean that they got upset when you used the application on your phone?InterviewerNo, because I tell them, “Look, I am going to use this app to help in the diagnosis,” and I show them my phone.Participantperceived loss of attention and negotiating the use of the device with the patient. The first category refers to the way the patient seems to interpret and resent that he has lost the physician’s attention when the latter is using the computer. The second category refers to the way physicians prevent complaints about themselves when using the phone during the encounter by telling the patients what they are using the phone for and sometimes including them in the process of using the system. The relationship between these categories allows us to recognize differential effects over the patient–health professional relationship of CDSSs implemented as mobile apps and as desktop applications.Later in the analysis, the review of the content in these codes led to development of two categories: Alert fatigue is a common problem for clinicians who use technology designed to improve patient safety. Evidence-based strategies to overcome alert fatigue are lacking, especially in the intensive care unit (ICU). There is an evidence gap, as discussed in a recent review and guidance document .The goal of the project in this use example is to provide effective strategies for the management of alert fatigue in the ICU. The behavioral change of interest is increasing clinician responsiveness to CDSS alerts provided during patient care . Formative research needs to be completed to understand the barriers and facilitators to clinicians’ responsiveness to alerts. To meet these goals, a mixed methods approach was applied using a survey and in-depth interviews conducted with critical care clinicians. Questions were developed based on BEAR at the domain level. A sample of selected questions is provided in skills, ability, and competence, whose definitions in the American Psychological Association Dictionary of Psychology [Out of 122 constructs identified in the literature review, 52 (42.6%) mapped to the TDF see . This suychology  are simiychology —was chaninner setting—implementation climate construct represents an organizational characteristic that influences the individual’s behavior. However, in other cases , we did not identify a direct influence over the individual. The same happened with the HOT-fit framework’s organization domain [The CFIR  and HOT-n domain . We recoThis is certainly not the first attempt to apply technology acceptance models in health care ,53. The On the other hand, literature reviews have found that the TAM has low explanatory power in health care environments ,53. The Finally, our objective of this study was to develop a conceptual framework, not a theory. A theory serves as an explanation of a phenomenon that in many cases allows for the prediction of outcomes. However, our objective was not to predict an outcome or the relative weight of each determinant in the explanation of an outcome, but to synthesize and organize all potential determinants reported in the reviewed literature. That is why we do not state any conclusion about the relationship between specific constructs, besides grouping them into domains to facilitate organization and presentation, nor their relative contribution to the success in the implementation of CDSSs. In this sense, BEAR is akin to other determinant frameworks , such as


The results support the conclusion that specialists exhibit higher competence than generalists, which allows proper application of the InfoSAS, possibly because of the practice of skills and reflective teaching styles in physical activity they experienced, along with their academic education in sport sciences.Teaching physical education requires competencies to conduct the classes and to assess the motor skills of practitioners. Specialists  and generalists (primary school teachers) differently experienced motor tasks during their academic education. This study aimed to compare the teachers’ ability in assessing the children’s forward and backward rolls from the analysis of the reliability of an evaluation grid of rolling abilities , which was investigated in a first study with teachers. A second study in young children explored the responsiveness of the InfoSAS to discriminate by skill level or by training effects. When administered by specialists, the InfoSAS resulted in being reliable (forward:  Competence is a pivotal concept of the teaching process at any school level. It can be defined as the individual capacity/ability to deal with job, study, professional, or personal requirements by applying all knowledge and skills previously acquired in formal, non-formal, and informal learning contests . KnowledFrom this perspective, the curricula of the degree courses in physical education (PE) are aimed to train proficient professionals to be skilled in finding appropriate solutions to apply to any educational scenarios they are facing by best summarizing what they have learned and what, especially, they have experienced during the instruction process, and by problem solving and didactical laboratory experiences in particular ,3. PractTo prepare a competent professional, the structure and planning of the curriculum should be set by considering the acquisition of different competences  to shiftA further issue to be considered is how task observation can be performed. Anyone perceives and pays attention differently to what she/he considers to be relevant to better complete a task, further interpreting in her/his own way what has been seen . For thiThe teacher’s attitude in approaching and assessing the rolling abilities in young children served in this study to compare specialists’ and generalists’ approaches while leading physical activity of primary school children. Rolling abilities were selected, as rolls and rotations on the transverse axis are frequent in gymnastics and in basic motor skills involved in improving fundamental motor skills (FMS), and are also very useful in several other sports or everyday life ,16. In pPractice based only on non-structured free play during childhood is not enough to determine the improvement of FMS . GuidingHence, learning forward and backward rolls in childhood appear to be suitable to enhance the sensory-motor system and motor control, to prevent injuries, to overcome proficiency barriers, and definitely to improve in particular the FMS and agility on the soil, promoting motor fitness  and lifeThe design of this study has been based on the hypothesis that the learning process and the “conceptual maps” coming from connections of multiple theoretical and practical experiences as depicted in The study was performed under the principles of the 1975 Declaration of Helsinki and was approved by the local Ethical Committee (Nr. 34/18).After a full explanation of the purpose of the study, all participants or parents/legal tutors signed a written informed consent before the study. They were allowed to withdraw from the study at any time. The reliability of the instrument to evaluate the execution of forward and backward rolls  and its suitability to be used by both generalist and specialist professionals to evaluate forward and backward rolls was assessed in a first study, while a second study involving two groups of young children served to confirm the responsiveness of the InfoSAS.The instrument InfoSAS to evaluate the execution of forward and backward rolls was arranged by two researchers in exercise sciences and sport. The InfoSAS considers seven fundamentals, attitudes, and skills  that performers have to manage and successfully achieve while doing rolls . The perThe reliability and responsiveness of the InfoSAS and its application by specialist and generalist teachers is one of the goals of this study addressed at teachers’ education.Five specialists  and five generalists  participated in this study. SPE were female students (age 23.8 ± 0.8 years) of the last year of the Master’s degree program in Individual and Team Sport Science at the University of Milan. Inclusion criteria were the possession of a Bachelor’s degree in Sport Sciences and no previous experience in PE teaching in primary school. GEN were female teachers (age 42.0 ± 2.5 years) of the public primary school. Inclusion criteria were a minimum of fifteen years of teaching experience and solid knowledge in didactics, teaching, and evaluation, including PE. However, GEN had poorer individual experience in PE than SPE, as their education programs spent a limited amount of time in practical experience compared to the education programs followed by SPE, in which methodologies, productive practice, laboratory involvements in specific training on motor control, and motor learning are integrated with more specific knowledge and competencies in exercise science . All par2) acting as models to make a data bank of rolling videos served to investigate the intra- and inter-rater reliability of the InfoSAS and to define whether it can be successfully used by both SPE and GEN.Video recordings from 13 primary school children  and some parameters defining the flexibility of the children were measured and correlated to the