Public health dashboards have been used in the past to communicate and guide local responses to outbreaks, epidemics, and a host of various health conditions. During the first year of the COVID-19 pandemic, dashboards proliferated but the availability and quality differed across the world. This study aimed to evaluate the quality, access, and end-user experience of one such dashboard in the Western Cape province, South Africa.We analysed retrospective aggregate data on viewership over time for the first year since launch of the dashboard (30 April 2020 – 29 April 2021) and conducted a cross-sectional survey targeting adult users of the dashboard at one year post the initial launch. The self-administered, anonymous questionnaire with a total of 13 questions was made available via an online digital survey tool for a 2-week period (6 May 2021 – 21 May 2021).After significant communication by senior provincial political leaders, adequate media coverage and two waves of COVID-19 the Western Cape public COVID-19 dashboard attracted a total of 2,248,456 views during its first year. The majority of these views came from Africa/South Africa with higher median daily views during COVID-19 wave periods. A total of 794 participants responded to the survey questionnaire. Reported devices used to access the dashboard differed statistically between occupational status groups with students tending toward using mobile devices whilst employed and retired participants tending toward using desktop computers/laptops. Frequency of use increases with increasing age with 65.1% of those > 70 years old viewing it daily. Overall, 76.4% of respondents reported that the dashboard influenced their personal planning and behaviour. High Likert score ratings were given for clarity, ease of use and overall end-user experience, with no differences seen across the various age groups surveyed.The study demonstrated that both the availability of data and an understanding of end-user need is critical when developing and delivering public health tools that may ultimately garner public trust and influence individual behaviour.The online version contains supplementary material available at 10.1186/s12889-022-14657-w. Public health dashboards have been used in the past to communicate, manage and guide local responses to outbreaks, epidemics, and a variety of health conditions . The conOver the last decade, public health officials have increasingly employed dashboards to guide their response to outbreaks and epidemics . Several. (2022) reported on the variability in COVID-19 reporting systems in Africa and noted that most African countries (98.1%) had some form of official national COVID-19 reporting system in place. Data was communicated in the form of situation reports, press releases, and official dashboards through websites and social media . Most participants (63.0%) were employed followed by retirees (28.3%). Far fewer participants reported being unemployed or students (5.0% and 3.7% respectively). Most respondents reported their country of origin as South Africa (96.1%) with smaller numbers from the other countries . Reported devices most often used to access the dashboard included a desktop or laptop (65.5%) followed by mobile phones (26.7%) and tablets (7.8%).A total of 794 individuals responded to the survey invitation during the two-week period of recruitment at one year post the initial launch. Table When comparing the association between devices used to access the dashboard and the occupational status of the participants Fig. , we see p < 0.001).We found that participants in the older age categories viewed the dashboard more frequently Fig. . Of thosp = 0.006).Overall, 76.4% of all respondents reported that the dashboard influenced their personal planning and behaviour Fig. . RespondMedian Likert-scale ratings of end-user experience were 4/5 for clarity and understanding, 4/5 for ease of use and 8/10 for overall rating with no difference by age category Table .Table 2CThe Western Cape public COVID-19 dashboard was well-used in the first year of the pandemic with 2,248,456 views, predominantly by South Africans, with positive end-user experiences.Firstly, it is important to acknowledge that the delivery of the dashboard was well-communicated to the public by senior political figures at the outset and only 7 weeks after the first case was identified in the Western Cape, South Africa. There was significant media coverage with a resultant highest median daily viewership in the first two weeks of launch. Secondly, most dashboard viewers were from Africa/South Africa. This was reflected in both the aggregate viewership over the first year and in the profile of respondents from the cross-sectional survey. This is reassuring as it reaffirms the statement from Ivanković et al. [Furthermore, the pattern of viewership over the wave 1, 2 and trough periods provide insight into the differentiated need based on public concern at the time. The increase in daily viewership over the wave 2 period, in particular, provided an indication that the dashboard was referred to as a trustworthy source of information with increased utility during a COVID-19 wave as opposed to between it. The pattern of access also showed an interesting weekday preponderance with lower access over weekends. This may relate to access to the relevant tools needed to view the dashboard as most respondents reported using a computer/laptop to view it or potentially to differences in weekday vs weekend behaviour patterns as it relates the utility of analytic type tools.The association between devices used most often and occupational status revealed an interesting phenomenon and reflected the characteristics within the underlying population groups. Students accessed the dashboard via their mobile devices far more than any other occupational group. This may be an indication of both comfort in using a mobile device for the purposes of health information and epidemic surveillance as well as access to a mobile device more so than a computer/laptop . A similThe association between frequency of viewing and age groups provided insight into the interest level from the various population groups and potentially toward their motivation for access. There was a consistent pattern of increasing frequency of viewership per increasing age band with the highest frequency of access by those aged > 70 years old. This is, in fact, contrary to research that reported a digital divide between younger groups having higher access and frequency of use to COVID-19 digital tools compared to elderly populations . During The fact that just over three quarters of respondents reported that the dashboard had an impact on their personal planning and behaviour speaks to the utility of tools like this amid a pandemic. When overlaying the association between those that reported impact and the element they most often used, it provided an indication to where the most value potentially lies. The results of this study showed that the additional analyses page conferred the highest association with a positive impact on personal planning and behaviour. When looking at what this area contains on the dashboard it becomes clear that it fulfils many of the criteria for a highly actionable dashboard as described by Ivanković et al.  includinIn terms of quality metrics, the dashboard was given relatively high scores in the areas of clarity and understanding, ease of use and overall rating of end-user experience. The study attempted to see if this experience of quality differed by age group but found no such difference with both elderly and young users reporting a similar positive experience.This evaluation provides insight into the necessity for development of public health data tools within an environment of a health priority as well as the end-user characteristics and needs as it pertains to access, understanding and quality of dashboard tools to communicate the relevant health data. This understanding has implications for public health officials and is pertinent as we transition out from a COVID-19 era and look to apply some of the lessons learnt during COVID-19 towards other communicable and non-communicable diseases.This study has several limitations. Firstly, although we did not specifically enquire about the end-users’ socio-economic status, we did ask their employment status and the proportions did not seem to align with the demographic characteristics of the Western Cape. Considering that the adult population in the province is ~ 4.8 million with an unemployment rate of 25.8%, the viewership was potentially skewed to higher income, employed cohorts, and low relative to the full adult population . This spThe need for research into the delivery of intuitive, easy-to-understand and timely health data for public use is rapidly growing. This is an important consideration as we emerge toward a post-COVID-19 era and need to leverage the lessons for an African context that is often sparse in terms of data availability. The present study findings, though, demonstrate that it is both availability of data and an understanding of end-user need that is important to develop and deliver quality and appropriate health data tools that may ultimately garner public trust, ensure accountability, and influence individual behaviour.Additional file 1: Supplementary Table 1. Questionnaire with possible response options. Supplementary Table 2. Number of views by continental region. Supplementary Table 3. Number of views per day by COVID-19 period (30 April 2020 – 29 April 2021). Supplementary Figure 1. Number of views per day by COVID-19 wave period. Supplementary Figure 2. Median number of views by day of week. Supplementary Figure 3. Number of views per day by weekday vs weekend*. Supplementary Table 4. Reported devices used by occupational status* (n=794). Supplementary Table 5. Reported frequency of use by age category* (n=794). Supplementary Table 6. Reported impact on personal planning and behaviour by element most often used* (n=794). Supplementary Table 7. Reported purpose of dashboard for participants* (n=1279).


