Rapid economic and societal development increases resource consumption. Understanding how to balance the discrepancy between economic and social water use and ecological water use is an urgent problem to be solved, especially in arid areas. The Heihe River is the second-largest inland river in China, and this problem is notable. To ensure the downstream ecological water use, the “Water Distribution Plan for the Mainstream of the Heihe River” (97 Water Diversion Scheme) controls the discharge of Yingluo Gorge and Zhengyi Gorge, while the “Opinions of applying the strictest water resources control system” (Three Red Lines) restricts the water use. With the development of the economy and agriculture in the midstream, Zhengyi Gorge’s discharge cannot reach the Heihe River’s ecological water downstream. This paper is under the constraints of the “97 Water Diversion Scheme” of Heihe River and the “Three Red Lines” of the total water use control index for Zhangye County. We constructed a water resource allocation model for the midstream of Heihe River to reasonably allocate water resources in the Heihe River’s midstream and downstream. This model is divided into three parts: Establish the mathematical equation, simulate the water consumption under the different inflow conditions, and ensure each water user’s demand. The result showed that if we fail to confine total water consumption in the midstream, through the reasonable allocation of water resources, the real water use and water consumption of the middle Heihe River will be greater than the “97 Water Diversion Scheme” and the “Three Red Lines.” If we confine water consumption, they will be within the “97 Water Diversion Scheme” and the “Three Red Lines,” at the same time, they can reach the downstream of the Heihe River’s ecological water. Besides, under the premise of satisfying the economic water and ecological water downstream of the Heihe River, returning farmland to wasteland and strengthening water-saving measures will improve water efficiency and be more conducive to allocating water resources. Water resources are essential to all life, primary natural resources, strategic economic resources, and ecological control factors ,2,3. MorThe Heihe River is the second-largest inland river in China. It is an essential water source in Northwest China; it is also the study area’s primary water source. The runoff from Yingluo Gorge is the main water source of the midstream of the Heihe River. The discharge from Zhengyi Gorge determines the ecological water demand downstream of the Heihe River. If the discharge cannot reach the Heihe River’s ecological water consumption downstream, it will affect the Heihe River’s ecological environment. With the rapid development of the economy and agriculture in the midstream, water resources demand has increased significantly ,38,39. TTo effectively curb the excessive development and utilization of water resources, China’s government established the “Three Red Lines” system that includes total water use control, water efficiency, and water functional areas that limit pollution capacity ,44,45. TWith the continued development of the economy and increasing demand for water resources, problems in controlling total water use and the “97 Water Diversion Scheme” gradually appeared ,46. In rIn this paper, we constructed a water resource allocation model for Heihe River based on the “97 Water Diversion Scheme”, simulated water consumption in the middle reaches of Heihe River under different inflow scenarios, and proposed the water resources regulation strategy to meet the needs of ecological water in the lower reaches of Heihe River.The Heihe River is the second-largest inland water body in Northwest China, with the midstream located at 38.6° N–39.8° N, 99.5° E–100.8° E . Heihe iThe Heihe River originates from the northern foothills of the Qilian Mountains. The upstream above Yingluo Gorge, the midstream between Yingluo Gorge and Zhengyi Gorge, and the downstream below Zhengyi Gorge. The “97 Water Diversion Scheme” strictly stipulates the inflow of water from Yingluo Gorge and the Zhengyi Gorge’s discharge under different inflow conditions.  “Three RThe midstream of the Heihe River includes three districts and counties  and 13 irrigated areas . Li Yuan River is a tributary of the Heihe River, the Liyuan River irrigated area irrigated by the Liyuan River, and the other 12 are irrigated by the Heihe River mainstream. Due to the development of the economy and agriculture in the midstream of the Heihe River, groundwater demand is also great, so groundwater is extracted by underground mechanical wells for agricultural irrigation.3, the Zhengyi Gorge discharges 1.32 billion m3. When the Yingluo Gorge’s 25% guaranteed rate of incoming water is 1.71 billion m3, the water discharged from Zhengyi Gorge is 1.09 billion m3. When Yingluo Gorge’s 75% guaranteed rate of incoming water is 1.42 billion m3, the discharged water will be 760 million m3. When Yingluo Gorge’s 90% guaranteed rate of incoming water is 1.29 billion m3, the discharge volume of Zhengyi Gorge will be 630 million m3 [The Ministry of Water Resources approved the “Water Distribution Plan for the Mainstream of the Heihe River.” This plan allocates the water volume of the Heihe River in the wet and dry years . When thllion m3 .3 in 2015, 2.011 billion m3 in 2020, and 2.71 billion m3 in 2030. The total water consumption control indexes for Ganzhou District were 779 million m3, 681 million m3, and 702 million m3. The total water consumption control indexes for Linze County were 464 million m3, 406 million m3, and 418 million m3. The total water consumption control indexes for Gaotai County were 389 million m3, 340 million m3, and 350 million m3 [In January 2013, the General Office of the State Council issued the most stringent “Three Red Lines” indicators for water resources management to all provinces, autonomous regions, and municipalities directly under the central government. In November, the general office of the Gansu Provincial People’s government issued water resources management control indicators for 2015, 2020, and 2030 for the Gansu Province prefecture-level administrative regions. According to the city water resources management target, the Heihe water diversion scheme, water resources allocation scheme, and water use practice for each county, Zhangye City issued its total water consumption targets in 2015, 2020, and 2030 . The totllion m3 ,56 . We obtained meteorological data from six weather stations in the Heihe River watershed from 1956 to 2017 from the China National Meteorological Information Center (http://cdc.cma.gov.cn accessed on 8 July 2020). We obtained data for irrigated area, average flow rate, and the closing time of the river in the section from the Zhangye Water Conservancy Annals (http://www.zhangye.gov.cn accessed on 8 July 2020) and Gansu Statistical Yearbook (http://tjj.gansu.gov.cn accessed on 8 July 2020).The Cold and Arid Region Scientific Data Center provided annually observed runoff discharge (1956–2017) and Digital Elevation Model (DEM)  is an empirical parameter, which is determined after many trials and is combined with the Heihe water resources deployment.In Equation (2), the coordination coefficient  and the main soil types (loam) in the midstream of the Heihe River Basin ,59, we sV, z, and Except for KAccording to the groundwater module in the midstream of the Heihe River Basin water resources allocation model, we established an objective function using the historical three-year  average water withdrawal data, multiyear average precipitation, multiyear average water surface evaporation, and predetermined parameters of each irrigation area in the middle reaches, and optimized the calculation of pending parameters.m is the number of irrigation area units, including subirrigation area units, m = 23; 8 m3; 8 m3; 8 m3; 8m3. We obtained the numerical values of undetermined parameters  according to the optimization calculations given in The objective function is as follows:The model simulated the groundwater level of each irrigation area and the flow of each river section from 2005 to 2012. It was evident from the main section flow fitting and the groundwater level simulation that the results were better.3 of water. We used the water allocation to simulate the completion of a long series of annual water diversion schemes under current water demand conditions. 