scores assigned by SPE and GEN. Studies have pointed out that, in early childhood, body composition and mobility significantly affect the acquisition of the FMS and, as a consequence, the overpassing of the proficiency barrier ,40. TherAll of the statistical procedures were performed using SPSS . The Shapiro–Wilk test revealed that the assumption of normal distribution of each set of data used was not met. To assess the intra-rater reliability for total scores of SPE and GEN, the non-parametric Friedman test with Dunn’s post hoc was applied, whether or not multiple comparisons were necessary, for both forward and backward roll. Similarly, the inter-rater reliability of the total scores was analyzed by applying the Kruskal–Wallis test with Dunn’s post hoc test. Linear regression analysis with Pearson’s coefficient was applied to analyze the correlations of SPE and GEN scores with children’s anthropometrics and flexibility. Means of the raters’ evaluations were used. The alpha value was set at a significance level of 0.05.From study 1, the InfoSAS resulted in being fully reliable only if administered by SPE. Therefore, we decided to complete the research with a second study to further investigate the responsiveness of the InfoSAS to discriminate the forward and backward rolls in children of different skill levels or to discriminate improvements resulting from a period of specific training in rolling. Therefore, three specialists from study 1 were further involved in this second study: two with the role of raters and one as a teacher to conduct the practice of the participants.2) and 12 female gymnasts from a one-year preparatory course practicing gymnastics two days per week  participated in the study. Inclusion criteria were the absence of pathologies or injuries of the locomotor system, no previous experience in gymnastics for PRI, and more than one year of previous experience in gymnastics for GYM.Nineteen young children from a primary school  twice a week, and the children were taught in rolls for a total of 100 min . They unThe Shapiro–Wilk test was applied to test the assumption of normal distribution of each set of data. As the assumption was not met, non-parametric statistics were applied. The reliability of total scores (TS) of PRI and GYM in both forward and backward rolls was assessed by the Friedman test with Dunn’s post hoc for multiple comparisons. To detect the effects of the teaching program on forward and backward rolls, the best TS of PRI before treatment was compared with TS post-training with the Wilcoxon test, whereas partial scores  were analyzed by applying the McNemar test. To compare GYM to PRI pre- and PRI post-TS, the Mann–Whitney U-test was applied, and partial scores were compared with the McNemar test. Further comparisons between GYM, PRI pre, and PRI post forward and backward roll TS were performed by applying the Mann–Whitney test. The alpha value for all performed analyses was set at 0.05.p > 0.05), and the same was for GEN, but only in backward rolling (p > 0.05). Conversely, the reliability of GEN in scoring the forward rolls was not confirmed because of the significant difference found among evaluations (p < 0.05) despite the fact that post hoc multiple comparisons did not differ.The results of the intra-rater reliability are presented in p > 0.05). Differently than SPE, GEN exhibited reliable scorings only in front rolling evaluations (p > 0.05), while backward rolling was differently observed among them (p < 0.05). In particular, the post hoc comparisons revealed differences in scores between raters #2 and 4, and between raters #3 and 4 (p < 0.05).The inter-rater reliability of the InfoSAS  was fullp > 0.05) with BMI nor with S&R in both SPE and GEN. Similarly, SPE and GEN scores of the forward rolling did not correlate with DS (p > 0.05), which, on the contrary, correlated with SPE ratings of backward rolling , and did not correlate with GEN ratings (p > 0.05).The scores of forward and backward rolling, as rated with InfoSAS application, did not correlate .Statistics confirmed the reliability of TS in both forward and backward roll , and backward rolling better than PRI pre . After training, PRI markedly increased TS in both forward and backward rolling . PRI also improved with training, and reached comparable results to GYM in backward rolling .p = 0.016 and p = 0.016, BA and OR, respectively; backward rolling: p = 0.008 and p = 0.008, BA and OR, respectively). HL further contributed to backward rolling improvements (p = 0.031). Markedly, looking at the forward rolling changes in the seven fundamentals, attitudes, and skills of PRI , PRI pre differed from GYM in BA, OR, HL, and CME , while PRI post almost reached GYM and only differed in HL and CME , which highlights and supports the positive effects the training had on PRI.The partial scores analysis (p > 0.05), whereas in both GYM and PRI pre, forward rolling was more highly scored than the backward one (p < 0.05).This explorative study was designed to investigate the reliability and responsiveness of the instrument InfoSAS to evaluate the execution of the forward and backward roll (agility on the soil) in young children, and to further verify its suitability and application by specialist or generalist teachers by comparing their proficiency in assessing the rolling motor task. From the results of study 1, the InfoSAS appeared to be reliable and therefore suitable to be administered only by specialists, hardly applicable by generalists. The second study, which was consequently carried out only with specialists’ support, confirmed that the InfoSAS was responsive in detecting differences due to expertise (gymnasts vs. beginners) or due to improvements resulting from specific training in rolling.The results of this study preliminarily suggest that: (i) the instrument is reliable and responsive to evaluate forward and backward rolling in children; (ii) being the instrument not reliable with generalists, it seems to require specific competence to be administered, and appears to be suitable only for specialists; and (iii) specialists and generalists highly differ in assessing the rolling abilities with the InfoSAS.The intra-rater analysis  confirmeEven if the InfoSAS thoroughly defines the procedure to evaluate the rolling performance, GEN hardly detected and realized all specifics of the motor tasks’ accomplishment, possibly because of the reduced individual experience in physical activity compared to SPE. Differently, SPE was confirmed to properly focus on all of the performers’ actions that are important to be observed and scored (and managed while teaching). The literature reported frequent use of thinking shortcuts when the resources to process information are limited, as in the case of generalist teachers without specific individual practical experience . Due to These memories from individual experiences are essential to expanding the conceptualization and the interpretation of situational constraints , distinguishing the daily professional activity of PE teachers ,48,49. FCompetence is the ability to correlate the “old to the new”, that is, to identify similarities between actual and previously solved problems, which enlarges the collection of mental representations at the disposal of competent experts to deal with specific situations in a wide range of resolutions and by most effective actions . RecogniThe learning process can be enhanced and promoted by widening and connecting physical, motor, social, and cognitive experiences among them . ThroughFormer learnings, attitudes, predispositions, and current contexts strongly affect the discernment of reality and how it is interpreted ,58. ActiLastly, the higher ability of SPE in evaluating the rolling abilities compared to GEN is further supported by the correlation that has been found in backward rolling between TS and DS, which was retrieved only in SPE. Therefore, the evaluation of backward rolling given by SPE seems to be somehow linked to DS. That can possibly be explained by ankle flexibility, which is particularly relevant in DS testing, and whose observation while scoring the backward rolling requires a level of knowledge, competence, and experience not familiar to and not possessed by GEN.Indeed, the DS testing procedure allows measurement of the flexibility of the back, knee, and ankle. These components have been confirmed to be fundamental to hold the right attitude while rolling. In particular, ankle flexibility is important in the final phase of backward rolling, when the performer has to return to the standing position from its lower point, having the hip and knees in their maximal bending . ContrarThe responsiveness of the InfoSAS and its logic validity that make it applicable by SPE to detect and discriminate the differences of rolling actions due to expertise or training effects were confirmed based on the results from Study 2 .Concerning expertise, the young female gymnasts composing the GYM group had at least one year of practice in gymnastics skills, while PRI were children attending the first grade of primary school and had non-specific motor experience coming from different sports practice. The results from the use of the InfoSAS by the