Due to the accessibility of omnidirectional cameras to record 360-degree videos and the technology to view the videos via mobile phones and other devices, 360-degree videos are being used more frequently to place people in different contexts and convey health-related information. Increasingly, 360-degree videos are being employed in health marketing because they have the potential to enhance health-related attitudes and behaviors. As a case study on how this technology may be used for health-related information and its effect on health care providers, we created a 360-degree video that portrays the experience of a migraine sufferer to be used as a stimulus in an online study. We describe the challenges and lessons learned in designing and implementing a 360-degree video as part of an online experiment focused on inducing empathy among clinicians for understanding patient experience. Given the rapid change in digital technology, future research can use this knowledge to design and implement 360-degree video studies more effectively. An accessible form of virtual reality (VR) technology is 360-degree video. For the public, 360-degree videos are more accessible than other VR technologies because they can be viewed on many common devices, including head-mounted display (HMD) VR headsets, computers, and mobile devices, and because of the technology used to create them. For content creators, 360-degree videos are more accessible because the equipment and labor costs to produce them are lower. For these reasons, 360-degree videos are becoming a more common mode of marketing and communicating information.To create 360-degree videos, omnidirectional cameras are used to simultaneously record a view from every direction. Users can replicate the way they would change their field of view using their body in the real world by moving the device or clicking the screen. The audience is limited to an omnidirectional view from predetermined standpoints in 360-degree videos, unlike other virtual reality systems that allow users to move throughout a virtual environment. Despite this limitation, 360-degree videos are capable of immersing their audience in a virtual world.One of the most important capabilities of VR is to transport and fully immerse viewers into a new experience or environment . SimilarBecause it can place people in unfamiliar environments and experiences, 360-degree video is especially relevant for research focused on understanding how people react to new information or diverse health situations . HoweverThis approach can be challenging because of the diverse settings and devices participants may use to view the stimuli . Some reIn time, 360-degree videos may become an important medium for studying how different environments and experiences affect behavior and other outcomes. For example, as 360-degree video messaging becomes more ubiquitous, research studies using 360-degree videos as stimuli will be necessary in order to understand how the medium engages viewers and influences their perceptions toward health-related products or influences behaviors among diverse populations. In order to use 360-degree videos as research stimuli in real-world settings, researchers first need to understand how 360-degree videos can be implemented in the real world and how these settings affect proximal outcomes such as immersion, presence, and empathy. To fill this gap, we present lessons learned from an online study examining the effect of a 360-degree video depicting a migraine episode from the sufferer’s point of view on physicians’ empathy compared with a first-person written narrative describing the same experience.We created a 360-degree video that portrays the light and sound sensitivity experienced by migraine sufferers. The content of the video was based on similar videos created by pharmaceutical companies. The video was filmed using an Odyssey (GoPro Inc), a custom camera rig using 16 individual, tethered GoPro cameras designed for Google’s Jump platform. The video was created internally by AK and the John Bollenbacher Multimedia Communication Services department at RTI International.We fielded the experiment for 3 weeks. Primary care physicians (PCPs) were recruited through an online survey panel vendor, Dynata . ParticiWe used After Effects (Adobe), a visual effects and compositing application, to portray the sensory experience of a migraine in a realistic manner. The monoscopic 4k footage was edited in Premiere Pro (Adobe), a video editing application, and then imported into After Effects. In After Effects, multiple stock “steam” elements were composited—layering video clips/instances with transparent background—on top of the espresso machine to represent a subtle increasing stressor to our “barista” neglecting the machine. All light sources in the scene, such as the overhead lights and the sun coming through windows, were masked and given a glow effect, making the light blurry, soft, and expansive to represent the visual sensitivity experienced of a migraine. This was gradually increased throughout the scene. Animated energy effects, such as separate looped video clips, were adjusted to represent white flaring and then composited directly over the already glowing light sources. They were timed to appear midway through the scene. Finally, toward the end of the scene, colorful “coma halo” animations were composited to emanate from the light sources but covered a broader area of the frame to represent a severe visual disturbance for the viewer/barista. The footage was then imported to Premiere for additional color correction, where the entire scene gradually becomes harsher with more contrast and unnatural hues.Our 360-degree video starts with a brief text introduction describing the scenario to orient the viewer to the context. Using text in 360-degree video stimuli is challenging because it needs to be placed in a location that is visible from all directions, small enough that the text can be read without changing perspective, and large enough that it can be easily read on mobile device screens. We decided to place the text at 0 and 180 degrees so that part of the text would be visible at any perspective, directing the viewer to change their orientation to view the entire message. We reached this decision after an attempt to place text at more locations in the 360 sphere, which resulted in multiple partial instances of the text being visible by the viewer at one time and proving to be a distraction and an improper introduction to the 360 experience.Although we captured our video at 8k (8192×8192) resolution, viewing platforms such as YouTube and Vimeo do not support such resolutions. Most internet connections and computer processors are also not capable of playing back the content in real time; consequently, 4k was selected as an appropriate and scalable resolution. Also, the file size had to be small enough to support streaming through Wi-Fi and cellular networks and to allow enough resolution to view the text and scenes. The 360-degree video was edited and exported to MP4 files, a common video and audio format supported by popular video platforms such as YouTube and Vimeo.We successfully conducted an online experiment using 360-degree video as stimuli with very few technical issues. In this section we describe the technical challenges of using 360-degree video in Web surveys and the evidence that we considered when deciding on eligibility criteria based on device type and headphone availability. The results describing the impact of device type and headphone use are shown in The 360-degree video technology is relatively new and is not supported by online survey platforms, including the survey platform Qualtrics used in this study. A 360-degree video must be hosted on an outside platform and also be embedded within the survey. We considered five 360-degree video hosting platforms, which are shown in Videos can be hosted on a private server and embedded using Google VR View or uploaded to an all-in-one hosting and 360-degree video player platform. To save on costs and mitigate technical issues playing the video, we wanted to use a platform that could also host 360-degree video. OmniVirt Premium provided video hosting.In terms of player customization, many video players allow users to click a logo embedded in the player to view the video on the platform’s website. On some platforms, clicking the full-screen option also opens the video in a new browser window. We needed to disable these options for three reasons. First, opening the video in a new window allows anyone to copy the link and share it with others. Second, participants may not be able to navigate back to the survey to complete the questionnaire. Third, participants would be able to view the video again while completing the survey. OmniVirt Premium allowed us to customize the player to disable the full-screen mode, remove the OmniVirt logo, and disable links.Regarding mobile device support, many 360-degree video platforms do not support embedded 360-degree video for iOS because of limitations inherent in the Apple iOS operating system. Users must often download the platform app to view the video. We decided to select a platform that supported iOS embedded video because of the popularity of iOS devices. OmniVirt was the only platform that supported Web-embedded 360-degree video playback on iOS devices.Participants can access an online survey using a wide array of devices, browsers, and internet connection speeds. Technical issues or limited bandwidth can cause videos to play choppily or to not play at all. Additionally, participants could potentially skip the video altogether and proceed with the survey. We added several safeguards to ensure that participants watched the video. We disabled the Next button from appearing on the survey screen for 110 seconds, the approximate length of our video. On the same survey screen, we used JavaScript to update a hidden variable each time the play button was clicked.We also added two questions immediately after the stimuli asking participants to confirm whether they were able to view the 360-degree video, and if they were not able to view it, what technical issues they encountered. All participants who answered that they were not able to view the video were terminated and excluded from analysis. Only one participant was unable to view the video. All participants randomized to the video condition clicked play at least once.The ability to play 360-degree videos on computers, mobile devices, tablets, and HMD has made 360-degree video more accessible. For research purposes, different device types can confound the results because the different mediums may provide varied levels of immersion and presence because of screen size and video controls. For example, laptop users must click and drag the screen to change perspective, whereas mobile device users only need to change the orientation of their device. Randomized assignment of device type is not an option when conducting online panel research, so eligibility criteria must be limited to participants using a certain device or the device type must be controlled for during analysis.Prior research has labeled computer monitors as nonimmersive  and founApproximately a third of our final participants  viewed the 360-degree video on a tablet or mobile phone, with a majority of participants  viewing the video on a laptop or desktop computer. Compared with participants who viewed the video on a tablet or mobile device, participants who viewed the video on a desktop or laptop computer reported significantly greater levels of immersion (mean 3.3 [SD 0.8] vs mean 2.8 [SD 0.8]) and agreement on one dimension of empathy, “I understand what the person in the video is going through” (mean 3.4 [SD 0.8] vs mean 2.9 [SD 1.0]), as shown in Some evidence indicates that using headphones can have an impact on viewers’ immersion in 360-degree videos . We consA majority of participants used headphones . Participants who wore headphones were more likely to agree that watching the 360-degree video changed the way they thought about migraines (mean 3.3 [SD 0.9]) as compared with participants who did not use headphones (mean 3.7 [SD 0.8]; Given the potential challenges that can arise when creating 360-degree video content and implementing an online experiment using 360-degree video as stimuli, our study found that 360-degree video stimuli must be designed so that essential elements are seen by all viewers, regardless of perspective. The video also must be edited and encoded in a format that allows for playback on common video players and in a resolution that allows participants using a variety of screen sizes to clearly view the scene. The file size of the video must also be small enough to avoid long download or buffering times. Researchers will need to carefully select the survey and video platforms so that all eligible participants can view the stimuli. It also is important to confirm participants actually viewed the stimuli by using embedded variables and follow-up questions. As 360-degree video technology advances, new challenges no doubt will be introduced; however, some of the challenges presented here can be addressed.Future research also should address the impact of device type and headphone use on empathy and engagement. In this study, we found that laptop and desktop users reported greater levels of empathy and engagement as compared with mobile and tablet device users. Prior research has compared HMD to these types of devices, but no research has looked at the difference between computers and mobile devices, two of most common ways that the general public accesses 360-degree video. Finally, we found that using headphones does not affect most outcomes.