3 of downstream water was owed annually. In more abundant years, 126 million m3 water was owed to downstream water. In a dry year and normal flow year, the discharge target of the Zhengyi Gorge could be met. The average annual groundwater extraction volume was 598 million m3, which was greater than the average annual extraction volume of 480 million m3 in the middle reaches of the Heihe River [In 2017, the midstream of Heihe needed 1.394 billion mhe River . The aveBy comparing the Zhengyi Gorge’s drainage index with the Zhengyi Gorge simulated discharge, under different incoming water conditions, the Zhengyi Gorge simulated discharge is less than the drainage index, and Midstream water consumption is greater than the water consumption index. Under such circumstances, the ecological water in the downstream will be insufficient, thus affecting the ecological environment in the downstream. Total water withdrawal (including Liyuan River) in the midstream is also greater than the Midstream water consumption index. With the increase of water consumption in the study area, the extraction of water from Heihe River will increase, and the discharge from Zhengyi Gorge will become smaller, which is more detrimental to the ecological environment of the downstream of the Heihe River.3), and the total water consumption also exceeded the midstream water consumption index. Therefore, under current water demand conditions, if the total water consumption was not taken into account, through the reasonable allocation of water resources, the water intake and consumption would exceed the indexes of in the middle reaches of “97 Water Diversion Scheme” in the Heihe River. As a result, the ecological water demand of the lower reaches of the Heihe river decreases, which affects the ecological development of the lower reaches of the Heihe River.In each inflow year, the total water intake was greater than the total water withdrawal index (1.63 billion m3), and total water consumption and total water consumption indicators were basically equivalent. Therefore, considering the water consumption, under current water requirements, through water allocation, the water intake and consumption in the middle reaches of Heihe River could be controlled within the “Three Red Lines” for water intake and consumption indicators.By comparing the Zhengyi Gorge’s drainage index with the Zhengyi Gorge simulated discharge, under different incoming water conditions, the Zhengyi Gorge simulated discharge is less than the drainage index. However difference between the results is not significant. Total water withdrawal (including Liyuan River) in the midstream is also less than the Midstream water consumption index. By the conditions, the discharge volume of Zhengyi Gorge can be satisfied to meet the downstream ecological water.3.Under these circumstances, the water shortage in the middle reaches was significantly greater than without the considering water consumption index. In years when the incoming water was 10%, 25%, 50%, 75%, and 90%, the water shortage was 1.64 and 1.61, 1.55, 1.58, and 1.56 million mIn the case of considering water consumption, the downstream ecological water demand can be satisfied through rational allocation of water resources, so as to achieve the purpose of water resources allocation, but the water shortage in the middle reaches was significantly greater than without considering the water consumption index. So some measures should be taken.2; (2) use advanced technologies to save water and improve the utilization efficiency of water resources, to increase the utilization coefficient of irrigation water to 0.68. Increasing water-saving measures can reduce water consumption and improve water efficiency.According to the results of water resources allocation, we obtained different results under different inflow conditions. If the total water consumption limit was not taken into account, the water consumption in the middle reaches of the Heihe River exceeded the water consumption target. If the total water consumption limit was not taken into account, the water shortage in the middle reaches significantly increased. We identified two solutions: (1) Returning farmland to 1200 Km2, and the corresponding water demand is 1.023 billion m3 (Heihe Recent Governance Plan) [3, with 10% over-extraction. The water consumption in the middle reaches of Heihe could be controlled within the “97 Water Diversion Scheme”.The increase in the cultivated land area has been the main factor driving water demand. Reducing midstream water demand can achieve the water diversion target in wet years. The cultivated land area in the middle reaches should be 1200 kmce Plan) . We simu3 under water-saving conditions. In addition, the reclaimed farmland in the middle reaches of the Heihe River to 1200 Km2 and the corresponding water demand was 1.046 billion m3 [3, with 14% over-extraction. The water consumption in the middle reaches of the Heihe River could be controlled within the “97 Water Diversion Scheme”.Strengthening water saving in the middle reaches can alleviate the water contradiction of water use in the middle reaches. In the middle reaches of Heihe River, the water demand was 1.15 billion mllion m3 . We simuAccording to our results, if water consumption and water use were controlled, farmland was returned, and water-saving measures were applied, the indicators of the middle reaches of the Heihe River could be met. The over-exploitation of groundwater could also be categorized as mild over-exploitation. The water shortage was not significant, and water resources allocation in the middle reaches of the Heihe River was well satisfied. So, the application of returning farmland and water-saving measures is an important measure for water resources allocation in the Heihe River, and controlling the scale of farmland is the most important.On the basis of our simulation, we made the following conclusions.The “97 Water Diversion Plan” and the “Three Red Lines” control the consumption and water use, respectively, in the middle reaches of the Heihe River. With improvements in water use level, and without exceeding the water use targets, water consumption still increased. This affected the ecological water of the lower reaches of the Heihe River. We constructed a water resource allocation model to satisfy the “97 Water Diversion Plan” and solve the shortage of ecological water of the lower reaches.Without considering the total water consumption index, even though the reasonable allocation of water resources, water intake, and consumption in the middle reaches of the Heihe River exceeded the water intake and consumption indicators. Considering water consumption in the middle reaches, and with the allocation of water resources, the water intake and consumption could be controlled within the indicators.2 and implement water-saving measures, especially the control of farmland area is of great significance for the Heihe river resources.After the rational allocation of water resources, it is necessary to control the farmland area to 1200 Km


In 2018, the most recent year for which data are available, dog bites ranked as the 13th leading cause of nonfatal emergency department visits in the United States. As dog ownership spirals upwards in the United States, it is important to continue to monitor the epidemiology of dog bite injuries. This study provides contemporary data on the incidence of dog bites injuries in the United States and in New York and profiles individuals who have been treated for dog bites in emergency departments. The study also examines the demographic correlates of the rate of injuries at the neighborhood level in New York City and maps the rate in each neighborhood.At the national level, the study examines longitudinal data on dog bite injuries from 2005 to 2018 gathered by the Centers for Disease Control and Prevention. For New York, the study analyzes data for 2005–2018 collected by the New York State Department of Health. A negative binomial regression analysis was performed on the state data to measure the simultaneous effects of demographic variables on the incidence of dog-related injuries. A thematically shaded map of the rate of dog bite injuries in New York City’s neighborhoods was created to identify neighborhoods with higher-than-average concentration of injuries.In both the United States and New York the rate of dog-bite injuries increased from 2005 to 2011 and then underwent a significant decline. Injuries due to dog bites, however, still remain a sizable public health problem. Injuries are more prevalent among school-age children, inhabitants of less-densely populated areas, and residents of poorer neighborhoods. In New York City, poorer neighborhoods are also associated with fewer dogs being spayed or neutered.To reduce the rate of dog bite injuries, prevention programs – particularly those which center on teaching the dangers of canine interactions with humans – should be targeted at children. Dog bite injuries tend to be clustered in identifiable neighborhoods. Dog bite prevention programs and stricter enforcement of dog laws can target these neighborhoods. While the appellation attached to dogs is “man’s best friend,” dog bite injuries are a common occurrence. Data covering the years 2001–2003 revealed that approximately 4.5 million individuals in the United States were bitten by dogs each year  to provide contemporary data on the incidence of dog bites in the United States and in New York, (2) to furnish a detailed profile of individuals who have been treated for dog bites in New York to describe individuals who are most at risk, (3) to present the socio-demographic correlates of the rate of dog bite injuries at the neighborhood level in New York City which can help to identify the characteristics of neighborhoods with a higher incidence of dog bite injuries, (4) to map the incidence of dog bite injuries at the local level which can be used to target neighborhoods which have a disproportionately large number of dog bite injuries, and (5) to provide data on the changing composition of the dog-owning population to help explain the epidemiological findings.The analyses conducted in this study rest principally on data collected from ED visits in the United States and New York. The national-level data are derived from the Web-based Injury Statistics Query and Reporting System (WISQARS) , which is under the auspices of the New York State Department of Health . SPARCS This study also draws upon data gathered by New York City’s Department of Health and Mental Hygiene (DOHMH) . The datInjury Code. For both the national and state data sets, identification of patients who were treated for a dog bite was