specialists agree with the literature: gymnastics practice improved the skills required to perform the rolling more than other motor activities . In partThis might depend on the specific expertise and practice of GYM in managing body control throughout the whole action because of the specific features of the sport, and because of the need to hold complete control until the end of the movement to be ready to continue in successive figures, which results in better agility on the soil of GYM than PRI. The development of motor abilities is age-related and not age-dependent . In our Concerning the training effects, the comparisons of pre- and post-training scores  also conFinally, from the results , panel cIn the didactics, an instrument such as the InfoSAS can be defined as an authentic task . AuthentBiggs and Tang  pointed Competence levels can be verified exclusively in situational conditions, such as in our studies 1 and 2. Indeed, competence is the ability to apply decision-making and properly operate and react to the specific and actual conditions that occur.Therefore, competence can be specifically assessed by means of reality tasks . Morin  pointed The differences between specialists’ and generalists’ outcomes, as resulting from our study, seem to confirm some insights from the literature. Vygotskij  highlighOur preliminary results suggest that specialists and generalists highly differ in the assessment of rolling abilities by applying the InfoSAS evaluation grid, possibly because of the different levels of competence and individual practice they previously experienced along with their academic education.The InfoSAS has been proved to be reliable and responsive in evaluating the rolling abilities of children in primary school. The InfoSAS can be applied by specialists who have competence in sport sciences and have previous experience of individual practice and teaching styles in physical activity, while generalist teachers failed to satisfactorily apply this instrument of evaluation. The specialists can apply this evaluating tool to properly and adequately judge the performances of the learners and to discriminate the outcomes in rolling abilities depending on expertise or on the effects of training the skills involved in rolling, even if lasting for a limited amount of time. The InfoSAS can also be applied to focus on skills defining agility on the soil.Application and validation of the reliability and responsiveness of the InfoSAS with older children are advisable, as well as investigations on the retention after training sessions and on the effects of specific additional exercises to improve ankle flexibility. Further research should also be aimed at comparing specialists and generalists in applying alternative tools to evaluate motor patterns other than rolling. It must be said that a larger sample size could be useful to reach the normal distribution of the data and to deepen and possibly confirm the results of the present exploring study. A further and not trivial limitation of the present research is represented by the lack of a control group in study 2; the presence of a control group could have been very useful to rule out that an improvement in the two experimental conditions had been by chance.From the results of our study, we can extend our assumptions to the needs of the teaching process of physical activity, and we can conclude that PE in primary school should be necessarily guided by competent professionals in sport sciences. In addition, this study exemplifies how the research itself might serve to assess and review some specific targets of academic education in sports sciences. This process can help institutions to find strengths or weaknesses of the curricula, to check whether their aims and teaching styles adequately succeed in making proficient professionals not exclusively in knowledge but in competences as well, and to make proficient specialists in assessing, judging, and managing the widest spectrum of motor patterns and skills.


Deoxyribonucleic acid (DNA) is a biological macromolecule. Its main function is information storage. At present, the advancement of sequencing technology had caused DNA sequence data to grow at an explosive rate, which has also pushed the study of DNA sequences in the wave of big data. Moreover, machine learning is a powerful technique for analyzing largescale data and learns spontaneously to gain knowledge. It has been widely used in DNA sequence data analysis and obtained a lot of research achievements. Firstly, the review introduces the development process of sequencing technology, expounds on the concept of DNA sequence data structure and sequence similarity. Then we analyze the basic process of data mining, summary several major machine learning algorithms, and put forward the challenges faced by machine learning algorithms in the mining of biological sequence data and possible solutions in the future. Then we review four typical applications of machine learning in DNA sequence data: DNA sequence alignment, DNA sequence classification, DNA sequence clustering, and DNA pattern mining. We analyze their corresponding biological application background and significance, and systematically summarized the development and potential problems in the field of DNA sequence data mining in recent years. Finally, we summarize the content of the review and look into the future of some research directions for the next step. We live in the era of the genome, advances in science have allowed humans to spy on the mysteries of life. In recent decades, the rapid expansion of biological data is a significant feature of the development of molecular biology, and a massive biological information database has rapidly formed. We must obtain useful knowledge from these huge data, and simultaneously bioinformatics was born. Bioinformatics is an interdisciplinary subject. It comprehensively uses mathematics, life sciences, and computer science to mine biological information in biological data , and furAccording to statistics, the amount of biological data approximately doubles every 18 months. In 1982, GenBank’s first nucleic acid sequence database had only 606 sequences, containing 680,000 nucleotide bases . As of FFor complex biological data, on the one hand, it is necessary to solve the problem of storage and management of massive data, and on the one hand, it is necessary to extract effective information from the data on the premise of ensuring that the data reflects the true meaning of biology. Machine learning is an important method to achieve artificial intelligence. It can handle the automatic learning of machines without explicit programming and has been widely used in the field of bioinformatics .DNA is a kind of biomacromolecule in organisms. It carries the genetic information of life and guides the development of biological development and the functioning of life functions. At present, machine learning has been widely used in sequence data analysis and has very broad application prospects in improving data processing capabilities and generating valuable biological information. The review focuses on DNA sequence data mining and machine learning. The review briefly introduces the development process of sequencing technology, DNA sequence data structure, and several sequence encoding methods in machine learning. And we clarify that sequence similarity is the basis of DNA sequence data mining. We have comprehensively analyzed the basic process of data mining and summarized the algorithms commonly used in machine learning. Then, we summarized four typical applications of machine learning in DNA sequence data: DNA sequence alignment, classification, clustering, and pattern mining. In summary, we have the following conclusions: distributed sequence alignment and parallel computing may be the research focus of DNA sequence alignment. How to effectively express sequence features and analyze DNA sequence classification is a difficult point in research. The two key points of DNA sequence clustering are how to extract characteristic subsequences in the DNA sequence. DNA sequence pattern mining will generate an explosion of candidate sequence patterns, which will consume a lot of time and space. How to design a suitable search strategy and eliminate redundant sequence patterns will be an important direction for future research.Gene sequencing is one of the most popular technologies in life sciences. At present, HiSeq X Ten is the sequencing platform with the highest sequencing throughput and the lowest cost. The introduction of equipment and its commercialization has greatly promoted the development of the sequencing industry. The rapid progress of sequencing technology and the continuous decline of sequencing costs have made sequencing more and more common.Sequence similarity is the basis of sequence data mining, and it is a research direction where sequence similarity bioinformatics is very meaningful. Sequence similarity refers to the degree of similarity between sequences. If the similarity between two sequences exceeds 30%, it is considered that they may have homology. The homologous sequences have a common evolutionary ancestor, and their structures and functions may