This retrospective cohort study investigates how parenting stress, measured at 4 months of age by use of a classic three-dimensional parent-reported scale , can predict anxiety symptoms and quality of sleep at 24 months in toddlers with congenital heart disease (CHD).Sixty-six toddlers with CHD followed at our cardiac neurodevelopmental follow-up clinic were included in this study. As part of their systematic developmental assessment program, parents completed questionnaires on their stress level (PSI-4) when their child was 4 months old, and on their child's anxiety symptoms and quality of sleep at 24 months. Eight multiple linear regression models were built on the two measures collected at 24 months using the PSI-4 scores collected at 4 months. For each measure, four models were built from the PSI-4 total score and its three subscales , controlling for sex and socioeconomic status.The PSI-4 Difficult Child subscale, which focuses on parenting anxiety related to the child's behavioral problems and poor psychosocial adjustment, accounted for 17% of the child's anxiety symptoms at 24 months. The two other PSI-4 subscales  and the PSI-4 total score did not contribute significantly to the models. None of the four regression models on perceived quality of sleep were significant. It is important to note that 33% of parents responded defensively to the PSI-4.Parenting stress related to the child's behavioral problems and poor psychosocial adjustment, measured when the child is 4 months old, is associated with the child's ulterior anxiety symptoms. As very few standardized tools are available to assess the behavioral and psychoaffective development of infants, this study highlights the importance of early psychosocial screening in parents of infants with CHD. The high rate of significant Defensive Responding Indices reminds us to not take parent reports at face value, as their actual stress levels might be higher. More spMeanwhile, up to 50% of parents of children with CHD exhibit significant emotional distress , 7, thusVisconti et al.  conducteHigh levels of parenting stress have also been associated with more sleep problems in healthy 5-year-olds who exhibit behavioral and psychoaffective problems , as wellMany sociodemographic and clinical factors related to CHD are known to influence neurodevelopmental outcomes , 22. Inv2.2.1.n = 281) seen at our cardiac neurodevelopmental follow-up clinic and whose parents had consented to their child's medical information being used for research purposes. In order to be included in the study, children had to have an ante- or perinatal diagnosis of CHD, have undergone at least one corrective surgery early in life  and have in their medical chart all parent-reported questionnaires of interest completed during the systematic follow-up assessments at 4 and 24 months. More specifically, parents filled the Parenting Stress Index, 4th Edition—Short Form (PSI-4-SF) at 4 months, and the Child Behavior Checklist 1.5–5 years old (CBCL 1.5–5 years old) and the Échelle de dépistage des troubles de sommeil pédiatriques  at 24 months .We performed a retrospective examination of all patients aged 24 months and older —see among others Ha et al. . This is a non-standardized parent-reported screening tool translated into French by Godbout and Martello  from the2.3.Analyses were conducted using the Statistical Package for Social Sciences . First, the data was explored to ensure assumptions of normality were met and Descriptive comparison statistics between participants and non-participants were performed using independent t-tests. Then, to determine the interrelatedness between parenting stress at 4 months  and our behavioral and psychoaffective measures of interest at 24 months  bivariate Pearson correlations were computed prior to the regression models. The correlation coefficients yielded the maximum degree of linear relationship that could be obtained between our variables. Finally, multiple linear regression analyses were performed to examine how parenting stress at 4 months could explain ulterior anxiety symptoms and quality of sleep at 24 months.Four regression models were built on the CBCL Anxiety Problems score at 24 months. The first model included the PSI-4 total stress score at 4 months as a predictor. The three other models each included a PSI-4 subscale score other than the total stress score . Since significant differences were found between the PSI-4 scores of parents who responded defensively (see below) to the questionnaire and those who did not, Defensive Responding was included as a dichotomic predictor in all models. Finally, sex , 39, socM = 43.16 months; SD = 7.07; range of 28–58 months), they were all roughly the same age when the questionnaires were completed  and the patients followed at the neurocardiac clinic who were excluded (n = 205). These results are presented in Among the 281 patients whose medical charts were reviewed, ten were excluded because they did not require corrective surgery and 205 were excluded because of incomplete questionnaires, interruptions in the follow-up process or late referrals to the clinic (past the 4-month assessment). A total of 66 patients with CHD  were ultimately included in this study. See p < .001, mean raw score of 48 ± 8 vs. 73 ± 15, respectively), PD  and DC  scores. However, no difference was found when comparing the PCDI scores . These results suggest that a variety of patterns could potentially emerge in our analyses, when considering the parents’ defensive responding. Thus, Defensive Responding was included as a controlled variable in all regression models. We also compared the PSI-4-SF scores of the patients’ parents who completed the questionnaire before their child underwent surgery to that of parents who completed it after the surgical intervention. No difference was found between the two groups in regard to the Total Stress score  nor any of the three subscale scores . No difference was found when comparing PSI Total Stress scores of parents who received an antenatal diagnosis of CHD to that of parents who received the diagnosis after birth .Results for the PSI-4 revealed that the patients' parents' stress levels were not above the clinical threshold and that 33% of them responded defensively to this questionnaire. Considering the potential influence of the defensive responding bias on parent-reported stress , we sepa3.2.R2 = .166; p = .048) of the CBCL Anxiety Problems subscale score. In fact, a closer look at the contribution of each variable included in the model shows that the DC subscale score accounts for 36%  of the variance in parent-reported anxiety symptoms at 24 months, independently of controlled variables. No significant predictive value of the PSI-4 PD, PCDI and total stress scores on CBCL Anxiety Problems at 24 months was found. No significant predictive value of PSI-4 scores on the HIBOU scores at 24 months was found either with any of the four regression models . See Bivariate Pearson correlation analyses were performed between the variables of interest see . When pr4.This longitudinal retrospective study aimed to investigate the predictive value of parenting stress at 4 months on anxiety outcomes and quality of sleep at 24 months in toddlers with CHD. Our results partly align with the hypothesis that was put forward. First, parenting stress scores at 4 months were significantly associated with the child's anxiety symptoms at 24 months. In fact, parenting stress related to the parent's perception of their child's behavioral problems and poor psychosocial adjustment  acted as a significant predictor of parent-reported anxiety in their toddler at 24 months. Even when controlling for the child's sex, the family's socioeconomic status, the duration of hospitalization at first corrective cardiac surgery and the Defensive Responding bias, the regression model remained significant. This result suggests that parenting stress is an independent predictor of ulterior anxiety in the child, above and beyond sociodemographic and medical factors known to impact the development of children with CHD , 45. KarAmong the four scores obtained with the PSI-4 , only the DC score was significantly correlated with the CBCL Anxiety Problems subscale score at 24 months. Since the total stress score represents the sum of the three dimensions of the PSI-4 scale, it is less specific than the three subscale scores considered individually. Perhaps parents of children with CHD have a generally higher threshold for what they perceive to be stressful, given what they have gone through with their child's medical condition , and thiFinally, parenting stress scores at 4 months were not associated with the child's perceived quality of sleep at 24 months. This could be due to the broad nature of the HIBOU screening tool, as explained by Martin et al.  in their4.1.Although the parents do not necessarily report clinically significant parenting stress levels, probably due to the high rate of significant Defensive Responding Indices, our results show that, when administered early, parenting stress scores could have a predictive value of the child's ulterior anxiety during toddlerhood (even when controlling for the tendency to answer defensively). The PSI scores could be used for early identification of parents and children potentially at risk of more important behavioral and psychoaffective challenges, which are difficult to assess with standardized tools at this young age . At our We also found that parenting stress scores at 4 months and the child's perceived quality of sleep at 24 months were both significantly associated with anxiety problems at 24 months. Since sleep difficulties are common in congenital heart disease , we must4.2.The current study has limitations that must be considered. First, significant differences were found between participants and non-participants regarding two medically-related variables: a smaller proportion of perinatal diagnosis and a shorter average duration of hospitalization at first corrective cardiac surgery were both found in our sample. Since an antenatal diagnosis is associated with more complex forms of CHD , we hypoSecond, all the questionnaires that we used are standardized except the HIBOU, which provides a measure of the parent's perception of the child's quality of sleep. Nonetheless, it is widely used in both clinical and research contexts throughout the province of Quebec. Its scoring procedure provides a relevant clinical cut-off and allows for early identification of potential sleep disturbances. We put forward the interesting alternative of an at-home sleep monitoring device that would provide objective data on specific parameters of the child's sleep . Using a device that targets cardiorespiratory parameters, Vézina et al.  obtained5.The present study shows that parenting stress can act as a predictor of anxiety symptoms in toddlers with CHD. Our results revealed that the parents’ perception of their child's poor behavioral and psychosocial adjustment at 4 months of age contributed significantly to the prediction of ulterior anxiety at 24 months. Accurate measures of the patients' parents' stress levels could therefore allow for early identification of distressed families. However, the defensive responding bias must be considered, as it might lead to an underestimation of the parenting stress levels during the first months of life. Normal or lower-than-normal stress levels therefore do not necessarily indicate a healthy adaptation to life with the disease, and measures that are more oriented towards the parents’ worries about the child, instead of toward themselves, might be more appropriate. The results of this study also bring into consideration the necessity for very early intervention. For families who receive an antenatal diagnosis of CHD, antenatal psychosocial interventions, such as stress management workshops, psychotherapy or psychoeducation follow-up sessions, should be considered in order to optimize developmental outcomes.


