With the growing popularity of online services such as online banking and online shopping, one of the essential research topics is how to build a privacy-preserving user abnormal behavior recommendation system. However, a machine-learning based system may present a dilemma. On one aspect, such system requires large volume of features to pre-train the model, but on another aspect, it is challenging to design usable features without looking to plaintext private data. In this paper, we propose an unorthodox approach involving graph analysis to resolve this dilemma and build a novel private-preserving recommendation system under a multilayer network framework. In experiments, we use a large, state-of-the-art dataset  to evaluate the recommendation ability of our system on abnormal user behavior, yielding an overall precision rate of around 0.9, a recall rate of 1.0, and an F1-score of around 0.94. Also, we have also reported a linear time complexity for our system. Last, we deploy our system on the “Wenjuanxing” crowd-sourced system and “Amazon Mechanical Turk” for other users to evaluate in all aspects. The result shows that almost all feedbacks have achieved up to 85% satisfaction. With the recent growth of web activity such as online shopping and banking, it is becoming more and more necessary to detect abnormal user behavior on the web. For example, we can safeguard privacy for real users by distinguishing instances of identity theft.In general, given the popularity of a wide variety of online services, machine learning (ML) algorithms including Support Vector Machine (SVM), decision trees, and deep neural networks have been applied to help operators maintain user privacy  which uniquely depicts the behavior patterns for each user. Hence, unlike ML algorithms which must first train a model, graph-based methods naturally capture dependencies among the various features to yield a topologically-based portrait of the user. In addition, we also use a multilayer network , 9 to deIn conclusion, by using multilayer network analysis algorithms, we here develop a privacy-preserving user abnormal behavior recommendation system. Below are five major contributions:Privacy preservation: As encrypted features are sufficient to construct a multilayer network, our system does not need access to unencrypted features and thus cannot leak information to host machines.Efficient feature use given limited features: Our system can work with only one feature to construct a multilayer network based on user timestamps, in contrast to ML based algorithms which generally are difficult to train given insufficient features. However, the more features provided to our system, the more complete the user portrait it can build, and the more accurate abnormal behavior recommendations it can provide.Unsupervised learning: As the multilayer network of a user captures user portraits automatically, we need no label information to train a model in the first place. Unsupervised network analysis algorithms such as community detection, sub-graph pattern matching, network embedding, and the like can also be applied to recommend abnormal behavior for the user automatically.Efficient performance: Since our algorithms are quite efficient (see Section 16), and the proposed system works for each user, it is efficient to recognize abnormal behavior(s) for each user in the real time.A Practical System: We have built a system that to visualize the results, demonstrate how to use the user’s devices and the encrypted features to build the multilayer network, and recommend the suspicious activities. The system is available at https://github.com/Liu-WeiYi/Private_Preserving_Outlier_Behavior_Detection.Techniques based on ML , 11 and However, as pointed out by Shmatikov et al. , ML modeUnlike typical solutions which focus on constructing a more accurate or more privacy-preserving model to depict the data and capture outliers, we propose an unorthodox approach that focuses only on a specific user. As pointed out in the survey , graph-bGenerally speaking, there are two ways to extract useful information from the network: multilayer network community detection derived from pure topology analysis, and network embedding to find an appropriate vector space onto which to project nodes. For the former, many approaches use multilayer network community detection –27. AmonWe use the following example to describe our private-preserving user abnormal behavior recommendation system. Part of protecting online accounts is carefully preserving user privacy. That is, without violating user privacy (or using encrypted features instead), a privacy-preserving system must distinguish from the user behavior whether the account is currently being used by the real owner or an imposter. Multilayer network construction: This module captures user behavior across time (Alg. 1) and constructs a multilayer network based on these timeslot groups (Alg. 2), where each layer depicts the user behavior corresponding to a specific timeslot group. See Section for details.Multilayer network analysis: This module detects abnormal users by using unsupervised graph analysis algorithms from two aspects: weighted multilayer network community detection based on the pure topology-based analysis (Alg. 3), and state-of-the-art multilayer network embedding (Alg. 4). See Section 12 for details.Outlier recommendation: This module calculates node scores based on community and cluster results from the previous module (Alg. 5), and recommends nodes given the corresponding scores from small to large. See Section 10 for details.Visualization system: This module builds a visualization system. See Section 16 for details.MN = {G1, G2, …, Gl} is a combination of layers, where each layer Gl =   represents a particular type of relationship among nodes. In order to capture user behavior across time, nodes within the multilayer network can be devices belonging to a given user, the edge weights can be the similarity among different devices, and the layers can represent topological information among nodes with different timestamps .A multilayer network Consider T), where the input is All_Time_List =  which contains all timestamps for the current user  and the output is m is the total number of groups; m ≤ l).It is not practical to simply create a layer for each timestamp, as this would yield layers that contain redundant information. One way to solve this problem is to first group timestamps, and then create a layer corresponding to the current timestamp group. Alg. 1 shows the process by which timestamps are grouped based on the mean time interval  − 1do4 t2_idx = t1_idx + 1;5  t1 = All_Time_List[t1_idx];6  t2 = All_Time_List[t2_idx];7  Interval_T.append(t2 − t1);8  end9 T = mean;10 ΔT;11 // 2. Group times by Δfort1, t2 ∈ All_Time_Listdo12 fort2 − t1 ≤ ΔTthen13  Time_Groups[−1].extend;14   end15  else16  Time_Groups.append;17   end18  end19 Time_Groups20 return Time_Grou −ps for the current user, the weights between the different devices in the current timeslot group W ∈  are calculated using B(⋅) contains all the behavior features (such as scrambled IP address and encrypted queried keywords) of the current device in the current timeslot group.After obtaining Time_Groups, where the inputs are Time_Groups and User_Logs and the output is a weighted multilayer network MN for the corresponding user. Note that Alg. 2 details the construction of the multilayer network based on Algorithm 2: Multilayer Network ConstructionInput: Time_Groups, User_LogsOutput: MNMN;1 Initialize a multilayer network fordo2 G in current timeslot group 3  Initialize a graph D in User_Logs;4  Extract device list ford1, d2 ∈ Ddo5  weight = W;6   G.add_node;7   G.add_edge;8   end9  MN.append(G);10  end11 MN12 return Given a multilayer network, unsupervised graph analysis algorithms such as multilayer network embedding  and multIn this section, we discuss identifying abnormal nodes by analyzing network topology. To this end, we propose a basic assumption from the behavior analysis field: “Abnormal  behavior is but a small fraction of all behavior of the current node —most useBelow, we leverage multilayer network embedding , a stateQ  and com = . neighbor indicates the neighbors of node i in layer R.Alg. 3 details the detection of communities in a multilayer network by maximizing Algorithm 3: Weighted Multilayer Network Community DetectionInput: MNOutput: ComsComs = ;1 stopFlag = False, Q = 0;2 whilestopFlag!do3 StableComFlag = False;4  whileStableComFlag!do5  MaxQ = Q;6   fori, R ∈ MNdo7   i and j ∈ neighbor into one Com by maximizing Q;8    Merge nodes MaxQ;9    Update ifComs becomes stablethen10    Coms corresponding to MaxQ; Q = MaxQ;11     Save StableComFlag = True;12     end13    end14   com ∈ Coms to one node, and update origin MN;15   Shrink each 16   // Stop when they are shrunk to one node;iflen(Coms) ⩵ 1then17   stopFlag = True;18    end19   end20  end21 Coms22 return p, q, and r to enable a second-order random walk. Parameters p and q control the local and global biases of the sample random walk, and r controls the layer traversals during the random walk; that is, the random walk stays on the current layer l′ with probability r, and moves along the edge of another layer l with probability 1 − r. P|ti−1 = ) among layers, where  represents the first random walk from node zl′ to node xl′ in layer l′, and  represents the second random walk from node xl to node yl in layer l. Note that xl′ and xl is the same name node x exists in different layers.This method uses the hyper-parameters p and q in node2vec , but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.2. Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc. http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and  there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to  PLOS ONE policies on sharing data and materials.” :[Note: HTML markup is below. Please do not edit.]Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: I am particularly satisfied with content and structure of this article. It suggest that the author has taken enough time to prepare the manuscript. The few observations from me is highlighted in the article.Reviewer #2: The manuscript presents important information and deserves to be published in Plos One. Before publication, I suggest the following modifications:Page 1, line 6-7Change “machine learning algorithms” for “machine learning (ML) algorithms”Page 1, line 7Change “SVM” for “Support Vector Machine (SVM)”Page 1, line 9Change “machine learning algorithms” for “ML algorithms”Page 1, line 13Change “IP adresses” for “Internet Protocol (IP) addresses”Page 1, line 15Change “machine learning models” for “ML models”Page 2, line 18Change “machine learning methods” for “ML methods”Page 2, line 27Change “machine learning algorithms” for “ML algorithms”Page 2, line 30“…over time1.”Confirm in the Plos One standards if footnotes can be used.Page 2, line 39Change “machine learning” for “ML”Page 2, line 55https://github.com/StoneSongLucky/Private Preserving Outlier Behavior Detection”“The system is available at The link is unavailable.Page 2, line 56-62Delete: “The remainder of the paper is organized as follows. In Section 2, we discuss recent work on user abnormal behavior recommendation. Section 3 describes the whole proposed model, and describes network construction (Section 3.3), network analysis (Section 3.4), outlier recommendation (Section 3.5) and a visualization practical system (Section 3.6). In Section 4, we present a thorough evaluation of the proposed privacy-preserving recommendation system. Finally, we conclude and describe future work in Section 5.”Page 3, line 65Change “machine learning” for “ML”Page 3, line 71Change “machine learning (ML) models” for “ML models” - The abbreviation should appear the first time the word is cited in the manuscript (as per the previous changes I am suggesting).Page 10, line 262Change “machine learning-based algorithms” for “ML-based algorithms”Page 10, line 266Change “machine learning-based algorithms” for “ML-based algorithms”Page 11, line 283Change “machine learning-based algorithms” for “ML-based algorithms”**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, AttachmentPONE-D-19-18509_reviewer.pdf-reveiwed.pdfSubmitted filename: Click here for additional data file. 8 Oct 2019Response to Reviewers and Editors' Comments & List of Changes in the Revised PaperTitle: Privacy-Preserving Recommendation of User Abnormal Behavior Under Multilayer NetworkAuthors: Chengyun Song, Weiyi Liu, Zhining Liu, Xiaoyang Liu-------------------------------------------------------------------------------------------------------The authors thank the reviewers and editors for their comments and suggestions. We have revised the paper following the comments and suggestions. Following is our response and also the list of changes that we have made in the revised version. The changes, including newly added parts, are in red font in the revised manuscript.Review Comments to Author:Reviewer 1:1. Reconcile the 15% and 85% satisfaction mentioned in the conclusionResponse:Sorry to bring some mistakes and ambiguous here. We meant to say that according to our proposed practical system is highly friendly to uses, as we use “85% satisfaction” to point out there are a large amount of users that do accept our proposed system, and only “15%” of users which are NOT satisfied. To eliminate the ambiguous, we use “up to 85% satisfaction” in the abstract and conclusion2. Citation missingResponse:In the paper, we define a simple and way  to evaluate the security scores in multilayer network. So, there is no citation here.3. From the decision box " If True". The flow chart did not say anything about 'if false". Marking the suspicious node is not enough but creating a library of suspicious nodes to be verify for subsequent loginsResponse:For “If false”, the program will ignore the current user. We have updated this figure in the revised paper. And if the system has found a “suspicious” node, it will raise an alert to alarm the user, as the final decision should be made by user himself.4. Give the scenario of medium level noise in this methodResponse:Here we use “medium” as the opposite of “High”. In fact, due to the fact that our proposed method (privacy-preserving) and the ML-based algorithms needs all the information to learn the user behavior, these methods have possibilities to absorb noises information as well. Hence, we have replaced term “medium” as “Not Effected”.5. Cite standard that support this assertionResponse: We have added the cite paper to support the assertion in the revised version.6. Reconcile with abstractResponse:Sorry to bring ambiguous here. We have used “up to 85% satisfaction” in the abstract which is same to the conclusion.Reviewer 2:1. Page 1, line 6-7Change “machine learning algorithms” for “machine learning (ML) algorithms”Response:I have added the abbreviation in the revision (Page 1 line 6-7).2. Page 1, line 7Change “SVM” for “Support Vector Machine (SVM)”Response:I have added the abbreviation of “SVM” in the revision (Page 1 line 7).3. Page 1, line 9Change “machine learning algorithms” for “ML algorithms”Response:I have used the abbreviation in the revision (Page 1 line 9).4. Page 1, line 13Change “IP adresses” for “Internet Protocol (IP) addresses”Response:I have given the full name of IP in the revision (Page 1 line 14).5. Page 1, line 15Change “machine learning models” for “ML models”Response:I have used the abbreviation of “ML” in the revision (Page 2 line 15).6. Page 2, line 18Change “machine learning methods” for “ML methods”Response:I have used the abbreviation of “ML” in the revision (Page 12 line 18).7. Page 2, line 27Change “machine learning algorithms” for “ML algorithms”Response:I have used the abbreviation of “ML” in the revision (Page 2 line 27).8. Page 2, line 30“…over time1.” Confirm in the Plos One standards if footnotes can be used.Response:We have not found the standards about the footnotes. However, this standard is used in other journals. So I do no changes and keep the original format in the revision (Page 2 line 30).9. Page 2, line 39Change “machine learning” for “ML”Response:I have used the abbreviation of “ML” in the revision (Page 2 line 39).10. Page 2, line 55https://github.com/StoneSongLucky/Private Preserving Outlier Behavior Detection” The link is unavailable.“The system is available at Response:The correct links are https://github.com/StoneSongLucky/Private_Preserving_Outlier_Behavior_Detectionand https://github.com/Liu-WeiYi/Private_Preserving_Outlier_Behavior_DetectionThe link in the paper is right and available.11. Page 2, line 56-62Delete: “The remainder of the paper is organized as follows. In Section 2, we discuss recent work on user abnormal behavior recommendation. Section 3 describes the whole proposed model, and describes network construction (Section 3.3), network analysis (Section 3.4), outlier recommendation (Section 3.5) and a visualization practical system (Section 3.6). In Section 4, we present a thorough evaluation of the proposed privacy-preserving recommendation system. Finally, we conclude and describe future work in Section 5.”Response:I have deleted those contents in the revision.12. Page 3, line 65Change “machine learning” for “ML”Response:I have used the abbreviation of “ML” to replace ‘machine learning’’ in the revision (Page 2 line 57).13. Page 3, line 71Change “machine learning (ML) models” for “ML models” - The abbreviation should appear the first time the word is cited in the manuscript (as per the previous changes I am suggesting).Response:I have revised those mistakes in the revision (Page 3 line 64).14. Page 10, line 262Change “machine learning-based algorithms” for “ML-based algorithms”Response:I have used the abbreviation of “ML” in the revision (Page 10 line 254).15. Page 10, line 266Change “machine learning-based algorithms” for “ML-based algorithms”Response:I have used the abbreviation of “ML” in the revision (Page 10 line 257).16. Page 11, line 283Change “machine learning-based algorithms” for “ML-based algorithms”Response:I have used the abbreviation of “ML” in the revision (Page 11 line 274).AttachmentResponse to Reviewers.pdfSubmitted filename: Click here for additional data file. 21 Oct 2019Privacy-Preserving Recommendation of User Abnormal Behavior Under Multilayer NetworkPONE-D-19-18509R1Dear Dr. liu,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. 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Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact With kind regards,He DebiaoAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.Reviewer #1: All comments have been addressedReviewer #2: All comments have been addressed**********2. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: I Don't KnowReviewer #2: Yes**********4. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********5. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********6. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Better in the current form since all questions and comment has been addressed. Also, to the best of my understanding comply with Plos One policy. From basic research writing and reporting, the paper has improved significantly. Assessment on the technical aspect is left for other reviewers and the editorial board.Reviewer #2: After the modifications, consider the appropriate manuscript for publication in Plos One in its current form.**********what does this mean?). If published, this will include your full peer review and any attached files.7. PLOS authors have the option to publish the peer review history of their article (If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? 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The resting metabolic rate  and aerobic metabolic capacity (spiroergometry) were assessed in 52 END  and 53 CON . METmax was calculated as the ratio of VO2max over VO2 during RMR (METmax_ind), and VO2max over the conventional 1-MET-value (METmax_fix). AEE was estimated by multiplying published MET values with the individual and conventional 1-MET-values. Dependent t-tests were used to compare the different modes for calculating METmax and AEE (α = 0.05). In women and men CON, men END METmax_fix was significantly higher than METmax_ind (p < 0.01), whereas, in women END, no difference was found (p > 0.05). The conventional 1-MET-value significantly underestimated AEE in men and women CON, and men END (p < 0.05), but not in women END (p > 0.05). The conventional 1-MET-value appears inappropriate for determining the aerobic metabolic capacity and AEE in active and endurance-trained persons.The maximum aerobic metabolic rate can be expressed in multiple metabolically equivalent tasks (MET), i.e., METmax. The purpose was to quantify the error when the conventional (3.5 mL∙kg However, expressing VO2max by normalizing for body mass (m) can be problematic since an m-based ratio is negatively correlated with m [m is not linear [2max is compared between groups that are not matched for body size and mass, or when body mass changes over time [m is to adjust VO2max by using the power function relationship VO2max = amk where a is the scaling constant and k is the scaling exponent [k = 2/3, 3/4 or >3/4) [m is also a function of body composition since muscle volume is an important determinant of metabolic capacity while fat tissue is comparatively metabolically inert. This would imply that fat mass changes would introduce a greater bias as compared to lean mass changes.The aerobic capacity  ,8. In ad−1·h−1 [2 kg−1·min−1 [2 kg−1 ·min−1, which is equivalent to ~10 kcal·kg−1·h−1.Alternatively, the aerobic capacity can be expressed as the maximum aerobic metabolic rate in a multiple of metabolic equivalent of tasks (MET), i.e., METmax. One MET is defined as the energy expended by a subject at rest  of ~1 kcal·kg−1·h−1 , and is −1·min−1 . The METThe Compendium of Physical Activities provides a five-digit coding scheme linking categories and types of physical activity with their respective intensity values in METs . It was Several authors have questioned the widespread application of the conventional 1-MET value ,18,19,20Purpose of the study was to quantify the error when the conventional compared to an individualized 1-MET value is used for (1) calculating METmax, and (2) estimating energy expenditure for various daily physical activities, in endurance trained women and men, and active healthy controls. It was further investigated whether the use of a predicted RMR by the Harris-Benedict equation would reduce such an error. It was hypothesized that the use of the conventional 1-MET value would lead to relevant errors in both calculating METmax and estimating energy expenditure in comparison with an individualized approach.