have similarities.With the development of biological information technology, sequencing technology has experienced three stages of development. The chain termination method proposed by Sanger and the chain degradation method proposed by Gibert is collectively called the first generation sequencing technology. At present, Sanger sequencing is still widely used in conventional sequencing applications and verification, but the sequencing cost is extremely high and the throughput is low, which seriously affects its truly large-scale application. After more than 40 years of technological development, sequencing technology has achieved considerable progress. The progress of sequencing technology is shown in After the continuous efforts of researchers, the second generation sequencing technology marked by 454 technology was born in 2005. These sequencing systems can analyze billions of sequencing reactions at the same time. The second-generation sequencing technology is a kind of connected sequencing, which greatly improves the speed of sequencing and greatly reduces the cost of sequencing. At present, the second-generation sequencing technology  is the mIn 2011, the third generation sequencing technology represented by Oxford single molecule sequencing technology and PacBio’s SMRT technology was born. Single molecule sequencing is the biggest feature of the third-generation sequencing technology. This technology needs to be continuously adjusted and upgraded for large-scale applications. Sequencing technology is revolutionizing personalized medicine by providing high throughput options with sequence capabilities for clinical diagnosis.Genomic big data analysis is becoming the next frontier in the field of biomedicine , which iBiological studies have shown that biological sequences are not random and unordered strings. They consist of a linear arrangement of smaller elements. The DNA sequence is connected by four kinds of deoxyribonucleotides (bases). Base order contributes to the diversity of DNA molecules.The structure of the DNA double helix is shown in DNA sequence data have different characteristics from other data, mainly including:1.DNA sequence data consists of non-numeric  characters;2.The length of different sequences varies greatly. Some sequences have only a few dozen characters, while others are very long, up to hundreds of megabytes;3.DNA sequence data contains its specific biological significance;4.Due to certain errors in the sequencing process and noise in the sequence data, it is necessary to perform corresponding data preprocessing before analyzing the data.When processing the DNA sequence, it is necessary to convert the string sequence into a numerical value, so as to form a matrix input model training. Generally speaking, there are three methods for sequence encoding: sequential encoding, one-hot encoding, and k-mer encoding . The chaThe main mining modes of machine learning include data characterization and differentiation, data frequent patterns, association and correlation, classification and regression of data predictive analysis, cluster analysis, and outlier analysis. Data mining for DNA sequences is generally carried out from these aspects, and research in these areas is inseparable from similarity analysis between sequences . It can Sequence similarity means that there are similar or identical sites between sequences. The sequence similarity can be a quantitative value or a qualitative description. If the degree of similarity between two sequences exceeds 30%, It is considered that the two sequences have a homologous relationship. Therefore, if the two sequences are highly similar, the two sequences are likely to have a common evolutionary ancestor. At the same time, if a sequence similar to the unknown sequence can be found from the sequences with known functions, we can further predict the function  of the uOne of the main problems of DNA sequence similarity research is to search for sequences whose similarity to a specified sequence exceeds a certain threshold. The most commonly used method is to establish a similarity matrix  and findSequence similarity is one of the key processes of DNA sequence analysis in computational biology and bioinformatics. In the study of gene function analysis, protein structure prediction and sequence retrieval, similarity calculations are required. We select the appropriate sequence similarity analysis method and improve it according to actual application requirements and biological background. This is the basis and key of DNA sequence data mining.In the past few decades, we have witnessed the revolutionary development of biomedical research and biotechnology and the explosive growth of biomedical data. The problem has changed from the accumulation of biomedical data to how to mine useful knowledge from the data. On the one hand, the rapid development of biotechnology and biological data analysis methods has led to the emergence of a challenging new field: bioinformatics. On the other hand, the continuous development of biological data mining technology has produced a large number of effective and well-scalable algorithms. How to build a bridge between the two fields of machine learning and bioinformatics to successfully analyze biomedical data is worthy of attention and research. In particular, we should analyze how to use data mining for effective biomedical data analysis, and outline some research questions that may stimulate the further development of powerful biological machine learning algorithms.Data mining is a discipline that combines classic statistical tools with computer science algorithms. This discipline aims to mine knowledge from large amounts of data for scientific, computational, or industrial use. As shown in 1.Data cleaning. Because of the increasing amount of heterogeneous data, data sets often have missing data and inconsistent data. Low data quality will have a serious negative impact on the information extraction process. Therefore, deleting incomplete, or inconsistent data is the first step in data mining;2.Data integration. If the source of the data to be studied is different, it must be aggregated consistently;3.Data selection. Accurately select relevant data based on the research content;4.Data conversion. Transform or merge data into a form suitable for mining, and integrate new attributes or functions useful for the data mining process;5.Data mining. Select the appropriate model according to the problem and make subsequent improvements;6.Mode evaluation. After acquiring knowledge from the data, select appropriate indicators to evaluate the model.The main task of the data mining step is to correctly select one or a combination of these steps and find an effective and reliable method to solve the given problem. In recent years, machine learning has been widely used in bioinformatics analysis. Each step of data mining is developed independently of other steps, and each step has a large number of machine learning algorithms.As one of the most important branches of data mining, association rule mining can identify the associations and frequent patterns of a set of items in a given database. It consists of two sub-problems: (1) Set the minimum support threshold and use the minimum support Find frequent itemsets from the database; (2) Use minimum confidence to find association rules that satisfy specified constraints on frequent itemsets. Association rule mining not only plays an important role in business data analysis but has also been successful in many other fields, such as virtual shopping basket analysis and medical data analysis.Apriori algorithm is a typical association rule-based mining algorithm, which has applications in sequence pattern mining and protein structure prediction. Many machine learning algorithms in data mining are derived based on Apriori  is an extension of double sequence alignment, but when the amount of sequences is large, it will face the problem of excessive data storage space occupation and high calculation complexity. MSA has a key characteristic: Since MSA is an NP-complete problem, MSA relies on approximate alignment heuristic algorithms. These heuristic algorithms depend to a certain extent on specific data attributes. This algorithm was proposed by Hogeweg, and later researchers developed sequence alignment packages based on it, such as CLUSTAL, T-Coffee, CLUSTALW. In recent years, the research and application of iterative algorithms in MSA have become common. Many studies have focused on heuristic techniques to solve MSA problems, among which stochastic methods are very effective methods. GA is a stochastic method, which can solve this type of optimization problem well. 1.You can find the optimal solution or the suboptimal solution of the sequence alignment problem in computing time;2.Regardless of the length of the sequence and the number of sequences, this method is applicable;3.There is much room for improvement in the optimization of the objective function, and the description of the objective function is crucial for the optimal solution of sequence alignment.The scale of biological sequence data continues to grow, and sequence alignment is a necessary step for sequence data