Finding a proper balance between video quality and the required bandwidth is an important issue, especially in networks of limited capacity. The problem of comparing the efficiency of video codecs and choosing the most suitable one in a specific situation has become very important. This paper proposes a method of comparing video codecs while also taking into account objective quality assessment metrics. The author shows the process of preparing video footage, assessing its quality, determining the rate–distortion curves, and calculating the bitrate saving for pairs of examined codecs. Thanks to the use of the spline interpolation method, the obtained results are better than those previously presented in the literature, and more resistant to the quality metric used. A huge growth in Internet traffic has been observed in recent years. According to the Cisco’s Visual Networking Index (VNI) forecast, by the year 2022, more Internet protocol (IP) traffic will cross global networks than all the traffic observed before 2017. Busy-hour Internet traffic (the busiest 60 min period in a day) has grown even more  than average Internet traffic (by a factor of 3.2) in the years 2016–2021. Moreover, video will account for up to 82 percent of global Internet traffic in 2022 [The Internet is currently the most popular and broadly available means of communication, and is it used for video streaming to almost every place in the world. Data streaming must be adapted to the dynamically varying circumstances, while also taking into account different network parameters. This idea was implemented in the Internet as adaptive streaming over the hypertext transport protocol (HAS). It was initially introduced by leading companies like Apple (HTTP live streaming) , Adobe  as the transport-layer protocols. The video is stored on the HTTP server as a stream of media segments (chunks) that are typically two to ten seconds long. The segments are encoded at multiple bitrate levels and listed in a media presentation description (MPD) file that provides an index for the available segments at the server. The adaptation streaming mechanism assumes that a DASH client estimates the available network bandwidth and uses information from the playback buffer to select a suitable bitrate of the video chunk to be requested (using GET message) from the server. This process, called bitrate switching, allows sufficient data to be kept in the playback buffer in order to avoid video stalls and to preserve a seamless, or at least smoother, streaming experience . When thThe dataset should be chosen so that efficient and unobtrusive switching between different video resolutions is possible. This is required in order to preserve a high, and possibly stable, video quality. The quality may be defined by objective parameters that describe quality of service (QoS) ,16,17,18Currently, H.264/AVC (audio video coding)  and H.26A comparison of two codecs may be performed by calculating the difference between the obtained video quality levels for a specific coding bitrate. The second approach assumes the opposite situation, i.e., determining the coding bitrates needed to achieve a required quality level. The calculated difference between these coding bitrates is called a delta bitrate (D-BR), which denotes the bitrate saving that can be achieved using one codec instead of the other one. Such a method of measuring the coding efficiency for the examined codec in comparison with the other one was proposed by Gisle Bjöntegaard in , where tThe Bjöntegaard model is a very, if not the most, common method used to compare video codecs performances, where the quality is represented by the PSNR metric. Thus, the results of comparing codecs’ performances are expressed by Bjöntegaard delta PSNR (BD-PSNR) or Bjöntegaard delta bit rate (BD-BR) values. It is known from the literature that PSNR is not always the best factor describing video quality, especially in comparison with subjectively measured quality—the quality perceived by the users. Objective quality metrics, especially PSNR, may give different results depending on the specific video content ,32. Laud(a)Presentation of a new approach to comparing the performance of video codecs;(b)Showing the whole video quality assessment process—the preparation of video footage and test material, the assessment of the quality of individual samples, and the presentation of results;(c)Implementation of the spline interpolation method for building R–D curves for the examined codecs;(d)Presentation of the results of comparing the H.264, H.265, and AV1 codecs, which are more quality-metric resistant than those previously presented in the literature.The main aspects of the paper are as follows:The paper is organized as follows. The next section presents the materials and methods used to compare video codecs, with the preparation process of video footages being shown step-by-step. Next, the methods of comparing codecs, based on a limited set of video samples, are discussed and the way of solving the problem is proposed. Validation of this approach is then conducted. The ‘Results’ section presents the main outcome of the author’s research on comparing codecs, and includes some experimental conclusions. Finally, an interpretation of the results is conducted and future research directions are highlighted.One issue that complicates the comparison process of two video codecs is that the relation between the curves, which present video quality as a function of the coding bitrate, is not a constant value for the two specific codecs. Thus, the coding efficiency of one codec in comparison with the other one may be represented as a function of the area between the R–D curves . Each cuN bitrates , with the corresponding video quality being represented by the appropriate PSNR or other measured quality values. In general, these measurement results are denoted as X,1D, …, X,NXD. The results may also show the quality distortion as a function of the coding bitrate. A functional relation between the coding bitrates and the corresponding quality values may be described by third-order logarithmic polynomial fitting, as presented in Equation (2):F is the fitted distortion (in Bjöntegaard model based on PSNR); R is the coding bitrate; and a, b, c, and d are the parameters.As presented in In order to validate the results based on the PSNR values, other objective quality measures, such as SSIM , may alsL and DH are the lower and higher integration limits, respectively, which may be designated as follows:Assuming that the logarithm of the coding rate may be expressed as a function of the distortion by a third–order polynomial like the following:A graphical explanation of this approach is presented in http://ultravideo.cs.tut.fi/#testsequences (accessed on 3 July 2021) by the Ultra Video Group (UVG) [The performance of the previously mentioned video codecs will be evaluated using an objective quality assessment method. In this paper, the FR method will be used in order to determine the video quality of the three examined codecs, i.e., H.264, H.265, and AV1, as a function of the coding bitrate. In the first step, the source video footage and a set of test samples of the same time and spatial resolutions (of different bitrates) should be prepared. It should be noted that, in order to have a reference sample of the highest quality, the source video must be lossless  footage. This often requires the use of a professional camera in order to record the video; however, most consumer class devices, including ubiquitous smartphones, usually save the captured video using a lossy compression. As a consequence, the evaluation of the performance of the video codecs presented in the paper was carried out using two kinds of video footage taken from an open database, which is provided on the website up (UVG) . The firup (UVG) , was preIn order to prepare the reference video files and a series of test samples, the ffmpeg framework  was used1920x1080 -i input.yuv -vf scale=858:480 -c:v rawvideo -pix_fmt yuv420p output.yuv,	  ffmpeg -s:v input.yuv and output.yuv are the analyzed video footages, i.e., ‘Beauty’ and ‘ReadyStGo’, before (beauty_raw1080p.yuv and readystgo_raw1080p.yuv) and after (beauty_raw480p.yuv and readystgo_raw480p.yuv) the rescaling operation, respectively. The two video footages of the lower resolution were used to prepare the test video files; three analyzed codecs, i.e., H.264, H.265, and AV1, and different coding bitrates, were taken into account.where ITU-T recommendations on the subjective video quality assessment methods propose the use of video samples with a length of no more than 10 s in order to reduce the test time. This takes into account that one test sequence may contain several video samples that should be assessed. However, it should be noted that such video footage may sometimes be too short in the case of using subjective methods, when it is actually humans that should assess the video quality. Therefore, the recommendation says that the presentation time  may be increased according to the content of the test material . Based o(a)for the H.264 encoded samplesThe video test samples were prepared and saved as mp4 files using the following ffmpeg commands:ref_file.yuv –b:v bitrate_in_bps -c:v libx264test_480p_h264_N_file.mp4			ffmpeg -f rawvideo -pix_fmt yuv420p -s:v 858x480 -r 25 -i(b)for the H.265 encoded samplesref_file.yuv –b:v bitrate_in_bps -c:v libx265test_480p_h265_N_file.mp4			ffmpeg -f rawvideo -pix_fmt yuv420p -s:v 858x480 -r 25 -i(c)for the AV1 encoded samplesref_file.yuv –b:v bitrate_in_bps -c:v libaom-av1 –strict -2test_480p_av1_N_file.mp4			ffmpeg -f rawvideo -pix_fmt yuv420p -s:v 858x480 -r 25 -iref_file.yuv, the previously prepared reference video samples, i.e., beauty_raw480p.yuv and readystgo_raw480p.yuv, were taken. The test files named as test_480p_h264_N_file.mp4, test_480p_h264_N_file.mp4, and test_480p_h264_N_file.mp4 were H.264-, H.265-, and