At the Denver site, which had the highest measured U.S. near-road 24-hr PM2.5 concentrations in 2014, concentrations exceeded the daily National Ambient Air Quality Standards (NAAQS) on three days during one week in 2014; the Indianapolis site had the second-highest number of daily exceedances of any near-road site in 2016 and the highest 3-year average PM2.5 of any near-road site during 2014–2016. Both sites had hourly pollutant, meteorological, and traffic data available, making them ideal for case studies. For both locations, we compared air pollution observations at the near-road site to observations at other sites in the urban area to calculate the near-road PM2.5 “increment” and evaluated the effects of changes in meteorology and traffic. The Denver near-road site consistently had the highest PM2.5 values in the Denver area, and was typically highest when winds were near-downwind, rather than directly downwind, to the freeway. Complex Denver site conditions  likely contributed to higher PM2.5 concentrations. The increment at Indianapolis was also highest under near-downwind, rather than directly downwind, conditions. At both sites, while the near-road site often had higher PM2.5 concentrations than nearby sites, there was no clear correlation between traffic conditions  and the high PM2.5 concentrations.We examined two near-road monitoring sites where the daily PM In this paper, we examine two cases—Denver, Colorado, and Indianapolis, Indiana—where some of the highest near-road PM2.5 concentrations have been observed under the U.S. near-road monitoring program. These in-depth case studies complement separately published national-scale reviews of EPA-mandated near-road air pollutant measurements [2.5 hot-spot assessments for certain transportation highway projects that involve significant levels of diesel vehicle traffic or are otherwise designated as being of localized air quality concern [2.5 next to roadways. Data from air quality monitors deployed next to major U.S. roadways in response to the EPA mandate offers an opportunity to examine how near-road PM2.5 varies with meteorology and traffic over short- and long-term periods.Air pollution is recognized to be elevated near major roads ,2,3,4,5 urements ,8. Since concern , there i2.5 comprises a mixture of carbonaceous, inorganic, semi-volatile, and metal species. In the near-road environment, it includes direct emissions of PM and gaseous PM precursors from tailpipe exhaust that are predominantly carbonaceous, as well as emissions of metals from brake or tire wear and lubricating oil [2.5 depend not only on the concentrations of gaseous precursors but also on the concentrations and composition of existing particles, contributing to a complicated, dynamic environment of carbonaceous particles next to a roadway. Resuspension of road dust also contributes to PM2.5 and PM10 concentrations. Road dust can include typical soil elements such as iron and calcium, as well as trace metals such as zinc, and its composition can vary from location to location [2.5, including whether they coincide with other mobile source-related species such as CO or NO2, is of considerable interest.PMting oil ,11,12,13ting oil ,21,22,23location . With thlocation ,26,27. Ilocation ; therefo2.5 and NO2 concentrations among near-road sites throughout the country [2.5 between a given near-road site and nearby sites was assessed on an annual basis. Here, we present a day-by-day look at PM2.5 concentrations at two near-road sites in comparison to nearby sites, traffic, and meteorological conditions. The cases examined represent the highest measured U.S. near-road 24-hr PM2.5 concentrations in 2014 (Denver), and the highest measured U.S. near-road 3-yr annual average PM2.5 concentrations for 2014–2016 (Indianapolis).Prior work focused on comparing CO, PM country ,8. As pa2.5 concentrations. Indianapolis lies at a confluence of major interstate freeways and is subject to a number of point sources within or near the city. Denver is on the eastern side of the Rocky Mountains, with a predominantly north-south wind pattern; when winds are from the west, wind speed is typically high. Sources of PM2.5 in Denver are predominantly mobile sources and small-scale point or area sources, and PM2.5 is typically a combination of carbonaceous particles and ammonium nitrate [Indianapolis and Denver have a different mix of sources and meteorology impacting local PM nitrate ,30,31.2.5 measurements paired with hourly measurements at a nearby, urban-scale monitoring site to examine whether PM2.5 concentrations next to these roadways are higher under direct downwind conditions or when the monitoring site is downwind at an angle across the roadway. Prior work has typically focused on annual average trends at multiple near-road sites or measurements at a single near-road site during a limited time of the year. This work combines short- and long-term data analysis to derive insights into roadway contributions to near-road PM2.5. Here, for each of Denver and Indianapolis, we complete a detailed examination of meteorology, air quality, and traffic data during multi-day PM2.5 episodes and we analyze a year’s worth of hourly near-road data. To our knowledge, this is the first study that systematically identifies high-concentration near-road PM2.5 episodes from across the United States near-road monitoring network, evaluates the relationship between traffic conditions and episodic PM2.5, and contrasts how short-term (episodic) and long-term  near-road PM2.5 concentrations relate to roadway traffic and regional background concentrations. Importantly, with a year’s worth of data to support statistical assessments, this work examines the variability of near-road PM2.5 increments under downwind compared to near-downwind conditions, and complements existing modeling and measurement literature. Given the U.S. EPA-mandated PM2.5 hot-spot analyses for certain transportation projects [2.5.For each Denver and Indianapolis near-road site, we use a year of hourly near-road PMprojects , this wo2.5 concentrations are higher at the near-road site, i.e., how changes in traffic or meteorological patterns impact PM2.5 concentrations next to the roadway. We examine data at the Denver Interstate Highway 25 (I-25) near-road site on three days during February 2014 when PM2.5 concentrations were above the NAAQS threshold of 35 µg/m3, and at the Indianapolis I-70 near-road site on three days during November 2016 when PM2.5 concentrations were above the NAAQS threshold of 35 µg/m3. Other near-road sites also exceeded this threshold on some days, but both the Denver and Indianapolis sites recorded hourly PM2.5, meteorological, and other pollutant data, plus hourly traffic data, making these sites ideal for case study analysis. In addition, when compared to other nearby sites, these two near-road sites have different estimated PM2.5 increments [2.5 increment (0–1 μg/m3 across three methods) than the Denver near-road site (approximately 3 μg/m3 across three methods); as a result, these sites can provide a contrast for examining the conditions under which PM2.5 concentrations are elevated at a near-road site compared to nearby urban sites.This case study analyses the conditions under which PMcrements . The Ind2.5 and NO/NO2/NOx data were downloaded from the EPA’s Air Quality System (AQS) for all air monitoring sites, including the near-road site, in the Denver and Indianapolis areas for 2014 and 2016, respectively. The near-road site (AQS ID 08-031-0027) in Denver is 9 m from the east side of I-25, with an annual average daily traffic (AADT) count of 249,000 and a fleet-equivalent AADT  of 263,118 (see the EPA’s list of near-road sites at https://www3.epa.gov/ttnamti1/nearroad.html). Data from the near-road site were compared to data from nearby monitors, which include CAMP  3 km to the northeast, 14th and Albion (AQS ID 08-031-0013) 6.5 km to the east, La Casa (AQS ID 08-031-0026) 5 km to the north and Alsup (AQS ID 08-001-0006) 12 km to the northeast. All sites are within the urbanized Denver area and can provide context for the near-road measurements. The PM2.5 was measured hourly via a GRIMM EDM 180 Federal Equivalent Method (FEM) at both the near-road and nearby CAMP site. The GRIMM at the near-road site has correlation (R) with the collocated 24-h FRM measurement of 0.95 and slope of 0.91, for the 2015–2017 .Hourly meteorological PM2.5, are within the urbanized Indianapolis area and are listed by the state monitoring agency as neighborhood- or middle-scale. The near-road site has hourly data via a Thermo Scientific 5030 SHARP instrument, and the nearby Washington Park site has hourly data via a MetOne BAM 1020, both of which are FEM. The daily averaged data at the near-road monitoring site has a correlation (R) of 0.93 and slope of 0.97 with the collocated FRM for 2015–2017, and the daily averaged data from the BAM at the Washington Park site has correlation (R) of 0.92 and slope of 0.89 with the collocated FRM.The Indianapolis near-road site (AQS ID 18-097-0087) is 24.5 m south of the I-70 freeway, with an AADT of 189,760 and an FE-AADT of 362,110. Nearby monitors used for comparison to the near-road site include Washington Park (AQS ID 18-097-0078), 3 km to the northeast, which is the monitor used to represent background in our analyses based on factors such as prevailing winds and site characteristics and W. 18th St. (AQS ID 18-097-0081) 7.1 km to the west. These monitors have hourly PMThe traffic monitor closest to the Denver near-road air monitoring site is located at I-25/6th Avenue, approximately 700 m south of the near-road monitoring site. Traffic data from this monitor were obtained via email contact with the Colorado Department of Transportation. Traffic data were the total number of vehicles and hourly measurements of northbound and southbound vehicle speed across ten bins. Hourly average vehicle speed was estimated by multiplying the number of vehicles in each speed bin by the midpoint of the bin, then summing these values and dividing by the total number of vehicles in the hour. Traffic data for Indianapolis were downloaded from the Indiana Department of Transportation’s traffic data repository (indot.ms2soft.com). Data were obtained by traffic direction  at the location of the near-road air quality monitor. Data were hourly values in FHWA-Scheme F classification; i.e., they included a count of motorcycles, cars, light trucks, buses, and heavy-duty trucks, as well as the number of vehicles in each of 14 speed bins. Data were aggregated to the hourly average speed of all vehicles and hourly counts of total vehicles and of heavy-duty trucks, defined as those with three or more axles (FHWA Classes 6 and above).2.5 and meteorological data are available, with a nearby site available to support comparisons to background. Modeling of roadway emissions suggests that, especially under stable meteorological conditions, maximum concentrations from roadway emissions do not occur under wind directions when a receptor is perpendicular to the roadway, but rather at angles when the wind direction is not perpendicular to the roadway [We examined how the contribution of emissions from the roadway vary by wind direction. At both sites, hourly PM roadway ,33,34. T roadway . Also, V roadway , and fou roadway ,33,34. I roadway .2.5, since relative humidity and atmospheric pressure can also modify the rapidly changing chemistry of particulate matter and its precursors. For example, in an urban street canyon, Richmond-Bryant et al. found that temperature was a significant predictor of near-road PM2.5 and black carbon (BC), in addition to background concentrations (as the strongest predictor), wind speed, wind direction and traffic [10 in a generalized additive model (GAM) [2.5 in a GAM [2.5 next to a roadway [Wind speed and direction are not the only meteorological factors that can impact near-road PM traffic . Pearce el (GAM) . At a nein a GAM . Togethe roadway ,45,46,472.5 increment, meaning the PM2.5 concentration difference between measurements taken next to the road compared to those from a nearby site used to represent background . The angle that the monitoring site was perpendicular to, and therefore at the minimum distance from, the adjacent roadway was determined in ArcGIS. At Denver, the perpendicular downwind compass point was at 255 degrees and at Indianapolis, it was 345 degrees. We defined the near-road monitor as being “downwind” of the road when winds flowed