analysis. Since the research of sequence alignment is very mature, a large number of excellent and open-source sequence alignment tools have appeared. At present, the research of sequence alignment focuses on improving the speed of the alignment. Faced with such a large amount of sequence data, traditional sequence comparison tools can no longer handle it, so highly distributed computers will be required. In recent years, a distributed computing framework called Hadoop can be used for big data processing and storage. It has two main components, MapReduce for programming model and Hadoop Distributed File System (HDFS) for storing data. Using the distributed platform of the MapReduce model, massive sequencing data can be effectively stored and analyzed. Evaluation of biological sequence alignment algorithms mainly considers the efficiency of the algorithm and the sensitivity to obtain the best alignment results. The Smith-Waterman algorithm for double-sequence alignment is highly sensitive, but its complexity is high. FASTA and BLAST are a decrease in predicted sensitivity in exchange for an increase in speed. The CLUSTALW algorithm is the most common and effective among multiple sequence alignment algorithms. The main problem in sequence alignment is whether the sensitivity of the alignment and the efficiency of the algorithm have been improved for sequences with large differences.Next-generation sequencing technology (NGS) has brought us a lot of biological data. Sequence alignment is always an indispensable step in finding the relationship between sequences. For fairly large input sequences, sequence alignment is a difficult task Currently, traditional sequence alignment tools are inefficient in terms of computing time. In the future, in the face of high throughput, biological sequence data, distributed sequence alignment, and parallel computing may be the focus of research in this field.Classification is an important mining task in machine learning. Its purpose is to learn a classification model from the training sample set to predict the category of unknown new samples. The classification of biological sequences as a special data type is a popular problem in data mining. It is a difficult problem, due to the non-numerical attributes of the biological sequence elements, the sequence relationship between the sequence elements, and the different sequence lengths of different events, etc. Sequence classification is to predict the type of DNA sequence based on the similarity of its structure or function, and then predict the sequence function and the relationship between other sequences, and assist in the identification of genes in DNA molecules.E. coli promoter sequences in DNA sequences. He Uses four different coding methods to encode the sequence and then uses the coding sequence to train four different neural networks. The classification results of the four individual neural networks were then combined through an aggregation function, which used a variation of the logarithmic opinion pool method. Experiments show that when the same data is provided to the neural network with different encoding methods, it can provide slightly different results that can be provided. At the same time, when the results of more classifiers with the same input data are integrated into a multi-classifier, the results we can obtain are better than the single performance of the neural network. However, the main disadvantage of the neural network design is that it is difficult to obtain the optimal parameters of the neural network. This will involve the deployment of the neural network and the optimization of the encoding method used.In recent years, the convolutional neural network is a widely used deep learning model. Convolutional neural networks can extract abstract features from data. The machine learning method used for supervised learning classification tasks depends on feature extraction. A key problem in genomics is the classification and annotation of sequences. In recent years, a variety of machine learning techniques have been used to complete this task. In any case, the main difficulty behind the problem is still the feature selection process. The sequence has no clear features And the general representation method easily introduces high-dimensional problems. How to effectively represent sequence features and analyze high dimensional data is the difficulty of research.Cluster analysis is one of the most commonly used methods of machine learning. It is different from the classification that we don’t know specific categories in advance. Cluster analysis is unsupervised learning of data patterns. DNA sequence clustering is based on sequence similarity analysis. Cluster analysis clusters DNA sequences with similar characteristics into a cluster and then analyzes biological sequence functions. How to determine whether there is a similarity between sequences is the key to DNA sequence clustering. At present, a lot of research in DNA sequence clustering is based on the local characteristics of DNA for clustering, and the clustering results of DNA sequences are affected by many factors Impact. If a clustering algorithm that considers the global characteristics of DNA sequences can be designed, the accuracy of clustering will be greatly improved, and it is of great significance for the further analysis of DNA sequence clusters.Early foreign scholars Some recent studies have proposed methods for converting DNA data into genomic digital signals. These studies will provide opportunities for existing digital signal processing methods to be used in genomic data. Mendizabal-Ruiz G proposed a method for clustering analysis of DNA sequences based on GSP and K-means clustering. He chose Euclidean distance as the similarity measure to be adopted by the K-means algorithm. This method can be used to evaluate the ability of markers or genes to distinguish organisms at different levels, identify subgroups in a group of organisms, and classify fragments of DNA sequences based on known sequences . MendizaAt present, the two key points of DNA sequence clustering are how to extract the characteristic subsequences in the DNA sequence, and how to design an effective similarity measure from the biological meaning. Based on the above two key points, the design of the DNA sequence clustering algorithm will get a more practical application of clustering results.During DNA evolution, its sequence patterns are well conserved, which is of great significance for biological research. The DNA sequence pattern is usually a sequence fragment in the DNA sequence that has a specific function. In the process of DNA evolution, the more conserved regions in most sequences will form specific sequence patterns, and the structure and function of these sequences play an important role. Therefore, identifying these patterns is an important research content of DNA sequence data analysis. This helps to predict DNA sequence function and explain the evolutionary relationship between sequences. The purpose of DNA sequence pattern mining is to find such sequence patterns from DNA sequences and to identify genes and their functions.Since, As shown in Apriori algorithm is commonly used to mine data association rules. It is used to find data sets that frequently appear in data values. Finding out the patterns of these sets helps us make some decisions. Srikant proposed a GSP  algorithm based on the Apriori algorithm. The GSP algorithm introduces time and conceptual level constraints and uses a bottom-up breadth-first strategy to mine all frequent patterns  One type uses a heuristic search strategy. This type of algorithm is usually an iterative process. The optimal solution is obtained through repeated iterations. The advantage of the solution is that the calculation complexity is reduced. This kind of method is suitable for the study of subdivided DNA sequences. The disadvantage is that its solution may fall into the local optimum; (2) Another type of algorithm uses an exhaustive search strategy to enumerate all possible solutions and evaluate them one by one to find the best solution.Existing sequential pattern mining algorithms can be roughly divided into two categories: (1) One type of sequential pattern mining algorithm is to search for patterns in the sequence that exceed a certain threshold: mining alternative patterns. They can only mine alternating patterns in a single sequence. (2) However, in the study of biological sequence analysis, we often require simultaneous analysis of sequence patterns in sequence sets, which cannot be achieved by this method. Therefore, another type of sequence pattern mining algorithm is needed. This type of sequence pattern mining algorithm mines repeated sequence patterns in data sets. When we face massive amounts of biological data, such algorithms usually search very slowly.In the biological sequence database composed of DNA sequences, the existing search algorithm is time-consuming and requires multiple scans of the database. To overcome these disadvantages, DNA sequence pattern mining is a necessary means to study the structure and function of DNA sequences. Traditional DNA sequence pattern mining algorithms will result in a large