AV1-encoded video samples, respectively. After these operations, six sets of video test samples were obtained—taking into account three codecs multiplied by two different reference files of eight coding bitrates. This makes 48 video test samples in total  tool . Based oUsually, the obtained video encoding bitrates are close to the specified ones, but not exactly the same (see Tables 3 and 4). Moreover, taking into account the limited number of compared video samples of different bitrates, the quality values of the samples presented by the first codec are usually not the same as the quality of the samples generated by the second codec. This is a consequence of the video sample preparation process based on ffmpeg. A real encoding bitrate of a video sample, obtained during ffmpeg conversion from a raw video, usually is close, but not equal, to the specified ‘target bitrate’. On the other hand, analysis of bitrates of video samples presenting the same quality level is also difficult because the obtained sets of footage are of little different bitrates and qualities.As presented in The appropriate curve fitting to the points presented in Finally, for each pair of codecs, the average value of bitrate distortion was calculated (see Tables 5 and 6). These calculations were done for two sets of video footage and two quality metrics. The interpolation uncertainty may lead to inequalities between the calculated and the real (measured) coding bitrate values corresponding to the appropriate quality levels for the analyzed video footages. Therefore, the author’s approach was validated by calculating the ‘interpolation delta rates’ (IDR), i.e., the differences between the values of the encoding bitrates of (a subset of) the real video footage and the corresponding values of the bitrates of (a set of) the points obtained from spline interpolation, which took into account appropriate quality (PSNR and SSIM) levels see .PSNR = 0.31% and IDRSSIM = 0.34%. Such low IDR values (less than 1%) show that the interpolated PSNR and SSIM values allow good R–D (rate–distortion) curves to be built for the H.264 and H.265 codecs. The research on the AV1 codec showed better interpolation results for the SSIM metric than for the PSNR metric. In this case, the obtained IDR values were IDRPSNR = 12.53% and IDRSSIM = 4.94%.The next section presents the interpolation R–D curves determined for the two sets of video footage, i.e., the ‘Beauty’ and ‘ReadyStGo’ footage, encoded using the H.264, H.265, and AV1 codecs. Next, based on these curves, the results of comparing these codecs are discussed.As already mentioned, the video quality assessment was conducted based on a limited number of prepared video footages, i.e., 8 samples per each examined codec . For each encoded video sample, the values of PSNR and SSIM were determined.Each table contains the PSNR and SSIM results obtained using video quality estimator—a software tool for video quality assessment provided by Elecard Company . The ‘TaA more detailed analysis could be carried out after the interpolation of the measured PSNR and SSIM values, and after drawing the appropriate R–D curves for each codec and video sample.a) slow motion video (‘Beauty’); (b) fast motion video (‘ReadyStGo’).Firstly, the observed video quality values, expressed by both the PSNR and SSIM metrics, are directly proportional to the coding bitrate. However, these relations are not linear;Secondly, the obtained results are consistent with those presented in the literature , where tThirdly, the R–D curves, describing a specific codec, differ from each other, depending on the metric and video footage used.From the curves presented in Thus, comparison of the codecs’ performances requires further discussion, as presented in the next section.According to DRΔ values are lower than the average BDRΔ, i.e., in the case of the ‘Beauty’ video, the avg. DRΔ = 0.81%, while the avg. BDRΔ = 1.98%, and in the case of the ‘ReadyStGo’ video, the avg. DRΔ = 2.43%, while the avg. BDRΔ = 2.63%. It can thus be noted that the author’s method of comparing codecs is more resistant to the metric used in the video quality assessment process.In connection with this, the author’s calculation method seems to produce better results than the Bjöntegaard model , becauseThe author’s method of comparing the performance of codecs, as well as the Bjöntegaard model, allow reasonable results to be achieved based on the interpolation of real video samples, even if their number is limited. Such a reduction in the number of the required samples allows the whole process to be accelerated, which is because the preparation of the test samples may take a long time ,26. ThisThe problem of choosing the right video coding method may also be discussed in much more detail. The author’s observations, as well as many reports in the literature ,53, show


Objective: This study aimed to assess the content, quality, and reliability of YouTube videos on colorectal cancer screening. Colorectal cancer is the third most common cause of death worldwide.TM website on November 19, 2020, using the English keywords "colorectal cancer screening," without using any filter. The videos were categorized into five groups according to the source and then each video was evaluated by three physicians. The videos were compared in terms of the quality of the information, and their reliability and comprehensiveness, according to the sources.Methods: A basic search was conducted on the YouTubeResults: Of the 137 videos included in the study, 74 (54%) were categorized in the useful information group and 63 (46%) in the misleading information group. It was found that most (47.3%) of the videos in the useful information group were uploaded by academic-based sources. Conversely, most (46%) of the videos in the misleading information group were uploaded by for-profit companies, private hospitals, and medical advertisements. An analysis of the video features according to the upload source revealed that the total views (p=0.023), likes (p=0.004), and viewer engagement with the video (p=0.026) were higher in the public spotlight videos group.Conclusionsː The quality of public spotlight videos with high viewing rates and audience interaction should be increased to inform the public. In addition, there is a need for videos containing comprehensive and accurate information to be uploaded to YouTube, which is an important source of information today, by universities, health organizations, and doctors who do not make a profit  from the results. Colorectal cancer (CRC) is the third most common cancer for both men and women and ranks third among cancer-related deaths. Every year there are approximately 900,000 CRC-related deaths globally . AccordiThe mortality rate of CRC  has been declining for decades in both men and women. The most important reason for this decline is that screening methods have been developed and are being widely used to reach the whole community. By means of screening methods, colorectal polyps can be detected before they turn into cancer or at an early enough stage whereby existing cancers can be fully cured. Although the overall mortality rate continues to decline, CRC-related mortality rates in people under the age of 55 years have purportedly increased by 1% each year from 2008 to 2017 . For thiDespite these recommendations, CRC screening rates in general and the frequency of colonoscopy screening, in particular, are below optimal . The typToday, the internet is almost the first point of reference for learning about health problems. YouTube offers an obvious advantage in this regard. As of the beginning of 2021, the total number of views of the top 10 most-watched YouTube health channels had exceeded 1.9 billion . StudiesA basic search was conducted on the YouTube website on November 19, 2020, using the English keywords "colorectal cancer screening," without using any filter. Anticipating that a normal YouTube user would not use any filters when searching for videos on CRC screening, the first 200 videos obtained by searching with no filter were saved as a playlist to be examined by the physicians participating in the study. The videos were saved as a playlist because search results on YouTube can change from day-to-day.Videos with no audio English narration (n=7), those not related to colorectal cancer screening (n=17), duplicate videos (n=16), image only videos without voice, or audio only without images (n=12), and videos of less than 30 seconds (n=10) were not included in the study. Videos consisting of multiple parts were regarded as a single video (n=1). As a result of the search, 137 videos were included in the study.The total number of views, the length, time elapsed after uploading and the numbers of likes, dislikes and comments were recorded for each video. The engagement of viewers with the video was calculated in terms of the number of views per day . A new playlist was created for the videos included in the study.We did not apply to any medical ethics committee for the approval of this study, according to the Declaration of Helsinki of the World Medical Association, as no patient data or material were used and all the videos used for the study were available on a public social media website (YouTube).Evaluation of the videos’ usefulnessAll 138 videos were independently evaluated using the Quality Criteria for Consumer Health Information (DISCERN) scale and Global Quality Scale (GQS), by three physicians . The three physicians blindly evaluated the videos and when there was a lack of consensus, a final decision was made by the fourth physician (EE). Subsequently, two groups were created. The group classifications were: (1) useful information (group 1) - the information was correct and they were helpful for learning CRC screening; (2) misleading information (group 2) - these included incorrect information about CRC screening Classification of the Video CharacteristicsThe videos