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Weather-Dependent Risk for Legionnaires' Disease, United States - Volume 23, Number 11—November 2017 - Emerging Infectious Disease journal - CDC
Volume 23, Number 11—November 2017
Weather-Dependent Risk for Legionnaires' Disease, United States
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Jacob E. Simmering, Linnea A. Polgreen, Douglas B. Hornick, Daniel K. Sewell, and Philip M. Polgreen
Author affiliations: The University of Iowa, Iowa City, Iowa, USA
Using the Nationwide Inpatient Sample and US weather data, we estimated the probability of community-acquired pneumonia (CAP) being diagnosed as Legionnaires' disease (LD). LD risk increases when weather is warm and humid. With warm weather, we found a dose-response relationship between relative humidity and the odds for LD. When the mean temperature was 60°–80°F with high humidity (>80.0%), the odds for CAP being diagnosed with LD were 3.1 times higher than with lower levels of humidity (<50.0%). Thus, in some regions (e.g., the Southwest), LD is rarely the cause of hospitalizations. In other regions and seasons (e.g., the mid-Atlantic in summer), LD is much more common. Thus, suspicion for LD should increase when weather is warm and humid. However, when weather is cold, dry, or extremely hot, empirically treating all CAP patients for LD might contribute to excessive antimicrobial drug use at a population level.
Legionellosis is associated with a mild febrile illness, Pontiac fever, or Legionnaires' disease (LD) (1), a cause of severe, atypical, community-acquired pneumonia (CAP) (2). Legionella spp. are aerobic, gram-negative bacilli, common in the environment, that were identified as pathogenic after an outbreak of illness among attendees of a 1976 American Legion convention (1,3). Although there are several species of Legionella and different serotypes, L. pneumophilia causes most LD cases (4,5). The case-fatality rate for LD among community-dwelling persons is as high as 10% (5). Delayed initiation of appropriate antimicrobial drug therapy further increases death rates (6,7), and the severity of LD drives the rationale for covering atypical organisms in the guidelines for empiric treatment of CAP. In developed countries, Legionella causes 1%–4% of CAP cases (4,8,9). Thus, because the rate of LD is low, many persons with CAP may be unnecessarily treated for LD. In fact, a recent noninferiority study, which included aggressive diagnostic testing, showed similar outcomes when treating and not treating for atypical organisms (10).
A striking epidemiologic feature of Legionella-associated CAP is its seasonality; more cases are reported during the summer (1). In contrast, hospital-associated cases do not exhibit seasonality (1). Seasonality has been described in the mid-Atlantic United States (11–14), England and Wales (15), and the Netherlands (16). Changes in use of cooling towers (17) or additional testing for pneumonia during the summer have been hypothesized as causes of this seasonality (1). However, strong evidence indicates that weather, particularly temperature and humidity, drive the summer spike in incidence (11,12,15,16). Although Legionella spp. are common in the environment, dry environments do not support them (1), and Legionella spp. are more sensitive than other pathogens to drying conditions (18). In contrast, warm and humid weather tends to support pathogen survival, growth, and the potential for aerosol exposures, increasing disease risk (1,13,19).
If the incidence of LD depends on local weather, the baseline rate of LD might be extremely low year-round in some locations and during specific seasons in other locations. Use of local weather data ultimately might provide information to help determine whether a specific CAP case is caused by Legionella. To establish the risk for LD across season, location, and weather conditions, we combined patient-level data on hospitalizations for pneumonia and LD from 26 US states with local weather data.
Data Source and Case Definition
Figure 1. Locations of Healthcare Cost and Utilization Project (HCUP) hospitals used in the analysis of risk for Legionnaires' disease, 26 US states, 1998–2011. Because many hospitals are near each other, each hospital...
We extracted individual-level inpatient-event data from the Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) for 1998–2011. The University of Iowa Institutional Review Board deems such studies as non–human subjects research. The NIS, a stratified 20% sample of discharges from nonfederal US hospitals, contains data from 47 states; after excluding the 21 NIS states that do not report the American Hospital Association identifier (AHA ID), patient race, or admission month, we used data from 26 states: Arizona, Arkansas, California, Colorado, Connecticut, Illinois, Iowa, Kentucky, Maryland, Massachusetts, Mississippi, Missouri, Montana, Nevada, New Hampshire, New Jersey, New York, North Carolina, Oregon, Pennsylvania, Rhode Island, Utah, Vermont, Virginia, Washington, and Wisconsin. Next, we mapped the hospitals in these 26 states to the AHA-reported addresses using the AHA ID; we then converted the addresses to geographic coordinates by using the US Census Bureau Geocoder (https://www.census.gov/geo/maps-data/data/geocoder.html) and Google Maps' Geocoding API (Google; Mountain View, CA, USA). We located 2,079 unique hospitals (Figure 1).
We identified LD cases as hospitalizations of persons with a primary diagnosis code of 482.84 (pneumonia due to Legionnaires' disease) from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). The primary diagnosis in the NIS is the condition chiefly responsible for the hospital admission (20). The sensitivity and specificity of the 482.84 code for LD was previously evaluated in a New York, NY, hospital for 2003–2013 (21); the authors reported high sensitivity (83.5%) and specificity (99.9%), a positive predictive value of 88.0%, a negative predictive value of 99.8%, and agreement between the estimated cases observed in the NIS for 2012 and the Centers for Disease Control and Prevention (CDC) data (21).
As study controls, we used hospitalizations of persons with a primary diagnosis ICD-9-CM code of 481 and subcodes (pneumococcal pneumonia) and 482 and subcodes (other bacterial pneumonia) excluding 482.84 (pneumonia due to Legionnaires' disease). We refer to the combination of the codes 481 and 482 as bacterial pneumonia. Because these codes were assigned to patients as their primary diagnosis code for admission, we assumed that this collection of codes identified cases of community-acquired bacterial pneumonia. Finally, using CDC surveillance results (22), we computed the correlation between the national-level estimated number of LD cases in our data and the number reported by CDC (23).
We excluded records for persons <18 years of age and records that omitted any of our variables of interest: age, sex, payer, race, admission month and year, and hospital location. We also required the hospital to have >1 weather station within 100 km (62 miles).
Weather Definition
We obtained weather observations from the Integrated Surface Database (ISD) provided and maintained by the National Climatic Data Center of the National Oceanic and Atmospheric Administration. Because the NIS database provides only the month of admission, we aggregated the average temperature, relative humidity, and total rainfall by month for each weather station. We recorded each hospital's monthly weather data as the mean of these values observed at nearby (within 100 km [62 miles]) weather stations. Using only the states with hospital location reported, we considered different definitions of "nearby." Average temperatures computed using only the nearest station and stations within 10 or 25 miles were highly correlated with the average temperature using a 62-mile radius (r>0.99).
Using logistic regression, we modeled whether a hospitalization for bacterial pneumonia had a diagnosis of LD on the basis of patient age, patient sex, payer, patient race, admission month, admission year, hospital latitude, total monthly rainfall, mean relative humidity, mean temperature, and an interaction between temperature and relative humidity. The interaction is required because relative humidity depends on temperature. We used mean temperature because it captures the nighttime and daytime temperature effects more accurately than does mean high temperature. However, the average high temperature and the average temperature for a month are highly correlated (r = 0.988). We used relative humidity rather than absolute humidity for 2 reasons. First, relative humidity ranges from 0% to 100% for any temperature, whereas absolute humidity ranges from 0 g/m3 to some temperature-specific maximum, which introduces problems because often the lowest observed absolute humidity at high temperatures is impossible at lower temperatures. Second, the values of absolute humidity were extremely correlated with temperature (r = 0.85), whereas the correlation was much lower with relative humidity (r = −0.23). The correlation of temperature with relative humidity is negative because relative humidity is often much higher at cold temperatures. To make the results easier to interpret and because the expected responses are not linear, we converted humidity and temperature into bins: relative humidity <50.0%, 50.0%–55.0%, 55.1%–60.0%, 60.1%–65.0%, 65.1%–70.0%, 70.1%–75.0%, 75.1%–80.0%, and >80.0%; and mean temperature <60°F, 60.1–80°F, >80°F. Additionally, monthly rainfall was binned into dry (<18 mm, the lowest 25%), normal (18–85.85 mm, the middle 50%), and wet (>85.85 mm, the top 25%). Patient age was binned by decade, and we included hospital latitude and squared hospital latitude. The squared hospital latitude was included to enable the effect of latitude to be nonlinear.
To visualize the model and how LD risk varies with space and season, we computed the fitted values from this model using location and weather information and set the demographic variables to their individual modal values for cases of bacterial pneumonia observed in the NIS data. Because the weather data are nationally complete, unlike the NIS data, we can take a given demographic profile (e.g., white man, 68 years of age, on Medicare) and estimate the probability of an LD diagnosis for any location, given the weather data for each location and month.
The NIS data provided a total of 5,172 LD cases from 447,132 hospitalizations for bacterial pneumonia (Table 1). After applying the discharge weights to produce a national-level estimate and before applying any exclusion rules other than reporting month and year, the NIS series compares favorably to CDC's reported monthly LD estimates (23), with a correlation of 0.74. After applying exclusion rules (age >18 years and provision of a complete set of predictor variables), we had data on 3,005 LD cases and 189,412 hospitalizations for pneumonia. The most common reasons for exclusion were lack of the AHA ID, admission month and year, and race, because some states elected not to report these variables.
We compared several demographic and severity measures between the portion of the data dropped because of missing values and the proportion retained (Table 2). Among the cases, the only substantial difference was in the percentage of patients not insured; for 10.8% of dropped records, no insurance was reported, compared with 7.4% of those used in the model. Many statistically significant differences existed between the controls retained and those lost; however, the large sample size (retained sample n = 189,412) meant many non–clinically relevant differences would be statistically significant.
In general, hospitalized persons with LD were younger than those with other bacterial pneumonia and more likely to be male (Table 3). We found a large unadjusted difference in the mean monthly environmental temperatures between cases (58.5°F) and controls (52.6°F), and monthly environmental relative humidity was higher on average for cases (70.0%) than for controls (67.3%). Additionally, there was nearly 20 mm more rain for cases (80.4 mm) than for controls (61.7 mm).
In the regression analysis, the primary variables of interest—mean temperature, mean relative humidity, and their interaction—were all significant (likelihood ratio test against model with only main effects, χ2 test statistic = 350.42; p<0.0001) (Table 4). Although total rainfall was independently a risk factor for LD, the effects of temperature and humidity were still significant.
The combination of the temperature and humidity main effects and interactions can make understanding the combined effect difficult. For this reason, we separately reported the estimated composite odds ratios for each combination (Table 5). Additionally, because odds ratios can be difficult to interpret, we provide the expected probabilities for a 61–70-year-old white man on Medicare admitted to a hospital at 42°N in April 2011 at the 3 different rainfall levels (Table 6). The relationship between temperature and relative humidity exhibits the Goldilocks principle: when it is too hot (>80°F) or too cold (<60°F), the odds of LD do not vary with humidity, but when the temperature is "just right" (60°–80°F), the odds have a dose-response pattern with humidity. The largest effect of this relationship between temperature and humidity was evident for warm and very humid months across all 3 rainfall levels.