number of redundant sequence patterns. These sequence patterns are usually short, and they have little biological significance, which makes the results of sequence pattern mining inefficient. At the same time, the long sequence always contains a considerable number of sub-sequences, so an explosive number of candidate sequence patterns will be generated, which will generate a lot of time and space consumption. How to design an appropriate search strategy and eliminate redundant sequence patterns will be an important direction for future research.DNA sequence analysis provides an opportunity to explore the genetic variation of organisms. The rapid growth of DNA sequence data has continuously expanded the demand for DNA sequence analysis. At present, there are still the following problems in DNA sequence data mining:1.There are still efficiency challenges when processing large-scale DNA sequence data;2.For different biological needs, suitable DNA sequence data mining algorithms should be designed according to the corresponding background knowledge and sequence characteristics;3.How to extract the sequence characteristics of DNA sequences and how to design an effective similarity measure to measure sequence similarity is very important;4.Due to the “black box” nature of machine learning, the output of machine learning is difficult to give a reasonable explanation from a biological perspective, which limits the application of the model to a certain extent.In the past few decades, the rapid development of hardware technology has opened up new possibilities for life scientists to collect data in various application fields, such as omics, biological imaging, medical imaging, etc. At the same time, the advancement of life science technology has brought Huge challenge. Today, how to apply numerous data mining technologies to bioinformatics analysis is a current research hotspot, including data mining architecture, machine learning algorithm development, and new data mining analysis function research suitable for biological information processing. At the same time, the interdisciplinary approach has promoted the development of machine learning. And artificial neural networks, deep learning, and reinforcement learning have made breakthroughs in machine intelligence. Besides, due to the growth of computing power, the acceleration of data storage speed and the reduction of computing costs,scientists in various fields have been able to apply these technologies to biological data. The close integration of machine learning and bioinformatics will result in more and more meaningful mining results, which will play a positive role in the progress of human society.Based on the above research, we believe that the research of machine learning in DNA sequence analysis has two aspects that deserve attention:On the one hand, it describes the biological significance of DNA sequences. At present, a large number of algorithms can achieve efficient performance when analyzing DNA sequences, but their mining results are highly sensitive and specific, which will make a large deviation during use. Therefore, how to integrate the biological significance of DNA sequences into the data mining process is a problem worthy of everyone’s attention and research.On the other hand, with the continuous expansion of data volume, traditional analysis tools are inefficient in terms of computing time, and how to design efficient calculation methods is an important research aspect. The integration of distributed computing and parallel computing will greatly improve mining efficiency.At the same time, it is very necessary to choose a suitable DNA sequence coding method for a specific task. This can improve the performance of the algorithm and reduce the training time.In summary, from the aspects of sequencing technology, DNA sequence data structure, and sequence similarity, this review comprehensively introduces the source and characteristics of DNA sequence data; we briefly summarize the machine learning algorithms and propose biological sequence data Challenges faced by machine learning algorithms in mining and possible solutions in the future. Then, we reviewed four typical applications of machine learning in DNA sequence data: DNA sequence alignment, classification, clustering, and pattern mining, analyzed and discussed their corresponding biological application background and significance, and systematically summarized recent years Research on the field of DNA sequence data mining by domestic and foreign scholars. We put forward several key issues in the future research field of DNA sequence data mining and some future research directions and trends. In future research, I believe that the biological field and machine learning will be more closely integrated, and more effective mining results will be obtained.AY proposed the idea of this manuscript. WZ searched the references. JW wrote this manuscript. KY and YH double checked this manuscript and formatted it as per the template of this journal. LZ polished the language. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To describe the epidemiological profile of victims of cardiac arrest assisted using a nontransporting emergency medical service vehicle and to determine whether these patients met the criteria for the use of extracorporeal membrane oxygenation.This study employed a retrospective, cohort, descriptive, and exploratory design. Data were collected in January 2018 in northern Portugal by consulting the records of nontransporting emergency medical service vehicles that provided assistance between 2012 and 2016. An observation grid was prepared that was supported by the instrument used for collecting data from the national registry of out-ofhospital cardiac arrests.After applying the inclusion criteria, the sample consisted of 36 victims. Extracorporeal membrane oxygenation could have been applied to 24 victims during the period analyzed, which might have increased the odds for transplantation, survival, or both, for either the victim or other individuals.Nontransporting emergency medical service vehicles have the potential for inclusion in the extracorporeal membrane oxygenation network of the study area. Currently, providing care to critically ill patients is a major challenge for health professionals. Recently increasing occurrences and complexity of health problems as well as the advances in technologies, for both diagnosis and treatment, have led to the need for updated knowledge and practices for the care management of critically ill patients. Different complications associated with critically ill patients can lead to cardiac arrest.A critically ill patient is defined as "one whose survival depends on advanced means of monitoring and therapy because of the severe dysfunction or failure of one or more organs or systems." Thus, a critically ill patient is one whose life is threatened and depends on advanced means of monitoring, surveillance, and therapy.Cardiac arrest is defined as the cessation of the mechanical activity of the heart as confirmed by the lack of signs of blood flow caused by several clinical conditions. Cardiac arrest is characterized by four patterns of irregular heartbeat; ventricular fibrillation is the most common, followed by pulseless ventricular tachycardia, pulseless electrical activity, and asystole. It is possible to recover spontaneous circulation with basic life support, but this protocol is not sufficient in most situations; rather, advanced life support maneuvers are required, which consist of achieving the most appropriate return of spontaneous circulation through advanced medical care. Ideally, advanced life support must be started during the out-of-hospital phase and continue in the hospital. Whenever successful resuscitation occurs, it is essential to continue to provide postresuscitation care to preserve the integrity of the organs. Extracorporeal membrane oxygenation (ECMO) has already been introduced in certain countries for this purpose.Cardiopulmonary resuscitation maneuvers are the most common clinical intervention methods used to reestablish the spontaneous circulation of critically ill patients. Cardiopulmonary resuscitation should be an early, appropriate, coordinated, and standardized intervention for successful clinical reversal.influenza A (H1N1) virus pandemic in 2009. Worldwide, numerous institutions have adopted the same type of intervention.In Portugal, ECMO began as the consequence of the Extracorporeal membrane oxygenation involves an extracorporeal circuit that directly oxygenates and removes carbon dioxide from the blood using an oxygenator. Gas exchange is performed via semipermeable membranes. The deoxygenated blood is drained into a drainage cannula using an external pump, which passes it through the oxygenator. The blood is then returned to the victim through a return cannula. When the blood is drained by the venous system and pumped into an artery, the circuit provides both respiratory and cardiac support. The mandatory criteria for the use of ECMO are a patient age between 18 and 60 years old; the absence of significant comorbidities; a shockable rhythm on first evaluation; evidence of pulmonary thromboembolism, hypothermia, or