−1 and participation in competitions) and 63 healthy, non-endurance-trained active controls  were recruited for this study. Inclusion criteria for all participants were weight stability (<2 kg of weight difference in the last 3 months), a Body Mass Index (BMI) between 18.5 and 25 kg∙m−2, and an age between 18 and 40 years. All participants were non-smokers, not pregnant or lactating, not dieting, not suffering from metabolic disease and/or eating disorders, and not taking medication (apart from contraceptives). Athletes did not change their training habits within the last four weeks before the experiments .After a public announcement, 68 competitive endurance athletes  approved all experimental procedures, and the study was carried out according to the recommendations of the latest Helsinki Declaration. Written informed consent of the participants was obtained before any testing.2max) in a non-fasted state. On the days between the two testing days, the participants wore the HR and movement sensor for at least 7 days. All tests were carried out in Magglingen (Switzerland) at an altitude of 950 m.The participants arrived to the testing center on two separate testing days and had refrained from strenuous physical activity for at least 24 h. On the first testing day, the participants completed, in a fasting state  measurements in the following order: (1) anthropometry and body composition, (2) RMR, and (3) individual calibration of a combined heart rate (HR) and movement sensor (see below). One week after the first testing day, the participants performed an incremental exercise test  and a calibrated beam scale , respectively, with the participants in light clothing and without shoes.Body composition was assessed using Lunar iDXA . The iDXA was calibrated on a daily basis using a calibration phantom before any testing. The participants were in underwear, bladder-voided, and all metal artefacts were removed. During the measurement, participants were in a supine position on the scanning table with their ankles and legs fixed using supports. Arms were positioned to the side with the palms flat on the table. Participants were requested not to move during the measurement. Whole body scans were performed, according to the manufacturer’s instructions. Adipose tissue mass, lean tissue mass, and bone mineral content were derived with the accompanying software . Estimation of total body composition with the Lunar iDXA has been reported to be excellent in other studies ,32.2 and carbon dioxide production (VCO2) were measured for 30 min at 10-s intervals with participants remaining motionless in a supine position in a thermo-neutral environment  <0.70 and >1.00, since values outside the plausible range for RQ suggest protocol violations or inaccurate gas measurements [2 and VCO2 were 3.6 ± 1.5% and 4.5 ± 1.9%, respectively. RMR measurements took place at a mean temperature of 21.9 ± 1.1 °C, 39.1 ± 10.3% humidity, and an air pressure of 914 ± 8 hpa.Following the body composition assessment, RMR was measured by indirect calorimetry using a ventilated hood system . Calibrations of the flowmeter and gas analyzer were carried out before each test, according to the manufacturer’s instructions. Participants were acclimatizing and relaxing for 30 min on a bed before the hood was placed over the participant’s head and measurements were started. VO20–25 °C ). The filculated . Pre-hocurements . Average−1, whereas participants from the athletic group started at 9 km∙h−1. The speed was increased by 1 km∙h−1 every minute for the first 3 min of the test, and, thereafter, by 0.5 km∙h−1 every 30 s until exhaustion. Treadmill inclines were set at 4° throughout the test [2 data was processed using 10-s time averages and VO2max was determined as the highest 30-s VO2 average for the test [2 <2.1 mL∙kg−1∙min−1 [n = 4), then the secondary criteria of a RQ value ≥1.10, and an HR close (± 10 bpm) to the age-predicted maximum HR [2max [2max-tests were carried out at a mean room temperature of 21.7 ± 1.2 °C, a humidity of 39.2 ± 9.8%, and an air pressure of 914 ± 7 hpa. In general, a temperature range of 20 to 22 °C in a cool, dry environment (<50% humidity) is considered comfortable for exercise testing [Before the test, each participant filled out the German  or Frencthe test . Gas excthe test . HR was the test . If the −1∙min−1 ) was notximum HR  were useHR [2max . VO2max- testing .2max (mL∙min−1) over VO2 during RMR measurement (METmax_ind), and (2) the VO2max (mL∙min−1) over the conventional 1-MET value . For calculating activity energy expenditure (AEE) activities, different MET intensities were chosen: light (<3 METs), moderate (3–5.99 METs), vigorous (6–8.99 METs), and very vigorous (≥9 METs) activity [METmax was calculated in two modes, which are the ratio of (1) VOactivity . MET valrate 3–5.9 METs, vactivity . The difTM Red DotTM Electrode 2560; 3M Health Care, St. Paul, USA) on the chest of the participant, according to the manufacturer’s instructions, and worn day and night [The PAL of the participants was assessed using a combined HR and movement sensor . The Actiheart was clipped onto two standard ECG electrodes (3Mnd night . The devnd night ). PAL wand night ,50,51,52−1), VO2max (L∙min−1), RQrest, and AEE/TEE calculated either by use of the conventional, predicted, or individual 1-MET value. Group differences were tested by independent t-tests and Mann-Whitney-U-tests (α = 0.05). The relationship between the two modes for calculating METmax and the relationship between VO2max and RMR were first investigated using the Pearson’s Product moment correlation analysis. The correlation coefficients (r) were classified, according to Cohen [r between 0.10–0.29 was considered small, between 0.30–0.49 was considered moderate, and between 0.50–1.0 was considered showing a strong association. The data were further analyzed using dependent t-tests. In addition, the mean absolute error (MAE) and the mean absolute percentage error (MAPE) of METmax_fix compared to METmax_ind were calculated. Since no standardized threshold exists for high or low MAPE, a MAPE ≥10% was considered an indicator of inaccuracy as suggested by other authors [Statistical analyses were performed with SPSS statistics version 24 for MS-Windows . Mean values and standard deviations (SD) were calculated and data was checked for normality using the Shapiro-Wilk-test. All parameters were normally distributed with the exception of age, body mass, body mass index (BMI), body fat (%), fat-free mass , RMR , 9 participants with invalid RMR tests , and 7 subjects without a valid VO2max test  were excluded from the analysis. One participant withdrew from the study due to personal reasons. In total, data of 57 women and 48 men were included in the analysis. The subjects were grouped, according to their aerobic fitness level (METmax_ind). Male and female participants with a METmax_ind above the 50th percentile were classified as endurance trained participants . Subjects with a METmax_ind below the 50th percentile served as healthy, non-endurance trained active controls . In 2max of the participants are displayed. Women END had a significantly lower body fat percentage and BMI and higher PAL than CON (p < 0.05). In men, no significant differences between groups were obtained for body composition, RMR, and PAL (p > 0.05). The individual 1-MET value was significantly higher than 1 kcal∙kg−1∙h−1 in men and women CON, and men END (p < 0.05). When RMR was predicted by the use of the Harris-Benedict equation, the RMR was significantly lower than 1 kcal∙kg−1∙h−1 in women CON, and higher in men CON and END (p < 0.05). The range of VO2max was 2.2–3.9 L∙min−1 or 34.4–62.0 mL∙kg−1∙min−1 for women and 2.5–5.3 L∙min−1 or 42.8–78.4 mL∙kg−1∙min−1 for men, respectively. Men and women END had significantly higher aerobic capacity compared to CON (p < 0.01). In 2max and RMR is displayed. There was a significant positive relationship between VO2max and RMR in all subgroups (p < 0.0001).Nine participants who did not meet the pre-defined inclusion criteria , whereas, in the women END, there was no difference (p > 0.05). When looking at the total sample, METmax_fix significantly overestimated METmax, compared to the use of the individual 1-MET value for its calculation . The range of MAPE was 6.6% to 11.3% across all groups. METmax_ind was significantly higher in men and women END compared to their non-athletic counterparts , whereas, in women END, no difference was observed .The conventional 1-MET value significantly underestimated the energy expenditure of all activities in men and women CON and men END .TEE was significantly underestimated in men and women CON, and men END when the conventional 1-MET value was used for estimating RMR and a PAL of 1.53, 1.76, or 2.25 was applied for calculating TEE (−1·h−1) was compared to an individualized RMR value measured by indirect calorimetry. Both values were used as the baseline for calculating METmax and they were compared to determine whether the use of a measured (rather than predicted) RMR would reduce the relative and absolute error of prediction of METmax.Aims of the study were to quantify the absolute and relative errors when the conventional 1-MET estimated value . Therefore, it can be concluded that the use of the conventional 1-MET value is inappropriate for determining the aerobic metabolic capacity and estimating the daily activity related energy expenditure (using a METs table) in active people and endurance trained athletes.In men and women controls, and endurance trained men, the individual 1-MET value was significantly