Figure 2. Time series of Legionnaires' disease as a percentage of bacterial pneumonia discharges in Healthcare Cost and Utilization Project hospitals, 26 US states, 1998–2011. The Legionnaires' disease series is highly seasonal in...
We determined the monthly percentage of bacterial pneumonia discharges for which an LD diagnosis had been given within HCUP by US Census region (Figure 2). Percentages were relatively high in the Northeast and somewhat lower in the Midwest and South. The frequencies in the West were the lowest of all 4 regions and appeared not to be seasonal. The changes around 2002–2003 in all of the series are present in other data sources (23). The exact cause is unknown but is thought to be related to increased vigilance, testing, and reporting of atypical pneumonia after the outbreak of severe acute respiratory syndrome (24).
Figure 3. Predicted probability of an inpatient hospitalization for bacterial pneumonia being coded as Legionnaires' disease by location and month in 2011 for 26 US states. The predicted risk is for a 61–70-year-old...
Figure 4. Predicted probability of an inpatient hospitalization for bacterial pneumonia being coded as Legionnaires' disease, all US states, 2011. The predicted risk is for a 61–70-year-old white man on Medicare (the most...
We also determined the probability of a case of bacterial pneumonia being diagnosed as LD in 2011 using the local weather data. We restricted this prediction to the states used to estimate the model (Figure 3). We set the nonweather, nonlocation covariates to their modal values for patients hospitalized with bacterial pneumonia (white 61–70-year-old man on Medicare) and used the weather station latitude and monthly average temperature, humidity, and rainfall. The estimated probabilities of LD (Figure 3) are the fitted values from the model described by Table 4 and these covariate values. Since the ISD is national in scope, we extrapolated from the model estimated using HCUP data to the entire United States, including non-HCUP regions (Figure 4). The risk for LD varied considerably by location (low risk along the Gulf Coast and relatively low risk in the West) and calendar month (high–risk areas such as the Mid-Atlantic region are only actually at high risk during June–September and are at low risk during December–April) (Figure 4).
Our results suggest that the incidence of LD varies considerably by season and local weather patterns. Specifically, LD is more likely to occur in warm (60°–80°F) and very humid (>80.0%) months. For example, the odds of LD being diagnosed in a pneumonia patient during a month when the rainfall is <18 mm and the temperature is 60°–80°F was 3.1 (1.70/0.55) times higher when the relative humidity was >80.0% than when it was <50.0%. When rainfall amounts were greater, the risk also increased; however, regardless of rainfall, warm and humid weather was a major risk factor. Also, we found a dose-response relationship between relative humidity and the odds of an LD diagnosis during periods of warm weather. In contrast, hot, cool, or dry weather patterns produce no meaningful increase in LD.
Previous work has demonstrated seasonality and the effects of weather patterns on LD (1,11–13,15,16). However, much of this work was based on regional investigations where LD is common. Regional investigations are limited in their ability to more fully describe the relationship between weather patterns and LD incidence. For instance, in the Rocky Mountains or the US Southwest, community-associated LD is comparatively rare, and the rate for LD is much lower than what would be expected given patient factors. In contrast, the Mid-Atlantic has a higher-than-expected risk during certain months of the year.
Using the national scope of our data, we estimated the differences that weather has on LD risk. The predicted probabilities of LD varied from 0% to 1% of all cases of bacterial pneumonia in the Southwest to nearly 8% during warm, humid, and rainy summer months in the mid-Atlantic. Weather appears to drive these differences because our model had no geographic information other than latitude. This finding is consistent with prior regionally and seasonally limited studies (11–13,15,16). Areas previously studied (e.g., the mid-Atlantic) vary considerably in risk depending on recent weather. Our results are biologically plausible because L. pneumophilia thrives in warm, wet environments (25), which support not only the pathogen's survival but also the existence of aerosolizations. In contrast, conditions are not as supportive for the pathogen in dry or excessively hot environments (1,18).
LD is difficult to diagnose on the basis of clinical manifestations alone (26). Furthermore, rapid diagnostic tests do not cover all strains (27), and some tests have relatively low sensitivity (28). More definitive culture results may take 3–5 days after therapeutic decisions are needed (1). Thus, incorporating local weather conditions into clinical decision-making ultimately might help increase or decrease clinical suspicion for LD, especially when combined with diagnostic testing. Current US CAP guidelines recommend empiric therapy routinely covering atypical pneumonias (29). Results of a recent noninferiority trial suggest that monotherapy with a β-lactam, aggressive diagnostic testing, and use of clinical judgment may safely avert the use of fluoroquinolones or dual therapy with a macrolide in patients with CAP (10). However, the same trial replicated elsewhere with a higher rate of LD might yield different results. Another study investigating the effect of a β-lactam alone versus a β-lactam with a macrolide found delays in clinical stability for persons treated with only 1 agent, but the authors failed to show that the β-lactam alone was not inferior (30). Our model suggests that warm, humid, and rainy summer months in the mid-Atlantic may exhibit predicted probabilities of LD of nearly 8%. Accordingly, abandoning initial empiric coverage for LD might yield a differential effect on outcomes depending on season, region, and weather, and treating all CAP cases for atypical pneumonia in areas and seasons when LD is relatively uncommon may result in the excessive use of antimicrobial agents.
The antimicrobial drugs most commonly used to treat LD include either a fluoroquinolone or a macrolide (with a β-lactam), and resistance has increased for both (10,31–34). Thus, treating for LD only when and where risk is higher, along with increased diagnostic testing and good clinical judgement, may help reduce antimicrobial drug use, providing a new antimicrobial drug stewardship target. The temporal, climatologic, and geographic variations in LD risk emphasize the potential importance of regionally relevant guidelines. Basing treatment guidelines on estimates in high- or low-risk areas will lead to overuse or underuse of LD treatment for CAP. However, future work with more detailed clinical information is required to determine when and where antimicrobial drug use may be initially restricted and in which patients, without significantly impairing the quality of care. Specifically, future work will need to determine under which conditions and in which patients initial monotherapy with a β-lactam will not be inferior to combination therapy. Furthermore, future work will need to consider and incorporate diagnostic testing for LD and the patient's clinical status (e.g., intensive care unit admission).
Our work has several limitations. First, our study uses administrative data for inpatient hospitalizations. Some CAP and LD cases are treated on an outpatient basis. Furthermore, state participation in the NIS is voluntary, and some states omit key variables (e.g., AHA IDs). Although we found no meaningful differences between the parts of the sample we retained and parts we dropped, it is possible that participation/reporting varies nonrandomly. In addition, we do not have information about medications, test results, or microbiology data, so we cannot confirm an LD diagnosis or determine whether and what microbiological testing was performed. Thus, future work should incorporate alternative case-finding approaches, including more granular information about cases, specifically the CDC legionellosis database. Alternative data sources may also enable the generation of more granular geographic estimates and age-based estimates of disease risk. We also are unable to confirm without more clinical data the extent to which having a primary diagnosis of pneumonia correlates with CAP for the control patients in our analysis. Second, some of the geographic differences in the predicted probabilities may be due to differences in propensity to test for, and therefore diagnose, LD. Third, the NIS reports only the month of admission. Thus, we aggregated weather information by month. Future work needs to consider more granular (e.g., daily) data. Fourth, we consider the weather around a hospital, not the weather experienced by the patients admitted to the hospital. Fifth, meteorologic variables are interdependent: relative humidity depends on temperature because the maximum amount of water suspended in the air rises with the temperature, and our model may inadequately specify these relationships. Finally, our analysis did not contain possibly important geographic differences (e.g., use of monochloramine in municipal water).
The experience of warm and humid weather patterns common during summer resulting in substantial increases in LD might have driven the current view about the frequency of LD in the United States. Our results demonstrate the need to investigate the effects of incorporating recent weather patterns, particularly wet, warm, and humid weather, as an additional consideration in the clinical decision-making process for CAP. Our results suggest that the risk for LD is highly related to temperature and humidity regionally. We found locations where LD relatively rarely causes hospitalization for CAP, such as the Southwest and Rocky Mountains, but also the Mid-Atlantic region during the winter. Information about weather exposures for patients also should help inform the design and interpretation of CAP-treatment trials. Future work examining more granular environmental data may ultimately enable clinicians to safely limit initial empiric antimicrobial drug selection for CAP to monotherapy with a β-lactam in specific seasons and regions.
Mr. Simmering is a data scientist at the University of Iowa Health Ventures' Signal Center for Health Innovation. His primary research interests include urinary tract infections, LD, influenza, and hospital-associated infections, using time series, computational, and network models.
L.A.P. received support from the National Heart, Lung and Blood Institute (grant #K25 HL 122305). P.M.P. received support from the University of Iowa Health Ventures' Signal Center for Health Innovation.
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Figure 1. Locations of Healthcare Cost and Utilization Project (HCUP) hospitals used in the analysis of risk for Legionnaires' disease, 26 US states, 1998–2011. Because many hospitals are near each other,...
Figure 2. Time series of Legionnaires' disease as a percentage of bacterial pneumonia discharges in Healthcare Cost and Utilization Project hospitals, 26 US states, 1998–2011. The Legionnaires' disease series is highly...
Figure 3. Predicted probability of an inpatient hospitalization for bacterial pneumonia being coded as Legionnaires' disease by location and month in 2011 for 26 US states. The predicted risk is for...
Figure 4. Predicted probability of an inpatient hospitalization for bacterial pneumonia being coded as Legionnaires' disease, all US states, 2011. The predicted risk is for a 61–70-year-old white man on Medicare...
Table 1. Sample sizes for Legionnaires' disease cases and other pneumonia controls in a study of weather-dependent risk for Legionnaires' disease, United States, 1998–2011
Table 2. Demographic and severity characteristics among dropped and retained records in a study of weather-dependent risk for Legionnaires' disease, United States, 1998–2011
Table 3. Key variables in the sample in a study of weather-dependent risk for Legionnaires' disease, United States, 1998–2011
Table 4. Effects of humidity and temperature on Legionnaires' disease risk, United States, 1998–2011
Table 5. Odds ratios for Legionnaires' disease based on the interaction between average monthly temperature and average monthly relative humidity, United States, 1998–2011
Table 6. Estimated probability of Legionnaires' disease given bacterial pneumonia in a 61–70-year-old white man on Medicare located at 42°N, admitted to a hospital in April 2011
DOI: 10.3201/eid2311.170137