Tobacco use is one of the major public health threats globally. Community pharmacists are uniquely positioned to offer tobacco cessation services owing to their easy accessibility by the public. To prepare Qatar community pharmacists to develop the competencies and skills required to offer smoking cessation services, an intensive tobacco control education program was designed and implemented. The study aimed to assess the impact of the tobacco education program on the pharmacists’ skills and competence.A random sample of community pharmacists in Qatar was chosen for participation in the program. Consenting participants were randomly assigned to either intervention or control groups. The intervention group received an intensive education program on treatment of tobacco-use disorder, while a short didactic session on a non-tobacco-related topic was delivered to the control group. The pharmacists’ tobacco cessation skills and competencies were assessed using an Objective Structured Clinical Examination (OSCE).p < 0.001) for cases one to six respectively.A total of 54 and 32 community pharmacists in the intervention group and the control group, respectively, completed the OSCE. The intensive tobacco education group achieved significantly higher total scores than the control group in all the OSCE cases. Specifically, the mean total scores for the intervention group were 15.2, 15.3, 14.2, 14.6, 16.3, and 15.2 compared to 8.8, 6.2, 7.7, 9.2, 8.3, and 11.3 for the control group  Registration date: May 8, 2018.Clinical Trials NCT03518476 ( Around 942 million men and 175 million women aged 15 or older currently smoke cigarettes globally [Community pharmacists are uniquely positioned healthcare professionals to offer tobacco cessation services using the 5A’s  approach, owing to the ease of their accessibility by the public. Evidence has shown that quit rates achieved when pharmacists recommend smoking cessation medications and provide counseling are similar to those achieved by other healthcare professionals . AdditioTherefore, in an effort to prepare Qatar community pharmacists to have the required knowledge and skills to offer tobacco cessation services, formal tobacco education program is required. The aim of this randomized controlled study is to design, implement, and evaluate an intensive education program on tobacco-use treatment for pharmacists provided by a team of health professional educators in Qatar. The effectiveness of the program was assessed using a multiple-choice-based evaluation instrument and an Objective Structured Clinical Examination (OSCE).OSCE is a performance-based assessment commonly used and described in the literature to assess clinical skills in health professional education programs –10. ManyPerformance of candidates being assessed via OSCE focuses on cognitive skills, including critical thinking, problem-solving, communication and interpersonal skills. In addition, elements of ethical and professional judgment can be assessed using a well-designed OSCE –10. In thttps://clinicaltrials.gov/ct2/show/NCT03518476) with the registration date: May 8, 2018.The study was a randomized controlled trial (RCT) aimed to evaluate the effectiveness of a tobacco-cessation education program for community pharmacists using an OSCE. Community pharmacists were randomly assigned to either an intensive education program on treatment of tobacco-use disorder group (intervention group) or didactic sessions on non-tobacco-related topics (control group). The tobacco dependence treatment skills of participants in both groups were assessed using a six-station OSCE, targeting evidence-based tobacco cessation core competencies. The study methodology is available in details in the published study protocol . The triAny licensed pharmacist who practiced in the community pharmacy setting in Qatar was eligible to participate in the study. Pharmacists in training or interns or unlicensed pharmacists were excluded.A random sample of 529 community pharmacists was chosen for recruitment from among a total list of 1000 pharmacists, provided by the Health Practitioners Registration and Licensing Section of the Qatar Ministry of Public Health.Consented pharmacists were randomly allocated to the intervention or control groups using permuted block randomization with blocks of size 2, 4, and 6. Randomization was made by the study statistician who was not involved in the recruitment process and was concealed from the research assistants recruiting the participants. Given the nature of the intervention, blinding of the study participants about their assigned groups was not feasible. No incentives were offered to participants in both groups other than the CE units.t test with a significance level of 2.5%. In addition, with 54 participants per group, the study will be able to detect a difference of at least 27.5% between the two study groups for any dichotomous outcome using the chi-square test with 80% power and a significance level of 5%. With a 10% loss-to-follow-up rate assumed, 60 pharmacists were required per group, resulting in 120 pharmacists to be randomized into groups.The sample size estimates were for 54 participants in each group with a power of 80% to detect an effect size of 0.60 between the study groups for the numeric primary outcomes  using the independent A team of educators, researchers, and clinicians with expertise in tobacco control and tobacco dependence treatment delivered an intensive tobacco education program to participants in the intervention group. The training program was conducted at Qatar University over four days with an average of eight contact hours per day . The program materials were developed de novo and targeted the Core Competencies for Evidence-based Treatment of Tobacco Dependence by the Association for the Treatment of Tobacco Use and Dependence (ATTUD) . The traThe training curriculum covered the following topics: (1) Tobacco-use epidemiology in Qatar and globally, (2) Risks and consequences of tobacco use, (3) Benefits of quitting tobacco use, (4) principles of nicotine addiction, 5) Non-pharmacological treatment of tobacco use and dependence 6) Pharmacological treatment of tobacco use and dependence 7) Alternative therapies 8) Counseling and communication skills 9) Relapse prevention 10) Treatment of tobacco use and dependence in special populations 11) Treatment of waterpipe dependence 12) Role of pharmacist in tobacco cessation 13) Establishing a tobacco cessation service 14) Tobacco cessation related professional development 15) Qatar law and ethics. The learning outcomes for each topic were mapped against ATTUD Core Competencies.The program was delivered through a combination of didactic lectures and active learning strategies including problem-based learning (PBL) exercises using case scenarios, group discussions, games, role plays, videos, simulated practical applications with peers and standardized patients, self and peer debriefing and performance feedback activities.A didactic lecture on women health and contraception was delivered to pharmacists in the control group. This was to prevent contaminating participants’ knowledge and skills with tobacco-related elements that could subsequently change their behavior to another level not representative of their tobacco-cessation-related “current practice” or “usual care.” Pharmacists in the control group received three CE units accredited by QCHP.The main outcome measure was the post-intervention tobacco-cessation skills, assessed through OSCE.A six-station OSCE, which targeted the core competencies and skills covered in the program, was completed by participants in both groups in the same location of the training program. OSCE cases were developed and validated for content and face validity, through several meetings, by a group of educators and researchers experienced in the field of tobacco cessation. The first case targeted tobacco use in a generally healthy female adult patient, the second included a pregnant woman contemplating to stop smoking, the third involved a teenager who smokes, the fourth targeted relapse prevention in a patient who recently quitted and experiencing withdrawal symptoms, the fifth case involved a smoker with cardiovascular disease, and the last case involved a smoker in the precontemplation stage of behavior change. During the OSCE, each participant was allocated 10 min for each case to interact with a standardized patient (SP) in a private counseling room. Each SP was extensively trained by the research team using a validated script for the corresponding case. Performance of participants was assessed by trained assessors (faculty members from the College of Pharmacy and advanced pharmacy practitioners) using assessment checklists designed and validated by the same group of experts. The checklists included both analytical and global assessment sections. The analytical assessment section evaluated the participants’ ability to establish rapport, gather relevant information, provide appropriate recommendations and follow-up evaluation for the patient. On the other hand, the global assessment part evaluated the participants’ communication skills in general, confidence, and body language using a 5-point rating scale. The number of items in the analytical checklist differed between one case and another. Given the complexity of cases, items were rated on two or three or four level scale (1 to 4 points). Assessors in every station were trained on how to use the checklists and were blinded regarding the participants’ groups. The standardized patient was not involved in rating or assessing the study participants.IBM SPSS software  was used for data analyses. The Consolidated Standards of Reporting Trials (CONSORT) guidelines were followed when analyzing the study data . DemograThe study protocol and all related instruments and forms were reviewed and granted ethical approval by the Qatar University Institutional Review Board (QU-IRB) .n = 77, 46.9%) and control  groups  and 32 participants (86.4%) in the control group completed the OSCE. The OSCE was conducted in the same week after the end of both the tobacco education and contraception training groups.The majority of the participants in the intervention group were male 56.1%), while most participants in the control group were female (55.9%)  were significantly greater in the group of pharmacists who received the intensive tobacco-related education in all the OSCE cases Table .The intensive tobacco education group achieved significantly higher overall total score than the control group for all the OSCE cases. Specifically, the mean total scores for the intervention group ranged from 14.2 to 16.3 compared to 6.2 to 11.3 for the control group for cases one to six  conducted in Qatar and the Middle East that involved designing, implementing, and evaluating an intensive education program on tobacco use and dependence disorder treatment for community pharmacists using an Objective Structured Clinical Examination (OSCE). Overall, the outcomes of this study showed that an intensive tobacco education program significantly enhances community pharmacists’ skills and competencies on tobacco cessation. Generally, the pharmacists who received the tobacco cessation training achieved higher scores in developing rapport, data gathering, disease management, and patient follow up/monitoring compared to those who did not. These results are comparable to the findings of other studies, which involved pharmacists or pharmacy students elsewhere , 15.The current study demonstrated the feasibility and the benefits of implementing an educational training on tobacco cessation for community pharmacists in Qatar. Evidence supports the real-life benefits of smoking cessation-training programs on pharmacists’ ability to offer smoking cessation –18. One p = 0.087), but a statistical significance was only found in one single counselling sequence  [p < 0.001) [p < 0.001; d = 2.3) [Different interventions and training programs related to tobacco cessation have been implemented and assessed for effectiveness among healthcare students and professionals using OSCE –26. For = 0.049) . A prosp< 0.001) . Moreove< 0.001) . MoreoveIt is worthwhile to note that no significant improvement was observed for the intervention group for the last OSCE case, which is about a patient who was not motivated in quitting smoking . Initiating discussions and establishing rapport with unmotivated smokers is essential for assisting patients to quit. Hence, future training efforts should emphasize more on how to deal with these patients and encourage them to stop smoking. This might be achieved by having more practical cases and simulated scenarios that target unmotivated patients in the training program.It is noteworthy to mention that the estimated sample size was achieved for the intervention group, but not for the control group. A possible reason for the low enrollment rate in the control group could be the topic itself. Pharmacists may not have been interested in the “women health and contraception” topic versus the “ tobacco cessation” topic.This study has some limitations. First, allocation concealment or blinding of participants was not possible due to the nature of the study design and program. This might have resulted in some bias as the intervention group might have better prepared for the OSCE. Second, the skills of the participants in both groups were not assessed at baseline before receiving the training programs. Third, the predetermined sample size was not achieved especially in the control group due to difficulties in recruiting participants for both the training and the OSCE as most community pharmacists in Qatar do not have flexible working schedules and many have very long working hours. Moreover, in this study we were not able to examine if the study results represent real differences in the pharmacists’ tobacco cessation activities in practice. Despite these limitations, this study showed the usefulness of an intensive tobacco cessation educational program on the skills of community pharmacists. Future studies are required to examine if pharmacists’ OSCE performances are translated into real-life smoking cessation counseling interventions in their practice settings. This can be assessed through actual unannounced visits to community pharmacies using standardized patients or simulated clients to evaluate if there are any changes in pharmacists’ actual competencies in their clinical settings. Moreover, additional research is warranted to assess the impact of the program on smokers’ quitting rates in Qatar.This study has demonstrated that an intensive tobacco cessation education program can improve pharmacists’ objectively measured tobacco cessation skills and increase their abilities of tobacco cessation counseling. Any improvements in pharmacists’ tobacco cessation related skills could ultimately decrease tobacco use and dependence among their patients. We believe that the tobacco cessation program that we designed and implemented can be incorporated into a wider and larger scale CPD program in Qatar to develop a critical mass of knowledgeable pharmacists with effective tobacco cessation intervention skills.