from the roadway toward the monitor, for all wind directions within a ± 15-degree arc of perpendicular to the roadway . Similarly, we defined a “near downwind” bin for wind directions that were a further 30 degrees away from the downwind bin. We also established “near parallel” and “parallel” bins by adding additional 30-degree arcs, with the remaining spatial area defined as the “upwind” bin. Data were limited to only those hours with stable conditions (defined here as wind speed of less than 1 m/s) and hours when traffic was typically high (06:00 to 20:00). We then examined median hourly PM2.5 increment by wind direction bin, and used the Kruskal–Wallis test to determine whether increment concentrations at non-downwind angles were significantly different than concentrations at downwind angles. At both sites, we examined the hourly near-road PM2.5 concentrations were measured at the Denver near-road site. Hourly concentrations were high overnight on 3–4 February, during the morning and afternoon of 4 February, at midday on 7 February, and throughout 9 and 10 February. The 24-hr PM2.5 concentrations exceeded 35 μg/m3 on three days: 7 February (35.4 µg/m3), 9 February (44.4 µg/m3), and 10 February (57.0 μg/m3). The 10 February PM2.5 concentration was the highest 24-hr PM2.5 value measured in 2014 across all of the near-road monitoring sites in the U.S.Between 2 and 12 February 2014, four periods of relatively high PM2.5, hourly NO/NO2/NOx, wind speed, and whether the near-road monitor was upwind or downwind of the freeway. Hourly PM2.5 concentrations do not exhibit a consistent diurnal cycle during this period (2−12 February 2014). In some cases , hourly PM2.5 concentrations peaked in the middle of the day, while in other cases , PM2.5 concentrations were highest during the overnight hours. Between 9 and 11 February, PM2.5 concentrations were consistently elevated for multiple consecutive days.2.5 concentrations were higher when wind speeds were lower. Although wind speeds vary from day to day, they are generally higher during midday and afternoon. When hourly PM2.5 concentrations were elevated, wind speeds were typically low, and winds were often from the north , varying between upwind and downwind conditions.In general, PM2.5 concentrations coincided with high hourly NOx concentrations; however, during other periods of high PM2.5 concentrations , NOx concentrations were not elevated. NO2 concentrations were consistently low at the near-road site between 2 and 12 February. There is no consistent relationship between NO/NO2/NOx or temperature and high PM2.5; when the temperature was below 20 °F from 5–6 February, PM2.5 was relatively low. PM2.5 had little to modest correlation with NO2 or NOx (r2 = 0.26–0.28).In some cases , high hourly PMx nor PM2.5 concentrations were correlated with vehicle speeds or traffic volumes during 2−12 February 2014 .Lastly, traffic volumes exhibit a typical diurnal activity pattern, with morning and afternoon peaks consistent with the morning and evening commute times. Traffic speeds are somewhat variable and do not track the diurnal signature of traffic volumes. Neither NO2.5 concentration values, especially when winds are out of the southwest.The Denver site has additional characteristics that likely influence the measured near-road concentrations. Three factors in particular are worth noting: the roadway alignment, the presence of buildings next to the monitor, and the location of an onramp. The analysis here characterizes wind conditions that result in the near-road monitor being upwind or downwind of the road; these conditions are defined by the roadway alignment adjacent to the monitor. However, 2.5 data from nearby sites to assess whether all sites varied together, indicating an urban-scale PM2.5 signature. 2.5 in February 2014 at the near-road site, as well as a time series of PM2.5 concentrations at the near-road site and at three nearby regional PM2.5 monitoring sites. The figure also shows the difference between the concentrations at the near-road site and the nearest site (CAMP) for February 2014. When winds are from the west, directly from the adjacent freeway, concentrations are typically lower than they are when winds come from other directions, particularly from north or south. The time series shows that hourly PM2.5 concentrations at the near-road site are closely correlated with concentrations at the nearby site . PM2.5 concentrations are typically higher at the near-road site than at the regional sites. Hourly PM2.5 concentrations were higher at the near-road site than at the nearby regional CAMP, 14th and Albion, and La Casa PM2.5 monitoring sites during 71% of the hours in February 2014; 24-hr concentrations were higher at the near-road site than at the nearby regional sites on 79% of the days in all of 2014. Furthermore, concentrations at the near-road site were typically higher than concentrations at other sites when PM2.5 concentrations were high. For example, on 9 and 10 February, hourly PM2.5 concentrations at the near-road site were 7−12 μg/m3 higher than at other nearby sites for multiple hours. On all but one day of February 2014, 24-hr concentrations at the near-road site were the highest of any of the sites in Denver. As seen in the pollution rise, concentrations at the near-road site were consistently higher when winds came from the north or south.Next, we examined PM2.5 with wind direction during February 2014, 2.5 concentrations at the near-road site and the nearby CAMP site. Observations are sized by wind speed and colored by PM concentration; note that wind direction measurements are highly uncertain under low-wind-speed conditions. The near-road site typically recorded the highest hourly concentrations of any site in Denver during February 2014. The roadway alignment at the monitor location may mean that the monitor is downwind from the roadway across roughly 180 degrees of wind directions, and adjacent buildings likely slow or prevent dispersion.We also examined whether the excess concentrations typically found at the near-road site occurred only when the near-road site was downwind of the freeway, and how they varied by wind direction. To better depict the variation of PM2.5 concentrations in the region were consistently measured at the near-road site during February, we examined whether this was true throughout the year. 2.5 concentrations during 2014 for the Denver near-road site, the concentrations at the nearby CAMP site, and the difference in concentration between the near-road site and the CAMP site. PM2.5 concentrations were higher at the near-road site than at the nearby site on 91% of days in 2014, and higher than any other site in Denver on 79% of days.Since the highest PM2.5 increment) varied by hour and wind direction , though with near-downwind winds from the northwest, the difference to downwind concentrations is not significant. In addition to the increased travel distance over the roadway, emissions from vehicles accelerating on the on-ramp to the southwest may also be part of the increment in this southwest direction and may be the difference between the southwest and northwest near-downwind concentrations. Even though we have filtered data for low wind speeds, it is likely that other differences in stability not captured in the wind speed have an effect, causing the concentrations in the northwest near-downwind bin to be lower than those in the southwest near-downwind bin. For example, median wind speed is higher from the northwest near-downwind bin than from the southwest near-downwind bin . The increments are consistently higher for these bins during lower wind speed conditions. The highest increments are seen for these bins when restricting for wind speed less than 1 m/s. Overall, we do see a higher near-road increment of PM2.5 when the monitor is not directly downwind of the roadway but located at an angle near to downwind of the roadway.We further examined how the difference in concentrations at the near-road site and the CAMP site  of any U.S. near-road site where three years of complete data through 2016 were available. In November 2016 at this site, PM2.5 was above 35 μg/m3 on three days, which are the focus of this case study: 5 November (38.5 µg/m3), 6 November (39.1 µg/m3), and 13 November (35.2 μg/m3). These days were all on weekends.The Indianapolis near-road site had the highest 3-year annual average (11.6 μg/m2.5 concentrations occurred during the evening and early morning hours (approximately 18:00 to 08:00 LST), when the wind directions were from the east and southeast, though winds from these directions were relatively infrequent. The orientation of the I-70 with respect to the Indianapolis near-road site means that when winds are from the northwest, the site is downwind of the freeway. When winds were from the north/northwest, i.e., from the direction of I-70, PM2.5 concentrations were typically lower than when winds were from other directions.On weekdays, the traffic along I-70 peaked twice throughout the day, from approximately 06:00 09:00 LST (morning peak), and from 15:00 to 18:00 LST (evening peak). This pattern is typical of major freeways in urban areas. The highest PM2.5 concentrations were measured at the near-road site  at some times, but not at others . Traffic volumes exhibited a typical diurnal weekday activity pattern, with morning and afternoon peaks consistent with the morning and evening commute times. Traffic volumes on weekend days peaked near midday. NOx concentrations and PM2.5 concentrations were poorly correlated with traffic volumes during these periods (r2 less than 0.10), and modestly correlated to each other (r2 = 0.47). Days when PM2.5 exceeded 35 μg/m3  were all weekend days, and while traffic measured on 5 November (Saturday) was higher than the mean traffic volume from October–December, the near-road monitoring site was predominantly upwind of I-70 on these days.One indicator to help determine whether high levels of PM2.5 concentrations at the Indianapolis-0087 near-road monitoring site during November 2016 was likely not caused by on-road mobile emission sources. Rather, the diurnal pattern of 1-h PM2.5 may be indicative of the influence of residential wood burning , overnight temperature inversions in the lower boundary layer , or both. To investigate this further, PM2.5 concentrations from the near-road monitoring site were compared to those from the nearby air quality monitoring site. Homogeneous PM2.5 concentrations would indicate a regional-scale influence on air quality, rather than impacts from local emission sources at the near-road site. During 3–18 November, 24-h PM2.5 concentrations at the near-road site were closely correlated with those at nearby sites   . PM2.5 c2.5 concentrations were higher at the Indianapolis near-road site than at the background site during 3–18 November 2016, this was not always the case in 2016. The 24-h PM2.5 concentrations measured at the near-road site were higher than the nearby Washington Park site on 68% of days in 2016. 