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Dentists and hygienists pressure state boards to close dental offices
As NC gets its first COVID-19 case believed to be spread through community contact, oral health care workers seek a statewide edict.
by Anne Blythe March 20, 2020 March 20, 2020
Photo credit: Rose Hoban.
By Anne Blythe
As North Carolina's workforce is being encouraged to hunker down at home and telecommute, dentist offices across the state have been given leeway in how they respond to the COVID-19 pandemic.
The state Board of Dental Examiners, which oversees licensing and disciplinary actions for dentists in this state, posted a notice on its website last week with guidance for the more than 5,100 dentists and 6,100 hygienists across the state.
Got a question about COVID-19? You can ask us and we'll try to find an answer! Click here.
"The board recognizes that circumstances vary deeply within our state and no two dental practices are alike," the notice states. "You as a dentist have the best information regarding your patients, your auxiliary personnel, and the community you serve."
The board encouraged all practices to postpone any non-urgent care and elective care until at least the end of the month.
Dentists, hygienists and other oral health care workers are considered to be at higher risk for transmitting and contracting the infection. Not only are they in close proximity to their patients, but they also handle drills that use water to cool the bit as well as aerosol rinsing devices that can propel germs and droplets from inside the mouth into the air.
On March 18, the state dental board posted a new notice in response to a barrage of pleas calling for a full shutdown.
"The NC State Board of Dental Examiners has received numerous requests for the Board to mandate the closure of all NC dental offices in response to the coronavirus pandemic," the post states. "However, the Dental Board lacks the authority to mandate the closure of dental offices. Notwithstanding certain federal authority, the authority to close North Carolina businesses resides with the Governor, certain state agencies, and county and local governments under the emergency powers of the state."
None of those agencies to date has ordered a full shutdown.
"In the meantime, dentists, who strictly adhere to all sterilization and disinfection guidelines, should consider the continuation of emergency care to prevent dental patients from adding stress to hospitals, hospital emergency rooms, and urgent care facilities," the March 18 notice states. "Likewise, rescheduling or postponing non-urgent, routine, and elective procedures is advised to lessen the risk of an inadvertent spread of the virus."
The American Dental Association issued a recommendation on March 16 suggesting a three-week pause, longer than the North Carolina board's guidelines initially suggested on delaying routine cleanings and non-urgent procedures.
"The ADA is deeply concerned for the health and well-being of the public and the dental team," Chad P. Gehani, ADA president, said in a March 16 statement recommending a three-week postponement of elective procedures.
"Concentrating on emergency dental care will allow us to care for our emergency patients and alleviate the burden that dental emergencies would place on hospital departments."
Board responds to rapid changes
Since the State Public Health Laboratory reported its first presumptive positive result for COVID-19 on March 3, the state has been in a constant state of flux, responding to quickly evolving events.
Gov. Roy Cooper declared a state of emergency on March 10 and then ordered that crowds be no larger than 100 people, while recommending gatherings be fewer than 50.
When he ordered the closing of bars and restaurants on March 17, the governor noted many establishments had not adhered to his guidance. He acknowledged the financial hardships that many businesses, households and individuals would have even as he pursued measures to blunt the impact of the contagious pathogen, especially if the situation goes on for a while.
Alec Parker, executive director of the N.C. Dental Society, said most of the offices across the state that he has heard from are not seeing patients for routine procedures such as cleanings and even in some cases not filling cavities.
Some, though, are continuing with urgent care. They're doing so, in part, to help keep those people out of emergency rooms where they might otherwise land as the state tries to lighten routine hospital loads to make room for the swelling needs predicted to come with the COVID-19 pandemic.
As of midday Thursday, North Carolina had 93 people who tested positive for the infection, but those numbers are expected to rise as the ability to test has expanded and more results come in. Secretary of Health and Human Services Mandy Cohen said the state has had its first "community-acquired" case, a key indicator that the disease has started spreading silently among the population.
The state has not reported any COVID-19 related deaths.
Precautions advised, guidelines provided
Parker said the Dental Society is advising any practices that continue seeing patients to take temperatures of all staff in the office first thing in the morning, again at midday and at the close of the day.
Though there is no order forcing offices to do that, Parker said he thought dentists would take the precautions. Too much is at risk if they don't, he said.
"Dentists are pretty compliant people," Parker added.
Parker said his organization is aware that some of these decisions might have negative financial consequences for many practices and their employees but said there are a number of efforts in the works that could help bring some relief created by the loss in revenue and any needed reductions to employee hours.
Now that North Carolina has its first case believed to be spread to someone who had no known contacts with anyone who had tested positive, there likely will be more guidelines issued in the coming weeks.
The Dental Society has set up a web page where providers can get information and training notices as the pandemic continues.
The American Dental Association sent out guidelines for how oral health care providers should determine what constitutes a dental emergency and urgent dental care.
"Dental emergencies are potentially life threatening and require immediate treatment to stop ongoing tissue bleeding, alleviate severe pain or infection, and include: Uncontrolled bleeding; cellulitis or a diffuse soft tissue bacterial
infection with intra-oral or extra-oral swelling that potentially compromise the patient's airway; and trauma involving facial bones, potentially
compromising the patient's airway."
by Anne Blythe, North Carolina Health News