Magnetic resonance imaging (MRI) is widely used for monitoring and measuring MS lesions. Automated methods for MS lesion segmentation have usually been performed on individual MRI scans. Recently, tracking lesion activity for quantifying and monitoring MS disease progression, especially detecting new lesions, has become an important biomarker. In this study, a unique pipeline with a deep neural network that combines U-Net, attention gate, and residual learning is proposed to perform better new MS lesion segmentation using baseline and follow-up 3D FLAIR MR images. The proposed network has a similar architecture to U-Net and is formed from residual units which facilitate the training of deep networks. Networks with fewer parameters are designed with better performance through the skip connections of U-Net and residual units, which facilitate information propagation without degradation. Attention gates also learn to focus on salient features of the target structures of various sizes and shapes. The MSSEG-2 dataset was used for training and testing the proposed pipeline, and the results were compared with those of other proposed pipelines of the challenge and experts who participated in the same challenge. According to the results over the testing set, the lesion-wise F1 and dice scores were obtained as a mean of 48 and 44.30%. For the no-lesion cases, the number of tested and volume of tested lesions were obtained as a mean of 0.148 and 1.488, respectively. The proposed pipeline outperformed 22 proposed pipelines and ranked 8 Multiple sclerosis (MS) is an autoimmune disease characterized by demyelinating axons in the central nervous system, resulting in white matter (WM) lesions  ” 3 to 1.2 ×1.2 ×1.2 mm3. The dataset was divided into two groups for training and testing. 40 image pairs were used for the training and the remaining were used for testing. For each patient, raw 3D T2/FLAIR MRI pairs were obtained from 15 different MRI scanners at 1.5T and 3T. A rigid registration was applied to these images to bring them into a middle point in which the ground truth data was calculated by the challenge organizers. Thereafter, a consensus delineated ground truth data for the follow-up images were formed by a majority voting among the four experts and validated by a senior expert neuroradiologist.In this study, a total of 100 patients' MRI data that was associated with MS disease provided by the MSSEG-2 challenge th percentile and Kernel Density Estimate (KDE) with the Gaussian kernel similar to one described by Reinhold et al. . To prepare input data, each 3D image was divided into its axial, sagittal, and coronal views. Two-channel input feature data was created using each corresponding 2D slice from both time points as discussed previously. Keras (version=2.4)gt denotes the ground truth, and pr indicates prediction. 0.25 and 2.0 default values were used for the parameters of α and γ, respectively.where Keras data generator was used for performing real-time data augmentation such as vertical flipping, horizontal flipping, random rotation, and shift range to increase the number of training samples. To compare components of the designed network, a testing subset was created from the MSSEG-2 test dataset provided by the challenge organizers. This subset comprised MRI data of 7 patients by considering the different scanners and new lesion loads. Satisfactory results with the MSSEG-2 dataset could not be obtained by the implementation of the original U-Net. Therefore, this implementation was modified with transpose upsampling instead of a simple upsampling operation, and batch normalization to make the neural network more stable. A hybrid loss function, the summation of binary focal and dice losses, was used for all models.The segmentation of new lesions was considered one of the two most important evaluation criteria for the challenge. This indicates how many new lesions are precisely overlapped in the ground truth which is also known as the Dice score  and lesion positive predictive (P) can be calculated with the following equations  and manual annotation mask (G) while surface metric computes the average symmetric surface distance. The surface metric considers contours obtained from the segmentation output and manual annotation mask. As described in Commowick et al. :Positive Predictive Value (Se):Sensitivity (Sp):Specificity :Mean Surface Distance  were realized for the new MS lesion segmentation. The results of U-Net, U-Net + AGs, U-Net + RUs, and U-Net + RUs + AGs are presented in In this study, a deep learning model was developed to handle the problem of identifying new MS lesions using the baseline and the follow-up 3D FLAIR MR images. Activity segmentation particularly for new lesions is a more challenging task compared to lesion segmentation in a single-time MR scan due to small lesion loads. MS lesion segmentation using traditional and deep learning approaches has usually been studied in a single MRI scan in recent years. However, deep learning approaches for MS lesion activity using the baseline and follow-up MR images still remain limited. In most of these studies, the researchers have been using their own datasets making it difficult to compare and reproduce their results with the proposed pipeline. Thus, in this study, comparisons were performed on the automated methods proposed in the challenge. Moreover, comparisons were performed among components used for building the designed network as well. The proposed network, which combines the strengths of U-Net, residual units, and attention gates, has outperformed other methods comprising different combinations of components in terms of dice and F1 scores.A whole-brain slice-based approach was used as patch-based CNNs lack spatial information about MS lesions due to the patch size limitation  was used to evaluate the segmentation performance of both automated and expert delineation outputs. As seen in The evaluation metrics for no new lesions are indicated in Instead of using a 3D segmentation approach requiring more computational power and learning parameters, the proposed method and the slice-based approach were used together for detecting and segmenting new lesions on the follow-up images. While the appearance of new lesions is of primary interest for the challenge, enlarged or disappearance of MS lesions could be also studied. Different MRI modalities such as T1-and T2-weighted can also be incorporated into the given task to extract more features related to the size or location of new MS lesions even though the FLAIR images reveal lesions as more intense. To achieve a robust automated model for the given task, large datasets from different scanners are needed; however, it is difficult to obtain such datasets.In this study, an automated pipeline for new MS lesion segmentation using the baseline and follow-up 3D FLAIR MRI has been designed with a deep learning-based network that fuses the strengths of U-Net, residual learning, and AG. For more accurate segmentation of new MS lesions, this network architecture was designed as a deep encoder-decoder network to enhance the U-Net by replacing plain blocks with residual blocks and adding attention gates. These residual blocks replaced with the plain blocks facilitate the training. Skip connections within both residual units and U-Net facilitates the propagation of information in both forward and backward phases during the training procedure. AGs integrated into the proposed model emphasize important features propagated over skip connections. A hybrid loss function was introduced as the addition of dice loss and 1 × binary focal loss. The input data for the proposed method was prepared by converting 3D scans into their plane orientations of axial, sagittal, and coronal views which yielded 2D slices. Baseline and follow-up slices were stacked to create a two-channel feature mapping for each plane orientation. Then, all slices extracted from all three planes were grouped into a single input to increase training samples and to use the contextual information in all directions. The predicted 2D slices for each view were aggregated using a majority voting to generate the final 3D binary output. Although new MS lesion segmentation and detection pose a difficult problem due to small lesion sizes, the proposed method has achieved comparable segmentation performance compared to the experts and top-ranked automated methods in the challenge. Finding the appropriate data sets and using the existing ones as publicly available will reduce the gap for the data required in these studies and the lack of which is frequently discussed, and will allow different studies to be carried out. This study provides clues about the recent techniques regarding the MS lesion activity segmentation that can be used as a guide for future studies in this field.https://portal.fli-iam.irisa.fr/msseg-2/data/.The dataset used and analyzed in this study can be accessed online at BS conducted the experiments and organized the main manuscript. DZS participated in the writing and modifying of the English grammar of the manuscript. Both authors analyzed the results and reviewed the manuscript. 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Multiple data augmentation methods are combined to improve the generalization ability of the model. Ultimately, the detection and recognition of traffic lights can be realized. The S2TLD dataset is selected for training and testing, and it can be proved that the recognition accuracy and model size are greatly optimized. Meanwhile, a self-made dataset is selected for training and testing. Compared with the conventional YOLOv4, the recognition accuracy of the proposed algorithm for traffic lights’ state information increases by 1.79%, and the model size decreases by 81.97%. Appropriate scenes are selected for real-vehicle testing and the results demonstrate that the detection speed of the presented algorithm increases by 16%, and the recognition effect for small targets increases by 37% in comparison with conventional YOLOv4.To resolve the issues of a deep backbone network, a large model, slow reasoning speed on a mobile terminal, low detection accuracy for small targets and difficulties detecting and recognizing traffic lights in real time and accurately with YOLOv4, a traffic lights recognition method based on improved YOLOv4 is proposed. The lightweight ShuffleNetv2 network is utilized to replace the CSPDarkNet53 network of YOLOv4 to satisfy the requirements of a mobile terminal. The reformed k-means clustering algorithm is applied to generate anchor boxes for avoiding the sensitivity issue of outliers and initial values. A novel attention mechanism named CS Nowadays, urban traffic networks are becoming more and more complex. To ensure autonomous vehicles can be integrated into urban transport safely it is essential for them to be able to recognize the state of traffic lights rapidly and accurately. Consequently, the detection and recognition of traffic lights have attracted more attention from research scholars ,2. CurreSince conventional image processing approaches are very sensitive to environment, the robustness for various environmental illuminations is rather poor, which cannot be conducive to applying it further in practice ,6,7. DeeBased on the abovementioned pioneering research, a traffic lights detection and recognition method based on the improved YOLOv4 is presented herein.