were separated into the following five groups according to the source of the upload: source 1 - academic-based channels ; source 2 - non-profit associations and physicians/physician groups/professional organizations; source 3 - for-profit companies, private hospital publications, medical advertisements; source 4 - public spotlight videos; source 5 - TV health programs.The total number of views, lengths, time elapsed after uploading and the numbers of likes, dislikes, and comments were recorded for each video. The engagement of the viewer with the video was calculated in terms of the number of views per day .A five-point DISCERN scale (0-5 points) was used to evaluate the reliability of the videos ,15, and Statistical analysisNormality testing was conducted for total views; video length; duration of stay on YouTube; the numbers of likes, dislikes, and comments; reliability score; comprehensiveness score, and global quality score; to check the assumption of normality of subgroups according to useful and non-useful video types. The normality hypotheses used in the statistical analysis were as follows: H0 - the variable is distributed normally; H1 - the variable is not distributed normally.Since the normality distribution was not met by any variables, the comparisons were performed by using the non-parametric Mann-Whitney test. In the statistical evaluation conducted according to the source of the video, the multiple non-parametric Kruskal-Wallis test was preferred because no variables met the normality distribution assumption. A value of p < 0.05 was accepted as statistically significant.The comparison test in terms of the total views revealed that the medians of the number of views in useful and misleading video groups were equal. No statistically significant difference was found between the two video groups in terms of the video length. There was no statistically significant difference between the two groups in terms of the duration of stay on YouTube. The difference in the number of likes was identified as significant between the two groups (p=0.013). It was understood that the number of likes was significantly higher in the videos containing useful information than the videos with misleading information. The comparison tests conducted for reliability, comprehensiveness, and quality scores revealed that the medians of the useful video group for all three were significantly higher Table .Since the p-value was below 0.05 in the chi-square test conducted according to the upload source, it was understood that the video source and information type variables were dependent variables. Hence, the useful/misleading ratio in videos from academic-based channels was prominently higher. In addition, the useful/misleading ratio was higher for the videos posted by non-profit associations and public spotlight videos. The videos of for-profit companies and TV health programs had a lower useful/misleading ratio Figure .There was a significant difference between the medians of the number of views according to the video source groups. In binary comparisons, it was seen that the public spotlight videos were watched significantly more than the non-profit association videos (p=0.01) Table .A significant difference was observed between the video upload source groups for the reliability score. In binary comparisons, videos from non-profit associations were found to be less reliable than videos posted on academic-based channels (p=0.026). It was observed that there was a significant difference between the video upload source groups in terms of the comprehensiveness score. The binary comparisons indicated that the comprehensiveness scores of the videos by for-profit companies were lower than that of the videos by academic-based channels (p<0.001). Similarly, it has been observed that the TV health programs were less reliable than the videos in academic-based channels (p=0.001). In addition, videos produced by the non-profit associations were found to be significantly less comprehensive than those in academic-based channels (p=0.025).It was observed that there was a significant difference between the video upload source groups in terms of the GQS quality score. Also, in the binary comparisons, it was detected that the quality scores of the videos based on TV health programs were lower than those posted by the academic-based channels (p=0.001 ). Similarly, it was found that there was a significant difference between the video source groups in terms of the number of likes. In the binary comparisons, it was established that non-profit organizations received a significantly lower number of likes than academic-based channels (p=0.023). In terms of video interaction, a significant difference was determined according to the video source. The binary comparison tests showed that the viewer interaction of the videos posted by the non-profit associations was significantly lower than that of the public spotlight videos (p=0.012). However, it was observed that the videos of not-for-profit associations received significantly lower interaction than the videos of for-profit companies (p=0.019). Additionally, it was also found that the academic-based videos had a significantly lower interaction than the public spotlight videos (p=0.044). It was detected by the chi-square test of independence that there was a significant relationship between groups (useful information group and misleading information group) and the video sources Figure .According to the results of the chi-square test, the rating of the videos of for-profit companies in the misleading information group was the highest. On the other hand, the rating of the public spotlight videos was the lowest. In the useful information group, the videos from academic-based channels had the highest rating, while the public spotlight videos had the lowest.YouTube, which has been operating since 2005, is the most popular video-sharing platform among internet users today [Inadequate public awareness on CRC screening and cultural resistance to CRC screening tests are known screening challenges . YouTubeAt the end of the study, we made deductions under three main topics. In the useful information group, the highest number of videos were posted by academic-based sources. The length of these videos was long, the narrative had excessive use of medical terms, the visual content was poor and the memorable features were inadequate. For these reasons, although they contained the most useful and reliable information, we could see that the number of likes given by the viewers as well as the video interactions was very low. We are of the opinion that to be viewed more by the users, the academic-based videos should be shortened and visually enriched.The for-profit companies uploaded the highest number of videos among the sources in the misleading information group. We consider that the reason for them to be included in the misleading information group is that for commercial reasons the videos are created to explain a single screening method, in order to highlight and market its own product. We conclude that the reason that the number of views is higher than those for the academic-based videos is due to the for-profit companies’ advertising agreements, as even YouTube users who are not searching with words associated with CRC screening see these videos on their pages.The source of the video that the users viewed the most, following a standard search, was the public spotlight videos. Those videos had the highest number of views and the highest interaction rate. We established that the rate of videos that were viewed so much and had so many interactions was the least among the useful information videos. The content of these videos is poor because they explain only one to two CRC screening procedures. We think that public spotlight videos are the most-watched group due to their short length and the high amount of visual content. Previous studies have reported similar results -19.In the present study, we ascertained to what extent videos belonging to which source were viewed. The public spotlight videos were the most viewed videos and it was concluded that their content should be increased. In addition, we detected that the academic-based videos contained comprehensive information, but even so were less viewed compared to videos from other sources. We believe that the artificial intelligence infrastructure of YouTube could be used to increase the number of views of those videos, and cooperation could be sought with the YouTube website management in this regard.In addition, we think that it will make a difference if high-quality videos that have received referee approval, in terms of CRC screening, are included in the suggestion videos section spontaneously, for YouTube users of the screening age using algorithms.Study limitationsFirst of all, the data included in the study were extracted at a certain time and as is known, view, like, dislike, and comments change every second. Second, only English language videos were evaluated, making generalization of the results difficult. Finally, we used the term "colorectal cancer" (CRC), which may not be exactly understood by layperson. However, we had to use it because the primary objective of the study was the evaluation of the videos about CRC. Nevertheless, we believe that our results will contribute to what is known on the issue in the literature.We think that the CRC screening rate could increase by collaborating with YouTube , enriching the public spotlight videos with useful information, keeping academic videos short and more viewable. In addition, healthcare professionals should be encouraged to upload accurate and useful videos to YouTube in order to direct those who want to inquire about screening to professionals. Furthermore, YouTube authorities should develop new strict policies and strategies to inspect and audit particularly health-related videos before they can be published.