Dentists and hygienists pressure state boards to close dental offices

by Anne Blythe, North Carolina Health News
March 20, 2020

By Anne Blythe

As North Carolina's workforce is being encouraged to hunker down at home and telecommute, dentist offices across the state have been given leeway in how they respond to the COVID-19 pandemic.

The state Board of Dental Examiners, which oversees licensing and disciplinary actions for dentists in this state, posted a notice on its website last week with guidance for the more than 5,100 dentists and 6,100 hygienists across the state.

"The board recognizes that circumstances vary deeply within our state and no two dental practices are alike," the notice states. "You as a dentist have the best information regarding your patients, your auxiliary personnel, and the community you serve."

The board encouraged all practices to postpone any non-urgent care and elective care until at least the end of the month.

Dentists, hygienists and other oral health care workers are considered to be at higher risk for transmitting and contracting the infection. Not only are they in close proximity to their patients, but they also handle drills that use water to cool the bit as well as aerosol rinsing devices that can propel germs and droplets from inside the mouth into the air.

On March 18, the state dental board posted a new notice in response to a barrage of pleas calling for a full shutdown.

"The NC State Board of Dental Examiners has received numerous requests for the Board to mandate the closure of all NC dental offices in response to the coronavirus pandemic," the post states. "However, the Dental Board lacks the authority to mandate the closure of dental offices. Notwithstanding certain federal authority, the authority to close North Carolina businesses resides with the Governor, certain state agencies, and county and local governments under the emergency powers of the state."

None of those agencies to date has ordered a full shutdown.

"In the meantime, dentists, who strictly adhere to all sterilization and disinfection guidelines, should consider the continuation of emergency care to prevent dental patients from adding stress to hospitals, hospital emergency rooms, and urgent care facilities," the March 18 notice states. "Likewise, rescheduling or postponing non-urgent, routine, and elective procedures is advised to lessen the risk of an inadvertent spread of the virus."

The American Dental Association issued a recommendation on March 16 suggesting a three-week pause, longer than the North Carolina board's guidelines initially suggested on delaying routine cleanings and non-urgent procedures.

"The ADA is deeply concerned for the health and well-being of the public and the dental team," Chad P. Gehani, ADA president, said in a March 16 statement recommending a three-week postponement of elective procedures.

"Concentrating on emergency dental care will allow us to care for our emergency patients and alleviate the burden that dental emergencies would place on hospital departments."

Board responds to rapid changes

Since the State Public Health Laboratory reported its first presumptive positive result for COVID-19 on March 3, the state has been in a constant state of flux, responding to quickly evolving events.

Gov. Roy Cooper declared a state of emergency on March 10 and then ordered that crowds be no larger than 100 people, while recommending gatherings be fewer than 50.

When he ordered the closing of bars and restaurants on March 17, the governor noted many establishments had not adhered to his guidance. He acknowledged the financial hardships that many businesses, households and individuals would have even as he pursued measures to blunt the impact of the contagious pathogen, especially if the situation goes on for a while.

Alec Parker, executive director of the N.C. Dental Society, said most of the offices across the state that he has heard from are not seeing patients for routine procedures such as cleanings and even in some cases not filling cavities.

Some, though, are continuing with urgent care. They're doing so, in part, to help keep those people out of emergency rooms where they might otherwise land as the state tries to lighten routine hospital loads to make room for the swelling needs predicted to come with the COVID-19 pandemic.

As of midday Thursday, North Carolina had 93 people who tested positive for the infection, but those numbers are expected to rise as the ability to test has expanded and more results come in. Secretary of Health and Human Services Mandy Cohen said the state has had its first "community-acquired" case, a key indicator that the disease has started spreading silently among the population.

The state has not reported any COVID-19 related deaths.

Precautions advised, guidelines provided

Parker said the Dental Society is advising any practices that continue seeing patients to take temperatures of all staff in the office first thing in the morning, again at midday and at the close of the day.

Though there is no order forcing offices to do that, Parker said he thought dentists would take the precautions. Too much is at risk if they don't, he said.

"Dentists are pretty compliant people," Parker added.

Parker said his organization is aware that some of these decisions might have negative financial consequences for many practices and their employees but said there are a number of efforts in the works that could help bring some relief created by the loss in revenue and any needed reductions to employee hours.

Now that North Carolina has its first case believed to be spread to someone who had no known contacts with anyone who had tested positive, there likely will be more guidelines issued in the coming weeks.

The Dental Society has set up a web page where providers can get information and training notices as the pandemic continues.

The American Dental Association sent out guidelines for how oral health care providers should determine what constitutes a dental emergency and urgent dental care.

"Dental emergencies are potentially life threatening and require immediate treatment to stop ongoing tissue bleeding, alleviate severe pain or infection, and include: Uncontrolled bleeding; cellulitis or a diffuse soft tissue bacterial

infection with intra-oral or extra-oral swelling that potentially compromise the patient's airway; and trauma involving facial bones, potentially

compromising the patient's airway."

This article first appeared on North Carolina Health News and is republished here under a Creative Commons license.
Tagged: Board of Dental Examiners, community spread, coronavirus, COVID-19, dentists, Gov. Roy Cooper, hygienists, N.C. Dental Society, NC health, oral health, pandemic, should my dentist close his office, state of emergency, State Public Health Laboratory, teeth cleanings, The American Dental Association
Anne Blythe
Anne Blythe, a reporter in North Carolina for more than three decades, writes about oral health care, children's health and other topics for North Carolina Health News.	More by Anne Blythe
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4 replies on "Dentists and hygienists pressure state boards to close dental offices"
Same for the optical industry!! We are being made to go to work right now (I personally work for a large retail optical company) with instructions to practice the heightened hygiene and cleaning protocols as well as social distancing, which is impossible in our industry. I cannot adjust glasses or measure for lenses from 6ft away. The industry standard is to be no more than 2ft away to get an accurate measurement. We are very concerned about performing our job duties and keeping ourselves and others safe at the same time. It can't be done properly. Not to mention, every product our company sells comes from Italy or China – where the virus originated and has been the most severe.
I don't know who to go to for guidance on how to get the concerns of our employees to be heard! We are still open, located inside a shopping mall where 70% of the stores are closed. From Sunday 3/15 to Friday 3/20, almost 900 people have walked thru our doors and not for emergency services. This is an alarming amount of traffic we are exposed to posing a threat for employees customers and their families!
JDeC says:
Is this a possible interim solution(given no policies yet about either dentistry or optical industries):
Require masks for both provider, patients during entire visit, and not allowing family members into the clinic or store area, unless "masked"….
Obviously not dental patients!!
I just misplaced my 2 upper front false teeth (on a retainer). I know, I know. I'm still gonna keep looking for sure….but, if I don't find 'em……? I know it's not an urgent health importance, if you have time, lemme know what dentists think, please. Thanks.


University, New Brunswick, for a history internship project.
(1836-1911), the son of her father's business partner.
family home in Morristown, New Jersey (Morris County).
Historical Nook," to the New Jersey Historical Society.
Martha Washington and castles of the Rhine.
Columbia and a physics teacher at Barnard College.
Coleridge, Byron, Milton, and Chaucer.
supplemented the lectures on Goethe and castles of the Rhine.
additional manuscript collections relating to Julia Keese Colles.
collection of Getrude and Julia Keese Colles's papers.
COLLES, JULIA KEESE (d. 1913) author, educator.
1027, for biographical data, see MG 958.