●To resolve the issues of deep backbone network, large model and slow reasoning speed on a mobile terminal, the CSPDarkNet53 network of YOLOv4 is replaced by the lightweight ShuffleNetv2 network.●The improved k-means clustering algorithm is deployed to obtain anchor boxes to avoid the influences of outlier points on clustering effects and compensate for the sensitivity issue of initial clustering center of the original algorithm.●2A is inserted to enhance effective features and augment the ability of feature extraction.A novel attention mechanism named CS●Multiple data augmentation methods are utilized to enrich samples and ameliorate the generalization and robustness of the model.The main content of this work contains eight sections. YOLOv4 is a target detection algorithm. The network structure is composed of backbone network and detection network, which are applied for feature extraction and multiscale prediction, respectively . The oveCSPDarkNet53 network is utilized as the backbone network of YOLOv4. The original 52 convolutional layers in DarkNet53 are retained in CSPDarkNet53, and the cross-stage feature fusion strategy is added in DarkNet53 . CompareUnlike DarkNet53, the Mish activation function is utilized in CSPDarkNet53. Mish is a smooth and non-monotonic activation function . The expCompared with LeakyReLU activation function utilized in DarkNet53, the Mish activation function is equipped with an outstanding ability of generalization and can stabilize network gradient flow better and ameliorate the quality of results.The detection network of YOLOv4 can be divided into a feature fusion module and a prediction network module named YOLO HEAD. The feature fusion module mainly consists of an SPP spatial pyramid pooling module, and the PANet feature fusion network is deployed to acquire three reinforcing feature layers by fusing multi-scale features. The prediction network module is applied to predict the results.The SPP spatial pyramid pooling module is applied to fix image size to equip the network for inputting images of arbitrary size and to avoid geometric distortion caused by cropping or stretching input image, which affects original features .The PANet network is a feature fusion network based on Feature Pyramid Network (FPN) . Large tThree enhanced feature layers are put into the prediction network module. One-time convolution of 3 × 3 is applied to integrate features. One-time convolution of 1 × 1 is used to adjust the number of channels. The prediction results are obtained by classification and regression in accordance with the number of classes in the training set.Based on YOLOv4, an improved lightweight target detection network is proposed to address the issues of deep backbone network, large model, slow reasoning speed on a mobile terminal, low detection accuracy for small targets and difficulties in detecting and recognizing traffic lights in real time and accurately. According to the structure of the algorithm, the lightweight ShuffleNetv2 network is utilized to replace the CSPDarkNet53 network of YOLOv4. A novel attention mechanism based on CA attention mechanism is added after three effective feature layers of the output of the backbone network. In addition, the ameliorated k-means clustering algorithm is adopted to obtain appropriate anchor boxes. Multiple data augmentation methods are combined to enhance the generalization ability of model. Ultimately, the network model can meet the requirements of a mobile terminal while having better detection and recognition effects for small targets like traffic lights. The overall structure of improved YOLOv4 is demonstrated in The lightweight ShuffleNetv2 network is regarded as a backbone network in the framework of YOLOv4. The ShuffleNetv2 network, which was proposed by improving the ShuffleNetv1 network  with a lTwo types of block unit for the ShuffleNetv2 are elaborated in As stride is equal to 1, the block unit in As stride is equal to 2, the block unit in Generally, ShuffleNetv2 is consistent with ShuffleNetv1 in the overall framework. To satisfy the four principles of lightweight network design propounded by Ma N et al. , a largeThe overall structure of ShuffleNetv2 is described in Human eyes can quickly scan a whole image to find the regions of interest (ROI) and then allocate attention to these regions. Finally, the ROI in an image can be perceived with high resolution with detailed information, while the surrounding background around it can be perceived with low resolution to suppress useless information . By intrAs for the two-dimensional image put into CNN, the two dimensions stand for scale space of image and channel, respectively. Traditionally, an attention mechanism can be divided into channel attention mechanism, spatial attention mechanism and a combination of the two attention mechanisms. The channel attention mechanism focuses on the enhancement or suppression of the importance of different feature channels, spatial attention mechanism focuses on the enhancement or suppression of the importance of different image features, and spatial and channel fusion attention mechanism focuses on both.2A is proposed, which further fuses the spatial attention and channel attention fused with location information. The CS2A is a spatial and channel fusion attention mechanism that pays more attention to scale space. The principle of CS2A is presented in Convolutional Block Attention Module (CBAM), which combines channel attention and spatial attention in parallel, can effectively increase the effect of target detection . CA embeThe channel attention fused with location information is utilized as the new channel attention mechanism.Average-pooling is applied in images along width and height directions, as indicated in Equations (2) and (3). c-channel with h-height. c-channel with w-width. c-channel.Of which, AF denotes nonlinear activation function; 1F represents convolution transformation function of 1 × 1; and f signifies intermediate feature mapping. The above outputs are concatenated and the nonlinear activation function is introduced to obtain intermediate feature mapping of spatial information coding, as expressed in Equation (4).f can be adjusted to get the weights of feature points along two directions, Equations (5) and (6) are presented as follows.The tensor Of which, Finally, the output of the new channel attention One-time maximum pooling and one-time average pooling are performed to the input channel attention feature layer 7F stands for convolution transformation function of 7 × 7.Among which, The sigmoid activation function can be introduced to get the weights of feature points, as represented in Equation (9).Eventually, the output of spatial attention, that is, the output of the whole attention mechanism, is expressed as the following Equation (10).K-means clustering algorithm is utilized in YOLOv4 to obtain anchor boxes for the PASCAL VOC dataset . As the C is the prediction border, G donates the actual border, and d represents the distance.Similar to the clustering algorithm in YOLOv4, Intersection over Union (IoU) is introduced as the distance in the improved k-median + + clustering algorithm, as expressed in Equations (11) and (12).To resolve the sensitivity issue of outliers, the median value of sample points is deployed to update clustering centers.c is randomly chosen. The distance D among each sample point ix and the existing initial clustering centers can be calculated to acquire the probability P that the sample point can be regarded as the next initial cluster center, as expressed in Equation (13).For the sensitivity issue of initial clustering centers, an initial clustering center The next initial clustering center is determined by roulette method and k initial clustering centers are obtained. Through continuous iterative update of clustering centers, the appropriate anchor boxes are generated.Mosaic data augmentation is employed in YOLOv4, which splices four images after flipping, scaling and color gamut transformation to enrich samples and ameliorate the generalization and robustness of the model. Since the detection and recognition of traffic lights mainly focus on colors and digitals, the original Mosaic data augmentation is not appropriate. Hence, the flipping and color gamut transformation modules of the original Mosaic data augmentation are deleted. The random difference and other modules are added, which are more suitable for the detection and recognition of colors and digitals. Moreover, Copy-Paste data augmentation  and MixuPrecision denotes precision rate, Recall represents recall rate, TP is the number of correctly identified positive samples, FP stands for the number of incorrectly identified positive samples, and FN is representative of the number of incorrectly identified negative samples.To evaluate overall performances of different algorithms, Average Precision (AP), mean Average Precision (mAP), model size, model parameters, floating point of operations (FLOPs) and detection speed are regarded as evaluation criteria.The test operating system is Ubuntu 18.04, the GPU is NVIDA GeForce RTX 3090 24G, and the CPU is Intel i9-11900K 3.5 GHz. The test environment is CUDA 11.2, cuDNN 8.4.0, Python 3.8. The deep learning framework is Pytorch 1.7.0.2TLD) with 5786 images from Shanghai Jiao Tong University is selected, including 14130 instances of five categories consisting of red, yellow, green, off and waiting. Some typical images are displayed in The open-source traffic lights dataset named Small Traffic signal light Dataset A new method for generating anchor boxes is adopted herein. Compared with the traditional k-means clustering algorithm, the k-median ++ clustering algorithm is less sensitive to outliers and initial values.(2)A novel attention mechanism is propounded herein. A spatial attention mechanism tightly coupled with channel attention further loosely couples with spatial attention, which makes the new attention module pay more attention to scale space information while making a compromise between scale space and feature channel to boost the recognition effect for small targets in a complex background.(3)A multiple data augmentation fusion method is adopted. Three data augmentation methods are fused and the hyper-parameters are adjusted by actual test to expand sample size and improve the generalization and robustness of the model.(4)Compared with YOLOv4, the proposed algorithm has better recognition effect for the state of traffic lights. The model size is significantly reduced, the detection speed is increased, the fitting ability is stronger and the recognition effect for small targets is better, which corresponds to the detection demands of actual autonomous vehicles for traffic lights. Compared with the similar lightweight algorithms YOLOv4-tiny and MobileNetv3-YOLOv4, the proposed algorithm has a slight decrease in how lightweight it is. Nevertheless, the effect on actual target detection can be significantly enhanced, and it is more suitable for target detection in actual autonomous driving.(5)The established traffic light dataset was artificially collected outdoors and contains 2872 images, 12 categories and 7862 examples, including sunny, cloudy, night, evening and other different lighting conditions scenes, as well as red, green, red LED light digitals within five and green LED light digitals within five. Rich target and superior image quality provide the foundation for subsequent research on the perception of traffic lights.The main contributions can be summarized as follows:However, the overall mAP of the models is not high owing to limited digital image collection, and future work will focus on the expanded self-made dataset. Meanwhile, in consideration of the poor fitting ability of YOLOv4, a network with better fitting ability can be selected by utilizing XAI tools  to enhan