2.5 increments at the near-road site during 2016, grouped by wind direction bins. Similar to results at Denver, the highest increment is seen when winds are near-downwind, in this case from the northerly near-downwind bin. Again, similar to Denver, there is asymmetry in the results by wind direction bin, where the increment is higher on the northern near-downwind side of the downwind bin but not on the western near-downwind side. Wind speed is also slightly lower in the northern near-downwind bin  compared to western near-downwind bin , which could also lead to the higher concentrations. Increments are consistently higher for these near-downwind bins during lower wind speed conditions, where the largest increments occur with winds less than 1 m/s; increments in both bins are significantly higher than those in the downwind bin under such conditions.While 24-h PM3 when comparing to the nearby site; during 3–18 November, the near-road site typically had the highest concentrations of any site in Indianapolis, with an average increment of 4.5 μg/m3 when compared to the background site. On average in 2016, the near-road site is 1.1 μg/m3 and 13% higher than the Washington Park site.Overall, the average estimated near-road increment at the Indianapolis near-road site during all of 2016 was 1.0 μg/m2.5 concentrations observed by the EPA-mandated near-road air quality monitoring network in 2014–2016. In both cases, PM2.5 concentrations at the near-road monitoring site were above NAAQS thresholds, and were higher than at nearby sites. However, in neither case was there a clear indicator in traffic data  that would suggest that PM2.5 should be higher at the near-road site than at other nearby sites. The near-road site was not downwind of the freeway more often than usual during these periods; an exception may be Denver, depending on the extent to which roadway alignment effectively broadened the wind conditions when the monitor was downwind of the road. Lastly, NO2, NO, and NOx varied widely during these periods and did not correspond to high PM2.5 . Rather, PM2.5 was consistently higher next to the roadway, with significant variations in its near-road increment depending on meteorology.These two case studies exhibit some of the highest U.S. near-road PM2.5 composition and the specific elements or particulate species that led to the PM2.5 near-road increment. Jeong et al. reported similar results in Toronto, where a near-road site had higher concentrations than a nearby site; by apportioning the components of PM2.5 through high-time-resolution measurements of PM2.5 species, they found that traffic-related sources of PM2.5 were two to three times higher next to the near-road site than at a nearby site [2.5). Results from Jeong et al. and our results are similar to those of other studies with a roadway/background site pair, where some organic compounds and black carbon are higher next to the roadway compared to a nearby or background site [2.5 mass. Over the course of 24 h, and relying on total PM2.5 mass as we have in our case studies here, any direct link between changes in traffic and the near-road increment of PM2.5 mass may be masked.No speciation data are available at these two near-road sites to further examine the PMrby site . The traund site ,16,48,492.5 increment is higher not when winds are directly perpendicular to the roadway, but at a slightly larger angle: near-downwind. Again, at both sites, there is asymmetry in the results, likely due to differences in wind speed and site characteristics such as the on-ramp next to the Denver site. These results support the concept that the combination of wind direction and distance is an important predictor of near-road concentrations, as seen in Bryzk et al., in modeling work by Venkatram et al., and elsewhere. In addition, examining hourly variations in other pollutants that have a larger near-road gradient than PM2.5, such as black carbon or ultrafine particles, could find further evidence of how concentrations may be higher at angles other than directly downwind. As additional, high frequency data are collected at multiple near-road sites over multiple years, future work can build on analyses presented here to further determine when emissions on a roadway impact nearby receptors and the implications for health impacts as well as meeting air quality standards.This is further seen in how the hourly increment varies by wind direction. At the Denver and Indianapolis sites, the PM2.5 episodes at two near-road monitoring sites with high frequency data collected for an entire year at both sites to understand under what conditions near-road PM2.5 is high. Near-road PM2.5 was consistently higher at two near-road locations in Indianapolis and in Denver than at nearby locations. No direct link between changes in traffic was found to explain the day-by-day PM2.5 increment between the near-road and nearby sites, and the increment was highest on an hourly basis when winds were near-perpendicular to the roadway, rather than directly perpendicular. The relatively small but consistent difference in near-road and nearby PM2.5 concentrations is consistent with literature finding that road dust, break/tire wear, and black carbon are the main components resulting in higher PM2.5 concentrations next to the roadway, and that near-road PM2.5 concentrations averaged over the long-term are dominated by background concentrations. Future work could further examine near-road-speciated PM2.5 concentration data, a broader array of sites with varying measured near-road increments, and the differences between measured and modeled concentration data in the near-road setting.We combined the analysis of short-term PM


This work discusses video communications over wireless networks (IEEE 802.11ac standard). The videos are in three different resolutions: 720p, 1080p, and 2160p. It is essential to study the performance of these media in access technologies to enhance the current coding and communications techniques. This study sets out a video quality prediction model that includes the different resolutions that are based on wireless network terms and conditions, an approach that has not previously been adopted in the literature. The model involves obtaining Service and Experience Quality Metrics, such as PSNR  and packet loss. This article outlines a methodology and mathematical model for video quality loss in the wireless network from simulated data and its accuracy is ensured through the use of performance metrics (RMSE and Standard Deviation). The methodology is based on two mathematical functions, , and their parameters are defined by linear regression. The model obtained RMSE values and standard deviation of 2.32 dB and 2.2 dB for the predicted values, respectively. The results should lead to a CODEC (Coder-Decoder) improvement and contribute to a better wireless networks design. According to CISCO reports , 2, a siIn wireless networks, the most widely used technology is IEEE 802.11 , which wThe standard 802.11ac was first applied in 2008 and operates in the 5 GHz band; it relies on two basic concepts/assumptions unlike other versions . This standard employs the Multiple Input and Multiple Output (MIMO) technique, and higher channel bandwidth. There has been a rapid growth in the use of video streaming because of the improved Internet connection speed . Thus itThe increased video streaming in last-mile technologies, especially WLANs, has required high transfer rates resulting from alterations in end-user profiles, which have led to the consumption of high and ultra-high-resolution video. One type of access technology employed to satisfy this demand, is IEEE 802.11ac, which is regarded as the “last mile” access technology candidate that can support the video streaming service at high and ultra-high resolutions to provide a better quality of service and experience . Hence, The ability of high-resolution videos to provide people with an improved quality of experience is beginning to attract a good deal of attention because this type of service is used by a large number of users. Thus, coding is in progress as well as the optimization of network planning. In the case of coding, the objective is to provide acceptable video quality, and hence reduce the storage space for information . Both quThis work discusses the video transmitted by the standard IEEE 802.11ac wireless communications network, and designs a model to predict the video quality loss caused by packet loss. This model is based on simulated and subsequently validated data based on video measurements in a real transmission environment. In the literature, mathematical modeling is not usually employed to predict the loss of quality of videos, since solutions can usually be found in computational intelligence. Moreover, in , methodoThe main contribution made by this article is to design a methodology and mathematical model to predict the video quality loss in a wireless network. As far as was verified by the research of the authors, no studies were found that recommend the use of mathematical models to predict video quality loss in wireless networks.This work consists of an introduction and five further sections. Some related work is examined in section 2. section 3 outlines the problem and the methodology adopted to employ the model for predicting the video quality loss. section 4 discusses how real data is obtained and how they are handled statistically for the validation of the model. section 5 discusses the performance of the model and its restricted use. Finally, the conclusions are summarized in section 6.There is a real need to adopt new approaches to Quality of Experience based on Quality of Service. In , two QuaIn , the strIn , 17, theAnother quality analysis involves the use of video streaming to make adjustments to the unfavorable conditions for transmission (in the communication channel). The authors in  studied On the basis of this study and other works like , 19, in From the above in the preceding paragraphs, there is a deficiency in research based on mathematical formalism to predict aspects of QoE from the QoS monitoring. The authors of  present It is necessary to check the performance of the videos transmitted when designing a video loss prediction model, based on the communication network standards (IEEE 802.11ac). A set of techniques is required such as simulation, modeling, and measurement. The modeling process uses computational tools such as the EVALVID framework  to transThe next subsections will describe in detail the methodology employed in this work to carry out the task of designing a video loss prediction model.Wireless networks are prone to error owing to some of the features of the communication channel, such as trajectory loss, diffraction, and refraction. These phenomena directly affect the quality of service when the video streaming over the wireless network is analyzed. It is necessary to evaluate user satisfaction by means of quality of experience metrics. For this reason, this work evaluates the transmission of videos in high and ultra-resolution in the wireless network. This study also provides a model that is capable of predicting the video quality loss.It is necessary to design predictive models that relate QoS and QoE metrics to specific video streaming applications in wireless networks, as discussed in the introduction and related work sections. These models can complement the development of QoE-driven wireless access technologies, as well as making improvements to the source encoders.The process of data collection constitutes the first stage in carrying out the study and modeling. An “ideal transmission” was conducted in a controlled environment, i.e., a lossless, simultaneous transmission between just two dedicated computers. Alterations were made to the original file with the aim of obtaining an encoded video file without losses, in which the packets transmitted would be equal to the amount received, i.e., some lines forming this file were removed, thus reducing its size and, hence, the number of packets.Thus, in this controlled environment (i.e. the simulation), a total of twelve transmissions were transmitted to four types of videos, in which checks were made of the packages sent by the server´s computer and the packages received by the client’s computer. The result was that the files which included the number of sent packets, was the same as the number received, and this faithfully represented each video.The number of packages sent and received during the stage of video transmission was determined by using the TCPDUMP tool, which, after selecting the send and receive port, captures the packages that have to be analyzed. In this way, the number of packages that are sent and received in each transmission, is obtained and saved in the file .Thus, a scenario was set up: this involved two notebooks (client and video server) connected to an access point see  below. Thttps://media.xiph.org/video/derf/) in three different resolutions: 720p, 1080p, 2160p (4k). The names of the videos used are: (a) Crowd Run, (b) Ducks Take Off, (c) Park Joy, and (d) IntoTree. A significant factor that differentiates the videos is the number of motions throughout their reproduction, where videos with small, average, and intense variations in motion are used.The videos were obtained from a multimedia base called “Xiph.Org Foundation”  and the packages sent by the server with the packages received by the client were analyzed. If the number of packages sent and received was equal see , it was The wireless communication channel is essentially random, so in this work, it was assumed that the relationship with packet loss can be described by a uniform and have a discrete distribution. On the basis of the coding files with no video loss, that were obtained in the preceding stage, the probability mass function was applied to define the lines that had to be removed from the received file, as in Thus, the