The COVID-19 pandemic prompted safety-net health care systems to rapidly implement telemedicine services with little prior experience, causing disparities in access to virtual visits. While much attention has been given to patient barriers, less is known regarding system-level factors influencing telephone versus video-visit adoption. As telemedicine remains a preferred service for patients and providers, and reimbursement parity will not continue for audio visits, health systems must evaluate how to support higher-quality video visit access.This study aimed to assess health system–level factors and their impact on telephone and video visit adoption to inform sustainability of telemedicine for ambulatory safety-net sites.We conducted a cross-sectional survey among ambulatory care clinicians at a hospital-linked ambulatory clinic network serving a diverse, publicly insured patient population between May 28 and July 14, 2020. We conducted bivariate analyses assessing health care system–level factors associated with (1) high telephone adoption (4 or more visits on average per session); and (2) video visit adoption (at least 1 video visit on average per session).P=.04). Video nonadopters requested more training in technical aspects than adopters . Primary or urgent care had the highest proportion of high telephone adoption . Medical specialties had the highest proportion of video adoption .We collected 311 responses from 643 eligible clinicians, yielding a response rate of 48.4%. Clinician respondents (N=311) included 34.7% (n=108) primary or urgent care, 35.1% (n=109) medical, and 7.4% (n=23) surgical specialties. Our sample included 178 (57.2%) high telephone adopters and 81 (26.05%) video adopters. Among high telephone adopters, 72.2% utilized personal devices for telemedicine (vs 59.0% of low telephone adopters, Personal device access and department specialty were major factors associated with high telephone and video visit adoption among safety-net clinicians. Desire for training was associated with lower video visit use. Secure device access, clinician technical trainings, and department-wide assessments are priorities for safety-net systems implementing telemedicine. The COVID-19 pandemic catalyzed a dramatic increase in telemedicine care, prompted by the need for physical distancing and Medicare and Medicaid reimbursement changes enabling parity in coverage for telemedicine . Many saPrior studies have found older adults, those insured by Medicaid, or low-income  and raciOur study setting was a large, hospital-linked ambulatory clinic network serving an ethnically diverse, publicly insured patient population. This network had limited telemedicine visit capacity prior to the COVID-19 pandemic and began providing telemedicine care on March 3, 2020, with support and infrastructure primarily for telephone visits. Departments and clinicians could provide video visits on an ad hoc basis. All clinic and charting rooms had landlines and access to local area network–connected client computers without webcams. The network developed a standardized electronic health record (EHR) visit type and video visit workflow on June 15, 2020. Details of EHR implementation for telemedicine during the COVID-19 pandemic in this network are published elsewhere .This quality improvement study was exempt from institutional review board review.We invited all 643 clinicians providing ambulatory telemedicine care to participate via email, with prompts from specialty-specific study champions. We aimed for a response rate greater than 40% given the challenges of surveying clinicians during the COVID-19 pandemic.We conducted a cross-sectional anonymous survey via the cloud-based platform Qualtrics. We developed our survey on the basis of key constructs from validated implementation science and telemedicine surveys such as the Telehealth Usability Questionnaire -25 and dTo assess telemedicine adoption, we asked respondents, “On average, how many telemedicine visits do you complete per half-day session? Think back to the last month of ambulatory care.” Answer options were 0, 1-3, 4-6, 7-9, or ≥10. We dichotomized responses for our two distinct outcomes of interest: (1) high telephone visit adoption  and (2) video visit adoption . We defined “high telephone adopter” as 4 visits or more because telemedicine was still a novel innovation for the delivery system at this time, with almost none prior to March 2020. In June 2020, ambulatory providers had 2-12 visits total per day across specialties (mean 5 visits per day); 4 visits comprise a majority of a day’s visits and therefore fits our definition of substantial use. The half day distinction is because of the high proportion of part-time clinicians in this network. For video visits, there was extremely low uptake (<1%) of video visit use across the network ; therefoWe assessed demographic characteristics including age, gender, and clinical specialty. Respondents reported their age range , which we collapsed to a binary variable of <50 versus >50. We categorized gender as male and nonmale . We groSystem-level factors included the following: perceived workload of telemedicine compared to in-person consultations , estimated time spent helping patients navigating their telemedicine visit (dichotomized to <5 or ≥5 minutes), ease of interpreter use during telemedicine , desire for additional telemedicine training , and adequacy of audio or video quality of telemedicine encounters (yes or no). We asked what devices were used for telemedicine encounters; respondents could select smartphone, landline phone, desktop, laptop, or tablet device and specify if these were personal devices or institutional (work-provided) devices; we dichotomized this to “any personal device use” versus “only institutional device use.”We conducted bivariate chi-square tests to assess if individual-level characteristics and system-level variables were associated with high telephone adoption, and, separately, video visit adoption. We conducted a sensitivity analysis including only respondents who had completed ≥50% of the survey for a “complete case” analysis. There were no major differences in including all responses versus “complete case” analysis; therefore, we report all responses available.Statistical analysis was conducted in SAS software (version 9.4). This was a voluntary study encompassing the entire source population; no power calculation was attempted given the lack of prior reference data on telemedicine during a pandemic to extrapolate to the analysis.We collected 311 responses from 643 eligible clinicians, which resulted in a response rate of 48.4%. Full demographic characteristics of respondents are listed in P=.04). Personal device use was higher among video visit nonadopters than among video visit adopters, but this was not significant .On bivariate analysis, we found that personal device use was associated with high telephone adoption . Among hP<.001).Many video adopters as well as nonadopters expressed interest in many training domains, especially supporting patients with low technical literacy, technical aspects of the visit, and teaching trainees telemedicine . Desire P<.001). Medical specialties had the highest proportion of video adoption , compared to 14.8% (16/108) of primary care and 12.5% (3/24) of surgical physicians (P<.001).We found that clinical specialty was significantly associated with high telephone adoption and video adoption B. PrimarWe did not find associations between telemedicine adoption and workflow-specific challenges . The perOur investigation is one of the few studies assessing system-level factors that might influence multispecialty clinicians’ telephone and video visit adoption at a safety-net site in the early phase of the COVID-19 pandemic. Like many safety-net clinics, our telemedicine services were largely telephone-based .We found that many clinicians utilize their personal devices to provide both telephone and video visits. A study of family planning clinicians found high personal smartphone use for telemedicine care during the COVID-19 pandemic, despite respondents’ preference for work-issued devices . This raDesire for more training in telemedicine was high for video visits, with almost half of video nonadopters requesting training in the conduct of the technical aspects of the visit. The extent and capacity to which clinicians have been trained in telemedicine modalities have been variable throughout the COVID-19 pandemic, and standardized trainings may influence adoption .Although we could not statistically compare telephone adopters against video adopters, we see that barriers such as increased workload, greater time spent supporting the patient, difficulty with the interpreter, and poor audiovisual quality were more common for video adopters than high telephone adopters. In this network, clinicians must provide their own time and support necessary to allow patients to engage in video visits. Direct patient supports, through patient orientations or external services assisting patients in logging into video visits and contacting an interpreter, would drastically reduce these barriers.Neither perceived workload of telemedicine (compared to in-person care), nor length of time spent supporting a patient to enter a telemedicine visit were associated with telemedicine adoption. Interpreter challenges were not associated with adoption, although this has been endorsed as a barrier by clinicians . AlthougWithin this network, there was wide variation in infrastructural support for video visits by department. Some surgical specialties may have needed in-person evaluation rather than virtual visits. These factors likely explain specialty-specific differences in our findings.Since our survey, telemedicine has become integrated into ambulatory care and is likely “here to stay,” both owing to ongoing pandemic surges because of new variants, as well as patient and provider preference for the convenience and access enabled by virtual visits. For example, in this network, telemedicine volume appears to be approaching a plateau of 20%-30% of primary and specialty care, although video visit uptake remains low . Learning health systems serving vulnerable populations must assess the first years of implementation to optimize video visit delivery for the long term. Without system-level investments, disparities in video visit access may worsen disparities in the safety-net . First, We almost reached a 50% response rate for this voluntary survey among diverse clinicians. Although this was greater than our prespecified target, participants may have differed in their experience of telemedicine compared to nonparticipants. There was high variation in the completeness of survey responses; this was likely owing to the survey burden early during the pandemic. However, we observed no differences in results when comparing all participants to complete case analysis. Owing to survey missingness, we could not generate a multivariable model nor assess for variable interactions. As findings are based on bivariate analysis, they are exploratory and hypothesis-generating for system-level factors that may be related to safety-net telemedicine implementation. As telemedicine workflows were evolving during the survey period, some telemedicine practices may have changed during our assessment; this may have confounded some of our findings regarding specialty-specific differences. However, although a specific workflow was disseminated in June 2020, the same video visit software was available to clinicians over the entire survey period. While our definitions of “high telephone adoption” and “video adoption” were appropriate when we conducted the survey, telemedicine volume will continue to shift and evolve. Comparing the proportion of telemedicine visits to that of in-person visits may be an alternative definition for future studies; qualitative work should also assess how varying specialties choose to optimize their ambulatory schedules between virtual and in-person visits.Clinical specialty type, personal device use, and desire for technical training were major factors associated with telephone and video visit adoption among safety-net clinicians. Department-level support, assessment of use of personal devices, and clinician training are priorities for safety-net systems.