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Translation in Online Dispute Resolution
Sometimes settling a dispute outside of court is preferable, especially when neither party wants to wait for a long court ruling or carry the cost of a lengthy trial. Logistically, meeting in court may be inconvenient especially for international cases. One technological solution was created which allows disputes to be settled online. While a more economic and quick way to deal with dispute cases, language barriers still exist in multilingual disputes. Therefore, legal translation in online dispute resolution still plays a major role.
What Is Online Dispute Resolution (ODR)
In such civil law cases, where a consumer and a trader are involved, often Alternative Dispute Resolution is sought out, that is to say hashing out the details by meeting face-to-face. In international business cases, however, this is often not logistically possible. Instead a very similar concept is used called Online Dispute Resolution (ODR), considered to be the online equivalent of Alternative Dispute Resolution.
This method was established in February of 2016 by the European Commission to allow consumers in the EU to submit their disputes online in any of the 23 EU official languages. It is also used among Asian countries, where China, Japan and India are major ODR players. This platform has made border-less dispute resolution possible. But what about the obvious language barriers?
Role of Translation in Online Dispute Resolution
Professional legal translation in online dispute resolution plays a vital role. Take for example, a dispute between an American customer and a Chinese trader in a business to business transaction. When Online Dispute Resolution is used to resolve this conflict, each company will have a different internal language. While certain key players might be able to freely communicate in either language, others added to the discussion later on might not be able to do so.
Moreover, in a dispute situation each word carries equal weight and there is absolutely no room for ambiguity. The first instinct might be to simply let an employee who speaks both languages handle the translations. However, even if the employee has bilingual proficiency, a legal text might still suffer due to the complexity of such content. Legal terminology is standardized in every language and must be adhered to, particularly when the interaction is between two businesses.
Currently, there are options for translation services in online dispute resolutions cases. One such example is the platform of the European Commission which offers users two ways to receive multilingual legal translation. Online traders or consumers involved in an ODR have the option of using machine translation to communicate with the other parties involved with the dispute, and sending documents off to professional legal translators at the end of the dispute (via the European Commissions' ODR portal). As stated on their website:
"This site also has an automatic translation tool to help you communicate with the trader and/or the resolution body if you do not speak the same language. At the end of the procedure, you can ask for the final outcome to be translated by a professional translator."
Both have the usual pros and cons of machine vs human translation – namely speed vs high accuracy. While convenient for quick communication, machine translation is still not ideal for legal material due to the technical, and particular, nature of legal language translation. Depending on the type of documents needed for translation in online dispute resolution occurring cross-boarders, using a professional translator who has a background in law (and specifically ODR) may be the best way to ensure smooth communication for both parties.
Who Uses Professional Translation in Online Dispute Resolution?
E-commerce appears to be a major area where ODR is most readily used to settle disputes, for obvious reasons of convenience. In such cases, an email can be prepared in the native language of one party and refined to convey their exact position in the matter. Then either a machine translation can be prepared, or a professional translator can translate the text into the target language, together with the writers of the original content, to ensure that nothing is lost in translation. While online dispute resolution can be used to facilitate online negotiation and or arbitration, the importance of accuracy and correct word usage is crucial in high level disputes. Not only would a professional legal translator ensure that nothing is misunderstood or miscommunicated, but it is beneficial to use a professional translator who can provide objectivity according to the code of ethics associated with the profession.
http://ec.europa.eu/consumers/solving_consumer_disputes/non-judicial_redress/adr-odr/index_en.htm
https://ec.europa.eu/consumers/odr/main/?event=main.help.faq
This post was published in General Law, Patent Law and tagged in alternative dispute resolution, legal translation, online dispute resolution, professional legal translation, professional legal translator, translation, translation services.


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Washington Redskins vs. Oakland Raiders - 9/24/17 NFL Pick, Odds, and Prediction
Photo by USA TODAY Sports
Chris Ruffolo September 24, 2017 12:59 am
Oakland Raiders (2-0) at Washington Redskins (1-1)
NFL Football: Sunday, September 24, 2017 at 8:30 pm (FedExField)
The Line: Washington Redskins +3 -- Over/Under: 54 Click to Get Latest Betting Odds
TV: NBC Stream live sports and exclusive originals on ESPN+. Sign up now!
The Oakland Raiders and the Washington Redskins face off in week 3's edition of Sunday Night Football on NBC.
The Oakland Raiders will look to continue their strong start after improving to 2-0 following their 45-20 beatdown of the Jets in week 2. Derek Carr threw for 230 yards and 3 TDs on 23 of 28 passing. Jalen Richard led the Raiders with 58 rushing yards and a touchdown. Cordarrelle Patterson added 57 yards on the ground and a touchdown as well while Marshawn Lynch chipped in 45 yards and a rushing score of his own with a team-high 12 carries. Michael Crabtree caught 6 passes for 80 yards while hauling in all 3 receiving TDs for the Raiders in the win. Amari Cooper and Jared Cook each pulled in 4 grabs for Oakland in the win. Defensively, the Raiders combined for 4 sacks, with Karl Joseph also adding a forced fumble which he also recovered as the Raiders held the Jets to just 271 yards of total offense in the win.
The Washington Redskins will look to build on their first victory of the season, a 27-20 win over the Rams in week 2. Kirk Cousins threw for 179 yards and a touchdown on 18 of 27 passing. Rob Kelley led the Redskins with 78 yards on 12 caries before leaving with a rib injury, while Chris Thompson followed right behind with 3 carries for 77 yards and a pair of touchdowns on the ground. Samaje Perine made the most out of the injury to Kelley, logging a team-high 21 carries for 67 yards in the win. Jordan Reed led the team with 6 catches for 48 yards, while Jamison Crowder was right behind with 47 yards on 4 catches and Ryan Grant caught the team's lone receiving TD in the win. Defensively, the 'Skins forced a total of 3 fumbles, recovering one, while alsonotching an interception and a pair of sacks as Washington held L.A. to 332 yards of total offense while dominating the time of possession battle with the ball behind in the Rams' hands for just under 24 minutes of the game.
Oakland is 10-3 ATS in their last 13 road games and 9-4 ATS in their last13 games overall while the over is 19-6-1 in their last 26 games following a win. Washington is 11-5 ATS in their last 16 games overall and 6-1 ATS in their last 7 games against a team with a winning record while the over is 8-3 in their last 11 home games. The road team is 5-0 ATS in the lat 5 meetings between these two teams.
As much as I'd like to think that Washington will step it's game up for primetime, I'm just not sure the 'Skins have what it takes to hang with the Raiders offensively. The Redskins have a slight advantage on the defensive side of the football, but is that enough to counter Oakland's dynamic offense? I personally don't think so, and I'll take the low line with Oakland as a gift and lay the field goal.
Chris's Pick Oakland -3
Los Angeles Chargers vs. Kansas City Chiefs - 9/24/17 NFL Pick, Odds, and Prediction
Jacksonville Jaguars vs. Baltimore Ravens - 9/24/17 NFL Pick, Odds, and Prediction
AUTHOR: Chris Ruffolo
Chris has written about professional and collegiate sports for more than 3 years.
Follow Chris Ruffolo on Twitter @ruffthepickdawg