Body representation distortion (BRD) is a core criterion of Anorexia Nervosa (AN), and is usually assessed subjectively, focusing on body shape. We aimed to develop a new assessment to evaluate body representation independently from socially-mediated body image, on a body part with low emotional salience (hands). In a monocentric open label pilot study, we measured hand representations based on explicit  and implicit (tactile) instructions. Participants, with eyes closed, had to point targeted locations (knuckles and nails of each finger) based on verbal instructions and tactile stimulations to evaluate body representations respectively. Ratios between hand width and finger length were compared between AN (n = 31) and controls (n = 31) and correlated with current body mass index, AN subtype and disease duration. To control that hand distortion was specific to body representation, we also assessed object representation. Hand representation’s width/length ratio was significantly increased in patients with AN, whereas no difference was found in object representation. We found no correlation between hand wideness and clinical traits related to eating disorders. Our results propose that BRD is not limited to body parts with high emotional salience, strengthening the hypothesis that anorexia nervosa is associated with profound unspecific BRD. AN is characterized by a number of clinical symptoms, including body representation distortion (BRD), sometimes called “dysmorphophobia”3. How we represent our body has consequences for definition of the self and mental health. In AN, body dissatisfaction and weight concern are linked to body representation, and take part in the development and the maintaining of the disease4. BRD often persists after weight restoration6 and taking it into account in care for AN remains a clinical challenge7. In AN, body distortion usually occurs through an overestimation of general body size9.Anorexia nervosa (AN), a concerning eating disorder (ED) in many countries, still lacks understanding of its physiopathology. Women are more affected by this disease with a sex ratio of 1/9, and the mortality rate is the highest among psychiatric disorders10. The resulting shape of hand representation shows an overestimation of hand width and an overall underestimation of finger length10. Similar distortion has been found11 for implicit body representation based on tactile instructions: participants had to indicate the location of different tactile stimuli applied on the hand on a silhouette of their hand. Moreover, these biases appeared to mirror known characteristics of primary somatosensory cortical maps11.Interestingly, body representation  is also distorted among healthy participants12, localization task based on tactile or verbal instructions as described above13 or indirect behavioural measures14.Different experimental paradigms have been used to investigate various aspects of body representation such as the rubber hand illusion15 and body image distortion is generally accepted as a main diagnostic criterion, but the origin of body image alteration has not been identified yet. BRD could be due to multisensorial integration impairments of body representation. Several studies support the idea that AN is associated with deficits of somatosensory integration22: tactile perception, vision, haptic perception and even action-oriented tasks seem to be impaired in AN. Indeed, perturbations in multisensory integration regarding the body itself may be an endophenotype of AN, and patients with this disease may use different strategies from healthy individuals in evaluating their own weight and size . Excessive or lack of focus on exteroceptive input  may play a role in AN patients’ body representation distortion. The allocentric-lock hypothesis posits that patients with AN have a defective multisensory integration and consequently have difficulties to update body representation after weight loss24. Consequently, incoming perceptual or sensory signals differ from the stored information, which produces a mismatch that could contribute to pathologically exaggerated body representation distortions.In patients with AN, body image concern has been widely studied25. Questionnaires used in ED, such as the BSQ (body shape questionnaire)26 and the EDI (eating disorder inventory)27, also focus on these body parts. However, as body representation disorders could be caused not only by cultural pressure and current thinness ideal, but also by a multisensorial integration defect of body representation, we hypothesized that BRD is not limited to body parts with high emotional salience such as hips, thighs or abdomen, and that it could be interesting to assess BRD in other body parts, such as hands. Indeed, hands are commonly assumed to be “less emotionally salient” in ED22, and patients do not usually report being disturbed by their hands’ appearance.Patients with AN usually describe body concern focused on hips, thighs, abdomen (feeling too fat and seeing themselves too large). Some approaches to assess BRD evaluate representation of these body parts, asking patients to place on a wall indicators reflecting the width of their waist and hips, the latter being compared with actual sizes28. While these approaches focus on conscious body image, in the present study we use a more objective approach which measures implicit distortion. The aim was to focus on hand representation based on explicit and implicit instructions in motionless AN patients, hypothesizing that patients with AN have a wider representation of their hand compared to healthy controls. We used a method inspired from localization tasks described above11: participants, with eyes closed, had to point on a tablet placed above their hand the location of their hand’s knuckles and nails following verbal (explicit) or tactile (implicit) instructions.Nowadays, body representation disorder is subjectively measured, with feminine shapes (shape’s silhouette), anthropometric diameters (rope test), or with numerical tools such as virtual realityIn a monocentric open label pilot study, patients were recruited in a specialized eating disorder clinical unit. Healthy controls were recruited in the Clinical Research Centre of the same hospital.Included participants were older than 18. Participants with hand injuries, pathologies affecting tactile sensitivity (as diabetes) or neurological or cognitive disorders were excluded from the study. AN patients were assessed by a psychiatrist following DSM5 criteria. Healthy controls (HC) were recruited among volunteers taking part in another validation trial of the task. Exclusion criteria for HC were: self-declared psychiatric diagnosis, neurological or cognitive disorder with sensorimotor impairment, assessed by an interview with the participant.The clinical data collected for AN participants were: current BMI, minimal BMI, subtype of AN, disease duration.Participants were matched for age, as hand width representation reduces with age . They were not matched for sex as there is no known differences in hand’s representation between male and female . AN patients were assessed at the beginning of outpatient treatment or during inpatient treatment.The number of participants was established from a power calculation  on preliminary sample of 20 participants by group to identify a potential difference between groups for tactile (HC mean: 1.26 (S.D. 0.36), AN mean: 1.53  and verbal (HC mean: 1.34 (S.D. 0.48), AN mean: 1.90 (S.D. 0.63), n = 11) conditions. Some hospitalized patients (N = 5) had an initial diagnosis of AN but gained weight during the course of treatment, resulting in normal weight at the time of the testing. Other symptoms of AN were still present. These patients did not differ on any of the outcome variables of the current experiment, and we chose to include them in the AN group.The study procedures were verbally explained at the beginning of the session. The evaluation lasted approximately 30 min. The different conditions  were assessed on the non-dominant hand, so the participant pointed with their dominant hand. The order of the conditions was randomized between participants.31 and also influence participant responses by using visual reference and/or explicit knowledge of hand characteristics32.Participants were seated on a chair. A graphic tablet was placed above the participant’s non-dominant tested hand . The task was to point with the stylus pen on the graphic tablet directly above where they perceived the target indicated by the experimenter, without time limitation.There were 10 possible targets, randomized and repeated 3 times per condition: the five nails and the five metacarpophalangeal joints. Then the coordinate was recorded from the graphic tablet.The experimenter could indicate the target verbally (VERBAL condition) or use tactile stimulation of the target using a stick with a foam tip (TACTILE condition).After VERBAL and TACTILE conditions, object distortion was assessed (CARD condition). This CARD condition was used to control that the potential body distortion was specific to the body and not due to a general pattern of space distortion with eyes closed. In this condition, participants had to successively point the four corners of an imagined asked card whose size is standard and known by the participant . The task was done twice: first with eyes closed (CARD no vision) and then with eyes open (CARD vision). The distortion was the difference between the card with eyes open and eyes closed . For each condition  and each participant, the barycentre of each target  repetition was computed. We used this barycentre as the responded location of the target.Hand width was computed as the distance between metacarpophalangeal joint of the little finger and the thumb. Hand width in TACTILE and VERBAL conditions was divided by PHYSICAL hand width in order to normalize data between participants. The length of each finger was computed as the distance between the nail and the metacarpophalangeal joint of each finger. Then the mean of the five finger lengths was computed.Finger length in TACTILE and VERBAL was divided by PHYSICAL finger length in order to normalize data between participants.The relative wideness of the hand corresponds to the ratio between hand width and finger length of the representation both normalized over the corresponding physical hand extent. In other words, rWideness is the relative wideness of the representation compared to the one of the real hand. rWideness was computed so that a value of 1 means that the representation has the same ratio between hand width and length of the participant’s physical hand. A rWideness lower than one means that the representation of the hand is narrower than the physical hand and a ratio greater than 1 means that the representation is wider.CARD width was the mean distance between the two edges in the frontal plane while CARD length was the mean distance between the two edges in the sagittal plane. Width and length in CARD