acute intoxication; cardiac arrest witnessed and promptly treated; a time from cardiac arrest to advanced life support of less than 10 minutes; a time from advanced life support to cannulation of between 10 and 30 minutes; and transport with a mechanical chest compression device during out-of-hospital cardiac arrest.Extracorporeal membrane oxygenation is indicated to treat refractory cardiogenic shock in the presence of severe biventricular dysfunction, heart failure, cardiac arrest, or malignant ventricular arrhythmias.,11Scientific evidence shows that the use of ECMO is important across different settings. ECMO reduces mortality rates and helps preserve organs for transplantation.Instituto Nacional de Emergência Médica - INEM). In the prehospital setting, clinical intervention aims to guarantee the population the provision of healthcare to injured victims and victims of sudden illness, ensuring the correct approach and stabilization of the victim at the site of the occurrence. One of the means of intervention in the prehospital setting is the use of a nontransporting emergency medical service vehicle, known in Portugal as Viatura Médica de Emergência e Reanimação (VMER). This prehospital intervention vehicle is intended for the rapid transport of a physician and a nurse with specific training in medical emergencies, particularly in advanced life support and advanced trauma life support.In particular, the organization of responses to emergency situations has been the responsibility of the National Institute of Medical Emergency ; a time from advanced life support to cannulation of between 10 and 30 minutes; and transport with a chest compression device during out-of-hospital cardiac arrest.Data were collected by consulting the medical records. A grid of the records was created that organized the information into groups. The first group consisted of sociodemographic data . The second group consisted of other variables . Finally, the third group referred to the inclusion criteria including the use of an automated external defibrillator (AED) on arrival; a time until VMER arrival of less than 15 minutes; and a distance between the place of occurrence and the emergency department of the reference center of less than 30 minutes.Escola Superior de Saúde do Vale do Ave  - ESSVA/ENF-Va-017/2017). Respect for all deontological assumptions inherent in the ethics of human research were fully respected. The information collected was organized and subsequently analyzed using descriptive analyses  and inferential analyses  using the Statistical Package for the Social Sciences (SPSS), version 23.The study was performed with the authorization of the ethics committee and board of directors of the institution where the information was collected  of whom were men. Patient age varied from 25 to 60 years old, with a mean age of 48.06 years and a standard deviation of 10.26 years. Regarding year of occurrence, 2013 had the highest rate of cardiac arrests , followed by 2014 , 2012 and 2016 , and 2015 .Mental illness and diabetes mellitus were the major previous diseases among women, followed by hypertension and respiratory problems; alcoholism and obesity were observed in only one victim. Among men, hypertension, diabetes mellitus, heart disease, obesity, and stroke were prevalent; mental illness, dyslipidemia, cancer, and liver disease as well as smoking and alcoholism were less frequent.rendez-vous less frequently (2.78%).We present data on the place and type of occurrence, diagnostic hypotheses, real time of occurrence, and heart rhythm. Regarding the place of occurrence , the vicRegarding heart rhythm, those with asystole were excluded from the sample ; the mosBased on the analysis shown in In the variable "year of occurrence", the value p = 0.165 and p > 0.05, it was accepted that there was not enough statistical evidence to state that the ocurrences were equally divided by the 5 years of occurrences. However, the evidence suggested that the cardiac arrests were not evenly distributed with regard to place of occurrence . The same result was found regarding heart rhythm (p < 0.001 and p < 0.05). Thus, the occurrences were not equally divided with regard to the different heart rhythms .The main objective of this study was to identify whether victims of cardiac arrest assisted by a hospital VMER met the criteria for ECMO application. The study was conducted in northern Portugal and used data from a VMER database with a target population of 8,330 victims. The final sample was 36 patients after applying the inclusion criteria. who also observed a higher ratio of male victims. This profile might be related to the personal histories of the victims, where men show a predominance of hypertension, diabetes mellitus, stroke, heart failure, and smoking, whereas women show a predominance of mental illness. According to data from the National Institute of Statistics, mental and behavioral disorders are predominant in women. On the other hand, cardiovascular diseases predominate in males, which helps to understand the characteristics of the victims.The mean age of these victims was 48 years old. Men predominated over women. This gender profile corroborates the results of Branco,More VMER activations were observed in 2013. Cardiac arrest predominantly occurred at home, and no comparison data exist for these occurrences. Given that the age of the victims varied from 20 to 60 years, most VMER activations occurred after work hours and during lunch time because victims of this age range are not institutionalized. who showed that ventricular fibrillation was the second most prevalent rhythm, whereas asystole was the main cardiac arrest rhythm observed. However, we did not include the asystole rhythm in our results because its exclusion is one of the criteria for ECMO activation. Thus, 310 victims with asystole rhythm were excluded from the sample. According to the study, this variable was related to the time in which the victim was without basic life support between the activation time of the INEM and the consequent emergency team arrival at the location of the victim.The most prevalent heart rhythm was ventricular fibrillation, followed by pulseless electrical activity. The current data were identical to those of Branco,The VMER response time was less than 15 minutes in most cases. Therefore, we believe that this rapid response was crucial for the observation of victims with rhythms other than asystole. These data also indicate that it might be possible to apply ECMO because a response time of less than 30 minutes is an inclusion criterion. The rhythm variables were related to the various procedures in the approach to victims in cardiac arrest, namely automatic external defibrillation.Automated external defibrillator was applied to some victims but not by the VMER; rather, this team uses a manual defibrillator. reported that basic life support was started no later than 10 minutes after cardiac arrest, ranging from 0 to 5 minutes.The time from cardiac arrest to the beginning of the application of basic life support maneuvers was less than 15 minutes for most of the occurrences; however, basic life support was started after more than 15 minutes in some cases. Additionally, some cases did not list information concerning the beginning of cardiac arrest; thus, this parameter could not be calculated. BrancoA primary criterion for ECMO activation is a time of less than 30 minutes from the place of occurrence to the emergency department. In this study, 24 events met this criterion, but 11 exceeded the maximum limit of 30 minutes. Therefore, ECMO could have been applied to 24 victims during the period analyzed, which might have increased the odds of transplantation, survival, or both for either the victim or other individuals. Therefore, it is important the inclusion of VMER in the ECMO program to increase the possibility of victim recovery.The development of this study involved certain limitations; thus, we suggest conducting additional studies on this topic. We were confronted with a scarcity of articles on the subject, a lack of records regarding the victims, and missing data regarding complication rates, survival rates, recovery rates, monetary costs, and professional training.This theme was pertinent given the assumption that more knowledge increases the individual's capacity to correctly evaluate and properly perform and implement treatment because this topic is in the early stages of research. Extracorporeal membrane oxygenation contributes to increased survival rates for different age groups and reduces mortality rates among victims of cardiogenic shock after acute myocardial infarction, cardiotomy, and  cardiac arrest. Furthermore, it enables and contributes to the recovery of organs for transplantation, thereby decreasing the numbers of cadaver donations and interventions needed among chronic patients over the medium term. We confirmed the importance of including the nontransporting emergency medical service vehicles of a peripheral hospital as an integral part of an extracorporeal membrane oxygenation program because some victims meet the criteria for its application. These data are essential for obtaining health gains.