based on two separate injury codes. The International Classification of Diseases, Ninth Revision (ICD-9) External Cause of Injury code (E-code) E906.0 – Dog Bite – was utilized for the years prior to 2015. Both the ICD-9 E-code E906.0 and the ICD-10CM E-code W54.0XXA – Bitten by dog  – were utilized for the year 2015. Just the ICD-10CM E-code W54.0XXA was used for the years 2016–2018.Sociodemographic Characteristics. Both the WISQARS and SPARCS data sets furnished information about the age and gender of patients. The SPARCS data sets also included two separate variables about the race and ethnicity of patients. A typology was created from these two variables with the following five values: “white, non-Hispanic,” “black, non-Hispanic,” “Asian, non-Hispanic,” “other, non-Hispanic,” and “Hispanic.” Importantly, the SPARCS database included the patient’s county of residence and his/her 5-digit zip code.To measure the combined effects of year, background characteristics , and geographic location on the incidence of dog bites, we conducted a negative binomial regression analysis using the patient records from New York. A negative binomial regression analysis was performed instead of a Poisson regression due to overdispersion of the data.The population-based counts of both the number of outpatients and inpatients who were bitten by a dog served as the dependent variable in this analysis. The predictor variables comprised the year, geographic location, and the demographic characteristics of the patients. Year was measured as an interval-level variable ranging in values from 1 (corresponding to the year 2005) to 14 (corresponding to the year 2018). To capture possible curvilinear effects of year on the incidence of dog bites, a multiplicative term created by squaring the year variable was also incorporated into the analysis. Geographic location was a dichotomous variable with a value of 1 indicating New York City and a value of 0 indicating New York State omitting New York City. Gender was also a dichotomous variable with a value of 1 indicating male and a value of 0 indicating female. The age variable consisted of 7 categories: under 5, 5 to 9, 10 to 14, 15 to 19, 20 to 44, 45 to 64, and 65 and older. The racial-ethnic background of patients was made up of 5 groups as mentioned above: non-Hispanic white, non-Hispanic black, non-Hispanic Asian, non-Hispanic other, and Hispanic.Since it can be assumed that the risk of being bitten by a dog varies by population sizes, an offset variable was introduced into the analysis. The offset variable was created in two steps. First, population counts were tallied for each combination of year, geographic location, gender, age group, and racial-ethnic category. So, for example, one count might comprise non-Hispanic Asian females between the ages of 10 to 14 living in New York City in 2014. Altogether, this step yielded 1960 different counts. Next, natural log transformations were carried out on each of these counts.N = 62), a three-step process was undertaken. First, the number of both outpatients and inpatients were combined for each county for the year 2018 (the most recent year for which data are available). Second, these figures were divided by the population of each county to obtain an injury rate. Finally, the rates were correlated with an array of socio-demographic variables at the county derived from the American Community Survey 2014–2018 (5-Year Estimates) (U.S. Census Bureau To measure the demographic correlates of the rate of dog bite injuries at the county level in New York State (N = 179). Next these figures were aggregated up to the United Health Fund (UHF) level (N = 42) and divided by the population of each UHF district to obtain an injury rate. These rates were then correlated with the same set of socio-demographic variables described above calculated for each UHF district.A similar procedure was conducted to examine the socio-demographic correlates associated with dog bite injuries at the neighborhood level in New York City. For this analysis, the number of outpatients and inpatients were combined for each 5 digit zip code in New York City  district in which the patient resided was created. A Global Moran’s I was computed to assess whether the spatial distribution of the residences of the patients was geographically clustered or dispersed.The rates of dog bite-related injuries by age and sex for the period 2005–2018 are presented in Table P = .046 for the curvilinear relationship).Table Noteworthy is that the relationship between the incidence of dog bite injuries varies by age group over time. Figure The results of the negative binomial regression analysis examining the simultaneous effects of time and key demographic variables on the incidence of dog bites treated in an ED are displayed in Table The effects of year and the multiplicative term of year squared are both significant. A graphic display of these terms indicates that from 2005 to 2012 the frequency of dog bite injuries increased and then from 2013 to 2018 decreased, controlling for the other variables in the analysis. The same general pattern emerges if the injury rate of just individuals who were admitted as inpatients serves as the dependent variable. Both trends mirror the results observed at the national level.Inspection of Table As expected, age is a major determinant of the risk of injury from a dog bite. Compared with patients who are 65 and older (the reference category), patients aged 5 to 9 are 2.7 times more likely to incur a dog bite injury and patients aged 10 to 14 are 2.3 more likely to sustain an injury. Individuals in the other age categories  are also significantly more likely to be injured by a dog bite than those in the reference category.Finally, the data reveal that non-Hispanic Asians are considerably less likely to be treated in an ED for a dog bite than Hispanics (the reference category). The odds ratios for the other racial-ethnic groups are not statistically significant.Table Table The table shows the socio-demographic correlates of both the percent of dogs which were spayed/neutered and the percent of dogs which were pit bulls in the 42 UHF districts. Of the breeds identified in the data set (84.6%), pit bulls were the most numerous (33.6%), followed in order by Shih Tzu (5.3%), Chihuahua (5.2%), German Shepherd (4.1%), and Yorkshire Terrier (3.1%). This finding is consistent with previous research showing that pit bulls are responsible for more bites than any other dog breed (McReynolds p < .001), indicating a pattern of spatial clustering .Coinciding with expectations, the rates of dog-bite injuries are not uniformly distributed across the UHF districts. A choropleth map of the rates shows that the Hunts Point-Mott Haven neighborhood in the Bronx, East Harlem neighborhood in Manhattan, the Sunset Park neighborhood in Brooklyn, and the Port Richard and Stapleton-St. George neighborhoods in Staten Island have notably higher rates than other UHF districts see Fig. . The MorSurveys of dog owners during the last decade reveal significant changes in their demographic characteristics Table . The datThis study has found that the rate of dog bite injuries has been declining in recent years. This decline is in evidence at both the national and state levels of analysis. The decline has been most visible among those under the age of 19 – particularly children under the age of 9.inpatients treated for dog bites in New York -- does not lend support to this explanation.One explanation for this downward trend might be that it is simply an artifact of the methodology employed in this study. Most of the findings contained in this study are based on dog bite injuries treated in emergency room departments. It may be the case, though, that in recent years individuals bitten by dogs have increasingly sought treatment in other venues such as private physicians’ offices or urgent care centers. While this may be a factor associated with the downward trend in dog bite injuries, another finding uncovered in this study -- a decline in the number of A second explanation for the recent decline in dog bite injuries centers on the change in the profile of dog owners and the characteristics of the dogs themselves. Survey data presented in this study indicates that there has been a decline in the presence of young children in dog-owning households over the past decade. Since young children are the most likely age group to be bitten by dogs and the overwhelming majority of injuries in the United States occur in the home, the reduction in the number of younger-aged children living at home would help to explain the drop off in dog-related injuries , it will be important to monitor the frequency of dog-bite related injuries to see if this positive trend persists.Though dog bite injuries have declined in recent years, the extent of these injuries still constitutes a major health problem. Young children especially are vulnerable to being bitten by a dog. Prevention programs – particularly those which center on teaching the dangers of canine interactions with humans – should be targeted at this age group. This study also has noted that residents of poorer neighborhoods in urban areas are more susceptible to being injured than residents of more affluent neighborhoods. Future research needs to be conducted to increase our understanding of why there is a negative association between a neighborhood’s socioeconomic status and injury rates from dog bites. Hopefully this greater understanding will lead to a reduction in the disparity of these rates.