Bee Tracker software consists of four trained deep learning networks to detect bees that enter or leave their nest and to recognize individual IDs on the bees’ thorax and the IDs of their nests according to their positions in the nesting unit. The software is able to identify each nest of each individual nesting bee, which permits to measure individual‐based measures of reproductive success. Moreover, the software quantifies the number of cavities a female enters until it finds its nest as a proxy of nest recognition, and it provides information on the number and duration of foraging trips. By training the software on 8 videos recording 24 nesting females per video, the software achieved a precision of 96% correct measurements of these parameters. The software could be adapted to various experimental setups by training it according to a set of videos. The presented method allows to efficiently collect large amounts of data on cavity‐nesting solitary bee species and represents a promising new tool for the monitoring and assessment of behavior and reproductive success under laboratory, semi‐field, and field conditions.The foraging and nesting performance of bees can provide important information on bee health and is of interest for risk and impact assessment of environmental stressors. While radiofrequency identification (RFID) technology is an efficient tool increasingly used for the collection of behavioral data in social bee species such as honeybees, behavioral studies on solitary bees still largely depend on direct observations, which is very time‐consuming. Here, we present a novel automated methodological approach of individually and simultaneously tracking and analyzing foraging and nesting behavior of numerous cavity‐nesting solitary bees. The approach consists of monitoring nesting units by video recording and automated analysis of videos by machine learning‐based software. This  The foraging and nesting performance of bees can provide important information on bee health and is of interest for risk and impact assessment of environmental stressors. Here, we present a novel video analysis software for the analysis of foraging and nesting behavior of cavity‐nesting solitary bees. The software could be adapted to various experimental setups and allows to efficiently collect large amounts of data on cavity‐nesting solitary bee species under laboratory, semi‐field, and field conditions. How the software can be trained to the setup in use and/or additional bee and cavity IDs is described in the manual , which returns well‐resolved images that generate outputs with a high measurement precision. A lower resolution could impair the precision, but the software can still process the input.2.3all_events_unfiltered: Inside this csv file, all detected events are listed containing the video timestamp, the bee ID, the event type (entering or leaving), and the cavity ID. This list is completely unfiltered and may contain errors.error_corrected_events: This csv file contains all events that remain after error correction: Events with unidentified bee IDs get removed. The software additionally identifies missing events within sequences of enter–leave–enter. Such sequences with missing events are not considered for the creation of below‐described output files . Besides the video timestamp, the bee ID, the cavity ID, and the type of event (entering or leaving a cavity), this file therefore also indicates for each event whether it was used for the output files address_book, nest_recognition, and flight_list.address_book: This csv file contains all bees that were assigned to a nest and lists the according bee and cavity IDs. These data  they are nesting in) are of interest for assessments of nesting progress and reproductive success of individual nesting females. In order to assign females to cavities that are used for nesting (in contrast to simply probed cavities not used for nesting), a cavity is only assigned to an individual bee if (i) the bee stays inside the cavity for a time span that is minimally required by a nesting bee to unload collected pollen for offspring provision, and (ii) the bee does not enter another cavity during a time span that is minimally required by a bee to collect pollen or material such as mud for nest construction . The default setting of these two time spans is both 40 s in the published open‐source version of the software. These values were chosen based on over 20 h of direct observation of Osmia bicornis females nesting in a natural habitat in Switzerland . However, the species under study or the experimental setting may require adjustment of these threshold values. This can be done in the “config” file of the software, which can be selected as an optional input file for the analysis  based on cavity IDs permits to measure individual reproductive success per female for this time period.Nesting progress, that is, the number of produced brood cells and offspring, can be tracked by repeatedly photographing the nest cavities Figure , for exanest_recognition: This csv file contains the number of cavities a female enters before finding its nest . Besides the bee ID and the number of probed cavities, the file also lists the video timestamp.flight_list: This csv file provides flight durations of individual females from leaving the nesting cavity until returning to it again . Besides the bee ID and the flight duration, the file also lists the video timestamp. If of interest, flight activity, defined here as females that perform at least one flight during the observation time, can be assessed by classifying females that are listed in the flight_list file as active. For this measurement, the number of total, alive females needs to be known, however, which can be assessed by taking pictures of the nest layer  and improve the precision.The software will create a new subfolder within the selected result folder for each input video. Inside each subfolder, the following outputs are stored by the software:2.4Precision = TP / (TP + FP), where TP is the number of true positives and FP the number of false positives. The measured precision is valid for all extracted measurements: assignment of females to their nest cavity, flight duration, and number of probed cavities.To measure the precision of the software in a set of videos, the created visualization videos can be manually checked and the correct assignment of the bee ID, cavity ID, and event type (entering or leaving) can be reviewed for single events. Only events listed in the error_corrected_events file as events that were used to create measurements in output files (last column contains a yes) should be checked and used for the measurement of precision. Precision can be calculated as the proportion of fully correct assignments as The software was designed to achieve a high precision at the expense of the recall (fraction of events that was retrieved), which is of minor interest in this type of analysis as it only affects the sample sizes but not the extracted measurements themselves. We therefore did not implement the possibility to assess the recall.2.5Bee Tracker software uses a combination of three machine‐learning algorithms to generate the above‐mentioned outputs: the Faster R‐CNN object detection pipeline  that contained sufficient floral sources for offspring provisioning by nesting female Osmia bicornis . A total of 24 females marked with the above‐described 24 unique digit–color ID tags were released into each of these flight cages, and videos were recorded after initiation of nesting. Each video was recorded between 9 a.m. and 3 p.m. when flight activity was high; recording times ranged between 2 and 4 h. Cameras were placed at a distance of 1 m from the nesting unit with frontal view (camera placed at same height as nesting unit). From the recorded videos, 8 randomly selected ones were used to train the software to this experimental setup, while the remaining 15 videos were used to measure precision. Precision was assessed by manually checking 180 randomly selected events (12 events per video) for their correctness using the visualization option of the software (see above). Only events that were used for the generation of output csv files (after error correction) were inspected as described above.To evaluate the software and measure the precision of the analyses and generated outputs, we recorded a total of 23 videos from 15 nesting units during two consecutive days using the nesting units as described in Figure For the comparability of bee health under different environmental conditions , a similar precision across videos is required. We therefore tested whether precision varied between videos in our set of evaluated videos in a generalized linear model with a binomial distribution. The correctness of the detected event (correct or wrong) was included as the response variable and the video ID as explanatory variable. As the software only assigns females to a nest that are active during recording and fulfill certain criteria (as described in the section address_book), we further fitted a generalized linear model with a binomial distribution to test whether the proportion of females that can be assigned to a nest cavity per video depends on the video recording time. The analysis was done in R 4.1.  of the alive females to a nest per video. The probability that a female gets assigned to a nest did not depend on video recording time , which ranged between 2 and 4 h. Per video, the software generated on average nest_recognition files with 80 measures of nest recognition  per hour and flight_list files with 61 flights per hour.On average, the 2 = 13.95, p = .45; Figure The precision of the software was evaluated by manually checking the created visualization videos and reviewing the correct assignment of the bee ID, cavity ID, and event type (entering or leaving) of single events. Per video, 96.1%  of the checked events were detected correctly on average, whereas precision did not depend on the video that was used for the analysis , where bees needed to be observed simultaneously. Researchers therefore used videos for the assessments of individual behavior in solitary bees  seemed to work equally precise when tested on the closely related species O. cornuta , further training may be required when working with other solitary bee species to obtain full precision of the software. Furthermore, the current version of the software can only analyze the above‐described 24 unique color–digit‐based bee IDs and identify cavities with a certain size and shape that are arranged in the nesting unit as described  are used as indicators of colony strength and vitality ; Data curation (lead); Funding acquisition ; Methodology ; Project administration (lead); Visualization ; Writing – original draft (lead). Johannes Gallmann: Conceptualization ; Methodology ; Software (lead); Visualization ; Writing – original draft (supporting). Matthias Albrecht: Conceptualization ; Funding acquisition ; Supervision (lead); Writing – original draft (supporting).Supplementary MaterialClick here for additional data file.