This article presents the details and findings of a practical implementation of a preliminary model for use in hearing conservation programmes (HCPs) in the mining sector in South Africa.The implementation is based on a previously published model, called feedback-based noise monitoring model (FBNMM), and represents its implementation as a tool of predicting occupational noise-induced hearing loss (ONIHL), as well as monitoring and managing HCPs in the mining sector within the South African context.The article, utilising real miners’ data, demonstrates this basic static feedback model with its practical applications such as estimating, monitoring and providing quantitative information to aid miners, mining administrators and policymakers in decision-making around HCPs, as recommended in the previous 2019 publication by Moroe et al. This study was conducted in a South African platinum mine. Evidence on the model’s sensitivity and practicability in early identification, intervention and management of ONIHL in the workplace is presented.Findings show how the use of the model within an HCP viewed as a complex intervention can allow for early prediction of ONIHL, consequently affording more accurate early intervention as part of preventive audiology within the health and safety goals of mines.The feedback-based model should be a useful tool for successful implementation and monitoring of HCPs within South African mines. Occupational noise-induced hearing loss (ONIHL) is a complex disease , where effective ways of assessing, monitoring and reducing or eliminating excessive workplace exposure to hazardous noise are adhered to Amedofu, . EfficacT]he underlying premise of risk management is that improvements in health and safety can be made by correctly identifying and addressing hazards or factors (which may be underlying or direct) that contribute to occupational risk. (p. 536)[According to Madahana, Ekoru and Nyandoro :From The current model shown in Subsystem 1, which is also the baseline or the reference, is interpreted as the tracking unit. It is a non-constant reference. With sufficient hearing protection, the mine worker’s threshold shift is expected to closely follow the baseline. This would then be interpreted that the miner is only being affected by presbycusis and not occupational noise.Actual values are used in the input noise intensity that is a disturbance to the system. The exposure levels for each mine worker are identified according to the drilling machine that the mine worker operates.To simplify the developed current model, the risk treatment is represented by the actuator (hearing protection) and the plant (ISO equations that represent hearing loss). These are also considered to be on the forward path.Data used for model validation were collected from all the individuals who work at the platinum mine. However, based on key findings by Edwards and Kritzinger , when thThe current study therefore targeted one of the worst-case scenarios of mine employees. These were the workers who are affected the most by occupational noise because of the nature of their tasks in the mine. Edwards and Kritzinger  proposedIn this study, the two forms of hearing protection used by the rock drill mine operators are described below:3M peltor H9A optime 98 over-the-Head earmuff. Noise reduction rating (NRR)*: 25 dB. CSA Class A. *The NRR may overestimate the hearing protection provided during typical use. 3M recommends reducing the NRR by 50% for estimating the amount of noise reduction provided .The mine workers are not only exposed to drilling noise but also to the different sources of noise within the mine, for instance, from transportation.It is assumed that all rock drillers use the 18.35 dBA and 25 dBA attenuation setting provided and not the worst-case scenario of 7.1 dBA.The data set did not indicate the exact hearing protection that the mine worker uses; hence, both case scenarios are simulated (i.e. the 25 dBA and 18.35 dBA).Two hundred and ten rock drill operators employed in a platinum mine were chosen. Their occupational exposure histories were collected. Five variables which can influence hearing loss were used in the development of the predictive model.Ethical approval to conduct the study was obtained from the Human Research Ethics Committee of the University of the Witwatersrand  on 18 April 2016. Data used in the current study were obtained from a bigger study entitled ‘Occupational noise-induced hearing loss in South African large-scale mines: From policy formulation to implementation and monitoring’ Moroe, . The datThe presented results in The predictive tool using the provided inputs presents a futuristic estimation of how the thresholds will shift as shown in quiet level of machines they use as well as the age of the machines being used. Although these strategies would be important for all mine workers, current findings suggest more active and age-specific interventions, and hence the suggestions made for the older miner in this article.In It is observed in The FBNMM that was put into action in this article clearly provided a prediction of how a mining employee’s hearing function would present with given attenuation at speech frequencies. Suppose that this tool had been employed at the beginning of the employee’s career, then correct measure to ensure zero harm as required by the Mine Health and Safety Council would have been taken by the mine administrators in the performance of administrative controls and personal hearing protection pillars of their HCP. The FBNMM is significant to the HCP because it is a tool that can be used to predict hearing impairment and allow for provision of early intervention to prevent ONIHL  and HIV, the presence of dust, ototoxic chemicals and cigarette smoking, are considered and managed as part of the HCP. These factors, which were not added into the FBNMM as inputs, are argued as the possible explanation as to why there was an inability of the graphs to closely track (follow) the baseline irrespective of the hearing protection provided. Current implementation of the FBNMM reveals that, over and above what Edwards and Kritzinger  list as Putting the FBNMM into action in this article yielded positive findings that have important implications for HCPs. Furthermore, actionalising the FBNMM raised the following recommendations for future studies, which will be tested, documented and reported on an already planned follow-up paper:inclusion of more dynamic aspects to the system, for example, the use of an ear model of a human beinginclusion of other aspects that affect the mining environment, for instance, acoustic environment, temperature and ventilation in account during the period values were selected versus, how it would be in the future? Does it consider, for example, number of drills in operation at a time, or the acoustic environment variations machinery in operation, faults or dykes, water, etc.?inclusion of other risk factors, over and above age, gender, ethnicity, nutrition, exposure to chemicals (in the workplace as well as because of medication), family history of exposure and the likelihood of developing ONIHL at a faster rate once exposed previously that influence susceptibility of an individual to ONIHL in the modeldevelopment of a preliminary working prototype to be used in monitoring of ONIHL in the mineinclusion of artificial intelligence and machine learning concepts in the working prototype.The use of metrics, as was performed in the current study, is argued by the authors to be advancement in the management of ONIHL and has significant value towards efficacious implementation and monitoring of HCPs. The close link of metrics with artificial intelligence places this advancement at the forefront of audiology’s engagement with the Fourth Industrial Revolution in combating ONIHL.