higher than the conventional and fixed 1-MET value . Indirect calorimetry measurements were made (1) with a comfortable hood system .These findings are in contrast to the majority of published studies, where the measured individual 1-MET value in women was mostly lower than 3.5 mL∙kgkg−1∙h−1 . However values, ), (2) unt adults . Adults −1·h−1, and men 1.13 kcal·kg−1·h−1 [n = 64) with a mean age of 27 years, more than two-thirds (70.3%) had a body fat percentage ≥30% [2 [In a previous study, the RMR of adolescents, pregnant and post-pregnant women, and active men was measured . A signikg−1·h−1 ,65,66,67kg−1·h−1 , which iage ≥30% , which i ≥30% [2 , such diestimated using established equations, such as the Harris-Benedict [2 of the subject prior to exercise testing while standing still on the treadmill or sitting quietly on a bike. However, it is unclear whether this resting VO2 is more appropriate for calculating METmax than using a conventional or estimated value for RMR.The present study addressed how much relative and absolute error the use of the conventional 1-MET value would introduce when used for calculating METmax. In 79% of the women and 88% of the men CON, and 83% of the endurance trained men (END), the conventional 1-MET value overestimated METmax, which resulted in a significant overestimation of the mean aerobic metabolic capacity in these groups. The MAPE was 6.6%, 10.3%, and 11.3% in both women and men CON, and men END, respectively, and 35%, 50%, and 46% of the women and men CON, and men END had a MAPE ≥10%, respectively. Generally, a MAPE ≥10% can be considered to be a marker for inaccurate measurements ,55,56,57Benedict  or the CBenedict  formulas−1, 326 kcal∙day−1, and 282 kcal∙day−1 for women and men CON, and men END, respectively, when the conventional 1-MET value of 1 kcal∙kg−1∙h−1 was used. In the endurance trained women of this study, no difference between the individual and conventional 1-MET value was found when energy requirements were calculated. This is because the difference between the individual and conventional 1-MET value was not significant.As the second purpose, the error when the conventional 1-MET value was used for estimating daily energy expenditures was investigated. Large differences using the individual and conventional 1-MET values for estimating energy expenditures were observed. In women and men CON, and men END, the conventional 1-MET value significantly underestimated the energy expenditure of several physical activities and total daily energy expenditure. For example, using a PAL of 2.25 for estimation of TEE, reflecting a rather active lifestyle, led to an underestimation of energy requirements of 108 kcal∙dayPAL and MET values are frequently used for estimating energy requirements in athletes. However, using the conventional 1-MET value for their calculation might underestimate (true) energy costs of their activities and might promote insufficient energy intake, especially in situations when athletes wish to control their energy balance . Besides the possible undesirable effects on body mass and body composition, the underestimation of energy costs and further advised erroneous energy intakes might also lead to a higher risk of suffering from Relative Energy Deficiency in Sport (RED-S), its concomitant symptoms, and a decrease in endurance performance . Therefooverestimating energy costs of activities, as shown previously by others [−1∙min−1 is divided by a predicted RMR obtained from the Harris-Benedict equation [−1∙min−1 [Most often in the general population, the individual 1-MET value is significantly lower than the conventional 1-MET value ,24,25,26y others ,23. Thisy others ,18. Hereequation . The aut−1∙min−1 . In the This is the first study with the purpose to assess the individual 1-MET value in endurance trained athletes. Expressing aerobic capacity as a ratio of maximum oxygen consumption divided by oxygen consumption at rest is a suitable measure in endurance trained athletes and healthy, active controls. A big advantage of the METmax calculation is that the denominator (RMR) already takes into account the metabolic and physiological characteristics of the individual at the baseline, so that inter-individual  and intra-individual  comparisons of METmax will not be biased by a difference or change of these characteristics. The readjustment of RMR (upward or downward) will not bias the validity of the new METmax recalculation. Another advantage of the present study is the focus on strict protocols for assessment of the variables, e.g., RMR was measured in the early morning after an overnight fast with subjects abstaining from vigorous exercise for ≥24 h. Assessment of body composition was performed with a gold standard method viz. dual x-ray absorptiometry.However, some limitations must be addressed. First of all, the control participants do not reflect the typical, less physically active Swiss population. For example, the METmax of the control participants was “only” two units less when compared to their endurance-trained counterparts. In addition, the fat mass percentage might be lower in the control participants compared to the general population. On the other hand, the physical activity level, body composition, and the aerobic capacity of the control participants might reflect the recommended “normal” human phenotype, i.e., physically active and “normal” BMI. Nevertheless, inclusion of a sedentary overweight (or obese) control group would give additional insights about the error when using the conventional 1-MET value for both determining the aerobic metabolic capacity and estimating the energy expenditure in these populations. Generally, it must be stated that estimating the energy expenditure using published MET values of the Compendium of Physical Activities must be taken with caution since the published MET values are often based on only one reference. Therefore, it can be expected that there is a wider variance in estimated AEE compared to the direct assessment of oxygen consumption during various physical activities . In the 2max), measuring RMR by indirect calorimetry is recommended or, if not possible, estimating RMR is recommended using published validated equations tailored to the characteristics of the group studied in terms of age, gender, body composition (FFM), physiological status , and ethnicity. For estimating energy requirements, it can be recommended to (1) either measure directly the energy expenditure by use of validated tools, or (2) measure (or at least estimate) RMR and use appropriately adjusted MET values published in the literature [The use of a conventional 1-MET value appears inappropriate for determining the aerobic metabolic capacity and estimating the daily energy expenditure in active and endurance-trained persons. When the conventional standard 1-MET value was used, the predicted resting energy expenditure was slightly but significantly underestimated . As a result, the calculation of METmax was significantly overestimated due to the underestimation of the denominator. Furthermore, the energy costs of non-maximal physical activities should also be underestimated when the conventional 1-MET value is used and this might lead to an underestimation of energy requirements for a given physical activity. For valid assessment of METmax (calculated from VOterature  for esti


This product contains nicotine which is a highly addictive substance. [It is not recommended for non-smokers.]”) and a comparative harm message (“Use of this product is much less harmful than smoking” [COMP]) on smokers’ and non-smokers’ perceptions and behavioural intentions.This study investigated the effects of the European Union Tobacco Products Directive [EU-TPD] Article 20 E-cigarette (EC) health warnings  self-reported perceived Non-smokers had higher harm, addictiveness and lower social acceptability perceptions. TPD presence increased, whilst COMP decreased, harm and addictiveness perceptions in both groups. For smokers only, harm perceptions were lower following exposure to COMP alone vs. no message. For non-smokers the TPD increased harm perceptions vs. no message. There were no effects on social acceptability, EC effectiveness or use intentions. In smokers only, purchase and quit intentions were higher following exposure to the COMP alone.TPD messages may be effective smoking prevention tools, although the COMP message was more effective in reducing harm perceptions and increasing use intentions in smokers. That COMP did not increase use intentions in non-smokers suggest that such exposures may potentially act as an effective harm reduction tool without resulting in increased uptake among non-smokers. The mosThis product contains nicotine which is a highly addictive substance’, or ii) ‘This product contains nicotine which is a highly addictive substance. It is not recommended for use by non-smokers’. Given current misperceptions and concerns around addiction per se, warning messages which focus solely on the potential of EC to lead or maintain addiction may maintain public misperceptions of the harms of EC and discourage use amongst smokers. Indeed, concerns over substituting one addiction for another is the most commonly cited reason in deterring never vaped smokers from EC use  while seeking to attract smokers to switch to a less harmful product, remains an ongoing question. Current messages on EC packaging present a standardised addiction health warning covering 30% of the pack. Article 20 of the EU Tobacco Products Directive [TPD]  requiresm EC use .To date, research exploring the impact of EC health warnings has been conducted outside of Europe, thus potential effects of the mandatory TPD nicotine addiction warning messages are underexplored. Experimental work has shown that exposure to EC advertisements  and EC h1.1This study aimed to investigate the effects of the EU-TPD EC health warnings and a comparative harm message (COMP) on smokers’ and non-smokers’ perceptions of harm, addictiveness, effectiveness and social acceptability of EC. We also aimed to explore the effects of the EU-TPD warnings on smokers’ and non-smokers’ intentions to purchase and use an EC, and on smokers’ quit intentions and intentions to use EC in future quit attempts. Lastly, the potential effects of providing a comparative harm message either in addition to the TPD warning or as a stand-alone message were also explored.22.1Ethical approval was granted by the School of Applied Sciences Ethics Committee at London South Bank University . Data were collected in the UK between December 2018 and January 2019. A 2 × 2 × 2 × 2 factorial design with Smoking status (Smoker vs. Non-smoker), TPD presence (Presence vs. Absence) and COMP presence (Presence vs. Absence) as between-subject factors, and Time  as