There are some problems in the process of video intelligent description and analysis of volleyball, such as poor effective information extraction rate and poor dynamic tracking effect. Based on this, combined with long-term and short-term memory network and attention mechanism, this paper designs an intelligent description model of volleyball video based on deep learning algorithm and studies how to improve the extraction rate of volleyball video information through intelligent detection hardware and image recognition technology. This paper first introduces the application of image recognition technology and deep learning algorithm in the intelligent description of volleyball video, then designs the volleyball video and image recognition model based on deep learning algorithm according to the requirements of volleyball video intelligent description, and selects three correlation factors related to the impact indicators of volleyball skills. This study selects three characteristic parameters associated with volleyball video analysis indexes, namely, take-off, bounce, and hand movement, combined with image sensing hardware assisted sensor network to realize real-time monitoring of action state in volleyball video analysis system. The experimental results show that, compared with the current mainstream sports video intelligent analysis and image recognition methods with data analysis as the core, the intelligent volleyball sports video intelligent description and image recognition system based on the integration of deep learning algorithm and sensor hardware assistance has the advantages of good detection effect, high data effectiveness, low cost, and high efficiency of volleyball sports video analysis. It can effectively improve the efficiency of volleyball video intelligent description. In recent years, China's sports industry has entered a new period of rapid development. Volleyball plays an important role in sports . IntelliIn view of the low degree of effective information in information description and feature extraction of volleyball video, data mining analysis method is used first. Combined with long-term and short-term memory network, attention mechanism, and image recognition technology, this paper studies the intelligent description model of volleyball video based on deep learning algorithm. This paper is divided into four parts. The first part briefly describes the research background and research framework. The second part summarizes and analyzes the research status of intelligent description and information extraction of volleyball video. In the third part, combined with long-term and short-term memory network, attention mechanism, and image recognition technology, three correlation feature parameters related to the impact index of volleyball video intelligent description quality are selected, and a volleyball video intelligent description model based on correlation factor feature parameters and deep learning algorithm is proposed. The fourth part designs relevant experiments to verify the feasibility of the intelligent model proposed in this study and makes an objective quantitative evaluation of the experimental results through the quantitative evaluation method to draw a conclusion.Compared with the current mainstream volleyball video intelligent description model , the innovation of this paper is to propose an improved deep learning algorithm combined with long-term and short-term memory network, attention mechanism, and image recognition technology. It can make full use of the information of volleyball video intelligent description technology to realize the overall capture and information extraction of key actions in volleyball. By combining image recognition technology, it can realize the extraction, analysis, and correlation coincidence of target information, complete the coincidence analysis of influencing factors with quantitative indicators, and realize the key information analysis of sports video intelligent description.Up to now, most volleyball video intelligent analysis and description in China have problems of weak foundation and low intelligence, especially in the construction of sports video intelligent description and detection system . MoreoveTo sum up, it can be seen that the current volleyball video analysis system and the traditional image recognition model generally have the problems of poor recognition effect, low recognition accuracy, poor stability, and low data utilization in volleyball video analysis , 19. On Image recognition technology is an important field of artificial intelligence. It refers to the technology of object recognition of images to identify targets and objects of different modes . If the The volleyball video recognition algorithm based on deep learning is used to analyze and process the image. The different motion trajectories of volleyball in different motion scenes are photographed, and several photos are input into the system as training sets for model training. Finally, the model is tested. The above types of photos different from the sample set are used as the test set to test the fitting degree of the model. According to the test results, the model can be continuously optimized to improve the recognition accuracy and system performance.The combination of attention mechanism and long-term and short-term memory network with attention mechanism can improve the accuracy of quality rating. This method overcomes the disadvantage of slow text model training and adapts to the end-to-end model analysis of data features. Through the methods of recognition, representation, and feature weighting, the long text is accurately classified by quality rating.This volleyball video analysis model based on deep learning algorithm combines long-term and short-term memory network and attention mechanism to simplify the processing of complex data in volleyball video information. Therefore, the intelligent description model needs to check many factors of multidata, so as to judge whether there are closely related factors in a variety of data. In this way, it can not only use the comprehensive average index of these closely related factors or one of them to represent a variety of such factors, but also make these multidata to be processed. The information carried by video and image is not seriously distorted. The data analysis process of its volleyball video intelligent description model is shown in n data objects to be processed , which are called implementation objects; then the position sequence expression of volleyball in volleyball video in three-dimensional space isx, y, z are the spatial trajectory coordinate information of volleyball, X, Y, Z are the motion position of volleyball in three-dimensional space, and n is the total amount of different volleyball video feature points.Firstly, we assume that there are K and the attention mechanism rule function L, the corresponding volleyball spatial position expression isAfter combining the long-term and short-term memory variable function K and attention mechanism rule function L are, respectively,The expressions of long-term and short-term memory variable function X, Y, and Z are the motion position functions of volleyball in three-dimensional space, n is the total amount of video feature points of different volleyball sports, and x, y, z are the spatial trajectory coordinate information of volleyball sports. When the set signal is different from the signal in the volleyball video, combined with the analysis of the long-term and short-term memory network function, the deviation proportion, integral, and differential can be combined to form the control quantity, and the integral expression P(t) in the volleyball video intelligent analysis model is obtained as follows:e(t) represents deviation, kp represents control quantity, and T represents time constant.Among them, In this study, once the intelligent description decision of sports video is determined, the depth learning equation required in volleyball video can be determined. In order to study the influence of deep learning times on video information feature extraction, without combining long-term and short-term memory and attention mechanism, the simulation results of feature information extraction and action recognition in two categories (10 groups each) of volleyball video under different deep learning times are shown in Figures It can be seen from Figures In this intelligent description model of volleyball video based on deep learning algorithm and sensor hardware assisted fusion, combined with the key factors of long-term and short-term memory variables and attention mechanism rules, the simulation results of feature information extraction and action recognition in volleyball video under different deep learning times are shown in Figures As can be seen from Figures When setting the confirmatory experiment, the database of in-depth learning is the volleyball event video, physical education teaching video, and other volleyball videos published on the Internet. The total amount of data is more than 10 million times. The learning rules include input layer, hidden layer, and output layer, in which the input layer is volleyball video. The hidden layer is to perform mathematical calculation on the input data. Its calculation method is mainly an adaptive iterative cyclic calculation mode based on successive error reduction, and the output layer is the target data result . When evaluating the application of deep rest learning algorithm in volleyball video intelligent description system, it needs to be evaluated from many aspects. Therefore, this study selects three associated characteristic parameters related to volleyball video analysis indicators, namely, starting, bouncing, and hand movement, combined with image sensing hardware assisted sensor network to realize the real-time monitoring of action status in volleyball video analysis system, and then transmits these monitored information data to the terminal database of detection system. Through the processing of dynamic algorithm and grey correlation entropy analysis method, the effective data information is extracted. The large data information (obtained by image sensing hardware assisted sensor network) generated in the system of the fusion of volleyball video intelligent description technology based on video intelligent description algorithm and sensing hardware assistance is obtained, and the corresponding quantitative evaluation results are obtained. In the experiment, for three types of data , when the long-term and short-term memory network and attention mechanism are not introduced, the preliminary experimental results on the effective information extraction rate in volleyball video are shown in After the introduction of long-term and short-term memory network and attention mechanism, the preliminary experimental results of effective information extraction rate in volleyball video are shown in As can be seen from Figures x volleyball video at the y time.With the three types of data in the experiment , the analysis results based on the depth learning algorithm are shown in The normalized quantitative evaluation and analysis results of the error degree of the experimental results are shown in It can be seen from With the popularity of Internet technology in the sports industry, how to use image recognition technology and intelligent analysis methods to intelligently describe and accurately identify volleyball video has become more and more important. On this basis, combined with long-term and short-term memory networks and attention mechanism, an intelligent description model of volleyball video based on deep learning algorithm is designed, and how to improve the extraction rate of volleyball video information through intelligent detection hardware and image recognition technology is studied. Compared with the current mainstream volleyball video intelligent description model , the innovation of this paper is to propose an improved deep learning algorithm combined with long-term and short-term memory network, attention mechanism, and image recognition technology. It can make full use of the information in volleyball video intelligent description technology to realize the overall capture and information extraction of volleyball key movements. This paper first introduces the application of image recognition technology and deep learning algorithm in volleyball video intelligent description, then designs the volleyball video and image recognition model based on deep learning algorithm according to the requirements of volleyball video intelligent description, and selects three related factors related to the influence index of volleyball technology. The experimental results show that the intelligent volleyball video intelligent description and image recognition system based on the combination of deep learning algorithm and sensor hardware assistance has the advantages of good detection effect, high data efficiency, low cost, and high volleyball video analysis efficiency. It can effectively improve the efficiency of volleyball video intelligent description. However, the system only considers the information analysis of volleyball video data but does not consider the influence of volleyball players' sports habits on volleyball trajectory, so it can be further optimized from this aspect.