no vision was divided with CARD vision in order to obtain the distortion of the card with eyes closed compared to what participants « think » the real size of the card is.Data analyses were computed using Wolfram Mathematica 11.3 for distance computation and R for statistical analyses.e were computed for significant results as re = Z/(N)1/2. For the AN group, we performed non-parametric correlation analyses (Spearman correlation) between rWideness and clinical data current BMI, minimal BMI, duration of the ED, and subtype of AN (binge/purge or restrictive). We did not record BMI for HC group.We used Shapiro–Wilk test to test for data normality. We found that rWideness did not follow normal distribution for VERBAL and TACTILE conditions. Consequently, we used Wilcoxon signed rank test to analyse between-group differences, corresponding effect size rThe device consisted of a graphic table HUON WH1409) connected to a PC through USB. The graphic tablet . Each participant received an information letter about the study. In accordance with the Helsinki declaration, written informed consent was obtained from each participant before inclusion.2 (range 12.4–20.6), and mean lower BMI was 13.44 ± 1.42 (mean ± SD) kg/m2 (range 9.9–16).We tested 62 participants, 31 female patients with AN  and 31 HC whom we matched as closely as possible for age and handedness . Mean body mass index (BMI) in the AN group was 15.49 ± 2.3 (mean ± SD) kg/mp = 0.1863). There were 5 males in the HC group, none in the AN group.Mean age of AN patients was 27.19 ± 8.99 (mean ± SD) years (range 18–51) and mean ED duration was 9.10 ± 8.47 (mean ± SD) years (range 1–37). Mean age of healthy controls was 27.74 (± 8.2) years (range 18–47). Table The relative wideness  and in the AN group , coherent with the general pattern of distortions previously found in healthy populations.As previous studies found shorter finger and wider hand representation in healthy populations, we controlled if rWideness was greater than 1. We found that rWideness was significantly greater than 1 in both conditions in the HC group . These results indicate that the increased distortion in AN is specific to the body since it does not affect object representation.We compared rWideness between AN and HC groups Table  and founp = 0.005) but not in the AN group . The rWideness Tactile and Verbal significantly differed in the AN group  but not in the HC group .There was a significant positive correlation between the rWideness Tactile and Verbal in the Healthy Control group .The difference between Verbal and Tactile condition was significantly greater in the AN group (median = 0.34) compared to HC  and Verbal conditions . There was also no difference between early weight-recovered AN patients (N = 5) and other AN patients (N = 26) for Tactile  and Verbal conditions .Analysis showed no significant differences between AN-BP subgroup (n = 14) and AN-R subgroup (n = 17) for Tactile , minimal BMI , or ED duration  in the AN group. Similarly, we found no significant correlation between rWideness Verbal and current BMI , minimal BMI , or ED duration .We found no significant correlation between rWideness in the Tactile condition and current BMI  and the tactile (implicit) conditions. There was less increased hand relative wideness in the Tactile condition compared to the Verbal condition in the AN group. Another difference between groups was that tactile and verbal hand distortions were correlated in controls, but not in the AN group. In contrast, no difference between groups was found for object representation. Finally, there was no correlation between hand representation relative wideness and BMI or ED duration for AN patients in the study. AN patients with recent normal weight showed the same distortion as underweight patients.11. We also found that body representations based on explicit and implicit instructions were distorted in healthy participants, with an increased hand relative wideness in this group. Both conscious body image and sensory perception correlates differ from the physical hand; however, in AN patients, this distortion is stronger.Hand representation is not identical to the physical hand, even in healthy persons. Previous studies on hand size estimation found that hand representation based on verbal instruction (explicit) was wider than the physical hand in healthy persons, but they did not report the ratio between hand width and finger length12. Only the explicit hand width representation was evaluated , and they did not compare the estimated hand width of AN patients with that in healthy controls. This experiment used the rubber hand illusion with therapeutic purpose: it was found that the rubber hand illusion improved estimation of hand width in AN patients, the hand width being less overestimated after the rubber hand illusion.An overestimation of hand width using a caliper with pointers was also previously found in AN patientsThe overestimation of hand wideness was less substantial in the Tactile condition compared to the Verbal condition: in other words, conscious hand representation is more impaired than pure implicit (or somatoperceptive) hand representation in the AN group. This difference between the two representations in AN can be related to the fact that a lack of somatoperceptive feedback increases the distortion. Clinically, patients often use body checking behaviours, such as trying to feel their bones on specific parts of their body, pinching or wrapping a hand around the stomach or thigh, etc. These behaviours could be related to attempts to balance their impaired explicitly-instructed body representation in the verbal condition. Moreover, both representation distortions based on implicit and explicit instructions correlated in the HC group and consequently, in this group, both representations seem to rely on similar processing and share neural mechanisms. In the AN group, this was not the case: implicitly and explicitly-instructed body representation distortions were not correlated, indicating an imbalance between the two body representations.7.Both explicitly and implicitly instructed representations were unrelated to BMI. Undernutrition is linked to several AN symptoms, but it does not appear to be related with BRD in our study. Our results are congruent with the clinical observation that BRD often persists after weight restoration, and with the allocentric-lock hypothesis (i.e. related to difficulties to update body representation after weight change). Patients may have difficulties in updating their body representation along with the weight changes, due to their defective multisensory integration. BRD is a strong risk factor of relapse and may play a main role in remission35. Body representation distortion could also take part in it. Further research using the hand representation spatial task in recovered AN and relatives is necessary to precise whether this body representation distortion is a stable vulnerability trait for eating disorders or if it is a symptom occurring during the period of the illness and lingering after weight restoration before improving.In this study, both explicitly and implicitly-instructed representations were unrelated to ED duration. The patient sample size was limited, and only composed of adults, with a wide ED duration range (1–37 years). Further research with more participants and younger patients is needed to investigate whether similar distortions are present early in AN and to confirm independency with ED duration. An endophenotype of AN, shared by patients and unaffected relatives, is supported by recent work; it usually concerns the intense and compulsive exercise exhibited by most patients and neurocognitive featuresOur results add to the growing evidence that somatoperception and conscious body image are impaired in AN, and strengthen the hypothesis that AN has profound and unspecific body distortion. The method used in this study can objectively evaluate these impairments with a simple test which is easy to perform, replicable and potentially compatible with neuroimaging. The assessment used in this study provided an indirect/unconscious measure of body representation as hand is not one of the most commonly affected body parts reported by patients affected by AN. Nevertheless, the assessment of the hand showed strong distortion, unveiling a new approach to measure body representation distortion in AN.First, our sample was not homogeneous in term of weight status, and some patients had recently gained weight. In our sample, we did not find any evidence for an effect of weight recovery, however, assessing hand representation in a third group made of weight-recovered AN patients would be necessary to confirm this observation. Second, participants did not have a subjective evaluation of body representation (with questionnaires). Third, some of our control subjects were men, while there were only women in the AN group. However, no gender difference in spatial hand representation distortions was found in previous work . Therefore, there should be no major impact of this difference between groups on the results. The originality of the assessment is also a limitation. Similar measurements on other parts of the body could have better characterized the specifics of these implicit hand measurements and should be incorporated into future studies.Using a novel method to evaluate body representation in AN, we targeted a low emotionally salient body part (i.e. hand) in AN patients and controls to assess body representation using explicit and implicit instructions. We found that hand representation was wider in patients with AN in both representations than age-matched healthy controls. Our results support a body representation disorder hypothesis and show that BRD is not limited to body parts with high emotional salience, as it also concerns hands. Our method is easy to use in clinical practice, allows for immediate quantification and implicit assessment. It allows quantification of body representation in both clinical care and research concerning anorexia nervosa and eating disorders.