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Home/Sports Tips/NBA Tips/NBA Tips & Previews, Tuesday 14th December
NBA Tips & Previews, Tuesday 14th December
It's NBA galore as we head into a plethora Tuesday matches as the BetDeluxe NBA team finds the best bets you need to be on.
Indiana and Golden State lead us into Tuesday NBA action from over in the USA.
The best bets from BetDeluxe are in for the games you cannot miss.
golden state @ indiana
december 14th 11:00am
Stephen Curry and his Golden State Warriors make the trek over to Indiana for a clash against the Pacers come Tuesday morning.
Golden State comes in off a 102-93 road loss to Philadelphia Saturday night in their most recent game, while Indiana defeated Dallas 106-93 at home in their last contest.
As we stand, the Warriors sit perched right up the top of the Western Conference with a dominant 21-5 record and will be keen to keep away from the chasing pack with a win here.
The Warriors were not their usual self against Philadelphia as they struggled shooting from the outside against the 76ers while fading in the fourth quarter.
The Warriors are the benchmark of the NBA at the moment, and it will be a challenge for an Indiana team that is mediocre on both ends to come up with enough plays to earn the victory.
The scales tip in favour of Golden State on the road here.
Suggested Bets: Golden State -3.5 at $1.91
Bet Now: golden state @ indiana
miami @ cleveland
Two Eastern Conference rivals go toe-to-toe on the parquet floor of Rocket Mortage FieldHouse when the Miami Heat hit the road to take on the Cleveland Cavaliers.
Miami comes in off a very impressive 118-92 home win over the Bulls over the weekend, while Cleveland dropped Sacramento 117-103 at home in the second game of a back-to-back set.
Even though Cleveland have been on a serious winning run having notched up three straight and seven of their last nine games, fatigue after such a long brutal set of fixtures is surely going to play a big part in this contest.
This marks the Cavaliers' third game in four nights after playing Minnesota on the road Friday and Sacramento at home Saturday.
The Heat are banged up, but Erik Spoelstra is known get the most out of his players and squad, regardless who is missing.
Siding with the Heat who look the value here.
Suggested Bet: Miami Heat head to head at $2.53
Bet Now: heat @ cavaliers
milwaukee @ boston
A healthier, fitter and in-form Milwaukee Bucks team gets a second dip at the Boston Celtics when the two meet at TD Garden on Tuesday.
The Celtics won the first meeting between these two teams back on November 12, but they have clearly fallen on hard times in recent outings with the team being on a three-game losing streak.
They didn't even come close to covering as 2.5-point dogs in their 111-90 loss to the Phoenix Suns.
Meanwhile, the Bucks covered as 5.5-point favourite in a 112-97 shellacking over the New York Knicks.
The Bucks are progressively starting to resemble the team that won the championship last season, and that should be a worrying sign for the Celtics.
Would much prefer to see the Celtics get back into form before backing them.
Siding the Bucks who look great value at the head-to-head and line.
Suggested Bet: Milwaukee Bucks head to head at $1.99
Bet Now: bucks @ celtics
phoenix @ la clippers
december 14th 2:30pm
The Phoenix Suns are looking to stand alone at the top of the Western Conference as they seek their third consecutive win when visiting the Los Angeles Clippers.
Phoenix's jaw-dropping 18-game winning streak came to a halt with a loss to the Golden State Warriors, but the Suns bounced back with a pair of victories over San Antonio and Boston.
The Clippers, on the other hand, are finding winning form and will on the hunt for their fourth consecutive win.
The main cause for concern for both teams is the injury woes they face coming into this clash.
That will determine who we back.
Devin Booker will certainly miss for the Suns, but Paul George is a chance to return from an elbow injury.
If George returns, we are heavily invested in the Clippers.
Suggested Bet: Los Angeles Clippers head to head at $2.18
Bet Now: suns @ clippers


Slot-based plasmonic waveguides have attracted significant attention owing to their unique ability to confine light within nanometer-scale. In this context, enhanced localized light-matter interaction and control have been exploited to demonstrate novel concepts in data communication and sensing applications revealing the immense potential of plasmonic slot waveguides. However, inherent light absorption in the metallic parts included is such structures hampers the scaling of plasmonic devices and limits their application diversity. A promising solution of such issues is the use of hybrid plasmo-photonic configurations. Hybrid slot waveguides have been introduced as the means to reduce such propagation losses while maintaining their functional advantages. In addition, their co-integration with low-loss photonic waveguides can enable the development of more complex structures with acceptable overall losses. In such scenario, light needs to be efficiently transferred from the photonic to the plasmonic components and/or backwards. Based on this rationale, in this work a hybrid slot-based structure is adopted to achieve highly efficient light transfer between photonic and plasmonic slot waveguides in the near-infrared spectrum region (λ=1550 nm). This transition is realized with the aid of a directional coupling scheme. For this purpose, a Si3N4 bus waveguide (photonic branch) is located below an Aubased metallic slot (plasmonic branch) forming a hybrid waveguide element. The combined configuration, as it is shown with the aid of numerical simulations , is capable of supporting two hybrid guided modes with quasi-even and odd symmetry allowing the development of a power exchange mechanism between the two branches. In this context, a new directional coupling structure has been designed which can achieve power transmission per transition over 68% within a coupling length of the order of just several microns.


text:
Home > Treatments & Services > Clinical Trials & Research > Available Trials
USOR 17188
High-Risk Early-Stage Estrogen Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative (ER+/HER2-) Breast Cancer
Available at 5 locations
STAR14059
HER2 Amplified Metastatic Breast Cancer
USOR18109
All Pathologies/Patients
Clinical Data to Evaluate New Gene Expression Profiles (A Registry), All Comers Applicable
HER2 Negative, Hormone Receptor Positive, Locally Advanced or Metastatic Breast Cancer (ER+/PR+/HER2- )
ER+/HER2- Breast Cancer
Advanced Endometrial Cancer
STAR 18016
Patients with Locally Advanced Unresectable or Metastatic Gastrointestinal Stromal Tumor (GIST)
Advanced Squamous Cell Carcinoma of the Head and Neck
AZA-MDS-006
Myelodysplastic syndromes (MDS) or Acute Myeloid Leukemia (AML) on Bone Marrow
Thyroid Cancer, Non-Small Cell Lung Cancer (NSCLC) and Other Advanced Solid Tumors with RET fusion
Advanced Anaplastic Lymphoma Kinase-Positive Non–Small-Cell Lung Cancer
Stage IIIB (locally advanced or recurrent and not a candidate for curative therapy) or stage IV Advanced Non–Small-Cell Lung Cancer
Non-Small Cell Lung Cancer (NSCLC), Locally Advanced or Metastatic, with EGFR or HER2 Exon 20 Insertion Mutation
Metastatic Non-Small Cell Lung Cancer Expressing High Levels of PD-L1
Non-Small Cell Lung Cancer High Tumor Mutation Burden
Follicular Lymphoma with EZH2 mutation
Hodkin Lymphoma (HL) in Adults Age 60 and Above
Follicular Lymphoma, Non-Hodgkin's Lymphoma, Mantle Cell Lymphoma
Myelofibrosis, post-poycythemia vera myelofibrosis, post essential thrombocytopenia myelo fibrosis
Metastatic Castration-resistant Prostate Cancer (mCRPC) with BRCA1/2 mutation or ATM
Metastatic Castration-Resistant Prostate Cancer (mCRPC)
Renal Cancer
Renal Cell Carcinoma Clear Cell Component
Clear Renal Cell Carcinoma
Rare Malignancies
Urothelial Cancer
urothelial or non-urothelial carcinoma of the urinary tract
Metastatic or Surgically Unresectable Urothelial Carcinoma Haboring FGF/FGFR Alterations
Advanced or Metastatic Urothelial Cancer


text:
Israel Frees Palestinian Detainee After Hunger Strike of Weeks
ABEL KERSHNER and FARES AKRAM, New York Times, April 1, 2012
The woman, Hana Shalabi, 30, from the northern West Bank, was the second Palestinian this year to have challenged and changed the terms of their "administrative detention," a practice of the Israeli military courts that allows imprisonment based on secret informants or information and that has been used against thousands of Palestinians over the years.
Both Ms. Shalabi's case and that of Khader Adnan, 33, who ended a 66-day fast
in February in return for a reduced term, drew international attention to the continuing use of administrative detention and prompted concerns in Israel that a hunger strike to the death could set off widespread unrest.
But both cases were resolved individually and have so far failed to produce any fundamental change in Israeli policy.
Ms. Shalabi had previously spent more than two years in administrative detention before being released in October 2011 as one of hundreds of Palestinian prisoners exchanged for Gilad Shalit, an Israeli soldier who had been held in Gaza. She was rearrested in February and handed a six-month detention order that was later reduced to four months.
The Israeli military said in a statement that she was originally placed in detention in 2009 on the basis of information that she intended to carry out a suicide attack against Israelis, and that intelligence reports had indicated that she had recently "resumed terrorist activity."
Ms. Shalabi's brother, Omar Shalabi, 42, rejected those accusations. "If they were true, she would have been sentenced," he said in a telephone interview shortly before Ms. Shalabi's deal with the authorities was made public toward the end of last week.
Israel defends its use of administrative detention as necessary for national security, and says it is used when a case is based on informants or intelligence material that cannot be revealed in court. Critics say the secret evidence makes it impossible for administrative detainees or their lawyers to mount a proper defense. Administrative detention orders can be issued for a maximum of six months, but can be renewed indefinitely. About 300 administrative detainees are currently in Israeli prisons, officials say.
Mr. Adnan was said to have been a leader of
Islamic Jihad, an extremist organization that has carried out suicide bombings and fired rockets from Gaza into southern Israel.
Ms. Shalabi is also said to belong to Islamic Jihad. When she entered Gaza on Sunday, supporters and leaders of the movement were waiting for her at the Erez crossing. Islamic Jihad did not welcome the deal made with the Israeli authorities, which will confine Ms. Shalabi to Gaza for three years, but said that it respected her decision.
This entry was posted in MRSCP and tagged Detention, Human Rights by admin. Bookmark the permalink.