Ear related concerns and symptoms represent the leading indication for seeking pediatric healthcare attention. Despite the high incidence of such encounters, the diagnostic process of commonly encountered diseases of the middle and external presents a significant challenge. Much of this challenge stems from the lack of cost effective diagnostic testing, which necessitates the presence or absence of ear pathology to be determined clinically. Research has, however, demonstrated considerable variation among clinicians in their ability to accurately diagnose and consequently manage ear pathology. With recent advances in computer vision and machine learning, there is an increasing interest in helping clinicians to accurately diagnose middle and external ear pathology with computer-aided systems. It has been shown that AI has the capacity to analyze a single clinical image captured during the examination of the ear canal and eardrum from which it can determine the likelihood of a pathognomonic pattern for a specific diagnosis being present. The capture of such an image can, however, be challenging especially to inexperienced clinicians. To help mitigate this technical challenge, we have developed and tested a method using video sequences. The videos were collected using a commercially available otoscope smartphone attachment in an urban, tertiary-care pediatric emergency department. We present a two stage method that first, identifies valid frames by detecting and extracting ear drum patches from the video sequence, and second, performs the proposed shift contrastive anomaly detection (SCAD) to flag the otoscopy video sequences as normal or abnormal. Our method achieves an AUROC of 88.0% on the patient level and also outperforms the average of a group of 25 clinicians in a comparative study, which is the largest of such published to date. We conclude that the presented method achieves a promising first step toward the automated analysis of otoscopy video. Ear related concerns constitute the leading cause for seeking pediatric healthcare attention in the USA. Given the current lack of cost-effective confirmatory testing, accurate diagnosis and subsequent management depend on visual detection of characteristic findings during otoscope examination. Despite the frequency of such encounters, the medical literature suggests that the diagnostic accuracy for such pathology is only around 46–56%. In alignment with the bias toward over-diagnosis of ear disease, it is currently estimated that between 25–50% of all antibiotics prescribed for ear disease are not indicated –3. BeyonFurthermore,  compare In the study by , the autThe accuracy reported by previous study, even in multi-class settings, is remarkable, yet, all previous approaches process high quality still images of the eardrum. Unfortunately, in a clinical setting the capture of high quality still images can be challenging in practice, especially given that pediatric patients are often moving and uncooperative with the examination. The use of a single image also increased the chance of failing to capture a clinically accurate representation of the anatomy due to an incomplete view. A method that analyzes otoscopy video sequences rather than still images could help overcome the aforementioned shortcomings.The straightforward approach to have the whole video sequence as input is to train a 3D  CNN mapping from videos to labels. However, that would require (1) heuristics to harmonize videos to a preset length, and (2) large amounts of labeled data to avoid over-fitting, especially considering that the class frequency in any otoscopy video dataset is likely to be heavily skewed toward normal cases. The present study addresses these challenges by framing otoscopy interpretation as a video anomaly detection problem. Under our setting, the model is trained with normal videos only and will flag videos that diverge from the normal as an anomaly during testing.The contribution of this study is 2-fold. First, we devise a two stage model to apply on full video sequences collected in clinics, which differentiates our approach from all previous methods that only considered high quality still images. A region detector first extracts eardrum patches from video frames that are then used to perform anomaly detection in the following stage. This architecture restricts anomaly detection to semantically useful regions. Second, we develop the shift contrastive anomaly detection (SCAD) method that leverages color-jitter-based distributional shift-transformation to improve the separability between normal and abnormal data. Tailored to our application, our self-supervision task enforces the model to leverage subtle color features to identify abnormalities during testing. Even when the amount of training videos is relatively small, our approach generates superior binary  otoscopy video screening results compared to both a baseline method and the average of a group of clinicians. We believe that this study constitutes a promising first step toward achieving algorithmic decision support for the diagnosis of pathology of the middle and external ear and the development of a smart tool that might aid clinicians in the accurate diagnosis and management of common ear disease.The task of anomaly detection , 14, alsA rich set of methods  has beenAnomalies in medical imaging tend to be more subtle and only reside in a small region of the whole image, which makes it particularly challenging to apply UAD in this field. To address this problem,  propose The otoscope video database is skewed toward the non-disease status given the practice of capturing bilateral ear examinations and occurrence of non-ear disease presenting with symptoms such as ear pain. Dataset imbalance such as this can present a significant challenge for the development of robust design boundaries, particularly when relying on small data sets. To overcome such limitations, we propose to use UAD approach to provide computer-aided screening for otoscopy video. The formal definition of this problem is as follows.A(v) with video v as input and detect the abnormal videos as an anomaly during testing. Ideally, the function learns to map normal videos to small anomaly scores and abnormal videos to large anomaly scores. During testing, we threshold the score, where A(s) >ψ indicates an anomaly.We are given a large training dataset x from given video v using provided labels. Step 2) Self-supervised representation learning on the labeled region of interest to obtain an embedding function that ideally projects normal and abnormal samples to distinct regions of the embedding space. During testing, we compute anomaly scores using the detected patches and the learned embedding function.In otoscopy video diagnosis, the temporal relation between frames is not as influential as in action recognition tasks. Whether or not a video is abnormal is directly determined by the existence of abnormal frames therein. With this observation, we turn the video anomaly detection problem into a frame anomaly detection problem with an additional video level aggregation function. Moreover, only some of the frames in the video are vital to diagnosis, and in those frames, only the region that visualizes the eardrum. Therefore, it would be desirable to only conduct anomaly detection on such frames, and the eardrum region in particular. As shown in L1 loss of the groundtruth bounding box and the predicted bounding box. Then, the overall loss is simply combining the classification loss and localization loss.To detect the frames and regions showing the eardrum, we use a convolutional neural network and train it in a supervised manner using image-label pairs. Different from the standard detection problem where each image may have multiple instances, we can only have at most one eardrum in each frame. Therefore, we do not use a standard object detection architecture and rather choose to have a single convolutional neural network to predict the binary label and corresponding bounding box. We define Given our problem setup, we train the detection module on the normal training videos only. Even though the model does not use abnormal videos during training, we find it generalizes well to abnormal frames during testing as shown in Section 4.2.Contrastive Learning: Contrastive learning . This makes anomaly samples more separable from normal samples in the ϕ(x) space.Because the mean-shifted representation is normalized around Distributional Shift Angular Loss: To further improve separability, MSC , where z from x and then pushes ϕ(z) away from the normalized training center c. Shift-transformed samples z are created by applying distributional shift-transformations on x so that z is no longer from the original data distribution. The intuition is straightforward: not only should the normal samples occupy a small region around the center but also the samples not drawn from the original distribution cannot be too close to the center either. Hinge loss is applied to the angle between center c and shift-augmented samples z. The loss function takes the form:To address this issue, we develop the distributional shift angular center loss to further enhance separability between normal and abnormal samples. As shown in z are semantically similar to real anomalies. In the context of otoscopy images, color is an important feature in that infection may lead to the change of color of the whole or part of the eardrum. Thus, we employ three variants of color-jitter as the distributional shift-transformation. They are color-jitter-random-cut (CJ-RC), color-jitter-random-region (CJ-RR), and color-jitter-whole-frame (CJ-WF). CJ-RC uses a random rectangle as a mask of transformation. CJ-RR is constructed by interpolating the color-jittered image and the original image using pixel-wise weights for each image. We first initialize the pixel-wise weights to be all zero, and then pick random points to be filled with one, and apply a Gaussian filter to obtain smoothed weights. CJ-WF simply applies color-jitter to the whole frame. Ideally, these shift-transformed samples Final Loss: We combine (i) the mean-shifted contrastive loss This loss function is tailored to this specific medical imaging application and enjoys the best of both objectives. The mean-shifted loss makes the features to be representative of the images and the angular loss encourages normal and abnormal instances to be distant from each other in the feature space. Therefore, the combination of these two losses achieves superior performances, which is demonstrated through ablation in Section 4.3.Frame Level Anomaly Score: In order to classify a sample as normal or abnormal, we use a simple criterion based on kNN. kNN predicts the label of a data point by ensembling the k closest labeled samples according to some distance measure. In this study, we use the cosine distance between the features of the target image x and those of all training images. The anomaly score is then given by:Nk(x) denotes the k nearest features to ϕ(x) in the training feature set where Video Level Anomaly Score: We consider a relatively simple method to aggregate the frame level anomaly score to construct the video level anomaly score. The aggregation function is simply taking the average of the frame level anomaly score across all detected eardrum patches. The video level anomaly score is defined asa(x)} represents the set of frame level anomaly scores for all frames in the given video s.where {A through averaging. Such a score is expected to be small for normal videos and large for abnormal videos. Any frame that is substantially different from normal frames in the training set  would lead to a large distance to training examples in embedding space and then produce a large image and video level anomaly score. The threshold can be selected to meet the desired clinical requirement  and in practice is selected using the validation dataset consisting of both normal and abnormal videos.In summary, during training, we learn a frame level embedding function ϕ with all individual normal frames in the training set. During testing, we compute the kNN-based anomaly score using the embedding feature from ϕ for all detected frames. We then aggregate the predicted anomaly score for