Dog bites are a significant health concern in the pediatric population. Few studies published to date have stratified the injuries caused by dog bites based on surgical severity to elucidate the contributing risk factors.We used an electronic hospital database to identify all patients ≤17 years of age treated for dog bites from 2013–2018. Data related to patient demographics, injury type, intervention, dog breed, and payer source were collected. We extracted socioeconomic data from the American Community Survey. Data related to dog breed was obtained from public records on dog licenses. We calculated descriptive statistics as well as relative risk of dog bite by breed.Of 1,252 injuries identified in 967 pediatric patients, 17.1% required consultation with a surgical specialist for repair. Bites affecting the head/neck region were most common (61.7%) and most likely to require operating room intervention (P = 0.002). The relative risk of a patient being bitten in a low-income area was 2.24, compared with 0.46 in a high-income area. Among cases where the breed of dog responsible for the bite was known, the dog breed most commonly associated with severe bites was the pit bull .The majority of injuries did not require repair and were sufficiently handled by an emergency physician. Repair by a surgical specialist was required <20% of the time, usually for bites affecting the head/neck region. Disparities in the frequency and characteristics of dog bites across socioeconomic levels and dog breeds suggest that public education efforts may decrease the incidence of pediatric dog bites. With over 4.5 million dog bite injuries reported each year in the United States, dog bites continue to be a significant public health concern.Many studies have identified trends in pediatric dog-bite injuries and interventions,Orange County, CA, where our institution resides, is the sixth largest county by population in the US, with many low-income and affluent communities in close proximity to one another. Our academic pediatric trauma center is the only pediatric hospital serving this diverse population of over three million. This makes our institution an ideal setting for an investigation of the etiology and treatment of pediatric dog-bite injuries. In this study, we describe our five-year experience and aim to characterize the settings in which a surgeon is required for the treatment of pediatric dog-bite injury. We also collected information from public records and healthcare databases to evaluate external risk factors that may increase risk for dog bites, such as socioeconomic status and breed of dog. Delineating the injury patterns in this high-risk population may both streamline care and guide future prevention efforts.International Classification of Diseases, Ninth Revision and Tenth Revision, Clinical Modification [ICD-9] E906.0 and ICD-10-CM W54.0). Exclusion criteria were bite wounds that had already received a procedure at another institution and transferred to our institution for delayed reconstruction, patients who presented > 24 hours after the injury, and any subsequent visits related to the same initial injury. Two unblinded abstractors were uniformly trained to use a pilot-tested, standardized, online data abstraction form with coding rules. Data abstraction was routinely monitored to ensure systematic data collection including refresher training and review of coding rules. We did not exclude records with missing data; missing values for categorical variables were documented as unknown.This was a retrospective cross-sectional study of all children aged 0 to 17 years treated for dog-bite injury during the period from 2013–2018 at our institution. The inclusion criteria were all pediatric patients presenting to the pediatric emergency department (ED) during the study period and identified in the electronic health record (EHR) as having an acute dog bite injury ; clinical variables ; and information on the dog . Wound depth was categorized as superficial , deep (full-thickness skin wounds without trauma to underlying tissue), and complex . Information on the dog breed, patient’s relationship to the dog, and location where the injury occurred were first abstracted from the provider notes in the EHR and then cross-referenced with information included in the Animal Bite Human Reporting Form sent to the county health department.Socioeconomic data such as median income was extracted from the American Community Survey (ACS).We calculated the relative risk of being bitten by a specific breed of dog, the relative risk of being bitten in a lower-income area, and the relative risk of sustaining a severe, rather than moderate or mild, dog-bite injury. The relative risk of being bitten by a specific breed of dog was calculated using dog population data collected by the animal shelters of our county, which collect data for all licensed dogs in the county. We ranked dog breeds according to relative risk of bite, compared to the risk of being bitten by any member of the dog population in the county. The relative risk of dog bite was mapped onto each breed in the phylogenetic tree. If no bite data was observed for a specific dog breed, the relative risk was set to one.P-values using the chi-square test for cell size >100 and Fisher’s exact test for cell size <100. In this study, the Fisher’s and chi-square P-values measured distribution of a given variable after stratification by another categorical variable, in comparison to the distribution of all other categories summed. For continuous measures such as bite diameter, a Wilcoxon rank-sum test was used to measure the difference in distribution among continuous measures. We used the R programming language to conduct these analyses. Income and dog-bite frequency were mapped using the Choroplethr package .20We calculated From 2013 to 2018, 967 pediatric patients at our children’s hospital were identified as victims of a dog bite. The mean and median ages of pediatric patients who sustained dog-bite injuries were six years and five years, respectively. The mode of the age variable in this study was three years. After stratification into age categories of 1–5 years, 6–10 years, and >10 years of age, the 1–5 age group was identified as the group of patients that made up the greatest proportion of those bitten (53.4%). The risk for dog-bite injury was inversely correlated with age, with a Pearson correlation coefficient of −0.76 . RegardlOur analysis of the sociodemographic data collected revealed that the racial distribution of pediatric patients who sustained dog-bite injuries was similar to the racial make-up of the community, with 64.6% of patients in the study identifying as White/Caucasian. It should be noted that patient families identifying as Latino were disproportionately represented in this survey. The 2017 ACS reported that 34.2% of the residents in the county identified as Latino, while 55.2% of the patient population in this study identified as Latino (with only 1.16% of study participants refusing to answer this question). It should also be noted that a large proportion of the patient families included in this study were covered by Medicare (22.4%) or Medicaid (29.5%); 41.4% were covered by private insurance, and the remaining 6.6% were self-pay .Most injuries did not require specialist or OR services; 71.8% of bites did not require wound repair, while 17.1% of patients required specialist consultation for wound repair in the ED or the OR. The distribution of bite severity mirrored this pattern, with 70.5% of bites classified as “superficial” ; 21.1% of bites classified as “deep” (full thickness without trauma to underlying structures); and 8.5% of bites classified as “complex” . Analysis of the data to determine which anatomical area was most commonly affected revealed that 61.7% of bites were inflicted on the head or neck, 20.6% on the hands or arms, and 13.0% on the feet or legs .P = <.0001). When stratifying injuries by different levels of repair  there were statistically significant differences in the proportion of observed injuries across different anatomic sites. The largest difference in proportion was observed in head and neck injuries, which contributed to 41.2% of cases not requiring repair, and 86.2%, 69.6%, and 88.5% to cases requiring repair by EPs, surgical specialists in the ED, and repair performed by specialists in the OR, respectively. This association persisted even when “no repair” patients were removed from the dataset (P = 0.002). This data is presented in When we investigated the relationship between anatomical site of injury  and type of intervention , we found that head and neck injuries were significantly more likely to require repair  were treated in the OR, 9.8% of complex wounds were treated by a specialist in the ED, and 1.9% of wounds were repaired by a general EP. This observed pattern contrasted with that observed for deep wounds (full thickness without trauma to underlying structures), for which the majority (79.4%) were treated by an EP. The majority of superficial wounds (76.3%) required no repair.P <.0001). These differences are illustrated in We used ZIP codes to map city-level reports of median income from the ACS. The ZIP code was used to approximate the economic status of a patient family to evaluate the association between economic status and the frequency of bites. According to the 2017 ACS, the median income in the county is $89,000. Analysis of the study data showed that 67.9% of patients lived in areas with median annual income greater than $42,000, and 32.1% of patients lived in areas with median income of $42,000 or less . Using pP <.0001)  . Medicai <.0001) .In 61.4% of cases included in the study, the breed of the dog that had bitten a particular patient was unknown. Among the cases where the breed of the dog responsible for the injury was reported, representation was as follows: Chihuahua mix, 7%; pit bull mix, 7.6%; German shepherd mix, 3.3%; other or mixed breed, 20.4%. No significant relationship was found between dog breed and anatomical site of injury, or between dog breed and median income in the area where the dog bite occurred. There was, however, a significant association between breed and the requirement for surgical treatment by a specialist . The likP <.0001) and of bite diameter (P <.0001). Pit bull bites were found to be significantly larger, deeper, and/or more complex than the average dog bites included in this study (Dog breed was a significant predictor of bite severity (is study . Patientis study .We constructed a phylogenetic tree of dog breeds to identify clades with an increased relative risk of bite, compared to the general dog population . This viDog bite injuries continue to be prevalent in the pediatric population, especially among young children. Similar to previous studies,A previous study by our group of dog-bite injuries in the county showed that 60% of dog bites in adult patients received no intervention.Our analysis goes further to reveal how socioeconomic factors influence the management of dog-bite injury. A median annual income below $42,000 conferred a 2.24 relative risk for pediatric dog-bite injury, compared to a 0.46 relative risk in regions with high median annual income. This trend is consistent with the findings of a study by Ruiz-Casares et al, which demonstrated that children in low-income families are the most vulnerable to unintentional injury.In our analysis, insurance type was used as an index for socioeconomic status. Our study shows that children in families with Medicaid or self-pay status were more likely to experience a dog-bite injury, but less likely to have their injuries repaired by specialists in the OR. It is unclear whether the difference in service utilization between private insurance payers vs Medicaid or self-payers reflects systemic obstacles or, rather, a parental preference for ED intervention based on financial concerns. While Essig et al showed that the surgical management of pediatric facial dog-bite injuries by specialists in either the ED or OR had no significant effect on the risk for surgical-site infection or reoperation,Many studies have attempted to elucidate the role of dog breed in bite injuries. In the literature the dog breeds most commonly associated with pediatric dog-bite injuries include the pit bull, Rottweiler, German shepherd, terrier, and mixed.35It should be noted that aggressive canine behavior is multifactorial, with genetic as well as human interference-related contributing factors.There were several limitations to our study. The socioeconomic data that we extracted from the ACS was not a true measure of family income, as these pooled data represent neighborhood-level rather than individualized patient information. The data presented in this analysis is specific to a high-volume, academic healthcare institution that serves a large and diverse community. The findings may, therefore, not be generalizable to all institutions and populations. We did not stratify the data used for analysis based on surgical subspecialty or type of dog-bite injury. Not all bites could be attributed to a specific breed or mixed breed of dog. As a result, the relative risk of bite in some breeds may have been under-reported. Additional bias may occur in breeds with small reported populations in the community; these breeds may have instability in the estimates of relative risk of bite due to small samples that are not representative of a given dog breed.Additional studies will be designed to elucidate whether plastic surgeons, otolaryngologists, or general surgeons are more frequently involved with certain types of pediatric dog-bite injuries. Such an investigation would help to streamline workflow and to increase the use of a multidisciplinary approach in pediatric EDs. With interest, we continue to monitor and study how trends in the etiology and management of pediatric dog-bite injuries may change as social distancing alters the way that children interact with their environments.Our findings support previous reports that pediatric dog-bite injuries occur more frequently in children aged 1–5 years. Most dog-bite injuries in this study were caused by encounters with large dogs, and bites from pit bulls were associated with significantly more severe injury. The anatomical site affected most commonly was the head and neck region. The dog-bite injuries that most frequently require subspecialist surgical intervention are those affecting the head and neck region and those involving extensive soft tissue damage. Low socioeconomic status may increase the risk of dog-bite injury. Pediatric patients with private health insurance were more likely than others to receive surgical intervention for dog-bite injuries.