Watched all the Star Wars episodes starting on the May the Fourth. I think kinesiologic ARV polls could be used to prevent some of the underlying issues in those movies, mainly the disconnect between the Jedi Council, the Senate and Chancellor Palpatine.
Let's say everyone received Jedi training during public education, say how to meditate, practice Qigong, muscle test, Remote view, etc.. So, everyone is at some level is a Jedi, who knows how to vote blindly, to get the correct response. This would prevent political manipulation by concealment of the truth.
Now let's say Palpatine was a formal Jedi Chancellor, with the council behind him. If everyone is Jedi, I would make the Jedi council up of previous Chancellors who've retired. An oligarch, similar to the Native American Chief and the tribal elders. The elders are there to advise the Chief, as they've made it in life and can't be bought. They can say, "Hey, this won't work.". The Chief is advised, but he doesn't have to listen, as he is still Chief.
This alone will take care of many issues most countries aren't really setup to handle. Rough draft idea. Voting 101 to prevent political deception.
That would change the world. There'd be a whole % of people who used to be politicians who'd have to take up waiting tables.


text:
#ifndef __H264DECAPI_H__
#define __H264DECAPI_H__

#include "basetype.h"
#include "decapicommon.h"
#include "h264hwd_dpb.h"
#include "h264hwd_container.h"
#include "dwl.h"
#include "vpu_mem.h"

namespace android {

#ifdef __cplusplus
extern "C"
{
#endif

/*------------------------------------------------------------------------------
    API type definitions
------------------------------------------------------------------------------*/

    /* Return values */
    typedef enum H264DecRet_
    {
        H264DEC_OK = 0,
        H264DEC_STRM_PROCESSED = 1,
        H264DEC_PIC_RDY = 2,
        H264DEC_PIC_DECODED = 3,
        H264DEC_HDRS_RDY = 4,
        H264DEC_ADVANCED_TOOLS = 5,
        H264DEC_PENDING_FLUSH = 6,
        H264DEC_NONREF_PIC_SKIPPED = 7,/* Skipped non-reference picture */

        H264DEC_PARAM_ERROR = -1,
        H264DEC_STRM_ERROR = -2,
        H264DEC_NOT_INITIALIZED = -3,
        H264DEC_MEMFAIL = -4,
        H264DEC_INITFAIL = -5,
        H264DEC_HDRS_NOT_RDY = -6,
        H264DEC_STREAM_NOT_SUPPORTED = -8,
        H264DEC_SIZE_TOO_LARGE = -9,

        H264DEC_HW_RESERVED = -254,
        H264DEC_HW_TIMEOUT = -255,
        H264DEC_HW_BUS_ERROR = -256,
        H264DEC_SYSTEM_ERROR = -257,
        H264DEC_DWL_ERROR = -258,

        H264DEC_EVALUATION_LIMIT_EXCEEDED = -999,
        H264DEC_FORMAT_NOT_SUPPORTED = -1000,
        H264DEC_NUMSLICE_ERROR = -384
    } H264DecRet;

    /* decoder  output picture format */
    typedef enum H264DecOutFormat_
    {
        H264DEC_SEMIPLANAR_YUV420 = 0x020001,
        H264DEC_TILED_YUV420 = 0x020002,
        H264DEC_YUV400 = 0x080000
    } H264DecOutFormat;

    /* decoder  output picture format */
    typedef enum ppOutFormat_
    {
        SCALE_PP = 0 ,
        ROTATE_PP = 2 ,
        CROPPING_PP = 4 ,
        DEINTELACE_PP = 8 ,
        DITHER_PP = 0X10 ,
        ALPHA_BLENDING_PP = 0X20 ,
        COLOR_CONVERSION_PP = 0X30,
    } ppOutFormat;

    /* Input structure */
    typedef struct H264DecInput_
    {
        u8 *pStream;         /* Pointer to the input */
        u32 streamBusAddress;   /* DMA bus address of the input stream */
        u32 dataLen;         /* Number of bytes to be decoded         */
        u32 picId;           /* Identifier for the picture to be decoded */
        u32 skipNonReference; /* Flag to enable decoder skip non-reference
                               * frames to reduce processor load */
        u32 TimeLow;
        u32 TimeHigh;
    } H264DecInput;

    /* Output structure */
    typedef struct H264DecOutput_
    {
        u32 dataLeft;        /* how many bytes left undecoded */
    } H264DecOutput;

    /* cropping info */
    typedef struct H264CropParams_
    {
        u32 croppingFlag;
        u32 cropLeftOffset;
        u32 cropOutWidth;
        u32 cropTopOffset;
        u32 cropOutHeight;
    } H264CropParams;

    /* Output structure for H264DecNextPicture */
    typedef struct H264DecPicture_
    {
        u32 picWidth;        /* pixels width of the picture as stored in memory */
        u32 picHeight;       /* pixel height of the picture as stored in memory */
        H264CropParams cropParams;  /* cropping parameters */
        u8 *pOutputPicture;  /* Pointer to the picture */
        u32 outputPictureBusAddress;    /* DMA bus address of the output picture buffer */
        u32 picId;           /* Identifier of the picture to be displayed */
        u32 isIdrPicture;    /* Indicates if picture is an IDR picture */
        u32 nbrOfErrMBs;     /* Number of concealed MB's in the picture  */
        u32 interlaced;      /* non-zero for interlaced picture */
        //u32 fieldPicture;    /* non-zero if interlaced and only one field present */
        //u32 topField;        /* if only one field, non-zero signals TOP field otherwise BOTTOM */
        //u32 viewId;
        u32 TimeLow;
        u32 TimeHigh;
        VPUMemLinear_t lineardata;
    } H264DecPicture;

    /* stream info filled by H264DecGetInfo */
    typedef struct H264DecInfo_
    {
        u32 picWidth;        /* decoded picture width in pixels */
        u32 picHeight;       /* decoded picture height in pixels */
        u32 videoRange;      /* samples' video range */
        u32 matrixCoefficients;
        H264CropParams cropParams;  /* display cropping information */
        H264DecOutFormat outputFormat;  /* format of the output picture */
        u32 sarWidth;        /* sample aspect ratio */
        u32 sarHeight;       /* sample aspect ratio */
        u32 monoChrome;      /* is sequence monochrome */
        u32 interlacedSequence;      /* is sequence interlaced */
        u32 picBuffSize;     /* number of picture buffers allocated&used by decoder */
        //u32 multiBuffPpSize; /* number of picture buffers needed in decoder+postprocessor multibuffer mode */
    } H264DecInfo;

/*------------------------------------------------------------------------------
    Prototypes of Decoder API functions
------------------------------------------------------------------------------*/

    H264DecRet H264DecInit(decContainer_t* H264deccont, u32 useVideoFreezeConcealment);

    void H264DecRelease(decContainer_t *pDecCont);

    H264DecRet H264DecDecode(decContainer_t *pDecCont,
                             const H264DecInput * pInput,
                             H264DecOutput * pOutput);

    void H264DecReset(decContainer_t *pDecCont);

    H264DecRet H264DecNextPicture(decContainer_t *pDecCont, H264DecPicture * pOutput);

    H264DecRet H264DecGetInfo(decContainer_t *pDecCont, H264DecInfo * pDecInfo);

	void *h264bsdAllocateDpbImage(storage_t * pStorage, dpbStorage_t * dpb);

	u32 h264bsdCheckGapsInFrameNum(storage_t * pStorage, dpbStorage_t * dpb, u32 frameNum,
												 u32 isRefPic, u32 gapsAllowed);

/*------------------------------------------------------------------------------
    Prototype of the API trace funtion. Traces all API entries and returns.
    This must be implemented by the application using the decoder API!
    Argument:
        string - trace message, a null terminated string
------------------------------------------------------------------------------*/
    void H264DecTrace(char *string);

    DispLinearMem *GetDispbuff(void);

    void DispbuffFree(void);

    void DispbuffMalloc(u32 width, u32 height);

#ifdef __cplusplus
}
#endif

}

#endif                       /* __H264DECAPI_H__ */

redpajama_set_name: RedPajamaGithub


text:
import { Person } from '@mattailes/types/StarWars'
import { gql } from 'apollo-server-express'

import { batchLoad, getAll, getOne, loader } from './helpers'

export const PersonTypeDef = gql`
  extend type Query {
    getAllPeople: [Person]
    getPerson(id: Int): Person
  }

  type Person {
    id: Int
    # The url of the planet resource that this person was born on.
    homeworld: Planet
    # The url of the species resource that this person is.
    species: [Species]
    # An array of urls of film resources that this person has been in.
    films: [Film]
    # The eye color of this person.
    eye_color: String
    # The gender of this person (if known).
    gender: String
    # The height of this person in meters.
    height: String
    # The hair color of this person.
    hair_color: String
    # An array of starship resources that this person has piloted
    starships: [Starship]
    # The birth year of this person. BBY (Before the Battle of Yavin) or ABY (After the Battle of Yavin).
    birth_year: String
    # The name of this person.
    name: String
    # The mass of this person in kilograms.
    mass: String
    # An array of vehicle resources that this person has piloted
    vehicles: [Vehicle]
    # The skin color of this person.
    skin_color: String
  }
`

export const personResolvers = {
  Query: {
    getAllPeople: () => getAll('people'),
    getPerson: (_: any, { id }: { id: number }) => getOne('people', id),
  },