each detected frame in the testing video to produce a video level anomaly score We collected a total of 100 otoscopy videos from pediatric patients that were seen for various conditions in an urban, tertiary-care pediatric emergency department that treats over 34,000 patients, aged between 0 and 21 years of age per year. Patients were recruited as a convenience sample based on their willingness to participate in the study. At this site, patient ages range from 0 to 22 years. Study protocols were approved by the local institutional review board. The length for each video ranges from 5 to 40 s. Twenty seven to thirty frames per second are extracted from the video. Two otolaryngologists  annotated every video, and consensus agreement was used as ground truth. While the expert annotations are more granular, due to the small size of the dataset, we decided to restrict the analysis to two classes and each video is assigned into either the normal or abnormal category. Additional to the binary categorical label on video level, we also annotate frame level binary labels and bounding boxes for each appearance of the eardrum, where the eardrum occupies at least 20% of the image size in width and height. Of the 100 videos, 80 are labeled normal and 20 are labeled abnormal. We assign 60 normal videos to the training set, 10 to the validation set, and 10 to the testing set. For anomaly videos, we assign 10 to the validation set and 10 to the testing set. The testing set is used for the evaluation of the algorithms and comparison with human clinicians. We do not have overly apparent abnormal examples  during testing which makes it a challenging comparative study with human clinicians.ad hoc selection of otoscopes used in the clinical setting and perhaps more importantly the overwhelming reliance on non-digital otoscopes in clinical practice. As a result, we opted to use Cellscope Oto as an affordable solution that can potentially be deployed at scale. The Cellscope Oto device is designed to attach to an ear speculum and provides an additional light source but relies on the iPhone camera to capture the exam. We do note that the image and video quality captured on this device is not as high as reported in previous studies  optimizer with a 1Results: Since under our setup there is at most one instance of a single class, we evaluate the performance of our model by the accuracy of eardrum detection under different intersection over union (IoU) (on (IoU)  threshole − 5 weight decay of 5e − 5 and no momentum. For each minibatch size of 60, we sample one frame from each video to prevent similar frames treated as negative pairs. During training, we use the groundtruth bounding box to extract patches from videos. We use KNN (k=2) in computing the frame-level anomaly score. We conduct experiments with three random seeds for evaluations.Following the architecture suggested by , we consResults: We evaluate our methods on the anomaly detection task in the previously introduced otoscopy video dataset. We adopt the Area Under the Receiver Operating Characteristic curve (AUROC) and the Area Under the Precision Recall Curve (AUPRC) as anomaly detection performance score. We compare our approaches against the recently proposed top performing method MSC , nurse practitioners (NP), and physician assistants (PAs). The is clinician group reflects frontline healthcare providers who typically diagnose and manage ear disease. Exclusion criteria were practicing outside of the United states, employment by the Johns Hopkins University or Johns Hopkins Hospital, visual impairment limiting video analysis, or non-English speakers. Recruitment took place through digital messaging via professional email mailing lists and professional social media groups during November 2020.Study Procedure: Ten normal and ten videos designated as acute otitis media (AOM) by ground truth were selected as the testing set of images, as described above. Clinicians completed an online survey in which they were instructed to review the 20 eardrum videos without any further clinical information provided. Following each video, participants were asked to indicate whether the eardrums appeared normal or abnormal. They were also asked to rate their level of confidence in the accuracy of their diagnosis using a Likert scale . Participants were also asked to report the number of times they viewed each video. Participant responses were collected and managed using REDCap electronic data capture tools  or with view count and score (P=.32) were found .In In Discussion: Our presented method shows promising performance in anomaly detection on otoscopy video sequences. The whole workflow runs at 15 ms per frame on a single NVIDIA Quadro RTX 6000 GPU. This suggests the feasibility of adopting computer-aided diagnostics to real time detect and report anomalies may assist front-line clinicians in primary care and eventually even patients at home.The eventual successful deployment of such technology necessitates more than the successful development of a diagnostic AI algorithm with high accuracy. For such technology to integrate within current healthcare models and work flow, there is a need to create a whole new technological infrastructure. The implementation of such technology beyond its obvious need to be adopted by healthcare providers, perhaps the more pressing questions where the required financial investment for the creation of this infrastructure will come from.Meanwhile, our study exhibits several limitations at the current stage. (i) The dataset is relatively small, skewed toward normal, and collected through convenience sampling. As such our data serve to provide some additional support for the proof of concept but does not negate the need for a larger and more rigorously collected data-set before such technology becomes clinically viable. The development of a large video database would likely further improve the algorithm's diagnostic performance and enable training of specific diagnoses as opposed to being limited to detecting normal vs. abnormal ear exams. (ii) Cellscope Oto does not permit pneumotoscopy  which can also serve as a critical step for accurate diagnosis of ear disease. (iii) In our comparative study, clinicians were not provided with patient history which might increase their diagnostic accuracy in a clinical setting.In this study, we present a two-stage anomaly detection method that is designed to perform normal/abnormal classification for otoscopy videos and that can be developed based on a small dataset and highly skewed class distribution. We demonstrate that our method outperforms baseline algorithms and the average of 25 clinicians, constituting a promising step toward a computer-aided diagnosis of the middle and external ear pathology. The further development of computer-aided diagnosis methods, like ours, could contribute to timely and high-quality management of ear disease.wwang121@jhu.edu.The datasets presented in this article are not readily available because the institutional review board restrict data to be accessed only by approved personals. Requests to access the datasets should be directed to Weiyao Wang, The studies involving human participants were reviewed and approved by the Johns Hopkins Medicine Institutional Review Boards. Written informed consent from the participants' legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.WW, AT, and MU contribute to this study on the algorithm design, implementation, and evaluation. CS, JC, and TC contribute to this study on the clinical comparative study's organizing, data collection, and processing. JC contributes to this study on the hardware integration. All authors contributed to the article and approved the submitted version.This study is gratefully supported by Leon Lowenstein Foundation.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Near visual impairment (VI) is a common disability in an aging population. Near vision is crucial in activity of daily living including reading, smartphone and computer use and meal preparation. This study was conducted to determine the association between near visual acuity (VA) and contrast sensitivity (CS) with activity of daily living (ADL) among visually impaired older adults.A total of 208 participants aged  ≥  60 were recruited from the population-based longitudinal study on neuroprotective model for healthy longevity. Habitual near VA and CS were measured using Lighthouse near VA chart and Pelli-Robson CS chart, respectively. Lawton instrumental activities of daily living (IADL) was used to assess ADL. There are 41.8% participants with near visual impairment and 28.7% among them had IADL disability. Independent t test showed significant lower mean IADL score among visually impaired participants . IADL score significantly correlated with near VA  but not with CS . Near VA  and age  significantly predicted IADL. The findings show poorer VA renders higher IADL disability, which may necessitate interventions to improve ADL among visually impaired older adults. Visual impairment (VI) is a common disability among older adults worldwide and its prevalence increases with advancing age . GloballActivity of daily living (ADL) refers to the fundamental skills necessary for daily self-care which further categorized into basic ADL (BADL) and instrumental ADL (IADL) . BADL isVI led to person-environment misfit, causing difficulty in handling daily tasks even at familiar environment . There w0 was calculated based on Cochran formula .Among VI group, independent t-test revealed no significant different in mean IADL score among gender  Table . ANOVA tPearson correlation showed higher IADL score (less disability) significantly correlated with better near VA (lower M score in Lighthouse near chart) , but not with CS  Table .Table 3P2 of 0.16. Participants’ predicted IADL score is equal to 17.83 − 0.44 − 0.07  +  0.06. Participants’ IADL score decreased by 0.44 M unit for each of near VA, 0.07 year for each of age and 0.06 point for each of M-MMSE-blind score. Better near VA  and increasing age  were significant predictors of IADL score whereas M-MMSE-blind (p  =  0.14) did not.A multiple linear regression was conducted to predict IADL score based on near VA, age and M-MMSE-blind score among VI group. A significant regression equation was found , with RTo our knowledge, this is the first study on the association between near VA and IADL among visually impaired older adults in Malaysia. The present study, conducted among community-dwelling older adults with VI, highlights a significant inverse relationship between near VA with IADL. The poorer the near VA, the more severe the IADL disability among older adults with VI. Previous studies found that self-reported poor near vision doubled the risk of IADL limitation   and the The findings of this study support previous study in which elderly with self-reported fair to poor vision experienced difficulty in any IADL activity, especially meals preparation, phone usage and money management . Older aRubin et al.  found thThe association between various subitems in MMSE with IADL have been commonly reported –39. For Our study suggests that age was a significant predictor of IADL score, which agrees with previous findings , 41. AgiThe strengths of this study include objective measurement of near VA and classification of near VI based on ICD-11 for Mortality and Morbidity Statistics . SecondlThis study suggested a significant inverse association between near VA and IADL. There may be a need for interventions appropriate to the visually impaired such as optical correction and low vision rehab, to optimize IADL and mitigate disability. Concomitant public health measures may include activities to improve public awareness regarding the availability of such resources and programs in the community.This study did not use a specific ADL questionnaire for VI population. Further study should be conducted using a specific ADL questionnaire designed for VI population in order to gain a more specific or focused finding. As this study was only conducted in a Malaysian context, its findings might only be relevant to the Malaysian older adult’s population. It is interesting to know if same findings could be found in other countries or cultural settings.