In this model, the cell uses the Ca2+ current to negatively regulate IFT injection. The recent discovery that IFT entry into flagella is regulated by the phosphorylation of kinesin through a calcium-dependent protein kinase has provided further impetus for the ion-current model. To test this model, we measured and manipulated the levels of Ca2+ inside of Chlamydomonas flagella and quantified IFT injection. Although the concentration of Ca2+ inside of flagella was weakly correlated with the length of flagella, we found that IFT injection was reduced in calcium-deficient flagella, rather than increased as the model predicted, and that variation in IFT injection was uncorrelated with the occurrence of flagellar Ca2+ spikes. Thus, Ca2+ does not appear to function as a negative regulator of IFT injection, hence it cannot form the basis of a stable length control system.Eukaryotic cilia and flagella are microtubule-based organelles whose relatively simple shape makes them ideal for investigating the fundamental question of organelle size regulation. Most of the flagellar materials are transported from the cell body via an active transport process called intraflagellar transport (IFT). The rate of IFT entry into flagella, known as IFT injection, has been shown to negatively correlate with flagellar length. However, it remains unknown how the cell measures the length of its flagella and controls IFT injection. One of the most-discussed theoretical models for length sensing to control IFT is the ion-current model, which posits that there is a uniform distribution of Ca Chlamydomonas reinhardtii adjusts the length of both flagella to a length suitable for swimming . Because no protein synthesis occurs in the flagellum, new materials must be brought into the flagellum and carried to the site of assembly at the flagellar tip. IFT mediates this transport. IFT is carried out by huge protein complexes called trains, which have an elongate shape and move on the flagellar axoneme back and forth like a train . IFT tra2+ are known to change flagellar length in Chlamydomonas . The ion-current model was first proposed by ydomonas . The callagellum . CAV2 islagellum . The pprresponse . TRP chaflagella .2+ contributes to flagellar length regulation, but a potential molecular mechanism has been proposed by Pan and colleagues  cells and removed the membrane from flagella so that Ca2+ can access the axoneme-associated DRC4-GCaMP (or DRC4-mCherry-GCaMP). Isolated axonemes were observed in various concentrations of free Ca2+ solutions  microscopy during flagellar regeneration. Chlamydomonas flagella were amputated with the pH shock method , allowing us to measure Ca2+ influx as a function of flagellar length as the flagella regrow. We observed intensity fluctuation of GCaMP in live cells, indicating the presence of Ca2+ influx . Longer flagella generally allowed more Ca2+ influx but also had greater variability. This variability is mainly caused by the fact that the Ca2+ influx does not occur consistently. The frequency of flagellar Ca2+ influx events (spikes) was not correlated with the flagellar length (2=0.02). The pattern of Ca2+ influx events varied from flagellum to flagellum and from time to time and did not match a Poisson distribution  and the magnitude of the influx was smaller than control on the average , which is a subunit of the heteromeric kinesin-2 motor (IFT kinesin), tagged with green fluorescent protein (GFP). We focused on the kinesin-2 motor because this is the component of IFT that is apparently regulated by Ca2+ , and rescued the phenotype of the fla3 mutant . A total of 150 nM free Ca2+ is comparable to the cytosolic Ca2+ concentration but was enough to eliminate detectable flagellar Ca2+ influx , the fla3-1 mutant expressing KAP-GFP (CC-4296)(fla10-1 mutant (CC-1919) (pf2-4 mutant (CC-4404)(Chlamydomonas cells were grown in liquid Tris-acetate-phosphate (TAP) medium media  ((CC-4296), the flaCC-1919) , and the(CC-4404) were obtoshimura . The iftechtreck . The genescribed . The prect (CLiP). Chlamydic acid)  at room Chlamydomonas liquid cultures were adjusted to pH 4.5 by adding 0.5 M acetic acid and incubated for 1 min to amputate flagella, and then the pH was returned to pH 7.0 with 0.5 M KOH. The pH shocked cells were pelleted by centrifugation and resuspended with fresh TAP media with or without 1 mM EGTA . For the flagellar disassembly assay, we used the fla10 temperature-sensitive kinesin mutant. The fla10 mutant and fla10 ppr2 double mutant cells were cultured in TAP media at room temperature for 1 day and then 1 mM EGTA was added, after which the cultures were transferred to a 33 °C incubator with continuous illumination. Cells were fixed with 2.5% glutaraldehyde before and at time points after pH shock or temperature shift. Fixed cells were imaged by differential interference contrast (DIC) microscopy using an inverted microscope  with an air objective  and a CCD camera . The lengths of 20 biflagellated cells at each time point and each sample were measured using ImageJ . We often observed the fla10 mutant and fla10 ppr2 double mutant cells with a single flagellum after the temperature shift but measured only the cells with two flagella. We did not observe any fla10 ppr2 double mutant cells with two flagella at 300 min after the temperature shift.Flagellar regeneration assays were performed in the same manner as previously described . BrieflyChlamydomonas flagella were amputated by the pH shock method as described above. #1 (22 x 22 mm) coverslips were coated with 0.1% poly-L-lysine solution  for 10 min and washed three times with deionized water. A culture of flagella-regenerating cells was placed on a poly-L-lysine-coated coverslip for 3 min and removed most of the media. Then, the coverslip was mounted on glass slides with 2% agarose/TAP with or without 1 mM EGTA and sealed with VALAP . Agarose embedding prevents cells from gliding. Images were acquired on an inverted microscope  configured for TIRF microscopy using an oil objective , MLC400 laser combiner with solid-state lasers of 405, 488, 561, and 647 nm  and an electron-multiplying CCD  camera controlled with Nikon Elements Software (v5.20.00 b1423). Image sequences were continuously acquired using triggered acquisition using the 488 nm and/or 561 nm lasers  and single band-pass  or dual band-pass emission filters  with a camera exposure of 50 ms and an EM gain of 300 used for both channels.All live-cell imaging was performed during the regeneration of flagella. pf2-4 DRC4-GCaMP or pf2-4 DRC4-mCherry-GCaMP strains. Flagellar isolation and demembranation were performed as previously described  or 10mM CaEGTA (39µM free Ca2+) from the calcium calibration buffer kit  and then observed on the TIRF microscope . GCaMP intensity was measured using ImageJ. Each axoneme was traced by hand using the Segmented line tool with a 7-pixel line width, analyzed by the Plot Profile function, and then average intensity per pixel was calculated. At least 13 axonemes were analyzed for each calcium concentration.To quantify the GCaMP reactivity, axonemes were isolated from the escribed . For Ca22+ influx was detected using the findpeaks function on MATLAB. The intensity of Ca2+ influx was calculated as the sum of the peak area of Ca2+ influx divided by imaging time.For GCaMP intensity analysis, kymographs were generated from movies of DRC4-GCaMP using ImageJ. GCaMP kymographs were analyzed in MATLAB . The kymographs were smoothed using a 2D Gaussian filter and photobleach-corrected. The peak of CaFor quantitative analysis of IFT injection and behavior, kymographs were generated from movies of KAP-GFP or mScarlet-IFT54 using Kymograph Clear . IFT tra2+ influx was calculated in the same manner as described above. To investigate the short-term effect of Ca2+ influx on IFT, we selected the kymographs without Ca2+ influx for more than 10 s before bundles of Ca2+ influx that had more than 2000 intensities. We calculated IFT injection, velocity, and frequency for 10 s and 15 s each before and after the start of the Ca2+ influx in these kymographs. The intensities of IFT injection and returning IFT were calculated as the sum of the intensity of IFT trajectories that started from or returned to the base within the time range. We also calculated the background intensity from the kymographs which removed both anterograde and retrograde IFT trajectories. All calculations were performed using custom-written routines in MATLAB.For analysis of dual-channel imaging, dual-channel movies were converted to kymographs for each channel. The mScarlet channel showed slight bleed-through to the GCaMP channel because we used the dual band-pass emission filter for imaging. To remove the weak bleed-through of mScaret-IFT54 signals from DRC4-GCaMP kymographs, 20% intensity of the mScaret-IFT54 kymograph was subtracted from the GCaMP kymograph. The intensity of Ca2+ influx between the control and the ppr2 mutant was tested using an unpaired t test. Statistical significances of IFT behaviors were tested using a paired t test.For statistical analyses and graph generation, we used GraphPad Prism . The statistical significance of CaWe constructed a computational model of flagellar length control based on the general concept proposed by where α is a proportionality constant.Second, we assume that CDPK1 activity depends on the instantaneous calcium concentration according to a saturable binding relation with some dissociation constant that is an adjustable parameter of the model. In effect, this assumption means that the kinetics of allosteric regulation of CDPK1 activity by calcium occur on a much faster time scale than the other events of our model. This yields the relation:CDPK is the