routine to simulate packet loss was established where certain lines of the source file could be basically removed (“RD Source”). The stages followed by the algorithm are: a) setting the initial loss at 2%, which represents the total number of lines to be removed, and b) adding 2% to that value up to 30%. This routine applies to each set of video in the files that correspond to the receipt of the file. Thus, there will be 15 new encoded files for each video, with losses, which now have different percentages. The procedure to remove the line is outlined in It is worth emphasizing the importance of this removal routine. Several files are created that correspond to the degraded videos are, after their application, and have a packet loss rate. These files are of crucial importance to extract the PSNR metric (in dB) of the simulated transmissions based on the percentage of packet losses. These values are obtained by reconstructing the video received through the EVALVID software . In totaOne of the most mathematical methods used to measure video quality is PSNR, which is based on statistical parameters for images. It defines the relationship between the potential maximum power of a signal and the noise affecting the representation of this signal between the original and degraded video frames.PSNR is the objective metric used to evaluate the video quality received by the user compared with that of the original video. It is measured in a logarithmic scale and depends on another metric called Mean Squared Error (MSE) to estimate the difference between the original video and the video received . The MSEMean Squared Error.MSE—Ii,j)(—matrix that forms a received frame.M and N—represent the dimension of the frame.m − 1)2 represents the frame of the highest possible value of a pixel in the image, where n is the number of bits required to represent a pixel of the image.Lref variable Note that, as the video consists of a set of frames and the EVALVID provides us with the PSNR value per frame, the average mean PSNR value of the video was taken to represent the video quality.L—mean PSNR loss in dB;Lref—mean PSNR values of the video transmitted in dB, without losses;Lsim—mean PSNR value of the video received in dB.In In the graphics, it can be seen that there are videos that are more susceptible to the loss of packages when the loss of PSNR is analyzed. This feature was determined by disregarding the resolution, and concluding that there are features of the type of video (mainly movement) that can influence the performance of the encoder during the transmission. This is probably due to the loss of P frames, which includes information on motion compensation –33.In simulated data, there are points that do not describe the loss of PSNR (Outliers), which means that they can be identified and removed. The standard deviation was used, while adding the upper cutting limit and subtracting the lower cut-off limit of the standard deviation of the samples, as expressed in In simulated data, there are points that do not describe the loss of PSNR (Outliers), so they are identified and removed. The standard deviation was used, adding the upper cutting limit and subtracting the lower cut-off limit of the standard deviation of the samples, according to Lfit—represents the mathematical function of the data;Dp—is the standard deviation of data.Thus, all the data for each resolution will be grouped and combined with a certain percentage of packet losses, with the aim of identifying the outliers. For example, the results (irrespective of the video) of the 720 p simulations provide all the values corresponding to 2% of the packet loss. Later, the outliers will be identified and removed through analysis and the other resolutions will follow this same procedure.The best estimate for describing the simulated data is the first degree polynomial function. After obtaining the mathematical representation, the standard deviation is applied to these samples and the upper and lower limits can be defined for data filtering, as displayed in After this filtering, a total of 37 samples of the three resolutions were rejected see . On averThe PSNR loss values for the four videos are known for each resolution, and the strategy was employed of grouping their values by resolution. Therefore, for each study resolution, the average loss of PSNR of the four videos is obtained as a function of the values of the packet losses, using PL—is the packet loss percentage;Lr—is the PSNR loss;F—is the value of the number of samples.After filtering the data, the best expression to represent the PSNR loss was obtained. As a result, a logarithmic function was defined by .LPSNRfiPL—is the packet loss percentage;a and b—are coefficients.To obtain the expression above, the mathematical formula of the Minimum Squares Method  was usedIn As a result of the application of the general equation, the following expressions were found with their appropriate coefficients:PL—is the packet loss percentage.Lp720—is the approximation for the loss values of PSNR for the resolution of 720p;Lp1080—is the approximation for the loss values of PSNR for the resolution of 1080p;Lp2160—is the approximation for the loss values of PSNR for the resolution of 2160p.Thus, it should be noted that at high resolutions , there was no significant loss of PSNR for the end user, i.e. the impact was very small. This means that the high and ultra-resolution are more robust than the loss of packets to the constant bit rate value of 32 Mbps.As the equations are based on the logarithmic function, when differentiated by the coefficients (a and b), and since each resolution has an equation Eqs  and 11, At this stage, an analysis must be made to determine the coefficients a and b. Initially, the values are determined in terms of the variable related to the resolution. In this study, the resolution will be represented by Rand and can assume the following values: one (1), one and a half (1.5) and three (3) for 720p, 1080p and 2160p, respectively. In this case, the expression that best describes the coefficient is the exponential , as bothC—is the function to analyze the data;D and G—are constant;R—is the type of resolution.Ca) and b (Cb), respectively.The expressions defining a and b are set out in Eqs R—is related to the resolution .With the aid of the simulated data and data handling, it is possible to describe the loss of PSNR, as there are packet losses for each resolution. This expression is:PL—is the packet loss percentage;Ca and Cb—are given by Eqs Expanding the equation above to cover the three video resolutions under examination:LPSNR—is the PSNR loss estimate, in dB;R—is the type of resolution PL—is the packet loss percentage (%).The equation above estimates the PSNR loss caused by the packet loss and video resolution. The results of the equation when applied to the simulated data are shown in Note that, (for the three resolutions), the approximate RMSE value is, on average, 0.97 dB and 0.29 dB of standard deviation. These are satisfactory results for the model obtained when based on the linear regression. The validation of the model will be compared with the real environment measurements and shown below.Realistic video data were obtained by conducting measurement campaigns, in which the video was transmitted over the WLAN standard. These data will be used to validate the methodology employed for modeling the video quality loss in the network.In this case study scenario, the measurement points were arranged in 16 radials around the focal point (access point). The points for each radial, were spaced about 2 meters apart and, at every point, three video transmissions were carried out of Crowd Run, and resolution 2160p in wireless IEEE 802.11ac standard. The measurement system was standardized to ensure there were uniform measured points and to enable the use of a greater area of the scenario . Fig 6 sIt was noted that, as the access point operates in the 5 GHz frequency band, some points that were far from the access point were disconnected which prevented data from being collected. This was due to the signal fading caused by the number of masonry walls. Owing to the fact that the wavelength is shorter than the standard working frequency (5 GHz), the distortions along the course tend to be higher, and so the 802.11ac network lacks a suitable coverage for the case study scenario. As a result, the effects on the videos transmitted at these points were so harmful that they prevented their reconstruction and, hence, no metrics were obtained to validate the video quality.The transmissions in real time were made with the “CrowdRun” video in resolution 2160p, encoded with H.264/AVC, in standard 802.11ac , which fThe outliers were also identified in the measurement, which was similar to that carried out in the simulation process. These samples do not represent the PSNR loss that is based on information about the standard deviation. Samples (after the measurement processing) are shown in The values obtained after the elimination of the outliers will be used to make a comparison with the values predicted by the model recommended for this work.The analysis of the PSNR loss of the transmitted videos (only for resolution 2160p) suggests that there is a connection between the video quality loss and the packet loss see . In addiThe values estimated by It could be noted in There are no studies in the literature to assess the strategy of predicting the video quality loss on the basis of the number of packets or by using simple mathematical mechanisms. In light of this, techniques to reduce packet loss were studied in , 37. HowThis study has set out a model to predict the video quality loss in a wireless communications network. This led to a simulated packet loss, but subsequently were obtained in a real environment during the video transmission. The same source coding was applied to standard H.264/AVC, although they were set with different resolutions for later transmission in an IEEE 802.11ac network. An ideal flow (without packet loss) was obtained for the files that are required to reconstruct videos. The mathematical model is used for the following: a) Simulating the packet loss on reception, b) reconstructing and obtaining video quality metrics, and (c) handling the simulated data. Additionally, the model links the quality loss and the packet loss in the three study resolutions.From the results obtained from the simulations, it can be concluded that in video transmissions, the video quality loss may be subject to variations while there is a loss of packets, depending on the characteristics of their movement . This observation, together with the CODEC parameters, such as bit rate and GOP size, will be investigated in future work.The model recommended here is based on the average patterns of PSNR losses in the studied videos, according to the quality loss of four videos in three resolutions. The leasts mean squares method applies to three means of obtaining relevant estimates through resolution. The mathematical expression that depends on video resolution, is obtained through the estimates, which describes the prediction of video quality loss based on the packet loss. In general, the logarithmic function describes the average mean of the PSNR loss. The coefficients of the logarithmic function are described through an exponential function based on the type of resolution. The methodology used in this work for the modeling strategy takes the form of a simple implementation at low computational cost, which can be used in both wireless network planning and improving source encoders (CODEC).The model recommended here is robust and achieved good results when compared with the mean simulated data, since it obtained a mean RMSE value of 0.97dB and standard deviation of 0.85dB. The equations derived were applied to real data to check the effectiveness of the model, i.e. a mean average RMSE of 2.32 dB and standard deviation of 2.2 dB were obtained for the data collected during an indoor measurement campaign. Thus, the results are satisfactory.As far as the authors searched it is not usual to apply mathematical formalism to develop the predictive model. As a result appears a mathematical expression can predict video quality loss values in wireless networks. Measurements are compared to evaluate the model’s performance, which proved to have good results. It is believed that the results of this study may help in the improvement of source encoders and streaming video services.