Fusobacteria, and an increase of the relative abundance of Actinobacteria and Proteobacteria. At the genus level, Fusobacterium, Leptotrichia, Oribacterium, and Veillonella decreased, whereas Neisseria and Rothia increased. However, at the end of the decompression, both the diversity and composition of the microbiota returned to pre-dive values. The results indicate that the hyperoxic conditions during saturation may suppress the activity of anaerobes, leaving a niche for other bacteria to fill. The transient nature of the change could imply that hyperbaric heliox saturation has no lasting effect on the oral microbiota, but it is unknown whether or how a shift in oral bacterial diversity and abundance during saturation might impact the divers’ health or well-being.During commercial saturation diving, divers live and work under hyperbaric and hyperoxic conditions. The myriads of bacteria that live in and on the human body must adjust to the resultant hyperbaric stress. In this study, we examined the shifts in bacterial content in the oral cavity of saturation divers, using a metagenomic approach to determine the diversity in the composition of bacterial phyla and genera in saliva from 23 male divers before, during, and immediately after 4 weeks of commercial heliox saturation diving to a working depth of circa 200 m. We found that the bacterial diversity fell during saturation, and there was a change in bacterial composition; with a decrease at the phylum level of obligate anaerobe  Commercial saturation diving is used to perform long-term subsea work at greater depths. During saturation diving operations, the divers live within a pressurized, hyperbaric chamber system in a heliox atmosphere (a mix of oxygen and helium) for longer periods, normally limited to 28 days . The divvia their essential function in the regulation of nitrate (NO3−) production , approval number 2018/1184. Divers who passed the pre-saturation medical examination and were committed to saturation onboard the Dive Support Vessel (DSV) Deep Arctic were eligible for participation. They were informed of the aim and scope of the study and provided written consent before inclusion. The experimental procedures were conducted according to the Declaration of Helsinki principles for ethical human experimentation.2max ≥ 40 L/min. Initially, 30 certified saturation divers, all male non-smokers, were enrolled in the study for the duration of a 28-day work assignment in hyperbaric heliox saturation. All held valid health certificates for work in saturation and fulfilled the operator’s requirement for aerobic fitness with maximum oxygen uptake capacity VOSaturation diving was performed according to the contractor’s procedures as previously described . In summ2) was kept at 40 kPa in the pressure chamber and 60–80 kPa during the bell-runs. During decompression, the ppO2 was increased to 50 kPa until a depth of 13 msw was reached, and from there on ppO2 was gradually reduced to 21%. After reaching the surface, the divers stayed on the vessel for another 24 h for observation for decompression sickness, before they left the vessel. The saturation profile is shown in During the bottom phase, the partial pressure of oxygen  were used as a template for PCR of the 16S V2–V3 and V4–V5 regions, in separate reactions using Illumina primers/reagents. The resulting 16S amplicons were purified using AMPure XP beads . Purified PCR products from each sample were pooled and thereafter subjected to a second PCR amplification step, where dual samples indices and Illumina sequencing adaptors were added, according to the manufacturer’s instructions. A second PCR clean-up step was performed using AMPure XP beads , before the validation of the sequencing library using an Agilent High Sensitivity DNA Kit on a BioAnalyzer . Individual sample libraries were pooled and normalized to 10 pM, prior to sequencing of 2 × 300 cycles with a MiSeq V3 flowcell on a MiSeq instrument, according to the manufacturer’s instructions . Sequence reads were demultiplexed and converted from BCL to fastq file format using bcl2fastq2 conversion software V2.20.0422 .16S metagenomic sequencing libraries were prepared according to the QIAseq 16S Region Panel protocol . In brief, 4 nanograms (ng) of genomic DNA extracted from the saliva samples  pipeline . All folp > 0.05), either directly or after the data were transformed. If the data were still non-normal after transformation, a non-parametric analysis was applied. A one-way repeated measures ANOVA, with Bonferroni post hoc adjustment for multiple comparisons, was used to assess within diver differences in Shannon index and relative abundance of phyla and genera between the four time-points. Differences were considered significant at p < 0.05. If the assumption of sphericity was violated, as assessed by Mauchly’s test which assumes homogeneity for p > 0.05, a Greenhouse-Geisser correction was applied. In the case of non-normal data, Friedman’s test was applied and pairwise comparisons were performed with a Bonferroni correction for multiple comparisons.To estimate the diversity of the oral microbiota within individuals, i.e., the richness and evenness of the bacterial community , differeThe data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB40804.p < 0.001), compared to the baseline . However, the alpha diversity returned to baseline after the decompression , Proteobacteria (27%), Bacteroidetes (17%), Actinobacteria (11%), and Fusobacteria (9%). Five other phyla were detected at lower levels (<1%), including Patescibacteria, Epsilonbacteræota, Tenericutes, Spirochaetes, and Synergistetes. Moving down the taxonomic tree, the most abundant significantly altered genera belonged to the phyla Firmicutes (genus Veillonella and Oribacterium), Proteobacteria (genus Neisseria), Actinobacteria (genus Rothia and Actinomyces), and Fusobacteria (genus Fusobacterium and Leptotrichia)  .Firmicutes, Fusobacteria, and Bacteroidetes decreased during the first 5 days of the bottom phase, but only significantly so for Fusobacteria. Within the phylum Firmicutes, one of the dominant genera, Veillonella, decreased at day 2 (p = 0.047) and day 5 (p = 0.004), as well as the genus Oribacterium, that also decreased but only at day 2 of the bottom phase  and day 5 (p = 0.027), and in genus Leptotrichia at day 2 (p = 0.0022). We also observed a decrease in the genus Prevotella from Bacteroidetes but non-significant  increased at day 2  increased at day 2 (p = 0.010) and day 5  to aerobic/facultative anaerobic bacteria (Neisseria and Rothia). To our knowledge, this is the first study to address the relationship between hyperbaric environments and oral microbiota.In this study, we surveyed the oral bacterial microbiota of divers during 4 weeks of commercial heliox saturation diving. Using a taxonomic analysis through 16S rRNA sequencing, we identified 10 abundant bacterial phyla in saliva collected before, during, and immediately after saturation, and this composition is coherent with other studies that describe the human oral microbiota . We founFusobacteria decreased during saturation, whereas Proteobacteria and Actinobacteria increased. The most probable explanation is that the evenness of the oral microbiota was impacted by the drop of Fusobacteria during the bottom phase together with the raise of Proteobacteria abundance, in particular the Neisseria genus – of which this phylum alone made up more than 30% of the total abundance during the bottom phase. Furthermore, the absence of change in the total abundance during saturation , whereas microaerophiles prosper best well-below normal atmospheric concentrations. Unlike obligate anaerobes that cannot tolerate oxygen and thrive only under anoxic conditions; facultative anaerobes can grow by fermentation, use other terminal electron acceptors for anaerobic respiration, or their ability to breathe aerobically. Aerotolerant anaerobes as the name suggests can tolerate the presence of oxygen, but do not benefit from aerobic respiration and thrive optimally without oxygen . Many faStaphylococcus aureus) in patients with respiratory failure who received high concentrations of therapeutic oxygen. In parallel, using neonatal and adult mouse models, the same authors demonstrated that lung and gut bacterial communities were altered within 24 and 72 h, respectively, in mice exposed to hyperoxia (Neisseria (Neisseria gonorrhoeae and Neisseria meningitidis), when even within the same genus, Neisserial species appeared to have different contents of antioxidant enzymes , making it a common microbial signature of states linked to various degrees with inflammation  can be found at: The studies involving human participants were reviewed and approved by Norwegian Regional Committee for Medical and Health Research Ethics (REK). The patients/participants provided their written informed consent to participate in this study.RM, KH, and IE designed the study. IE collected the material. RM, JL, AH, and AF conducted the analyses. RM initiated the manuscript. All authors contributed in the writing and approval of the final version.TechnipFMC sponsored helicopter transfers and boarding on the DSV Deep Arctic for IE.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Trypanosoma cruzi, the etiologic agent of Chagas disease. In vitro and in vivo studies have shown that P21 plays an important role in the invasion of mammalian host cells and establishment of infection in a murine model. P21 functions as a signal transducer, triggering intracellular cascades in host cells and resulting in the remodeling of the actin cytoskeleton and parasite internalization. Furthermore, in vivo studies have shown that P21 inhibits angiogenesis, induces inflammation and fibrosis, and regulates intracellular amastigote replication. In this study, we used the CRISPR/Cas9 system for P21 gene knockout and investigated whether the ablation of P21 results in changes in the phenotypes associated with this protein. Ablation of P21 gene resulted in a lower growth rate of epimastigotes and delayed cell cycle progression, accompanied by accumulation of parasites in G1 phase. However, P21 knockout epimastigotes were viable and able to differentiate into metacyclic trypomastigotes, which are infective to mammalian cells. In comparison with wild-type parasites, P21 knockout cells showed a reduced cell invasion rate, demonstrating the role of this protein in host cell invasion. However, there was a higher number of intracellular amastigotes per cell, suggesting that P21 is a negative regulator of amastigote proliferation in mammalian cells. Here, for the first time, we demonstrated the direct correlation between P21 and the replication of intracellular amastigotes, which underlies the chronicity of T. cruzi infection.P21 is an immunomodulatory protein expressed throughout the life cycle of  Trypanosoma cruzi. It has been estimated that 6–7 million people are infected with T. cruzi worldwide, mainly in Latin America. Moreover, approximately 10,000 people die from Chagas disease each year, thus constituting an important public health problem , which can eventually lead to severe disease. The persistence of the parasite in host tissues