Wearable robotic devices are designed to assist, enhance or restore human muscle performance. Understanding how a wearable robotic device changes human biomechanics through complex interaction is important to guide its proper design, parametric optimization and functional success. The present work develops a human-machine-interaction simulation platform for closed loop dynamic analysis with feedback control and to study the effect of soft-robotic wearables on human physiology. The proposed simulation platform incorporates Computed Muscle Control (CMC) algorithm and is implemented using the MATLAB -OpenSim interface. The framework is generic and will allow incorporation of any advanced control strategy for the wearable devices. As a demonstration, a Gravity Compensation (GC) controller has been implemented on the wearable device and the resulting decrease in the joint moments, muscle activations and metabolic costs during a simple repetitive load lifting task with two different speeds is investigated. Exoskeletons and exosuits are the terms used interchangeably to refer to a class of wearable assistive devices which work in tandem with the human body to provide assistance. The assistance provided by these human joint force amplifiers can augment, reinforce or even restore human performance. Muscle strength augmentation for workers or soldiers , assistaIn recent literature, Zhang et al. employed an inverse dynamics and optimization-based technique using the Anybody Inc. software  to obserIn this paper, the position and velocity data of the joint from the forward dynamics are fed back, so that the inverse dynamics-based motor command generator can compensate for the error terms. The present simulation framework implements the open-source platform OpenSim , that is1) inverse dynamics, 2) musculoskeletal model, 3) static optimization, and 4) activation dynamics. The muscle excitations are combined with the exosuit’s actuator signal that is generated by the controller implemented in MATLAB. The combined signal  is then sent as an input to the forward dynamics block in OpenSim which generates the human motion and computes the metabolic cost. The joint angles from the motion are fed back to the controller to close the loop. This closed-loop system involves continuous exchange of information between MATLAB and OpenSim and is possible due to the OpenSim application programming interface (API) commands. The functional blocks within these modules are the following:The present simulation framework creates a digital model of the human musculoskeletal system integrated with an external assistive device. The overall system architecture is divided into the brain computer interface (BCI), the musculoskeletal biomechanics, actuator and controller modules. This overall Computed Muscle Control (CMC) architecture  is presented in The purpose of MCG is to use the error between the desired and actual trajectories to calculate the required excitations in the muscles such that the trajectory error is also minimized. The MCG also uses the position and velocity feedback from the simulated output trajectory. Inverse dynamics simulator calculates the joint moments from the input kinematics, followed by the static optimization to solve for the muscle redundancy problem and obtain the muscle activations. Finally, the equations involved in activation dynamics are used to calculate the corresponding muscle excitation values. The inverse dynamics and musculoskeletal models within the MCG use the OpenSim API commands while the static optimization and activation dynamics are carried out entirely using MATLAB algorithm. The MCG functionality is explained using the following:Inverse Dynamics: The desired joint trajectory as well as the error between the simulated and desired trajectories is used to calculate the joint moments  such that the joint moment,p and kv values from p and kv respectively) provided the best error compensation, especially for the simulations where the exosuit provided assistance. Musculoskeletal Model: The musculoskeletal model comprises a forward simulation of the arm26 musculoskeletal model, which is well documented in the open literature , involved in the simulation. The objective function J is chosen as a square of the activations, as previous literature suggests that the quadratic criterion provides a reasonable to good estimate of the muscle activations   in MATLAB, where ai is the activation corresponding to the i th muscle.signals) . The optActivation Dynamics: The forward dynamics simulation in the system framework requires the muscle control signals (excitations) as input. The individual muscle excitations are calculated from their activation values using the activation dynamics equation  is the time constant whose magnitude depends upon whether the muscle activation is increasing or decreasing.equation  shown bea and u are the muscle activations and excitations, respectively. The parameters The parameters The forward dynamics  uses OpenSim API commands to determine the joint reaction forces and exosuit-human interaction forces, along with the error between the desired and output joint trajectories, thus refining the accuracy of estimated muscle excitations.assisted condition (actuator ON) the exosuit provides assistance to the elbow joint, while in the unassisted condition (actuator OFF) the exosuit doesn’t provide any assistance to the elbow joint. The GC controller compensates for the gravity dependent component of the moment acting at the elbow joint, given by:A gravity compensation (GC) based control strategy is implemented in the assistive device. In the present study, the assistive moment at the joint is directly proportional to the angle between the forearm and the direction of gravitational force at the elbow joint. In the h et al. . In a phThis module is used to calculate the forces in the extensor and flexor cables such that the desired moment can be transmitted to the elbow joint. In the simulation, these forces are directly generated using the path actuators (OpenSim API). The actuator module is based on the current value of moment arms of flexor-extensor cables, in addition to the magnitude and direction of the desired moment (to be transmitted to the elbow joint), and computes the value of the tension required in the cables. These forces in the simulation are directly applied to the path actuators in the forward dynamics block.For Actuator moment >0:For Actuator moment <0:The functionality of BCI module in the simulation environment is to test and validate the different methods of desired motion estimation in sync with the other modules of exosuit like controller and actuator by taking data from a pre-recorded EEG dataset. In the current simulation study though, the control system uses gravity compensation control which only requires the joint angle data as input, and the BCI module comprises a ready-made reference trajectory as input to the simulation.large scale interior point algorithm. The algorithms for controller and actuator modules used MATLAB for calculations. A sampling rate of 121 Hz was used for the joint angle data from 0 to 1 s. The simulation presents robust results with variations of step-sizes between 0.004s < We developed a MATLAB based platform in the present study, to access the OpenSim functionalities using the OpenSim API, and perform the forward and inverse dynamics of the musculoskeletal system. MATLAB provided the requisite functionalities for the calculations involving controller, actuator and muscle activations. The simulation platform is versatile and provides integration with multiple software, and subsequently other utilities such as brain machine interface and finite element analysis for study of force interaction can be interfaced into the simulation. The simulation framework involved the OpenSim Inverse Dynamics Solver as well as the Forward Integrator (using a Runge-Kutta-Merson integrator) by default. The static optimization carried out in MATLAB uses a 1) wearable fabric and straps, 2) actuator unit, 3) controller unit, 4) cable routing, and 5) battery pack. As shown in For its digital counterpart, we used a right upper body model “arm26” as the musculoskeletal model, representing the digital human, as shown in path actuators, which apply equal and opposite force on the segments to which they are attached. These path actuators are attached agonistically and antagonistically, in between the upper-arm and forearm straps, as shown in prescribed controller, control signals were sent to the path actuators to create the desired level of tension. All the geometrical input parameters of the model are given in As a part of the exosuit, the CAD designs of exosuit straps were imported and attached to their appropriate locations over the digital human model. The force transmission system from the motor to the anchorage points are modeled directly as force generating elements between the two straps in the digital model. The force generating elements are the 1) a fast and 2) slow flexion motion, with and without assistance from the exosuit. In the current simulation study, we constrained our analysis to elbow joint only, and locked the shoulder degree of motion (at a 0° orientation). Following are the parameters varied in different simulation iterations:The simulation was repeated for two conditions of the elbow flexion: External load in hand: An external mass has been attached at a distance of 29 cm from the elbow joint, as shown in Reference trajectory: The elbow joint of the simulation model undergoes motion from an initial angular position of 0° to a final position of 90°, following a minimum jerk trajectory path for a specified time duration. As shown in Metrics of evaluation: We calculated the joint moment, activation and metabolic cost parameters from the simulation platform and observed the changes in magnitudes for different parametric