fraction of enzyme that is active, and KD is the dissociation constant of calcium binding by CDPK1.where FThird, we assume that phosphorylation of kinesin by CDPK1, as well as subsequent dephosphorylation of kinesin, both take place on a fast timescale relative to the timescale of flagellar length change, such that the fraction of active kinesin is at a quasi-state in the model. Given this assumption, we represent the fraction of phosphorylated kinesin as a function of the relative activity of CDPK1 according to a Michaelis-Menten equation:M is the Michaelis constant for the interaction of the enzyme CDPK1 with the kinesin substrate.where KFourth, based on the results of where A is a proportionality constant describing the total pool of kinesins, such that AK is the pool of kinesins currently active, as well as the incremental increase in length that occurs when a kinesin delivers cargo to the tip. D is the rate of disassembly, which is assumed to be constant based on prior measurements .https://github.com/ishikawaUCSF/IonCurrentModel, (copy archived at swh:1:rev:c916c6101e469bab1b1f0b6fa2a79bc56266c390).The net growth rate is used to update the length using the Euler method with a timestep of 0.02 s. Output values are stored every 50 timesteps (1 second of simulation). Source codes of the model and data analyses used in this work  can be f This paper is valuable and of interest to scientists studying primary cilia/flagellar formation and regulation. It addresses how ciliary/flagellar length is controlled and whether calcium negatively regulates Intraflagellar transport (IFT) injection. The study convincingly demonstrates that calcium influx correlates with flagellar length, but calcium does not appear to work as a negative regulator of IFT injection, which challenges a previous model. The models and methods are generally sound. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Piali Sengupta as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Testing the ion-current model for flagellar length sensing and IFT regulation" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) The authors only examined the impact of reducing ciliary calcium influx. To further support the conclusions, it is recommended that the authors examine IFT injection in a condition where ciliary calcium level is increased. Using a calcium ionophore may not be a good choice as it may change the global cellular calcium level. One approach to consider is to use mutants of a calcium pump present in cilia.2) The authors conclude that calcium does not act as a negative regulator of IFT injection. However, close examination of the calcium flux in Figure 3B and IFT injection in Figure 4B of cilia less than 6 microns, suggests that calcium influx is higher in the ppr2 mutant than in control cilia and IFT injection is reduced in the mutant compared to the control. Thus, this analysis shows the opposite result of the authors' conclusion, and is supporting the previous model. These data should therefore be re-analysed focusing on the early stages of ciliary assembly. In addition, the graphs in Figure 4b, c, d are very hard to read and should be edited for clarity.3) The conclusion on line 272-273 needs more evidence. The authors showed that addition of 1 mM CaCl2 does not change ciliary assembly, and used this to argue against the ion-current model. The addition of calcium extracellularly may not alter intracellular/intraciliary calcium level given that cells have robust systems to control calcium homeostasis. To support the authors' conclusion, one should measure the changes of calcium level in the cell/cilia under these conditions; alternatively, authors should temper their conclusion.2+. Prior to each DRC4-GCaMP cluster of pulses one can observe stalled mScarlet-IFT54 which is then cleared with the pulse of ca2+. Is this relationship observed in the other data-sets used in this figure or is this an artifact of this particular image? Earlier in the paper the frequency of ca2+ appeared random in relation to length. Does it instead relate to stalled IFT trains in the leading or following flagella during gliding? How were the chlamy blocked from gliding during imaging? It is not apparent from the methods.4) In figure 7A the authors show an example kymogram to illustrate the time course used to measure rapid changes in response to changes in ca5) In figure 4B the authors show "the mean KAP-GFP intensity of each flagellum" as a measure of total levels of IFT in the cilia at one time. In the main text this is described as "injection intensity." While total levels of IFT in the cilia are certainly influenced by injection rates it is also influenced by other factors such as IFT retrograde trafficking rates, turn around at the tip and the number of stalled trains. Were any of these other factors changed in their conditions? The authors should discuss what exactly they are measuring when they refer to injections in this paper, especially to make the paper more accessible to a broader audience. Furthermore, the authors may want to cite and discuss the Nievergelt et al. 2022 paper from Gaia Pigino's lab, which is listed in the references but not cited in the text.Reviewer #1 (Recommendations for the authors):1. Figure 1 B. It has been reported by Liang et al. (Dev Cell 2014) that phosphorylation of FLA8 dissociates FLA8 from IFT complex. Thus, it is unknown whether kinesin-2 is inactivated. I suggest that active/inactive kinesin-2 is changed to active/inactive IFT.2. Using a condition where intraciliary calcium can be increased to analyze IFT injection, which is critical for drawing a conclusion on calcium regulation of IFT injection.3. For Figure 3B and 4B, I suggest the authors do another mathematical analysis but only include the data for cilia shorter than about 6 micron.Reviewer #3 (Recommendations for the authors):I would like to start by thanking the authors for the work they have done.1) In figure 4B the authors show "the mean KAP-GFP intensity of each flagellum" a measure of total levels of IFT in the cilia at one time. In the main text this is described as "injection intensity." While total levels of IFT in the cilia are certainly influenced by injection rates it is also influenced by other factors such as IFT retrograde trafficking rates, turn around at the tip and the number of stalled trains. Were any of these other factors changed in their conditions? The authors should discuss what exactly they are measuring when they refer to injections in this paper, especially to make the paper more accessible to a broader audience.2+. Prior to each DRC4-GCaMP cluster of pulses one can observe stalled mScarlet-IFT54 which is then cleared with the pulse of ca2+. Is this relationship observed in the other data-sets used in this figure or is this an artifact of this particular image? Earlier in the paper the frequency of ca2+ appeared random in relation to length. Does it instead relate to stalled IFT trains in the leading or following flagella during gliding? How were the chlamy blocked from gliding during imaging? I could not find it in the methods.2) In figure 7A the authors show an example kymogram to illustrate the time course used to measure rapid changes in response to changes in ca3) The graphs in Figure 4 b,c,d are incredibly hard to read. Color changes or redesign would be much appreciated. Perhaps turn the individual data point opacity down so the trend lines are more visible? Essential revisions:1) The authors only examined the impact of reducing ciliary calcium influx. To further support the conclusions, it is recommended that the authors examine IFT injection in a condition where ciliary calcium level is increased. Using a calcium ionophore may not be a good choice as it may change the global cellular calcium level. One approach to consider is to use mutants of a calcium pump present in cilia.Chlamydomonas flagella proteome  and ordered several mutant strains from Chlamydomonas Library Project (CLiP) which are annotated as affecting these pumps. We measured the flagellar length of these potential calcium pump mutant strains, but none showed a statistically significant difference in length relative to control cells. We have now included this data as Figure 5 —figure supplement 1. Because no length change was observed, we did not perform the extremely time consuming process of constructing strains that contain these mutations along with DRC4-GCaMP and KAP-GFP.We thank the reviewers for this suggestion. The calcium current model would predict that if a calcium pump mutant failed to export calcium, the increased calcium building up inside the flagellum should lead to decreased IFT entry and a shorter flagellar length. We found at least two calcium pumps in the published As an alternative strategy to get at this reviewer's suggestion, we measured DRC4-GCaMP and KAP-GFP intensity in 1 mM CaCl2 treated flagella and found that CaCl2 treatment increases both the flagellar calcium level  and IFT injection . This increase in IFT injection is the opposite of what the calcium current model predicts.Based on these results, we think the calcium pump experiment is not necessary because of the following reasons. 1. These calcium pump mutants might not increase the flagellar calcium level. 2. Even if the flagellar calcium was increased in these mutants, it does not affect the flagellar length and thus our conclusions would still hold. 3. These mutant strains might still have functional calcium pumps since the existing data on calcium pumps in flagella is likely to be incomplete. 