a within-subjects factor. Dependent variables comprised self-reported perceived harm, addictiveness, social acceptability, effectiveness, intentions to purchase and use of EC, intentions to quit and intentions to use ECs in future quit attempts.2.22495 UK residents were recruited  and others e.g. .Participants were classified as ‘non-smoker’, ‘daily smoker’ and ‘occasional smoker’ see , daily aFor smokers, motivation to quit was measured using the Motivation to Stop Scale ([MTSS] ). CigareThe COMP message was generated in a pilot study  and selected for its higher than average overall mean rating and its brevity see  for a stWhich e-cigarette did you think looked most like a cigarette?”) were presented following exposure of the message stimuli in addition to outcome measures.Prior to, and following the experimental exposure to one of the EC health messages see , percept2.3.2The full procedure is outlined in our protocol paper . Upon pr2.3.3As per our published protocol, a series of ANCOVAs were conducted with 3 between-factors: Smoking Status (Smoker vs. Non-smoker), TPD Presence (Present vs. Absent), COMP Presence (Present vs. Absent) with DVs consisting of Time 2 perceived ratings of EC i) harm, ii) addictiveness, iii) social acceptability, iv) effectiveness, v) intention to quit, vi) intention to purchase and vii) intention to use ECs to observe the main effects and interactions whilst controlling for Time 1 measurements of the DV. All main and interactive simple effects were tested, with planned a-priori comparisons between the TPD alone condition against i) TPD + COMP, ii) no TPD/no COMP iii) no TPD/COMP, and the COMP alone condition against i) TPD + COMP, ii) no TPD/no COMP, iii) TPD/no COMP. These were conducted at each level of smoking status. As per protocol, for secondary analyses, the same ANCOVAs were repeated i) controlling for cigarette dependence, previous e-cigarette exposure, and baseline intentions to quit and, ii) exploring the interactions with demographic variables . For i) only significant main effects and interactions are reported here and for ii) adding demographics co-variates did not change the overall pattern of main effects and interactions reported here; these results will be reported separately and made available via the Open Science Framework: osf.io/ta4vx. As per our published protocol, outliers were calculated (±2.5 SD) for each variable before removal, the total numbers discarded differed per variable and ranged between 15 and 69. When outliers were included the effects of the COMP vs. no message on smokers’ harm perceptions and the effect of the TPD vs. no message on smokers’ perceptions of EC addictiveness fell short of statistical significance. In addition, a further effect of COMP vs TPD was found. Smokers’ intentions to use an EC in a quit attempt next month were higher after exposure to the COMP vs. the TPD.33.1ps > 0.05; See TPD1 and 2 were combined in the analyses as scores of risk perceptions and intentions did not differ (ps > 0.05), thus subsequent references to TPD are to both TPD messages combined. All confidences intervals reported are at 95%.3.1.1p2 = 0.03; Non-smokers perceived EC as more harmful compared to smokers . There was also a main effect of TPD Presence F = 31.89, p < .001, ƞp2 = 0.01. Harm-related perceptions were greater in those exposed to TPD (M = 5.23 [CI: 5.19–5.27]) compared to TPD absence conditions (M = 5.01 [CI: 4.95–5.07]). After adjusting for Motivation to Quit and Cigarette Dependence, a main effect of COMP was shown F = 7.11, p = .008, ƞp2 = 0.006. Following exposure of the COMP message, smokers perceived EC as less harmful compared to exposure to other messages. A-priori comparisons revealed that both smokers and non-smokers exposed to the TPD perceived EC as significantly more harmful compared to those exposed to the COMP alone  compared to when it was absent (M = 5. 05 [CI: 4.97–5.12]). Non-smokers’ harm perceptions were higher than that of smokers; when COMP was present (M = 5.29 [CI: 5.21–5.37]) harm perceptions were slightly higher compared to when COMP was absent (M = 5.26 [CI: 5.18–5.33]).ANCOVAs revealed a main effect of Smoking Status F = 63.21, p < .001, ƞlone see . In non-sage see . A signi3.1.2p2 = 0.03; Smokers perceived EC as less addictive than non-smokers . There was a main effect of TPD presence F = 65.31, p < .001, ƞp2 = 0.03; significantly increased scores were shown for those who had been exposed to the TPD compared to no presence conditions (TPD present M = 5.60 [CI: 5.56–5.64] vs. TPD absent M = 5.32 [CI: 4.95–5.07]). A-priori comparisons revealed that compared to the COMP alone and No message conditions, exposure to the TPD increased perceptions of addictiveness. In addition, exposure to the COMP alone compared to both the TPD and the COMP combined (TPD + COMP) led to significantly lower perceptions of addictiveness in both smokers and non-smokers  = 68.55, p < .001, ƞkers see .3.1.3ps > 0.05)  see .3.1.4p2 = 0.004; Smokers (M = 4.36 [CI: 4.30–4.41]) perceived EC to be more socially acceptable compared to non-smokers (M = 4.22 [CI: 4.16–4.28]). All remaining main effect, interaction and a-priori comparisons were not significant (ps > 0.05)  = 11.01, p < .001, ƞ.05) see .3.1.5p2 = 0.02 and F = 55.47, p < .001, ƞp2 = 0.02. Smokers reported higher intentions to purchase an EC compared to non-smokers (Smokers M = 1.93 [CI: 1.88–1.98] vs. non-smokers M = 1.66 [CI: 1.60–1.72]) and in the next 6 months respectively (Smokers M = 2.04 [CI: 1.98–2.09] vs. non-smokers M = 1.73 [CI: 1.67–1.79]). A-priori comparisons revealed higher purchase intentions in those exposed to the COMP alone compared to the TPD alone in smokers only  = 42.46, p < .001, ƞonly see . All rem3.1.6p2 = 0.02. Smokers reported higher intentions to use an EC compared to non-smokers (Smokers M = 1.97 [CI: 1.92–2.02] vs. non-smokers M = 1.70 [CI: 1.63–1.75]). All remaining main and interaction effects were not significant (ps > 0.05).A main effect of smoking status was shown only after adjusting for EC past exposure, F = 42.06, p < .001, ƞ3.1.7p2 = 0.004; When present, smokers’ intentions to quit were lower (M = 2.63 [CI: 2.57–2.70]) compared to when TPD was absent (M = 2.77 [CI: 2.67–2.87]). There was no main effect of COMP presence . There was no significant interaction (p = .201). A-priori comparisons revealed that smokers reported increased intentions to quit in the next month when showed the COMP alone. The COMP message significantly differed from exposure to the TPD and exposure to both messages combined (TPD + COMP)  = 5.26, p = .022, ƞ + COMP) .Table 5MFor intentions to quit within 6 months, and intentions to use an EC in a quit attempt in the next month and in the next 6 months, no significant differences between conditions was shown (ps < 0.05).4This study compared the effects of the EU-TPD EC nicotine addiction health warning to a comparative harm (COMP) message on smokers’ and non-smokers’ perceptions of EC. We also examined the effects of these messages on purchase intentions, and on smokers’ intentions to quit and to use an EC in a quit attempt. The potential effects of a comparative risk message either in addition to the TPD warnings or as a stand-alone message were also explored.Smokers perceived EC as less harmful, less addictive and more socially acceptable than non-smokers. EC were perceived as more harmful following exposure to the TPD messages by both smokers and non-smokers. Following exposure to the COMP alone EC were perceived as less harmful by both smokers and non-smokers when compared to both messages combined. However, when compared with no message, reduction in perceptions of EC harm was shown after exposure to the COMP alone only in smokers. This suggests that adding the COMP as a stand-alone message to EC packs, may reduce smokers’ harm perceptions of EC whilst leaving non-smokers’ EC harm perceptions unaffected. For perceptions of EC addictiveness, the TPD and the COMP alone conditions differed significantly. Perceptions of EC addictiveness increased in both smokers and non-smokers following exposure to the TPD relative to those exposed to no messages and those exposed to the COMP. Similarly, smokers and non-smokers exposed to the COMP alone perceived EC as less addictive compared to those exposed to both the TPD and COMP messages combined. For EC purchase intentions smokers reported increased intentions to buy in the next month  compared to non-smokers. Critically, in smokers (not non-smokers), exposure to the COMP message increased intentions to purchase an EC in the next month compared to exposure to the TPD messages; this was the case only when the COMP message was presented alone. The COMP message led to higher quit intentions compared to the TPD in smokers only.That non-smokers were more likely to endorse beliefs about EC addictiveness and harm compared to smokers corroborates previous evidence . It is uThe finding that perceptions of harm differed between smokers and non-smokers following exposure to the COMP message alone compared to no messages suggests that smokers may be more receptive to the COMP than non-smokers. This is encouraging to the extent that reduced harm messages which convey relative risks and encourage a switch away from smoking is important for smokers, but at the same time should not encourage use among non-smokers. In other words, it suggests specificity for better targeting these individuals by increasing engagement about the relative risks of EC. However, when comparing the TPD messages to no message, the increase in harm perceptions was only present for non-smokers. To some extent, this demonstrates the effectiveness of the TPD messages as a smoking prevention tool without also increasing harm perceptions among smokers.Exposure to the COMP alone was also associated with reduced scores on perceptions of addictiveness of EC compared to the TPD and the TPD + COMP. Thus, COMP may act as a stand-alone message as opposed to as an adjunct to the TPD. Previous work which paired relative risks health statements with addiction warnings has failed to show any influence on addictiveness perceptions and use intentions . Given tThe TPDs and the COMP messages did not differ in their influence on perceptions of EC as effective stop smoking aids nor did they affect perceptions on social acceptability, perhaps because none focus on effectiveness or social acceptability. Because such a focus may influence switching amongst smokers, they may require separate attention from messages focusing on harm.As expected, the COMP led to higher purchase intentions compared to the TPD in smokers only, but these effects were lost when participants were asked to rate their intentions over 6 months. There were no effects or differences in intentions to use EC in the