Spirogyra jaoensis in starter feed on growth performance, pectoralis muscle (PM) growth, and small intestine morphology of broiler chickens.This study aimed to examine the effect of dietary S. jaoensis at doses of 0%, 0.5%, 1%, or 2%. The treatment was carried out until the chickens were 18 days old to examine growth of broiler chicks at starter period (8-21 days old).One hundred twenty one-day-old Cobb-500 broilers (body weight 46±2.6 g) were divided into four equal groups with 3 replicates in each group and given basal feed supplemented with dried S. jaoensis at doses of 0.5% and 1% resulted in increased weight and improved feed conversion ratio compared to the control group. At the end of treatment, chickens fed with 0.5% and 1% S. jaoensis weighed 428.3±47.8 g and 426.9±31.8 g, respectively, and were significantly heavier than the control group (373.1±44.1 g). Furthermore, parameters related to PM growth and small intestine morphology of chickens supplemented with 0.5% S. jaoensis in basal feed were improved compared to the control group.Supplementation with S. jaoensis at a dose of 0.5% improves growth performance, PM growth, and small intestine morphology in broiler chickens.The results of this research indicate that  Gallus gallus domesticus L.) with a consumption rate of 4.01 kg/person/year or 85% of national fresh meat consumption per capita ). The feed conversion ratio (FCR) was calculated as the FI needed to gain 1 kg in weight (WG) ,21:FCR = FI/WGOn the final day of treatment (day 18), all chicks were fasted and eight from each group were euthanized. The small intestine  was excised and prepared for histological examination using the paraffin method . HistoloThe PM was excised, and the left half of the muscle was weighed while the right half was photographed and measured for PM area with ImageJ software (NIH-USA). For histological analysis, 3 cm×3 cm of muscle tissue was processed using the paraffin method and hematoxylin-eosin staining. The histological slides of muscle were then observed and measured for fasciculus and myofiber size/area using ImageJ .Data on body weight, FCR, small intestine morphology, and PM parameters were analyzed using one-way analysis of variance (ANOVA), followed by Tukey’s test at a confidence threshold of 5%.S. jaoensis on growth performance of broiler chickens are shown in S. jaoensis at 0.5% and 1% (P1 and P2) had higher WG compared to both the K0 group and the treatment group supplemented with 2% (P3) S. jaoensis. The increased WG with 0.5% and 1% S. jaoensis supplementation compared to the K0 group began at the age of 6 days old and continued to 18 days old. WG of broilers fed with S. jaoensis at doses of 0.5%, 1%, and 2% was 29.7±18 g/day, 25.5±11.8 g/day, and 23.7±16.6 g/day, respectively. These rates are greater than that of the K0 group (19.3±10.5 g/day), although none of these differences are significant. The FCR values of all four groups were similar.The effects of various doses of S. jaoensis feed supplementation on PM growth of broiler chickens are presented in 2, 53.6±8.4 mm2, and 53.9±7.8 mm2 for P1, P2, and P3, respectively, and 48.4±4.2 mm2 for K0. The PM area values of P1, P2, and P3 were all significantly higher than that of K0 (p<0.05).The effects of various doses of S. jaoensis in basal feed on intestinal morphology of broiler chickens is shown in The effect of Algae have many benefits as food sources and animal feed additives -29. In cS. jaoensis in broiler chickens could enhance weight increase and improve FCR compared to the control group. These results were consistent with previous research reporting that the administration of S. ellipsospora could also stimulate WG in broiler chickens [S. jaoensis used in this research has a content of 16% protein, 36% carbohydrate, and 11% lipid (The previous studies reported that the protein content in chicken feed had a considerable influence on the growth of chickens and also increased the efficiency of the digestive system ,34. In ochickens . S. jaoe1% lipid , which wS. jaoensis in feed was improved (S. jaoensis. These components increased the calorie count in basal feed (PM growth is an important indicator of the success of broiler chicken growth . The PM improved . Even thsal feed , which, S. jaoensis had a positive impact on intestinal morphology, which, in turn, made the small intestine more effective in nutrient absorption. This result is in agreement with a previous report that the presence of high free fiber from Trichoderma fermented wheat bran in diet could improve small intestine morphology in broiler chickens [The small intestine is an important site for the absorption process in chickens. It consists of the duodenum, jejunum, and ileum, all of which have absorption functions. Efficiency in the absorption of food nutrients is influenced by tissue structure in the small intestine, especially in villus length, crypt depth, and optimal goblet cell numbers for secreting mucin. Villi in the small intestine play a role in the absorption of nutrients by increasing the surface area of the intestine . The rolchickens .S. jaoensis increased the number and area of goblet cells (Goblet cells also have an important role in synthesizing mucin, a glycoprotein that helps to maintain the ecology of the intestine by excluding germs and preventing epithelial degradation . The widet cells , suggestS. jaoensis at a dose of 0.5% has the potential to improve growth performance, PM growth, and small intestine morphology in broiler chickens.This research indicates that supplementing feed with the alga HTS, AAKM, AA, and FV contributed equally to the experimentation. HTS, IL, and ZR wrote and edited the article. HTS, LFU, and HW equally designed the experiment. All authors read and approved the final manuscript.