  Person: {
    homeworld: ({ homeworld }: Person) => loader.load({ id: homeworld, type: 'people' }),
    films: ({ films }: Person) => batchLoad(films as number[], 'films'),
    species: ({ species }: Person) => batchLoad(species as number[], 'species'),
    starships: ({ starships }: Person) => batchLoad(starships as number[], 'starships'),
    vehicles: ({ vehicles }: Person) => batchLoad(vehicles as number[], 'vehicles'),
  },
}


text:
What is a chaotic society?
What causes chaos in society?
What is chaos theory in sociology?
What are synonyms for chaos?
What is opposite to chaos?
What's the opposite of chaotic?
Is chaos the opposite of order?
What is an antonym for chaotic?
What is a synonym of chaotic?
What is the modern meaning of chaos?
What is chaos physics?
Is organized chaos an oxymoron?
Is chaos the natural order?
What is order and chaos?
Is there a symbol for chaos?
Is Yin order or chaos?
Can you get order from chaos?
Are humans chaotic?
Why do we need chaos?
What is the relationship between order and chaos?
Who said out of chaos comes order?
Who won order vs chaos?
What came after chaos?
A purely chaotic society is therefore the ideal mature community, where no regulations are necessary, because everyone gets along well without them.
The major causes of chaos in the world is first and foremost: the lack of love. Greed, fame, and desires is what is destroying us all. People 3rd world countries desire killing other people: boom!
In the social sciences, chaos theory is the study of complex non-linear systems of social complexity. Chaos theory looks at this unpredictability of nature and tries to make sense of it. Chaos theory aims to find the general order of social systems and particularly social systems that are similar to each other.
other words for chaos
anarchy.
disarray.
discord.
disorder.
lawlessness.
pandemonium.
tumult.
turmoil.
English Synonyms and Antonyms chaos. Antonyms: manner, method, mode, order, regularity, rule, system. Synonyms: confusion, derangement, disarrangement, disorder, irregularity.
What is the opposite of chaotic?
peaceful quiet
easeful relaxing
tranquil unhurried
relaxed serene
Chaos is the complete opposite of Order.
Antonyms: organized, tame. Synonyms: disorderly, helter-skelter, topsy-turvy, hugger-mugger, jumbled, higgledy-piggledy.
In this page you can discover 30 synonyms, antonyms, idiomatic expressions, and related words for chaotic, like: disorderly, muddled, uncontrolled, formless, turbulent, disorganized, disordered, confused, anarchic, helter-skelter and quiet.
Our most common uses of chaos today imply either a confused mass or jumble of things, or a state of utter confusion. English speakers borrowed this meaning of chaos, then broadened it into the word we recognize today: one that denotes utter confusion or disorganization.
In continuous time dynamical systems, chaos is the phenomenon of the spontaneous breakdown of topological supersymmetry, which is an intrinsic property of evolution operators of all stochastic and deterministic (partial) differential equations.
The above phrase is packed with oxymorons, including "organized mess," "controlled chaos," and "same difference." For something to be organized, it cannot be a mess. The answer is the oxymoron. An oxymoron is a figure of speech that puts together opposite elements.
All living organisms are guided by unwritten laws, which provide guidance and natural order even for the animals in the jungle. It is this order that prevents self-harm and obliteration of our entire race thereby protecting us from extinction.
Order and Chaos is a variant of the game tic-tac-toe on a 6×6 gameboard . It was invented by Stephen Sniderman and introduced by him in Games magazine in 1981. The player Order strives to create a five-in-a-row of either Xs or Os. The opponent Chaos endeavors to prevent this.
In them, the Symbol of Chaos comprises eight arrows in a radial pattern. It is also called the Arms of Chaos, the Arrows of Chaos, the Chaos Star, the Chaos Cross, the Chaosphere (when depicted as a three-dimensional sphere), or the Symbol of Eight.
In Chinese numerology, even numbers are yin (order) and odd numbers are yang (chaos). 6 is the quintessential yin number and 9 is the quintessential yang number. That's because, of the even numbers: 2, 4, 6, 8, 10, we see 6 at the centre, establishing order, therefore characteristic of yin the feminine.
There is order emerging from the chaos, but we can only say roughly which direction the temperatures will change, not precisely by how much. Chaos theory applies even to economics. This very small change created a huge amount of chaos and unpredictability, and led to the market dominance of the VCR.
Despite this regulation, our systems are aperiodic and unpredictable in the long term. We are incredibly ordered on several scales of magnitude, but irregularly so. Our bodies conform to a set of non-linear, dynamic rules. The human body is not a simple machine, but an amazingly complex chaotic system.
Why are the chaos theories important for you? Linear mechanics could be said to be at a human scale. It allows us to analyze systems and phenomena that are not too different from the human scale: neither too small nor too large. In very small and very large cases, we'll realize that it does not hold any longer.
Each situation moves from something chaotic to something organized. Order cannot exist without chaos; it is born out chaos. Chaos is the mother and order is born from within it. Both exist in everything and in every situation – it is a primary hierarchy.
The votes have been tallied and the Splatocalypse has come to its ultimate conclusion. With a 3-0 win, Team Chaos are your Final Splatfest victors. Congratulations to these patrons of pandemonium! Thank you to everyone who participated in this Splatfest and all the others, and thank you for supporting Splatoon 2!
From Chaos came Eros, the potent concept of Love, which is fundamental. TARTARUS [tar'ta-rus], or TARTAROS. Tartarus, which came out of Chaos, was an area in the depths of the earth. It became a place of punishment in the Underworld; EREBUS [er'e-bus], or EREBOS, its darkness, became another name for Tartarus itself.
Previous Article What makes a human truly human?
Next Article Why was Sherlock Holmes so popular?


Today I started my new Day "C" workouts and it felt great! I worked really hard and I think I hit the spots that I may be missing in the "A" and "B" days. I also did one of the walking DVDs for cardio and have several new ones coming today so I can keep doing cardio almost every day and not get bored with it!


text:
Q: Sort an array of array by 2 conditions I have to sort this
ary = [[5, "e", "2"], [2, "r", "="], [2, "y", "2"], [2, "h", "="]]

to obtain :
# => [[5, "e", "2"], [2, "y", "2"], [2, "h", "="], [2, "r", "="]]

If the last element (index 2) is equal to "=" it must be after the array which has the same first element even if the letter is before.
Something like this :
ary.each_with_index do |array, index|
  if ary[index][2] == "=" && ary[index][0] == ary[index +1][2]
    a = ary[index]
    b = ary[index +1]
    ary[index] = b
    ary[index+1] = a
  end
end

A: I assume that, for elements ending with "=", sorting is by decreasing value of the first element (integers) and when there are ties, by increasing order of the second element (strings). Further, I assume sorting is the same for elements not ending with "=", except when the first two elements both tie, sorting is by increasing order of the last element (strings).
def sort_em(arr)
  arr.sort_by { |n, s1, s2| [s2 == "=" ? 1 : 0, -n, s1, s2] }
end

sort_em [[5, "e", "2"], [2, "r", "="], [2, "y", "2"], [2, "h", "="]]
  #=> [[5, "e", "2"], [2, "y", "2"], [2, "h", "="], [2, "r", "="]]

See the third paragraph of the doc for Array#<=> for an explanation of how arrays are ordered when sorting.
To ensure elements ending with "=" come last in the sort, I've simply added 1 (0) at the beginning of array in sort_by's block for arrays ending (not ending) with "=".

A: You can use sort and provide your own block for sorting:
ary.sort do |a, b|
  if a[2] == '=' && b[2] != '='
    # a has '=' => a > b
    1
  elsif b[2] == '=' && a[2] != '='
    # b has '=' => a < b
    -1
  else
    # This is hit if neither a nor b have a '=' OR when both do.
    # Use default comparison operator
    # but restrict it to the second element of the array
    a[1] <=> b[1]
  end
end

The block needs to return a value that is 1, -1 or 0. Based on this, the values are placed in order.

*

*1: a > b

*-1: a < b

*0: a = b

redpajama_set_name: RedPajamaStackExchange