future and overall ratings were low. Although previous studies , found tIdeally, a comparative harm message would reduce perceptions of harm in both smokers and non-smokers, while increasing intentions to use for smokers. However, we found that although the COMP showed lower harm and addictiveness perceptions of EC in both smokers and non-smokers, the COMP alone against no message did not affect non-smokers’ perceptions whilst increasing purchase intentions in smokers only. Furthermore, the null effects of COMP on use intentions in non-smokers is a promising finding in that it suggests such exposure might not result in unintended effects.4.1The study has several limitations. Although the sample was matched to the ONS data to represent the smoking population, it was self-selecting, potentially introducing response bias. Secondly, whilst findings of this study are informative and help further our understanding of communication of risks related to EC, these are confined to intentions. Prospective studies could focus on exploring how to translate intentions into behaviours.5The findings suggest that messages such as the COMP can help redress harm perceptions associated with EC and encourage smokers to switch to EC. When communicating health risks, it is important that messages are clear and unambiguous. Any future communication strategy to include a comparative message, such as the one developed here, has greater potential to impact as a stand-alone message.Cancer Research UK's Tobacco Advisory Group (CRUK Grant Number: 25855). The funder played no role in the design of the study and drafting of the manuscript.This study was funded by LD and DF are joint lead principal researchers and grant holders for this project. Along with SC and IA, LD and DF conceived the original idea for the project, designed the study, refined the methodology and contributed to the grant application. CK led on the drafting of the manuscript and was responsible for the day-to-day running of the project and data collection. All authors contributed significantly to and edited drafts of this manuscript. All authors have read and approved the final manuscript.CK has no conflict of interest to declare.DF is principal investigator on a randomised controlled trial funded by Allen Carr’s Easyway Ltd (ISRCN number: ISRCTN23584477). This trial is comparing the Allen Carr Easyway stop-smoking method to local NHS 1-1 stop smoking counselling service. The trial is being conducted independently, the protocol and analysis plan are both pre-registered and the research team are contractually free to independently publish the results of the trial regardless of the study outcome. DF has no other conflicts of interest to declare.SC has provided consultancy services to UK life insurers on smoking cessation and reduce risk products prevalence rates.IA is an investigator on a randomised controlled trial funded by Allen Carr’s Easyway Ltd (ISRCN number: ISRCTN23584477). This trial is comparing the Allen Carr Easyway stop-smoking method to local NHS 1-1 stop smoking counselling service. The trial is being conducted independently, the protocol and analysis plan are both pre-registered and the research team are contractually free to independently publish the results of the trial regardless of the study outcome.LD has provided consultancy for the pharmaceutical industry  and acted as an expert witness for an EC patent infringement case (2015). Between 2011 and 2013 she conducted research for several independent electronic cigarette companies for which the University of East London received funds. The EC companies involved had no input into the design, conduct or write up of these projects.


Giardia 18S rDNA probe. Faecal samples were further characterised with taxon assemblage-specific triose phosphate isomerase (TPI) Taqman® probes and by sequence characterisation of the β-giardin gene. While less sensitive than the 18S rDNA assay, general prevalence by TPI probes was 52.4%, with prevalence by taxon assemblage of 8.3% (assemblage A), 35.8% (assemblage B) and 8.3% co-infection (A & B assemblages). While assemblage B was dominant across the sample, proportions of assemblages A and B, and co-infections thereof, varied by school and by age of child; mixed infections were particularly common at Runga school  and in children aged 6 and under . Infection with assemblage B was associated with underweight children . The presence of each assemblage was also confirmed by sequence analysis of the β-giardin gene finding sub-assemblage AII and further genetic diversity within assemblage B. To better explore the local epidemiology of giardiasis and its impact on child health, additional sampling of school children with assemblage typing would be worthwhile.As part of an epidemiological survey for gastrointestinal parasites in school children across five primary schools on the shoreline of Lake Albert, the prevalence of giardiasis was 87.0% (n = 254) as determined by real-time PCR analysis of faecal samples with a genus-specific  Assemblcognised ; each ascognised . Despitecognised .Helicobacter pylori infection in apparently healthy children aged 0–12 living in Kampala, the capital. Assemblage B was found dominant and a risk factor for H. pylori infection , and wasting, weight-for-age Z-score (WAZ). Children were defined as stunted if their height-for-age Z score was −2 ≤ SD and underweight if their weight-for-age Z score was −2 ≤ SD  that were initially collected within the epidemiological survey of 254 school children from five primary schools  as reported by Al-Shehri et al. . Each sa −2 ≤ SD . Finger- −2 ≤ SD .During the surveys, all sampled stools were tested for faecal occult blood  but owing to a limited supply of rapid diagnostic tests (RDTs), only stools collected from Bugiogo and Runga were tested in-field with Quik-Chek RDTs for giardiasis and cryptosporidiosis . Stools were then stored in absolute ethanol for later DNA analysis.2.2Giardia 18S rDNA was performed using TaqMan® assay following primers, probes and protocols of Verweij et al. (Giardia DNA template) from a heavily infected individual excreting approximately 1000 cysts per gram of faeces as estimated by microscopy. As a further quality control, reamplification of 10% of samples was undertaken to assess assemblage assay reliability. Assays were performed in a Chromo-4 with Opticon monitor™ version 3.1. . The infection was determined according to Ct values; for the 18S rDNA TaqMan® assay no-infection was Ct ≥  40 and positive infection Ct ≤ 39 while for assemblages-specific probes was Ct ≤ 45.After transfer to the UK and each faecal sample was spiked with Phocine Herpes Virus to act as an internal control for genomic DNA extraction and amplification performance of later real-time PCR assays. Genomic DNA was extracted, and detection of j et al. . These ej et al. . The reaβ-giardin gene was amplified from samples from six children using nested PCR following protocols of Minetti et al. .To further confirm assemblage A and B, the i et al. . PCR pro2.3Statistical analysis was performed using Minitab Ltd.® . Binary logistic regression tests were performed to compare data from each school and as well as risk variables as an independent indicator to assess any associations with specific assemblages.3Giardia 18S rDNA assay while 133 (52.3%) tested positive with TPI assemblage-specific probes. Across Bugoigo and Runga schools, the prevalence of giardiasis by Quik-Chek RDT was 41.6%. Of the 133-tested positive by TPI probes, 21 samples were positive for assemblage A (15.8%) only, 91 positives for assemblage B (68.4%) only and 21 positives for both assemblage A and B (15.8%), mixed assemblage infections.Out of the 254 samples examined, 221 tested positive (87.0%) by targeting Giardia 18S rDNA TaqMan® probe and the corresponding Ct value of assemblage A TPI TaqMan® probe , and positive correlation (r = 0.60)); P < 0.005), and positive correlation (r = 0.54)). The performance of each TaqMan® assay appeared equivalent. Of note, however, is that mixed assemblage infections appear more common at Runga school where the local prevalence of assemblage A was also much higher.Across these samples assemblage, A was less common than assemblage B, an approximate ratio of 1: 2.7, with assemblage B dominant. To ascertain if there was any amplification bias in assemblage detection, β-giardin gene, finding an exact match with sub-assemblage AII and no sequence within the three sample inspected. By contrast, each of the three samples for assemblage B was different and did not match either BIII and BIV precisely. The sequence from Sample 102 is particularly notable as there appeared to be allelic variation within the TPI gene as evidenced by split-peak chromatograms of A/G or T/C at three locations present within this region  demonstrates that children living on the shoreline of Lake Albert are at very high risk of both acute and more likely, chronic infections. The high burden of giardiasis was also corroborated in field by the Quik-Chek RDT at Runga and Bugoigo schools confirming that some 41.6% of children were patently shedding copious amounts of  disease . Nonethe disease , althoug methods . More br methods , and als methods .Giardia 18S rDNA probe assay is approaching 10 pg DNA/μL (Given the multi-copy nature of the 18S rDNA against the lower copy number of TPI, the diagnostic sensitivity of TPI probes is lower, such that just under a half of the infected cases detected by 18S rDNA were missed. It has been stated previously that the detection limit of g DNA/μL , presumag DNA/μL  such thag DNA/μL  which hoNonetheless, in this sample assemblage B dominates upon comparison to assemblage A. Notably this 1:2.7 ratio varied by school with Runga having a greater proportion of assemblage A, as well as co-infection with assemblage B thereof, see β-giardin, the three samples selected from assemblage A were identical and could be further unequivocally assigned to sub-lineage AII, which has been reported in other studies (Giardia is complex, being binucleate and sometimes aneuploid (It is an interesting observation that of the six samples subjected to sequence analysis of  studies . By contneuploid  which miGiardia, we encourage future studies that track each assemblage within local livestock and undertake environmental sampling of lake water where domestic water is drawn.To conclude, additional sampling of school children would be worthwhile if putative associations between assemblage B and detrimental health outcomes reported here are to be fully verified statistically. Furthermore to better monitor local transmission cycles of DNA chromatograms illustrative of genetic variation at each variable position.Supplemental Fig. 1The following is the supplementary data related to this article.