Malnutrition in older persons is a public health concern. This study aimed to estimate the prevalence of malnutrition and its associated factors among community-dwelling older persons in Sri Lanka.A cross-sectional study was conducted in the Kandy district, Sri Lanka. The nutritional status of older persons was assessed using the Mini Nutritional Assessment –Short Form (MNASF). A standardised questionnaire was used to record factors associated with malnutrition: demographic characteristics, financial characteristics, food and appetite, lifestyle, psychological characteristics, physical characteristics, disease and care, oral health, and social factors. Complex sample multinomial logistic regression analysis was performed.Among the 999 participants included in the study, 748 (69.3%) were females and 251 (25.1%) were males. The mean age was 70.80 years . The prevalence of malnutrition, risk of malnutrition and well-nutrition was 12.5%, 52.4% and 35.1% respectively. In the multivariate model, hypertension , alcohol consumption , and increased age  were positively associated with malnutrition. An increased number of people living with the older person  was a protective factor among those at risk for malnutrition.Both the prevalence of malnutrition and risk of malnutrition were commonly observed among community-dwelling older persons in Sri Lanka. The associated factors identified in this study might help public health professionals to implement necessary interventions that improve the nutritional status of this population. Sri Lanka is experiencing rapid growth of its older population due to the improvement of health care facilities. In the South Asian region, Sri Lanka has the fastest-growing ageing population . By yearMalnutrition is often defined as ‘a faulty or inadequate nutrition status’ . MalnutrThe prevalence of malnutrition among older persons varies according to the setting in which they reside. In Sri Lanka, the prevalence of malnutrition among older persons who resided in nursing homes was 30% . It rangMalnutrition among older persons is a multifactorial condition. A recent systematic review identified several domains of malnutrition: demographic characteristics, financial characteristics, food and appetite, lifestyle, psychological characteristics, physical functioning, disease and care, oral health, and social factors . PreviouAs most studies were conducted in high-income countries, where lifestyles and foods differ from that in Sri Lanka , 6, 17, This cross-sectional study was conducted to determine the prevalence and associated factors of malnutrition among community-dwelling older persons in Sri Lanka. A total of 1267 respondents were recruited and 1015 of them completed the questionnaires (response rate of 80.1%). Sixteen cases were subsequently excluded because of missing data. The final sample used for analysis was 999.This study was conducted in Kandy district, Sri Lanka from July 2015 to May 2016. The Open Source Epidemiologic Statistics for Public Health software (version 3.01) was used to calculate the sample size. Sample size was calculated using the odds ratios of selected factors associated with malnutrition among older persons in previous studies and the largest sample size was taken for the present study. Nazemi and colleagues (2015) reported that older persons with diabetes mellitus were twice more likely  to have malnutrition compared to older persons without diabetes. The estimated sample size was 1056. Considering the 20% non-response rate, 1267 older persons were needed for the sample size of the study. Participants were included in the study if they were aged 60 years and older, stayed in the community and consented to participate in the study. Exclusion criteria included being physically frail and/or mentally unable to answer the questionnaire. Participants were selected using multistage sampling from twenty-six Grama Niladari (GN) divisions, among seven randomly-selected Divisional Secretariats in Kandy district. At the GN level, a list of older persons was obtained from the 2012 electoral registers utilizing the simple random sample approach.The selected participants completed the questionnaires at a community centre. All participants were informed about the aim and content of the study and each participant signed an informed consent form prior to data collection. Anthropometric measurements (height and weight) were recorded using standardized procedures.The nutritional status was assessed using the Sinhala version of the Mini Nutritional Assessment – short form (MNA-SF), which is an instrument specifically designed for elderly people; it has been validated in many settings including hospitals, nursing homes and communities , 28. The2). The World Health Organization classifies BMI < 18.5 as underweight, BMI 18.5–24.9 as normal weight, BMI ≥ 25 as overweight [Body mass index (BMI) was calculated to assess the nutritional status of participants. BMI is calculated as weight in kilograms (kg) divided by height in square meters , the middle old (70 to 79 years), and the very old (80 years and older) . With reParticipants’ financial status was determined by whether their last monthly income was below or above the poverty level, which was Rs. 3999 (Sri Lankan rupees). Living arrangements and the number of people living with the older person were considered as social factors. Participants were divided into three groups based on social factors: alone, living with children/spouse or living with relatives/friends.Loss of appetite, eating/swallowing difficulties, food allergies and vegetarian diet were included under food and appetite. Lifestyle factors included alcohol consumption, cigarette smoking and betel chewing. Disease and care included suffering from chronic illnesses, usage of medicines, and chronic conditions such as diabetes mellitus, hypertension and hypercholesterolemia. Tooth loss and use of dentures were considered oral factors. For these four explanatory variables – food and appetite, lifestyle, disease and care, and oral factors, participants were classified based on self-reported responses of ‘yes’ or ‘no’.Physical characteristics included physical activity, which was measured using the Sinhala version of the International Physical Activity Questionnaire (IPAQ) short form validated for Sri Lankans . All parThe depression level of the participants was considered as a psychological factor. It was measured using the Sinhala version of the geriatric depression scale (GDS) . The GDSData analysis was performed using the IBM Statistical Program for Social Sciences (SPSS) version 21. This sampling utilized multistage sampling of Divisional Secretariats (DS), Grama Niladari (GN) Divisions and households. Households within clusters are more similar than households randomly sampled from the population as a whole . Complexp < .05) with malnutrition and risk of malnutrition in the univariate analysis, while those with p > .25 in the univariate analysis were excluded from the multivariate model [Initially, descriptive analyses was conducted to describe socio-demographic characteristics. The mean and 95% confidence interval (CI) were calculated for continuous variables. Unweighted counts and weighted percentages for categorical variables were reported. Then the independent variables were compared between nutritional categories  using one way analysis of variance for continuous variables and Chi-square tests for categorical study variables. Then, complex samples multinomial logistic regression analyses, in the form of univariate and multivariate, were used to examine the associations between malnutrition and risk of malnutrition and study variables. Well-nutrition was used as the reference category. Variables entered into the multivariate model were those found to have significant associations . Almost half of the participants (48.1%) were young old, 42.3% were middle old and 9.5% were very old. Two-thirds (71.9%) were females. The majority were Sinhalese (94.0%). About half of them (55.6%) had completed secondary or tertiary education, while 10.2% had no formal education. The majority (73.9%) of the participants earned an income below the poverty line and lived with family members (88.8%) . Based on the MNA scores, 12.5% of the participants were malnourished, about half (52·4%) were at risk of malnutrition and one-third (35.1%) were well-nourished. Table In the univariate analyses, age, hypertension, cigarette smoking and alcohol consumption were significantly associated with malnutrition. The number of people living with the older person was statistically significant for risk of malnutrition Table .Table 2CFactors associated with malnutrition and at risk of malnutrition in the multinomial logistic regression analysis after adjusting for age, number of people living with, alcohol use, smoking, betel chewing, usage of medication and gender are shown in Table This study was conducted among community-dwelling older persons in the Kandy district in Sri Lanka. About half of the participants were young old, while a small proportion consisted of very old participants. This pattern is similar to the age structure of the Sri Lankan population and represents the Sri Lankan older population . The majWe found that the prevalence of malnutrition among community-dwelling older persons was 12.5%, with more than half of the population being at risk of malnutrition; this corroborates the findings of other studies in India which used MNA , 47. SevIn comparison to studies conducted among older persons in other care settings of Sri Lanka, the community-dwelling older persons of the current study were less likely to be malnourished , 13. ThiIn the multivariate analysis, age was associated with malnutrition. The physiological changes of aging directly affect the metabolism of nutrients. Also, physiological conditions of aging, such as sarcopenia and osteoporosis, might progressively limit the mobility of older individuals, further limiting their ability in shopping, preparing foods and even consuming foods . This fiAlcohol consumption was also found to contribute to the prevalence of malnutrition. Alcohol and malnutrition may be linked to impaired liver function which directly affects protein metabolism . FurtherTraditionally, Sri Lankan society holds older persons in reverence and provides social and financial support for them . The majWhile previous studies have suggested that marital status, level of education, income, tooth loss, use of dentures, chronic illnesses, diabetes, dyslipidaemia, depression, physical activity, eating difficulty, food allergy, loss of appetite and vegetarianism are associated with malnutrition among older persons, our study did not find these factors to be associated with malnutrition –19. A reThere were a few limitations which warrant consideration in the present study. First, this was a cross-sectional study, which limits the establishment of causality. Second, as the majority of the participants were Sinhalese, our sample may not be representative of the community-dwelling older persons in the country. Third, recall bias may be another limitation. However, the questionnaire was interviewer-administered and probing was used to ensure that the participants recalled information as well as they could. Fourth, non-respondents are likely to be malnourished or at risk of malnutrition . ExcludiOn the other hand, to the best of our knowledge, this is the first study on malnutrition conducted among community-dwelling older persons in Sri Lanka. The use of validated and internationally-accepted questionnaires is a strength of the current study. Finally, the use of multistage sampling ensured that a representative sample was recruited from the community.Prevalence of risk of malnutrition is high among older persons in the community-dwelling setting. Age, having hypertension and alcohol consumption were significantly associated with malnutrition among the participants. Moreover, the number of people living with the older persons decreased the probability of being at risk of malnutrition. Public health professionals should be encouraged to develop screening strategies according to the identified factors to improve the nutritional status of this vulnerable population.