The immune response to invasive carcinoma has been the focus of published work, but little is known about the adaptive immune response to bronchial premalignant lesions (PMLs), precursors of lung squamous cell carcinoma. This study was designed to characterize the T cell receptor (TCR) repertoire in PMLs and its association with clinical, pathological, and molecular features.Endobronchial biopsies (n=295) and brushings (n=137) from high-risk subjects (n=50), undergoing lung cancer screening at approximately 1-year intervals via autofluorescence bronchoscopy and CT, were profiled by RNA-seq. We applied the TCR Repertoire Utilities for Solid Tissue/Tumor tool to the RNA-seq data to identify TCR CDR3 sequences across all samples. In the biopsies, we measured the correlation of TCR diversity with previously derived immune-associated PML transcriptional signatures and PML outcome. We also quantified the spatial and temporal distribution of shared and clonally expanded TCRs. Using the biopsies and brushes, the ratio of private  and public  TCRs was quantified, and the CDR3 sequences were compared with those found in curated databases with known antigen specificities.We detected 39,303 unique TCR sequences across all samples. In PML biopsies, TCR diversity was negatively associated with a transcriptional signature of T cell mediated immune activation (p=4e-4) associated with PML outcome. Additionally, in lesions of the proliferative molecular subtype, TCR diversity was decreased in regressive versus progressive/persistent PMLs (p=0.045). Within each patient, TCRs were more likely to be shared between biopsies sampled at the same timepoint than biopsies sampled at the same anatomic location at different times. Clonally expanded TCRs, within a biopsied lesion, were more likely to be expanded at future time points than non-expanded clones. The majority of TCR sequences were found in a single sample, with only 3396 (8.6%) found in more than one sample and 1057 (2.7%) found in two or more patients ; however, when compared with a public database of CDR3 sequences, 4543 (11.6%) of TCRs were identified as public. TCRs with known antigen specificities were enriched among public TCRs (p<0.001).Decreased TCR diversity may reflect nascent immune responses that contribute to PML elimination. Further studies are needed to explore the potential for immunoprevention of PMLs. Lung cancer is the second most common cancer in the USA and accounts for approximately one in four cancer deaths.5–8By transcriptionally profiling endobronchial biopsies of PMLs obtained from high-risk smokers undergoing lung cancer screening, we have identified distinct molecular subtypes of squamous PMLs.T cells are able to recognize aberrant peptides, including neoepitopes that are presented by tumor cells as a result of mutational events acquired during cancer initiation and progression. This endogenous T cell response to malignancy can be harnessed to mediate tumor regression using adoptive cell therapies16Previous studies have demonstrated that the TCR repertoire can be partially assembled from bulk RNA-sequencing (RNA-seq) data.Endobronchial biopsies and brushings from high-risk subjects, undergoing lung cancer screening at approximately 1-year intervals via auto fluorescence bronchoscopy and CT, were profiled by RNA-seq as previously described .We applied the TCR Repertoire Utilities for Solid Tissue/Tumor  tool10.1136/jitc-2021-002647.supp1Supplementary dataIn the bulk RNA-seq data, TCR diversity was measured as the number of unique clonotypes per 1000 TCR reads, previously described as clonotypes per kilo-reads (CPK).We used a linear mixed effects models adjusting for patient as the random effect to evaluate the association between CPK and the expression of an immune-associated gene set we previously identified and found to be associated with squamous PML regression.We compared the overlap of TCR repertoires in biopsies: (1) between PMLs from different subjects, (2) within the same subject between PMLs acquired during the same procedure at different anatomic locations, and (3) within the same subject between PMLs acquired at the same anatomic location across different procedures. Fisher’s exact test was used to identify pairs of samples enriched in shared TCRs, comparing the number of CDR3 sequences shared and unique, against a background of all 39,303 TCRs. The mean odds ratios (ORs) of these Fisher’s exact tests grouped by shared location, time, and subject were compared using the Tukey test.To identify TCRs undergoing clonal expansion, we normalized counts of all TCRs to counts per thousand reads within each sample and defined expanded TCRs as those with normalized counts more than three SD greater than the mean calculated across all samples. Samples with fewer than 250 reads total were removed from this analysis, as low total counts in a sample resulted in inflated normalized counts for individual TCRs. With a threshold of 250 reads, there was no longer a significant linear association between total reads and number of expanded clones (p>0.05 Pearson). In order to identify whether the same TCR sequences were repeatedly expanded more often than expected by chance, we conducted a permutation test. We shuffled ‘expanded’ versus ‘not expanded’ labels of TCRs present 1000 times, then compared how often the same TCR sequence was expanded at multiple timepoints at each location within each patient.TCRs from both biopsies and brushes were defined as public if they were found in more than one patient or, for TCR beta, if they were also found in the peripheral blood of a previously described cohort of 666 healthy donors.We detected a total of 39,303 unique TCR sequences of at least six amino acids in length using the TRUST tool and RNA-seq from 294 biopsies of PMLs (one biopsy did not generate TCR sequences) and 137 brushings of normal appearing airway epithelium from 50 subjects at high-risk for lung cancer. The TRUST TCR sequences were used for further analyses as MiXCR assembled fewer sequences , the sequences from both methods were highly overlapping  TCRs were shared, p<1.5e-21 by hypergeometric test), and the methods produced similar results. Eleven representative biopsy samples underwent targeted RNA sequencing of the TCR loci, yielding an average of 585,589 TCR reads per sample  and 1618 unique CDR3 sequences per sample (range 462–2772). Both the total number of TCR reads identified and TCR diversity were significantly correlated across bulk and targeted sequencing results  in comparison with other commonly used TCR diversity metrics   . In our On average across the subjects, endobronchial biopsies were obtained from different anatomic locations during multiple screening bronchoscopies. Out of the 34,251 CDR3 sequences identified in biopsy samples, 2494 (7.3%) were present in more than one biopsy in our cohort. In order to identify patterns in CDR3 sharing between biopsies, we identified enrichment of shared CDR3 sequences for all possible pairs of PML biopsies using Fisher’s exact test. Only 0.01% of pairs of samples from different patients showed significant enrichment  <0.01) of shared CDR3 sequences. Among samples from the same patients, we detected higher TCR clone sharing among samples found at different anatomic locations at identical time points  and among samples taken from the same anatomic location at separate time points  versus samples at different locations and time points  . The meaAmong the 74 lesions with at least 250 total TCR counts, we identified 344 TCRs likely to have undergone clonal expansion in PML biopsies based on increased normalized abundance three SD above the mean across all samples. Among TCRs that were only found in a single sample, 3.1% of TCRs were expanded, significantly less than in TCRs that were identified in multiple samples within the same patient . Furthermore, we identified 10 TCRs that were expanded at two time points in the same lesion at the same location, more than would be expected if all TCRs recurring in a patient were equally likely to be expanded (p<0.001) . The 10 Among all TCRs identified in both brushes and biopsies, 4543 (11.6%) were considered public, meaning shared between multiple patients in our cohort or, for TCR beta sequences, identified in a previously described cohort of 666 people.Lung squamous cell carcinoma arises in the epithelial layer of the bronchial airways and is often preceded by the development of bronchial PMLs. Bronchial PMLs have variable outcome and biomarkers of PML progression, and therapies to intercept lung cancer development at this early stage are needed. We have previously reported the identification of four molecular subtypes of PMLs based on bulk RNA-seq profiling of endobronchial biopsies and brushes from high-risk smokers undergoing lung cancer screening.Given that bulk RNA sequencing is a relatively insensitive method of characterizing the TCR repertoire, capturing only a fraction of the TCRs, we confirmed on a subset of samples that total TCR reads and TCR diversity was significantly correlated between TCRs derived from bulk RNA-seq versus targeted TCR-seq data. Using the TCRs derived from bulk RNA-seq and the gene expression data, we found that in the biopsies, a decrease in TCR diversity or an increase in TCR clonal selection is significantly associated with a metagene score for the gene signature associated with interferon and antigen processing and presentation pathways. Interestingly, we also found increased TCR clonal selection in regressive versus progressive/persistent lesions belonging to the proliferative subtype, although a larger sample size is needed to properly adjust for patient effects. These results suggest that regressive high-grade lesions are recognizing neoantigens, and further single T cell sequencing and functional studies are needed to evaluate the utility of these for immunoprevention of lung squamous cell carcinoma.Since the biopsies in this study were serially collected at different anatomic locations in the lung, we were also able to quantify the amount of TCR clone sharing between or within patients as a function of space and time. We detected a high degree of clone sharing between biopsies from the same patients and the degree of sharing was higher for samples obtained from the same time point versus the same anatomic location at separate time points. Shared TCRs within a patient were more likely to be expanded than TCRs found in a single sample. We observed 10 TCRs that were expanded at multiple time points in the same lesion at the same location suggesting the potential existence of lesion-specific neoantigens. Additionally, our results show that TCR repertoires significantly overlap in different areas of the airway, suggesting a field immunity effect may exist concurrently with lesion-specific immunity. This finding parallels our prior work showing field-level transcriptomic changes associated with PML and lung cancer development in distant, normal-appearing tissue from the nasal turbinate and the mainstem bronchus.15 27KRAS exon 12 G12D mutation in colorectal cancer and can be leveraged for adoptive T cell therapy.29In order to further characterize the TCR repertoire, we classified TCRs as public (found in more than one patient) and private (found in only one patient). Public TCRs were more likely to be clonally expanded than TCR sequences found only in one patient. Additionally, they were more likely to have known infectious antigen specificity in our dataset, consistent with previous evidence of shared TCRs in response to infectious agents.Our study provides TCR characterization of bronchial lesions that are precursors of lung squamous cell carcinoma suggesting that TCR diversity measured via RNA may help predict the host immune response at the earliest stages of cancer development and may, in the future, aid in the development of immunoprevention strategies for lung cancer.