variations within the simulation, i.e., varying speeds, external loads and actuator assistance (ON/OFF). The metabolic cost function for the activity has been calculated as the sum of rate of heat liberated from the body and the rate at which work is done  and maintain the desired trajectory. In this situation, the antagonistic muscles (triceps group) present relatively higher activations  to distribute joint reaction forces, the soft exosuit envisioned in the present work does not have any such joint. Interestingly, the simulation results illustrate that there is a reduction in the joint reaction force when external assistance is provided . As showThe interaction force between the exosuit strap and the human limb is an important parameter in the design of the exosuit. The dimensions of straps and the padding material to be used in the strap, can be calculated based on the force applied by the strap to the body, in order to bring the contact pressure within the required tolerance . The sim1) incorporation of only six muscles about the elbow joint and omitting the muscles at the shoulder and the forearm, 2) the metabolic cost reduction values are those calculated considering these six muscles only. We have used the arm26 musculoskeletal model in the present study, whereas a more realistic simulation should consider a full-body model with all muscle definitions. However, the MATLAB-OpenSim framework provides easy access and utilization of multiple musculoskeletal models. Further, the force interaction between the actuator straps and the human twin may be carried out with the addition of a FEA module. The simulation framework described in this paper utilizes the best features of OpenSim and MATLAB to develop a system that acts as a digital model of the human physiology. Overall, the reduction in effort of the human muscle may be estimated using the framework, thereby indicating the efficiency of an exosuit implementing the proposed control strategy.The metabolic cost plots presented in


Changes in speech can be detected objectively before and during migraine attacks. The goal of this study was to interrogate whether speech changes can be detected in subjects with post-traumatic headache (PTH) attributed to mild traumatic brain injury (mTBI) and whether there are within-subject changes in speech during headaches compared to the headache-free state.Using a series of speech elicitation tasks uploaded via a mobile application, PTH subjects and healthy controls (HC) provided speech samples once every 3 days, over a period of 12 weeks. The following speech parameters were assessed: vowel space area, vowel articulation precision, consonant articulation precision, average pitch, pitch variance, speaking rate and pause rate. Speech samples of subjects with PTH were compared to HC. To assess speech changes associated with PTH, speech samples of subjects during headache were compared to speech samples when subjects were headache-free. All analyses were conducted using a mixed-effect model design.Longitudinal speech samples were collected from nineteen subjects with PTH  who were an average of 14 days (SD = 32.2) from their mTBI at the time of enrollment and thirty-one HC . Regardless of headache presence or absence, PTH subjects had longer pause rates and reductions in vowel and consonant articulation precision relative to HC. On days when speech was collected during a headache, there were longer pause rates, slower sentence speaking rates and less precise consonant articulation compared to the speech production of HC. During headache, PTH subjects had slower speaking rates yet more precise vowel articulation compared to when they were headache-free.Compared to HC, subjects with acute PTH demonstrate altered speech as measured by objective features of speech production. For individuals with PTH, speech production may have been more effortful resulting in slower speaking rates and more precise vowel articulation during headache vs. when they were headache-free, suggesting that speech alterations were related to PTH and not solely due to the underlying mTBI. Individuals with migraine report changes in speech during migraine attacks and several studies have documented speech difficulty during the aura phase of the attack as well as prior to and during the attack –3. AlthoPost-traumatic headache (PTH) due to mild traumatic brain injury (mTBI) commonly has symptoms that are similar to those of migraine –7. In faThe overarching goal of this study was to determine whether objective features measured from speech samples obtained from individuals with acute PTH could provide a surrogate measure of headache burden, which could have utility in the future for tracking headache persistence and recovery.Study questionnaires: Subjects with PTH completed a detailed headache symptom questionnaire. All subjects completed the Ohio State University TBI Identification Method, a standardized questionnaire assessing the lifetime history of TBI for an individual (available at www.brainline.org) [This study received approval from the Mayo Clinic IRB in 2019. All subjects completed written informed consent. Subjects had to be native English-speakers aged 18–65 years. All subjects were required to have a mobile device with capability for downloading an application used to collect speech and had to be willing and able to provide a speech sample once every 3 days over a period of 3 months. Subjects with PTH were eligible for enrollment starting on the day of mTBI and until 59 days post-mTBI. Subjects had to meet criteria for acute PTH attributable to mTBI in accordance with the ICHD-3 criteria [ine.org) , 11, theine.org) , the Becine.org)  for asseine.org) . The SymFor the RAVLT, a list of 15 words is read out loud by the examiner and the examinee is asked immediately afterward to recall as many words as they can. The list is read five times and each time, the examinee is asked to recall as many words from the list as they can, in any order. Next, a distractor list is read out loud, and the participant is asked to recall only the words from the distractor list. Afterward, the participant is then asked to recall only those words from the first list, which was read 5 times. After a delay of about 20 min , the examinee is asked again to recall as many words as possible from the first list. Only the delayed recall z-scores, which are a measure of episodic memory performance were included in this study.At the first study visit, subjects were taught to download the speech application to their mobile devices. The study coordinator modeled the completion of speech elicitation tasks and the correct procedure for using the speech app. This included, selecting a time and place that is comfortable, without distractions and with minimal background noise. All subjects were asked to submit a speech sample every 3 days, beginning on the day of the first study visit and continuing over a period of the subsequent 12 weeks. As it was assumed that subjects with PTH would show the most significant speech changes during the acute phase of mTBI, therefore only the speech samples submitted during the first 30 days were used for comparison between subjects with PTH and healthy controls. When comparing subjects with PTH during headache to the headache-free phase, speech samples submitted over the first 90 days were used to increase the number of available samples.The speech application was specifically developed for the objective evaluation of the following measures: sentence speaking rate, average pitch, pitch variance, vowel space area vowel and consonant articulation precision, and the spontaneous pause rate. As part of the speech app, subjects were asked to read out loud five sentences (sentence reading task) and to use spontaneous speech to describe activities of the previous day (spontaneous speaking task). The entire speech elicitation task took approximately 3 min to complete. Prior to starting the speech task, all subjects indicated whether they currently had a headache or whether they were headache free. If a current headache was reported then individuals were prompted to rate their headache intensity on a scale ranging from 1 (mild headache) to 10 (most severe headache imaginable). Table The methodology for extracting and normalizing speech features is shown in Fig. the supermarket chain shut down because of poor management” for 4.01 s. As there are a total of 15 syllables in the sentence: “the su-per-mar-ket chain shut down be-cause of poor man-age-ment”, the speaking rate was calculated as 15/4.01 = 3.74 syllables/second.First, the total speaking time in a sentence audio sample was detected by using a Voice Activity Detection (VAD) algorithm  that idehttps://github.com/google/REAPER) pitch estimator, was used to extract the pitch contour from the raw audio waveform for calculating the average pitch and pitch variance. The average pitch was estimated by calculating the sample mean from the pitch contour; similarly, the pitch variance was estimated by calculating the sample variance from the pitch contour.The REAPER (The sentence reading audio files and corresponding sentence texts were estimated using the goodness of pronunciation (GOP) score evaluation algorithm  to generAll five sentence reading samples were concatenated into a continuous audio stream. The vowel space area was estimated using a extraction algorithm .The VAD algorithm was used to detect the timepoints during which the participant was speaking. The total speaking time was measured as the period from the speech start point to the speech stop point; the pause time was measured as the non-speech periods during spontaneous speech. The spontaneous pause rate was then calculated as the ratio of pause time over speaking time. For example, if a subject provided a spontaneous speech sample lasting 10.82 s seconds, and paused for 2.13 s during the task, then the