Proctocolectomy with ileal pouch–anal anastomosis (IPAA) is the surgical procedure of choice for medically refractory ulcerative colitis and familial adenomatous polyposis. While rare, a pouch volvulus can occur. We aimed to determine the frequency, presentation, and management approach of pouch volvulus in patients with IPAA.A systematic search of published literature was performed by a medical reference librarian on 10 August 2018 and two independent reviewers identified relevant publications, extracted data, and assessed the methodological quality based on a validated tool. A retrospective review of the Mayo Clinic electronic medical records identified one case of pouch volvulus between January 2008 and August 2018.The frequency of pouch volvulus from one large published study reporting long-term outcomes of IPAA was 0.18% . A total of 22 patients (18 ulcerative colitis) were included . Median time to volvulus after IPAA was 36 months while median interval to volvulus diagnosis from symptom onset was 24 hours. Abdominal pain was the most commonly reported symptom (76%). The diagnosis was made primarily by abdominal computed tomography . Endoscopic treatment was successful in 1 of 11 patients (9%). Surgery was performed in 20 patients and pouch-pexy and pouch excision were the most frequent surgical operations. A redo IPAA was performed in five patients (25%).Pouch volvulus is a rare but serious complication of IPAA and should be suspected even in the absence of obstruction symptoms. Endoscopic treatment often fails and surgery is effective when performed early. Proctocolectomy with ileal pouch–anal anastomosis (IPAA) is the surgical procedure of choice for the treatment of medically refractory ulcerative colitis (UC) and familial adenomatous polyposis (FAP). Since its first description in 1978 by Parks and Nicholls, the procedure evolved to include the creation of three types of pouch configurations . The overall morbidity of pouch construction is reported at between 8% and 28%, with a low mortality rate between 0% and 4% .Pouch volvulus is a rare mechanical complication of IPAA surgery and little is known about its frequency, natural history, and optimal treatment algorithm. This unique complication may portend sinister outcomes such as pouch necrosis and ischemia, leading to pouch excision and permanent stoma. Prompt and early diagnosis of pouch volvulus is critical and increased clinical awareness of this entity is important. In this systematic review and pooled analysis, we aim to delineate the frequency, clinical presentation, diagnostic approach, management, and outcomes of IPAA volvulus.This systematic review is reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) with an a priori study protocol .A medical reference librarian conducted a comprehensive, language-unrestricted search of several databases from each database's inception to 10 August 2018. The data sources and search strategies are provided in Given the rarity of pouch volvulus, the estimation of its frequency was based on published studies including at least 1,000 patients with IPAA and reporting the long-term outcome. In the case of multiple publications emanating from the same center at different periods of time, we elected to include the most recent or the most relevant publication to estimate the frequency.We included case reports and case series that reported the diagnosis of pouch volvulus in patients with IPAA, affirming this diagnosis by different imaging modalities, endoscopy, or surgery with sufficient data to be reported individually.We considered that there was a delay in diagnosis of pouch volvulus when the diagnosis was made more than 12 hours after the onset of symptoms because the intestine can compensate for approximately 75% reduction of its mesenteric blood flow during this period without substantial injury [We excluded duplicated studies and studies with insufficient clinical data. In addition, we excluded cases of small-bowel volvulus without actual pouch volvulus in patients with IPAA and cases of pouch twist encountered during the surgical intervention .Two independent reviewers (M.J. and S.H.) evaluated the studies based on the selection criteria and extracted the relevant data onto a standardized form. The data included year of publication, country of origin, publication language, publication format , type of study , age, gender, ethnicity, medical history, body mass index (BMI), indication of IPAA, type of pouch , IPAA creation-to-volvulus interval, clinical symptoms at presentation , serological exams, radiological exams , endoscopic findings, endoscopic and surgical treatment, axis of volvulus, complications of volvulus, post-surgical complications, duration of follow-up after therapy, recurrence of volvulus, and the final outcome. Disagreements between the reviewers were settled by discussion and adjudication by the corresponding author (F.B.).et al., since all included studies were non-comparative single case reports or case series [The quality of the included reports was determined using the methodological quality and synthesis of case series and case reports tool designed by Murad e series . Accordie series . The samThree studies reporting the long-term outcome of at least 1,000 patients with IPAA were published . Pouch vThe flow diagram of different phases of this systematic review is shown in et al. [2 and was not reported in the remaining patients.et al.  with a mThe median interval between the performance of IPAA and occurrence of pouch volvulus was 36  months. Abdominal pain was the most commonly reported symptom followed by obstipation, abdominal distention, and vomiting . MetabolEndoscopic treatment was applied in 12 patients. Endoscopic detorsion was performed in 11 patients and was successful in 7, partially successful in 2, and unsuccessful in 2 patients. In one patient, intra-operative endoscopic detorsion coupled with laparoscopic manipulation of the pouch allowed reduction of the volvulus . When im weeks, showing resolution of the obstruction [One patient received circumferential electro-incision/cauterization by endoscopic needle knife of the pouch to relieve a partial stenosis at the IPAA-anastomosis. In this patient, pouchoscopy was performed after 2truction .Twenty patients were treated surgically. An open laparotomy was performed in 10 patients, laparoscopic surgery in 2, and the surgical approach was not reported in 8 patients. In three patients, detorsion of the pouch was performed and the mesenteric defect was closed, without concomitant pouch-pexy , 26 May.n = 1), ischemia and fistula of the pouch (n = 1), gangrenous pouch with minor perforation and metabolic acidosis (n = 1), pouch perforation (n = 1), and small-bowel infarction with septic shock (n = 1). Two post-operative complications were reported. One patient developed a mild anastomotic stricture treated by digital dilation and a second patient developed an anastomotic leak, pelvic sepsis, and entero-cutaneous fistula after pouch excision and redo IPAA [The volvulus resulted in secondary complications in 5 of 22 patients (23%): mild ischemic pouch  months. Recurrence of pouch volvulus after successful endoscopic detorsion was observed in two of seven patients (28.5%). Recurrence of pouch volvulus was observed in 2 of 20 (10%) patients treated surgically. It occurred 2 and 10 patient , 29. No The small sample size limits the generalizability of results of this review to all cases of pouch volvulus after IPAA. However, the rarity of this complication, the good-quality assessment of the majority of included studies, and the fact that the included patients belonged to different ethnicities could make these results applicable in the appropriate context.et al. [ years, which may reflect an increased awareness of this rare complication of IPAA. We include in this systematic review 16 studies published between 1993 and 2018 and a total of 21 cases of pouch volvulus with sufficient clinical data, adhering to rigorous a priori inclusion criteria, in addition to one patient from the Mayo Clinic, identified within 10 years.Poggioli et al.  reportedet al. , 21. Altet al. , this syet al. [Pouch twisting has also been described in the literature, at times distinctly differentiated from pouch volvulus, as suggested by Lipman et al. . A pouchet al. . In our Pouch volvulus can occur following the creation of any pouch configuration, as demonstrated in this systematic review, although the majority occurred following J-pouch creation, which likely reflects the popularity of the J figure for IPAA . WhetherIt is noteworthy to mention that the majority of patients did not develop intestinal obstruction symptoms and rather presented with nonspecific manifestations such as abdominal pain or nausea. Obstructive features, such as abdominal distention, obstipation, or vomiting, occurred in fewer than half of patients. Moreover, laboratory tests were within normal limits at the time of emergent presentation in all reported cases. The totality of nonspecific symptoms, lack of laboratory abnormalities, in addition to the possibility of a normal digital rectal exam (the axis of rotation may not be reachable) may incur a serious delay in diagnosis, leading to catastrophic results, such as necrosis or ischemia, leading to pouch excision. This was reported in one case in this systematic review .Several risk factors may contribute to the development of pouch volvulus and can be divided into patient-related factors, procedure-related factors, and combined factors. Patient-related factors include body habitus (e.g. low BMI) , pelvic In this review, the diagnosis of IPAA volvulus could not be based on clinical symptoms and appropriate endoscopic/imaging studies should be requested early. In this systematic review, computed tomography was the modality of choice for the diagnosis of pouch volvulus, demonstrating typical small-bowel torsion signs around the mesenteric axis  Figure . InteresSurgical intervention was performed in 91% of patients. Although no patient mortality was reported, pouch excision was pursued in 36%. The management of the volvulus depends on the viability of the pouch, which could be determined initially via endoscopy. If the pouch is viable and a successful endoscopic reduction of the volvulus was performed, the placement of a decompression tube could optimize the outcome of definitive operative management [This systematic review has several inherent shortcomings. First, it is based on case series and case reports with a very high likelihood of selection bias . Second,To the best of our knowledge, this is the first systematic review that synthesizes the evidence on pouch volvulus in patients with IPAA. This review followed an a priori protocol with evaluation of multiple databases by pairs of independent reviewers. Prospective longitudinal studies, examining the natural history following pouch surgery will be able to offer more accurate data for the frequency, risk factors, and therapeutic options for IPAA complications.In conclusion, pouch volvulus is a rare but serious complication of IPAA. Heralding signs and symptoms, such as recurrent vague abdominal pain after IPAA surgery and acute obstipation, should initiate a prompt workup for a pouch volvulus. Quick and early application of barium enema, endoscopy, or cross-sectional imaging should be pursued. Endoscopic therapy often fails and salvage surgical pouch detorsion with pouch-pexy has a good outcome when performed early.No source of funding has been declared by the authors. The guidelines of the PRISMA 2009 statement were adopted.L.H.R.: Pfizer Pharmaceuticals, consultant, honorarium paid to Mayo. P.D.: Speaker's bureau: Abbvie; Advisory board: Pfizer and Janssen; Research grant: Takeda. A.H.L.: Takeda, consultant. All other authors have no conflict of interest or disclosures.goz045_Supplementary_DataClick here for additional data file.


Dear Editor,A 28-year-old woman who had been taking an oral contraceptive (OC) for 10 years underwentmagnetic resonance imaging (MRI), which showed multiple tumors. The tumors wereisoattenuating in the pre-contrast phase, showed homogeneous enhancement in the arterialphase, and were isoattenuating again (with enhancement comparable to that of the liverparenchyma) in the subsequent phases .The pat(1). The annual incidence of HCAis 3-4 cases/100,000 women who have used OCs for an extended period of time.Approximately 25% of patients with HCA experience bleeding, the risk of which increasesin parallel with an increase in tumor diameter. Malignant transformation occurs in up to4% of all cases of HCA. The risk of malignant transformation also increases astumor diameter increases, and excision is generally recommended for tumors that arestill larger than 5 cm in diameter after OC discontinuation(4).Hepatocellular adenoma (HCA) is a rare benign tumor of the liver that is commonly seen inwomen of reproductive age and is associated with the use of OCs(5-7). Given that the risk of malignant transformation isdirectly proportional to the size of the adenoma(7), TAE can reduce this risk. However, the roleof TAE as an elective therapy in HCA is unclear, because it is not known whether itreduces the risk of hemorrhage or malignant transformation of residual HCA, despitereports of a reduction in tumor size(8).Transarterial embolization (TAE) is widely used for the treatment of bleeding adenomasand can be performed before elective surgery to reduce intraoperative blood loss. In HCApatients, TAE can reduce the size of large adenomas, multiple adenomas, or adenomas thatare in a surgically inaccessible location, in order to reduce symptoms and the risk ofbleeding(8). In the case presented here,the patient evolved to late liver abscess after embolization of the adenoma. To ourknowledge, there have been no previous reports of this complication. The treatment ofpyogenic liver abscess includes intravenous antibiotic therapy and percutaneous drainageguided by ultrasound or CT.In patients with HCA, the most common complication of TAE is post-embolization syndrome,followed by transient renal insufficiency and cyst formationAcute or elective TAE seems to be a safe procedure for the management of HCA. Because ofits minimally invasive and parenchyma-preserving properties, together with its abilityto reduce the size of tumors located at anatomical sites that make surgery difficult,elective TAE offers a reasonable alternative to surgery.