could be attributed to the perpetuation of the disease and its chronicity, a stage for which there is no effective treatment. Studies have reported the presence of dormant amastigotes or amastigote nests with a reduced replication rate in host tissues and how the replicative status of these parasites contributes to resistance to benznidazole therapy . HoweverT. cruzi circulates in the bloodstream of infected humans or reservoir mammals and invades several types of nucleated cells, within which it replicates and remains infectious. T. cruzi invasion and replication processes involve interactions between the parasite and host cell molecules in a coevolutionary arms race  were grown at 28°C in liver infusion tryptose (LIT) medium supplemented with 10% or 20% fetal bovine serum  and manipulated in level 2 biosafety cabinets following institutional safety procedures. To differentiate epimastigotes into metacyclic forms, the epimastigotes were maintained in LIT medium (10% FBS) for 14 days, and metacyclic trypomastigotes were purified as previously described (2.escribed . Human cT. cruzi epimastigotes (Y strain) were seeded (2 × 105 cells/well) into a 96-well plate and incubated with 200 µL of LIT medium supplemented with antibiotics . The parasites were kept at 28°C, and the LIT medium (containing the antibiotics) was replaced as needed. After 10 days of incubation, the number of parasites was determined by counting in a Neubauer chamber.Wild-type (WT) P21 gene we used CRISPR/Cas9-mediated genome editing, as previously described (P21 gene (TriTrypDB ID: TcYC6_0032830), which encodes the T. cruzi P21 protein.To knockout escribed . Brieflyhttp://grna.ctegd.uga.edu/). This in silico analysis allows to choose a specific protospacer, containing a unique on-target site, and no off-targets in the genome of T. cruzi Y C6 TritrypDB-46 release (https://tritrypdb.org’ dataset). The protospacer sequence is shown in the T. cruzi epimastigotes and disrupt P21 ORF with a blasticidin resistance gene . In addition, parasites were transfected with Cas9/pTREX-n vector containing Scrambled sgRNA sequence as previously described  in the early exponential phase were washed with phosphate-buffered saline  and transfected with 25 µg of plasmid (sgRNA/Cas9/pTREX-n) diluted in P3 Primary Cell 4D-Nucleofector (V4XP-3024) solution according to the manufacturer’s instructions. The parasites were electroporated with two pulses (EH100 program) delivered by the 4D Nucleofector system (Lonza) ( (Lonza) . Then, tP21 and Bsd genes with specific sets of oligonucleotides  and used in PCR to verify the presence or absence of the 7 epimastigotes) was extracted using the PureLink RNA Mini Kit  and treated with DNAse I (Sigma-Aldrich) according to the manufacturer’s instructions. First-strand cDNA was synthesized from total RNA (1 µg) and oligo(dT) primers using the Superscript III First-Strand Synthesis System  following the manufacturer’s instructions. Next, PCR was performed in a separate microtube using the primers listed in P21 knockout clones and controls, as described in the previous section.Total RNA from WT, Scrambled, and knockout clones    diluted To evaluate the cell cycle progression, WT and knockout clone parasites in log phase growth were incubated with 20 mM hydroxyurea (HU) for 18 h as previously described . Thus, c3VO4, 2 mM NaF, 20% glycerol, 1% NP-40, and complete-C protease inhibitors). The extracts were incubated on ice for 30 min and centrifuged at 13,000 × g for 10 min at 4°C. The supernatant was recovered and frozen at -20°C.Metacyclic trypomastigotes were obtained from axenic cultures of epimastigotes in the stationary phase maintained in LIT medium (pH 7.2) for 14 days at 28°C. After differentiation, the number of parasites was counted in a Neubauer chamber. Then, the samples were washed twice with PBS, and the pellet was resuspended in 50 µL of lysis buffer   and anti5 cells/well for invasion and 5 × 104 cells/well for replication assays and maintained overnight under optimal conditions for adhesion. Then, the cells were incubated for 2h with purified metacyclic forms of the WT and P21 knockout clones at a multiplicity of infection (MOI) of 20 (20 parasites:1 host cell) for invasion and 10 (10 parasites:1 host cell) for replication.HeLa cells were seeded onto 24-well plates containing 13 mm coverslips to a final density of 1 × 10® (Merck Millipore), and the number of infected cells was quantified for 100 cells.The cells were washed three times with PBS to remove non-internalized parasites, fixed with Bouin’s solution  for 5 min, washed with PBS, and stained with Giemsa (1:3)  for 1 h. Then, the stained cells were dehydrated sequentially in acetone, acetone:xylol, and xylol and mounted on slides with EntellanFor the replication assays, after 2 h, HeLa cells were washed and maintained in optimal conditions for 24h, 48h or 72 h. Then, the cells were fixed with 2% paraformaldehyde and incubated with anti-chagasic human serum (1:100) diluted in permeabilization solution overnight. The cells were washed with PBS and incubated with secondary antibodies conjugated to Alexa Fluor 568 (1:200) and DAPI (1:500) for 1 h. The number of parasites per cell was quantified for 100 infected cells. Representative images of intracellular amastigotes were captured using a confocal microscope (Leica SP5), and Imaris software (Bitplane) was used to perform the analysis.p < 0.05. Significant differences were determined by one-way ANOVA and Tukey’s test for multiple comparisons of parametric data, Kruskal-Wallis and Dunn’s test for multiple comparisons of nonparametric data, or two-way ANOVA and Tukey’s or Sidak’s test for multiple comparisons.Statistical analyses were performed using GraphPad Prism software version 7.0. All values are expressed as the mean ± standard deviation of two independent assays performed at least in duplicate. Differences were considered significant at P21 encodes a 154-amino acid protein (TcYC6_0032830-RA) and it was mapped (position:58.538-59.002 nt) near one end of chromosome 17. P21 is located within a polycistronic unit of about 230,000 nt belonging to a syntenic region conserved in different strains of T. cruzi. Nucleotide blast analysis demonstrate that P21 protein is coded by a pair of alleles and no orthologs were found in other trypanosomatids (https://tritrypdb.org/tritrypdb/app/record/gene/TcYC6_0032830).In this work, gene P21 from strain Y clone C6 (Y C6) (Gene ID: TcYC6_0032830) was selected for functional analysis. The genome of Y C6 was organized into 40 chromosome-scale scaffolds, many of which represent near-complete chromosomes, such as the chromosome 17 where gene P21 has been assigned . The genFirstly, we determined the antibiotic concentration of G418 and blasticidin for selecting the epimastigotes of the Y strain under culture conditions. They were incubated with different concentrations of antibiotics for 10 days, and the number of parasites was estimated by counting in a Neubauer chamber. We found that 250 µg/mL G418 and 25 µg/mL blasticidin could be used to select the parasites after electroporation   and the pGEM-Bsd vector as a template. The plasmids were used to electroporate epimastigotes, generating lines that express the Cas9-GFP fusion protein in the parasite nucleus  . The P21P21, Bsd, and P21 UTR regions ; while the four knockout clones did not show amplification    and hypo (TcMVK) . The resT. cruzi, we evaluated the growth curve of P21 knockout clones compared with that of WT parasites for 14 days by counting the number of parasites every 2 days. We found that P21 knockout cells exhibit a slower growth rate than WT parasites (To investigate whether P21 deficiency hinders the growth of P21 knockout parasites by flow cytometry after synchronization cells with hydroxyurea treatment. The first graph (0H) demonstrated that most of the parasites from both groups were synchronized in the G1 phase of the cell cycle. At other times the data demonstrated that P21 ablation altered the cell cycle progression compared with WT parasites. P21 knockout parasites accumulated in G1 and decreased in S phase throughout the cell cycle while WT cells presented normal progression  of WT and P21 ablation in T. cruzi metacyclogenesis. We analyzed the expression of the glycoprotein gp82, a molecular marker of the differentiation of epimastigotes into metacyclic trypomastigotes, after 14 days of growth. The results showed that there was no difference in the expression of gp82 between the knockout clones and controls  is homozygous for the P21 locus, which has been assigned to one end of the in silico assembled chromosome 17 of the T. cruzi Y C6 strain (TriTrypDB ID: TcYC6_0032830). In this study, we used the CRISPR/Cas9 system, a robust genetic tool, to investigate the biological roles of the P21 protein. We obtained four double-knockout clones, which the P21 gene locus was sequenced, confirming the insertion of the Bsd gene. No other rearrangements were identified at this particular locus.Bioinformatics analysis of the genome of P21 in different strains of P21 ablation. First, we determined the growth curve of knockout clones and WT control for 14 days. From day 8 onwards, knockout clones grew slower than WT parasites, and on day 14, their growth rate was around twice slower than that of WT epimastigotes. Interestingly, this result showed that P21 gene knockout impaired the growth of epimastigotes. We also analyzed the cell cycle progression after synchronization of cells with hydroxyurea treatment. The results showed that P21 ablation induces an increase in the number of parasites in the G1 and S phases of the cycle at most analyzed times, indicating a delayed cell cycle progression, which corroborates the reduced growth of epimastigotes observed. A previous study reported that the incubation of epimastigotes for 72 h with recombinant P21 protein (rP21) caused an increase in the number of G1 and S phase parasites with no interference in the G2 phase of the cell cycle  and 2019/05049-4 ; FAPEMIG process number: APQ-00971-17.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