Eosinophilia may cause organ dysfunction, but an exact relation between eosinophil blood counts and adverse outcomes has not been described. The aim of the study is to associate in one model both normal and increased blood eosinophil counts to the subsequent development of common conditions in internal medicine, in which eosinophil granulocytes may play a role for the symptoms.CopDiff) Database, we identified 359,950 individuals with at least one differential cell count (DIFF) during 2000–2007. From these, one DIFF was randomly chosen. From the Danish National Patient Register we ascertained organ damage, within four years following the DIFF. Using multivariable logistic regression, odds ratios were calculated and adjusted for previous eosinophilia, sex, age, year, month, CRP and comorbid conditions.From the Copenhagen Primary Care Differential Count (9/l and reached a plateau around 1.0 × 109/l. Furthermore, risks of most outcomes also increased when the eosinophil count approached zero.Risks for skin- and respiratory disease were increased from above the median eosinophil count of 0.16 × 109/l indicates that the risk for symptoms due to eosinophilia do not increase proportionate at higher counts. This study demonstrates for the first time that there is indeed an increased risk below median count of 0.16 × 109/l for an increased risk for the same manifestations. Clinically, it means that a normal or even low count of eosinophils do not rule out a risk for organ affection by eosinophils, and may contribute to explain, why patients may have normal eosinophil counts in e.g. asthma or allergy and still have symptoms from the lungs and skin, most likely explained by the extravasation of eosinophils.The observed U-shaped association with a plateau of risks around 1 × 10 •Blood eosinophilia may cause end-organ symptoms.•An exact relation between eosinophil count and outcome has not been demonstrated.•Eosinophil numbers correlate to organ damage even below the definition of eosinophilia.•This association is U-shaped between organ manifestations and eosinophil count in blood.•9/l.A plateau of risks is observed around 1 × 10 It may arise from either clonal intrinsic disorders or from reactive extrinsic conditions [In healthy individuals, eosinophilic granulocytes (eosinophils) constitute less than five percent of all white blood cells . Blood enditions . Reactivnditions .idiopathic hypereosinophilia [For the prognostic evaluation and management of patients presenting with eosinophilia it is important to identify both the many patients with reactive eosinophilia and those patients with the rarer specific clonal diseases. This leaves a very small subgroup of patients with nophilia ,4,7,8, wThe eosinophilic granulocyte may have diverse physiological functions, which in principle are beneficial in the immune reaction against exogenous (infections) and endogenous (inflammation and cancer) intruders ,10. Howeidiopathic hypereosinophilic syndrome [9/l-1.5 × 109/l), moderate (≥1.5 × 109/l-5.0 × 109/l) and severe (≥5.0 × 109/l) is arbitrary and not based on risk stratification for organ manifestations [So far, no association between levels of blood eosinophils and organ manifestations has been demonstrated. However, it seems as if eosinophils show a predilection for certain organ systems such as heart , lungs , gastroisyndrome ,42, but stations .This is the first study to examine both normal and increased number of blood eosinophils and the development of various common medical conditions. The rationale for this study was to investigate the number of eosinophil granulocytes in blood samples and the subsequent risk in the same individual subjects in a large population-based cohort to develop disorders with organ involvement, where eosinophils may play a pathophysiological role, in order to reflect a functional context, which has not been established previously.2CopDiff) database contains results from all differential cell counts (DIFFs) requested by GPs in Copenhagen from July 1, 2000 to January 25, 2010. From each of the 359,950 unique individuals (aged 18–80 years) with at least one DIFF in the period January 1, 2001 to December 31, 2007, a single DIFF encompassing the eosinophil count was randomly chosen by computer-generated random numbers . Where available, the level of C-reactive-protein (CRP), categorized as “increased” (≥10 mg/l) vs. “normal” (<10 mg/l) was also obtained from the database . Furthermore, we recorded whether another DIFF was made during the 6 months before our request  and whether eosinophilia was present in this DIFF. In November 2013, the CopDiff database was linked to The Danish National Patient Register (NPR) which has recorded information on all contacts since 1977 with hospitals in Denmark, including discharge diagnoses, outpatient clinic contacts and surgical procedures performed; and to which reporting is mandatory by law in Demark [The Copenhagen General Practitioners’ Laboratory (CGPL) served the general practitioners (GPs) in the Copenhagen area up until 2016 and covered approximately 1.3 million inhabitants. CGPL had International Organization for Standardization (ISO) accreditation and has registered all analytical results since May 1, 2000. The Copenhagen Primary Care Differential Count  over the 4-year period following the DIFF defined according to the ICD nomenclature and grouped as: 39/l which was the median eosinophil count in our data. This OR was adjusted for previous eosinophilia, sex, age, year, month, CRP and competing comorbid conditions (CCI), and modelled as a restricted cubic spline [de novo cases of potential eosinophil-related end-organ damage, individuals who had already experienced organ damage (since three years prior to index DIFF blood sampling) were excluded from risk analyses. A Chi-squared test was used for comparison of the observed distributions of incident disease within the five organ damage groups between the eosinophil groups of “ < 0.16x109/l” and “≥0.16x109/l”. All analyses and calculations were performed with SAS version 9.4 . This study was registered by The Danish Data Protection Agency J.nr. 2013-231-0053 and by www.researchregistry.com UIN 4955.We used multivariable logistic regression to calculate the odds ratio (OR) for the 4 -year incidences of the outcomes between the eosinophil count and a baseline count of 0.16 × 10c spline . In orde49/l). Compared with the baseline count of 0.16 × 109/l which was the median eosinophil count in our data, risks for skin- and respiratory disease were increased both above and below the definition of eosinophilia  of 2.11 . The corresponding risk increase estimate for skin disease was 1.88 .In the full cohort of 359,950 individuals there was a female/male sex ratio of 1.38  and a mean age (SD) of 48.3 (16.7) years. Of these, 14,406 individuals (4%) had eosinophilia  can be interpreted independently of the frequency of the outcomes in the study. The OR is therefore a valid estimate for excess risk in the general population as well [The  reports ,56. Thir as well .de novo cases of potential end-organ disease as reported in the NPR. We cannot rule out, however, that some conditions have evolved since prior to blood sampling due to developmental latency of some disease entities. Likewise, we did not have information about drug treatment. Some types of drugs are known to cause eosinophilia [This study also has several limitations. Firstly, we were not able to identify the different causes of eosinophilia in the DIFFs. However, eosinophil-related organ damage may occur irrespective of the cause of eosinophilia which renders this information less important in the present study. Certainly, most patients in this study registered with eosinophilia have a reactive condition, because primary causes are so rare. Secondly, we performed risk analysis on nophilia  whereas nophilia . Systeminophilia ,60, or dnophilia .9/l may indicate to the physician managing eosinophilia that the risk do not increase proportionate at higher counts , and Christen Lykkegaard Andersen (MD).www.researchregistry.com UIN 4955.No author has any conflict of interest.None.