An 87-year-old woman presented to the emergency department of our hospital with acute-onset chest pain and a presyncopal episode. She was haemodynamically stable with sinus tachycardia and 93% oxygen saturation. Urgent transthoracic echocardiography and transoesophageal echocardiography Fig. a, b. ConUrgent transthoracic and transesophageal echocardiogram depicting enormous, mobile masses in the cardiac chambers


We present an update to the previously published dataset known as SFU-HW-Objects-v1. The new dataset is called SFU-HW-Tracks-v1 and contains object annotations with unique object identities (IDs) for the High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) sequences. For each video frame, ground truth annotations include object class ID, object ID, and bounding box location and its dimensions. The dataset can be used to evaluate object tracking performance on uncompressed video sequences and study the relationship between video compression and object tracking, which was not possible using SFU-HW-Objects-v1. For each video frame, ground truth annotations include object class ID, object ID, and bounding box location and its dimensions. The dataset SFU-HW-Tracks-v1 has separate folders for each class of sequences , which differ in resolution, and each class folder contains individual sequence folders. Each sequence folder contains one annotation file per frame, which is a text file and can be viewed in any text editor. Each row in the annotation file corresponds to an object in the corresponding frame, and contains the following information: [Class ID, Object ID, ,,,We prepared object tracking annotations for 13 high efficiency video coding (HEVC) v1 common test conditions (CTC) video sequences in the YUV420 format Similarly, relative bounding box width and height, The conversion between relative coordinates and absolute coordinates is also explained in The folder and file structure of SFU-HW-Tracks-v1 is illustrated on the example of the BasketballDrive sequence in 2,,,Tracking annotations in SFU-HW-Tracks-v1 were created based on object detection annotations in SFU-HW-Objects-v1 We used normalized cross-correlation (NCC) If a particular object does not exist in frame No human, animal subjects, and data from social media platforms were involved in this work.Takehiro Tanaka: Methodology, Software, Data curation, Writing - Original draft, Visualization. Hyomin Choi: Conceptualization, Methodology, Software, Data curation, Supervision, Writing - Review & Editing. Ivan V. Bajić: Conceptualization, Supervision, Writing - Review & Editing, Project administration, Funding acquisition.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Biological nitrification inhibition (BNI)–enabled wheats can improve soil ammonium levels by slowing down its oxidation and generate significant synergistic benefits from assimilating dual nitrogen forms and improving adaptation to low N systems. Deploying BNI-enabled wheat on a significant proportion of current global wheat area (ca. 225 M ha) could be a powerful nature-based solution for reducing N fertilizer use and nitrogen losses while maintaining productivity.Globally, wheat farming is a major source of nitrogen pollution. Rapid generation of soil nitrates cause nitrogen leakage and damage ecosystems and human health. Here, we show the 3Ns N losses from agriculture. Production and release of nitrification inhibitors from plant roots is termed “biological nitrification inhibition” (BNI). Here, we report the discovery of a chromosome region that controls BNI production in “wheat grass” Leymus racemosus (Lam.) Tzvelev, located on the short arm of the “Lr#3Nsb” (Lr#n), which can be transferred to wheat as T3BL.3NsbS (denoted Lr#n-SA), where 3BS arm of chromosome 3B of wheat was replaced by 3NsbS of L. racemosus. We successfully introduced T3BL.3NsbS into the wheat cultivar “Chinese Spring” , which resulted in the doubling of its BNI capacity. T3BL.3NsbS from BNI-CS was then transferred to several elite high-yielding hexaploid wheat cultivars, leading to near doubling of BNI production in “BNI-MUNAL” and “BNI-ROELFS.” Laboratory incubation studies with root-zone soil from field-grown BNI-MUNAL confirmed BNI trait expression, evident from suppression of soil nitrifier activity, reduced nitrification potential, and N2O emissions. Changes in N metabolism included reductions in both leaf nitrate, nitrate reductase activity, and enhanced glutamine synthetase activity, indicating a shift toward ammonium nutrition. Nitrogen uptake from soil organic matter mineralization improved under low N conditions. Biomass production, grain yields, and N uptake were significantly higher in BNI-MUNAL across N treatments. Grain protein levels and breadmaking attributes were not negatively impacted. Wide use of BNI functions in wheat breeding may combat nitrification in high N input–intensive farming but also can improve adaptation to low N input marginal areas.Active nitrifiers and rapid nitrification are major contributing factors to nitrogen losses in global wheat production. Suppressing nitrifier activity is an effective strategy to limit  This optimizes the utilization of biochemical machinery for N assimilation, improving stability and possibly enhancing yield potential  than NO3− assimilation  have been shown to suppress Nng wheat , 19–21.ng wheat , 19, 20.Leymus racemosus (hereafter referred to as “wild grass”), a perennial Triticeae evolutionarily related to wheat, produces extensive root systems  and was discovered to have a high BNI capacity several times higher than cultivated wheat. It was also effective in suppressing soil nitrifier activity and in reducing soi -nitrate formation   is a plant function whereby nitrification inhibitors (BNIs) are produced from root systems to suppress soil nitrifier activity –26. EFig. S2) . Subsequresearch , 27, 28.research .bS) controlling BNI capacity (hereafter referred to as BNI trait) into the cultivated wheat, Chinese Spring (CS). The results of the transfer of this BNI trait into several elite wheat types with a grain-yield (GY) potential >10 t ha−1, resulting in substantial improvements of BNI capacity in root systems, are reported in this paper.We transferred the Lr#n chromosome . We observed no clear trend in the 20 varieties’ BNI capacity . There were significant differences (P < 0.001) in the BNI capacities of wheat varieties , but none showed higher BNI capacity than the standard wheat genetic stock, CS, an old landrace from China . Some elite varieties released in 2000 (such as ROELFS) as well as “SONORA-64” (an early GR era variety released in 1964) have BNI capacity akin to CS . Also, these results indicate that wheat breeding had no directional impact  on the BNI capacity of root systems  . This, in turn, could be due to a lack of adequate allelic variation for this trait  in wheat or close wheat relatives used in breeding.We evaluated 20 International Maize and Wheat Improvement Center (CIMMYT)–derived wheat varieties released between 1950 and 2010 (belonging to both pre-Green Revolution [GR] and post-GR era wheat varieties) to determine the impact of five decades of breeding under high nitrogen input conditions on the BNI capacity of wheat root systems , only the Leymus mollis (Trin.) Pilger  amphiploid (generated by crossing with Triticum turgidum) showed BNI capacity several times (around seven times) higher than CS . This is akin to what was reported earlier for another wild grass species L. racemosus (Lam.) Tzvelev  and from the results reported here . Negative BNI activity was detected in the root exudates of most amphiploids derived from wild wheats, implying that they are likely to stimulate nitrifying bacteria, thus accelerating soil nitrification . So far, only two wild grasses (L. racemosus and L. mollis) have been identified as genetic sources for improving the BNI capacity in the root systems of cultivated wheat.Of 20 amphiploids (generated by crossing various wild wheat species and wild grasses with cultivated wheat) evaluated for BNI capacity (ted here B. Both L. racemosus was controlled by chromosome Lr#n (P < 0.001) of BNI capacity when the short arm was introduced into CS but not with long-arm translocation . A previous study established that the short arm of Lr#n corresponds to a Leymus chromosome from homoeologous group 3 (3Ns) . The BNI trait from Lr#n fully expressed (P < 0.001) only on wheat 3B translocation (T3BL.3NsbS), not on 7B translocation (T7BL.3NsbS)  were developed in CS using the ph1b mutation . BNI activity release rates in two recombinant chromosomes were 20 to 40% higher (P < 0.001) compared to T3BL.3NsbS  . In addiL.3NsbS) . Three 3BL.3NsbS , suggestL.racemosus T3BL.3NsbS chromosome was successfully transferred from CS into several elite hexaploid wheat cultivars: “ROELFS,” “MUNAL,” “NAVOJOA,” and “QUAIU,” with a GY potential of >10 t ⋅ ha−1 . This was achieved by utilizing at least four backcrosses and selection for T3BL.3NsbS using fluorescence in situ hybridization following Kishii et al. . We conformed an enhanced BNI capacity, as there were significant improvements (P < 0.001) in BNI activity release from the root systems of the most BNI elite wheats . We also observed a near doubling (P < 0.001) in the release of BNI activity from root systems of BNI-MUNAL and BNI-ROELFS (compared to MUNAL control and ROELFS control). For “BNI-QUAIU,” we observed only a 50% increase (P < 0.001) in BNI activity release. For “BNI-NAVOJOA,” there was no significant improvement in BNI release compared to “NAVOJOA control”  with BNI-MUNAL revealed that BNI release rates were between two and five times higher than in MUNAL control , indicating enormous plasticity in the phenotypic expression of BNI trait (4+ available at soil sites) for suppressing and/or moderating nitrifier activity  with maximum BNI activity release rates of 182 ATU ⋅ g−1 root dry weight d−1 , measured in hydroponics  indicated a 30% reduction in soil nitrate levels (P < 0.05) and substantial improvements in soil ammonium levels (P < 0.001) (in core soil samples taken at a 20-cm depth near plant roots)  (compared to MUNAL control field plots), indicating the expression of