MR-guided focused ultrasound (MRgFUS) is increasingly being used to treat patients with essential tremor (ET) and Parkinson's disease (PD) with thalamotomy and pallidotomy, respectively. Pallidotomy is performed off-center within the cranium compared to thalamotomy and may present challenges to therapeutic lesioning due to this location. However, the impact of target location on treatment efficiency and ability to create therapeutic lesions has not been studied. This study aimed to compare the physical efficiency of MRgFUS thalamotomy and pallidotomy.n = 20) or pallidotomy (n = 20), matched by skull density ratios (SDR). Aspects of treatment efficiency were compared between these groups. Demographic and comparative statistics were conducted to assess these differences. Acoustic field simulations were performed to compare and validate the simulated temperature profile for VIM and GPi ablation.Treatment characteristics were compared between patients treated with thalamotomy  and pallidotomy . The impact of low SDR on efficiency reduction was greater for pallidotomy, approaching significance (p = 0.061). A nearly two-fold increase in energy was needed to reach 50°C in pallidotomy (10.9kJ) than in thalamotomy (5.7kJ), (p = 0.002). Despite lower energy requirement, the maximum average temperature reached was higher in thalamotomy (56.7°C) than in pallidotomy (55.0°C), (p = 0.017). Mean incident angle of acoustic beams was lesser in thalamotomy (12.7°) than in pallidotomy (18.6°), (p < 0.001). For all patients, a lesser mean incident angle correlated with a higher maximum average temperature reached , and less energy needed to reach 50°C . Greater skull thickness was associated with a higher maximum energy for a single sonication for thalamotomy  and pallidotomy . An acoustic and temperature field simulation validated similar findings for thalamotomy and pallidotomy in a single patient.Lower SDR values were associated with greater energy requirement for thalamotomy (RThe centrally located VIM offers a more efficient location for therapeutic lesioning compared to GPi pallidotomy in SDR matched cohort of patients. The impact on therapeutic lesioning with lower SDR may be greater for pallidotomy patients. As newer off-center targets are investigated, these findings can inform patient selection and treatment requirements for lesion production. Magnetic resonance imaging-guided focused ultrasound (MRgFUS) is a promising, non-invasive technology that is being increasingly applied to treat various neurological disorders, including essential tremor and Parkinson's disease . HoweverHowever, the efficiency of transcranial ablation using a hemispherical array of transducers is known to vary by target location, with a small treatment envelope being present in the center of the brain. In this envelope, the incident angles of individual transducer elements at the calvarium are optimal for current mid-frequency systems . As one We therefore elected to compare the physical efficiency of MRgFUS ablation of the near-center thalamic ventral intermediate (VIM) nucleus for treatment of essential tremor (ET) to that of ablation of the more laterally and anteriorly located globus pallidus internus (GPi) for dyskinesias or motor fluctuations of Parkinson's disease (PD). Data were collected from individual cases performed at our institution, and partnering institutions, and matched by patients' skull density ratio (SDR), as SDR is the most significant determinant of treatment efficiency , 10. AcoTo compare treatment characteristics, 20 patients treated with MRgFUS unilateral VIM thalamotomy for ET and 20 patients treated with unilateral GPi pallidotomy for PD were selected in pairs with matching SDRs, defined as values within 0.02 of each other. All 20 of 20 PD patients with available data were included, and 20 ET patients with similar SDRs were manually selected from a larger repository of treated patients. Patients were treated using the ExAblate 4,000 mid-frequency (670 kHz) head transducer system . All thalamotomies and 13 of the pallidotomies were performed in an identical fashion with the same neurosurgeon (H.M.E.), with low-level sonications to start for correction and alignment of the focal spot, followed by a relatively rapid rises in power to achieve ablative temperatures. The remaining pallidotomies were performed at partnering institutions. Parkinson's disease patients were treated in a prospective, open-label, multicenter trial of unilateral MRgFUS ablation of globus pallidus interna (NCT02263885). Institutional review board (IRB) approval was obtained at respective institutions and anonymized data were available in accordance with a prior data sharing agreement (NCT03100474). ET patients were all treated at the University of Maryland, Baltimore, and their data were entered into a prospectively maintained IRB-approved database . All data were anonymized.max), the maximum energy for a single sonication , and the energy required to reach a temperature of 50 °C (kJ) were recorded  on the outer table of the skull emitted by ultrasound elements, the number of sonications for treatment, the sonication time (minutes), the maximum average temperature reached  (IBM Corp).Demographic and comparative statistics were conducted to assess differences between the treatment groups and to develop regression models to assess relationships between different parameters (independent samples ficient) . Energy ficient) , 14. TheTo better understand the differences of lesion formation between MRgFUS thalamotomy and pallidotomy, acoustic field simulations were performed to evaluate the resulting acoustic profile. The acoustic fields within the head were simulated using a 3-D finite differences algorithm, which aims to solve the full Westervelt equation, a method to estimate temperature rise through heterogenous tissues . The acon = 20) were older than patients treated with pallidotomy (n = 20) (p < 0.001). The proportions of patients in each treatment group did not differ by sex (p = 0.490). The mean SDR for thalamotomy was 0.54 (SD: 0.071) and for pallidotomy was 0.55 (SD: 0.067), . The mean of absolute differences in SDR between treatment groups was 0.013 (SD: 0.0092). The mean skull thickness, an average of thickness calculated at every element point across the skull area treated, was 6.1 mm (SD: 1.1) for thalamotomy and 6.4 mm (SD: 1.1) for pallidotomy. The distribution of skull thickness between these groups was not different . There was no difference in mean skull surface area between thalamotomy, 340.3 cm2 (SD: 30.2), and pallidotomy, 336.7 cm2 (SD: 22.9), .Data on 40 patients were collected and analyzed . Patientt-test, p < 0.001). The mean number of elements of 1,024 maximum elements that emitted beams with incident angles <25 degrees, previously shown to be an optimal incident angle, was greater for thalamotomy  than for pallidotomy    than in pallidotomy , (< 0.001)  .p = 0.233) or the mean sonication time (p = 0.679). The mean maximum average temperatures reached was higher in the thalamotomy group  than in the pallidotomy , . Mean maximum energy for a single sonication was higher in pallidotomy  than in thalamotomy , approaching significance . The mean energy needed to reach 50 °C was nearly two-fold higher in pallidotomy  than in thalamotomy , .Treatment groups did not differ in the mean number of sonications , and less energy needed to reach 50 °C   than for pallidotomy . The slope of this relationship was steeper for pallidotomy, approaching significance on comparison between thalamotomy and pallidotomy (p = 0.061)  . A lower= 0.061) .2 = 0.324, p = 0.009). Additionally, a greater skull thickness was associated with a higher maximum energy for a single sonication for thalamotomy  and pallidotomy , but the slopes of these relationships did not differ (p = 0.345) (p = 0.002), SDR , and skull thickness  correlated with energy needed to reach 50 °C  = 12.719, p < 0.001, R2 = 0.515). Age and sex did not correlate with each other, or with SDR, incident angle, maximum average temperature reached, or energy needed to reach 50 °C.A greater skull thickness was associated with more energy needed to reach 50°C among all patients, however analysis by treatment revealed this held true for pallidotomy, not for thalamotomy,  . Skull tSimulated temperature maps for both VIM  and GPi Our results demonstrate that MRgFUS ablation of the GPi is less efficient than ablation of the thalamic VIM nucleus in patient populations with matched skull density ratios. The energy required to reach 50 °C during pallidotomy was nearly twice that of thalamotomy. This is likely due to the greater incident angles of individual ultrasound elements when targeting the more antero-laterally located GPi, which in turn results in decreased transmission and greater reflection of acoustic energy at the skull outer table  1, 18, , 18, 19.Similar to prior studies, SDR and skull thickness significantly influenced treatment efficiency at both targets in our cohort, SDR more so than skull thickness , 10, 19.An acoustic and temperature field simulation demonstrated a higher peak temperature for thalamotomy than pallidotomy in the same ET patient, an internal control. The close estimated peak temperature by simulation compared with the treatment data indicated the accuracy of the simulation model. The simulated thermal profiles also closely resemble those observed in ET and PD treatment cases , 23. GPiAlthough it was beyond the scope of the current study, there was no evidence to suggest that the lower treatment efficiency of pallidotomy impacted the ability to generate a lesion at the GPi in our cohort. Specifically, all Parkinson's patients included in the current investigation underwent successful GPi ablation with evidence of the expected T2 hyperintense, diffusion restricting lesion on post-procedure MRI . One potIn one attempt to perform MRgFUS lesioning of the hippocampus for mesial temporal lobe epilepsy, adequate temperatures could not be reached, likely secondary to the peripherality of the target . There aThis study has several important limitations, including its retrospective design and smaller cohort size; the latter was due to the limited number of pallidotomy patients with data available for analysis. Also, SDRs are specific between individuals, and were unable to be matched exactly between cohort groups, which may have influenced our results. Our data was not granular enough to determine the effect of local SDR on treatment efficiency, so we used average SDR as prior studies have done. Although most of our patients were treated at a single institution, differences in treatment practices between institutions can be difficult to parse out when grouping individuals treated for the same disease. Similarly, treatment practices and strategies can vary between teams affecting treatment duration. Finally, we did not include any patients with lower SDRs (i.e. <0.4), which would pose a significantly greater challenge for successful MRgFUS lesioning due to reduced treatment efficiency. Future studies should focus on this cohort of patients to understand the true impact of larger incident angles while treating off-center locations.MRgFUS thalamotomy of the VIM for essential tremor has higher treatment efficiency characteristics than pallidotomy of the GPi for Parkinson's disease. This is likely due to the central location of the VIM. As new off-center targets within the skull are investigated for MRgFUS thermal ablation and treatments are considered in low SDR subjects, these findings can inform appropriate patient selection and physical treatment requirements .The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by University of Maryland, Baltimore Institutional Review Board (Exemption HP-00093725). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.A-KA organized the database. A-KA and TM performed the statistical analysis. A-KA, DG, and TM wrote the first draft of the manuscript. SG and AK wrote sections of the manuscript. All authors contributed to manuscript revision, conception, read, design of the study, and approved the submitted version.Authors NK and RC were employed by company Insightec. HE and DG were on the advisory board of Insightec. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.