4. The CaCl2 experiment clearly increased the flagellar calcium level inside flagella, directly addressing the point that the reviewer is getting at.2) The authors conclude that calcium does not act as a negative regulator of IFT injection. However, close examination of the calcium flux in Figure 3B and IFT injection in Figure 4B of cilia less than 6 microns, suggests that calcium influx is higher in the ppr2 mutant than in control cilia and IFT injection is reduced in the mutant compared to the control. Thus, this analysis shows the opposite result of the authors' conclusion, and is supporting the previous model. These data should therefore be re-analysed focusing on the early stages of ciliary assembly. In addition, the graphs in Figure 4b, c, d are very hard to read and should be edited for clarity.Thank you for pointing this out, but this result of apparently low calcium influx in shorter flagella in control cells is due to bias from technical issues: it is relatively difficult to image shorter flagella in our TIRF imaging setup, because shorter flagella have less flagellar surface area to attach the coverslip. The more motile the flagella are, the more likely are the cells to detach when their flagella are short, because the bending force of the flagella is strong enough to pull them away from their small area of adhesion. This effect is much stronger in control cells than in either the ppr2 mutants or EGTA treated cells, whose flagella are less motile. This led to a reduced number of cells examined with flagella shorter than 6 μm . To overcome the difficulties and biased result, we observed more flagella in control cells. The new data was integrated with previous data and shown in Figure 3. The new result shows that calcium influx in control cells is indeed higher than in the ppr2 mutant cells. So, our result is remains consistent with our conclusion, and we believe that we do not have to analyze the shorter flagella separately.Regarding the clarify of the graphs, we have updated the graphs in Figures 3 and 4 by reducing the size and opacity of each point to increase the visibility of the trend lines.3) The conclusion on line 272-273 needs more evidence. The authors showed that addition of 1 mM CaCl2 does not change ciliary assembly, and used this to argue against the ion-current model. The addition of calcium extracellularly may not alter intracellular/intraciliary calcium level given that cells have robust systems to control calcium homeostasis. To support the authors' conclusion, one should measure the changes of calcium level in the cell/cilia under these conditions; alternatively, authors should temper their conclusion.This is an excellent point. We have now quantitatively measured the GCaMP intensity inside flagella when 1 mM CaCl2 was added to the media. The data has now been integrated with previous data and shown in Figure 3. It is apparent in this graph that 1 mM CaCl2 treatment produces a statistically significant increase in flagellar calcium levels. We also measured KAP-GFP intensity in CaCl2 treated flagella, and found that CaCl2 addition increased IFT injection . We believe that these new results strengthen our conclusions, and we thank the reviewer for encouraging us to do these experiments.2+. Prior to each DRC4-GCaMP cluster of pulses one can observe stalled mScarlet-IFT54 which is then cleared with the pulse of ca2+. Is this relationship observed in the other data-sets used in this figure or is this an artifact of this particular image? Earlier in the paper the frequency of ca2+ appeared random in relation to length. Does it instead relate to stalled IFT trains in the leading or following flagella during gliding? How were the chlamy blocked from gliding during imaging? It is not apparent from the methods.4) In figure 7A the authors show an example kymogram to illustrate the time course used to measure rapid changes in response to changes in ca2+ influx in many cells. Fort et al. (2021), whom we cite, also showed similar results, which makes us confident that it is a general result. Nevertheless, we have now added kymographs as a supplemental figure .We have observed the clearing of stalled trains occurring at the same time as caIt is an interesting idea that stalled trains might influence the calcium spike frequency. Glen Wheeler’s group found that gliding motility induces calcium influx in trailing flagella , and we also observed calcium influx in trailing flagella when cells first start gliding. However, because calcium influx was observed in the absence of gliding motility, other stimuli or signals can also induce calcium influx. In any case, we found that the frequency of calcium influx was not correlated with the flagellar length. We have now added discussed about calcium influx to the Discussion section.Chlamydomonas cells in an agarose gel to prevent the moving of the cell body, and found that this form of mounting greatly reduced the number of cells that showed gliding. We confirmed that this embedded method did not differ in results from the non-embedded method. We have added a note about blocking gliding with agarose in the discussion when we discuss the potential effect of gliding, and we also now note this in the Methods section.We embedded 5) In figure 4B the authors show "the mean KAP-GFP intensity of each flagellum" as a measure of total levels of IFT in the cilia at one time. In the main text this is described as "injection intensity." While total levels of IFT in the cilia are certainly influenced by injection rates it is also influenced by other factors such as IFT retrograde trafficking rates, turn around at the tip and the number of stalled trains. Were any of these other factors changed in their conditions? The authors should discuss what exactly they are measuring when they refer to injections in this paper, especially to make the paper more accessible to a broader audience. Furthermore, the authors may want to cite and discuss the Nievergelt et al. 2022 paper from Gaia Pigino's lab, which is listed in the references but not cited in the text.We thank the reviewers for this comment, which allowed us to see that our description of our method was not clear. In fact, what we did was to extract the anterograde IFT trajectories from the kymograph, and then use only those pixels for calculating IFT injection intensity. So, we are not measuring total flagellar IFT intensity, just the intensity found in the anterograde tracks. The intensity of anterograde IFT trajectories can reflect only the IFT injection, not retrograde IFT, etc.We also thank the reviewer for noticing our accidental failure ot cite Nievergelt et al. (2022) in the Discussion section, which we had intended to do as evidenced by our inclusion of the citation in the references. We have now added a discussion of this paper.Reviewer #1 (Recommendations for the authors):1. Figure 1 B. It has been reported by Liang et al. (Dev Cell 2014) that phosphorylation of FLA8 dissociates FLA8 from IFT complex. Thus, it is unknown whether kinesin-2 is inactivated. I suggest that active/inactive kinesin-2 is changed to active/inactive IFT.Liang’s paper not only discusses dissociation of FLA8 from the IFT complex, they also mention that phosphorylation inactivates kinesin and inhibits IFT entry. Nevertheless, we have replaced the words, active and inactive, with "can bind with IFT" and "can't bind with IFT" just to avoid any potential confusion.2. Using a condition where intraciliary calcium can be increased to analyze IFT injection, which is critical for drawing a conclusion on calcium regulation of IFT injection.This is an excellent suggestion, given that we have shown results for calcium depletion. To this end, we have now used our fluorescent strains to measure the GCaMP intensity of 1 mM CaCl2 treated flagella and found that 1 mM CaCl2 treatment does indeed increase the calcium level in flagella. Furthermore, we now show that CaCl2 treatment increases IFT injection, again in contradiction of the prediction of the calcium ion current model that more calcium should decrease IFT injection. This new experimental data has been added as Figure 3D and Figure 4BC. These results are all consistent with our original conclusions.3. For Figure 3B and 4B, I suggest the authors do another mathematical analysis but only include the data for cilia shorter than about 6 micron.We address this point in the reviewer's general comments above. In brief, there were very few control cells in the short length range due to increased flagellar motility in controls. Prompted by the reviewer suggestion, we analyzed additional control cells with short flagella and found that our conclusion holds that control cells have higher calcium influx even for short flagella.Reviewer #3 (Recommendations for the authors):I would like to start by thanking the authors for the work they have done.1) In figure 4B the authors show "the mean KAP-GFP intensity of each flagellum" a measure of total levels of IFT in the cilia at one time. In the main text this is described as "injection intensity." While total levels of IFT in the cilia are certainly influenced by injection rates it is also influenced by other factors such as IFT retrograde trafficking rates, turn around at the tip and the number of stalled trains. Were any of these other factors changed in their conditions? The authors should discuss what exactly they are measuring when they refer to injections in this paper, especially to make the paper more accessible to a broader audience.We thank the reviewers for this comment, which allowed us to see that our description of our method was not clear. In fact, what we did was to extract the anterograde IFT trajectories from the kymograph, and then use only those pixels for calculating IFT injection intensity. So, we are not measuring total flagellar IFT intensity, just the intensity found in the anterograde tracks. The intensity of anterograde IFT trajectories can reflect only the IFT injection, not retrograde IFT, stalled trains, etc.We have now defined injection in both the abstract and introduction, which we agree is important to do.2+. Prior to each DRC4-GCaMP cluster of pulses one can observe stalled mScarlet-IFT54 which is then cleared with the pulse of ca2+. Is this relationship observed in the other data-sets used in this figure or is this an artifact of this particular image? Earlier in the paper the frequency of ca2+ appeared random in relation to length. Does it instead relate to stalled IFT trains in the leading or following flagella during gliding? How were the chlamy blocked from gliding during imaging? I could not find it in the methods.2) In figure 7A the authors show an example kymogram to illustrate the time course used to measure rapid changes in response to changes in ca2+ influx in many cells. Fort et al. (2021), whom we cite, also showed similar results, which makes us confident that it is a general result. Nevertheless, we have now added kymographs as a supplemental figure .We have observed the clearing of stalled trains occurring at the same time as caIt is an interesting idea that stalled trains might influence the calcium spike frequency. Glen Wheeler’s group found that gliding motility induces calcium influx in trailing flagella , and we also observed calcium influx in trailing flagella when cells first start gliding. However, because calcium influx was observed in the absence of gliding motility, other stimuli or signals can also induce calcium influx. In any case, we found that the frequency of calcium influx was not correlated with the flagellar length. We have now added discussed about calcium influx to the Discussion section.Chlamydomonas cells in an agarose gel to prevent the moving of the cell body, and found that this form of mounting greatly reduced the number of cells that showed gliding. We confirmed that this embedded method did not differ in results from the non-embedded method. We have added a note about blocking gliding with agarose in the discussion when we discuss the potential effect of gliding, and we also now note this in the Methods.We embedded 3) The graphs in Figure 4 b,c,d are incredibly hard to read. Color changes or redesign would be much appreciated. Perhaps turn the individual data point opacity down so the trend lines are more visible?We have now modified the plots by reducing both the size and the opacity of the markers for individual data points. We agree that this makes the graph more readable.