spontaneous pause rate was calculated as 2.13/10.82 = 0.197. The pause rate was measured from the spontaneous speaking task. , feature normalization was used to control for these potential confounding variables. Speech features were normalized by subject age and sex using the Mozilla common voice English database, a large open-source corpus for speech data . This daTo normalize the features of a study participant, a nonparametric estimate of the cumulative distribution function (CDF) was computed from a subset of age/sex matched individuals in Mozilla. The features were converted to percentiles relative to this CDF and the normalized percentiles were then used as features.t-tests or Fisher-exact tests, as appropriate.Speech patterns of subjects with PTH and healthy controls were compared using a mixed-effects model with random (unique) intercepts for each participant and controlled for age and sex. The effect of healthy controls/PTH was tested on each speech measure. Age, sex, and group differences were treated as fixed effects. Differences on cohort demographics were assessed via two-sided mean difference in the metrics when subjects had a headache compared to when they were headache-free but it also tests the extent to which participants differed on their changes on speech measures .Speech patterns between subjects with PTH during headache were compared to speech patterns of subjects with PTH when they were headache-free using a mixed-effects model with random (unique) intercepts for each participant and random (unique) slope for headache status. Age, sex, and headache status were modeled using fixed effects. The use of random slopes tests not only whether there was a p-value may indicate mean differences in a measure when headache.Therefore, a significant is present versus absent or indicate that participants vary in terms of how their scores differ when they have a headache or are headache-free. Given the limited sample size, the models for some speech metrics did not converge. Non-convergence occurs when the model is too complex, the sample size is too small, or the model is not supported by the data, which results in the model being unable to reach a stable solution. Therefore, only the models that converged are reported.p = 0.32) or sex (p = 0.55); see Table Subject Demographics. Among those with PTH, 9 had mTBI that were due to motor vehicle accidents, 3 that were due to falls, 5 that were due to sports-related injuries, and 2 that were due to hitting their head in home-related accidents. Fifteen subjects with PTH had no prior mTBI, 1 subject had one prior mTBI, and 3 subjects had two prior mTBIs. Twelve subjects reported no loss of consciousness, and 7 had loss of consciousness. As part of completing the headache questionnaire, individuals reported medication use for treating headache. Nine individuals reported treating headache with NSAIDs, and ten patients did not treat headache with medication. There were significant differences between groups on the symptom assessment of the SCAT-5, with individuals with PTH reporting more severe symptoms  and they had significantly lower delayed recall z-scores compared to healthy controls . Symptoms of aura including difficulty with speech was only reported by one individual with PTH. Although there were significant group differences on raw scores of the BDI, the mean raw scores of both groups were in the ‘normal, non-depressed’ range. On average, subjects with PTH were seen two-weeks post-mTBI .Nineteen subjects with PTH  and 31 healthy controls  participated. There were no significant differences between groups for age .p = 0.008; normalized: p = 0.0015) and vowel precision  and longer pause rates (0.0098) relative to healthy controls. On days when PTH subjects had headache, subjects had significantly longer pause rates (p = 0.0043), slower sentence speaking rates  and less precise vowel  and consonant articulation  compared to healthy controls.Regardless of headache presence or absence, individuals with PTH had significantly reduced consonant precision , the mean values  for the two groups, and the difference between the two groups. A significant p-value indicates that the mean speech measure differed significantly between the control and PTH groups.Table The means and differences are based on the sample cohorts and not based on the mixed-effects model and are provided in order to give context to the p-value and to evaluate the directionality of the effect.p = 0.002; normalized: p < 0.0001) but more precise vowel articulation  compared to when they were headache-free. Table p-values for the differences between the headache states, the mean values for the two headache states, and the mean differences between the two headache states. The means and differences are based on the raw scores of the sample and not based on the mixed-effects model and are provided in order to give context to the p-values and to evaluate the directionality of the effect. Two sets of p-values are provided: p-values for the random-intercepts models and p-values for random-intercepts-random-slopes models. As previously explained, significant p-value in the random-intercepts model indicates that the mean speech measure differed significantly between the headache states, while a significant p-value in the random-intercepts-random-slopes indicates significant mean differences and between-participant variability in the differences between the two states.During headache, PTH subjects had significantly slower sentence speaking rates  in individuals with chronic back pain  and chanCompared to healthy controls, individuals with acute PTH demonstrated alterations in speech rate and rhythm . There is emerging data that individuals with PTH have difficulty understanding and performing cognitive-linguistics tasks, have difficulty understanding and processing rapid speech  and showAlthough not the focus of this study, participants with acute PTH did show significantly worse performance on a delayed word recall task  and more cognitive, behavioral and mood related symptoms (SCAT-5). Therefore, pause rates in individuals with PTH could be an indication of word-finding difficulties and may serve as a proxy for cognitive function in individuals with PTH. However, future studies are needed that specifically relate post-mTBI symptoms including cognitive function to changes in speech.In the current study, individuals with PTH during headache also showed alterations in the precision of articulation, specifically reduced vowel space area relative to healthy controls.Vowel space area is an acoustic metric commonly used for measuring articulatory function. Previous data have shown reduced vowel space area in patients with motor speech disorders  includinThe disruption in speech pattern in subjects with PTH might be a result of brain structural or functional changes in auditory and language pathways such as the posterior thalamic fasciculus and the superior and inferior longitudinal fasciculus. However, the neural underpinnings of speech changes will need to be further interrogated by associating brain structural and functional data with speech features in subjects with PTH.Individuals with PTH had more precise vowel articulation during headache compared to when they were headache-free. It may be hypothesized that during headache, when speech production requires more effort (hence resulting in slower speaking rates), individuals need to pay more attention to the production of speech and thus paradoxically produce more precise vowel articulation.It is possible that several factors may have influenced individuals’ speech patterns and introduced variance to our result including 1) mTBI mechanism , or 2) the number of previous mTBIs. Additionally, future studies are needed that assess speech features in subjects with mTBI without headache to subjects with PTH to specifically disentangle speech changes due to mTBI from speech changes due to headache. Additionally, future studies are needed to isolate speech alterations in individuals with PTH without history of PTSD from individuals who suffer from PTSD without history of mTBI. In the current study, the model for pause rate did not converge in the within-subject analysis which is likely due to the relatively small sample size in the study. We posit that a larger study, with more speech samples captured during periods of headache and no-headache per individual, would further show that reductions in pause rate are apparent in the within-subject analysis as well.Our results indicated changes in speech rate and rhythm and alterations in precision of articulation in individuals with PTH to due mTBI relative to healthy controls as well as a reduction in sentence speaking rate and alterations in vowel articulation precision when individuals with PTH had a headache compared to when they were headache-free -- potentially suggesting that PTH-related pain can modify healthy speech patterns. Currently, there is not a way to predict when and whether an individual with PTH will recover. The current results indicate that speech detection using a speech application downloaded on a mobile device might be a practical, objective, and early rapid screening tool for assessing headache-related burden and may have potential for predicting headache recovery in subjects with acute PTH. Additionally, the recognition of speech changes in individuals with acute PTH could be important for identifying those individuals at ‘high risk’ for developing persistent post-traumatic headache and may allow physicians to begin headache treatment early, when it might be most effective, in order to prevent headache chronification.Relative to healthy controls, individuals with acute PTH show aberrations in objective speech features.Speech changes are exacerbated in PTH subjects during headache.Speech pattern analysis might have utility for assessing headache burden and recovery.