BNI function in BNI-MUNAL root systems. In root-zone soils (defined as “soil that is in close proximity to roots”) , the nitrate percentage of inorganic N pool (%) declined by 26% (P < 0.001), potential net nitrification rates declined by 17% (P < 0.05) . The slopes of regression lines are significantly different (P < 0.001) based on analysis of covariance (ANCOVA). Also, N2O emissions based on laboratory incubation studies declined by 25% (P < 0.01)  and soil archaea (AOA) populations declined by 20 to 36% (P < 0.005) . This lends support to recent reports of BNIs being more potent on AOAs  with alkaline soils suggested a 45% decline (P < 0.05) in AOBs with BNI-MUNAL but did not influence AOAs .Field studies on acidic soils (soil pH 5.0 to 5.5) at Japan International Research Center for Agricultural Sciences  , and pot< 0.005) B. BNI f on AOAs , 33, whe on AOAs . FurtherHowever, this requires additional studies because most available evidence indicates that BNI function is mostly effective in soils that are acidic or neutral , 23–26SI Appendix, Study 6a). The slopes of regression lines are significantly different (P < 0.001) based on ANCOVA  and leaf NRA   and an increase  in glutamine synthetase activity (GSA) in leaves; GSA is an enzyme that is at the forefront of ammonium assimilation . Likely due to enhanced ammonium nutrition (uptake and assimilation), the root-zone soil pH was consistently lower  in BNI-MUNAL .In addition, nitrogen metabolism in BNI-MUNAL was fundamentally altered. This is evident from radical changes in the relationship between leaf nitrate levels and nitrate reductase activity (NRA) in BNI-MUNAL (compared to MUNAL control) (n ANCOVA . FurtherP < 0.001) and GY (P < 0.001) across treatments  based on field evaluations at the JIRCAS experimental station in 2019, with improved (P < 0.001) agronomic attributes: harvest index, tiller numbers, and 100 seed wt. . The biggest impact from introducing a BNI trait is evident in no N application field plots in which N deficiency symptoms are visible  only in MUNAL control but not in BNI-MUNAL . Its biomass production and GYs were 50% higher (P < 0.001) than MUNAL control . With N fertilization (100 to 250 kg ⋅ N ⋅ ha−1), BNI-MUNAL yielded about 10 to 14% (P < 0.001) higher than MUNAL control  (ranging from 9 to 58% depending on N fertilizer treatment) in BNI-MUNAL . It is likely that the root systems of perennial wild grass have the ability to mineralize N more efficiently from soil organic matter (SOM) than cultivated wheat, as they are highly adapted to low fertility and low N environments  supports the hypothesis that T3BL.3NsbS, in addition to the BNI trait, is also carrying genes that improve the uptake of native soil N by efficient SOM mineralization, introduced as part of the BNI trait package. SOM mineralization rates were nearly doubled (P < 0.05) in root-zone soils of BNI-MUNAL compared to MUNAL control within low to medium N treatments but not under high N treatment (250 kg ⋅ N ⋅ ha−1) , further supporting such a hypothesis. Nevertheless, the potential impact of BNI function on SOM mineralization beyond root-zone soils  remains unknown at this stage.Nitrogen uptake (aboveground biomass that includes grain) improved by about 28%  values (P < 0.002) in wheat grain  indicated the likelihood of higher ammonium assimilation. Lower δ15N values indicate reduced soil microbial nitrification, a process known for strong isotopic N fractionation, which results in 15N enrichment of source N (soil NH4+)  than in MUNAL control , suggested reduced nitrification and consequently lower NO3− availability and improved NH4+ assimilation during the growth period. In addition to yield, the introduction of T3BL.3NsbS into MUNAL and ROELFS did not affect the grain protein profile, protein levels, or breadmaking attributes  and therefore should facilitate a broader deployment of BNI traits in wheat production.Enhanced BNI production from BNI-MUNAL root systems decreased soil NOl NH4+) , 40. Braenotypes . Lower gP < 0.05) higher (in Obregon-2 in 2019)  under high N input systems (≥250 kg ⋅ N ⋅ ha−1). However, for BNI-ROELFS, GYs were significantly (P < 0.05) lower than ROELFS control in three of the Obregon trials and were not significantly different in one trial . It should be noted that BNI-ROELFS is at the BC5 stage, whereas BNI-MUNAL is at the BC6 stage. These results suggest that introduction of a BNI trait into these elite wheats did not negatively impact yield potential under high N inputs, even though soil conditions  were not best suited for optimum BNI trait expression  or significantly P < 0.0 higher  into modern wheats without disrupting their agronomic performance, grain protein content, or breadmaking quality . Introducing a BNI trait into MUNAL resulted in substantial improvements in N uptake and significant yield gains across N inputs, particularly under low nitrogen inputs. The potential for further genetic gains in enhancing BNI capacity is evident from reducing the size of T3BL.3NsbS . Also, L. mollis can provide an additional genetic platform for introducing BNI traits into cultivated wheat. These field studies are part of establishing a proof of concept of trait expression in the field and potential beneficial impacts. Further studies are needed in agricultural settings, using multilocations with varying N inputs, to assess elite BNI wheats’ productivity gains. Similarly, future research should investigate how the BNI trait interacts with diverse environmental conditions  as part of identifying farmlands suitable for deploying BNI wheats.The last five decades of wheat breeding did not result in improvements in the BNI capacity of root systems, thus necessitating the use of wild grasses to source this trait. Two perennial wild grasses, N pollution reaching the present crisis .A major shift to “all nitrate” nutrition is the hallmark of modern production systems , 7 and it crisis , 43. By utrition –17, 44.utrition , 46, whilability , 47, 48.N input systems. Thus, T3BL.3NsbS can be transferred into wheat varieties adapted to diverse geographical regions to improve the BNI capacity of cultivated wheats. In addition, the increasing CO2 levels in the atmosphere favors assimilation of NH4+ over NO3− in wheat . The enhanced BNI release from root systems of T3BL.3NsbS (about two- to fivefold increase) resulted in the suppression of soil nitrifier activity, reduced levels of soil nitrates, enhanced soil NH4+ availability, and accelerated SOM mineralization, which led to substantial gains in N uptake and GY. Also, the introduction of BNI traits led to major changes to N metabolism, indicating a shift toward more NH4+ assimilation. The newly developed BNI elite wheats retained their yield potential with an enhanced ability to uptake N from SOM mineralization (as evident from 58% higher N uptake in BNI-MUNAL under no N treatment), which has implications for the adaptation to low N input systems.We have demonstrated the feasibility of enhancing BNI capacity in elite wheats by transferring a chromosome arm 3NsN fertilization without losing productivity. The success of BNI wheats will be judged on their ability to maintain or improve productivity with reduced N inputs. This will drive their adoption.Nearly 60% of wheat area is in the Global South , mostly N cycling processes  needs to be validated and quantified in field settings closer to farmer conditions. A comparative advantage of BNI wheats needs to be assessed based on operational-scale field plots in multilocations (representing wheat production environments) to understand the potential value  of BNI trait introduction. Like most production technologies or plant traits, it is likely that BNI traits will function only in certain agro-ecosystems and soil types. Based on current knowledge, BNI function is best expressed under mild acidic soil conditions, which potentially limits areas where BNI wheats can be deployed with current technology. If BNI traits could, in the future, be sourced from other wild grasses that can express well under alkaline soils, BNI wheat deployment could be expanded.The positive impact from BNI wheats on soil 2O–emitting production systems. Being a seed-based technology and representing a nature-based solution, the potential for BNI wheats’ greater scalability and wider adoption can be high , which suppress different segments of nitrifier populations . The results presented here may have the potential to usher in a new era of BNI-enabled elite wheats based on genetic resources of wild grass. Realizing the full potential of BNI wheats, however, depends on a successful transfer of BNI trait (T3BL.3NsbS) into elite varieties adapted to diverse agro-climatic conditions representative of wheat growing areas. In our view, this requires a major initiative by the global wheat research community, private sector, policy makers, and research funders  were conducted to characterize wheat genetic stocks for BNI capacity using hydroponically grown plants. Plants are grown for about 50 to 60 d, and root exudates are collected using aerated trap solutions  for 24 h to collect BNI activity. Root exudates are evaporated to dryness, and BNI activity is extracted and determined using luciferous recombinant Nitrosomonas assay described earlier . Detailed methodology involved in developing wheat alien addition/translocation lines are described in SI Appendix, Study 3 . During 2019, two BNI isogenic MUNAL genetic stocks (MUNAL control and BNI-MUNAL) were evaluated at JIRCAS experimental station in Tsukuba, Japan, to validate the effectiveness of BNI traits in suppressing soil nitrification, nitrogen uptake, and productivity gains in a range of nitrogen inputs . A soil microcosm study was undertaken with BNI isogenic MUNAL genetic stocks at the University of the Basque Country, Spain  to validate BNI impact on soil nitrifier populations using plants grown in pots in a greenhouse . The yield potential of BNI isogenic lines of MUNAL control, BNI-MUNAL, ROELFS control, and BNI-ROELFS were measured in the CIMMYT field station in Obregon, Mexico, during 2018 and 2019 .Several growth-chamber experiments ( earlier . The BNISupplementary File