Progressive supranuclear palsy (PSP) is a rare neurodegenerative disease for which the genetic contribution is incompletely understood.We conducted a joint analysis of 5,523,934 imputed SNPs in two newly-genotyped progressive supranuclear palsy cohorts, primarily derived from two clinical trials  and a previously published genome-wide association study (GWAS), in total comprising 1646 cases and 10,662 controls of European ancestry.P < 5 × 10− 8, including replication of 3 loci from previous studies and 2 novel loci at 6p21.1 and 12p12.1 . At the 17q21.31 locus, stepwise regression analysis confirmed the presence of multiple independent loci . An additional 4 loci were highly suggestive of association (P < 1 × 10− 6). We analyzed the genetic correlation with multiple neurodegenerative diseases, and found that PSP had shared polygenic heritability with Parkinson’s disease and amyotrophic lateral sclerosis.We identified 5 associated loci at a genome-wide significance threshold In total, we identified 6 additional significant or suggestive SNP associations with PSP, and discovered genetic overlap with other neurodegenerative diseases. These findings clarify the pathogenesis and genetic architecture of PSP.The online version of this article (10.1186/s13024-018-0270-8) contains supplementary material, which is available to authorized users. Tau pathology is a prominent hallmark of neurodegenerative diseases, including Alzheimer’s disease (AD) and frontotemporal dementia (FTD). Progressive supranuclear palsy (PSP) is a relatively pure tauopathy associated with parkinsonism - dementia, characterized by pathological tau aggregation and a clinical syndrome of postural instability, falls, and supranuclear ophthalmoplegia . It sharMAPT (the gene encoding the tau protein), and is homozygous in almost all PSP patients [MAPT, MOBP, STX6, and EIF2AK3, suggesting a strong contribution of common variation in its genetic architecture [The major known genetic risk factor is an extended H1 haplotype on chromosome 17q21.31, which includes itecture . We reasThree cohorts of primarily European ancestry were included in the study – “UCLA”, a combination of 349 PSP patients and 130 controls from the UCSF Memory and Aging Center , 9 and tGenotyping in the UCLA study cohort was performed as a prelude to whole-genome sequencing, and was performed by Illumina (using the Illumina HumanOmni 2.5 Array) and the New York Genome Center (using the Illumina HumanCore Array). Genotyping calls were made using the Illumina GenomeStudio software.Genotypes from the Hoglinger et al. GWAS  , and sex-matching was performed in PLINK v1.90b3.28 [Genotypes for all datasets were converted to the forward strand, and converted into coordinates based on the hg19 reference sequence using UCSC liftOver . The gen.90b3.28  and used.90b3.28 . Ancestr.90b3.28  were incImputation was performed separately for each genotyping platform using the IMPUTE v2.3.2 algorithm . Prephasp-values were chosen for downstream anaylsis. Odds ratios were calculated as exp. (beta). Because some of the individual cohort sizes violate the large sample size assumptions of BOLT-LMM, odds ratios for association  were computed using a logistic regression model in PLINK, using the first 5 eigenvectors, derived from Principal Component Analysis (PCA), as covariates. Power calculations were performed using the Genetic Power Calculator [− 8, using a genotypic, 2 df case-control test. QQ and Manhattan plots were constructed using the R package “qqman” [Association was performed using a linear mixed model to correct for population structure, using BOLT-LMM . The genlculator , assumin “qqman” . Forest  “qqman” . The gen “qqman” . To cont “qqman”  as a covThe explained variance in liability at each of the genome-wide significant loci was calculated according to the method of So et al., which requires the frequency of the risk allele, the relative risk of the heterozygous genotype, the relative risk of the homozygous risk genotype, and the prevalence of the disease in the population . The allP < 1 × 10− 5.Genetic associations with PSP may be due to genetic control of gene expression. We used TWAS to predict differential gene expression in PSP from the joint analysis summary statistics, integrating paired genotyping and gene expression data from the GTEx Consortium . Correctp-value was chosen as the index SNP, and variants with p-value < 10− 5 and in LD (r2 > 0.6) with the index SNP were input into CAVIAR. The CAVIAR-identified credible set contains potential causal variants  that could explain the association at each locus.A credible set  was identified at each of total of seven genome-wide significant loci identified in this study using the CAusal Variants Identification in Associated Regions (CAVIAR) software package . BecauseGenetic variation can result in changes to the coding sequence of a gene  or can regulate the gene’s expression . We first identified credible SNPs as “functional” . Of the remaining credible SNPs, we identified those in the promoter region of a gene, defined as the range 2 kb upstream to 1 kb downstream relative to the transcription start site (TSS). Finally, the remaining credible SNPs were considered possible regulatory variants and tested for short- or long- range interaction with other regions of chromatin to identify potential downstream target genes. The interactions were determined by Hi-C experiments in IMR90 and embryonic stem cells from public data , 30, andGenetic correlation was assessed from GWAS summary statistics using the Linkage Disequilibrium Score Regression method (LDSC) . SummaryFull and imputed genotyping results from the UCLA and NNIPPS cohorts will be made available on the NIAGADS database.r2 < 0.9) or low minor allele frequency (< 0.01) were filtered, and genotypes across all cohorts were combined in a joint analysis. In total, we examined 6,419,662 SNPs in 1646 PSP cases and 10,662 controls.We analyzed subjects from three GWAS cohorts, including 1) a multi-center cohort , 6 in whMAPT and largely corresponded to the haplotype region , 3p22.1 , 1q25.3 , 6p21.1 , and 12p12.1 , reached genome-wide significance (p < 5 × 10− 8) , the direction of the association was consistent with the previous association in each cohort. In order to decrease the likelihood that the results were influenced by population stratification, we assessed association at the loci in each of the study cohorts , in 1q41 , 12q13.13 , 8q24.21 , and 1p22.3   and rs9904290 (P = 8.9 × 10− 12) in the haplotype-regressed and H1/H1 only datasets, respectively  that was filtered from this dataset in variant QC; however, they were highly correlated with each other (r2 = 0.996). Additionally, both variants and rs242557 are within the first intron of the MAPT gene.The association between PSP and the chr17q21 haplotype (H1/H2) has been widely characterized, but independent SNPs in the chr17q21 region may also contribute to disease susceptibility. To test this, we performed linear regression, taking haplotype as a covariate. Additionally, we identified subjects that were homozygous for the risk allele (H1/H1), and performed association in this subset of patients. Both approaches identified similar independent associations from the H1 haplotype in the 17q21.31 region, with the most significant SNPs at rs8078967  was among the genes predicted to be differentially expressed, as well as STX6 (within the associated 1q24 locus), SP1 (within the suggestive 12q13.13 locus), SKIV2L , and RPSA (within the associated 3p22.1 locus). Other genes that were pinpointed outside of association regions were CEP57 (in 11q21) and RPS6KL1 (in 14q24.3).To supplement the mapping information from Hi-C, we also identified the functional consequences of GWAS hits using the TWAS method to predict genes that may be affected by risk alleles . TWAS esP = 9.7 × 10− 5) and ALS (P = 1.8 × 10− 3), but not for non-neurodegenerative disease control GWAS  were reported significant in a previous GWAS [As a prototypical tauopathy, insight into PSP susceptibility alleles can help to illuminate the downstream molecular effects of tau pathology, which is a major component of many common neurodegenerative diseases. Altogether, from a joint analysis of three disease cohorts, we have found 2 novel genome-wide significant susceptibility loci in PSP and replicated 3 previously reported loci. Of the loci identified in this study, three  was also previously identified; however, the reported SNP did not reach genome-wide significance in this joint analysis or in the new “Hoglinger” cohort (using different controls).An additional locus near the RUNX2 and SLCO1A2, respectively). At 6p21.1, we identified a lead SNP as well as several coding SNPs in the credible set within RUNX2, a gene thought to be a transcriptional factor involved in regulation of osteoblastic differentiation [TREM2, which has been linked to AD and Nasu-Hakola disease [VCP, linked to amyotrophic lateral sclerosis and Paget’s disease of bone [SLCO1A2, a transporter present (among other places) at the blood-brain barrier, where it regulates solute trafficking [DUSP10, SP1, ASAP1, and WDR63/MIR4423) were suggestive of association, but did not reach genome-wide significance. While this study raises the possibility of involvement of these genes in PSP pathogenesis, further fine-mapping and functional studies are needed to confirm their possible roles.We also identified 2 novel genome-wide significant susceptibility loci at 6p21.1 and 12p12.1 . Atfficking . An addiMOBP. This locus has previously been implicated in differential expression of the SLC25A38/appoptosin gene, which may regulate tau cleavage [MYRIP and EIF1B that could also explain this association. Similarly, at 1q25.3, the gene closest to the GWAS lead SNP was STX6; by Hi-C, we have also identified XPR1 as a possible candidate gene. Interestingly, our group has previously demonstrated XPR1 mutations in primary familial brain calcification [RPSA at 3p22.1 and SKIV2L near 6p21.1 may also be the causal genes but the tissue-relevant datasets were relatively underpowered.Our results also implicate possible alternative causal genes in previously reported genome-wide significant loci. At 3p22.1, the gene closest to the GWAS lead SNP was reported as cleavage . Using Hfication , though MOBP gene have been recently associated with ALS [Aside from identifying additional associated loci and highlighting potential PSP susceptibility genes, we analyzed the polygenic overlap between neurodegenerative diseases, identifying shared heritability with PD and ALS. Curiously, these diseases do not have predominant tau neuropathology, as PSP and other tauopathies do. Typically, PD is associated with aggregation of α-synuclein, and ALS with aggregation of TDP43 and other proteins, while tau pathology is prominent in AD. However, there are known shared genetic risk factors among these diseases. The 17q21.31 haplotype is highly associated with PD, in the same direction as in PSP , and SNPwith ALS . Our resA limitation of the study includes the case-control matching design. While this design allows for matching by array platforms and avoids stratification due to technical artifacts, stratification based on the ancestral differences may be present. The potential for stratification was reduced by strict filtering based on multidimensional scaling to limit the sample to subjects of European ancestry, and linear mixed model methods to further reduce confounding. Combining multiple cohorts as we have done may also reduce the degree of population stratification in the joint sample. Overall, the genomic inflation factor (λ = 1.05) suggested an acceptable level of population stratification. The validity of the results and replication of the original GWAS are further reinforced by the consistency of the identified associations across the multiple platform-matched sub-cohorts.Here, we have increased the number of significant genetic risk locus for PSP, an important advance for understanding its pathophysiology. The power of this study to identify novel loci at genome wide significance and a large unexplained heritability suggests that PSP may be highly amenable to genetic association studies in larger sample cohorts using next generation sequencing. Overall, by establishing the genetic correlations of PSP with PD and ALS and identifying novel genome-wide significant and suggestive associations, we shed insight into the mechanisms of neurodegenerative disease.Additional file 1:Tables S1-S5. (XLSX 636 kb)Supplementary Additional file 2:Figures S1-S5, Supplementary Methods. (DOCX 8021 kb)Supplementary