Self-ATtentiOn based model to detect Regulatory element Interactions. Our approach combines convolutional layers with a self-attention mechanism that helps us capture a global view of the landscape of interactions between regulatory elements in a sequence. A comprehensive evaluation demonstrates the ability of SATORI to identify numerous statistically significant TF-TF interactions, many of which have been previously reported. Our method is able to detect higher numbers of experimentally verified TF-TF interactions than existing methods, and has the advantage of not requiring a computationally expensive post-processing step. Finally, SATORI can be used for detection of any type of feature interaction in models that use a similar attention mechanism, and is not limited to the detection of TF-TF interactions.Deep learning has demonstrated its predictive power in modeling complex biological phenomena such as gene expression. The value of these models hinges not only on their accuracy, but also on the ability to extract biologically relevant information from the trained models. While there has been much recent work on developing feature attribution methods that discover the most important features for a given sequence, inferring cooperativity between regulatory elements, which is the hallmark of phenomena such as gene expression, remains an open problem. We present SATORI, a  High-throughput sequencing techniques are producing an abundance of transcriptomic and epigenetic datasets that can be used to generate genome-wide maps of different aspects of regulatory activity. The complexity and magnitude of these data has made deep neural networks an appealing choice for a variety of modeling tasks, including transcription factor (TF) binding prediction , chromatDFIM) uses a network attribution method called DeepLIFT  in the input sequences (for caveats and architecture choices that affect this ability see (In this work we propose lity see ). The seWe test SATORI on several simulated and real datasets, including data on chromatin accessibility in 164 cell lines in all human promoters and genome-wide chromatin accessibility data across 36 samples in Arabidopsis. To compare our method to DFIM, we incorporated their Feature Interaction Scores (FIS)  into ourL is transformed into a matrix of size 4 × L where each position in the sequence is represented by a column in the matrix with a single non-zero element corresponding to the nucleotide in that position.We present a self-attention based deep neural network to capture interactions between regulatory features in genomic sequences. Figure X′ defined by:X is the input matrix, i is the position at which convolution is performed, j is the index of the filter, and ωj) and B is the number of input channels (four in the case of one-hot encoded input DNA sequences). After the convolution operation, we apply the Rectified Linear Unit activation function (ReLU), which is given by:The first component of our model is a one dimensional CNN layer where a set of filters are scanned against the input sequence/matrix. Formally, we can express the result of one dimensional convolution as a matrix Following the CNN layer we use an optional RNN layer  layer dimensions through addition and normalized by the mean and the standard deviation of the result. Empirically we find that this final step is not only computationally efficient but also leads to better model accuracy in comparison to flattening the attention layer output. For explicit details, please refer to the Supplementary Material. The final fully connected read-out layer outputs the model’s prediction: either binary or multi-label classification, depending on the experiment. For binary classification, we use the standard cross entropy loss function. For multi-label classification, we use the binary cross entropy with logits loss function:For model selection and optimization, we employ a random search algorithm to tune the network’s hyperparameters. For the convolutional layers we considered filter size, number of filters, and size of window over which pooling is performed. For the multi-head attention layer we tuned the dimensionality of the features generated, and the size of the output of the multi-head attention layer. Details are provided in  PyTorch  and all To interpret the deep learning model, we extract sequence motifs from the weight matrices (filters) of the first convolutional layer, similarly to the methodology used in . For bink heads to a single d × d matrix by taking the maximum at each position  so that sufficient positional information is maintained to accurately detect active filters. The max-pooling operation was useful for reducing the length of the resulting sequences to reduce the computational overhead. Finally, for each identified filter-filter interaction, we generate the attention profile across all testing examples. For a given pair, this profile consists of a vector of its attention values at positions where the corresponding filters were active. An interaction pair is discarded if its maximum attention value is below a certain threshold. By default we used the value 0.10; in the human promoter data we used 0.08 to increase sensitivity. In the Filter-filter interactions are then translated to TF interactions by picking the most significant TomTom hits in the appropriate TF database. Filters often learn redundant motifs and, as a consequence, a single TF-TF interaction can be captured by multiple distinct filter-filter interactions. Nevertheless, we observe that for a given TF-TF interaction, the corresponding interacting filters have very similar attention scores see . Note thP-values are adjusted for multiple hypothesis testing using Benjamini-Hochberg method . Then the non-parametric Mann-Whitney U Test is used to calculate their significance. All the g method .As mentioned above, to test the statistical significance of regulatory interactions, we need to compare them to a background. We use a biologically relevant background depending on the experiment:For binary classification problems, the negative test set is used as the background.For multi-label, multi-class or regression problems we generate a background set by shuffling the test set sequences while preserving their di-nucleotide frequencies. Next, in the shuffled sequences, we randomly embed motifs that are generated based on the CNN filters, interpreted as probability distributions, taking into account the number of times a filter is active above a given threshold in the original test sequences, using the same threshold used for motif extraction.To infer interactions between motifs using the FIS method, we closely follow the strategy described in . Given ap = 0.70 in our experiments. For the background examples, we pick all the negative test examples that score below 1 − p. In case of the multi-label classification problem, we pick our test examples based on the precision of the model’s prediction probabilities: for a test example to qualify, the precision value—calculated using the given labels and their model assigned probabilities—must be above a specified threshold (default precision threshold =0.50). We note that for FIS scoring of multi-label classification problems, we only use the attribution values of the true positive predictions. These values are summed and used in calculating the final FIS score.To quantify interactions using SATORI or the FIS-based approach, we use the high-confidence predictions of the model. For binary classification, we pick all positive examples that are assigned prediction confidence above a specified threshold. We use a threshold of We use four datasets to evaluate the ability of SATORI to capture interactions between regulatory elements. The datasets are summarized in Table et al.  across 164 immortalized cell lines and tissues. This experiment was based on the work of Kelley et al. . SATORI P-value of 5.98 × 10−4 using the hypergeometric test. We also found support for nine out of the 123 interactions in the HIPPIE database of human protein-protein interactions . Among those 93 TFs, our model identified 123 unique pairs of motifs that interact. The 15 most frequent interactions are shown in Figure ractions , which hIn this work we only analyzed interactions between motifs of known TFs. Not all filters can be mapped to characterized regulatory proteins. In this dataset, TomTom returned no significant matches for 80 out of the 200 CNN filters with motif information content >3.0. The interactions of these filters require further investigation to discover the regulatory molecules associated with them.As mentioned above, the motif matching results returned by TomTom are noisy and imperfect. For example, some of the statistically significant matches are clearly incorrect, as shown in In the next experiment we evaluated the ability of SATORI to detect interactions on a genome-wide scale. For this task we chose to focus on regions of accessible chromatin in arabidopsis in a manner similar to our experiment in human promoter regions. More specifically, we predict chromatin accessibility from sequence across 36 arabidopsis samples from recently published arabidopsis DNase I-Seq and ATAC-Seq studies (GEO accession numbers provided in the In the next step, we investigated genome-wide regulatory interactions in those regions of open-chromatin. The trained network yielded 189 filters with information content above 3.0, and we obtained 100 unique matches for those filters in the DAP-Seq arabidopsis TF database . Among tTo compare our model to DFIM , we incoNext, we compared the computation times for the two methods. As discussed earlier, unlike the FIS method, SATORI does not require re-calculation of the gradients to estimate the interactions, leading to much faster computation times: it processed all motif interactions 8–20 times faster than FIS (see Figure In summary, we find a very high level of overlap between the results of the two methods, which use very different approaches. This is important in view of the relatively small number of experimentally verified interactions that are available. Further wet-lab validation is needed to test the quality of the reported interactions by the two methods. High-frequency interactions consistently detected by both methods can be used as the most promising candidates for experimental follow-up.In this work we presented SATORI—a method for extracting interactions between the learned features of an attention-based deep learning model. Unlike existing methods, it only requires minimal post-processing and uses the sparsity of the attention matrix to infer the most salient interactions. We compared SATORI to the FIS interaction estimation method and reported a 10× speed-up in its computation time in most cases. Furthermore, the top predictions made by both methods show very high overlap, suggesting such interactions as promising targets for follow-up biological experiments. This high overlap, despite the big difference in the approach provides good evidence for their potential biological relevance.The proposed method can be extended in several ways. In this work, we focused on globally scoring interactions between TFs with known PWMs. This is in contrast to feature attribution methods that score the contribution of features in genomic regions of interest. We believe that the sparsity of the attention matrix could make it useful as an attribution method as well, but further experiments are required in order to validate that. SATORI is able to detect interactions between filters, even if they do not correspond to known TFs. Furthermore, the proposed methodology is flexible enough to be applied to deep networks that integrate multiple data modalities, and has potential applications outside of computational biology. For example, it can allow discovery of interactions between different characteristics of chromatin structure to provide a better understanding of the relationship between epigenetic markers such as histone modifications, DNA methylation, and nucleosome positioning and their contribution to the regulation of gene expression.https://github.com/fahadahaf/satori.The source code for SATORI and the processed data and results are available at gkab349_Supplemental_FileClick here for additional data file.